Statistical Analysis of Microarray Data to Identify Key Gene Expression Patterns in Primary Hyperoxaluria

Background: Non-obstructive azoospermia (NOA) is the most severe form of male infertility. Currently, the molecular mechanisms underlying NOA pathology have not yet been elucidated. Hence, elucidation of the mechanisms of NOA and exploration of potential biomarkers are essential for accurate diagnosis and treatment of this disease. In the present study, we aimed to screen for biomarkers and pathways involved in NOA and reveal their potential molecular mechanisms using integrated bioinformatics.Methods: We downloaded two gene expression datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in NOA and matched the control group tissues were identified using the limma package in R software. Subsequently, Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), protein-protein interaction (PPI) network, gene-microRNAs network, and transcription factor (TF)-hub genes regulatory network analyses were performed to identify hub genes and associated pathways. Finally, we conducted immune infiltration analysis using CIBERSORT to evaluate the relationship between the hub genes and the NOA immune infiltration levels.Results: We identified 698 common DEGs, including 87 commonly upregulated and 611 commonly downregulated genes in the two datasets. GO analysis indicated that the most significantly enriched gene was protein polyglycylation, and KEGG pathway analysis revealed that the DEGs were most significantly enriched in taste transduction and pancreatic secretion signaling pathways. GSEA showed that DEGs affected the biological functions of the ribosome, focaladhesion, and protein_expor. We further identified the top 31 hub genes from the PPI network, and friends analysis of hub genes in the PPI network showed that NR4A2 had the highest score. In addition, immune infiltration analysis found that CD8+ T cells and plasma cells were significantly correlated with ODF3 expression, whereas naive B cells, plasma cells, monocytes, M2 macrophages, and resting mast cells showed significant variation in the NR4A2 gene expression group, and there were differences in T cell regulatory immune cell infiltration in the FOS gene expression groups.Conclusion: The present study successfully constructed a regulatory network of DEGs between NOA and normal controls and screened three hub genes using integrative bioinformatics analysis. In addition, our results suggest that functional changes in several immune cells in the immune microenvironment may play an important role in spermatogenesis. Our results provide a novel understanding of the molecular mechanisms of NOA and offer potential biomarkers for its diagnosis and treatment.

We aimed to give a systematic hypothesis on the functions of exercise on circulating monocytes by identifying a discrete set of genes in circulating monocytes that were altered by exercise. The microarray expression profile of GSE51835 was downloaded from gene expression omnibus (GEO) database for the identification of differentially expressed genes (DEGs) using limma and affy packages in R language. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for DEGs, followed by the construction of co-expression network and protein-protein interaction (PPI) network. The top 10 nodes in PPI network were screened, and subnetwork was constructed for the key genes identification. Totally, 35 DEGs, including 2 upregulated genes and 33 downregulated genes, were identified. The enriched GO terms were mainly linked to immune response and defence response, and the enriched KEGG pathways were mainly associated with natural killer cell-mediated cytotoxicity and graft-versus-host disease. Dual-specificity phosphatase 2 (DUSP2) was identified as a key node in the co-expression network. In the PPI network, CD247 module (CD247), chemokine (C-X-C motif) receptor 4 (CXCR4), granzyme B (GZMB) and perforin 1 (PRF1) were identified as key nodes. An important interaction, GZMB/PRF1, was detected. Five key genes, including DUSP2, CD247, CXCR4, GZMB and PRF1, and an interaction of GZMB/PRF1, were significant factors in the immune processes of circulating monocytes, which might be regulated by brief exercises, leading to the enhancement of immune function.

