Abstract

   Background: Seminoma accounts for the most part of cases of testicular
   germ cell tumor, which is the most common malignancy among males
   between ages 15 and 44 years. Understanding the molecular mechanism of
   tumorigenesis is important for better clinical diagnosis and treatment.

   Purpose: We performed bioinformatics analysis to better understand
   seminoma at the genetic level and to explore potential candidate genes
   or molecules for diagnosis, treatment, and prognosis.

   Methods: A gene expression profile ([44]GSE8607), containing 40
   seminoma samples and three healthy testes samples, was analyzed to
   identify differentially expressed genes (DEGs) associated with the
   occurrence of seminoma. Functional annotation was then performed using
   gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway
   enrichment analyses. Cytoscape with Search Tool for the Retrieval of
   Interacting Genes was used to construct a protein–protein interaction
   (PPI) network of the DEGs and select hub genes. Moreover, validation of
   expression level and Kaplan–Meier analysis for overall survival were
   conducted to those hub genes.

   Results: A total of 1,636 DEGs were identified between seminoma and
   healthy samples, including 701 up-regulated in seminoma that were
   enriched in the regulation of immune responses, defense responses,
   receptor activity, and signal transducer activity; 935 were
   down-regulated in seminoma and were associated with reproductive
   processes, kinase activity, and carbohydrate derivative binding. Five
   hub genes were selected from the PPI network according to the degree of
   connectivity: IL6, VEGFA, IL10, CCR5, and CXCR4. Among them, high
   expression levels of CCR5 and CXCR4 were associated with poor prognosis
   for seminoma patients. Four modules selected from the PPI network
   revealed that seminoma was connected with the Janus kinase-signal
   transducers and activators of transcription signaling pathway,
   chemokine signaling pathway, endocytosis, and cytokine–cytokine
   receptor interaction.

   Conclusion: These identified DEGs and hub genes facilitate our
   knowledge of the underlying molecular mechanism of seminoma and have
   the potential to be used as diagnostic biomarkers or therapeutic
   targets for seminoma.

   Keywords: seminoma, bioinformatics analysis, DEGs, key genes, pathways

Introduction

   Testicular germ cell tumors (TGCT) are rare tumors in the general
   population but are the most commonly occurring malignancy among males
   between ages 15 and 44 years.[45]^1 Radical orchidectomy is the
   standard treatment method.[46]^2^,[47]^3 Recently, immunotherapy and
   gene therapy are suggested to be potential therapeutic
   strategies.[48]^4^–[49]^6 Understanding the molecular mechanism of
   tumorigenesis is important for a better clinical application of the
   novel treatments, as well as early diagnosis of disease and prognostic
   prediction. In the present study, we aim to identify key genes
   associated with the tumorigenesis of seminoma, based on the data from
   the Gene Expression Omnibus (GEO) database, for a better knowledge
   about the mechanism at the genetic level and exploring potential
   candidate genes or molecules for diagnosis, treatment, and prognosis.

Materials and methods

Microarray data

   The gene expression profile ([50]GSE8607), containing 40 seminoma
   samples and three healthy testes samples, was downloaded from the GEO
   database. [51]GSE8607, which was submitted by Gashaw, was based on the
   Affymetrix [52]GPL8300 platform ([HG_U95Av2Affymetrix] Human Genome U95
   Version 2 Array). The average age of the 40 patients with seminoma was
   37.6 (23–56) years old, while 53 years old in the control group. The
   American Joint Committee on Cancer (AJCC) stage distribution of these
   seminoma samples is as follows: 35 with stage I, 3 with stage II, and 2
   with stage III.

Identification of DEGs

   GEO2R ([53]http://www.ncbi.nlm.nih.gov/geo/geo2r/) is a free online
   tool for comparing several groups of gene expression data in a GEO
   Series.[54]^7 We applied it to detect the differentially expressed
   genes (DEGs) between seminoma samples and normal testes samples. Adjust
   P-value<0.05 was thought significant statistically and |logFC| ≥2 was
   set as the cut-off criterion for a better accuracy and significance as
   described earlier.[55]^8^,[56]^9

GO and KEGG pathway analysis

   Gene ontology (GO) analysis is a useful method for functional studies
   of high-throughput genomes or transcriptome data,[57]^10^,[58]^11 and
   Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment
   analysis is employed for the systematic analysis of gene functions by
   linking genomic information with higher-order functional
   information.[59]^12 To visualize core biological processes (BP),
   molecular functions (MF), and cellular component (CC) pathways among
   the DEGs, we used Functional Annotation Tool of Database for
   Annotation, Visualization and Integrated Discovery (DAVID) version 6.8
   ([60]https://david.ncifcrf.gov/).[61]^13 Adjust P-value<0.05 was
   considered statistically significant.

