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Zhu J, Luo J, Ma Y. Screening of serum exosome markers for colorectal cancer based on Boruta and multi-cluster feature selection algorithms. Mol Cell Toxicol 2023. [DOI: 10.1007/s13273-023-00348-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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Wei J, Wu Y, Zhang X, Sun J, Li J, Li J, Yang X, Qiao H. Type 2 diabetes is more closely associated with risk of colorectal cancer based on elevated DNA methylation levels of ADCY5. Oncol Lett 2022; 24:206. [PMID: 35720494 PMCID: PMC9178693 DOI: 10.3892/ol.2022.13327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 04/19/2022] [Indexed: 11/24/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) has an increased risk of cancer. In the present study, the relationship between T2DM and 13 types of cancer was analyzed and key methylation genes were searched. First, DNA methylation and mRNA expression were obtained data for T2DM and 13 types of cancer from The Cancer Genome Atlas and Gene Expression Omnibus. The t-test was used to screen the differentially methylated expression overlapping genes (DE-MGs) in T2DM and cancer on both methylation and expression levels. DE-MGs are weighted based on the methylation and projected into the human protein interaction network. The correlation between T2DM and each type of cancer was analyzed, and key genes were identified. The results showed that 293 DE-MGs were related to T2DM and 3307 were related to cancer. The network found that T2DM is more related to colorectal cancer (CRC) compare with the other 12 types of cancer. A total of 5 from 8 candidate genes were associated with CRC. A total of 28 clinical patients were used to validate these 5 genes. A CRC tissue sample was collected from each patient, as well as a paracancerous sample that served as a control. A total of 56 tissue samples were divided into 4 groups: control group, T2DM group, CRC group and T2DM with CRC group (combination group). Compared with the control group, the methylation level of adenylate cyclase 5 (ADCY5), neuregulin 1 and ELAV-like RNA-binding protein 4 in the combination group was significantly upregulated, and the mRNA level was significantly downregulated. Furthermore, based on the methylation level of ADCY5, the correlation coefficient between the combination group and the T2DM group was greater than that of the CRC group. In conclusion, T2DM is most likely to be associated with CRC among 13 common types of cancer based on methylation characteristics. An upregulated methylation of ADCY5 in T2DM may have a higher risk of CRC.
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Affiliation(s)
- Jiaxing Wei
- Department of Endocrinology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Yanmeizhi Wu
- Department of Endocrinology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Xiaona Zhang
- Department of Endocrinology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Jingxue Sun
- Department of Endocrinology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Jian Li
- Department of Endocrinology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Jingjing Li
- Department of Endocrinology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Xu Yang
- Department of Endocrinology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Hong Qiao
- Department of Endocrinology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
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Li Z, Guo W, Zeng T, Yin J, Feng K, Huang T, Cai YD. Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer's Disease. Front Neurosci 2022; 16:895181. [PMID: 35585924 PMCID: PMC9108872 DOI: 10.3389/fnins.2022.895181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 04/11/2022] [Indexed: 01/01/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive disease that leads to irreversible behavioral changes, erratic emotions, and loss of motor skills. These conditions make people with AD hard or almost impossible to take care of. Multiple internal and external pathological factors may affect or even trigger the initiation and progression of AD. DNA methylation is one of the most effective regulatory roles during AD pathogenesis, and pathological methylation alterations may be potentially different in the various brain structures of people with AD. Although multiple loci associated with AD initiation and progression have been identified, the spatial distribution patterns of AD-associated DNA methylation in the brain have not been clarified. According to the systematic methylation profiles on different structural brain regions, we applied multiple machine learning algorithms to investigate such profiles. First, the profile on each brain region was analyzed by the Boruta feature filtering method. Some important methylation features were extracted and further analyzed by the max-relevance and min-redundancy method, resulting in a feature list. Then, the incremental feature selection method, incorporating some classification algorithms, adopted such list to identify candidate AD-associated loci at methylation with structural specificity, establish a group of quantitative rules for revealing the effects of DNA methylation in various brain regions (i.e., four brain structures) on AD pathogenesis. Furthermore, some efficient classifiers based on essential methylation sites were proposed to identify AD samples. Results revealed that methylation alterations in different brain structures have different contributions to AD pathogenesis. This study further illustrates the complex pathological mechanisms of AD.
