Prognostic value of LRRC4C in Colon and Gastric Cancers correlates with Tumour Microenvironment Immunity.
Yang XiaoFeng,Lei Purun,Huang Lijun,Tang Xiao,Wei Bo,Wei HongBo
International journal of biological sciences
In this study, we aimed to use ESTIMATE and CIBERSORT computational methods to analyse transcriptional information on COAD and STAD in TCGA. We downloaded transcriptome RNA-seq data of 446 patients with colon cancer from TCGA and estimated the amount of immune and stromal components in the COAD samples using CIBERSORT algorithms. We analysed differentially expressed genes in 446 TCGA samples and 585 Series GSE39582 samples, in high- and low-scoring groups, using Cox regression. The expression of , correlated positively with clinicopathological characteristics and negatively with the survival of patients with COAD. Single-gene survival analysis using Gene Expression Profiling Interactive Analysis 2.0 and Kaplan-Meier plotter revealed an association between high levels of expression and poor prognosis in patients with colon and gastric cancers. Gene set enrichment analysis of COAD and STAD samples indicated that genes in groups expressing high and low levels were mainly enriched in immune-related activities and metabolic pathways, respectively. Difference and correlation analyses of the relationship between expression and tumour-infiltrating immune cells, determined using CIBERSORT algorithms, revealed that monocytes, resting mast cells, and M2 macrophages were positively correlated with expression.
Integrated analysis reveals the participation of IL4I1, ITGB7, and FUT7 in reshaping the TNBC immune microenvironment by targeting glycolysis.
Xu Tao,Liu Jiahao,Xia Yu,Wang Zhi,Li Xingrui,Gao Qinglei
Annals of medicine
BACKGROUND:The overall response rate of immunotherapy in triple-negative breast cancer (TNBC) remains unsatisfactory. Accumulating evidence indicated that glucose metabolic reprogramming could modulate immunotherapy efficacy. However, transcriptomic evidence remains insufficient. METHODS:Genes' relationship with glucose metabolism and TNBC-specific immune was demonstrated by weighted gene co-expression network analysis (WGCNA). The glucose metabolic capability was estimated by standardised uptake value (SUV), an indicator of glucose uptake in 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET), and a reflection of cancer metabolic behaviour. PD-(L)1 expression was used to reflect the efficacy of immunotherapy. Additionally, immune infiltration, survival, and gene coexpression profiles were provided. RESULTS:Comprehensive analysis revealing that IL4I1, ITGB7, and FUT7 hold the potential to reinforce immunotherapy by reshaping glucose metabolism in TNBC. These results were verified by functional enrichment analysis, which demonstrated their relationships with immune-related signalling pathways and extracellular microenvironment reprogramming. Their expressions have potent positive correlations with Treg and Macrophage cell infiltration and exhausted T cell markers. Meanwhile, their overexpression also lead to poor prognosis. CONCLUSION:IL4I1, ITGB7, and FUT7 may be the hub genes that link glucose metabolism, and cancer-specific immunity. They may be potential targets for enhancing ICB treatment by reprogramming the tumour microenvironment and remodelling tumour metabolism.
Research on the circadian clock gene HNF4a in different malignant tumors.
Qiu Meng-Jun,Zhang Li,Fang Xie-Fan,Li Qiu-Ting,Zhu Li-Sheng,Zhang Bin,Yang Sheng-Li,Xiong Zhi-Fan
International journal of medical sciences
The circadian rhythm is produced by multiple feedback loops formed by the core clock genes after transcription and translation, thus regulating various metabolic and physiological functions of the human body. We have shown previously that the abnormal expression of 14 clock genes is related closely to the occurrence and development of different malignant tumors, and these genes may play an anti-cancer or pro-cancer role in different tumors. HNF4a has many typical properties of clock proteins involved in the clock gene negative feedback loop regulation process. We need to explore the function of HNF4a as a circadian clock gene in malignant tumors further. We used The Cancer Genome Atlas (TCGA) database to download the clinicopathological information of twenty malignant tumors and the corresponding RNA-seq data. The HNF4a RNA-seq data standardized by R language and clinical information were integrated to reveal the relationship between HNF4a and prognosis of patients. Analysis of TCGA data showed that the prognosis of HNF4a was significantly different in BLCA, KIRC, LUSC, and READ. High HNF4a expression is correlated with good prognosis in BLCA, KIRC, and READ but poor prognosis in LUSC. However, HNF4a was associated with the stages, T stages, and lymph node status only in BLCA. HNF4a plays different roles in different malignancies, and the abnormal expression of HNF4a has a great correlation with the biological characteristics of BLCA. The low expression of HNF4a could be a reference index for the metastasis, recurrence, and prognosis of BLCA.
Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC).
Yang Xin,Liu Qiong,Zou Juan,Li Yu-Kun,Xie Xia
Cancer management and research
Background:Metabolic disorders have attracted increasing attention from scientists who conduct research on various tumours, especially hepatocellular carcinoma (HCC). The purpose of this study was to assess the prognostic significance of metabolism in HCC. Methods:The expression profiles of metabolism-related genes (MRGs) of 349 surviving HCC patients were extracted from The Cancer Genome Atlas (TCGA) database. Subsequently, a series of biomedical computational algorithms were used to identify a seven-MRG signature as a prognostic model. GSEA indicated the function and pathway enrichment of these MRGs. Then, drug sensitivity analysis was used to identify the hub gene, which was tested using IHC staining. Results:A total of 420 differential MRGs and 116 differentially expressed transcription factors (TFs) were identified in HCC patients based on data from the TCGA database. The GO and KEGG enrichment analyses indicated that metabolic disturbance might be involved in the development of HCC. LASSO regression analysis was used to construct a seven-MRG signature (DHDH, ENO1, G6PD, LPCAT1, PDE6D, PIGU and PPAT) that could predict the prognosis of HCC patients. GSEA revealed the functional and pathway enrichment of these seven MRGs. Then, drug sensitivity analysis indicated that G6PD might play a key role in the prognosis of HCC by promoting chemoresistance. Finally, we used IHC staining to demonstrate the relationship between G6PD expression levels and clinical parameters in HCC patients. Conclusion:The results of this study provide a potential method for predicting the prognosis of HCC patients and avenues for further studies of HCC metabolism. Moreover, the function of G6PD may play a key role in the development and progression of HCC.
A novel recurrence-associated metabolic prognostic model for risk stratification and therapeutic response prediction in patients with stage I lung adenocarcinoma.
Liu Chengming,Wang Sihui,Zheng Sufei,Wang Xinfeng,Huang Jianbin,Lei Yuanyuan,Mao Shuangshuang,Feng Xiaoli,Sun Nan,He Jie
Cancer biology & medicine
OBJECTIVE:The proportion of patients with stage I lung adenocarcinoma (LUAD) has dramatically increased with the prevalence of low-dose computed tomography use for screening. Up to 30% of patients with stage I LUAD experience recurrence within 5 years after curative surgery. A robust risk stratification tool is urgently needed to identify patients who might benefit from adjuvant treatment. METHODS:In this first investigation of the relationship between metabolic reprogramming and recurrence in stage I LUAD, we developed a recurrence-associated metabolic signature (RAMS). This RAMS was based on metabolism-associated genes to predict cancer relapse and overall prognoses of patients with stage I LUAD. The clinical significance and immune landscapes of the signature were comprehensively analyzed. RESULTS:Based on a gene expression profile from the GSE31210 database, functional enrichment analysis revealed a significant difference in metabolic reprogramming that distinguished patients with stage I LUAD with relapse from those without relapse. We then identified a metabolic signature (i.e., RAMS) represented by 2 genes ( and ) significantly related to recurrence-free survival and overall survival times of patients with stage I LUAD using transcriptome data analysis of a training set. The training set was well validated in a test set. The discriminatory power of the 2 gene metabolic signature was further validated using protein values in an additional independent cohort. The results indicated a clear association between a high risk score and a very poor patient prognosis. Stratification analysis and multivariate Cox regression analysis showed that the RAMS was an independent prognostic factor. We also found that the risk score was positively correlated with inflammatory response, the antigen-presenting process, and the expression levels of many immunosuppressive checkpoint molecules (e.g., PD-L1, PD-L2, B7-H3, galectin-9, and FGL-1). These results suggested that high risk patients had immune response suppression. Further analysis revealed that anti-PD-1/PD-L1 immunotherapy did not have significant benefits for high risk patients. However, the patients could respond better to chemotherapy. CONCLUSIONS:This study is the first to highlight the relationship between metabolic reprogramming and recurrence in stage I LUAD, and is the first to also develop a clinically feasible signature. This signature may be a powerful prognostic tool and help further optimize the cancer therapy paradigm.
Establishment of the Prognostic Index Reflecting Tumor Immune Microenvironment of Lung Adenocarcinoma Based on Metabolism-Related Genes.
Zhang Jianguo,Zhang Jianzhong,Yuan Cheng,Luo Yuan,Li Yangyi,Dai Panpan,Sun Wenjie,Zhang Nannan,Ren Jiangbo,Zhang Junhong,Gong Yan,Xie Conghua
Journal of Cancer
The incidence of lung adenocarcinoma (LUAD) increased substantially in recent years. A systematic investigation of the metabolic genomics pattern is critical to improve the treatment and prognosis of LUAD. This study aimed to analyze the relationship between tumor microenvironment (TME) and metabolism-related genes of LUAD. The data was extracted from TCGA and GEO datasets. The metabolism-related gene expression profile and the corresponding clinical data of LUAD patients were then integrated. The survival-related genes were screened out using univariate COX regression and lasso regression analysis. The latent properties and molecular mechanisms of these LUAD-specific metabolism-related genes were investigated by computational biology. A novel prognostic model was established based on 8 metabolism-related genes, including TYMS, ALDH2, PKM, GNPNAT1, LDHA, ENTPD2, NT5E, and MAOB. The immune infiltration of LUAD was also analyzed using CIBERSORT algorithms and TIMER database. In addition, the high- and low-risk groups exhibited distinct layout modes in the principal component analysis. In summary, our studies identified clinically significant metabolism-related genes, which were potential signature for LUAD diagnosis, monitoring, and prognosis.
