Integrating Bulk and Single-Cell RNA Sequencing to Identify Prognostic Genes Associated with Adaptive Immunity and the Tumor Microenvironment in Cholangiocarcinoma for Risk Model Construction.

BACKGROUND

Cholangiocarcinoma (CCA) has a complex tumor microenvironment (TME) and poor immunotherapy response. Understanding how the adaptive immune response (AIR) affects the TME and prognosis is critical.

METHODS

All data were obtained from publicly available resources. Differentially expressed genes (DEGs) were identified by comparing tumor vs. normal and stromal/immune score-based samples, and were then intersected with an AIR gene set to obtain candidate genes. Prognostic genes identified by univariate Cox regression were used to build a risk model. Analyses included functional enrichment, immune landscape, drug sensitivity, and single-cell RNA sequencing (scRNA-seq) to identify key cell types, cell-cell communication, and pseudotime trajectories.

RESULTS

Intersection analysis yielded 68 candidate genes, from which eight prognostic genes (C1QA, CD55, ZAP70, CLEC4M, FCAMR, LILRA1, CD80, C17orf99) were identified. The risk model stratified patients into high-risk (HRG) and low-risk (LRG) groups with significantly different overall survival. LRG was enriched for immune activation pathways, whereas HRG exhibited an immune-excluded phenotype (lower CD8+ T cell infiltration, higher exclusion score). LRG showed higher sensitivity to gemcitabine and ABT-737. Macrophages were identified as key cells, with enhanced malignant cell-macrophage communication via the MIF-(CD74 + CXCR4) axis in CCA. Macrophages comprised seven heterogeneous subtypes and five distinct differentiation states. Firmicutes abundance was lower in the HRG. A nomogram integrating risk score and Firmicutes demonstrated favorable predictive performance.

CONCLUSION

An AIR-related gene signature stratifies CCA prognosis by reflecting TME composition rather than tumor-intrinsic drivers, highlighting immune cell-centric mechanisms and intratumoral microbiota. The nomogram may aid prognostic stratification in research settings.