Translational cancer research · 2026
Tertiary lymphoid structure-related genes drive tumor microenvironment heterogeneity and prognostic disparities in left- vs. right-sided colon cancer.
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Zhou Hongyi, Qian Xiaohong, Dai Qiujun, Zhu Qiqi, Li Peiyuan, Cui Wei, Zhao Yibin
Аннотация
BACKGROUND: Colorectal cancer (CRC) exhibits significant heterogeneity between left-sided (LCC) and right-sided (RCC) subtypes, influenced by tertiary lymphoid structures-related genes (TLSRGs). This study aimed to explore TLSRGs' roles in LCC/RCC differences, including expression, mutations, prognosis, and molecular pathways, while developing a prognostic risk model.
METHODS: Data were integrated from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) (324 samples: 135 LCC, 189 RCC) and Gene Expression Omnibus (GEO) datasets. TLSRGs were analyzed for differential expression, mutations, and prognostic associations using Wilcoxon tests, Cox regression, and gene set variation analysis (GSVA). Consensus clustering identified molecular subtypes. Single-cell analysis involved Seurat/Harmony for clustering and annotation, single-sample gene set enrichment analysis (ssGSEA) for scoring, and CellChat for cell communication. A machine learning-based risk model was constructed by screening ten algorithms (including SuperPC, RSF, CoxBoost, etc.) using the Mime package. The SuperPC algorithm demonstrated the best performance and was built on TLSRGs and related pathways, validated via Concordance index (C-index), Kaplan-Meier (KM) curves, and receiver operating characteristic (ROC) analysis.
RESULTS: Integrated analysis revealed significant heterogeneity between LCC and RCC, characterized by distinct somatic mutation profiles, differential expression of 27 TLSRGs, and subtype stratification via TLSRG-based consensus clustering (C1 enriched in RCC, P<0.001, with higher GSVA activity, P<0.001). Single-cell transcriptomics demonstrated reduced T-cell proportions, elevated epithelial cells, and lower tertiary lymphoid structure (TLS) scores in RCC, alongside weakened cell-cell communication strength (notably epithelial-T/B cell interactions). Prognostic genes exhibited location-specificity: FBLN7 [hazard ratio (HR) =7.09 for disease-specific survival (DSS)] and MS4A1 [HR =7.79 for disease-free interval (DFI)] predicted poor survival in LCC, while ICOS was protective in RCC [HR =0.42 for overall survival (OS)]. Drug sensitivity screening identified 22 agents with differential half-maximal inhibitory concentration (IC50) correlated to TLSRG expression, and immune infiltration analysis revealed reduced CD8+ T cells/follicular helper T cells in RCC. An 8-gene prognostic signature (HSPB1, NCOR2, STIL, etc.) derived via SuperPC effectively stratified high-risk patients in TCGA (HR =2.06, P=0.001) and GSE103479 (HR =1.86, P=0.02), with consistent performance across LCC/RCC subgroups.
CONCLUSIONS: TLSRGs underpin LCC/RCC heterogeneity in mutations, expression, immunity, and cell interactions. The developed risk model offers robust prognostic value, highlighting potential therapeutic targets for sidedness-specific CRC management.
METHODS: Data were integrated from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) (324 samples: 135 LCC, 189 RCC) and Gene Expression Omnibus (GEO) datasets. TLSRGs were analyzed for differential expression, mutations, and prognostic associations using Wilcoxon tests, Cox regression, and gene set variation analysis (GSVA). Consensus clustering identified molecular subtypes. Single-cell analysis involved Seurat/Harmony for clustering and annotation, single-sample gene set enrichment analysis (ssGSEA) for scoring, and CellChat for cell communication. A machine learning-based risk model was constructed by screening ten algorithms (including SuperPC, RSF, CoxBoost, etc.) using the Mime package. The SuperPC algorithm demonstrated the best performance and was built on TLSRGs and related pathways, validated via Concordance index (C-index), Kaplan-Meier (KM) curves, and receiver operating characteristic (ROC) analysis.
RESULTS: Integrated analysis revealed significant heterogeneity between LCC and RCC, characterized by distinct somatic mutation profiles, differential expression of 27 TLSRGs, and subtype stratification via TLSRG-based consensus clustering (C1 enriched in RCC, P<0.001, with higher GSVA activity, P<0.001). Single-cell transcriptomics demonstrated reduced T-cell proportions, elevated epithelial cells, and lower tertiary lymphoid structure (TLS) scores in RCC, alongside weakened cell-cell communication strength (notably epithelial-T/B cell interactions). Prognostic genes exhibited location-specificity: FBLN7 [hazard ratio (HR) =7.09 for disease-specific survival (DSS)] and MS4A1 [HR =7.79 for disease-free interval (DFI)] predicted poor survival in LCC, while ICOS was protective in RCC [HR =0.42 for overall survival (OS)]. Drug sensitivity screening identified 22 agents with differential half-maximal inhibitory concentration (IC50) correlated to TLSRG expression, and immune infiltration analysis revealed reduced CD8+ T cells/follicular helper T cells in RCC. An 8-gene prognostic signature (HSPB1, NCOR2, STIL, etc.) derived via SuperPC effectively stratified high-risk patients in TCGA (HR =2.06, P=0.001) and GSE103479 (HR =1.86, P=0.02), with consistent performance across LCC/RCC subgroups.
CONCLUSIONS: TLSRGs underpin LCC/RCC heterogeneity in mutations, expression, immunity, and cell interactions. The developed risk model offers robust prognostic value, highlighting potential therapeutic targets for sidedness-specific CRC management.