The short isoform of PRLR suppresses the pentose phosphate pathway and nucleotide synthesis through the NEK9-Hippo axis in pancreatic cancer.
Nie Huizhen,Huang Pei-Qi,Jiang Shu-Heng,Yang Qin,Hu Li-Peng,Yang Xiao-Mei,Li Jun,Wang Ya-Hui,Li Qing,Zhang Yi-Fan,Zhu Lei,Zhang Yan-Li,Yu Yanqiu,Xiao Gary Guishan,Sun Yong-Wei,Ji Jianguang,Zhang Zhi-Gang
Prolactin binding to the prolactin receptor exerts pleiotropic biological effects in vertebrates. The prolactin receptor (PRLR) has multiple isoforms due to alternative splicing. The biological roles and related signaling of the long isoform (PRLR-LF) have been fully elucidated. However, little is known about the short isoform (PRLR-SF), particularly in cancer development and metabolic reprogramming, a core hallmark of cancer. Here, we reveal the role and underlying mechanism of PRLR-SF in pancreatic ductal adenocarcinoma (PDAC). A human PDAC tissue array was used to investigate the clinical relevance of PRLR in PDAC. The implications of PRLR-SF in PDAC were examined in a subcutaneous xenograft model and an orthotopic xenograft model. Immunohistochemistry was performed on tumor tissue obtained from genetically engineered KPC (Kras; Trp53; Pdx1-Cre) mice with spontaneous tumors. C-labeled metabolite measures, LC-MS, EdU incorporation assays and seahorse analyses were used to identify the effects of PRLR-SF on the pentose phosphate pathway and glycolysis. We identified the molecular mechanisms by immunofluorescence, coimmunoprecipitation, proximity ligation assays, chromatin immunoprecipitation and promoter luciferase activity. Public databases (TCGA, GEO and GTEx) were used to analyze the expression and survival correlations of the related genes. We demonstrated that PRLR-SF is predominantly expressed in spontaneously forming pancreatic tumors of genetically engineered KPC mice and human PDAC cell lines. PRLR-SF inhibits the proliferation of PDAC cells (AsPC-1 and BxPC-3) and tumor growth . We showed that PRLR-SF reduces the expression of genes in the pentose phosphate pathway (PPP) and nucleotide biosynthesis by activating Hippo signaling. TEAD1, a downstream transcription factor of Hippo signaling, directly regulates the expression of G6PD and TKT, which are PPP rate-limiting enzymes. Moreover, NEK9 directly interacts with PRLR-SF and is the intermediator between PRLR and the Hippo pathway. The PRLR expression level is negatively correlated with overall survival and TNM stage in PDAC patients. Additionally, pregnancy and lactation increase the ratio of PRLR-SF:PRLR-LF in the pancreas of wild-type mice and subcutaneous PDAC xenograft tumors. Our characterization of the relationship between PRLR-SF signaling, the NEK9-Hippo pathway, PPP and nucleotide synthesis explains a mechanism for the correlation between PRLR-SF and metabolic reprogramming in PDAC progression. Strategies to alter this pathway might be developed for the treatment or prevention of pancreatic cancer.
Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer.
Zhu Jun,Wang Shuai,Bai Han,Wang Ke,Hao Jun,Zhang Jian,Li Jipeng
Frontiers in oncology
Metabolic changes, especially in glucose metabolism, are widely established during the occurrence and development of tumors and regarded as biological markers of pan-cancer. The well-known 'Warburg effect' demonstrates that cancer cells prefer aerobic glycolysis even if there is sufficient ambient oxygen. Accumulating evidence suggests that aerobic glycolysis plays a pivotal role in colorectal cancer (CRC) development. However, few studies have examined the relationship of glycolytic gene clusters with prognosis of CRC patients. Here, our aim is to build a glycolysis-associated gene signature as a biomarker for colorectal cancer. The mRNA sequencing and corresponding clinical data were downloaded from TCGA and GEO databases. Gene set enrichment analysis (GSEA) was performed, indicating that four gene clusters were significantly enriched, which revealed the inextricable relationship of CRC with glycolysis. By comparing gene expression of cancer and adjacent samples, 236 genes were identified. Univariate, multivariate, and LASSO Cox regression analyses screened out five prognostic-related genes (ENO3, GPC1, P4HA1, SPAG4, and STC2). Kaplan-Meier curves and receiver operating characteristic curves (ROC, AUC = 0.766) showed that the risk model could become an effective prognostic indicator ( < 0.001). Multivariate Cox analysis also revealed that this risk model is independent of age and TNM stages. We further validated this risk model in external cohorts (GES38832 and GSE39582), showing these five glycolytic genes could emerge as reliable predictors for CRC patients' outcomes. Lastly, based on five genes and risk score, we construct a nomogram model assessed by C-index (0.7905) and calibration plot. In conclusion, we highlighted the clinical significance of glycolysis in CRC and constructed a glycolysis-related prognostic model, providing a promising target for glycolysis regulation in CRC.
Identification and validation of ADME genes as prognosis and therapy markers for hepatocellular carcinoma patients.
Wang Jukun,Han Ke,Zhang Chao,Chen Xin,Li Yu,Zhu Linzhong,Luo Tao
PURPOSE:ADME genes are genes involved in drug absorption, distribution, metabolism, and excretion (ADME). Previous studies report that expression levels of ADME-related genes correlate with prognosis of hepatocellular carcinoma (HCC) patients. However, the role of ADME gene expression on HCC prognosis has not been fully explored. The present study sought to construct a prediction model using ADME-related genes for prognosis of HCC. METHODS:Transcriptome and clinical data were retrieved from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), which were used as training and validation cohorts, respectively. A prediction model was constructed using univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analysis. Patients were divided into high- and low-risk groups based on the median risk score. The predictive ability of the risk signature was estimated through bioinformatics analyses. RESULTS:Six ADME-related genes (CYP2C9, ABCB6, ABCC5, ADH4, DHRS13, and SLCO2A1) were used to construct the prediction model with a good predictive ability. Univariate and multivariate Cox regression analyses showed the risk signature was an independent predictor of overall survival (OS). A single-sample gene set enrichment analysis (ssGSEA) strategy showed a significant relationship between risk signature and immune status. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed differentially expressed genes (DEGs) in the high- and low-risk groups were enriched in biological process (BP) associated with metabolic and cell cycle pathways. CONCLUSION:A prediction model was constructed using six ADME-related genes for prediction of HCC prognosis. This signature can be used to improve HCC diagnosis, treatment, and prognosis in clinical use.
A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer.
Guo Kaixuan,Lai Cong,Shi Juanyi,Tang Zhuang,Liu Cheng,Li Kuiqing,Xu Kewei
Frontiers in oncology
Background:Prostate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa. Methods:Differential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the "edgeR" package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells. Results:Five glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown. Conclusion:The risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR.
Identification of glycolysis related pathways in pancreatic adenocarcinoma and liver hepatocellular carcinoma based on TCGA and GEO datasets.
Li Ji,Zhu Chen,Yue Peipei,Zheng Tianyu,Li Yan,Wang Biao,Meng Xin,Zhang Yao
Cancer cell international
BACKGROUND:Abnormal energy metabolism is one of the characteristics of tumor cells, and it is also a research hotspot in recent years. Due to the complexity of digestive system structure, the frequency of tumor is relatively high. We aim to clarify the prognostic significance of energy metabolism in digestive system tumors and the underlying mechanisms. METHODS:Gene set variance analysis (GSVA) R package was used to establish the metabolic score, and the score was used to represent the metabolic level. The relationship between the metabolism and prognosis of digestive system tumors was explored using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Volcano plots and gene ontology (GO) analyze were used to show different genes and different functions enriched between different glycolysis levels, and GSEA was used to analyze the pathway enrichment. Nomogram was constructed by R package based on gene characteristics and clinical parameters. qPCR and Western Blot were applied to analyze gene expression. All statistical analyses were conducted using SPSS, GraphPad Prism 7, and R software. All validated experiments were performed three times independently. RESULTS:High glycolysis metabolism score was significantly associated with poor prognosis in pancreatic adenocarcinoma (PAAD) and liver hepatocellular carcinoma (LIHC). The STAT3 (signal transducer and activator of transcription 3) and YAP1 (Yes1-associated transcriptional regulator) pathways were the most critical signaling pathways in glycolysis modulation in PAAD and LIHC, respectively. Interestingly, elevated glycolysis levels could also enhance STAT3 and YAP1 activity in PAAD and LIHC cells, respectively, forming a positive feedback loop. CONCLUSIONS:Our results may provide new insights into the indispensable role of glycolysis metabolism in digestive system tumors and guide the direction of future metabolism-signaling target combined therapy.
Prognostic Implication of a Novel Metabolism-Related Gene Signature in Hepatocellular Carcinoma.
