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Kajiwara N, Kakihana M, Maeda J, Kaneko M, Ota S, Enomoto A, Ikeda N, Sugimoto M. Salivary metabolomic biomarkers for non-invasive lung cancer detection. Cancer Sci 2024; 115:1695-1705. [PMID: 38417449 DOI: 10.1111/cas.16112] [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: 11/28/2023] [Revised: 01/23/2024] [Accepted: 02/01/2024] [Indexed: 03/01/2024] Open
Abstract
Identifying novel biomarkers for early detection of lung cancer is crucial. Non-invasively available saliva is an ideal biofluid for biomarker exploration; however, the rationale underlying biomarker detection from organs distal to the oral cavity in saliva requires clarification. Therefore, we analyzed metabolomic profiles of cancer tissues compared with those of adjacent non-cancerous tissues, as well as plasma and saliva samples collected from patients with lung cancer (n = 109 pairs). Additionally, we analyzed plasma and saliva samples collected from control participants (n = 83 and 71, respectively). Capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry were performed to comprehensively quantify hydrophilic metabolites. Paired tissues were compared, revealing 53 significantly different metabolites. Plasma and saliva showed 44 and 40 significantly different metabolites, respectively, between patients and controls. Of these, 12 metabolites exhibited significant differences in all three comparisons and primarily belonged to the polyamine and amino acid pathways; N1-acetylspermidine exhibited the highest discrimination ability. A combination of 12 salivary metabolites was evaluated using a machine learning method to differentiate patients with lung cancer from controls. Salivary data were randomly split into training and validation datasets. Areas under the receiver operating characteristic curve were 0.744 for cross-validation using training data and 0.792 for validation data. This model exhibited a higher discrimination ability for N1-acetylspermidine than that for other metabolites. The probability of lung cancer calculated using this model was independent of most patient characteristics. These results suggest that consistently different salivary biomarkers in both plasma and lung tissues might facilitate non-invasive lung cancer screening.
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Affiliation(s)
- Naohiro Kajiwara
- Department of Thoracic Surgery, Hachioji Medical Center of Tokyo Medical College Hospital, Hachioji, Tokyo, Japan
- Department of Surgery, Tokyo Medical University, Tokyo, Japan
| | | | - Junichi Maeda
- Department of Surgery, Tokyo Medical University, Tokyo, Japan
- Division of Thoracic Surgery, Mitsui Memorial Hospital, Tokyo, Japan
| | - Miku Kaneko
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Sana Ota
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Ayame Enomoto
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Norihiko Ikeda
- Department of Surgery, Tokyo Medical University, Tokyo, Japan
| | - Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
- Institute of Medical Science, Tokyo Medical University, Tokyo, Japan
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ZOU S, LI N, ZHANG T, GENG Q. [Research Progress on Tumor Metabolic Biomarkers in Liquid Biopsy of Lung Cancer]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2024; 27:126-132. [PMID: 38453444 PMCID: PMC10918242 DOI: 10.3779/j.issn.1009-3419.2023.106.29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Indexed: 03/09/2024]
Abstract
Liquid biopsy is gradually being applied in the clinical diagnosis and treatment of lung cancer. At present, with the development of metabolomics, more and more metabolic biomarkers are considered as potential sensitive markers reflecting the occurrence and development of tumors. This article summarizes the changes in the main metabolic pathways of lung cancer, including glucose metabolism, amino acid metabolism, lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and purine metabolism. Meanwhile, this article reviews the role of metabolic biomarkers in the early diagnosis of lung cancer, predicting disease progression, and evaluating the efficacy of chemotherapy and immunotherapy, aiming to provide effective biomarkers for tumor diagnosis and treatment.
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Abstract
Current lung cancer screening protocols use low-dose computed tomography scans in selected high-risk individuals. Unfortunately, utilization is low, and the rate of false-positive screens is high. Peripheral biomarkers carry meaningful promise in diagnosing and monitoring cancer with added potential advantages reducing invasive procedures and improving turnaround time. Herein, the use of such blood-based assays is considered as an adjunct to further utilization and accuracy of lung cancer screening.
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Affiliation(s)
- Nathaniel Deboever
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Edwin J Ostrin
- Department of General Internal Medicine, Pulmonary Medicine, University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Mara B Antonoff
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
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Liu Z, Wang L, Gao S, Xue Q, Tan F, Li Z, Gao Y. Plasma metabolomics study in screening and differential diagnosis of multiple primary lung cancer. Int J Surg 2023; 109:297-312. [PMID: 36928390 PMCID: PMC10389222 DOI: 10.1097/js9.0000000000000006] [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: 09/14/2022] [Accepted: 11/28/2022] [Indexed: 03/18/2023]
Abstract
BACKGROUND Multiple primary lung cancer (MPLC) is becoming increasingly common in clinical practice. Imaging examination is sometimes difficult to differentiate from intrapulmonary metastasis (IM) or single primary lung cancer (SPLC) before surgery. There is a lack of effective blood biomarkers as an auxiliary diagnostic method. PARTICIPANTS AND METHODS A total of 179 patients who were hospitalized and operated in our department from January to June 2019 were collected, and they were divided into SPLC with 136 patients, MPLC with 24 patients, and IM with 19 patients. In total, 96 healthy people without lung cancer were enrolled. Medical history, imaging, and pathology data were assembled from all participants. Plasma metabolomics analysis was performed by quadrupole time-of-flight tandem mass spectrometry, and data were analyzed using SPSS19.0/Simca 14.1/MetaboAnalyst5.0 software. Significant metabolites were selected by variable importance in projection, P value, and fold change. The area under the receiver operating characteristic curve was used to evaluate their diagnostic ability. RESULTS There were significant differences in plasma metabolite profiles between IM and MPLC. Seven metabolites were screened out. Two metabolites had higher levels in IM, and five metabolites had higher levels in MPLC. All had favorable discriminating capacity. Phosphatidyl ethanolamine (38:5) showed the highest sensitivity (0.95) and specificity (0.92). It was followed by l -histidine with sensitivity 0.92 and specificity 0.84. l -tyrosine can be used to identify SPLC and MPLC. The panel composed of related metabolites exhibited higher diagnostic ability. Eight principal metabolites caused remarkable differences between healthy people and MPLC, and five of them had area under the curves greater than 0.85, showing good discriminating power. CONCLUSION Through the study of plasma metabolomics, it was found that there were obvious differences in the metabolite profiles of MPLC, IM, SPLC, and the healthy population. Some discovered metabolites possessed excellent diagnostic competence with high sensitivity and specificity. They had the potential to act as biomarkers for the screening and differential diagnosis of MPLCs.
