{"paper_id":"59c361be-9d6b-4668-b713-273422d5460c","body_text":"Differentially expressed male infertility-associated genes in sperm as prospective diagnostic biomarkers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differentially expressed male infertility-associated genes in sperm as prospective diagnostic biomarkers Amir Ebrahimi, Davood Ghavi, Zohreh Mirzaei, Tahereh Barati, Sima Mansoori This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3138032/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Genetic defects in sperm are responsible for a great percentage of male infertility. The association between numerous genes and spermatogenesis has been observed. Dysregulation of these genes greatly influence sperm morphology, motility and viability. Therefore, analyzing gene expression aberrancies is a must in male infertility. Microarray analysis is practically used for several aspects in male infertility including detection of differentially expressed genes (DEGs), selection of finest sperm for assisted reproductive therapy (ART) and identification of potential infertility biomarkers. Methods We conducted a meta-analysis using microarray datasets from NCBI.GEO. We have included datasets containing sperm tissues from both healthy and infertile males. Seven datasets qualified for inclusion in this study. These data were then transformed into a single set of meta-data. For these genes, expression and diagnostic analyses were conducted. In addition, enrichment analysis revealed the role and function of these genes in cellular processes. Results Six genes, including S100Z, SLC2A2, IMPG1, HOXD12, RAPGEFL1, and DMBX1, were identified as being significantly down-regulated in infertile men's sperm. Notably, the expression of these genes was highly correlated in sterile sperm. In addition, an analysis of the receiver operating curve indicated that these genes may serve as useful biomarkers for infertility diagnosis. The role of these genes in transporting glucose, vitamins and fructose as the sperm's primary fuel source, was suggested by pathway analysis. Conclusion Overall, our results suggest genes with expression abnormalities that may mediate the underlying mechanisms of infertility and also offer promising diagnostic values. Differentially Expressed genes Infertility Biomarker Sperm Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights Genetic dysregulations are undeniably significant in male infertility. We have identified six genes (S100Z, SLC2A2, IMPG1, DMBX1, RAPGEFL1 and HOXD12) to be particularly down-regulated in sperm of infertile men. We have also observed, identified differentially expressed genes, are strongly correlated in infertile cases. In addition to expression aberrancy, these genes are also capable of providing advantageous diagnostically biomarkers. Introduction Infertility is a developing concern, affecting between 10 and 15 percent of couples worldwide ( 1 ). Male factor infertility accounts for approximately fifty percent of all sterility cases, with sperm abnormalities being the most common cause ( 2 ). The traditional analysis of sperm provides fundamental information regarding sperm count, motility, and morphology but fails to identify the underlying molecular abnormalities ( 3 – 5 ). Microarray analysis is a high-throughput technique that permits the simultaneous identification of numerous gene expression alterations ( 6 ). Recently, it has been utilized to investigate the genetic basis of male infertility and sperm dysfunction ( 7 , 8 ). Sperm microarray analysis can provide a comprehensive picture of the sperm’s molecular landscape, including gene expression patterns and DNA copy number variations (CNVs) that may be responsible for infertility ( 9 ). The identification of specific genes and pathways involved in sperm function and fertilization can lead to the development of novel therapeutic targets for male infertility ( 10 ). So far, over 2000 genes and pathways are recognized to be involved in spermatogenesis; hence, spotting differentially expressed genes (DEGs) may disclose infertility etiology ( 11 , 12 ). Among the genes associated with male infertility and the disorder caused by their defect include: CFTR (congenital unilateral/bilateral absence of vas deferens), AR (non-obstructive azoospermia) ( 13 ), LRRC6 (primary ciliary dyskinesia) ( 14 ), APOA1 (testicular amyloidosis) ( 15 ), SRY (sexual development disorders) ( 16 ). Signaling pathways such as JAKs, MAPKs, etc. are also involved in this disease (Fig. 1 ). Genetic factors account for approximately 15% of the causes of male infertility, which may manifest as deficiencies in sperm count or sperm quality ( 17 ). However, the genetic cause of male infertility remains unknown in about 40% of cases ( 18 ). Several DEGs and CNVs have been confirmed to play a role in spermatogenesis and fertilization as a result of microarray analysis of sperm from infertile men. For instance, Hashemi et al. benefited microarray analysis to reveal multiple gene dysregulations such as RAD23B, OBFC2A, CHEK2, TRIP13, and POLD4, which primarily mediate DNA damage detection and repair and regulate cell proliferation ( 19 ). The identification of biomarkers for male infertility is one of the most important applications for sperm microarray analysis ( 20 – 22 ). Moreover, microarray is also advantageous in selecting the finest sperm for assisted reproduction technology (ART) ( 23 , 24 ). Identifying sperm with high DNA integrity and normal gene expression patterns can increase the success rate of ART ( 25 ). However, there are limitations to the use of microarray analysis on sperm samples. First, for accurate analysis, the integrity of the sample is essential. Any contamination with somatic cells can bias the results by introducing variations unrelated to fertility ( 26 ). Second, there is no standard protocol for sperm microarray analysis, which makes comparing results across studies challenging. Conducting a broad microarray meta-analysis can help to overcome these complications. In this study, we use a meta-analysis approach using NCBI.GEO ( 27 ) microarray studies to discover DEGs in the sperm of infertile men compared to the fertile control group and assess their potency as novel diagnostic biomarkers. In fact, data from various sources are analyzed as meta-data to enhance the validity and reliability of results. Materials and Methods Data Filtration Data regarding microarrays were downloaded from the NCBI.GEO database. We have searched for sperm tissue datasets. Our search phrase included: “infertile OR sterile” AND “sperm OR semen” AND “healthy OR control OR fertile OR normal”. Both fertile and infertile datasets were integrated into a single meta-data set. This investigation included ten datasets (GSE14078, GSE6872, GSE6967, GSE44133, GSE4797, GSEGSE6969, GSE26982, GSE9210, GSE160749, and GSE34514) ( 28 – 33 ) that met our inclusion criteria. The characteristics of the dataset are listed in Table 1 . We used the GEOquery package to import data from the NCBI repository into R 4.2.1 ( 34 ). Table 1 Characteristics of selected studies. Dataset GPL Sample Size Reproductively status Decision GSE14078 GPL6104 46 F Included GSE160749 GPL17692 24 F and InF Excluded GSE26982 GPL6244 GPL8490 39 only Inf Included GSE34514 GPL570 8 F and InF Excluded GSE44133 GPL4133 11 F Included GSE4797 GPL10558 28 F and InF Included GSE6872 GPL570 21 F and InF Included GSE6967 GPL2507 13 F and InF Included GSE6969 GPL570 GPL2507 GPL2700 44 F and InF Included GSE9210 GPL887 58 InF (non-obstructive and obstructive) Excluded Table 1. Accession number for each dataset (GSE) and used microarray chip (GPL), sample size of datasets, reproductive status including Fertility (F) or Infertility (InF) and presence in final meta-data is mentioned in this table. Quality Control and Assurance Each dataset's preprocessed form was utilized for further analysis. Using a boxplot, imaging of microarray chips, and an RNA degradation plot ( 35 ), the integrity of the selected data was evaluated. Using normalizebetweenarray from the LIMMA program, the data was normalized. A boxplot of meta-data before and after normalization is depicted in Fig. 2 . Each dataset's boxplot can be obtained from the supplementary file. Defining Meta-Data Using the ComBat function from the SVA package, selected datasets were merged into a singular meta-data record and the batch effect was removed ( 36 ). On this meta-data, expression and diagnostic analysis were then performed. We have used venn diagram to detect the intersection of probes in each platform. There are 14228 probes in the best-case scenario. (Supplementary Fig. 1). Moreover, GSE9210 was excluded from the aforementioned datasets due to its small number of evaluated genes (Supplementary Fig. 2). Principal Component Investigation Principal component analysis (PCA) was utilized to assess the expression profiles of various datasets and meta-data. PCA is the most prevalent method for evaluating the similarity of genetic profiles among various samples ( 37 ). The PCA graphic groups samples into clusters based on their resemblance to one another. We used principal component analysis to separate these samples into a fertile and an infertile group. Neither GSE160749 nor GSE34514 presented sufficiently differentiated samples to be included in the meta-data. In addition, samples exhibiting abnormal behavior were omitted from the study on the basis of the PCA performed on the meta-data. Analyzing Gene Expression Variation We have highlighted DEGs between infertile and fertile groups using the