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Xu Y, Ding K, Peng Z, Ding L, Li H, Fan Y. Evaluating for Correlations between Specific Metabolites in Patients Receiving First-Line or Second-Line Immunotherapy for Metastatic or Recurrent NSCLC: An Exploratory Study Based on Two Cohorts. Mol Cancer Ther 2024; 23:733-742. [PMID: 38346938 PMCID: PMC11063768 DOI: 10.1158/1535-7163.mct-23-0459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 11/07/2023] [Accepted: 02/06/2024] [Indexed: 05/03/2024]
Abstract
Immune checkpoint inhibitors (ICI) have displayed impressive clinical efficacy in the context of non-small cell lung cancer (NSCLC). However, most patients do not achieve long-term survival. Minimally invasive collected samples are attracting significant interest as new fields of biomarker study, and metabolomics is one of these growing fields. We concentrated on the augmented value of the metabolomic profile in differentiating long-term survival from short-term survival in patients with NSCLC subjected to ICIs. We prospectively recruited 97 patients with stage IV NSCLC who were treated with anti-PD-1 inhibitor, including patients treated with monoimmunotherapy as second-line treatment (Cohort 1), and patients treated with combination immunotherapy as first-line treatment (Cohort 2). Each cohort was divided into long-term and short-term survival groups. All blood samples were collected before beginning immunotherapy. Serum metabolomic profiling was performed by UHPLC-Q-TOF MS analysis. Pareto-scaled principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis were performed. In Cohort 1, the mPFS and mOS of long-survival patients are 27.05 and NR months, respectively, and those of short-survival patients are 2.79 and 10.59 months. In Cohort 2, the mPFS and mOS of long-survival patients are 27.35 and NR months, respectively, and those of short-survival patients are 3.77 and 12.17 months. A total of 41 unique metabolites in Cohort 1 and 47 in Cohort 2 were screened. In Cohorts 1 and 2, there are 6 differential metabolites each that are significantly associated with both progression-free survival and overall survival. The AUC values for all groups ranged from 0.73 to 0.95. In cohort 1, the top 3 enriched KEGG pathways, as determined through significant different metabolic pathway analysis, were primary bile acid biosynthesis, African trypanosomiasis, and choline metabolism in cancer. In Cohort 2, the top 3 enriched KEGG pathways were the citrate cycle (TCA cycle), PPAR signaling pathway, and primary bile acid biosynthesis. The primary bile acid synthesis pathway had significant differences in the long-term and short-term survival groups in both Cohorts 1 and 2. Our study suggests that peripheral blood metabolomic analysis is critical for identifying metabolic biomarkers and pathways responsible for the patients with NSCLC treated with ICIs.
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Affiliation(s)
- Yanjun Xu
- Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Kaibo Ding
- Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Zhongsheng Peng
- Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Ling Ding
- Institute of Pharmacology and Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Hui Li
- Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Yun Fan
- Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
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Madama D, Carrageta DF, Guerra-Carvalho B, Botelho MF, Oliveira PF, Cordeiro CR, Alves MG, Abrantes AM. Impact of Different Treatment Regimens and Timeframes in the Plasmatic Metabolic Profiling of Patients with Lung Adenocarcinoma. Metabolites 2023; 13:1180. [PMID: 38132862 PMCID: PMC10744969 DOI: 10.3390/metabo13121180] [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: 10/27/2023] [Revised: 11/20/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023] Open
Abstract
In recent years, the treatment of advanced non-small cell lung cancer (NSCLC) has suffered a variety of alterations. Chemotherapy (CTX), immunotherapy (IT) and tyrosine kinase inhibitors (TKI) have shown remarkable results. However, not all patients with NSCLC respond to these drug treatments or receive durable benefits. In this framework, metabolomics has been applied to improve the diagnosis, treatment, and prognosis of lung cancer and particularly lung adenocarcinoma (AdC). In our study, metabolomics was used to analyze plasma samples from 18 patients with AdC treated with CTX or IT via 1H-NMR spectroscopy. Relevant clinical information was gathered, and several biochemical parameters were also evaluated throughout the treatments. During the follow-up of patients undergoing CTX or IT, imaging control is recommended in order to assess the effectiveness of the therapy. This evaluation is usually performed every three treatments. Based on this procedure, all the samples were collected before the beginning of the treatment and after three and six treatments. The identified and quantified metabolites in the analyzed plasma samples were the following: isoleucine, valine, alanine, acetate, lactate, glucose, tyrosine, and formate. Multivariate/univariate statistical analyses were performed. Our data are in accordance with previous published results, suggesting that the plasma glucose levels of patients under CTX become higher throughout the course of treatment, which we hypothesize could be related to the tumor response to the therapy. It was also found that alanine levels become lower during treatment with CTX regimens, a fact that could be associated with frailty. NMR spectra of long responders' profiles also showed similar results. Based on the results of the study, metabolomics can represent a potential option for future studies, in order to facilitate patient selection and the monitoring of therapy efficacy in treated patients with AdC. Further studies are needed to improve the prospective identification of predictive markers, particularly glucose and alanine levels, as well as confer guidance to NSCLC treatment and patient stratification, thus avoiding ineffective therapeutic strategies.
