scholarly journals Identification and validation of a prognostic 5-protein signature for biochemical recurrence following radical prostatectomy in prostate cancer

2020 ◽  
Author(s):  
Xiangkun Wu ◽  
Wenjie Li ◽  
Daojun Lv ◽  
Yongda Liu ◽  
Di Gu
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Biochemical Recurrence ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Medical Decision ◽  
Cox Regression Analysis ◽  
Protein Signature

Abstract Background : Biochemical recurrence (BCR) is considered as an indicator for prostate cancer (PCa)-specific recurrence and mortality. However, lack of effective prediction model to assess the prognosis of patients for optimization of treatment. The aim of this work was to construct a protein-based nomogram that could predict BCR for PCa.Materials and methods: Univariate Cox regression analysis was conducted to identify candidate proteins from the Cancer Genome Atlas (TCGA) database. LASSO Cox regression was further conducted to pick out the most significant prognostic proteins and formulate the proteins signature for predicting BCR. Additionally, a nomogram was constructed by multivariate Cox proportional hazards regression.Results: We established a 5‐protein-based signature which was well used to identify PCa patients into high‐ and low‐risk groups. Kaplan-Meier analysis demonstrated patients with higher BCR generally had significantly worse survival than those with lower BCR (p<0.0001). Time-dependent receiver operating characteristic curve expounded that ours signature had excellent prognostic efficiency for 1‐, 3‐ and 5‐year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariable and multivariate Cox regression analysis showed that this 5‐protein signature was an independent of several clinical signatures including age, Gleason score, T stage, N status, PSA and residual tumor. Moreover, a nomogram was constructed and calibration plots confirmed the its predictive value in 3-, 5- and 10-year BCR overall survival.Conclusion: Our study identified a 5-protein-based signature and constructed a prognostic nomogram that reliably predicts BCR in prostate cancer. The findings might be of paramount importance in tumor prognosis and medical decision-making.

scholarly journals Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Daojun Lv ◽  
Zanfeng Cao ◽  
Wenjie Li ◽  
Haige Zheng ◽  
Xiangkun Wu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Survival Analysis ◽  
Biochemical Recurrence ◽  
Cox Regression ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Medical Decision ◽  
Regression Analyses ◽  
Cox Regression Analysis ◽  
Protein Signature

Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p &lt; 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.Subjects: Bioinformatics, oncology, urology.

scholarly journals Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382096357
Author(s):  
Xiaoyong Gong ◽  
Bobin Ning
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Normal Prostate ◽  
Cox Regression Analysis ◽  
Incidence And Mortality

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

scholarly journals Does perineural invasion in a radical prostatectomy specimen predict biochemical recurrence in men with prostate cancer?

2015 ◽  
Vol 9 (5-6) ◽  
pp. 252 ◽  
Author(s):  
Fairleigh Reeves ◽  
Christopher M. Hovens ◽  
Laurence Harewood ◽  
Shayne Battye ◽  
Justin S. Peters ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Radical Prostatectomy ◽  
Gleason Score ◽  
Biochemical Recurrence ◽  
Perineural Invasion ◽  
Cox Regression ◽  
Clinicopathological Parameters ◽  
Positive Surgical Margin ◽  
Cox Regression Analysis

Introduction: The ability of perineural invasion (PNI) in radical prostatectomy (RP) specimens to predict biochemical recurrence (BCR) is unclear. This study investigates this controversial question in a large cohort.Methods: A retrospective analysis was undertaken of prospectively collected data from 1497 men who underwent RP (no neoadjuvant therapy) for clinically localized prostate cancer. The association of PNI at RP with other clinicopathological parameters was evaluated. The correlation of clinicopathological factors and BCR (defined as prostate-specific antigen [PSA] >0.2 ng/mL) was investigated with univariable and multivariable Cox regression analysis in 1159 men.Results: PNI-positive patients were significantly more likely to have a higher RP Gleason score, pT3 disease, positive surgical margins, and greater cancer volume (p < 0.0005). The presence of PNI significantly correlated with BCR on univariable (hazard ratio 2.30, 95% confidence interval 1.50–3.55, p < 0.0005), but not multivariable analysis (p = 0.602). On multivariable Cox regression analysis the only independent prognostic factors were preoperative PSA, RP Gleason score, pT-stage, and positive surgical margin status. These findings are limited by a relatively short follow-up time and retrospective study design.Conclusions: PNI at RP is not an independent predictor of BCR. Therefore, routine reporting of PNI is not indicated. Future research should be targeted at the biology of PNI to increase the understanding of its role in prostate cancer progression.

