Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0376358 (prostate cancer)
 59,338 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The three main types of urological cancers are mostly curable by surgical resection, if early detected. We aimed to identify novel DNA methylation biomarkers common to these three urological cancers, potentially suitable for non-invasive testing. From a candidate list of markers created after gene expression assessment of pharmacologically treated cell lines and tissue samples, two genes were selected for further validation. Methylation levels of these genes were quantified in a total of 12 cancer cell lines and 318 clinical samples. PCDH17 and TCF21 methylation levels provided a sensitivity rate of 92% for bladder cancer, 67% for renal cell tumors and 96% for prostate cancer. Methylation levels were significantly different from those of cancer free individuals (n = 37) for all tumor types (p < 0.001), providing 83% sensitivity and 100% specificity for cancer detection. Although in urine samples the sensitivity was 60%, 32% and 26% for bladder, renal, and prostate tumors, respectively (39% overall), absolute specificity was retained. We identified novel and highly specific methylation markers common to the three main urological cancers. However, additional efforts are required to increase the assay's sensitivity, enabling the simultaneous non-invasive screening of urological tumors in a single voided urine analysis.
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PMID:TCF21 and PCDH17 methylation: An innovative panel of biomarkers for a simultaneous detection of urological cancers. 2184 11

To develop a routine and effectual procedure of detecting bladder cancer (BlCa), an optimized combination of epigenetic biomarkers that work synergistically with high sensitivity and specificity is necessary. In this study, methylation levels of seven biomarkers (EOMES, GDF15, NID2, PCDH17, POU4F2, TCF21, and ZNF154) in 148 individuals-which including 58 urothelial cell carcinoma (UCC) patients, 20 infected urinary calculi (IUC) patients, 20 kidney cancer (KC) patients,20 prostate cancer (PC) patients, and 30 healthy volunteers (HV)-were quantified by qMSP using the urine sediment DNA. Receiver operating characteristic (ROC) curves were generated for each biomarker. The combining predictors of possible combinations were calculated through logistic regression model. Subsequently, ROC curves of the three best performing combinations were constructed. Then, we validated the three best performing combinations and POU4F2 in another 72 UCC, 21 IUC, 26 KC and 22 PC, and 23 HV urine samples. The combination of POU4F2/PCDH17 has yielded the highest sensitivity and specificity of 90.00% and 93.96% in all the 312 individuals, showing the capability of detecting BlCa effectively among pathologically varied sample groups.
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PMID:An epigenetic biomarker combination of PCDH17 and POU4F2 detects bladder cancer accurately by methylation analyses of urine sediment DNA in Han Chinese. 2670 Jun 20

The present study aimed to screen potential genes associated with metastatic prostate cancer (PCa), in order to improve the understanding of the mechanisms underlying PCa metastasis. The GSE3325 microarray dataset, which was downloaded from the Gene Expression Omnibus database, consists of seven clinically localized PCa samples, six hormone-refractory metastatic PCa samples and six benign prostate tissue samples. The Linear Models for Microarray Data package was used to identify differentially-expressed genes (DEGs) and a hierarchical cluster analysis for DEGs was performed with the pheatmap package. Furthermore, potential functions for the DEGs were predicted by a functional enrichment analysis. Subsequently, microRNAs (miRNAs) potentially involved in the regulation of PCa metastasis were identified by WebGestalt software, and the miRNA-DEG regulatory network was visualized using Cytoscape. In addition, a pathway enrichment analysis for DEGs in the regulatory network was performed. A total of 306 and 2,073 genes were differentially expressed in the clinically localized PCa and the metastatic PCa groups, respectively, as compared with the benign prostate group, of which 174 were differentially expressed in both groups. A number of the DEGs, including CAMK2D and SH3BP4, were significantly enriched in the cell cycle, and others, such as MAF, were associated with the regulation of cell proliferation. Furthermore, some DEGs (CAMK2D and PCDH17) were observed to be regulated by miR-30, whereas others (ADCY2, MAF, SH3BP4 and PCDH17) were modulated by miR-182. Additionally, ADCY2 and CAMK2D were distinctly enriched in the calcium signaling pathway. The present study identified novel DEGs, including ADCY2, CAMK2D, MAF, SH3BP4 and PCDH17, that may be involved in the metastasis of PCa.
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PMID:Investigation of the molecular mechanisms underlying metastasis in prostate cancer by gene expression profiling. 2744 97