Abstract Background Cryptorchidism is one of the most common causes of non-obstructive azoospermia (NOA) leading to male infertility. Despite various medical approaches been utilised, many patients still suffer from infertility. MicroRNAs (miRNAs) play vital roles in the progress of spermatogenesis; however, little is known about the miRNA expression profile in the testes. Therefore, the miRNA profile was assessed in the testis of post-cryptorchidopexy patients. Methods Three post-cryptorchidopexy testicular tissue samples from patients aged 23, 26 and 28 years old and three testis tissues from patients with obstructive azoospermia (controls) aged 24, 25 and 36 years old were used in this study. Next-generation sequencing (NGS) was used to perform the miRNA expression profiling. Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) assays were subsequently used to confirm the results of several randomly-selected and annotated miRNAs. Results A series of miRNAs were found to be altered between post-cryptorchidopexy testicular tissues and control tissues, including 297 downregulated and 152 upregulated miRNAs. In the subsequent qRT-PCR assays, the expression levels of most of the selected miRNAs (9/12, P < 0.05) were consistent with the results of NGS technology. Furthermore, signal transduction, adaptive immune response and biological regulation were associated with the putative target genes of the differentially-expressed miRNAs via GO analysis. In addition, oxidative phosphorylation, Parkinson’s disease and ribosomal pathways were shown to be enriched using KEGG pathway analysis of the differentially-expressed genes. Conclusions This study provides a global view of the miRNAs involved in post-cryptorchidopexy testicular tissues as well as the altered expression of miRNAs compared to control tissues, thus confirming the vital role of miRNAs in cryptorchidism. Electronic supplementary material The online version of this article (10.1186/s12958-018-0393-3) contains supplementary material, which is available to authorized users. Keywords: miRNA, Cryptorchidism, Cryptorchidopexy, Spermatogenesis, Next-generation small RNA sequencing Background Male factors account for approximately 50% of infertility cases, which affect 10–15% of couples around the world [[37]1]. Although most cases of male infertility are idiopathic with no known etiological factor, some causes (i.e. varicocele, sexual dysfunction etc.) are known [[38]2]. Among these causes, cryptorchidism is a relatively common anomaly in the male genitalia that affects approximately 2–4% of male infants. Despite various medical approaches (i.e. surgical operations and hormone administration) being applied for years, many patients still suffer from infertility [[39]3, [40]4], and little is known about the clear mechanism of spermatogenesis arrest in these patients. Spermatogenesis is a complex process consisted of three phases including mitotic, meiotic and haploid processes [[41]5]. These cellular events require highly regulated spatiotemporal expression of specific protein-coding genes, especailly at the post-transcriptional levels [[42]6]. MicroRNAs (miRNAs) are a series of small noncoding RNAs that negatively regulate gene expression after transcription [[43]7]. Research has shown that miRNAs play crucial roles in spermatogenesis [[44]5, [45]6, [46]8–[47]13]; for example, Lian et al. identified a series of altered miRNAs in patients with non-obstructive azoospermia (NOA) using microarray technology. These identified 154 significantly downregulated and 19 upregulated miRNAs indicated the important role of miRNAs in spermatogenesis [[48]10]. It was