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

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 m...

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Vydané v:	Briefings in bioinformatics Ročník 24; číslo 1
Hlavní autori:	Liu, Chun-Jie, Hu, Fei-Fei, Xie, Gui-Yan, Miao, Ya-Ru, Li, Xin-Wen, Zeng, Yan, Guo, An-Yuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Oxford University Press 19.01.2023 Oxford Publishing Limited (England)
Predmet:	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
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Abstract	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.
AbstractList	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. 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.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. 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.
Author	Hu, Fei-Fei Xie, Gui-Yan Miao, Ya-Ru Liu, Chun-Jie Zeng, Yan Li, Xin-Wen Guo, An-Yuan
Author_xml	– sequence: 1 givenname: Chun-Jie orcidid: 0000-0002-3008-3375 surname: Liu fullname: Liu, Chun-Jie email: chunjie.sam.liu@gmail.com – sequence: 2 givenname: Fei-Fei orcidid: 0000-0002-8884-3568 surname: Hu fullname: Hu, Fei-Fei email: guoay@hust.edu.cn – sequence: 3 givenname: Gui-Yan orcidid: 0000-0002-2924-5306 surname: Xie fullname: Xie, Gui-Yan email: xieguiyan@hust.edu.cn – sequence: 4 givenname: Ya-Ru orcidid: 0000-0002-1982-3141 surname: Miao fullname: Miao, Ya-Ru email: miaoyr@hust.edu.cn – sequence: 5 givenname: Xin-Wen surname: Li fullname: Li, Xin-Wen email: lixinwen2020@163.com – sequence: 6 givenname: Yan surname: Zeng fullname: Zeng, Yan email: zengyan68@wust.edu.cn – sequence: 7 givenname: An-Yuan orcidid: 0000-0002-5099-7465 surname: Guo fullname: Guo, An-Yuan email: guoay@hust.edu.cn
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36549921$$D View this record in MEDLINE/PubMed
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Snippet	Abstract Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the... Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise...
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SubjectTerms	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
Title	GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels
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