A novel inflammatory signature for evaluating immune microenvironment status in soft tissue sarcoma

Zhehong Li; Honghong Zheng; Lirui Liu; Zhen Fen; Haiying Cao; Jilong Yang; Junqiang Wei

doi:10.3389/fonc.2022.990670

A novel inflammatory signature for evaluating immune microenvironment status in soft tissue sarcoma

Front Oncol. 2022 Oct 13:12:990670. doi: 10.3389/fonc.2022.990670. eCollection 2022.

Authors

Zhehong Li^{1

2}, Honghong Zheng³, Lirui Liu⁴, Zhen Fen¹, Haiying Cao¹, Jilong Yang^{5

6}, Junqiang Wei^{1

5

6}

Affiliations

¹ Department of Traumatology and Orthopaedics, Affiliated Hospital of Chengde Medical University, Chengde, China.
² Department of General Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
³ Department of General Surgery, Affiliated Hospital of Chengde Medical University, Chengde, China.
⁴ Department of Neonatal Department, Affiliated Hospital of Chengde Medical University, Chengde, China.
⁵ Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
⁶ National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: cancer; immune; inflammatory landscape; soft tissue sarcoma; tumor microenvironment.