Multiomics identification of programmed cell death-related characteristics for nonobstructive azoospermia based on a 675-combination machine learning computational framework

被引:1
作者
Huang, Shuqiang [1 ]
Tan, Cuiyu [1 ]
Chen, Wanru [2 ]
Zhang, Tongtong [1 ]
Xu, Liying [1 ]
Li, Zhihong [1 ]
Chen, Miaoqi [1 ]
Yuan, Xiaojun [1 ]
Chen, Cairong [1 ,3 ]
Yan, Qiuxia [1 ,3 ]
机构
[1] Guangzhou Med Univ, Affiliated Qingyuan Hosp, Qingyuan Peoples Hosp, Ctr Reprod Med, 35 Yinquan North Rd, Qingyuan 511518, Guangdong, Peoples R China
[2] Guangzhou Med Univ, Sch Clin Med 3, Guangzhou 511436, Guangdong, Peoples R China
[3] Guangzhou Med Univ, Affiliated Qingyuan Hosp, Qingyuan Peoples Hosp, Guangdong Engn Technol Res Ctr Urinary Continence, Qingyuan 511518, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Nonobstructive azoospermia; Programmed cell death; Multiomics; Machine learning; PCDscore; Biomarker; GENE-EXPRESSION; SERTOLI-CELLS; R PACKAGE; TESTIS; INFERTILITY; SPERMATOGENESIS; TRANSCRIPTOME; FERTILITY; DIAGNOSIS; MICE;
D O I
10.1016/j.ygeno.2024.110977
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Background: Abnormal programmed cell death (PCD) plays a central role in spermatogenic dysfunction. However, the molecular mechanisms and biomarkers of PCD in patients with nonobstructive azoospermia (NOA) remain unclear. Methods: The genetic conditions of NOA patients were analysed using bulk transcriptomic, single-cell transcriptomic, single nucleotide polymorphism (SNP), and clinical data from multiple centres. A total of 675 machine learning methods were applied to construct models from 12 different PCDs and to screen for distinctive genes. A new PCDscore system was created to measure the degree of PCD in patients. Using the NOA mouse model, TUNEL, qRT-PCR, Western blotting, and immunohistochemistry (IHC) were utilized to validate the PCD status in NOA testes and the expression levels of hub PCD-related genes (PCDRGs). Mouse testicular samples were used for sequencing of the whole transcriptome. The sequencing results were used to evaluate the correlation between PCD scores and expression of hub genes. Results: A PCDscore system was built using 12 characteristic PCDRGs chosen by machine learning. PCD scores correlated with gene interaction and immune activity changes. Leydig, Sertoli, and T cells were prominent in cell interactions with PCDscore changes. PCDscore in the NOA mouse testis was increased. Among the 12 PCDRGs, BCL2L14, GGA1, GPX4, PHKG2, and SLC39A8 were strongly linked to spermatogenesis. BCL2L14, GGA1, GPX4, and PHKG2 strongly correlated with PCD statuses. The changes in the expression of these genes may be due to the effects of SNPs, which may lead to the male reproductive system disorders. Conclusions: Our study provides new insights into PCD-related mechanisms in NOA patients via multiomics and proposes reliable models for the diagnosis of NOA via the use of PCD biomarkers. A deeper understanding of these mechanisms may aid in the clinical diagnosis and treatment of NOA.
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页数:14
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