reported that during mouse testicular development, up-regulation of miR-449 coincided with initiation of meiotic, and miR-449 was predominantly expressed in spermatocytes and spermatids during adult spermatogenesis. Furthermore, Cdc20b/miR-449 cluster activity was documented to be cooperatively mediated by CREMT and SOX5 during postnatal testes development [[49]5]. Later on, Comazzetto et al. have identified the miR-34 family consisted of miR-34b/c and miR-449a/b/c as upregulated from late meiosis to sperm stage. miR-34b/c and miR-449 deletion led to sterility due to abnormal spermatozoa production with reduced motility [[50]11]. With regards to the effects of miRNAs in cryptorchidism, Duan et al. found that miR-210, a significantly upregulated miRNA in patients with NOA, was also highly expressed in patients with cryptorchidism [[51]12]. In addition, Moritoki et al. demonstrated that miR-135a was downregulated in unilateral undescended testes in a rat model of cryptorchidism [[52]13]. Although some miRNAs were shown to be involved in the regulation of spermatogenesis in patients with cryptorchidism, no studies have yet investigated miRNA expression in the testis of post-cryptorchidopexy patients with NOA. Therefore this study investigated the miRNA profile in the testis of post-cryptorchidopexy patients and aimed to provide a platform to expound the mechanism of spermatogenesis arrest in post-cryptorchidopexy patients with NOA. Methods Ethics statement Three patients (23, 26 and 28 years old) who underwent cryptorchidopexy but were still experiencing NOA, as well as three patients (24, 25 and 36 years old) suffering from obstructive azoospermia (OA) signed informed consent and approved the use of their tissues for research purposes. The local medical ethics committee approved this study. Clinical specimen collection Testes tissues were collected by testicular biopsy from all six subjects between July 2017 and January 2018 at the Reproductive Medicine Center, First Affiliated Hospital of Anhui Medical University (Hefei, Anhui, China). For post-cryptorchidopexy patients, all cases were bilateral. Case one was 23 years old and underwent the operation 1 year ago, case two was 26 years old and underwent the operation 18 years ago and case three was 28 years old and underwent the operation 12 years ago. Testes samples were frozen at − 80 °C in RNAlater (Ambion, USA) immediately after surgery. Haematoxylin and eosin (HE) staining and the Johnson score system were used to assess testicular spermatogenic function. Construction of a smRNA library and next-generation sequencing (NGS) Total RNA was extracted from the six samples using TRIzol (Life Technologies, USA) and was used to construct miRNA libraries using the NEBNext® Multiplex Small RNA Library Prep Set (Illumina®) according to the manufacturer’s instructions. Sequencing was performed on a Hiseq X (Illumina) using the HiSeq X Reagent Kit v2. Data analyses and novel miRNA exploration Data were analysed according to previously-reported methods. Known miRNAs were identified by mapping reads to miRBase (version 21.0) in Homo sapiens, whilst nonmatched reads were subsequently aligned against other noncoding RNAs within the Ensembl database [[53]14]. The remaining nonannotated sequences were selected for alignment with the integrated human transcriptome to explore novel miRNAs. All hairpin-like structures containing unclassified smRNA reads (no less than 45 reads) were predicted using miRDeep2 [[54]15] following the criteria described previously [[55]16]. Bioinformatic analyses for miRNAs with differential expression patterns The target genes of the