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

<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>

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Identification of the Key Genes and Pathways in Esophageal Carcinoma

Gastroenterology Research and Practice ◽

10.1155/2016/2968106 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Peng Su ◽

Shiwang Wen ◽

Yuefeng Zhang ◽

Yong Li ◽

Yanzhao Xu ◽

...

Keyword(s):

Cell Cycle ◽

Esophageal Carcinoma ◽

Gene Expression Omnibus ◽

Expression Level ◽

Ppi Network ◽

Protein Protein Interaction ◽

Go Enrichment ◽

Key Genes ◽

Microarray Datasets ◽

Upregulated Genes

Objective. Esophageal carcinoma (EC) is a frequently common malignancy of gastrointestinal cancer in the world. This study aims to screen key genes and pathways in EC and elucidate the mechanism of it.Methods. 5 microarray datasets of EC were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened by bioinformatics analysis. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network construction were performed to obtain the biological roles of DEGs in EC. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression level of DEGs in EC.Results. A total of 1955 genes were filtered as DEGs in EC. The upregulated genes were significantly enriched in cell cycle and the downregulated genes significantly enriched in Endocytosis. PPI network displayed CDK4 and CCT3 were hub proteins in the network. The expression level of 8 dysregulated DEGs including CDK4, CCT3, THSD4, SIM2, MYBL2, CENPF, CDCA3, and CDKN3 was validated in EC compared to adjacent nontumor tissues and the results were matched with the microarray analysis.Conclusion. The significantly DEGs including CDK4, CCT3, THSD4, and SIM2 may play key roles in tumorigenesis and development of EC involved in cell cycle and Endocytosis.

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Bioinformatics study on genes related to a high-risk postoperative recurrence of lung adenocarcinoma

Science Progress ◽

10.1177/00368504211018053 ◽

2021 ◽

Vol 104 (3) ◽

pp. 003685042110180

Author(s):

Xiao Lin ◽

Meng Zhou ◽

Zehong Xu ◽

Yusheng Chen ◽

Fan Lin

Keyword(s):

Cell Cycle ◽

Gene Ontology ◽

High Risk ◽

Lung Adenocarcinoma ◽

Candidate Genes ◽

Postoperative Recurrence ◽

High Expression ◽

Ppi Network ◽

Hub Genes ◽

Expression Levels

In this study, we aimed to screen out genes associated with a high risk of postoperative recurrence of lung adenocarcinoma and investigate the possible mechanisms of the involvement of these genes in the recurrence of lung adenocarcinoma. We identify Hub genes and verify the expression levels and prognostic roles of these genes. Datasets of GSE40791, GSE31210, and GSE30219 were obtained from the Gene Expression Omnibus database. Enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the screened candidate genes using the DAVID database. Then, we performed protein–protein interaction (PPI) network analysis through the database STRING. Hub genes were screened out using Cytoscape software, and their expression levels were determined by the GEPIA database. Finally, we assessed the relationships of Hub genes expression levels and the time of survival. Forty-five candidate genes related to a high-risk of lung adenocarcinoma recurrence were screened out. Gene ontology analysis showed that these genes were enriched in the mitotic spindle assembly checkpoint, mitotic sister chromosome segregation, G2/M-phase transition of the mitotic cell cycle, and ATP binding, etc. KEGG analysis showed that these genes were involved predominantly in the cell cycle, p53 signaling pathway, and oocyte meiosis. We screened out the top ten Hub genes related to high expression of lung adenocarcinoma from the PPI network. The high expression levels of eight genes (TOP2A, HMMR, MELK, MAD2L1, BUB1B, BUB1, RRM2, and CCNA2) were related to short recurrence-free survival and they can be used as biomarkers for high risk of lung adenocarcinoma recurrence. This study screened out eight genes associated with a high risk of lung adenocarcinoma recurrence, which might provide novel insights into researching the recurrence mechanisms of lung adenocarcinoma as well as into the selection of targets in the treatment of the disease.

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

BioMed Research International ◽

10.1155/2020/4505720 ◽

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.

