Department of Hematology and Oncology, Beilun District People’s Hospital, Ningbo, China.
Department of Oncology, Weifang People’s Hospital, Weifang, China.
Aging (Albany NY). 2023 Oct 18;15(20):11092-11113. doi: 10.18632/aging.205099.
Cancer-associated fibroblasts (CAFs) regulate the malignant biological behaviour of hepatocellular carcinoma (HCC) as a significant component of the tumour immune microenvironment (TIME). This study aimed to develop a CAFs-based scoring system to predict the prognosis and TIME of patients with HCC.
Data for the TCGA-LIHC and GSE14520 cohorts were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Single-cell RNA-sequencing data for HCC samples were retrieved from the GSE166635 cohort. The Least Absolute Shrinkage and Selection Operator algorithm was employed to develop a CAFs-related scoring system (CAFRss). The predictive value of the CAFRss was determined using Kaplan-Meier, Cox regression and Receiver Operating Characteristic curves. Additionally, the TIMER platform, single sample Gene Set Enrichment Analysis and the Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data algorithms were performed to determine the TIME landscape. Finally, the pRRophic algorithm was utilised for drug sensitivity analysis.
The evaluation of the CAFRss system demonstrated its superior ability to predict the clinical outcome of patients with HCC. Additionally, CAFRss effectively distinguished HCC populations with distinct TIME landscapes. Furthermore, CAFRss-based risk stratification identified individuals with immune 'hot tumours' and predicted the survival of patients treated with ICBs.
The developed CAFRss can serve as a predictive tool for determining the clinical outcome of HCC and differentiating populations with diverse TIME characteristics.
癌症相关成纤维细胞（CAFs）作为肿瘤免疫微环境（TIME）的重要组成部分，调节肝癌（HCC）的恶性生物学行为。本研究旨在开发一种基于 CAFs 的评分系统，以预测 HCC 患者的预后和 TIME。
从癌症基因组图谱和基因表达综合数据库下载 TCGA-LIHC 和 GSE14520 队列的数据。从 GSE166635 队列中检索 HCC 样本的单细胞 RNA 测序数据。采用最小绝对收缩和选择算子算法（Least Absolute Shrinkage and Selection Operator algorithm）开发 CAFs 相关评分系统（CAFs-related scoring system，CAFRss）。采用 Kaplan-Meier、Cox 回归和Receiver Operating Characteristic 曲线确定 CAFRss 的预测价值。此外，还通过 TIMER 平台、单样本基因集富集分析（Single sample Gene Set Enrichment Analysis）以及使用表达数据算法估计恶性肿瘤组织中的基质和免疫细胞（Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data algorithms）来确定 TIME 图谱。最后，利用 pRRophic 算法进行药物敏感性分析。
CAFRss 系统的评估表明，其具有预测 HCC 患者临床结局的优异能力。此外，CAFRss 还能有效区分具有不同 TIME 特征的 HCC 人群。此外，CAFRss 基于风险分层的方法可以识别具有免疫“热肿瘤”的个体，并预测接受 ICB 治疗的患者的生存情况。
开发的 CAFRss 可作为预测 HCC 临床结局和区分具有不同 TIME 特征人群的工具。
