Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering - 13/11/21
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Abstract |
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.
Le texte complet de cet article est disponible en PDF.Highlights |
• | We used ten clustering algorithms-based R packages “MOVICS” to reveal the molecular subtypes for gastric cancer with multi-omics data. |
• | We recognized two molecular subtypes CS1 and CS2, with diverse prognosis, immune infiltration status, genetic alteration. |
• | CS1 group had a poorer prognosis with lesser genetic alterations and activation of EMT-associated pathways. |
• | CS2 group with high TMB, more mutations, and CNAs, had a favorable prognosis and high responsiveness to immunotherapy. |
• | CS1 and CS2 subtypes was successfully validated in a total of 1357 GC patients from four different cohorts. |
Keywords : Gastric cancer, Molecular classification, Multi-omics, Overall survival, Gene mutation
Plan
Vol 144
Article 112222- décembre 2021 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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