Molecular profiling of transcription factors pinpoints MYC‑estrogen related receptor α‑regulatory factor X5 panel for characterizing the immune microenvironment and predicting the efficacy of immune checkpoint inhibitors in renal cell carcinoma

Liu,Fei; Zhang,Hongxia; Xue,Lihua; Yang,Qiankun; Yan,Wanchun

doi:10.3892/ol.2019.10523

Molecular profiling of transcription factors pinpoints MYC‑estrogen related receptor α‑regulatory factor X5 panel for characterizing the immune microenvironment and predicting the efficacy of immune checkpoint inhibitors in renal cell carcinoma

Authors:
- Fei Liu
- Hongxia Zhang
- Lihua Xue
- Qiankun Yang
- Wanchun Yan
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Affiliations: Department of Nephrology, The 940th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army, Lanzhou, Gansu 730000, P.R. China, Department of Emergency Medicine, The First Hospital of the Chinese People's Liberation Army, Lanzhou, Gansu 730000, P.R. China, Department of Obstetrics and Gynecology, The Family Planning Service Center for Maternal and Child Health in Zhouqu County, Lanzhou, Gansu 730000, P.R. China, Department of Bone and Soft Tissue, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning 110042, P.R. China, Department of Geriatrics, The First Hospital of the Chinese People's Liberation Army, Lanzhou, Gansu 730000, P.R. China
Published online on: June 24, 2019 https://doi.org/10.3892/ol.2019.10523
Pages: 1895-1903
Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Transcription factors (TFs) play key roles in biological processes, and previous studies revealed that they can control oncogenic processes. However, the functional impact of TFs on the prognosis of patients with cancer has not been extensively elucidated. In the context of The Cancer Genome Atlas, few studies have focused on the roles of TFs in tumorigenesis. In the present study, a TF‑based robust MYC‑estrogen related receptor α‑regulatory factor X5 (MYC‑ESRRA‑RFX5) signature was developed for predicting the survival of patients with renal cell carcinoma. Functional enrichment analysis of this signature revealed that it was associated with the immune system of these patients. Further analysis demonstrated that this panel could characterize the immune microenvironment and potentially predicts the effectiveness of immune checkpoint inhibitors. Therefore, the present study recommends future exploration on TF‑based biomarkers for their potential as prognostic predictors. Overall, the highlights of this study are: i) This novel study pinpoints a TF panel for the robust prediction of renal cell carcinoma prognosis, and ii) the MYC‑ESRRA‑RFX5 panel is proposed as a signature for characterizing the immune microenvironment, and to potentially predict the effectiveness of immune checkpoint inhibitors.