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Affiliation(s)
- ZhanDong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Jie Yin
- Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Human Genetics, Institute of Genetics, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - KaiYan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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Fan J, Chen M, Cao S, Yao Q, Zhang X, Du S, Qu H, Cheng Y, Ma S, Zhang M, Huang Y, Zhang N, Shi K, Zhan S. Identification of a ferroptosis-related gene pair biomarker with immune infiltration landscapes in ischemic stroke: a bioinformatics-based comprehensive study. BMC Genomics 2022; 23:59. [PMID: 35033021 PMCID: PMC8761271 DOI: 10.1186/s12864-022-08295-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/29/2021] [Indexed: 12/14/2022] Open
Abstract
Background Ischemic stroke (IS) is a principal contributor to long-term disability in adults. A new cell death mediated by iron is ferroptosis, characterized by lethal aggregation of lipid peroxidation. However, a paucity of ferroptosis-related biomarkers early identify IS until now. This study investigated potential ferroptosis-related gene pair biomarkers in IS and explored their roles in immune infiltration. Results In total, we identified 6 differentially expressed ferroptosis-related genes (DEFRGs) in the metadata cohort. Of these genes, 4 DEFRGs were incorporated into the competitive endogenous RNA (ceRNA) network, including 78 lncRNA-miRNA and 16 miRNA-mRNA interactions. Based on relative expression values of DEFRGs, we constructed gene pairs. An integrated scheme consisting of machine learning algorithms, ceRNA network, and gene pair was proposed to screen the key DEFRG biomarkers. The receiver operating characteristic (ROC) curve witnessed that the diagnostic performance of DEFRG pair CDKN1A/JUN was superior to that of single gene. Moreover, the CIBERSORT algorithm exhibited immune infiltration landscapes: plasma cells, resting NK cells, and resting mast cells infiltrated less in IS samples than controls. Spearman correlation analysis confirmed a significant correlation between plasma cells and CDKN1A/JUN (CDKN1A: r = − 0.503, P < 0.001, JUN: r = − 0.330, P = 0.025). Conclusions Our findings suggested that CDKN1A/JUN could be a robust and promising gene-pair diagnostic biomarker for IS, regulating ferroptosis during IS progression via C9orf106/C9orf139-miR-22-3p-CDKN1A and GAS5-miR-139-5p/miR-429-JUN axes. Meanwhile, plasma cells might exert a vital interplay in IS immune microenvironment, providing an innovative insight for IS therapeutic target. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08295-0.
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Affiliation(s)
- Jiaxin Fan
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Mengying Chen
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Shuai Cao
- Department of Orthopedics, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Qingling Yao
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Xiaodong Zhang
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Shuang Du
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Huiyang Qu
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Yuxuan Cheng
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Shuyin Ma
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Meijuan Zhang
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Yizhou Huang
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Nan Zhang
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Kaili Shi
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China
| | - Shuqin Zhan
- Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China.
