Table7_Retinoic Acid Metabolism-Related Enzyme Signature Identified Prognostic and Immune Characteristics in Sarcoma.xlsx

Growing evidence indicates a link between retinoic acid (RA) metabolism and sarcoma progression or immunity in laboratory studies. However, a comprehensive analysis of RA abnormality in the sarcoma population is still lacking. Herein, we systematically analyzed the molecular features of 19 retinoic acid metabolism-related enzymes and sarcoma patients’ clinical information based on TCGA/TARGET/GSE datasets. We identified two RA expression subtypes, which were related to distinct clinical survival outcomes and exhibited different biological features. Gene set enrichment analysis indicated a set of immune pathways were enriched in G1 while oncogenic pathways were enriched in G2. Immune cell infiltration analysis using the TIMER algorithm revealed more CD4+ and CD8+ T cell infiltration in G1 subgroups than in G2. Moreover, we generated a seven genes signature to predict the RA metabolism index based on the LASSO-penalized Cox regression model. Survival analysis demonstrated the significant prognostic differences between high- and low-risk groups among different bone and soft tissue datasets. A higher risk index was associated with less T cell CD8+ infiltration. The predictive ability of the RA risk score was validated in 71 bone or soft tissue sarcoma clinical samples. These results indicated that RA-based classification could distinguish sarcoma patients with different clinical outcomes and immune statuses, which may help to explore better treatment decision-making for sarcoma patients.越来越多的实验室研究证据表明，视黄酸（retinoic acid, RA）代谢与肉瘤的进展或免疫状态存在关联。然而，目前仍缺乏针对肉瘤人群中RA异常情况的系统性分析。在此，我们基于TCGA、TARGET及GSE数据集，系统性分析了19种视黄酸代谢相关酶的分子特征以及肉瘤患者的临床信息。我们鉴定出两种RA表达亚型，它们与不同的临床生存结局相关，并呈现出各异的生物学特征。基因集富集分析结果显示，G1亚型富集了一系列免疫通路，而G2亚型则富集了致癌通路。基于TIMER算法的免疫细胞浸润分析显示，G1亚组的CD4+及CD8+ T细胞浸润水平高于G2亚组。此外，我们基于LASSO惩罚Cox回归模型构建了用于预测RA代谢指数的7基因特征。生存分析结果证实，在不同骨与软组织肉瘤数据集的高低风险组间，均存在显著的预后差异。较高的风险指数与较低的CD8+ T细胞浸润水平相关。我们在71例骨或软组织肉瘤临床样本中验证了RA风险评分的预测能力。上述结果表明，基于RA的分类能够区分具有不同临床结局与免疫状态的肉瘤患者，这可为肉瘤患者优化治疗决策提供参考依据。