Bioinformatic analysis and experimental validation of six cuproptosis-associated genes as a prognostic signature of breast cancer. PeerJ Background:Breast carcinoma (BRCA) is a life-threatening malignancy in women and shows a poor prognosis. Cuproptosis is a novel mode of cell death but its relationship with BRCA is unclear. This study attempted to develop a cuproptosis-relevant prognostic gene signature for BRCA. Methods:Cuproptosis-relevant subtypes of BRCA were obtained by consensus clustering. Differential expression analysis was implemented using the 'limma' package. Univariate Cox and multivariate Cox analyses were performed to determine a cuproptosis-relevant prognostic gene signature. The signature was constructed and validated in distinct datasets. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were also conducted using the prognostic signature to uncover the underlying molecular mechanisms. ESTIMATE and CIBERSORT algorithms were applied to probe the linkage between the gene signature and tumor microenvironment (TME). Immunotherapy responsiveness was assessed using the Tumor Immune Dysfunction and Exclusion (TIDE) web tool. Real-time quantitative PCR (RT-qPCR) was performed to detect the expressions of cuproptosis-relevant prognostic genes in breast cancer cell lines. Results:Thirty-eight cuproptosis-associated differentially expressed genes (DEGs) in BRCA were mined by consensus clustering and differential expression analysis. Based on univariate Cox and multivariate Cox analyses, six cuproptosis-relevant prognostic genes, namely SAA1, KRT17, VAV3, IGHG1, TFF1, and CLEC3A, were mined to establish a corresponding signature. The signature was validated using external validation sets. GSVA and GSEA showed that multiple cell cycle-linked and immune-related pathways along with biological processes were associated with the signature. The results ESTIMATE and CIBERSORT analyses revealed significantly different TMEs between the two Cusig score subgroups. Finally, RT-qPCR analysis of cell lines further confirmed the expressional trends of SAA1, KRT17, IGHG1, and CLEC3A. Conclusion:Taken together, we constructed a signature for projecting the overall survival of BRCA patients and our findings authenticated the cuproptosis-relevant prognostic genes, which are expected to provide a basis for developing prognostic molecular biomarkers and an in-depth understanding of the relationship between cuproptosis and BRCA. 10.7717/peerj.17419

Anoikis-related genes as potential prognostic biomarkers in gastric cancer: A multilevel integrative analysis and predictive therapeutic value. IET systems biology BACKGROUND:Gastric cancer (GC) is a frequent malignancy of the gastrointestinal tract. Exploring the potential anoikis mechanisms and pathways might facilitate GC research. PURPOSE:The authors aim to determine the significance of anoikis-related genes (ARGs) in GC prognosis and explore the regulatory mechanisms in epigenetics. METHODS:After describing the genetic and transcriptional alterations of ARGs, we searched differentially expressed genes (DEGs) from the cancer genome atlas and gene expression omnibus databases to identify major cancer marker pathways. The non-negative matrix factorisation algorithm, Lasso, and Cox regression analysis were used to construct a risk model, and we validated and assessed the nomogram. Based on multiple levels and online platforms, this research evaluated the regulatory relationship of ARGs with GC. RESULTS:Overexpression of ARGs is associated with poor prognosis, which modulates immune signalling and promotes anti-anoikis. The consistency of the DEGs clustering with weighted gene co-expression network analysis results and the nomogram containing 10 variable genes improved the clinical applicability of ARGs. In anti-anoikis mode, cytology, histology, and epigenetics could facilitate the analysis of immunophenotypes, tumour immune microenvironment (TIME), and treatment prognosis. CONCLUSION:A novel anoikis-related prognostic model for GC is constructed, and the significance of anoikis-related prognostic genes in the TIME and the metabolic pathways of tumours is initially explored. 10.1049/syb2.12088

Prognostic costimulatory molecule-related signature risk model correlates with immunotherapy response in colon cancer. Scientific reports Costimulatory molecules can promote the activation and proliferation of T cells and play an essential role in immunotherapy. However, their role in the prognosis of colon adenocarcinoma remains elusive. In this study, the expression data of costimulatory molecules and clinicopathological information of 429 patients with colon adenocarcinoma were obtained from The Cancer Genome Atlas database. The patients were divided into training and verification cohorts. Correlation, Cox regression, and Lasso regression analyses were performed to identify costimulatory molecules related to prognosis. After mentioning the construction of the risk mode, a nomogram integrating the clinical characteristics and risk scores of patients was constructed to predict prognosis. Eventually, three prognostic costimulatory molecules were identified and used for constructing a risk model. High expression of these three molecules indicated a poor prognosis. The predictive accuracy of the risk model was verified in the GSE17536 dataset. Subsequently, multivariate regression analysis showed that the signature based on the three costimulatory molecules was an independent risk factor in the training cohort (HR = 2.12; 95% CI = 1.26, 3.56). Based on the risk model and clinicopathological data, the AUC values for predicting the 1-, 3-, and 5-year survival probability of patients with colon adenocarcinoma were 0.77, 0.77, and 0.71, respectively. To the best of our knowledge, this study is the first to report a risk signature constructed based on the costimulatory molecules TNFRSF10c, TNFRSF13c, and TNFRSF11a. This risk signature can serve as a prognostic biomarker for colon adenocarcinoma and is related to the immunotherapeutic response of patients. 10.1038/s41598-023-27826-7