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Hub Genes Related to the Pathogenesis and Progression of Colorectal Cancer and Adenoma by Integrative Bioinformatics Approaches

10.21203/rs.3.rs-637295/v1 ◽

2021 ◽

Author(s):

Yuxuan HUANG ◽

Ge CUI

Keyword(s):

Colorectal Cancer ◽

Cell Cycle ◽

Differentially Expressed Genes ◽

Protein Interaction ◽

Biological Networks ◽

Interaction Network ◽

Differentially Expressed ◽

Venn Diagram ◽

Hub Genes ◽

Protein Protein Interaction

Abstract Aims: To utilize the bioinformatics to analyze the differentially expressed genes (DEGs), interaction proteins, perform gene enrichment analysis, protein-protein interaction network (PPI) and map the hub genes between colorectal cancer(CRC) and colorectal adenocarcinomas(CA).Methods: We analyzed a microarray dataset (GSE32323 and GSE4183) from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in tumor tissues and non-cancerous tissues were identified using the dplyr and Venn diagram packages of the R Studio software. Functional annotation of the DEGs was performed using the Gene Ontology (GO) website. Pathway enrichment (KEGG) used the WebGestalt to analyze the data and R Studio to generate the graph. We constructed a protein–protein interaction (PPI) network of DEGs using STRING and Cytoscape software was used for visualization. Survival analysis of the hub genes and was performed using the online platform GEPIA to determine the prognostic value of the expression of hub genes in cell lines from CRC patients. The expression of molecules with prognostic values was validated on the UALCAN database. The expression of hub genes was examined using the Human Protein Atlas. Results: Applying the GEO2R analysis and R studio, we identified a total of 471 upregulated and 278 downregulated DEGs. By using the online database WebGestalt, we identified the most relevant biological networks involving DEGs with statistically significant differences in expression were mainly associated with biological processes involved in the cell proliferation, cell cycle transition, cell homeostasis and indicated the role of each DEGs in cell cycle regulation pathways. We found 10 hub genes with prognostic values were overexpressed in the CRC and CA samples.Conclusion: we found out ten hub genes and three core genes closely associated with the pathogenesis and prognosis of CRC and CA, which is of great significance for colorectal tumor early detection and prognosis evaluation.

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Bioinformatics Analysis of Potential Key Genes in Trastuzumab-Resistant Gastric Cancer

Disease Markers ◽

10.1155/2019/1372571 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Guangda Yang ◽

Liumeng Jian ◽

Xiangan Lin ◽

Aiyu Zhu ◽

Guohua Wen

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Prognostic Value ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Trastuzumab Resistance ◽

Hub Genes ◽

Her2 Positive ◽

Protein Protein Interaction ◽

Stem Cell Pluripotency

Background. This study was performed to identify genes related to acquired trastuzumab resistance in gastric cancer (GC) and to analyze their prognostic value. Methods. The gene expression profile GSE77346 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by using GEO2R. Functional and pathway enrichment was analyzed by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, and MCODE were then used to construct the protein-protein interaction (PPI) network and identify hub genes. Finally, the relationship between hub genes and overall survival (OS) was analyzed by using the online Kaplan-Meier plotter tool. Results. A total of 327 DEGs were screened and were mainly enriched in terms related to pathways in cancer, signaling pathways regulating stem cell pluripotency, HTLV-I infection, and ECM-receptor interactions. A PPI network was constructed, and 18 hub genes (including one upregulated gene and seventeen downregulated genes) were identified based on the degrees and MCODE scores of the PPI network. Finally, the expression of four hub genes (ERBB2, VIM, EGR1, and PSMB8) was found to be related to the prognosis of HER2-positive (HER2+) gastric cancer. However, the prognostic value of the other hub genes was controversial; interestingly, most of these genes were interferon- (IFN-) stimulated genes (ISGs). Conclusions. Overall, we propose that the four hub genes may be potential targets in trastuzumab-resistant gastric cancer and that ISGs may play a key role in promoting trastuzumab resistance in GC.

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ITGB2 as a Novel Biomarker Correlating With Prognosis and Immune Infiltrates in Ovarian Cancer

10.21203/rs.3.rs-263949/v1 ◽

2021 ◽

Author(s):

chanyuan li ◽

Ting Wan ◽

Ting Deng ◽

Junya Cao ◽

He Huang ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Protein Interaction ◽

