The gene expression pattern as a tool for assessment of components of microenvironment and response to anti-cancer therapy of prostate tumors

TitleThe gene expression pattern as a tool for assessment of components of microenvironment and response to anti-cancer therapy of prostate tumors
Publication TypeJournal Article
Year of Publication2019
AuthorsGerashchenko, GV, Chashchina, LI, Rynditch, AV, Kashuba, VI
Abbreviated Key TitleDopov. Nac. akad. nauk Ukr.
Date Published04/2019

We have analyzed the putative value of the pattern of relative expression (RE) of several genes that might be involved in a response to anti-cancer therapy, namely AR, PTEN, COX2, FASN, HMGCR, LDLR, and CTLA4, in samples of prostate adenoma, adenocarcinoma, and the paired conventional normal tissues. We could propose three subtypes of adenocarcinomas that show the distinct pattern of expression of the above-mentioned genes, characteristics for (1) cancer-associated fibroblasts (CAFs), (2) tumor-associated macrophages (TAMs), and (3) markers of immune response. These groups correlate with the prostate cancer subtypes, that were determined earlier, based on the analysis of RE of the epithelial-to-mesenchymal cell transition (EMT) genes and prostate cancer-associated genes. Noteworthy, the highest correlation was found for genes characteristic of CAFs. This emphasizes the importance of the simultaneous analysis of genes, involved in various intercellular interactions between tumor cells and cells of tumor microenvironment, in prediction of efficacy of anti-cancer therapy. To confirm the presented data, the additional studies on a larger cohort of the prostate cancer patients are required.

Keywordscancerassociated fibroblasts, pharmacological markers, prostate tumors, relative gene expression, tumor microenvironment, tumor-associated macrophages

1. Gerashchenko, G. V., Mankovska, O. S., Dmitriev, A. A., Mevs, L. V., Rosenberg, E. E., Pikul, M. V., Marynychenko, M. V., Gryzodub, O. P., Stakhovsky, E. O. & Kashuba, V. I. (2017). Expression of epithelial-mesenchymal transition-related genes in prostate tumours. Biopolym. Cell, 33, No. 5, pp. 335-355. doi:
2. Gerashchenko, G. V., Rynditch, A. V. & Kashuba, V. I. (2019). Development of gene expression panels to determine prostate cancer. Dopov. Nac. acad. Nauk Ukr., No. 1, pp. 100-106. doi:
3. Gerashchenko, G. V., Mevs, L. V., Chashchina, L. I., Pikul, M. V., Gryzodub, O. P., Stakhovsky, E. O. & Kashuba, V. I. (2018). Expression of steroid and peptide hormone receptors, metabolic enzymes and EMT-related genes in prostate tumors in relation to the presence of the TMPRSS2/ERG fusion. Exp Oncol., 40, No. 2, pp. 101-108. doi:
4. Gerashchenko, G. V., Grygoruk, O. V., Kononenko, O. A., Gryzodub, O. P., Stakhovsky, E. O. & Kashuba, V. I. (2018). Expression pattern of genes, associated with tumor microenvironment in prostate tumors. Exp. Oncol., 40, No. 4, pp. 315-322. doi:
5. Aoun, F., Bourgi, A., Ayoub, E., El Rassy, E., van Velthoven, R. & Peltier, A. (2017). Androgen deprivation therapy in the treatment of locally advanced, nonmetastatic prostate cancer: practical experience and a review of the clinical trial evidence. Ther. Adv. Urol., 9, No. 3-4, pp. 73-80. doi:
6. Matsumoto, C. S., Almeida, L. O., Guimarães, D. M., Martins, M. D., Papagerakis, P., Papagerakis, S., Leopoldino, A. M., Castilho, R. M. & Squarize, C. H. (2016). PI3K-PTEN dysregulation leads to mTOR-driven upregulation of the core clock gene BMAL1 in normal and malignant epithelial cells. Oncotarget, 7, No. 27, pp. 42393-42407. doi:
7. Jamaspishvili, T., Berman, D. M., Ross, A. E., Scher, H. I., De Marzo, A. M., Squire, J. A. & Lotan, T. L. (2018). Clinical implications of PTEN loss in prostate cancer. Nat. Rev. Urol., 15, No. 4, pp. 222-234. doi:
8. New Drugs at FDA: CDER’s New Molecular Entities and New Therapeutic Biological Products. Retrieved from druginnovation/default.htm
9. Turanli, B., Grøtli, M., Boren, J., Nielsen, J., Uhlen, M., Arga, K. Y. & Mardinoglu, A. (2018). Drug repositioning for effective prostate cancer treatment. Front. Physiol., 15, No. 9, 500. doi:
10. Montironi, R., Santoni, M., Sotte, V., Cheng, L., Lopez-Beltran, A., Massari, F., Matrana, M. R., Moch, H., Berardi, R. & Scarpelli, M. (2016). Emerging immunotargets and immunotherapies in prostate cancer. Curr. Drug. Targets, 17, No. 7, pp. 777-782. doi:
11. Komohara, Y. & Takeya, M. (2017). CAFs and TAMs: maestros of the tumour microenvironment. J. Pathol., 241, No. 3, pp. 313-315. doi:
12. Livak, K. & Schmittgen, T. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods, 25, No. 4, pp. 402-408. doi:
13. Benjamini, Y. & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B (Methodological), 57, No. 1, pp. 289-300. doi:
14. Gerashchenko, G. V., Kononenko, O. A., Bondarenko, Yu. M., Stakhovsky, E. O. & Kashuba, V. I. (2018). Expression patterns of genes, regulating lipid metabolism in prostate tumors. Biopolym. Cell, 34, No. 6, pp. 445–460. doi:
15. Takahashi, H., Sakakura, K., Kudo, T., Toyoda, M., Kaira, K., Oyama, T. & Chikamatsu, K. (2017). Cancerassociated fibroblasts promote an immunosuppressive microenvironment through the induction and accumulation of protumoral macrophages. Oncotarget, 8, No. 5, pp. 8633-8647. doi: