Dataset Information

Identification of Prognostic Biomarkers and Drugs Targeting Them in Colon Adenocarcinoma: A Bioinformatic Analysis.

ABSTRACT: Objective: To identify prognostic biomarkers and drugs that target them in colon adenocarcinoma (COAD) based on the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Methods: The TCGA dataset was used to identify the top 50 upregulated differentially expressed genes (DEGs), and Gene Expression Omnibus profiles were used for validation. Survival analyses were conducted with the TCGA dataset using the RTCGAToolbox package in the R software environment. Drugs targeting the candidate prognostic biomarkers were searched in the DrugBank and herbal databases. Results: Among the top 50 upregulated DEGs in patients with COAD in the TCGA dataset, the Wnt signaling pathway and cytokine-cytokine receptor interactions and pathways in cancer Kyoto Encyclopedia of Genes and Genomes pathway analysis were enriched in DEGs. Tissue development and regulation of cell proliferation were the main Gene Ontology biological processes associated with upregulated DEGs. MYC and KLK6 were overexpressed in tumors validated in the TCGA, GSE41328, and GSE113513 databases (all P < .001) and were significantly associated with overall survival in patients with COAD (P = .021 and P = .047). Nadroparin and benzamidine were identified as inhibitors of MYC and KLK6 in DrugBank, and 8 herbs targeting MYC, including Da Huang (Radix Rhei Et Rhizome), Hu Zhang (Polygoni Cuspidati Rhizoma Et Radix), Huang Lian (Coptidis Rhizoma), Ban Xia (Arum Ternatum Thunb), Tu Fu Ling (Smilacis Glabrae Rhixoma), Lei Gong Teng (Tripterygii Radix), Er Cha (Catechu), and Guang Zao (Choerospondiatis Fructus), were identified. Conclusion: MYC and KLK6 may serve as candidate prognostic predictors and therapeutic targets in patients with COAD.

SUBMITTER: Dong S

PROVIDER: S-EPMC6681251 | biostudies-literature | 2019 Jan-Dec

REPOSITORIES: biostudies-literature

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Identification of Prognostic Biomarkers and Drugs Targeting Them in Colon Adenocarcinoma: A Bioinformatic Analysis.

Dong Shu S Ding Zhimin Z Zhang Hao H Chen Qiwen Q

Integrative cancer therapies 20190101

<b>Objective:</b> To identify prognostic biomarkers and drugs that target them in colon adenocarcinoma (COAD) based on the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. <b>Methods:</b> The TCGA dataset was used to identify the top 50 upregulated differentially expressed genes (DEGs), and Gene Expression Omnibus profiles were used for validation. Survival analyses were conducted with the TCGA dataset using the RTCGAToolbox package in the R software environment. Drugs targeting ...[more]

PMID: 31370719

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Project description:BackgroundColon adenocarcinoma (COAD) is one of the leading causes of death worldwide. Cancer stem cells (CSCs) are vital for COAD chemoresistance and recurrence, however little is known about stem cell-related biomarkers in drug resistance and COAD prognosis prediction.MethodsTo uncover the roles of CSC in COAD tumorigenesis, chemoresistance, and prognosis, we retrieved COAD patients' RNAseq data from TCGA (The Cancer Genome Atlas). We further performed analysis of differentially expressed genes (DEGs) and mRNA expression-based stemness index (mRNAsi) to identify stemness-related COAD biomarkers. We then evaluated the roles of mRNAsi in tumorigenesis, clinical-stage, overall survival (OS), and chemoresistance. Afterward, we used identified prognostic stemness-related genes (PSRGs) to construct a prediction model. After constructing the prediction model, we used elastic Net regression and area under the curve (AUC) to explore the prediction value of PSRGs based on risk scores and the receiver operator characteristic (ROC) curve. To elucidate the underlying interconnected systems, we examined relationships between the levels of TFs, PSRGs, and 50 cancer hallmarks by a Pearson correlation analysis.ResultsTwelve thousand one hundred eight DEGs were identified by comparing 456 primary COADs and 41 normal solid tissue samples. Furthermore, we identified 4351 clinical stage-related DEGs, 16,516 stemness-associated DEGs, and 54 chemoresistance-related DEGs from cancer stages: mRNAsi, and COAD chemoresistance. Compared to normal tissue samples, mRNAsi in COAD patients were marked on an elevation and involved in prognosis (p = 0.027), stemness-related DEGs based on chemoresistance (OR = 3.28, p ≤ 0.001) and AJCC clinical stage relating (OR = 4.02, p ≤ 0.001) to COAD patients. The prediction model of prognosis were constructed using the 6 PSRGs with high accuracy (AUC: 0.659). The model identified universal correlation between NRIP2 and FDFT1 (key PRSGs), and some cancer related transcription factors (TFs) and trademarks of cancer gene were in the regulatory network.ConclusionWe found that mRNAsi is a reliable predictive biomarker of tumorigenesis and COAD prognosis. Our established prediction model of COAD chemoresistance, which includes the six PSRGs, is effective, as the model provides promising therapeutic targets in the COAD.

Dataset Information

Identification of Prognostic Biomarkers and Drugs Targeting Them in Colon Adenocarcinoma: A Bioinformatic Analysis.

Publications

Identification of Prognostic Biomarkers and Drugs Targeting Them in Colon Adenocarcinoma: A Bioinformatic Analysis.

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