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Open Access 01-12-2021 | Colorectal Cancer | Research

Identification of prognostic immune-related gene signature associated with tumor microenvironment of colorectal cancer

Authors: Yuanyuan Wang, Wei Li, Xiaojing Jin, Xia Jiang, Shang Guo, Fei Xu, Xingkai Su, Guiqi Wang, Zengren Zhao, Xiaosong Gu

Published in: BMC Cancer | Issue 1/2021

Abstract

Background

The tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC).

Methods

Transcriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database.

Results

Gene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C-index of 0.769 (95%CI, 0.717–0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen-IRGs signature. The AUC for 1-, 3-, and 5-year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates.

Conclusions

In this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.

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Schreuders EH, Ruco A, Rabeneck L, Schoen RE, Sung JJ, Young GP, et al. Colorectal cancer screening: a global overview of existing programmes. Gut. 2015;64(10):1637–49. https://doi.org/10.1136/gutjnl-2014-309086.CrossRefPubMed

Bahrami A, Shahidsales S, Khazaei M, Ghayour-Mobarhan M, Maftouh M, Hassanian SM, et al. C-met as a potential target for the treatment of gastrointestinal cancer: current status and future perspectives. J Cell Physiol. 2017;232(10):2657–73. https://doi.org/10.1002/jcp.25794.CrossRefPubMed

Gholamin S, Fiuji H, Maftouh M, Mirhafez R, Shandiz FH, Avan A. Targeting c-MET/HGF signaling pathway in upper gastrointestinal cancers: rationale and progress. Curr Drug Targets. 2014;15(14):1302–11. https://doi.org/10.2174/1389450115666141107105456.CrossRefPubMed

Bahrami A, Khazaei M, Hasanzadeh M, ShahidSales S, Joudi Mashhad M, Farazestanian M, et al. Therapeutic potential of targeting PI3K/AKT pathway in treatment of colorectal Cancer: rational and Progress. J Cell Biochem. 2018;119(3):2460–9. https://doi.org/10.1002/jcb.25950.CrossRefPubMed

Bahrami A, Hesari A, Khazaei M, Hassanian SM, Ferns GA, Avan A. The therapeutic potential of targeting the BRAF mutation in patients with colorectal cancer. J Cell Physiol. 2018;233(3):2162–9. https://doi.org/10.1002/jcp.25952.CrossRefPubMed

Bahrami A, Amerizadeh F, ShahidSales S, Khazaei M, Ghayour-Mobarhan M, Sadeghnia HR, et al. Therapeutic potential of targeting Wnt/beta-catenin pathway in treatment of colorectal Cancer: rational and Progress. J Cell Biochem. 2017;118(8):1979–83. https://doi.org/10.1002/jcb.25903.CrossRefPubMed

Petersen VC, Baxter KJ, Love SB, Shepherd NA. Identification of objective pathological prognostic determinants and models of prognosis in Dukes' B colon cancer. Gut. 2002;51(1):65–9. https://doi.org/10.1136/gut.51.1.65.CrossRefPubMedPubMedCentral

Roxburgh CS, McMillan DC, Richards CH, Atwan M, Anderson JH, Harvey T, et al. The clinical utility of the combination of T stage and venous invasion to predict survival in patients undergoing surgery for colorectal cancer. Ann Surg. 2014;259(6):1156–65. https://doi.org/10.1097/SLA.0000000000000229.CrossRefPubMed

Roxburgh CS, McMillan DC. The role of the in situ local inflammatory response in predicting recurrence and survival in patients with primary operable colorectal cancer. Cancer Treat Rev. 2012;38(5):451–66. https://doi.org/10.1016/j.ctrv.2011.09.001.CrossRefPubMed

10.

Freeman MR, Li Q, Chung LW. Can stroma reaction predict cancer lethality? Clinical Cancer Res. 2013;19(18):4905–7. https://doi.org/10.1158/1078-0432.CCR-13-1694.CrossRef

11.

Kalluri R, Weinberg RA. The basics of epithelial-mesenchymal transition. J Clin Invest. 2009;119(6):1420–8. https://doi.org/10.1172/JCI39104.CrossRefPubMedPubMedCentral

12.

Senbabaoglu Y, Gejman RS, Winer AG, Liu M, Van Allen EM, de Velasco G, et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 2016;17(1):231. https://doi.org/10.1186/s13059-016-1092-z.CrossRefPubMedPubMedCentral

13.

Winslow S, Lindquist KE, Edsjo A, Larsson C. The expression pattern of matrix-producing tumor stroma is of prognostic importance in breast cancer. BMC Cancer. 2016;16(1):841. https://doi.org/10.1186/s12885-016-2864-2.CrossRefPubMedPubMedCentral

14.

