scholarly journals Integrated bioinformatics analysis of core regulatory elements involved in keloid formation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chuying Li ◽  
Meitong Jin ◽  
Yinli Luo ◽  
Zhehu Jin ◽  
Longquan Pi
Keyword(s):  
Gene Expression ◽  
G Protein ◽  
Normal Skin ◽  
Regulatory Genes ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Protein Subunit ◽  
Keloid Formation ◽  
Skin Samples ◽  
Core Genes

Abstract Background Keloid is a benign fibro-proliferative dermal tumor formed by an abnormal scarring response to injury and characterized by excessive collagen accumulation and invasive growth. The mechanism of keloid formation has not been fully elucidated, especially during abnormal scarring. Here, we investigated the regulatory genes, micro-RNAs (miRNAs) and transcription factors (TFs) that influence keloid development by comparing keloid and normal scar as well as keloid and normal skin. Methods Gene expression profiles (GSE7890, GSE92566, GSE44270 and GSE3189) of 5 normal scar samples, 10 normal skin samples and 18 keloid samples from the Gene Expression Omnibus (GEO) database were interrogated. Differentially expressed genes (DEGs) were identified between keloid and normal skin samples as well as keloid and normal scar samples with R Project for Statistical Computing. Gene Ontology (GO) functional enrichment analysis was also performed with R software. DEG-associated protein–protein interaction (PPI) network was constructed by STRING, followed by module selection from the PPI network based on the MCODE analysis. Regulatory relationships between TF/miRNA and target genes were predicted with miRnet and cytoscape. Core regulatory genes were verified by RT-qPCR. Results We identified 628 DEGs, of which 626 were up-regulated and 2 were down-regulated. Seven core genes [neuropeptide Y(NPY), 5-hydroxytryptamine receptor 1A(HTR1A), somatostatin (SST), adenylate cyclase 8 (ADCY8), neuromedin U receptor 1 (NMUR1), G protein subunit gamma 3 (GNG3), and G protein subunit gamma 13 (GNG13)] all belong to MCODE1 and were enriched in the “G protein coupled receptor signaling pathway” of the GO biological process category. Furthermore, nine core miRNAs (hsa-mir-124, hsa-let-7, hsa-mir-155, hsa-mir-26a, hsa-mir-941, hsa-mir-10b, hsa-mir-20, hsa-mir-31 and hsa-mir-372), and two core TFs (SP1 and TERT) were identified to play important roles in keloid formation. In the TF/miRNA-target gene network, both hsa-mir-372 and hsa-mir-20 had a regulatory effect on GNG13, ADCY8 was predicted to be target by hsa-mir-10b, and HTR1A and NPY were potentially by SP1. Furthermore, the expression of core regulatory genes (GNG13, ADCY8, HTR1A and NPY) was validated in clinical samples. Conclusions GNG13, ADCY8, NPY and HTR1A may act as core genes in keloid formation and these core genes establish relationship with SP1 and miRNA (hsa-mir-372, hsa-mir-20, hsa-mir-10b), which may influence multiple signaling pathways in the pathogenesis of keloid.

scholarly journals Identification of Potentially Therapeutic Target Genes of Hepatocellular Carcinoma

2020 ◽  
Vol 17 (3) ◽  
pp. 1053
Author(s):  
Chengzhang Li ◽  
Jiucheng Xu
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Gene Expression Profiles ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Differential Expression ◽  
Oncomine Database

