ArticleFrontiers in surgery2022
A seven-gene prognosis model to predict biochemical recurrence for prostate cancer based on the TCGA database.
Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 18 citations in OpenAlex.
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- Glycolysis‑related lncRNA may be associated with prognosis and immune activity in grade II‑III glioma.Oncology letters · 2024Article
- Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients.Heliyon · 2024Article
- Analysis of risk factors affecting the prognosis of angiosarcoma patients: a retrospective study.American journal of cancer research · 2024Article
- A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer.PeerJ · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The incidence rate of prostate cancer is increasing rapidly. This study aims to explore the gene-associated mechanism of prostate cancer biochemical recurrence (BCR) after radical prostatectomy and to construct a biochemical recurrence of prostate cancer prognostic model. Methods: The DEseq2 R package was used for the differential expression of mRNA. The ClusterProfiler R package was used to analyze the functional enrichment of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore related mechanisms. The Survival, Survminer, and My.stepwise R packages were used to construct the prognostic model to predict the biochemical recurrence-free probability. The RMS R package was used to draw the nomogram. For evaluating the prognostic model, the timeROC R package was used to draw the time-dependent ROC curve (receiver operating characteristic curve). Result: To investigate the association between mRNA and prostate cancer, we performed differential expression analysis on the TCGA (The Cancer Genome Atlas) database. Seven protein-coding genes ( Conclusions: This study shows that establishing a biochemical recurrence prediction prognostic model comprising seven protein-coding genes is an effective and precise method for predicting the progression of prostate cancer.
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