Bioinformatics Analysis Reveals Diagnostic Markers and Vital Pathways Involved in Acute Coronary Syndrome

Background. Acute coronary syndrome (ACS) has a high incidence and mortality rate. Early detection and intervention would provide clinical benefits. This study aimed to reveal hub genes, transcription factors (TFs), and microRNAs (miRNAs) that affect plaque stability and provide the possibility for the early diagnosis and treatment of ACS. Methods. We obtained gene expression matrix GSE19339 for ACS patients and healthy subjects from public database. The differentially expressed genes (DEGs) were screened using Limma package in R software. The biological functions of DEGs were shown by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Protein-protein interaction (PPI) network was mapped in Cytoscape, followed by screening of hub genes based on the Molecular Complex Detection (MCODE) plug-in. Functional Enrichment analysis tool (FunRich) and Database for Annotation, Visualization and Integrated Discovery (DAVID) were used to predict miRNAs and TFs, respectively. Finally, GSE60993 expression matrix was chosen to plot receiver operating characteristic (ROC) curves with the aim of further assessing the reliability of our findings. Results. We obtained 176 DEGs and further identified 16 hub genes by MCODE. The results of functional enrichment analysis showed that DEGs mediated inflammatory response and immune-related pathways. Among the predicted miRNAs, hsa-miR-4770, hsa-miR-5195, and hsa-miR-6088 all possessed two target genes, which might be closely related to the development of ACS. Moreover, we identified 11 TFs regulating hub gene transcriptional processes. Finally, ROC curves confirmed three genes with high confidence (area under the curve > 0.9), including VEGFA, SPP1, and VCAM1. Conclusion. This study suggests that three genes (VEGFA, SPP1, and VCAM1) were involved in the molecular mechanisms of ACS pathogenesis and could serve as biomarkers of disease progression.

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Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

Cancer Cell International ◽

10.1186/s12935-021-01933-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Huan Deng ◽

Qingqing Hang ◽

Dijian Shen ◽

Yibi Zhang ◽

Ming Chen

Keyword(s):

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Transforming Growth Factor ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Prognostic Indicators ◽

Hub Genes ◽

Coexpressed Genes

Abstract Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

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Identification of Key Modules and Hub Genes in Hypertrophic Cardiomyopathy Based on Integrative Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-915958/v1 ◽

2021 ◽

Author(s):

Jun Jiang ◽

Delong Chen ◽

Siyuan Xie ◽

Qichao Dong ◽

Yi Yu ◽

...

Keyword(s):

Network Analysis ◽

Hedgehog Signaling ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Receptor Interaction ◽

Hub Genes ◽

Remodeling Of Extracellular Matrix

Abstract BackgroundHypertrophic cardiomyopathy (HCM) is a heterogeneously inherited cardiac disorder with unclear biological pathogenesis. This study aims to identify the key modules and genes involved in the development of HCM.MethodsUsing weighted gene co-expression network analysis (WGCNA) algorithm, we constructed integrative co-expression networks for the two large sample HCM datasets separately. After selecting clinically significant modules with the same clinical trait, functional enrichment analysis was performed to detect their common pathways. Based on the intramodular connectivity (IC), the shared hub genes were generated, validated, and further explored in gene set enrichment analysis (GSEA).ResultsThe orange and pink modules in GSE141910, the green and brown modules in GSE36961 were mostly related to HCM. Functional enrichment analysis suggested that HCM might exhibit enhanced processes including remodeling of extracellular matrix, activation of abnormal protein signaling, aggregation of calcium ion, and organization of cytoskeleton. SMOC2, COL16A1, RASL11B, TUBA3D, IL18R1 were defined as real hub genes due to their top IC values, significantly different expression levels, and excellent diagnostic performance in both datasets. Moreover, GSEA analysis demonstrated that pathways of the five hub genes were mainly involved in neuroactive ligand-receptor interaction, ECM-receptor interaction, Hedgehog signaling pathway.ConclusionOur study provides more comprehensive insights into the molecular mechanisms of HCM, identifies five hub genes as candidate biomarkers for HCM, which might be theoretically feasible for targeted therapy against HCM.