Development and Validation of a Novel Ferroptosis-Related Gene Signature for Predicting Prognosis and the Immune Microenvironment in Gastric Cancer. Wang Feng,Chen Cheng,Chen Wei-Peng,Li Zu-Ling,Cheng Hui BioMed research international Ferroptosis is a mode of regulated cell death that depends on iron and plays pivotal roles in regulating various biological processes in human cancers. However, the role of ferroptosis in gastric cancer (GC) remains unclear. In our study, a total of 2721 differentially expressed genes (DEGs) were filtered based on The Cancer Genome Atlas (TCGA) ( = 375) dataset. Weighted gene coexpression network (WGCNA) analysis was then used and identified 7 modules, of which the blue module with the most significant enrichment result was selected. By taking the intersections of the blue module and ferroptosis-related genes (FRGs), we obtained 23 common genes. Functional analysis was performed to explore the biological function of the genes of interest, and with univariate Cox regression (UCR) analysis, survival genes were screened to construct a prognostic model based on 3 genes (SLC1A5, ANGPTL4, and CGAS), which could play a role in predicting the survival of GC patients. UCR and multivariate Cox regression (MCR) analysis revealed that the prognostic index could be used as an independent prognostic indicator and validated using another GSE84437 dataset. Notably, patients in the high-risk group had higher mutation frequencies, such as TTN and TP53. TIMER analysis demonstrated that the risk score strongly correlated with macrophage and CD4+ T cell infiltration. In addition, the high- and low-risk groups illustrated different distributions of different immune statuses. Furthermore, the low-risk group had a higher immunophenoscore (IPS), which meant a better response to immune checkpoint inhibitors (ICIs). Finally, gene set enrichment analysis (GSEA) revealed several significant pathways involved in GC. In this study, a novel FRG signature was built that could predict GC prognosis and reflect the status of the tumor immune microenvironment. 10.1155/2021/6014202

Identification of the cuproptosis-related molecular subtypes and an immunotherapy prognostic model in hepatocellular carcinoma. BMC bioinformatics BACKGROUND:Cuproptosis, a newly discovered mode of cell death, has been less studied in hepatocellular carcinoma (HCC). Exploring the molecular characteristics of different subtypes of HCC based on cuproptosis-related genes (CRGs) is meaningful to HCC. In addition, immunotherapy plays a pivotal role in treating HCC. Exploring the sensitivity of immunotherapy and building predictive models are critical for HCC. METHODS:The 357 HCC samples from the TCGA database were classified into three subtypes, Cluster 1, Cluster 2, and Cluster 3, based on the expression levels of ten CRGs genes using consensus clustering. Six machine learning algorithms were used to build models that identified the three subtypes. The molecular features of the three subtypes were analyzed and compared from some perspectives. Moreover, based on the differentially expressed genes (DEGs) between Cluster 1 and Cluster 3, a prognostic scoring model was constructed using LASSO regression and Cox regression, and the scoring model was used to predict the efficacy of immunotherapy in the IMvigor210 cohort. RESULTS:Cluster 3 had the worst overall survival compared to Cluster 1 and Cluster 2 (P = 0.0048). The AUC of the Catboost model used to identify Cluster 3 was 0.959. Cluster 3 was significantly different from the other two subtypes in gene mutation, tumor mutation burden, tumor microenvironment, the expression of immune checkpoint inhibitor genes and N-methyladenosine regulatory genes, and the sensitivity to sorafenib. We believe Cluster 3 is more sensitive to immunotherapy from the above analysis results. Therefore, based on the DEGs between Cluster 1 and Cluster 3, we obtained a 7-gene scoring prognostic model, which achieved meaningful results in predicting immunotherapy efficacy in the IMvigor210 cohort (P = 0.013). CONCLUSIONS:Our study provides new ideas for molecular characterization and immunotherapy of HCC from machine learning and bioinformatics. Moreover, we successfully constructed a prognostic model of immunotherapy. 10.1186/s12859-022-04997-0