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OncoTargets and Therapy Dovepress open access to scientific and medical research Open Access Full Text Article ORIGINAL RESEARCH Identification of key genes and pathways in seminoma by bioinformatics analysis This article was published in the following Dove Press journal: OncoTargets and Therapy Ye-Hui Chen* Background: Seminoma accounts for the most part of cases of testicular germ cell tumor, Ting-Ting Lin* which is the most common malignancy among males between ages 15 and 44 years. Yu-Peng Wu* Understanding the molecular mechanism of tumorigenesis is important for better clinical Xiao-Dong Li diagnosis and treatment. Shao-Hao Chen Purpose: We performed bioinformatics analysis to better understand seminoma at the Xue-Yi Xue genetic level and to explore potential candidate genes or molecules for diagnosis, treatment, and prognosis. Yo n g We i Methods: A gene expression profile (GSE8607), containing 40 seminoma samples and three Qing-Shui Zheng healthy testes samples, was analyzed to identify differentially expressed genes (DEGs) Jin-Bei Huang associated with the occurrence of seminoma. Functional annotation was then performed Ning Xu using gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment Departments of Urology, the First analyses. Cytoscape with Search Tool for the Retrieval of Interacting Genes was used to Affiliated Hospital of Fujian Medical construct a protein–protein interaction (PPI) network of the DEGs and select hub genes. ’ University, Fuzhou 350005, People s – Republic of China Moreover, validation of expression level and Kaplan Meier analysis for overall survival were conducted to those hub genes. *These authors contributed equally to Results: A total of 1,636 DEGs were identified between seminoma and healthy samples, this work 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 under- lying 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 Correspondence: Ning Xu Introduction Department of Urology, the First Testicular germ cell tumors (TGCT) are rare tumors in the general population but Affiliated Hospital of Fujian Medical University, 20 Chazhong Road, Fuzhou are the most commonly occurring malignancy among males between ages 15 and ’ 350005, People s Republic of China 44 years.1 Radical orchidectomy is the standard treatment method.2,3 Recently, Tel +8 605 918 798 1687 Email [email protected] immunotherapy and gene therapy are suggested to be potential therapeutic submit your manuscript | www.dovepress.com OncoTargets and Therapy 2019:12 3683–3693 3683 DovePress © 2019 Chen et al. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work http://doi.org/10.2147/OTT.S199115 you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Chen et al Dovepress strategies.4–6 Understanding the molecular mechanism of Hub genes selection and analysis of tumorigenesis is important for a better clinical application modules from PPI networks of the novel treatments, as well as early diagnosis of Search Tool for the Retrieval of Interacting Genes (STRING) disease and prognostic prediction. In the present study, is a biological database for building protein–protein interac- we aim to identify key genes associated with the tumor- tion (PPI) networks, providing a system-wide view of inter- igenesis of seminoma, based on the data from the Gene actions between each member.14 DEGs were mapped to Expression Omnibus (GEO) database, for a better knowl- STRING to analyze their relationships with each other, and edge about the mechanism at the genetic level and explor- a combined score of >0.4 was set as the cut-off criterion ing potential candidate genes or molecules for diagnosis, described before. PPI networks were then established using treatment, and prognosis. Cytoscape software,15 which visually explores biomolecular interaction networks composed of proteins, genes, and other Materials and methods molecules. The plug-in Centiscape was used to search for the Microarray data most important nodes in a network by calculating centrality The gene expression profile (GSE8607), containing 40 semi- parameters for each node.15 Hub genes were selected from noma samples and three healthy testes samples, was down- the PPI network according to the degree of connectivity. The loaded from the GEO database. GSE8607, which was plug-in Molecular Complex Detection (MCODE) was used submitted by Gashaw, was based on the Affymetrix GPL8300 to select modules from the PPI network with the criteria as platform ([HG_U95Av2Affymetrix] Human Genome U95 described before: node score cutoff=0.2, degree cutoff=2, Version 2 Array). The average age of the 40 patients with max depth=100, and k-core=2.9 Pathway analyses of genes seminoma was 37.6 (23–56) years old, while 53 years old in in each module were performed using DAVID, with adjust the control group. The American Joint Committee on Cancer P-value<0.05 considered statistically significant. (AJCC) stage distribution of these seminoma samples is as follows: 35 with stage I, 3 with stage II, and 2 with stage III. Comparison of the hub genes expression fi level