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5
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Guo D, Fan Y, Yue JR, Lin T. A regulatory miRNA-mRNA network is associated with transplantation response in acute kidney injury. Hum Genomics 2021; 15:69. [PMID: 34886903 PMCID: PMC8656037 DOI: 10.1186/s40246-021-00363-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 10/11/2021] [Indexed: 02/08/2023] Open
Abstract
Background Acute kidney injury (AKI) is a life-threatening complication characterized by rapid decline in renal function, which frequently occurs after transplantation surgery. However, the molecular mechanism underlying the development of post-transplant (post-Tx) AKI still remains unknown. An increasing number of studies have demonstrated that certain microRNAs (miRNAs) exert crucial functions in AKI. The present study sought to elucidate the molecular mechanisms in post-Tx AKI by constructing a regulatory miRNA–mRNA network. Results Based on two datasets (GSE53771 and GSE53769), three key modules, which contained 55 mRNAs, 76 mRNAs, and 151 miRNAs, were identified by performing weighted gene co-expression network analysis (WGCNA). The miRDIP v4.1 was applied to predict the interactions of key module mRNAs and miRNAs, and the miRNA–mRNA pairs with confidence of more than 0.2 were selected to construct a regulatory miRNA–mRNA network by Cytoscape. The miRNA–mRNA network consisted of 82 nodes (48 mRNAs and 34 miRNAs) and 125 edges. Two miRNAs (miR-203a-3p and miR-205-5p) and ERBB4 with higher node degrees compared with other nodes might play a central role in post-Tx AKI. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that this network was mainly involved in kidney-/renal-related functions and PI3K–Akt/HIF-1/Ras/MAPK signaling pathways. Conclusion We constructed a regulatory miRNA–mRNA network to provide novel insights into post-Tx AKI development, which might help discover new biomarkers or therapeutic drugs for enhancing the ability for early prediction and intervention and decreasing mortality rate of AKI after transplantation. Supplementary Information The online version contains supplementary material available at 10.1186/s40246-021-00363-y.
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Affiliation(s)
- Duan Guo
- Department of Palliative Medicine, West China School of Public Health and West China fourth Hospital, Sichuan University, Chengdu, 610041, China.,Palliative Medicine Research Center, West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, 610041, China
| | - Yu Fan
- Department of Urology, National Clinical Research Center for Geriatrics and Organ Transplantation Center, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Ji-Rong Yue
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Tao Lin
- Department of Urology, National Clinical Research Center for Geriatrics and Organ Transplantation Center, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
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Zhang H, Xu L, Zhong Z, Liu Y, Long Y, Zhou S. Lower-Grade Gliomas: Predicting DNA Methylation Subtyping and its Consequences on Survival with MR Features. Acad Radiol 2021; 28:e199-e208. [PMID: 32241714 DOI: 10.1016/j.acra.2020.02.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 02/20/2020] [Accepted: 02/20/2020] [Indexed: 11/26/2022]
Abstract
RATIONALE AND OBJECTIVES To explore associations between MR imaging features, DNA methylation subtyping, and survival in lower-grade gliomas (LGG). MATERIALS AND METHODS The MR data from 170 patients generated with the Cancer Imaging Archive were reviewed. The correlation was evaluated by Fisher's Exact Test, Pearson Chi-Square and binary regression analysis. Survival analysis was conducted by using time-dependent ROC analysis and the Kaplan-Meier method (the worst prognosis subgroup). RESULTS Identified were 9 (5.3%) M1-subtype, 18 (10.6%) M2-subtype, 48 (28.2%) M3-subtype, 31 (18.2%) M4-subtype and 64 (37.6%) M5-subtype. Patients with M4-subtype had the shortest median OS (49.3 vs. 28.4) months(p < 0.05). The time-dependent ROC for the M4-subtype was 0.83 (95% confidence interval 0.72-0.95) for survival at 12 months, 0.82 (95% confidence interval 0.70-0.94) for survival at 24 months, and 0.74 (95% confidence interval 0.62-0.86) for survival at 36 months. After uni- and multivariate analysis, a nomogram was built based on proportion contrast-enhanced (CE) tumor, extranodular growth, volume_cutoff_median, and location. For the prediction of M4-subtype, the nomogram showed good discrimination, with an area under the curve (AUC) of 0.886 (95% CI: 0.820-952) and was well calibrated. On multivariate logistic regression analysis, volume ≥60cm3 (OR: 0.200; p < 0.001; 95%CI: 0.048-0.834) was associated with M1-subtype (AUC: 0.690). Hemorrhage (OR: 5.443; p = 0.002; 95%CI: 1.844-16.069) and volume > median (OR: 3.256; p = 0.05; 95%CI: 0.992-10.686) were associated with M2-subtype (AUC: 0.733). Proportion CE tumor<=5% (OR: 3.968; P=0.002; 95%CI: 1.634-9.635) was associated with M3-subtype (AUC: 0.632). Poorly-defined (OR: 2.258; p = 0.05; 95%CI: 1.000-5.101) and volume > median (OR: 2.447; p = 0.01; 95%CI: 1.244-4.813) were associated with M5-subtype (AUC: 0.645). Decision curve analysis indicated predictions for all models were clinically useful. CONCLUSION This preliminary radiogenomics analysis of lower-grade gliomas demonstrated associations between MR features and DNA methylation subtyping. The shortest survival was observed in patients with M4-subtype. And we have constructed nomogram that enables more accurate predictions of M4-subtype.