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Affiliation(s)
- Daniela Madama
- Clinical Academic Centre of Coimbra (CACC), Department of Pulmonology, Faculty of Medicine, University Hospitals of Coimbra, University of Coimbra, 3004-504 Coimbra, Portugal
| | - David F. Carrageta
- Clinical and Experimental Endocrinology, UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal (M.G.A.)
- Laboratory for Integrative and Translational Research in Population Health (ITR), University of Porto, 4050-600 Porto, Portugal
| | - Bárbara Guerra-Carvalho
- Clinical and Experimental Endocrinology, UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal (M.G.A.)
- Laboratory for Integrative and Translational Research in Population Health (ITR), University of Porto, 4050-600 Porto, Portugal
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal;
| | - Maria F. Botelho
- Clinical Academic Centre of Coimbra (CACC), Centre for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal
| | - Pedro F. Oliveira
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal;
| | - Carlos R. Cordeiro
- Clinical Academic Centre of Coimbra (CACC), Department of Pulmonology, Faculty of Medicine, University Hospitals of Coimbra, University of Coimbra, 3004-504 Coimbra, Portugal
| | - Marco G. Alves
- Clinical and Experimental Endocrinology, UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal (M.G.A.)
| | - Ana M. Abrantes
- Clinical Academic Centre of Coimbra (CACC), Centre for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal
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Cao K, Lyu Y, Chen J, He C, Lyu X, Zhang Y, Chen L, Jiang Y, Xiang J, Liu B, Wu C. Prognostic Implication of Plasma Metabolites in Gastric Cancer. Int J Mol Sci 2023; 24:12774. [PMID: 37628957 PMCID: PMC10454100 DOI: 10.3390/ijms241612774] [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: 07/04/2023] [Revised: 08/02/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients' quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan-Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients.
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Affiliation(s)
- Kang Cao
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Yanping Lyu
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Jingwen Chen
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Chenzhou He
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Xuejie Lyu
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Yuling Zhang
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Liangping Chen
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Yu Jiang
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Jianjun Xiang
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Baoying Liu
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Chuancheng Wu
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
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Liao Z, Zhao L, Zhong F, Zhou Y, Lu T, Liu L, Gong X, Li J, Rao J. Serum and urine metabolomics analyses reveal metabolic pathways and biomarkers in relation to nasopharyngeal carcinoma. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2023; 37:e9469. [PMID: 36593223 DOI: 10.1002/rcm.9469] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/01/2023] [Accepted: 01/01/2023] [Indexed: 06/17/2023]
Abstract
RATIONALE Nasopharyngeal carcinoma (NPC) is a malignant tumor that is endemic in Southeast Asia, North Africa, and southern China. There is an urgent need for effective early diagnosis and treatment of this disease since NPC is currently often detected at advanced stages. METHODS To reveal the underlying metabolic mechanisms and discover potential diagnostic biomarkers of NPC, we employed ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) and UHPLC-Q-Exactive Orbitrap MS, respectively, to analyze 54 serum samples and 54 urine samples from 27 patients with NPC and 27 healthy control individuals. RESULTS A total of 1230 metabolites were determined in serum samples, and 181 of the 1230 metabolites were significantly changed in NPC patients. The 181 metabolites were enriched in 16 pathways, including biosynthesis of unsaturated fatty acids, cholesterol metabolism, and ferroptosis. A total of 2509 metabolites were detected in the urine samples. Among them, 179 metabolites were significantly altered in NPC patients, and these metabolites were enriched in eight pathways, including the tricarboxylic acid (TCA) cycle and caffeine metabolism. Seven metabolites, including creatinine and paraxanthine, were found to be significantly changed in both NPC serum and urine samples. Based on them, further biomarker analysis revealed that the panel of three serum metabolites, octanoylcarnitine, creatinine, and decanoyl-l-carnitine, displayed a perfect diagnostic performance (area under the curve [AUC] = 0.973) to distinguish NPC patients from controls, while the other three-metabolite biomarker panel, consisting of stachydrine, decanoyl-l-carnitine, and paraxanthine, had an AUC = 0.809 to distinguish NPC and control in urine samples. CONCLUSION This work highlights the key metabolites and metabolic pathways disturbed in NPC and presents potential biomarkers for effective diagnosis of this disease.