Ppi Network ◽

Hub Genes ◽

Immune Infiltration ◽

Protein Protein Interaction ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Abstract Background: Epithelial ovarian cancer is nowadays one of the malignancies in women, this study aimed to identify novel biomarkers to predict prognosis and immunotherapy efficacy.Methods: The differentially expressed genes (DEGs) obtained from online database Gene Expression Omnibus (GEO)were screened via GEO2R and Venn diagram software, gene enrichment was analysed by Gene Ontology(GO) function and Kyoto Encyclopedia of Genes and Genomes(KEGG), then protein protein interaction(PPI)network and Cytoscape software were used to confirm the genes closely related to ovarian cancer. Survival analysis of hub genes were obtained from Kaplan–Meier plotter, with their differential expression in specimen validated by Gene Expression Profiling Interactive Analysis (GEPIA) and an integrated repository portal for tumor-immune system interactions (TISIDB). Finally, we used the Tumor Immune Estimation Resource 2.0 (TIMER2.0) and application Estimate the Proportion of Immune and Cancer cells (EPIC) to search the immune infiltration characteristics of the genes.Results: 355 DEGs between epithelial ovarian cancer and normal ovarian tissue were screened out. These DEGs were associated with extracellular exosome, bicellular tight junction and cell-cell junction, and remarkably enriched in molecules of cell adhesion and leukocyte transendothelial migration activity. Ten hub genes were identified via protein protein interaction (PPI) network: PTAFR, HLA-DRA, OAS2, OAS3, PTPN6, LYN, VAMP8, IRF6, ITGB2, CD47. Furthermore, the Kaplan–Meier plotter was conducted, overexpression of four genes was positively connected to poor prognosis in ovarian cancer:OAS2, OAS3, ITGB2, CD47,which were also correlated with immune infiltrates in ovarian cancer and had the highest degree of correlation with tumor associated macrophages (TAMs) infiltration, among which ITGB2 was highly correlated with TAMs infiltration level.Conclusion: ITGB2, OAS2, OAS3, and CD47 are upregulated with unfavorable prognosis in ovarian cancer, and ITGB2 may act as a novel prognostic biomarker with immune infiltration values.

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Identification of Pulpitis-Related Potential Biomarkers Using Bioinformatics Approach

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/1808361 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Bingchang Xin ◽

Yuxiang Lin ◽

He Tian ◽

Jia Song ◽

Liwei Zhang ◽

...

Keyword(s):

Inflammatory Reaction ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Hub Genes ◽

Pulp Tissue ◽

Network Nodes ◽

Protein Protein Interaction ◽

Pathogen Elimination ◽

Potential Biomarkers ◽

Geo Database

Inflammatory reaction of pulp tissue plays a role in the pathogen elimination and tissue repair. The evaluation of severity of pulpitis can serve an instructive function in therapeutic scheme. However, there are many limitations in the traditional evaluation methods for the severity of pulpitis. Based on the Gene Expression Omnibus (GEO) database, our study discovered 843 differentially expressed genes (DEGs) related to pulpitis. Afterwards, we constructed a protein-protein interaction (PPI) network of DEGs and used MCODE plugin to determine the key functional subset. Meanwhile, genes in the key functional subset were subjected to GO and KEGG enrichment analyses. The result showed that genes were mainly enriched in inflammatory reaction-related functions. Next, we screened out intersections of PPI network nodes and pulpitis-related genes. Then, 20 genes were obtained as seed genes. In the PPI network, 50 genes that had the highest correlation with seed genes were screened out using random walk with restart (RWR). Furthermore, 4 pulpitis-related hub genes were obtained from the intersection of the top 50 genes and genes in the key functional subset. Finally, GeneMANIA was utilized to predict genes coexpressed with hub genes, and expression levels of the 4 hub genes in normal and pulpitis groups were analyzed based on GEO data. The result demonstrated that the 4 hub genes were mainly coexpressed with chemokine-related genes and were remarkably upregulated in the pulpitis group. In short, we eventually determined 4 potential biomarkers of pulpitis.

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Cell Adhesion-Related Molecules Play a Key Role in Renal Cancer Progression by Multinetwork Analysis

BioMed Research International ◽

10.1155/2019/2325765 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Anbang Wang ◽

Ming Chen ◽

Hui Wang ◽

Jinming Huang ◽

Yi Bao ◽

...

Keyword(s):

Cell Adhesion ◽

Cancer Progression ◽

Kegg Pathway ◽

Ppi Network ◽

Hub Genes ◽

Genetic Characteristics ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Underlying Mechanisms ◽

Upregulated Genes

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system. The study aimed to identify genetic characteristics and reveal the underlying mechanisms in RCC. GSE53757, GSE46699, and TCGA KIRC database (n = 897) were analyzed to screen differentially expressed genes (DEGs) in RCC. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by the analysis of the protein-protein interaction (PPI) network of the DEGs by Cytoscape software. In all, 834 DEGs were identified in RCC, including 416 upregulated genes and 418 downregulated genes. The top 10 hub genes, VEGFA, EGFR, EGF, CD44, CD86, FN1, ITGAM, ITGB2, TLR2, and PTPRC, were identified from the PPI network according to the core degree. The following subnetwork revealed that these significant modules were enriched in positive regulation of response to external stimulus, regulation of leukocyte-mediated immunity, and regulation of exocytosis. The expressions of these hub genes were also validated using qRT-PCR and IHC in Changzheng RCC database (n = 160). We especially found that half of the top ten hub genes were cell adhesion-related molecules, which were associated with RCC progression and poor prognosis. In conclusion, these hub genes, particularly cell adhesion-related molecules, could be used as prognostic biomarkers and potential therapeutic targets for RCC.