Yuan Chaoyan,Yuan Mengqin,Chen Mingqian,Ouyang Jinhua,Tan Wei,Dai Fangfang,Yang Dongyong,Liu Shiyi,Zheng Yajing,Zhou Chenliang,Cheng Yanxiang
Frontiers in oncology
Background:Hepatocellular carcinoma (HCC) is one of the main causes of cancer-associated deaths globally, accounts for 90% of primary liver cancers. However, further studies are needed to confirm the metabolism-related gene signature related to the prognosis of patients with HCC. Methods:Using the "limma" R package and univariate Cox analysis, combined with LASSO regression analysis, a metabolism-related gene signature was established. The relationship between the gene signature and overall survival (OS) of HCC patients was analyzed. RT-qPCR was used to evaluate the expression of metabolism-related genes in clinical samples. GSEA and ssGSEA algorithms were used to evaluate differences in metabolism and immune status, respectively. Simultaneously, data downloaded from ICGC were used as an external verification set. Results:From a total of 1,382 metabolism-related genes, a novel six-gene signature (G6PD, AKR1B15, HMMR, CSPG5, ELOVL3, FABP6) was constructed based on data from TCGA. Patients were divided into two risk groups based on risk scores calculated for these six genes. Survival analysis showed a significant correlation between high-risk patients and poor prognosis. ROC analysis demonstrated that the gene signature had good predictive capability, and the mRNA expression levels of the six genes were upregulated in HCC tissues than those in adjacent normal liver tissues. Independent prognosis analysis confirmed that the risk score and tumor grade were independent risk factors for HCC. Furthermore, a nomogram of the risk score combined with tumor stage was constructed. The calibration graph results demonstrated that the OS probability predicted by the nomogram had almost no deviation from the actual OS probability, especially for 3-year OS. Both the C-index and DCA curve indicated that the nomogram provides higher reliability than the tumor stage and risk scores. Moreover, the metabolic and immune infiltration statuses of the two risk groups were significantly different. In the high-risk group, the expression levels of immune checkpoints, TGF-β, and C-ECM genes, whose functions are related to immune escape and immunotherapy failure, were also upregulated. Conclusions:In summary, we developed a novel metabolism-related gene signature to provide more powerful prognostic evaluation information with potential ability to predict the immunotherapy efficiency and guide early treatment for HCC.
Transcriptomic Analyses of the Adenoma-Carcinoma Sequence Identify Hallmarks Associated With the Onset of Colorectal Cancer.
Hong Qin,Li Bing,Cai Xiumei,Lv Zhengtao,Cai Shilun,Zhong Yunshi,Wen Bo
Frontiers in oncology
The concept of the adenoma-carcinoma sequence in colorectal cancer (CRC) is widely accepted. However, the relationship between the characteristics of the transcriptome and the adenoma-carcinoma sequence in CRC remains unclear. Here, the transcriptome profiles of 15 tissue samples from five CRC patients were generated by RNAseq. Six specific dynamic expression patterns of differentially expressed genes (DEGs) were generated by mFuzz. Weighted correlation network analysis showed that DEGs in cluster 4 were associated with carcinoma tissues, and those in cluster 6 were associated with non-normal tissues. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses identified metabolic dysregulation as a consistent finding throughout the transition process, whereas downregulation of the immune response occurred during normal to adenoma transition, and the upregulation of canonical pathways was associated with adenoma to carcinoma transition. Overall survival analysis of patients in cluster 6 identified as a marker of poor prognosis, and cell proliferation, colony formation, wound healing, and Transwell invasion assays showed that high expression levels of promoted malignant behaviors. In total, 70 proteins were identified as potential partners of hD53 by mass spectrometry. CRC formation was associated with three cancer hallmarks: dysregulation of metabolism, inactivation of the immune response, and activation of canonical cancer pathways. The gene was identified as a potential marker to track tumor formation in CRC and as an indicator of poor patient prognosis.
Potential role of glucosamine-phosphate N-acetyltransferase 1 in the development of lung adenocarcinoma.
Zhang Shengqiang,Zhang Hongyan,Li Huawei,Guo Jida,Wang Jun,Zhang Linyou
Glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1) is a key enzyme associated with glucose metabolism and uridine diphosphate-N-acetylglucosamine biosynthesis. Abnormal expression might be associated with carcinogenesis. We analyzed multiple lung adenocarcinoma (LUAD) gene expression databases and verified higher expression in LUAD tumor tissues than in normal tissues. Moreover, we analyzed the survival relationship between LUAD patients' clinical status and expression, and found higher expression in LUAD patients with unfavorable prognosis. We built gene co-expression networks and further annotated the co-expressed genes' Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and various associated regulatory factors. These co-expression genes' functional networks mainly participate in chromosome segregation, RNA metabolic process, and RNA transport. We analyzed genetic alterations and co-occurrence networks, and the functional networks of these genes showed that participates in multiple steps of cell cycle transition and in the development of some cancers. We assessed the correlation between expression and cancer immune infiltrates and showed that expression is correlated with several immune cells, chemokines, and immunomodulators in LUAD. We found that correlates with LUAD development and prognosis, laying a foundation for further research, especially in immunotherapy.
Combined analysis of RNA-sequence and microarray data reveals effective metabolism-based prognostic signature for neuroblastoma.
Meng Xinyao,Feng Chenzhao,Fang Erhu,Feng Jiexiong,Zhao Xiang
Journal of cellular and molecular medicine
The relationship between metabolism reprogramming and neuroblastoma (NB) is largely unknown. In this study, one RNA-sequence data set (n = 153) was used as discovery cohort and two microarray data sets (n = 498 and n = 223) were used as validation cohorts. Differentially expressed metabolic genes were identified by comparing stage 4s and stage 4 NBs. Twelve metabolic genes were selected by LASSO regression analysis and integrated into the prognostic signature. The metabolic gene signature successfully stratifies NB patients into two risk groups and performs well in predicting survival of NB patients. The prognostic value of the metabolic gene signature is also independent with other clinical risk factors. Nine metabolism-related long non-coding RNAs (lncRNAs) were also identified and integrated into the metabolism-related lncRNA signature. The lncRNA signature also performs well in predicting survival of NB patients. These results suggest that the metabolic signatures have the potential to be used for risk stratification of NB. Gene set enrichment analysis (GSEA) reveals that multiple metabolic processes (including oxidative phosphorylation and tricarboxylic acid cycle, both of which are emerging targets for cancer therapy) are enriched in the high-risk NB group, and no metabolic process is enriched in the low-risk NB group. This result indicates that metabolism reprogramming is associated with the progression of NB and targeting certain metabolic pathways might be a promising therapy for NB.
Prediction and analysis of novel key genes ITGAX, LAPTM5, SERPINE1 in clear cell renal cell carcinoma through bioinformatics analysis.
Sui Yingli,Lu Kun,Fu Lin
Background:Clear Cell Renal Cell Carcinoma (CCRCC) is the most aggressive subtype of Renal Cell Carcinoma (RCC) with high metastasis and recurrence rates. This study aims to find new potential key genes of CCRCC. Methods:Four gene expression profiles (GSE12606, GSE53000, GSE68417, and GSE66272) were downloaded from the Gene Expression Omnibus (GEO) database. The TCGA KIRC data was downloaded from The Cancer Genome Atlas (TCGA). Using GEO2R, the differentially expressed genes (DEG) in CCRCC tissues and normal samples were analyzed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed in DAVID database. A protein-protein interaction (PPI) network was constructed and the hub gene was predicted by STRING and Cytoscape. GEPIA and Kaplan-Meier plotter databases were used for further screening of Key genes. Expression verification and survival analysis of key genes were performed using TCGA database, GEPIA database, and Kaplan-Meier plotter. Receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of key genes in CCRCC, which is plotted by R software based on TCGA database. UALCAN database was used to analyze the relationship between key genes and clinical pathology in CCRCC and the methylation level of the promoter of key genes in CCRCC. Results:A total of 289 up-regulated and 449 down-regulated genes were identified based on GSE12606, GSE53000, GSE68417, and GSE66272 profiles in CCRCC. The upregulated DEGs were mainly enriched with protein binding and PI3K-Akt signaling pathway, whereas down-regulated genes were enriched with the integral component of the membrane and metabolic pathways. Next, the top 35 genes were screened out from the PPI network according to Degree, and three new key genes ITGAX, LAPTM5 and SERPINE1 were further screened out through survival and prognosis analysis. Further results showed that the ITGAX, LAPTM5, and SERPINE1 levels in CCRCC tumor tissues were significantly higher than those in normal tissues and were associated with poor prognosis. ROC curve shows that ITGAX, LAPTM5, and SERPINE1 have good diagnostic value with good specificity and sensitivity. The promoter methylation levels of ITGAX, LAPTM5 and SERPINE1 in CCRCC tumor tissues were significantly lower than those in normal tissues. We also found that key genes were associated with clinical pathology in CCRCC. Conclusion:ITGAX, LAPTM5, and SERPINE1 were identified as novel key candidate genes that could be used as prognostic biomarkers and potential therapeutic targets for CCRCC.
Lactate Dehydrogenase-A (LDH-A) Preserves Cancer Stemness and Recruitment of Tumor-Associated Macrophages to Promote Breast Cancer Progression.
