A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma.

ABSTRACT:

Background

Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions.

Methods

RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK ce

SUBMITTER: Yao Q 

PROVIDER: S-EPMC10807100 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature