Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients

Objective: To explore the molecular mechanism and search for the candidate differentially expressed genes (DEGs) with the predictive and prognostic potentiality that is detectable in the whole blood of patients with ST-segment elevation (STEMI) and those with post-STEMI HF.Methods: In this study, we downloaded GSE60993, GSE61144, GSE66360, and GSE59867 datasets from the NCBI-GEO database. DEGs of the datasets were investigated using R. Gene ontology (GO) and pathway enrichment were performed via ClueGO, CluePedia, and DAVID database. A protein interaction network was constructed via STRING. Enriched hub genes were analyzed by Cytoscape software. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics analyses were performed to build machine learning models for predicting STEMI. Hub genes for further validated in patients with post-STEMI HF from GSE59867.Results: We identified 90 upregulated DEGs and nine downregulated DEGs convergence in the three datasets (|log2FC| ≥ 0.8 and adjusted p < 0.05). They were mainly enriched in GO terms relating to cytokine secretion, pattern recognition receptors signaling pathway, and immune cells activation. A cluster of eight genes including ITGAM, CLEC4D, SLC2A3, BST1, MCEMP1, PLAUR, GPR97, and MMP25 was found to be significant. A machine learning model built by SLC2A3, CLEC4D, GPR97, PLAUR, and BST1 exerted great value for STEMI prediction. Besides, ITGAM and BST1 might be candidate prognostic DEGs for post-STEMI HF.Conclusions: We reanalyzed the integrated transcriptomic signature of patients with STEMI showing predictive potentiality and revealed new insights and specific prospective DEGs for STEMI risk stratification and HF development.

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Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients

10.21203/rs.3.rs-118025/v1 ◽

2020 ◽

Author(s):

Jing Xu ◽

Yuejing Yang

Keyword(s):

Machine Learning ◽

Gene Ontology ◽

Risk Stratification ◽

Molecular Mechanisms ◽

Interaction Network ◽

Hub Genes ◽

Machine Learning Model ◽

Transcriptomic Signature ◽

Logistic Regression Algorithm ◽

Geo Database

Abstract Objective To explore the molecular mechanism and search for the candidate biomarkers with predictive and prognostic potentiality that detectable in the whole blood of STEMI patients and post-STEMI HF patients.Methods In this study, we downloaded GSE60993, GSE61144, GSE66360, and GSE59867 datasets from the NCBI-GEO database. Differentially expressed genes (DEGs) of the datasets were investigated using R. Gene ontology and pathway enrichment were performed via ClueGO, CluePedia, and DAVID database. Protein interaction network was constructed via STRING. Enriched hub genes were analyzed by Cytoscape software. LASSO logistic regression algorithm and ROC analysis were performed to build machine learning models for predicting STEMI. Hub genes for further validated in post-STEMI HF patients from GSE59867.Results We identified 90 up-regulated DEGs and 9 down-regulated DEGs convergence in the three datasets (|log2FC| ≥ 0.8 and adjusted p value < 0.05). They were mainly enriched in Gene Ontology terms relating to cytokine secretion, pattern recognition receptors signaling pathway, and immune cells activation. A cluster of 8 genes including ITGAM, CLEC4D, SLC2A3, BST1, MCEMP1, PLAUR, GPR97, and MMP25 was found to be significant. A machine learning model built by SLC2A3, CLEC4D, GPR97, PLAUR, and BST1 exerted great value for STEMI prediction. Besides, ITGAM and BST1 might be candidate prognostic biomarkers for post-STEMI HF.Conclusions We re-analyzed the integrated transcriptomic signature of STEMI patients showing predictive potentiality and revealed new insights and specific prospective biomarkers for STEMI risk stratification and HF development.