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Affiliation(s)
- Zixu Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Langfang, People’s Republic of China
| | - Ling Wang
- Department of Hematology, Beijing Chuiyangliu Hospital, Beijing
| | - Shugeng Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Qi Xue
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Fengwei Tan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Zhili Li
- Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College
| | - Yushun Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Langfang, People’s Republic of China
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Oh S, Jo S, Kim HS, Mai VH, Endaya B, Neuzil J, Jung KH, Hong SS, Kim JM, Park S. Chemical Biopsy for GNMT as Noninvasive and Tumorigenesis-Relevant Diagnosis of Liver Cancer. Anal Chem 2023; 95:1184-1192. [PMID: 36602057 DOI: 10.1021/acs.analchem.2c03944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Early diagnosis of hepatocellular carcinoma (HCC) is difficult; the lack of convenient biomarker-based diagnostic modalities renders high-risk HCC patients burdened by life-long periodical examinations. Here, a new chemical biopsy approach was developed for noninvasive diagnosis of HCC using urine samples. Bioinformatic screening for tumor suppressors yielded glycine N-methyltransferase (GNMT) as a biomarker with clinical relevance to HCC tumorigenesis. A liquid chromatography-mass spectrometry (LC-MS)-based chemical biopsy detecting nonradioactive 13C-sarcosine from 13C-glycine was designed to noninvasively assess liver GNMT activity extrahepatically. 13C-Sarcosine showed a strong correlation with GNMT in normal and cancerous liver cells. In an autochthonous animal model developing visible cancer nodules at 17 weeks, the urinary 13C-sarcosine chemical biopsy exhibited notable changes as early as 8 weeks, showing significant correlations with liver GNMT and molecular pathological changes. Our chemical biopsy approach should facilitate early and noninvasive diagnosis of HCC, with direct relevance to tumorigenesis, which can be straightforwardly applied to other diseases.
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Affiliation(s)
- Sehyun Oh
- College of Pharmacy, Natural Products Research Institute, Seoul National University, Seoul 08826, Korea
| | - Sihyang Jo
- College of Pharmacy, Natural Products Research Institute, Seoul National University, Seoul 08826, Korea
| | - Han Sun Kim
- College of Pharmacy, Natural Products Research Institute, Seoul National University, Seoul 08826, Korea
| | - Van-Hieu Mai
- College of Pharmacy, Natural Products Research Institute, Seoul National University, Seoul 08826, Korea
| | - Berwini Endaya
- School of Pharmacy and Medical Science, Griffith University, Southport 4222, Qld, Australia
| | - Jiri Neuzil
- School of Pharmacy and Medical Science, Griffith University, Southport 4222, Qld, Australia.,Institute of Biotechnology, Czech Academy of Sciences, Prague-West 252 50, Czech Republic.,Faculty of Science, Charles University, Prague 128 00, Czech Republic
| | - Kyung Hee Jung
- Department of Biomedical Sciences, College of Medicine, Inha University, 3-ga, Sinheung-dong, Jung-gu, Incheon 22332, Korea
| | - Soon-Sun Hong
- Department of Biomedical Sciences, College of Medicine, Inha University, 3-ga, Sinheung-dong, Jung-gu, Incheon 22332, Korea
| | - Jin-Mo Kim
- College of Pharmacy, Natural Products Research Institute, Seoul National University, Seoul 08826, Korea
| | - Sunghyouk Park
- College of Pharmacy, Natural Products Research Institute, Seoul National University, Seoul 08826, Korea
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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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García-Pardo M, Makarem M, Li JJN, Kelly D, Leighl NB. Integrating circulating-free DNA (cfDNA) analysis into clinical practice: opportunities and challenges. Br J Cancer 2022; 127:592-602. [PMID: 35347327 PMCID: PMC9381753 DOI: 10.1038/s41416-022-01776-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 12/12/2022] Open
Abstract
In the current era of precision medicine, the identification of genomic alterations has revolutionised the management of patients with solid tumours. Recent advances in the detection and characterisation of circulating tumour DNA (ctDNA) have enabled the integration of liquid biopsy into clinical practice for molecular profiling. ctDNA has also emerged as a promising biomarker for prognostication, monitoring disease response, detection of minimal residual disease and early diagnosis. In this Review, we discuss current and future clinical applications of ctDNA primarily in non-small cell lung cancer in addition to other solid tumours.
