Research Article

Identification of potential crucial genes associated with the pathogenesis and prognosis of prostate cancer

Department of Urology, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China

^‡Authors contributed equally

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Zhi-Qiang Liang^‡

Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

Shanghai TCM-Integrated Institute of Vascular Anomalies, Shanghai, China

^‡Authors contributed equally

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Qi-Peng Xie

Department of Laboratory Medicine, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China

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Wei Han

Cancer Research Institute, Southern Medical University, Guangzhou, Guangdong, China

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Sen Yang

Department of Urology, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China

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Shuai-Bin Wang

Department of Urology, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China

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Cheng Zhao

Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

Shanghai TCM-Integrated Institute of Vascular Anomalies, Shanghai, China

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Ye-Min Cao

Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

Shanghai TCM-Integrated Institute of Vascular Anomalies, Shanghai, China

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You-Hua He

*Author for correspondence:

E-mail Address: heyouhua304@163.com

Department of Urology, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China

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Jian Chen

**Author for correspondence:

E-mail Address: alexandercj@126.com

https://orcid.org/0000-0001-5230-7141

Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

Shanghai TCM-Integrated Institute of Vascular Anomalies, Shanghai, China

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Published Online:7 Apr 2020https://doi.org/10.2217/bmm-2019-0318

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Abstract

Aim: Prostate cancer (PCa) is the sixth leading cause of cancer-related deaths in men throughout the world. This study aimed to investigate genes associated with the pathogenesis and prognosis of PCa. Materials & methods: Data of PCa cases were obtained from public datasets and were analyzed using an integrated bioinformatics strategy. Results: A total of 969 differential expression genes were identified. Moreover, GSE16560 and The Cancer Genome Atlas (TCGA) data showed a prognostic prompt function of the nine-gene signature, as well as in PCa with Gleason 7. Finally, majority of the nine hub genes were associated with drug sensitivity, mutational landscape, immune infiltrates and clinical characteristics of PCa. Conclusion: The nine-gene signature was correlated with drug sensitivity, mutational landscape, immune infiltrates, clinical characteristics and survival from PCa.

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Papers of special note have been highlighted as: • of interest

