Integrative Bioinformatics Analysis to Identify Novel Biomarkers Associated with Non-obstructive Azoospermia

Overview

Journal Front Immunol

Specialty Allergy & Immunology

Date 2023 Mar 27

PMID 36969237

Authors

Yucheng Zhong

Jun Zhao

Hao Deng

Yaqin Wu

Li Zhu

Meiqiong Yang

Qianru Liu

Guoqun Luo

Wenmin Ma

Huan Li

Affiliations

Soon will be listed here.

Abstract

Aim: This study aimed to identify autophagy-related genes (ARGs) associated with non-obstructive azoospermia and explore the underlying molecular mechanisms.

Methods: Two datasets associated with azoospermia were downloaded from the Gene Expression Omnibus database, and ARGs were obtained from the Human Autophagy-dedicated Database. Autophagy-related differentially expressed genes were identified in the azoospermia and control groups. These genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, protein-protein interaction (PPI) network, and functional similarity analyses. After identifying the hub genes, immune infiltration and hub gene-RNA-binding protein (RBP)-transcription factor (TF)-miRNA-drug interactions were analyzed.

Results: A total 46 differentially expressed ARGs were identified between the azoospermia and control groups. These genes were enriched in autophagy-associated functions and pathways. Eight hub genes were selected from the PPI network. Functional similarity analysis revealed that may play a key role in azoospermia. Immune cell infiltration analysis revealed that activated dendritic cells were significantly decreased in the azoospermia group compared to those in the control groups. Hub genes, especially , , , and were strongly correlated with immune cell infiltration. Finally, a hub gene-miRNA-TF-RBP-drug network was constructed.

Conclusion: The eight hub genes, including , , , , and , may serve as biomarkers for the diagnosis and treatment of azoospermia. The study findings suggest potential targets and mechanisms for the occurrence and development of this disease.

Citing Articles

Integrative bioinformatics analysis to identify ferroptosis-related genes in non-obstructive azoospermia.

Hong Y, Yuan Q, Wang L, Yang Z, Xu P, Guan X J Assist Reprod Genet. 2024; 41(8):2145-2161.

PMID: 38902567 PMC: 11339017. DOI: 10.1007/s10815-024-03155-0.

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