Hub genes selection and analysis of modules from PPI networks

   Search Tool for the Retrieval of Interacting Genes (STRING) is a
   biological database for building protein–protein interaction (PPI)
   networks, providing a system-wide view of interactions between each
   member.[62]^14 DEGs were mapped to STRING to analyze their
   relationships with each other, and a combined score of >0.4 was set as
   the cut-off criterion described before. PPI networks were then
   established using Cytoscape software,[63]^15 which visually explores
   biomolecular interaction networks composed of proteins, genes, and
   other molecules. The plug-in Centiscape was used to search for the most
   important nodes in a network by calculating centrality parameters for
   each node.[64]^15 Hub genes were selected from the PPI network
   according to the degree of connectivity. The plug-in Molecular Complex
   Detection (MCODE) was used to select modules from the PPI network with
   the criteria as described before: node score cutoff=0.2, degree
   cutoff=2, max depth=100, and k-core=2.[65]^9 Pathway analyses of genes
   in each module were performed using DAVID, with adjust P-value<0.05
   considered statistically significant.

Comparison of the hub genes expression level

   GEPIA ([66]http://gepia.cancer-pku.cn/index.html) is a web server for
   cancer and normal gene expression profiling and interactive analyses,
   based on the data from TCGA and the GTEx project.[67]^16 We used it to
   validate the different expression levels of the hub genes between
   seminoma tissues and normal testes tissues. Then, the box plot was
   performed to visualize the relationship.

Survival analyses of hub genes

   The expression profiles of 134 seminoma samples, along with the
   clinical data were downloaded from the TCGA database
   ([68]http://tcga-data.nci.nih.gov) for survival analyses of hub genes.
   The hazard ratio (HR) with 95% confidence intervals and log-rank
   P-value were calculated and displayed. Log-rank P-value<0.05 was
   considered statistically significant.

Results

Identification of DEGs

   The study included 40 seminoma samples and three healthy testes
   samples. A total of 1,636 DEGs were selected after the analysis of
   [69]GSE8607 by GEO2R. Of these, 701 were up-regulated and 935 were
   down-regulated in seminoma samples compared with healthy controls. A
   volcano plot and heat map of DEGs are shown in [70]Figure 1A and B,
   respectively.

Figure 1.

   [71]Figure 1
   [72]Open in a new tab

   Selection of DEGs and function annotation. (A) Volcano plot of the DEGs
   (adjust P-value <0.05 and |logFC| ≥2 were set as the cut-off criteria).
   (B) Heat map of the top 100 DEGs (top 50 up-regulated and
   down-regulated genes). (C) Genes ontology analysis of up-regulated
   DEGs. (D) Genes ontology analysis of down-regulated DEGs. (E) KEGG
   pathway analysis of DEGs.

GO term and KEGG pathway analyses

   For a deeper insight into the DEGs, we performed GO and KEGG pathway
   enrichment analyses. GO BP analysis revealed that up-regulated DEGs
   were mainly involved in the regulation of immune system processes and
   defense responses; while down-regulated DEGs were mainly associated
   with reproductive processes. GO MF analysis showed that the
   up-regulated DEGs were mainly enriched in receptor binding, signal
   transducer activity, receptor activity, molecular transducer activity,
   and protein complex binding; while down-regulated DEGs were
   significantly enriched in kinase activity, carbohydrate derivative
   binding, protein serine/threonine kinase activity, adenyl nucleotide
   binding, and adenyl ribonucleotide binding. GO CC analysis showed that
   up-regulated DEGs were involved with intrinsic components of plasma
   membranes, cell surface, side of membranes, integral components of
   plasma membranes, and external side of plasma membranes; while
   down-regulated DEGs were mainly associated with cilium and neuron
   projections. GO analysis findings are shown in [73]Figure 1C and D, and
   [74]Table 1.

