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Hasan MAM, Maniruzzaman M, Shin J. Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning. Sci Rep 2023; 13:3771. [PMID: 36882493 PMCID: PMC9992474 DOI: 10.1038/s41598-023-30851-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 03/02/2023] [Indexed: 03/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.
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Affiliation(s)
- Md Al Mehedi Hasan
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.,Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh
| | - Md Maniruzzaman
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.,Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh
| | - Jungpil Shin
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.
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He L, Wang H, He P, Jiang Y, Ma F, Wang J, Hu J. Serum Long Noncoding RNA H19 and CKD Progression in IgA Nephropathy. J Nephrol 2023; 36:397-406. [PMID: 36574208 DOI: 10.1007/s40620-022-01536-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/20/2022] [Indexed: 12/28/2022]
Abstract
BACKGROUND IgA nephropathy (IgAN) is one of the most common primary glomerular diseases worldwide, especially in young Asian adults. Long RNA H19 is associated with renal pathologies, such as renal cell injury; however, a connection between serum H19 expression and kidney disease progression has not been demonstrated. METHOD Our cohort consisted of 204 patients with IgAN. Serum H19 levels were determined with reverse-transcription quantitative polymerase between 1 May, 2014 and 1 May, 2015. H19 levels were log-transformed and categorical variables were categorized according to cutoff points of a ROC curve. Restricted cubic spline and generalized estimating equation analyses were performed to determine the association between serum H19 and kidney disease progression. RESULTS H19 expression was significantly downregulated in patients with IgAN compared to healthy controls. Restricted cubic spline analyses showed that the relationship was negatively and linearly correlated (P for nonlinearly = 0.256). After adjusting for other potential clinical, pathologic, and treatment factors, H19 was found to be a protective factor for prognosis in IgAN (HR, 0.52; 95% CI 0.32-0.84; P = 0.008). ROC curve analysis showed that the clinical value of lncRNA H19 with CKD and area under the ROC curve was 0.746 (95% CI 0.663-0.829; P < 0.001) of the clinical prognostic value of H19. Serum restricted cubic spline analyses showed that the relationship was negatively and linearly correlated (P for non-linearly = 0.256). H19 > 0.097 in patients in IgAN was associated with a reduction of the risk of kidney progression by approximately 70% within 5 years compared to H19≤0.097 (HR, 0.30;95% CI 0.12-0.74; P = 0.009). CONCLUSION H19 is an independent protective factor, and a high level of H19 often indicates better renal outcome within 5 years.
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Affiliation(s)
- Lijie He
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Hanmin Wang
- Department of Nephrology, First Hospital of Xi'an City, Northwest University, Xi'an, 710054, Shaan'xi Province, China
| | - Peng He
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Yali Jiang
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Feng Ma
- Department of Nephrology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaan'xi Province, China
| | - Jing Wang
- Department of Nephrology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaan'xi Province, China
| | - Jinping Hu
- Department of Nephrology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaan'xi Province, China.
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Li J, Sun G, Ma H, Wu X, Li C, Ding P, Lu S, Li Y, Yang P, Li C, Yang J, Peng Y, Meng Z, Wang L. Identification of immune-related hub genes and miRNA-mRNA pairs involved in immune infiltration in human septic cardiomyopathy by bioinformatics analysis. Front Cardiovasc Med 2022; 9:971543. [PMID: 36204577 PMCID: PMC9530044 DOI: 10.3389/fcvm.2022.971543] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract Septic cardiomyopathy (SCM) is a serious complication caused by sepsis that will further exacerbate the patient's prognosis. However, immune-related genes (IRGs) and their molecular mechanism during septic cardiomyopathy are largely unknown. Therefore, our study aims to explore the immune-related hub genes (IRHGs) and immune-related miRNA-mRNA pairs with potential biological regulation in SCM by means of bioinformatics analysis and experimental validation. Method Firstly, screen differentially expressed mRNAs (DE-mRNAs) from the dataset GSE79962, and construct a PPI network of DE-mRNAs. Secondly, the hub genes of SCM were identified from the PPI network and the hub genes were overlapped with immune cell marker genes (ICMGs) to further obtain IRHGs in SCM. In addition, receiver operating characteristic (ROC) curve analysis was also performed in this process to determine the disease diagnostic capability of IRHGs. Finally, the crucial miRNA-IRHG regulatory network of IRHGs was predicted and constructed by bioinformatic methods. Real-time quantitative reverse transcription-PCR (qRT-PCR) and dataset GSE72380 were used to validate the expression of the key miRNA-IRHG axis. Result The results of immune infiltration showed that neutrophils, Th17 cells, Tfh cells, and central memory cells in SCM had more infiltration than the control group; A total of 2 IRHGs were obtained by crossing the hub gene with the ICMGs, and the IRHGs were validated by dataset and qRT-PCR. Ultimately, we obtained the IRHG in SCM: THBS1. The ROC curve results of THBS1 showed that the area under the curve (AUC) was 0.909. Finally, the miR-222-3p/THBS1 axis regulatory network was constructed. Conclusion In summary, we propose that THBS1 may be a key IRHG, and can serve as a biomarker for the diagnosis of SCM; in addition, the immune-related regulatory network miR-222-3p/THBS1 may be involved in the regulation of the pathogenesis of SCM and may serve as a promising candidate for SCM therapy.