Identi cation of DEGs GEPIA (http://gepia.cancer-pku.cn/index.html) is a web GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) is a free server for cancer and normal gene expression profiling online tool for comparing several groups of gene expres- and interactive analyses, based on the data from TCGA 7 sion data in a GEO Series. We applied it to detect the and the GTEx project.16 We used it to validate the differ- differentially expressed genes (DEGs) between seminoma ent expression levels of the hub genes between seminoma samples and normal testes samples. Adjust P-value<0.05 tissues and normal testes tissues. Then, the box plot was fi ≥ was thought signi cant statistically and |logFC| 2 was set performed to visualize the relationship. as the cut-off criterion for a better accuracy and signifi- cance as described earlier.8,9 Survival analyses of hub genes fi GO and KEGG pathway analysis The expression pro les of 134 seminoma samples, along with the clinical data were downloaded from the TCGA Gene ontology (GO) analysis is a useful method for functional database (http://tcga-data.nci.nih.gov) for survival ana- studies of high-throughput genomes or transcriptome data,10,11 lyses of hub genes. The hazard ratio (HR) with 95% and Kyoto Encyclopedia of Genes and Genomes (KEGG) confidence intervals and log-rank P-value were calculated pathway enrichment analysis is employed for the systematic and displayed. Log-rank P-value<0.05 was considered sta- analysis of gene functions by linking genomic information with tistically significant. higher-order functional information.12 To visualize core biolo- gical processes (BP), molecular functions (MF), and cellular component (CC) pathways among the DEGs, we used Results Functional Annotation Tool of Database for Annotation, Identification of DEGs Visualization and Integrated Discovery (DAVID) version 6.8 The study included 40 seminoma samples and three (https://david.ncifcrf.gov/).13 Adjust P-value<0.05 was consid- healthy testes samples. A total of 1,636 DEGs were ered statistically significant. selected after the analysis of GSE8607 by GEO2R. Of 3684 submit your manuscript | www.dovepress.com OncoTargets and Therapy 2019:12 DovePress Dovepress Chen et al these, 701 were up-regulated and 935 were down- reproductive processes. GO MF analysis showed that the up- regulated in seminoma samples compared with healthy regulated DEGs were mainly enriched in receptor binding, controls. A volcano plot and heat map of DEGs are signal transducer activity, receptor activity, molecular transdu- shown in Figure 1A and B, respectively. cer activity, and protein complex binding; while down- regulated DEGs were significantly enriched in kinase activity, GO term and KEGG pathway analyses carbohydrate derivative binding, protein serine/threonine kinase For a deeper insight into the DEGs, we performed GO and activity, adenyl nucleotide binding, and adenyl ribonucleotide KEGG pathway enrichment analyses. GO BP analysis revealed binding. GO CC analysis showed that up-regulated DEGs were that up-regulated DEGs were mainly involved in the regulation involved with intrinsic components of plasma membranes, cell of immune system processes and defense responses; while surface, side of membranes, integral components of plasma down-regulated DEGs were mainly associated with membranes, and external side of plasma membranes; while Figure 1 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).
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  • Supplementary Table 2

    Supplementary Table 2

    Supplementary Table 2. Differentially Expressed Genes following Sham treatment relative to Untreated Controls Fold Change Accession Name Symbol 3 h 12 h NM_013121 CD28 antigen Cd28 12.82 BG665360 FMS-like tyrosine kinase 1 Flt1 9.63 NM_012701 Adrenergic receptor, beta 1 Adrb1 8.24 0.46 U20796 Nuclear receptor subfamily 1, group D, member 2 Nr1d2 7.22 NM_017116 Calpain 2 Capn2 6.41 BE097282 Guanine nucleotide binding protein, alpha 12 Gna12 6.21 NM_053328 Basic helix-loop-helix domain containing, class B2 Bhlhb2 5.79 NM_053831 Guanylate cyclase 2f Gucy2f 5.71 AW251703 Tumor necrosis factor receptor superfamily, member 12a Tnfrsf12a 5.57 NM_021691 Twist homolog 2 (Drosophila) Twist2 5.42 NM_133550 Fc receptor, IgE, low affinity II, alpha polypeptide Fcer2a 4.93 NM_031120 Signal sequence receptor, gamma Ssr3 4.84 NM_053544 Secreted frizzled-related protein 4 Sfrp4 4.73 NM_053910 Pleckstrin homology, Sec7 and coiled/coil domains 1 Pscd1 4.69 BE113233 Suppressor of cytokine signaling 2 Socs2 4.68 NM_053949 Potassium voltage-gated channel, subfamily H (eag- Kcnh2 4.60 related), member 2 NM_017305 Glutamate cysteine ligase, modifier subunit Gclm 4.59 NM_017309 Protein phospatase 3, regulatory subunit B, alpha Ppp3r1 4.54 isoform,type 1 NM_012765 5-hydroxytryptamine (serotonin) receptor 2C Htr2c 4.46 NM_017218 V-erb-b2 erythroblastic leukemia viral oncogene homolog Erbb3 4.42 3 (avian) AW918369 Zinc finger protein 191 Zfp191 4.38 NM_031034 Guanine nucleotide binding protein, alpha 12 Gna12 4.38 NM_017020 Interleukin 6 receptor Il6r 4.37 AJ002942