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Affiliation(s)
- Zhaohui Liao
- Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, People's Republic of China
| | - Li Zhao
- School of Nursing, Nanchang University, Nanchang, Jiangxi, China
| | - Fangyan Zhong
- Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, People's Republic of China
- National Health Commission Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma (Jiangxi Cancer Hospital), Nanchang, Jiangxi, China
| | - Yumeng Zhou
- Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, People's Republic of China
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Nanchang, Jiangxi, China
| | - Tianzhu Lu
- Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, People's Republic of China
- National Health Commission Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma (Jiangxi Cancer Hospital), Nanchang, Jiangxi, China
| | - Lijuan Liu
- Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, People's Republic of China
- National Health Commission Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma (Jiangxi Cancer Hospital), Nanchang, Jiangxi, China
| | - Xiaochang Gong
- Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, People's Republic of China
- National Health Commission Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma (Jiangxi Cancer Hospital), Nanchang, Jiangxi, China
| | - Jingao Li
- Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, People's Republic of China
- National Health Commission Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma (Jiangxi Cancer Hospital), Nanchang, Jiangxi, China
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Nanchang, Jiangxi, China
| | - Jun Rao
- Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, People's Republic of China
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Nanchang, Jiangxi, China
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Plasm Metabolomics Study in Pulmonary Metastatic Carcinoma. JOURNAL OF ONCOLOGY 2022; 2022:9460019. [PMID: 36046366 PMCID: PMC9420632 DOI: 10.1155/2022/9460019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/15/2022] [Indexed: 11/18/2022]
Abstract
Background The lung is one of the most common metastatic sites of malignant tumors. Early detection of pulmonary metastatic carcinoma can effectively reduce relative cancer mortality. Human metabolomics is a qualitative and quantitative study of low-molecular metabolites in the body. By studying the plasm metabolomics of patients with pulmonary metastatic carcinoma or other lung diseases, we can find the difference in plasm levels of low-molecular metabolites among them. These metabolites have the potential to become biomarkers of lung metastases. Methods Patients with pulmonary nodules admitted to our department from February 1, 2019, to May 31, 2019, were collected. According to the postoperative pathological results, they were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Moreover, healthy people who underwent physical examination were enrolled as the healthy population group (HPG) during the same period. On the one hand, to study lung metastases screening in healthy people, PMC was compared with HPG. The multivariate statistical analysis method was used to find the significant low-molecular metabolites between the two groups, and their discriminating ability was verified by the ROC curve. On the other hand, from the perspective of differential diagnosis of lung metastases, three groups with different pulmonary lesions (PMC, BPN, and PLC) were compared as a whole, and then the other two groups were compared with PMC, respectively. The main low-molecular metabolites were selected, and their discriminating ability was verified. Results In terms of lung metastases screening for healthy people, four significant low-molecular metabolites were found by comparison of PMC and HPG. They were O-arachidonoyl ethanolamine, adrenoyl ethanolamide, tricin 7-diglucuronoside, and p-coumaroyl vitisin A. In terms of the differential diagnosis of pulmonary nodules, the significant low-molecular metabolites selected by the comparison of the three groups as a whole were anabasine, octanoylcarnitine, 2-methoxyestrone, retinol, decanoylcarnitine, calcitroic acid, glycogen, and austalide L. For the comparison of PMC and BPN, L-tyrosine, indoleacrylic acid, and lysoPC (16 : 0) were selected, while L-octanoylcarnitine, retinol, and decanoylcarnitine were selected for the comparison of PMC and PLC. Their AUCs of ROC are all greater than 0.80. It indicates that these substances have a strong ability to differentiate between pulmonary metastatic carcinoma and other pulmonary nodule lesions. Conclusion Through the research of plasm metabolomics, it is possible to effectively detect the changes in some low-molecular metabolites among primary lung cancer, pulmonary metastatic carcinoma, and benign pulmonary nodule patients and healthy people. These significant metabolites have the potential to be biomarkers for screening and differential diagnosis of lung metastases.
