Analysis of immune microenvironment and potential sensitive drugs in esophageal squamous cell carcinoma based on GEO database and bioinformatics method_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

PAN Shize , LI Ning , SONG Congkuan , HAO Bo , LU Zilong , FAN Tao , LI Donghang , ZHANG Lin , MENG Heng , LAI Kai ,  GENG Qing

Department of Thoracic Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, P. R. China;

Corresponding author：

GENG Qing, Email: gengqingwhu@whu.edu.cn

Keywords：

Esophageal squamous cell carcinoma; bioinformatics analysis; immune checkpoint; risk model; drug sensitivity; tumor microenvironment

DOI：

10.7507/1007-4848.202110062

Video：

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Objective To construct a prognostic model of esophageal squamous cell carcinoma (ESCC) based on immune checkpoint-related genes and explore the potential relationship between these genes and the tumor microenvironment (TME). Methods The transcriptome sequencing data and clinical information of immune checkpoint genes of samples from GSE53625 in GEO database were collected. The difference of gene expression between ESCC and normal paracancerous tissues was evaluated, and the drug sensitivity of differentially expressed genes in ESCC was analyzed. We then constructed a risk model based on survival-related genes and explored the prognostic characteristics, enriched pathway, immune checkpoints, immune score, immune cell infiltration, and potentially sensitive drugs of different risk groups. Results A total of 358 samples from 179 patients were enrolled, including 179 ESCC samples and 179 corresponding paracancerous tissues. There were 33 males and 146 females, including 80 patients≤60 years and 99 patients>60 years. 39 immune checkpoint genes were differentially expressed in ESCC, including 14 low expression genes and 25 high expression genes. Drug sensitivity analysis of 8 highly expressed genes (TNFRSF8, CTLA4, TNFRSF4, CD276, TNFSF4, IDO1, CD80, TNFRSF18) showed that many compounds were sensitive to these immunotherapy targets. A risk model based on three prognostic genes (NRP1, ICOSLG, HHLA2) was constructed by the least absolute shrinkage and selection operator analysis. It was found that the overall survival time of the high-risk group was significantly lower than that of the low-risk group (P<0.001). Similar results were obtained in different ESCC subtypes. The risk score based on the immune checkpoint gene was identified as an independent prognostic factor for ESCC. Different risk groups had unique enriched pathways, immune cell infiltration, TME, and sensitive drugs. Conclusion A prognostic model based on immune checkpoint gene is established, which can accurately stratify ESCC and provide potential sensitive drugs for ESCC with different risks, thus providing a possibility for personalized treatment of ESCC.

Citation： PAN Shize, LI Ning, SONG Congkuan, HAO Bo, LU Zilong, FAN Tao, LI Donghang, ZHANG Lin, MENG Heng, LAI Kai, GENG Qing. Analysis of immune microenvironment and potential sensitive drugs in esophageal squamous cell carcinoma based on GEO database and bioinformatics method. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(9): 1251-1260. doi: 10.7507/1007-4848.202110062 Copy

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29.	Ge PL, Li SF, Wang WW, et al. Prognostic values of immune scores and immune microenvironment-related genes for hepatocellular carcinoma. Aging (Albany NY), 2020, 12(6): 5479-5499.
30.	Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun, 2013, 4: 2612.
31.	Zhang C, Cheng W, Ren X, et al. Tumor purity as an underlying key factor in glioma. Clin Cancer Res, 2017, 23(20): 6279-6291.
32.	Mao Y, Feng Q, Zheng P, et al. Low tumor purity is associated with poor prognosis, heavy mutation burden, and intense immune phenotype in colon cancer. Cancer Manag Res, 2018, 10: 3569-3577.

