School of Pharmacy and Life Science, Jiujiang University, Jiujiang, Jiangxi 332005, China.
School of Basic Medicine, Jiujiang University, Jiujiang, Jiangxi 332005, China.
Gene. 2021 Jan 5;764:145105. doi: 10.1016/j.gene.2020.145105. Epub 2020 Aug 31.
Sarcoma (SARC) represents a group of highly histological and molecular heterogeneous rare malignant tumors with poor prognosis. There are few proposed classifiers for predicting patient's outcome. The Cancer Proteome Atlas (TPCA) and The Cancer Genome Atlas (TCGA) databases provide multi-omics datasets that enable a comprehensive investigation for this disease. The proteomic expression profile of SARC patients along with the clinical information was downloaded. 55 proteins were found to be associated with overall survival (OS) of patients using univariate Cox regression analysis. We developed a prognostic risk signature that comprises seven proteins (AMPKALPHA, CHK1, S6, ARID1A, RBM15, ACETYLATUBULINLYS40, and MSH6) with robust predictive performance using multivariate Cox stepwise regression analysis. Additionally, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters. Patients in high-risk group also have worse progression free intervals (PFI) than that of patients in low-risk group, but not for disease free intervals (DFI). The signature was validated using transcriptomic profile of SARC patients from TCGA. Potential mechanisms between high- and low-risk groups were identified using differentially expressed genes (DEGs) analysis. These DEGs were primarily enriched in RAS and MPAK signaling pathways. The signature protein molecules are candidate biomarkers for SARC, and the analysis of computational biology in tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinctly immune landscapes of high- and low-risk patients. Together, we constructed a prognostic signature for predicting outcomes for SARC integrating proteomic and transcriptomic profiles, this might have value in guiding clinical practice.
肉瘤（SARC）是一组具有高度组织学和分子异质性的罕见恶性肿瘤，预后不良。目前提出的预测患者预后的分类器很少。癌症蛋白质组图谱（TPCA）和癌症基因组图谱（TCGA）数据库提供了多组学数据集，使我们能够对这种疾病进行全面研究。下载了 SARC 患者的蛋白质组表达谱以及临床信息。使用单因素 Cox 回归分析发现 55 种蛋白与患者的总生存期（OS）相关。我们使用多因素 Cox 逐步回归分析开发了一个包含七个蛋白（AMPKALPHA、CHK1、S6、ARID1A、RBM15、ACETYLATUBULINLYS40 和 MSH6）的预后风险签名，具有稳健的预测性能。此外，该签名在调整临床病理参数后也可以作为独立的预后预测因子。高风险组的患者无进展间隔（PFI）比低风险组的患者差，但无疾病间隔（DFI）没有差异。该签名使用 TCGA 中 SARC 患者的转录组谱进行了验证。使用差异表达基因（DEGs）分析鉴定了高风险组和低风险组之间的潜在机制。这些 DEGs 主要富集在 RAS 和 MPAK 信号通路中。该签名蛋白分子是 SARC 的候选生物标志物，对肿瘤浸润淋巴细胞和免疫检查点分子的计算生物学分析揭示了高风险和低风险患者明显不同的免疫景观。综上所述，我们构建了一个基于蛋白质组学和转录组学的预测 SARC 患者预后的风险签名，这可能对指导临床实践具有价值。
