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Identification of Potential Diagnostic and Prognostic Biomarkers for Prostate Cancer

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Identification of Potential Diagnostic and Prognostic Biomarkers for Prostate Cancer ONCOLOGY LETTERS 18: 4237-4245, 2019 Identification of potential diagnostic and prognostic biomarkers for prostate cancer QIANG ZHANG1*, XIUJUAN YIN1*, ZHIWEI PAN2, YINGYING CAO3, SHAOJIE HAN4, GUOJUN GAO5, ZHIQIN GAO1, Zhifang PAN1 and WEIGUO FENG1 1College of Bioscience and Technology, Weifang Medical University, Weifang, Shandong 261053; 2Department of Medicine, Laizhou Development Zone Hospital, Yantai, Shandong 261400; 3College of Clinical Medicine, Weifang Medical University; 4Changle County Bureau of Animal Health and Production; 5Urology Department, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261053, P.R. China Received April 4, 2019; Accepted July 25, 2019 DOI: 10.3892/ol.2019.10765 Abstract. Prostate cancer (PCa) is one of the most common was associated with poor survival in patients with PCa. In malignant tumors worldwide. The aim of the present study was conclusion, this study identified GOLM1 and ACLY in PCa, to determine potential diagnostic and prognostic biomarkers which may be potential diagnostic and prognostic biomarker for PCa. The GSE103512 dataset was downloaded, and the of PCa. differentially expressed genes (DEGs) were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes Introduction (KEGG) and protein-protein interaction (PPI) analyses of DEGs were performed. The result of GO analysis suggested that Prostate cancer (PCa) is the most common cancer world- the DEGs were mostly enriched in ‘carboxylic acid catabolic wide, and its incidence rate has increased continuously in process’, ‘cell apoptosis’, ‘cell proliferation’ and ‘cell migra- recent years; the number of new cases of PCa worldwide tion’. KEGG analysis results indicated that the DEGs were increased from 1.11 million in 2015 to 1.28 million mostly concentrated in ‘metabolic pathways’, ‘ECM-receptor in 2018 (1-3). It has been estimated that >1 million men interaction’, the ‘PI3K-Akt pathway’ and ‘focal adhesion’. The are diagnosed and >300,000 succumb to the disease annu- PPI analysis results showed that Golgi membrane protein 1 ally (4). Although radical prostatectomy is an effective (GOLM1), melanoma inhibitory activity member 3 (MIA3), treatment, early detection of PCa is difficult (2). Therefore, ATP citrate lyase (ACLY) and G protein subunit β2 (GNB2) early diagnosis is extremely important for PCa treatment. were the key genes in PCa, and the Module analysis revealed At present, prostate-specific antigen (PSA) is used as a that they were associated with ‘ECM-receptor interaction’, biomarker for PCa diagnosis (5). however, this method has ‘focal adhesion’, the ‘PI3K-Akt pathway’ and the ‘metabolic numerous defects. For example, the specificity is low when pathway’. Subsequently, the gene expression was confirmed PSA is moderately elevated (6,7). As a result, identification using Gene Expression Profiling Interactive Analysis and the of more specific biomarkers is essential in order to detect Human Protein Atlas. The results demonstrated that GOLM1 patients at an early stage of PCa and to provide patients with and ACLY expression was significantly upregulated (P<0.05) an optimal treatment. in PCa compared with that in normal tissues. Receiver oper- Microarrays are an efficient tool for analysis of differ- ating characteristic and survival analyses were performed. entially expressed genes (DEGs) and could be applied to The results showed that area under the curve values of these identify potential biomarkers for the diagnosis and prognosis genes all exceeded 0.85, and high expression of these genes of cancer (8,9). During the past decade, various DEGs in colorectal cancer and pancreatic carcinoma have been identi- fied using microarrays (10,11). However, the results indicated that these biomarkers are not enough for the diagnosis and prognosis of PCa, and no reliable biomarker was validated for Correspondence to: Dr Weiguo Feng or Professor Zhifang Pan, clinical use (12). Therefore, potential diagnostic and prognostic College of Bioscience and Technology, Weifang Medical University, biomarkers need to be further identified using microarrays and 7166 Baotong Street, Weifang, Shandong 261053, P.R. China E-mail: [email protected] bioinformatics. E-mail: [email protected] The aim of the present study was to determine poten- tial diagnostic and prognostic biomarkers of PCa. Firstly, *Contributed equally the GSE103512 dataset was analyzed and the DEGs were screened. Secondly, Gene Ontology (GO), Kyoto Encyclopedia Key words: biomarker, prostate cancer, bioinformatics analysis, of Genes and Genomes (KEGG) and protein-protein interac- differentially expressed gene, identification tion (PPI) analyses