Wang Shengnan,Ma Lingyu,Wang Ziyuan,He Huiwen,Chen Huilin,Duan Zhaojun,Li Yuyang,Si Qin,Chuang Tsung-Hsien,Chen Chong,Luo Yunping
Frontiers in oncology
Increasing evidence reveals that breast cancer stem cells (BCSCs) subtypes with distinct properties are regulated by their abnormal metabolic changes; however, the specific molecular mechanism and its relationship with tumor microenvironment (TME) are not clear. In this study, we explored the mechanism of lactate dehydrogenase A (LDHA), a crucial glycolytic enzyme, in maintaining cancer stemness and BCSCs plasticity, and promoting the interaction of BCSCs with tumor associated macrophages (TAMs). Firstly, the expression of LDHA in breast cancer tissues was much higher than that in adjacent tissues and correlated with the clinical progression and prognosis of breast cancer patients based on The Cancer Genome Atlas (TCGA) data set. Moreover, the orthotopic tumor growth and pulmonary metastasis were remarkable inhibited in mice inoculated with 4T1-shLdha cells. Secondly, the properties of cancer stemness were significantly suppressed in MDA-MB-231-shLDHA or A549-shLDHA cancer cells, including the decrease of ALDH cells proportion, the repression of sphere formation and cellular migration, and the reduction of stemness genes (SOX2, OCT4, and NANOG) expression. However, the proportion of ALDH cells (epithelial-like BCSCs, E-BCSCs) was increased and the proportion of CD44 CD24 cells (mesenchyme-like BCSCs, M-BCSCs) was decreased after LDHA silencing, suggesting a regulatory role of LDHA in E-BCSCs/M-BCSCs transformation in mouse breast cancer cells. Thirdly, the expression of epithelial marker E-cadherin, proved to interact with LDHA, was obviously increased in LDHA-silencing cancer cells. The recruitment of TAMs and the secretion of CCL2 were dramatically reduced after LDHA was knocked down and . Taken together, LDHA mediates a vicious cycle of mutual promotion between BCSCs plasticity and TAMs infiltration, which may provide an effective treatment strategy by targeting LDHA for breast cancer patients.
Prognostic value of the tumor-specific ceRNA network in epithelial ovarian cancer.
Li Gailing,Han Liping,Ren Fang,Zhang Ruitao,Qin Guijun
Journal of cellular physiology
Epithelial ovarian cancer is one of the leading causes of cancer-related death worldwide. Growing evidence indicates that multiple complex altered pathways play important regulatory roles in the development and progression of a variety of cancers, including epithelial ovarian cancer. However, the underlying mechanisms remain unclear. First, we identified differentially expressed messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) in epithelial ovarian cancer by comparing the expression profiles between epithelial ovarian cancer samples and normal tissue samples in different GEO datasets. Then, GO- and KEGG-pathway-enrichment analyses were applied to investigate the primary functions of the overlapped differentially expressed mRNAs. Moreover, the primary enriched genes were used to construct the signal-network with Cytoscape software. In addition, we integrated the relationship among lncRNAs-miRNAs-mRNAs to create a competing endogenous RNA network. Finally, mRNAs that were associated with patient prognosis in epithelial ovarian cancer were selected using univariate Cox regression analysis. A total of 2,225 mRNAs, 336 lncRNAs, and 14 miRNAs were shown to be differentially expressed in epithelial ovarian cancer compared with normal tissues. The dysregulated mRNAs were primarily enriched in cell division and signal transduction, according to Gene Ontology, whereas, according to KEGG, they were primarily enriched in metabolic pathways and pathways in cancer. A total of 10 mRNAs were associated with patient prognosis in ovarian cancer. This study identifies a novel lncRNA-miRNA-mRNA network, which may suggest potential molecular mechanisms underlying the development of epithelial ovarian cancer, providing new insights for survival prediction and interventional strategies for epithelial ovarian cancer.
, , and as Prognostic Factors in Head and Neck Squamous Cell Carcinoma Are Involved in the Remodeling of the Tumor Microenvironment.
Meng Liangliang,He Xiaoxi,Hong Quan,Qiao Bo,Zhang Xiao,Wu Bin,Zhang Xiaobo,Wei Yingtian,Li Jing,Ye Zhaoxiang,Xiao Yueyong
Frontiers in oncology
The tumor microenvironment (TME) plays a critical role in the initiation and progression of cancer. However, the specific mechanism of its regulation in head and neck squamous cell carcinoma (HNSCC) remains unclear. In this study, we first applied the ESTIMATE method to calculate the immune and stromal scores in patients' tumor tissues from The Cancer Genome Atlas (TCGA) database. GSE41613, GSE30784, and GSE37991 data sets from the Gene Expression Omnibus (GEO) database were recruited for further validation. Differentially expressed genes (DEGs) were identified and then analyzed by Cox regression analysis and protein-protein interaction (PPI) network construction. DEGs significantly associated with prognosis and TME will be identified as hub genes. These genes were also validated at the protein level by immunohistochemical analysis of 10 pairs of primary tumor tissues and the adjacent normal tissues from our institution. The relationship between hub genes expression and immune cell fraction estimated by CIBERSORT software was also examined. 275 DEGs were significantly associated with TME. , and have then identified as hub genes by intersection Cox and PPI analysis. Further investigation revealed that the expression of , and was negatively correlated with clinicopathological characteristics (clinical stage, T stage) and positively associated with survival in HNSCC patients, especially in male patients. The expression of and was lower in males than in females. and were differentially expressed in tumor tissues than normal tissues, and the results were validated at the protein level by immunohistochemistry experiments. Gene set enrichment analysis (GSEA) showed that the high expression groups' hub genes were mainly enriched for immune-related activities. In the low-expression groups, genes were primarily enriched in metabolic pathways. CIBERSORT results showed that the expression of these genes was all negatively correlated with the fraction of memory B cells and positively correlated with the fraction of the other four cells, including naive B cells, resting T cells CD4 memory, T cells follicular helper, and T cells regulatory (Tregs). The results suggest that , and may be responsible for maintaining the immune dominance of TME, thus leading to a better prognosis.
Bioinformatics analysis of DNMT1 expression and its role in head and neck squamous cell carcinoma prognosis.
Cui Jili,Zheng Lian,Zhang Yuanyuan,Xue Miaomiao
Head and neck squamous cell carcinoma (HNSCC) is the sixth most common type of malignancy in the world. DNA cytosine-5-methyltransferase 1 (DNMT1) play key roles in carcinogenesis and regulation of the immune micro-environment, but the gene expression and the role of DNMT1 in HNSCC is unknown. In this study, we utilized online tools and databases for pan-cancer and HNSCC analysis of DNMT1 expression and its association with clinical cancer characteristics. We also identified genes that positively and negatively correlated with DNMT1 expression and identified eight hub genes based on protein-protein interaction (PPI) network analysis. Enrichment analyses were performed to explore the biological functions related with of DNMT1. The Tumor Immune Estimation Resource (TIMER) database was performed to explore the relationship between DNMT1 expression and immune-cell infiltration. We demonstrated that DNMT1 gene expression was upregulated in HNSCC and associated with poor prognosis. Based on analysis of the eight hub genes, we determined that DNMT1 may be involved in cell cycle, proliferation and metabolic related pathways. We also found that significant difference of B cells infiltration based on TP 53 mutation. These findings suggest that DNMT1 related epigenetic alterations have close relationship with HNSCC progression, and DNMT1 could be a novel diagnostic biomarker and a promising therapeutic target for HNSCC.
Identification of Mitochondrial-Related Prognostic Biomarkers Associated With Primary Bile Acid Biosynthesis and Tumor Microenvironment of Hepatocellular Carcinoma.
Zhang Tao,Nie Yingli,Gu Jian,Cai Kailin,Chen Xiangdong,Li Huili,Wang Jiliang
Frontiers in oncology
Hepatocellular carcinoma (HCC) is one of the leading causes of tumor-associated deaths worldwide. Despite great progress in early diagnosis and multidisciplinary tumor management, the long-term prognosis of HCC remains poor. Currently, metabolic reprogramming during tumor development is widely observed to support rapid growth and proliferation of cancer cells, and several metabolic targets that could be used as cancer biomarkers have been identified. The liver and mitochondria are the two centers of human metabolism at the whole organism and cellular levels, respectively. Thus, identification of prognostic biomarkers based on mitochondrial-related genes (Mito-RGs)-the coding-genes of proteins located in the mitochondria-that reflect metabolic changes associated with HCC could lead to better interventions for HCC patients. In the present study, we used HCC data from The Cancer Genome Atlas (TCGA) database to construct a classifier containing 10 Mito-RGs (ACOT7, ADPRHL2, ATAD3A, BSG, FAM72A, PDK3, PDSS1, RAD51C, TOMM34, and TRMU) for predicting the prognosis of HCC by using 10-fold Least Absolute Shrinkage and Selection Operation (LASSO) cross-validation Cox regression. Based on the risk score calculated by the classifier, the samples were divided into high- and low-risk groups. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), t-distributed stochastic neighbor embedding (t-SNE), and consensus clusterPlus algorithms were used to identify metabolic pathways that were significantly different between the high- and low-risk groups. We further investigated the relationship between metabolic status and infiltration of immune cells into HCC tumor samples by using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm combined with the Tumor Immune Estimation Resource (TIMER) database. Our results showed that the classifier based on Mito-RGs could act as an independent biomarker for predicting survival of HCC patients. Repression of primary bile acid biosynthesis plays a vital role in the development and poor prognosis of HCC, which provides a potential approach to treatment. Our study revealed cross-talk between bile acid and infiltration of tumors by immune cells, which may provide novel insight into immunotherapy of HCC. Furthermore, our research may provide a novel method for HCC metabolic therapy based on modulation of mitochondrial function.
A Metabolic Gene Signature to Predict Overall Survival in Head and Neck Squamous Cell Carcinoma.
