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    A nine-gene signature related to tumor microenvironment predicts overall survival with ovarian cancer. Ding Qi,Dong Shanshan,Wang Ranran,Zhang Keqiang,Wang Hui,Zhou Xiao,Wang Jing,Wong Kee,Long Ying,Zhu Shuai,Wang Weigang,Ren Huayi,Zeng Yong Aging Mounting evidence suggests that immune cell infiltration within the tumor microenvironment (TME) is a crucial regulator of carcinogenesis and therapeutic efficacy in ovarian cancer (OC). In this study, 593 OC patients from TCGA were divided into high and low score groups based on their immune/stromal scores resulting from analysis utilizing the ESTIMATE algorithm. Differential expression analysis revealed 294 intersecting genes that influencing both the immune and stromal scores. Further Cox regression analysis identified 34 differentially expressed genes (DEGs) as prognostic-related genes. Finally, the nine-gene signature was derived from the prognostic-related genes using a Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. This nine-gene signature could effectively distinguish the high-risk patients in the training (TCGA database) and validation (GSE17260) cohorts (all p < 0.01). A time-dependent receiver operating characteristic (ROC) analysis showed that the nine-gene signature had a reasonable predictive accuracy (AUC = 0.707, AUC =0.696) in both cohorts. In addition, this nine-gene signature is associated with immune infiltration in TME by Gene Set Variation Analysis (GSVA), and can be used to predict the survival of patients with OC. 10.18632/aging.102914
    Integrating genomic, epigenomic, and transcriptomic features reveals modular signatures underlying poor prognosis in ovarian cancer. Zhang Wei,Liu Yi,Sun Na,Wang Dan,Boyd-Kirkup Jerome,Dou Xiaoyang,Han Jing-Dong Jackie Cell reports Ovarian cancer has a poor prognosis, with different outcomes for different patients. The mechanism underlying this poor prognosis and heterogeneity is not well understood. We have developed an unbiased, adaptive clustering approach to integratively analyze ovarian cancer genome-wide gene expression, DNA methylation, microRNA expression, and copy number alteration profiles. We uncovered seven previously uncategorized subtypes of ovarian cancer that differ significantly in median survival time. We then developed an algorithm to uncover molecular signatures that distinguish cancer subtypes. Surprisingly, although the good-prognosis subtypes seem to have not been functionally selected, the poor-prognosis ones clearly have been. One subtype has an epithelial-mesenchymal transition signature and a cancer hallmark network, whereas the other two subtypes are enriched for a network centered on SRC and KRAS. Our results suggest molecular signatures that are highly predictive of clinical outcomes and spotlight "driver" genes that could be targeted by subtype-specific treatments. 10.1016/j.celrep.2013.07.010
    Identification and verification of a ten-gene signature predicting overall survival for ovarian cancer. Liu Jinwei,Xu Fei,Cheng Weiye,Gao Leilei Experimental cell research BACKGROUND:This study was aimed to identify an accurate gene expression signature to predict overall survival (OS) in patients with ovarian cancer (OC). METHODS:Expression data and corresponding clinical information were obtained from two independent databases: the Cancer Genome Atlas (TCGA) dataset and International Cancer Genome Consortium (ICGC) dataset. Multiple analysis methods including univariate and multivariate COX regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were utilized to build the signature. Receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analyses were used to assess the predictive accuracy of this gene signature. RESULTS:A novel 10-gene signature with high predictive accuracy for OS in OC patients was constructed and validated in the training and validation set. Based on the results of univariate and multivariate analyses, the presence of risk Score was identified as an independent prognostic factor for survival of OC patients. Moreover, we developed a nomogram model based on these 10 genes in the signature, which also displayed a favorable predictive efficacy for prognosis in OC. CONCLUSIONS:Our results identified a robust 10-gene signature for OC prognosis prediction, which might be applied to assist clinical decision-making and individualized treatment. 10.1016/j.yexcr.2020.112235
