Liu Shanshan, Yu Guangchuang, Liu Li, Zou Xuejing, Zhou Lang, Hu Erqiang, Song Yang
Country Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Front Genet. 2021 Feb 11;12:625236. doi: 10.3389/fgene.2021.625236. eCollection 2021.
A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the liver cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to identify stromal-immune score-based potential prognostic biomarkers for hepatocellular carcinoma. Stromal and immune scores were estimated from transcriptomic profiles of a liver cancer cohort from The Cancer Genome Atlas using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select prognostic genes. Favorable overall survivals and progression-free interval were found in patients with high stromal score and immune score, and 828 differentially expressed genes were identified. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. (matrix metallopeptidase 9) was identified as a prognostic tumor microenvironment-associated gene by using LASSO and TIMER (Tumor IMmune Estimation Resource) algorithms and was found to be positively correlated with immunosuppressive molecules and drug response.
越来越多的证据表明,基质细胞和免疫细胞在肝癌微环境中具有临床重要性。然而,基于基质和免疫成分评估的可靠预后特征尚未得到很好的确立。本研究旨在识别基于基质-免疫评分的肝细胞癌潜在预后生物标志物。使用ESTIMATE(利用表达数据估计恶性肿瘤中的基质和免疫细胞)算法,从癌症基因组图谱的肝癌队列转录组谱中估计基质和免疫评分。应用最小绝对收缩和选择算子(LASSO)算法来选择预后基因。在基质评分和免疫评分高的患者中发现了良好的总生存期和无进展生存期,并鉴定出828个差异表达基因。功能富集分析和蛋白质-蛋白质相互作用网络进一步表明,这些基因主要参与免疫反应、细胞外基质和细胞粘附。基质金属蛋白酶9通过LASSO和TIMER(肿瘤免疫估计资源)算法被鉴定为预后肿瘤微环境相关基因,并发现其与免疫抑制分子和药物反应呈正相关。