Background: Hepatocellular carcinoma (HCC) is a major threat to public health. However, few effective therapeutic strategies exist. We aimed to identify potentially therapeutic target genes of HCC by analyzing three gene expression profiles. Methods: The gene expression profiles were analyzed with GEO2R, an interactive web tool for gene differential expression analysis, to identify common differentially expressed genes (DEGs). Functional enrichment analyses were then conducted followed by a protein-protein interaction (PPI) network construction with the common DEGs. The PPI network was employed to identify hub genes, and the expression level of the hub genes was validated via data mining the Oncomine database. Survival analysis was carried out to assess the prognosis of hub genes in HCC patients. Results: A total of 51 common up-regulated DEGs and 201 down-regulated DEGs were obtained after gene differential expression analysis of the profiles. Functional enrichment analyses indicated that these common DEGs are linked to a series of cancer events. We finally identified 10 hub genes, six of which (OIP5, ASPM, NUSAP1, UBE2C, CCNA2, and KIF20A) are reported as novel HCC hub genes. Data mining the Oncomine database validated that the hub genes have a significant high level of expression in HCC samples compared normal samples (t-test, p < 0.05). Survival analysis indicated that overexpression of the hub genes is associated with a significant reduction (p < 0.05) in survival time in HCC patients. Conclusions: We identified six novel HCC hub genes that might be therapeutic targets for the development of drugs for some HCC patients.

scholarly journals Identification of Potentially Functional Circular RNAs hsa_circ_0070934 and hsa_circ_0004315 as Prognostic Factor of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Pejman Morovat ◽  
Saman Morovat ◽  
Arash M. Ashrafi ◽  
Shahram Teimourian
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Factor ◽  
Sequence Data ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Ppi Network ◽  
Hub Genes ◽  
Cerna Network

Abstract Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide, which has a high mortality rate and poor treatment outcomes with yet unknown molecular basis. It seems that gene expression plays a pivotal role in the pathogenesis of the disease. Circular RNAs (circRNAs) can interact with microRNAs (miRNAs) to regulate gene expression in various malignancies by acting as competitive endogenous RNAs (ceRNAs). However, the potential pathogenesis roles of the ceRNA network among circRNA/miRNA/mRNA in HCC are unclear. In this study, first, the HCC circRNA expression data were obtained from three Gene Expression Omnibus microarray datasets (GSE164803, GSE94508, GSE97332), and the differentially expressed circRNAs (DECs) were identified using R limma package. Also, the liver hepatocellular carcinoma (LIHC) miRNA and mRNA sequence data were retrieved from TCGA, and differentially expressed miRNAs (DEMIs) and mRNAs (DEGs) were determined using the R DESeq2 package. Second, CSCD website was used to uncover the binding sites of miRNAs on DECs. The DECs' potential target miRNAs were revealed by conducting an intersection between predicted miRNAs from CSCD and downregulated DEMIs. Third, some related genes were uncovered by intersecting targeted genes predicted by miRWalk and targetscan online tools with upregulated DEGs. The ceRNA network was then built using the Cytoscape software. The functional enrichment and the overall survival time of these potential targeted genes were analyzed, and a PPI network was constructed in the STRING database. Network visualization was performed by Cytoscape, and ten hub genes were detected using the CytoHubba plugin tool. Four DECs (hsa_circ_0000520, hsa_circ_0008616, hsa_circ_0070934, hsa_circ_0004315) were obtained and six miRNAs (hsa-miR-542-5p, hsa-miR-326, hsa-miR-511-5p, hsa-miR-195-5p, hsa-miR-214-3p, and hsa-miR-424-5p) which are regulated by the above DECs were identified. Then 543 overlapped genes regulated by six miRNAs mentioned above were predicted. Functional enrichment analysis showed that these genes are mostly associated with cancer regulation functions. Ten hub genes (TTK،AURKB, KIF20A، KIF23، CEP55، CDC6، DTL، NCAPG، CENPF، PLK4) have been screened from the PPI network of the 204 survival-related genes. KIF20A, NCAPG, TTK, PLK4, and CDC6 were selected for the highest significant p-values. In the end, a circRNA-miRNA-mRNA regulatory axis was established for five final selected hub genes. This study implies the potential pathogenesis of the obtained network and proposes that the two DECs (has_circ_0070934 and has_circ_0004315) may be important prognostic factor for HCC.

scholarly journals Using Weighted Gene Co-Expression Network Analysis to Identify Key Genes Related to the Onset of Preeclampsia

2020 ◽  
Author(s):  
Xinyang Shen ◽  
Zhijian Wang ◽  
Zhirui Zeng ◽  
Zhenqin Huang ◽  
Xiaowei Huang ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Immune Response Gene ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Ontology Term ◽  
Gene Data ◽  
Core Genes

