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Published in: Cancer Cell International 1/2021

Open Access 01-12-2021 | Kidney Cancer | Primary research

GNRH1 and LTB4R might be novel immune-related prognostic biomarkers in clear cell renal cell carcinoma (ccRCC)

Authors: Hua-Hui Wu, Xin Yan, Zhao Chen, Guo-Wei Du, Xiao-Jie Bai, Kurerban Tuoheti, Tong-Zu Liu

Published in: Cancer Cell International | Issue 1/2021

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Abstract

Background

Clear cell renal cell carcinoma (ccRCC) occupied most of renal cell carcinoma (RCC), which associated with poor prognosis. The purpose of this study is to screen novel and prognostic biomarkers for patients with ccRCC.

Methods and results

Firstly, Gene Expression Omnibus database was used to collect microarray data for weighted gene co-expression network construction. Gene modules related to prognosis which interest us most were picked out. 90 hub genes were further chosen in the key modules, two of which including gonadotropin releasing hormone 1 (GNRH1) and leukotriene B4 receptor (LTB4R) were screened and validated as immune-related prognostic biomarkers. Based on several public databases and ccRCC tissues collected by ourselves, we performed survival analysis, spearman correlation analysis, receiver operating characteristic (ROC) analysis, quantitative real-time PCR (qRT-PCR), western blotting, immunofluorescence (IF) and immunohistochemistry (IHC) staining for the validation of immune-related prognostic biomarkers. We further explored the relationship between immune-related prognostic biomarker expressions and immunocytes. Finally, gene set enrichment analysis (GSEA) demonstrated that the two immune-related prognostic biomarkers were significantly correlated with cell cycle.

