Gene Set Cancer Analysis (GSCA) is an integrated platform for genomic, pharmacogenomic, and immunogenomic gene set cancer analysis.
The alterations of DNA, RNA, and immune microenvironment could contribute to the cancer initiation, progress, diagnosis, prognosis and therapy. In the bio-big data era, individual causal gene signals could be masked by massive background noises. A set of genes or compiled gene set scores from multiple dimensional data across a large number of patients per stage could represent a snapshot of the underlying cancer process.
In this enhanced GSCA, we provide a series of services to perform gene set genomic (Expression, SNV, CNV and methylation) and immunogenomic (24 immune cells) analyses. Besides, combining clinical information and small molecular drugs, a user could mine candidate biomarkers and valuable small drugs for better experimental design and further clinical trials. GSCA integrates over 10,000 multi-dimensional genomic data across 33 cancer types from TCGA and over 750 small molecule drugs from GDSC and CTRP. Immunogenomic analysis was performed by our ImmuCellAI algorithm with 24 immunes cells.
GSEA: expression of gene set enrichment analysis across pan-cancers.
GSVA: exploring the association between immune infiltrate/clinical and expression score compiled by GSVA of your gene set.
Immune: investigating correlations between immune cell abundance and genomic variations of your gene set in cancer.
Survival: genomic associations with 4 survival types.
Drug sensitivity: update new drugs and correlate expression with drug sensitivity (IC50).
SNV: update mutational distribution, lolipop plot, gene set SNV, and survival analysis.
CNV: update gene set CNV and correlates with expression and survival.
Methylation: update correlats with gene set expression and survival.
- 2023-4-4
- 2023-4-4
- 2023-3-23
- 2023-2-2
Boxplots were optimized.
Optimized the figure that showing the trend of gene expression with stages.
The method of gene ranking in the figure showing drug-gene corr elation was added to the help page of Drug module.
Fixed the bugs because of server maintenance and restart.
Fixed the bug that SNV data cannot be extracted from .gz format.
Added the citation information.
Added sample count information in cancer type selection box of Expression module.
Added a prompt of quick entrance of biological pathway databases to get gene sets.
Corrected grammar errors.
Fixed bugs.
All tables were sorted.
Searchable gene set size changed from 2-200 to 2-500.
Re-calculated methylation & survival results. DETAILS:
Corrected several error results. The results after this update shall prevail.
Added DFS and DSS survival results. DETAILS:
1) Used DFS and DSS data from Liu et al., Cell, 2018.
2) Supplemented DFS and DSS results.
Updated survival data and results. DETAILS:
1) Used OS and PFS data from Liu et al., Cell, 2018.
2) Sample counts were updated.
3) Re-calculated all results related to survival using updated survival data. The results after this update shall prevail.
Updated stage data and results. DETAILS:
1) Added clinical, igcccg, and masaoka stage data from the TCGA database. The previous GSCA used only the pathologic stage for calculation.
2) Supplemented stage results using clinical, igcccg, and masaoka stage data.
- 2022-11-21
- 2018-11-01
Chun-Jie Liu, Fei-Fei Hu, Gui-Yan Xie, Ya-Ru Miao, Xin-Wen Li, Yan Zeng, An-Yuan Guo. GSCA: an Integrated Platform for Gene Set Cancer Analysis at Genomic, Pharmacogenomic, and Immunogenomic Levels. Briefings in bioinformatics, 2022, bbac558. Click here to read the article
Chun-Jie Liu, Fei-Fei Hu, Meng-Xuan Xia, Leng Han, Qiong Zhang, An-Yuan Guo. GSCALite: A Web Server for Gene Set Cancer Analysis. Bioinformatics, 2018, 34(21): 3771–72. Click here to read the article