ArticleAmerican journal of translational research2023
A necroptosis gene signature predicts prostate cancer recurrence, and is linked to somatic mutation, therapeutic landscape, and immune infiltration.
Article in American journal of translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 8 citations in OpenAlex.
- A GIMAP family signature for prognostic prediction and immune characterization in hepatocellular carcinoma patients.Discover oncology · 2026Article
- Targeting RPS6KC1 to overcome enzalutamide resistance in prostate cancer.Biomarker research · 2025Article
- SLC39A14 Is a Potential Therapy Target and Prognostic Biomarker for Acute Myeloid Leukemia.Genes · 2025Article
- Using gene and gene-set association tests to identify lethal prostate cancer genes.Prostate cancer and prostatic diseases · 2025Article
- Angiogenesis-related gene signature for prognostic prediction and immune microenvironment characterization in diffuse large B-cell lymphoma.Clinical and experimental medicine · 2025Article
- Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experiments.Discover oncology · 2025Article
- Immunological characterization and prognostic of colon cancer evaluated by angiogenesis-related features: a computational analysis and in vitro experiments.Discover oncology · 2025Article
- Downregulation of SALL1 and its association with poor prognosis and the immune cell landscape in clear cell renal cell carcinoma.Frontiers in oncology · 2025Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveNecroptosis, a type of programmed necrotic cell death, has been implicated in cancer biology and therapeutics. Improved risk stratification is required for prostate carcinoma in individuals. In view of the importance of necroptosis, this work proposed a necroptosis-based genetic model for recurrence prediction, and clarified its characteristics.
methodsA least absolute shrinkage and selection operator (LASSO) regression analysis was conducted based upon transcriptome data of necroptosis genes with clinical information in the Cancer Genome Atlas (TCGA) prostate carcinoma samples, which were externally verified in the GSE116918 cohort. Somatic mutation was characterized by Maftools method. The drug sensitivity was estimated via OncoPredict algorithm. T-cell inflammation score and tumor mutational burden (TMB) score were computed for inferring immunotherapy response. CIBERSORT was adopted for scoring the infiltration of immune cell compositions.
resultsThe necroptosis gene model was defined, composed of BCL2, BCL2L11, BNIP3, CASP8, CYLD, HDAC9, IDH2, IPMK, MYC, PLK1, TNF, TNFRSF1A, and TSC1. Considering external verification, this model effectively predicted recurrence-free survival, notably within one year (area under the curve (AUC) = 0.841, 0.706, 0.776, and 0.893 in the discovery, verification, total and external independent sets, respectively). Patients who had a risk score > median value were defined as high risk, while those who had risk score ≤ median value were defined as low risk. Older age, more advanced T, N, M stage, shorter disease-free survival, and more recurred/progressed statuses were found in high-risk patients (all P<0.05). Moreover, the signature independently predicted patient recurrence (P<0.05). High-risk specimens had more frequent somatic mutation, especially of TP53, BSN, APC, TRANK1, DNAH9, and SALL1 (all P<0.05). The heterogeneity in sensitivity to small-molecule compounds was investigated in low- and high-risk patients. Also, high-risk individuals responded better to immunotherapy (P<0.05).
conclusionAltogether, the necroptosis gene signature may effectively predict prostatic carcinoma recurrence and therapeutic responses, but its clinical feasibility must be verified.
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37193176PMC10182515W4376871546What OpenQuestion holds
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