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Bhat A, Irizar H, Thygesen JH, Kuchenbaecker K, Pain O, Adams RA, Zartaloudi E, Harju-Seppänen J, Austin-Zimmerman I, Wang B, Muir R, Summerfelt A, Du XM, Bruce H, O'Donnell P, Srivastava DP, Friston K, Hong LE, Hall MH, Bramon E. Transcriptome-wide association study reveals two genes that influence mismatch negativity. Cell Rep 2021; 34:108868. [PMID: 33730571 PMCID: PMC7972991 DOI: 10.1016/j.celrep.2021.108868] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 12/09/2020] [Accepted: 02/24/2021] [Indexed: 01/22/2023] Open
Abstract
Mismatch negativity (MMN) is a differential electrophysiological response measuring cortical adaptability to unpredictable stimuli. MMN is consistently attenuated in patients with psychosis. However, the genetics of MMN are uncharted, limiting the validation of MMN as a psychosis endophenotype. Here, we perform a transcriptome-wide association study of 728 individuals, which reveals 2 genes (FAM89A and ENGASE) whose expression in cortical tissues is associated with MMN. Enrichment analyses of neurodevelopmental expression signatures show that genes associated with MMN tend to be overexpressed in the frontal cortex during prenatal development but are significantly downregulated in adulthood. Endophenotype ranking value calculations comparing MMN and three other candidate psychosis endophenotypes (lateral ventricular volume and two auditory-verbal learning measures) find MMN to be considerably superior. These results yield promising insights into sensory processing in the cortex and endorse the notion of MMN as a psychosis endophenotype.
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Affiliation(s)
- Anjali Bhat
- Division of Psychiatry, University College London, London, UK; Wellcome Centre for Human Neuroimaging, University College London, London, UK; Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK.
| | - Haritz Irizar
- Division of Psychiatry, University College London, London, UK
| | | | - Karoline Kuchenbaecker
- Division of Psychiatry, University College London, London, UK; UCL Genetics Institute, University College London, London, UK
| | - Oliver Pain
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Rick A Adams
- Division of Psychiatry, University College London, London, UK; Institute of Cognitive Neuroscience, University College London, London, UK
| | | | - Jasmine Harju-Seppänen
- Division of Psychiatry, University College London, London, UK; Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | | | - Baihan Wang
- Division of Psychiatry, University College London, London, UK
| | - Rebecca Muir
- Division of Psychiatry, University College London, London, UK
| | - Ann Summerfelt
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA
| | - Xiaoming Michael Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA
| | - Patricio O'Donnell
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Deepak P Srivastava
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA
| | - Mei-Hua Hall
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Psychosis Neurobiology Laboratory, McLean Hospital, Belmont, MA, USA
| | - Elvira Bramon
- Division of Psychiatry, University College London, London, UK; Institute of Cognitive Neuroscience, University College London, London, UK; Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK.
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Peng X, Chen L, Zhou JP. Identification of Carcinogenic Chemicals with Network Embedding and Deep Learning Methods. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200414084317] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background:
Cancer is the second leading cause of human death in the world. To date,
many factors have been confirmed to be the cause of cancer. Among them, carcinogenic chemicals
have been widely accepted as the important ones. Traditional methods for detecting carcinogenic
chemicals are of low efficiency and high cost.
Objective:
The aim of this study was to design an efficient computational method for the
identification of carcinogenic chemicals.
