Table_7_Tumor purity–associated genes influence hepatocellular carcinoma prognosis and tumor microenvironment.xls

IntroductionTumor purity takes on critical significance to the progression of solid tumors. The aim of this study was at exploring potential prognostic genes correlated with tumor purity in hepatocellular carcinoma (HCC) by bioinformatics analysis.
MethodsThe ESTIMATE algorithm was applied for determining the tumor purity of HCC samples from The Cancer Genome Atlas (TCGA). The tumor purity–associated genes with differential expression (DEGs) were identified based on overlap analysis, weighted gene co-expression network analysis (WGCNA), and differential expression analysis. The prognostic genes were identified in terms of the prognostic model construction based on the Kaplan–Meier (K–M) survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses. The expression of the above-described genes was further validated by the GSE105130 dataset from the Gene Expression Omnibus (GEO) database. We also characterized the clinical and immunophenotypes of prognostic genes. Gene set enrichment analysis (GSEA) was carried out for exploring the biological signaling pathway.
ResultsA total of 26 tumor purity–associated DEGs were identified, which were involved in biological processes such as immune/inflammatory responses and fatty acid elongation. Ultimately, we identified ADCK3, HK3, and PPT1 as the prognostic genes for HCC. Moreover, HCC patients exhibiting higher ADCK3 expression and lower HK3 and PPT1 expressions had a better prognosis. Furthermore, high HK3 and PPT1 expressions and low ADCK3 expression resulted in high tumor purity, high immune score, high stromal score, and high ESTIMATE score. GSEA showed that the abovementioned prognostic genes showed a significant correlation with immune-inflammatory response, tumor growth, and fatty acid production/degradation.
DiscussionIn conclusion, this study identified novel predictive biomarkers (ADCK3, HK3, and PPT1) and studied the underlying molecular mechanisms of HCC pathology initially.

Introduction
肿瘤纯度对于实体瘤的进展具有至关重要的意义。本研究旨在通过生物信息学分析，探索肝细胞癌（hepatocellular carcinoma, HCC）中与肿瘤纯度相关的潜在预后基因。
Methods
本研究采用ESTIMATE算法对癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库中的肝细胞癌样本进行肿瘤纯度计算。通过重叠分析、加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）以及差异表达分析，筛选出与肿瘤纯度相关的差异表达基因（differentially expressed genes, DEGs）。基于卡普兰-迈耶（Kaplan–Meier, K-M）生存分析及最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）回归分析构建预后模型，以此筛选预后相关基因。通过基因表达综合数据库（Gene Expression Omnibus, GEO）中的GSE105130数据集，对上述基因的表达水平进行验证。此外，本研究还对预后相关基因的临床表型及免疫表型进行了表征。通过基因集富集分析（gene set enrichment analysis, GSEA）探究其潜在的生物学信号通路。
Results
本研究共筛选出26个与肿瘤纯度相关的差异表达基因，这些基因主要参与免疫/炎症反应、脂肪酸延伸等生物学过程。最终，本研究确定ADCK3、HK3及PPT1为肝细胞癌的预后相关基因。此外，ADCK3表达水平较高、HK3与PPT1表达水平较低的肝细胞癌患者预后更佳。进一步分析发现，HK3与PPT1高表达、ADCK3低表达的样本往往具有更高的肿瘤纯度、免疫评分、基质评分及ESTIMATE评分。基因集富集分析结果显示，上述预后相关基因与免疫炎症反应、肿瘤生长及脂肪酸合成/降解过程显著相关。
Discussion
综上，本研究筛选出了全新的肝细胞癌预后生物标志物（ADCK3、HK3及PPT1），并初步阐明了肝细胞癌病理发生的潜在分子机制。