Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling

被引:4
作者
Chen, Haiqing [1 ,2 ]
Zuo, Haoyuan [1 ,3 ]
Huang, Jinbang [2 ]
Liu, Jie [1 ,4 ]
Jiang, Lai [2 ]
Jiang, Chenglu [2 ]
Zhang, Shengke [2 ]
Hu, Qingwen [2 ]
Lai, Haotian [2 ]
Yin, Bangchao [5 ]
Yang, Guanhu [6 ]
Mai, Gang [1 ,3 ]
Li, Bo [1 ]
Chi, Hao [2 ]
机构
[1] Southwest Med Univ, Affiliated Hosp, Dept Gen Surg Hepatopancreatobiliary Surg, Luzhou, Peoples R China
[2] Southwest Med Univ, Affiliated Hosp, Sch Clin Med, Luzhou, Peoples R China
[3] Deyang Peoples Hosp, Dept Gen Surg Hepatopancreatobiliary Surg, Deyang, Peoples R China
[4] Dazhou Cent Hosp, Dept Gen Surg, Dazhou, Peoples R China
[5] Sixth Peoples Hosp Yibin, Dept Pathol, Yibin, Peoples R China
[6] Ohio Univ, Dept Specialty Med, Athens, OH 45701 USA
关键词
immune microenvironment; machine learning; multi-omics; single-cell analysis; spatial transcriptome; T cells; tumour heterogeneity; INHIBITORY FACTOR MIF; ACTIVATION; PROGNOSIS;
D O I
10.1111/jcmm.18403
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single-cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high-density weighted gene co-expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell-associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK-8 assays, plate cloning assays, wound-healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid- to late-stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG-based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T-cell heterogeneity in KIRC using single-cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.
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页数:29
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