Table 1.

   Gene ontology analysis of DEGs associated with seminoma
   Expression Category Term Count % P-value FDR
   Up-regulated GOTERM_BP_FAT Immune response 206 36.20 6.12E−76 1.19E−72
   GOTERM_BP_FAT Regulation of immune system process 166 29.17 4.71E−53
   9.19E−50
   GOTERM_BP_FAT Defense response 168 29.53 1.39E−48 2.71E−45
   GOTERM_BP_FAT Positive regulation of immune system process 130 22.85
   4.61E−46 9.00E−43
   GOTERM_BP_FAT Regulation of immune response 126 22.14 1.75E−45 3.41E−42
   GOTERM_MF_FAT Receptor binding 112 19.68 1.01E−17 1.62E−14
   GOTERM_MF_FAT Signal transducer activity 113 19.86 2.74E−12 4.42E−09
   GOTERM_MF_FAT Receptor activity 104 18.28 2.08E−10 3.36E−07
   GOTERM_MF_FAT Molecular transducer activity 104 18.28 2.08E−10 3.36E−07
   GOTERM_MF_FAT Protein complex binding 61 10.72 3.30E−10 5.33E−07
   GOTERM_CC_FAT Intrinsic component of plasma membrane 132 23.20 1.36E−19
   2.02E−16
   GOTERM_CC_FAT Cell surface 81 14.24 2.44E−19 3.60E−16
   GOTERM_CC_FAT Side of membrane 60 10.54 2.75E−19 4.06E−16
   GOTERM_CC_FAT Integral component of plasma membrane 128 22.50 2.89E−19
   4.27E−16
   GOTERM_CC_FAT External side of plasma membrane 44 7.73 8.94E−19
   1.32E−15
   Down-regulated GOTERM_BP_FAT Reproductive process 145 19.03 9.16E−27
   1.79E−23
   GOTERM_BP_FAT Reproduction 145 19.03 1.06E−26 2.08E−23
   GOTERM_BP_FAT Single organism reproductive process 133 17.45 2.37E−25
   4.63E−22
   GOTERM_BP_FAT Sexual reproduction 98 12.86 1.32E−23 2.58E−20
   GOTERM_BP_FAT Multi-organism reproductive process 107 14.04 4.11E−22
   8.04E−19
   GOTERM_MF_FAT Kinase activity 74 9.71 6.64E−08 1.08E−04
   GOTERM_MF_FAT Carbohydrate derivative binding 141 18.50 2.01E−07
   3.27E−04
   GOTERM_MF_FAT Protein serine/threonine kinase activity 43 5.64 9.12E−07
   1.49E−03
   GOTERM_MF_FAT Adenyl nucleotide binding 103 13.52 1.06E−06 1.72E−03
   GOTERM_MF_FAT Adenyl ribonucleotide binding 102 13.39 1.35E−06 2.20E−03
   GOTERM_CC_FAT Cilium 55 7.22 1.78E−09 2.70E−06
   GOTERM_CC_FAT Neuron projection 75 9.84 1.04E−06 1.58E−03
   GOTERM_CC_FAT Ciliary part 37 4.86 1.40E−06 2.12E−03
   GOTERM_CC_FAT Cytosol 196 25.72 5.46E−06 8.26E−03
   GOTERM_CC_FAT Neuron part 92 12.07 7.43E−06 1.12E−02
   [75]Open in a new tab

   [76]Table 2 and [77]Figure 1E show the results of KEGG analysis.
   Up-regulated DEGs were mainly enriched in cytokine–cytokine receptor
   interactions, cell adhesion molecules (CAMs), phagosomes, osteoclast
   differentiation, leukocyte transendothelial migration, natural killer
   cell-mediated cytotoxicity, chemokine signaling pathways, and antigen
   processing and presentation; down-regulated DEGs were significantly
   enriched in tight junctions, pyruvate metabolism, the cell cycle,
   glutamatergic synapses, metabolic pathways, ovarian steroidogenesis,
   axon guidance, and calcium signaling pathways.

Table 2.

