Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma

Wang,Sen; Zheng,Xiangguang; Chen,Xinglu; Shi,Xiaojun; Chen,Sansan

doi:10.3892/ol.2020.11574

Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma

Authors:
- Sen Wang
- Xiangguang Zheng
- Xinglu Chen
- Xiaojun Shi
- Sansan Chen
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Affiliations: Department of Urology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong 510000, P.R. China, Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China
Published online on: April 24, 2020 https://doi.org/10.3892/ol.2020.11574
Pages: 308-316
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Immune/stromal-associated genes may be promising biomarkers for cancer diagnosis and the determination of clinical cancer treatment options. The aim of the present study was to identify prognostic stromal/immune-associated genes in renal cell carcinoma (RCC). RCC gene expression data (885 cases) were obtained from The Cancer Genome Atlas database. Immune/stromal scores were calculated by using the ESTIMATE package in R. Immune/stromal scores were significantly associated with Tumor-Node-Metastasis stage, clinical stage and overall survival rate (P<0.05). There were 419 differentially expressed genes (DEGs) based on immune scores and 738 DEGs based on stromal scores. Among these DEGs, 406 DEGs based on stromal scores and 252 DEGs based on immune scores were significantly associated with overall survival rate (P<0.05). The biological functions of these DEGs were primarily enriched in the ‘immune response’ and ‘regulation of cell migration and proliferation’. These DEGs were observed in a protein-protein interaction network. A LASSO Cox regression model was used to build a prognostic 6 gene-based classifier, including the IL21R, ATP6V1C2, GBP1, P2RY10, GBP4 and TNNC2 genes [area under the curve (AUC) =0.776]. The predictive model which combined this classifier with clinical prognostic factors had a high accuracy in predicting patient survival in RCC (combined AUC =0.899). Taken together, these results demonstrated that there are significant associations between immune/stromal scores and clinicopathological staging. A set of tumor microenvironment-associated genes that have powerful prognostic value in patients with RCC were identified in the present study.