LIMMA package in R4.2.1 ( 38 ). Study design was based on the difference between average expression of infertile and fertile. Using the FDR approach, statistically significant DEGs were isolated. In addition, the DEGs' correlation was assessed with the use of the cor function (Pearson Coefficient) and heatmap visualization. Receiver Operating Characteristics Curve Analysis We evaluated the efficacy of these genes as diagnostic biomarkers by doing a Receiver Operating Characteristic (ROC) curve analysis on the meta-data using GraphPad Prism 9 ( 39 ). The area under the curve (AUC) for each DEG was calculated independently for each gene. Further, sensitivity (Sen) and specificity (Spe) of each biomarker was also calculated. Enrichment The EnrichR ontology and pathway analysis were used to conduct an investigation of the molecular function, biological processes, and cellular components of the enrichment DEGs ( 40 ). Odds ratio and combined score of significant results (Adj.P.Val < 0.05) were then included. Results Sample Distinctions Due to their high degree of similarity, two datasets were excluded from the meta-data for further analysis. Figure 3 illustrates the PCA plot of meta-data. The PCA of the meta-data is likely to distinguish between the Fertile and Infertile categories, making it suitable for expression analysis. The PCA results for individual datasets are available in Fig. 3 of Supplemental File. Expression Analysis According to the FDR method, among more than 14228 genes a total number of six genes were found out to be dysregulated in the infertile group comparing to the healthy fertile, including: S100Z (S100 Calcium Binding Protein Z), SLC2A2 (solute carrier family 2 member 2), IMPG1 (interphotoreceptor matrix proteoglycan 1), HOXD12 (homeobox D12), RAPGEFL1 (Rap guanine nucleotide exchange factor like 1) and DMBX1 (diencephalon/mesencephalon homeobox 1). These genes were significantly (Adjusted P.Value < 0.05) down-taken in the infertile group almost four times (Log Fold Change < -2) of normal values. DEG analysis results are presented comprehensively in Table 2 . Among these DEGs, HOXD12 was the most down-regulated (LogFC = − 2.6) while DMBX1 was the least down-regulated among these (LogFC = -2). Table 2 Dysregulated genes in sperm of infertile men. Genes LogFC AveExpr t P.Value Adj.P.Val S100Z -2.502583752 4.540991924 -7.869844153 8.68E-12 1.26E-07 SLC2A2 -2.394007223 4.458132888 -7.570613989 3.51E-11 2.55E-07 IMPG1 -2.327159937 4.639426893 -7.199985918 1.96E-10 9.47E-07 HOXD12 -2.639138043 4.649579351 -6.766454857 1.42E-09 5.16E-06 RAPGEFL1 -2.382114253 5.281461005 -5.512379259 3.51E-07 0.001019646 DMBX1 -2.007710761 4.753559015 -5.229184865 1.14E-06 0.002768283 Table 2. These genes are significantly (adj.P.Val < 0.05) down-regulated in great amounts (LogFC < -2). In addition, average expression of these genes are also mentioned. Furthermore, t and p values are presented. Correlation Analysis Figure 4 A illustrates the heatmap of correlation analysis between fertile and infertile groups, while Fig. 4 B depicts the correlation between identified DEGs. Based on our findings, the correlation between these DEGs and sperm tissue is high (ρ > 0.75). Among the identified DEGs, IMPG1 has the lowest correlation to other genes, while other genes are nearly as highly correlated as > 0.90. In other words, the expression of these genes tends to decrease simultaneously in infertile sperm based on the correlation results. ROC Analysis According to ROC curve analysis, all six DEGs show promising and significant usage in distinguishing infertility cases. Based on the ROC, SLC2A2 was the most potential diagnostic biomarker (AUC = 0.79, Sen: 0.91 and Spe: 0.68). Moreover, S100Z, RAPGEFL1, HOXD12, DMBX1 and IMPG1 may provide favorable diagnostic biomarkers respectively. Figure 5 provides additional information about the results above. Enrichment Analysis Functional analysis indicated the role of these genes in monosaccharide transmembrane transporter activity (GO: 0015415), Fructose transmembrane transporter activity (GO: 005353) and dehydroascorbic acid transmembrane transporters (GO: 0033300). Moreover, pathway analysis pointed out these genes are responsible for mediating onset of diabetes in young, type II diabetes mellitus, carbohydrate digestion and absorption. Full results are available in Table 3 . Table 3 Pathway and ontology analysis. KEGG Pathways Term Adjusted P-value Odds Ratio Combined Score Fructose Transmembrane Transport (GO:0015755) 0.020967582 666.2666667 4108.590826 Dehydroascorbic Acid Transport (GO:0070837) 0.020967582 571.0571429 3445.289983 Glucose Import (GO:0046323) 0.020967582 363.3272727 2044.882815 Hexose Transmembrane Transport (GO:0008645) 0.020967582 190.2190476 955.5316922 Glucose Transmembrane Transport (GO:1904659) 0.020967582 190.2190476 955.5316922 Nervous System Development (GO:0007399) 0.020967582 22.69489559 113.8706689 Vitamin Transport (GO:0051180) 0.031606356 105.0315789 467.6978365 Molecular Function Monosaccharide Transmembrane Transporter Activity (GO:0015145) 0.012590324 799.56 5053.709148 Fructose Transmembrane Transporter Activity (GO:0005353) 0.012590324 666.2666667 4108.590826 Dehydroascorbic Acid Transmembrane Transporter Activity (GO:0033300) 0.012590324 666.2666667 4108.590826 D-glucose Transmembrane Transporter Activity (GO:0055056) 0.014372827 399.68 2284.210592 Hyaluronic Acid Binding (GO:0005540) 0.014372827 307.4 1682.802968 Hexose Transmembrane Transporter Activity (GO:0015149) 0.014372827 266.3866667 1422.778691 Glucose Transmembrane Transporter Activity (GO:0005355) 0.018460915 173.6608696 857.2874885 Double-Stranded DNA Binding (GO:0003690) 0.031480234 14.92746914 63.19504037 Sequence-Specific Double-Stranded DNA Binding (GO:1990837) 0.031480234 13.52103787 54.78051148 Sequence-Specific DNA Binding (GO:0043565) 0.031480234 13.48181818 54.54989117 Biological Process Fructose Transmembrane Transport (GO:0015755) 0.020967582 666.2666667 4108.590826 Dehydroascorbic Acid Transport (GO:0070837) 0.020967582 571.0571429 3445.289983 Glucose Import (GO:0046323) 0.020967582 363.3272727 2044.882815 Hexose Transmembrane Transport (GO:0008645) 0.020967582 190.2190476 955.5316922 Glucose Transmembrane Transport (GO:1904659) 0.020967582 190.2190476 955.5316922 Nervous System Development (GO:0007399) 0.020967582 22.69489559 113.8706689 Vitamin Transport (GO:0051180) 0.031606356 105.0315789 467.6978365 Table 3. Pathway analysis for identified DEGs based on KEGG are presented. Moreover, molecular function and biological processes mediated by these genes are also provided alongside their adj.p.val, odds ratio and combined score. Discussion The World Health Organization (WHO) defines infertility as the inability to attain a clinical pregnancy after at least 12 months of unprotected sexual intercourse( 41 ), and this definition applies to both genders. Globally, approximately 15% of all couples in their reproductive years suffer from this disease( 42 ). In 20–30% of couples, the masculine factor is the only cause of infertility, whereas in 50% of cases, it is one of several factors that contribute to infertility( 43 ). Despite the fact that the majority of cases of infertility are primarily caused by genetic and epigenetic abnormalities, the causes of 70% of male infertility cases are still unknown( 44 ). Recent reproductive applications of microarray include the identification of genes with aberrant expression, the selection of the most fertile sperm for fertilization, and, of course, as a potential fertility diagnostic tool. We have performed a meta-analysis on the microarray data of gene expression in sperm from two categories of men: healthy men and infertile men. We discovered six dysfunctional genes in the sperm of males who are unable to fertilize. The fact that these genes (S100Z, SLC2A2, IMPG1, HOXD12, RAPGEFL1 and DMBX1) exhibited significant down-regulation in the meta-analysis suggests that they are of the uttermost importance. The S100 protein family consists of calcium-binding proteins. Despite their diminutive size, these proteins play a crucial role in a vast array of cellular processes, including cell proliferation, differentiation, and mortality( 45 ). For instance, the overexpression of S100A12, as a member of S100 family, was observed in semen of infertile men ( 46 ). S100Z protein is another member of the S100 proteins which our results suggest to be decreased in infertile sperm, however; so far not many studies are conducted to evaluate the expression and role of S100Z in male infertility which emphasizes on the concept of unrevealed underlying mechanisms. The majority of interphotoreceptor matrix proteoglycans are encoded by IMPG1, a member of the IMPG gene family ( 47 ). IMPG1 has been shown to code a protein called SPACR which is highly expressed in the testis ( 48 ). Frame shift mutations can lead to aberrant expression as well as the deletion of the C-terminal portion of the IMPG1 protein, resulting in infertility ( 49 ), but as like S100Z, further studies are required for role identification and expression assessment. Homeobox genes, also known as HOX genes, are a family of genes responsible for regulating anterior–posterior axis. HOXD has been observed to play crucial roles in female infertility but further examinations are required in case of male sterility ( 50 ). For a long time, it was believed HOXD genes are necessary for uterine receptivity ( 51 ), but our findings suggest remarkable influence in infertile sperm too. SLC2A2 codes a protein mostly known as GLUT2 which is not only a glucose