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Miller HA, Rai SN, Yin X, Zhang X, Chesney JA, van Berkel VH, Frieboes HB. Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival. Metabolomics 2022; 18:31. [PMID: 35567637 PMCID: PMC9724684 DOI: 10.1007/s11306-022-01891-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 04/19/2022] [Indexed: 01/06/2023]
Abstract
INTRODUCTION Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive. OBJECTIVES This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS. METHODS Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events. RESULTS Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates). CONCLUSION Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
| | - Shesh N Rai
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA
| | - Xinmin Yin
- Department of Chemistry, University of Louisville, Louisville, USA
| | - Xiang Zhang
- Department of Chemistry, University of Louisville, Louisville, USA
| | - Jason A Chesney
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA
- Division of Medical Oncology and Hematology, Department of Medicine, University of Louisville, Louisville, USA
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, USA
| | - Victor H van Berkel
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, USA
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA.
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, USA.
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Li Y, Shi R, Zhu G, Chen C, Huang H, Gao M, Xu S, Cao P, Zhang Z, Wu D, Li X, Liu H, Chen J. Construction of a circular RNA-microRNA-messenger RNA regulatory network of hsa_circ_0043256 in lung cancer by integrated analysis. Thorac Cancer 2021; 13:61-75. [PMID: 34806315 PMCID: PMC8720627 DOI: 10.1111/1759-7714.14226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/23/2021] [Accepted: 10/25/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Patients with non-small cell lung cancer (NSCLC) are diagnosed in advanced stages and with a poor 5-year survival rate. There is a critical need to identify novel biomarkers to improve the therapy and overall prognosis of this disease. METHODS Differentially expressed genes (DEGs) were identified from three profiles of GSE101586, GSE101684 and GSE112214 using Venn diagrams. hsa_circ_0043256 were validated using quantitative real-time polymerase chain reaction (RT-qPCR). The circular RNA-microRNA-messenger RNA (circRNA-miRNA-mRNA) regulatory network was constructed with Cytoscape 3.7.0. Hub genes were identified with protein interaction (PPI) and validated with the Gene Expression Profiling Interactive Analysis (GEPIA), Human Protein Atlas (HPA) databases, and immunohistochemistry. Survival analyses were also performed using a Kaplan-Meier (KM) plotter. The effects of hsa_circ_0043256 on cell proliferation and cell cycles were evaluated by EdU staining and flow cytometry, respectively. RESULTS hsa_circ_0043256, hsa_circ_0029426 and hsa_circ_0049271 were obtained. Following RT-qPCR validation, hsa_circ_0043256 was selected for further analysis. In addition, functional experiment results indicated that hsa_circ_0043256 could inhibit cell proliferation and cell-cycle progression of NSCLC cells in vitro. Prediction by three online databases and combining with DEGs identified from The Cancer Genome Atlas (TCGA), a network containing one circRNAs, three miRNAs, and 209 mRNAs was developed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated DEGs might be associated with lung cancer onset and progression. A PPI network based on the 209 genes was established, and five hub genes (BIRC5, SHCBP1, CCNA2, SKA3, and GINS1) were determined. Following verification of five hub genes using GEPIA database, HPA database, and immunohistochemistry. High expression of all five hub genes led to poor overall survival. CONCLUSION Our study constructed a circRNA-miRNA-mRNA network of hsa_circ_0043256. hsa_circ_0043256 may be a potential therapeutic target for lung cancer.
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Affiliation(s)
- Yongwen Li
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Ruifeng Shi
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Guangsheng Zhu
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Chen Chen
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Hua Huang
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Min Gao
- Department of Thoracic Surgery, The Affiliated Hospital of Inner Mongolia Medical University, Huhhot, China
| | - Songlin Xu
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Peijun Cao
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Zihe Zhang
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Di Wu
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Xuanguang Li
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Hongyu Liu
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jun Chen
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Department of Thoracic Surgery, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
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Madama D, Martins R, Pires AS, Botelho MF, Alves MG, Abrantes AM, Cordeiro CR. Metabolomic Profiling in Lung Cancer: A Systematic Review. Metabolites 2021; 11:630. [PMID: 34564447 PMCID: PMC8471464 DOI: 10.3390/metabo11090630] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 12/25/2022] Open
Abstract
Lung cancer continues to be a significant burden worldwide and remains the leading cause of cancer-associated mortality. Two considerable challenges posed by this disease are the diagnosis of 61% of patients in advanced stages and the reduced five-year survival rate of around 4%. Noninvasively collected samples are gaining significant interest as new areas of knowledge are being sought and opened up. Metabolomics is one of these growing areas. In recent years, the use of metabolomics as a resource for the study of lung cancer has been growing. We conducted a systematic review of the literature from the past 10 years in order to identify some metabolites associated with lung cancer. More than 150 metabolites have been associated with lung cancer-altered metabolism. These were detected in different biological samples by different metabolomic analytical platforms. Some of the published results have been consistent, showing the presence/alteration of specific metabolites. However, there is a clear variability due to lack of a full clinical characterization of patients or standardized patients selection. In addition, few published studies have focused on the added value of the metabolomic profile as a means of predicting treatment response for lung cancer. This review reinforces the need for consistent and systematized studies, which will help make it possible to identify metabolic biomarkers and metabolic pathways responsible for the mechanisms that promote tumor progression, relapse and eventually resistance to therapy.
