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Herth J, Schmidt F, Basler S, Sievi NA, Kohler M. Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study. J Breath Res 2024; 18:036003. [PMID: 38718786 DOI: 10.1088/1752-7163/ad48a9] [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: 02/04/2024] [Accepted: 05/08/2024] [Indexed: 05/18/2024]
Abstract
Exhaled breath analysis has emerged as a non-invasive and promising method for early detection of lung cancer, offering a novel approach for diagnosis through the identification of specific biomarkers present in a patient's breath. For this longitudinal study, 29 treatment-naive patients with lung cancer were evaluated before and after surgery. Secondary electrospray ionization high-resolution mass spectrometry was used for exhaled breath analysis. Volatile organic compounds with absolute log2fold change ⩾1 andq-values ⩾ 0.71 were selected as potentially relevant. Exhaled breath analysis resulted in a total of 3482 features. 515 features showed a substantial difference before and after surgery. The small sample size generated a false positive rate of 0.71, therefore, around 154 of these 515 features were expected to be true changes. Biological identification of the features with the highest consistency (m/z-242.18428 andm/z-117.0539) revealed to potentially be 3-Oxotetradecanoic acid and Indole, respectively. Principal component analysis revealed a primary cluster of patients with a recurrent lung cancer, which remained undetected in the initial diagnostic and surgical procedures. The change of exhaled breath patterns after surgery in lung cancer emphasizes the potential for lung cancer screening and detection.
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Affiliation(s)
- Jonas Herth
- Department of Pulmonology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Felix Schmidt
- Faculty of Medicine, University of Zurich, 8032 Zurich, Switzerland
- Department of Pulmonology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Sarah Basler
- Faculty of Medicine, University of Zurich, 8032 Zurich, Switzerland
- Department of Pulmonology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Noriane A Sievi
- Department of Pulmonology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Malcolm Kohler
- Faculty of Medicine, University of Zurich, 8032 Zurich, Switzerland
- Department of Pulmonology, University Hospital Zurich, 8091 Zurich, Switzerland
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Gao M, Wang M, Chen Y, Wu J, Zhou S, He W, Shu Y, Wang X. Identification and validation of tryptophan metabolism-related lncRNAs in lung adenocarcinoma prognosis and immune response. J Cancer Res Clin Oncol 2024; 150:171. [PMID: 38558328 PMCID: PMC10984901 DOI: 10.1007/s00432-024-05665-x] [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: 12/07/2023] [Accepted: 02/23/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Tryptophan (Trp) is an essential amino acid. Increasing evidence suggests that tryptophan metabolism plays a complex role in immune escape from Lung adenocarcinoma (LUAD). However, the role of long non-coding RNAs (lncRNAs) in tryptophan metabolism remains to be investigated. METHODS This study uses The Cancer Genome Atlas (TCGA)-LUAD dataset as the training cohort, and several datasets from the Gene Expression Omnibus (GEO) database are merged into the validation cohort. Genes related to tryptophan metabolism were identified from the Molecular Signatures Database (MSigDB) database and further screened for lncRNAs with Trp-related expression. Subsequently, a prognostic signature of lncRNAs related to tryptophan metabolism was constructed using Cox regression analysis, (Least absolute shrinkage and selection operator regression) and LASSO analysis. The predictive performance of this risk score was validated by Kaplan-Meier (KM) survival analysis, (receiver operating characteristic) ROC curves, and nomograms. We also explored the differences in immune cell infiltration, immune cell function, tumor mutational load (TMB), tumor immune dysfunction and exclusion (TIDE), and anticancer drug sensitivity between high- and low-risk groups. Finally, we used real-time fluorescence quantitative PCR, CCK-8, colony formation, wound healing, transwell, flow cytometry, and nude mouse xenotransplantation models to elucidate the role of ZNF8-ERVK3-1 in LUAD. RESULTS We constructed 16 tryptophan metabolism-associated lncRNA prognostic models in LUAD patients. The risk score could be used as an independent prognostic indicator for the prognosis of LUAD patients. Kaplan-Meier survival analysis, ROC curves, and risk maps validated the prognostic value of the risk score. The high-risk and low-risk groups showed significant differences in phenotypes, such as the percentage of immune cell infiltration, immune cell function, gene mutation frequency, and anticancer drug sensitivity. In addition, patients with high-risk scores had higher TMB and TIDE scores compared to patients with low-risk scores. Finally, we found that ZNF8-ERVK3-1 was highly expressed in LUAD tissues and cell lines. A series of in vitro experiments showed that knockdown of ZNF8-ERVK3-1 inhibited cell proliferation, migration, and invasion, leading to cell cycle arrest in the G0/G1 phase and increased apoptosis. In vivo experiments with xenografts have shown that knocking down ZNF8-ERVK3-1 can significantly inhibit tumor size and tumor proliferation. CONCLUSION We constructed a new prognostic model for tryptophan metabolism-related lncRNA. The risk score was closely associated with common clinical features such as immune cell infiltration, immune-related function, TMB, and anticancer drug sensitivity. Knockdown of ZNF8-ERVK3-1 inhibited LUAD cell proliferation, migration, invasion, and G0/G1 phase blockade and promoted apoptosis.
