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Zhang X, Tong X, Chen Y, Chen J, Li Y, Ding C, Ju S, Zhang Y, Zhang H, Zhao J. A metabolomics study on carcinogenesis of ground-glass nodules. Cytojournal 2024; 21:12. [PMID: 38628288 PMCID: PMC11021118 DOI: 10.25259/cytojournal_68_2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/03/2023] [Indexed: 04/19/2024] Open
Abstract
Objective This study aimed to identify differential metabolites and key metabolic pathways between lung adenocarcinoma (LUAD) tissues and normal lung (NL) tissues using metabolomics techniques, to discover potential biomarkers for the early diagnosis of lung cancer. Material and Methods Forty-five patients with primary ground-glass nodules (GGN) identified on computed tomography imaging and who were willing to undergo surgery at Shanghai General Hospital from December 2021 to December 2022 were recruited to the study. All participants underwent video thoracoscopy surgery with segmental or wedge resection of the lung. Tissue samples for pathological examination were collected from the site of ground-glass nodules (GGN) lesion and 3 cm away from the lesion (NL). The pathology results were 35 lung adenocarcinoma (LUAD) cases (13 invasive adenocarcinoma, 14 minimally invasive adenocarcinoma, and eight adenocarcinoma in situ), 10 benign samples, and 45 NL tissues. For the untargeted metabolomics technique, 25 LUAD samples were assigned as the case group and 30 NL tissues as the control group. For the targeted metabolomics technique, ten LUAD samples were assigned as the case group and 15 NL tissues as the control group. Samples were analyzed by untargeted and targeted metabolomics, with liquid chromatography-tandem mass spectrometry detection used as part of the experimental procedure. Results Untargeted metabolomics revealed 164 differential metabolites between the case and control groups, comprising 110 up regulations and 54 down regulations. The main metabolic differences found by the untargeted method were organic acids and their derivatives. Targeted metabolomics revealed 77 differential metabolites between the case and control groups, comprising 69 up regulations and eight down regulations. The main metabolic changes found by the targeted method were fatty acids, amino acids, and organic acids. The levels of organic acids such as lactic acid, fumaric acid, and malic acid were significantly increased in LUAD tissue compared to NL. Specifically, an increased level of L-lactic acid was found by both untargeted (variable importance in projection [VIP] = 1.332, fold-change [FC] = 1.678, q = 0.000) and targeted metabolomics (VIP = 1.240, FC = 1.451, q = 0.043). Targeted metabolomics also revealed increased levels of fumaric acid (VIP = 1.481, FC = 1.764, q = 0.106) and L-malic acid (VIP = 1.376, FC = 1.562, q = 0.012). Most of the 20 differential fatty acids identified were downregulated, including dodecanoic acid (VIP = 1.416, FC = 0.378, q = 0.043) and tridecane acid (VIP = 0.880, FC = 0.780, q = 0.106). Furthermore, increased levels of differential amino acids were found in LUAD samples. Conclusion Lung cancer is a complex and heterogeneous disease with diverse genetic alterations. The study of metabolic profiles is a promising research field in this cancer type. Targeted and untargeted metabolomics revealed significant differences in metabolites between LUAD and NL tissues, including elevated levels of organic acids, decreased levels of fatty acids, and increased levels of amino acids. These metabolic features provide valuable insights into LUAD pathogenesis and can potentially serve as biomarkers for prognosis and therapy response.