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Identification of Key Genes and Pathways Associated with Differences of Subchondral Bone in Osteoarthritis

10.21203/rs.3.rs-51799/v1 ◽

2020 ◽

Author(s):

Yiyuan Zhang ◽

Rongguo Yu ◽

Jiayu Zhang ◽

Eryou Feng ◽

Haiyang Wang ◽

...

Keyword(s):

Subchondral Bone ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Hub Genes ◽

Common Chronic Disease ◽

Drug Candidates ◽

Therapeutic Modalities ◽

Pathogenic Genes

Abstract BackgroundOsteoarthritis (OA) is a common chronic disease worldwide. Subchondral bone is an important pathological change in OA and responds more rapidly to adverse loading and events compared to cartilage. However, the pathogenic genes and pathways of subchondral bone are largely unclear.ObjectiveThis study aimed to identify signature differences in genes involved in knee lateral tibial (LT) and medial tibial (MT) plateaus of subchondral bone tissue while exploring their potential molecular mechanisms via bioinformatics analysis.MethodsFirst, the gene expression data of GSE51588 was downloaded from the GEO database. Differentially expressed genes (DEGs) between knee LT and MT were identified, and functional enrichment analyses were performed. Then, a protein-protein interactive network was constructed in order to acquire the hub genes, and modules analysis was conducted using STRING and Cytoscape for further analysis. The enriched hub genes were queried in DGIdb database to find suitable drug candidates in OA.ResultsA total of 202 DEGs (112 upregulated genes and 84 downregulated genes) were determined. In the PPI network, ten hub genes were identified. Five significant modules were identified using the MCODE plugin unit. Functional enrichment analysis revealed the most important signaling pathways. Six of the ten hub genes were targetable by a total of 35 drugs, suggesting their possible therapeutic use for OA .ConclusionsThe identified hub genes and functional enrichment pathways were implicated in the development and progression of subchondral bone in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic modalities.

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Identification of key genes and biological processes contributing to colitis associated dysplasia in ulcerative colitis

PeerJ ◽

10.7717/peerj.11321 ◽

2021 ◽

Vol 9 ◽

pp. e11321

Author(s):

Di Zhang ◽

Pengguang Yan ◽

Taotao Han ◽

Xiaoyun Cheng ◽

Jingnan Li

Keyword(s):

Ulcerative Colitis ◽

Mitochondrial Dysfunction ◽

Single Gene ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Cell Junction ◽

Biological Processes ◽

Hub Genes ◽

Key Genes

Background Ulcerative colitis-associated colorectal cancer (UC-CRC) is a life-threatening complication of ulcerative colitis (UC). The mechanisms underlying UC-CRC remain to be elucidated. The purpose of this study was to explore the key genes and biological processes contributing to colitis-associated dysplasia (CAD) or carcinogenesis in UC via database mining, thus offering opportunities for early prediction and intervention of UC-CRC. Methods Microarray datasets (GSE47908 and GSE87466) were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between groups of GSE47908 were identified using the “limma” R package. Weighted gene co-expression network analysis (WGCNA) based on DEGs between the CAD and control groups was conducted subsequently. Functional enrichment analysis was performed, and hub genes of selected modules were identified using the “clusterProfiler” R package. Single-gene gene set enrichment analysis (GSEA) was conducted to predict significant biological processes and pathways associated with the specified gene. Results Six functional modules were identified based on 4929 DEGs. Green and blue modules were selected because of their consistent correlation with UC and CAD, and the highest correlation coefficient with the progress of UC-associated carcinogenesis. Functional enrichment analysis revealed that genes of these two modules were significantly enriched in biological processes, including mitochondrial dysfunction, cell-cell junction, and immune responses. However, GSEA based on differential expression analysis between sporadic colorectal cancer (CRC) and normal controls from The Cancer Genome Atlas (TCGA) indicated that mitochondrial dysfunction may not be the major carcinogenic mechanism underlying sporadic CRC. Thirteen hub genes (SLC25A3, ACO2, AIFM1, ATP5A1, DLD, TFE3, UQCRC1, ADIPOR2, SLC35D1, TOR1AIP1, PRR5L, ATOX1, and DTX3) were identified. Their expression trends were validated in UC patients of GSE87466, and their potential carcinogenic effects in UC were supported by their known functions and other relevant studies reported in the literature. Single-gene GSEA indicated that biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to angiogenesis and immune response were positively correlated with the upregulation of TFE3, whereas those related to mitochondrial function and energy metabolism were negatively correlated with the upregulation of TFE3. Conclusions Using WGCNA, this study found two gene modules that were significantly correlated with CAD, of which 13 hub genes were identified as the potential key genes. The critical biological processes in which the genes of these two modules were significantly enriched include mitochondrial dysfunction, cell-cell junction, and immune responses. TFE3, a transcription factor related to mitochondrial function and cancers, may play a central role in UC-associated carcinogenesis.