Although the main treatments of inflammatory bowel disease (IBD) aim at the induction and maintenance of clinical remission, several studies have demonstrated inflammation still present in clinical remission. The goal of this study was to analyze the gene expression profiles between the pediatric IBD and control samples, aiming to further investigate the underlying therapeutic target in remission. Gene expression profiles data of GSE33943 were downloaded from Gene Expression Omnibus, which included 45 pediatric IBD samples and 13 control samples. The differentially expressed genes (DEGs) between IBD and control samples were identified by LIMMA package in R and the function of DEGs were predicted by Gene Ontology and KEGG pathway enrichment analyses using GeneAnswer package. Furthermore, a protein-protein interaction (PPI) network was constructed through the STRING database and functional module was obtained using ClusterONE. A total of 224 DEGs were screened between IBD and control samples. These DEGs (e.g. up-regulated FAS and down-regulated CCL5) were mainly enriched in cytokine‒cytokine receptor interaction and chemokine signaling pathway. In addition, some hub genes (e.g. up-regulated PSMA2 and PSMA6) were obtained through PPI network and functional module. These two genes were involved in Proteasome alpha-subunit and conserved site by functional module analysis. The immune and Proteasome mechanisms are still active during remission and FAS, PMSF6 and PMSF2 may be underlying targets for therapy of this disease.

Tuberculosis (TB) is the world's most prevalently infectious disease. Molecular mechanisms behind tuberculosis remain unknown. microRNA (miRNA) is involved in a wide variety of diseases. To validate the significant genes and miRNAs in the current sample, two messenger RNA (mRNA) expression profile datasets and three miRNA expression profile datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed (DE) genes (DEGs) and miRNAs (DE miRNAs) between healthy and TB patients were filtered out. Enrichment analysis was executed, and a protein-protein interaction (PPI) network was developed to understand the enrich pathways and hub genes of TB. Additionally, the target genes of miRNA were predicted and overlapping target genes were identified. We studied a total of 181 DEGs (135 downregulated and 46 upregulated genes) and two DE miRNAs (2 downregulated miRNAs) from two gene profile datasets and three miRNA profile datasets, respectively. 10 hub genes were defined based on high degree of connectivity. A PPI network's top module was constructed. The 23 DEGs identified have a significant relationship with miRNAs. 25 critically significant Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were discovered. The detailed study revealed that, in tuberculosis, the DE miRNA and DEGs form an interaction network. The identification of novel target genes and main pathways would aid with our understanding of miRNA's function in tuberculosis progression.

