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Exploration of the mechanism of colorectal cancer metastasis using microarray analysis.

ABSTRACT: The aim of the present study was to investigate the mechanism of metastasis in colorectal cancer (CRC) using microRNA (miRNA) and mRNA expression profiles. The mRNA and miRNA expression profiles of the GSE2509 and GSE56350 datasets were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified using the limma software package. The Database for Annotation, Visualization and Integrated Discovery was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs. The predicted target genes associated with the DEMs were identified using the miRWalk database and the enrichment analysis was conducted using the clusterProfiler package. The miRNA-gene molecular interaction network was visualized using the Cytoscape software platform. A total of 544 DEGs and 42 DEMs were identified. DEGs were annotated in 320 GO terms and 11 KEGG pathways. Overall, 366 miRNA-gene pairs were identified and the miRNA-gene network was visualized. Furthermore, the predicted target genes were mainly classified in 12 pathways. The results of the present study suggest that fibronectin type III domain-containing 3B, cysteine rich transmembrane BMP regulator 1 and forkhead box J2 may be potential therapeutic and prognostic targets of metastatic CRC. In addition, pathways in cancer, the Wnt signaling pathway and extracellular matrix-receptor interaction may play a critical role in CRC metastasis.

SUBMITTER: Chen S

PROVIDER: S-EPMC5691382 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Exploration of the mechanism of colorectal cancer metastasis using microarray analysis.

Chen Shuo S Wang Yan Y Zhang Lin L Su Yinan Y Zhang Mingqing M Wang Juan J Zhang Xipeng X

Oncology letters 20170925 6

The aim of the present study was to investigate the mechanism of metastasis in colorectal cancer (CRC) using microRNA (miRNA) and mRNA expression profiles. The mRNA and miRNA expression profiles of the GSE2509 and GSE56350 datasets were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified using the limma software package. The Database for Annotation, Visualization and Integrated Discovery was used ...[more]

PMID: 29163694

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Project description:Objectives: To evaluate whether incorporating the radiomics, genomics, and clinical features allows prediction of metastasis in colorectal cancer (CRC) and to develop a preoperative nomogram for predicting metastasis. Methods: We retrospectively analyzed radiomics features of computed tomography (CT) images in 134 patients (62 in the primary cohort, 28 in the validation cohort, and 44 in the independent-test cohort) clinicopathologically diagnosed with CRC at Dazhou Central Hospital from February 2018 to October 2019. Tumor tissues were collected from all patients for RNA sequencing, and clinical data were obtained from medical records. A total of 854 radiomics features were extracted from enhanced venous-phase CT of CRC. Least absolute shrinkage and selection operator regression analysis was utilized for data dimension reduction, feature screen, and radiomics signature development. Multivariable logistic regression analysis was performed to build a multiscale predicting model incorporating the radiomics, genomics, and clinical features. The receiver operating characteristic curve, calibration curve, and decision curve were conducted to evaluate the performance of the nomogram. Results: The radiomics signature based on 16 selected radiomics features showed good performance in metastasis assessment in both primary [area under the curve (AUC) = 0.945, 95% confidence interval (CI) 0.892-0.998] and validation cohorts (AUC = 0.754, 95% CI 0.570-0.938). The multiscale nomogram model contained radiomics features signatures, four-gene expression related to cell cycle pathway, and CA 19-9 level. The multiscale model showed good discrimination performance in the primary cohort (AUC = 0.981, 95% CI 0.953-1.000), the validation cohort (AUC = 0.822, 95% CI 0.635-1.000), and the independent-test cohort (AUC = 0.752, 95% CI 0.608-0.896) and good calibration. Decision curve analysis confirmed the clinical application value of the multiscale model. Conclusion: This study presented a multiscale model that incorporated the radiological eigenvalues, genomics features, and CA 19-9, which could be conveniently utilized to facilitate the individualized preoperatively assessing metastasis in CRC patients.

Dataset Information

Exploration of the mechanism of colorectal cancer metastasis using microarray analysis.

Publications

Exploration of the mechanism of colorectal cancer metastasis using microarray analysis.

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