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Affiliation(s)
- Miguel García-Pardo
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Maisam Makarem
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Janice J N Li
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Deirdre Kelly
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Natasha B Leighl
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
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Zhao Y, Huang B, Zhou L, Cai L, Qian J. Challenges in diagnosing hydatidiform moles: a review of promising molecular biomarkers. Expert Rev Mol Diagn 2022; 22:783-796. [PMID: 36017690 DOI: 10.1080/14737159.2022.2118050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Hydatidiform moles (HMs) are pathologic conceptions with unique genetic bases and abnormal placental villous tissue. Overlapping ultrasonographical and histological manifestations of molar and non-molar (NM) gestations and HMs subtypes makes accurate diagnosis challenging. Currently, immunohistochemical analysis of p57 and molecular genotyping have greatly improved the diagnostic accuracy. AREAS COVERED The differential expression of molecular biomarkers may be valuable for distinguishing among the subtypes of HMs and their mimics. Thus, biomarkers may be the key to refining HMs diagnosis. In this review, we summarize the current challenges in diagnosing HMs, and provide a critical overview of the recent literature about potential diagnostic biomarkers and their subclassifications. An online search on PubMed, Web of Science, and Google Scholar databases was conducted from the inception to 1 April 2022. EXPERT OPINION the emerging biomarkers offer new possibilities to refine the diagnosis for HMs and pregnancy loss. Although the additional studies are required to be quantified and investigated in clinical trials to verify their diagnostic utility. It is important to explore, validate, and facilitate the wide adoption of newly developed biomarkers in the coming years.
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Affiliation(s)
- Yating Zhao
- Department of Gynaecology and Obstetrics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou City, 310003, Zhejiang Province, People's Republic of China
| | - Bo Huang
- Department of Gynaecology and Obstetrics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou City, 310003, Zhejiang Province, People's Republic of China
| | - Lin Zhou
- Department of Gynaecology and Obstetrics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou City, 310003, Zhejiang Province, People's Republic of China
| | - Luya Cai
- Department of Gynaecology and Obstetrics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou City, 310003, Zhejiang Province, People's Republic of China
| | - Jianhua Qian
- Department of Gynaecology and Obstetrics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou City, 310003, Zhejiang Province, People's Republic of China
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Zang X, Zhang J, Jiao P, Xue X, Lv Z. Non-Small Cell Lung Cancer Detection and Subtyping by UPLC-HRMS-Based Tissue Metabolomics. J Proteome Res 2022; 21:2011-2022. [PMID: 35856400 DOI: 10.1021/acs.jproteome.2c00316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Non-small cell lung cancer (NSCLC) is the prevalent histological subtype of lung cancer. In this study, we performed ultraperformance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS)-based metabolic profiling of 227 tissue samples from 79 lung cancer patients with adenocarcinoma (AC) or squamous cell carcinoma (SCC). Orthogonal partial least squares-discriminant analysis (oPLS-DA) analyses showed that AC, SCC, and NSCLC tumors were discriminated from adjacent noncancerous tissue (ANT) and distant noncancerous tissue (DNT) samples with good accuracies (91.3, 100, and 88.3%), sensitivities (85.7, 100, and 83.9%), and specificities (94.3, 100, and 90.7%), using 12, 4, and 7 discriminant metabolites, respectively. The discriminant panel for AC detection included valine, sphingosine, glutamic acid γ-methyl ester, and lysophosphatidylcholine (LPC) (16:0), levels of which in tumor tissues were significantly altered. Valine, sphingosine, LPC (18:1), and leucine derivatives were used for SCC detection. The discrimination between AC and SCC had 96.8% accuracy, 98.2% sensitivity, and 85.7% specificity using a five-metabolite panel, of which valine and creatine had significant differences. The classification models were further verified with external validation sets, showing a promising prospect for NSCLC tissue detection and subtyping.
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Affiliation(s)
- Xiaoling Zang
- School of Medicine and Pharmacy, Ocean University of China, Qingdao, Shandong 266003, P. R. China
| | - Jie Zhang
- School of Medicine and Pharmacy, Ocean University of China, Qingdao, Shandong 266003, P. R. China
| | - Peng Jiao
- Department of Thoracic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P. R. China
| | - Xuyan Xue
- College of Physics, Qingdao University, Qingdao, Shandong 266071, P. R. China
| | - Zhihua Lv
- School of Medicine and Pharmacy, Ocean University of China, Qingdao, Shandong 266003, P. R. China
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Liang TL, Li RZ, Mai CT, Guan XX, Li JX, Wang XR, Ma LR, Zhang FY, Wang J, He F, Pan HD, Zhou H, Yan PY, Fan XX, Wu QB, Neher E, Liu L, Xie Y, Leung ELH, Yao XJ. A method establishment and comparison of in vivo lung cancer model development platforms for evaluation of tumour metabolism and pharmaceutical efficacy. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2022; 96:153831. [PMID: 34794861 DOI: 10.1016/j.phymed.2021.153831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/15/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Currently, the identification of accurate biomarkers for the diagnosis of patients with early-stage lung cancer remains difficult. Fortunately, metabolomics technology can be used to improve the detection of plasma metabolic biomarkers for lung cancer. In a previous study, we successfully utilised machine learning methods to identify significant metabolic markers for early-stage lung cancer diagnosis. However, a related research platform for the investigation of tumour metabolism and drug efficacy is still lacking. HYPOTHESIS/PURPOSE A novel methodology for the comprehensive evaluation of the internal tumour-metabolic profile and drug evaluation needs to be established. METHODS The optimal location for tumour cell inoculation was identified in mouse chest for the non-traumatic orthotopic lung cancer mouse model. Microcomputed tomography (micro-CT) was applied to monitor lung tumour growth. Proscillaridin A (P.A) and cisplatin (CDDP) were utilised to verify the anti-lung cancer efficacy of the platform. The top five clinically valid biomarkers, including proline, L-kynurenine, spermidine, taurine and palmitoyl-L-carnitine, were selected as the evaluation indices to obtain a suitable lung cancer mouse model for clinical metabolomics research by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). RESULTS The platform was successfully established, achieving 100% tumour development rate and 0% surgery mortality. P.A and CDDP had significant anti-lung cancer efficacy in the platform. Compared with the control group, four biomarkers in the orthotopic model and two biomarkers in the metastatic model had significantly higher abundance. Principal component analysis (PCA) showed a significant separation between the orthotopic/metastatic model and the control/subcutaneous/KRAS transgenic model. The platform was mainly involved in arginine and proline metabolism, tryptophan metabolism, and taurine and hypotaurine metabolism. CONCLUSION This study is the first to simulate clinical metabolomics by comparing the metabolic phenotype of plasma in different lung cancer mouse models. We found that the orthotopic model was the most suitable for tumour metabolism. Furthermore, the anti-tumour drug efficacy was verified in the platform. The platform can very well match the clinical reality, providing better lung cancer diagnosis and securing more precise evidence for drug evaluation in the future.