Our study aimed to investigate the gene expression at different myocardial infarction (MI) phases and to understand the development mechanisms of congestive heart failure (CHF) after MI. Dataset GSE1957 including 24 samples of rat left ventricles at 1-day post MI or sham operation and 7-day post MI or sham operation was downloaded from Gene Expression Ominibus. The data were normalized with an affyPLM package and differentially expressed genes (DEGs) were identified with a Linear Models for Microarray Data package. Heat maps of the DEGs were constructed using Cluster 3.0. GO (Gene Ontology) enrichment analysis of the DEGs was performed in Database for Annotation, Visualization, and Integrated Discovery. A protein-protein interaction (PPI) network was constructed by Biomolecular Interaction Network Database and visualized by Cytoscape, and a subnetwork was analyzed using plugin ClusterONE in Cytoscape. A total of 5 DEGs at 1-day post-MI, 5 DEGs at 7-day post-MI, and 7 DEGs between the MI and sham groups at 1-day and 7-day post-MI were identified. For the GO category analysis, DEGs at 1-day post-MI were enriched in response to cytokine stimulus. DEGs at 7-day post-MI were enriched in response to inorganic substance and chemical homeostasis. DEGs between 1-day and 7-day post-MI including CDK2 and CDC20 were significantly enriched in mitosis. CDK2, ANXA1, CDC20, and AQP2 were included in the PPI network, and CDK2 was the only DEG included in the subnetwork. In conclusion, the induction of DEGs at 7-day post-MI might participate in the response to a hormone and endogenous stimulus to regulate the development of CHF after MI.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major respiratory disorder that is characterized by persistent airflow limitation and an abnormal inflammation response to noxious particles, such as cigarette smoke and environmental pollution. COPD is a leading cause of mortality, causing a significant economic and social burden in many countries worldwide. Umbelliferone, which is a coumarin derivative, has been shown to possess anti-inflammatory and antioxidant properties, which may be useful in alleviating COPD. METHODS: An integrative bioinformatics approach was adopted to analyze gene expression datasets from gene expression omnibus (GSE275503 for bulk RNA sequencing and GSE183974 for single-cell RNA sequencing). Differentially expressed genes (DEGs) were identified and overlapped with COPD-associated genes from GeneCards. Common genes were analyzed using the STRING database to construct a protein-protein interactions (PPI) network, and further processed in Cytoscape using the “cytoHubba” plug-in to identify the top 10 hub genes. Molecular docking of these genes, along with three key genes from single-cell analysis, was performed using AutoDock vina in SAMSON software. A molecular dynamic (MD) simulation study was carried out using Desmond and Schrodinger software. The MD simulations were performed to investigate the stability and dynamic behavior of the UMB with 6E3K and 1S9V over time 200 ns. RESULTS: From bulk and single-cell RNA sequencing analyses, 50 and 1053 DEGs were extracted, respectively, with the cut-off value of LogFC > 1 and [Formula: see text]-value < 0.05. A total number of 562 overlapping genes were identified between the DEGs and COPD-associated genes from GeneCards. The top 10 hub genes from the PPI network were ITGAM, CCL2, IFN-[Formula: see text], CCL5, FN1, IL-1B, BCL2, CDH1, IL-1A and CXCL8. Among these, molecular docking revealed the highest binding affinities for IFN-[Formula: see text] (PDB ID: 6E3K, −7.4 kcal/mol), ITGAM (PDB ID: 1NA5, −6.6 kcal/mol), IL-1B (PDB ID: 8C3U, −6.2 kcal/mol), FN1 (PDB ID: 3M7P, −6.1 kcal/mol) and BCL2 (PDB ID: 6YLD, −5.9 kcal/mol). Additionally, single-cell RNA analysis identified HLA-DQA2 (PDB ID: 1H15, −2.7 kcal/mol), HLA-DRB5 (PDB ID: 1S9V, −4.9 kcal/mol) and S100A9 (PDB ID: 6ZDY, −5.3 kcal/mol) as the top three genes. An MD simulation study evaluated protein-ligand stability, binding dynamics and conformational changes. Root mean square deviation (RMSD) and Root mean square fluctuation (RMSF) analyses identified key interacting residues involved in hydrogen bonding, hydrophobic interactions and water bridges. The 6E3K complex exhibited protein backbone RMSD of 5−7 Å, ligand RMSD of 2−5 Å and residue fluctuations <3 Å. The 1S9V complex showed protein RMSD of 2.85 Å, ligand RMSD of 1.95−2.30 Å and residue fluctuations around 1.25−3.25 Å. CONCLUSION: The top 10 Genes from the PPI network and the top 3 genes from the single-cell RNA analysis were associated with COPD-related pathways. Molecular docking with the UMB showed strong binding affinities, indicating its potential to inhibit these pathways and serve as a therapeutic option for COPD. The MD simulation confirmed stable binding, key interactions and structural flexibility.

Improved insight into the molecular characteristics of hepatocellular carcinoma (HCC) is required to predict prognosis and to develop a new rationale for targeted therapeutic strategy. Bioinformatics methods, including functional enrichment and network analysis combined with survival analysis, are required to process a large volume of data to obtain further information on differentially expressed genes (DEGs). The RNA sequencing data related to HCC in The Cancer Genome Atlas (TCGA) database were analyzed to screen DEGs, which were separately submitted to perform gene enrichment analysis to identify gene sets and signaling pathways, and to construct a protein-protein interaction (PPI) network. Subsequently, hub genes were selected by the core level in the network, and the top hub genes were focused on gene expression analysis and survival analysis. A total of 610 DEGs were identified, including 444 upregulated and 166 downregulated genes. The upregulated DEGs were significantly enriched in the Gene Ontology analysis (GO): Cell division and in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway: Cell cycle, whereas the downregulated DEGs were enriched in GO: Negative regulation of growth and in the KEGG pathway: Retinol metabolism, with significant differences. Cyclin-dependent kinase (CDK)1 was selected as the top hub gene by the PPI network, which exhibited a similar expression trend with the data from the Gene Expression Omnibus (GEO) database. Survival analysis revealed a significantly negative correlation between CDK1 expression level and overall survival in the TCGA group (P<0.01) and the GEO group (P<0.01). Therefore, high-throughput TCGA data analysis appears to be an effective method for screening tumor molecular markers, and high expression of CDK1 is a prognostic factor for HCC.