scholarly journals Identification of a Novel Immune-Related CpG Methylation Signature to Predict Prognosis in Stage II/III Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Feng Chen ◽  
Lijuan Pei ◽  
Siyao Liu ◽  
Yan Lin ◽  
Xinyin Han ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Stage Ii ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Methylation Patterns

With the increasing incidence of colorectal cancer (CRC) and continued difficulty in treating it using immunotherapy, there is an urgent need to identify an effective immune-related biomarker associated with the survival and prognosis of patients with this disease. DNA methylation plays an essential role in maintaining cellular function, and changes in methylation patterns may contribute to the development of autoimmunity, aging, and cancer. In this study, we aimed to identify a novel immune-related methylated signature to aid in predicting the prognosis of patients with CRC. We investigated DNA methylation patterns in patients with stage II/III CRC using datasets from The cancer genome atlas (TCGA). Overall, 182 patients were randomly divided into training (n = 127) and test groups (n = 55). In the training group, five immune-related methylated CG sites (cg11621464, cg13565656, cg18976437, cg20505223, and cg20528583) were identified, and CG site-based risk scores were calculated using univariate Cox proportional hazards regression in patients with stage II/III CRC. Multivariate Cox regression analysis indicated that methylated signature was independent of other clinical parameters. The Kaplan–Meier analysis results showed that CG site-based risk scores could significantly help distinguish between high- and low-risk patients in both the training (P = 0.000296) and test groups (P = 0.022). The area under the receiver operating characteristic curve in the training and test groups were estimated to be 0.771 and 0.724, respectively, for prognosis prediction. Finally, stratified analysis results suggested the remarkable prognostic value of CG site-based risk scores in CRC subtypes. We identified five methylated CG sites that could be used as an efficient overall survival (OS)-related biomarker for stage II/III CRC patients.

scholarly journals 5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Ke Wang ◽  
Weibo Zhong ◽  
Zining Long ◽  
Yufei Guo ◽  
Chuanfan Zhong ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Biochemical Recurrence ◽  
Prognostic Model ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Lasso Regression ◽  
Clinical Value ◽  
Cox Regression Analysis

The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p &lt; 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA–protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, MAFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.

scholarly journals Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Yutao Wang ◽  
Jiaxing Lin ◽  
Kexin Yan ◽  
Jianfeng Wang
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Test Set ◽  
Cox Regression Analysis ◽  
Significant Difference ◽  
Robust Likelihood

Aim. In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer. Method. We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P<0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the “Rbsurv” package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032. Results. FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗FAM72D + 1.737∗ARHGAP33 − 0.737∗TACR2 − 0.651∗PLEK2 − 0.793∗FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC=0.6039) and GSE21032 GPL5188 (140 samples; AUC=0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups. Conclusion. A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.

scholarly journals 5 Gleason score-associated gene signatures serve as novel biomarkers for identifying early recurring events and contributing to early diagnosis for Prostate Adenocarcinoma

2020 ◽  
Author(s):  
Lingyu Zhang ◽  
Yu Li ◽  
Weiwei Liu ◽  
Xuchu Wang ◽  
Ying Ping ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Gleason Score ◽  
Cox Regression ◽  
Tnm Classification ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Diagnostic Efficiency ◽  
Cox Regression Analysis ◽  
Gene Signatures

Abstract Background: Prostate cancer (PCa) recurrence leads to much higher mortality than those without recurring events. Early and accurate laboratory diagnosis is particularly important for early identification of patients at high risk of recurrence and to benefit from additional systemic intervention. This study aimed to develop efficient and accurate Prostate Cancer diagnostic and prognostic biomarkers for the identification of initial tumor new events. Methods: Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) data portal were utilized to obtain differentially expressed genes (DEGs) and clinical trait information in PCa. WGCNA analysis obtained the most relevant clinical traits and genes enriched in several modules. Univariate Cox regression analysis and multivariate Cox proportional hazards (Cox-PH) model was employed to candidate gene signatures related to Disease-Free Interval (DFI). Internal and external cohort was utilized to test and validate the validity, accuracy, and clinical utility of prognostic models.Results: We constructed and optimized a valid and credible model for predicting patient outcomes, based on 5 Gleason score-associated gene signatures (ZNF695, CENPA, TROAP, BIRC5, KIF20A). Furthermore, ROC and Kaplan-Meier analysis revealed higher diagnostic efficiency for PCa and predictive effectiveness in tumor recurrence and metastasis. Calibration curve also revealed high prediction accuracy in internal TCGA cohort and external GEO cohort. The model was prognostically significant in the stratified cohort, including TNM classification and Gleason score, and was deemed to be an independent PCa prognostic factor, and superioring to other clinicopathological characteristics. On the other hand, we also measured the correlation between gene signatures’ expression and inflammation landscape. 5 gene signatures were significantly positively correlated with tumor purity and negatively correlated with the immersion levels of CD8+ T cells. Conclusions: Our study identified and validated 5 gene signatures as biomarkers for prostate cancer diagnosis, providing an assessment of DFI while predicting tumor progression, possibly providing novel theories for the treatment of prostate cancer.