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Affiliation(s)
- Tu-Liang Liang
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Run-Ze Li
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Chu-Tian Mai
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Xiao-Xiang Guan
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Jia-Xin Li
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Xuan-Run Wang
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Lin-Rui Ma
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Fang-Yuan Zhang
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Jian Wang
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Fan He
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Hu-Dan Pan
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Hua Zhou
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Pei-Yu Yan
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Xing-Xing Fan
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Qi-Biao Wu
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Erwin Neher
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Liang Liu
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China
| | - Ying Xie
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China.
| | - Elaine Lai-Han Leung
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China; Zhuhai Hospital of Traditional Chinese and Western Medicine, Zhuhai City, Guangdong, PR China.
| | - Xiao-Jun Yao
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau (S.A.R.), China; State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou, China.
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Peila C, Sottemano S, Cesare Marincola F, Stocchero M, Pusceddu NG, Dessì A, Baraldi E, Fanos V, Bertino E. NMR Metabonomic Profile of Preterm Human Milk in the First Month of Lactation: From Extreme to Moderate Prematurity. Foods 2022; 11:foods11030345. [PMID: 35159496 PMCID: PMC8834565 DOI: 10.3390/foods11030345] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/20/2022] [Accepted: 01/21/2022] [Indexed: 02/06/2023] Open
Abstract
Understanding the composition of human milk (HM) can provide important insights into the links between infant nutrition, health, and development. In the present work, we have longitudinally investigated the metabolome of milk from 36 women delivering preterm at different gestational ages (GA): extremely (<28 weeks GA), very (29–31 weeks GA) or moderate (32–34 weeks GA) premature. Milk samples were collected at three lactation stages: colostrum (3–6 days post-partum), transitional milk (7–15 days post-partum) and mature milk (16–26 days post-partum). Multivariate and univariate statistical data analyses were performed on the 1H NMR metabolic profiles of specimens in relation to the degree of prematurity and lactation stage. We observed a high impact of both the mother’s phenotype and lactation time on HM metabolome composition. Furthermore, statistically significant differences, although weak, were observed in terms of GA when comparing extremely and moderately preterm milk. Overall, our study provides new insights into preterm HM metabolome composition that may help to optimize feeding of preterm newborns, and thus improve the postnatal growth and later health outcomes of these fragile patients.
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Affiliation(s)
- Chiara Peila
- Neonatal Unit, University of Turin, City of Health and Science of Turin, 10126 Turin, Italy; (C.P.); (S.S.); (E.B.)
| | - Stefano Sottemano
- Neonatal Unit, University of Turin, City of Health and Science of Turin, 10126 Turin, Italy; (C.P.); (S.S.); (E.B.)
| | - Flaminia Cesare Marincola
- Department of Chemical and Geological Sciences, Cittadella Universitaria di Monserrato, University of Cagliari, Monserrato, 09042 Cagliari, Italy;
- Correspondence: (F.C.M.); (M.S.)
| | - Matteo Stocchero
- Department of Women’s and Children’s Health, University of Padova, 35128 Padova, Italy;
- Institute of Pediatric Research (IRP), Fondazione Città della Speranza, 35128 Padova, Italy
- Correspondence: (F.C.M.); (M.S.)
| | - Nicoletta Grazia Pusceddu
- Department of Chemical and Geological Sciences, Cittadella Universitaria di Monserrato, University of Cagliari, Monserrato, 09042 Cagliari, Italy;
| | - Angelica Dessì
- Neonatal Intensive Care Unit, Neonatal Pathology and Neonatal Section, Azienda University Polyclinic, University of Cagliari, 09042 Cagliari, Italy; (A.D.); (V.F.)
| | - Eugenio Baraldi
- Department of Women’s and Children’s Health, University of Padova, 35128 Padova, Italy;
- Institute of Pediatric Research (IRP), Fondazione Città della Speranza, 35128 Padova, Italy
| | - Vassilios Fanos
- Neonatal Intensive Care Unit, Neonatal Pathology and Neonatal Section, Azienda University Polyclinic, University of Cagliari, 09042 Cagliari, Italy; (A.D.); (V.F.)
| | - Enrico Bertino
- Neonatal Unit, University of Turin, City of Health and Science of Turin, 10126 Turin, Italy; (C.P.); (S.S.); (E.B.)
