Identification of Key Genes and Pathways in Colorectal Cancer by Integrated Bioinformatics Analysis

Gene Expression Omnibus ◽

Diagnosis And Treatment ◽

Receptor Interaction ◽

Mineral Absorption ◽

Visual Interactions

Abstract PurposeIn order to understand the mechanism of colorectal cancer occurrence and development, we screened related core genes and provided new targets for clinical diagnosis and treatment of colorectal cancer.MethodsWe downloaded CRC-associated gene expression profile of GSE110233 from Gene Expression Omnibus (GEO) dataset. There were 26 samples in this dataset, all the differentially expressed genes (DEGs) with p<0.05 and fold change ≥1 or ≤-1 were identified. Gene ontology (GO) and "Kyoto Encyclopedia of Genes and Genomes" (KEGG) were used to search for these DEG enrichment methods. In addition, the protein-protein interaction (PPI) network was also used to construct visual interactions between proteins. At last, we used GEPIA to conduct the survival analysis 4 down-regulation and 8 up-regulation genes for clarify the potential effects on CRC.ResultsA total of 866 differentially expressed genes were obtained, including 360 up-regulated genes and 506 down-regulated genes. These genes were involve in Cell proliferation; Extracellular exosome; Protein binding; Chemokine activity. Genes were mainly involved in the KEGG pathway termed Cell cycle; PI3K-Akt signaling pathway; Mineral absorption; MicroRNAs in cancer; Cytokine-cytokine receptor interaction. We finally found 12 hubgenes by PPI connective degree whom named PRKACB, FGFR2, FGFR3, CKB, TIMP1, CCNA2, CCNB1, CDC20, CDC6, CCND1, CDK4 and CDK1.ConclusionBioinformatics is helpful for comprehensive and in-depth study of the occurrence and development mechanism of diseases, to screen possible core targets, and to provide a reliable basis for clinical diagnosis and treatment of colorectal cancer.

AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.1773 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 1037.2-1038

Author(s):

X. Sun ◽

S. X. Zhang ◽

S. Song ◽

T. Kong ◽

C. Zheng ◽

...

Keyword(s):

Gene Expression ◽

Signaling Pathways ◽

Enrichment Analysis ◽

Shanxi Province ◽

Receptor Interaction ◽

Term Therapy ◽

Pathway Enrichment Analysis ◽

Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

Gene expression and DNA methylation analyses suggest that two immune related genes are prognostic factors of colorectal cancer

BMC Medical Genomics ◽

10.1186/s12920-021-00966-3 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Xiao-Liang Xing ◽

Zhi-Yong Yao ◽

Chaoqun Xing ◽

Zhi Huang ◽

Jing Peng ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Risk Assessment ◽

Dna Methylation ◽

Risk Score ◽

Immune Cells ◽

Assessment Model ◽

Risk Assessment Model

Abstract Background Colorectal cancer (CRC) is the second most prevalent cancer, as it accounts for approximately 10% of all annually diagnosed cancers. Studies have indicated that DNA methylation is involved in cancer genesis. The purpose of this study was to investigate the relationships among DNA methylation, gene expression and the tumor-immune microenvironment of CRC, and finally, to identify potential key genes related to immune cell infiltration in CRC. Methods In the present study, we used the ChAMP and DESeq2 packages, correlation analyses, and Cox regression analyses to identify immune-related differentially expressed genes (IR-DEGs) that were correlated with aberrant methylation and to construct a risk assessment model. Results Finally, we found that HSPA1A expression and CCRL2 expression were positively and negatively associated with the risk score of CRC, respectively. Patients in the high-risk group were more positively correlated with some types of tumor-infiltrating immune cells, whereas they were negatively correlated with other tumor-infiltrating immune cells. After the patients were regrouped according to the median risk score, we could more effectively distinguish them based on survival outcome, clinicopathological characteristics, specific tumor-immune infiltration status and highly expressed immune-related biomarkers. Conclusion This study suggested that the risk assessment model constructed by pairing immune-related differentially expressed genes correlated with aberrant DNA methylation could predict the outcome of CRC patients and might help to identify those patients who could benefit from antitumor immunotherapy.

