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Joblin-Mills A, Wu ZE, Sequeira-Bisson IR, Miles-Chan JL, Poppitt SD, Fraser K. Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study. Metabolites 2024; 14:313. [PMID: 38921448 PMCID: PMC11205627 DOI: 10.3390/metabo14060313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/20/2024] [Accepted: 05/27/2024] [Indexed: 06/27/2024] Open
Abstract
Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.
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Affiliation(s)
- Aidan Joblin-Mills
- Food Chemistry & Structure Team, AgResearch, Palmerston North 4410, New Zealand; (Z.E.W.); (K.F.)
- High-Value Nutrition National Science Challenge, Auckland 1145, New Zealand; (I.R.S.-B.); (J.L.M.-C.); (S.D.P.)
| | - Zhanxuan E. Wu
- Food Chemistry & Structure Team, AgResearch, Palmerston North 4410, New Zealand; (Z.E.W.); (K.F.)
- High-Value Nutrition National Science Challenge, Auckland 1145, New Zealand; (I.R.S.-B.); (J.L.M.-C.); (S.D.P.)
- School of Food and Nutrition, Massey University, Palmerston North 4410, New Zealand
| | - Ivana R. Sequeira-Bisson
- High-Value Nutrition National Science Challenge, Auckland 1145, New Zealand; (I.R.S.-B.); (J.L.M.-C.); (S.D.P.)
- Human Nutrition Unit, School of Biological Sciences, University of Auckland, Auckland 1024, New Zealand
| | - Jennifer L. Miles-Chan
- High-Value Nutrition National Science Challenge, Auckland 1145, New Zealand; (I.R.S.-B.); (J.L.M.-C.); (S.D.P.)
- Human Nutrition Unit, School of Biological Sciences, University of Auckland, Auckland 1024, New Zealand
| | - Sally D. Poppitt
- High-Value Nutrition National Science Challenge, Auckland 1145, New Zealand; (I.R.S.-B.); (J.L.M.-C.); (S.D.P.)
- Human Nutrition Unit, School of Biological Sciences, University of Auckland, Auckland 1024, New Zealand
- Department of Medicine, University of Auckland, Auckland 1145, New Zealand
| | - Karl Fraser
- Food Chemistry & Structure Team, AgResearch, Palmerston North 4410, New Zealand; (Z.E.W.); (K.F.)
- High-Value Nutrition National Science Challenge, Auckland 1145, New Zealand; (I.R.S.-B.); (J.L.M.-C.); (S.D.P.)
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Liu Y, Li T, Zhu H, Cao L, Liang L, Liu D, Shen Q. Methionine inducing carbohydrate esterase secretion of Trichoderma harzianum enhances the accessibility of substrate glycosidic bonds. Microb Cell Fact 2024; 23:120. [PMID: 38664812 PMCID: PMC11046756 DOI: 10.1186/s12934-024-02394-1] [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: 01/20/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The conversion of plant biomass into biochemicals is a promising way to alleviate energy shortage, which depends on efficient microbial saccharification and cellular metabolism. Trichoderma spp. have plentiful CAZymes systems that can utilize all-components of lignocellulose. Acetylation of polysaccharides causes nanostructure densification and hydrophobicity enhancement, which is an obstacle for glycoside hydrolases to hydrolyze glycosidic bonds. The improvement of deacetylation ability can effectively release the potential for polysaccharide degradation. RESULTS Ammonium sulfate addition facilitated the deacetylation of xylan by inducing the up-regulation of multiple carbohydrate esterases (CE3/CE4/CE15/CE16) of Trichoderma harzianum. Mainly, the pathway of ammonium-sulfate's cellular assimilates inducing up-regulation of the deacetylase gene (Thce3) was revealed. The intracellular metabolite changes were revealed through metabonomic analysis. Whole genome bisulfite sequencing identified a novel differentially methylated region (DMR) that existed in the ThgsfR2 promoter, and the DMR was closely related to lignocellulolytic response. ThGsfR2 was identified as a negative regulatory factor of Thce3, and methylation in ThgsfR2 promoter released the expression of Thce3. The up-regulation of CEs facilitated the substrate deacetylation. CONCLUSION Ammonium sulfate increased the polysaccharide deacetylation capacity by inducing the up-regulation of multiple carbohydrate esterases of T. harzianum, which removed the spatial barrier of the glycosidic bond and improved hydrophilicity, and ultimately increased the accessibility of glycosidic bond to glycoside hydrolases.
