scholarly journals A New Sight In The Key Prognosis-Related Proto-Oncogene FYN In Hepatocellular Carcinoma Based On WCGNA Hub-Gene Screening Strategy

2021 ◽  
Author(s):  
Chenkai Huang ◽  
Juanjuan Zhou ◽  
Yuan Nie ◽  
Guihai Guo ◽  
Anjiang Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Treatment Strategies ◽  
Xenograft Model ◽  
Screening Strategy ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Protein Protein Interaction ◽  
Promote Tumor Cell ◽  
New Treatment

Abstract Background: Hepatocellular Carcinoma (HCC) is the third deadly cancer worldwide. More breakthroughs are needed in the clinical practice of liver cancer, and new treatment strategies are required to carry out to benefit patients. This study aims to screen out other significant differences in genes associated with LIHC and analyze its prognostic value further. Methods: Here, we use the TCGA-LIHC database and the profiles of GSE25097 from GEO to explore the differential co-expression genes in HCC tissues compared with para-tumor (or healthy) tissues. Then, we utilized WGCNA to screen out differential co-expression genes. Finally, we explored the function of FYN in HCC cells and xenograft tumor models.Results: We identified ten hub genes in the protein-protein interaction (PPI) network, but only three (COLEC10, TGFBR3, and FYN) of them seem to have a close-related to the prognosis. The expression of FYN was negatively correlated with the prognosis of HCC patients. The xenograft model showed that overexpression of FYN could significantly inhibit malignant tumor behaviors and promote tumor cell apoptosis.Conclusion: Thus, FYN may be central to the development of LIHC and maybe a novel biomarker for clinical diagnosis and treatment.

scholarly journals The Identification of Core Gene Expression Signature in Hepatocellular Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ning Li ◽  
Ling Li ◽  
Yongshun Chen
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Financial Burden ◽  
Gene Expression Signature ◽  
Core Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Hepatocellular carcinoma (HCC) is one of the most common malignancies, which causes serious financial burden worldwide. This study aims to investigate the potential mechanisms contributing to HCC and identify core biomarkers. The HCC gene expression profile GSE41804 was picked out to analyze the differentially expressed genes (DEGs). Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out using DAVID. We constructed a protein-protein interaction (PPI) network to visualize interactions of the DEGs. The survival analysis of these hub genes was conducted to evaluate their potential effects on HCC. In this analysis, 503 DEGs were captured (360 downregulated genes and 143 upregulated genes). Meanwhile, 15 hub genes were identified. GO analysis showed that the DEGs were mainly enriched in oxidative stress, cell cycle, and extracellular structure. KEGG analysis suggested the DEGs were enriched in the absorption, metabolism, and cell cycle pathway. PPI network disclosed that the top3 modules were mainly enriched in cell cycle, oxidative stress, and liver detoxification. In conclusion, our analysis uncovered that the alterations of oxidative stress and cell cycle are two major signatures of HCC. TOP2A, CCNB1, and KIF4A might promote the development of HCC, especially in proliferation and differentiation, which could be novel biomarkers and targets for diagnosis and treatment of HCC.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

2022 ◽  
Vol 12 (3) ◽  
pp. 523-532
Author(s):  
Xin Yan ◽  
Chunfeng Liang ◽  
Xinghuan Liang ◽  
Li Li ◽  
Zhenxing Huang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Adrenocortical Carcinoma ◽  
Gene Expression Omnibus ◽  
Heparin Binding ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

scholarly journals Molecular profile reveals immune-associated markers of medulloblastoma for different subtypes

2021 ◽  
Author(s):  
shenglan li ◽  
Zhuang Kang ◽  
jinyi Chen ◽  
Can Wang ◽  
Zehao Cai ◽  
...  
Keyword(s):  
Immune Cell ◽  
Intracranial Tumor ◽  
Cell Infiltration ◽  
Molecular Profile ◽  
Immune Cell Infiltration ◽  
Ppi Network ◽  
Hub Genes ◽  
Data Set ◽  
Hub Gene ◽  
External Data

Abstract Background Medulloblastoma is a common intracranial tumor among children. In recent years, research on cancer genome has established four distinct subtypes of medulloblastoma: WNT, SHH, Group3, and Group4. Each subtype has its own transcriptional profile, methylation changes, and different clinical outcomes. Treatment and prognosis also vary depending on the subtype. Methods Based on the methylation data of medulloblastoma samples, methylCIBERSORT was used to evaluate the level of immune cell infiltration in medulloblastoma samples and identified 10 kinds of immune cells with different subtypes. Combined with the immune database, 293 Imm-DEGs were screened. Imm-DEGs were used to construct the co-expression network, and the key modules related to the level of differential immune cell infiltration were identified. Three immune hub genes (GAB1, ABL1, CXCR4) were identified according to the gene connectivity and the correlation with phenotype in the key modules, as well as the PPI network involved in the genes in the modules. Results The subtype marker was recognized according to the immune hub, and the subtype marker was verified in the external data set, the methylation level of immune hub gene among different subtypes was compared and analyzed, at the same time, tissue microarray was used for immunohistochemical verification, and a multi-factor regulatory network of hub gene was constructed. Conclusions Identifying subtype marker is helpful to accurately identify the subtypes of medulloblastoma patients, and can accurately evaluate the treatment and prognosis, so as to improve the overall survival of patients.

