Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas

Ao-Yu Li; Jie Bu; Hui-Ni Xiao; Zi-Yue Zhao; Jia-Lin Zhang; Bin Yu; Hui Li; Jin-Ping Li; Tao Xiao

doi:10.1016/j.heliyon.2024.e38253

Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas

Heliyon. 2024 Oct 1;10(20):e38253. doi: 10.1016/j.heliyon.2024.e38253. eCollection 2024 Oct 30.

Authors

Ao-Yu Li^{1

2}, Jie Bu³, Hui-Ni Xiao⁴, Zi-Yue Zhao^{1

2}, Jia-Lin Zhang^{1

2}, Bin Yu^{1

2}, Hui Li^{1

2}, Jin-Ping Li⁵, Tao Xiao^{1

2}

Affiliations

¹ Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China.
² Orthopedic Biomedical Materials Engineering Laboratory of Hunan Province, Changsha, China.
³ Department of Orthopedics, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
⁴ Department of Gastroenterology, The Second Affiliated Hospital, University of South China, Hengyang, China.
⁵ Department of Orthopedics, Changsha Central Hospital, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.

Abstract

To conduct a comprehensive investigation of the sarcoma immune cell infiltration (ImmCI) patterns and tumoral microenvironment (TME). We utilized transcriptomic, clinical, and mutation data of sarcoma patients (training cohort) obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) server. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were applied to decipher the immune cell infiltration landscape and TME profiles of sarcomas. An unsupervised clustering method was utilized for classifying ImmCI clusters (initial clustering) and ImmCI-based differentially expressed gene-driven clusters (secondary clustering). Mortality rates and immune checkpoint gene levels was analyzed among the identified clusters. We calculated the ImmCI score through principal component analysis. The tumor immune dysfunction evaluation (TIDE) score was also employed to quantify immunotherapy efficacy between two ImmCI score groups. We further validated the biomarkers for ImmCI and gene-driven clusters via experimental verification and the accuracy of the ImmCI score in predicting survival outcomes and immunotherapy efficacy by external validation cohorts (testing cohort). We demonstrated that ImmCI cluster A and gene-driven cluster A, were beneficial prognostic biomarkers and indicators of immune checkpoint blockade response in sarcomas via in-silico and laboratory experiments. Additionally, the ImmCI score exhibited independent prognostic significance and was predictive of immunotherapy response. Our research underscores the clinical significance of ImmCI scores in identifying sarcoma patients likely to respond to immunotherapy.

Keywords: Immune cell infiltration; Immune checkpoint blade; Prognosis; Sarcomas; Tumor microenvironment.