Introduction: This study on lung adenocarcinoma (LUAD), a common lung cancer subtype with high mortality. Aims: This study focuses on how tumor cell interactions affect immunotherapy responsiveness. Methods: Using public databases, we used non-negative matrix factorization clustering method, ssGSEA, CIBERSORT algorithm, immunophenotype score, survival analysis, protein–protein interaction network method to analyze gene expression data and coagulation-related genes. Results: We divided LUAD patients into three coagulation-related subgroups with varying immune characteristics and survival rates. A cluster of three patients, having the highest immune infiltration and survival rate, also showed the most potential for immunotherapy. We identified five key genes influencing patient survival using a protein–protein interaction network. Conclusion: This research offers valuable insights for forecasting prognosis and immunotherapy responsiveness in LUAD patients, helping to inform clinical treatment strategies.

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References

1. Sung H, Ferlay J, Siegel RL et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209–249 (2021).
MedlineGoogle Scholar
2. Mulder FI, Horvath-Puho E, Van Es N et al. Venous thromboembolism in cancer patients: a population-based cohort study. Blood 137(14), 1959–1969 (2021).
Medline CASGoogle Scholar
3. Prandoni P, Lensing AW, Piccioli A et al. Recurrent venous thromboembolism and bleeding complications during anticoagulant treatment in patients with cancer and venous thrombosis. Blood 100(10), 3484–3488 (2002).
Medline CASGoogle Scholar
4. Chew HK, Davies AM, Wun T, Harvey D, Zhou H, White RH. The incidence of venous thromboembolism among patients with primary lung cancer. J. Thromb. Haemost. 6(4), 601–608 (2008).
Medline CASGoogle Scholar
5. Ruiz-Artacho P, Lecumberri R, Trujillo-Santos J et al. Cancer histology and natural history of patients with lung cancer and venous thromboembolism. Cancers (Basel) 14(17), 4127 (2022).
MedlineGoogle Scholar
6. Blann AD, Lip GY. Venous thromboembolism. BMJ 332(7535), 215–219 (2006).
MedlineGoogle Scholar
7. Fernandes CJ, Morinaga LTK, Alves JL Jr et al. Cancer-associated thrombosis: the when, how and why. Eur. Respir. Rev. 28(151), (2019).
MedlineGoogle Scholar
8. Magnus N, Garnier D, Meehan B et al. Tissue factor expression provokes escape from tumor dormancy and leads to genomic alterations. Proc. Natl Acad. Sci. USA 111(9), 3544–3549 (2014).
Medline CASGoogle Scholar
9. Monreal M, Lensing AW, Prins MH et al. Screening for occult cancer in patients with acute deep vein thrombosis or pulmonary embolism. J. Thromb. Haemost. 2(6), 876–881 (2004).
Medline CASGoogle Scholar
10. Zhang W, Ou X, Wu X. Proteomics profiling of plasma exosomes in epithelial ovarian cancer: a potential role in the coagulation cascade, diagnosis and prognosis. Int. J. Oncol. 54(5), 1719–1733 (2019).
Medline CASGoogle Scholar
11. Shaker H, Bundred NJ, Landberg G et al. Breast cancer stromal clotting activation (tissue factor and thrombin): a pre-invasive phenomena that is prognostic in invasion. Cancer Med. 9(5), 1768–1778 (2020).
Medline CASGoogle Scholar
12. Desai C, Koupenova M, Machlus KR, Sen Gupta A. Beyond the thrombus: platelet-inspired nanomedicine approaches in inflammation, immune response, and cancer. J. Thromb. Haemost. 20(7), 1523–1534 (2022).
Medline CASGoogle Scholar