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A Network Pharmacology-Based Study on the Anti-Lung Cancer Effect of Dipsaci Radix

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/7424061 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Jiayan Wu ◽

Shengkun Hong ◽

Xiankuan Xie ◽

Wangmi Liu

Keyword(s):

Lung Cancer ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Molecular Mechanisms ◽

Target Genes ◽

Interaction Network ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Active Compounds

Objective. Dipsaci Radix (DR) has been used to treat fracture and osteoporosis. Recent reports have shown that myeloid cells from bone marrow can promote the proliferation of lung cancer. However, the action and mechanism of DR has not been well defined in lung cancer. The aim of the present study was to define molecular mechanisms of DR as a potential therapeutic approach to treat lung cancer. Methods. Active compounds of DR with oral bioavailability ≥30% and drug-likeness index ≥0.18 were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform. The potential target genes of the active compounds and bone were identified by PharmMapper and GeneCards, respectively. The compound-target network and protein-protein interaction network were built by Cytoscape software and Search Tool for the Retrieval of Interacting Genes webserver, respectively. GO analysis and pathway enrichment analysis were performed using R software. Results. Our study demonstrated that DR had 6 active compounds, including gentisin, sitosterol, Sylvestroside III, 3,5-Di-O-caffeoylquinic acid, cauloside A, and japonine. There were 254 target genes related to these active compounds as well as to bone. SRC, AKT1, and GRB2 were the top 3 hub genes. Metabolisms and signaling pathways associated with these hub genes were significantly enriched. Conclusions. This study indicated that DR could exhibit the anti-lung cancer effect by affecting multiple targets and multiple pathways. It reflects the traditional Chinese medicine characterized by multicomponents and multitargets. DR could be considered as a candidate for clinical anticancer therapy by regulating bone physiological functions.

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Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

PLoS ONE ◽

10.1371/journal.pone.0257343 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257343

Author(s):

Shaoshuo Li ◽

Baixing Chen ◽

Hao Chen ◽

Zhen Hua ◽

Yang Shao ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Network Analysis ◽

Postmenopausal Osteoporosis ◽

Molecular Mechanism ◽

Molecular Mechanisms ◽

Functional Enrichment ◽

Support Vector ◽

Hub Genes ◽

Genetic Biomarkers

Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.

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Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

BioMed Research International ◽

10.1155/2022/1776082 ◽

2022 ◽

Vol 2022 ◽

pp. 1-17

Author(s):

Md. Rakibul Islam ◽

Lway Faisal Abdulrazak ◽

Mohammad Khursheed Alam ◽

Bikash Kumar Paul ◽

Kawsar Ahmed ◽

...

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Clinical Information ◽

Gene Expression Omnibus ◽

Hub Genes ◽

New Treatments

Background. Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods. A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣ log fold change ∣ > 1 and P < 0.05 . The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results. Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

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Identification of key genes and their microRNAs in glioma: Evidence from bioinformatic analysis

10.21203/rs.3.rs-27708/v1 ◽

2020 ◽

Author(s):

Xichao Wen ◽

Meijuan Fu ◽

Wensong Wu ◽

Zhaomu Zeng ◽

Kebin Zheng

Keyword(s):

Survival Analysis ◽

Molecular Mechanisms ◽

Process Analysis ◽

Interaction Network ◽

Tumor Grade ◽

Bioinformatic Analysis ◽

Sh3 Domain ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Condensed Chromosome

Abstract Background Glioma is one of the most common primary intracranial tumors. Although a lot of studies have been conducted to elucidate the pathogeny of glioma, the molecular mechanisms are still unclear because of its complex biological functions. Methods To identify the candidate genes in the carcinogenesis and progression of glioma, microarray datasets GSE4290, GSE122498 and GSE2223 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and performed function enrichment of DEGs by Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction network (PPI) was constructed using STRING and Cytoscape to find hub genes. Survival analysis and GEPIA database was conducted to screen and validate critical genes. Analysis of miRNA and genetic alteration was used to explore and predict the molecule mechanism. Results A total of 150 DEGs were identified, consisting of 54 downregulated genes and 96 upregulated genes. The enriched functions and pathways of the DEGs include regulation of transportation, synaptic transmission and SH3 domain binding. Fifteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in SH3 domain binding, neuron projection terminus, mitotic nuclear division, condensed chromosome and affected the brain development. Survival analysis showed that VAMP2, PPP3CA, DLGAP5, KIF14, REPS2, CENPU, KNTC1 and SMC4, may be involved in the carcinogenesis, invasion or recurrence of glioma. These 8 hub genes, which were related miRNAs and genetic changes were commonly involved in the development of glioma, were closely associated with tumor grade. Conclusion DEGs and hub genes identified in the present study help us understand the molecular mechanisms of carcinogenesis and progression of glioma, and provide candidate targets for diagnosis and treatment of glioma.