transporter but also in control of fructose transportation. GLUT2 is mainly dysregulated in diabetes II ( 52 ), this is important due to the fact that fructose is the primary energy source of semen ( 53 ). Down-regulation of DMBX1 is perhaps the most interesting dysregulation among these DEGs, as DMBX1 is a homeodomain transcription factor specially expressed in brain. It seems like DMBX1 is mainly expressed during embryogenesis and therefore, expression aberrancies of this gene can lead to severe damages. Additionally, we postulate down-regulation of DMBX1 is greatly correlated with male infertility( 54 ). Based on our findings, RAPGEFL1, also known as Link-GEFII, is a vague down-regulated gene which is predicted to facilitate the function of guanine nucleotide exchange factors. According to the enrichment analysis, these genes play crucial roles, most notably in the transportation of glucose, vitamins and especially fructose, which is considered the primary source of energy for the mitochondria in sperm cells. In this regard, it seems like adjusting defects in transportation system may do well in infertility treatment. Interestingly, identified genes in the sperm of infertile males appear to be strongly interrelated. This suggests that the simultaneous down-regulation of six genes in sperm tissue is what causes infertility. According to the ROC analysis, all of these genes met the requirements for functioning as diagnostic indicators of infertility; however, additional research may be required to validate these results prior to their clinical application. Future Prospective Advantages of using microarrays for infertile sperm include the discovery of underlying genetic pathways and the suggestion of novel diagnostic biomarkers. Our findings may prove useful in the treatment and accurate diagnosis of male infertility. Limitations Although the study of microarrays is a high-throughput method, we recommend verifying the data with a gold-standard method, such as real-time polymerase chain reaction (RT-PCR). Given the scarcity of samples, we would be overjoyed if subsequent research confirmed the molecular level findings reported here. As for the diagnostics, commenting on ideal biomarkers require several studies with a wide sample size, therefore, testing our findings on a cohort will disclose the accurate and real ability of these DEGs in identifying infertility. Abbreviations ART: Assisted Reproductive Therapy AUC: Area Under Curve CNV: Copy Number Variations DEG: Differentially Expressed Genes FDR: False Discovery Rate GEO: Gene Expression Omnibus LogFC: Logarithm Fold Change PCA: Principal Components Analysis ROC: Receiver Operating Characteristic RT-PCR: Real-time Polymerase Chain Reaction Sen: Sensitivity Spe: Specificity Declarations Ethics approval and consent to participate: This article uses an in-silico approache and does not include any experiments on human participants or animal specimen. This study was reviewed and approved by the Tabriz Research Ethics Committee. (Ethics code: IR.TBZMED.VCR.REC.1401.347) Consent for publication: Not applicable. Availability of data and materials: The dataset analyzed in this study are publicly reachable in NCBI.GEO repository. Any requested data will be available on reasonable requests. Competing Interests: The authors have declared no conflicts of interest for this article. Funding: This work was supported by grants from the Student Research Committee, Tabriz University of Medical Sciences (grant number: 70804). Author’s Contribution: The research was conceived by A. Ebrahimi and S. Mansoori. Analysis was done by Ghavi D. and Ebrahimi A. Data collection was performed with the help of Mirzaei Z and Barati T. The intellectual content of the manuscript was revised critically by Mirzaei Z. Both A. Ebrahimi and T. Barati wrote portions of the draft script. The research was overseen by S. Mansoori. The final paper was read and approved by all authors named. References Wasilewski T, Łukaszewicz-Zając M, Wasilewska J, Mroczko B. Biochemistry of infertility. Clin Chim Acta. 2020;508:185-90. 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Biochim Biophys Acta Mol Cell Res. 2020;1867(6):118677. Bagheri V, Hassanshahi G, Zeinali M, Abedinzadeh M, Khorramdelazad H. Elevated levels of S100A12 in the seminal plasma of infertile men with varicocele. International Urology and Nephrology. 2016;48(3):343-7. Meunier I, Manes G, Bocquet B, Marquette V, Baudoin C, Puech B, et al. Frequency and Clinical Pattern of Vitelliform Macular Dystrophy Caused by Mutations of Interphotoreceptor Matrix IMPG1 and IMPG2 Genes. Ophthalmology. 2014;121(12):2406-14. Fagerberg L, Hallström BM, Oksvold P, Kampf C, Djureinovic D, Odeberg J, et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteomics. 2014;13(2):397-406. Manes G, Meunier I, Avila-Fernández A, Banfi S, Le Meur G, Zanlonghi X, et al. Mutations in IMPG1 Cause Vitelliform Macular Dystrophies. The American Journal of Human Genetics. 2013;93(3):571-8. Akbas GE, Taylor HS. HOXC and HOXD gene expression in human endometrium: lack of redundancy with HOXA paralogs. Biol Reprod. 2004;70(1):39-45. Du H, Taylor HS. The Role of Hox Genes in Female Reproductive Tract Development, Adult Function, and Fertility. Cold Spring Harb Perspect Med. 2015;6(1):a023002. Thorens B. GLUT2, glucose sensing and glucose homeostasis. Diabetologia. 2015;58(2):221-32. Helsley RN, Moreau F, Gupta MK, Radulescu A, DeBosch B, Softic S. Tissue-Specific Fructose Metabolism in Obesity and Diabetes. Current Diabetes Reports. 2020;20(11):64. Hirono S, Lee EY, Kuribayashi S, Fukuda T, Saeki N, Minokoshi Y, et al. Importance of Adult Dmbx1 in Long-Lasting Orexigenic Effect of Agouti-Related Peptide. Endocrinology. 2016;157(1):245-57. Supplementary information Supplementary file and Supplementary Figures are not available with this version. 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Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3138032\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":217650792,\"identity\":\"02ac23d7-01c5-4be9-a910-7cb3e99c198e\",\"order_by\":0,\"name\":\"Amir Ebrahimi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tabriz University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Amir\",\"middleName\":\"\",\"lastName\":\"Ebrahimi\",\"suffix\":\"\"},{\"id\":217650793,\"identity\":\"b1f23db5-4823-4af3-8ac3-10c9ff7be6d0\",\"order_by\":1,\"name\":\"Davood Ghavi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tabriz University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Davood\",\"middleName\":\"\",\"lastName\":\"Ghavi\",\"suffix\":\"\"},{\"id\":217650794,\"identity\":\"36e986b9-bb7e-474c-9943-36ea6fb42296\",\"order_by\":2,\"name\":\"Zohreh Mirzaei\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tabriz University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zohreh\",\"middleName\":\"\",\"lastName\":\"Mirzaei\",\"suffix\":\"\"},{\"id\":217650795,\"identity\":\"a2cea1f5-e028-45e1-85e2-0814743ad0cc\",\"order_by\":3,\"name\":\"Tahereh Barati\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tabriz University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tahereh\",\"middleName\":\"\",\"lastName\":\"Barati\",\"suffix\":\"\"},{\"id\":217650796,\"identity\":\"bad86a79-a5da-4d6a-baee-d4de87afde2b\",\"order_by\":4,\"name\":\"Sima Mansoori\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBAC+QYog02+gfEBkObhI6TF4ABMi8QBZgOQFjaCWuAsiQQ2CbBeglrEDj97+KPCJp9PIvlY5dccOxk2BuaHj27g0SI/O83cmOdMmmWbRFrabdltyUCHsRkb5+Cz5naCmTRj22EDNokcs9uS25iBWnjYpPFrSf8m+ROsJf9bseS2emK05JhJ8EJsYWP8uO0wYS0Gt3PKpIF+AWo5ZizNuO04DxszAb/Iz07fJgkMMQP5+c0PP/7cVm3Pz9788DFehyEDZh4wSaxyEGD8QYrqUTAKRsEoGDEAADuQQVdF/B/PAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Tabriz Medical University: Tabriz University of Medical Sciences\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sima\",\"middleName\":\"\",\"lastName\":\"Mansoori\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2023-07-04 07:40:14\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3138032/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3138032/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":40055415,\"identity\":\"b4e98726-4b99-45cb-909c-1b593be4093d\",\"added_by\":\"auto\",\"created_at\":\"2023-07-14 19:05:14\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":681149,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe influence of genetic factors in male infertility. Several mutations in genes such as PLCZ1, AZF family and CFTR and chromosomal structural defects can lead to decreased sperm count, motility and vitality.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3138032/v1/cbbf94265b637205a8544523.png\"},{\"id\":40055818,\"identity\":\"adfaad6b-d082-45ec-80bc-953da10843f1\",\"added_by\":\"auto\",\"created_at\":\"2023-07-14 19:21:14\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":449093,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eBoxplot of the meta-data. Picture above (red boxes) indicate the distribution of samples before normalization where the picture below (blue boxes) depicts the normalized meta-data.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3138032/v1/7190a0075490f7b8d0fbb5eb.png\"},{\"id\":40055744,\"identity\":\"a05d7b59-de4f-42e1-a874-1f68a4799a55\",\"added_by\":\"auto\",\"created_at\":\"2023-07-14 19:13:14\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":90548,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePCA plot of the meta-data. Red and blue dots are representative of fertile and infertile samples respectively. Healthy fertile men tend to locate right side of the PCA plot while infertile are mostly located at the left. Accession number of each sample is also added to the plot to identify samples with high dispersion.