   KEGG pathway analysis of DEGs associated with seminoma
   Expression Pathway ID Term Count % P-value FDR Genes
   Up-regulated hsa04060 Cytokine−cytokine receptor interaction 44 7.73
   5.35E−15 6.77E-12 IL1R2, CCL3, CCL2, PDGFB, TNFRSF25, CCR1, CXCL9,
   FASLG, CCL8, KIT, CCL5, CXCL11, CCL4, TNFRSF4, IL10, FLT3LG, CXCL10,
   TNFRSF1B, TNFRSF11B, IL23A, CXCR5, CXCR4, IFNA5, IL10RA, CXCR6, IFNG,
   CSF2RB, IL1B, IL2RG, LTB, CSF2RA, CD27, IL6, IL2RB, CCL19, IL6R,
   IFNAR2, CCR8, TNFSF10, CCR5, CXCL13, CCR2, VEGFA, ACVR1
   hsa04514 Cell adhesion molecules (CAMs) 27 4.75 2.78E−09 3.54E−06
   ITGAL, CLDN7, CD8A, CDH1, L1CAM, ITGB2, HLA-DMB, CDH3, HLA-DMA, ITGAM,
   NRCAM, CD2, HLA-DRB4, CD4, HLA-DPB1, HLA-DOA, CD28, ICAM1, PTPRC, SELL,
   ICAM2, ITGA4, HLA-E, HLA-F, PECAM1, HLA-DPA1, HLA-DRA
   hsa04145 Phagosome 28 4.92 3.17E−09 4.03E−06 TUBB2B, TLR2, ITGB2,
   HLA-DMB, ATP6V1B1, HLA-DMA, ITGAM, TUBB, TAP2, HLA-DRB4, HLA-DPB1,
   FCGR3A, THBS1, HLA-DOA, FCGR3B, NCF2, NCF4, CTSS, HLA-E, HLA-F, CYBA,
   CORO1A, CYBB, ITGA5, HLA-DPA1, FCGR2A, CD14, HLA-DRA
   hsa04380 Osteoclast differentiation 25 4.39 1.15E−08 1.46E−05 NCF2,
   NCF4, ACP5, NFKB2, STAT1, JUNB, BTK, STAT2, LILRB1, IRF9, IFNAR2,
   LILRB2, CYBA, TNFRSF11B, CYBB, LILRB3, LCK, IFNG, LILRB4, IL1B, FCGR2A,
   FCGR3A, FCGR3B, TYROBP, LCP2
   hsa04670 Leukocyte transendothelial migration 22 3.87 1.63E−07 2.07E−04
   ICAM1, ITGAL, CLDN7, MYL7, ITK, NCF2, MMP9, NCF4, SIPA1, ACTN1, ITGB2,
   ITGA4, ITGAM, VASP, THY1, CYBA, CYBB, RAC2, CXCR4, PECAM1, MSN, RHOH
   hsa04650 Natural killer cell mediated cytotoxicity 22 3.87 2.94E−07
   3.73E−04 ICAM1, ITGAL, PTPN6, ICAM2, CD247, FASLG, ITGB2, GZMB, NCR1,
   CD48, IFNAR2, TNFSF10, SH2D1A, RAC2, IFNA5, LCK, IFNG, FCER1G, FCGR3A,
   FCGR3B, TYROBP, LCP2
   hsa04062 Chemokine signaling pathway 27 4.75 8.07E−07 1.03E−03 CCL3,
   CCL2, CCR1, CXCL9, CCL8, CXCL11, CCL5, CCL4, CXCL10, RAC2, CXCR5,