    Cox-LASSO Analysis Reveals a Ten-lncRNA Signature to Predict Outcomes in Patients with High-Grade Serous Ovarian Cancer. Xu Lu,Wu Ying,Che Xiaofang,Zhao Jia,Wang Fang,Wang Pengshuo,Qu Xiujuan,Liu YunPeng,Li Zhi DNA and cell biology High-grade serous ovarian cancer (HGSOC) is one of the most common and lethal gynecological cancers. Long noncoding RNAs (lncRNAs) play important roles and act as prognostic biomarkers of ovarian cancer. However, few studies have focused on the prognostic prediction of lncRNAs solely in HGSOC. In this study, we identified candidate lncRNAs for a prognostic evaluation by examining reannotated lncRNA expression profiles and clinical data of 343 patients with HGSOC from The Cancer Genome Atlas. We built a 10-lncRNA signature using Cox-LASSO regression to predict the prognosis of patients with HGSOC. Trichotomized by the 10-lncRNA signature, high-risk patients experienced significantly shorter disease-free survival and overall survival (OS). Our novel 10-lncRNA signature showed superior predictive capacity compared to the other 2 published lncRNA signature models and clinicopathological parameters. We developed a nomogram for clinical use by integrating the 10-lncRNA signature and two clinicopathological risk factors to predict 1-, 3-, and 5-year OS. In addition, gene set enrichment analysis suggested that the group of high-risk patients was associated with mitotic spindle pathways. This model was also compatible with patients with or without BRCA1/2 mutations and had the potential to predict the response to platinum-based adjuvant chemotherapy. Our findings provide a novel 10-lncRNA prognostic signature for further clinical application in patients with HGSOC and indicate the underlying mechanisms of HGSOC progression. 10.1089/dna.2019.4826
    Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers. Kawakami Eiryo,Tabata Junya,Yanaihara Nozomu,Ishikawa Tetsuo,Koseki Keita,Iida Yasushi,Saito Misato,Komazaki Hiromi,Shapiro Jason S,Goto Chihiro,Akiyama Yuka,Saito Ryosuke,Saito Motoaki,Takano Hirokuni,Yamada Kyosuke,Okamoto Aikou Clinical cancer research : an official journal of the American Association for Cancer Research PURPOSE:We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers. EXPERIMENTAL DESIGN:Overall, 334 patients with epithelial ovarian cancer (EOC) and 101 patients with benign ovarian tumors were randomly assigned to "training" and "test" cohorts. Seven supervised machine learning classifiers, including Gradient Boosting Machine (GBM), Support Vector Machine, Random Forest (RF), Conditional RF (CRF), Naïve Bayes, Neural Network, and Elastic Net, were used to derive diagnostic and prognostic information from 32 parameters commonly available from pretreatment peripheral blood tests and age. RESULTS:Machine learning techniques were superior to conventional regression-based analyses in predicting multiple clinical parameters pertaining to EOC. Ensemble methods combining weak decision trees, such as GBM, RF, and CRF, showed the best performance in EOC prediction. The values for the highest accuracy and area under the ROC curve (AUC) for segregating EOC from benign ovarian tumors with RF were 92.4% and 0.968, respectively. The highest accuracy and AUC for predicting clinical stages with RF were 69.0% and 0.760, respectively. High-grade serous and mucinous histotypes of EOC could be preoperatively predicted with RF. An ordinal RF classifier could distinguish complete resection from others. Unsupervised clustering analysis identified subgroups among early-stage EOC patients with significantly worse survival. CONCLUSIONS:Machine learning systems can provide critical diagnostic and prognostic prediction for patients with EOC before initial intervention, and the use of predictive algorithms may facilitate personalized treatment options through pretreatment stratification of patients. 10.1158/1078-0432.CCR-18-3378
