Lung adenocarcinoma (LUAD), the most common subtype of lung cancer, is a global health challenge with high recurrence and mortality rates. The coagulation cascade plays an essential role in tumor disease progression and leads to death in LUAD. We differentiated two coagulation-related subtypes in LUAD patients in this study based on coagulation pathways collected from the KEGG database. We then demonstrated significant differences between the two coagulation-associated subtypes regarding immune characteristics and prognostic stratification. For risk stratification and prognostic prediction, we developed a coagulation-related risk score prognostic model in the Cancer Genome Atlas (TCGA) cohort. The GEO cohort also validated the predictive value of the coagulation-related risk score in terms of prognosis and immunotherapy. Based on these results, we identified coagulation-related prognostic factors in LUAD, which may serve as a robust prognostic biomarker for therapeutic and immunotherapeutic efficacy. It may contribute to clinical decision-making in patients with LUAD.
Citation: Mengyang Han, Xiaoli Wang, Yaqi Li, Jianjun Tan, Chunhua Li, Wang Sheng. Identification of coagulation-associated subtypes of lung adenocarcinoma and establishment of prognostic models[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10626-10658. doi: 10.3934/mbe.2023470
Lung adenocarcinoma (LUAD), the most common subtype of lung cancer, is a global health challenge with high recurrence and mortality rates. The coagulation cascade plays an essential role in tumor disease progression and leads to death in LUAD. We differentiated two coagulation-related subtypes in LUAD patients in this study based on coagulation pathways collected from the KEGG database. We then demonstrated significant differences between the two coagulation-associated subtypes regarding immune characteristics and prognostic stratification. For risk stratification and prognostic prediction, we developed a coagulation-related risk score prognostic model in the Cancer Genome Atlas (TCGA) cohort. The GEO cohort also validated the predictive value of the coagulation-related risk score in terms of prognosis and immunotherapy. Based on these results, we identified coagulation-related prognostic factors in LUAD, which may serve as a robust prognostic biomarker for therapeutic and immunotherapeutic efficacy. It may contribute to clinical decision-making in patients with LUAD.
[1] | F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, A. Jemal, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J. Clin., 68 (2018), 394–424. https://doi.org/10.3322/caac.21492 doi: 10.3322/caac.21492 |
[2] | W. Chen, R. Zheng, P. D. Baade, S. Zhang, H. Zeng, F. Bray, et al., Cancer statistics in China, 2015, CA Cancer J. Clin., 66 (2016), 115–132. https://doi.org/10.3322/caac.21338 doi: 10.3322/caac.21338 |
[3] | D. Yang, Y. Liu, C. Bai, X. Wang, C. A. Powell, Epidemiology of lung cancer and lung cancer screening programs in China and the United States, Cancer Lett., 468 (2020), 82–87. https://doi.org/10.1016/j.canlet.2019.10.009 doi: 10.1016/j.canlet.2019.10.009 |
[4] | R. Ruiz-Cordero, W. P. Devine, Targeted therapy and checkpoint immunotherapy in lung cancer, Surg. Pathol. Clin., 13 (2020), 17–33. https://doi.org/10.1016/j.path.2019.11.002 doi: 10.1016/j.path.2019.11.002 |