   CXCR4, CXCR6, ITK, LYN, HCK, CCL19, STAT1, CCL18, STAT2, CCR8, CCR5,
   ARRB2, CXCL13, CCR2, GRK5, XCL2
   hsa04612 Antigen processing and presentation 16 2.81 2.59E−06 3.29E−03
   CD8A, CTSS, HLA-DMB, HLA-E, HLA-DMA, CD74, HLA-F, B2M, TAP2, IFNG,
   HLA-DRB4, CD4, HLA-DPA1, HLA-DPB1, HLA-DOA, HLA-DRA
   Down-regulated hsa04530 Tight junction 17 2.23 5.54E−04 7.16E−01
   PPP2R1B, PRKCZ, CLDN18, MYL5, MAGI1, GNAI1, MYH3, MYLPF, MYH7, ACTN3,
   CTNNA2, CSNK2A2, EPB41L3, MYH11, EXOC3, TJP3, TJP2
   hsa00620 Pyruvate metabolism 8 1.05 2.13E−03 2.73E+00 HAGH, LDHC, ME1,
   ME3, ACYP1, PDHA2, ACACB, PC
   hsa04110 Cell cycle 14 1.84 4.68E−03 5.90E+00 CCNE2, CDKN1C, E2F2,
   FZR1, CDKN1B, RBL2, CDKN2B, CCNH, DBF4, CDKN2D, CDK6, PTTG1, CDC25C,
   CCNA1
   hsa04724 Glutamatergic synapse 12 1.57 1.61E−02 1.90E+01 PRKACG,
   SLC1A2, DLGAP1, GNAO1, GRIA2, ADCY9, GNAI1, GRM8, SLC1A6, PPP3CC,
   CACNA1C, GRK3
   hsa01100 Metabolic pathways 72 9.45 2.27E−02 2.58E+01 LDHC, KYNU,
   CYP2J2, PGAM2, ITPKA, CKB, AKR1C3, TYR, AKR1C4, AGPS, PTGES, RGN,
   PDHA2, PCYT2, NT5E, NMNAT2, DDC, ALDH6A1, HYAL3, PDXK, GATM, CYP11A1,
   PFKP, CDS1, GMPS, NME5, ALOX15, ADO, PTGDS, CHPF, RFK, ABAT, PLA2G6,
   INPP4B, INPP1, GALNT3, ME1, CHKA, ME3, MVD, GK2, HMGCS1, HPRT1,
   ALDH1A1, TYMS, CKMT2, PLCH1, HSD17B3, PLCD1, PCYT1B, PRPS1L1, UGT8,
   MTMR6, SPAM1, GAD1, ODC1, PLA2G16, UAP1, SPHK2, MAOA, BCKDHB, MGAT4C,
   SI, ACACB, GAPDHS, CYP17A1, ACSM1, PYGM, HMGCS2, GAMT, PC, LIPF
   hsa04913 Ovarian steroidogenesis 7 0.92 2.46E−02 2.76E+01 AKR1C3,
   PRKACG, CYP17A1, CYP2J2, ADCY9, CYP11A1, STAR
   hsa04360 Axon guidance 12 1.57 3.29E−02 3.52E+01 EPHA5, DCC, EPHA4,
   PAK3, GNAI1, ROBO1, EFNB2, SEMA3D, SRGAP3, PPP3CC, SEMA3C, EPHB1
   hsa04020 Calcium signaling pathway 15 1.97 3.76E−02 3.92E+01
   SPHK2EPHB1 ERBB4, SLC25A4, PHKG2, ITPKA, PRKACG, CAMK4, ADCY9, PLN,
   PDE1A, PPP3CC, PLCD1, CACNA1C, MYLK, HTR2A
   [78]Open in a new tab

