GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels

• Published in: Briefings in bioinformatics Vol. 24; no. 1
• Main Authors: Liu, Chun-Jie, Hu, Fei-Fei, Xie, Gui-Yan, Miao, Ya-Ru, Li, Xin-Wen, Zeng, Yan, Guo, An-Yuan
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19.01.2023; Oxford Publishing Limited (England)
• Subjects: 1-Phosphatidylinositol 3-kinase; Cancer; Cell cycle; Clinical outcomes; Gene expression; Genes; Genomic analysis; Genomics; Genomics - methods; Humans; Immune system; Infiltration; Metastases; Mutation; Neoplasms - drug therapy; Neoplasms - genetics; Oncogenes; Pharmacogenetics; Pharmacogenomics; Phosphatidylinositol 3-Kinases - genetics; Sensitivity analysis; Signal to noise ratio; Tumor suppressor genes; Tumors; immunogenomics; clinical outcomes; gene set; cancer analysis; web server
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbac558

Abstract Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.