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Affiliation(s)
- Mingjun Gao
- Dalian Medical University, Dalian, 116000, China
| | | | - Yong Chen
- Dalian Medical University, Dalian, 116000, China
| | - Jun Wu
- Clinical Medical College, Yangzhou University, Yangzhou, 225000, China
| | - Siding Zhou
- Clinical Medical College, Yangzhou University, Yangzhou, 225000, China
| | - Wenbo He
- Clinical Medical College, Yangzhou University, Yangzhou, 225000, China
| | - Yusheng Shu
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital, No. 98 Nantong West Road, Yangzhou, 225000, Jiangsu, China.
| | - Xiaolin Wang
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital, No. 98 Nantong West Road, Yangzhou, 225000, Jiangsu, China.
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Correnti S, Preianò M, Gamboni F, Stephenson D, Pelaia C, Pelaia G, Savino R, D'Alessandro A, Terracciano R. An integrated metabo-lipidomics profile of induced sputum for the identification of novel biomarkers in the differential diagnosis of asthma and COPD. J Transl Med 2024; 22:301. [PMID: 38521955 PMCID: PMC10960495 DOI: 10.1186/s12967-024-05100-2] [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: 12/21/2023] [Accepted: 03/15/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND Due to their complexity and to the presence of common clinical features, differentiation between asthma and chronic obstructive pulmonary disease (COPD) can be a challenging task, complicated in such cases also by asthma-COPD overlap syndrome. The distinct immune/inflammatory and structural substrates of COPD and asthma are responsible for significant differences in the responses to standard pharmacologic treatments. Therefore, an accurate diagnosis is of central relevance to assure the appropriate therapeutic intervention in order to achieve safe and effective patient care. Induced sputum (IS) accurately mirrors inflammation in the airways, providing a more direct picture of lung cell metabolism in comparison to those specimen that reflect analytes in the systemic circulation. METHODS An integrated untargeted metabolomics and lipidomics analysis was performed in IS of asthmatic (n = 15) and COPD (n = 22) patients based on Ultra-High-Pressure Liquid Chromatography-Mass Spectrometry (UHPLC-MS) and UHPLC-tandem MS (UHPLC-MS/MS). Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to resulting dataset. The analysis of main enriched metabolic pathways and the association of the preliminary metabolites/lipids pattern identified to clinical parameters of asthma/COPD differentiation were explored. Multivariate ROC analysis was performed in order to determine the discriminatory power and the reliability of the putative biomarkers for diagnosis between COPD and asthma. RESULTS PLS-DA indicated a clear separation between COPD and asthmatic patients. Among the 15 selected candidate biomarkers based on Variable Importance in Projection scores, putrescine showed the highest score. A differential IS bio-signature of 22 metabolites and lipids was found, which showed statistically significant variations between asthma and COPD. Of these 22 compounds, 18 were decreased and 4 increased in COPD compared to asthmatic patients. The IS levels of Phosphatidylethanolamine (PE) (34:1), Phosphatidylglycerol (PG) (18:1;18:2) and spermine were significantly higher in asthmatic subjects compared to COPD. CONCLUSIONS This is the first pilot study to analyse the IS metabolomics/lipidomics signatures relevant in discriminating asthma vs COPD. The role of polyamines, of 6-Hydroxykynurenic acid and of D-rhamnose as well as of other important players related to the alteration of glycerophospholipid, aminoacid/biotin and energy metabolism provided the construction of a diagnostic model that, if validated on a larger prospective cohort, might be used to rapidly and accurately discriminate asthma from COPD.
