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Bioinformatics-based screening and analysis of the key genes involved in the influence of antiangiogenesis on myeloid-derived suppressor cells and their effects on the immune microenvironment

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Abstract

This study aimed to screen differentially expressed genes (DEGs) involved in the influence of antiangiogenic therapy on myeloid-derived suppressor cell (MDSC) infiltration and investigate their mechanisms of action. Data on DEGs after the action of antiangiogenic drugs in a pan-cancer context were obtained from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler package in R software. Single-sample gene set enrichment analysis was performed using the gene set variation analysis package to evaluate the levels of immune cells and the activity of immune-related pathways. The relationships of DEGs with the infiltration levels of MDSCs and specific immune cell subpopulations were investigated via gene module analysis. The top 10 key genes were subsequently obtained from PPI network analysis using the cytoHubba plugin of the Cytoscape platform. When the DEGs of the four datasets were intersected, a DEG in the intersection of three datasets and 12 DEGs in the intersection of two datasets were upregulated, and 28 DEGs in the intersection of two datasets were downregulated. GO and KEGG pathway enrichment analyses revealed that the DEGs were associated with multiple important signaling pathways closely related to tumor onset and development, including cell differentiation, cell proliferation, the cell cycle, and immune responses. Most downregulated genes in lung adenocarcinoma (LUAD) were positively correlated with MDSC expression. Only MGP was negatively correlated; the correlation between CACNG6 and MDSC expression was statistically insignificant. In lung squamous cell carcinoma (LUSC), the relationships of PMEPA1, PCDH7, NEURL1B, and CACNG6 with MDSC expression were statistically insignificant; MGP was negatively correlated with MDSC expression. The top 10 key genes with the highest degree scores obtained using the cytoHubba plugin of Cytoscape were AURKB, RRM2, BUB1, NUSAP1, PRC1, TOP2A, NCAPH, CENPA, KIF2C, and CCNA2. Most of these genes were upregulated in LUAD and associated with immune cell infiltration and prognosis in tumors. An analysis of the relationships between DEGs and infiltration by other specific immune cells revealed the presence of consistent patterns in the downregulated genes, which exhibited positive correlations with the levels of Th2 cells, γδ T cells, and CD56dim NK cells, and negative correlations with other infiltrating immune cells. Antiangiogenic therapy may regulate MDSC infiltration through multiple important signaling pathways closely associated with tumor onset and development, such as cell differentiation, cell proliferation, the cell cycle, and immune responses. Antiangiogenic drugs may exert effects by affecting various types of infiltrating cells associated with immune suppression.

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Data availability

These data were derived from the following resources available in the public domain: GEO (Gene Expression Omnibus) https://www.ncbi.nlm.nih.gov/gds, TCGA (The cancer genome atlas) https://portal.gdc.cancer.gov/, TIMER (Tumor Immune Estimation Resource) https://cistrome.shinyapps.io/timer/.

References

  1. Erratum: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2020;70:313. https://doi.org/10.3322/caac.21609

  2. Yasuda S, Sho M, Yamato I, Yoshiji H, Wakatsuki K, Nishiwada S, et al. Simultaneous blockade of programmed death 1 and vascular endothelial growth factor receptor 2 (VEGFR2) induces synergistic anti-tumour effect in vivo. Clin Exp Immunol. 2013;172:500–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Zhao X, Zhao R, Wen J, Zhang X, Wu S, Fang J, et al. Anlotinib reduces the suppressive capacity of monocytic myeloid-derived suppressor cells and potentiates the immune microenvironment normalization window in a mouse lung cancer model. Antocancer Drugs. 2022. https://doi.org/10.1097/CAD.0000000000001481.

    Article  PubMed  Google Scholar 

  4. Smyth GK. Limma: linear models for microarray data. In: Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S, editors. Bioinformatics and computational biology solutions using R and bioconductor. Statistics for biology and health. New York: Springer; 2005. p. 397–420. https://doi.org/10.1007/0-387-29362-0_23

    Chapter  Google Scholar 

  5. Davis S, Meltzer PS. GEOquery: a bridge between the gene expression Omnibus (GEO) and BioConductor. Bioinformatics. 2007;23:1846–7. https://doi.org/10.1093/bioinformatics/btm254.