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Affiliation(s)
- Xiaomiao Zhang
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xin Tong
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yuan Chen
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jun Chen
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yu Li
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Cheng Ding
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Sheng Ju
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yi Zhang
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Hang Zhang
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jun Zhao
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
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Shi W, Cheng Y, Zhu H, Zhao L. Metabolomics and lipidomics in non-small cell lung cancer. Clin Chim Acta 2024; 555:117823. [PMID: 38325713 DOI: 10.1016/j.cca.2024.117823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/09/2024]
Abstract
Due to its insidious nature, lung cancer remains a leading cause of cancer-related deaths worldwide. Therefore, there is an urgent need to identify sensitive/specific biomarkers for early diagnosis and monitoring. The current study was designed to provide a current metabolic profile of non-small cell lung cancer (NSCLC) by systematically reviewing and summarizing various metabolomic/ lipidomic studies based on NSCLC blood samples, attempting to find biomarkers in human blood that can predict or diagnose NSCLC, and investigating the involvement of key metabolites in the pathogenesis of NSCLC. We searched all articles on lung cancer published in Elsevier, PubMed, Web of Science and the Cochrane Library between January 2012 and December 2022. After critical selection, a total of 31 studies (including 2768 NSCLC patients and 9873 healthy individuals) met the inclusion criteria, and 22 were classified as "high quality". Forty-six metabolites related to NSCLC were repeatedly identified, involving glucose metabolism, amino acid metabolism, lipid metabolism and nucleotide metabolism. Pyruvic acid, carnitine, phenylalanine, isoleucine, kynurenine and 3-hydroxybutyrate showed upward trends in all studies, citric acid, glycine, threonine, cystine, alanine, histidine, inosine, betaine and arachidic acid showed downward trends in all studies. This review summarizes the existing metabolomic/lipidomic studies related to the identification of blood biomarkers in NSCLC, examines the role of key metabolites in the pathogenesis of NSCLC, and provides an important reference for the clinical diagnosis and treatment of NSCLC. Due to the limited size and design heterogeneity of the existing studies, there is an urgent need for standardization of future studies, while validating existing findings with more studies.
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Affiliation(s)
- Wei Shi
- Shenyang Pharmaceutical University, 103 Wenhua Road Shenhe District, 110016 Shenyang, Liaoning Province, PR China
| | - Yizhen Cheng
- Shenyang Pharmaceutical University, 103 Wenhua Road Shenhe District, 110016 Shenyang, Liaoning Province, PR China
| | - Haihua Zhu
- Betta Pharmaceuticals Co., Ltd, 24 Wuzhou Road Yuhang Economic and Technological Development Area, Hangzhou, Zhejiang Province, PR China
| | - Longshan Zhao
- Shenyang Pharmaceutical University, 103 Wenhua Road Shenhe District, 110016 Shenyang, Liaoning Province, PR China.
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Almalki AH. Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer. Metabolites 2023; 13:1037. [PMID: 37887362 PMCID: PMC10609104 DOI: 10.3390/metabo13101037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 10/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.
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Affiliation(s)
- Atiah H. Almalki
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
- Addiction and Neuroscience Research Unit, Health Science Campus, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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Shestakova KM, Moskaleva NE, Boldin AA, Rezvanov PM, Shestopalov AV, Rumyantsev SA, Zlatnik EY, Novikova IA, Sagakyants AB, Timofeeva SV, Simonov Y, Baskhanova SN, Tobolkina E, Rudaz S, Appolonova SA. Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer. Sci Rep 2023; 13:11072. [PMID: 37422585 PMCID: PMC10329697 DOI: 10.1038/s41598-023-38140-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/04/2023] [Indexed: 07/10/2023] Open
Abstract
Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed new ratios of the metabolites that significantly distinguished the considered groups of participants. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC.
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Affiliation(s)
- Ksenia M Shestakova
- World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
| | - Natalia E Moskaleva
- World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
| | - Andrey A Boldin
- Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
| | - Pavel M Rezvanov
- Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
| | | | - Sergey A Rumyantsev
- Pirogov Russian National Research Medical University, Moscow, Russia, 117997
| | - Elena Yu Zlatnik
- National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019
| | - Inna A Novikova
- National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019
| | - Alexander B Sagakyants
- National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019
| | - Sofya V Timofeeva
- National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019
| | - Yuriy Simonov
- Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435
| | - Sabina N Baskhanova
- World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
| | - Elena Tobolkina
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1206, Geneva 4, Switzerland.