13. Stoiber D, Assinger A. Platelet–leukocyte interplay in cancer development and progression. Cells 9(4), 855 (2020).
Medline CASGoogle Scholar
14. Agrawal S, Ganguly S, Hajian P, Cao JN, Agrawal A. PDGF upregulates CLEC-2 to induce T regulatory cells. Oncotarget 6(30), 28621–28632 (2015).
MedlineGoogle Scholar
15. NIH. National Cancer Institute. National GDC Portal. Harmonized Cancer Datasets. Genomic Data Commons Data Portal. https://portal.gdc.cancer.gov/
Google Scholar
16. NCBI.Gene Expression Omnibus (GEO). www.ncbi.nlm.nih.gov/geo/
Google Scholar
17. He Q, Yang J, Jin Y. Immune infiltration and clinical significance analyses of the coagulation-related genes in hepatocellular carcinoma. Brief. Bioinform. 23(4), (2022).
Google Scholar
18. KEGG: Kyoto Encyclopedia of Genes and Genomes. www.genome.jp/kegg/
Google Scholar
19. Robinson MD, Mccarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics (Oxford, England) 26(1), 139–140 (2010).
Medline CASGoogle Scholar
20. STRING.Protein-Protein Interaction Networks Functional Enrichment Analysis. https://cn.string-db.org/cgi/input.pl
Google Scholar
21. Zhang H, Meltzer P, Davis S. RCircos: an R package for Circos 2D track plots. BMC Bioinformatics 14, 244 (2013).
MedlineGoogle Scholar
22. Skidmore ZL, Wagner AH, Lesurf R et al. GenVisR: genomic visualizations in R. Bioinformatics (Oxford, England) 32(19), 3012–3014 (2016).
Medline CASGoogle Scholar
23. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16(5), 284–287 (2012).
Medline CASGoogle Scholar
24. Terry M. Therneau PMG. Modeling Survival Data: Extending the Cox Model. Springer, NY, USA (2000).
Google Scholar
25. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
MedlineGoogle Scholar
26. Yoshihara K, Shahmoradgoli M, Martinez E et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).
MedlineGoogle Scholar
27. Liu J, Shen H, Gu W et al. Prediction of prognosis, immunogenicity and efficacy of immunotherapy based on glutamine metabolism in lung adenocarcinoma. Front. Immunol. 13, 960738 (2022).
Medline CASGoogle Scholar
28. TCIA. The Cancer Immunome Atlas. https://tcia.at
Google Scholar
29. TIDE. http://tide.dfci.harvard.edu/login/
Google Scholar
30. Chew HK, Wun T, Harvey D, Zhou H, White RH. Incidence of venous thromboembolism and its effect on survival among patients with common cancers. Arch. Intern. Med. 166(4), 458–464 (2006).
MedlineGoogle Scholar
31. Timp JF, Braekkan SK, Versteeg HH, Cannegieter SC. Epidemiology of cancer-associated venous thrombosis. Blood 122(10), 1712–1723 (2013).
Medline CASGoogle Scholar
32. Mahajan A, Brunson A, Adesina O, Keegan THM, Wun T. The incidence of cancer-associated thrombosis is increasing over time. Blood Adv. 6(1), 307–320 (2022).
MedlineGoogle Scholar
33. Tsubata Y, Hotta T, Hamai K et al. A new risk-assessment tool for venous thromboembolism in advanced lung cancer: a prospective, observational study. J. Hematol. Oncol. 15(1), 40 (2022).
Medline CASGoogle Scholar
34. Dvorak HF. Tumors: wounds that do not heal-a historical perspective with a focus on the fundamental roles of increased vascular permeability and clotting. Semin. Thromb. Hemost. 45(6), 576–592 (2019).
Medline CASGoogle Scholar
35. Burzynski LC, Humphry M, Pyrillou K et al. The coagulation and immune systems are directly linked through the activation of interleukin-1alpha by thrombin. Immunity 50(4), 1033–1042; e1036 (2019).
Medline CASGoogle Scholar
36. Esmon CT. Inflammation and thrombosis. J. Thromb. Haemost. 1(7), 1343–1348 (2003).
