A Robust Prognostic Gene Signature Based on eRNAs-Driven Genes in Prostate Cancer

Prostate cancer (PCa) is the second most common malignancy in men, but its exact pathogenetic mechanisms remain unclear. This study explores the effect of enhancer RNAs (eRNAs) in PCa. Firstly, we screened eRNAs and eRNA -driven genes from The Cancer Genome Atlas (TCGA) database, which are related to the disease-free survival (DFS) of PCa patients;. screening methods included bootstrapping, Kaplan–Meier (KM) survival analysis, and Pearson correlation analysis. Then, a risk score model was established using multivariate Cox analysis, and the results were validated in three independent cohorts. Finally, we explored the function of eRNA-driven genes through enrichment analysis and analyzed drug sensitivity on datasets from the Genomics of Drug Sensitivity in Cancer database. We constructed and validated a robust prognostic gene signature involving three eRNA-driven genes namely MAPK15, ZNF467, and MC1R. Moreover, we evaluated the function of eRNA-driven genes associated with tumor microenvironment (TME) and tumor mutational burden (TMB), and identified remarkable differences in drug sensitivity between high- and low-risk groups. This study identified a prognostic gene signature, which provides new insights into the role of eRNAs and eRNA-driven genes while assisting clinicians to determine the prognosis and appropriate treatment options for patients with PCa.

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Identification of a candidate prognostic gene signature by transcriptome analysis of matched pre- and post-treatment prostatic biopsies from patients with advanced prostate cancer

BMC Cancer ◽

10.1186/1471-2407-14-977 ◽

2014 ◽

Vol 14 (1) ◽

Cited By ~ 18

Author(s):

Prabhakar Rajan ◽

Jacqueline Stockley ◽

Ian M Sudbery ◽

Janis T Fleming ◽

Ann Hedley ◽

...

Keyword(s):

Prostate Cancer ◽

Transcriptome Analysis ◽

Advanced Prostate Cancer ◽

Gene Signature ◽

Post Treatment ◽

Prognostic Gene ◽

Prognostic Gene Signature

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Abstract 4346: Prognostic gene signature for intermediate risk prostate cancer

10.1158/1538-7445.am2015-4346 ◽

2015 ◽

Author(s):

Brian Li ◽

Robin Hallet ◽

Ying Wu ◽

Greg Pong ◽

John Hassell ◽

...

Keyword(s):

Prostate Cancer ◽

Gene Signature ◽

Intermediate Risk ◽

Prognostic Gene ◽

Prognostic Gene Signature ◽

Risk Prostate Cancer

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Identification of potential crucial genes associated with the pathogenesis and prognosis of prostate cancer

Biomarkers in Medicine ◽

10.2217/bmm-2019-0318 ◽

2020 ◽

Vol 14 (5) ◽

pp. 353-369

Author(s):

Hai-Qi Mu ◽

Zhi-Qiang Liang ◽

Qi-Peng Xie ◽

Wei Han ◽

Sen Yang ◽

...

Keyword(s):

Prostate Cancer ◽

Clinical Characteristics ◽

Drug Sensitivity ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Mutational Landscape ◽

Differential Expression Genes ◽

Cancer Genome Atlas ◽

Public Datasets

Aim: Prostate cancer (PCa) is the sixth leading cause of cancer-related deaths in men throughout the world. This study aimed to investigate genes associated with the pathogenesis and prognosis of PCa. Materials & methods: Data of PCa cases were obtained from public datasets and were analyzed using an integrated bioinformatics strategy. Results: A total of 969 differential expression genes were identified. Moreover, GSE16560 and The Cancer Genome Atlas (TCGA) data showed a prognostic prompt function of the nine-gene signature, as well as in PCa with Gleason 7. Finally, majority of the nine hub genes were associated with drug sensitivity, mutational landscape, immune infiltrates and clinical characteristics of PCa. Conclusion: The nine-gene signature was correlated with drug sensitivity, mutational landscape, immune infiltrates, clinical characteristics and survival from PCa.

