Identification of potential diagnostic and prognostic biomarkers for prostate cancer

Zhang,Qiang; Yin,Xiujuan; Pan,Zhiwei; Cao,Yingying; Han,Shaojie; Gao,Guojun; Gao,Zhiqin; Pan,Zhifang; Feng,Weiguo

doi:10.3892/ol.2019.10765

Identification of potential diagnostic and prognostic biomarkers for prostate cancer

Authors:
- Qiang Zhang
- Xiujuan Yin
- Zhiwei Pan
- Yingying Cao
- Shaojie Han
- Guojun Gao
- Zhiqin Gao
- Zhifang Pan
- Weiguo Feng
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Affiliations: College of Bioscience and Technology, Weifang Medical University, Weifang, Shandong 261053, P.R. China, Department of Medicine, Laizhou Development Zone Hospital, Yantai, Shandong 261400, P.R. China, College of Clinical Medicine, Weifang Medical University, Weifang, Shandong 261053, P.R. China, Changle County Bureau of Animal Health and Production, Weifang, Shandong 261053, P.R. China, Urology Department, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261053, P.R. China
Published online on: August 16, 2019 https://doi.org/10.3892/ol.2019.10765
Pages: 4237-4245
Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Prostate cancer (PCa) is one of the most common malignant tumors worldwide. The aim of the present study was to determine potential diagnostic and prognostic biomarkers for PCa. The GSE103512 dataset was downloaded, and the differentially expressed genes (DEGs) were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein‑protein interaction (PPI) analyses of DEGs were performed. The result of GO analysis suggested that the DEGs were mostly enriched in ‘carboxylic acid catabolic process’, ‘cell apoptosis’, ‘cell proliferation’ and ‘cell migration’. KEGG analysis results indicated that the DEGs were mostly concentrated in ‘metabolic pathways’, ‘ECM‑receptor interaction’, the ‘PI3K‑Akt pathway’ and ‘focal adhesion’. The PPI analysis results showed that Golgi membrane protein 1 (GOLM1), melanoma inhibitory activity member 3 (MIA3), ATP citrate lyase (ACLY) and G protein subunit β2 (GNB2) were the key genes in PCa, and the Module analysis revealed that they were associated with ‘ECM‑receptor interaction’, ‘focal adhesion’, the ‘PI3K‑Akt pathway’ and the ‘metabolic pathway’. Subsequently, the gene expression was confirmed using Gene Expression Profiling Interactive Analysis and the Human Protein Atlas. The results demonstrated that GOLM1 and ACLY expression was significantly upregulated (P<0.05) in PCa compared with that in normal tissues. Receiver operating characteristic and survival analyses were performed. The results showed that area under the curve values of these genes all exceeded 0.85, and high expression of these genes was associated with poor survival in patients with PCa. In conclusion, this study identified GOLM1 and ACLY in PCa, which may be potential diagnostic and prognostic biomarker of PCa.