Identification of hub genes and analysis of modules from PPI networks

   According to the information from STRING, the top five hub nodes were
   selected, including interleukin 6 (IL6), vascular endothelial growth
   factor A (VEGFA), including interleukin 10 (IL10), C-C motif chemokine
   receptor 5 (CCR5), and C-X-C motif chemokine receptor 4 (CXCR4).
   Analysis of the relationship between 1,074 nodes and 4,633 edges by
   plug-in MCODE enabled four modules to be selected, and then functional
   annotation of the genes from these modules was conducted by DAVID
   ([79]Figure 2, [80]Tables 3–[81]6). The genes in these modules were
   mainly involved in the Janus kinase-signal transducers and activators
   of transcription (Jak–STAT) signaling pathway, chemokine signaling
   pathway, endocytosis, and cytokine–cytokine receptor interactions.

Table 4.

   The enriched pathways of module 2
   Term P-value FDR Genes
   Endocytosis 8.30E−07 8.76E−04 SH3GL3, ADRB2, ARRB2, CBL, DNAJC6, GRK5,
   HLA-E, BIN1, CLTCL1, SH3GL2, HLA-F
   Antigen processing and presentation 3.03E−06 3.19E−03 CD4, HLA-DPA1,
   HLA-DPB1, HLA-E, HLA-DRA, HLA-F, B2M
   Cell adhesion molecules (CAMs) 1.09E−04 1.15E−01 ICAM1, CD4, HLA-DPA1,
   HLA-DPB1, HLA-E, HLA-DRA, HLA-F
   Neuroactive ligand-receptor interaction 6.68E−04 7.03E−01 P2RY6,
   P2RY10, ADRB2, AVPR1B, F2RL1, NMBR, PTAFR, HTR2A
   Phagosome 9.50E−03 9.58E+00 HLA-DPA1, HLA-DPB1, HLA-E, HLA-DRA, HLA-F
   [82]Open in a new tab

Table 5.

   The enriched pathways of module 3
   Term P-value FDR Genes
   Cytokine−cytokine receptor interaction 3.34E−05 3.56E−02 IL5, CCL2,
   PDGFB, VEGFA, IFNG, IL1B, CCL4
   Phagosome 8.49E−04 9.01E−01 TLR2, THBS1, FCGR3A, FCGR3B, ITGAM
   Osteoclast differentiation 6.00E−03 6.21E+00 IFNG, IL1B, FCGR3A, FCGR3B
   Complement and coagulation cascades 1.66E−02 1.64E+01 FGA, SERPING1,
   SERPINA1
   Rap1 signaling pathway 2.15E−02 2.07E+01 PDGFB, VEGFA, THBS1, ITGAM
   [83]Open in a new tab

Figure 2.

   [84]Figure 2
   [85]Open in a new tab

   Top four modules from the PPI network. (A) modules 1; (B) modules 2;
   (C) modules 3; (D) modules 4.

Table 3.

   The enriched pathways of module 1
   Term P-value FDR Genes
   Chemokine signaling pathway 4.19E−17 4.35E−14 GNAI1, CCR1, CXCL9,
   CCL19, CXCL11, CCL5, CXCL10, CCR8, CXCR5, ADCY9, CCR5, CXCR4, CXCL13,
   CCR2, CXCR6
   Cytokine−cytokine receptor interaction 1.36E−12 1.41E−09 CCR1, CXCL9,
   CCL19, CXCL11, CCL5, CXCL10, CCR8, CCR5, CXCR5, CXCL13, CXCR4, CCR2,
   CXCR6
   Neuroactive ligand-receptor interaction 2.23E−04 2.31E−01 OPRM1, GRM8,
   P2RY14, FPR3, HTR1D, ADORA1, NPY5R
   Toll-like receptor signaling pathway 4.97E−03 5.04E+00 CXCL9, CCL5,
   CXCL11, CXCL10
   Regulation of lipolysis in adipocytes 1.46E−02 1.42E+01 ADCY9, GNAI1,
   ADORA1
   [86]Open in a new tab

Table 6.

   The enriched pathways of module 4
   Term P-value FDR Genes
   Jak-STAT signaling pathway 9.16E−08 9.76E−05 IL6, IL23A, CCND2, IL7,
   CSF2RB, IL13, IL2RG, IL10, CSF2RA
   Cytokine-cytokine receptor interaction 2.40E−07 2.56E−04 IL6, CCL3,
   IL23A, IL7, CSF2RB, IL13, IL2RG, IL10, CSF2RA, ACVR1
   Cell cycle 1.66E−03 1.75E+00 FZR1, CCND2, CCNH, CDK6, CDK4
   Ubiquitin mediated proteolysis 2.39E−03 2.52E+00 FZR1, NEDD4, SMURF1,
   PARK2, UBE2H
   PI3K-Akt signaling pathway 1.33E−02 1.33E+01 IL6, CCND2, IL7, IL2RG,
   CDK6, CDK4
   [87]Open in a new tab

Expression level and survival analysis of hub genes

   Compared with the normal tissues, CCR5 and CXCR4 revealed higher
   expression levels in seminoma tissues (P<0.05), but IL6, VEGFA and IL10
   not ([88]Figure 3A–E). Besides, 134 seminoma samples from TCGA
   database, grouped by the different expression of CCR5 and CXCR4, were
   used to conduct survival analyses. [89]Table 7 shows the
   clinicopathological characteristics in patients with seminoma from TCGA
   cohort. It was found that increased expression level of CCR5 (HR 0.72
   [0.52–0.98], P=0.047) was associated with poor overall survival for
   seminoma patients, as well as CXCR4 (HR 0.73 [0.59–0.92], P=0.012)
   ([90]Figure 3F and G).