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Affiliation(s)
- Serena Correnti
- Department of Health Sciences, Magna Græcia University, 88100, Catanzaro, Italy.
| | | | - Fabia Gamboni
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Daniel Stephenson
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Corrado Pelaia
- Department of Medical and Surgical Sciences, Magna Græcia University, 88100, Catanzaro, Italy
| | - Girolamo Pelaia
- Department of Health Sciences, Magna Græcia University, 88100, Catanzaro, Italy
| | - Rocco Savino
- Department of Medical and Surgical Sciences, Magna Græcia University, 88100, Catanzaro, Italy
| | - Angelo D'Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Rosa Terracciano
- Department of Experimental and Clinical Medicine, Magna Græcia University, 88100, Catanzaro, Italy.
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Bae HL, Jeong K, Yang S, Jun H, Kim K, Chai YJ. Expression Profiles of Hypoxia-Related Genes of Cancers Originating from Anatomically Similar Locations Using TCGA Database Analysis. MEDICINES (BASEL, SWITZERLAND) 2023; 11:2. [PMID: 38248716 PMCID: PMC10819830 DOI: 10.3390/medicines11010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 11/15/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
Background: Hypoxia is a well-recognized characteristic of the tumor microenvironment of solid cancers. This study aimed to analyze hypoxia-related genes shared by groups based on tumor location. Methods: A total of 9 hypoxia-related pathways from the Kyoto Encyclopedia of Genes and Genomes database or the Reactome database were selected, and 850 hypoxia-related genes were analyzed. Based on their anatomical locations, 14 tumor types were categorized into 6 groups. The group-specific genetic risk score was classified as high- or low-risk based on mRNA expression, and survival outcomes were evaluated. Results: The risk scores in the Female Reproductive group and the Lung group were internally and externally validated. In the Female Reproductive group, CDKN2A, FN1, and ITGA5 were identified as hub genes associated with poor prognosis, while IL2RB and LEF1 were associated with favorable prognosis. In the Lung group, ITGB1 and LDHA were associated with poor prognosis, and GLS2 was associated with favorable prognosis. Functional enrichment analysis showed that the Female Reproductive group was enriched in relation to cilia and skin, while the Lung group was enriched in relation to cytokines and defense. Conclusions: This analysis may lead to better understanding of the mechanisms of cancer progression and facilitate establishing new biomarkers for prognosis prediction.
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Affiliation(s)
- Hye Lim Bae
- Department of Surgery, Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Kyeonghun Jeong
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea;
| | - Suna Yang
- Department of Clinical Medical Science, Seoul National University, Seoul 08826, Republic of Korea;
| | - Hyeji Jun
- Seoul National University Hospital Biomedical Research Institute, Seoul 03080, Republic of Korea;
| | - Kwangsoo Kim
- Department of Transdisciplinary Department of Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul 03080, Republic of Korea
- Department of Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Young Jun Chai
- Department of Surgery, Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
- Department of Transdisciplinary Department of Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul 03080, Republic of Korea
- Department of Surgery, Seoul Metropolitan Government—Seoul National University Boramae Medical Center, Seoul 07061, Republic of Korea
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Thafar MA, Albaradei S, Uludag M, Alshahrani M, Gojobori T, Essack M, Gao X. OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features. Front Genet 2023; 14:1139626. [PMID: 37091791 PMCID: PMC10117673 DOI: 10.3389/fgene.2023.1139626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 03/24/2023] [Indexed: 04/08/2023] Open
Abstract
Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.