    Article  CAS  PubMed  Google Scholar 

  6. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32:2847–9.

    Article  CAS  PubMed  Google Scholar 

  7. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 2013;14:7.

    Article  Google Scholar 

  8. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39:782–95.

    Article  CAS  PubMed  Google Scholar 

  9. 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:e108–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 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;17:174.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.

    Article  CAS  PubMed  Google Scholar 

  12. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72:7–33. https://doi.org/10.3322/caac.21708.

    Article  PubMed  Google Scholar 

  14. Han B, Li K, Zhao Y, Li B, Cheng Y, Zhou J, et al. Anlotinib as a third-line therapy in patients with refractory advanced non-small-cell lung cancer: a multicentre, randomised phase II trial (ALTER0302). Br J Cancer. 2018;118:654–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gridelli C, de Castro CJ, Dingemans AC, Griesinger F, Grosssi F, Langer C, et al. Safety and efficacy of bevacizumab plus standard-of-care treatment beyond disease progression in patients with advanced non-small cell lung cancer: the AvaALL randomized clinical trial. JAMA Oncol. 2018;4:e183486.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Itatani Y, Kawada K, Yamamoto T, Sakai Y. Resistance to antiangiogenic therapy in cancer-alterations to anti-VEGF pathway. Int J Mol Sci. 2018;19:E1232.

    Article  Google Scholar 

  17. Doroshow DB, Sanmamed MF, Hastings K, Politi K, Rimm DL, Chen L, et al. Immunotherapy in non-small cell lung cancer: facts and hopes. Clin Cancer Res. 2019;25:4592–602.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Yu Y, Zeng D, Ou Q, Liu S, Li A, Chen Y, et al. Association of survival and immune-related biomarkers with immunotherapy in patients with non-small cell lung cancer: a meta-analysis and individual patient level analysis. JAMA Netw Open. 2019;2:e196879.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ren S, Xiong X, You H, Shen J, Zhou P. The combination of immune checkpoint blockade and angiogenesis inhibitors in the treatment of advanced non-small cell lung cancer. Front Immunol. 2021;12:689132.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Farsaci B, Donahue RN, Coplin MA, Grenga I, Lepone LM, Molinolo AA, et al. Immune consequences of decreasing tumor vasculature with antiangiogenic tyrosine kinase inhibitors in combination with therapeutic vaccines. Cancer Immunol Res. 2014;2:1090–102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Viallard C, Larrivée B. Tumor angiogenesis and vascular normalization: alternative therapeutic targets. Angiogenesis. 2017;20:409–26.

    Article  CAS  PubMed  Google Scholar 

  22. Ramjiawan RR, Griffioen AW, Duda DG. Antiangiogenesis for cancer revisited: is there a role for combinations with immunotherapy. Angiogenesis. 2017;20:185–204.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Okła K, Czerwonka A, Wawruszak A, Bobinski M, Bilska M, Tarkowski R, et al. Clinical relevance and immunosuppressive pattern of circulating and infiltrating subsets of myeloid-derived suppressor cells (MDSCs) in epithelial ovarian cancer. Front Immunol. 2019;10:691.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Solito S, Falisi E, Diaz-Montero CM, Doni A, Pinton L, Rosato A, et al. A human promyelocytic-like population is responsible for the immune suppression mediated by myeloid-derived suppressor cells. Blood. 2011;118:2254–65. https://doi.org/10.1182/blood-2010-12-325753.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gabrilovich DI, Bronte V, Chen SH, Colombo MP, Ochoa A, Ostrand-Rosenberg S, et al. The terminology issue for myeloid-derived suppressor cells. Cancer Res. 2007;67:425 (author reply 426).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bronte V, Brandau S, Chen SH, Colombo MP, Frey AB, Greten TF, et al. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat Commun. 2016;7:12150.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Condamine T, Dominguez GA, Youn JI, Kossenkov AV, Mony S, Alicea-Torres K, et al. Lectin-type oxidized LDL receptor-1 distinguishes population of human polymorphonuclear myeloid-derived suppressor cells in cancer patients. Sci Immunol. 2016;1:aaf8943.