Figure 3.

   [91]Figure 3
   [92]Open in a new tab

   Expression levels and survival analyses of DEGs. (A–E) Expression
   levels of the five hub genes. *P-value <0.05. (F–G) Kaplan-Meier plot
   of overall survival for CCR5 and CXCR4 in seminoma patients.

   Abbreviation: HR, hazard ratio.

Table 7.

   The clinicopathological characteristics in patients with seminoma from
   TCGA cohort
   Expression of CCR5 P-value Expression of CXCR4 P-value
   Low (n=107) High (n=27) Low (n=47) High (n=87)
   Age 0.305 0.239
   <40 y 88 25 42 71
   ≥40 y 19 2 5 16
   Race 0.442 0.133
   White 95 23 45 74
   Yellow 7 1 1 7
   Black 5 3 1 6
   AJCC stage 0.288 0.588
   I 76 23 33 68
   II 14 2 7 9
   III 17 2 7 10
   Survival 0.380 0.297
   Yes 105 25 47 83
   No 2 2 0 4
   [93]Open in a new tab

   Note: *p<0.05.

Discussion

   Seminoma accounts for the most part of cases of TGCT, which is the most
   common malignancy among males between ages 15 and 44 years.[94]^1
   Understanding its molecular mechanism in genetic aspects is important
   for diagnosis and treatment. In the present study, we analyzed the gene
   expression profile [95]GSE8607, containing 40 seminoma samples and
   three healthy testes samples, by bioinformatics methods, to explore the
   hub genes which may play crucial roles in tumorigenesis. We identified
   1,636 DEGs, of which 701 were up-regulated and 935 were down-regulated
   in seminomas compared with control testes.

   GO analysis showed that up-regulated DEGs were mainly enriched in
   defense responses, receptor activity, regulation of immune response
   processes, and signal transducer activity, while down-regulated DEGs
   were mainly enriched in reproductive processes, kinase activity, and
   carbohydrate derivative binding. Regarding KEGG pathway enrichment
   analysis, up-regulated DEGs were enriched in CAMs, natural killer
   cell-mediated cytotoxicity, cytokine–cytokine receptor interactions,
   and chemokine signaling pathways. Previous studies have suggested that
   cell adhesion plays an important part in the growth, progression, and
   metastasis of tumors. Moreover, high expression level of CAMs was
   reported to be associated with poor prognosis in lung and breast
   cancer, and many other tumor types.[96]^17^–[97]^19 In recent years,
   natural killer T cells were found to be an effective treatment for
   several cancers, but their efficacy in seminoma remains unknown.[98]^20

   We showed that down-regulated DEGs were mainly associated with tight
   junctions, metabolic pathways, axon guidance, the cell cycle, and
   calcium signaling pathways. Tight junctions of healthy testes separate
   the internal and external environment of the testis and protect it from
   harmful substances. However, it is difficult to maintain a normal
   structure and function in cancer tissue. Moreover, studies have
   suggested that the loss of cell cycle regulation leads to genomic
   instability, and the cell cycle is thought to play an important role in
   the etiology of spontaneous cancers.[99]^21 Recent evidence indicated
   that physiological calcium signaling regulated aerobic metabolism, but
   that pathological calcium overload contributed to cell death.[100]^22
   Therefore, monitoring these processes and pathways may aid the
   diagnosis or treatment of seminoma.