scholarly journals MicroRNA expression analysis for identification of biomarkers in inflammatory bowel disease subtypes

2020 ◽  
Author(s):  
Manisha Mandal ◽  
Shyamapada Mandal
Keyword(s):  
Ribosome Biogenesis ◽  
Target Genes ◽  
Proteasomal Degradation ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Hub Gene ◽  
Protein Protein Interaction ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Protein Protein Interaction Networks

Abstract The potential biomarkers in inflammatory bowel diseases (IBDs) were analyzed from GSE53867 dataset. Differentially expressed microRNAs (DEMs)-genes and protein-protein interaction networks were constructed, and hub genes selected using Cytoscape. Differentially expressed genes were analyzed for GO and Reactome-pathway. Seven DEMs were upregulated in Crohn's disease (CD), 4 downregulated in ulcerative colitis (UC), 8 upregulated and 2 downregulated in IBD. A 620, 2377, and 1821 target-genes were in CD, UC, and IBD, respectively. SOCS3, upregulated by miR-650, was hub gene in CD, induced by cytokines, through NFKB-signalling pathway to mediate ubiquitin-proteasomal degradation. CIRH1A, downregulated by miR-16, was hub gene of UC, acted by impairing ribosome-biogenesis. SKP2 and ASB1, up- and downregulated, by miR-142 and miR-665, respectively, were hub genes of IBD, induced cytokines through activation of TLR- and TNF-signalling pathways to mediate ubiquitin-proteasomal degradation. SOCS3, CIRH1A, SKP2 and ASB1 genes might serve as valuable biomarkers to differentiate CD, UC and IBD.

scholarly journals Identification of hub genes and their clinical value for predicting the development of prostate cancer from benign prostate hyperplasia by bioinformatic analysis

2021 ◽  
Author(s):  
Xi Chen ◽  
Junjie Ma ◽  
Chengdang Xu ◽  
Licheng Wang ◽  
Yicong Yao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Survival Data ◽  
Benign Prostate Hyperplasia ◽  
Disease Free Survival ◽  
Ppi Network ◽  
Hub Genes ◽  
Prostate Hyperplasia ◽  
Hub Gene ◽  
Benign Prostate ◽  
Geo Database

Abstract BackgroundProstate cancer (PCa) and benign prostate hyperplasia (BPH) are commonly encountered diseases in elderly males. The two diseases have some commonalities: both are growth depend on hormone and respond to antiandrogen therapy. Some studies have shown that genetic factors are responsible for the occurrences of both diseases. There may be a correlation between BPH and PCa. MethodsThe GEO database can help determine the differentially expressed genes (DEGs) between BPH and PCa. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were utilized to find pathways in which the DEGs were enriched. The STRING database can provide a protein–protein interaction (PPI) network, and Cytoscape software can find hub genes in PPI network. GEPIA can be used to analyze expression and survival data for hub genes. R software was used to progress regression analysis, decision curve analysis and built nomograph. UALCAN and The Human Protein Atlas was utilized to test the results. Finally, we made clinical and cell experiments to verify the results.ResultsSixty DEGs, consisting of 15 up-regulated and 45 down-regulated genes, were found based on the GEO database. Using Cytoscape, we found 7 hub gene in the PPI network. The hub gene expression was tested on TCGA database. Except CXCR4, all hub genes expressed differently between tumor and normal samples. Meanwhile, all hub genes exclude CXCR4 has diagnostic value in predicting PCa and their mutations are risk factors leading to PCa. The expression of CSRP1, MYL9 and SNAI2 changed in different tumor stage. CSRP1 and MYH11 could affect the disease-free survival (DFS). The same results reflected in different database. In addition, we also chose three hub gene, MYC, MYL9, and SNAI2, to validate their functions in clinical specimens and cells.ConclusionThese identified hub genes can help us to understand the process and mechanism by which BPH develops into PCa and provide achievable targets for predicting which BPH patients may later develop PCa.

scholarly journals Weighted gene co-expression network analysis to define pivotal modules and genes in diabetic heart failure

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Weiwei Liang ◽  
FangFang Sun
Keyword(s):  
Heart Failure ◽  
Kegg Pathway ◽  
Diabetic Heart ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Light Yellow ◽  
Go Enrichment ◽  
Key Genes ◽  
Functional Analyses