Wu Zeng-Hong,Tang Yun,Zhou Yue
Mediators of inflammation
Background:Head and neck squamous cell carcinoma (HNSCC) is a common malignancy that emanates from the lips, mouth, paranasal sinuses, oropharynx, larynx, nasopharynx, and from other pharyngeal cancers. The availability of high-throughput expression data has made it possible to use global gene expression data to analyze the relationship between metabolic-related gene expression and clinical outcomes in HNSCC patients. Method:In this study, we used RNA sequencing (RNA-seq) data from the cancer genome atlas (TCGA), with validation in the GEO dataset to profile the metabolic microenvironment and define potential biomarkers for metabolic therapy. Results:We extracted data for 529 patients and 327 metabolic genes (198 upregulated and 129 downregulated genes) in the TCGA database. Carbonic anhydrase 9 (CA9) and CA6 had the largest logFCs in the upregulated and downregulated genes, respectively. Our Cox regression model data showed 51 prognostic-related genes with lysocardiolipin acyltransferase 1 () and choline dehydrogenase () being associated with the highest risk (HR = 1.144, 95% CI = 1.044 ~ 1.251) and the lowest risk (HR = 0.580, 95% CI = 0.400 ~ 0.839) in HNSCC, respectively. We next used the ROC curve to evaluate whether the differentially expressed metabolic-related genes could serve as early predictors of HNSCC. The findings showed an AUC of 0.745 and 0.618 in the TCGA and GEO analysis, respectively. Besides, the ability for the genes to predict clinicopathological HNSCC status was analyzed and the data showed that the AUC for age, gender, grade, stage, T, M, and N was 0.520, 0.495, 0.568, 0.606, 0.577, 0.476, and 0.673, respectively, in the TCGA dataset. On the other hand, the AUC for age, gender, stage, T, M, N, smoking, and HPV16-pos was 0.599, 0.531, 0.622, 0.606, 0.616, 0.550, 0.614, 0.519, and 0.397, respectively, in the GEO dataset. Conclusion:Taken together, our study unearths a novel metabolic gene signature for the prediction of HNSCC prognosis based on the TCGA dataset. Our signature might point out the metabolic microenvironment disorders and provides potential treatment targets and prognostic biomarkers.
Comprehensive analysis of the association between tumor glycolysis and immune/inflammation function in breast cancer.
Li Wenhui,Xu Ming,Li Yu,Huang Ziwei,Zhou Jun,Zhao Qiuyang,Le Kehao,Dong Fang,Wan Cheng,Yi Pengfei
Journal of translational medicine
BACKGROUND:Metabolic reprogramming, immune evasion and tumor-promoting inflammation are three hallmarks of cancer that provide new perspectives for understanding the biology of cancer. We aimed to figure out the relationship of tumor glycolysis and immune/inflammation function in the context of breast cancer, which is significant for deeper understanding of the biology, treatment and prognosis of breast cancer. METHODS:Using mRNA transcriptome data, tumor-infiltrating lymphocytes (TILs) maps based on digitized H&E-stained images and clinical information of breast cancer from The Cancer Genome Atlas projects (TCGA), we explored the expression and prognostic implications of glycolysis-related genes, as well as the enrichment scores and dual role of different immune/inflammation cells in the tumor microenvironment. The relationship between glycolysis activity and immune/inflammation function was studied by using the differential genes expression analysis, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene set enrichment analyses (GSEA) and correlation analysis. RESULTS:Most glycolysis-related genes had higher expression in breast cancer compared to normal tissue. Higher phosphoglycerate kinase 1 (PGK1) expression was associated with poor prognosis. High glycolysis group had upregulated immune/inflammation-related genes expression, upregulated immune/inflammation pathways especially IL-17 signaling pathway, higher enrichment of multiple immune/inflammation cells such as Th2 cells and macrophages. However, high glycolysis group was associated with lower infiltration of tumor-killing immune cells such as NKT cells and higher immune checkpoints expression such as PD-L1, CTLA4, FOXP3 and IDO1. CONCLUSIONS:In conclusion, the enhanced glycolysis activity of breast cancer was associated with pro-tumor immunity. The interaction between tumor glycolysis and immune/inflammation function may be mediated through IL-17 signaling pathway.
Prognostic value of Glypican family genes in early-stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy and possible mechanisms.
Liu Jun-Qi,Liao Xi-Wen,Wang Xiang-Kun,Yang Cheng-Kun,Zhou Xin,Liu Zheng-Qian,Han Quan-Fa,Fu Tian-Hao,Zhu Guang-Zhi,Han Chuang-Ye,Su Hao,Huang Jian-Lu,Ruan Guo-Tian,Yan Ling,Ye Xin-Ping,Peng Tao
BACKGROUND:This study explored the prognostic significance of Glypican (GPC) family genes in patients with pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). METHODS:A total of 112 PDAC patients from TCGA and 48 patients from GEO were included in the analysis. The relationship between overall survival and the expression of GPC family genes as well as basic clinical characteristics was analyzed using the Kaplan-Meier method with the log-rank test. Joint effects survival analysis was performed to further examine the relationship between GPC genes and prognosis. A prognosis nomogram was established based on clinical characteristics and prognosis-related genes. Prognosis-related genes were investigated by genome-wide co-expression analysis and gene set enrichment analysis (GSEA) was carried out to identify potential mechanisms of these genes affecting prognosis. RESULTS:In TCGA database, high expression of GPC2, GPC3, and GPC5 was significantly associated with favorable survival (log-rank P = 0.031, 0.021, and 0.028, respectively; adjusted P value = 0.005, 0.022, and 0.020, respectively), and joint effects analysis of these genes was effective for prognosis prediction. The prognosis nomogram was applied to predict the survival probability using the total scores calculated. Genome-wide co-expression and GSEA analysis suggested that the GPC2 may affect prognosis through sequence-specific DNA binding, protein transport, cell differentiation and oncogenic signatures (KRAS, RAF, STK33, and VEGFA). GPC3 may be related to cell adhesion, angiogenesis, inflammatory response, signaling pathways like Ras, Rap1, PI3K-Akt, chemokine, GPCR, and signatures like cyclin D1, p53, PTEN. GPC5 may be involved in transcription factor complex, TFRC1, oncogenic signatures (HOXA9 and BMI1), gene methylation, phospholipid metabolic process, glycerophospholipid metabolism, cell cycle, and EGFR pathway. CONCLUSION:GPC2, GPC3, and GPC5 expression may serve as prognostic indicators in PDAC, and combination of these genes showed a higher efficiency for prognosis prediction.
Subgroup-specific prognostic signaling and metabolic pathways in pediatric medulloblastoma.
Park Ae Kyung,Lee Ji Yeoun,Cheong Heesun,Ramaswamy Vijay,Park Sung-Hye,Kool Marcel,Phi Ji Hoon,Choi Seung Ah,Cavalli Florence,Taylor Michael D,Kim Seung-Ki
BACKGROUND:Using a pathway-focused approach, we aimed to provide a subgroup-specific basis for finding novel therapeutic strategies and further refinement of the risk stratification in pediatric medulloblastoma. METHOD:Based on genome-wide Cox regression and Gene Set Enrichment Analysis, we investigated prognosis-related signaling pathways and core genes in pediatric medulloblastoma subgroups using 530 patient data from Medulloblastoma Advanced Genomic International Consortium (MAGIC) project. We further examined the relationship between expression of the prognostic core genes and frequent chromosome aberrations using broad range copy number change data. RESULTS:In SHH subgroup, relatively high expression of the core genes involved in p53, PLK1, FOXM1, and Aurora B signaling pathways are associated with poor prognosis, and their average expression synergistically increases with co-occurrence of losses of 17p, 14q, or 10q, or gain of 17q. In Group 3, in addition to high MYC expression, relatively elevated expression of PDGFRA, IGF1R, and FGF2 and their downstream genes in PI3K/AKT and MAPK/ERK pathways are related to poor survival outcome, and their average expression is increased with the presence of isochromosome 17q [i(17q)] and synergistically down-regulated with simultaneous losses of 16p, 8q, or 4q. In Group 4, up-regulation of the genes encoding various immune receptors and those involved in NOTCH, NF-κB, PI3K/AKT, or RHOA signaling pathways are associated with worse prognosis. Additionally, the expressions of Notch genes correlate with those of the prognostic immune receptors. Besides the Group 4 patients with previously known prognostic aberration, loss of chromosome 11, those with loss of 8q but without i(17q) show excellent survival outcomes and low average expression of the prognostic core genes whereas those harboring 10q loss, 1q gain, or 12q gain accompanied by i(17q) show bad outcomes. Finally, several metabolic pathways known to be reprogrammed in cancer cells are detected as prognostic pathways including glutamate metabolism in SHH subgroup, pentose phosphate pathway and TCA cycle in Group 3, and folate-mediated one carbon-metabolism in Group 4. CONCLUSIONS:The results underscore several subgroup-specific pathways for potential therapeutic interventions: SHH-GLI-FOXM1 pathway in SHH subgroup, receptor tyrosine kinases and their downstream pathways in Group 3, and immune and inflammatory pathways in Group 4.
Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer.
