Integrated bioinformatics analysis identifies an immune-related gene signature associated with non-obstructive azoospermia
- 1Department of Urology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China
- 2Assisted Reproductive Technology Center, The Affiliated Foshan Women and Children Hospital, Guangdong Medical University, Foshan, China
- 3Department of Urology, Affiliated Hospital Group of Guangdong Medical University Shenzhen Baoan Central Hospital, Shenzhen, Guangdong, China
Abstract
Non-obstructive azoospermia (NOA) is a severe male infertility disorder. This study aims to identify immune-related biomarkers for explore immune-related molecular signatures associated with NOA. Here, we integrated transcriptomic data from four independent cohorts (GSE145467, GSE45885, GSE45887, GSE9210) using an integrative pipeline combining differential expression analysis, weighted gene co-expression network analysis (WGCNA), and consensus-based multi-machine learning to identify an immune-related gene signature associated with NOA. We identified 16 immune-related differentially expressed genes through WGCNA, which were refined into a 12-gene panel—Hub NOA.Sig (CD14, CD19, CMTM2, CXCL16, HSPA1L, HSPA2, IGF1, IL13, POMC, RBP1, STAB2, TYROBP)—using five feature selection algorithms (Bagged Trees, Bayesian, Boruta, LASSO, LVQ). A Random Forest classifier based on Hub NOA.Sig yielded good classification performance with an AUC of 0.87. This signature offers a transcriptomic reference for characterizing NOA heterogeneity and exploring immune-related molecular features. Single-sample gene set enrichment analysis (ssGSEA) revealed significant alterations in six immune cell populations—including effector memory CD4 ⁺ /CD8 ⁺ T cells, regulatory T cells, and plasmacytoid dendritic cells—and demonstrated strong correlations between hub genes and specific immune infiltrates. Furthermore, drug-gene interaction screening and molecular docking predicted loperamide-POMC, acitretin-RBP1, and retinol-RBP1 as candidate drug-gene pairs for subsequent experimental validation. This work provides transcriptomic clues and candidate molecular targets for future mechanistic investigations into NOA-related male infertility.
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BibTeX
@article{zhong2026integrated,
title = {{Integrated bioinformatics analysis identifies an immune-related gene signature associated with non-obstructive azoospermia}},
author = {Yucheng Zhong and Songqing Ye and Hongshen Lai and Xiangyu Zeng and Ge Song and Xiaofu Qiu},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0360305},
url = {https://doi.org/10.1371/journal.pone.0360305},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrated bioinformatics analysis identifies an immune-related gene signature associated with non-obstructive azoospermia
AU - Yucheng Zhong
AU - Songqing Ye
AU - Hongshen Lai
AU - Xiangyu Zeng
AU - Ge Song
AU - Xiaofu Qiu
PY - 2026
DA - 2026/10/12
JO - PLOS One
DO - 10.1371/journal.pone.0360305
UR - https://doi.org/10.1371/journal.pone.0360305
AB - Non-obstructive azoospermia (NOA) is a severe male infertility disorder. This study aims to identify immune-related biomarkers for explore immune-related molecular signatures associated with NOA. Here, we integrated transcriptomic data from four independent cohorts (GSE145467, GSE45885, GSE45887, GSE9210) using an integrative pipeline combining differential expression analysis, weighted gene co-expression network analysis (WGCNA), and consensus-based multi-machine learning to identify an immune-related gene signature associated with NOA. We identified 16 immune-related differentially expressed genes through WGCNA, which were refined into a 12-gene panel—Hub NOA.Sig (CD14, CD19, CMTM2, CXCL16, HSPA1L, HSPA2, IGF1, IL13, POMC, RBP1, STAB2, TYROBP)—using five feature selection algorithms (Bagged Trees, Bayesian, Boruta, LASSO, LVQ). A Random Forest classifier based on Hub NOA.Sig yielded good classification performance with an AUC of 0.87. This signature offers a transcriptomic reference for characterizing NOA heterogeneity and exploring immune-related molecular features. Single-sample gene set enrichment analysis (ssGSEA) revealed significant alterations in six immune cell populations—including effector memory CD4 ⁺ /CD8 ⁺ T cells, regulatory T cells, and plasmacytoid dendritic cells—and demonstrated strong correlations between hub genes and specific immune infiltrates. Furthermore, drug-gene interaction screening and molecular docking predicted loperamide-POMC, acitretin-RBP1, and retinol-RBP1 as candidate drug-gene pairs for subsequent experimental validation. This work provides transcriptomic clues and candidate molecular targets for future mechanistic investigations into NOA-related male infertility.
SN - 1932-6203
ER -