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Affiliation(s)
- Daniela Madama
- Clinical Academic Center of Coimbra (CACC), Department of Pulmonology, Faculty of Medicine, University Hospitals of Coimbra, University of Coimbra, 3004-504 Coimbra, Portugal;
| | - Rosana Martins
- Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal;
| | - Ana S. Pires
- Clinical Academic Center of Coimbra (CACC), Center for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal; (A.S.P.); (M.F.B.); (A.M.A.)
| | - Maria F. Botelho
- Clinical Academic Center of Coimbra (CACC), Center for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal; (A.S.P.); (M.F.B.); (A.M.A.)
| | - Marco G. Alves
- Department of Anatomy, Unit for Multidisciplinary Research in Biomedicine (UMIB), Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, 4099-002 Porto, Portugal;
| | - Ana M. Abrantes
- Clinical Academic Center of Coimbra (CACC), Center for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal; (A.S.P.); (M.F.B.); (A.M.A.)
| | - Carlos R. Cordeiro
- Clinical Academic Center of Coimbra (CACC), Department of Pulmonology, Faculty of Medicine, University Hospitals of Coimbra, University of Coimbra, 3004-504 Coimbra, Portugal;
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Metabolites as Prognostic Markers for Metastatic Non-Small Cell Lung Cancer (NSCLC) Patients Treated with First-Line Platinum-Doublet Chemotherapy. Cancers (Basel) 2020; 12:cancers12071926. [PMID: 32708705 PMCID: PMC7409233 DOI: 10.3390/cancers12071926] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 07/01/2020] [Accepted: 07/10/2020] [Indexed: 11/16/2022] Open
Abstract
The metabolic requirements of metastatic non-small cell lung (mNSCLC) tumors from patients receiving first-line platinum-doublet chemotherapy are hypothesized to imprint a blood signature suitable for survival prediction. Pre-treatment samples prospectively collected at baseline from a randomized phase III trial were assayed using nuclear magnetic resonance (NMR) spectroscopy (n = 341) and ultra-high performance liquid chromatography – mass spectrometry (UPLC-MS) (n = 297). Distributions of time to event outcomes were estimated by Kaplan-Meier analysis, and baseline characteristics adjusted Cox regression modeling was used to correlate markers’ levels to time to event outcomes. Sixteen polar metabolites were significantly correlated with overall survival (OS) by univariate analysis (p < 0.025). Formate, 2-hydroxybutyrate, glycine and myo-inositol were selected for a multivariate model. The median OS was 6.6 months in the high-risk group compared to 11.4 months in the low risk group HR (Hazard Ratio) = 1.99, 95% C.I. (Confidence Interval) 1.45–2.68; p < 0.0001). Modeling of lipids by class (sphingolipids, acylcarnitines and lysophosphatidylcholines) revealed a median OS = 5.7 months vs. 11. 9 months for the high vs. low risk group. (HR: 2.23, 95% C.I. 1.55–3.20; p < 0.0001). These results demonstrate that metabolic profiles from pre-treatment samples may be useful to stratify clinical outcomes for mNSCLC patients receiving chemotherapy. Genomic and longitudinal measurements pre- and post-treatment may yield addition information to personalize treatment decisions further.
