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Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA‐lncRNA co‐expression network analysis
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) a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7667366/ https://www.ncbi.nlm.nih.gov/pubmed/33002344 http://dx.doi.org/10.1002/mgg3.1512 |
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author | Li, Qun Liu, Xiaofeng Gu, Jia Zhu, Jinming Wei, Zhi Huang, Hua |
author_facet | Li, Qun Liu, Xiaofeng Gu, Jia Zhu, Jinming Wei, Zhi Huang, Hua |
author_sort | Li, Qun |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7667366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76673662020-11-20 Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA‐lncRNA co‐expression network analysis Li, Qun Liu, Xiaofeng Gu, Jia Zhu, Jinming Wei, Zhi Huang, Hua Mol Genet Genomic Med Original Articles 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. John Wiley and Sons Inc. 2020-10-01 /pmc/articles/PMC7667366/ /pubmed/33002344 http://dx.doi.org/10.1002/mgg3.1512 Text en © 2020 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals LLC. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles Li, Qun Liu, Xiaofeng Gu, Jia Zhu, Jinming Wei, Zhi Huang, Hua Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA‐lncRNA co‐expression network analysis |
title | Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA‐lncRNA co‐expression network analysis |
title_full | Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA‐lncRNA co‐expression network analysis |
title_fullStr | Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA‐lncRNA co‐expression network analysis |
title_full_unstemmed | Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA‐lncRNA co‐expression network analysis |
title_short | Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA‐lncRNA co‐expression network analysis |
title_sort | screening lncrnas with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mrna‐lncrna co‐expression network analysis |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7667366/ https://www.ncbi.nlm.nih.gov/pubmed/33002344 http://dx.doi.org/10.1002/mgg3.1512 |
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