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Affiliation(s)
- Maha A. Thafar
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- College of Computers and Information Technology, Computer Science Department, Taif University, Taif, Saudi Arabia
| | - Somayah Albaradei
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Mona Alshahrani
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- *Correspondence: Xin Gao, ; Magbubah Essack,
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- *Correspondence: Xin Gao, ; Magbubah Essack,
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Blood Plasma Metabolome Profiling at Different Stages of Renal Cell Carcinoma. Cancers (Basel) 2022; 15:cancers15010140. [PMID: 36612136 PMCID: PMC9818272 DOI: 10.3390/cancers15010140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/28/2022] Open
Abstract
Early diagnostics significantly improves the survival of patients with renal cell carcinoma (RCC), which is the prevailing type of adult kidney cancer. However, the absence of clinically obvious symptoms and effective screening strategies at the early stages result to disease progression and survival rate reducing. The study was focused on revealing of potential low molecular biomarkers for early-stage RCC. The untargeted direct injection mass spectrometry-based metabolite profiling of blood plasma samples from 51 non-cancer volunteers (control) and 78 patients with different RCC subtypes and stages (early stages of clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chrRCC) and advanced stages of ccRCC) was performed. Comparative analysis of the blood plasma metabolites between the control and cancer groups provided the detection of metabolites associated with different tumor stages. The designed model based on the revealed metabolites demonstrated high diagnostic power and accuracy. Overall, using the metabolomics approach the study revealed the metabolites demonstrating a high value for design of plasma-based test to improve early ccRCC diagnosis.
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Liu Y, Xiang J, Liao Y, Peng G, Shen C. Identification of tryptophan metabolic gene-related subtypes, development of prognostic models, and characterization of tumor microenvironment infiltration in gliomas. Front Mol Neurosci 2022; 15:1037835. [PMID: 36407768 PMCID: PMC9673907 DOI: 10.3389/fnmol.2022.1037835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/13/2022] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Epigenetic regulation and immunotherapy of tumor microenvironment (TME) is a hot topic in recent years. However, the potential value of tryptophan metabolism genes in regulating TME and immunotherapy is still unclear. MATERIALS AND METHODS A comprehensive study of glioma patients was carried out based on 40 tryptophan metabolic genes. Subsequently, these prognostic tryptophan metabolic genes are systematically associated with immunological characteristics and immunotherapy. A risk score model was constructed and verified in the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) cohorts to provide guidance for prognosis prediction and immunotherapy of glioma patients. RESULTS We described the changes of tryptophan metabolism genes in 966 glioma samples from genetic and transcriptional fields and evaluated their expression patterns from two independent data sets. We identified two different molecular subtypes and found that two subtypes were associated with clinicopathological features, prognosis, TME cell infiltration, and immune checkpoint blockers (ICBs). Then, four genes (IL4I1, CYP1A1, OGDHL, and ASMT) were screened out by univariate and multivariate cox regression analysis of tryptophan metabolism genes, and a risk score model for predicting the overall survival (OS) of glioma patients was constructed. And its predictive ability is verified using the CGGA database. At the same time, we verified the expression of IL4I1, CYP1A1, OGDHL, and ASMT four genes in glioma specimens and cell lines in GES4260 and GES15824. Therefore, we constructed a nomogram to improve the clinical applicability of the risk assessment model. The high risk score group, characterized by increased TMB and immune cell infiltration, was also sensitive to temozolomide immunotherapy. Our comprehensive analysis of tryptophan metabolic genes in gliomas shows that they play a potential role in tumor immune stromal microenvironment, clinicopathological features, and prognosis. CONCLUSION Tryptophan metabolism genes play an indispensable role in the complexity, diversity, and prognosis of TME. This risk score model based on tryptophan metabolism gene is a new predictor of clinical prognosis and immunotherapy response of glioma, and guides a more appropriate immunotherapy strategy for glioma patients.