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1	Yan G, Ru Y, Wu K, Yan F, Wang Q, Wang J, Pan T, Zhang M, Han H, Li X and Zou L: GOLM1 promotes prostate cancer progression through activating PI3K-AKT-mTOR signaling. Prostate. 78:166–177. 2018. View Article : Google Scholar : PubMed/NCBI
2	Zhao H, Zhao X, Lei T and Zhang M: Screening, identification of prostate cancer urinary biomarkers and verification of important spots. Invest New Drugs. Jan 4–2019.(Epub ahead of print). View Article : Google Scholar
3	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2018. CA Cancer J Clin. 68:7–30. 2018. View Article : Google Scholar : PubMed/NCBI
4	Kudryavtseva AV, Lukyanova EN, Kharitonov SL, Nyushko KM, Krasheninnikov AA, Pudova EA, Guvatova ZG, Alekseev BY, Kiseleva MV, Kaprin AD, et al: Bioinformatic identification of differentially expressed genes associated with prognosis of locally advanced lymph node-positive prostate cancer. J Bioinform Computat Biol. 17:19500032019. View Article : Google Scholar
5	Gadzinski AJ and Cooperberg MR: Prostate cancer markers. Cancers Treat Res. 175:55–86. 2018. View Article : Google Scholar
6	Fujita K and Nonomura N: Urinary biomarkers of prostate cancer. Int J Urol. 25:770–779. 2018. View Article : Google Scholar : PubMed/NCBI
7	Thompson IM, Pauler DK, Goodman PJ, Tangen CM, Lucia MS, Parnes HL, Minasian LM, Ford LG, Lippman SM, Crawford ED, et al: Prevalence of prostate cancer among men with a prostate-specific antigen level < or =4.0 ng per milliliter. N Engl J Med. 350:2239–2246. 2004. View Article : Google Scholar : PubMed/NCBI
8	Kulasingam V and Diamandis EP: Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat Clin Pract Oncol. 5:588–599. 2008. View Article : Google Scholar : PubMed/NCBI
9	Liang B, Li C and Zhao J: Identification of key pathways and genes in colorectal cancer using bioinformatics analysis. Med Oncol. 33:1112016. View Article : Google Scholar : PubMed/NCBI
10	Lascorz J, Hemminki K and Försti A: Systematic enrichment analysis of gene expression profiling studies identifies consensus pathways implicated in colorectal cancer development. J Carcinog. 10:72011. View Article : Google Scholar : PubMed/NCBI
11	Cheng Y, Wang K, Geng L, Sun J, Xu W, Liu D, Gong S and Zhu Y: Identification of candidate diagnostic and prognostic biomarkers for pancreatic carcinoma. EBioMedicine. 40:382–393. 2019. View Article : Google Scholar : PubMed/NCBI
12	Shinichi Y, Sian J, Ivana B, Antal T, Leary R, Fu B, Kamiyama M, Hruban RH, Eshleman JR, Nowak MA, et al: Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature. 467:1114–1117. 2010. View Article : Google Scholar : PubMed/NCBI
13	Brouwer-Visser J, Cheng WY, Bauer-Mehren A, Maisel D, Lechner K, Andersson E, Dudley JT and Milletti F: Regulatory T-cell genes drive altered immune microenvironment in adult solid cancers and allow for immune contextual patient subtyping. Cancer Epidemiol Biomarkers Prev. 27:103–112. 2018. View Article : Google Scholar : PubMed/NCBI
14	Mi B, Liu G, Zhou W, Lv H, Liu Y and Liu J: Identification of genes and pathways in the synovia of women with osteoarthritis by bioinformatics analysis. Mol Med Rep. 17:4467–4473. 2018.PubMed/NCBI
15	Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C and Jensen LJ: STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41((Database Issue)): D808–D815. 2013.PubMed/NCBI
16	Fan S, Liang Z, Gao Z, Pan Z, Han S, Liu X, Zhao C, Yang W, Pan Z and Feng W: Identification of the key genes and pathways in prostate cancer. Oncol Lett. 16:6663–6669. 2018.PubMed/NCBI
17	Zhang B, Wu Q, Wang Z, Xu R, Hu X, Sun Y, Wang Q, Ju F, Ren S, Zhang C, et al: The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of highthroughput data. Mol Genet Genomic Med. 7:e6072019. View Article : Google Scholar : PubMed/NCBI
18	Jin Q, Dai Y, Wang Y, Zhang S and Liu G: High kinesin family member 11 expression predicts poor prognosis in patients with clear cell renal cell carcinoma. J Clin Pathol. 72:354–362. 2019. View Article : Google Scholar : PubMed/NCBI
19	Perez R, Wu N, Klipfel AA and Beart RW Jr: A better cell cycle target for gene therapy of colorectal cancer: Cyclin G. J Gastroint Surg. 7:884–889. 2003. View Article : Google Scholar