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3138032/v1/32482ad27f0aaf8d443cb4d7.png\"},{\"id\":40055419,\"identity\":\"2bf77520-8a09-4154-b9f9-1b6f097a042f\",\"added_by\":\"auto\",\"created_at\":\"2023-07-14 19:05:14\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":555312,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(A) Correlation analysis between fertile and infertile samples displayed as a heatmap. (B) Correlation of identified DEGs indicate they tend to dysregulate consistently in sperm tissue of infertile men. Therefore, down-regulation observed in one of these gene increases the chance of down-regulation in other DEGs.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3138032/v1/8ad888ed243efbd7f0b3ead6.png\"},{\"id\":40055416,\"identity\":\"3523aa01-66df-4413-9d87-fa448fd020ab\",\"added_by\":\"auto\",\"created_at\":\"2023-07-14 19:05:14\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":170176,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eROC curve analysis of identified DEGs. The diagnostic ability of identified DEGs were assessed via application of ROC curve analysis. All six genes, are significantly favorable in case of diagnosis (AUC \\u0026gt; 0.7). The evaluation of sensitivity and specificity revealed even though these genes are greatly sensitive in identifying infertile men, they may lack enough specificity of an ideal diagnostic biomarker.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3138032/v1/3767ed3d8718271487a92385.png\"},{\"id\":40490865,\"identity\":\"294005b9-fd8c-426d-a178-4c0a983c985d\",\"added_by\":\"auto\",\"created_at\":\"2023-07-24 18:09:30\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1565923,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3138032/v1/641ee140-dea2-444e-a49d-9a2a3ad5c61b.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Differentially expressed male infertility-associated genes in sperm as prospective diagnostic biomarkers\",\"fulltext\":[{\"header\":\"Highlights\",\"content\":\"\\u003cul\\u003e\\n \\u003cli\\u003eGenetic dysregulations are undeniably significant in male infertility.\\u003c/li\\u003e\\n \\u003cli\\u003eWe have identified six genes (S100Z, SLC2A2, IMPG1, DMBX1, RAPGEFL1 and HOXD12) to be particularly down-regulated in sperm of infertile men.\\u003c/li\\u003e\\n \\u003cli\\u003eWe have also observed, identified differentially expressed genes, are strongly correlated in infertile cases.\\u003c/li\\u003e\\n \\u003cli\\u003eIn addition to expression aberrancy, these genes are also capable of providing advantageous diagnostically biomarkers.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\"},{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eInfertility is a developing concern, affecting between 10 and 15 percent of couples worldwide (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). Male factor infertility accounts for approximately fifty percent of all sterility cases, with sperm abnormalities being the most common cause (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). The traditional analysis of sperm provides fundamental information regarding sperm count, motility, and morphology but fails to identify the underlying molecular abnormalities (\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMicroarray analysis is a high-throughput technique that permits the simultaneous identification of numerous gene expression alterations (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Recently, it has been utilized to investigate the genetic basis of male infertility and sperm dysfunction (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Sperm microarray analysis can provide a comprehensive picture of the sperm\\u0026rsquo;s molecular landscape, including gene expression patterns and DNA copy number variations (CNVs) that may be responsible for infertility (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). The identification of specific genes and pathways involved in sperm function and fertilization can lead to the development of novel therapeutic targets for male infertility (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e). So far, over 2000 genes and pathways are recognized to be involved in spermatogenesis; hence, spotting differentially expressed genes (DEGs) may disclose infertility etiology (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Among the genes associated with male infertility and the disorder caused by their defect include: CFTR (congenital unilateral/bilateral absence of vas deferens), AR (non-obstructive azoospermia) (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e), LRRC6 (primary ciliary dyskinesia) (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e), APOA1 (testicular amyloidosis) (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e), SRY (sexual development disorders) (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). Signaling pathways such as JAKs, MAPKs, etc. are also involved in this disease (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Genetic factors account for approximately 15% of the causes of male infertility, which may manifest as deficiencies in sperm count or sperm quality (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). However, the genetic cause of male infertility remains unknown in about 40% of cases (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). Several DEGs and CNVs have been confirmed to play a role in spermatogenesis and fertilization as a result of microarray analysis of sperm from infertile men. For instance, Hashemi et al. benefited microarray analysis to reveal multiple gene dysregulations such as RAD23B, OBFC2A, CHEK2, TRIP13, and POLD4, which primarily mediate DNA damage detection and repair and regulate cell proliferation (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe identification of biomarkers for male infertility is one of the most important applications for sperm microarray analysis (\\u003cspan additionalcitationids=\\\"CR21\\\" citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). Moreover, microarray is also advantageous in selecting the finest sperm for assisted reproduction technology (ART) (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). Identifying sperm with high DNA integrity and normal gene expression patterns can increase the success rate of ART (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eHowever, there are limitations to the use of microarray analysis on sperm samples. First, for accurate analysis, the integrity of the sample is essential. Any contamination with somatic cells can bias the results by introducing variations unrelated to fertility (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). Second, there is no standard protocol for sperm microarray analysis, which makes comparing results across studies challenging. Conducting a broad microarray meta-analysis can help to overcome these complications.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we use a meta-analysis approach using NCBI.GEO (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e) microarray studies to discover DEGs in the sperm of infertile men compared to the fertile control group and assess their potency as novel diagnostic biomarkers. In fact, data from various sources are analyzed as meta-data to enhance the validity and reliability of results.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eData Filtration\\u003c/h2\\u003e\\n \\u003cp\\u003eData regarding microarrays were downloaded from the NCBI.GEO database. We have searched for sperm tissue datasets. Our search phrase included: \\u0026ldquo;infertile OR sterile\\u0026rdquo; AND \\u0026ldquo;sperm OR semen\\u0026rdquo; AND \\u0026ldquo;healthy OR control OR fertile OR normal\\u0026rdquo;. Both fertile and infertile datasets were integrated into a single meta-data set. This investigation included ten datasets (GSE14078, GSE6872, GSE6967, GSE44133, GSE4797, GSEGSE6969, GSE26982, GSE9210, GSE160749, and GSE34514) (\\u003cspan class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e) that met our inclusion criteria. The characteristics of the dataset are listed in Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. We used the GEOquery package to import data from the NCBI repository into R 4.2.1 (\\u003cspan class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eCharacteristics of selected studies.