Conclusions

Generally speaking, the present study has identified two novel prognostic biomarkers for patients with ccRCC, which showed strong correlation with prognosis of patients with ccRCC, could further be used as potential prognostic biomarkers in ccRCC.
Appendix
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Literature
1.
go back to reference Wettersten HI, Aboud OA, Lara PJ, Weiss RH. Metabolic reprogramming in clear cell renal cell carcinoma. Nat Rev Nephrol. 2017;13(7):410–9.CrossRef Wettersten HI, Aboud OA, Lara PJ, Weiss RH. Metabolic reprogramming in clear cell renal cell carcinoma. Nat Rev Nephrol. 2017;13(7):410–9.CrossRef
2.
go back to reference Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33.CrossRef Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33.CrossRef
3.
go back to reference Cerbone L, Cattrini C, Vallome G, Latocca MM, Boccardo F, Zanardi E. Combination therapy in metastatic renal cell carcinoma: back to the future? Semin Oncol. 2020;47(6):361–6.CrossRef Cerbone L, Cattrini C, Vallome G, Latocca MM, Boccardo F, Zanardi E. Combination therapy in metastatic renal cell carcinoma: back to the future? Semin Oncol. 2020;47(6):361–6.CrossRef
4.
go back to reference Braun DA, Bakouny Z, Hirsch L, Flippot R, Van Allen EM, Wu CJ, Choueiri TK. Beyond conventional immune-checkpoint inhibition—novel immunotherapies for renal cell carcinoma. Nat Rev Clin Oncol. 2021;18:199–214.CrossRef Braun DA, Bakouny Z, Hirsch L, Flippot R, Van Allen EM, Wu CJ, Choueiri TK. Beyond conventional immune-checkpoint inhibition—novel immunotherapies for renal cell carcinoma. Nat Rev Clin Oncol. 2021;18:199–214.CrossRef
5.
go back to reference Marabelle A, Tselikas L, de Baere T, Houot R. Intratumoral immunotherapy: using the tumor as the remedy. Ann Oncol. 2017;28(suppl_12):i33–43.CrossRef Marabelle A, Tselikas L, de Baere T, Houot R. Intratumoral immunotherapy: using the tumor as the remedy. Ann Oncol. 2017;28(suppl_12):i33–43.CrossRef
6.
go back to reference Sprooten J, Ceusters J, Coosemans A, Agostinis P, De Vleeschouwer S, Zitvogel L, Kroemer G, Galluzzi L, Garg AD. Trial watch: dendritic cell vaccination for cancer immunotherapy. Oncoimmunology. 2019;8(11):e1638212.CrossRef Sprooten J, Ceusters J, Coosemans A, Agostinis P, De Vleeschouwer S, Zitvogel L, Kroemer G, Galluzzi L, Garg AD. Trial watch: dendritic cell vaccination for cancer immunotherapy. Oncoimmunology. 2019;8(11):e1638212.CrossRef
7.
go back to reference Xie F, Xu M, Lu J, Mao L, Wang S. The role of exosomal PD-L1 in tumor progression and immunotherapy. Mol Cancer. 2019;18(1):146.CrossRef Xie F, Xu M, Lu J, Mao L, Wang S. The role of exosomal PD-L1 in tumor progression and immunotherapy. Mol Cancer. 2019;18(1):146.CrossRef
8.
go back to reference Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.CrossRef Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.CrossRef
9.
go back to reference Edeline J, Mottier S, Vigneau C, Jouan F, Perrin C, Zerrouki S, Fergelot P, Patard JJ, Rioux-Leclercq N. Description of 2 angiogenic phenotypes in clear cell renal cell carcinoma. Hum Pathol. 2012;43(11):1982–90.CrossRef Edeline J, Mottier S, Vigneau C, Jouan F, Perrin C, Zerrouki S, Fergelot P, Patard JJ, Rioux-Leclercq N. Description of 2 angiogenic phenotypes in clear cell renal cell carcinoma. Hum Pathol. 2012;43(11):1982–90.CrossRef
10.
go back to reference Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.CrossRef Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.CrossRef
11.
go back to reference Gautier L, Cope LBolstad BM, Irizarry RA. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004;20(3):307–15.CrossRef Gautier L, Cope LBolstad BM, Irizarry RA. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004;20(3):307–15.CrossRef
12.
go back to reference Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.CrossRef Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.CrossRef
13.
go back to reference Ivliev AE, Pa TH, Sergeeva MG. Coexpression network analysis identifies transcriptional modules related to proastrocytic differentiation and sprouty signaling in glioma. Cancer Res. 2010;70(24):10060.CrossRef Ivliev AE, Pa TH, Sergeeva MG. Coexpression network analysis identifies transcriptional modules related to proastrocytic differentiation and sprouty signaling in glioma. Cancer Res. 2010;70(24):10060.CrossRef
14.
go back to reference Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284–7.CrossRef Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284–7.CrossRef
15.
go back to reference Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98–102.CrossRef Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98–102.CrossRef
16.
go back to reference Hothorn T, Lausen B. On the exact distribution of maximally selected rank statistics. Comput Stat Data Anal. 2002;43:121–37.CrossRef Hothorn T, Lausen B. On the exact distribution of maximally selected rank statistics. Comput Stat Data Anal. 2002;43:121–37.CrossRef
17.
go back to reference Therneau TM. Survival: survival analysis. Technometrics. 2015;46(1):111–2. Therneau TM. Survival: survival analysis. Technometrics. 2015;46(1):111–2.
18.
go back to reference Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12:77.CrossRef Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12:77.CrossRef
19.
go back to reference Patil I. ggstatsplot: ‘ggplot2’ based plots with statistical details. 2018. Patil I. ggstatsplot: ‘ggplot2’ based plots with statistical details. 2018.
20.