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1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2019. CA Cancer J Clin. 69:7–34. 2019. View Article : Google Scholar : PubMed/NCBI
2	Zhang Q, Shi J, Yuan F, Wang H, Fu W, Pan J, Huang Y, Yu J, Yang J and Chen Z: Higher expression of XPF is a critical factor in intrinsic chemotherapy resistance of human renal cell carcinoma. Int J Cancer. 139:2827–2837. 2016. View Article : Google Scholar : PubMed/NCBI
3	Dos Santos M, Brachet PE, Chevreau C and Joly F: Impact of targeted therapies in metastatic renal cell carcinoma on patient-reported outcomes: Methodology of clinical trials and clinical benefit. Cancer Treat Rev. 53:53–60. 2017. View Article : Google Scholar : PubMed/NCBI
4	Chen L, Yuan L, Qian K, Qian G, Zhu Y, Wu CL, Dan HC, Xiao Y and Wang X: Identification of biomarkers associated with pathological stage and prognosis of clear cell renal cell carcinoma by co-expression network analysis. Front Physiol. 9:3992018. View Article : Google Scholar : PubMed/NCBI
5	Goto-Yamaguchi L, Yamamoto-Ibusuki M, Yamamoto Y, Fujiki Y, Tomiguchi M, Sueta A, Takeshita T and Iwase H: Therapeutic predictors of neoadjuvant endocrine therapy response in estrogen receptor-positive breast cancer with reference to optimal gene expression profiling. Breast Cancer Res Treat. 172:353–362. 2018. View Article : Google Scholar : PubMed/NCBI
6	Tse LA, Dai J, Chen M, Liu Y, Zhang H, Wong TW, Leung CC, Kromhout H, Meijer E, Liu S, et al: Prediction models and risk assessment for silicosis using a retrospective cohort study among workers exposed to silica in China. Sci Rep. 5:110592015. View Article : Google Scholar : PubMed/NCBI
7	Gatto F, Blum KA, Hosseini SS, Ghanaat M, Kashan M, Maccari F, Galeotti F, Hsieh JJ, Volpi N, Hakimi AA, et al: Plasma glycosaminoglycans as diagnostic and prognostic biomarkers in surgically treated eenal cell carcinoma. Eur Urol Oncol. 1:364–377. 2018. View Article : Google Scholar : PubMed/NCBI
8	Wang SC, Wang ST, Liu HT, Wang XY, Wu SC, Chen LC and Liu YW: Trichostatin A induces bladder cancer cell death via intrinsic apoptosis at the early phase and Sp1-survivin downregulation at the late phase of treatment. Oncol Rep. 38:1587–1596. 2017. View Article : Google Scholar : PubMed/NCBI
9	Sweis RF, Spranger S, Bao R, Paner GP, Stadler WM, Steinberg G and Gajewski TF: Molecular drivers of the non-T-cell-inflamed tumor microenvironment in urothelial bladder cancer. Cancer Immunol Res. 4:563–568. 2016. View Article : Google Scholar : PubMed/NCBI
10	Chen F, Zhang Y, Bossé D, Lalani AA, Hakimi AA, Hsieh JJ, Choueiri TK, Gibbons DL, Ittmann M and Creighton CJ: Pan-urologic cancer genomic subtypes that transcend tissue of origin. Nat Commun. 8:1992017. View Article : Google Scholar : PubMed/NCBI
11	Maniecki MB, Etzerodt A, Ulhøi BP, Steiniche T, Borre M, Dyrskjøt L, Orntoft TF, Moestrup SK and Møller HJ: Tumor-promoting macrophages induce the expression of the macrophage-specific receptor CD163 in malignant cells. Int J Cancer. 131:2320–2331. 2012. View Article : Google Scholar : PubMed/NCBI
12	Tsai S, McOlash L, Palen K, Johnson B, Duris C, Yang Q, Dwinell MB, Hunt B, Evans DB, Gershan J, et al: Development of primary human pancreatic cancer organoids, matched stromal and immune cells and 3D tumor microenvironment models. BMC Cancer. 18:3352018. View Article : Google Scholar : PubMed/NCBI
13	Garcia-Gomez A, Rodríguez-Ubreva J and Ballestar E: Epigenetic interplay between immune, stromal and cancer cells in the tumor microenvironment. Clin Immunol. 196:64–71. 2018. View Article : Google Scholar : PubMed/NCBI
14	Jia D, Li S, Li D, Xue H, Yang D and Liu Y: Mining TCGA database for genes of prognostic value in glioblastoma microenvironment. Aging (Albany NY). 10:592–605. 2018. View Article : Google Scholar : PubMed/NCBI
15	Gan TQ, Chen WJ, Qin H, Huang SN, Yang LH, Fang YY, Pan LJ, Li ZY and Chen G: Clinical value and prospective pathway signaling of microRNA-375 in lung adenocarcinoma: A study based on the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and bioinformatics analysis. Med Sci Monit. 23:2453–2464. 2017. 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	Nagy Á, Lánczky A, Menyhárt O and Győrffy B: Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep. 8:92272018. View Article : Google Scholar : PubMed/NCBI
