Wu Ronghuo, Deng Xiaoxia, Wang Xiaomin, Li Shanshan, Su Jing, Sun Xiaoyan
Department of Economics, Jinan University, Guangzhou, 510632, China.
School of Mathematics and Statistics, Yulin Normal University, Yulin, 537000, China.
Heliyon. 2024 Aug 22;10(17):e36561. doi: 10.1016/j.heliyon.2024.e36561. eCollection 2024 Sep 15.
Recent studies reveal that necroptosis is pivotal in tumorigenesis, cancer metastasis, cancer immunity, and cancer subtypes. Apoptosis or necroptosis of hepatocytes in the liver microenvironment can determine the subtype of liver cancer. However, necroptosis-related genomes have rarely been analyzed in hepatocellular carcinoma (HCC). Therefore, this study aims to construct an HCC risk scoring model based on necroptosis-related genes and to validate its predictive performance in overall survival prediction and immunotherapy efficacy evaluation in HCC, as well as to analyze drug treatment responses.
This study analyzed clinical information and RNA-seq expression data of liver cancer patients from TCGA public data, identified necroptosis-related genes, and conducted GO and KEGG enrichment analyses. Using Cox regression analysis and LASSO analysis to identify independent prognostic factors, a predictive model was established and validated in clinical subgroups, and correlation analysis with immune cells and ssGSEA differential analysis were conducted. Finally, potential drugs for HCC were screened to explore the drug sensitivity of different subtypes.
We identified 19 differentially expressed necroptosis-related genes and constructed a predictive model with 3 independent prognostic factors through stepwise Cox regression. Validation results from clinical subgroups showed that the constructed model performed well in risk prediction, and ssGSEA differential analysis results were significant. We analyzed 55 immunotherapy drugs, and clustered them by distinct IC50 values to guide drug selection for HCC patients. Notable, Bleomycin, Obatoclax. Mesylate, PF.562271, PF.02341066, QS11, X17. AAG, and Bl. D1870 exhibited significantly different sensitivities in different subtypes, providing references for clinical practice in HCC patients.
近期研究表明，坏死性凋亡在肿瘤发生、癌症转移、癌症免疫及癌症亚型中起关键作用。肝微环境中肝细胞的凋亡或坏死性凋亡可决定肝癌的亚型。然而，肝细胞癌（HCC）中与坏死性凋亡相关的基因组很少被分析。因此，本研究旨在构建基于坏死性凋亡相关基因的HCC风险评分模型，并验证其在HCC总生存预测和免疫治疗疗效评估中的预测性能，以及分析药物治疗反应。
本研究分析了来自TCGA公共数据的肝癌患者的临床信息和RNA测序表达数据，鉴定了坏死性凋亡相关基因，并进行了基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析。使用Cox回归分析和套索分析确定独立预后因素，建立预测模型并在临床亚组中进行验证，同时进行与免疫细胞的相关性分析和单样本基因集富集分析（ssGSEA）差异分析。最后，筛选HCC的潜在药物，以探索不同亚型的药物敏感性。
我们鉴定出19个差异表达的坏死性凋亡相关基因，并通过逐步Cox回归构建了一个包含3个独立预后因素的预测模型。临床亚组的验证结果表明，构建的模型在风险预测方面表现良好，且ssGSEA差异分析结果显著。我们分析了55种免疫治疗药物，并根据不同的半数抑制浓度（IC50）值对它们进行聚类，以指导HCC患者的药物选择。值得注意的是，博来霉素、甲磺酸奥巴克拉、PF.562271、PF.02341066、QS11、X17.AAG和Bl.D1870在不同亚型中表现出显著不同的敏感性，为HCC患者的临床实践提供了参考。