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Affiliation(s)
- Yang Liu
- Key Lab of Organic-Based Fertilizers of China and Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
- College of Resource and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
| | - Tuo Li
- Key Lab of Organic-Based Fertilizers of China and Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
- College of Resource and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
| | - Han Zhu
- Key Lab of Organic-Based Fertilizers of China and Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
- College of Resource and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
| | - Linhua Cao
- Key Lab of Organic-Based Fertilizers of China and Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
- College of Resource and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
| | - Lebin Liang
- Key Lab of Organic-Based Fertilizers of China and Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
- College of Resource and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
| | - Dongyang Liu
- Key Lab of Organic-Based Fertilizers of China and Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China.
- College of Resource and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China.
| | - Qirong Shen
- Key Lab of Organic-Based Fertilizers of China and Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
- College of Resource and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
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Bin Masud S, Jenkins C, Hussey E, Elkin-Frankston S, Mach P, Dhummakupt E, Aeron S. Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset. PLoS One 2021; 16:e0255240. [PMID: 34324558 PMCID: PMC8320926 DOI: 10.1371/journal.pone.0255240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 07/12/2021] [Indexed: 12/26/2022] Open
Abstract
Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we introduce one such recently developed technique called aggregate knockoff filtering to untargeted metabolomic analysis. When applied to a publicly available dataset, aggregate knockoff filtering combined with typical p-value filtering improves the number of significantly changing metabolites by 25% when compared to conventional untargeted metabolomic data processing. By using this method, features that would normally not be extracted under standard processing would be brought to researchers’ attention for further analysis.
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Affiliation(s)
- Shoaib Bin Masud
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA, United States of America
| | - Conor Jenkins
- DEVCOM Chemical Biological Center, Aberdeen Proving Ground, Aberdeen, MD, United States of America
| | - Erika Hussey
- DEVCOM Soldier Center, Natick, MA, United States of America
| | | | - Phillip Mach
- DEVCOM Chemical Biological Center, Aberdeen Proving Ground, Aberdeen, MD, United States of America
| | - Elizabeth Dhummakupt
- DEVCOM Chemical Biological Center, Aberdeen Proving Ground, Aberdeen, MD, United States of America
- * E-mail: (ED); (SA)
| | - Shuchin Aeron
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA, United States of America
- * E-mail: (ED); (SA)
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A 12-immune cell signature to predict relapse and guide chemotherapy for stage II colorectal cancer. Aging (Albany NY) 2020; 12:18363-18383. [PMID: 32855365 PMCID: PMC7585080 DOI: 10.18632/aging.103707] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 07/06/2020] [Indexed: 01/24/2023]
Abstract
The management of stage II colorectal cancer is still difficult. We aimed to construct a new immune cell-associated signature for prognostic evaluation and guiding chemotherapy in stage II colorectal cancer. We used the "Cell Type Identification by Estimating Relative Subsets of RNA Transcripts" (CIBERSORT) method to estimate the fraction of 22 immune cells by analyzing bulk tumor transcriptomes and a LASSO Cox regression model to select the prognostic immune cells. A 12-immune cell prognostic classifier, ISCRC, was built, which could successfully discriminate the high-risk patients in the training cohort (GSE39582: HR = 3.16, 95% CI: 1.85-5.40, P < 0.0001) and another independent cohorts (GSE14333: HR = 3.47, 95% CI: 1.18-10.15, P =0.0167). The receiver operating characteristic analysis revealed that the AUC of the ISCRC model was significantly greater than that of oncotypeDX model (0.7111 versus 0.5647, p=0.0152). We introduced the propensity score matching analysis to eliminate the selection bias; survival analysis showed relatively poor prognosis after chemotherapy in stage II CRC patients. Furthermore, a nomogram was built for clinicians and did well in the calibration plots. In conclusion, this immune cell-based signature could improve prognostic prediction and may help guide chemotherapy in stage II colorectal cancer patients.
