MicroRNAs in seminal plasma are able to discern infertile men at increased risk of developing testicular cancer

Abstract

Male infertility is a risk factor for the development of testicular germ cell tumors. In this study, we investigated microRNA profiles in seminal plasma to identify potential noninvasive biomarkers able to discriminate the men at highest risk of developing cancer among the infertile population. We compared the microRNA profiles of individuals affected by testicular germ cell tumors and healthy individuals with normal or impaired spermiograms using high-throughput technology and confirmed the results by single-assay digital PCR. We found that miR-221-3p and miR-222-3p were downregulated and miR-126-3p was upregulated in cancer patients compared to both infertile and fertile men. ROC curve analysis confirmed that miR-126 upregulation is able to identify cancer patients among the infertile male population. In addition, in-depth bioinformatics analysis based on weighted gene co-expression networks showed that the identified miRNAs regulate cellular pathways involved in cancer.

Keywords: circulating microRNA; liquid biopsy; male infertility; testicular germ cell tumor.

Fig. 1

Bar plot of differentially expressed miRNAs from Digital PCR analysis. (A) differential expression of miR‐221‐3p (*P < 0.05, evaluated using ANOVA. Error bars indicate SD). (B) differential expression of miR‐222‐3p (*P < 0.05, evaluated using ANOVA. Error bars indicate SD). (C) differential expression of miR‐126‐3p (*P < 0.05, evaluated using ANOVA. Error bars indicate SD). CTRL IS, healthy subjects with impaired spermiogram; CTRL NS, healthy subjects with normal spermiogram; TGCT, patients affected by testicular germ cell tumors. In Y‐axis, values are reported as ratio copies·μL⁻¹.

Fig. 2

Classical univariate ROC curve analyses and correlation scatterplot. (A) miR‐126‐3p in the comparisons between TGCTs and CTRLs IS. (B) miR‐126‐3p in the comparisons between TGCTs and CTRLs NS. (C) miR‐221‐3p and (D) miR‐222‐3p in the comparisons between TGCTs and CTRLs NS. (E) miR‐221‐3p in the comparisons between CTRLs IS and CTRLs NS. (F) correlation between miR‐221‐3p normalized copies and sperm count. (G) correlation between miR‐222‐3p normalized copies and sperm count. (H) Correlation between miR‐221‐3p and miR‐222‐3p normalized copies. Statistical significance for correlation analysis was calculated using t‐test. CTRL IS, healthy subjects with impaired spermiogram; CTRL NS, healthy subjects with normal spermiogram; TGCT = patients affected by testicular germ cell tumors. Area under the ROC curve (AUC) and P‐values are shown, P‐value < 0.05 were considered statistically significant, the light blue curves in ROC curves indicate the interval of confidence. Statistical significance for ROC curve analysis was calculated using DeLong's method.

Fig. 3

Regulatory network of DE miRNA–mRNA interactions. Nodes are represented with a color scheme according to their betweenness centrality, from dark blue (high centrality) to lilac (low centrality). DE, differentially expressed.

Fig. 4

Network of DE miRNAs' target and correlated genes interactions in Cyan module. Nodes are represented with a color scheme according to their degree, from dark blue (high degree) to yellow (low degree). The edges' color indicates the weight of the interaction, with a color scheme from dark red (higher weight) to pale red (lower weight). Rhomboidal nodes contain miRNAs that regulate their respective targets connected to them with an unweighted edge. In the up‐right corner is showed a scatterplot showing MM‐GS correlations for the Cyan module. DE, differentially expressed; GS, gene significance; MM, module membership.

Fig. 5

Network of DE miRNAs' target and correlated genes interactions in Dark magenta module. Nodes are represented with a color scheme according to their degree, from dark purple (high degree) to lilac (low degree). The edges' color indicates the weight of the interaction, with a color scheme from dark red (higher weight) to pale red (lower weight). Rhomboidal nodes contain miRNAs that regulate their respective targets connected to them with an unweighted edge. In the upright corner is showed a scatterplot showing MM‐GS correlations for the dark magenta module. DE, differentially expressed; GS, gene significance; MM, module membership.

Fig. 6

Network of DE miRNAs' target and correlated genes interactions in orange module. Nodes are represented with a color scheme according to their degree, from dark orange (high degree) to pale orange (low degree). The edges' color indicates the weight of the interaction, with a color scheme from dark green (higher weight) to pale green (lower weight). Rhomboidal nodes contain miRNAs that regulate their respective targets connected to them with an unweighted edge. In the up‐right corner is showed a scatterplot showing MM‐GS correlations for the orange module. DE, differentially expressed; GS, gene significance; MM, module membership.

Fig. 7

Boxplot of genes differentially expressed in TGCT tissue and belonging to the identified modules. CTRL, healthy subjects; TGCT, patients affected by testicular germ cell tumors. All the samples are derived from the GEO dataset GSE1818. In the Y axes, values are shown as log₂ of normalized expression values. The whiskers represent the highest and lowest values. *P < 0.05, **P < 0.01, ***P < 0.001. Statistical significance was calculated using the moderated t‐test. P‐values were adjusted for multiple testing using the Benjamini‐Hochberg (BH) method.

Fig. 8

Significant pathways associated with the four DE miRNAs. The point size indicates the number of genes target of DE miRNAs involved in the pathway, while the gradient color indicates the relative P‐value. DE, differentially expressed.