differentially-expressed miRNAs in the two groups were predicted using TargetScan [[56]17] and miRanda [[57]18]. Enriched GO terms and KEGG pathway analysis was subsequently applied to predict the target genes of miRNAs with differential expression patterns in the two groups of specimens. QRT-PCR verification for altered miRNA expression cDNA synthesis was performed using a PrimeScript RT reagent kit following the manufacturer’s instructions (Takara, Japan). The abundance of individual miRNAs was subsequently assessed via an Applied Biosystems 7500 PCR System (Applied Biosystems) using SYBR Premix Ex Taq II (Tli RNaseH Plus, Takara) under optimised reaction conditions. The specific reverse transcription and qPCR primers for all miRNAs are listed in Additional file [58]1. The processes were performed in accordance with the protocols supplied by the manufacturers. Briefly, for qPCR, triplicate reactions were performed at 95 °C for 10 min, and the subsequent 40 amplification cycles were conducted at 95 °C for 15 s and 60 °C for 60 s. Meanwhile, 18S rRNA was used as an internal normalised control. Relative miRNA abundances were calculated using 2^−△△Ct (threshold cycle) formula, where △Ct = Ct[miRNA] − Ct[18S rRNA] and △△Ct = (△Ct[post-cryptorchidopexy] − △Ct[obstructive azoospermia]). The miRNA concentration differences between post-cryptorchidopexy and control tissues were analysed using unpaired t-tests. P < 0.05 indicated a statistically significant difference. Results Histopathological characteristics of post-cryptorchidopexy testicular tissue and control tissue To clarify the histopathological characteristics of the post-cryptorchidopexy testicular tissue (hereafter referred to as ‘cryptorchid tissue’) and control tissue (hereafter referred to as ‘normal tissue’), HE staining and the Johnson scoring system were used to assess the function of spermatogenesis (Fig. [59]1). The Johnson scores were 3, 3 and 3 in cryptorchid tissues, which indicated maturation arrest, and 9, 9 and 10 in normal control tissues, which indicated normal spermatogenesis. Fig. 1. [60]Fig. 1 [61]Open in a new tab HE-staining of cryptorchid tissue and control tissue, which clarify the histopathological characteristics of cryptorchid tissues (a) and control tissues (b). Comprehensive overview of whole genome smRNAs in cryptorchid and normal tissues All smRNAs [18–32 nucleotides (nt)] acquired from cryptorchid and normal tissues were deep sequenced by NGS. A total of 19,931,698 (out of 21,212,215) and 20,243,124 (out of 21,524,351) sequence reads that aligned to the human genome sequence dataset were obtained in the cryptorchid and normal tissues, respectively. MiRNAs accounted for 85.5% and 71.19% in cryptorchid and normal tissues, respectively (Fig. [62]2). Fig. 2. [63]Fig. 2 [64]Open in a new tab Results of geneome mapping and distribution of RNAs among different categories. The most abundant of these smRNAs in cryptorchid tissue were 21 nt in length, and these smRNAs were more abundant than the 22-nt and 23-nt RNAs which were in second and third place, respectively. However, the most abundant smRNAs in normal tissue were 22 nt in length, and these were more abundant than the 21-nt and 23-nt RNAs which were in second and third place, respectively (see Additional file [65]2). Understanding the distribution pattern of miRNA genes may help to elucidate their roles, therefore the chromosomal locations of miRNA genes were evaluated. In cryptorchid tissue, most miRNA genes were located on chromosomes X, 9, 3 and 21. Similarly, in normal tissues, most miRNA genes were located on chromosome X, 15, 9 and 5 (see Additional file [66]3). Features