Suppr超能文献

肝细胞癌中一种新型三级淋巴结构相关预后基因特征的鉴定与验证

Identification and Validation of a Novel Tertiary Lymphoid Structures-Related Prognostic Gene Signature in Hepatocellular Carcinoma.

作者信息

Liu Yin, Li Chao Bo, Zhai Yun Peng, Zhang Shao Kang, Li Ding Yang, Gao Zhi Qiang, Liang Ruo Peng

机构信息

Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Yin Liu and Chao Bo Li contributed equally to this work.

出版信息

World J Oncol. 2024 Aug;15(4):695-710. doi: 10.14740/wjon1893. Epub 2024 Jul 5.

Abstract

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors originating from the digestive system. Tertiary lymphoid structures (TLS), non-lymphoid tissues outside of the lymphoid organs, are closely connected to chronic inflammation and tumorigenesis. However, the detailed relationship between TLS and HCC prognosis remained unclear. In this study, we aimed to construct a TLS-related gene signature for predicting the prognosis of HCC patients.

METHODS

The Cancer Genome Atlas (TCGA) clinical data from 369 HCC tissues and 50 normal liver tissues were utilized to examine the differential expression of TLS-related genes. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, the prognostic model was constructed using the TCGA cohort and validated in the GSE14520 cohort and International Cancer Genome Consortium (ICGC) cohort. The Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were employed to validate the predictive ability of the prognostic model. Furthermore, Cox regression analysis was applied to identify whether the TLS score could be employed as an independent prognosis factor. A nomogram was developed to predict the survival probability of HCC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for TLS-related genes. Genetic mutation analysis, the CIBERSORT algorithm, and single-sample gene set enrichment analysis (ssGSEA) were used to assess the tumor mutation landscape and immune infiltration. Finally, the role of the TLS score in HCC therapy was investigated.

RESULTS

Six genes were included in the construction of our prognostic model (CETP, DNASE1L3, PLAC8, SKAP1, C7, and VNN2), and we validated its accuracy. Survival analysis showed that patients in the high-TLS score group had a significantly better overall survival than those in the low-TLS score group. Univariate, multivariate Cox regression analysis and the establishment of a nomogram indicated that the TLS score could independently function as a potential prognostic marker. A significant association between TLS score and immunity was revealed by an analysis of gene alterations and immune cell infiltration. In addition, two subtypes of the TLS score could accurately predict the effectiveness of sorafenib, transcatheter arterial chemoembolization (TACE), and immunotherapy in HCC patients.

CONCLUSION

In this research, we conducted and validated a prognostic model associated with TLS that may be helpful for predicting clinical outcomes and treatment responsiveness for HCC patients.

摘要

背景

肝细胞癌(HCC)是消化系统最常见的恶性肿瘤之一。三级淋巴结构(TLS)是淋巴器官外的非淋巴组织,与慢性炎症和肿瘤发生密切相关。然而,TLS与HCC预后的具体关系仍不清楚。在本研究中,我们旨在构建一个与TLS相关的基因特征,用于预测HCC患者的预后。

方法

利用来自369例HCC组织和50例正常肝组织的癌症基因组图谱(TCGA)临床数据,检测TLS相关基因的差异表达。基于最小绝对收缩和选择算子(LASSO)Cox回归分析,使用TCGA队列构建预后模型,并在GSE14520队列和国际癌症基因组联盟(ICGC)队列中进行验证。采用Kaplan-Meier(KM)曲线和受试者工作特征(ROC)曲线验证预后模型的预测能力。此外,应用Cox回归分析确定TLS评分是否可作为独立的预后因素。绘制列线图以预测HCC患者的生存概率。对TLS相关基因进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)通路分析。使用基因突变分析、CIBERSORT算法和单样本基因集富集分析(ssGSEA)评估肿瘤突变图谱和免疫浸润。最后,研究TLS评分在HCC治疗中的作用。

结果

我们的预后模型构建中纳入了6个基因(CETP、DNASE1L3、PLAC8、SKAP1、C7和VNN2),并验证了其准确性。生存分析表明,高TLS评分组患者的总生存期明显优于低TLS评分组。单因素、多因素Cox回归分析和列线图的建立表明,TLS评分可独立作为潜在的预后标志物。通过基因改变和免疫细胞浸润分析,揭示了TLS评分与免疫之间的显著关联。此外,TLS评分的两种亚型可以准确预测索拉非尼、经动脉化疗栓塞(TACE)和免疫治疗对HCC患者的疗效。

结论

在本研究中,我们构建并验证了一个与TLS相关的预后模型,该模型可能有助于预测HCC患者的临床结局和治疗反应性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b170/11236367/8af04f07d9f5/wjon-15-695-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验