Identification of biomarkers, pathways and potential therapeutic agents for white adipocyte insulin resistance using bioinformatics analysis

Comparative genomics analysis to explore the biodiversity and mining novel target genes of Listeria monocytogenes strains from different regions

As a common foodborne pathogen, infection with L. monocytogenes poses a significant threat to human life and health. The objective of this study was to employ comparative genomics to unveil the biodiversity and evolutionary characteristics of L. monocytogenes strains from different regions, screening for potential target genes and mining novel target genes, thus providing significant reference value for the specific molecular detection and therapeutic targets of L. monocytogenes strains. Pan-genomic analysis revealed that L. monocytogenes from different regions have open genomes, providing a solid genetic basis for adaptation to different environments. These strains contain numerous virulence genes that contribute to their high pathogenicity. They also exhibit relatively high resistance to phosphonic acid, glycopeptide, lincosamide, and peptide antibiotics. The results of mobile genetic elements indicate that, despite being located in different geographical locations, there is a certain degree of similarity in bacterial genome evolution and adaptation to specific environmental pressures. The potential target genes identified through pan-genomics are primarily associated with the fundamental life activities and infection invasion of L. monocytogenes, including known targets such as inlB, which can be utilized for molecular detection and therapeutic purposes. After screening a large number of potential target genes, we further screened them using hub gene selection methods to mining novel target genes. The present study employed eight different hub gene screening methods, ultimately identifying ten highly connected hub genes (bglF_1, davD, menE_1, tilS, dapX, iolC, gshAB, cysG, trpA, and hisC), which play crucial roles in the pathogenesis of L. monocytogenes. The results of pan-genomic analysis showed that L. monocytogenes from different regions exhibit high similarity in bacterial genome evolution. The PCR results demonstrated the excellent specificity of the bglF_1 and davD genes for L. monocytogenes. Therefore, the bglF_1 and davD genes hold promise as specific molecular detection and therapeutic targets for L. monocytogenes strains from different regions.

The identification of a novel shared therapeutic target and drug across all insulin-sensitive tissues under insulin resistance

Background

To identify key and shared insulin resistance (IR) molecular signatures across all insulin-sensitive tissues (ISTs), and their potential targeted drugs.

Methods

Three datasets from Gene Expression Omnibus (GEO) were acquired, in which the ISTs (fat, muscle, and liver) were from the same individual with obese mice. Integrated bioinformatics analysis was performed to obtain the differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was carried out to determine the "most significant trait-related genes" (MSTRGs). Enrichment analysis and PPI network were performed to find common features and novel hub genes in ISTs. The shared genes of DEGs and genes between DEGs and MSTRGs across four ISTs were identified as key IR therapeutic target. The Attie Lab diabetes database and obese rats were used to verify candidate genes. A medical drug-gene interaction network was conducted by using the Comparative Toxicogenomics Database (CTD) to find potential targeted drugs. The candidate drug was validated in Hepa1-6 cells.

Results

Lipid metabolic process, mitochondrion, and oxidoreductase activity as common features were enriched from ISTs under an obese context. Thirteen shared genes (Ubd, Lbp, Hp, Arntl, Cfd, Npas2, Thrsp., Tpx2, Pkp1, Sftpd, Mthfd2, Tnfaip2, and Vnn3) of DEGs across ISTs were obtained and confirmed. Among them, Ubd was the only shared gene between DEGs and MSTRGs across four ISTs. The expression of Ubd was significantly upregulated across four ISTs in obese rats, especially in the liver. The IR Hepa1-6 cell models treated with dexamethasone (Dex), palmitic acid (PA), and 2-deoxy-D-ribose (dRib) had elevated expression of Ubd. Knockdown of Ubd increased the level of p-Akt. A lowing Ubd expression drug, promethazine (PMZ) from CTD analysis rescued the decreased p-Akt level in IR Hepa1-6 cells.

Conclusion

This study revealed Ubd, a novel and shared IR molecular signature across four ISTs, as an effective biomarker and provided new insight into the mechanisms of IR. PMZ was a candidate drug for IR which increased p-Akt level and thus improved IR by targeting Ubd and downregulation of Ubd expression. Both Ubd and PMZ merit further clinical translational investigation to improve IR.

Network pharmacology based research into the effect and potential mechanism of Portulaca oleracea L. polysaccharide against ulcerative colitis

BACKGROUND

Ulcerative colitis (UC) as a chronic inflammatory bowel disease (IBD) has received extensive concerns worldwide. As a traditional herbal medicine, Portulaca oleracea L. (POL) has a wide application in gastrointestinal diseases such as diarrhea and dysentery. This study aims to investigate the target and potential mechanisms of Portulaca oleracea L. polysaccharide (POL-P) in the treatment of UC.