of the most abundant miRNAs in cryptorchid and normal tissues The NGS results were used to compile a list of the 20 most abundant and known miRNAs in cryptorchid tissue and the 10 most abundant and novel miRNAs in normal tissue. In cryptorchid tissue, miR-514a-3p, miR-143-3p, miR-26a-5p, miR-99a-5p, miR-202-5p, miR-509-3-5p, miR-10b-5p, miR-508-3p, let-7 g-5p and let-7f-5p were the most abundant known miRNAs. In normal tissue, miR-514a-3p, miR-143-3p, miR-26a-5p, miR-509-3-5p, miR-99a-5p, miR-202-5p, miR-10b-5p, let-7f-5p, miR-508-3p and let-7 g-5p were the most abundant known miRNAs (Table [67]1). Detailed information is shown in Table [68]1. Of the 10 most abundant novel miRNAs, only one was different between cryptorchid and normal tissues. Detailed information is shown in Table [69]2. Table 1. The top 20 most abundant known miRNAs expressed in cryptorchid and normal tissues miRNA name Cryptorchid miRNA name Control Reads count Normalized reads count Reads count Normalized reads count hsa-miR-514a-3p 2,313,282 109,499 hsa-miR-514a-3p 1,008,914 98,435 hsa-miR-143-3p 864,140 45,306 hsa-miR-143-3p 677,433 65,245 hsa-miR-26a-5p 829,953 46,035 hsa-miR-26a-5p 407,763 39,613 hsa-miR-99a-5p 705,575 38,041 hsa-miR-509-3-5p 392,074 37,711 hsa-miR-202-5p 616,580 30,770 hsa-miR-99a-5p 346,310 33,871 hsa-miR-509-3-5p 593,363 28,054 hsa-miR-202-5p 258,855 25,790 hsa-miR-10b-5p 428,806 24,984 hsa-miR-10b-5p 248,352 24,218 hsa-miR-508-3p 303,445 14,625 hsa-let-7f-5p 154,806 15,116 hsa-let-7 g-5p 296,741 15,472 hsa-miR-508-3p 153,631 15,059 hsa-let-7f-5p 266,118 14,707 hsa-let-7 g-5p 151,745 14,851 hsa-let-7a-5p 265,013 14,644 hsa-let-7a-5p 137,886 13,412 hsa-miR-21-5p 248,959 12,378 hsa-miR-21-5p 132,197 12,931 hsa-miR-509-5p 194,212 9293 hsa-miR-148a-3p 119,576 11,671 hsa-miR-148a-3p 188,828 10,645 hsa-miR-100-5p 103,191 10,071 hsa-miR-125b-5p 172,432 9013 hsa-miR-125b-5p 93,627 9134 hsa-miR-100-5p 169,375 9160 hsa-miR-27b-3p 92,637 8927 hsa-miR-199a-3p 154,971 8592 hsa-miR-509-5p 81,898 8124 hsa-miR-27b-3p 144,132 7610 hsa-miR-126-3p 79,980 7687 hsa-let-7i-5p 140,689 7772 hsa-miR-125a-5p 72,130 7013 hsa-let-7b-5p 112,327 6040 hsa-miR-34c-5p 69,568 6885 hsa-miR-125a-5p 107,449 5594 hsa-let-7i-5p 66,915 6560 [70]Open in a new tab Table 2. The list of top 10 most abundant novel miRNAs expressed in cryptorchid and normal tissues Cryptorchid tissues miRNA ID Mature Sequence Reads count Location of novel miRNA precusor chrX_47246 AUUGACACUUCUGUGAGUAGA 2,280,438 chrX:146366172..146366230:- chr12_27425 UUCAAGUAAUCCAGGAUAGGCU 826,714 chr12:58218403..58218462:- chr3_5958 UUCAAGUAAUCCAGGAUAGGCU 826,558 chr3:38010903..38010964:+ chr21_44054 AACCCGUAGAUCCGAUCUUGU 693,017 chr21:17911420..17911480:+ chrX_47235 UACUGCAGACGUGGCAAUCAUG 592,879 chrX:146341178..146341235:- chr10_23103 UUCCUAUGCAUAUACUUCUUU 586,995 chr10:135061041..135061097:- chr5_9937 UGAGAUGAAGCACUGUAGCUC 534,462 chr5:148808506..148808561:+ chr2_3766 UACCCUGUAGAACCGAAUUUGU 428,617 chr2:177015056..177015117:+ chrX_47228 UGAUUGUAGCCUUUUGGAGUAGA 298,225 chrX:146318462..146318520:- chr3_7283 UGAGGUAGUAGUUUGUACAGUU 295,643 chr3:52302295..52302373:- Normal tissues miRNA ID Mature Sequence Reads count Location of novel miRNA precusor chrX_47246 AUUGACACUUCUGUGAGUAGA 996,346 chrX:146366172..146366230:- chr5_9937 UGAGAUGAAGCACUGUAGCUC 419,103 chr5:148808506..148808561:+ chr12_27425 UUCAAGUAAUCCAGGAUAGGCU 405,817 chr12:58218403..58218462:- chr3_5958 UUCAAGUAAUCCAGGAUAGGCU 405,621 chr3:38010903..38010964:+ chrX_47235 UACUGCAGACGUGGCAAUCAUG 391,755 chrX:146341178..146341235:- chr21_44054 AACCCGUAGAUCCGAUCUUGU 340,009 chr21:17911420..17911480:+ chr2_3766 UACCCUGUAGAACCGAAUUUGU 