Zhang Qiangnu, Hu Weibin, Xiong Lingfeng, Wen Jin, Wei Teng, Yan Lesen, Liu Quan, Zhu Siqi, Bai Yu, Zeng Yuandi, Yin Zexin, Yang Jilin, Zhang Wenjian, Wu Meilong, Zhang Yusen, Peng Gongze, Bao Shiyun, Liu Liping
Division of Hepatobiliary and Pancreas Surgery, Department of General Surgery, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), 518020 Shenzhen, China.
Integrated Chinese and Western Medicine Postdoctoral Research Station, Jinan University, 510632 Guangzhou, China.
Comput Struct Biotechnol J. 2023 Aug 6;21:3987-3998. doi: 10.1016/j.csbj.2023.08.003. eCollection 2023.
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.
挖掘基因表达数据对于发现肝细胞癌（HCC）的新型生物标志物和治疗靶点具有重要价值。尽管有新兴的数据挖掘工具可用于泛癌相关基因数据分析，但专门针对HCC的工具却很少。此外，专门为HCC设计的工具存在数据规模小和功能有限等限制。因此，我们开发了IHGA，这是一个新的交互式网络服务器，用于大规模、全面地发现HCC中感兴趣的基因。综合HCC基因分析（IHGA）包含100多个独立的HCC患者来源数据集（超过10,000个组织样本）和90多个细胞模型。IHGA允许用户基于基因mRNA水平进行一系列大规模、全面的分析和数据可视化，包括表达比较、相关性分析、临床特征分析、生存分析、免疫系统相互作用分析和药物敏感性分析。这种方法显著提高了IHGA中临床数据的丰富度。此外，IHGA整合了基于自然语言模型的人工智能（AI）辅助基因筛选。IHGA免费、用户友好，能有效减少数据收集、整理和分析过程中花费的时间。总之，IHGA在数据规模、数据多样性和功能方面具有竞争力。它有效缓解了HCC异质性给数据挖掘工作带来的障碍，有助于推动HCC分子机制的研究。