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDataset\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSample Size\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eReproductively status\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDecision\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSE14078\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL6104\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncluded\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSE160749\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL17692\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eF and InF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eExcluded\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSE26982\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL6244\\u003c/p\\u003e\\n \\u003cp\\u003eGPL8490\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eonly Inf\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncluded\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSE34514\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL570\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eF and InF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eExcluded\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSE44133\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL4133\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncluded\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSE4797\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL10558\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eF and InF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncluded\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSE6872\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL570\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eF and InF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncluded\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSE6967\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL2507\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eF and InF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncluded\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSE6969\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL570\\u003c/p\\u003e\\n \\u003cp\\u003eGPL2507\\u003c/p\\u003e\\n \\u003cp\\u003eGPL2700\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eF and InF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncluded\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSE9210\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGPL887\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eInF (non-obstructive and obstructive)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eExcluded\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eTable 1. Accession number for each dataset (GSE) and used microarray chip (GPL), sample size of datasets, reproductive status including Fertility (F) or Infertility (InF) and presence in final meta-data is mentioned in this table.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eQuality Control and Assurance\\u003c/h2\\u003e\\n \\u003cp\\u003eEach dataset\\u0026apos;s preprocessed form was utilized for further analysis. Using a boxplot, imaging of microarray chips, and an RNA degradation plot (\\u003cspan class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e), the integrity of the selected data was evaluated. Using \\u003cem\\u003enormalizebetweenarray\\u003c/em\\u003e from the LIMMA program, the data was normalized. A boxplot of meta-data before and after normalization is depicted in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Each dataset\\u0026apos;s boxplot can be obtained from the supplementary file.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eDefining Meta-Data\\u003c/h2\\u003e\\n \\u003cp\\u003eUsing the ComBat function from the SVA package, selected datasets were merged into a singular meta-data record and the batch effect was removed (\\u003cspan class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e). On this meta-data, expression and diagnostic analysis were then performed. We have used venn diagram to detect the intersection of probes in each platform. There are 14228 probes in the best-case scenario. (Supplementary Fig.\\u0026nbsp;1). Moreover, GSE9210 was excluded from the aforementioned datasets due to its small number of evaluated genes (Supplementary Fig.\\u0026nbsp;2).\\u003c/p\\u003e\\n \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003ePrincipal Component Investigation\\u003c/h2\\u003e\\n \\u003cp\\u003ePrincipal component analysis (PCA) was utilized to assess the expression profiles of various datasets and meta-data. PCA is the most prevalent method for evaluating the similarity of genetic profiles among various samples (\\u003cspan class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e). The PCA graphic groups samples into clusters based on their resemblance to one another. We used principal component analysis to separate these samples into a fertile and an infertile group. Neither GSE160749 nor GSE34514 presented sufficiently differentiated samples to be included in the meta-data. In addition, samples exhibiting abnormal behavior were omitted from the study on the basis of the PCA performed on the meta-data.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003eAnalyzing Gene Expression Variation\\u003c/h2\\u003e\\n \\u003cp\\u003eWe have highlighted DEGs between infertile and fertile groups using the LIMMA package in R4.2.1 (\\u003cspan class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e). Study design was based on the difference between average expression of infertile and fertile. Using the FDR approach, statistically significant DEGs were isolated. In addition, the DEGs\\u0026apos; correlation was assessed with the use of the cor function (Pearson Coefficient) and heatmap visualization.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eReceiver Operating Characteristics Curve Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eWe evaluated the efficacy of these genes as diagnostic biomarkers by doing a Receiver Operating Characteristic (ROC) curve analysis on the meta-data using GraphPad Prism 9 (\\u003cspan class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e). The area under the curve (AUC) for each DEG was calculated independently for each gene. Further, sensitivity (Sen) and specificity (Spe) of each biomarker was also calculated.\\u003c/p\\u003e\\n \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003eEnrichment\\u003c/h2\\u003e\\n \\u003cp\\u003eThe EnrichR ontology and pathway analysis were used to conduct an investigation of the molecular function, biological processes, and cellular components of the enrichment DEGs (\\u003cspan class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e). Odds ratio and combined score of significant results (Adj.P.Val\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) were then included.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eSample Distinctions\\u003c/h2\\u003e\\n \\u003cp\\u003eDue to their high degree of similarity, two datasets were excluded from the meta-data for further analysis. Figure\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e illustrates the PCA plot of meta-data. The PCA of the meta-data is likely to distinguish between the Fertile and Infertile categories, making it suitable for expression analysis. The PCA results for individual datasets are available in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e of Supplemental File.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eExpression Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eAccording to the FDR method, among more than 14228 genes a total number of six genes were found out to be dysregulated in the infertile group comparing to the healthy fertile, including: S100Z (S100 Calcium Binding Protein Z), SLC2A2 (solute carrier family 2 member 2), IMPG1 (interphotoreceptor matrix proteoglycan 1), HOXD12 (homeobox D12), RAPGEFL1 (Rap guanine nucleotide exchange factor like 1) and DMBX1 (diencephalon/mesencephalon homeobox 1). These genes were significantly (Adjusted P.Value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) down-taken in the infertile group almost four times (Log Fold Change \\u0026lt; -2) of normal values. DEG analysis results are presented comprehensively in Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Among these DEGs, HOXD12 was the most down-regulated (LogFC\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;2.6) while DMBX1 was the least down-regulated among these (LogFC = -2).\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eDysregulated genes in sperm of infertile men.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGenes\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLogFC\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAveExpr\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003et\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP.Value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdj.P.Val\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eS100Z\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-2.502583752\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.540991924\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-7.869844153\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.68E-12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.26E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSLC2A2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-2.394007223\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.458132888\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-7.570613989\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.51E-11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.55E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIMPG1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-2.327159937\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.639426893\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-7.199985918\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.96E-10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.47E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHOXD12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-2.639138043\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.649579351\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-6.766454857\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.42E-09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.16E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRAPGEFL1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-2.382114253\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.281461005\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-5.512379259\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.51E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.001019646\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDMBX1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-2.007710761\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.753559015\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-5.229184865\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.14E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002768283\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003eTable 2. These genes are significantly (adj.P.Val \\u0026lt; 0.05) down-regulated in great amounts (LogFC \\u0026lt; -2). In addition, average expression of these genes are also mentioned. Furthermore, t and p values are presented.