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12
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Haince JF, Joubert P, Bach H, Ahmed Bux R, Tappia PS, Ramjiawan B. Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome. Int J Mol Sci 2022; 23:ijms23031215. [PMID: 35163138 PMCID: PMC8835988 DOI: 10.3390/ijms23031215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/17/2022] [Accepted: 01/17/2022] [Indexed: 12/19/2022] Open
Abstract
The five-year survival rate of lung cancer patients is very low, mainly because most newly diagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosis is key to the successful treatment and management of lung cancer. Unfortunately, early detection methods of lung cancer are not ideal. In this brief review, we described early detection methods such as chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, and low-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomic fingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost, high-throughput blood-based test that is both feasible and affordable for early-stage lung cancer screening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as an alternative to molecular and proteomic-based markers in lung cancer screening, which will enable blood-based routine testing and be accessible to those patients at the highest risk for lung cancer.
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Affiliation(s)
| | - Philippe Joubert
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Department of Pathology, Laval University, Quebec, QC G1V 4G5, Canada;
| | - Horacio Bach
- Department of Medicine, Division of Infectious Diseases, University of British Columbia, Vancouver, BC V6H 3Z6, Canada;
| | - Rashid Ahmed Bux
- BioMark Diagnostics Inc., Richmond, BC V6X 2W8, Canada; (J.-F.H.); (R.A.B.)
| | - Paramjit S. Tappia
- Asper Clinical Research Institute, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada;
- Correspondence: ; Tel.: +1-204-258-1230
| | - Bram Ramjiawan
- Asper Clinical Research Institute, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada;
- Department of Pharmacology & Therapeutics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada
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13
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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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14
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Nooreldeen R, Bach H. Current and Future Development in Lung Cancer Diagnosis. Int J Mol Sci 2021; 22:8661. [PMID: 34445366 PMCID: PMC8395394 DOI: 10.3390/ijms22168661] [Citation(s) in RCA: 230] [Impact Index Per Article: 76.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/05/2021] [Accepted: 08/10/2021] [Indexed: 12/16/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths in North America and other developed countries. One of the reasons lung cancer is at the top of the list is that it is often not diagnosed until the cancer is at an advanced stage. Thus, the earliest diagnosis of lung cancer is crucial, especially in screening high-risk populations, such as smokers, exposure to fumes, oil fields, toxic occupational places, etc. Based on the current knowledge, it looks that there is an urgent need to identify novel biomarkers. The current diagnosis of lung cancer includes different types of imaging complemented with pathological assessment of biopsies, but these techniques can still not detect early lung cancer developments. In this review, we described the advantages and disadvantages of current methods used in diagnosing lung cancer, and we provide an analysis of the potential use of body fluids as carriers of biomarkers as predictors of cancer development and progression.
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Affiliation(s)
| | - Horacio Bach
- Division of Infectious Diseases, Faculty of Medicine, The University of British Columbia, Vancouver, BC V6H 3Z6, Canada;
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15
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Zheng Y, He Z, Kong Y, Huang X, Zhu W, Liu Z, Gong L. Combined Metabolomics with Transcriptomics Reveals Important Serum Biomarkers Correlated with Lung Cancer Proliferation through a Calcium Signaling Pathway. J Proteome Res 2021; 20:3444-3454. [PMID: 34056907 DOI: 10.1021/acs.jproteome.0c01019] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Lung cancer (LC) is one of the most malignant cancers in the world, but currently, it lacks effective noninvasive biomarkers to assist its early diagnosis. Our study aims to discover potential serum diagnostic biomarkers for LC. In our study, untargeted serum metabolomics of a discovery cohort and targeted analysis of a test cohort were performed based on gas chromatography-mass spectrometry. Both univariate and multivariate statistical analyses were employed to screen for differential metabolites between LC and healthy control (HC), followed by the selection of candidate biomarkers through multiple algorithms. The results showed that 15 metabolites were significantly dysregulated between LC and HC, and a panel, comprising cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid, was demonstrated to have excellent differentiating capability for LC based on multiple classification modelings. In addition, the molecular interaction analysis combined with transcriptomics revealed a close correlation between the candidate biomarkers and LC proliferation via a Ca2+ signaling pathway. Our study discovered that cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid in combination could be a promising diagnostic biomarker for LC, and most importantly, our results will shed some light on the pathophysiological mechanism underlying LC to understand it deeply. The data that support the findings of this study are openly available in MetaboLights at https://www.ebi.ac.uk/metabolights/, reference number MTBLS1517.
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Affiliation(s)
- Yuan Zheng
- Department of Cardiothoracic Surgery, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Zhuoru He
- International Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Yu Kong
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Plant Science Research Centre, Chinese Academy of Sciences, Shanghai Chenshan Botanical Garden, Shanghai 201602, PR China
| | - Xinjie Huang
- Department of Cardiothoracic Surgery, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Wei Zhu
- Department of Cardiothoracic Surgery, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Zhongqiu Liu
- International Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Lingzhi Gong
- International Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
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16
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Jiang W, Qiao L, Zuo D, Qin D, Xiao J, An H, Wang Y, Zhang X, Jin Y, Ren L. Aberrant lactate dehydrogenase A signaling contributes metabolic signatures in pancreatic cancer. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:358. [PMID: 33708985 PMCID: PMC7944301 DOI: 10.21037/atm-21-295] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Background Pancreatic cancer (PC) has the lowest 5-year survival rate; therefore, new early screening methods and therapeutic targets are still urgently required. Emerging technologies such as metabolomic-based liquid biopsy may contribute to the field. We found aberrant lactate dehydrogenase A (LDHA) signaling to be an unfavorable biomarker for PC. Methods A total of 9 genes of the glycolysis pathway were detected by enrichment analysis in the PC Gene Expression Omnibus (GEO) dataset. The relationship between LDHA/pyruvate kinase (PKM)/fructose biphosphate aldolase A (ALDOA)/glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and patient survival was analyzed by Kaplan-Meier plotting analysis of The Cancer Genome Atlas (TCGA). The detection of changing metabolites in the serum of PC patients was performed using a nuclear magnetic resonance (NMR) spectrometer. Results We found LDHA was an independent predictor of overall survival (OS) in PC patients (P<0.001). Consistent with genetic aberrance of LDHA, we identified significant alterations in patients’ glycolysis-related metabolites, including upregulation of lactic acid and downregulation of pyruvic acid. A 0.956 area under the curve (AUC) was achieved using the combinative metabolites score of lactic acid, pyruvic acid, citric acid, and glucose to distinguish PC from healthy controls. Conclusions Aberrant LDHA signaling is an unfavorable biomarker for PC and consequential metabolic changes constitute potential diagnostic signatures of PCs.