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Zhou C, Zhao Y, Yin Y, Hu Z, Atyah M, Chen W, Meng Z, Mao H, Zhou Q, Tang W, Wang P, Li Z, Weng J, Bruns C, Popp M, Popp F, Dong Q, Ren N. A robust 6-mRNA signature for prognosis prediction of pancreatic ductal adenocarcinoma. Int J Biol Sci 2019; 15:2282-2295. [PMID: 31595147 PMCID: PMC6775308 DOI: 10.7150/ijbs.32899] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Accepted: 08/03/2019] [Indexed: 01/04/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide. PDAC prognostic and diagnostic biomarkers are still being explored. The aim of this study is to establish a robust molecular signature that can improve the ability to predict PDAC prognosis. 155 overlapping differentially expressed genes between tumor and non-tumor tissues from three Gene Expression Omnibus (GEO) datasets were explored. A least absolute shrinkage and selection operator method (LASSO) Cox regression model was employed for selecting prognostic genes. We developed a 6-mRNA signature that can distinguish high PDAC risk patients from low risk patients with significant differences in overall survival (OS). We further validated this signature prognostic value in three independent cohorts (GEO batch, P < 0.0001; ICGC, P = 0.0036; Fudan, P = 0.029). Furthermore, we found that our signature remained significant in patients with different histologic grade, TNM stage, locations of tumor entity, age and gender. Multivariate cox regression analysis showed that 6-mRNA signature can be an independent prognostic marker in each of the cohorts. Receiver operating characteristic curve (ROC) analysis also showed that our signature possessed a better predictive role of PDAC prognosis. Moreover, the gene set enrichment analysis (GSEA) analysis showed that several tumorigenesis and metastasis related pathways were indeed associated with higher scores of risk. In conclusion, identifying the 6-mRNA signature could provide a valuable classification method to evaluate clinical prognosis and facilitate personalized treatment for PDAC patients. New therapeutic targets may be developed upon the functional analysis of the classifier genes and their related pathways.
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Affiliation(s)
- Chenhao Zhou
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China
| | - Yue Zhao
- Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany.,Department of Surgery, Otto-von-Guericke University, Magdeburg, Germany
| | - Yirui Yin
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China
| | - Zhiqiu Hu
- Institute of Fudan Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
| | - Manar Atyah
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China
| | - Wanyong Chen
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China.,Institute of Fudan Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhefeng Meng
- Institute of Fudan Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
| | - Huarong Mao
- Institute of Fudan Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
| | - Qiang Zhou
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China
| | - Weiguo Tang
- Institute of Fudan Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
| | - Pengcheng Wang
- Institute of Fudan Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhanming Li
- Institute of Fudan Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
| | - Jialei Weng
- Institute of Fudan Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
| | - Christiane Bruns
- Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany
| | - Marie Popp
- Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany
| | - Felix Popp
- Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany
| | - Qiongzhu Dong
- Institute of Fudan Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China.,Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Ning Ren
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China.,Institute of Fudan Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
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NMR-Based Plasma Metabolomics at Set Intervals in Newborn Dairy Calves with Severe Sepsis. Mediators Inflamm 2018; 2018:8016510. [PMID: 29743812 PMCID: PMC5883973 DOI: 10.1155/2018/8016510] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 01/09/2018] [Indexed: 12/13/2022] Open
Abstract
The aim of this first study was to reveal the new potential biomarkers by a metabolomics approach in severe septic calves. Sepsis is a common cause of morbidity and mortality in newborn dairy calves. The main challenges with the use of biomarkers of sepsis in domestic animals are their availability, cost, and time required to obtain a result. Metabolomics may offer the potential to identify biomarkers that define calf sepsis in terms of combined clinical, physiological, and pathobiological abnormalities. To our knowledge, this is the first study presenting an NMR- (nuclear magnetic resonance-) based plasma metabolomics at set intervals in neonatal septic calves. Twenty neonatal dairy calves with severe sepsis and ten healthy calves were used. Hematological and biochemical health profiles were gathered in plasma samples at set intervals. Similarly, NMR spectra were acquired. All diseased animals (except one) died after 72 hours. Clinical and laboratory results were in accordance with those of severe septic animals. Multivariate analysis on NMR plasma spectra proved to be an excellent tool for faster identification of calves with severe sepsis from healthy animals. The NMR-based metabolomic profile may contribute to the better understanding of severe sepsis in newborn calves.