[5] | J. Vansteenkiste, L. Crinò, C. Dooms, J. Y. Douillard, C. Faivre-Finn, E. Lim, et al., 2nd ESMO consensus conference on lung cancer: early-stage non-small-cell lung cancer consensus on diagnosis, treatment and follow-up, Ann. Oncol. Off. J. Eur. Soc. Med. Oncol., 25 (2014), 1462–1474. https://doi.org/10.1093/annonc/mdu089 doi: 10.1093/annonc/mdu089 |
[6] | F. R. Hirsch, P. A. Bunn Jr, Adjuvant TKIs in NSCLC: what can we learn from RADIANT, Nat. Rev. Clin. Oncol., 12 (2015), 689–690. https://doi.org/10.1038/nrclinonc.2015.202 doi: 10.1038/nrclinonc.2015.202 |
[7] | S. Sampath, Treatment: Radiation therapy, in Lung Cancer, Springer, 170 (2016), 105–118. https://doi.org/10.1007/978-3-319-40389-2_5 |
[8] | M. Ahn, J. M. Sun, S. H. Lee, J. S. Ahn, K. Park, EGFR TKI combination with immunotherapy in non-small cell lung cancer, Expert Opin. Drug Saf., 16 (2017), 465–469. https://doi.org/10.1080/14740338.2017.1300656 doi: 10.1080/14740338.2017.1300656 |
[9] | J. F. Gainor, A. M. Varghese, S. H. Ignatius Ou, S. Kabraji, M. M. Awad, R. Katayama, et al., ALK rearrangements are mutually exclusive with mutations in EGFR or KRAS: an analysis of 1683 patients with non-small cell lung cancer, Clin. Cancer Res., 19 (2013), 4273–4281. https://doi.org/10.1158/1078-0432.CCR-13-0318 doi: 10.1158/1078-0432.CCR-13-0318 |
[10] | Z. Wang, K. S. Embaye, Q. Yang, L. Qin, C. Zhang, L. Liu, et al., Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism-related genes, Cancer Cell Int., 21 (2021). https://doi.org/10.1186/s12935-021-01915-x doi: 10.1186/s12935-021-01915-x |
[11] | P. E. Serrano, S. Parpia, L. A. Linkins, L. Elit, M. Simunovic, L. Ruo, et al., Venous thromboembolic events following major pelvic and abdominal surgeries for cancer: A prospective cohort study, Ann. Surg. Oncol., 25 (2018), 3214–3221. https://doi.org/10.1245/s10434-018-6671-7 doi: 10.1245/s10434-018-6671-7 |
[12] | A. Falanga, M. Marchetti, L. Russo, The mechanisms of cancer-associated thrombosis, Thromb. Res., 135 (2015), 8–11. https://doi.org/10.1016/S0049-3848(15)50432-5 doi: 10.1016/S0049-3848(15)50432-5 |
[13] | L. Bao, S. Zhang, X. Gong, G. Cui, Trousseau syndrome related cerebral infarction: Clinical manifestations, laboratory findings and radiological features, J. Stroke Cerebrovasc. Dis., 29 (2020), 104891. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.104891 doi: 10.1016/j.jstrokecerebrovasdis.2020.104891 |
[14] | Y. Li, S. Wei, J. Wang, L. Hong, L. Cui, C. Wang, Analysis of the factors associated with abnormal coagulation and prognosis in patients with non-small cell lung cancer (in Chinese), Zhongguo fei ai za zhi, 17 (2014), 789–796. https://doi.org/10.3779/j.issn.1009-3419.2014.11.04 doi: 10.3779/j.issn.1009-3419.2014.11.04 |
[15] | M. J. Goldman, M. J. Craft, M. Hastie, K. Repečka, F. McDade, A. Kamath, et al., Visualizing and interpreting cancer genomics data via the Xena platform, Nat. Biotechnol., 38 (2020), 675–678. https://doi.org/10.1038/s41587-020-0546-8 doi: 10.1038/s41587-020-0546-8 |
[16] | Q. He, J. Yang, Y. Jin, Immune infiltration and clinical significance analyses of the coagulation-related genes in hepatocellular carcinoma, Briefings Bioinf., 23 (2022). https://doi.org/10.1093/bib/bbac291 doi: 10.1093/bib/bbac291 |
[17] | C. Ren, J. Li, Y. Zhou, S. Zhang, Q. Wang, Typical tumor immune microenvironment status determine prognosis in lung adenocarcinoma, Transl. Oncol., 18 (2022), 101367. https://doi.org/10.1016/j.tranon.2022.101367 doi: 10.1016/j.tranon.2022.101367 |
