Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Wang Z

PROVIDER: S-EPMC8575581 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Wang Zhifeng Z Chen Zihao Z Zhao Hongfan H Lin Hao H Wang Junjie J Wang Ning N Li Xiqing X Ding Degang D

Translational andrology and urology 20211001 10

<h4>Background</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 34804821

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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