Peddareddigari VG, Wang D, Dubois RN. The tumor microenvironment in colorectal carcinogenesis. Cancer Microenviron. 2010;3(1):149–66. https://doi.org/10.1007/s12307-010-0038-3.CrossRefPubMedPubMedCentral

15.

Li B, Severson E, Pignon JC, Zhao H, Li T, Novak J, et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 2016;17(1):174. https://doi.org/10.1186/s13059-016-1028-7.CrossRefPubMedPubMedCentral

16.

Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7. https://doi.org/10.1038/nmeth.3337.CrossRefPubMedPubMedCentral

17.

Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4(1):2612. https://doi.org/10.1038/ncomms3612.CrossRefPubMed

18.

Shah N, Wang P, Wongvipat J, Karthaus WR, Abida W, Armenia J, et al. Regulation of the glucocorticoid receptor via a BET-dependent enhancer drives antiandrogen resistance in prostate cancer. eLife. 2017;11(6):e27861. https://doi.org/10.7554/eLife.27861.

19.

Jia D, Li S, Li D, Xue H, Yang D, Liu Y. Mining TCGA database for genes of prognostic value in glioblastoma microenvironment. Aging (Albany NY). 2018;10(4):592–605. https://doi.org/10.18632/aging.101415.CrossRef

20.

Yang S, Liu T, Nan H, Wang Y, Chen H, Zhang X, et al. Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of cutaneous melanoma. J Cell Physiol. 2020;235(2):1025-35. https://doi.org/10.1002/jcp.29018.

21.

Staub E, Groene J, Heinze M, Mennerich D, Roepcke S, Klaman I, et al. An expression module of WIPF1-coexpressed genes identifies patients with favorable prognosis in three tumor types. J Mole Med. 2009;87(6):633–44.CrossRef

22.

Marisa L, de Reynies A, Duval A, Selves J, Gaub MP, Vescovo L, et al. Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value. PLoS Med. 2013;10(5):e1001453. https://doi.org/10.1371/journal.pmed.1001453.CrossRefPubMedPubMedCentral

23.

Sheffer M, Bacolod MD, Zuk O, Giardina SF, Pincas H, Barany F, et al. Association of survival and disease progression with chromosomal instability: a genomic exploration of colorectal cancer. Proc Natl Acad Sci U S A. 2009;106(17):7131–6. https://doi.org/10.1073/pnas.0902232106.CrossRefPubMedPubMedCentral

24.

Del Rio M, Mollevi C, Bibeau F, Vie N, Selves J, Emile JF, et al. Molecular subtypes of metastatic colorectal cancer are associated with patient response to irinotecan-based therapies. Eur J Cancer. 2017;76:68–75.CrossRefPubMed

25.

Allen WL, Dunne PD, McDade S, Scanlon E, Loughrey M, Coleman H, et al. Transcriptional subtyping and CD8 immunohistochemistry identifies poor prognosis stage II/III colorectal cancer patients who benefit from adjuvant chemotherapy. JCO Precision Oncol. 2018;6(2018):PO.17.00241. https://doi.org/10.1200/PO.17.00241.

26.

Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487(7407):330–7. https://doi.org/10.1038/nature11252.

27.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.CrossRefPubMedPubMedCentral

28.

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. https://doi.org/10.1089/omi.2011.0118.CrossRefPubMedPubMedCentral

29.

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue):D447–52. https://doi.org/10.1093/nar/gku1003.CrossRefPubMed

30.

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. https://doi.org/10.1101/gr.1239303.CrossRefPubMedPubMedCentral

31.

Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–e10. https://doi.org/10.1158/0008-5472.CAN-17-0307.CrossRefPubMedPubMedCentral

32.

Aran D, Sirota M, Butte AJ. Systematic pan-cancer analysis of tumour purity. Nat Commun. 2015;6(1):8971. https://doi.org/10.1038/ncomms9971.CrossRefPubMed

33.

Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10(21):7252–9. https://doi.org/10.1158/1078-0432.CCR-04-0713.CrossRefPubMed

34.

Loi S, Drubay D, Adams S, Pruneri G, Francis PA, Lacroix-Triki M, et al. Tumor-infiltrating lymphocytes and prognosis: a pooled individual patient analysis of early-stage triple-negative breast cancers. J Clin Oncol. 2019;37(7):559–69. https://doi.org/10.1200/JCO.18.01010.CrossRefPubMedPubMedCentral

35.

Quail DF, Joyce JA. Microenvironmental regulation of tumor progression and metastasis. Nat Med. 2013;19(11):1423–37. https://doi.org/10.1038/nm.3394.CrossRefPubMedPubMedCentral

36.

Lim YZ, South AP. Tumour-stroma crosstalk in the development of squamous cell carcinoma. Int J Biochem Cell Biol. 2014;53:450–8. https://doi.org/10.1016/j.biocel.2014.06.012.CrossRefPubMed

37.