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The Role of Circular RNA CDR1as/ciRS-7 in Regulating Tumor Microenvironment: A Pan-Cancer Analysis

Biomolecules ◽

10.3390/biom9090429 ◽

2019 ◽

Vol 9 (9) ◽

pp. 429 ◽

Cited By ~ 24

Author(s):

Zou ◽

Zheng ◽

Deng ◽

Yang ◽

Xie ◽

...

Keyword(s):

Tumor Microenvironment ◽

Signaling Pathway ◽

Malignant Tumors ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Circular Rna ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Receptor Interaction

Circular RNA CDR1as/ciRS-7 functions as an oncogenic regulator in various cancers. However, there has been a lack of systematic and comprehensive analysis to further elucidate its underlying role in cancer. In the current study, we firstly performed a bioinformatics analysis of CDR1as among 868 cancer samples by using RNA-seq datasets of the MiOncoCirc database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), CIBERSORT, Estimating the Proportion of Immune and Cancer cells (EPIC), and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were applied to investigate the underlying functions and pathways. Functional enrichment analysis suggested that CDR1as has roles associated with angiogenesis, extracellular matrix (ECM) organization, integrin binding, and collagen binding. Moreover, pathway analysis indicated that it may regulate the TGF-β signaling pathway and ECM-receptor interaction. Therefore, we used CIBERSORT, EPIC, and the ESTIMATE algorithm to investigate the association between CDR1as expression and the tumor microenvironment. Our data strongly suggest that CDR1as may play a specific role in immune and stromal cell infiltration in tumor tissue, especially those of CD8+ T cells, activated NK cells, M2 macrophages, cancer-associated fibroblasts (CAFs) and endothelial cells. Generally, systematic and comprehensive analyses of CDR1as were conducted to shed light on its underlying pro-cancerous mechanism. CDR1as regulates the TGF-β signaling pathway and ECM-receptor interaction to serve as a mediator in alteration of the tumor microenvironment.

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Integrated Analyses Identify Immune-Related Signature Associated with Qingyihuaji Formula for Treatment of Pancreatic Ductal Adenocarcinoma Using Network Pharmacology and Weighted Gene Co-Expression Network

Journal of Immunology Research ◽

10.1155/2020/7503605 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Xiang Qian ◽

Zhuo Chen ◽

Sha Sha Chen ◽

Lu Ming Liu ◽

Ai Qin Zhang

Keyword(s):