Lin Dagui,Fan Wenhua,Zhang Rongxin,Zhao Enen,Li Pansong,Zhou Wenhao,Peng Jianhong,Li Liren
Journal of translational medicine
BACKGROUND:Metabolic reprograming have been associated with cancer occurrence and progression within the tumor immune microenvironment. However, the prognostic potential of metabolism-related genes in colorectal cancer (CRC) has not been comprehensively studied. Here, we investigated metabolic transcript-related CRC subtypes and relevant immune landscapes, and developed a metabolic risk score (MRS) for survival prediction. METHODS:Metabolism-related genes were collected from the Molecular Signatures Database and metabolic subtypes were identified using an unsupervised clustering algorithm based on the expression profiles of survival-related metabolic genes in GSE39582. The ssGSEA and ESTIMATE methods were applied to estimate the immune infiltration among subtypes. The MRS model was developed using LASSO Cox regression in the GSE39582 dataset and independently validated in the TCGA CRC and GSE17537 datasets. RESULTS:We identified two metabolism-related subtypes (cluster-A and cluster-B) of CRC based on the expression profiles of 539 survival-related metabolic genes with distinct immune profiles and notably different prognoses. The cluster-B subtype had a shorter OS and RFS than the cluster-A subtype. Eighteen metabolism-related genes that were mostly involved in lipid metabolism pathways were used to build the MRS in GSE39582. Patients with higher MRS had worse prognosis than those with lower MRS (HR 3.45, P < 0.001). The prognostic role of MRS was validated in the TCGA CRC (HR 2.12, P = 0.00017) and GSE17537 datasets (HR 2.67, P = 0.039). Time-dependent receiver operating characteristic curve and stratified analyses revealed the robust predictive ability of the MRS in each dataset. Multivariate Cox regression analysis indicted that the MRS could predict OS independent of TNM stage and age. CONCLUSIONS:Our study provides novel insight into metabolic heterogeneity and its relationship with immune landscape in CRC. The MRS was identified as a robust prognostic marker and may facilitate individualized therapy for CRC patients.
Identified lung adenocarcinoma metabolic phenotypes and their association with tumor immune microenvironment.
Wu Xian-Ning,Su Dan,Mei Yi-De,Xu Mei-Qing,Zhang Hao,Wang Ze-Yu,Li Li-Ling,Peng Li,Jiang Jun-Yi,Yang Jia-Yi,Li Dong-Jie,Cao Hui,Xia Zhi-Wei,Zeng Wen-Jing,Cheng Quan,Zhang Nan
Cancer immunology, immunotherapy : CII
BACKGROUND:Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), causes high mortality around the world. Previous studies have suggested that the metabolic pattern of tumor is associated with tumor response to immunotherapy and patient's survival outcome. Yet, this relationship in LUAD is still unknown. METHODS:Therefore, in this study, we identified the immune landscape in different tumor subtypes classified by metabolism-related genes expression with a large-scale dataset (tumor samples, n = 2181; normal samples, n = 419). We comprehensively correlated metabolism-related phenotypes with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in LUAD patients. RESULTS:And we confirmed tumors with activated lipid metabolism tend to have higher immunocytes infiltration and better response to checkpoint immunotherapy. This work highlights the connection between the metabolic pattern of tumor and tumor immune infiltration in LUAD. A scoring system based on metabolism-related gene expression is not only able to predict prognosis of patient with LUAD but also applied to pan-cancer. LUAD response to checkpoint immunotherapy can also be predicted by this scoring system. CONCLUSIONS:This work revealed the significant connection between metabolic pattern of tumor and tumor immune infiltration, regulating LUAD patients' response to immunotherapy.
Identification of Prognosis-Related Genes in Bladder Cancer Microenvironment across TCGA Database.
Zhao Xin,Tang Yu,Ren Haoyu,Lei Yi
BioMed research international
Background:Bladder cancer (BCa) is a common urothelial malignancy. The Cancer Genome Atlas (TCGA) database allows for an opportunity to analyze the relationship between gene expression and clinical outcomes in bladder cancer patients. This study is aimed at identifying prognosis-related genes in the bladder cancer microenvironment. Methods:Immune scores and stromal scores were calculated by applying the ESTIMATE algorithm. We divided bladder cancer patients into high and low groups based on their immune/stromal scores. Then, differentially expressed genes (DEGs) were identified in bladder cancer patients based on the TCGA database. We evaluated the correlation between immune/stromal scores and clinical characteristics as well as prognosis. Finally, we validated identified genes associated with bladder cancer prognosis through a cohort study in the Gene Expression Omnibus (GEO) database. Results:A higher stromal score was associated with female (vs. male = 0.037), age > 65 (vs.age ≤ 65 = 0.015), T3/4 (vs. T1/2, < 0.001), N status( = 0.016), and pathological high grade (vs. low grade < 0.001). By analyzing DEGs, there were 1125 genes commonly upregulated, and 209 genes were commonly downregulated. Protein-protein interaction networks further showed the important protein that may be involved in the biological behavior and prognosis of BCa, such as FN1, CXCL12, CD3E, LCK, and ZAP70. A total of 14 DEGs were found to be associated with overall survival of bladder cancer. After validation by a cohort of 165 BCa cases with detailed follow-up information from GSE13507, 10 immune-associated DEGs were demonstrated to be predictive of prognosis in BCa. Among them, 5 genes have not been reported previously associated with the prognosis of BCa, including BTBD16, OLFML2B, PRRX1, SPINK4, and SPON2. Conclusions:Our study elucidated tight associations between stromal score and clinical characteristics as well as prognosis in BCa. Moreover, we obtained a group of genes closely related to the prognosis of BCa in the tumor microenvironment.
Integrative genomics analysis of hub genes and their relationship with prognosis and signaling pathways in esophageal squamous cell carcinoma.
Chen Fang-Fang,Zhang Shi-Rong,Peng Hao,Chen Yun-Zhao,Cui Xiao-Bin
Molecular medicine reports
The main purpose of the present study was to recognize the integrative genomics analysis of hub genes and their relationship with prognosis and signaling pathways in esophageal squamous cell carcinoma (ESCC). The mRNA gene expression profile data of GSE38129 were downloaded from the Gene Expression Omnibus database, which included 30 ESCC and 30 normal tissue samples. The differentially expressed genes (DEGs) between ESCC and normal samples were identified using the GEO2R tool. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify the functions and related pathways of the genes. The protein‑protein interaction (PPI) network of these DEGs was constructed with the Search Tool for the Retrieval of Interacting Genes and visualized with a molecular complex detection plug‑in via Cytoscape. The top five important modules were selected from the PPI network. A total of 928 DEGs, including ephrin‑A1 (EFNA1), collagen type IV α1 (COL4A1), C‑X‑C chemokine receptor 2 (CXCR2), adrenoreceptor β2 (ADRB2), P2RY14, BUB1B, cyclin A2 (CCNA2), checkpoint kinase 1 (CHEK1), TTK, pituitary tumor transforming gene 1 (PTTG1) and COL5A1, including 498 upregulated genes, were mainly enriched in the 'cell cycle', 'DNA replication' and 'mitotic nuclear division', whereas 430 downregulated genes were enriched in 'oxidation‑reduction process', 'xenobiotic metabolic process' and 'cell‑cell adhesion'. The KEGG analysis revealed that 'ECM‑receptor interaction', 'cell cycle' and 'p53 signaling pathway' were the most relevant pathways. According to the degree of connectivity and adjusted P‑value, eight core genes were selected, among which those with the highest correlation were CHEK1, BUB1B, PTTG1, COL4A1 and CXCR2. Gene Expression Profiling Interactive Analysis in The Cancer Genome Atlas database for overall survival (OS) was applied among these genes and revealed that EFNA1 and COL4A1 were significantly associated with a short OS in 182 patients. Immunohistochemical results revealed that the expression of PTTG1 in esophageal carcinoma tissues was higher than that in normal tissues. Therefore, these genes may serve as crucial predictors for the prognosis of ESCC.
Metabolic classification of bladder cancer based on multi-omics integrated analysis to predict patient prognosis and treatment response.
Tang Chaozhi,Yu Meng,Ma Jiakang,Zhu Yuyan
Journal of translational medicine
BACKGROUND:Currently, no molecular classification is established for bladder cancer based on metabolic characteristics. Therefore, we conducted a comprehensive analysis of bladder cancer metabolism-related genes using multiple publicly available datasets and aimed to identify subtypes according to distinctive metabolic characteristics. METHODS:RNA-sequencing data of The Cancer Genome Atlas were subjected to non-negative matrix fractionation to classify bladder cancer according to metabolism-related gene expression; Gene Expression Omnibus and ArrayExpress datasets were used as validation cohorts. The sensitivity of metabolic types to predicted immunotherapy and chemotherapy was assessed. Kaplan-Meier curves were plotted to assess patient survival. Differentially expressed genes between subtypes were identified using edgeR. The differences among identified subtypes were compared using the Kruskal-Wallis non-parametric test. To better clarify the subtypes of bladder cancer, their relationship with clinical characteristics was examined using the Fisher's test. We also constructed a risk prediction model using the random survival forest method to analyze right-censored survival data based on key metabolic genes. To identify genes of prognostic significance, univariate Cox regression, lasso analysis, and multivariate regression were performed sequentially. RESULTS:Three bladder cancer subtypes were identified according to the expression of metabolism-related genes. The M1 subtype was characterized by high metabolic activity, low immunogenicity, and better prognosis. M2 exhibited moderate metabolic activity, high immunogenicity, and the worst prognosis. M3 was associated with low metabolic activity, low immunogenicity, and poor prognosis. M1 showed the best predicted response to immunotherapy, whereas patients with M1 were predicted to be the least sensitive to cisplatin. By contrast, M2 showed the worst predicted response to immunotherapy but was predicted to be more sensitive to cisplatin, doxorubicin, and other first-line anticancer drugs. M3 was the most sensitive to gemcitabine. The risk model based on metabolic genes effectively predicted the prognosis of bladder cancer patients. CONCLUSIONS:Metabolic classification of bladder cancer has potential clinical value and therapeutic feasibility by inhibiting the associated pathways. This classification can provide valuable insights for developing precise bladder cancer treatment.
