Construction of a prognostic prediction model for invasive lung adenocarcinoma based on machine learning_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

CUI Yanqi ¹ , YANG Jingrong ² , NI Lin ³ , LIAN Duohuang ² , YE Shixin ² , LIAO Yi ¹ , ZHANG Jincan ² ,  ZENG Zhiyong ^1,2

1. Fuzong Clinical College of Fujian Medical University, Fuzhou, 350025, P. R. China;
2. Department of Cardiothoracic Surgery, The 900th Hospital of the Joint Support Force, Fuzhou, 350025, P. R. China;
3. Department of General Surgery, The 900th Hospital of the Joint Support Force, Fuzhou, 350025, P. R. China;

Corresponding author：

ZENG Zhiyong, Email: 13295916182@163.com

Keywords：

Invasive lung adenocarcinoma; machine learning; biomarker; risk assessment model; nomogram

DOI：

10.7507/1007-4848.202303059

Video：

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Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD patients. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which were obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA), were employed to analyze the data. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to establish an assessment model. Based on this model, we constructed a nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as metabolism of xenobiotics by cytochrome P450, natural killer cell-mediated cytotoxicity, antigen processing and presentation, and regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the curve (AUC) of 0.675. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.

Citation： CUI Yanqi, YANG Jingrong, NI Lin, LIAN Duohuang, YE Shixin, LIAO Yi, ZHANG Jincan, ZENG Zhiyong. Construction of a prognostic prediction model for invasive lung adenocarcinoma based on machine learning. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(1): 80-86. doi: 10.7507/1007-4848.202303059 Copy

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, 2021, 71(3): 209-249.
2.	Travis WD, Brambilla E, Nicholson AG, et al. The 2015 World Health Organization classification of lung tumors: Impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol, 2015, 10(9): 1243-1260.
3.	Woo W, Yang YH, Cha YJ, et al. Prognosis of resected invasive mucinous adenocarcinoma compared with the IASLC histologic grading system for invasive nonmucinous adenocarcinoma: Surgical database study in the TKIs era in Korea. Thorac Cancer, 2022, 13(23): 3310-3321.
4.	Zheng M, Hu Y, Gou R, et al. Integrated multi-omics analysis of genomics, epigenomics, and transcriptomics in ovarian carcinoma. Aging (Albany NY), 2019, 11(12): 4198-4215.
5.	Szalontai K, Gémes N, Furák J, et al. Chronic obstructive pulmonary disease: Epidemiology, biomarkers, and paving the way to lung cancer. J Clin Med, 2021, 10(13): 2889.
6.	Stipp MC, Acco A. Involvement of cytochrome P450 enzymes in inflammation and cancer: A review. Cancer Chemother Pharmacol, 2021, 87(3): 295-309.
7.	Zeng Y, Lv X, Du J. Natural killer cell-based immunotherapy for lung cancer: Challenges and perspectives (Review). Oncol Rep, 2021, 46(5): 232.
8.	Souza-Fonseca-Guimaraes F, Cursons J, Huntington ND. The emergence of natural killer cells as a major target in cancer immunotherapy. Trends Immunol, 2019, 40(2): 142-158.
9.	Li C, Long Q, Zhang D, et al. Identification of a four-gene panel predicting overall survival for lung adenocarcinoma. BMC Cancer, 2020, 20(1): 1198.
10.	Sawaki K, Kanda M, Umeda S, et al. Level of melanotransferrin in tissue and sera serves as a prognostic marker of gastric cancer. Anticancer Res, 2019, 39(11): 6125-6133.
11.	Ji L, Zhao G, Zhang P, et al. Knockout of MTF1 inhibits the epithelial to mesenchymal transition in ovarian cancer cells. J Cancer, 2018, 9(24): 4578-4585.
12.	Anvari K, Pakdel AF, Memar B, et al. The prevalence and expression pattern of melanoma-associated antigen 1 in esophageal squamous cell carcinoma: A historical cohort study. Electron Physician, 2017, 9(2): 3756-3763.
13.	Fanipakdel A, Seilanian Toussi M, Rezazadeh F, et al. Overexpression of cancer-testis antigen melanoma-associated antigen A1 in lung cancer: A novel biomarker for prognosis, and a possible target for immunotherapy. J Cell Physiol, 2019, 234(7): 12080-12086.
14.	Liu Y, Cao M, Cai Y, et al. Dissecting the role of the FGF19-FGFR4 signaling pathway in cancer development and progression. Front Cell Dev Biol, 2020, 8: 95.
15.	Chen J, Shao J, Shen A, et al. Enhanced expression of FGF19 predicts poor prognosis in patients with non-small cell lung cancer. J Thorac Dis, 2021, 13(3): 1769-1784.
16.	Lang L, Teng Y. Fibroblast growth factor receptor 4 targeting in cancer: New insights into mechanisms and therapeutic strategies. Cells, 2019, 8(1): 31.
17.	Lee S, Choi J, Mohanty J, et al. Structures of β-klotho reveal a 'zip code'-like mechanism for endocrine FGF signalling. Nature, 2018, 553(7689): 501-505.
18.	Yang X, Liu Y, Li W, et al. DKK4-knockdown enhances chemosensitivity of A549/DTX cells to docetaxel. Acta Biochim Biophys Sin (Shanghai), 2017, 49(10): 899-906.
19.	Lou X, Meng Y, Hou Y. A literature review on function and regulation mechanism of DKK4. J Cell Mol Med, 2021, 25(6): 2786-2794.
20.	Huang SL, Chang TC, Chao CCK, et al. Role of the TLR4-androgen receptor axis and genistein in taxol-resistant ovarian cancer cells. Biochem Pharmacol, 2020, 177: 113965.