References

1. Baade PD, Youlden DR, Krnjacki LJ. International epidemiology of prostate cancer: geographical distribution and secular trends. Mol. Nutr. Food Res. 53(2), 171–184 (2009).Crossref, Medline, CAS, Google Scholar
2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J. Clin. 66(1), 7–30 (2016). • Epidemiological data on prostate cancer.Crossref, Medline, Google Scholar
3. D'elia C, Cerruto MA, Cioffi A, Novella G, Cavalleri S, Artibani W. Upgrading and upstaging in prostate cancer: from prostate biopsy to radical prostatectomy. Mol. Clin. Oncol. 2(6), 1145–1149 (2014).Crossref, Medline, Google Scholar
4. Zhou CK, Check DP, Lortet-Tieulent J et al. Prostate cancer incidence in 43 populations worldwide: an analysis of time trends overall and by age group. Int. J. Cancer 138(6), 1388–1400 (2016).Crossref, Medline, CAS, Google Scholar
5. Feng S, Shao L, Yu W, Gavine P, Ittmann M. Targeting fibroblast growth factor receptor signaling inhibits prostate cancer progression. Clin. Cancer Res. 18(14), 3880–3888 (2012).Crossref, Medline, CAS, Google Scholar
6. Al-Azayzih A, Gao F, Goc A, Somanath PR. TGFβ1 induces apoptosis in invasive prostate cancer and bladder cancer cells via Akt-independent, p38 MAPK and JNK/SAPK-mediated activation of caspases. Biochem. Biophys. Res. Commun. 427(1), 165–170 (2012).Crossref, Medline, CAS, Google Scholar
7. Fleischmann A, Huland H, Mirlacher M et al. Prognostic relevance of Bcl-2 overexpression in surgically treated prostate cancer is not caused by increased copy number or translocation of the gene. Prostate 72(9), 991–997 (2012).Crossref, Medline, CAS, Google Scholar
8. Vo BT, Morton D Jr, Komaragiri S, Millena AC, Leath C, Khan SA. TGF-beta effects on prostate cancer cell migration and invasion are mediated by PGE2 through activation of PI3K/AKT/mTOR pathway. Endocrinology 154(5), 1768–1779 (2013).Crossref, Medline, CAS, Google Scholar
9. Li D, Hao X, Song Y. Identification of the key microRNAs and the miRNA–mRNA regulatory pathways in prostate cancer by bioinformatics methods. Biomed. Res. Int. 2018, 6204128 (2018).Medline, Google Scholar
10. Anker JF, Naseem AF, Mok H, Schaeffer AJ, Abdulkadir SA, Thumbikat P. Multi-faceted immunomodulatory and tissue-tropic clinical bacterial isolate potentiates prostate cancer immunotherapy. Nat. Commun. 9(1), 1591 (2018).Crossref, Medline, Google Scholar
11. Tumeh PC, Harview CL, Yearley JH et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515(7528), 568–571 (2014). • The importance of immunotherapy on prostate cancer treatment.Crossref, Medline, CAS, Google Scholar
12. Champiat S, Ferte C, Lebel-Binay S, Eggermont A, Soria JC. Exomics and immunogenics: bridging mutational load and immune checkpoints efficacy. Oncoimmunology 3(1), e27817 (2014).Crossref, Medline, Google Scholar
13. Bezzi M, Seitzer N, Ishikawa T et al. Diverse genetic-driven immune landscapes dictate tumor progression through distinct mechanisms. Nat. Med. 24(2), 165–175 (2018).Crossref, Medline, CAS, Google Scholar
14. Sinnott JA, Peisch SF, Tyekucheva S et al. Prognostic utility of a new mRNA expression signature of Gleason score. Clin. Cancer Res. 23(1), 81–87 (2017).Crossref, Medline, CAS, Google Scholar
15. BarrettT, Wilhite SE, Ledoux P et al. NCBIGEO: archive for functional genomics data sets – update. Nucleic Acids Res. 41, D991–D995 (2013).Crossref, Medline, Google Scholar
16. Simon R, Lam A, Li MC, Ngan M, Menenzes S, Zhao Y. Analysis of gene expression data using BRB-ArrayTools. Cancer Inform. 3, 11–17 (2007). • An important tool for microarray data analysis.Crossref, Medline, Google Scholar
17. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16(5), 284–287 (2012).Crossref, Medline, CAS, Google Scholar
18. Szklarczyk D, Franceschini A, Wyder S et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).Crossref, Medline, CAS, Google Scholar
19. Kohl M, Wiese S, Warscheid B. Cytoscape: software for visualization and analysis of biological networks. Methods Mol. Biol. 696, 291–303 (2011).Crossref, Medline, CAS, Google Scholar
20. Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 2 (2003).Crossref, Medline, Google Scholar
21. Cline MS, Smoot M, Cerami E et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2(10), 2366–2382 (2007).Crossref, Medline, CAS, Google Scholar
22. Wu G, Feng X, Stein L. A human functional protein interaction network and its application to cancer data analysis. Genome Biol. 11(5), R53 (2010).Crossref, Medline, Google Scholar