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Affiliation(s)
- Yi Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Juan Xiang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yiwei Liao
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Gang Peng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chenfu Shen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Kim J, Suresh B, Lim MN, Hong SH, Kim KS, Song HE, Lee HY, Yoo HJ, Kim WJ. Metabolomics Reveals Dysregulated Sphingolipid and Amino Acid Metabolism Associated with Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis 2022; 17:2343-2353. [PMID: 36172036 PMCID: PMC9511892 DOI: 10.2147/copd.s376714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 09/09/2022] [Indexed: 11/25/2022] Open
Abstract
Purpose Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease presenting as multiple phenotypes, such as declining lung function, emphysema, or persistent airflow limitation caused by several risk factors, including cigarette smoking and air pollution. The inherent complexity of COPD phenotypes propounds difficulties for accurate diagnosis and prognosis. Although metabolomic profiles on COPD have been reported, the role of metabolism in COPD-related phenotypes is yet to be determined. In this study, we investigated the association between plasma sphingolipids and amino acids, and between COPD and COPD-related phenotypes in a Korean cohort. Patients and Methods Blood samples were collected from 120 patients with COPD and 80 control participants who underwent spirometry and quantitative computed tomography. The plasma metabolic profiling was carried out using LC-MS/MS analysis. Results Among the evaluated plasma sphingolipids, an increase in the metabolism of two specific sphingomyelins, SM (d18:1/24:0) and SM (d18:1/24:1) were significantly associated with COPD. There was no significant correlation between any of the SMs and the emphysema index, FVC and FEV1 in the COPD cohort. Meanwhile, Cer (d18:1/18:0) and Cer (d18:1/24:1) were significantly associated with reduced FEV1. Furthermore, the levels of several amino acids were altered in the COPD group compared to that in the non-COPD group; glutamate and alpha AAA were substantial associated with emphysema in COPD. Kynurenine was the only amino acid significantly associated with reduced FEV1 in COPD. In contrast, there was no correlation between FVC and the elevated metabolites. Conclusion Our results provide dysregulated plasma metabolites impacting COPD phenotypes, although more studies are needed to explore the underlying mechanism related to COPD pathogenesis.
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Affiliation(s)
- Jeeyoung Kim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Korea
| | - Bharathi Suresh
- Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, South Korea
| | - Myoung Nam Lim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Korea
| | - Seok-Ho Hong
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, South Korea
| | - Kye-Seong Kim
- Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, South Korea.,College of Medicine, Hanyang University, Seoul, South Korea
| | - Ha Eun Song
- Department of Convergence Medicine, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, South Korea
| | - Hyo Yeong Lee
- Department of Convergence Medicine, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, South Korea
| | - Hyun Ju Yoo
- Department of Convergence Medicine, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, South Korea
| | - Woo Jin Kim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Korea
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Amino Acid Profiles and Nutritional Evaluation of Fresh Sweet–Waxy Corn from Three Different Regions of China. Nutrients 2022; 14:nu14193887. [PMID: 36235541 PMCID: PMC9572857 DOI: 10.3390/nu14193887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
This study conducted a comparative analysis of the amino acid compositions of Chinese Huangnuo 9 fresh sweet–waxy corn from three different provinces in China—Inner Mongolia, Jilin, and Heilongjiang Province. Moreover, we established a nutritive evaluation system based on amino acid profiles to evaluate, compare, and rank the fresh sweet–waxy corn planted in different regions. A total of 17 amino acids were quantified, and the amino acid composition of fresh sweet–waxy corn was analyzed and evaluated. The amino acid quality was determined by the amino acid pattern spectrum, chemical evaluations (including CS, AAS, EAAI, BV, U(a,u), NI, F, predict PER, and PDCAAS), flavor evaluation, amino acid matching degree evaluation, and the results of the factor analysis. The results showed that the protein content of fresh corn 1–1 from Inner Mongolia was the highest (40.26 ± 0.35 mg/g), but the factor analysis results, digestion, and absorption efficiency of fresh corn 1–2 were the best. The amino acid profile of fresh corn 1–1 was closest to each evaluation’s model spectrum. The results of the diversity evaluations in fresh corn 3–2 were the best, and fresh corn 3–3 had the most essential amino acid content. A total of 17 amino acids in fresh corn were divided into three principal component factor analyses: functional principal components (Leu, Pro, Glu, His, Ile, Ser, Met, Val, Tyr, Thr), regulatory principal components (Lys, Gly, Ala, Asp, Arg, Trp), and protection principal components (Phe). The scores of the three principal components and the comprehensive score in fresh corn 1–2 were all the highest, followed by 3–3 and 1–1. The amino acid nutritional values of fresh corn 1–2 were the highest in 12 samples.
