Dataset Information

Integrated Analysis of Oncogenic Networks in Colorectal Cancer Identifies GUCA2A as a Molecular Marker.

ABSTRACT: Colorectal cancer (CRC) is one of the most common and deadly malignancies in the world. In China, the morbidity rate of CRC has increased during the period 2000 to 2011. Biomarker detection for early CRC diagnosis can effectively reduce the mortality of patients with CRC. To explore the underlying mechanisms of effective biomarkers and identify more of them, we performed weighted correlation network analysis (WGCNA) on a GSE68468 dataset generated from 378 CRC tissue samples. We screened the gene set (module), which was significantly associated with CRC histology, and analyzed the hub genes. The key genes were identified by obtaining six colorectal raw data (i.e., GSE25070, GSE44076, GSE44861, GSE21510, GSE9348, and GSE21815) from the GEO database (https://www.ncbi.nlm.nih.gov/geo). The robust differentially expressed genes (DEGs) in all six datasets were calculated and obtained using the library "RobustRankAggreg" package in R 3.5.1. An integrated analysis of CRC based on the top 50 downregulated DEGs and hub genes in the red module from WGCNA was conducted, and the intersecting genes were screened. The Kaplan-Meier plot was further analyzed, and the genes associated with CRC prognosis based on patients from the TCGA database were determined. Finally, we validated the candidate gene in our clinical CRC specimens. We postulated that the candidate genes screened from the database and verified by our clinical pathological data may contribute to understanding the molecular mechanisms of tumorigenesis and may serve as potential biomarkers for CRC diagnosis and treatment.

SUBMITTER: Zhang H

PROVIDER: S-EPMC6701329 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Integrated Analysis of Oncogenic Networks in Colorectal Cancer Identifies GUCA2A as a Molecular Marker.

Zhang Hui H Du Yuanyuan Y Wang Zhuo Z Lou Rui R Wu Jianzhong J Feng Jifeng J

Biochemistry research international 20190728

Colorectal cancer (CRC) is one of the most common and deadly malignancies in the world. In China, the morbidity rate of CRC has increased during the period 2000 to 2011. Biomarker detection for early CRC diagnosis can effectively reduce the mortality of patients with CRC. To explore the underlying mechanisms of effective biomarkers and identify more of them, we performed weighted correlation network analysis (WGCNA) on a GSE68468 dataset generated from 378 CRC tissue samples. We screened the gen ...[more]

PMID: 31467713

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Project description:BackgroundColorectal cancer (CRC) is the third most lethal and malignant type of cancer in the world. Abnormal expression of human microRNA-200a (hsa-miRNA-200a or miR-200a) has previously been characterized as a clinically noticeable biomarker in several cancers, but its role in CRC is still unclear.MethodsThree CRC miRNA expression datasets were integratively analyzed by Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms. Nine candidate miRNAs were identified and validated for diagnostic and prognostic capability with the prediction model. The potential roles of the tumor suppressor miR-200a-3p in invasion, migration, and epithelial-mesenchymal transition of CRC cells were elaborated by in vitro studies.ResultsNine miRNAs (miR-492, miR-200a, miR-338, miR-29c, miR-101, miR-148a, miR-92a, miR-424, and miR-210) were identified as potentially useful diagnostic biomarkers in the clinic. The overall accuracy rate of the nine miRNAs in the diagnostic model was 0.94, 0.89, and 0.978 in the testing, validation, and independent validation dataset, respectively. CRC patients in the GSE29622 cohort were separated by the prognostic model into the low-risk score group and the high-risk score group. The area under the receiver operating characteristic curve (AUC) was 0.872 and 0.783 for predicting the 1- to 10-year survival of CRC patients. The performance of the prognostic model was validated by an independent TCGA-Colon Adenocarcinoma (COAD) dataset with AUC values between 0.911 and 0.796 in predicting 1- to 10-year survival. Nomograms comprising risk scores, tumor stage, and TNM staging were generated for predicting 1-, 3-, and 5-year overall survival (OS) in the GSE29622 and TCGA-COAD datasets. Colony formation, invasion, and migration in DLD1 and SW480 cells were suppressed by overexpression of miR-200a-3p. Inhibition of miR-200a-3p function contributed to abnormal colony formation, migration, invasion, and epithelial-mesenchymal transition (EMT). miR-200a-3p binding sites were located within the 3'-untranslated region (3'-UTR) of the Forkhead box protein A1 (FOXA1) mRNA.ConclusionWe developed and validated a diagnostic and prognostic prediction model for CRC. miR-200a-3p was determined to be a potential diagnostic and prognostic biomarker for CRC.

Project description:BackgroundWith persistent inconsistencies in colorectal cancer (CRC) miRNAs expression data, it is crucial to shift toward inclusion of a "pre-laboratory" integrated analysis to expedite effective precision medicine and translational research. Aberrant expression of hsa-miRNA-195 (miR-195) which is distinguished as a clinically noteworthy miRNA has previously been observed in multiple cancers, yet its role in CRC remains unclear.MethodsIn this study, we performed an integrated analysis of seven CRC miRNAs expression datasets. The expression of miR-195 was validated in The Cancer Genome Atlas (TCGA) datasets, and an independent validation sample cohort. Colon cancer cells were transfected with miR-195 mimic and inhibitor, after which cell proliferation, colony formation, migration, invasion, and dual luciferase reporter were assayed. Xenograft mouse models were used to determine the role of miR-195 in CRC tumorigenicity in vivo.ResultsFour downregulated miRNAs (hsa-let-7a, hsa-miR-125b, hsa-miR-145, and hsa-miR-195) were demonstrated to be potentially useful diagnostic markers in the clinical setting. CRC patients with a decreased level of miR-195-5p in tumor tissues had significantly shortened survival as revealed by the TCGA colon adenocarcinoma (COAD) dataset and our CRC cohort. Overexpression of miR-195-5p in DLD1 and HCT116 cells repressed cell growth, colony formation, invasion, and migration. Inhibition of miR-195-5p function contributed to aberrant cell proliferation, migration, invasion, and epithelial mesenchymal transition (EMT). We identified miR-195-5p binding sites within the 3'-untranslated region (3'-UTR) of the human yes-associated protein (YAP) mRNA. YAP1 expression was downregulated after miR-195-5p treatment by qRT-PCR analysis and western blot.ConclusionsFour downregulated miRNAs were shown to be prime candidates for a panel of biomarkers with sufficient diagnostic accuracy for CRC in a clinical setting. Our integrated microRNA profiling approach identified miR-195-5p independently associated with prognosis in CRC. Our results demonstrated that miR-195-5p was a potent suppressor of YAP1, and miR-195-5p-mediated downregulation of YAP1 significantly reduced tumor development in a mouse CRC xenograft model. In the clinic, miR-195-5p can serve as a prognostic marker to predict the outcome of the CRC patients.

Dataset Information

Integrated Analysis of Oncogenic Networks in Colorectal Cancer Identifies GUCA2A as a Molecular Marker.

Publications

Integrated Analysis of Oncogenic Networks in Colorectal Cancer Identifies GUCA2A as a Molecular Marker.

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