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eCorrelation Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eFigure \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA illustrates the heatmap of correlation analysis between fertile and infertile groups, while Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB depicts the correlation between identified DEGs. Based on our findings, the correlation between these DEGs and sperm tissue is high (\\u0026rho;\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.75). Among the identified DEGs, IMPG1 has the lowest correlation to other genes, while other genes are nearly as highly correlated as \\u0026gt;\\u0026thinsp;0.90. In other words, the expression of these genes tends to decrease simultaneously in infertile sperm based on the correlation results.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eROC Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eAccording to ROC curve analysis, all six DEGs show promising and significant usage in distinguishing infertility cases. Based on the ROC, SLC2A2 was the most potential diagnostic biomarker (AUC\\u0026thinsp;=\\u0026thinsp;0.79, Sen: 0.91 and Spe: 0.68). Moreover, S100Z, RAPGEFL1, HOXD12, DMBX1 and IMPG1 may provide favorable diagnostic biomarkers respectively. Figure\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e provides additional information about the results above.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eEnrichment Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eFunctional analysis indicated the role of these genes in monosaccharide transmembrane transporter activity (GO: 0015415), Fructose transmembrane transporter activity (GO: 005353) and dehydroascorbic acid transmembrane transporters (GO: 0033300). Moreover, pathway analysis pointed out these genes are responsible for mediating onset of diabetes in young, type II diabetes mellitus, carbohydrate digestion and absorption. Full results are available in Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003ePathway and ontology analysis.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eKEGG Pathways\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTerm\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAdjusted P-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eOdds Ratio\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCombined Score\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFructose Transmembrane Transport (GO:0015755)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e666.2666667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4108.590826\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDehydroascorbic Acid Transport (GO:0070837)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e571.0571429\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3445.289983\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGlucose Import (GO:0046323)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e363.3272727\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2044.882815\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHexose Transmembrane Transport (GO:0008645)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e190.2190476\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e955.5316922\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGlucose Transmembrane Transport (GO:1904659)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e190.2190476\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e955.5316922\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNervous System Development (GO:0007399)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e22.69489559\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e113.8706689\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVitamin Transport (GO:0051180)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.031606356\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e105.0315789\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e467.6978365\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMolecular Function\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMonosaccharide Transmembrane Transporter Activity (GO:0015145)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.012590324\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e799.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5053.709148\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFructose Transmembrane Transporter Activity (GO:0005353)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.012590324\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e666.2666667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4108.590826\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDehydroascorbic Acid Transmembrane Transporter Activity (GO:0033300)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.012590324\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e666.2666667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4108.590826\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD-glucose Transmembrane Transporter Activity (GO:0055056)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.014372827\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e399.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2284.210592\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHyaluronic Acid Binding (GO:0005540)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.014372827\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e307.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1682.802968\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHexose Transmembrane Transporter Activity (GO:0015149)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.014372827\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e266.3866667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1422.778691\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGlucose Transmembrane Transporter Activity (GO:0005355)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.018460915\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e173.6608696\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e857.2874885\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDouble-Stranded DNA Binding (GO:0003690)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.031480234\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14.92746914\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e63.19504037\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSequence-Specific Double-Stranded DNA Binding (GO:1990837)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.031480234\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13.52103787\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e54.78051148\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSequence-Specific DNA Binding (GO:0043565)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.031480234\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13.48181818\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e54.54989117\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBiological Process\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFructose Transmembrane Transport (GO:0015755)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e666.2666667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4108.590826\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDehydroascorbic Acid Transport (GO:0070837)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e571.0571429\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3445.289983\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGlucose Import (GO:0046323)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e363.3272727\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2044.882815\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHexose Transmembrane Transport (GO:0008645)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e190.2190476\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e955.5316922\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGlucose Transmembrane Transport (GO:1904659)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e190.2190476\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e955.5316922\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNervous System Development (GO:0007399)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020967582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e22.69489559\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e113.8706689\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVitamin Transport (GO:0051180)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.031606356\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e105.0315789\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e467.6978365\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003eTable 3. Pathway analysis for identified DEGs based on KEGG are presented. Moreover, molecular function and biological processes mediated by these genes are also provided alongside their adj.p.val, odds ratio and combined score.