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Miller HA, van Berkel VH, Frieboes HB. Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data. Metabolomics 2022; 18:57. [PMID: 35857204 PMCID: PMC9737952 DOI: 10.1007/s11306-022-01918-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 06/30/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value. OBJECTIVES Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies. METHODS Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility. RESULTS Overall survival (OS) is predicted in external validation cohort with AUROCTEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROCTEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased. CONCLUSION This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
| | - Victor H van Berkel
- UofL Health-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.
- UofL Health-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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Application of Surface-Enhanced Raman Spectroscopy in the Screening of Pulmonary Adenocarcinoma Nodules. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4368928. [PMID: 35782079 PMCID: PMC9246604 DOI: 10.1155/2022/4368928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/03/2022] [Accepted: 06/09/2022] [Indexed: 11/24/2022]
Abstract
This study is aimed at evaluating the feasibility of a screening method for the pulmonary adenocarcinoma nodules through surface-enhanced Raman spectroscopy (SERS). Objective. Using SERS to measure serum from pulmonary nodules and healthy subjects, intraoperative biopsy pathological diagnosis was regarded as the gold standard for labeling serum samples. To explore the application value of SERS in the differential diagnosis of pulmonary adenocarcinoma nodules, benign nodules, and healthy, we build a machine learning model. Method. We collected 116 serum samples from patients. Radiographically confirmed nodules less than 3 cm in maximum diameter in all patients, including 58 cancer (pathologic diagnosis: adenocarcinoma nodules, labeled as cancer) patients, 58 pathologic diagnoses as benign nodule (labeled as benign) patients, and 63 healthy (labeled as normal) people from the clinical laboratory of Sichuan Cancer Hospital. Gold nanorods were employed as SERS substrates. Support vector machine (SVM) was used to classify the normal, benign, and cancer sample groups, and SVM model evaluated using cross-validation. Results. The average SERS spectra of serum were significantly different between the normal group and the cancer/benign group. While the average SERS spectra of the cancer group and the benign group differed slightly, for the cancer, benign, and normal groups, SVM models can predict with 93.33% accuracy. Conclusion. This exploratory study demonstrates that the SERS technique based on nanoparticles in conjunction with SVM has great potential as a clinical auxiliary diagnosis and screening for pulmonary adenocarcinoma nodules.
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Fahrmann JF, Tanaka I, Irajizad E, Mao X, Dennison JB, Murage E, Casabar J, Mayo J, Peng Q, Celiktas M, Vykoukal JV, Park S, Taguchi A, Delgado O, Tripathi SC, Katayama H, Soto LMS, Rodriguez-Canales J, Behrens C, Wistuba I, Hanash S, Ostrin EJ. Mutational Activation of the NRF2 Pathway Upregulates Kynureninase Resulting in Tumor Immunosuppression and Poor Outcome in Lung Adenocarcinoma. Cancers (Basel) 2022; 14:2543. [PMID: 35626147 PMCID: PMC9139317 DOI: 10.3390/cancers14102543] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 11/17/2022] Open
Abstract
Activation of the NRF2 pathway through gain-of-function mutations or loss-of-function of its suppressor KEAP1 is a frequent finding in lung cancer. NRF2 activation has been reported to alter the tumor microenvironment. Here, we demonstrated that NRF2 alters tryptophan metabolism through the kynurenine pathway that is associated with a tumor-promoting, immune suppressed microenvironment. Specifically, proteomic profiles of 47 lung adenocarcinoma (LUAD) cell lines (11 KEAP1 mutant and 36 KEAP1 wild-type) revealed the tryptophan-kynurenine enzyme kynureninase (KYNU) as a top overexpressed protein associated with activated NRF2. The siRNA-mediated knockdown of NFE2L2, the gene encoding for NRF2, or activation of the NRF2 pathway through siRNA-mediated knockdown of KEAP1 or via chemical induction with the NRF2-activator CDDO-Me confirmed that NRF2 is a regulator of KYNU expression in LUAD. Metabolomic analyses confirmed KYNU to be enzymatically functional. Analysis of multiple independent gene expression datasets of LUAD, as well as a LUAD tumor microarray demonstrated that elevated KYNU was associated with immunosuppression, including potent induction of T-regulatory cells, increased levels of PD1 and PD-L1, and resulted in poorer survival. Our findings indicate a novel mechanism of NRF2 tumoral immunosuppression through upregulation of KYNU.