  28. Zahoor H, Mir MC, Barata PC, Stephenson AJ, Campbell SC, Fergany A, et al. Phase II trial of continuous treatment with sunitinib in patients with high-risk (BCG-refractory) non-muscle invasive bladder cancer. Invest New Drugs. 2019;37:1231–8.

    Article  CAS  PubMed  Google Scholar 

  29. Bauer R, Udonta F, Wroblewski M, Ben-Batalla I, Santos IM, Taverna F, et al. Blockade of myeloid-derived suppressor cell expansion with all-trans retinoic acid increases the efficacy of antiangiogenic therapy. Cancer Res. 2018;78:3220–32. https://doi.org/10.1158/0008-5472.CAN-17-3415.

    Article  CAS  PubMed  Google Scholar 

  30. Sun Y, Mo Y, Jiang S, Shang C, Feng Y, Zeng X. CXC chemokine ligand-10 promotes the accumulation of monocyte-like myeloid-derived suppressor cells by activating p38 MAPK signaling under tumor conditions. Cancer Sci. 2023;114:142–51. https://doi.org/10.1111/cas.15598.

    Article  CAS  PubMed  Google Scholar 

  31. Gu H, Deng W, Zheng Z, Wu K, Sun F. CCL2 produced by pancreatic ductal adenocarcinoma is essential for the accumulation and activation of monocytic myeloid-derived suppressor cells. Immun Inflamm Dis. 2021;9:1686–95. https://doi.org/10.1002/iid3.523.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yu J, Li H, Zhang Z, Lin W, Wei X, Shao B. Targeting the MDSCs of tumors in situ with inhibitors of the MAPK signaling pathway to promote tumor regression. Front Oncol. 2021;11:647312. https://doi.org/10.3389/fonc.2021.647312.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zeng X, Zhou J, Xiong Z, Sun H, Yang W, Mok MTS, et al. Cell cycle-related kinase reprograms the liver immune microenvironment to promote cancer metastasis. Cell Mol Immunol. 2021;18:1005–15. https://doi.org/10.1038/s41423-020-00534-2.

    Article  CAS  PubMed  Google Scholar 

  34. Zhou L, Lin X, Zhang L, Chen S, Chen J, Zhou Z, et al. Neddylation pathway promotes myeloid-derived suppressor cell infiltration via NF-κB-mCXCL5 signaling in lung cancer. Int Immunopharmacol. 2022;113:109329. https://doi.org/10.1016/j.intimp.2022.109329.

    Article  CAS  PubMed  Google Scholar 

  35. Galetta D, Cortes-Dericks L. Promising therapy in lung cancer: spotlight on Aurora kinases. Cancers (Basel). 2020;12:3371.

    Article  CAS  PubMed  Google Scholar 

  36. Gao X, Jiang A, Shen Y, Lu H, Chen R. Expression and clinical significance of AURKB gene in lung adenocarcinoma: analysis based on the data-mining of bioinformatic database. Medicine (Baltimore). 2021;100:e26439.

    Article  CAS  PubMed  Google Scholar 

  37. Jin CY, Du L, Nuerlan AH, Wang XL, Yang YW, Guo R. High expression of RRM2 as an independent predictive factor of poor prognosis in patients with lung adenocarcinoma. Aging (Albany NY). 2020;13:3518–35.

    Article  PubMed  Google Scholar 

  38. Wang L, Yang X, An N, Liu J. Bioinformatics analysis of BUB1 expression and gene regulation network in lung adenocarcinoma. Transl Cancer Res. 2020;9:4820–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yu Z, Li XM, Huai M, Cao SS, Han HY, Bi YH. NUSAP1 promotes lung cancer progression by activating AKT/mTOR signaling pathway. Zhonghua Zhong Liu Za Zhi. 2020;42:551–5 (in Chinese).

    CAS  PubMed  Google Scholar 

  40. Xu Z, Wang Y, Xiong J, Cui F, Wang L, Peng H. NUSAP1 knockdown inhibits cell growth and metastasis of non-small-cell lung cancer through regulating BTG2/PI3K/Akt signaling. J Cell Physiol. 2020;235:3886–93.