   IL6, VEGFA, IL10, CCR5, and CXCR4 were selected as hub genes because of
   their high degree of connectivity. Existing evidence suggests that
   immunologic factors may affect the development of seminoma, with the
   inflammatory cytokines IL6 and IL10 thought to promote tumor immune
   evasion through local immunosuppression. Parker et al reported that the
   extent of lymphocyte infiltration in seminomas was associated with a
   reduced risk of disease recurrence,[101]^23 while Klein et al
   documented major roles for IL6 in shaping the surrounding tumor
   microenvironment by influencing local immune responses.[102]^24 IL6
   may, therefore, have the potential to become a novel diagnostic and
   immunotherapeutic factor for seminoma.[103]^25 However, details about
   signaling and intercellular interaction require further investigation.
   Mohamed reported that IL10 secreted by tumor-infiltrating
   monocytes/macrophages (CD14+/CD16+) separated from inflammatory breast
   cancer patients positively correlated with the expression level of
   CPB2, which was crucial to lymphovascular invasion in inflammatory
   breast cancer.[104]^26 However, further studies are required to clarify
   the role of IL10 in seminoma.

   Angiogenesis is crucial to the progression of many kinds of tumors
   because of the need for nutrition from blood vessels.[105]^27 VEGFA is
   a subtype of VEGF that functions as a potent angiogenic factor in blood
   vessel formation and regulates the progression of
   tumors.[106]^28^,[107]^29 VEGFA promotes tumor proliferation and
   angiogenesis by activating phosphoinositide 3-kinase (PI3K)/AKT and
   extracellular-signal-regulated kinase (ERK) signaling
   pathways.[108]^30^–[109]^32 Although anti-angiogenesis therapies
   targeting VEGFA inhibit the progression of many tumor types, their
   efficacy in seminoma needs to be investigated. The gene CCR5, encoding
   one of the receptors of C-C chemokine ligand 5 (CCL5), promotes
   carcinogenesis, stroma genesis, and tumor progression.[110]^33
   CCL5/CCR5 operates via PI3K/AKT, mitogen-activated protein kinase
   kinase, and ERK, which in turn activate nuclear factor-κB, leading to
   activation of αvβ3 integrin and contributing to cell
   migration.[111]^34^–[112]^36 CCL5/CCR5 was found to be a biomarker of
   poor prognosis in various cancer types such as pancreas, prostate,
   breast, ovarian, and renal cancers.[113]^37^–[114]^41 CXCR4 is one of
   the most commonly overexpressed cytokine receptors in malignant tumors,
   and it accepts the signal of its ligand CXCL12 to mediate cell
   adhesion, angiogenesis, proliferation, metastasis, and
   survival.[115]^42 Both CXCR4 and CXCL12 are important components of the
   signaling mechanisms that facilitate the normal migration of primordial
   germ cells from the hindgut to the genital ridges in fish and
   mammals.[116]^43 Although they were reported to play an important role
   in the metastasis of seminoma by activating MAP kinase and PI3K
   pathways, they have a limited role in tumor survival and
   proliferation.[117]^44 In the present study, we found that high
   expression levels of CCR5 and CXCR4 were associated with poor prognosis
   for seminoma patients. Maybe they are potential diagnostic biomaker or
   predictor for prognosis when more studies confirm their values.

   Analysis of the four modules selected from the PPI network in the
   present study suggested that the Jak–STAT signaling pathway, chemokine
   signaling pathway, endocytosis, and cytokine–cytokine receptor
   interactions may be associated with the occurrence of seminoma. The
   Jak–STAT pathway is an evolutionarily conserved signaling cascade which
   mediates the response to cytokines and growth factors. Cellular
   responses to the activation of this pathway include differentiation,
   proliferation, migration, apoptosis, and cell survival. Moreover,
   Jak–STAT signaling is integral to homeostatic and developmental
   processes such as hematopoiesis, immune development, stem cell
   maintenance, and organismal growth,[118]^45 while its activation by
   dysregulated chemokines induces the occurrence and growth of cancer.
   Palagani et al reported that inhibiting the Jak–STAT pathway might
   prevent pancreatic cancer progression,[119]^46 and Li et al showed that
   its inhibition would promote the antiproliferative effect of
   methotrexate in small cell lung cancer.[120]^47 However, the
   relationship between seminoma and the Jak–STAT pathway requires further
   investigation. Maybe the Jak–STAT pathway is a promising candidate
   signaling pathway which affects the tumorigenesis of seminoma.

   In conclusion, we used bioinformatics analysis of DEGs to identify key
   genes and pathways that are closely associated with the occurrence of
   seminoma. Our work identified several potential targets for biomarkers
   and for understanding underlying molecular mechanisms. However, further
   study is required to determine the functions of these candidates.

Acknowledgments