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Affiliation(s)
- Johannes F. Fahrmann
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | - Ichidai Tanaka
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya 464-8601, Japan;
| | - Ehsan Irajizad
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA;
| | - Xiangying Mao
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | - Jennifer B. Dennison
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | - Eunice Murage
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | - Julian Casabar
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | - Jeffrey Mayo
- Department of General Internal Medicine, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.M.); (Q.P.)
| | - Qian Peng
- Department of General Internal Medicine, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.M.); (Q.P.)
| | - Muge Celiktas
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | - Jody V. Vykoukal
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | - Soyoung Park
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | - Ayumu Taguchi
- Division of Molecular Diagnostics, Aichi Cancer Center, Kanokoden, Chikusa-ku, Nagoya 464-8681, Japan;
| | - Oliver Delgado
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | | | - Hiroyuki Katayama
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | - Luisa Maren Solis Soto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (L.M.S.S.); (J.R.-C.); (C.B.); (I.W.)
| | - Jaime Rodriguez-Canales
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (L.M.S.S.); (J.R.-C.); (C.B.); (I.W.)
| | - Carmen Behrens
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (L.M.S.S.); (J.R.-C.); (C.B.); (I.W.)
| | - Ignacio Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (L.M.S.S.); (J.R.-C.); (C.B.); (I.W.)
| | - Samir Hanash
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.F.F.); (X.M.); (J.B.D.); (E.M.); (J.C.); (M.C.); (J.V.V.); (S.P.); (O.D.); (H.K.); (S.H.)
| | - Edwin J. Ostrin
- Department of General Internal Medicine, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA; (J.M.); (Q.P.)
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Qian H, Wang Y, Ma Z, Qian L, Shao X, Jin D, Cao M, Liu S, Chen H, Pan J, Xue W. Surface-Enhanced Raman Spectroscopy of Pretreated Plasma Samples Predicts Disease Recurrence in Muscle-Invasive Bladder Cancer Patients Undergoing Neoadjuvant Chemotherapy and Radical Cystectomy. Int J Nanomedicine 2022; 17:1635-1646. [PMID: 35411143 PMCID: PMC8994599 DOI: 10.2147/ijn.s354590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/14/2022] [Indexed: 01/01/2023] Open
Abstract
Objective To explore the value of surface-enhanced Raman spectroscopy analysis of pretreated plasma samples in prediction of bladder cancer (BCa) recurrence after neoadjuvant chemotherapy (NAC) and radical cystectomy (RC). Patients and Methods SERS was used to analyze plasma samples collected before biopsy and treatment in BCa patients undergoing NAC and RC. The value of clinicopathological parameters and distinctive SERS peaks in the prediction of disease recurrence were analyzed in Cox regression proportional hazard analysis and Log rank test. Principal component analysis and linear discriminant analysis (PCA-LDA) were employed to process spectral data and construct diagnostic algorithms. Results A total of 88 patients with 440 plasma SERS spectra were collected. The SRES spectra from recurrent patients were compared with patients who remained recurrence free. The SERS demonstrated higher levels of circulating free nucleic acid components in recurrent population, which is represented by significantly higher intensities at SERS peaks of 725 cm−1, 1328 cm−1 and 1455 cm−1. The SERS also detected significantly lower levels of tryptophan shown as lower significantly intensities at the 1558 cm−1, which is proved to be an independent predictor of BCa recurrence. The addition of SERS peaks of 1558 cm−1 to classic clinicopathological predictors including pathological tumor stage, lymph node metastasis and pathological downstaging can significantly enhance the power of the predictive model from 0.66 to 0.76 in the area under curve (AUC) of receiver operating characteristic (ROC) curves. Meanwhile, the PCA-LDA diagnostic model based on SERS spectra reveals a high accuracy of 85.2% in prediction of disease recurrence and the AUC of 0.92 in the ROC curve. When validated in the leave-one-out cross-validation method, the accuracy of the model remained 84.1%. Conclusion We show that SERS analysis of plasma before NAC treatment can accurately classify patients with different risks of disease recurrence after surgery and improve the power of clinicopathological predictive models, thus refining clinical decision-making.