Diabetes-specific modulation of peripheral blood gene expression signatures in colorectal cancer

Current Molecular Medicine ◽

10.2174/1566524020666200504084626 ◽

2020 ◽

Vol 20 ◽

Author(s):

Zsuzsanna Molnár ◽

Zsófia Bánlaki ◽

Anikó Somogyi ◽

Zoltán Herold ◽

Magdolna Herold ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Peripheral Blood ◽

Expression Patterns ◽

Enrichment Analysis ◽

Colorectal Carcinogenesis ◽

Blood Leukocytes ◽

Significant Expression

Background: Type 2 diabetes (T2DM) and colorectal cancer (CRC) are both known to modulate gene expression patterns in peripheral blood leukocytes (PBLs). Objective : As T2DM has been shown to increase the incidence of CRC, we were prompted to check whether diabetes affects mRNA signatures in PBLs isolated from CRC patients. Methods : 22 patients were recruited to the study and classified into four cohorts (healthy controls; T2DM; CRC; CRC and T2DM). Relative expression levels of 573 cell signaling gene transcripts were determined by reverse transcription real-time PCR assays run on low-density OpenArray platforms. Enrichment analysis was performed with the g:GOSt profiling tool to order differentially expressed genes into functional pathways. Results : 49 genes were found to be significantly up- or downregulated in tumorous diabetic individuals as compared to tumor-free diabetic controls, while 11 transcripts were differentially regulated in patients with CRC versus healthy, tumor-free and non-diabetic controls. Importantly, these gene sets were completely distinct, implying that diabetes exerts profound influence on the transcription of signaling genes in CRC. The top 5 genes showing most significant expression differences in both contexts were PCK2, MAPK9, CCND1, HMBS, TLR3 (p≤ 0.0040) and CREBBP, PPIA, NFKBIL1, MDM2 and SELPLG (p0.0121), respectively. Functional analysis revealed that most significantly affected pathways were cytokine, interleukin and PI3K/Akt/mTOR signaling cascades as well as mitotic regulation. Conclusions : We propose that differentially expressed genes listed above might be potential biomarkers of CRC and should be studied further on larger patient groups. Diabetes might promote colorectal carcinogenesis by impairing signaling pathways in PBLs.

Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

10.21203/rs.2.17512/v1 ◽

2019 ◽

Author(s):

ChenChen Yang ◽

Aifeng Gong

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Kegg Pathway ◽

Gene Expression Omnibus ◽

Mrna Levels ◽

Pcr Assay ◽

Microarray Datasets ◽

Geo Database

Abstract Background Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis.Methods Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1and MMP1 in GC tissues and cell lines, respectively.Results We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1.Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines.Conclusion In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

Differential Analysis of Expressed Genes in Diabetic Nephropathy Based on Bioinformatics Technology

10.21203/rs.3.rs-770285/v1 ◽

2021 ◽

Author(s):

Yu Liu ◽

Jundong Wang ◽

wencheng Chi ◽

Jing Xie ◽

LaiKuan Teh ◽

...

Keyword(s):

