Integrating Immunogenetics, Single-Cell Transcriptomics, and AI-Assisted Drug Discovery Reveals CXCL10-Associated Immune Remodelling in Breast Cancer.

Authors

Hanmo Wang, Yaxin Li, Yanming Chen, Housheng Chen, Yucang He, Yu Zhou

Published in

International journal of immunogenetics. Oct 31, 2026. Epub Oct 31, 2026.

Abstract

Breast cancer is a heterogeneous malignancy shaped by genetic alterations and cytokine-mediated immune interactions. However, integrative characterization of cytokine-related regulators using genetic, single-cell, and computational approaches remains limited. This study aimed to identify genetically supported cytokine-related candidates in breast cancer, characterize their immune and cellular contexts, and explore their potential regulatory and therapeutic relevance. Bulk transcriptomic datasets were integrated to identify differentially expressed cytokine-cytokine receptor interaction-related genes, followed by two-sample Mendelian randomization (MR), functional enrichment, immune infiltration, single-cell RNA sequencing, CellChat analysis, in silico gene perturbation, AI-assisted compound screening, and molecular docking. A total of 882 differentially expressed genes and 20 cytokine-related candidates were identified. MR provided the most consistent support for CXCL10, with genetically predicted higher expression associated with increased breast cancer risk (OR = 1.112, 95% CI: 1.026-1.206, p = 0.010). PRLR was significant only in the IVW analysis and was therefore considered a candidate requiring further validation. The CXCL10/PRLR model achieved an AUC of 0.810 (95% CI: 0.774-0.846) for discriminating breast cancer from normal samples. CXCL10 was predominantly associated with immune-cell populations, interferon-related programs, and predicted chemokine communication. In silico perturbation, AI-assisted screening, and docking further generated computational hypotheses regarding CXCL10-associated regulation and candidate compounds. CXCL10 represents a genetically supported immune-associated candidate in breast cancer. Overall, the findings are computational and hypothesis-generating and require validation in independent cohorts and experimental models.

PMID:
42965880
Bibliographic data and abstract were imported from PubMed on 01 Nov 2026.

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