Identification of interleukin-11 as a comorbid risk factor for prostate cancer and Alzheimer's disease using integrated bioinformatics and machine learning.

Overview

1. School of Medicine, Southeast University, Nanjing, China
2. Department of Pathology, Zhongda Hospital, Southeast University, Nanjing, China
3. Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School of Nanjing Medical University, Suzhou, China
4. Department of Urology, Zhongda Hospital, Southeast University, Nanjing, China

Abstract

Background: Prostate cancer (PCa) and Alzheimer’s disease (AD) are age-related disorders with a complex epidemiological association and limited therapeutic options. Identifying shared molecular drivers may reveal new treatment targets.

Objective: To identify common transcriptomic signatures between PCa and AD and validate the role of interleukin-11 (IL11) as a functional comorbidity factor.

Methods: Multi-cohort transcriptomic datasets (GSE48350, GSE5281 and GSE28146 for AD; TCGA-PRAD, DKFZ2018 and MSKCC for PCa) were analyzed. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed, followed by a two-tier machine learning pipeline (Random Forest and LASSO Cox regression) to screen overlapping genes. Immune infiltration was evaluated by CIBERSORT and single-cell transcriptomics for PCa. The functional role of IL11 was assessed in RM-1 murine and DU145 human PCa cells using colony formation, wound healing, Transwell assays, and immunocompetent C57BL/6 orthotopic and subcutaneous xenograft models. The cognitive effects of IL11 were evaluated using Morris water maze (MWM) tests, and the neuropathological changes were assessed by detecting hippocampal amyloid-β (Aβ) deposition. Additionally, a cross-sectional analysis was performed in a population cohort (n=215) to investigate the associations between IL11 levels and AD pathological biomarkers.

Results: A total of 455 shared candidate genes were identified, and a 10-gene signature (NDRG4, ISG15, IL11, ENO2, DYNC1I1, DNASE1, ATP6V1G2, ATCAY, ANLN, AGAP9) was established. The risk score effectively stratified PCa patients with poor progression-free interval (log-rank P < 0.001; AUC for 1-,3-,5-year = 0.78,0.73,0.70), validated in two external cohorts (DKFZ2018, MSKCC). IL11 was the top candidate and was significantly upregulated in both diseases. High IL11 expression correlated with an immunosuppressive microenvironment, characterized by increased M2 macrophages and regulatory T cells, and with higher tumor mutation burden. Single-cell analysis localized IL11 to a subset of cancer-associated fibroblasts. In vitro, IL11 treatment enhanced PCa cell proliferation, migration, and invasion. In vivo, intraperitoneal IL11 accelerated PCa tumor growth in mice. In the MWM test, IL11-treated mice exhibited significantly longer escape latencies and fewer platform crossings, which were accompanied by increased hippocampal Aβ deposition, suggesting that IL11 induced cognitive dysfunction. Cross-sectional cohort analyses further linked higher serum IL11 to reduced CSF Aβ42 and elevated p-tau181.

Conclusion: IL11 acts as a shared risk factor related to PCa progression and cognitive decline in AD, representing a convergent molecular pathway and a potential therapeutic target for both age-related diseases.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• geo:GSE5281, at NCBI GEO; found in the text, “Data acquisition and preprocessing”

Other data links

• ncbi.nlm.nih.gov/geo, NCBI; found in the text, “Data acquisition and preprocessing”

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

• Funding: added National Natural Science Foundation of China; Southeast University

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 15 authors, 5 keywords, 16 MeSH terms, 55 references, 1 RRID.

Cite

This paper

Wang, M., Qian, Y., Huang, E., Chu, C., Gao, T., Chen, S., Zhao, N., Luo, C., Liu, Y., Zheng, X., Hu, H., Han, B., Chen, M., Mao, W., & Li, W. (2026). Identification of interleukin-11 as a comorbid risk factor for prostate cancer and Alzheimer's disease using integrated bioinformatics and machine learning. Frontiers in immunology, 17, 1911726. https://doi.org/10.3389/fimmu.2026.1911726

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