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Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model

Frontiers in Oncology ◽

10.3389/fonc.2020.598801 ◽

2020 ◽

Vol 10 ◽

Author(s):

Yanlong Zhang ◽

Ruiqiao Zhang ◽

Fangzhi Liang ◽

Liyun Zhang ◽

Xuezhi Liang

Keyword(s):

Prostate Cancer ◽

Drug Sensitivity ◽

Risk Model ◽

External Validation ◽

Disease Free Survival ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Predictive Values ◽

Cox Analysis

BackgroundDespite being the second most common tumor in men worldwide, the tumor metabolism-associated mechanisms of prostate cancer (PCa) remain unclear. Herein, this study aimed to investigate the metabolism-associated characteristics of PCa and to develop a metabolism-associated prognostic risk model for patients with PCa.MethodsThe activity levels of PCa metabolic pathways were determined using mRNA expression profiling of The Cancer Genome Atlas Prostate Adenocarcinoma cohort via single-sample gene set enrichment analysis (ssGSEA). The analyzed samples were divided into three subtypes based on the partitioning around medication algorithm. Tumor characteristics of the subsets were then investigated using t-distributed stochastic neighbor embedding (t-SNE) analysis, differential analysis, Kaplan–Meier survival analysis, and GSEA. Finally, we developed and validated a metabolism-associated prognostic risk model using weighted gene co-expression network analysis, univariate Cox analysis, least absolute shrinkage and selection operator, and multivariate Cox analysis. Other cohorts (GSE54460, GSE70768, genotype-tissue expression, and International Cancer Genome Consortium) were utilized for external validation. Drug sensibility analysis was performed on Genomics of Drug Sensitivity in Cancer and GSE78220 datasets. In total, 1,039 samples and six cell lines were concluded in our work.ResultsThree metabolism-associated clusters with significantly different characteristics in disease-free survival (DFS), clinical stage, stemness index, tumor microenvironment including stromal and immune cells, DNA mutation (TP53 and SPOP), copy number variation, and microsatellite instability were identified in PCa. Eighty-four of the metabolism-associated module genes were narrowed to a six-gene signature associated with DFS, CACNG4, SLC2A4, EPHX2, CA14, NUDT7, and ADH5 (p <0.05). A risk model was developed, and external validation revealed the strong robustness our risk model possessed in diagnosis and prognosis as well as the association with the cancer feature of drug sensitivity.ConclusionsThe identified metabolism-associated subtypes reflected the pathogenesis, essential features, and heterogeneity of PCa tumors. Our metabolism-associated risk model may provide clinicians with predictive values for diagnosis, prognosis, and treatment guidance in patients with PCa.

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A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients

BioMed Research International ◽

10.1155/2019/9506461 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Ziming Hou ◽

Jun Yang ◽

Hao Wang ◽

Dongyuan Liu ◽

Hongbing Zhang

Keyword(s):

Prognostic Model ◽

Cox Regression ◽

Risk Groups ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Prognostic Models ◽

Training Set ◽

Prognostic Gene ◽

Prognostic Gene Signature ◽

Genome Atlas

Objective. This study aimed to screen prognostic gene signature of glioblastoma (GBM) to construct prognostic model.Methods. Based on the GBM information in the Cancer Genome Atlas (TCGA, training set), prognostic genes (Set X) were screened by Cox regression. Then, the optimized prognostic gene signature (Set Y) was further screened by the Cox-Proportional Hazards (Cox-PH). Next, two prognostic models were constructed: model A was based on the Set Y; model B was based on part of the Set X. The samples were divided into low- and high-risk groups according to the median prognosis index (PI). GBM datasets in Gene Expression Ominous (GEO, GSE13041) and Chinese Glioma Genome Atlas (CGGA) were used as the testing datasets to confirm the prognostic models constructed based on TCGA.Results. We identified that the prognostic 14-gene signature was significantly associated with the overall survival (OS) in the TCGA. In model A, patients in high- and low-risk groups showed the significantly different OS (P = 7.47 × 10−9, area under curve (AUC) 0.995) and the prognostic ability were also confirmed in testing sets (P=0.0098 and 0.037). The model B in training set was significant but failed in testing sets.Conclusion. The prognostic model which was constructed based on the prognostic 14-gene signature presented a high predictive ability for GBM. The 14-gene signature may have clinical implications in the subclassification of GBM.

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Identification and Validation of a PPP1R12A-Related Five-Gene Signature Associated With Metabolism to Predict the Prognosis of Patients With Prostate Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.703210 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhihao Zou ◽

Ren Liu ◽

Yingke Liang ◽

Rui Zhou ◽

Qishan Dai ◽

...