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Cheng F, Zhou Y, Wang M, Guo C, Cao Z, Zhang R, Peng C. A review of pharmacological and pharmacokinetic properties of stachydrine. Pharmacol Res 2020; 155:104755. [PMID: 32173585 DOI: 10.1016/j.phrs.2020.104755] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/10/2020] [Accepted: 03/10/2020] [Indexed: 12/15/2022]
Abstract
Stachydrine is extracted from the leaves of Leonurus japonicus Houtt (or Motherwort, "Yi Mu Cao" in Traditional Chinese Medicine) and is the major bioactive ingredient. So far, stachydrine has demonstrated various bioactivities for the treatment of fibrosis, cardiovascular diseases, cancers, uterine diseases, brain injuries, and inflammation. The pharmacological and pharmacokinetic properties of stachydrine up to 2019 have been comprehensively searched and summarized. This review provides an updated summary of recent studies on the pharmacological activities of stachydrine. Many studies have demonstrated that stachydrine has strong anti-fibrotic properties (on various types of fibrosis) by inhibiting ECM deposition and decreasing inflammatory and oxidative stress through multiple molecular mechanisms (including TGF-β, ERS-mediated apoptosis, MMPs/TIMPs, NF-κB, and JAK/STAT). The cardioprotective and vasoprotective activities of stachydrine are related to its inhibition of β-MHC, excessive autophagy, SIRT1, eNOS uncoupling and TF, promotion of SERCA, and angiogenesis. In addition to its anticancer action, regulation of the uterus, neuroprotective effects, etc. the pharmacokinetic properties of stachydrine are also discussed.
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Affiliation(s)
- Fang Cheng
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Key Laboratory of Standardization for Chinese Herbal Medicine, Ministry of Education, Chengdu, China; School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yanxi Zhou
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Key Laboratory of Standardization for Chinese Herbal Medicine, Ministry of Education, Chengdu, China; Library, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Miao Wang
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Key Laboratory of Standardization for Chinese Herbal Medicine, Ministry of Education, Chengdu, China
| | - Chuanjie Guo
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Key Laboratory of Standardization for Chinese Herbal Medicine, Ministry of Education, Chengdu, China; School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhixing Cao
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Key Laboratory of Standardization for Chinese Herbal Medicine, Ministry of Education, Chengdu, China
| | - Ruoqi Zhang
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Key Laboratory of Standardization for Chinese Herbal Medicine, Ministry of Education, Chengdu, China.
| | - Cheng Peng
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Key Laboratory of Standardization for Chinese Herbal Medicine, Ministry of Education, Chengdu, China; School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
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11
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Wu D, Li X, Zhang X, Han F, Lu X, Liu L, Zhang J, Dong M, Yang H, Li H. Pharmacometabolomics Identifies 3-Hydroxyadipic Acid, d-Galactose, Lysophosphatidylcholine (P-16:0), and Tetradecenoyl-l-Carnitine as Potential Predictive Indicators of Gemcitabine Efficacy in Pancreatic Cancer Patients. Front Oncol 2020; 9:1524. [PMID: 32064236 PMCID: PMC7000527 DOI: 10.3389/fonc.2019.01524] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 12/18/2019] [Indexed: 12/28/2022] Open
Abstract
Gemcitabine (GEM)-based chemotherapy is the standard regimen for the treatment of pancreatic cancer (PC). However, chemoresistance is a major challenge in PC treatment. Reliable biomarkers are urgently needed to predict the response to GEM-based therapies. GEM-sensitive (GEM-S) and GEM-resistant (GEM-R) pancreatic carcinoma xenograft models were established, and GEM monotherapy and GEM plus nanoparticle albumin-bound paclitaxel (nab-PTX) doublet therapy were administered to GEM-S/R tumor-bearing mice. Metabolomic mass spectrometry (MS) analysis of serum, liver, and tumor samples was performed using an ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometer. The results showed that both GEM monotherapy and combination therapy significantly inhibited the tumor growth in GEM-S subgroup. However, in the GEM-R subgroup, tumor growth was not significantly inhibited by GEM monotherapy, but was significantly suppressed by GEM combination therapy. Metabolic profiling analysis by hierarchical cluster analysis and partial least squares discriminant analysis showed that the differences in metabolites were most significant in serum of three types of samples in the GEM-S/R subgroups, regardless of the administration of GEM monotherapy or combination therapy. The differential metabolite analysis of serum samples revealed 38 and 26 differential metabolites between the GEM-R and GEM-S subgroups treated with GEM monotherapy or combination therapy, and four common discriminating metabolites were investigated: 3-hydroxyadipic acid, d-galactose, lysophosphatidylcholine (LysoPC) (P-16:0), and tetradecenoyl-l-carnitine. The relative amounts of the four metabolites changed significantly and consistently after GEM monotherapy or combination therapy. The levels of these four metabolites were significantly different in the GEM-S and GEM-R pancreatic carcinoma xenograft models; thus, these metabolites could be effective predictive indicators of the efficacy of chemotherapy in PC patients, regardless of the administration of GEM alone or GEM plus nab-PTX.
