ISPRF: a machine learning model to predict the immune subtype of kidney cancer samples by four genes

Background: Clear 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. Methods: A 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. Results: In 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/). Conclusions: The 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.

Automated summary

This study identified immune subtypes in ccRCC through clustering and network analysis, established a machine learning model, and discovered hub genes associated with immune subtypes. With this model, patient's immune subtype can be predicted, providing important reference for personalized treatment.