[18] | A. Mayakonda, D. C. Lin, Y. Assenov, C. Plass, H. P. Koeffler, Maftools: efficient and comprehensive analysis of somatic variants in cancer, Genome Res., 28 (2018), 1747–1756. https://doi.org/10.1101/gr.239244.118 doi: 10.1101/gr.239244.118 |
[19] | Y. Zhou, T. O. Sharpee, Using global t-SNE to preserve intercluster data structure, Neural Comput., 34 (2022), 1637–1651. https://doi.org/10.1162/neco_a_01504 doi: 10.1162/neco_a_01504 |
[20] | M. I. Love, W. Huber, S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol., 15 (2014), 550. https://doi.org/10.1186/s13059-014-0550-8 doi: 10.1186/s13059-014-0550-8 |
[21] | The Gene Ontology Consortium, Gene Ontology Consortium: going forward, Nucleic Acids Res., 43 (2015), 1049–1056. https://doi.org/10.1093/nar/gku1179 doi: 10.1093/nar/gku1179 |
[22] | G. Yu, L. G. Wang, Y. Han, Q. Y. He, clusterProfiler: an R package for comparing biological themes among gene clusters, OMICS: J. Integr. Biol., 16 (2012), 284–287. https://doi.org/10.1089/omi.2011.0118 doi: 10.1089/omi.2011.0118 |
[23] | J. Friedman, T. Hastie, R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, J. Stat. Software, 33 (2010), 1–22. |
[24] | P. J. Heagerty, T. Lumley, M. S. Pepe, Time-dependent ROC curves for censored survival data and a diagnostic marker, Biometrics, 56 (2000), 337–344. https://doi.org/10.1111/j.0006-341x.2000.00337.x doi: 10.1111/j.0006-341x.2000.00337.x |
[25] | Z. Gu, R. Eils, M. Schlesner, Complex heatmaps reveal patterns and correlations in multidimensional genomic data, Bioinformatics, 32 (2016), 2847–2849. https://doi.org/10.1093/bioinformatics/btw313 doi: 10.1093/bioinformatics/btw313 |
[26] | B. Chen, M. S. Khodadoust, C. L. Liu, A. M. Newman, A. A. Alizadeh, Profiling tumor infiltrating immune cells with CIBERSORT, in Cancer Systems Biology, Springer Nature, 1711 (2018), 243–259. https://doi.org/10.1007/978-1-4939-7493-1_12 |
[27] | H. T. Sørensen, L. Mellemkjaer, J. H. Olsen, J. A. Baron, Prognosis of cancers associated with venous thromboembolism, N. Engl. J. Med., 343 (2000), 1846–1850. https://doi.org/10.1056/NEJM200012213432504 doi: 10.1056/NEJM200012213432504 |
[28] | Y. B. Yu, J. P. Gau, C. Y. Liu, M. Yang, S. Chiang, H. Hsu, et al., A nation-wide analysis of venous thromboembolism in 497,180 cancer patients with the development and validation of a risk-stratification scoring system, Thromb. Haemostasis, 108 (2012), 225–235. https://doi.org/10.1160/TH12-01-0010 doi: 10.1160/TH12-01-0010 |
[29] | N. S. Kwon, K. J. Baek, D. S. Kim, H. Y. Yun, Leucine-rich glioma inactivated 3: Integrative analyses reveal its potential prognostic role in cancer, Mol. Med. Rep., 17 (2018), 3993–4002. https://doi.org/10.3892/mmr.2017.8279 doi: 10.3892/mmr.2017.8279 |
[30] | A. P. Wolffe, Architectural transcription factors, Science, 264 (1994), 1100–1101. https://doi.org/10.1126/science.8178167 doi: 10.1126/science.8178167 |
[31] | Z. Shang, X. Niu, K. Zhang, Z. Qiao, S. Liu, X. Jiang, et al., FGA isoform as an indicator of targeted therapy for EGFR mutated lung adenocarcinoma, J. Mol. Med., 97 (2019), 1657–1668. https://doi.org/10.1007/s00109-019-01848-z doi: 10.1007/s00109-019-01848-z |
[32] | M. Majesky, Vascular development, Arterioscler. Thromb. Vasc. Biol., 38 (2018), 17–24. https://doi.org/10.1161/ATVBAHA.118.310223 doi: 10.1161/ATVBAHA.118.310223 |