23. MizunoH, Kitada K, Nakai K, Sarai A. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med. Genomics 2(1), 18 (2009). http://www.abren.net/PrognoScan/ • An important tool for survival analysis based on public database.Crossref, Medline, Google Scholar
24. Aguirre-Gamboa R, Gomez-Rueda H, Martinez-Ledesma E et al. SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis. PLoS ONE 8(9), e74250 (2013).Crossref, Medline, CAS, Google Scholar
25. Liu CJ, Hu FF, Xia MX, Han L, Zhang Q, Guo AY. GSCALite: a web server for gene set cancer analysis. Bioinformatics 34(21), 3771–3772 (2018).Crossref, Medline, CAS, Google Scholar
26. GaoJ, Aksoy BA, Dogrusoz U et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal Sci. Signal. 6(269), pl1 (2013). http://www.cbioportal.org Medline, Google Scholar
27. Li T, Fan J, Wang B et al. TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells Cancer Res. 77(21), e108–e110 (2017). https://cistrome.shinyapps.io/timer/ • An important tool for cancer immunotherapy analysis.Crossref, Medline, Google Scholar
28. KochA, De Meyer T, Jeschke J, Van Criekinge W. MEXPRESS: visualizing expression, DNA methylation and clinical TCGA data. BMC Genomics 16(1), 636 (2015). http://mexpress.be Crossref, Medline, Google Scholar
29. Taylor BS, Schultz N, Hieronymus H et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18(1), 11–22 (2010).Crossref, Medline, CAS, Google Scholar
30. Huang Y, Liu X, Wang YH et al. The prognostic value of ribonucleotide reductase small subunit M2 in predicting recurrence for prostate cancers. Urol. Oncol. 32(1), 51.e9–51.e19 (2014).Crossref, CAS, Google Scholar
31. Alinezhad S, Vaananen RM, Mattsson J et al. Validation of novel biomarkers for prostate cancer progression by the combination of bioinformatics, clinical and functional studies. PLoS ONE 11(5), e0155901 (2016).Crossref, Medline, Google Scholar
32. Huang YQ, Han ZD, Liang YX et al. Decreased expression of myosin light chain MYL9 in stroma predicts malignant progression and poor biochemical recurrence-free survival in prostate cancer. Med. Oncol. 31(1), 820 (2014).Crossref, Medline, Google Scholar
33. Knox AJ, Graham C, Bleskan J, Brodsky G, Patterson D. Mutations in the Chinese hamster ovary cell GART gene of de novo purine synthesis. Gene 429(1–2), 23–30 (2009).Crossref, Medline, CAS, Google Scholar
34. Liu X, Ding Z, Liu Y et al. Glycinamide ribonucleotide formyl transferase is frequently overexpressed in glioma and critically regulates the proliferation of glioma cells. Pathol. Res. Pract. 210(4), 256–263 (2014).Crossref, Medline, CAS, Google Scholar
35. Cong X, Lu C, Huang X et al. Increased expression of glycinamide ribonucleotide transformylase is associated with a poor prognosis in hepatocellular carcinoma, and it promotes liver cancer cell proliferation. Hum. Pathol. 45(7), 1370–1378 (2014).Crossref, Medline, CAS, Google Scholar
36. Taniguchi CM, Winnay J, Kondo T et al. The phosphoinositide 3-kinase regulatory subunit p85alpha can exert tumor suppressor properties through negative regulation of growth factor signaling. Cancer Res. 70(13), 5305–5315 (2010).Crossref, Medline, CAS, Google Scholar
37. Cizkova M, Vacher S, Meseure D et al. PIK3R1 underexpression is an independent prognostic marker in breast cancer. BMC Cancer 13, 545 (2013).Crossref, Medline, Google Scholar
38. Munkley J, Livermore KE, Mcclurg UL et al. The PI3K regulatory subunit gene PIK3R1 is under direct control of androgens and repressed in prostate cancer cells. Oncoscience 2(9), 755–764 (2015).Crossref, Medline, Google Scholar
39. Matsuoka R, Yoshida MC, Furutani Y et al. Human smooth muscle myosin heavy chain gene mapped to chromosomal region 16q12. Am. J. Med. Genet. 46(1), 61–67 (1993).Crossref, Medline, CAS, Google Scholar
40. Wang RJ, Wu P, Cai GX et al. Down-regulated MYH11 expression correlates with poor prognosis in stage II and III colorectal cancer. Asian Pac. J. Cancer Prev. 15(17), 7223–7228 (2014).Crossref, Medline, Google Scholar
41. Leveille N, Fournier A, Labrie C. Androgens down-regulate myosin light chain kinase in human prostate cancer cells. J. Steroid Biochem. Mol. Biol. 114(3–5), 174–179 (2009).Crossref, Medline, CAS, Google Scholar
42. Gu LZ, Hu WY, Antic N, Mehta R, Turner JR, De Lanerolle P. Inhibiting myosin light chain kinase retards the growth of mammary and prostate cancer cells. Eur. J. Cancer 42(7), 948–957 (2006).Crossref, Medline, CAS, Google Scholar
43. Pas J, Wyszko E, Rolle K et al. Analysis of structure and function of tenascin-C. Int. J. Biochem. Cell Biol. 38(9), 1594–1602 (2006).Crossref, Medline, CAS, Google Scholar