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Abstract
The tele-ICU is designed to leverage, not replace, the need for bedside clinical expertise in the diagnosis, treatment, and assessment of various critical illnesses. Tele-ICUs are primarily decentralized or centralized models with differing advantages and disadvantages. The centralized model has sufficiently powered published data to be associated with improved mortality and ICU length of stay in a cost-effective manner. Factors associated with improved clinical outcomes include improved compliance with best practices; providing off-hours implementation of the bedside physician's care plan; and identification of and rapid response to physiological instability (initial clinical review within 1 hour) and rapid response to alerts, alarms, or direct notification by bedside clinicians. With improved communication and frequent review of patients between the tele-ICU and the bedside clinicians, the bedside clinician can provide the care that only they can provide. Although technology continues to evolve at a rapid pace, technology alone will most likely not improve clinical outcomes. Technology will enable us to process real or near real-time data into complex and powerful predictive algorithms. However, the remote and bedside teams must work collaboratively to develop care processes to better monitor, prioritize, standardize, and expedite care to drive greater efficiencies and improve patient safety.
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Tian X, Zhu X, Yan T, Yu C, Shen C, Hu Y, Hong J, Chen H, Fang JY. Recurrence-associated gene signature optimizes recurrence-free survival prediction of colorectal cancer. Mol Oncol 2017; 11:1544-1560. [PMID: 28796930 PMCID: PMC5664005 DOI: 10.1002/1878-0261.12117] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 07/27/2017] [Accepted: 07/29/2017] [Indexed: 12/28/2022] Open
Abstract
High throughput gene expression profiling has showed great promise in providing insight into molecular mechanisms. Metastasis‐related mRNAs may potentially enrich genes with the ability to predict cancer recurrence, therefore we attempted to build a recurrence‐associated gene signature to improve prognostic prediction of colorectal cancer (CRC). We identified 2848 differentially expressed mRNAs by analyzing CRC tissues with or without metastasis. For the selection of prognostic genes, a LASSO Cox regression model (least absolute shrinkage and selection operator method) was employed. Using this method, a 13‐mRNA signature was identified and then validated in two independent Gene Expression Omnibus cohorts. This classifier could successfully discriminate the high‐risk patients in discovery cohort [hazard ratio (HR) = 5.27, 95% confidence interval (CI) 2.30–12.08, P < 0.0001). Analysis in two independent cohorts yielded consistent results (GSE14333: HR = 4.55, 95% CI 2.18–9.508, P < 0.0001; GSE33113: HR = 3.26, 95% CI 2.16–9.16, P = 0.0176). Further analysis revealed that the prognostic value of this signature was independent of tumor stage, postoperative chemotherapy and somatic mutation. Receiver operating characteristic (ROC) analysis showed that the area under ROC curve of this signature was 0.8861 and 0.8157 in the discovery and validation cohort, respectively. A nomogram was constructed for clinicians, and did well in the calibration plots. Furthermore, this 13‐mRNA signature outperformed other known gene signatures, including oncotypeDX colon cancer assay. Single‐sample gene‐set enrichment analysis revealed that a group of pathways related to drug resistance, cancer metastasis and stemness were significantly enriched in the high‐risk patients. In conclusion, this 13‐mRNA signature may be a useful tool for prognostic evaluation and will facilitate personalized management of CRC patients.
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Affiliation(s)
- Xianglong Tian
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai JiaoTong University, China
| | - Xiaoqiang Zhu
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai JiaoTong University, China
| | - Tingting Yan
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai JiaoTong University, China
| | - Chenyang Yu
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai JiaoTong University, China
| | - Chaoqin Shen
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai JiaoTong University, China
| | - Ye Hu
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai JiaoTong University, China
| | - Jie Hong
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai JiaoTong University, China
| | - Haoyan Chen
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai JiaoTong University, China
| | - Jing-Yuan Fang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai JiaoTong University, China
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Tian X, Zhu X, Yan T, Yu C, Shen C, Hong J, Chen H, Fang JY. Differentially Expressed lncRNAs in Gastric Cancer Patients: A Potential Biomarker for Gastric Cancer Prognosis. J Cancer 2017; 8:2575-2586. [PMID: 28900495 PMCID: PMC5595087 DOI: 10.7150/jca.19980] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 05/10/2017] [Indexed: 12/16/2022] Open
Abstract
Current studies indicate that long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers and implicated with prognosis in gastric cancer (GC). We intended to generate a multi-lncRNA signature to improve prognostic prediction of GC. By analyzing ten paired GC and adjacent normal mucosa tissues, 339 differentially expressed lncRNAs were identified as the candidate prognostic biomarkers in GC. Then we used LASSO Cox regression method to build a 12-lncRNA signature and validated it in another independent GEO dataset. An innovative 12-lncRNA signature was established, and it was significantly associated with the disease free survival (DFS) in the training dataset. By applying the 12-lncRNA signature, the training cohort patients could be categorized into high-risk or low-risk subgroup with significantly different DFS (HR = 4.52, 95%CI= 2.49-8.20, P < 0.0001). Similar results were obtained in another independent GEO dataset (HR=1.58, 95%CI=1.05 - 2.38, P=0.0270). Further analysis showed that the prognostic value of this 12-lncRNA signature was independent of AJCC stage and postoperative chemotherapy. Receiver operating characteristic (ROC) analysis showed that the area under receiver operating characteristic curve (AUC) of combined model reached 0.869. Additionally, a well-performed nomogram was constructed for clinicians. Moreover, single-sample gene-set enrichment analysis (ssGSEA) showed that a group of pathways related to drug resistance and cancer metastasis significantly enriched in the high risk patients. A useful innovative 12-lncRNA signature was established for prognostic evaluation of GC. It might complement clinicopathological features and facilitate personalized management of GC.
