Clinical value of metabolomics in assessing the malignant risk of pulmonary nodules

Abstract

Abstract:

ObjectiveTo evaluate the diagnostic value of non-targeted detection of metabolic fingerprinting in pulmonary nodules and to analyze the clinical effective model of multi-omics for assessing the malignant risk of pulmonary nodules.MethodsA total of 73 patients who underwent chest CT and completed pathological diagnosis and non-targeted detection of metabolic fingerprinting at Shandong Provincial Hospital Affiliated to Shandong First Medical University from November 2021 to October 2024 were selected as the research subjects. According to the postoperative histopathological diagnosis，the patients were divided into the lung malignant nodule group （61 cases） and the lung benign nodule group （12 cases）. General clinical data of the patients，including sex，age，smoking history，and family history of tumors，as well as imaging data，including nodule density，nodule size，nodule location，nodule number，and special imaging manifestations （spiculation，lobulation，vacuole sign，vascular convergence sign，etc.），and non-targeted detection of metabolic fingerprinting results were collected. The above data were compared between the two groups of patients，and the receiver operator characteristic （ROC） curve was drawn to evaluate the predictive value of each model.ResultsThere were statistically significant differences in age （t=4.41，P<0.001），nodule size （Z=2.67，P=0.008），nodule density （χ²=4.64，P=0.031），and spiculation （χ²=7.67，P=0.006） between the lung malignant nodule group and the lung benign nodule group. There were no statistically significant differences in sex，smoking history，family history of lung cancer，nodule number，nodule location，lobulation，vacuole sign，vascular convergence sign，pleural indentation sign，calcification sign，bronchial truncation sign，vascular supply sign，and bronchial air sign （allP>0.05）. The number of non-targeted detection of metabolic fingerprinting high-risk patients in the lung malignant nodule group （36 cases） was significantly higher than that in the lung benign nodule group （0 case）（χ²=13.97，P<0.001）. ROC curve analysis showed that the area under the curve of the Brock model combined with non-targeted detection of metabolic fingerprinting was 0.930 （95%CI： 0.872-0.988），which was greater than that of the Brock model （0.856，95%CI： 0.769-0.942，Z=0.27，P=0.040） and non-targeted detection of metabolic fingerprinting （0.768，95%CI： 0.650-0.887，Z=0.30，P=0.004） alone.ConclusionsNon-targeted detection of metabolic fingerprinting risk assessment may serve as a non-invasive method to assist the Brock model in the diagnosis of pulmonary nodules and has good application value. The combination of the Brock model and non-targeted detection of metabolic fingerprinting can more accurately distinguish the benign and malignant nature of pulmonary nodules.

Key words:Lung neoplasms,Early diagnosis,Metabolomics

Li Xiaoxuan, Xia Zhipeng, Luan Rumei, Wan Yunyan, Yao Zhouhong, Lin Xinshan, Lin Dianjie. Clinical value of metabolomics in assessing the malignant risk of pulmonary nodules[J]. Journal of International Oncology, 2025, 52(7): 409-413.

Figures/Tables4

项目	肺恶性结节组（n=61）	肺良性结节组（n=12）	χ²/t值	P值
性别
男	25（41）	7（58）	1.22	0.268
女	36（59）	5（42）
年龄（岁）	55.48±11.49	38.92±13.81	4.41	<0.001
吸烟史
有	14（23）	3（25）	<0.01	>0.999
无	47（77）	9（75）
肺癌家族史
有	8（13）	0（0）	0.67	0.410
无	53（87）	12（100）

项目	肺恶性结节组（n=61）	肺良性结节组（n=12）	Z/χ²值	P值
结节大小（mm）	1.0（0.8，1.5）	0.8（0.6，1.0）	2.67	0.008
结节数量（个）
≤4	29（47）	4（33）	0.82	0.366
≥5	32（53）	8（67）	0.82	0.366
结节位置
上叶	29（48）	6（50）	0.02	0.876
中叶及下叶	32（52）	6（50）	0.02	0.876
结节密度
亚实性结节	54（89）	7（58）	4.64	0.031
实性结节	7（11）	5（42）	4.64	0.031
毛刺征
有	51（84）	5（42）	7.67	0.006
无	10（16）	7（58）	7.67	0.006
分叶征
有	60（98）	10（83）		0.068
无	1（2）	2（17）		0.068
空泡征
有	28（46）	6（50）	0.07	0.795
无	33（54）	6（50）
血管集束征
有	31（51）	4（33）
无	30（49）	8（67）
胸膜凹陷征
有	27（44）	9（75）
无	34（56）	3（25）
钙化征
有	1（1）	0（0）
无	60（99）	12（100）
支气管截断征
有	11（18）	1（8）
无	50（82）	11（92）
血管供应征
有	59（96）	12（100）
无	2（4）	0（0）
支气管充气征
有	19（32）	4（33）
无	42（68）	8（67）