20	Tsunoda T, Nakamura T, Ishimoto K, Yamaue H, Tanimura H, Saijo N and Nishio K: Upregulated expression of angiogenesis genes and down regulation of cell cycle genes in human colorectal cancer tissue determined by cDNA macroarray. Anticancer Res. 21:137–143. 2001.PubMed/NCBI
21	Dong W, Keibler MA and Stephanopoulos G: Review of metabolic pathways activated in cancer cells as determined through isotopic labeling and network analysis. Metab Eng. 43:113–124. 2017. View Article : Google Scholar : PubMed/NCBI
22	Zarrinpar A: Metabolic pathway inhibition in liver cancer. SLAS Technol. 22:237–244. 2017.PubMed/NCBI
23	Zhang HJ, Tao J, Sheng L, Hu X, Rong RM, Xu M and Zhu TY: Twist2 promotes kidney cancer cell proliferation and invasion by regulating ITGA6 and CD44 expression in the ECM-receptor interaction pathway. OncoTargets Ther. 9:1801–1812. 2016.
24	Eke I and Cordes N: Focal adhesion signaling and therapy resistance in cancer. Semin Cancer Biol. 31:65–75. 2015. View Article : Google Scholar : PubMed/NCBI
25	Yang L, Zha TQ, He X, Chen L, Zhu Q, Wu WB, Nie FQ, Wang Q, Zang CS, Zhang ML, et al: Placenta-specific protein 1 promotes cell proliferation and invasion in non-small cell lung cancer. Oncol Rep. 39:53–60. 2018.PubMed/NCBI
26	Varambally S, Laxman B, Mehra R, Cao Q, Dhanasekaran SM, Tomlins SA, Granger J, Vellaichamy A, Sreekumar A, Yu J, et al: Golgi protein GOLM1 is a tissue and urine biomarker of prostate cancer. Neoplasia. 10:1285–1294. 2008. View Article : Google Scholar : PubMed/NCBI
27	Byrne AM, Bekiaris S, Duggan G, Prichard D, Kirca M, Finn S, Reynolds JV, Kelleher D and Long A: Golgi phosphoprotein 2 (GOLPH2) is a novel bile acid-responsive modulator of oesophageal cell migration and invasion. Br J Cancer. 113:1332–1342. 2015. View Article : Google Scholar : PubMed/NCBI
28	Ye QH, Zhu WW, Zhang JB, Qin Y, Lu M, Lin GL, Guo L, Zhang B, Lin ZH, Roessler S, et al: GOLM1 modulates EGFR/RTK cell-surface recycling to drive hepatocellular carcinoma metastasis. Cancer Cell. 30:444–458. 2016. View Article : Google Scholar : PubMed/NCBI
29	Gao H, Cong X, Zhou J and Guan M: MicroRNA-222 influences migration and invasion through MIA3 in colorectal cancer. Cancer Cell Int. 17:78–87. 2017. View Article : Google Scholar : PubMed/NCBI
30	Arndt S and Bosserhoff AK: TANGO is a tumor suppressor of malignant melanoma. Int J Cancer. 119:2812–2820. 2010. View Article : Google Scholar
31	Fu Y, Lu R, Cui J, Sun H, Yang H, Meng Q, Wu S, Aschner M, Li X and Chen R: Inhibition of ATP citrate lyase (ACLY) protects airway epithelia from PM_2.5-induced epithelial-mesenchymal transition. Ecotoxicol Environmen Saf. 167:309–316. 2019. View Article : Google Scholar
32	Icard P and Lincet H: The reduced concentration of citrate in cancer cells: An indicator of cancer aggressiveness and a possible therapeutic target. Drug Resist Updat. 29:47–53. 2016. View Article : Google Scholar : PubMed/NCBI
33	Hanai J, Doro N, Sasaki AT, Kobayashi S, Cantley LC, Seth P and Sukhatme VP: Inhibition of lung cancer growth: ATP citrate lyase knockdown and statin treatment leads to dual blockade of mitogen-activated protein kinase (MAPK) and phosphatidylinositol-3-kinase (PI3K)/AKT pathways. J Cell Physiol. 227:1709–1720. 2012. View Article : Google Scholar : PubMed/NCBI
34	Kotani S, Yoda A, Kon A, Kataoka K, Ochi Y, Shiozawa Y, Hirsch C, Takeda J, Ueno H, Yoshizato T, et al: Molecular pathogenesis of disease progression in MLL-rearranged AML. Leukemia. 33:612–624. 2019. View Article : Google Scholar : PubMed/NCBI
35	Yoda A, Adelmant G, Tamburini J, Chapuy B, Shindoh N, Yoda Y, Weigert O, Kopp N, Wu SC, Kim SS, et al: Mutations in G protein β subunits promote transformation and kinase inhibitor resistance. Nat Med. 21:71–75. 2015. View Article : Google Scholar : PubMed/NCBI
36	Arthurs C, Murtaza BN, Thomson C, Dickens K, Henrique R, Patel HRH, Beltran M, Millar M, Thrasivoulou C and Ahmed A: Expression of ribosomal proteins in normal and cancerous human prostate tissue. PLoS One. 12:e01860472017. View Article : Google Scholar : PubMed/NCBI
37	Fawcett T: An introduction to ROC analysis. Pattern Recog Lett. 27:861–874. 2006. View Article : Google Scholar