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IHGA: An interactive web server for large-scale and comprehensive discovery of genes of interest in hepatocellular carcinoma

Authors :
Qiangnu Zhang
Weibin Hu
Lingfeng Xiong
Jin Wen
Teng Wei
Lesen Yan
Quan Liu
Siqi Zhu
Yu Bai
Yuandi Zeng
Zexin Yin
Jilin Yang
Wenjian Zhang
Meilong Wu
Yusen Zhang
Gongze Peng
Shiyun Bao
Liping Liu
Source :
Computational and Structural Biotechnology Journal, Vol 21, Iss , Pp 3987-3998 (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

Mining gene expression data is valuable for discovering novel biomarkers and therapeutic targets in hepatocellular carcinoma (HCC). Although emerging data mining tools are available for pan-cancer–related gene data analysis, few tools are dedicated to HCC. Moreover, tools specifically designed for HCC have restrictions such as small data scale and limited functionality. Therefore, we developed IHGA, a new interactive web server for discovering genes of interest in HCC on a large-scale and comprehensive basis. Integrative HCC Gene Analysis (IHGA) contains over 100 independent HCC patient-derived datasets (with over 10,000 tissue samples) and more than 90 cell models. IHGA allows users to conduct a series of large-scale and comprehensive analyses and data visualizations based on gene mRNA levels, including expression comparison, correlation analysis, clinical characteristics analysis, survival analysis, immune system interaction analysis, and drug sensitivity analysis. This method notably enhanced the richness of clinical data in IHGA. Additionally, IHGA integrates artificial intelligence (AI)–assisted gene screening based on natural language models. IHGA is free, user-friendly, and can effectively reduce time spent during data collection, organization, and analysis. In conclusion, IHGA is competitive in terms of data scale, data diversity, and functionality. It effectively alleviates the obstacles caused by HCC heterogeneity to data mining work and helps advance research on the molecular mechanisms of HCC.

Details

Language :
English
ISSN :
20010370
Volume :
21
Issue :
3987-3998
Database :
Directory of Open Access Journals
Journal :
Computational and Structural Biotechnology Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.48e85807c4a418a96032159b74ac9fc
Document Type :
article
Full Text :
https://doi.org/10.1016/j.csbj.2023.08.003