scholarly journals Clinical Prognosis of Keratin Family Genes in Patients with LUAD a Study Based on TCGA Database

2021 ◽  
Author(s):  
Xia Liu ◽  
Hangzhou Zhu ◽  
Xiaojiu Zha ◽  
Yan Rui ◽  
Miao Li ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Cause Of Death ◽  
Prognostic Value ◽  
Cox Regression ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Clinical Prognosis ◽  
Cox Regression Analysis

Abstract Background: Malignant tumor is the main cause of death in the world, among which lung cancer is the main cause of death. The incidence rate and mortality of lung cancer are increasing year by year. This study aims to elucidate the potential prognostic value of keratin (KRT) gene family members in patients with lung adenocarcinoma (LUAD).Materials and methods: RNA sequencing data were obtained from the Cancer Genome Atlas (TCGA) database of LUAD tumors and paired normal tissues. Multivariate Cox proportional hazards regression analysis was used to evaluate the prognostic value of KRT family member genes. Analyze the screening variables to construct the risk score. The time-dependent ROC curve is used to evaluate the predicted results. Finally, nomograms were used to assess individualized prognostic risk.Result: From the differentially expressed genes, 14 KRT genes with significant imbalance in LUAD tumors and adjacent non-cancerous tissues were screened. Receiver operating characteristic curve (ROC) analysis confirmed that these 14 KRT genes can be used as potential diagnostic markers for the diagnosis of lung adenocarcinoma. Multivariate Cox regression analysis showed that six KRT genes were related to the prognosis of lung cancer. The variables were screened by multivariate Cox regression model. The final results showed that KRT8 and KRT6A were independent risk factors for the prognosis of lung adenocarcinoma.Conclusion: KRT8 and KRT6A can be used as prognostic markers of LUAD. The high expression of KRT8 and KRT6A suggests that the prognosis of LUAD patients is poor.

Patient experience with service quality: Implications for survival outcomes in prostate cancer.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 39-39
Author(s):  
Christopher G. Lis ◽  
Maurie Markman ◽  
Mark Rodeghier ◽  
Digant Gupta
Keyword(s):  
Prostate Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Service Quality ◽  
Patient Experience ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Risk Of Mortality ◽  
Patient Reported

39 Background: Prostate cancer is the second leading cause of cancer death among U.S. men. While self-reported quality of life has been shown to be prognostic of survival, there has been limited exploration of whether a patient’s assessment of the overall quality-of-care received might influence survival in prostate cancer. We evaluated the relationship between patient-reported experience with service quality and overall survival in prostate cancer. Methods: 832 returning prostate cancer patients treated at Cancer Treatment Centers of America between July 2007 and December 2010. Overall patient experience (“considering everything, how satisfied are you with your overall experience?”) was measured on a 7-point Likert scale ranging from “completely dissatisfied” to “completely satisfied”. It was dichotomized into 2 categories: top box response (7) versus all others (1-6). Cox regression was used to evaluate the association between patient experience and survival. Results: 560 patients were newly diagnosed while 272 had been previously treated. Majority of patients (n=570, 68.5%) had stage II disease at diagnosis. The mean age was 63.6 years. By the time of this analysis, 93 (11.2%) patients had expired. 710 (85.3%) patients were “completely satisfied” with the service quality they received while 122 (14.7%) patients were not. Median overall survival was 47.9 months. On univariate Cox regression analysis, “completely satisfied” patients had a significantly lower risk of mortality compared to those not “completely satisfied” (HR=0.48; 95% CI: 0.30-0.78; p=0.003). On multivariate Cox regression analysis controlling for stage at diagnosis, treatment history and age, “completely satisfied” patients demonstrated significantly lower mortality (HR=0.50; 95% CI: 0.29-0.87; p=0.01) compared to those not “completely satisfied”. Conclusions: Patient experience with service quality was an independent predictor of survival in prostate cancer. Based on this provocative observation, it is reasonable to suggest that further exploration of a possible meaningful relationship between patient perceptions of the care they have received and outcome in prostate cancer is indicated.

Sign in / Sign up

Export Citation Format

Share Document