Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering

Background: Globally, gastric cancer (GC) is the fifth most common tumor. It is necessary to identify novel molecular subtypes to guide patient selection for specific target therapeutic benefits. Methods: Multi-omics data, including transcriptomics RNA-sequencing (mRNA, LncRNA, miRNA), DNA methylati...

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Autores principales:	Xianyu Hu, Zhenglin Wang, Qing Wang, Ke Chen, Qijun Han, Suwen Bai, Juan Du, Wei Chen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Gastric cancer Molecular classification Multi-omics Overall survival Gene mutation Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/141e76a7211c444ab15b459d35d71973
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spelling	oai:doaj.org-article:141e76a7211c444ab15b459d35d719732021-11-14T04:28:40ZMolecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering0753-332210.1016/j.biopha.2021.112222https://doaj.org/article/141e76a7211c444ab15b459d35d719732021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0753332221010064https://doaj.org/toc/0753-3322Background: Globally, gastric cancer (GC) is the fifth most common tumor. It is necessary to identify novel molecular subtypes to guide patient selection for specific target therapeutic benefits. Methods: Multi-omics data, including transcriptomics RNA-sequencing (mRNA, LncRNA, miRNA), DNA methylation, and gene mutations in the TCGA-STAD cohort were used for the clustering. Ten classical clustering algorithms were executed to recognize patients with different molecular features using the “MOVICS” package in R. The activated signaling pathways were evaluated using the single-sample gene set enrichment analysis. The differential distribution of gene mutations, copy number alterations, and tumor mutation burden was compared, and potential responses to immunotherapy and chemotherapy were also assessed. Results: Two molecular subtypes (CS1 and CS2) were recognized by ten clustering algorithms with consensus ensembles. Patients in the CS1 group had a shorter average overall survival time (28.5 vs. 68.9 months, P = 0.016), and progression-free survival (19.0 vs. 63.9 months, P = 0.008) as compared to those in the CS2 group. Extracellular associated biological process activation was higher in the CS1 group, while the CS2 group displayed the enhanced activation of cell cycle-associated pathways. Significantly higher total mutation numbers and neoantigens were observed in the CS2 group, along with specific mutations in TTN, MUC16, and ARID1A. Higher infiltration of immunocytes was also observed in the CS2 group, reflective of the potential immunotherapeutic benefits. Moreover, the CS2 group could also respond to 5-fluorouracil, cisplatin, and paclitaxel. The similar diversity in clinical outcomes between CS1 and CS2 groups was successfully validated in the external cohorts, GSE62254, GSE26253, GSE15459, and GSE84437. Conclusion: The findings provided novel insights into the GC subtypes through integrative analysis of five -omics data by ten clustering algorithms. These could provide potential clinical therapeutic targets based on the specific molecular features.Xianyu HuZhenglin WangQing WangKe ChenQijun HanSuwen BaiJuan DuWei ChenElsevierarticleGastric cancerMolecular classificationMulti-omicsOverall survivalGene mutationTherapeutics. PharmacologyRM1-950ENBiomedicine & Pharmacotherapy, Vol 144, Iss , Pp 112222- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Gastric cancer Molecular classification Multi-omics Overall survival Gene mutation Therapeutics. Pharmacology RM1-950
spellingShingle	Gastric cancer Molecular classification Multi-omics Overall survival Gene mutation Therapeutics. Pharmacology RM1-950 Xianyu Hu Zhenglin Wang Qing Wang Ke Chen Qijun Han Suwen Bai Juan Du Wei Chen Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering
description	Background: Globally, gastric cancer (GC) is the fifth most common tumor. It is necessary to identify novel molecular subtypes to guide patient selection for specific target therapeutic benefits. Methods: Multi-omics data, including transcriptomics RNA-sequencing (mRNA, LncRNA, miRNA), DNA methylation, and gene mutations in the TCGA-STAD cohort were used for the clustering. Ten classical clustering algorithms were executed to recognize patients with different molecular features using the “MOVICS” package in R. The activated signaling pathways were evaluated using the single-sample gene set enrichment analysis. The differential distribution of gene mutations, copy number alterations, and tumor mutation burden was compared, and potential responses to immunotherapy and chemotherapy were also assessed. Results: Two molecular subtypes (CS1 and CS2) were recognized by ten clustering algorithms with consensus ensembles. Patients in the CS1 group had a shorter average overall survival time (28.5 vs. 68.9 months, P = 0.016), and progression-free survival (19.0 vs. 63.9 months, P = 0.008) as compared to those in the CS2 group. Extracellular associated biological process activation was higher in the CS1 group, while the CS2 group displayed the enhanced activation of cell cycle-associated pathways. Significantly higher total mutation numbers and neoantigens were observed in the CS2 group, along with specific mutations in TTN, MUC16, and ARID1A. Higher infiltration of immunocytes was also observed in the CS2 group, reflective of the potential immunotherapeutic benefits. Moreover, the CS2 group could also respond to 5-fluorouracil, cisplatin, and paclitaxel. The similar diversity in clinical outcomes between CS1 and CS2 groups was successfully validated in the external cohorts, GSE62254, GSE26253, GSE15459, and GSE84437. Conclusion: The findings provided novel insights into the GC subtypes through integrative analysis of five -omics data by ten clustering algorithms. These could provide potential clinical therapeutic targets based on the specific molecular features.
format	article
author	Xianyu Hu Zhenglin Wang Qing Wang Ke Chen Qijun Han Suwen Bai Juan Du Wei Chen
author_facet	Xianyu Hu Zhenglin Wang Qing Wang Ke Chen Qijun Han Suwen Bai Juan Du Wei Chen
author_sort	Xianyu Hu
title	Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering
title_short	Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering
title_full	Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering
title_fullStr	Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering
title_full_unstemmed	Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering
title_sort	molecular classification reveals the diverse genetic and prognostic features of gastric cancer: a multi-omics consensus ensemble clustering
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/141e76a7211c444ab15b459d35d71973
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_version_	1718430000968892416

Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering

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