Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms

Abstract

ObjectiveThe cause and mechanism of non-obstructive azoospermia (NOA) is complicated; therefore, an effective therapy strategy is yet to be developed. This study aimed to analyse the pathogenesis of NOA at the molecular biological level and to identify the core regulatory genes, which could be utilised as potential biomarkers.MethodsThree NOA microarray datasets (GSE45885, GSE108886, and GSE145467) were collected from the GEO database and merged into training sets; a further dataset (GSE45887) was then defined as the validation set. Differential gene analysis, consensus cluster analysis, and WGCNA were used to identify preliminary signature genes; then, enrichment analysis was applied to these previously screened signature genes. Next, 4 machine learning algorithms (RF, SVM, GLM, and XGB) were used to detect potential biomarkers that are most closely associated with NOA. Finally, a diagnostic model was constructed from these potential biomarkers and visualised as a nomogram. The differential expression and predictive reliability of the biomarkers were confirmed using the validation set. Furthermore, the competing endogenous RNA network was constructed to identify the regulatory mechanisms of potential biomarkers; further, the CIBERSORT algorithm was used to calculate immune infiltration status among the samples.ResultsA total of 215 differentially expressed genes (DEGs) were identified between NOA and control groups (27 upregulated and 188 downregulated genes). The WGCNA results identified 1123 genes in the MEblue module as target genes that are highly correlated with NOA positivity. The NOA samples were divided into 2 clusters using consensus clustering; further, 1027 genes in the MEblue module, which were screened by WGCNA, were considered to be target genes that are highly correlated with NOA classification. The 129 overlapping genes were then established as signature genes. The XGB algorithm that had the maximum AUC value (AUC=0.946) and the minimum residual value was used to further screen the signature genes. IL20RB, C9orf117, HILS1, PAOX, and DZIP1 were identified as potential NOA biomarkers. This 5 biomarker model had the highest AUC value, of up to 0.982, compared to other single biomarker models; additionally, the results of this biomarker model were verified in the validation set.ConclusionsAs IL20RB, C9orf117, HILS1, PAOX, and DZIP1 have been determined to possess the strongest association with NOA, these five genes could be used as potential therapeutic targets for NOA patients. Furthermore, the model constructed using these five genes, which possessed the highest diagnostic accuracy, may be an effective biomarker model that warrants further experimental validation.

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

“…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]…”

“…Certain studies have the limitation of being vaguely defined [54,58,59,66,70,71], thereby posing challenges in assessing the robustness of predictive models. It is crucial to clearly delineate the limitations of the study for appropriate interpretation of the results.…”

Non-invasive biomarkers for sperm retrieval in non-obstructive patients: a comprehensive review

“…-Extracellular matrix protein 1 (ECM1): has been identified as a significant biomarker in the diagnosis of NOA. A study involving 119 seminal plasma samples from men with both normal spermatogenesis and azoospermia identified ECM1, along with TEX101, as key in differentiating between OA and NOA ( 74 ). They found that at a cutoff level of 2.3 μg/ml of ECM1 could distinguish OA from normal spermatogenesis with 100% specificity and OA from NOA with 73% specificity at 100% sensitivity.…”

“…In the context of identifying non-invasive biomarkers for sperm retrieval in cases of NOA, the integration of artificial intelligence (AI) with clinical and molecular data presents a transformative approach ( Figure 1 ). Some studies demonstrate the application of AI in this field, illustrating how machine learning and bioinformatics can significantly enhance the understanding and diagnosis of NOA ( 74 , 75 ). The integration of non-invasive biomarkers into the diagnostic and therapeutic algorithms for NOA patients holds the potential to revolutionize the clinical approach to male infertility, and to transform male infertility management into a realm of personalized medicine.…”

“…In the study by Tang et al. ( 74 ), five different algorithms using machine learning, including the XG Boost (XGB) algorithm, were employed to identify potential biomarkers for NOA. This approach led to the discovery of a strong correlation of genes such as IL20RB, C9orf117, HILS1, PAOX , and DZIP1 with NOA.…”