Medline CASGoogle Scholar
37. Tsukamoto J, Monteiro M, Vale S et al. Thromboembolic events related to treatment with checkpoint inhibitors: report of two cases. Case Rep. Oncol. 11(3), 648–653 (2018).
MedlineGoogle Scholar
38. Boutros C, Scoazec JY, Mateus C, Routier E, Roy S, Robert C. Arterial thrombosis and anti-PD-1 blockade. Eur. J. Cancer 91, 164–166 (2018).
MedlineGoogle Scholar
39. Horio Y, Takamatsu K, Tamanoi D et al. Trousseau's syndrome triggered by an immune checkpoint blockade in a non-small cell lung cancer patient. Eur. J. Immunol. 48(10), 1764–1767 (2018).
Medline CASGoogle Scholar
40. Holmes VA. Changes in haemostasis during normal pregnancy: does homocysteine play a role in maintaining homeostasis? Proc. Nutr. Soc. 62(2), 479–493 (2003).
Medline CASGoogle Scholar
41. Xu D, Xu R, He L et al. Comparison of pathogenic mechanisms underlying single and recurrent venous thromboembolism based on gene expression profiling. Ann. Vasc. Surg. 36, 252–259 (2016).
MedlineGoogle Scholar
42. Wang Z, Lu Z, Lin S et al. Leucine-tRNA-synthase-2-expressing B cells contribute to colorectal cancer immunoevasion. Immunity 55(6), 1067–1081; e1068 (2022).
Medline CASGoogle Scholar
43. Cochran BJ, Ong KL, Manandhar B, Rye KA. APOA1: a protein with multiple therapeutic functions. Curr. Atheroscler. Rep. 23(3), 11 (2021).
Medline CASGoogle Scholar
44. Sun Y, Ren D, Yang C et al. TRIM15 promotes the invasion and metastasis of pancreatic cancer cells by mediating APOA1 ubiquitination and degradation. Biochim. Biophys. Acta Mol. Basis Dis. 1867(11), 166213 (2021).
Medline CASGoogle Scholar
45. Ochieng J, Nangami G, Sakwe A et al. Impact of fetuin-A (AHSG) on tumor progression and type 2 diabetes. Int. J. Mol. Sci. 19(8), (2018).
MedlineGoogle Scholar
46. Niu L, Song X, Wang N, Xue L, Song X, Xie L. Tumor-derived exosomal proteins as diagnostic biomarkers in non-small cell lung cancer. Cancer Sci. 110(1), 433–442 (2019).
Medline CASGoogle Scholar
47. Borgquist S, Butt T, Almgren P et al. Apolipoproteins, lipids and risk of cancer. Int. J. Cancer 138(11), 2648–2656 (2016).
Medline CASGoogle Scholar
48. Batsuli G, Kouides P. Rare coagulation factor deficiencies (factors VII, X, V, and II). Hematol. Oncol. Clin. North Am. 35(6), 1181–1196 (2021).
MedlineGoogle Scholar
49. Bayleyegn B, Adane T, Getawa S, Aynalem M, Kifle ZD. Coagulation parameters in lung cancer patients: a systematic review and meta-analysis. J. Clin. Lab. Anal. 36(7), e24550 (2022).
Medline CASGoogle Scholar

Vol. 21, No. 1

Supplemental Materials

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History

Received 17 August 2023

Accepted 31 October 2023

Published online 1 December 2023

Published in print January 2024

Information

Keywords

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/suppl/10.2217/pme-2023-0094

Author contributions

Y Zhou contributed to the study design. W Fan conducted the literature search. S Zhong and J Yang acquired the data. Y Zhong wrote the article. J Zhou performed data analysis. G Huang revised the article. All authors read and approved the final manuscript.

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Conflict of interest disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Availability of data and materials

The data and materials in the current study are available from the corresponding author on reasonable request.

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