Li Zhehong, Zheng Honghong, Liu Lirui, Fen Zhen, Cao Haiying, Yang Jilong, Wei Junqiang
Department of Traumatology and Orthopaedics, Affiliated Hospital of Chengde Medical University, Chengde, China.
Department of General Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Front Oncol. 2022 Oct 13;12:990670. doi: 10.3389/fonc.2022.990670. eCollection 2022.
Tumorigenesis and progression are intimately associated with inflammation. However, the inflammatory landscape in soft tissue sarcoma (STS) and its clinical consequences are yet unknown, and more investigation is needed.
RNA-seq expression data for STS and corresponding normal tissues were downloaded from The Cancer Genome Atlas database and the Genotype-Tissue Expression Portal. Differential and prognostic analyses were performed based on known inflammatory response genes from Gene Set Enrichment Analysis (GSEA). We utilized LASSO-Cox analysis to determine hub genes and built an inflammatory score (INFscore) and risk stratification model. Furthermore, a nomogram, including the risk stratification model, was established to predict the prognosis. We further elucidated the characteristics among different risk STS patients by GSEA, gene set variation analysis, and detailed immune infiltration analysis. Finally, the INFscore and risk stratification model in predicting prognosis and depicting immune microenvironment status were verified by pan-cancer analysis.
Five hub genes (HAS2, IL1R1, NMI, SERPINE1, and TACR1) were identified and were used to develop the INFscore. The risk stratification model distinguished the immune microenvironment status and evaluated the efficacy of immunotherapy and chemotherapy in STS. The novel nomogram had good efficacy in predicting the prognosis of STS patients. Finally, a pan-cancer investigation verified the association of INFscore with prognosis and immunity.
According to the present study, the risk stratification model can be used to evaluate STS prognosis, tumor microenvironment status, immunotherapy, and chemotherapy efficacy. The novel nomogram has an excellent predictive value. Thus, the INFscore and risk stratification model has potential value in assessing the prognosis and immune status of multiple malignancies.
肿瘤发生和进展与炎症密切相关。然而，软组织肉瘤（STS）中的炎症格局及其临床后果尚不清楚，需要更多的研究。
从癌症基因组图谱数据库和基因型-组织表达门户下载STS及相应正常组织的RNA测序表达数据。基于基因集富集分析（GSEA）中已知的炎症反应基因进行差异分析和预后分析。我们利用LASSO-Cox分析确定枢纽基因，并构建炎症评分（INFscore）和风险分层模型。此外，建立了包括风险分层模型的列线图以预测预后。我们通过GSEA、基因集变异分析和详细的免疫浸润分析进一步阐明不同风险STS患者的特征。最后，通过泛癌分析验证了INFscore和风险分层模型在预测预后和描绘免疫微环境状态方面的作用。
鉴定出五个枢纽基因（HAS2、IL1R1、NMI、SERPINE1和TACR1），并用于开发INFscore。风险分层模型区分了免疫微环境状态，并评估了免疫治疗和化疗在STS中的疗效。新型列线图在预测STS患者预后方面具有良好的疗效。最后，泛癌研究验证了INFscore与预后和免疫的关联。
根据本研究，风险分层模型可用于评估STS预后、肿瘤微环境状态、免疫治疗和化疗疗效。新型列线图具有出色的预测价值。因此，INFscore和风险分层模型在评估多种恶性肿瘤的预后和免疫状态方面具有潜在价值。
