Identification and immuno-infiltration analysis of cuproptosis regulators in human spermatogenic dysfunction

Zhao, Ming; Yu, Wen-Xiao; Liu, Shengjing; Deng, Ying-Jun; Zhao, Ziwei; Guo, Jun; Gao, Qinghe

doi:10.3389/fgene.2023.1115669

Cited by 5 publications

(6 citation statements)

References 42 publications

(42 reference statements)

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“…Therefore, to answer the PICO questions formulated in the previous section, we organized and schematically summarized them in the upcoming tables. Indeed, the studies were divided based on the general topic they dealt with; therefore, the specific variables considered in each model included sperm retrieval (Table 2, four studies [37][38][39][40]); sperm quality, further divided into the investigation of sperm quality and morphology (Table 3a, seventeen studies [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]) and quality of sperm and environmental factors (Table 3b, four studies [58][59][60][61]); non-obstructive azoospermia (Table 4, three studies [62][63][64]); IVF outcome (Table 5, three studies [65][66][67]); environmental and medical factors (Table 6, twelve studies [68][69][70][71][72][73][74][75]…”

Section: Resultsmentioning

confidence: 99%

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Schmeis Arroyo,

Iosa,

Antonucci

et al. 2024

Healthcare

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Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.

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Section: Resultsmentioning

confidence: 99%

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Schmeis Arroyo,

Iosa,

Antonucci

et al. 2024

Healthcare

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show abstract

“…Therefore, we hypothesize that there is a intricate connection between GlnMRGs and the occurrence of DFU. In our study, we explored our hypothesis through bioinformatics methods to aid in understanding DFU [ 31 ]. In this study, we have explored the potential of GlnMRGs as a biomarker in DFU, aiming to contribute to the diagnosis, detection, and treatment of DFU.…”

Section: Discussionmentioning

confidence: 99%

A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration

Shi,

Yuan,

Yang

et al. 2024

BMC Genomics

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Background Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis. Methods We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset. Results In both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929). Conclusion This study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.

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“… 9 10 11 Recently, a predictive model constructed by Zhao et al . 12 illustrated the relationship between human spermatogenic dysfunction and cuproptosis. In this study, we verified the existence of cuproptosis in the testis using copper-overloaded mice.…”

Section: Discussionmentioning

confidence: 99%

Cuproptosis mediates copper-induced testicular spermatogenic cell death

Zhang,

Yu,

et al. 2024

Asian Journal of Andrology

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Cuproptosis, a novel mechanism of programmed cell death, has not been fully explored in the context of spermatogenic cells. This study investigated the potential involvement of cuproptosis in spermatogenic cell death using a mouse model of copper overload. Sixty male Institute of Cancer Research (ICR) mice were randomly divided into four groups that received daily oral gavage with sodium chloride (control) or copper sulfate (CuSO4) at 50 mg kg−1, 100 mg kg−1, or 200 mg kg−1, for 42 consecutive days. Mice subjected to copper overload exhibited a disruption in copper homeostasis. Additionally, significant upregulated expression of key cuproptosis factors was accompanied by a significant rise in the rates of testicular tissue cell apoptosis. Immunohistochemical analysis revealed the presence of ferredoxin 1 (Fdx1) in Sertoli cells, Leydig cells, and spermatogenic cells at various stages of testicular development, and the Fdx1-positive staining area was significantly increased in copper-overloaded mice. Mitochondrial dysfunction and decreased adenosine triphosphate levels were also observed, further implicating mitochondrial damage under cuproptosis. Further analyses revealed pathological lesions and blood−testis barrier destruction in the testicular tissue, accompanied by decreased sperm concentration and motility, in copper-overloaded mice. In summary, our results indicate that copper-overloaded mice exhibit copper homeostasis disorder in the testicular tissue and that cuproptosis participates in spermatogenic cell death. These findings provide novel insights into the pathogenic mechanisms underlying spermatogenic cell death and provide initial experimental evidence for the occurrence of cuproptosis in the testis.

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Identification and immuno-infiltration analysis of cuproptosis regulators in human spermatogenic dysfunction

Cited by 5 publications

References 42 publications

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration

Cuproptosis mediates copper-induced testicular spermatogenic cell death

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