Department of General Surgery, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, 236000, Anhui, China.
Department of Anesthesiology, Chaohu Hospital Affiliated to Anhui Medical University, Chaohu, 238000, Anhui, China.
Hepatol Int. 2022 Aug;16(4):906-917. doi: 10.1007/s12072-022-10354-3. Epub 2022 Jun 14.
This study clarified whether EMT-related genes can predict immunotherapy efficacy and overall survival in patients with HCC.
The RNA-sequencing profiles and patient information of 370 samples were derived from the Cancer Genome Atlas (TCGA) dataset, and EMT-related genes were obtained from the Molecular Signatures database. The signature model was constructed using the least absolute shrinkage and selection operator Cox regression analysis in TCGA cohort. Validation data were obtained from the International Cancer Genome Consortium (ICGC) dataset of patients with HCC. Kaplan-Meier analysis and multivariate Cox analyses were employed to estimate the prognostic value. Immune status and tumor microenvironment were estimated using a single-sample gene set enrichment analysis (ssGSEA). The expression of prognostic genes was verified using qRT-PCR analysis of HCC cell lines.
A signature model was constructed using EMT-related genes to determine HCC prognosis, based on which patients were divided into high-risk and low-risk groups. The risk score, as an independent factor, was related to tumor stage, grade, and immune cells infiltration. The results indicated that the most prognostic genes were highly expressed in the HCC cell lines, but GADD45B was down-regulated. Enrichment analysis suggested that immunoglobulin receptor binding and material metabolism were essential in the prognostic signature.
Our novel prognostic signature model has a vital impact on immune status and prognosis, significantly helping the decision-making related to the diagnosis and treatment of patients with HCC.
本研究旨在阐明 EMT 相关基因能否预测 HCC 患者的免疫治疗疗效和总生存期。
从癌症基因组图谱（TCGA）数据库中获取了 370 个样本的 RNA-seq 图谱和患者信息，从分子特征数据库中获得了 EMT 相关基因。使用 TCGA 队列中的最小绝对收缩和选择算子 Cox 回归分析构建了特征模型。验证数据来自国际癌症基因组联盟（ICGC）的 HCC 患者数据集。使用 Kaplan-Meier 分析和多变量 Cox 分析来估计预后价值。使用单样本基因集富集分析（ssGSEA）来评估免疫状态和肿瘤微环境。使用 qRT-PCR 分析 HCC 细胞系来验证预后基因的表达。
构建了一个基于 EMT 相关基因的signature 模型来确定 HCC 的预后，根据该模型将患者分为高风险和低风险组。风险评分作为一个独立因素与肿瘤分期、分级和免疫细胞浸润有关。结果表明，最具预后意义的基因在 HCC 细胞系中高表达，但 GADD45B 下调。富集分析表明，免疫球蛋白受体结合和物质代谢在预后signature 中至关重要。
我们的新型预后 signature 模型对免疫状态和预后具有重要影响，显著有助于 HCC 患者诊断和治疗相关决策的制定。
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