Abstract Background: Preeclampsia is a form of hypertension in pregnancy, which induced by complicated factors. However, the pathogenesis of the disease is unclear. The present study was aimed to discover the critical biomarkers associated with the occurrence and development of preeclampsia. Methods：Gene data profile GSE75010 was downloaded from the Gene Expression Omnibus (GEO) database and used as discovery cohort to establish a WGCNA network determining significant modules which associated with clinical traits. Subsequently, functional enrichment analysis, pathway analysis and protein-protein interaction (PPI) network construction were performed on the core genes in significant modules to identify hub genes. Then, gene data profile GSE25906 was used as verified cohort to determine their diagnostic value of hub genes. The protein expression levels of these hub genes in preeclampsia and control placental tissues were verified using immunohistochemistry method. Finally, GSEA was performed to analyze their enrichment pathways. Results: Total 33 co-expression modules were identified after the establishment of WGCNA, of which 4 gene modules were identified as significant modules because they were related to multiple (>3) clinical traits. Total 75 core genes in significant modules were analyzed, and results showed that they were mainly enriched in adaptive immune response (Gene Ontology term) and platelet activation (Kyoto Encyclopedia of Genes and Genomes term). Finally, a total of 5 genes including TYROBP, PLEK, LCP2, HCK, ITGAM were identified as hub genes which scored high in PPI network and had high diagnostic value. Furthermore, the protein level of these 5 genes in placental tissues of preeclampsia was lower than that of the control group. Moreover, these 5 genes were all enriched in 17 pathways, including autoimmunity pathway. Conclusions：These 5 genes (TYROBP, PLEK, LCP2, HCK, ITGAM) may be closely related to the pathogenesis of preeclampsia, which may also help the diagnosis and therapy of preeclampsia.

scholarly journals Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning

2021 ◽  
Vol 18 (6) ◽  
pp. 8997-9015
Author(s):  
Ahmed Hammad ◽  
◽  
Mohamed Elshaer ◽  
Xiuwen Tang ◽  
◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Machine Learning ◽  
Roc Curve ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Survival Analyses ◽  
Potential Biomarkers

<abstract> <p>Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explore their diagnostic values. Meanwhile, TCGA transcriptomics data in Gene Expression Profiling Interactive Analysis (GEPIA) database and the pathology data presented by in the human protein atlas (HPA) database were used to verify our transcriptomic analyses. A total of 105 DEGs were identified in this study. Functional enrichment analysis showed that these genes were significantly enriched in biological processes related to cancer progression. Thereafter, PPI network explored a total of 10 significant hub genes. The ROC curve was used to predict the potential application of biomarkers in CRC diagnosis, with an area under ROC curve (AUC) of these genes exceeding 0.92 suggesting that this risk classifier can discriminate between CRC patients and normal controls. Moreover, the prognostic values of these hub genes were confirmed by survival analyses using different CRC patient cohorts. Our results demonstrated that these 10 differentially expressed hub genes could be used as potential biomarkers for CRC diagnosis.</p> </abstract>

Identification of Potential Biomarkers in Synovial Tissue of Rheumatoid Arthritis via Integrated Microarray Analysis. Identification of Biomarkers of Rheumatoid Arthritis

2021 ◽  
Author(s):  
Li Tao ◽  
ChaoLiang Xiong ◽  
Li Xue
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Pathway Analysis ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Markers