In the present study, gene expression profiles of patients with dilated cardiomyopathy (DCM) were re-analyzed with bioinformatics tools to investigate the molecular mechanisms underlying DCM. Gene expression dataset GSE3585 was downloaded from Gene Expression Omnibus, which included seven heart biopsy samples obtained from patients with DCM and five healthy controls. Differential analysis was performed using a Limma package in R to screen for differentially expressed genes (DEGs). Functional enrichment analysis was subsequently conducted for DEGs using the Database for Annotation, Visualization and Integration Discovery. A protein-protein interaction (PPI) network was constructed using information from Search Tool for the Retrieval of Interacting Genes software. A total of 89 DEGs were identified in the patients with DCM, including 67 upregulated and 22 downregulated genes. Functional enrichment analysis demonstrated that the downregulated genes predominantly encoded chromosomal proteins and transport-related proteins, which were significantly associated with the biological processes of ‘nucleosome assembly’, ‘chromatin assembly’, ‘protein-DNA complex assembly’, ‘nucleosome organization’ and ‘DNA packaging’ (H1 histone family member 0, histone cluster 1 H1c, histone cluster 1 H2bd and H2A histone family member Z). The upregulated genes detected in the present study encoded secreted proteins or phosphotransferase, which were associated with biological processes including ‘cell adhesion’ [connective tissue growth factor (CTGF)], ‘skeletal system development’ [CTGF and insulin-like growth factor binding protein 3 (IGFBP3)], ‘muscle organ development’ (SMAD7) and ‘regulation of cell migration’ [SMAD7, IGFBP3 and insulin receptor (INSR)]. Notably, signal transducer and activator of transcription 3, SMAD7, INSR, CTGF, exportin 1, IGFBP3 and phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha were hub nodes with the higher degree in the PPI network. Therefore, the results of the present study suggested that DEGs may alter the biological processes of ‘nucleosome formation’, ‘cell adhesion’, ‘skeletal system development’, ‘muscle organ development’ and ‘regulation of cell migration’ in the development of DCM.

ObjectiveTo investigate the gene-expression profile of chronic obstructive pulmonary disease (COPD) patients and explore the possible therapeutic targets.MethodsThe microarray raw dataset GSE29133, including three COPD samples and three normal samples, was obtained from Gene Expression Omnibus. After data preprocessing with the Affy package, Student’s t-test was employed to identify the differentially expressed genes (DEGs). The up- and downregulated DEGs were then pooled for gene-ontology and pathway-enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The upstream regulatory elements of these DEGs were also explored by using Whole-Genome rVISTA. Furthermore, we constructed a protein–protein interaction (PPI) network for DEGs. The surfactant protein D (SP-D) serum level and HLA-A gene frequency in COPD patients and healthy controls were also measured by enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction, respectively.ResultsA total of 39 up- and 15 downregulated DEGs were screened. Most of the upregulated genes were involved in the immune response process, while the downregulated genes were involved in the steroid metabolic process. Moreover, we also found that HLA-A has the highest degree in the PPI network. The SP-D serum level and HLA-A gene frequency in COPD patients were significantly higher than those in healthy controls (13.62±2.09 ng/mL vs 10.28±2.86 ng/mL; 62.5% vs 12.5%; P<0.05).ConclusionOur results may help further the understanding of the mechanisms of COPD. The identified DEGs, especially HLA-A, may serve as diagnosis markers for COPD.