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe World Health Organization (WHO) defines infertility as the inability to attain a clinical pregnancy after at least 12 months of unprotected sexual intercourse(\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e), and this definition applies to both genders. Globally, approximately 15% of all couples in their reproductive years suffer from this disease(\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e). In 20\\u0026ndash;30% of couples, the masculine factor is the only cause of infertility, whereas in 50% of cases, it is one of several factors that contribute to infertility(\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e). Despite the fact that the majority of cases of infertility are primarily caused by genetic and epigenetic abnormalities, the causes of 70% of male infertility cases are still unknown(\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eRecent reproductive applications of microarray include the identification of genes with aberrant expression, the selection of the most fertile sperm for fertilization, and, of course, as a potential fertility diagnostic tool.\\u003c/p\\u003e \\u003cp\\u003eWe have performed a meta-analysis on the microarray data of gene expression in sperm from two categories of men: healthy men and infertile men. We discovered six dysfunctional genes in the sperm of males who are unable to fertilize. The fact that these genes (S100Z, SLC2A2, IMPG1, HOXD12, RAPGEFL1 and DMBX1) exhibited significant down-regulation in the meta-analysis suggests that they are of the uttermost importance. The S100 protein family consists of calcium-binding proteins. Despite their diminutive size, these proteins play a crucial role in a vast array of cellular processes, including cell proliferation, differentiation, and mortality(\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e). For instance, the overexpression of S100A12, as a member of S100 family, was observed in semen of infertile men (\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e). S100Z protein is another member of the S100 proteins which our results suggest to be decreased in infertile sperm, however; so far not many studies are conducted to evaluate the expression and role of S100Z in male infertility which emphasizes on the concept of unrevealed underlying mechanisms. The majority of interphotoreceptor matrix proteoglycans are encoded by IMPG1, a member of the IMPG gene family (\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e). IMPG1 has been shown to code a protein called SPACR which is highly expressed in the testis (\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e). Frame shift mutations can lead to aberrant expression as well as the deletion of the C-terminal portion of the IMPG1 protein, resulting in infertility (\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e), but as like S100Z, further studies are required for role identification and expression assessment. Homeobox genes, also known as HOX genes, are a family of genes responsible for regulating anterior\\u0026ndash;posterior axis. HOXD has been observed to play crucial roles in female infertility but further examinations are required in case of male sterility (\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e). For a long time, it was believed HOXD genes are necessary for uterine receptivity (\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e), but our findings suggest remarkable influence in infertile sperm too. SLC2A2 codes a protein mostly known as GLUT2 which is not only a glucose transporter but also in control of fructose transportation. GLUT2 is mainly dysregulated in diabetes II (\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e), this is important due to the fact that fructose is the primary energy source of semen (\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e). Down-regulation of DMBX1 is perhaps the most interesting dysregulation among these DEGs, as DMBX1 is a homeodomain transcription factor specially expressed in brain. It seems like DMBX1 is mainly expressed during embryogenesis and therefore, expression aberrancies of this gene can lead to severe damages. Additionally, we postulate down-regulation of DMBX1 is greatly correlated with male infertility(\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e). Based on our findings, RAPGEFL1, also known as Link-GEFII, is a vague down-regulated gene which is predicted to facilitate the function of guanine nucleotide exchange factors. According to the enrichment analysis, these genes play crucial roles, most notably in the transportation of glucose, vitamins and especially fructose, which is considered the primary source of energy for the mitochondria in sperm cells. In this regard, it seems like adjusting defects in transportation system may do well in infertility treatment.\\u003c/p\\u003e \\u003cp\\u003eInterestingly, identified genes in the sperm of infertile males appear to be strongly interrelated. This suggests that the simultaneous down-regulation of six genes in sperm tissue is what causes infertility. According to the ROC analysis, all of these genes met the requirements for functioning as diagnostic indicators of infertility; however, additional research may be required to validate these results prior to their clinical application.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFuture Prospective\\u003c/h2\\u003e \\u003cp\\u003eAdvantages of using microarrays for infertile sperm include the discovery of underlying genetic pathways and the suggestion of novel diagnostic biomarkers. Our findings may prove useful in the treatment and accurate diagnosis of male infertility.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLimitations\\u003c/h2\\u003e \\u003cp\\u003eAlthough the study of microarrays is a high-throughput method, we recommend verifying the data with a gold-standard method, such as real-time polymerase chain reaction (RT-PCR). Given the scarcity of samples, we would be overjoyed if subsequent research confirmed the molecular level findings reported here. As for the diagnostics, commenting on ideal biomarkers require several studies with a wide sample size, therefore, testing our findings on a cohort will disclose the accurate and real ability of these DEGs in identifying infertility.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eART: Assisted Reproductive Therapy\\u003c/p\\u003e\\n\\u003cp\\u003eAUC: Area Under Curve\\u003c/p\\u003e\\n\\u003cp\\u003eCNV: Copy Number Variations\\u003c/p\\u003e\\n\\u003cp\\u003eDEG: Differentially Expressed Genes\\u003c/p\\u003e\\n\\u003cp\\u003eFDR: False Discovery Rate\\u003c/p\\u003e\\n\\u003cp\\u003eGEO: Gene Expression Omnibus\\u003c/p\\u003e\\n\\u003cp\\u003eLogFC: Logarithm Fold Change\\u003c/p\\u003e\\n\\u003cp\\u003ePCA: Principal Components Analysis\\u003c/p\\u003e\\n\\u003cp\\u003eROC: Receiver Operating Characteristic\\u003c/p\\u003e\\n\\u003cp\\u003eRT-PCR: Real-time Polymerase Chain Reaction\\u003c/p\\u003e\\n\\u003cp\\u003eSen: Sensitivity\\u003c/p\\u003e\\n\\u003cp\\u003eSpe: Specificity\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis article uses an in-silico approache and does not include any experiments on human participants or animal specimen.\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was reviewed and approved by the Tabriz Research Ethics Committee. (Ethics code: IR.TBZMED.VCR.REC.1401.347)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe dataset analyzed in this study are publicly reachable in NCBI.GEO repository. Any requested data will be available on reasonable requests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have declared no conflicts of interest for this article.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by grants from the Student Research Committee, Tabriz University of Medical Sciences (grant number: 70804).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor\\u0026rsquo;s Contribution:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe research was conceived by A. Ebrahimi and S. Mansoori. Analysis was done by Ghavi D. and Ebrahimi A. Data collection was performed with the help of Mirzaei Z and Barati T. The intellectual content of the manuscript was revised critically by Mirzaei Z. Both A. Ebrahimi and T. Barati wrote portions of the draft script. The research was overseen by S. Mansoori. The final paper was read and approved by all authors named.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eWasilewski T, Łukaszewicz-Zając M, Wasilewska J, Mroczko B. Biochemistry of infertility. Clin Chim Acta. 2020;508:185-90.\\u003c/li\\u003e\\n\\u003cli\\u003eKumar N, Singh AK. Trends of male factor infertility, an important cause of infertility: A review of literature. J Hum Reprod Sci. 2015;8(4):191-6.\\u003c/li\\u003e\\n\\u003cli\\u003eBoitrelle F, Shah R, Saleh R, Henkel R, Kandil H, Chung E, et al. The Sixth Edition of the WHO Manual for Human Semen Analysis: A Critical Review and SWOT Analysis. Life (Basel). 2021;11(12).\\u003c/li\\u003e\\n\\u003cli\\u003eVasan SS. Semen analysis and sperm function tests: How much to test? Indian J Urol. 2011;27(1):41-8.