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Affiliation(s)
- Wenna Jiang
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Lu Qiao
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Duo Zuo
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Di Qin
- Tianjin Key Laboratory of Clinical Multi-omics, Airport Economy Zone, Tianjin, China
| | - Jiawei Xiao
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Haohua An
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yanhui Wang
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Xinwei Zhang
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yu Jin
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Li Ren
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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17
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Early lung cancer diagnostic biomarker discovery by machine learning methods. Transl Oncol 2020; 14:100907. [PMID: 33217646 PMCID: PMC7683339 DOI: 10.1016/j.tranon.2020.100907] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/21/2020] [Accepted: 09/25/2020] [Indexed: 02/07/2023] Open
Abstract
Early diagnosis could improve lung cancer survival rate. The availability of blood-based screening could increase lung cancer patient uptake. An interdisciplinary mechanism combines metabolomics and machine learning methods. Metabolic biomarkers could be potential screening biomarkers for early detection of lung cancer. Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction.
Early diagnosis has been proved to improve survival rate of lung cancer patients. The availability of blood-based screening could increase early lung cancer patient uptake. Our present study attempted to discover Chinese patients’ plasma metabolites as diagnostic biomarkers for lung cancer. In this work, we use a pioneering interdisciplinary mechanism, which is firstly applied to lung cancer, to detect early lung cancer diagnostic biomarkers by combining metabolomics and machine learning methods. We collected total 110 lung cancer patients and 43 healthy individuals in our study. Levels of 61 plasma metabolites were from targeted metabolomic study using LC-MS/MS. A specific combination of six metabolic biomarkers note-worthily enabling the discrimination between stage I lung cancer patients and healthy individuals (AUC = 0.989, Sensitivity = 98.1%, Specificity = 100.0%). And the top 5 relative importance metabolic biomarkers developed by FCBF algorithm also could be potential screening biomarkers for early detection of lung cancer. Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction. This research will provide strong support for the feasibility of blood-based screening, and bring a more accurate, quick and integrated application tool for early lung cancer diagnostic. The proposed interdisciplinary method could be adapted to other cancer beyond lung cancer.
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Marquette CH, Boutros J, Benzaquen J, Ferreira M, Pastre J, Pison C, Padovani B, Bettayeb F, Fallet V, Guibert N, Basille D, Ilie M, Hofman V, Hofman P. Circulating tumour cells as a potential biomarker for lung cancer screening: a prospective cohort study. THE LANCET RESPIRATORY MEDICINE 2020; 8:709-716. [PMID: 32649919 DOI: 10.1016/s2213-2600(20)30081-3] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 02/11/2020] [Accepted: 02/12/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND Lung cancer screening with low-dose chest CT (LDCT) reduces the mortality of eligible individuals. Blood signatures might act as a standalone screening tool, refine the selection of patients at risk, or help to classify undetermined nodules detected on LDCT. We previously showed that circulating tumour cells (CTCs) could be detected, using the isolation by size of epithelial tumour cell technique (ISET), long before the cancer was diagnosed radiologically. We aimed to test whether CTCs could be used as a biomarker for lung cancer screening. METHODS We did a prospective, multicentre, cohort study in 21 French university centres. Participants had to be eligible for lung cancer screening as per National Lung Screening Trial criteria and have chronic obstructive pulmonary disease with a fixed airflow limitation defined as post-bronchodilator FEV1/FVC ratio of less than 0·7. Any cancer, other than basocellular skin carcinomas, detected within the previous 5 years was the main exclusion criterion. Participants had three screening rounds at 1-year intervals (T0 [baseline], T1, and T2), which involved LDCT, clinical examination, and a blood test for CTCs detection. Participants and investigators were masked to the results of CTC detection, and cytopathologists were masked to clinical and radiological findings. Our primary objective was to test the diagnostic performance of CTC detection using the ISET technique in lung cancer screening, compared with cancers diagnosed by final pathology, or follow up if pathology was unavailable as the gold standard. This study is registered with ClinicalTrials.gov identifier, number NCT02500693. FINDINGS Between Oct 30, 2015, and Feb 2, 2017, we enrolled 614 participants, predominantly men (437 [71%]), aged 65·1 years (SD 6·5), and heavy smokers (52·7 pack-years [SD 21·5]). 81 (13%) participants dropped out between baseline and T1, and 56 (11%) did between T1 and T2. Nodules were detected on 178 (29%) of 614 baseline LDCTs. 19 participants (3%) were diagnosed with a prevalent lung cancer at T0 and 19 were diagnosed with incident lung cancer (15 (3%) of 533 at T1 and four (1%) of 477 at T2). Extrapulmonary cancers were diagnosed in 27 (4%) of participants. Overall 28 (2%) of 1187 blood samples were not analysable. At baseline, the sensitivity of CTC detection for lung cancer detection was 26·3% (95% CI 11·8-48·8). ISET was unable to predict lung cancer or extrapulmonary cancer development. INTERPRETATION CTC detection using ISET is not suitable for lung cancer screening. FUNDING French Government, Conseil Départemental 06, Fondation UNICE, Fondation Aveni, Fondation de France, Ligue Contre le Cancer-Comité des Alpes-Maritimes, ARC (Canc'Air Genexposomics), Claire de Divonne-Pollner, Enca Faidhi, Basil Faidhi, Fabienne Mourou, Michel Mourou, Leonid Fridlyand, cogs4cancer, and the Fondation Masikini.
