南方医科大学学报

南方医科大学学报 ›› 2025, Vol. 45 ›› Issue (9): 1967-1979.doi: 10.12122/j.issn.1673-4254.2025.09.16

• • 上一篇    

原发性肝癌患者的临床结局与治疗反应预测模型：基于失巢凋亡和免疫基因

王莹1(), 李静1, 王伊迪2, 华明钰1, 胡玮彬1, 张晓智1()   

  1. 1.西安交通大学第一附属医院，放射肿瘤科，陕西 西安 710061
    2.西安交通大学第一附属医院，乳腺外科，陕西 西安 710061
  • 收稿日期:2025-02-19 出版日期:2025-09-20 发布日期:2025-09-28
  • 通讯作者: 张晓智 E-mail:wangying123456@stu.xjtu.edu.cn;zhangxiaozhi@xjtu.edu.cn
  • 作者简介:王 莹，在读硕士研究生，E-mail: wangying123456@stu.xjtu.edu.cn
  • 基金资助:
    陕西省自然科学基金(2025JC-YBQN-1105)

Construction and verification of a prognostic model combining anoikis and immune prognostic signatures for primary liver cancer

Ying WANG1(), Jing LI1, Yidi WANG2, Mingyu HUA1, Weibin HU1, Xiaozhi ZHANG1()   

  1. 1.Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
    2.Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
  • Received:2025-02-19 Online:2025-09-20 Published:2025-09-28
  • Contact: Xiaozhi ZHANG E-mail:wangying123456@stu.xjtu.edu.cn;zhangxiaozhi@xjtu.edu.cn

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摘要：

目的 利用生物信息学方法构建原发性肝癌（PLC）的预后模型。 方法 在The Cancer Genome Atlas （TCGA）数据库中纳入了404名PLC患者。使用单变量 Cox 回归及LASSO-Cox方法构建失巢凋亡和免疫相关基因（DAIs）的预后模型。Kaplan-Meier法和受试者工作特征曲线用于评估模型的预测能力，建立列线图以方便临床应用。进行基因集富集分析（GSEA）揭示相关通路，并使用CIBERSORT和TIDE方法进一步探讨DAIs与肿瘤免疫微环境之间的关系。使用“pRRophetic”R包来计算PLC药物的半数最大抑制浓度（IC50）。检测了PLC患者组织中SEMA7A的表达。 结果 构建并验证了基于7个DAIs（NR4A3、SEMA7A、IL11、AR、BIRC5、EGF和SPP1）的预后模型，在TCGA训练队列和GEO验证队列（GSE14520）中均发现一致的结果，即低风险组患者具有更好的临床预后和良好的免疫状态。将该预后模型与临床信息相结合，生成复合列线图以促进临床实践。体细胞突变分析显示TTN、TP53和CTNNB1突变占总突变比例最大，低风险-低TMB组生存率更高。药物敏感性分析显示高风险与低风险组之间以及TP53突变与非突变之间对化疗药物的敏感性存在差异。免疫组化结果显示SEMA7A在PLC中的表达高于癌旁正常肝组织（P<0.05）。 结论 本研究建立了一个基于DAIs的新的预测模型，用于预测PLC患者的临床结局和治疗反应，为个体化治疗提供新思路。

关键词: 肝癌, 失巢凋亡, 免疫相关基因, 预后模型, 生物信息学

Abstract:

Objective To establish a prognostic model for primary liver cancer (PLC) using bioinformatics methods. Methods Based on the data from 404 patients in the Cancer Genome Atlas (TCGA) database, we constructed a prognostic model integrating the differentially expressed genes, anoikis, and immune-related genes (DAIs) using univariate Cox regression and the LASSO-Cox approach. The predictive ability of the model was evaluated using Kaplan-Meier method and receiver-operating characteristic curves, and a nomogram was developed to facilitate its clinical applications. Gene set enrichment analysis (GSEA) was performed to explore the associated pathways and relationship between the DAIs and the tumor immune microenvironment, and the half-maximal inhibitory concentration (IC50) of liver cancer drugs was calculated using the "pRRophetic" R package. We also detected the expression of SEMA7A in paired tumor and adjacent tissues from liver cancer patients. Results We constructed and validated a prognostic model based on 7 DAIs (NR4A3, SEMA7A, IL11, AR, BIRC5, EGF, and SPP1), and obtained consistent results in both the TCGA training cohort and GEO validation cohort (GSE14520), where the patients in the low-risk group were characterized by more favorable clinical outcomes and immune status. By integrating this prognostic signature with clinical information, a composite nomogram was generated. Somatic mutation analysis showed that TTN, TP53, and CTNNB1 mutations accounted for the largest proportion of total mutations, and the patients in the low-risk-low-TMB group had higher survival rate. Drug sensitivity analysis revealed differences in sensitivity to chemotherapeutic agents between high- and low-risk groups and between TP53 mutations and non-mutations. In clinical tissue specimens, SEMA7A expression was significantly higher in liver cancer tissues than in the adjacent tissues. Conclusions We established a new prognostic model based on DAIs for predicting clinical outcomes and therapeutic response of patients with primary liver cancer.

Key words: liver cancer, anoikis, immune-related genes, prognosis model, bioinformatics