ArticleCancer medicine2020
A four-gene signature associated with clinical features can better predict prognosis in prostate cancer.
Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- Integrative analysis reveals SRD5A2 as a central mediator in PPCPs induced modulation of the prostate cancer immune microenvironment.Discover oncology · 2025Article
- Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients.Discover oncology · 2023Article
- Therapeutic implications for localized prostate cancer by multiomics analyses of the ageing microenvironment landscape.International journal of biological sciences · 2023Article
- The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer.Journal of clinical medicine · 2022Article
- A novel 8-gene panel for prediction of early biochemical recurrence in patients with prostate cancer after radical prostatectomy.American journal of cancer research · 2022Article
- Characterization of a Pyroptosis-Related Signature for Prognosis Prediction and Immune Microenvironment Infiltration in Prostate Cancer.Computational and mathematical methods in medicine · 2022Article
- Article
- Identification and Validation of a Prognostic Signature for Prostate Cancer Based on Ferroptosis-Related Genes.Frontiers in oncology · 2021Article
- A four-gene signature associated with clinical features can better predict prognosis in prostate cancer.Cancer medicine · 2020Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) is one of the most deadly urinary tumors in men globally, and the 5-year over survival is poor due to metastasis of tumor. It is significant to explore potential biomarkers for early diagnosis and personalized therapy of PCa. In the present study, we performed an integrated analysis based on multiple microarrays in the Gene Expression Omnibus (GEO) dataset and obtained differentially expressed genes (DEGs) between 510 PCa and 259 benign issues. The weighted correlation network analysis indicated that prognostic profile was the most relevant to DEGs. Then, univariate and multivariate COX regression analyses were conducted and four prognostic genes were obtained to establish a four-gene prognostic model. And the predictive effect and expression profiles of the four genes were well validated in another GEO dataset, The Cancer Genome Atlas and the Human Protein Atlas datasets. Furthermore, combination of four-gene model and clinical features was analyzed systematically to guide the prognosis of patients with PCa to a largest extent. In summary, our findings indicate that four genes had important prognostic significance in PCa and combination of four-gene model and clinical features could achieve a better prediction to guide the prognosis of patients with PCa.
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