of DEGs were performed. The expression of key genes was verified using Gene Expression Profiling 4238 ZHANG et al: DIAGNOSTIC AND PROGNOSTIC BIOMARKERS IN PCa Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) conducted. The results of GO analysis indicated that analysis. Finally, receiver operating characteristic (ROC) and the DEGs were mostly enriched in biological processes, survival analyses were performed to evaluate the diagnostic including ‘carboxylic acid catabolic process’, ‘negative and prognostic value of these genes. regulation of cell death’, ‘apoptotic process’, ‘cell prolifera- tion’ and ‘cell migration’ (Table I). For cell component, the Materials and methods DEGs were mostly concentrated in ‘Golgi apparatus part’, ‘endoplasmic reticulum’, ‘extracellular matrix’, ‘receptor Differential expression analysis. The mRNA expression complex’ and ‘anchoring junction’. For molecular function, profile of the GSE103512 dataset was downloaded from the DEGs were mainly concentrated in ‘coenzyme binding’, Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih. ‘transferase activity’, ‘transferring acyl groups’, ‘calcium ion gov/geo/). The dataset included 60 PCa samples and 7 normal binding’ and ‘phosphatidylserine binding’. In addition, the prostate samples (13). The expression profile of GSE103512 KEGG analysis results suggested that the DEGs were mostly was subsequently analyzed using Morpheus online tool concentrated in ‘metabolic pathways’, ‘ECM-receptor inter- (https://software.broadinstitute.org/morpheus/) (14). A clas- action’, the ‘PI3K-Akt pathway’, ‘pathways in cancer’ and sical t-test was used to identify the DEGs between PCa and ‘focal adhesion’ (Table II). normal prostate tissue. A fold‑change >2 and P<0.05 were considered to indicate a statistically significant difference. PPI analysis of DEGs. A total of four key genes with high degrees of interaction were selected using the Cytoscape GO and KEGG pathway analysis. To characterize the function software. The key genes included Golgi membrane protein 1 and pathway of DEGs, GO and KEGG pathway analyses were (GOLM1), melanoma inhibitory activity member 3 (MIA3), performed using the Database for Annotation, Visualization ATP citrate lyase (ACLY) and G protein subunit β2 (GNB2). and Integrated Discovery (https://david.ncifcrf.gov/) (9). Furthermore, the top three modules were obtained using P<0.05 was considered as statistically significant. plug-ins MCODE analysis (Fig. 2). The results of enrichment analysis revealed that the genes in the modules were associated PPI network and module analysis. To assess the interactive with ‘ECM-receptor interaction’, ‘focal adhesion’, ‘PI3K-Akt associations among DEGs, the DEGs were analyzed using the pathway’, ‘PPAR pathway’, ‘AMPK pathway’, ‘metabolic Search Tool for the Retrieval of Interacting Genes/Proteins pathways’, ‘pathways in cancer’, ‘gap junction’ and ‘cell cycle’ database, and a score >0.4 was considered significant (15). (Table III). Subsequently, PPI network was built by the Cytoscape software (version 3.3.0) (16). Finally, the modules were selected using Validation of key genes expression. The expression of key the plug-in Molecular Complex Detection (MCODE), and the genes was identified by GEPIA and HPA analysis. The results pathway analysis was conducted in the modules. P<0.05 was of GEPIA analysis suggested that the mRNA expression of considered as significant (16). MIA3 and GNB2 was slightly upregulated in PCa (P>0.05), and the expression of GOLM1 and ACLY was upregulated Validation of gene expression. In this study, GEPIA significantly (P<0.05). In addition, IHC results from HPA (http://gepia.cancer-pku.cn/) was used to verify the reli- showed that GOLM1 and ACLY expression was upregulated ability of the mRNA expression of genes (17). HPA database significantly in PCa compared with that in normal prostate (https://www.proteinatlas.org/) was applied to confirm the tissues (Fig. 3). protein expression of genes between PCa and normal prostate tissues based on immunohistochemistry (IHC) (17). Identification of key genes for diagnosis and prognosis of PCa. To evaluate the potential efficiency of these four genes as diag- ROC analysis and survival analysis. To assess the sensitivity nostic biomarkers, ROC curves were prepared. AUC values of and specificity of the key genes for PCa diagnosis, ROC GOLM1, MIA3, ACLY and GNB2 were >0.85, suggesting that curves were created by GraphPad Prism software (version 7.0, these four genes had high sensitivity and specificity for PCa GraphPad Software, Inc.), based on the data of GSE103512, diagnosis (Fig. 4A-D). These results indicated that GOLM1, and the area under the curve (AUC) was used to evaluate the MIA3, ACLY and GNB2 may be used as biomarkers for the ROC effect (11). In addition, GEPIA was used to further verify diagnosis of PCa. the prognostic
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