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1	Iwafuchi-Doi M and Zaret KS: Pioneer transcription factors in cell reprogramming. Genes Dev. 28:2679–2692. 2014. View Article : Google Scholar : PubMed/NCBI
2	Filtz TM, Vogel WK and Leid M: Regulation of transcription factor activity by interconnected post-translational modifications. Trends Pharmacol Sci. 35:76–85. 2014. View Article : Google Scholar : PubMed/NCBI
3	Johnston SJ and Carroll JS: Transcription factors and chromatin proteins as therapeutic targets in cancer. Biochim Biophys Acta. 1855:183–192. 2015.PubMed/NCBI
4	Yeh JE, Toniolo PA and Frank DA: Targeting transcription factors: Promising new strategies for cancer therapy. Curr Opin Oncol. 25:652–658. 2013. View Article : Google Scholar : PubMed/NCBI
5	Chen H, Li C, Peng X, Zhou Z, Weinstein JN; Cancer Genome Atlas Research Network, ; Liang H: A pan-cancer analysis of enhancer expression in nearly 9,000 patient samples. Cell. 173:386–399.e12. 2018. View Article : Google Scholar : PubMed/NCBI
6	Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, Dimitriadoy S, Liu DL, Kantheti HS, Saghafinia S, et al: Oncogenic signaling pathways in The Cancer Genome Atlas. Cell. 173:321–337.e10. 2018. View Article : Google Scholar : PubMed/NCBI
7	Chiu HS, Somvanshi S, Patel E, Chen TW, Singh VP, Zorman B, Patil SL, Pan Y, Chatterjee SS; Cancer Genome Atlas Research Network, ; Sood AK, Gunaratne PH and Sumazin P: Pan-cancer analysis of lncRNA regulation supports their targeting of cancer genes in each tumor context. Cell Rep. 23:297–312.e12. 2018. View Article : Google Scholar : PubMed/NCBI
8	Knaack SA, Siahpirani AF and Roy S: A pan-cancer modular regulatory network analysis to identify common and cancer-specific network components. Cancer Inform. 13 (Suppl 5):S69–S84. 2014.
9	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2015. CA Cancer J Clin. 65:5–29. 2015. View Article : Google Scholar : PubMed/NCBI
10	Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI, Ostrand-Rosenberg S, Hedrick CC, et al: Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 24:541–550. 2018. View Article : Google Scholar : PubMed/NCBI
11	Massari F, Santoni M, Ciccarese C, Santini D, Alfieri S, Martignoni G, Brunelli M, Piva F, Berardi R, Montironi R, et al: PD-1 blockade therapy in renal cell carcinoma: Current studies and future promises. Cancer Treat Rev. 41:114–121. 2015. View Article : Google Scholar : PubMed/NCBI
12	An N, Yu Z and Yang X: Expression differentiation is not helpful in identifying prognostic genes based on TCGA datasets. Mol Ther Nucleic Acids. 11:292–299. 2018. View Article : Google Scholar : PubMed/NCBI
13	Tang H, Wang S, Xiao G, Schiller J, Papadimitrakopoulou V, Minna J, WistubaI I and Xie Y: Comprehensive evaluation of published gene expression prognostic signatures for biomarker-based lung cancer clinical studies. Ann Oncol. 28:733–740. 2017. View Article : Google Scholar : PubMed/NCBI
14	Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI
15	Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA, et al: The immune landscape of cancer. Immunity. 48:812–830.e14. 2018. View Article : Google Scholar : PubMed/NCBI
16	Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird PW, Levine DA, et al: Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 4:26122013. View Article : Google Scholar : PubMed/NCBI
17	Liu Z, Dai J and Shen H: Systematic analysis reveals long noncoding RNAs regulating neighboring transcription factors in human cancers. Biochim Biophys Acta. 1864:2785–2792. 2018. View Article : Google Scholar
18	Jiang P, Freedman ML, Liu JS and Liu XS: Inference of transcriptional regulation in cancers. Proc Natl Acad Sci USA. 112:7731–7736. 2015. View Article : Google Scholar : PubMed/NCBI
19	Issa AM, Chaudhari VS and Marchant GE: The value of multigene predictors of clinical outcome in breast cancer: An analysis of the evidence. Expert Rev Mol Diagn. 15:277–286. 2015. View Article : Google Scholar : PubMed/NCBI
20	Lokeshwar SD, Talukder A, Yates TJ, Hennig MJ, Garcia-Roig M, Lahorewala SS, Mullani NN, Klaassen Z, Kava BR, Manoharan M, et al: Molecular characterization of renal cell carcinoma: A potential three-MicroRNA prognostic signature. Cancer Epidemiol Biomarkers Prev. 27:464–472. 2018. View Article : Google Scholar : PubMed/NCBI
21	Marziali G, Buccarelli M, Giuliani A, Ilari R, Grande S, Palma A, D'Alessandris QG, Martini M, Biffoni M, Pallini R and Ricci-Vitiani L: A three-microRNA signature identifies two subtypes of glioblastoma patients with different clinical outcomes. Mol Oncol. 11:1115–1129. 2017. View Article : Google Scholar : PubMed/NCBI
22	Mei S, Meyer CA, Zheng R, Qin Q, Wu Q, Jiang P, Li B, Shi X, Wang B, Fan J, et al: Cistrome cancer: A web resource for integrative gene regulation modeling in cancer. Cancer Res. 77:e19–e22. 2017. View Article : Google Scholar : PubMed/NCBI
23	Kim HD, Yu SJ, Kim HS, Kim YJ, Choe JM, Park YG, Kim J and Sohn J: Interleukin-4 induces senescence in human renal carcinoma cell lines through STAT6 and p38 MAPK. J Biol Chem. 288:28743–28754. 2013. View Article : Google Scholar : PubMed/NCBI
24	Cassiday LA: Having it both ways: Transcription factors that bind DNA and RNA. Nucleic Acids Research. 30:4118–4126. 2002. View Article : Google Scholar : PubMed/NCBI
25	Cyriac G and Gandhi L: Emerging biomarkers for immune checkpoint inhibition in lung cancer. Semin Cancer Biol. 52:269–277. 2018. View Article : Google Scholar : PubMed/NCBI
26	Schaub FX, Dhankani V, Berger AC, Trivedi M, Richardson AB, Shaw R, Zhao W, Zhang X, Ventura A, Liu Y, et al: Pan-cancer alterations of the MYC oncogene and its proximal network across the Cancer Genome Atlas. Cell Syst. 6:282–300.e2. 2018. View Article : Google Scholar : PubMed/NCBI
27	Rau A, Flister M, Rui H and Auer PL: Exploring drivers of gene expression in the Cancer Genome Atlas. Bioinformatics. 35:62–68. 2019.PubMed/NCBI
28	Zhu B, Tang L, Chen S, Yin C, Peng S, Li X, Liu T, Liu W, Han C, Stawski L, et al: Targeting the upstream transcriptional activator of PD-L1 as an alternative strategy in melanoma therapy. Oncogene. 37:4941–4954. 2018. View Article : Google Scholar : PubMed/NCBI
29	Blum A, Wang P and Zenklusen JC: SnapShot: TCGA-analyzed tumors. Cell. 173:5302018. View Article : Google Scholar : PubMed/NCBI
30	Kim EY, Kim A, Kim SK and Chang YS: MYC expression correlates with PD-L1 expression in non-small cell lung cancer. Lung Cancer. 110:63–67. 2017. View Article : Google Scholar : PubMed/NCBI
31	Datta S, Le-Rademacher J and Datta S: Predicting patient survival from microarray data by accelerated failure time modeling using partial least squares and LASSO. Biometrics. 63:259–271. 2007. View Article : Google Scholar : PubMed/NCBI