248,236 chr2:177015056..177015117:+ chr10_23103 UUCCUAUGCAUAUACUUCUUU 246,558 chr10:135061041..135061097:- chr9_18744 UGAGGUAGUAGAUUGUAUAGUU 154,925 chr9:96938634..96938712:+ chr3_7283 UGAGGUAGUAGUUUGUACAGUU 151,154 chr3:52302295..52302373:- [71]Open in a new tab Differential expression of miRNAs between cryptorchid and normal tissues As described previously by Zhang et al. [[72]16], miRNAs were considered to be significantly differentially expressed between cryptorchid and normal tissues if they were altered by at least two-fold with P < 0.05 on the t-test. The results showed that 449 miRNAs were significantly differentially expressed in cryptorchid tissue (Fig. [73]3). Of these, 297 were downregulated and 152 were upregulated compared to normal tissue. The 30 most downregulated and upregulated known miRNAs are listed in Tables [74]3 and [75]4, respectively. Fig. 3. Fig. 3 [76]Open in a new tab The overview of the volcano plot generated by miRNAs profile in cryptorchid tissues and control tissues. Table 3. A collection of the top 30 most downregulated known miRNAs detected by deep sequencing in cryptorchid tissues MiRNA name baseMean log2FoldChange lfcSE stat p Adjust p hsa-miR-3663-5p 41.936 −4.426 0.624 −7.089 1.35E-12 2.39E-10 hsa-miR-1233-3p 25.216 −4.227 0.679 −6.225 4.79E-10 1.84E-08 hsa-miR-552-5p 66.556 −4.055 0.563 −7.195 6.24E-13 1.21E-10 hsa-miR-449b-5p 392.523 −3.972 0.496 −8.001 1.23E-15 5.26E-13 hsa-miR-7153-5p 108.897 − 3.812 0.634 −6.010 1.84E-09 5.18E-08 hsa-miR-122-5p 525.785 −3.790 0.562 −6.741 1.57E-11 1.60E-09 hsa-miR-552-3p 65.189 −3.760 0.562 −6.680 2.38E-11 2.31E-09 hsa-miR-449a 5575.001 −3.740 0.511 −7.317 2.52E-13 5.97E-11 hsa-miR-122-3p 4.738 −3.722 1.011 −3.679 0.00023 0.0016 hsa-miR-34b-5p 123.524 −3.688 0.558 −6.610 3.84E-11 3.56E-09 hsa-miR-449c-5p 2234.173 −3.637 0.465 −7.816 5.42E-15 1.93E-12 hsa-miR-34c-5p 39,328.272 −3.553 0.440 −8.060 7.58E-16 5.26E-13 hsa-miR-449c-3p 7.961 −3.441 0.902 −3.812 0.00014 0.0011 hsa-miR-375 491.449 −3.408 0.362 −9.416 4.68E-21 9.99E-18 hsa-miR-3663-3p 37.612 −3.385 0.676 −5.001 5.68E-07 9.63E-06 hsa-miR-7159-5p 20.897 −3.259 0.705 −4.618 3.87E-06 5.29E-05 hsa-miR-449b-3p 142.460 −3.212 0.610 −5.262 1.42E-07 2.75E-06 hsa-miR-4700-5p 4.985 −3.208 0.951 −3.370 0.00075 0.0043 hsa-miR-522-3p 121.036 −3.153 0.465 −6.768 1.30E-11 1.46E-09 hsa-miR-1273a 38.566 −3.118 0.508 −6.135 8.47E-10 2.44E-08 hsa-miR-1295a 11.735 −3.075 0.760 −4.041 5.31E-05 0.0005 hsa-miR-34b-3p 1137.731 −2.970 0.516 −5.753 8.72E-09 2.16E-07 hsa-miR-1283 139.436 −2.798 0.488 −5.731 9.95E-09 2.41E-07 hsa-miR-3150b-3p 3.547 −2.768 0.991 −2.791 0.0052 0.020 hsa-miR-4423-3p 16.582 −2.702 0.755 −3.578 0.00035 0.0023 hsa-miR-6507-5p 7.696 −2.698 0.811 −3.325 0.00088 0.0049 hsa-miR-7154-5p 406.827 −2.646 0.981 −2.697 0.0070 0.025 hsa-miR-517c-3p 95.074 −2.639 0.386 −6.832 8.37E-12 9.92E-10 hsa-miR-3925-3p 10.324 −2.613 0.735 −3.553 0.00038 0.0025 hsa-miR-515-5p 84.007 −2.600 0.379 −6.856 7.04E-12 8.84E-10 [77]Open in a new tab Table 4. A collection of the top 30 most upregulated known miRNAs detected by deep sequencing in cryptorchid tissues MiRNA name baseMean log2FoldChange lfcSE stat p Adjust p hsa-miR-7151-3p 6.026 2.634 0.892 2.953 0.0031 0.014 hsa-miR-376a-2-5p 10.918 2.202 0.724 3.042 0.0023 0.011 hsa-miR-1224-5p 17.708 2.193 0.615 3.565 0.00036 0.0024 hsa-miR-1299 187.854 1.958 0.426 4.600 4.22E-06 5.73E-05 hsa-miR-142-5p 697.547 1.898 0.583 3.255 0.0011 0.0060 hsa-miR-543 1281.559 1.869 0.450 4.152 3.29E-05 0.00036 hsa-miR-487a-3p 80.564 1.865 0.591 3.155 0.0016 0.0079 hsa-miR-584-3p 19.666 1.829 0.562 3.254 0.0011 0.0060 hsa-miR-665 18.416 1.798 0.710 2.534 