Eight-gene metabolic signature related with tumor-associated macrophages predicting overall survival for hepatocellular carcinoma.
Huo Junyu,Wu Liqun,Zang Yunjin,Dong Hongjing,Liu Xiaoqiang,He Fu,Zhang Xiao
BACKGROUND:In recent years, the relationship between tumor-associated macrophages (TAMs) and solid tumors has become a research hotspot. This study aims to explore the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma (HCC) to provide new methods of treatment for HCC. METHODS:The study selected 343 HCC patients with complete survival information (survival time > = 1 month) in the Cancer Genome Atlas (TCGA) as study subjects. Kaplan-Meier survival analysis assisted in determining the relationship between macrophage infiltration and overall survival (OS), and Pearson correlation tests were used to identify metabolic reprogramming genes (MRGs) associated with tumor macrophage abundance. Lasso regression algorithms were used on prognosis-related MRGs identified by Kaplan-Meier survival analysis and univariate Cox regression analysis to construct a risk score; another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) was used to verify prognostic signature externally. RESULTS:A risk score composed of 8 metabolic genes could accurately predict the OS of a training cohort (TCGA) and a testing cohort (ICGC). The risk score could be widely used for people with different clinical characteristics, and it is a predictor that is independent of other clinical factors that affect prognosis. As expected, compared with the low-risk group, the high-risk group exhibited an obviously higher macrophage abundance, together with a positive correlation between the risk score and the expression levels of three commonly used immune checkpoints (PD1, PDL1, and CTLA4). CONCLUSION:Our study constructed and validated a novel eight-gene signature for predicting HCC patient OS, which may contribute to clinical treatment decisions.
Integrated Analysis of the Functions and Prognostic Values of RNA Binding Proteins in Lung Squamous Cell Carcinoma.
Li Wei,Li Xue,Gao Li-Na,You Chong-Ge
Frontiers in genetics
Lung cancer is the leading cause of cancer-related deaths worldwide. Dysregulation of RNA binding proteins (RBPs) has been found in a variety of cancers and is related to oncogenesis and progression. However, the functions of RBPs in lung squamous cell carcinoma (LUSC) remain unclear. In this study, we obtained gene expression data and corresponding clinical information for LUSC from The Cancer Genome Atlas (TCGA) database, identified aberrantly expressed RBPs between tumors and normal tissue, and conducted a series of bioinformatics analyses to explore the expression and prognostic value of these RBPs. A total of 300 aberrantly expressed RBPs were obtained, comprising 59 downregulated and 241 upregulated RBPs. Functional enrichment analysis indicated that the differentially expressed RBPs were mainly associated with mRNA metabolic processes, RNA processing, RNA modification, regulation of translation, the TGF-beta signaling pathway, and the Toll-like receptor signaling pathway. Nine RBP genes (, and ) were identified as prognosis-associated hub genes by univariate, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier survival, and multivariate Cox regression analyses, and were used to construct the prognostic model. Further analysis demonstrated that high risk scores for patients were significantly related to poor overall survival according to the model. The area under the time-dependent receiver operator characteristic curve of the prognostic model was 0.712 at 3 years and 0.696 at 5 years. We also developed a nomogram based on nine RBP genes, with internal validation in the TCGA cohort, which showed a favorable predictive efficacy for prognosis in LUSC. Our results provide novel insights into the pathogenesis of LUSC. The nine-RBP gene signature showed predictive value for LUSC prognosis, with potential applications in clinical decision-making and individualized treatment.
Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung cancers.
Wang You-Tyun,Lin Min-Ru,Chen Wei-Chen,Wu Wu-Hsiung,Wang Feng-Sheng
FEBS open bio
Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint-based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA-Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue-specific genome-scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well-known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin-converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS-CoV-2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment.
Investigation of miR-136-5p key target genes and pathways in lung squamous cell cancer based on TCGA database and bioinformatics analysis.
Xie Zu-Cheng,Li Tian-Tian,Gan Bin-Liang,Gao Xiang,Gao Li,Chen Gang,Hu Xiao-Hua
Pathology, research and practice
BACKGROUND:Lung squamous cell cancer (LUSC) is a common but challenging malignancy. It is important to illuminate the molecular mechanism of LUSC. Thus, we aim to explore the molecular mechanism of miR-136-5p in relation to LUSC. METHODS:We used the Cancer Genome Atlas (TCGA) database to investigate the expression of miR-136-5p in relation to LUSC. Then, we identified the possible miR-136-5p target genes through intersection of the predicted miR-136-5p target genes and LUSC upregulated genes from TCGA. Bioinformatics analysis was performed to determine the key miR-136-5p targets and pathways associated with LUSC. Finally, the expression of hub genes, correlation between miR-136-5p and hub genes, and expected significance of hub genes were evaluated via the TCGA and Genotype-Tissue Expression (GTEx) project. RESULTS:MiR-136-5p was significantly downregulated in LUSC patients. Glucuronidation, glucuronosyltransferase, and the retinoic acid metabolic process were the most enriched metabolic interactions in LUSC patients. Ascorbate and aldarate metabolism, pentose and glucuronate interconversions, and retinol metabolism were identified as crucial pathways. Seven hub genes (UGT1A1, UGT1A3, UGT1A6, UGT1A7, UGT1A10, SRD5A1, and ADH7) were found to be upregulated, and UGT1A1, UGT1A3, UGT1A6, UGT1A7, and ADH7 were negatively correlated with miR-136-5p. UGT1A7 and ADH7 were the most significantly involved miR-136-5p target genes, and high expression of these genes was correlated with better overall survival and disease-free survival of LUSC patients. CONCLUSIONS:Downregulated miR-136-5p may target UGT1A7 and ADH7 and participate in ascorbate and aldarate metabolism, pentose and glucuronate interconversions, and retinol metabolism. High expression of UGT1A7 and ADH7 may indicate better prognosis of LUSC patients.
Comprehensive analysis of tumor microenvironment and identification of an immune signature to predict the prognosis and immunotherapeutic response in lung squamous cell carcinoma.
Wu Jinlong,Xu Chengfeng,Guan Xin,Ni Da,Yang Xuhui,Yang Zhiyin,Wang Mingsong
Annals of translational medicine
Background:Tumor mutation burden (TMB) and immune microenvironment are important determinants of prognosis and immunotherapeutic efficacy for cancer patients. The aim of the present study was to develop an immune signature to effectively predict prognosis and immunotherapeutic response in patients with lung squamous cell carcinoma (LUSC). Methods:TMB and immune microenvironment characteristics were comprehensively analyzed by multi-omics data in LUSC. The immune signature was further constructed and validated in multiple independent datasets by LASSO Cox regression analysis. Next, the value of immune signature in predicting the response of immunotherapy was evaluated. Finally, the possible mechanism of immune signature was also investigated. Results:A novel immune signature based on 5 genes was constructed and validated to predict the prognosis of LUSC patients. These genes were filamin-C, Rho family GTPase 1, interleukin 4-induced gene-1, transglutaminase 2, and prostaglandin I2 synthase. High-risk patients had significantly poorer survival than low-risk patients. A nomogram was also developed based on the immune signature and tumor stage, which showed good application. Furthermore, we found that the immune signature had a significant correlation with immune checkpoint, microsatellite instability, tumor infiltrating lymphocytes, cytotoxic activity scores, and T-cell-inflamed score, suggesting low-risk patients are more likely to benefit from immunotherapy. Finally, functional enrichment and pathway analyses revealed several significantly enriched immune-related biological processes and metabolic pathways. Conclusions:In the present study, we developed a novel immune signature that could predict prognosis and immunotherapeutic response in LUSC patients. The results not only help identify LUSC patients with poor survival, but also increase our understanding of the immune microenvironment and immunotherapy in LUSC.
Upregulation of HOXA13 as a potential tumorigenesis and progression promoter of LUSC based on qRT-PCR and bioinformatics.
Zhang Rui,Deng Yun,Zhang Yu,Zhai Gao-Qiang,He Rong-Quan,Hu Xiao-Hua,Wei Dan-Ming,Feng Zhen-Bo,Chen Gang
International journal of clinical and experimental pathology
In this study, we investigated the levels of homeobox A13 (HOXA13) and the mechanisms underlying the co-expressed genes of HOXA13 in lung squamous cancer (LUSC), the signaling pathways in which the co-expressed genes of HOXA13 are involved and their functional roles in LUSC. The clinical significance of 23 paired LUSC tissues and adjacent non-tumor tissues were gathered. HOXA13 levels in LUSC were detected by quantitative real-time polymerase chain reaction (qRT-PCR). HOXA13 levels in LUSC from The Cancer Genome Atlas (TCGA) and Oncomine were analyzed. We performed receiver operator characteristic (ROC) curves of various clinicopathological features of LUSC. Co-expressed of HOXA13 were collected from MEM, cBioPortal and GEPIA. The functions and pathways of the most reliable overlapped genes were achieved from the Gene Otology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, respectively. The protein-protein interaction (PPI) networks were mapped using STRING. HOXA13 in LUSC were markedly upregulated compared with those in the non-cancerous controls as demonstrated by qRT-PCR (LUSC: 0.330±0.360; CONTROLS: 0.155±0.142; =0.021). TCGA (LUSC: 6.388±2.097, CONTROLS: 1.157±0.719; <0.001) and Hou's study from Oncomine (LUSC: 1.154±0.260; CONTROLS: 0.957±0.065; =0.001) showed the same tendency. Meanwhile, the area under the curve (AUC) of TNM was calculated as 0.877 with =0.002. Based on the HOXA13 expression data from TCGA, the ROC of the tissue types was calculated as AUC=0.971 (<0.001). In addition, 506 genes were filtered as co-expression genes of HOXA13. The 3 most significant KEGG pathways were metabolic pathways (=5.41E-15), the calcium signaling pathway (=3.01E-11), and the cAMP signaling pathway (=5.63E-11). MAPK1, GNG7, GNG12, PRKCA were selected as the hub genes. In conclusion, HOXA13 was upregulated and related to the TNM stage in LUSC. The expression of hub genes in LUSC might be deregulated by HOXA13. Moreover, the 4 co-expressed hub genes of HOXA13 might be crucial biomarkers for the diagnosis and prognosis of LUSC, as well as the development of novel therapeutic targets against LUSC.