1. Shah MA, Kojima T, Hochhauser D, et al. Efficacy and safety of pembrolizumab for heavily pretreated patients with advanced, metastatic adenocarcinoma or squamous cell carcinoma of the esophagus: The phase 2 KEYNOTE-180 study. JAMA Oncol, 2019, 5(4): 546-550.
2. Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin, 2015, 65(2): 87-108.
3. Aquino JL, Said MM, Pereira DA, et al. Complications of the rescue esophagectomy in advanced esophageal cancer. Arq Bras Cir Dig, 2013, 26(3): 173-178.
4. Jiao R, Luo H, Xu W, et al. Immune checkpoint inhibitors in esophageal squamous cell carcinoma: Progress and opportunities. Onco Targets Ther, 2019, 12: 6023-6032.
5. Baba Y, Nomoto D, Okadome K, et al. Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma. Cancer Sci, 2020, 111(9): 3132-3141.
6. Manson G, Norwood J, Marabelle A, et al. Biomarkers associated with checkpoint inhibitors. Ann Oncol, 2016, 27(7): 1199-1206.
7. Savas P, Salgado R, Denkert C, et al. Clinical relevance of host immunity in breast cancer: From TILs to the clinic. Nat Rev Clin Oncol, 2016, 13(4): 228-241.
8. Mantovani A, Marchesi F, Malesci A, et al. Tumour-associated macrophages as treatment targets in oncology. Nat Rev Clin Oncol, 2017, 14(7): 399-416.
9. Porta C, Sica A, Riboldi E. Tumor-associated myeloid cells: New understandings on their metabolic regulation and their influence in cancer immunotherapy. FEBS J, 2018, 285(4): 717-733.
10. Barbie DA, Tamayo P, Boehm JS, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 2009, 462(7269): 108-112.
11. Becht E, Giraldo NA, Lacroix L, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol, 2016, 17(1): 218.
12. Geeleher P, Cox N, Huang RS. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One, 2014, 9(9): e107468.
13. Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun, 2019, 10(1): 1523.
14. Cheng YF, Chen HS, Wu SC, et al. Esophageal squamous cell carcinoma and prognosis in Taiwan. Cancer Med, 2018, 7(9): 4193-4201.
15. Hirano H, Kato K. Systemic treatment of advanced esophageal squamous cell carcinoma: Chemotherapy, molecular-targeting therapy and immunotherapy. Jpn J Clin Oncol, 2019, 49(5): 412-420.
16. Hu X, Wu D, He X, et al. circGSK3β promotes metastasis in esophageal squamous cell carcinoma by augmenting β-catenin signaling. Mol Cancer, 2019, 18(1): 160.
17. Kojima T, Shah MA, Muro K, et al. Randomized phaseⅢKEYNOTE-181 study of pembrolizumab versus chemotherapy in advanced esophageal cancer. J Clin Oncol, 2020, 38(35): 4138-4148.
18. Zhang J, Shi Z, Xu X, et al. The influence of microenvironment on tumor immunotherapy. FEBS J, 2019, 286(21): 4160-4175.
19. Delgoffe GM, Woo SR, Turnis ME, et al. Stability and function of regulatory T cells is maintained by a neuropilin-1-semaphorin-4a axis. Nature, 2013, 501(7466): 252-256.
20. Overacre-Delgoffe AE, Chikina M, Dadey RE, et al. Interferon-γ drives Treg fragility to promote anti-tumor immunity. Cell, 2017, 169(6): 1130-1141.
21. Liu C, Somasundaram A, Manne S, et al. Neuropilin-1 is a T cell memory checkpoint limiting long-term antitumor immunity. Nat Immunol, 2020, 21(9): 1010-1021.
22. Iwata R, Hyoung Lee J, Hayashi M, et al. ICOSLG-mediated regulatory T-cell expansion and IL-10 production promote progression of glioblastoma. Neuro Oncol, 2020, 22(3): 333-344.
23. Janakiram M, Shah UA, Liu W, et al. The third group of the B7-CD28 immune checkpoint family: HHLA2, TMIGD2, B7x, and B7-H3. Immunol Rev, 2017, 276(1): 26-39.
24. Flajnik MF, Tlapakova T, Criscitiello MF, et al. Evolution of the B7 family: Co-evolution of B7H6 and NKp30, identification of a new B7 family member, B7H7, and of B7's historical relationship with the MHC. Immunogenetics, 2012, 64(8): 571-590.
25. Xiao Y, Freeman GJ. A new B7: CD28 family checkpoint target for cancer immunotherapy: HHLA2. Clin Cancer Res, 2015, 21(10): 2201-2203.
26. Boor PPC, Sideras K, Biermann K, et al. HHLA2 is expressed in pancreatic and ampullary cancers and increased expression is associated with better post-surgical prognosis. Br J Cancer, 2020, 122(8): 1211-1218.
27. Xu G, Shi Y, Ling X, et al. HHLA2 predicts better survival and exhibits inhibited proliferation in epithelial ovarian cancer. Cancer Cell Int, 2021, 21(1): 252.
28. Zhou QH, Li KW, Chen X, et al. HHLA2 and PD-L1 co-expression predicts poor prognosis in patients with clear cell renal cell carcinoma. J Immunother Cancer, 2020, 8(1): e000157.
29. Ge PL, Li SF, Wang WW, et al. Prognostic values of immune scores and immune microenvironment-related genes for hepatocellular carcinoma. Aging (Albany NY), 2020, 12(6): 5479-5499.
30. Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun, 2013, 4: 2612.
31. Zhang C, Cheng W, Ren X, et al. Tumor purity as an underlying key factor in glioma. Clin Cancer Res, 2017, 23(20): 6279-6291.
32. Mao Y, Feng Q, Zheng P, et al. Low tumor purity is associated with poor prognosis, heavy mutation burden, and intense immune phenotype in colon cancer. Cancer Manag Res, 2018, 10: 3569-3577.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Analysis of immune microenvironment and potential sensitive drugs in esophageal squamous cell carcinoma based on GEO database and bioinformatics method

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