Abstract Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovitis and subsequent destruction of cartilage and bone. This study aimed to explore RA-related gene markers and the underlying molecular mechanism.Material and Methods: The expression profiles of GSE77298, GSE55235 and GSE12021 were obtained from the Gene Expression Omnibus database. Then, the differential gene expression analysis was conducted between GSE77298 and GSE55235 datasets. Limma package and a Venn diagram were utilized to screen the overlapping differentially expressed genes (DEGs), and Functional enrichment and pathway analysis were performed by using DAVID database. Subsequently, a protein-protein interaction (PPI) network was established, and candidate hub genes were recognized by using STRING and Cytoscape software. Finally, another dataset (GSE12021) was used for the validation of diagnostic value of the candidate hub genes and to identify real hub genes by using receiver operating characteristic (ROC) curves.Results: A total of 385 DEGs were detected, which include 19 downregulated genes and 366 upregulated genes. GO and KEGG pathway analysis showed that DEGs was mainly enriched in various immune and inflammatory response-related functions and pathways. The PPI network was composed of 374 nodes and 767 edges. A total of 8 real hub genes (HLA-DRA, HLA-DRB1, LCK, VAV1, HLA-DPA1, HLA-DPB1, C3AR1 and CD3D) which displayed an excellent diagnostic value for RA were identified.Conclusion: these findings may provide novel and reliable biomarkers for RA, which have some interesting implications for early diagnosis, prognosis and targeted therapy.

scholarly journals The c-MYC Protooncogene Expression in Cholesteatoma

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Enikő Palkó ◽  
Szilárd Póliska ◽  
Zsuzsanna Csákányi ◽  
Gábor Katona ◽  
Tamás Karosi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Epidermoid Cyst ◽  
Normal Skin ◽  
Neck Region ◽  
Chromosome 8 ◽  
Significant Difference ◽  
The Matrix ◽  
Number Variation ◽  
Myc Gene ◽  
Skin Samples

Cholesteatoma is an epidermoid cyst, which is most frequently found in the middle ear. The matrix of cholesteatoma is histologically similar to the matrix of the epidermoid cyst of the skin (atheroma); their epithelium is characterized by hyperproliferation. The c-MYC protooncogene located on chromosome 8q24 encodes a transcription factor involved in the regulation of cell proliferation and differentiation. Previous studies have found aneuploidy of chromosome 8, copy number variation of c-MYC gene, and the presence of elevated level c-MYC protein in cholesteatoma. In this study we have compared the expression of c-MYC gene in samples taken from the matrix of 26 acquired cholesteatomas (15 children and 11 adults), 15 epidermoid cysts of the skin (atheromas; head and neck region) and 5 normal skin samples (retroauricular region) using RT-qPCR, providing the first precise measurement of the expression of c-MYC gene in cholesteatoma. We have found significantly elevated c-MYC gene expression in cholesteatoma compared to atheroma and to normal skin samples. There was no significant difference, however, in c-MYC gene expression between cholesteatoma samples of children and adults. The significant difference in c-MYC gene expression level in cholesteatoma compared to that of atheroma implies a more prominent hyperproliferative phenotype which may explain the clinical behavior typical of cholesteatoma.

scholarly journals Interactome Profile of Visceral Adipose Tissue in Obesity Links Key Genes to Cancer Pathogenesis

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A51-A52
Author(s):  
Dipayan Roy ◽  
Anupama Modi ◽  
Purvi Purohit
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Adipose Tissue ◽  
Visceral Adipose Tissue ◽  
Functional Enrichment ◽  
Regulatory Subunit ◽  
Ppi Network ◽  
Cancer Pathogenesis ◽  
Visceral Adipose ◽  
Risk Of Cancer