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Affiliation(s)
- Hongyang Qian
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Yiqiu Wang
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Zehua Ma
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Lei Qian
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Xiaoguang Shao
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Di Jin
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Ming Cao
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Shupeng Liu
- Shanghai Institute for Advanced Communication and Data Science, Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, People’s Republic of China
| | - Haige Chen
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Jiahua Pan
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Wei Xue
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
- Correspondence: Wei Xue; Jiahua Pan, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 1630 Dongfang Road, Shanghai, 200127, People’s Republic of China, Tel +86 21 6838 3375, Email ;
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Song X, Si Q, Qi R, Liu W, Li M, Guo M, Wei L, Yao Z. Indoleamine 2,3-Dioxygenase 1: A Promising Therapeutic Target in Malignant Tumor. Front Immunol 2022; 12:800630. [PMID: 35003126 PMCID: PMC8733291 DOI: 10.3389/fimmu.2021.800630] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 12/03/2021] [Indexed: 12/13/2022] Open
Abstract
Tumorigenesis is a complex multifactorial and multistep process in which tumors can utilize a diverse repertoire of immunosuppressive mechanisms to evade host immune attacks. The degradation of tryptophan into immunosuppressive kynurenine is considered an important immunosuppressive mechanism in the tumor microenvironment. There are three enzymes, namely, tryptophan 2,3-dioxygenase (TDO), indoleamine 2,3-dioxygenase 1 (IDO1), and indoleamine 2,3-dioxygenase 2 (IDO2), involved in the metabolism of tryptophan. IDO1 has a wider distribution and higher activity in catalyzing tryptophan than the other two; therefore, it has been studied most extensively. IDO1 is a cytosolic monomeric, heme-containing enzyme, which is now considered an authentic immune regulator and represents one of the promising drug targets for tumor immunotherapy. Collectively, this review highlights the regulation of IDO1 gene expression and the ambivalent mechanisms of IDO1 on the antitumoral immune response. Further, new therapeutic targets via the regulation of IDO1 are discussed. A comprehensive analysis of the expression and biological function of IDO1 can help us to understand the therapeutic strategies of the inhibitors targeting IDO1 in malignant tumors.
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Affiliation(s)
- Xiaotian Song
- Department of Immunology, Hebei Medical University, Shijiazhuang, China.,Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Shijiazhuang, China
| | - Qianqian Si
- Department of Immunology, Hebei Medical University, Shijiazhuang, China.,Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Shijiazhuang, China
| | - Rui Qi
- Department of Immunology, Hebei Medical University, Shijiazhuang, China.,Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Shijiazhuang, China
| | - Weidan Liu
- Department of Clinical Laboratory, The People's Hospital, Pingxiang County, Xingtai, China
| | - Miao Li
- Department of Immunology, Hebei Medical University, Shijiazhuang, China.,Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Shijiazhuang, China
| | - Mengyue Guo
- Department of Immunology, Hebei Medical University, Shijiazhuang, China.,Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Shijiazhuang, China
| | - Lin Wei
- Department of Immunology, Hebei Medical University, Shijiazhuang, China.,Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Shijiazhuang, China
| | - Zhiyan Yao
- Department of Immunology, Hebei Medical University, Shijiazhuang, China.,Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Shijiazhuang, China
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