go back to reference Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia. 2004;6(1):1–6.CrossRef Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia. 2004;6(1):1–6.CrossRef
21.
go back to reference Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419.CrossRef Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419.CrossRef
22.
go back to reference Uhlen M, Zhang C, Lee S, Sjöstedt E, Fagerberg L, Bidkhori G, Benfeitas R, Arif M, Liu Z, Edfors F, et al. A pathology atlas of the human cancer transcriptome. Science. 2017;357(6352):eaan2507.CrossRef Uhlen M, Zhang C, Lee S, Sjöstedt E, Fagerberg L, Bidkhori G, Benfeitas R, Arif M, Liu Z, Edfors F, et al. A pathology atlas of the human cancer transcriptome. Science. 2017;357(6352):eaan2507.CrossRef
23.
go back to reference Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait BH, Alm T, Asplund A, Björk L, Breckels LM, et al. A subcellular map of the human proteome. Science. 2017;356(6340):eaal3321.CrossRef Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait BH, Alm T, Asplund A, Björk L, Breckels LM, et al. A subcellular map of the human proteome. Science. 2017;356(6340):eaal3321.CrossRef
24.
go back to reference Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50.CrossRef Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50.CrossRef
25.
go back to reference Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18(1):248–62.CrossRef Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18(1):248–62.CrossRef
26.
go back to reference Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 2013;14:7.CrossRef Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 2013;14:7.CrossRef
27.
go back to reference 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. 2018;24(5):541–50.CrossRef 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. 2018;24(5):541–50.CrossRef
28.
go back to reference Ghatalia P, Gordetsky J, Kuo F, Dulaimi E, Cai KQ, Devarajan K, Bae S, Naik G, Chan TA, Uzzo R, et al. Prognostic impact of immune gene expression signature and tumor infiltrating immune cells in localized clear cell renal cell carcinoma. J Immunother Cancer. 2019;7(1):139.CrossRef Ghatalia P, Gordetsky J, Kuo F, Dulaimi E, Cai KQ, Devarajan K, Bae S, Naik G, Chan TA, Uzzo R, et al. Prognostic impact of immune gene expression signature and tumor infiltrating immune cells in localized clear cell renal cell carcinoma. J Immunother Cancer. 2019;7(1):139.CrossRef
29.
go back to reference Xu WH, Xu Y, Wang J, Wan FN, Wang HK, Cao DL, Shi GH, Qu YY, Zhang HL, Ye DW. Prognostic value and immune infiltration of novel signatures in clear cell renal cell carcinoma microenvironment. Aging. 2019;11(17):6999–7020.CrossRef Xu WH, Xu Y, Wang J, Wan FN, Wang HK, Cao DL, Shi GH, Qu YY, Zhang HL, Ye DW. Prognostic value and immune infiltration of novel signatures in clear cell renal cell carcinoma microenvironment. Aging. 2019;11(17):6999–7020.CrossRef
30.
go back to reference Atkins MB, Tannir NM. Current and emerging therapies for first-line treatment of metastatic clear cell renal cell carcinoma. Cancer Treat Rev. 2018;70:127–37.CrossRef Atkins MB, Tannir NM. Current and emerging therapies for first-line treatment of metastatic clear cell renal cell carcinoma. Cancer Treat Rev. 2018;70:127–37.CrossRef
31.
go back to reference Jala VR, Bodduluri SR, Satpathy SR, Chheda Z, Sharma RK, Haribabu B. The yin and yang of leukotriene B(4) mediated inflammation in cancer. Semin Immunol. 2017;33:58–64.CrossRef Jala VR, Bodduluri SR, Satpathy SR, Chheda Z, Sharma RK, Haribabu B. The yin and yang of leukotriene B(4) mediated inflammation in cancer. Semin Immunol. 2017;33:58–64.CrossRef
32.
go back to reference Seo JM, Park S, Kim JH. Leukotriene B4 receptor-2 promotes invasiveness and metastasis of ovarian cancer cells through signal transducer and activator of transcription 3 (STAT3)-dependent up-regulation of matrix metalloproteinase 2. J Biol Chem. 2012;287(17):13840–9.CrossRef Seo JM, Park S, Kim JH. Leukotriene B4 receptor-2 promotes invasiveness and metastasis of ovarian cancer cells through signal transducer and activator of transcription 3 (STAT3)-dependent up-regulation of matrix metalloproteinase 2. J Biol Chem. 2012;287(17):13840–9.CrossRef
33.
go back to reference Kim H, Choi JA, Park GS, Kim JH. BLT2 up-regulates interleukin-8 production and promotes the invasiveness of breast cancer cells. PLoS ONE. 2012;7(11):e49186.CrossRef Kim H, Choi JA, Park GS, Kim JH. BLT2 up-regulates interleukin-8 production and promotes the invasiveness of breast cancer cells. PLoS ONE. 2012;7(11):e49186.CrossRef
34.
go back to reference Andrusiewicz M, Szczerba A, Wołuń-Cholewa M, Warchoł W, Nowak-Markwitz E, Gąsiorowska E, Adamska K, Jankowska A. CGB and GNRH1 expression analysis as a method of tumor cells metastatic spread detection in patients with gynecological malignances. J Transl Med. 2011;9:130.CrossRef Andrusiewicz M, Szczerba A, Wołuń-Cholewa M, Warchoł W, Nowak-Markwitz E, Gąsiorowska E, Adamska K, Jankowska A. CGB and GNRH1 expression analysis as a method of tumor cells metastatic spread detection in patients with gynecological malignances. J Transl Med. 2011;9:130.CrossRef
Metadata
Title
GNRH1 and LTB4R might be novel immune-related prognostic biomarkers in clear cell renal cell carcinoma (ccRCC)
Authors
Hua-Hui Wu
Xin Yan
Zhao Chen
Guo-Wei Du
Xiao-Jie Bai
Kurerban Tuoheti
Tong-Zu Liu
Publication date
01-12-2021
Publisher
BioMed Central
Published in
Cancer Cell International / Issue 1/2021
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-021-02052-1

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