Integrated Multi-Omics Profiling Reveals Immune-Related Biomarkers and Regulatory Networks for Early Prediction of Tuberculosis in Type 2 Diabetes Mellitus

Zhaoyang Ye¹Guangliang Bai²Peng Cheng³Cong Peng⁴Yang Ling¹Li Zhuang¹Linsheng Li¹Yufeng Li¹Ruizi Ni¹Shuang Zhou¹Yajing An¹Mingming Zhang¹Yuan Tian¹Liang Wang⁴Wenping Gong^1*

• ¹Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
• ²Department of Clinical Laboratory, The Eighth Medical Center of PLA General Hospital, Beijing, China
• ³Handan Municipal Centre for Disease Prevention and Control, Hebei, China
• ⁴Department of Geriatrics, the Eighth Medical Center of PLA General Hospital, Beijing, China

Background: T2DM significantly elevates the risk of tuberculosis (TB); however, early detection in T2DM patients is still insufficient. This study aimed to identify immune-based early-warning biomarkers, develop robust prognostic models, and elucidate the immune-metabolic circuitry underlying the T2DM-TB. Methods: A prospective cohort study (n = 198; HC 71, T2DM 67, T2DM-TB 60) was conducted, involving whole-transcriptome and plasma-proteome profiling. Differential expression analysis, WGCNA, and mining of the ImmPort database facilitated the extraction of immune-relevant genes. PPI and competing endogenous RNA (ceRNA) networks were utilized to delineate core regulators. Eleven logistic regression models were developed based on 13 cross-platform biomarkers. The robustness of these models was evaluated through 5-fold cross-validation, and feature selection was optimized using LASSO regression. External validation was performed using GEO datasets (GSE181143, GSE114192) and RT-qPCR. Functional annotation and xCell immune-infiltration analyses were employed to characterize microenvironmental shifts, while dual-luciferase assays confirmed ceRNA interactions. Results: Thirteen immune-related biomarkers were identified, comprising 4 mRNAs (IRF1, FPR1, LILRB3, SECTM1), 2 microRNAs (miRNAs) (hsa-miR-4726-5p, novel-miR-109), 3 lncRNAs (MSTRG.128052.1, MSTRG.4908.1, MSTRG.37670.90), and 4 proteins (IFN-γ, IL-6, CXCL10, CXCL6). Eleven models demonstrated high diagnostic efficacy, with area under the curve (AUC) values ranging from 0.93 to 0.99, and exhibited stable performance in 5-fold cross-validation, yielding AUC values between 0.77 and 0.95. LASSO-derived concise biomarker subsets overlapped with primary model features, thereby confirming robust discriminative stability. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses underscored the significance of immune response, inflammation, and metabolic regulation, highlighting key pathways such as Toll-like receptors, NF-κB, and JAK-STAT. Immune infiltration analysis revealed a "pro-inflammatory-suppressive-reconstructive" imbalance characterized by overactivated innate immunity, including M1/M2 macrophages and NKT cells, alongside compromised adaptive immunity, evidenced by reduced CD4⁺/CD8⁺ T cells and B cells. Additionally, ceRNA networks and dual-luciferase assays confirmed that novel-miR-109 inhibits the translation of FPR1, LILRB3, and MSTRG.4908.1, while hsa-miR-4726-5p targets the 3' UTR of SECTM1. Conclusions: This study establishes a validated multi-omics framework for the early detection of T2DM-TB, elucidates key regulatory axes (IRF1/IFN-γ, ceRNA circuitry, CXCL10/CXCL6), and provides actionable biomarkers and high-performance models for precision intervention in T2DM-TB management.