    Development and validation of an immune gene-set based Prognostic signature in ovarian cancer. Shen Sipeng,Wang Guanrong,Zhang Ruyang,Zhao Yang,Yu Hao,Wei Yongyue,Chen Feng EBioMedicine BACKGROUND:Ovarian cancer (OV) is the most lethal gynecological cancer in women. We aim to develop a generalized, individualized immune prognostic signature that can stratify and predict overall survival for ovarian cancer. METHODS:The gene expression profiles of ovarian cancer tumor tissue samples were collected from 17 public cohorts, including 2777 cases totally. Single sample gene set enrichment (ssGSEA) analysis was used for the immune genes from ImmPort database to develop an immune-based prognostic score for OV (IPSOV). The signature was trained and validated in six independent datasets (n = 519, 409, 606, 634, 415, 194). FINDINGS:The IPSOV significantly stratified patients into low- and high-immune risk groups in the training set and in the 5 validation sets (HR range: 1.71 [95%CI: 1.32-2.19; P = 4.04 × 10] to 2.86 [95%CI: 1.72-4.74; P = 4.89 × 10]). Further, we compared IPSOV with nine reported ovarian cancer prognostic signatures as well as the clinical characteristics including stage, grade and debulking status. The IPSOV achieved the highest mean C-index (0.625) compared with the other signatures (0.516 to 0.602) and clinical characteristics (0.555 to 0.583). Further, we integrated IPSOV with stage, grade and debulking, which showed improved prognostic accuracy than clinical characteristics only. INTERPRETATION:The proposed clinical-immune signature is a promising biomarker for estimating overall survival in ovarian cancer. Prospective studies are needed to further validate its analytical accuracy and test the clinical utility. FUND: This work was supported by National Key Research and Development Program of China, National Natural Science Foundation of China and Natural Science Foundation of the Jiangsu Higher Education Institutions of China. 10.1016/j.ebiom.2018.12.054
    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. 10.1002/jcp.28770
    Identification of molecular marker associated with ovarian cancer prognosis using bioinformatics analysis and experiments. Zheng Ming-Jun,Li Xiao,Hu Yue-Xin,Dong Hui,Gou Rui,Nie Xin,Liu Qing,Ying-Ying Hao,Liu Juan-Juan,Lin Bei Journal of cellular physiology BACKGROUND:Ovarian cancer is one of the three major malignant tumors of the female reproductive system, and the mortality associated with ovarian cancer ranks first among gynecologic malignant tumors. The pathogenesis of ovarian cancer is not yet clearly defined but elucidating this process would be of great significance for clinical diagnosis, prevention, and treatment. For this study, we used bioinformatics to identify the key pathogenic genes and reveal the potential molecular mechanisms of ovarian cancer; we used immunohistochemistry to validate them. METHODS:We analyzed and integrated four gene expression profiles (GSE14407, GSE18520, GSE26712, and GSE54388), which were downloaded from the Gene Expression Omnibus (GEO) database, with the aim of obtaining a common differentially expressed gene (DEG). Then, we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We then established a protein-protein interaction (PPI) network of the DEGs through the Search Tool for the Retrieval of Interacting Genes (STRING) database and selected hub genes. Finally, survival analysis of the hub genes was performed using a Kmplotter online tool. RESULTS:A total of 226 DEGs were detected after the analysis of the four gene expression profiles; of these, 87 were upregulated genes and 139 were downregulated. GO analysis results showed that DEGs were significantly enriched in biological processes including the G2/M transition of the mitotic cell cycle, the apoptotic process, cell proliferation, blood coagulation, and positive regulation of the canonical Wnt signaling pathway. KEGG analysis results showed that DEGs were particularly enriched in the cell cycle, the p53 signaling pathway, the Wnt signaling pathway, the Ras signaling pathway, the Rap1 signaling pathway, and tyrosine metabolism. We selected 50 hub genes from the PPI network, which had 147 nodes and 655 edges, and 30 of them were associated with the prognosis of ovarian cancer. We performed immunohistochemistry on phosphoserine aminotransferase 1 (PSAT1). PSAT1 was highly expressed in cancer tissues, and its expression level was related to clinical stage and tissue differentiation in ovarian cancer. A Cox proportional risk model suggested that high expression of PSAT1 and late clinical stage were independent risk factors for survival and prognosis of ovarian cancer patients. CONCLUSION:The detection of DEGs using bioinformatics analysis might be crucial to understanding the pathogenesis of ovarian cancer, especially the molecular mechanisms of its development. The association between PSAT1 expression and the occurrence, development, and prognosis of ovarian cancer was further verified by immunohistochemistry. The PSAT1 expression can be used as a prognostic marker to provide a potential target for the diagnosis and treatment of ovarian cancer. 10.1002/jcp.27926