Dataset Information

Identification of novel prognosis-related genes associated with cancer using integrative network analysis.

ABSTRACT: Prognosis identifies the seriousness and the chances of survival of a cancer patient. However, it remains a challenge to identify the key cancer genes in prognostic studies. In this study, we collected 2064 genes that were related to prognostic studies by using gene expression measurements curated from published literatures. Among them, 1820 genes were associated with copy number variations (CNVs). The further functional enrichment on 889 genes with frequent copy number gains (CNGs) revealed that these genes were significantly associated with cancer pathways including regulation of cell cycle, cell differentiation and mitogen-activated protein kinase (MAPK) cascade. We further conducted integrative analyses of CNV and their target genes expression using the data from matched tumour samples of The Cancer Genome Atlas (TCGA). Ultimately, 95 key prognosis-related genes were extracted, with concordant CNG events and increased up-regulation in at least 300 tumour samples. These genes, and the number of samples in which they were found, included: ACTL6A (399), ATP6V1C1 (425), EBAG9 (412), FADD (308), MTDH (377), and SENP5 (304). This study provides the first observation of CNV in prognosis-related genes across pan-cancer. The systematic concordance between CNG and up-regulation of gene expression in these novel prognosis-related genes may indicate their prognostic significance.

SUBMITTER: Wee Y

PROVIDER: S-EPMC5818516 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Identification of novel prognosis-related genes associated with cancer using integrative network analysis.

Wee YongKiat Y Liu Yining Y Lu Jiachun J Li Xiaoyan X Zhao Min M

Scientific reports 20180219 1

Prognosis identifies the seriousness and the chances of survival of a cancer patient. However, it remains a challenge to identify the key cancer genes in prognostic studies. In this study, we collected 2064 genes that were related to prognostic studies by using gene expression measurements curated from published literatures. Among them, 1820 genes were associated with copy number variations (CNVs). The further functional enrichment on 889 genes with frequent copy number gains (CNGs) revealed tha ...[more]

PMID: 29459674

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Project description:BackgroundThe immune system significantly participates in the pathologic process of atrial fibrillation (AF). However, the molecular mechanisms underlying this participation are not completely explained. The current research aimed to identify critical genes and immune cells that participate in the pathologic process of AF.MethodsCIBERSORT was utilized to reveal the immune cell infiltration pattern in AF patients. Meanwhile, weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules that were significantly correlated with AF. The characteristic genes correlated with AF were identified by the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithm.ResultsIn comparison to sinus rhythm (SR) individuals, we observed that fewer activated mast cells and regulatory T cells (Tregs), as well as more gamma delta T cells, resting mast cells, and M2 macrophages, were infiltrated in AF patients. Three significant modules (pink, red, and magenta) were identified to be significantly associated with AF. Gene enrichment analysis showed that all 717 genes were associated with immunity- or inflammation-related pathways and biological processes. Four hub genes (GALNT16, HTR2B, BEX2, and RAB8A) were revealed to be significantly correlated with AF by the SVM-RFE algorithm and LASSO logistic regression. qRT-PCR results suggested that compared to the SR subjects, AF patients exhibited significantly reduced BEX2 and GALNT16 expression, as well as dramatically elevated HTR2B expression. The AUC measurement showed that the diagnostic efficiency of BEX2, HTR2B, and GALNT16 in the training set was 0.836, 0.883, and 0.893, respectively, and 0.858, 0.861, and 0.915, respectively, in the validation set.ConclusionsThree novel genes, BEX2, HTR2B, and GALNT16, were identified by WGCNA combined with machine learning, which provides potential new therapeutic targets for the early diagnosis and prevention of AF.

Project description:Improved understanding of the molecular mechanisms and immunoregulation of muscle-invasive bladder cancer (MIBC) is essential to predict prognosis and develop new targets for therapies. In this study, we used the cancer genome atlas (TCGA) MIBC and GSE13507 datasets to explore the differential co-expression genes in MIBC comparing with adjacent non-carcinoma tissues. We firstly screened 106 signature genes by Weighted Gene Co-expression Network Analysis (WGCNA) and further identified 15 prognosis-related genes of MIBC using the univariate Cox progression analysis. Then we systematically analyzed the genetic alteration, molecular mechanism, and clinical relevance of these 15 genes. We found a different expression alteration of 15 genes in MIBC comparing with adjacent non-carcinoma tissues and normal tissues. Meanwhile, the biological functions and molecular mechanisms of them were also discrepant. Among these, we observed the ANLN was highly correlated with multiple cancer pathways, molecular function, and cell components, revealing ANLN may play a pivotal role in MIBC development. Next, we performed a consensus clustering of 15 prognosis-related genes; the results showed that the prognosis, immune infiltration status, stage, and grade of MIBC patients were significantly different in cluster1/2. We further identified eight-genes risk signatures using the least absolute shrinkage and selection operator (LASSO) regression analysis based on the expression values of 15 prognosis-related genes, and also found a significant difference in the prognosis, immune infiltration status, stage, grade, and age in high/low-risk cohort. Moreover, the expression of PD-1, PD-L1, and CTLA4 was significantly up-regulated in cluster1/high-risk-cohort than that in cluster2/low-risk-cohort. High normalized enrichment score of the Mitotic spindle, mTORC1, Complement, and Apical junction pathway suggested that they might be involved in the distinct tumor immune microenvironment (TIME) of cluster1/2 and high-/low-risk-cohort. Our study identified 15 prognosis-related genes of MIBC, provided a feasible stratification method to help for the future immunotherapy strategies of MIBC patients.

Dataset Information

Identification of novel prognosis-related genes associated with cancer using integrative network analysis.

Publications

Identification of novel prognosis-related genes associated with cancer using integrative network analysis.

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