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Affiliation(s)
- Charles-Hugo Marquette
- Department of Pulmonary Medicine and Oncology, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, University Hospital Federation OncoAge, Nice, France; Institute of Research on Cancer and Aging, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Nice, France.
| | - Jacques Boutros
- Department of Pulmonary Medicine and Oncology, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, University Hospital Federation OncoAge, Nice, France
| | - Jonathan Benzaquen
- Department of Pulmonary Medicine and Oncology, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, University Hospital Federation OncoAge, Nice, France; Institute of Research on Cancer and Aging, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Nice, France
| | - Marion Ferreira
- Department of Pulmonary Medicine, Centre Hospitalier Régional Universitaire Tours, Tours, France
| | - Jean Pastre
- Department of Pulmonary Medicine, Hôpital Européen Georges Pompidou, Paris, France
| | - Christophe Pison
- Centre Hospitalier Universitaire Grenoble Alpes, Service Hospitalier Universitaire Pneumologie Physiologie, Université Grenoble Alpes, Grenoble, France
| | - Bernard Padovani
- Department of Radiology, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Nice, France
| | - Faiza Bettayeb
- Clinique des bronches, allergies, et sommeil, Centre Hospitalier Universitaire de Marseille, Institut National de la Santé et de la Recherche Médicale, Centre Recherche en Cardiovasculaire et Nutrition, Aix Marseille Université, Marseille, France
| | - Vincent Fallet
- Sorbonne Université, Groupe de Recherche Clinique 4, Theranoscan, Assistance Publique - Hôpitaux de Paris, Service de Pneumologie, Hôpital Tenon, Paris, France
| | - Nicolas Guibert
- Department of Pulmonary Medicine, Centre Hospitalier Universitaire Toulouse, Toulouse, France
| | - Damien Basille
- Department of Pulmonary Medicine, Centre Hospitalier Universitaire d'Amiens, Amiens, France
| | - Marius Ilie
- Laboratory of Clinical and Experimental Pathology, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, University Hospital Federation OncoAge, Nice, France; Hospital-Related Biobank, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, University Hospital Federation OncoAge, Nice, France; Institute of Research on Cancer and Aging, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Nice, France
| | - Véronique Hofman
- Laboratory of Clinical and Experimental Pathology, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, University Hospital Federation OncoAge, Nice, France; Hospital-Related Biobank, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, University Hospital Federation OncoAge, Nice, France; Institute of Research on Cancer and Aging, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Nice, France
| | - Paul Hofman
- Laboratory of Clinical and Experimental Pathology, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, University Hospital Federation OncoAge, Nice, France; Hospital-Related Biobank, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, University Hospital Federation OncoAge, Nice, France; Institute of Research on Cancer and Aging, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Nice, France
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Callejón-Leblic B, Arias-Borrego A, Rodríguez-Moro G, Navarro Roldán F, Pereira-Vega A, Gómez-Ariza JL, García-Barrera T. Advances in lung cancer biomarkers: The role of (metal-) metabolites and selenoproteins. Adv Clin Chem 2020; 100:91-137. [PMID: 33453868 DOI: 10.1016/bs.acc.2020.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Lung cancer (LC) is the second most common cause of death in men after prostate cancer, and the third most recurrent type of tumor in women after breast and colon cancers. Unfortunately, when LC symptoms begin to appear, the disease is already in an advanced stage and the survival rate only reaches 2%. Thus, there is an urgent need for early diagnosis of LC using specific biomarkers, as well as effective therapies and strategies against LC. On the other hand, the influence of metals on more than 50% of proteins is responsible for their catalytic properties or structure, and their presence in molecules is determined in many cases by the genome. Research has shown that redox metal dysregulation could be the basis for the onset and progression of LC disease. Moreover, metals can interact between them through antagonistic, synergistic and competitive mechanisms, and for this reason metals ratios and correlations in LC should be explored. One of the most studied antagonists against the toxic action of metals is selenium, which plays key roles in medicine, especially related to selenoproteins. The study of potential biomarkers able to diagnose the disease in early stage is conditioned by the development of new analytical methodologies. In this sense, omic methodologies like metallomics, proteomics and metabolomics can greatly assist in the discovery of biomarkers for LC early diagnosis.
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Affiliation(s)
- Belén Callejón-Leblic
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Ana Arias-Borrego
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Gema Rodríguez-Moro
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Francisco Navarro Roldán
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Integrated Sciences-Cell Biology, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | | | - José Luis Gómez-Ariza
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Tamara García-Barrera
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain.
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Zhang L, Zheng J, Ahmed R, Huang G, Reid J, Mandal R, Maksymuik A, Sitar DS, Tappia PS, Ramjiawan B, Joubert P, Russo A, Rolfo CD, Wishart DS. A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection. Cancers (Basel) 2020; 12:cancers12030622. [PMID: 32156060 PMCID: PMC7139410 DOI: 10.3390/cancers12030622] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/02/2020] [Accepted: 03/05/2020] [Indexed: 12/19/2022] Open
Abstract
The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.
