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Integrated Analysis Revealed Prognostic Factors for Prostate Cancer Patients.

ABSTRACT: BACKGROUND Prostate cancer (PCa) is one of the major causes of cancer-induced death among males. Here, we applied integrated bioinformatics analysis to identify key prognostic factors for PCa patients. MATERIAL AND METHODS The gene expression data were obtained from the UCSC Xena website. We calculated the differentially expressed genes between PCa tissues and normal controls. Pathway enrichment analyses found cell cycle-related pathways might play crucial roles during PCa tumorigenesis. The genes were assigned into 22 modules established via weighted gene co-expression network analysis (WGCNA). RESULTS The results indicated that the purple and red modules were obviously linked to the Gleason score, pathological N, pathological T, recurrence, and recurrence-free survival (RFS). In addition, Kaplan-Meier curve analysis found 8 modules were markedly correlated with RFS, serving as prognostic markers for PCa patients. Then, the hub genes in the most 2 critical modules (purple and red) were visualized by Cytoscape software. Pathway enrichment analyses confirmed the above findings that cell cycle-related pathways might play vital roles during PCa initiation and progression. Lastly, we randomly chose the PILRß (also termed PILRB) in the red module for clinical validation. The immunohistochemistry (IHC) results showed that PILRß was significantly increased in the high-risk PCa population compared with low-/middle-risk patients. CONCLUSIONS We used integrated bioinformatics approaches to identify hub genes that can serve as prognosis markers and potential treatment targets for PCa patients.

SUBMITTER: Che H

PROVIDER: S-EPMC6944160 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Integrated Analysis Revealed Prognostic Factors for Prostate Cancer Patients.

Che Hong H Liu Yi Y Zhang Meng M Meng Jialin J Feng Xingliang X Zhou Jun J Liang Chaozhao C

Medical science monitor : international medical journal of experimental and clinical research 20191226

BACKGROUND Prostate cancer (PCa) is one of the major causes of cancer-induced death among males. Here, we applied integrated bioinformatics analysis to identify key prognostic factors for PCa patients. MATERIAL AND METHODS The gene expression data were obtained from the UCSC Xena website. We calculated the differentially expressed genes between PCa tissues and normal controls. Pathway enrichment analyses found cell cycle-related pathways might play crucial roles during PCa tumorigenesis. The gen ...[more]

PMID: 31876269

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Project description:BackgroundTo guide the selection of treatments for spinal metastases, the expected survival time is one of the most important determinants. Few scoring systems are fully applicable for spinal metastasis secondary to prostate cancer (PCa). This study aimed to identify the independent factors to predict the overall survival (OS) of patients with spinal metastases from PCa.MethodsThe PubMed, Embase and CENTRAL were retrieved by two reviewers independently, to identify studies analyzed the prognostic effect of different factors in spinal metastasis from PCa. A systematic review and quantitative meta-analysis was conducted with hazard ratio (HR) and 95% confidence interval (95%CI) as the effect size.ResultsA total of 12 retrospective cohort studies (1566 patients) were eligible for qualitative synthesis and 10 for quantitative meta-analyses. The OS was significantly influenced by performance status, visceral metastasis, ambulatory status and time from PCa diagnosis in more than half of the available studies. The meta-analyses demonstrated that OS was significantly influenced by visceral metastasis (HR = 2.24, 95%CI:1.53-3.27, p < 0.001), pre-treatment ambulatory status (HR = 2.64, 95%CI:1.82-3.83, p < 0.001), KPS (HR = 4.45, 95%CI:2.01-9.85, p < 0.001), ECOG (HR = 2.96, 95%CI:2.02-4.35, p < 0.001), extraspinal bone metastasis (HR = 2.04, 95%CI:1.13-3.68, p = 0.018), time developing motor deficit (HR = 1.57, 95%CI:1.30-1.88, p < 0.001) and time from PCa diagnosis (HR = 1.37, 95%CI:1.17-1.59, p < 0.001).ConclusionsVisceral metastasis, ambulatory status, extraspinal bone metastasis, performance status, time developing motor deficit and time interval from primary tumor diagnosis were significantly associated with the OS for spinal metastasis from PCa. When selecting the treatment modality, clinicians should fully consider the patients' systematic status based on all potential prognostic factors.Level of evidenceI Meta-analysis.

Project description:ObjectiveUsing the latest cohort study of prostate cancer patients, explore the epidemiological trend and prognostic factors, and develop a new nomogram to predict the specific survival rate of prostate cancer patients.MethodsPatients with prostate cancer diagnosed from January 1, 1975 to December 31, 2019 in the Surveillance, Epidemiology, and End Results Program (SEER) database were extracted by SEER stat software for epidemiological trend analysis. General clinical information and follow-up data were also collected from 105 135 patients with pathologically diagnosed prostate cancer from January 1, 2010 to December 1, 2019. The factors affecting patient-specific survival were analyzed by Cox regression, and the factors with the greatest influence on specific survival were selected by stepwise regression method, and nomogram was constructed. The model was evaluated by calibration plots, ROC curves, Decision Curve Analysis and C-index.ResultsThere was no significant change in the age-adjusted incidence of prostate cancer from 1975 to 2019, with an average annual percentage change (AAPC) of 0.45 (95% CI:-0.87~1.80). Among the tumor grade, the most significant increase in the incidence of G2 prostate cancer was observed, with an AAPC of 2.99 (95% CI:1.47~4.54); the most significant decrease in the incidence of G4 prostate cancer was observed, with an AAPC of -10.39 (95% CI:-13.86~-6.77). Among the different tumor stages, the most significant reduction in the incidence of localized prostate cancer was observed with an AAPC of -1.83 (95% CI:-2.76~-0.90). Among different races, the incidence of prostate cancer was significantly reduced in American Indian or Alaska Native and Asian or Pacific Islander, with an AAPC of -3.40 (95% CI:-3.97~-2.82) and -2.74 (95% CI:-4.14~-1.32), respectively. Among the different age groups, the incidence rate was significantly increased in 15-54 and 55-64 age groups with AAPC of 4.03 (95% CI:2.73~5.34) and 2.50 (95% CI:0.96~4.05), respectively, and significantly decreased in ≥85 age group with AAPC of -2.50 (95% CI:-3.43~-1.57). In addition, age, tumor stage, race, PSA and gleason score were found to be independent risk factors affecting prostate cancer patient-specific survival. Age, tumor stage, PSA and gleason score were most strongly associated with prostate cancer patient-specific survival by stepwise regression screening, and nomogram prediction model was constructed using these factors. The Concordance indexes are 0.845 (95% CI:0.818~0.872) and 0.835 (95% CI:0.798~0.872) for the training and validation sets, respectively, and the area under the ROC curves (AUC) at 3, 6, and 9 years was 0.7 or more for both the training and validation set samples. The calibration plots indicated a good agreement between the predicted and actual values of the model.ConclusionsAlthough there was no significant change in the overall incidence of prostate cancer in this study, significant changes occurred in the incidence of prostate cancer with different characteristics. In addition, the nomogram prediction model of prostate cancer-specific survival rate constructed based on four factors has a high reference value, which helps physicians to correctly assess the patient-specific survival rate and provides a reference basis for patient diagnosis and prognosis evaluation.

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Integrated Analysis Revealed Prognostic Factors for Prostate Cancer Patients.

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Integrated Analysis Revealed Prognostic Factors for Prostate Cancer Patients.

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