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Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias

10.1101/2021.01.14.425874 ◽

2021 ◽

Author(s):

Nikoleta Vavouraki ◽

James E. Tomkins ◽

Eleanna Kara ◽

Henry Houlden ◽

John Hardy ◽

...

Keyword(s):

Machine Learning ◽

Protein Interaction ◽

Protein Interaction Network ◽

Molecular Mechanisms ◽

Topological Analysis ◽

Interaction Network ◽

Protein Protein Interaction ◽

Hereditary Spastic Paraplegias ◽

Core Proteins ◽

Protein Protein Interaction Network

AbstractThe Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Despite the identification of causative mutations in over 70 genes, the molecular aetiology remains unclear. Due to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human protein-protein interactions were used to create a protein-protein interaction network based on the causative Hereditary Spastic Paraplegia genes. Network evaluation as a combination of both topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, suggesting there is scope to further classify conditions currently described under the same umbrella term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease.

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Identification of Hub Genes in Anaplastic Thyroid Carcinoma: Evidence From Bioinformatics Analysis

Technology in Cancer Research & Treatment ◽

10.1177/1533033820962135 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096213

Author(s):

Liqi Li ◽

Mingjie Zhu ◽

Hu Huang ◽

Junqiang Wu ◽

Dong Meng

Keyword(s):

Thyroid Carcinoma ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Quantitative Polymerase Chain Reaction ◽

Interaction Network ◽

Anaplastic Thyroid Carcinoma ◽

Rank Aggregation ◽

Differentially Expressed ◽

Hub Genes ◽

Rare Type

Anaplastic thyroid carcinoma (ATC) is a rare type of thyroid cancer that results in fatal clinical outcomes; the pathogenesis of this life-threatening disease has yet to be fully elucidated. This study aims to identify the hub genes of ATC that may play key roles in ATC development and could serve as prognostic biomarkers or therapeutic targets. Two microarray datasets (GSE33630 and GSE53072) were obtained from the Gene Expression Omnibus database; these sets included 16 ATC and 49 normal thyroid samples. Differential expression analyses were performed for each dataset, and 420 genes were screened as common differentially expressed genes using the robust rank aggregation method. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to explore the potential bio-functions of these differentially expressed genes (DEGs). The terms and enriched pathways were primarily associated with cell cycle, cell adhesion, and cancer-related signaling pathways. Furthermore, a protein-protein interaction network of DEG expression products was constructed using Cytoscape. Based on the whole network, we identified 7 hub genes that included CDK1, TOP2A, CDC20, KIF11, CCNA2, NUSAP1, and KIF2C. The expression levels of these hub genes were validated using quantitative polymerase chain reaction analyses of clinical specimens. In conclusion, the present study identified several key genes that are involved in ATC development and provides novel insights into the understanding of the molecular mechanisms of ATC development.

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Discovery and validation of a cross-platform 21-gene prognostic signature in neuroblastoma.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.10035 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 10035-10035

Author(s):

Mehul Gupta ◽

Sunand Nageswaran Kannappan ◽

Aru Narendran ◽

Pinaki Bose

Keyword(s):