Abstract Obesity increases the risk of the development of several malignancies. The visceral adipose tissue (VAT) depot is one of the pivotal contributors behind the obesity-related pathogenetic mechanisms. In this study, we analyzed the differential gene expression profile in the VAT of obese children using two Gene Expression Omnibus datasets. GSE29718 and GSE9624 were sorted and 68 common differentially expressed genes (DEG) with fold change 1.5 upregulation or downregulation (cutoff |logFC|≥0.58496) were obtained. Gene ontology and functional enrichment and protein-protein interaction (PPI) network for the DEG were analyzed in Search Tool for the Retrieval of Interacting Genes (STRING), which revealed 37 biological processes, 3 cellular components, and 1 molecular function to be significantly associated. Reactome pathway analysis showed the DEG to be involved in- one carbon pool by folate, glycine degradation, transcriptional regulation by TP53, ERK inactivation, G1/S-specific transcription, Fanconi anemia pathway, beta-catenin phosphorylation cascade, RAF activation, and negative regulation of the MAPK pathway. The PPI network was set with a minimum interaction score of 0.400 and a maximum of 10 interactions, and it was significantly enriched (p-value 0.047) with 66 nodes and 46 edges. Target prediction was performed using miRNet. Several miRNA, including hsa-miR-1-3p, hsa-let-7b-5p, hsa-miR-16-5p, hsa-miR-27a-3p and hsa-miR-34a-5p were part of the mRNA-miRNA interaction network. Using the CytoHubba plugin in Cytoscape, the top 10 hub genes from the PPI network were discovered. Thymidine phosphorylase (TYMP) and dihydrofolate reductase (DHFR), essential components of nucleic acid metabolism, have been shown to be involved in angiogenesis and endothelial cell growth, and correlated to p53 mutations, respectively. Protein phosphatase 2, regulatory subunit A & regulatory subunit B (PPP2R1A and PPP2R1B) mutations are involved in ovarian, endometrial, lung and colorectal cancers. HLA-DQA1 mutation is involved cervical cancer, and it is involved in increased immune sensitivity and liver damage in breast cancer patients. The RAB7Ab and RAB7-interacting lysosomal protein (RILP) are regulators of endo-lysosomal trafficking and suppresses breast cancer cell invasion. To conclude, this study identifies several genes and their regulatory pathways in VAT which may contribute to the increased risk of cancer pathogenesis in obese individuals.

scholarly journals RNA-Seq Identifies Marked Th17 Cell Activation and Altered CFTR Expression in Different Atopic Dermatitis Subtypes in Chinese Han Populations

2021 ◽  
Vol 12 ◽  
Author(s):  
Xin Tian ◽  
Baoyi Liu ◽  
Lijie Chen ◽  
Yongyi Xie ◽  
Jingyao Liang ◽  
...  
Keyword(s):  
Atopic Dermatitis ◽  
Healthy Volunteers ◽  
Cell Activation ◽  
Phenotypic Variability ◽  
Normal Skin ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Software Environment ◽  
Altered Expression ◽  
Skin Samples

BackgroundPatients with atopic dermatitis (AD) exhibit phenotypic variability in ethnicity and IgE status. In addition, some patients develop other allergic conditions, such as allergic rhinitis (AR), in subsequent life. Understanding the heterogeneity of AD would be beneficial to phenotype-specific therapies.MethodsTwenty-eight Chinese AD patients and 8 healthy volunteers were enrolled in the study. High-throughput transcriptome sequencing was conducted on lesional and nonlesional skin samples from 10 AD patients and matched normal skin samples from 5 healthy volunteers. Identification of differentially expressed genes (DEGs), KEGG pathway analyses, and sample cluster analyses were conducted in the R software environment using the DEseq2, ClusterProfiler, and pheatmap R packages, respectively. qRT-PCR, Western blotting, and ELISA were used to detect gene expression levels among subtypes. Correlation analysis was performed to further investigate their correlation with disease severity.ResultsA total of 25,798 genes were detected per sample. Subgroup differential expression analysis and functional enrichment analysis revealed significant changes in the IL17 signaling pathway in Chinese EAD patients but not in IAD patients. DEGs enriched in cytokine-cytokine receptor interactions and gland secretion were considered to be associated with atopic march. Further investigations confirmed a marked IL17A upregulation in Chinese EAD with a positive relationship with total IgE level and AD severity. In addition, increased IL17A in AD patients with AR demonstrated a closer association with AR severity than IL4R. Moreover, AQP5 and CFTR were decreased in the lesions of AD patients with AR. The CFTR mRNA expression level was negatively associated with the skin IL17A level and AR severity.ConclusionOur research characterized marked Th17 activation in Chinese EAD patients, and altered expression of IL17A, IL4R, AQP5, and CFTR in AD patients with AR was associated with AR severity. It partially explained the phenotypic differences of AD subtypes and provided potential references for endotype-targeted therapy.

scholarly journals Identification of potential crucial genes and pathways associated with vein graft restenosis based on gene expression analysis in experimental rabbits