Gene Expression ◽

Diabetic Nephropathy ◽

Signaling Pathway ◽

Biological Effects ◽

Cytokine Receptor ◽

Receptor Interaction ◽

Differential Analysis ◽

Core Genes

Abstract Objective: Bioinformatics technology was used in this study to analyze the expression data of patients with diabetic nephropathy (DN) and normal subjects from the microarray. The purpose of this study was to screen the differentially expressed genes in DN and to explore the pathogenesis and potential therapeutic targets of DN. Methods: The data of gene expression in the gse142153 gene chip was downloaded from the gene expression database (GEO). The up-regulated and down-regulated expressed genes were analyzed by R language. The core genes of differentially expressed genes were analyzed by string database, Cytoscape software and its plug-in. The differentially expressed genes were analyzed by gene ontology and Kyoto Encyclopedia of genes and genomes. Results: A total of 112 differentially expressed genes were screened, including 50 down-regulated genes and 62 up-regulated genes. There are 10 up-regulated core genes including CXCL8, MMP9, IL1B, IL6, IL10, CXCL2, CCL20, ATF3, CXCL3, F3. Their biological effects are mainly concentrated in the IL-17 signaling pathway, rheumatoid arthritis, viral protein interaction with cytokine and cytokine receptor, Amoebiasis, TNF signaling pathway, Legionellosis, Cytokine-cytokine receptor interaction, Lipid, and atherosclerosis, Malaria, NOD-like receptor signaling pathway, etc. Conclusion: Analysis of differentially expressed genes and core genes enhanced the understanding of the pathogenesis of DN and provided a potential train of thought for the treatment of DN.

Investigation of Aberrantly Methylated Differentially Expressed Genes in Pancreatic Cancer by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-132250/v1 ◽

2021 ◽

Author(s):

Cailin xue ◽

Peng gao ◽

Xudong zhang ◽

Xiaohan cui ◽

Lei jin ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Signaling Pathways ◽

Dna Sequences ◽

Receptor Interaction ◽

Biological Processes ◽

Ppi Network ◽

Hub Genes

Abstract Background: Abnormal methylation of DNA sequences plays an important role in the development and progression of pancreatic cancer (PC). The purpose of this study was to identify abnormal methylation genes and related signaling pathways in PC by comprehensive bioinformatic analysis of three datasets in the Gene Expression Omnibus (GEO). Methods: Datasets of gene expression microarrays (GSE91035, GSE15471) and gene methylation microarrays (GSE37480) were downloaded from the GEO database. Aberrantly methylated-differentially expressed genes (DEGs) were analysis by GEO2R software. GO and KEGG enrichment analyses of selected genes were performed using DAVID database. A protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. Core module analysis was performed by Mcode in Cytoscape. Hub genes were obtained by CytoHubba app. in Cytoscape software. Results: A total of 267 hypomethylation-high expression genes, which were enriched in biological processes of cell adhesion, biological adhesion and regulation of signaling were obtained. KEGG pathway enrichment showed ECM-receptor interaction, Focal adhesion and PI3K-Akt signaling pathway. The top 5 hub genes of PPI network were EZH2, CCNA2, CDC20, KIF11, UBE2C. As for hypermethylation-low expression genes, 202 genes were identified, which were enriched in biological processes of cellular amino acid biosynthesis process and positive regulation of PI3K activity, etc. The pathways enriched were the pancreatic secretion and biosynthesis of amino acids pathways, etc. The five significant hub genes were DLG3, GPT2, PLCB1, CXCL12 and GNG7. In addition, five genes, including CCNA2, KIF11, UBE2C, PLCB1 and GNG7, significantly associated with patient's prognosis were also identified. Conclusion: Novel genes with abnormal expression were identified, which will help us further understand the molecular mechanism and related signaling pathways of PC, and these aberrant genes could possibly serve as biomarkers for precise diagnosis and treatment of PC.

Identification of key methylation differentially expressed genes in posterior fossa ependymoma based on epigenomic and transcriptome analysis

Journal of Translational Medicine ◽

10.1186/s12967-021-02834-1 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Guanyi Wang ◽

Yibin Jia ◽

Yuqin Ye ◽

Enming Kang ◽

Huijun Chen ◽

...

Keyword(s):