0.011 0.036 hsa-miR-134-3p 29.541 1.778 0.598 2.975 0.0029 0.013 hsa-miR-369-3p 500.851 1.692 0.432 3.916 8.99E-05 0.00082 hsa-miR-377-3p 96.245 1.665 0.551 3.023 0.0025 0.011 hsa-miR-33a-5p 28.103 1.664 0.550 3.025 0.0025 0.011 hsa-miR-376a-3p 112.0733 1.602 0.436 3.704 0.00021 0.0015 hsa-miR-758-3p 520.1303 1.589 0.439 3.620 0.00029 0.0020 hsa-miR-654-3p 4175.568 1.587 0.388 4.095 4.22E-05 0.00044 hsa-miR-134-5p 2747.859 1.558 0.424 3.675 0.00024 0.0017 hsa-miR-889-3p 740.3619 1.552 0.468 3.312 0.00093 0.0052 hsa-miR-127-3p 40,871.646 1.548 0.392 3.955 7.65E-05 0.00071 hsa-miR-1185-1-3p 161.457 1.539 0.506 3.039 0.0024 0.011 hsa-miR-1185-2-3p 38.541 1.534 0.587 2.614 0.0089 0.030 hsa-miR-154-5p 267.267 1.516 0.346 4.385 1.16E-05 0.00014 hsa-miR-381-3p 7512.422 1.511 0.382 3.957 7.57E-05 0.00070 hsa-miR-127-5p 768.176 1.511 0.401 3.765 0.00017 0.0013 hsa-miR-337-5p 44.570 1.510 0.439 3.437 0.00059 0.0036 hsa-miR-379-3p 262.022 1.508 0.401 3.756 0.00017 0.0013 hsa-miR-136-3p 937.135 1.506 0.389 3.868 0.00011 0.00096 hsa-miR-376c-3p 327.216 1.492 0.402 3.713 0.00020 0.0015 hsa-miR-495-3p 884.797 1.443 0.390 3.696 0.00022 0.0016 hsa-miR-376b-5p 24.828 1.442 0.590 2.445 0.014 0.045 [78]Open in a new tab Validating the altered expression level of miRNAs by qRT-PCR QRT-PCR was performed to validate the altered miRNA expression. Among these deregulated miRNAs, we firstly selected two well-established spermatogenesis-associated miRNAs, miR-449a and miR-34c-5p [[79]5, [80]11]. Additionally, to better proving the accuracy of NGS, the other validated miRNAs were picked from the non-top 30 most deregulated known miRNAs (see Additional file [81]4 and Additional file [82]5), so that the relatively small fold changes could be validated. According to the previous studies, ten miRNAs were picked for qRT-PCR validation randomly [[83]16, [84]19]. Eventually, a total of 12 differentially-expressed miRNAs (seven upregulated and five downregulated) were selected for qRT-PCR analysis. The results showed that the expression levels of most miRNAs (9/12; P < 0.05) were consistent with the results of NGS technology. Detailed information is shown in Fig. [85]4. Fig. 4. [86]Fig. 4 [87]Open in a new tab Confirmation of differentially expressed miRNAs between cryptorchid tissues and control tissues obtained by NGS using qRT-PCR. (* P <0.05) GO enrichment analysis of differentially-expressed genes in cryptorchid and normal tissues After predicting the target genes of differentially-expressed miRNAs in cryptorchid and normal tissues, GO enrichment analysis was conducted. The 10 most enriched GO terms, including signal transduction and adaptive immune response, are shown in Table [88]5. Table 5. Top 30 most enriched GO terms for predicted targets of differentially expressed miRNAs between cryptorchid and normal tissues GO number Term^* GO process Ratio in study Ratio in pop p GO:0007165 BP signal transduction 19.68% 23.63% 1.04E-05 GO:0002250 BP adaptive immune response 0.70% 1.83% 1.56E-05 GO:0050789 BP regulation of biological process 47.95% 52.34% 4.14E-05 GO:0050794 BP regulation of cellular process 45.10% 49.34% 7.66E-05 GO:0008150 BP biological_process 78.42% 81.73% 8.25E-05 GO:0065007 BP biological regulation 51.05% 55.25% 8.51E-05 GO:0006956 BP complement activation 0.25% 0.98% 0.000114 GO:0006958 BP complement activation, classical pathway 0.20% 0.88% 0.000134 GO:0048518 BP positive regulation of biological process 21.53% 25.03% 0.00014 GO:0050776 BP regulation of immune response 3.95% 5.72% 0.000229 GO:0044425 CC membrane part 28.37% 34.86% 1.03E-10 GO:0005886 CC plasma membrane 17.68% 23.38% 1.11E-10 GO:0031224 CC intrinsic