44. Nong Y, Wu D, Lin Y, Zhang Y, Bai L, Tang H. Tenascin-C expression is associated with poor prognosis in hepatocellular carcinoma (HCC) patients and the inflammatory cytokine TNF-α-induced TNC expression promotes migration in HCC cells. Am. J. Cancer Res. 5(2), 782–791 (2015).Medline, CAS, Google Scholar
45. Tastekin D, Tas F, Karabulut S et al. Clinical significance of serum tenascin-C levels in breast cancer. Tumour Biol. 35(7), 6619–6625 (2014).Crossref, Medline, CAS, Google Scholar
46. Yang ZT, Yeo SY, Yin YX et al. Tenascin-C, a prognostic determinant of esophageal squamous cell carcinoma. PLoS ONE 11(1), e0145807 (2016).Medline, Google Scholar
47. Pilch H, Schaffer U, Schlenger K et al. Expression of tenascin in human cervical cancer--association of tenascin expression with clinicopathological parameters. Gynecol. Oncol. 73(3), 415–421 (1999).Crossref, Medline, CAS, Google Scholar
48. Wiksten JP, Lundin J, Nordling S et al. Tenascin-C expression correlates with prognosis in gastric cancer. Oncology 64(3), 245–250 (2003).Crossref, Medline, CAS, Google Scholar
49. Gebauer F, Gelis S, Zander H et al. Tenascin-C serum levels and its prognostic power in non-small-cell lung cancer. Oncotarget 7(15), 20945–20952 (2016).Crossref, Medline, Google Scholar
50. Bloch M, Ousingsawat J, Simon R et al. KCNMA1 gene amplification promotes tumor cell proliferation in human prostate cancer. Oncogene 26(17), 2525–2534 (2007).Crossref, Medline, CAS, Google Scholar
51. Khaitan D, Sankpal UT, Weksler B et al. Role of KCNMA1 gene in breast cancer invasion and metastasis to brain. BMC Cancer 9, 258 (2009).Crossref, Medline, Google Scholar
52. Bury M, Girault A, Megalizzi V et al. Ophiobolin A induces paraptosis-like cell death in human glioblastoma cells by decreasing BKCa channel activity. Cell Death Dis. 4, e561 (2013).Crossref, Medline, CAS, Google Scholar
53. Cambien B, Rezzonico R, Vitale S et al. Silencing of hSlo potassium channels in human osteosarcoma cells promotes tumorigenesis. Int. J. Cancer 123(2), 365–371 (2008).Crossref, Medline, CAS, Google Scholar
54. Ma G, Liu H, Hua Q et al. KCNMA1 cooperating with PTK2 is a novel tumor suppressor in gastric cancer and is associated with disease outcome. Mol. Cancer 16(1), 46 (2017).Crossref, Medline, Google Scholar
55. Zhang Z, Liu J, Wang Y et al. Phosphatidylinositol 3-kinase β and δ isoforms play key roles in metastasis of prostate cancer DU145 cells. FASEB J. 32(11), 5967–5975 (2018).Crossref, Medline, CAS, Google Scholar
56. Nguyen EV, Centenera MM, Moldovan M et al. Identification of novel response and predictive biomarkers to Hsp90 inhibitors through proteomic profiling of patient-derived prostate tumor explants. Mol. Cell. Proteomics 17(8), 1470–1486 (2018).Crossref, Medline, CAS, Google Scholar
57. Canovas B, Igea A, Sartori AA et al. Targeting p38α increases DNA damage, chromosome instability, and the anti-tumoral response to taxanes in breast cancer cells. Cancer Cell 33(6), 1094–1110; e1098 (2018).Crossref, Medline, CAS, Google Scholar
58. Negrini S, Gorgoulis VG, Halazonetis TD. Genomic instability – an evolving hallmark of cancer. Nat. Rev. Mol. Cell Biol. 11(3), 220–228 (2010).Crossref, Medline, CAS, Google Scholar
59. Palucka AK, Coussens LM. The basis of oncoimmunology. Cell 164(6), 1233–1247 (2016).Crossref, Medline, CAS, Google Scholar

Vol. 14, No. 5

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History

Received 23 July 2019

Accepted 29 January 2020

Published online 7 April 2020

Published in print April 2020

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Availability of data & materials

Author(s) declare the data and materials are available.

Financial & competing interests disclosure

This work was supported by the grants from National Natural Science Foundation of China (no. 81601849), the Shanghai Natural Science Foundation (17ZR1427600), National Science and Technology Major Projects for ´Major New Drugs Innovation and Development’ (2018ZX09201008-002-091), Shanghai Science and Technology Support Project in Biomedicine Field (18401932900), Budgetary Projects of Shanghai University of Traditional Chinese Medicine (no. 2019LK046) and Special Clinical Research Project of Health Profession of Shanghai Municipal Commission of Health (20194Y0081). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

No institutional review board approval or patient consent was required as the data enrolled in the present study were obtained from public databases, GEO or TCGA.

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