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Affiliation(s)
- Xianglong Tian
- Division of Gastroenterology and Hepatology; Key Laboratory of Gastroenterology and Hepatology, Ministry of Health; State Key Laboratory for Oncogenes and Related Genes; Renji Hospital, School of Medicine, Shanghai JiaoTong University; Shanghai Institute of Digestive Disease; 145 Middle Shandong Road, Shanghai 200001, China
| | - Xiaoqiang Zhu
- Division of Gastroenterology and Hepatology; Key Laboratory of Gastroenterology and Hepatology, Ministry of Health; State Key Laboratory for Oncogenes and Related Genes; Renji Hospital, School of Medicine, Shanghai JiaoTong University; Shanghai Institute of Digestive Disease; 145 Middle Shandong Road, Shanghai 200001, China
| | - Tingting Yan
- Division of Gastroenterology and Hepatology; Key Laboratory of Gastroenterology and Hepatology, Ministry of Health; State Key Laboratory for Oncogenes and Related Genes; Renji Hospital, School of Medicine, Shanghai JiaoTong University; Shanghai Institute of Digestive Disease; 145 Middle Shandong Road, Shanghai 200001, China
| | - Chenyang Yu
- Division of Gastroenterology and Hepatology; Key Laboratory of Gastroenterology and Hepatology, Ministry of Health; State Key Laboratory for Oncogenes and Related Genes; Renji Hospital, School of Medicine, Shanghai JiaoTong University; Shanghai Institute of Digestive Disease; 145 Middle Shandong Road, Shanghai 200001, China
| | - Chaoqin Shen
- Division of Gastroenterology and Hepatology; Key Laboratory of Gastroenterology and Hepatology, Ministry of Health; State Key Laboratory for Oncogenes and Related Genes; Renji Hospital, School of Medicine, Shanghai JiaoTong University; Shanghai Institute of Digestive Disease; 145 Middle Shandong Road, Shanghai 200001, China
| | - Jie Hong
- Division of Gastroenterology and Hepatology; Key Laboratory of Gastroenterology and Hepatology, Ministry of Health; State Key Laboratory for Oncogenes and Related Genes; Renji Hospital, School of Medicine, Shanghai JiaoTong University; Shanghai Institute of Digestive Disease; 145 Middle Shandong Road, Shanghai 200001, China
| | - Haoyan Chen
- Division of Gastroenterology and Hepatology; Key Laboratory of Gastroenterology and Hepatology, Ministry of Health; State Key Laboratory for Oncogenes and Related Genes; Renji Hospital, School of Medicine, Shanghai JiaoTong University; Shanghai Institute of Digestive Disease; 145 Middle Shandong Road, Shanghai 200001, China
| | - Jing-Yuan Fang
- Division of Gastroenterology and Hepatology; Key Laboratory of Gastroenterology and Hepatology, Ministry of Health; State Key Laboratory for Oncogenes and Related Genes; Renji Hospital, School of Medicine, Shanghai JiaoTong University; Shanghai Institute of Digestive Disease; 145 Middle Shandong Road, Shanghai 200001, China
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