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Ductal Adenocarcinoma ◽

Network Pharmacology ◽

Active Ingredients ◽

Kaplan Meier ◽

Human Protein Atlas

The study aimed to clarify the potential immune-related targets and mechanisms of Qingyihuaji Formula (QYHJ) against pancreatic cancer (PC) through network pharmacology and weighted gene co-expression network analysis (WGCNA). Active ingredients of herbs in QYHJ were identified by the TCMSP database. Then, the putative targets of active ingredients were predicted with SwissTargetPrediction and the STITCH databases. The expression profiles of GSE32676 were downloaded from the GEO database. WGCNA was used to identify the co-expression modules. Besides, the putative targets, immune-related targets, and the critical module genes were mapped with the specific disease to select the overlapped genes (OGEs). Functional enrichment analysis of putative targets and OGEs was conducted. The overall survival (OS) analysis of OGEs was investigated using the Kaplan-Meier plotter. The relative expression and methylation levels of OGEs were detected in UALCAN, human protein atlas (HPA), Oncomine, DiseaseMeth version 2.0 and, MEXPRESS database, respectively. Gene set enrichment analysis (GSEA) was conducted to elucidate the key pathways of highly-expressed OGEs further. OS analyses found that 12 up-regulated OGEs, including CDK1, PLD1, MET, F2RL1, XDH, NEK2, TOP2A, NQO1, CCND1, PTK6, CTSE, and ERBB2 that could be utilized as potential diagnostic indicators for PC. Further, methylation analyses suggested that the abnormal up-regulation of these OGEs probably resulted from hypomethylation, and GSEA revealed the genes markedly related to cell cycle and proliferation of PC. This study identified CDK1, PLD1, MET, F2RL1, XDH, NEK2, TOP2A, NQO1, CCND1, PTK6, CTSE, and ERBB2 might be used as reliable immune-related biomarkers for prognosis of PC, which may be essential immunotherapies targets of QYHJ.

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Integrated analysis identifies a pathway-related competing endogenous RNA network in the progression of pancreatic cancer

BMC Cancer ◽

10.1186/s12885-020-07470-4 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Fuqiang Zu ◽

Peng Liu ◽

Huaitao Wang ◽

Ting Zhu ◽

Jian Sun ◽

...

Keyword(s):

Pancreatic Cancer ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Tumor Formation ◽

Functional Enrichment ◽

Integrated Analysis ◽

Competing Endogenous Rna ◽

Hub Genes ◽

Cerna Network ◽

Competing Endogenous Rna Network

Abstract Background It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Employing Integrated analysis, we aim to identify the pathway-related ceRNA network associated with PC progression. Methods We divided eight GEO datasets into three groups according to their platform, and combined TCGA and GTEx databases as a group. Additionally, we screened out the differentially expressed genes (DEGs) and performed functional enrichment analysis in each group, and recognized the top hub genes in the most enriched pathway. Furthermore, the upstream of miRNAs and lncRNAs were predicted and validated according to their expression and prognostic roles. Finally, the co-expression analysis was applied to identify a pathway-related ceRNA network in the progression of PC. Results A total of 51 significant pathways that common enriched in all groups were spotted. Enrichment analysis indicated that pathway in cancer was greatly linked with tumor formation and progression. Next, the top 20 hug genes in this pathway were recognized, and stepwise prediction and validation from mRNA to lncRNA, including 11 hub genes, 4 key miRNAs, and 2 key lncRNAs, were applied to identify a meaningful ceRNA network according to ceRNA rules. Ultimately, we identified the PVT1/miR-20b/CCND1 axis as a promising pathway-related ceRNA axis in the progression of PC. Conclusion Overall, we elucidate the pathway-related ceRNA regulatory network of PVT1/miR-20b/CCND1 in the progression of PC, which can be considered as therapeutic targets and encouraging prognostic biomarkers for PC.

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Xiaoyu Xiezhuo Drink Protects against Ischemia-Reperfusion Acute Kidney Injury in Aged Mice through Inhibiting the TGF-β1/Smad3 and HIF1 Signaling Pathways

BioMed Research International ◽

10.1155/2021/9963732 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Qingqing Ye ◽

Hongbo Chen ◽

Hongzhen Ma ◽

Xiaojun Xiang ◽

Shouci Hu ◽

...

Keyword(s):