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4704 ◽  
Author(s):  
Qiang Liu ◽  
Xiujie Yin ◽  
Mingzhu Li ◽  
Li Wan ◽  
Liqiao Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Vein Graft ◽  
Molecular Mechanisms ◽  
Rapid Development ◽  
Bypass Graft ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Network Analyses

Occlusive artery disease (CAD) is the leading cause of death worldwide. Bypass graft surgery remains the most prevalently performed treatment for occlusive arterial disease, and veins are the most frequently used conduits for surgical revascularization. However, the clinical efficacy of bypass graft surgery is highly affected by the long-term potency rates of vein grafts, and no optimal treatments are available for the prevention of vein graft restenosis (VGR) at present. Hence, there is an urgent need to improve our understanding of the molecular mechanisms involved in mediating VGR. The past decade has seen the rapid development of genomic technologies, such as genome sequencing and microarray technologies, which will provide novel insights into potential molecular mechanisms involved in the VGR program. Ironically, high throughput data associated with VGR are extremely scarce. The main goal of the current study was to explore potential crucial genes and pathways associated with VGR and to provide valid biological information for further investigation of VGR. A comprehensive bioinformatics analysis was performed using high throughput gene expression data. Differentially expressed genes (DEGs) were identified using the R and Bioconductor packages. After functional enrichment analysis of the DEGs, protein–protein interaction (PPI) network and sub-PPI network analyses were performed. Finally, nine potential hub genes and fourteen pathways were identified. These hub genes may interact with each other and regulate the VGR program by modulating the cell cycle pathway. Future studies focusing on revealing the specific cellular and molecular mechanisms of these key genes and pathways involved in regulating the VGR program may provide novel therapeutic targets for VGR inhibition.

scholarly journals Identification of Flap endonuclease 1 as a potential core gene in hepatocellular carcinoma by integrated bioinformatics analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7619 ◽  
Author(s):  
Chuanfei Li ◽  
Feng Qin ◽  
Hao Hong ◽  
Hui Tang ◽  
Xiaoling Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Expression Profiles ◽  
Core Gene ◽  
Functional Enrichment ◽  
Potential Core ◽  
Potential Biomarker ◽  
Cancer Tissues ◽  
Core Genes

Hepatocellular carcinoma (HCC) is a common yet deadly form of malignant cancer. However, the specific mechanisms involved in HCC diagnosis have not yet fully elucidated. Herein, we screened four publically available Gene Expression Omnibus (GEO) expression profiles (GSE14520, GSE29721, GSE45267 and GSE60502), and used them to identify 409 differentially expressed genes (DEGs), including 142 and 267 up- and down-regulated genes, respectively. The DAVID database was used to look for functionally enriched pathways among DEGs, and the STRING database and Cytoscape platform were used to generate a protein-protein interaction (PPI) network for these DEGs. The cytoHubba plug-in was utilized to detect 185 hub genes, and three key clustering modules were constructed with the MCODE plug-in. Gene functional enrichment analyses of these three key clustering modules were further performed, and nine core genes including BIRC5, DLGAP5, DTL, FEN1, KIAA0101, KIF4A, MCM2, MKI67, and RFC4, were identified in the most critical cluster. Subsequently, the hierarchical clustering and expression of core genes in TCGA liver cancer tissues were analyzed using the UCSC Cancer Genomics Browser, and whether elevated core gene expression was linked to a poor prognosis in HCC patients was assessed using the GEPIA database. The PPI of the nine core genes revealed an interaction between FEN1, MCM2, RFC4, and BIRC5. Furthermore, the expression of FEN1 was positively correlated with that of three other core genes in TCGA liver cancer tissues. FEN1 expression in HCC and other tumor types was assessed with the FIREBROWSE and ONCOMINE databases, and results were verified in HCC samples and hepatoma cells. FEN1 levels were also positively correlated with tumor size, distant metastasis and vascular invasion. In conclusion, we identified nine core genes associated with HCC development, offering novel insight into HCC progression. In particular, the aberrantly elevated FEN1 may represent a potential biomarker for HCC diagnosis and treatment.

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