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Affiliation(s)
- Lun Zhang
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada; (L.Z.); (J.Z.); (J.R.); (R.M.)
| | - Jiamin Zheng
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada; (L.Z.); (J.Z.); (J.R.); (R.M.)
| | - Rashid Ahmed
- BioMark Diagnostics Inc., Richmond, BC V6X 2W8, Canada; (R.A.); (G.H.)
| | - Guoyu Huang
- BioMark Diagnostics Inc., Richmond, BC V6X 2W8, Canada; (R.A.); (G.H.)
| | - Jennifer Reid
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada; (L.Z.); (J.Z.); (J.R.); (R.M.)
| | - Rupasri Mandal
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada; (L.Z.); (J.Z.); (J.R.); (R.M.)
| | - Andrew Maksymuik
- Cancer Care Manitoba, Winnipeg, MB R3E 0V9, Canada;
- Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada;
| | - Daniel S. Sitar
- Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada;
- Department of Pharmacology & Therapeutics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada
| | - Paramjit S. Tappia
- Asper Clinical Research Institute & Office of Clinical Research, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada; (P.S.T.); (B.R.)
| | - Bram Ramjiawan
- Asper Clinical Research Institute & Office of Clinical Research, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada; (P.S.T.); (B.R.)
| | - Philippe Joubert
- Department of Pathology, University of Laval, Quebec, QC G1V 4G5, Canada;
| | - Alessandro Russo
- Medical Oncology Unit A.O. Papardo & Department of Human Pathology, University of Messina, 98158 Messina, Italy;
- Thoracic Medical Oncology Program Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD 21201, USA;
| | - Christian D. Rolfo
- Thoracic Medical Oncology Program Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD 21201, USA;
| | - David S. Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada; (L.Z.); (J.Z.); (J.R.); (R.M.)
- Correspondence:
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Chen Q, Zhu C, Jin Y, Si X, Jiao W, He W, Mao W, Li M, Luo G. Plasma Long Non-Coding RNA RP11-438N5.3 as a Novel Biomarker for Non-Small Cell Lung Cancer. Cancer Manag Res 2020; 12:1513-1521. [PMID: 32184656 PMCID: PMC7055527 DOI: 10.2147/cmar.s237024] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 01/10/2020] [Indexed: 12/18/2022] Open
Abstract
Background Lung cancer is one of the most common malignancies around the world. The lack of early diagnosis and effective treatment strategies contributes to the poor prognosis of patients with lung cancer. Recent studies have implied the role of long non-coding RNAs (lncRNAs) in oncogenesis. The purpose of our study was to identify specific lncRNAs which were correlated with non-small cell lung cancer (NSCLC) and their potential functions. Materials and Methods The global plasma lncRNA profiling was performed using LncPathTM Human Cancer Array, and 11 lncRNAs were then selected for quantitative reverse transcription PCR (qRT-PCR) validation in 138 plasma samples from 69 NSCLC patients and 69 healthy controls (HCs). A noteworthy lncRNA, RP11-438N5.3, the function of which was previously unknown, was further explored on the aspect of the correlation of its expression level with clinicopathological factors. Results The results revealed that plasma level of RP11-438N5.3 was significantly lower in NSCLCs than that in HCs (p <0.01). Receiver operating characteristic (ROC) analyses showed that the area under the ROC curve (AUC) for plasma RP11-438N5.3 was 0.814 (95% CI, 0.743–0.885; p<0.01). High expression of RP11-438N5.3 in plasma correlated with favorable prognosis for NSCLC patients (Hazard ratio = 2.827; 95% CI: 1.036 to 7.718; p = 0.024; Cox regression analysis). Moreover, we found that the plasma level of stromal interaction molecule 1 (STIM1) mRNA was remarkably higher in NSCLC compared with HC (p<0.01), and the AUC for STIM1 was 0.753 (95% CI, 0.673–0.833; p<0.01), RP11-438N5.3 and STIM1 were inversely correlated with each other. Conclusion Our results indicated that RP11-438N5.3 and STIM1 might provide a new strategy for NSCLC diagnosis. Furthermore, increased circulating RP11-438N5.3 level holds great potential in indicating a beneficial prognosis in NSCLC patients. ![]()
Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use: https://youtu.be/cZTolLw-1og
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Affiliation(s)
- Qingjuan Chen
- Department of Oncology, Yongchuan Hospital of Chongqing Medical University, Chongqing 40016, People's Republic of China
| | - Chenjing Zhu
- Department of Radiation Oncology, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu 210009, People's Republic of China
| | - Yingying Jin
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province 710004, People's Republic of China
| | - Xiaomin Si
- Department of Oncology, Xianyang Center Hospital, Xi'an, Shaanxi Province 712000, People's Republic of China
| | - Wan Jiao
- Department of Oncology, Xianyang Center Hospital, Xi'an, Shaanxi Province 712000, People's Republic of China
| | - Wenjing He
- Department of Oncology, Xianyang Center Hospital, Xi'an, Shaanxi Province 712000, People's Republic of China
| | - Wei Mao
- Department of Oncology, Xianyang Center Hospital, Xi'an, Shaanxi Province 712000, People's Republic of China
| | - Ming Li
- Department of Oncology, Yongchuan Hospital of Chongqing Medical University, Chongqing 40016, People's Republic of China
| | - Guomin Luo
- Department of Oncology, Yongchuan Hospital of Chongqing Medical University, Chongqing 40016, People's Republic of China
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