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MMP1 and MMP9 are potential prognostic biomarkers and targets for uveal melanoma

10.21203/rs.3.rs-354059/v1 ◽

2021 ◽

Author(s):

Tianyu Wang ◽

Yuanyuan Zhang ◽

Jianhao Bai ◽

Yawen Xue ◽

Qing Peng

Keyword(s):

Gene Expression ◽

Uveal Melanoma ◽

Protein Interaction ◽

Disease Free Survival ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Hub Genes ◽

Protein Protein Interaction ◽

Normal Tissues

Abstract Background: Uveal melanoma (UVM) is the leading cause of eye-related mortality worldwide. This study aimed to explore the expression and prognostic value of matrix metalloproteinases (MMPs) in UVM.Methods: Gene expression levels were obtained from the Gene Expression Omnibus (GEO) and Oncomine databases. Functional and pathway enrichment analyses were performed using the Metascape database. GeneMANIA was then applied to construct a protein-protein interaction network and identify the hub genes. Moreover, overall (OS) and disease-free survival (DFS) analysis for the hub genes was performed using the UALCAN and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Furthermore, TRRUST was used to predict the targets of the MMPs. Results: Our results revealed that the transcriptional levels of MMP1, MMP9, MMP10, MMP11, MMP13, MMP14, and MMP17 were upregulated in UVM tissues compared to normal tissues. A protein-protein interaction (PPI) network was constructed, and the top 50 hub genes were identified. The functions of MMPs and their neighboring proteins are mainly associated with ECM-receptor interaction, proteoglycans in cancer, the IL-17 signaling pathway, and microRNAs in cancer. Among the MMPs, MMP1/2/9/11/14/15/16/17/24 played significant roles in the progression of UVM from stage 3 to stage 4. We also found that the expression of MMP1, MMP 2, MMP 9, and MMP 16 was positively correlated with OS and DFS in patients with UVM. Additionally, 18 transcription factors associated with nine MMPs were identified.Conclusions: The results of this study may provide potential biomarkers and targets for UVM. However, further studies are required to confirm these results.

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Bioinformatics Analysis of C3 and CXCR4 act as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma (ccRCC)

10.21203/rs.3.rs-319333/v1 ◽

2021 ◽

Author(s):

Jing Quan ◽

YUCHEN BAI ◽

YunBei Yang ◽

ErLei Han ◽

Hong Bai ◽

...

Keyword(s):

Candidate Genes ◽

Interaction Network ◽

Gene Expression Omnibus ◽

High Expression ◽

Individual Treatment ◽

Hub Genes ◽

Cell Renal Cell Carcinoma ◽

Protein Protein Interaction ◽

Microarray Datasets ◽

Upregulated Genes

Abstract Background: The molecular pathogenesis of ccRCC was still unknown. Hence, the ccRCC-associated genes needs to explored.Methods: Three ccRCC expression microarray datasets (GSE14762, GSE66270 and GSE53757) downloaded from gene expression omnibus (GEO) database. The distinguish of expressed genes (DEGs) between ccRCC and normal tissue was discuss and explored. the function of our DEGs was analyzed by Gene Ontology (GO) ,Kyoto Encyclopedia of Genes and Genomes (KEGG) .Then the protein‑protein interaction network (PPI) was established in order to screen the hub genes. Then the expressions of hub genes were identified by oncomine database.The prognostic values of hub genes were analyzed by GEPIA database in ccRCC patients. Result: A total of 137 DREs were analyzed, which including 63 upregulated genes and 74 downregulated genes. According to our result,137 DREs were mainly enriched in 82 functional terms and 24 pathways. 14 highest-scoring genes were screened as hub gene in the PPI network which including 12 upregulated candidate genes and 2 downregulated candidate genes. The result reveals that patients with higher C3 expression related to poor OS, while patients with high expression of CTSS and TLR3 related to better OS. Patients with high C3 and CXCR4 expression had a poor DFS, while ccRCC patients with high expression of TLR3 had better DFS. At last, C3 and CXCR4 were selected to detect the prognosis of patients with ccRCC.Conclusion: The result identified the C3 and CXCR4 as candidate biomarkers and potential therapeutic targets in the molecular mechanism and individual treatment of ccRCC.

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