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, 2021, 71(3): 209-249.
2. Travis WD, Brambilla E, Nicholson AG, et al. The 2015 World Health Organization classification of lung tumors: Impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol, 2015, 10(9): 1243-1260.
3. Woo W, Yang YH, Cha YJ, et al. Prognosis of resected invasive mucinous adenocarcinoma compared with the IASLC histologic grading system for invasive nonmucinous adenocarcinoma: Surgical database study in the TKIs era in Korea. Thorac Cancer, 2022, 13(23): 3310-3321.
4. Zheng M, Hu Y, Gou R, et al. Integrated multi-omics analysis of genomics, epigenomics, and transcriptomics in ovarian carcinoma. Aging (Albany NY), 2019, 11(12): 4198-4215.
5. Szalontai K, Gémes N, Furák J, et al. Chronic obstructive pulmonary disease: Epidemiology, biomarkers, and paving the way to lung cancer. J Clin Med, 2021, 10(13): 2889.
6. Stipp MC, Acco A. Involvement of cytochrome P450 enzymes in inflammation and cancer: A review. Cancer Chemother Pharmacol, 2021, 87(3): 295-309.
7. Zeng Y, Lv X, Du J. Natural killer cell-based immunotherapy for lung cancer: Challenges and perspectives (Review). Oncol Rep, 2021, 46(5): 232.
8. Souza-Fonseca-Guimaraes F, Cursons J, Huntington ND. The emergence of natural killer cells as a major target in cancer immunotherapy. Trends Immunol, 2019, 40(2): 142-158.
9. Li C, Long Q, Zhang D, et al. Identification of a four-gene panel predicting overall survival for lung adenocarcinoma. BMC Cancer, 2020, 20(1): 1198.
10. Sawaki K, Kanda M, Umeda S, et al. Level of melanotransferrin in tissue and sera serves as a prognostic marker of gastric cancer. Anticancer Res, 2019, 39(11): 6125-6133.
11. Ji L, Zhao G, Zhang P, et al. Knockout of MTF1 inhibits the epithelial to mesenchymal transition in ovarian cancer cells. J Cancer, 2018, 9(24): 4578-4585.
12. Anvari K, Pakdel AF, Memar B, et al. The prevalence and expression pattern of melanoma-associated antigen 1 in esophageal squamous cell carcinoma: A historical cohort study. Electron Physician, 2017, 9(2): 3756-3763.
13. Fanipakdel A, Seilanian Toussi M, Rezazadeh F, et al. Overexpression of cancer-testis antigen melanoma-associated antigen A1 in lung cancer: A novel biomarker for prognosis, and a possible target for immunotherapy. J Cell Physiol, 2019, 234(7): 12080-12086.
14. Liu Y, Cao M, Cai Y, et al. Dissecting the role of the FGF19-FGFR4 signaling pathway in cancer development and progression. Front Cell Dev Biol, 2020, 8: 95.
15. Chen J, Shao J, Shen A, et al. Enhanced expression of FGF19 predicts poor prognosis in patients with non-small cell lung cancer. J Thorac Dis, 2021, 13(3): 1769-1784.
16. Lang L, Teng Y. Fibroblast growth factor receptor 4 targeting in cancer: New insights into mechanisms and therapeutic strategies. Cells, 2019, 8(1): 31.
17. Lee S, Choi J, Mohanty J, et al. Structures of β-klotho reveal a 'zip code'-like mechanism for endocrine FGF signalling. Nature, 2018, 553(7689): 501-505.
18. Yang X, Liu Y, Li W, et al. DKK4-knockdown enhances chemosensitivity of A549/DTX cells to docetaxel. Acta Biochim Biophys Sin (Shanghai), 2017, 49(10): 899-906.
19. Lou X, Meng Y, Hou Y. A literature review on function and regulation mechanism of DKK4. J Cell Mol Med, 2021, 25(6): 2786-2794.
20. Huang SL, Chang TC, Chao CCK, et al. Role of the TLR4-androgen receptor axis and genistein in taxol-resistant ovarian cancer cells. Biochem Pharmacol, 2020, 177: 113965.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Construction of a prognostic prediction model for invasive lung adenocarcinoma based on machine learning

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