ArticleEvidence-based complementary and alternative medicine : eCAM2021
A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs.
Article in Evidence-based complementary and alternative medicine : eCAM, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- FAM189A2 activates Hippo signaling pathway by abrogating WWP2-mediated LATS1 ubiquitination, to inhibit the glycolysis and proliferation processes of lung adenocarcinoma.Journal of translational medicine · 2025Article
- Crotonylation-Related Prognostic Model of Esophageal Squamous Cell Carcinoma Based on Transcriptome Analysis and Single-Cell Sequencing Analysis.International journal of general medicine · 2025Article
- Overexpression of TMEM79 combined with SMG5 is related to prognosis, tumor immune infiltration and drug sensitivity in hepatocellular carcinoma.European journal of medical research · 2023Article
- Endocrine Disrupting Chemicals Influence Hub Genes Associated with Aggressive Prostate Cancer.International journal of molecular sciences · 2023Article
- PRLR and CACNA2D1 Impact the Prognosis of Breast Cancer by Regulating Tumor Immunity.Journal of personalized medicine · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
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Abstract
backgroundProstate cancer (PC) is one of the most critical cancers affecting men's health worldwide. The development of many cancers involves dysregulation or mutations in key transcription factors. This study established a transcription factor-based risk model to predict the prognosis of PC and potential therapeutic drugs. MATERIALS AND
methodsIn this study, RNA-sequencing data were downloaded and analyzed using The Cancer Genome Atlas dataset. A total of 145 genes related to the overall survival rate of PC patients were screened using the univariate Cox analysis. The Kdmist clustering method was used to classify prostate adenocarcinoma (PRAD), thereby determining the cluster related to the transcription factors. The support vector machine-recursive feature elimination method was used to identify genes related to the types of transcription factors and the key genes specifically upregulated or downregulated were screened. These genes were further analyzed using Lasso to establish a model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the functional analysis. The TIMER algorithm was used to quantify the abundance of immune cells in PRAD samples. The chemotherapy response of each GBM patient was predicted based on the public pharmacogenomic database, Genomics of Drug Sensitivity in Cancer (GDSC, http://www.cancerrxgene.org). The R package "pRRophetic" was applied to drug sensitivity (IC50) value prediction.
resultsWe screened 10 genes related to prognosis, including eight low-risk genes and two high-risk genes. The receiver operating characteristic (ROC) curve was 0.946. Patients in the high-risk score group had a poorer prognosis than those in the low-risk score group. The average area under the curve value of the model at different times was higher than 0.8. The risk score was an independent prognostic factor. Compared with the low-risk score group, early growth response-1 (
conclusionsTen genes of both high-and low-risk scores were associated with the prognosis of PC. PC prognosis may be related to immune disorders.
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