Smoking is considered the major risk factor for lung cancer, but only a small portion of female lung adenocarcinoma patients are associated with smoking. Thus, identifying crucial genes and pathways related to nonsmoking female lung cancer patients is of great importance. Gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. The R software packages were applied to screen the differentially expressed genes (DEGs). GO term enrichment and KEGG pathway analyses were carried out using DAVID tools. The protein-protein interaction (PPI) network was constructed by Cytoscape software. In total, 487 downregulated and 199 upregulated DEGs were identified. The down-regulated DEGs were mainly enriched for behavior and response to wounding, and the upregulated DEGs were significantly enriched for multicellular organismal metabolic process and cell division. The KEGG pathway analysis revealed that the downregulated DEGs were significantly enriched for cell adhesion molecules and neuroactive ligand-receptor interaction, while the upregulated DEGs were mainly enriched for cell cycle and the p53 signaling pathway. The top 10 hub genes and top 3 gene interaction modules were selected from the PPI network. Of the ten hub genes, a high expression of five genes was related to a poor OS in female lung cancer patients who never smoked, including IL6, CXCR2, FPR2, PPBP and HBA1. However, a low expression of GNG11, LRRK2, CDH5, CAV1 and SELE was associated with a worse OS for the female lung cancer patients who never smoked. In conclusion, our study provides novel insight for a better understanding of the pathogenesis of nonsmoking female lung cancer, and these identified DEGs may serve as biomarkers for diagnostics and treatment.

Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 and GSE51088) investigating OC gene expression were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) in samples from patients with non-recurrent and recurrent OC were revealed via a homogeneity test and quality control analysis. A protein-protein interaction (PPI) network was subsequently established for the DEGs using data from Biological General Repository for Interaction Datasets, Human Protein Reference Database and Database of Interacting Proteins. Degrees of interaction and betweenness centrality (BC) scores were calculated for each node in the PPI network. The top 100 genes ranked by BC scores were selected to identify feature genes via recursive feature elimination using the GSE17260 dataset. Following this, a SVM classifier was constructed and further validated using the GSE44104 and GSE51088 datasets and independent gene expression data obtained from the Cancer Genome Atlas (TCGA). A total of 639 DEGs were identified from the three gene expression datasets, and a PPI network including 249 nodes and 354 edges was constructed. A SVM classifier consisting of 39 feature genes (including cullin 3, mouse double minute 2 homolog, aurora kinase A, WW domain containing oxidoreducatase, large tumor suppressor kinase 2, sirtuin 6, staphylococcal nuclease and tudor domain containing 1, leucine rich repeats and immunoglobulin like domains 1 and aurora kinase 1 interacting protein 1) was subsequently constructed. The prediction accuracies of the SVM classifier for GSE17260, GSE44104 and GSE51088 datasets as well as data downloaded from TCGA were revealed to be 92.7, 93.3, 96.6 and 90.4%, respectively. Furthermore, the results of the present study revealed that patients with predicted non-recurrent OC survived significantly longer compared with the patients with predicted recurrent OC (P=6.598×10−6). A SVM classifier consisting of 39 feature genes was established for predicting the recurrence and prognosis of OC. Therefore, the results of the present study suggested that the 39 feature genes may serve important roles in the development of OC and may represent therapeutic biomarkers of OC.