Drug Targets ◽

Ischemia Reperfusion ◽

Kidney Injury ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Aged Mice ◽

Tgf Β1

Acute kidney injury (AKI) is responsible for significant mortality among hospitalized patients that is especially troubling aged people. An effective self-made Chinese medicine formula, Xiaoyu Xiezhuo Drink (XXD), displayed therapeutic effects on AKI. However, the compositions and underlying mechanisms of XXD remain to be elucidated. In this study, we used the ultra-high-performance liquid chromatography method coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) to investigate the chemical components in XXD. Then, the absorbable components of XXD were identified based on the five principles and inputted into the SwissTargetPrediction and STITCH databases to identify the drug targets. AKI-related targets were collected from the GenCLiP 3, GeneCards, and DisGeNET databases. The crossover genes of XXD and AKI were identified for functional enrichment analysis. The protein-protein interaction (PPI) network of crossover genes was constructed, followed by the identification of hub genes. Subsequently, the effects and potential mechanisms of XXD on AKI predicted by the network pharmacology and bioinformatics analyses were experimentally validated in ischemia-reperfusion (I/R) injury-induced AKI aged mouse models. A total of 122 components in XXD were obtained; among them, 58 components were found that could be absorbed in the blood. There were 800 potential drug targets predicted from the 58 absorbable components in AKI which shared 36 crossover genes with AKI-related targets. The results of functional enrichment analysis indicated that crossover genes mostly associated with the response to oxidative stress and the HIF1 signaling pathway. In the PPI network analysis, 12 hub genes were identified, including ALB, IL-6, TNF, TP53, VEGFA, PTGS2, TLR4, NOS3, EGFR, PPARG, HIF1A, and HMOX1. In AKI aged mice, XXD prominently alleviated I/R injury-induced renal dysfunction, abnormal renal pathological changes, and cellular senescence, inflammation, and oxidative damage with a reduction in the expression level of the inflammatory mediator, α-SMA, collagen-1, F4/80, TP53, VEGFA, PTGS2, TLR4, NOS3, EGFR, PPARG, HIF1A, ICAM-1, TGF-β1, Smad3, and p-Smad3 and an increase of nephridial tissue p-H3, Ki67, HMOX1, MMP-9, and Smad7 levels. In summary, our findings suggest that XXD has renoprotective effects against AKI in aged mice via inhibiting the TGF-β1/Smad3 and HIF1 signaling pathways.

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Improved cancer biomarkers identification using network-constrained infinite latent feature selection

PLoS ONE ◽

10.1371/journal.pone.0246668 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246668

Author(s):

Lihua Cai ◽

Honglong Wu ◽

Ke Zhou

Keyword(s):

Feature Selection ◽

Expression Profiles ◽

Biological Significance ◽

Feature Selection Method ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Gene Sets ◽

Significant Gene

Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.

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Weighted Gene Correlation Network Analysis Identifies Specific Functional Modules and Genes in Esophageal Cancer

Journal of Oncology ◽

10.1155/2021/8223263 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Wei Xu ◽

Jian Xu ◽

Zhiqiang Wang ◽

Yuequan Jiang

Keyword(s):

Esophageal Cancer ◽

Network Analysis ◽

Cell Cycle Progression ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Future Research ◽

Hub Genes ◽

Pathological Staging

Objective. Esophageal cancer (ESCA) is one of the most aggressive malignancies globally with an undesirable five-year survival rate. Here, this study was conducted for determining specific functional genes linked with ESCA initiation and progression. Methods. Gene expression profiling of ESCA was curated from TCGA (containing 160 ESCA and 11 nontumor specimens) and GSE38129 (30 paired ESCA and nontumor tissues) datasets. Differential expression analysis was conducted between ESCA and nontumor tissues with adjusted p value <0.05 and |log2fold-change|>1. Weighted gene coexpression network analysis (WGCNA) was conducted for determining the ESCA-specific coexpression modules and genes. Thereafter, ESCA-specific differentially expressed genes (DEGs) were intersected. Functional enrichment analysis was then presented with clusterProfiler package. Protein-protein interaction was conducted, and hub genes were determined. Association of hub genes with pathological staging was evaluated, and survival analysis was presented among ESCA patients. Results. This study determined 91 ESCA-specific DEGs following intersection of DEGs and ESCA-specific genes in TCGA and GSE38129 datasets. They were remarkably linked to cell cycle progression and carcinogenic pathways like the p53 signaling pathway, cellular senescence, and apoptosis. Ten ESCA-specific hub genes were determined, containing ASPM, BUB1B, CCNA2, CDC20, CDK1, DLGAP5, KIF11, KIF20 A, TOP2A, and TPX2. They were prominently associated with pathological staging. Among them, KIF11 upregulation was in relation to undesirable prognosis of ESCA patients. Conclusion. Collectively, we determined ESCA-specific coexpression modules and hub genes, which offered the foundation for future research concerning the mechanistic basis of ESCA.

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