ArticleTranslational andrology and urology2021
ISPRF: a machine learning model to predict the immune subtype of kidney cancer samples by four genes.
Article in Translational andrology and urology, 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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5 citing papers in PubMed, 10 citations in OpenAlex.
- Comprehensive analysis and identification of subtypes and hub genes of high immune response in lung adenocarcinoma.BMC pulmonary medicine · 2024Article
- A Novel, Simple, and Low-Cost Approach for Machine Learning Screening of Kidney Cancer: An Eight-Indicator Blood Test Panel with Predictive Value for Early Diagnosis.Current oncology (Toronto, Ont.) · 2022Article
- Classification of Muscle Invasive Bladder Cancer to Predict Prognosis of Patients Treated with Immunotherapy.Journal of immunology research · 2022Article
- Identification and Verification of Immune Subtype-Related lncRNAs in Clear Cell Renal Cell Carcinoma.Frontiers in oncology · 2022Article
- Construction of an Epithelial-Mesenchymal Transition-Related Model for Clear Cell Renal Cell Carcinoma Prognosis Prediction.Disease markers · 2022Article
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8 authors at 4 institutions in 1 country.
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Abstract
backgroundClear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma (RCC). Immunotherapy, especially anti-PD-1, is becoming a pillar of ccRCC treatment. However, precise biomarkers and robust models are needed to select the proper patients for immunotherapy.
methodsA total of 831 ccRCC transcriptomic profiles were obtained from 6 datasets. Unsupervised clustering was performed to identify the immune subtypes among ccRCC samples based on immune cell enrichment scores. Weighted correlation network analysis (WGCNA) was used to identify hub genes distinguishing subtypes and related to prognosis. A machine learning model was established by a random forest (RF) algorithm and used on an open and free online website to predict the immune subtype.
resultsIn the identified immune subtypes, subtype2 was enriched in immune cell enrichment scores and immunotherapy biomarkers. WGCNA analysis identified four hub genes related to immune subtypes, CTLA4, FOXP3, IFNG, and CD19. The RF model was constructed by mRNA expression of these four hub genes, and the value of area under the receiver operating characteristic curve (AUC) was 0.78. Subtype2 patients in the independent validation cohort had a better drug response and prognosis for immunotherapy treatment. Moreover, an open and free website was developed by the RF model (https://immunotype.shinyapps.io/ISPRF/).
conclusionsThe current study constructs a model and provides a free online website that could identify suitable ccRCC patients for immunotherapy, and it is an important step forward to personalized treatment.
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