Taddei ML, Giannoni E, Comito G, Chiarugi P. Microenvironment and tumor cell plasticity: an easy way out. Cancer Lett. 2013;341(1):80–96. https://doi.org/10.1016/j.canlet.2013.01.042.CrossRefPubMed

38.

Galon J, Angell HK, Bedognetti D, Marincola FM. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity. 2013;39(1):11–26. https://doi.org/10.1016/j.immuni.2013.07.008.CrossRefPubMed

39.

Zhang Y, Xu J, Zhang N, Chen M, Wang H, Zhu D. Targeting the tumour immune microenvironment for cancer therapy in human gastrointestinal malignancies. Cancer Lett. 2019;458:123–35. https://doi.org/10.1016/j.canlet.2019.05.017.CrossRefPubMed

40.

Fong W, To KKW. Drug repurposing to overcome resistance to various therapies for colorectal cancer. Cell Mole Life Sci. 2019;76(17):3383–406. https://doi.org/10.1007/s00018-019-03134-0.CrossRef

41.

Vincent KM, Findlay SD, Postovit LM. Assessing breast cancer cell lines as tumour models by comparison of mRNA expression profiles. Breast Cancer Res. 2015;17(1):114. https://doi.org/10.1186/s13058-015-0613-0.CrossRefPubMedPubMedCentral

42.

Huijbers A, Tollenaar RA, GW VP, Zeestraten EC, Dutton S, CC MC, et al. The proportion of tumor-stroma as a strong prognosticator for stage II and III colon cancer patients: validation in the VICTOR trial. Ann Oncol. 2013;24(1):179–85. https://doi.org/10.1093/annonc/mds246.CrossRefPubMed

43.

Danielsen HE, Hveem TS, Domingo E, Pradhan M, Kleppe A, Syvertsen RA, et al. Prognostic markers for colorectal cancer: estimating ploidy and stroma. Ann Oncol. 2018;29(3):616–23. https://doi.org/10.1093/annonc/mdx794.CrossRefPubMed

44.

Lind GE, Danielsen SA, Ahlquist T, Merok MA, Andresen K, Skotheim RI, et al. Identification of an epigenetic biomarker panel with high sensitivity and specificity for colorectal cancer and adenomas. Mol Cancer. 2011;10(1):85. https://doi.org/10.1186/1476-4598-10-85.CrossRefPubMedPubMedCentral

45.

Wang H, Luo C, Zhu S, Fang H, Gao Q, Ge S, et al. Serum peptidome profiling for the diagnosis of colorectal cancer: discovery and validation in two independent cohorts. Oncotarget. 2017;8(35):59376–86. https://doi.org/10.18632/oncotarget.19587.CrossRefPubMedPubMedCentral

46.

Chang CM, Chia VM, Gunter MJ, Zanetti KA, Ryan BM, Goodman JE, et al. Innate immunity gene polymorphisms and the risk of colorectal neoplasia. Carcinogenesis. 2013;34(11):2512–20. https://doi.org/10.1093/carcin/bgt228.CrossRefPubMedPubMedCentral

47.

Guo J, Jin H, Xi Y, Guo J, Jin Y, Jiang D. The miR-582/CD1B Axis is involved in regulation of dendritic cells and is associated with clinical outcomes in advanced lung adenocarcinoma. Biomed Res Int. 2020;2020:4360930.PubMedPubMedCentral

48.

Lee CH, Chen LC, Yu CC, Lin WH, Lin VC, Huang CY, et al. Prognostic value of CD1B in localised prostate cancer. Int J Environ Res Public Health. 2019;16(23):4723. https://doi.org/10.3390/ijerph16234723.

49.

Lou Q, Liu R, Yang X, Li W, Huang L, Wei L, et al. miR-448 targets IDO1 and regulates CD8(+) T cell response in human colon cancer. J Immunother Cancer. 2019;7(1):210. https://doi.org/10.1186/s40425-019-0691-0.

50.

Takada K, Kohashi K, Shimokawa M, Haro A, Osoegawa A, Tagawa T, et al. Co-expression of IDO1 and PD-L1 in lung squamous cell carcinoma: potential targets of novel combination therapy. Lung Cancer. 2019;128:26–32. https://doi.org/10.1016/j.lungcan.2018.12.008.CrossRefPubMed

Title: Identification of prognostic immune-related gene signature associated with tumor microenvironment of colorectal cancer
Authors: Yuanyuan Wang
Wei Li
Xiaojing Jin
Xia Jiang
Shang Guo
Fei Xu
Xingkai Su
Guiqi Wang
Zengren Zhao
Xiaosong Gu
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Colorectal Cancer
Colorectal Cancer
Published in: BMC Cancer / Issue 1/2021
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-021-08629-3

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Background

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