Integration analysis of senescence-related genes to predict prognosis and immunotherapy response in soft-tissue sarcoma: evidence based on machine learning and experiments

Lin Qi; Fangyue Chen; Lu Wang; Zhimin Yang; Wenchao Zhang; Zhihong Li

doi:10.3389/fphar.2023.1229233

Integration analysis of senescence-related genes to predict prognosis and immunotherapy response in soft-tissue sarcoma: evidence based on machine learning and experiments

Front Pharmacol. 2023 Jul 11:14:1229233. doi: 10.3389/fphar.2023.1229233. eCollection 2023.

Authors

Lin Qi^{1

2}, Fangyue Chen³, Lu Wang^{1

2}, Zhimin Yang^{1

2

4}, Wenchao Zhang^{1

2}, Zhihong Li^{1

2}

Affiliations

¹ Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China.
² Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China.
³ Department of General Surgery, Changhai Hospital, Navy Military Medical University, Shanghai, China.
⁴ Department of Microbiology, Immunology, and Molecular Genetics, University of Texas Long School of Medicine, UT Health Science Center, San Antonio, TX, United States.

Abstract

Background: Soft tissue sarcoma (STS) is the malignancy that exhibits remarkable histologic diversity. The diagnosis and treatment of STS is currently challenging, resulting in a high lethality. Chronic inflammation has also been identified as a key characteristic of tumors, including sarcomas. Although senescence plays an important role in the progression of various tumors, its molecular profile remains unclear in STS. Methods: We identified the senescence-related genes (SRGs) in database and depicted characteristics of genomic and transcriptomic profiling using cohort within TCGA and GEO database. In order to investigate the expression of SRGs in different cellular subtypes, single-cell RNA sequencing data was applied. The qPCR and our own sequencing data were utilized for further validation. We used unsupervised consensus clustering analysis to establish senescence-related clusters and subtypes. A senescence scoring system was established by using principal component analysis (PCA). The evaluation of clinical and molecular characteristics was conducted among distinct groups. Results: These SRGs showed differences in SCNV, mutation and mRNA expression in STS tissues compared to normal tissues. Across several cancer types, certain shared features of SRGs were identified. Several SRGs closely correlated with immune cell infiltration. Four clusters related to senescence and three subtypes related to senescence, each with unique clinical and biological traits, were established. The senescence scoring system exhibited effectiveness in predicting outcomes, clinical traits, infiltrations of immune cells and immunotherapy responses. Conclusion: Overall, the current study provided a comprehensive review of molecular profiling for SRGs in STS. The SRGs based clustering and scoring model could help guiding the clinical management of STS.

Keywords: immunotherapy response; molecular profiling; prediction model; senescence; soft-tissue sarcoma.

Grants and funding

This work was supported by the National Natural Science Foundation of China (82172500), the Science and Technology Development Fund Guided by Central Government (2021Szvup169) and Major Project of Technology Innovation of Hunan Province (2021SK1060).