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Affiliation(s)
- Dongyuan Wu
- Department of Biochemistry and Molecular Biology, Basic Medical Science College, Harbin Medical University, Harbin, China.,Department of Pharmacy, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xinyuan Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiaohan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Fang Han
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xin Lu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Lei Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junsheng Zhang
- College of Basic Medicine, Harbin Medical University, Harbin, China
| | - Mei Dong
- Department of Pharmacy, Harbin Medical University Cancer Hospital, Harbin, China
| | - Huanjie Yang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Hui Li
- Department of Biochemistry and Molecular Biology, Basic Medical Science College, Harbin Medical University, Harbin, China
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12
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Mu Y, Zhou Y, Wang Y, Li W, Zhou L, Lu X, Gao P, Gao M, Zhao Y, Wang Q, Wang Y, Xu G. Serum Metabolomics Study of Nonsmoking Female Patients with Non-Small Cell Lung Cancer Using Gas Chromatography-Mass Spectrometry. J Proteome Res 2019; 18:2175-2184. [PMID: 30892048 DOI: 10.1021/acs.jproteome.9b00069] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The incidence of nonsmoking female patients with non-small cell lung cancer (NSCLC) has increased in recent decades; however, the pathogenesis of patients is unclear, and early diagnosis biomarkers are in urgent need. In this study, 136 nonsmoking female subjects (65 patients with NSCLC, 6 patients with benign lung tumors, and 65 healthy controls) were enrolled, and their metabolic profiling was investigated by using pseudotargeted gas chromatography-mass spectrometry. A total of 56 annotated metabolites were found and verified to be significantly different in nonsmoking females with NSCLC compared with the control. The metabolic profiling was featured by disturbed energy metabolism, amino acid metabolism, oxidative stress, lipid metabolism, and so on. Cysteine, serine, and 1-monooleoylglycerol were defined as the biomarker panel for the diagnosis of NSCLC patients. 98.5 and 91.4% of subjects were correctly distinguished in the discovery and validation sets, respectively. The biomarker panel was also useful for the diagnosis of in situ malignancy patients, with an accuracy of 97.7 and 97.8% in the discovery and validation sets, respectively. The study provides a biomarker panel for the auxiliary diagnosis of nonsmoking females with NSCLC.
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Affiliation(s)
- Ying Mu
- The First Affiliated Hospital of Dalian Medical University , Dalian Medical University , Dalian 116000 , China.,The Dalian Branch, the Library of Liaoning University of Traditional Chinese Medicine , Dalian 116600 , China
| | - Yang Zhou
- CAS Key Laboratory of Separation Science for Analytical Chemistry , Dalian Institute of Chemical Physics, Chinese Academy of Sciences , Dalian 116023 , China.,The Second Affiliated Hospital of Dalian Medical University , Dalian Medical University , Dalian 116027 , China
| | - Yanfeng Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry , Dalian Institute of Chemical Physics, Chinese Academy of Sciences , Dalian 116023 , China.,University of Chinese Academy of Sciences , Beijing 100049 , China
| | - Wei Li
- The First Affiliated Hospital of Dalian Medical University , Dalian Medical University , Dalian 116000 , China
| | - Lina Zhou
- CAS Key Laboratory of Separation Science for Analytical Chemistry , Dalian Institute of Chemical Physics, Chinese Academy of Sciences , Dalian 116023 , China
| | - Xin Lu
- CAS Key Laboratory of Separation Science for Analytical Chemistry , Dalian Institute of Chemical Physics, Chinese Academy of Sciences , Dalian 116023 , China
| | - Peng Gao
- Clinical Laboratory, Dalian Sixth People's Hospital , Dalian 116031 , China
| | - Mingyang Gao
- The First Affiliated Hospital of Dalian Medical University , Dalian Medical University , Dalian 116000 , China
| | - Yanhui Zhao
- The Dalian Branch, the Library of Liaoning University of Traditional Chinese Medicine , Dalian 116600 , China
| | - Qi Wang
- The Second Affiliated Hospital of Dalian Medical University , Dalian Medical University , Dalian 116027 , China
| | - Yanfu Wang
- The First Affiliated Hospital of Dalian Medical University , Dalian Medical University , Dalian 116000 , China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry , Dalian Institute of Chemical Physics, Chinese Academy of Sciences , Dalian 116023 , China
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13
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Ballester LY, Lu G, Zorofchian S, Vantaku V, Putluri V, Yan Y, Arevalo O, Zhu P, Riascos RF, Sreekumar A, Esquenazi Y, Putluri N, Zhu JJ. Analysis of cerebrospinal fluid metabolites in patients with primary or metastatic central nervous system tumors. Acta Neuropathol Commun 2018; 6:85. [PMID: 30170631 PMCID: PMC6117959 DOI: 10.1186/s40478-018-0588-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 08/21/2018] [Indexed: 12/30/2022] Open