Integrated multiomic analysis reveals comprehensive tumour heterogeneity and novel immunophenotypic classification in hepatocellular carcinomas.
Zhang Qi,Lou Yu,Yang Jiaqi,Wang Junli,Feng Jie,Zhao Yali,Wang Lin,Huang Xing,Fu Qihan,Ye Mao,Zhang Xiaozhen,Chen Yiwen,Ma Ce,Ge Hongbin,Wang Jianing,Wu Jiangchao,Wei Tao,Chen Qi,Wu Junqing,Yu Chengxuan,Xiao Yanyu,Feng Xinhua,Guo Guoji,Liang Tingbo,Bai Xueli
OBJECTIVE:Hepatocellular carcinoma (HCC) is heterogeneous, especially in multifocal tumours, which decreases the efficacy of clinical treatments. Understanding tumour heterogeneity is critical when developing novel treatment strategies. However, a comprehensive investigation of tumour heterogeneity in HCC is lacking, and the available evidence regarding tumour heterogeneity has not led to improvements in clinical practice. DESIGN:We harvested 42 samples from eight HCC patients and evaluated tumour heterogeneity using whole-exome sequencing, RNA sequencing, mass spectrometry-based proteomics and metabolomics, cytometry by time-of-flight, and single-cell analysis. Immunohistochemistry and quantitative polymerase chain reactions were performed to confirm the expression levels of genes. Three independent cohorts were further used to validate the findings. RESULTS:Tumour heterogeneity is considerable with regard to the genomes, transcriptomes, proteomes, and metabolomes of lesions and tumours. The immune status of the HCC microenvironment was relatively less heterogenous. Targeting local immunity could be a suitable intervention with balanced precision and practicability. By clustering immune cells in the HCC microenvironment, we identified three distinctive HCC subtypes with immunocompetent, immunodeficient, and immunosuppressive features. We further revealed the specific metabolic features and cytokine/chemokine expression levels of the different subtypes. Determining the expression levels of CD45 and Foxp3 using immunohistochemistry facilitated the correct classification of HCC patients and the prediction of their prognosis. CONCLUSION:There is comprehensive intratumoral and intertumoral heterogeneity in all dimensions of HCC. Based on the results, we propose a novel immunophenotypic classification of HCCs that facilitates prognostic prediction and may support decision making with regard to the choice of therapy.
Whole-genome sequencing reveals novel tandem-duplication hotspots and a prognostic mutational signature in gastric cancer.
Xing Rui,Zhou Yong,Yu Jun,Yu Yingyan,Nie Yongzhan,Luo Wen,Yang Chao,Xiong Teng,Wu William K K,Li Zhongwu,Bing Yang,Lin Shuye,Zhang Yaping,Hu Yingqi,Li Lin,Han Lijuan,Yang Chen,Huang Shaogang,Huang Suiping,Zhou Rui,Li Jing,Wu Kaichun,Fan Daiming,Tang Guangbo,Dou Jianhua,Zhu Zhenggang,Ji Jiafu,Fang Xiaodong,Lu Youyong
Genome-wide analysis of genomic signatures might reveal novel mechanisms for gastric cancer (GC) tumorigenesis. Here, we analysis structural variations (SVs) and mutational signatures via whole-genome sequencing of 168 GCs. Our data demonstrates diverse models of complex SVs operative in GC, which lead to high-level amplification of oncogenes. We find varying proportion of tandem-duplications (TDs) among individuals and identify 24 TD hotspots involving well-established cancer genes such as CCND1, ERBB2 and MYC. Specifically, we nominate a novel hotspot involving the super-enhancer of ZFP36L2 presents in approximately 10% GCs from different cohorts, the oncogenic role of which is further confirmed by experimental data. In addition, our data reveal a mutational signature, specifically occurring in noncoding region, significantly enriched in tumors with cadherin 1 mutations, and associated with poor prognoses. Collectively, our data suggest that TDs might serve as an important mechanism for cancer gene activation and provide a novel signature for stratification.
Tissue-specific and convergent metabolic transformation of cancer correlates with metastatic potential and patient survival.
Gaude Edoardo,Frezza Christian
Cancer cells undergo a multifaceted rewiring of cellular metabolism to support their biosynthetic needs. Although the major determinants of this metabolic transformation have been elucidated, their broad biological implications and clinical relevance are unclear. Here we systematically analyse the expression of metabolic genes across 20 different cancer types and investigate their impact on clinical outcome. We find that cancers undergo a tissue-specific metabolic rewiring, which converges towards a common metabolic landscape. Of note, downregulation of mitochondrial genes is associated with the worst clinical outcome across all cancer types and correlates with the expression of epithelial-to-mesenchymal transition gene signature, a feature of invasive and metastatic cancers. Consistently, suppression of mitochondrial genes is identified as a key metabolic signature of metastatic melanoma and renal cancer, and metastatic cell lines. This comprehensive analysis reveals unexpected facets of cancer metabolism, with important implications for cancer patients' stratification, prognosis and therapy.
Phosphoglucose isomerase gene expression as a prognostic biomarker of gastric cancer.
Huang Han-Chen,Wen Xian-Zi,Xue Hua,Chen Run-Sheng,Ji Jia-Fu,Xu Lei
Chinese journal of cancer research = Chung-kuo yen cheng yen chiu
Objective:Tumor heterogeneity renders identification of suitable biomarkers of gastric cancer (GC) challenging. Here, we aimed to identify prognostic genes of GC using computational analysis. Methods:We first used microarray technology to profile gene expression of GC and paired nontumor tissues from 198 patients. Based on these profiles and patients' clinical information, we next identified prognostic genes using novel computational approaches. Phosphoglucose isomerase, also known as glucose-6-phosphate isomerase (), which ranked first among 27 candidate genes, was further investigated by a new analytical tool namely enviro-geno-pheno-state (E-GPS) analysis. Suitability of as a prognostic marker, and its relationship with physiological processes such as metabolism, epithelial-mesenchymal transition (EMT), as well as drug sensitivity were evaluated using both our own and independent public datasets. Results:We found that higher expression of in GC correlated with prolonged survival of patients. Particularly, a combination of and expression effectively stratified the outcomes of patients with TNM stage II/III. Down-regulation of in tumor tissues correlated well with depressed glucose metabolism and fatty acid synthesis, as well as enhanced fatty acid oxidation and creatine metabolism, indicating that represents a suitable marker for increased probability of EMT in GC cells. Conclusions:Our findings strongly suggest that acts as a novel biomarker candidate for GC prognosis, allowing greatly enhanced clinical management of GC patients. The potential metabolic rewiring correlated with also provides new insights into studying the relationship between cancer metabolism and patient survival.
Glycolysis gene expression profilings screen for prognostic risk signature of hepatocellular carcinoma.
Jiang Longyang,Zhao Lan,Bi Jia,Guan Qiutong,Qi Aoshuang,Wei Qian,He Miao,Wei Minjie,Zhao Lin
Metabolic changes are the markers of cancer and have attracted wide attention in recent years. One of the main metabolic features of tumor cells is the high level of glycolysis, even if there is oxygen. The transformation and preference of metabolic pathways is usually regulated by specific gene expression. The aim of this study is to develop a glycolysis-related risk signature as a biomarker via four common cancer types. Only hepatocellular carcinoma was shown the strong relationship with glycolysis. The mRNA sequencing and chip data of hepatocellular carcinoma, breast invasive carcinoma, renal clear cell carcinoma, colorectal adenocarcinoma were included in the study. Gene set enrichment analysis was performed, profiling three glycolysis-related gene sets, it revealed genes associated with the biological process. Univariate and multivariate Cox proportional regression models were used to screen out prognostic-related gene signature. We identified six mRNAs (DPYSL4, HOMER1, ABCB6, CENPA, CDK1, STMN1) significantly associated with overall survival in the Cox proportional regression model for hepatocellular carcinoma. Based on this gene signature, we were able to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis showed that prognostic power of this six gene signature is independent of clinical variables. Further, we validated this data in our own 55 paired hepatocellular carcinoma and adjacent tissues. The results showed that these proteins were highly expressed in hepatocellular carcinoma tissues compared with adjacent tissue. The survival time of high-risk group was significantly shorter than that of low-risk group, indicating that high-risk group had poor prognosis. We calculated the correlation coefficients between six proteins and found that these six proteins were independent of each other. In conclusions, we developed a glycolysis-related gene signature that could predict survival in hepatocellular carcinoma patients. Our findings provide novel insight to the mechanisms of glycolysis and it is useful for identifying patients with hepatocellular carcinoma with poor prognoses.