References27

[1]	Detterbeck FC, Chansky K, Groome P, et al. The IASLC lung cancer staging project: methodology and validation used in the development of proposals for revision of the stage classification of NSCLC in the forthcoming (eighth) edition of the TNM classification of lung cancer[J].J Thorac Oncol,2016,11(9): 1433-1446. DOI:10.1016/j.jtho.2016.06.028. pmid:27448762
[2]	Silvestri GA, Vachani A, Whitney D, et al. A bronchial genomic classifier for the diagnostic evaluation of lung cancer[J].N Engl J Med,2015,373(3): 243-251. DOI:10.1056/NEJMoa1504601.
[3]	梁新雨, 危志刚, 叶欣. 肺部磨玻璃结节的过度诊断与过度治疗现状及对策[J].国际肿瘤学杂志,2024,51(7): 432-440. DOI:10.3760/cma.j.cn371439-20240509-00071.
[4]	Jiang ZF, Wang M, Xu JL. Thymidine kinase 1 combined with CEA, CYFRA21-1 and NSE improved its diagnostic value for lung cancer[J].Life Sci,2018,194: 1-6. DOI:10.1016/j.lfs.2017.12.020. pmid:29247745
[5]	Rinschen MM, Ivanisevic J, Giera M, et al. Identification of bioactive metabolites using activity metabolomics[J].Nat Rev Mol Cell Biol,2019,20(6): 353-367. DOI:10.1038/s41580-019-0108-4.
[6]	Yu L, Li K, Zhang X. Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review[J].Oncotarget,2017,8(70): 115774-115786. DOI:10.18632/oncotarget.22404.
[7]	McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT[J].N Engl J Med,2013,369(10): 910-919. DOI:10.1056/NEJMoa1214726.
[8]	Nair VS, Sundaram V, Desai M, et al. Accuracy of models to identify lung nodule cancer risk in the national lung screening trial[J].Am J Respir Crit Care Med,2018,197(9): 1220-1223. DOI:10.1164/rccm.201708-1632LE.
[9]	Massion PP, Antic S, Ather S, et al. Assessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules[J].Am J Respir Crit Care Med,2020,202(2): 241-249. DOI:10.1164/rccm.201903-0505OC.
[10]	Wang L, Zhang M, Pan X, et al. Integrative serum metabolic fingerprints based multi-modal platforms for lung adenocarcinoma early detection and pulmonary nodule classification[J].Adv Sci (Weinh),2022,9(34): e2203786. DOI:10.1002/advs.202203786.
[11]	Filho AM, Laversanne M, Ferlay J, et al. The GLOBOCAN 2022 cancer estimates: data sources, methods, and a snapshot of the cancer burden worldwide[J].Int J Cancer, 2025,156(7): 1336-1346. DOI:10.1002/ijc.35278.
[12]	Leiter A, Veluswamy RR, Wisnivesky JP. The global burden of lung cancer: current status and future trends[J].Nat Rev Clin Oncol,2023,20(9): 624-639. DOI:10.1038/s41571-023-00798-3. pmid:37479810
[13]	郑荣寿, 张思维, 孙可欣, 等. 2016年中国恶性肿瘤流行情况分析[J].中华肿瘤杂志,2023,45(3): 212-220. DOI:10.3760/cma.j.cn112152-20220922-00647.
[14]	张彩云, 韩志海. 2024年版肺结节诊治中国专家共识解读[J].河北医科大学学报,2025,46(4): 373-378.
[15]	Mosmann MP, Borba MA, de Macedo FP, et al. Solitary pulmonary nodule and¹⁸F-FDG PET/CT. Part 1: epidemiology, morphological evaluation and cancer probability[J].Radiol Bras,2016,49(1): 35-42. DOI:10.1590/0100-3984.2014.0012.
[16]	中华医学会呼吸病学分会, 中国肺癌防治联盟专家组. 肺结节诊治中国专家共识(2024年版)[J].中华结核和呼吸杂志,2024,47(8): 716-729. DOI:10.3760/cma.j.cn112147-20231109-00300.
[17]	Cruickshank A, Stieler G, Ameer F. Evaluation of the solitary pulmonary nodule[J].Intern Med J,2019,49(3): 306-315. DOI:10.1111/imj.14219. pmid:30897667
[18]	Horeweg N, van Rosmalen J, Heuvelmans MA, et al. Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening[J].Lancet Oncol,2014,15(12): 1332-1341. DOI:10.1016/s1470-2045(14)70389-4. pmid:25282285
[19]	Hammer MM, Hatabu H. Subsolid pulmonary nodules: controversy and perspective[J].Eur J Radiol Open,2020,7: 100267. DOI:10.1016/j.ejro.2020.100267.
[20]	Lee SM, Park CM, Goo JM, et al. Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as ground-glass nodules: differentiation by using CT features[J].Radiology,2013,268(1): 265-273. DOI:10.1148/radiol.13120949. pmid:23468575
[21]	Swensen SJ, Silverstein MD, Ilstrup DM, et al. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules[J].Arch Intern Med,1997,157(8): 849-855. pmid:9129544
[22]	Phua CK, Sim WY, Sen TK, et al. Evaluation of pulmonary nodules in Asian population[J].J Thorac Dis,2016,8(5): 950-957. DOI:10.21037/jtd.2016.03.12. pmid:27162671
[23]	MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017[J].Radiology,2017,284(1): 228-243. DOI:10.1148/radiol.2017161659. pmid:28240562
[24]	Zhang X, Tong X, Chen Y, et al. A metabolomics study on carcinogenesis of ground-glass nodules[J].Cytojournal,2024,21: 12. DOI:10.25259/Cytojournal_68_2023. pmid:38628288
[25]	Huang L, Wang L, Hu X, et al. Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma[J].Nat Commun,2020,11(1): 3556. DOI:10.1038/s41467-020-17347-6.
[26]	Yao Y, Wang X, Guan J, et al. Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera[J].Nat Commun,2023,14(1): 2339. DOI:10.1038/s41467-023-37875-1.
[27]	Liang W, Tao J, Cheng C, et al. A clinically effective model based on cell-free DNA methylation and low-dose CT for risk stratification of pulmonary nodules[J].Cell Rep Med,2024,5(10): 101750. DOI:10.1016/j.xcrm.2024.101750.