Methods:
A new computational model was proposed for detecting carcinogenic chemicals. As a
data-driven model, carcinogenic and non-carcinogenic chemicals were obtained from Carcinogenic
Potency Database (CPDB). These chemicals were represented by features extracted from five
chemical networks, representing five types of chemical associations, via a network embedding
method, Mashup. Obtained features were fed into a powerful deep learning method, recurrent
neural network, to build the model.
Results:
The jackknife test on such model provided the F-measure of 0.971 and AUROC of 0.971.
Conclusion:
The proposed model was quite effective and was superior to the models with
traditional machine learning algorithms, classic chemical encoding schemes or direct usage of
chemical associations.
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Affiliation(s)
- Xuefei Peng
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Jian-Peng Zhou
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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Gu C, Shi X, Dang X, Chen J, Chen C, Chen Y, Pan X, Huang T. Identification of Common Genes and Pathways in Eight Fibrosis Diseases. Front Genet 2021; 11:627396. [PMID: 33519923 PMCID: PMC7844395 DOI: 10.3389/fgene.2020.627396] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 12/15/2020] [Indexed: 01/05/2023] Open
Abstract
Acute and chronic inflammation often leads to fibrosis, which is also the common and final pathological outcome of chronic inflammatory diseases. To explore the common genes and pathogenic pathways among different fibrotic diseases, we collected all the reported genes of the eight fibrotic diseases: eye fibrosis, heart fibrosis, hepatic fibrosis, intestinal fibrosis, lung fibrosis, pancreas fibrosis, renal fibrosis, and skin fibrosis. We calculated the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment scores of all fibrotic disease genes. Each gene was encoded using KEGG and GO enrichment scores, which reflected how much a gene can affect this function. For each fibrotic disease, by comparing the KEGG and GO enrichment scores between reported disease genes and other genes using the Monte Carlo feature selection (MCFS) method, the key KEGG and GO features were identified. We compared the gene overlaps among eight fibrotic diseases and connective tissue growth factor (CTGF) was finally identified as the common key molecule. The key KEGG and GO features of the eight fibrotic diseases were all screened by MCFS method. Moreover, we interestingly found overlaps of pathways between renal fibrosis and skin fibrosis, such as GO:1901890-positive regulation of cell junction assembly, as well as common regulatory genes, such as CTGF, which is the key molecule regulating fibrogenesis. We hope to offer a new insight into the cellular and molecular mechanisms underlying fibrosis and therefore help leading to the development of new drugs, which specifically delay or even improve the symptoms of fibrosis.
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Affiliation(s)
- Chang Gu
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xin Shi
- Department of Cardiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xuening Dang
- Department of Colorectal and Anal Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Colorectal Cancer Research Center, Shanghai, China
| | - Jiafei Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chunji Chen
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yumei Chen
- Department of Nuclear Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xufeng Pan
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
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Yu X, Wang Z, Zeng T. Essential gene expression pattern of head and neck squamous cell carcinoma revealed by tumor-specific expression rule based on single-cell RNA sequencing. Biochim Biophys Acta Mol Basis Dis 2020; 1866:165791. [PMID: 32234410 DOI: 10.1016/j.bbadis.2020.165791] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 03/14/2020] [Accepted: 03/25/2020] [Indexed: 01/05/2023]
Abstract
Head and neck squamous cell carcinoma (HNSCC) has been widely reported and considered as one of the most threatening diseases to human health. Derived from complicated tissue subtypes, HNSCC has diverse symptoms and pathogenesis. They make the identification of the core carcinogenic factors of such diseases at the multi-cell level difficult. With the development of single-cell sequencing technologies, the effects of non-malignant cells on traditional bulk sequencing data can be eliminated directly. On the basis of fresh single-cell RNA-seq data, we set up a computational filtering strategy for tumor cell identification in an expression rule manner. This strategy can reveal the accurate expression distinction between tumor cells and adjacent tumor microenvironment, which are all supported by literature reports. Validated by several independent datasets, these rule genes can further group HNSCC patients with significant difference on survival risks. Thus, the establishment of our computational approach may not only provide an efficient tool to identify malignant cells in the tumor ecosystem but also deepen our understanding of tumor heterogeneity and tumorigenesis.
