Biopolym. Cell. 2026; 42(2):150-157.
https://doi.org/10.7124/bc.000B40
Short Communications
Validation of prostate cancer molecular subtyping approach, based on the cluster analysis of cancer cell and tumor microenvironment gene expression patterns
1Gerashchenko G. V., 1Kashuba V. I.
  1. Institute of Molecular Biology and Genetics, NAS of Ukraine
    150, Akademika Zabolotnoho Str., Kyiv, Ukraine, 03143

Abstract

Aim. To verify of previously studied gene sets of cancer associated, tumor microenvironment related and lipid metabolism genes for molecular subtypes identifying of prostate cancer by analyzing TCGA prostate cancer gene expression data. Methods. Analysis of prostate cancer gene expression data of TCGA RNA-sequencing based expression profiles of 55 previously studied genes. Statistical and K-means clustering methods were used for molecular subtyping of prostate cancer samples. Results. Cluster analysis revealed two and three potentially significant clusters of prostate cancer based on the expression levels of three gene groups which is 27 cancer-associated genes, 23 tumor microenvironment related genes and 5 lipid metabolism genes. Among three clusters, the first one has the most aggressive prostate cancer samples and has elevated levels of mesenchymal markers and high levels of inflammation markers and tumor microenvironment elements. The second and third clusters of tumor samples showed signs of presumably luminal and basal subtypes with lower levels of inflammation markers. The highest level of correlation in the distribution of samples across clusters was found for cancer-associated and tumor microenvironment-related gene groups. Conclusions. The results revealed correlations and a high degree of dispersion in the expression of the studied genes, which made it possible to identify several molecular clusters. A more detailed statistical analysis is needed to determine clinicaly relevant molecular subtypes and to establish the most significant expression markers in biological modules of the studied genes for the diagnosis, prognosis, and effective treatment of prostate cancer.
Keywords: prostate cancer, gene expression patterns, TCGA, cluster analysis, molecular subtypes, prostate cancer-associated genes, tumor microenvironment-related genes, lipid metabolism genes
Full text: (PDF, in English)

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