Keyword(s):

Prostate Cancer ◽

Cox Regression ◽

Expression Profiles ◽

Affinity Purification ◽

Disease Free Survival ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Free Survival ◽

Cox Regression Analysis

BackgroundProstate cancer (PCa) is the most common malignant male neoplasm in the American male population. Our prior studies have demonstrated that protein phosphatase 1 regulatory subunit 12A (PPP1R12A) could be an efficient prognostic factor in patients with PCa, promoting further investigation. The present study attempted to construct a gene signature based on PPP1R12A and metabolism-related genes to predict the prognosis of PCa patients.MethodsThe mRNA expression profiles of 499 tumor and 52 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. We selected differentially expressed PPP1R12A-related genes among these mRNAs. Tandem affinity purification-mass spectrometry was used to identify the proteins that directly interact with PPP1R12A. Gene set enrichment analysis (GSEA) was used to extract metabolism-related genes. Univariate Cox regression analysis and a random survival forest algorithm were used to confirm optimal genes to build a prognostic risk model.ResultsWe identified a five-gene signature (PPP1R12A, PTGS2, GGCT, AOX1, and NT5E) that was associated with PPP1R12A and metabolism in PCa, which effectively predicted disease-free survival (DFS) and biochemical relapse-free survival (BRFS). Moreover, the signature was validated by two internal datasets from TCGA and one external dataset from the Gene Expression Omnibus (GEO).ConclusionThe five-gene signature is an effective potential factor to predict the prognosis of PCa, classifying PCa patients into high- and low-risk groups, which might provide potential novel treatment strategies for these patients.

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A prognostic gene signature for predicting survival outcome in diffuse large B-cell lymphoma

Cancer Genetics ◽

10.1016/j.cancergen.2021.01.001 ◽

2021 ◽

Vol 252-253 ◽

pp. 87-95

Author(s):

Santosh Khanal ◽

Todd Bradley

Keyword(s):

B Cell ◽

Cell Lymphoma ◽

B Cell Lymphoma ◽

Gene Signature ◽

Survival Outcome ◽

Prognostic Gene ◽

Prognostic Gene Signature ◽

Large B Cell Lymphoma ◽

Large B Cell

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A novel pyroptosis-associated gene signature for immune status and prognosis of cutaneous melanoma

PeerJ ◽

10.7717/peerj.12304 ◽

2021 ◽

Vol 9 ◽

pp. e12304

Author(s):

Zhengyuan Wu ◽

Leilei Chen ◽

Chaojie Jin ◽

Jing Xu ◽

Xingqun Zhang ◽

...

Keyword(s):

Cutaneous Melanoma ◽

Immune Status ◽

Immune Cell ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Tumor Cell Death ◽

Prognostic Gene ◽

Prognostic Gene Signature

Background Cutaneous melanoma (CM) is a life-threatening destructive malignancy. Pyroptosis significantly correlates with programmed tumor cell death and its microenvironment through active host-tumor crosstalk. However, the prognostic value of pyroptosis-associated gene signatures in CM remains unclear. Methods Gene profiles and clinical data of patients with CM were downloaded from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes associated with pyroptosis and overall survival (OS). We constructed a prognostic gene signature using LASSO analysis, then applied immune cell infiltration scores and Kaplan-Meier, Cox, and pathway enrichment analyses to determine the roles of the gene signature in CM. A validation cohort was collected from the Gene Expression Omnibus (GEO) database. Results Four pyroptosis-associated genes were identified and incorporated into a prognostic gene signature. Integrated bioinformatics findings showed that the signature correlated with patient survival and was associated with tumor growth and metastasis. The results of Gene Set Enrichment Analysis of a risk signature indicated that several enriched pathways are associated with cancer and immunity. The risk signature for immune status significantly correlated with tumor stem cells, the immune microenvironment, immune cell infiltration and immune subtypes. The expression of four pyroptosis genes significantly correlated with the OS of patients with CM and was related to the sensitivity of cancer cells to several antitumor drugs. A signature comprising four genes associated with pyroptosis offers a novel approach to the prognosis and survival of patients with CM and will facilitate the development of individualized therapy.

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