Anthracyclines are among the most effective and commonly used chemotherapeutic agents. However, the development of acquired anthracycline resistance is a major limitation to their clinical application. The aim of the present study was to identify differentially expressed genes (DEGs) and biological processes associated with the acquisition of anthracycline resistance in human breast cancer cells. We performed a meta-analysis of publically available microarray datasets containing data on stepwise-selected, anthracycline‑resistant breast cancer cell lines using the RankProd package in R. Additionally, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to analyze GO term enrichment and pathways, respectively. A protein-protein interaction (PPI) network was also generated using Cytoscape software. The meta-analysis yielded 413 DEGs related to anthracycline resistance in human breast cancer cells, and 374 of these were not involved in individual DEGs. GO analyses showed the 413 genes were enriched with terms such as 'response to steroid metabolic process', 'chemical stimulus', 'external stimulus', 'hormone stimulus', 'multicellular organismal process', and 'system development'. Pathway analysis revealed significant pathways including steroid hormone biosynthesis, cytokine-cytokine receptor interaction, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, and arachidonic acid metabolism. The PPI network indicated that proteins encoded by TRIM29, VTN, CCNA1, and karyopherin α 5 (KPNA5) participated in a significant number of interactions. In conclusion, our meta-analysis provides a comprehensive view of gene expression patterns associated with acquired resistance to anthracycline in breast cancer cells, and constitutes the basis for additional functional studies.

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

Backgrounds:HER-2 positive breast cancer is a subtype of breast cancer with poor clinical outcome. The aim of this study was to identify differentially expressed genes (DEGs) for HER-2 positive breast cancer and elucidate the potential interactions among them.Material and methods:Three gene expression profiles (GSE29431, GSE45827, and GSE65194) were derived from the Gene Expression Omnibus (GEO) database. GEO2R tool was applied to obtain DEGs between HER-2 positive breast cancer and normal breast tissues. Gene ontology (GO) annotation analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery (David) online tool. Protein-protein interaction (PPI) network, hub gene identification and module analysis was conducted by Cytoscape software. Online Kaplan–Meier plotter survival analysis tool was also used to investigate the prognostic values of hub genes in HER-2 positive breast cancer patients.Results:A total of 54 upregulated DEGs and 269 downregulated DEGs were identified. Among them, 10 hub genes including CCNB1, RAC1, TOP2A, KIF20A, RRM2, ASPM, NUSAP1, BIRC5, BUB1B, and CEP55 demonstrated by connectivity degree in the PPI network were screened out. In Kaplan–Meier plotter survival analysis, the overexpression of RAC1 and RRM2 were shown to be associated with an unfavorable prognosis in HER-2 positive breast cancer patients.Conclusions:This present study identified a number of potential target genes and pathways which might impact the oncogenesis and progression of HER-2 positive breast cancer. These findings could provide new insights into the detection of novel diagnostic and therapeutic biomarkers for this disease.

BackgroundColon cancer is one of the most common cancers in the world. Targeting biomarkers is helpful for the diagnosis and treatment of colon cancer. This study aimed to identify biomarkers in colon cancer, in addition to those that have already been reported, using microarray datasets and bioinformatics analysis.MethodsWe downloaded two mRNA microarray datasets (GSE44076 and GSE47074) for colon cancer from the Gene Expression Omnibus (GEO) database and the most recent colon cancer data (COAD) from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) between colon cancer and adjacent normal tissues were determined based on these three datasets. Additionally, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and protein-protein interaction (PPI) network analysis. The hub genes in the PPI network were then selected and analysed.ResultsWe identified 150 DEGs and the GO enrichment analysis revealed that these DEGs were enriched in functions related to accelerating the cell cycle, promoting tumour cell accumulation, promoting cell division, positively regulating cell division, and negatively regulating apoptosis. The KEGG pathway analysis indicated that the DEGs were also involved in the cell cycle pathway. In the PPI network, 34 hub genes were found to be enriched in cell division. Prognostic analysis of the 34 hub genes revealed that eight genes (CCNB1, CHEK1, DEPDC1, ECT2, GINS2, HMMR, KIF14, and KIF18A) were associated with the prognosis of colon cancer. And our qRT-PCR results confirmed that DEPDC1, ECT2, GINS2, HMMR and KIF18A were highly expressed in colon cancer cells.ConclusionsThe genes DEPDC1, ECT2, GINS2, HMMR, and KIF18A could serve as novel diagnostic biomarkers of colon cancer.