| | - Serge Rudaz
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1206, Geneva 4, Switzerland
| | - Svetlana A Appolonova
- Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
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Hu S, Zhao C, Wang Z, Li Z, Kong X. Clinical diagnostic value of amino acids in laryngeal squamous cell carcinomas. PeerJ 2023; 11:e15469. [PMID: 37283897 PMCID: PMC10239616 DOI: 10.7717/peerj.15469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/05/2023] [Indexed: 06/08/2023] Open
Abstract
Background Early diagnosis and treatment can improve the survival rates of patients with laryngeal squamous cell carcinoma (LSCC). Therefore, it is necessary to discover new biomarkers for laryngeal cancer screening and early diagnosis. Methods We collected fasting plasma from LSCC patients and healthy volunteers, as well as cancer and para-carcinoma tissues from LSCC patients, and performed quantitative detection of amino acid levels using liquid chromatography-mass spectrometry. We used overall analysis and multivariate statistical analysis to screen out the statistically significant differential amino acids in the plasma and tissue samples, conducted receiver operating characteristic (ROC) analysis of the differential amino acids to evaluate the sensitivity and specificity of the differential amino acids, and finally determined the diagnostic value of amino acids for laryngeal cancer. Additionally, we identified amino acids in the plasma and tissue samples that are valuable for the early diagnosis of laryngeal cancer classified according to the tumor-node-metastasis (TNM) classification. Results Asparagine (Asp) and homocysteine (Hcy) were two amino acids of common significance in plasma and tissue samples, and their specificity and sensitivity analysis showed that they may be new biomarkers for the diagnosis and treatment of LSCC. According to the TNM staging system, phenylalanine (Phe) and isoleucine (Ile) were screened out in the plasma of LSCC patients at early (I and II) and advanced (III and IV) stages; ornithine hydrochloride (Orn), glutamic acid (Glu), and Glycine (Gly) were selected in the tissue. These dysregulated amino acids found in LSCC patients may be useful as clinical biomarkers for the early diagnosis and screening of LSCC.
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Affiliation(s)
- Shousen Hu
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chang Zhao
- Department of Otorhinolaryngology, Zhengzhou Seventh People’s Hospital, Zhengzhou, China
| | - Zi’an Wang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Zeyun Li
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Xiangzhen Kong
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
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Li J, Liu K, Ji Z, Wang Y, Yin T, Long T, Shen Y, Cheng L. Serum untargeted metabolomics reveal metabolic alteration of non-small cell lung cancer and refine disease detection. Cancer Sci 2022; 114:680-689. [PMID: 36310111 PMCID: PMC9899604 DOI: 10.1111/cas.15629] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/09/2022] [Accepted: 10/14/2022] [Indexed: 01/07/2023] Open
Abstract
This study was performed to characterize the metabolic alteration of non-small-cell lung cancer (NSCLC) and discover blood-based metabolic biomarkers relevant to lung cancer detection. An untargeted metabolomics-based approach was applied in a case-control study with 193 NSCLC patients and 243 healthy controls. Serum metabolomics were determined by using an ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method. We screened differential metabolites based on univariate and multivariate analysis, followed by identification of the metabolites and related pathways. For NSCLC detection, machine learning was employed to develop and validate the model based on the altered serum metabolite features. The serum metabolic pattern of NSCLC was definitely different from the healthy condition. In total, 278 altered features were found in the serum of NSCLC patients comparing with healthy people. About one-fifth of the abundant differential features were identified successfully. The altered metabolites were enriched in metabolic pathways such as phenylalanine metabolism, linoleic acid metabolism, and biosynthesis of bile acids. We demonstrated a panel of 10 metabolic biomarkers which representing excellent discriminating capability for NSCLC discrimination, with a combined area under the curve (AUC) in the validation set of 0.95 (95% CI: 0.91-0.98). Moreover, this model showed a desirable performance for the detection of NSCLC at an early stage (AUC = 0.95, 95% CI: 0.92-0.97). Our study offers a perspective on NSCLC metabolic alteration. The finding of the biomarkers might shed light on the clinical detection of lung cancer, especially for those cancers in an early stage in Chinese population.
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Affiliation(s)
- Jiaoyuan Li
- Department of Laboratory MedicineTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Ke Liu
- Department of Laboratory MedicineTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Zhi Ji
- Department of Laboratory MedicineTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Yi Wang
- Department of Laboratory MedicineTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Tongxin Yin
- Department of Laboratory MedicineTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Tingting Long
- Department of Laboratory MedicineTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Ying Shen
- Department of Laboratory MedicineTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Liming Cheng
- Department of Laboratory MedicineTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
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