{"title":"基于675组合机器学习计算框架的非阻塞性无精子症程序性细胞死亡相关特征的多组学鉴定。","authors":"Shuqiang Huang, Cuiyu Tan, Wanru Chen, Tongtong Zhang, Liying Xu, Zhihong Li, Miaoqi Chen, Xiaojun Yuan, Cairong Chen, Qiuxia Yan","doi":"10.1016/j.ygeno.2024.110977","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":12521,"journal":{"name":"Genomics","volume":" ","pages":"110977"},"PeriodicalIF":3.4000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiomics identification of programmed cell death-related characteristics for nonobstructive azoospermia based on a 675-combination machine learning computational framework.\",\"authors\":\"Shuqiang Huang, Cuiyu Tan, Wanru Chen, Tongtong Zhang, Liying Xu, Zhihong Li, Miaoqi Chen, Xiaojun Yuan, Cairong Chen, Qiuxia Yan\",\"doi\":\"10.1016/j.ygeno.2024.110977\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":12521,\"journal\":{\"name\":\"Genomics\",\"volume\":\" \",\"pages\":\"110977\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genomics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.ygeno.2024.110977\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/9 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genomics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.ygeno.2024.110977","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/9 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
Multiomics identification of programmed cell death-related characteristics for nonobstructive azoospermia based on a 675-combination machine learning computational framework.
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.
期刊介绍：
Genomics is a forum for describing the development of genome-scale technologies and their application to all areas of biological investigation.
As a journal that has evolved with the field that carries its name, Genomics focuses on the development and application of cutting-edge methods, addressing fundamental questions with potential interest to a wide audience. Our aim is to publish the highest quality research and to provide authors with rapid, fair and accurate review and publication of manuscripts falling within our scope.
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