pubmed.ncbi.nlm.nih.gov

A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs - PubMed

  • ️Fri Jan 01 2021

A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs

Ruixiang Luo et al. Evid Based Complement Alternat Med. 2021.

Abstract

Background: Prostate cancer (PC) is one of the most critical cancers affecting men's health worldwide. The development of many cancers involves dysregulation or mutations in key transcription factors. This study established a transcription factor-based risk model to predict the prognosis of PC and potential therapeutic drugs.

Materials and methods: In this study, RNA-sequencing data were downloaded and analyzed using The Cancer Genome Atlas dataset. A total of 145 genes related to the overall survival rate of PC patients were screened using the univariate Cox analysis. The Kdmist clustering method was used to classify prostate adenocarcinoma (PRAD), thereby determining the cluster related to the transcription factors. The support vector machine-recursive feature elimination method was used to identify genes related to the types of transcription factors and the key genes specifically upregulated or downregulated were screened. These genes were further analyzed using Lasso to establish a model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the functional analysis. The TIMER algorithm was used to quantify the abundance of immune cells in PRAD samples. The chemotherapy response of each GBM patient was predicted based on the public pharmacogenomic database, Genomics of Drug Sensitivity in Cancer (GDSC, http://www.cancerrxgene.org). The R package "pRRophetic" was applied to drug sensitivity (IC50) value prediction.

Results: We screened 10 genes related to prognosis, including eight low-risk genes and two high-risk genes. The receiver operating characteristic (ROC) curve was 0.946. Patients in the high-risk score group had a poorer prognosis than those in the low-risk score group. The average area under the curve value of the model at different times was higher than 0.8. The risk score was an independent prognostic factor. Compared with the low-risk score group, early growth response-1 (EGR1), CACNA2D1, AC005831.1, SLC52A3, TMEM79, IL20RA, CRACR2A, and FAM189A2 expressions in the high-risk score group were decreased, while AC012181.1 and TRAPPC8 expressions were increased. GO and KEGG analyses showed that prognosis was related to various cancer signaling pathways. The proportion of B_cell, T_cell_CD4, and macrophages in the high-risk score group was significantly higher than that in the low-risk score group. A total of 25 classic immune checkpoint genes were screened out to express abnormally high-risk scores, and there were significant differences. Thirty mutant genes were identified; in the high- and low-risk score groups, SPOP, TP53, and TTN had the highest mutation frequency, and their mutations were mainly missense mutations. A total of 36 potential drug candidates for the treatment of PC were screened and identified.

Conclusions: Ten genes of both high-and low-risk scores were associated with the prognosis of PC. PC prognosis may be related to immune disorders. SPOP, TP53, and TTN may be potential targets for the prognosis of PC.

Copyright © 2021 Ruixiang Luo et al.

PubMed Disclaimer

Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1

PRAD clustering and analysis. (a) Kdmist clustering. (b) Survival analysis. p < 0.05 indicated that there was a significant difference in the survival rate between the two groups. (c) Volcano map. (d) Functional analysis. PRAD, prostate adenocarcinoma.

Figure 2
Figure 2

Screening of genes closely related to the prognosis of PC. (a) A different analysis of the two types of transcription factors. (b) ROC curve. (c) and (d) Lasso analysis. (e) Kaplan-Meier analysis. p < 0.05 indicated that there was a significant difference in survival rate between the two groups. (f) AUC value. PC, prostate cancer; ROC, receiver operating characteristic; AUC, area under the curve.

Figure 3
Figure 3

Correlation analysis of genes closely related to the prognosis of PC with risk score model. (a) Heat map. (b) Univariate factor analysis. (c) Analysis of related gene enrichment function.

Figure 4
Figure 4

The correlation between risk score model and immune cells. (a) Heat map. (b) The proportion of cells in each sample. (c) Survival analysis. p < 0.05 indicated that there was a significant difference in the survival rate between the two groups. (d) The relative percentage of CR/PR and SD/PD in the high- and low-risk score. (e) Analysis of immune checkpoints at different levels. ∗p < 0.05. ∗∗p < 0.01. ∗∗∗p < 0.001. ∗∗∗∗p < 0.0001. CR, complete response; PR, partial response; PD; SD.

Figure 5
Figure 5

The waterfall chart showed the top 30 mutant genes. (a) Low-risk score group. (b) High-risk score group.

Figure 6
Figure 6

Drug candidates for the treatment of PC. (a) CTRP. (b) PRISM. ∗p < 0.05. ∗∗p < 0.01. ∗∗∗p < 0.001. ∗∗∗∗p < 0.0001. PC, prostate cancer.

Similar articles

Cited by

References

    1. Lorenc T., Klimczyk K., Michalczewska I., Słomka M., Kubiak-Tomaszewska G., Olejarz W. Exosomes in prostate cancer diagnosis, prognosis and therapy. International Journal of Molecular Sciences . 2020;21(6) doi: 10.3390/ijms21062118. - DOI - PMC - PubMed
    1. Evans A. J. Treatment effects in prostate cancer. Modern Pathology: An Official Journal of the United States and Canadian Academy of Pathology, Inc . 2018;31(S1):S110–S121. doi: 10.1038/modpathol.2017.158. - DOI - PubMed
    1. Saad F., Shore N., Zhang T., Sharma S., Cho H. K., Jacobs I. A. Emerging therapeutic targets for patients with advanced prostate cancer. Cancer Treatment Reviews . 2019;76:1–9. doi: 10.1016/j.ctrv.2019.03.002. - DOI - PubMed
    1. Lambert S. A., Abraham G., Inouye M. Towards clinical utility of polygenic risk scores. Human Molecular Genetics . 2019;28(R2):R133–r142. doi: 10.1093/hmg/ddz187. - DOI - PubMed
    1. Osses D. F., Roobol M. J., Schoots I. G. Prediction medicine: biomarkers, risk calculators and magnetic resonance imaging as risk stratification tools in prostate cancer diagnosis. International Journal of Molecular Sciences . 2019;20(7) doi: 10.3390/ijms20071637. - DOI - PMC - PubMed