Machine Learning ◽

Risk Stratification ◽

Clinical Decision Making ◽

Proportional Hazards ◽

Risk Model ◽

Learning Algorithm ◽

Clinical Decision ◽

Gene Signature ◽

Cox Proportional Hazards ◽

Transcriptomic Signature

10035 Background: Neuroblastoma (NB) is the most common extracranial solid tumor in children. Despite the development of risk stratification tools to improve prognostication, prediction of patient survival outcomes in NB remains poor. In this study we used an unbiased machine-learning algorithm to develop and validate a transcriptomic signature capable of predicting 5-year overall (OS) and event-free survival (EFS) in these patients. Methods: The TARGET-Neuroblastoma dataset (n = 243) was used as the training set. Two independent NB cohorts, E-MTAB-179 (n = 478) and GSE85047 (n = 266) were used as validation sets. Elastic net regression was employed to identify transcripts associated with EFS. Association of the developed signature with EFS and OS was evaluated using univariate Cox proportional hazards (CoxPH), Kaplan-Meier, and 5-year receiver-operator characteristic curves in validation cohorts. Further, the independent prognostic value of the signature was assessed using multivariate CoxPH models with relevant clinicopathologic variables including age, INSS stage, and N-Myc amplification status in both validation sets. Finally, a nomogram was developed to integrate the signature with prognostic clinicopathologic variables to evaluate their combined efficacy for prediction of 5-year EFS and OS. Results: We identified a 21-gene signature that demonstrates significant association with EFS and OS in both E-MTAB-178 and GSE49710 validation cohorts. Moreover, the signature is independent of clinicopathological variables and can be effectively incorporated into a risk model, improving the prognostic performance. Several genes within the signature have been previously implicated in NB, including ECEL1, HOXC9 and ARAF1. Conclusions: To the best of our knowledge, we are the first to use an unbiased machine learning approach to generate a transcriptomic gene signature for neuroblastoma prognosis externally validated in multiple cohorts across platforms. This 21-gene transcriptomic signature significantly associated with EFS and OS in this disease. Combining this signature with current prognostic clinicopathologic variables will improve risk stratification of affected patients and may inform effective clinical decision-making.[Table: see text]

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Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways

Genetics Research ◽

10.1155/2021/2728757 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Linjie Fang ◽

Tingyu Tang ◽

Mengqi Hu

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Cellular Response ◽

Molecular Mechanisms ◽

Interaction Network ◽

Differentially Expressed ◽

Type I ◽

Hub Genes ◽

Gene Set ◽

Go Terms

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases’ lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.

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Investigation of molecular mechanisms using integrated analysis of transcriptomes and cytokinome in dermatomyositis

10.21203/rs.2.24841/v2 ◽

2020 ◽

Author(s):

Jingjing Bai ◽

Chanyuan Wu ◽

Danli Zhong ◽

Dong Xu ◽

Qian Wang ◽

...

Keyword(s):

Disease Activity ◽

Signaling Pathways ◽

Molecular Mechanisms ◽

Type I Interferon ◽

Interaction Network ◽

Integrated Analysis ◽

Type I ◽

Hub Genes ◽

Activity Assessment ◽

Ifn Γ

Abstract Background Pathomechanism of dermatomyositis (DM) remains yet fully elucidated. While several cytokines have been proved to participate in the progress of DM, few studies provided a comprehensive analysis of cytokinome in different DM clinical-serological subgroups and correlation with disease activity as well as interaction with DM tissue lesions.Methods Transcriptome datasets of DM skin and muscle were obtained from public database. Hub genes and signaling pathways were filtered by bioinformatic software. Serum cytokinome was measured in DM patients with different clinical-serological subgroups and correlation with disease activity indicators was analyzed. Cytokine interaction network was constructed.Results 6 hub genes, including STAT1, MX1, ISG15, IFIT3, GBP1 and OAS2 were identified as IFN signature in DM. Differently expressed genes (DEGs) identified in the skin and muscle datasets were significantly enriched in the type I interferon signaling pathway, defense response to virus and chemotaxis. 11 cytokines were significantly elevated in patients positive for melanoma differentiation-associated protein (MDA5) antibody. IFN-α, IFN-γ, MIP-1α, IP-10, MCP1, GRO-α, IL-6, IL-18 and IL-1RA were correlated with disease activity. MCP1/MIP-1α/RANTES/MCP2/CCR1 axes were filtered from cytokine interaction network.Conclusions The complexity of DM immunopathogenesis is mediated through interactions of multiple cytokines and signaling pathways. Type I interferon is the core participant in DM tissue damage. Serum upregulation of IFN-α, IFN-γ, MIP-1α, IP-10, MCP1, GRO-α, IL-6, IL-18 and IL-1RA could be used for disease activity assessment in DM patients positive for MDA5 antibody. Finally, MCP1/MIP-1α/RANTES/MCP2/CCR1 axes mediated monocytes attraction might be novel therapeutic targets in DM by chemokine network analysis.

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