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Identification of prognostic biomarkers and drug target prediction for colon cancer according to a competitive endogenous RNA network.

ABSTRACT: Colorectal cancer is one of the commoner digestive tract malignant tumor types, and its incidence and mortality rate are high. Accumulating evidence indicates that long?chain non?coding RNAs (lncRNAs) and protein?coding RNAs interact with each other by competing with the same micro(mi)RNA response element (MREs) and serve an important role in the regulation of gene expression in a variety of tumor types. However, the regulatory mechanism and prognostic role of lncRNA?mediated competing endogenous (ce)RNA networks in colon cancer have yet to be elucidated. The expression profiles of mRNAs, lncRNAs and miRNAs from 471 colon cancer and 41 paracancerous tissue samples were downloaded from The Cancer Genome Atlas database. A lncRNA?miRNA?mRNA ceRNA network in colon cancer was constructed and comprised 17 hub lncRNAs, 87 hub miRNA and 144 hub mRNAs. The topological properties of the network were analyzed, and the random walk algorithm was used to identify the nodes significantly associated with colon cancer. Survival analysis using the UALCAN database indicated that 2/17 lncRNAs identified [metastasis?associated lung adenocarcinoma transcript (MALAT1) and maternally expressed gene 3 (MEG3)] and 5/144 mRNAs [FES upstream region (FURIN), nuclear factor of activated T?cells 5 (NFAT5), RNA Binding Motif Protein 12B (RBM12B), Ras related GTP binding A (RRAGA) and WD repeat domain phosphoinositide?interacting protein 2 (WIPI2)] were significantly associated with the overall survival of patients with colon cancer, and may therefore be used as potential prognostic biomarkers of colon cancer. According to extracted lncRNA?miRNA?mRNA interaction pairs, the GSE26334 dataset was used to confirm that the lncRNA MALAT1/miR?129?5p/NFAT5 axis may represent a novel regulatory mechanism concerning the progression of colon cancer. The clusterProfiler package was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in colon cancer. Finally, drugs that significantly interact with the core genes identified in colon cancer were predicted using a hypergeometric test. Of these, fostamatinib was identified to be a targeted drug for colon cancer therapy. The present findings provide a novel perspective for improved understanding of the lncRNA?associated ceRNA network and may facilitate the development of novel targeted therapeutics in colon cancer.

SUBMITTER: Hu D

PROVIDER: S-EPMC7339803 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Identification of prognostic biomarkers and drug target prediction for colon cancer according to a competitive endogenous RNA network.

Hu Daojun D Zhang Boke B Yu Miao M Shi Wenjie W Zhang Li L

Molecular medicine reports 20200522 2

Colorectal cancer is one of the commoner digestive tract malignant tumor types, and its incidence and mortality rate are high. Accumulating evidence indicates that long‑chain non‑coding RNAs (lncRNAs) and protein‑coding RNAs interact with each other by competing with the same micro(mi)RNA response element (MREs) and serve an important role in the regulation of gene expression in a variety of tumor types. However, the regulatory mechanism and prognostic role of lncRNA‑mediated competing endogenou ...[more]

PMID: 32468035

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Project description:Background:Long non-coding RNA (lncRNA) functions as a competitive endogenous RNA (ceRNA) and plays an important role in the biological processes underlying tumorigenesis. However, studies describing the function of lncRNA in pheochromocytoma and paraganglioma (PCPG) remain largely unknown. Our study aims to construct a regulatory ceRNA network and explore prognostic biomarkers for PCPG through a comprehensive analysis. Methods:PCPG data from The Cancer Genome Atlas (TCGA) were utilized to obtain differentially expressed lncRNAs (DElncRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs). Kaplan-Meier analysis was used to detect prognostic biomarkers and Cytoscape was utilized to construct a regulatory network of ceRNA. Potential lncRNA-miRNA-mRNA axes were inferred by correlation analysis. GO and KEGG pathways were constructed using "clusterProfiler" and "DOSE" R-packages. Immunohistochemistry (IHC) staining was performed to validate differential protein expression levels of genes in the axes. Finally, the GSE19422 dataset and Pan-Cancer data were applied to validate the expression pattern and survival status of mRNAs, respectively. Results:A total of 334 DElncRNAs, 116 DEmiRNAs, and 3496 DEmRNAs were identified and mainly enriched in hormone secretion, metabolism signaling, metastatic and proliferative pathways. Among these differentially expressed genes, 16 mRNAs, six lncRNAs, and two miRNAs were associated with overall survival of patients with PCPG and sequentially enrolled in the ceRNA network. Two lncRNA-miRNA-mRNA regulatory axes were predicted: AP001486.2/hsa-miR-195-5p/RCAN3 and AP006333.2/hsa-miR-34a-5p/PTPRJ. The GSE19422 dataset and IHC analysis validated that mRNA and protein levels of RCAN3 and PTPRJ were upregulated in PCPG tissues compared with adjacent adrenal gland medulla tissues. Pan-Cancer data showed that the upregulated expression of RCAN3 and PTPRJ was associated with favorable overall survival and disease-free survival. Conclusion:A regulatory lncRNA-miRNA-mRNA ceRNA network was successfully constructed and 24 prognostic biomarkers were identified for PCPG patients. These findings may contribute toward a better understanding of the biological mechanism of tumorigenesis and enable further evaluation of the prognosis of patients with PCPG.

Project description:Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA-miRNA-mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.

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.

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Identification of prognostic biomarkers and drug target prediction for colon cancer according to a competitive endogenous RNA network.

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Identification of prognostic biomarkers and drug target prediction for colon cancer according to a competitive endogenous RNA network.

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