Dataset Information

Identifying a new microRNA signature as a prognostic biomarker in colon cancer.

ABSTRACT: BACKGROUND:The aim was to identify a novel prognostic miRNA signature for colon cancer (CC) in silico. METHODS:Data on the expression of miRNAs and relevant clinical information for 407 patients were obtained from The Cancer Genome Atlas (TCGA), and the samples were randomly split into a validation set (n = 203) and training set (n = 204). The differential expression of miRNAs between normal tissues and patients with CC was analyzed. We detected a miRNA expression signature in the training dataset by using a Cox proportional hazard regression model. Then, we verified the signature in the validation set. Association of the miRNA signature with overall survival was assessed in the validation cohort and combined cohort by log-rank test and based on Kaplan-Meier curves. The receiver operating characteristic and disease-free survival analyses were performed to evaluate the miRNA signature of CC in the combined cohort. Multivariate and univariate Cox analyses related to survival for the miRNA signature were performed, and a nomogram was built as a prognostic model for CC. To explore the function of target genes of the miRNA signature, Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were used. RESULTS:Between the matched normal tissues and colon cancer tissues, 267 differentially expressed miRNAs were detected, and a single-factor CoxPH model showed that 13 miRNAs were related to overall survival in the training cohort. Then, a five-miRNA signature was identified using a CoxPH regression model with multiple factors. The five-miRNA signature had significant prognostic value in the training cohort and was validated in the validation cohort and combined cohort. A total of 193 target genes of the miRNA signature were identified. According to the results of functional analysis of the target genes, the signaling pathways MAPK, AMPK and PI3K-Akt, focal adhesion, and microRNAs in cancer were remarkably enriched. CONCLUSION:A five-miRNA signature had increased prognostic value for CC, which may provide important biological insights for the discovery and development of molecular predictors to improve the prognosis of patients with CC.

SUBMITTER: Lv Y

PROVIDER: S-EPMC7015317 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identifying a new microRNA signature as a prognostic biomarker in colon cancer.

Lv Yunxia Y Duanmu Jinzhong J Fu Xiaorui X Li Taiyuan T Jiang Qunguang Q

PloS one 20200212 2

<h4>Background</h4>The aim was to identify a novel prognostic miRNA signature for colon cancer (CC) in silico.<h4>Methods</h4>Data on the expression of miRNAs and relevant clinical information for 407 patients were obtained from The Cancer Genome Atlas (TCGA), and the samples were randomly split into a validation set (n = 203) and training set (n = 204). The differential expression of miRNAs between normal tissues and patients with CC was analyzed. We detected a miRNA expression signature in the ...[more]

PMID: 32049961

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Project description:Mounting evidence has demonstrated that a lot of miRNAs are overexpressed or downregulated in colorectal cancer (CRC) tissues and play a crucial role in tumorigenesis, invasion, and migration. The aim of our study was to screen new biomarkers related to CRC prognosis by bioinformatics analysis. By using the R language edgeR package for the differential analysis and standardization of miRNA expression profiles from The Cancer Genome Atlas (TCGA), 502 differentially expressed miRNAs (343 up-regulated, 159 down-regulated) were screened based on the cut-off criteria of p < 0.05 and |log2FC|>1, then all the patients (421) with differentially expressed miRNAs and complete survival time, status were then randomly divided into train group (212) and the test group (209). Eight miRNAs with p < 0.005 were revealed in univariate cox regression analysis of train group, then stepwise multivariate cox regression was applied for constituting a five-miRNA (hsa-miR-5091, hsa-miR-10b-3p, hsa-miR-9-5p, hsa-miR-187-3p, hsa-miR-32-5p) signature prognostic biomarkers with obviously different overall survival. Test group and entire group shown the same results utilizing the same prescient miRNA signature. The area under curve (AUC) of receiver operating characteristic (ROC) curve for predicting 5 years survival in train group, test group, and whole cohort were 0.79, 0.679, and 0.744, respectively, which demonstrated better predictive power of prognostic model. Furthermore, Univariate cox regression and multivariate cox regression considering other clinical factors displayed that the five-miRNA signature could serve as an independent prognostic factor. In order to predict the potential biological functions of five-miRNA signature, target genes of these five miRNAs were analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and Gene Ontology (GO) enrichment analysis. The top 10 hub genes (ESR1, ADCY9, MEF2C, NRXN1, ADCY5, FGF2, KITLG, GATA1, GRIA1, KAT2B) of target genes in protein protein interaction (PPI) network were screened by string database and Cytoscape 3.6.1 (plug-in cytoHubba). In addition, 19 of target genes were associated with survival prognosis. Taken together, the current study showed the model of five-miRNA signature could efficiently function as a novel and independent prognosis biomarker and therapeutic target for CRC patients.

Project description:BackgroundColon cancer (CC) is a common gastrointestinal malignant tumor with high heterogeneity in clinical behavior and response to treatment, making individualized survival prediction challenging. Ferroptosis is a newly discovered iron-dependent cell death that plays a critical role in cancer biology. Therefore, identifying a prognostic biomarker with ferroptosis-related genes provides a new strategy to guide precise clinical decision-making in CC patients.MethodsAlteration in the expression profile of ferroptosis-related genes was initially screened in GSE39582 dataset involving 585 CC patients. Univariate Cox regression analysis and LASSO-penalized Cox regression analysis were combined to further identify a novel ferroptosis-related gene signature for overall survival prediction. The prognostic performance of the signature was validated in the GSE17536 dataset by Kaplan-Meier survival curve and time-dependent ROC curve analyses. Functional annotation of the signature was explored by integrating GO and KEGG enrichment analysis, GSEA analysis and ssGSEA analysis. Furthermore, an outcome risk nomogram was constructed considering both the gene signature and the clinicopathological features.ResultsThe prognostic signature biomarker composed of 9 ferroptosis-related genes accurately discriminated high-risk and low-risk patients with CC in both the training and validation datasets. The signature was tightly linked to clinicopathological features and possessed powerful predictive ability for distinct clinical subgroups. Furthermore, the risk score was confirmed to be an independent prognostic factor for CC patients by multivariate Cox regression analysis (p < 0.05). Functional annotation analyses showed that the prognostic signature was closely correlated with pivotal cancer hallmarks, particularly cell cycle, transcriptional regulation, and immune-related functions. Moreover, a nomogram with the signature was also built to quantify outcome risk for each patient.ConclusionThe novel ferroptosis-related gene signature biomarker can be utilized for predicting individualized prognosis, optimizing survival risk assessment and facilitating personalized management of CC patients.

Dataset Information

Identifying a new microRNA signature as a prognostic biomarker in colon cancer.

Publications

Identifying a new microRNA signature as a prognostic biomarker in colon cancer.

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