Gene Expression ◽

Posterior Fossa ◽

Poor Prognosis ◽

Cpg Island ◽

Adaptive Immune Response ◽

R Package ◽

Gene Expression Omnibus ◽

Hub Genes

Abstract Background Posterior fossa ependymoma (EPN-PF) can be classified into Group A posterior fossa ependymoma (EPN-PFA) and Group B posterior fossa ependymoma (EPN-PFB) according to DNA CpG island methylation profile status and gene expression. EPN-PFA usually occurs in children younger than 5 years and has a poor prognosis. Methods Using epigenome and transcriptome microarray data, a multi-component weighted gene co-expression network analysis (WGCNA) was used to systematically identify the hub genes of EPN-PF. We downloaded two microarray datasets (GSE66354 and GSE114523) from the Gene Expression Omnibus (GEO) database. The Limma R package was used to identify differentially expressed genes (DEGs), and ChAMP R was used to analyze the differential methylation genes (DMGs) between EPN-PFA and EPN-PFB. GO and KEGG enrichment analyses were performed using the Metascape database. Results GO analysis showed that enriched genes were significantly enriched in the extracellular matrix organization, adaptive immune response, membrane raft, focal adhesion, NF-kappa B pathway, and axon guidance, as suggested by KEGG analysis. Through WGCNA, we found that MEblue had a significant correlation with EPN-PF (R = 0.69, P = 1 × 10–08) and selected the 180 hub genes in the blue module. By comparing the DEGs, DMGs, and hub genes in the co-expression network, we identified five hypermethylated, lower expressed genes in EPN-PFA (ATP4B, CCDC151, DMKN, SCN4B, and TUBA4B), and three of them were confirmed by IHC. Conclusion ssGSEA and GSVA analysis indicated that these five hub genes could lead to poor prognosis by inducing hypoxia, PI3K-Akt-mTOR, and TNFα-NFKB pathways. Further study of these dysmethylated hub genes in EPN-PF and the pathways they participate in may provides new ideas for EPN-PF treatment.

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Experimental Biology and Medicine ◽

10.1177/15353702211008809 ◽

2021 ◽

pp. 153537022110088

Author(s):

Jinyi Tian ◽

Yizhou Bai ◽

Anyang Liu ◽

Bin Luo

Keyword(s):

Gene Expression ◽

Thyroid Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Thyroid Tissue ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Tissue Samples

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

INTEGRATED BIOINFORMATICS ANALYSIS OF DIFFERENTIALLY EXPRESSED GENES (DEGS) OF ALZHEIMER'S DISEASE (AD) DATASETS FROM GENE EXPRESSION OMNIBUS (GEO)

Alzheimer s & Dementia ◽

10.1016/j.jalz.2017.06.1270 ◽

2017 ◽

Vol 13 (7) ◽

pp. P953 ◽

Cited By ~ 3

Author(s):

Ashwani Kumar ◽

Ankush Bansal

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Bioinformatics Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed

High SLC7A11 Expression Is Correlated With Poor Prognosis and Associated With Ferroptosis in Ovarian Cancer

10.21203/rs.3.rs-233820/v1 ◽

2021 ◽

Author(s):

Mengqi Deng ◽

Yanqin Zhang ◽

Xiangyu Chang ◽

Di Wu ◽

Chunyu Xu ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Protein Expression ◽

Poor Prognosis ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Normal Tissues ◽

And Control

Abstract The current treatments of ovarian cancer (OC) do not yield satisfactory outcomes. Hence, it is necessary to find new treatment targets for OC. In this study, a comprehensive bioinformatic analysis was conducted to identify differentially expressed genes (DEGs) between OC and control tissues. Five datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by comparing gene expression between OC and control tissues. Module analysis of DEGs was performed on the STRING database and GEPIA. Kaplan Meier plotter and GEPIA database analysis the overall survival. Finally, SLC7A11 was found to be is the hubgene. And we confirm that the protein expression of SLC7A11 was increased in OC tissues. Analysis of a variety of tumor gene databases showed that SLC7A11 gene regulated the processes of OC. The low mutation rate of the gene (which were of amplified type) and high mRNA expression were associated with poor prognosis of OC patients.Using erastin-treated ovarian cancer (OC) cell lines, we examined the relationship between ferroptosis and OC. Results showed that OC tissues contained higher malondialdehyde (MDA) levels than normal tissues. Unlike normal ovarian epithelial cells which are not sensitive to erastin, the OC cell line, ES-2 is very sensitive to erastin. Here, we found that ferrostatin-1 treatment increased levels of reactive oxygen species (ROS), malondialdehyde, and SLC7A11 protein expression. These results provide an important theoretical basis for further studies into the role of SLC7A11, the effective biomarker and potential drug target, in the occurrence and development of OC.