BackgroundProstate cancer is one of the most common malignant tumors of the male genitourinary system. The impaired activity of natural killer (NK) cells observed in prostate cancer may contribute to immune evasion.
This study aimed to develop robust NK cell-related diagnostic signatures.MethodsBased on NK related genes identified by scRNA-seq analysis, weighted gene co-expression network analysis, least absolute shrinkage and selection operator regression analysis, and machine learning algorithms were used to develop a novel diagnostic model. The expression of diagnostic genes was validated using tumor and adjacent normal tissues collected from prostate cancer patients.
The biological functions of KIT as an NK cell related gene were further evaluated in prostate cancer cells.ResultsA nine-gene NK cell-related diagnostic signature was developed, including HSPD1, HSPE1, CLU, KIT, LAPTM4A, SLC18A2, TUBA4A, VWA5A, and ZFP36L1. These genes were validated in independent datasets and showed strong predictive ability for prostate cancer diagnosis (AUC >0.8).
Based on the expression profiles of these genes, nine compounds were identified that may influence drug sensitivity in prostate cancer.
Frontiers in Immunology published a clinical update in Infectious Disease on 03 Jun 2026.
The item focuses on A diagnostic signature derived from NK cell related genes in prostate cancer: insights from integrated scRNA-seq and bulk RNA-seq analyses with functional validation of KIT.
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