Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA‐lncRNA co‐expression network analysis

• Published in: Molecular genetics & genomic medicine Vol. 8; no. 11; pp. e1512 - n/a
• Main Authors: Li, Qun, Liu, Xiaofeng, Gu, Jia, Zhu, Jinming, Wei, Zhi, Huang, Hua
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.11.2020; John Wiley and Sons Inc; Wiley
• Subjects: Accuracy; Adenocarcinoma; Algorithms; Annotations; Bioinformatics; Biomarkers; Cancer therapies; Cell cycle; Datasets; Decision trees; Deoxyribonucleic acid; Diagnostic systems; DNA; DNA biosynthesis; Enrichment; Extracellular matrix; Gene expression; Gene set enrichment analysis; Genomes; Learning algorithms; Machine learning; Malignancy; Medical prognosis; mRNA; Network analysis; Non-coding RNA; Original; p53 Protein; Polymerase chain reaction; Signal transduction; Software; Stomach; Support vector machines; Survival; Survival analysis; China
• ISSN: 2324-9269; 2324-9269
• DOI: 10.1002/mgg3.1512

Background Stomach adenocarcinoma (STAD), is one of the most lethal malignancies around the world. The aim of this study was to find the long noncoding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of STAD. Methods Base on TCGA dataset, the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified between STAD and normal tissue. The machine learning and survival analysis were performed to evaluate the potential diagnostic and prognostic value of lncRNAs for STAD. We also build the co‐expression network and functional annotation. The expression of selected candidate mRNAs and lncRNAs were validated by Quantitative real‐time polymerase chain reaction (qRT‐PCR) and GSE27342 dataset. GSE27342 dataset were also to perform gene set enrichment analysis. Results A total of 814 DEmRNAs and 106 DElncRNAs between STAD and normal tissue were obtained. FOXD2‐AS1, LINC01235, and RP11‐598F7.5 were defined as optimal diagnostic lncRNA biomarkers for STAD. The area under curve (AUC) of the decision tree model, random forests model, and support vector machine (SVM) model were 0.797, 0.981, and 0.983, and the specificity and sensitivity of the three model were 75.0% and 97.1%, 96.9% and 96%, and 96.9% and 97.1%, respectively. Among them, LINC01235 was not only an optimal diagnostic lncRNA biomarkers, but also related to survival time. The expression of three DEmRNAs (ESM1, WNT2, and COL10A1) and three optimal diagnostic lncRNAs biomarkers (FOXD2‐AS1, RP11‐598F7.5, and LINC01235) in qRT‐PCR validation was were consistent with our integrated analysis. Except for FOXD2‐AS1, ESM1, WNT2, COL10A1, and LINC01235 were upregulated in STAD, which was consistent with our integration results. Gene set enrichment analysis results indicated that DNA replication, Cell cycle, ECM‐receptor interaction, and P53 signaling pathway were four significantly enriched pathways in STAD. Conclusion Our study identified three DElncRNAs as potential diagnostic biomarkers of STAD. Among them, LINC01235 also was a prognostic lncRNA biomarkers.