Abstract
Cancer cells have altered cellular metabolism. Mutations in genes associated with key metabolic pathways (e.g., isocitrate dehydrogenase 1 and 2, IDH1/IDH2) are important drivers of cancer, including central nervous system (CNS) tumors. Therefore, we hypothesized that the abnormal metabolic state of CNS cancer cells leads to abnormal levels of metabolites in the CSF, and different CNS cancer types are associated with specific changes in the levels of CSF metabolites. To test this hypothesis, we used mass spectrometry to analyze 129 distinct metabolites in CSF samples from patients without a history of cancer (n = 8) and with a variety of CNS tumor types (n = 23) (i.e., glioma IDH-mutant, glioma-IDH wildtype, metastatic lung cancer and metastatic breast cancer). Unsupervised hierarchical clustering analysis shows tumor-specific metabolic signatures that facilitate differentiation of tumor type from CSF analysis. We identified differences in the abundance of 43 metabolites between CSF from control patients and the CSF of patients with primary or metastatic CNS tumors. Pathway analysis revealed alterations in various metabolic pathways (e.g., glycine, choline and methionine degradation, dipthamide biosynthesis and glycolysis pathways, among others) between IDH-mutant and IDH-wildtype gliomas. Moreover, patients with IDH-mutant gliomas demonstrated higher levels of D-2-hydroxyglutarate in the CSF, in comparison to patients with other tumor types, or controls. This study demonstrates that analysis of CSF metabolites can be a clinically useful tool for diagnosing and monitoring patients with primary or metastatic CNS tumors.
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Affiliation(s)
- Leomar Y Ballester
- Department of Pathology and Laboratory Medicine, University of Texas Health Science Center at Houston, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA.
- Department of Neurosurgery, University of Texas Health Science Center at Houston, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA.
- Memorial Hermann Hospital, Houston, TX, 77030, USA.
| | - Guangrong Lu
- Department of Neurosurgery, University of Texas Health Science Center at Houston, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
| | - Soheil Zorofchian
- Department of Pathology and Laboratory Medicine, University of Texas Health Science Center at Houston, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
| | - Venkatrao Vantaku
- Department of Molecular and Cellular Biology, Baylor College of Medicine, 120D, Jewish Building, One Baylor Plaza, Houston, TX, 77030, USA
| | - Vasanta Putluri
- Advanced Technology Core, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yuanqing Yan
- Department of Neurosurgery, University of Texas Health Science Center at Houston, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
| | - Octavio Arevalo
- Department of Radiology, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Ping Zhu
- Department of Neurosurgery, University of Texas Health Science Center at Houston, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
| | - Roy F Riascos
- Department of Radiology, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Memorial Hermann Hospital, Houston, TX, 77030, USA
| | - Arun Sreekumar
- Department of Molecular and Cellular Biology, Baylor College of Medicine, 120D, Jewish Building, One Baylor Plaza, Houston, TX, 77030, USA
| | - Yoshua Esquenazi
- Department of Neurosurgery, University of Texas Health Science Center at Houston, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
- Memorial Hermann Hospital, Houston, TX, 77030, USA
| | - Nagireddy Putluri
- Department of Molecular and Cellular Biology, Baylor College of Medicine, 120D, Jewish Building, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Jay-Jiguang Zhu
- Department of Neurosurgery, University of Texas Health Science Center at Houston, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
- Memorial Hermann Hospital, Houston, TX, 77030, USA
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López-López Á, López-Gonzálvez Á, Barker-Tejeda TC, Barbas C. A review of validated biomarkers obtained through metabolomics. Expert Rev Mol Diagn 2018; 18:557-575. [PMID: 29808702 DOI: 10.1080/14737159.2018.1481391] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.
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Affiliation(s)
- Ángeles López-López
- a Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia , Universidad CEU San Pablo , Madrid , Spain
| | - Ángeles López-Gonzálvez
- a Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia , Universidad CEU San Pablo , Madrid , Spain
| | - Tomás Clive Barker-Tejeda
- a Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia , Universidad CEU San Pablo , Madrid , Spain
| | - Coral Barbas
- a Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia , Universidad CEU San Pablo , Madrid , Spain
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