18	R Core Team, . 2012.R: A language and environment for statistical computing. R Foundation for Statistical Computing. (Vienna, Austria). Available online at https://www.R-project.org/.
19	RStudio Team, . RStudio: Integrated Development for R. RStudio, Inc.; Boston, MA: 2015, http://www.rstudio.com/
20	Haque M, Sarmah R and Bhattacharyya DK: A common neighbor based technique to detect protein complexes in PPI networks. J Genet Eng Biotechnol. 16:227–238. 2018. View Article : Google Scholar : PubMed/NCBI
21	Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, et al: STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47(D1): D607–D613. 2019. View Article : Google Scholar : PubMed/NCBI
22	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
23	Patel H, Nilendu P, Jahagirdar D, Pal JK and Sharma NK: Modulating secreted components of tumor microenvironment: A masterstroke in tumor therapeutics. Cancer Biol Ther. 19:3–12. 2018. View Article : Google Scholar : PubMed/NCBI
24	Chevrier S, Levine JH, Zanotelli VRT, Silina K, Schulz D, Bacac M, Ries CH, Ailles L, Jewett MAS, Moch H, et al: An Immune Atlas of Clear Cell Renal Cell Carcinoma. Cell. 169:736–749.e18. 2017. View Article : Google Scholar : PubMed/NCBI
25	Herting CJ, Chen Z, Pitter KL, Szulzewsky F, Kaffes I, Kaluzova M, Park JC, Cimino PJ, Brennan C, Wang B, et al: Genetic driver mutations define the expression signature and microenvironmental composition of high-grade gliomas. Glia. 65:1914–1926. 2017. View Article : Google Scholar : PubMed/NCBI
26	Deng M, Brägelmann J, Schultze JL and Perner S: Web-TCGA: An online platform for integrated analysis of molecular cancer data sets. BMC Bioinformatics. 17:722016. View Article : Google Scholar : PubMed/NCBI
27	Andreeva E, Bobyleva P, Gornostaeva A and Buravkova L: Interaction of multipotent mesenchymal stromal and immune cells: Bidirectional effects. Cytotherapy. 19:1152–1166. 2017. View Article : Google Scholar : PubMed/NCBI
28	Schelker M, Feau S, Du J, Ranu N, Klipp E, MacBeath G, Schoeberl B and Raue A: Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nat Commun. 8:20322017. View Article : Google Scholar : PubMed/NCBI
29	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. 7:1392019. View Article : Google Scholar : PubMed/NCBI
30	Dai J, Lu Y, Roca H, Keller JM, Zhang J, McCauley LK and Keller ET: Immune mediators in the tumor microenvironment of prostate cancer. Chin J Cancer. 36:292017. View Article : Google Scholar : PubMed/NCBI
31	Kamal Y, Cheng C, Frost HR and Amos CI: Predictors of disease aggressiveness influence outcome from immunotherapy treatment in renal clear cell carcinoma. OncoImmunology. 8:e15001062018. View Article : Google Scholar : PubMed/NCBI
32	Faurobert E, Bouin AP and Albiges-Rizo C: Microenvironment, tumor cell plasticity, and cancer. Curr Opin Oncol. 27:64–70. 2015. View Article : Google Scholar : PubMed/NCBI
33	Wang L, Wen W, Yuan J, Helfand B, Li Y, Shi C, Tian F, Zheng J, Wang F, Chen L, et al: Immunotherapy for human renal cell carcinoma by adoptive transfer of autologous transforming growth factor beta-insensitive CD8+ T cells. Clin Cancer Res. 16:164–173. 2010. View Article : Google Scholar : PubMed/NCBI
34	Li M, Li M, Yin T, Shi H, Wen Y, Zhang B, Chen M, Xu G, Ren K and Wei Y: Targeting of cancer-associated fibroblasts enhances the efficacy of cancer chemotherapy by regulating the tumor microenvironment. Mol Med Rep. 13:2476–2484. 2016. View Article : Google Scholar : PubMed/NCBI
35	Raguraman R, Parameswaran S, Kanwar JR, Khetan V, Rishi P, Kanwar RK and Krishnakumar S: Evidence of tumour microenvironment and stromal cellular components in retinoblastoma. Ocul Oncol Pathol. 5:85–93. 2019. View Article : Google Scholar : PubMed/NCBI
36	Zhang J, Späth SS, Marjani SL, Zhang W and Pan X: Characterization of cancer genomic heterogeneity by next-generation sequencing advances precision medicine in cancer treatment. Precis Clin Med. 1:29–48. 2018. View Article : Google Scholar : PubMed/NCBI