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 < 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 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 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 Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

2020 ◽  
Author(s):  
Xinxin Xia ◽  
Hui Liu ◽  
Yuejun Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Immune Gene ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Biological Mechanisms ◽  
Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS. Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

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 Exploration of the Tumor Mutational Burden as a Prognostic Biomarker and Related Hub Gene Identification in Prostate Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382110521
Author(s):  
Licheng Wang ◽  
Yicong Yao ◽  
Chengdang Xu ◽  
Xinan Wang ◽  
Denglong Wu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
T Cells ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Mutational Burden ◽  
Clinicopathologic Feature ◽  
Tumor Mutational Burden

To explore the signature function of the tumor mutational burden (TMB) and potential biomarkers in prostate cancer (PCa), transcriptome profiles, somatic mutation data, and clinicopathologic feature information were downloaded from The Cancer Genome Atlas (TCGA) database. R software package was used to generate a waterfall plot to summarize the specific mutation information and calculate the TMB value of PCa. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select the hub genes related to the TMB from the ImmPort network to build a risk score (RS) model to evaluate prognostic values and plot Kaplan–Meier (K-M) curves to predict PCa patients survival. The results showed that PCa patients with a high TMB exhibited higher infiltration of CD8+ T cells and CD4+ T cells and better overall survival (OS) than those with a low TMB. The anti-Mullerian hormone (AMH), baculoviral IAP repeat-containing 5 (BIRC5), and opoid receptor kappa 1 (OPRK1) genes were three hub genes and their copy number variation (CNV) was relatively likely to affect the infiltration of immune cells. Moreover, PCa patients with low AMH or BIRC5 expression had a longer survival time and lower cancer recurrence, while elevated AMH or BIRC5 expression favored PCa progression. In contrast, PCa patients with low OPRK1 expression had poorer OS in the early stage of PCa and a higher recurrent rate than those with high expression. Taken together, these results suggest that the TMB may be a promising prognostic biomarker for PCa and that AMH, OPRK1, and BIRC5 are hub genes affecting the TMB; AMH, OPRK1, and BIRC5 could serve as potential immunotherapeutic targets for PCa treatment.

scholarly journals An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Qian Xu ◽  
Yurong Chen
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Staging System ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Net Benefit ◽  
Cox Regression Analysis

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

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 GNG7 and ADCY1 as diagnostic and prognostic biomarkers for pancreatic adenocarcinoma through bioinformatic-based analyses

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youfu Zhang ◽  
Jinran Yang ◽  
Xuyang Wang ◽  
Xinchang Li
Keyword(s):  
Survival Analysis ◽  
Pancreatic Adenocarcinoma ◽  
Cox Regression ◽  
Malignant Tumors ◽  
The Cancer Genome Atlas ◽  
Prognostic Biomarkers ◽  
Cox Regression Analysis ◽  
Protein Protein Interaction ◽  
Kegg Analysis ◽  
Upregulated Genes

AbstractPancreatic adenocarcinoma (PAAD) is one of the most lethal malignant tumors in the world. The GSE55643 and GSE15471 microarray datasets were downloaded to screen the diagnostic and prognostic biomarkers for PAAD. 143 downregulated genes and 118 upregulated genes were obtained. Next, we performed gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on these genes and constructed a protein–protein interaction (PPI) network. We screened out two important clusters of genes, including 13 upregulated and 5 downregulated genes. After the survival analysis, 3 downregulated genes and 10 upregulated genes were identified as the selected key genes. The KEGG analysis on 13 selected genes showed that GNG7 and ADCY1 enriched in the Pathway in Cancer. Next, the diagnostic and prognostic value of GNG7 and ADCY1 was investigated using independent cohort of the Cancer Genome Atlas (TCGA), GSE84129 and GSE62452. We observed that the expression of the GNG7 and ADCY1 was decreased in PAAD. The diagnostic receiver operating characteristic (ROC) analysis indicated that the GNG7 and ADCY1 could serve as sensitive diagnostic markers in PAAD. Survival analysis suggested that expression of GNG7, ADCY1 were significantly associated with PAAD overall survival (OS). The multivariate cox regression analysis showed that the expression of GNG7, ADCY1 were independent risk factors for PAAD OS. Our study indicated GNG7 and ADCY1 may be potential diagnostic and prognostic biomarkers in patients with PAAD.

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 Novel Diagnostic Biomarkers in Prostate Adenocarcinoma Based on the Stromal-Immune Score and Analysis of the WGCNA and ceRNA Network

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Tengfei Zhang ◽  
Yaxuan Wang ◽  
Yiming Dong ◽  
Lei Liu ◽  
Yikai Han ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Differentially Expressed Gene ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Prognostic Biomarkers ◽  
Mechanism Study ◽  
Cox Regression Analysis ◽  
Immune Score

Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.

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