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Affiliation(s)
- Jingru Li
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Guihu Sun
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Haocheng Ma
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xinyu Wu
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chaozhong Li
- Department of Emergency, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Peng Ding
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Si Lu
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yanyan Li
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ping Yang
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chaguo Li
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jun Yang
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yunzhu Peng
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhaohui Meng
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
- *Correspondence: Zhaohui Meng
| | - Luqiao Wang
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
- Luqiao Wang
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Qing J, Zheng F, Zhi H, Yaigoub H, Tirichen H, Li Y, Zhao J, Qiang Y, Li Y. Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning. Biomolecules 2022; 12:biom12091276. [PMID: 36139115 PMCID: PMC9496457 DOI: 10.3390/biom12091276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/05/2022] [Accepted: 09/06/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Objective: Identification of potential genetic biomarkers for various glomerulonephritis (GN) subtypes and discovering the molecular mechanisms of GN. (2) Methods: four microarray datasets of GN were downloaded from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression profiles of eight GN subtypes. Then, differentially expressed immune-related genes (DIRGs) were identified to explore the molecular mechanisms of GN, and single-sample gene set enrichment analysis (ssGSEA) was performed to discover the abnormal inflammation in GN. In addition, a nomogram model was generated using the R package "glmnet", and the calibration curve was plotted to evaluate the predictive power of the nomogram model. Finally, deep learning (DL) based on a multilayer perceptron (MLP) network was performed to explore the characteristic genes for GN. (3) Results: we screened out 274 common up-regulated or down-regulated DIRGs in the glomeruli and tubulointerstitium. These DIRGs are mainly involved in T-cell differentiation, the RAS signaling pathway, and the MAPK signaling pathway. ssGSEA indicates that there is a significant increase in DC (dendritic cells) and macrophages, and a significant decrease in neutrophils and NKT cells in glomeruli, while monocytes and NK cells are increased in tubulointerstitium. A nomogram model was constructed to predict GN based on 7 DIRGs, and 20 DIRGs of each subtype of GN in glomeruli and tubulointerstitium were selected as characteristic genes. (4) Conclusions: this study reveals that the DIRGs are closely related to the pathogenesis of GN and could serve as genetic biomarkers in GN. DL further identified the characteristic genes that are essential to define the pathogenesis of GN and develop targeted therapies for eight GN subtypes.
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Affiliation(s)
- Jianbo Qing
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan 030001, China
| | - Fang Zheng
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030001, China
| | - Huiwen Zhi
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan 030001, China
| | - Hasnaa Yaigoub
- Institutes of Biomedical Sciences, Shanxi University, Taiyuan 030001, China
| | - Hasna Tirichen
- Institutes of Biomedical Sciences, Shanxi University, Taiyuan 030001, China
| | - Yaheng Li
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan 030001, China
- Laboratory for Molecular Diagnosis and Treatment of Kidney Disease, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan 030001, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030001, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030001, China
| | - Yafeng Li
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan 030001, China
- Core Laboratory, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan 030001, China
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan 030001, China
- Academy of Microbial Ecology, Shanxi Medical University, Taiyuan 030001, China
- Correspondence:
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Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models. Sci Rep 2022; 12:13963. [PMID: 35978028 PMCID: PMC9385868 DOI: 10.1038/s41598-022-18273-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/08/2022] [Indexed: 11/08/2022] Open
Abstract
Immunoglobulin-A-nephropathy (IgAN) is a kidney disease caused by the accumulation of IgAN deposits in the kidneys, which causes inflammation and damage to the kidney tissues. Various bioinformatics analysis-based approaches are widely used to predict novel candidate genes and pathways associated with IgAN. However, there is still some scope to clearly explore the molecular mechanisms and causes of IgAN development and progression. Therefore, the present study aimed to identify key candidate genes for IgAN using machine learning (ML) and statistics-based bioinformatics models. First, differentially expressed genes (DEGs) were identified using limma, and then enrichment analysis was performed on DEGs using DAVID. Protein-protein interaction (PPI) was constructed using STRING and Cytoscape was used to determine hub genes based on connectivity and hub modules based on MCODE scores and their associated genes from DEGs. Furthermore, ML-based algorithms, namely support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA) were applied to identify the discriminative genes of IgAN from DEGs. Finally, the key candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) were identified as overlapping genes among the selected hub genes, hub module genes, and discriminative genes from SVM, LASSO, and PLS-DA, respectively which can be used for the diagnosis and treatment of IgAN.
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