    Article  CAS  PubMed  Google Scholar 

  41. Hanselmann S, Gertzmann D, Shin WJ, Ade CP, Gaubatz S. Expression of the cytokinesis regulator PRC1 results in p53-pathway activation in A549 cells but does not directly regulate gene expression in the nucleus. Cell Cycle. 2023;22:419–32.

    Article  CAS  PubMed  Google Scholar 

  42. Zhu P, Cui N, Song ZY, Yong WX, Luo XX, Wang GC, et al. PRC1 plays an important role in lung adenocarcinoma and is potentially targeted by fostamatinib. Eur Rev Med Pharmacol Sci. 2022;26:8924–34.

    CAS  PubMed  Google Scholar 

  43. Kou F, Sun H, Wu L, Li B, Zhang B, Wang X, et al. TOP2A promotes lung adenocarcinoma cells’ malignant progression and predicts poor prognosis in lung adenocarcinoma. J Cancer. 2020;11:2496–508.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kim B, Kim SW, Lim JY, Park SJ. NCAPH is required for proliferation, migration and invasion of non-small-cell lung cancer cells. Anticancer Res. 2020;40:3239–46.

    Article  CAS  PubMed  Google Scholar 

  45. Li C, Meng J, Zhang T. NCAPH is a prognostic biomarker and associated with immune infiltrates in lung adenocarcinoma. Sci Rep. 2022;12:9578.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhou H, Bian T, Qian L, Zhao C, Zhang W, Zheng M, et al. Prognostic model of lung adenocarcinoma constructed by the CENPA complex genes is closely related to immune infiltration. Pathol Res Pract. 2021;228:153680.

    Article  CAS  PubMed  Google Scholar 

  47. Chen B, Xie X, Lan F, Liu W. Identification of prognostic markers by weighted gene co-expression network analysis in non-small cell lung cancer. Bioengineered. 2021;12:4924–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Chen S, Zhao Z, Wang X, Zhang Q, Lyu L, Tang B. The predictive competing endogenous rna regulatory networks and potential prognostic and immunological roles of cyclin A2 in pan-cancer analysis. Front Mol Biosci. 2022;9:809509.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Espinosa Gonzalez M, Volk-Draper L, Bhattarai N, Wilber A, Ran S. Th2 cytokines IL-4, IL-13, and IL-10 promote differentiation of pro-lymphatic progenitors derived from bone marrow myeloid precursors. Stem Cells Dev. 2022;31:322–33. https://doi.org/10.1089/scd.2022.0004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mensurado S, Blanco-Domínguez R, Silva-Santos B. The emerging roles of γδ T cells in cancer immunotherapy. Nat Rev Clin Oncol. 2023;20:178–91. https://doi.org/10.1038/s41571-022-00722-1.

    Article  CAS  PubMed  Google Scholar 

  51. Cózar B, Greppi M, Carpentier S, Narni-Mancinelli E, Chiossone L, Vivier E. Tumor-infiltrating natural killer cells. Cancer Discov. 2021;11:34–44. https://doi.org/10.1158/2159-8290.CD-20-0655.

    Article  PubMed  Google Scholar 

  52. Wen Z, Zhang H, Zhang H, Huang J, Yang J, She X, et al. Differences in tumor immune microenvironment and clinical outcomes between right and left colon cancer. ESMO Virtual Congress 2020, Abstract 498P Ann Oncol. 2020;31:S451.

    Google Scholar 

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Acknowledgements

The authors thank and extend their appreciation to all the staff members of Department of Oncology of PLA General Hospital and Devices for their assistance and support. We also sincerely acknowledge the anonymous reviewers for their insights and comments to improve the quality of the manuscript.

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Correspondence to Yi Hu.

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Zhao, X., Zhao, R., Wen, J. et al. Bioinformatics-based screening and analysis of the key genes involved in the influence of antiangiogenesis on myeloid-derived suppressor cells and their effects on the immune microenvironment. Med Oncol 41, 96 (2024). https://doi.org/10.1007/s12032-024-02357-x

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