Abstract This research was carried out to reveal specific hub genes involved in diabetic heart failure, as well as remarkable pathways that hub genes locate. The GSE26887 dataset from the GEO website was downloaded. The gene co-expression network was generated and central modules were analyzed to identify key genes using the WGCNA method. Functional analyses were conducted on genes of the clinical interest modules via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene ontology (GO) enrichment, associated with protein–protein interaction (PPI) network construction in a sequence. Centrality parameters of the PPI network were determined using the CentiScape plugin in Cytoscape. Key genes, defined as genes in the ≥95% percentile of the degree distribution of significantly perturbed networks, were identified. Twenty gene co-expression modules were detected by WGCNA analysis. The module marked in light yellow exhibited the most significant association with diabetes (P=0.08). Genes involved in this module were primarily located in immune response, plasma membrane and receptor binding, as shown by the GO analysis. These genes were primarily assembled in endocytosis and phagosomes for KEGG pathway enrichment. Three key genes, STK39, HLA-DPB1 and RAB5C, which may be key genes for diabetic heart failure, were identified. To our knowledge, our study is the first to have constructed the co-expression network involved in diabetic heart failure using the WGCNA method. The results of the present study have provided better understanding the molecular mechanism of diabetic heart failure.

Identification of critical genes and pathways involved in intervertebral disc degeneration (IDD) An integrated bioinformatics analysis

2020 ◽  
Author(s):  
Jingyang Zhou ◽  
Feng Xin ◽  
Chuyu Xiao ◽  
Wuyuan Zhou
Keyword(s):  
Daily Life ◽  
Vital Role ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Diagnosis And Prognosis ◽  
Study Results ◽  
Validation Experiment ◽  
The One

Abstract Background: In western countries and China, back and neck pain has become a common problem that bothers daily life and severely influences the quality of our daily life. Among all factors that lead to chronic neck and back pain, IDD is the one that couldn’t be easily neglected. Methods: This study aims to figure out the critical genes and pathways involved in the development of IDD and provide a new aspect of following investigations on the etiology of IDD. We firstly systemically searched the GEO database and identified the differentially expressed genes (DEGs) from the expression profile dataset we selected. We secondly constructed the protein-protein interaction (PPI) network for DEGs, identified the top ten hub genes from the whole PPI network and found two statically and medically significant modules from the network, we then performed the GO and KEGG analysis on the DEGs, top ten hub genes, the PPI network and the two statically and medically modules. In the end, we provided the primers of the mRNAs of all DEGs, which will be useful for the validation experiment of this study. Results: FN1, MMP2, POSTN, COL3A1, TIMP3, FBN1, GJA1, TGFBI, EFEMP1 and ID1 were top ten hub genes identified from this study, and they may play a vital role in the development of IDD. Angiogenesis and integrin binging are crucial biological process and molecular function defined in this study, which are worthy of being intensely investigated.Conclusion: More studies on the top ten hub genes, the role of angiogenesis and integrin binding in IDD are urgently needed, which will benefit the prevention, screening, diagnosis and prognosis of IDD.

scholarly journals Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Meng Wang ◽  
Licheng Wang ◽  
Shusheng Wu ◽  
Dongsheng Zhou ◽  
Xianming Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Expression Profiles ◽  
Cyclin B1 ◽  
Ppi Network ◽  
Hub Genes ◽  
Altered Expression ◽  
Value Analysis ◽  
Kaplan Meier ◽  
Key Genes

Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα (TOP2A) predicted poor overall survival in HCC patients (all log-rank P<0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.

scholarly journals Bioinformatics analysis of different candidate genes involved in hepatocellular carcinoma induced by HepG2 cells or tumor cells of patients

2020 ◽  
Vol 48 (6) ◽  
pp. 030006052093211
Author(s):  
Xiang Zhang ◽  
Songna Yin ◽  
Ke Ma
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepg2 Cells ◽  
Liver Parenchyma ◽  
Preclinical Study ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Peroxisome Proliferator ◽  
Ppi Network ◽  
Peroxisome Proliferator Activated Receptor ◽  
Protein Protein Interaction

Objective Hepatocellular carcinoma (HCC) is a common cancer with a high mortality rate; the molecular mechanism involved in HCC remain unclear. We aimed to provide insight into HCC induced with HepG2 cells and identify genes and pathways associated with HCC, as well as potential therapeutic targets. Methods Dataset GSE72581 was downloaded from the Gene Expression Omnibus, including samples from mice injected in liver parenchyma with HepG2 cells, and from mice injected with cells from patient tumor explants. Differentially expressed genes (DEGs) between the two groups of mice were analyzed. Then, gene ontology and Kyoto Encyclopedia of Gene and Genomes pathway enrichment analyses were performed. The MCODE plug-in in Cytoscape was applied to create a protein–protein interaction (PPI) network of DEGs. Results We identified 1,405 DEGs (479 upregulated and 926 downregulated genes), which were enriched in complement and coagulation cascades, peroxisome proliferator-activated receptor signaling pathway, and extracellular matrix–receptor interaction. The top 4 modules and top 20 hub genes were identified from the PPI network, and associations with overall survival were determined using Kaplan–Meier analysis. Conclusion This preclinical study provided data on molecular targets in HCC that could be useful in the clinical treatment of HCC.

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