Comparative transcriptome analysis in the caput segment of yak and cattleyak epididymis

Adjei, Michael; Yan, Yan; Li, Chunhai; Pan, Cheng; Pan, Meilan; Wang, Peng; Li, Kerui; Shahzad, Khuram; Chen, Xiaoying; Zhao, Wangsheng

doi:10.1016/j.theriogenology.2022.10.039

Cited by 3 publications

(2 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PAOX was considered to be related to male yellow cattle infertility. The upregulation of PAOX may be associated with toxicity and apoptosis resistance in cattleyak ( 43 , 44 ). But there is still a lack of relevant evidence on the impact of PAOX on human infertility.…”

Section: Discussionmentioning

confidence: 99%

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

Tang,

Su,

Wei

et al. 2023

Front. Endocrinol.

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Tang,

Su,

Wei

et al. 2023

Front. Endocrinol.

View full text Add to dashboard Cite

show abstract

“…Moreover, ADAM28 have a potential ability to participate in tooth development (Zhao et al 2006). ADAMTSL3 (also known as punctin-2 or KIAA1233) as a member of the ADAMTS superfamily of proteins (Adjei et al 2023) is widely expressed, with highest expression in liver, kidney, heart and skeletal muscle, playing play a role in cell-matrix interactions by northern blot analysis of RNA from human adult tissues (Zhang et al 2023). However, it is not yet fully understood whether ADAM28 and ADAMTSL3 play a significant role in periodontitis.…”

Section: A N U S C R I P Tmentioning

confidence: 99%

Differential Expressions of ADAM28 and ADAMTSL3 in Gingival Tissue of Patients with Periodontitis

Zhu

Zhong

2023

Tohoku J. Exp. Med.

View full text Add to dashboard Cite

The modulation of gene expression via DNA methylation modifications is relevant to the pathogenesis of periodontitis. This study aimed at identifying novel biomarkers in gingival tissues from periodontitis by integrally analyzing methylation profiles and gene expression data. Differential gene expressions (DGEs) of dataset GSE106090 were obtained from the Gene Expression Omnibus (GEO) database for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. DNA methylation DGEs (DM-DGEs) were analyzed from dataset GSE173082.After integrating these two datasets, expressions of common genes were validated in gingival tissues from healthy controls and periodontitis patients by real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. GO analysis of 748 upregulated and 847 downregulated DEGs from the GSE106090 dataset revealed that immune response-regulating signaling pathway, cell-cell junction and signaling receptor activator activity as the top enriched biological process (BP), cellular component (CC) and molecular function (MF), respectively. DEGs were mainly enriched in cytokinecytokine receptor interaction, Ras signaling pathway, and chemokine signaling pathway. There was one up-regulated mRNA with hypo-methylated gene [ADAM28 (a disintegrin and metalloproteinase 28)] and one down-regulated mRNA with hyper-methylated gene [ADAMTSL3 (a disintegrin-like and metalloprotease domain with thrombospondin type I motifs-like-3)] after integrating GSE106090 and GSE173082 datasets. Increased ADAM28 expression was validated in gingival tissues from periodontitis patients as compared to the healthy controls with decreased ADAMTSL3 expression, which were correlated with disease stage. ADAM28 and ADAMTSL3 as novel biomarkers in gingival tissues from periodontitis by a comprehensive analysis of bioinformatics methods and executed validation.

show abstract

Single-cell RNA sequencing unveils dynamic transcriptional profiles during the process of donkey spermatogenesis and maturation

Jin,

Zhang,

et al. 2025

Genomics

View full text Add to dashboard Cite

Comparative transcriptome analysis in the caput segment of yak and cattleyak epididymis

Cited by 3 publications

References 62 publications

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

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

Differential Expressions of ADAM28 and ADAMTSL3 in Gingival Tissue of Patients with Periodontitis

Single-cell RNA sequencing unveils dynamic transcriptional profiles during the process of donkey spermatogenesis and maturation

Contact Info

Product

Resources

About