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Affiliation(s)
- Xiangtian Yu
- Clinical Research Center, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
| | - Zhenjia Wang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China.
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Liu F, Dong H, Mei Z, Huang T. Investigation of miRNA and mRNA Co-expression Network in Ependymoma. Front Bioeng Biotechnol 2020; 8:177. [PMID: 32266223 PMCID: PMC7096354 DOI: 10.3389/fbioe.2020.00177] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 02/20/2020] [Indexed: 12/18/2022] Open
Abstract
Ependymoma (EPN) is a rare primary tumor of the central nervous system (CNS) that affects both children and adults. Despite the definition and classification of distinct molecular subgroups, there remains a group of EPNs with a balanced genome, which makes it difficult to predict a prognosis of patients with EPN. The role of miRNA-mRNA network on EPN is still poorly understood. We assessed the involvement of miRNA-mRNA pairs in EPN by applying a weighted co-expression network analysis (WGCNA) approach. Using whole genome expression profile analysis followed by functional enrichment, we detected hub genes involved in active proliferation and DNA replication of nerve cells. Key genes including CYP11B1, KRT33B, RUNX1T1, SIK1, MAP3K4, MLANA, and SFRP5 identified in co-expression networks were regulated by miR-15a and miR-24-1. These seven miRNA-mRNA pairs were considered to influence not only pathways in cancer and tumor suppression process, but also MAPK, NF-kappaB, and WNT signaling pathways which were associated with tumorigenesis and development. This study provides a novel insight into potential diagnostic biomarkers of EPN and may have value in choosing therapeutic targets with clinical utility.
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Affiliation(s)
- Feili Liu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Hang Dong
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Zi Mei
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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Miao Y, Li Q, Wang J, Quan W, Li C, Yang Y, Mi D. Prognostic implications of metabolism-associated gene signatures in colorectal cancer. PeerJ 2020; 8:e9847. [PMID: 32953273 PMCID: PMC7474523 DOI: 10.7717/peerj.9847] [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: 05/07/2020] [Accepted: 08/11/2020] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identified differentially expressed PRMGs by R and Perl software. Hub genes were filtered out by univariate Cox analysis and least absolute shrinkage and selection operator Cox analysis. We used functional enrichment analysis methods, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, to identify involved signaling pathways of PRMGs. The nomogram predicted overall survival (OS). Calibration traces were used to evaluate the consistency between the actual and the predicted survival rate. Finally, a prognostic model was constructed based on six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5), and the risk score was an independent prognostic prognosticator. Genetic expression and risk score were significantly correlated with clinicopathologic characteristics of CRC. A nomogram based on the clinicopathological feature of CRC and risk score accurately predicted the OS of individual CRC cancer patients. We also validated the results in the independent colorectal cancer cohorts GSE39582 and GSE87211. Our study demonstrates that the risk score is an independent prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics of CRC patients. We also determined some metabolic genes associated with the survival and clinical stage of CRC as potential biomarkers for CRC diagnosis and treatment.
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Affiliation(s)
- Yandong Miao
- The First Clinical Medical College, Lanzhou University, Lanzhou City, Gansu Province, PR China
| | - Qiutian Li
- Department of Oncology, The 920th Hospital of the Chinese People’s Liberation Army Joint Logistic Support Force, Kunming City, Yunnan Province, PR China
| | - Jiangtao Wang
- The First Clinical Medical College, Lanzhou University, Lanzhou City, Gansu Province, PR China
| | - Wuxia Quan
- Qingyang People’s Hospital, Qingyang City, Gansu Province, PR China
| | - Chen Li
- The 3rd Affiliated Hospital, Kunming Medical College, Tumor Hospital of Yunnan Province, Kunming City, Yunnan Province, PR China
| | - Yuan Yang
- The First Clinical Medical College, Lanzhou University, Lanzhou City, Gansu Province, PR China
| | - Denghai Mi
- The First Clinical Medical College, Lanzhou University, Lanzhou City, Gansu Province, PR China
- Gansu Academy of Traditional Chinese Medicine, Lanzhou City, Gansu Province, PR China
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