Development of gene expression panels to determine prostate cancer

Gerashchenko, GV, Rynditch, AV, Kashuba, VI
Dopov. Nac. akad. nauk Ukr. 2019, 1:100-106
https://doi.org/10.15407/dopovidi2019.01.100
Section: Biology
Language: English
Abstract: 

The aim of this investigation is to prove a modified algorithm for statistical approaches to develop gene expression panels for the detection of prostate tumors. According to Classification and Regression tree models and RE differences between adenocarcinoma (T) and adenoma (A) groups, we have chosen 31 transcripts for MDR analysis. Among them, there were 15 transcripts of (epithelial-mesenchymal transition (EMT) and prostate-cancer associated (PrCa-associated) genes and 16 transcripts of cancer-associated fibroblasts (CAF), tumor-associated macrophages (TAM), immune-associated genes (IAG)), which have shown some datasets with high statistical parameters. The highest diagnostic levels are manifested by expression panels developed from all 5 gene groups: PCA3, HOTAIR, ESR1, IL1R1 (Se = 0.97, Sp = 0.85, Ac = 0.93, OR = 204); CDH2, KRT18, PCA3, HOTAIR, ESR1, IL1R1 (Se = 1.0, Sp = 0.8, Ac = 0.93, OR > 500). We propose an improved algorithm for the gene expression data analysis to develop diagnostic panels with good and excellent diagnostic levels for the prostate tumor stratification in a group of patients from the Ukrainian population. Our data require a more detailed analysis and a larger cohort of patients with prostate tumor.

Keywords: cancer-associated genes, gene expression panels, MDR analysis, prostate tumors
References: 

1. Ray, P., Le Manach, Y., Riou, B. & Houle, T. T. (2010). Statistical evaluation of a biomarker. Anesthesiology, 112, No. 4, pp. 1023-1040. doi: https://doi.org/10.1097/ALN.0b013e3181d47604
2. Martinez-Ledesma, E., Verhaak, R. G. &Trevino, V. (2015) Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm. Sci Rep., 23, No. 5, 11966. doi: https://doi.org/10.1038/srep11966
3. Rubicz, R., Zhao, S., Wright, J. L., Coleman, I., Grasso, C., Geybels, M. S., Leonardson, A., Kolb, S., April, C., Bibikova, M., Troyer, D., Lance, R., Lin, D. W., Ostrander, E. A., Nelson, P.S ., Fan, J. B., Feng, Z. & Stanford, J. L. (2017). Gene expression panel predicts metastatic-lethal prostate cancer outcomes in men diagnosed with clinically localized prostate cancer. Mol. Oncol., 11, No. 2, pp. 140-150. doi: https://doi.org/10.1002/1878-0261.12014
4. Goossens, N., Nakagawa, S., Sun, X. & Hoshida, Y. (2015). Cancer biomarker discovery and validation. Transl Cancer Res., 4, No. 3, pp. 256-269. doi: https://doi.org/10.3978/j.issn.2218-676X.2015.06.04
5. Looney, S. W. & Hagan, J. L. (2008). Statistical methods for assessing biomarkers and analyzing biomarker data. Handbook of Statistics, 27, pp. 109-147. doi: https://doi.org/10.1016/S0169-7161(07)27004-X
6. Mazzara, S., Rossi, R.L., Grifantini, R., Donizetti, S., Abrignani, S. & Bombaci, M. (2017). CombiROC: an interactive web tool for selecting accurate marker combinations of omics data. Sci. Rep., 30, No. 7, 45477. doi: https://doi.org/10.1038/srep45477
7. Beam, C. A. (2015). Statistical considerations when analyzing biomarker data. Clin. Immunol., 161, No. 1, pp. 31-36. doi: https://doi.org/10.1016/j.clim.2015.05.019
8. Gola, D., Mahachie, J. J. M., van Steen, K. & Konig, I. R. (2016). A roadmap to multifactor dimensionality reduction methods. Brief. Bioinform., 17, No. 2, pp. 293-308. doi: https://doi.org/10.1093/bib/bbv038
9. Motsinger, A. A. & Ritchie, M. D. (2006). Multifactor dimensionality reduction: an analysis strategy for modelling and detecting gene-gene interactions in human genetics and pharmacogenomics studies. Hum. Genomics, 2, No. 5, pp. 318-328.
10. Pan, Q., Hu, T. & Moore, J. H. (2013). Epistasis, complexity, and multifactor dimensionality reduction. Methods Mol. Biol., 1019, pp. 465-77. doi: https://doi.org/10.1007/978-1-62703-447-0_22
11. 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: https://doi.org/10.7124/bc.00095E
12. 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-relatedgenes in prostate tumors in relation to the presence of the TMPRSS2/ERG fusion. Exp. Oncol., 40, No. 2, pp. 101-108. doi: https://doi.org/10.31768/2312-8852.2018.40(2):101-108
13. 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 cancer. Exp. Oncol., 40, No. 4, pp. 315-322. doi: https://doi.org/10.31768/2312-8852.2018.40(4):315-322
14. 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: https://doi.org/10.1006/meth.2001.1262
15. 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: https://doi.org/10.1111/j.2517-6161.1995.tb02031.x