component of membrane 23.23% 29.30% 2.06E-10 GO:0016021 CC integral component of membrane 22.68% 28.69% 2.24E-10 GO:0005575 CC cellular_component 84.22% 88.01% 1.38E-07 GO:0005794 CC Golgi apparatus 3.35% 5.84% 1.42E-07 GO:0005840 CC ribosome 2.20% 1.09% 7.38E-06 GO:0000139 CC Golgi membrane 1.85% 3.41% 1.92E-05 GO:0044459 CC plasma membrane part 9.84% 12.81% 1.93E-05 GO:0004872 MF receptor activity 5.39% 8.48% 5.53E-08 GO:0060089 MF molecular transducer activity 5.39% 8.48% 5.53E-08 GO:0005179 MF hormone activity 1.55% 0.54% 6.49E-08 GO:0004871 MF signal transducer activity 5.79% 8.78% 2.25E-07 GO:0038023 MF signaling receptor activity 4.45% 7.13% 3.21E-07 GO:0099600 MF transmembrane receptor activity 4.25% 6.85% 4.09E-07 GO:0003823 MF antigen binding 0.50% 1.75% 4.18E-07 GO:0004888 MF transmembrane signaling receptor activity 4.15% 6.63% 9.33E-07 GO:0032553 MF ribonucleotide binding 6.34% 8.94% 1.10E-05 GO:0003674 MF molecular_function 77.82% 81.17% 7.79E-05 [89]Open in a new tab *BP Biological process; CC Cellular component; MF Molecular function KEGG pathway analysis of differentially-expressed genes in cryptorchid and normal tissues After GO analysis, KEGG pathway enrichment analysis was performed. A total of five KEGG pathways were enriched, including oxidative phosphorylation, Parkinson’s disease, Ribosomal pathways, Huntington’s disease and Alzheimer’s disease. The results are presented in Table [90]6. Table 6. KEGG pathway analysis for predicted target genes of differentially expressed miRNAs between cryptorchid and normal tissues Pathway ID Description GeneRatio BgRatio p Adjust p GeneName hsa00190 Oxidative phosphorylation 28/664 133/7297 1.80E-05 0.0053 ATP5G2;COX6C;SDHD;COX7A2L; COX8C;ATP6V1D hsa05012 Parkinson’s disease 28/664 142/7297 6.29E-05 0.0093 ATP5G2;COX6C;SDHD;UBB;UBE2L6;COX7A2L;GNAL;COX8C hsa03010 Ribosome 29/664 154/7297 0.0001112 0.0110 MRPL16;RPL38;RPS4X;MRPL35; RPS6;MRPS18C;RPL26;RPS27L hsa05016 Huntington’s disease 33/664 193/7297 0.0002633 0.0187 ATP5G2;COX6C;UCP1;SDHD; POLR2J3;COX7A2L;COX8C;POLR2K hsa05010 Alzheimer’s disease 30/664 171/7297 0.0003147 0.0187 ATP5G2;COX6C;CASP12;SDHD; PPP3CC;COX7A2L;LPL;COX8C [91]Open in a new tab Discussion As one of the most common congenital defects in newborn boys, cryptorchidism influences male fertility and increases the risk of testicular cancer. Reductions in seminiferous tubules and germ cells are common histological changes in cryptorchid testis [[92]20]. Despite surgery being recommended for many patients with cryptorchidism, the success of orchidopexy depends on the timing of the procedure and the position of the testis: some may not benefit from cryptorchidopexy [[93]21, [94]22]. Although research has identified some biological processes involved in spermatogenic arrest in cryptorchid testis (i.e. significant apoptotic changes in germ cells), the causative roles of genes in spermatogenic arrest or apoptosis remain unclear [[95]23–[96]26]. This is the first study to investigate the possible mechanisms of spermatogenic arrest in cryptorchid testes by assessing the miRNA profiles in post-cryptorchidopexy testes. Many rodent and primate models were developed to identify altered miRNAs in cryptorchid testis. For example, Duan et al. established a mouse model of cryptorchidism and showed that miR-210 was highly expressed in cryptorchid testes compared with control testes. Moreover, they showed that this miRNA regulated spermatogenesis by inhibiting the expression of NR1D2 [[97]12]. Moritoki et al. compared the miRNA expression profiles of unilateral undescended testes with contralateral descended testes in a rat model of cryptorchidism using microarray analysis. These authors found that only miR-135a expression was lower in unilateral undescended testes and that its target, FoxO1, played essential roles in stem cell maintenance [[98]13]. Furthermore, Duan et al., also found that miR-210 was upregulated in human cryptorchidism, thus suggesting a vital role for miRNAs in humans [[99]12]. In this study, 297 downregulated and 152 upregulated miRNAs were identified in post-cryptorchidopexy testicular tissue compared with normal testis tissue. However, miR-210 was not significantly altered, which may be due to the different types of human cryptorchid tissue. For example, Duan et al. used cryptorchid testis tissue obtained during the cryptorchidopexy, whilst this study used post-cryptorchidopexy testicular tissue. Some miRNA expression levels may change after the operation. Despite the insights gained into cryptorchidism over the years, the mechanism of spermatogenesis arrest in patients with this disease remains largely elusive. Germ cell apoptosis is commonly seen at the histological level in cryptorchid testes. Yin et al. revealed that cryptorchidism induced germ cell apoptosis in an experimental mouse model via p53-dependent and p53-independent pathways [[100]23]. Liu et al. also found that the Hsf1/Phlda1 pathway participated in primary spermatocyte apoptosis in surgery-induced cryptorchid testes of rats [[101]27]. The expression of many apoptosis-related miRNAs was also shown to be altered in post-cryptorchidopexy testicular tissues. It was reported that miR-299-5p could modulate apoptosis through autophagy in neurons and ameliorate the cognitive capacity of APPswe/PS1dE9 mice [[102]28]. In addition, miR-299-5p was significantly upregulated in post-cryptorchidopexy testicular tissue. Similar results were also found for miR-217, miR-206 etc. Li et al. also found that miR-217 could regulate apoptosis by targeting TNFSF11 in human podocyte cells [[103]29]. This study also identified a significant downregulation of miR-217 in post-cryptorchidopexy testicular tissue. Similarly, miR-206 was significantly upregulated in post-cryptorchidopexy testicular tissue and was shown to promoted cell apoptosis in Legg–Calvé–Perthes disease [[104]30]. Conclusions In summary, miRNA expression in post-cryptorchidopexy testicular tissue was profiled using NGS and compared with that of OA men with normal spermatogenesis. Several signalling pathways that are likely to be involved in spermatogenesis arrest in these patients were addressed. The results provide an important platform for future investigations into the roles of miRNAs in the progression of cryptorchidism as well as therapeutic targets to help these patients recover fertility. However, the comprehensive modulating behaviours of genes remain unclear, therefore determining the target genes and regulatory networks of these differentially-expressed miRNAs is essential in future investigations. Additional files [105]Additional file 1:^ (23KB, xls) Primers used for the quantification of representative deregulated miRNAs. (XLS 23 kb) [106]Additional file 2:^ (2.8MB, tif) Length distribution of clean reads from smRNA next-generation deep sequencing. (TIF 2836 kb) [107]Additional file 3:^ (3.5MB, tif) Number of smRNA sequencing tags that locate on each chromosome. (TIF 3575 kb) [108]Additional file 4:^ (45KB, xls) A collection of all the downregulated known miRNAs detected by deep sequencing in cryptorchid tissues. (XLS 45 kb) [109]Additional file 5:^ (35.5KB, xls) A collection of all the upregulated known miRNAs detected by deep sequencing in cryptorchid tissues. (XLS 35 kb) Acknowledgements