\\u003c/li\\u003e\\n\\u003cli\\u003eAuger J. Assessing human sperm morphology: top models, underdogs or biometrics? Asian J Androl. 2010;12(1):36-46.\\u003c/li\\u003e\\n\\u003cli\\u003eMutch DM, Berger A, Mansourian R, Rytz A, Roberts M-A. Microarray data analysis: a practical approach for selecting differentially expressed genes. Genome Biology. 2001;2(12):preprint0009.1.\\u003c/li\\u003e\\n\\u003cli\\u003eGarrido N, Garc\\u0026iacute;a-Herrero S, Meseguer M. Assessment of sperm using mRNA microarray technology. Fertility and sterility. 2013;99(4):1008-22.\\u003c/li\\u003e\\n\\u003cli\\u003eGarrido N, Martinez-Conejero J, Jauregui J, Horcajadas J, Simon C, Remohi J, et al. Microarray analysis in sperm from fertile and infertile men without basic sperm analysis abnormalities reveals a significantly different transcriptome. Fertility and sterility. 2009;91(4):1307-10.\\u003c/li\\u003e\\n\\u003cli\\u003eWaclawska A, Kurpisz M. Key functional genes of spermatogenesis identified by microarray analysis. 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NCBI GEO: archive for functional genomics data sets\\u0026mdash;update. Nucleic Acids Research. 2012;41(D1):D991-D5.\\u003c/li\\u003e\\n\\u003cli\\u003eLalancette C, Platts AE, Johnson GD, Emery BR, Carrell DT, Krawetz SA. Identification of human sperm transcripts as candidate markers of male fertility. J Mol Med (Berl). 2009;87(7):735-48.\\u003c/li\\u003e\\n\\u003cli\\u003ePlatts AE, Dix DJ, Chemes HE, Thompson KE, Goodrich R, Rockett JC, et al. Success and failure in human spermatogenesis as revealed by teratozoospermic RNAs. Hum Mol Genet. 2007;16(7):763-73.\\u003c/li\\u003e\\n\\u003cli\\u003eMetzler-Guillemain C, Victorero G, Lepoivre C, Bergon A, Yammine M, Perrin J, et al. Sperm mRNAs and microRNAs as candidate markers for the impact of toxicants on human spermatogenesis: an application to tobacco smoking. Syst Biol Reprod Med. 2015;61(3):139-49.\\u003c/li\\u003e\\n\\u003cli\\u003ePacheco SE, Houseman EA, Christensen BC, Marsit CJ, Kelsey KT, Sigman M, et al. Integrative DNA methylation and gene expression analyses identify DNA packaging and epigenetic regulatory genes associated with low motility sperm. PLoS One. 2011;6(6):e20280.\\u003c/li\\u003e\\n\\u003cli\\u003eOkada H, Tajima A, Shichiri K, Tanaka A, Tanaka K, Inoue I. Genome-wide expression of azoospermia testes demonstrates a specific profile and implicates ART3 in genetic susceptibility. PLoS Genet. 2008;4(2):e26.\\u003c/li\\u003e\\n\\u003cli\\u003eJodar M, Kalko S, Castillo J, Ballesc\\u0026agrave; JL, Oliva R. Differential RNAs in the sperm cells of asthenozoospermic patients. Hum Reprod. 2012;27(5):1431-8.\\u003c/li\\u003e\\n\\u003cli\\u003eDavis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics. 2007;23(14):1846-7.\\u003c/li\\u003e\\n\\u003cli\\u003eRaman T, O\\u0026apos;Connor TP, Hackett NR, Wang W, Harvey B-G, Attiyeh MA, et al. Quality control in microarray assessment of gene expression in human airway epithelium. BMC Genomics. 2009;10(1):493.\\u003c/li\\u003e\\n\\u003cli\\u003eLeek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882-3.\\u003c/li\\u003e\\n\\u003cli\\u003eBro R, Smilde AK. Principal component analysis. Analytical methods. 2014;6(9):2812-31.\\u003c/li\\u003e\\n\\u003cli\\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.\\u003c/li\\u003e\\n\\u003cli\\u003eHajian-Tilaki K. Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Caspian J Intern Med. 2013;4(2):627-35.\\u003c/li\\u003e\\n\\u003cli\\u003eKuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90-7.\\u003c/li\\u003e\\n\\u003cli\\u003eBarak S, Baker HWG. Clinical Management of Male Infertility. In: Feingold KR, Anawalt B, Blackman MR, Boyce A, Chrousos G, Corpas E, et al., editors. Endotext. South Dartmouth (MA): MDText.com, Inc. Copyright \\u0026copy; 2000-2023, MDText.com, Inc.; 2000.\\u003c/li\\u003e\\n\\u003cli\\u003eSaha S, Roy P, Corbitt C, Kakar SS. Application of Stem Cell Therapy for Infertility. Cells. 2021;10(7).\\u003c/li\\u003e\\n\\u003cli\\u003eMasoumi SZ, Parsa P, Darvish N, Mokhtari S, Yavangi M, Roshanaei G. An epidemiologic survey on the causes of infertility in patients referred to infertility center in Fatemieh Hospital in Hamadan. Iran J Reprod Med. 2015;13(8):513-6.\\u003c/li\\u003e\\n\\u003cli\\u003eWinters BR, Walsh TJ. The epidemiology of male infertility. Urol Clin North Am. 2014;41(1):195-204.\\u003c/li\\u003e\\n\\u003cli\\u003eGonzalez LL, Garrie K, Turner MD. Role of S100 proteins in health and disease. Biochim Biophys Acta Mol Cell Res. 2020;1867(6):118677.\\u003c/li\\u003e\\n\\u003cli\\u003eBagheri V, Hassanshahi G, Zeinali M, Abedinzadeh M, Khorramdelazad H. Elevated levels of S100A12 in the seminal plasma of infertile men with varicocele. International Urology and Nephrology. 2016;48(3):343-7.\\u003c/li\\u003e\\n\\u003cli\\u003eMeunier I, Manes G, Bocquet B, Marquette V, Baudoin C, Puech B, et al. Frequency and Clinical Pattern of Vitelliform Macular Dystrophy Caused by Mutations of Interphotoreceptor Matrix IMPG1 and IMPG2 Genes. Ophthalmology. 2014;121(12):2406-14.\\u003c/li\\u003e\\n\\u003cli\\u003eFagerberg L, Hallstr\\u0026ouml;m BM, Oksvold P, Kampf C, Djureinovic D, Odeberg J, et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteomics. 2014;13(2):397-406.\\u003c/li\\u003e\\n\\u003cli\\u003eManes G, Meunier I, Avila-Fern\\u0026aacute;ndez A, Banfi S, Le Meur G, Zanlonghi X, et al. Mutations in IMPG1 Cause Vitelliform Macular Dystrophies. The American Journal of Human Genetics. 2013;93(3):571-8.\\u003c/li\\u003e\\n\\u003cli\\u003eAkbas GE, Taylor HS. HOXC and HOXD gene expression in human endometrium: lack of redundancy with HOXA paralogs. Biol Reprod. 2004;70(1):39-45.\\u003c/li\\u003e\\n\\u003cli\\u003eDu H, Taylor HS. The Role of Hox Genes in Female Reproductive Tract Development, Adult Function, and Fertility. Cold Spring Harb Perspect Med. 2015;6(1):a023002.\\u003c/li\\u003e\\n\\u003cli\\u003eThorens B. GLUT2, glucose sensing and glucose homeostasis. Diabetologia. 2015;58(2):221-32.\\u003c/li\\u003e\\n\\u003cli\\u003eHelsley RN, Moreau F, Gupta MK, Radulescu A, DeBosch B, Softic S. Tissue-Specific Fructose Metabolism in Obesity and Diabetes. Current Diabetes Reports. 2020;20(11):64.\\u003c/li\\u003e\\n\\u003cli\\u003eHirono S, Lee EY, Kuribayashi S, Fukuda T, Saeki N, Minokoshi Y, et al. Importance of Adult Dmbx1 in Long-Lasting Orexigenic Effect of Agouti-Related Peptide. Endocrinology. 2016;157(1):245-57.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Supplementary information\",\"content\":\"\\u003cp\\u003eSupplementary file and Supplementary Figures are not available with this version.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Differentially Expressed genes, Infertility, Biomarker, Sperm\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3138032/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3138032/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eGenetic defects in sperm are responsible for a great percentage of male infertility. The association between numerous genes and spermatogenesis has been observed. Dysregulation of these genes greatly influence sperm morphology, motility and viability. Therefore, analyzing gene expression aberrancies is a must in male infertility. Microarray analysis is practically used for several aspects in male infertility including detection of differentially expressed genes (DEGs), selection of finest sperm for assisted reproductive therapy (ART) and identification of potential infertility biomarkers.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe conducted a meta-analysis using microarray datasets from NCBI.GEO. We have included datasets containing sperm tissues from both healthy and infertile males. Seven datasets qualified for inclusion in this study. These data were then transformed into a single set of meta-data. For these genes, expression and diagnostic analyses were conducted. In addition, enrichment analysis revealed the role and function of these genes in cellular processes.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eSix genes, including S100Z, SLC2A2, IMPG1, HOXD12, RAPGEFL1, and DMBX1, were identified as being significantly down-regulated in infertile men's sperm. Notably, the expression of these genes was highly correlated in sterile sperm. In addition, an analysis of the receiver operating curve indicated that these genes may serve as useful biomarkers for infertility diagnosis. The role of these genes in transporting glucose, vitamins and fructose as the sperm's primary fuel source, was suggested by pathway analysis.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eOverall, our results suggest genes with expression abnormalities that may mediate the underlying mechanisms of infertility and also offer promising diagnostic values.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Differentially expressed male infertility-associated genes in sperm as prospective diagnostic biomarkers\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2023-07-14 19:05:09\",\"doi\":\"10.21203/rs.3.rs-3138032/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"552494b7-20d5-4c05-a31b-82e247adcfe3\",\"owner\":[],\"postedDate\":\"July 14th, 2023\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2023-07-24T18:01:23+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2023-07-14 19:05:09\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3138032\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3138032\",\"identity\":\"rs-3138032\",\"version\":[\"v1\"]},\"buildId\":\"pf3fE39SIOqb-0xH_OWvX\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}