METHOD

The active ingredients and relevant targets of POL-P were searched through the TCMSP and Swiss Target Prediction databases. UC related targets were collected through the GeneCards and DisGeNET databases. The intersection of POL-P targets with UC targets was done using Venny. Then, protein-protein interaction (PPI) network of the intersection targets was constructed through the STRING database and analyzed using Cytohubba to identify the key targets of POL-P in the treatment of UC. In addition, GO and KEGG enrichment analyses were performed on the key targets and the binding mode of POL-P to the key targets was further analyzed by molecular docking technology. Finally, the efficacy and target of POL-P were verified using animal experiments and immunohistochemical staining.

RESULTS

A total of 316 targets were obtained based on POL-P monosaccharide structures, among which 28 were related to UC. Cytohubba analysis showed that VEGFA, EGFR, TLR4, IL-1β, STAT3, IL-2, PTGS2, FGF2, HGF, and MMP9 were the key targets for UC treatment and were mainly involved in multiple signaling pathways such as proliferation, inflammation, and immune response. Molecular docking results revealed that POL-P had a good binding potential to TLR4. In vivo validation results showed that POL-P significantly reduced the overexpression of TLR4 and its downstream key proteins (MyD88 and NF-κB) in intestinal mucosa of UC mice, which indicated that POL-P improved UC by mediating TLR4 related proteins.

CONCLUSION

POL-P may be a potential therapeutic agent for UC and its mechanism is closely related to the regulation of TLR4 protein. This study will provide novel insights for the treatment of UC with POL-P.

Identification of Copper Metabolism Related Biomarkers, Polygenic Prediction Model, and Potential Therapeutic Agents in Alzheimer's Disease

BACKGROUND

The underlying pathogenic genes and effective therapeutic agents of Alzheimer's disease (AD) are still elusive. Meanwhile, abnormal copper metabolism is observed in AD brains of both human and mouse models.

OBJECTIVE

To investigate copper metabolism-related gene biomarkers for AD diagnosis and therapy.

METHODS

The AD datasets and copper metabolism-related genes (CMGs) were downloaded from GEO and GeneCards database, respectively. Differentially expressed CMGs (DE-CMGs) performed through Limma, functional enrichment analysis and the protein-protein interaction were used to identify candidate key genes by using CytoHubba. And these candidate key genes were utilized to construct a prediction model by logistic regression analysis for AD early diagnosis. Furthermore, ROC analysis was conducted to identify a single gene with AUC values greater than 0.7 by GSE5281. Finally, the single gene biomarker was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in AD clinical samples. Additionally, immune cell infiltration in AD samples and potential therapeutic drugs targeting the identified biomarkers were further explored.

RESULTS

A polygenic prediction model for AD based on copper metabolism was established by the top 10 genes, which demonstrated good diagnostic performance (AUC values). COX11, LDHA, ATOX1, SCO1, and SOD1 were identified as blood biomarkers for AD early diagnosis. 20 agents targeting biomarkers were retrieved from DrugBank database, some of which have been proven effective for the treatment of AD.

CONCLUSIONS

The five blood biomarkers and copper metabolism-associated model can differentiate AD patients from non-demented individuals and aid in the development of new therapeutic strategies.

SPRY4 promotes adipogenic differentiation of human mesenchymal stem cells through the MEK-ERK1/2 signaling pathway

Obesity is a chronic metabolic disorder characterized by the accumulation of excess fat in the body. Preventing and controlling obesity by inhibiting the adipogenic differentiation of mesenchymal stem cells (MSCs) and thereby avoiding the increase of white adipose tissue is safe and effective. Recent studies have demonstrated that Sprouty proteins (SPRYs) are involved in cell differentiation and related diseases. However, the role and mechanism of SPRY4 in MSC adipogenic differentiation remain to be explored. Here, we found that SPRY4 positively correlates with the adipogenic differentiation of human adipose-derived MSCs (hAMSCs). Via gain- and loss-of-function experiments, we demonstrated that SPRY4 promotes hAMSC adipogenesis both in vitro and in vivo. Mechanistically, SPRY4 functioned by activating the MEK-ERK1/2 pathway. Our findings provide new insights into a critical role for SPRY4 as a regulator of adipogenic differentiation, which may illuminate the underlying mechanisms of obesity and suggest the potential of SPRY4 as a novel treatment option.

Identification of key genes and pathways revealing the central regulatory mechanism of brain-derived glucagon-like peptide-1 on obesity using bioinformatics analysis

Objective

Central glucagon-like peptide-1 (GLP-1) is a target in treating obesity due to its effect on suppressing appetite, but the possible downstream key genes that GLP-1 regulated have not been studied in depth. This study intends to screen out the downstream feeding regulation genes of central GLP-1 neurons through bioinformatics analysis and verify them by chemical genetics, which may provide insights for future research.

Materials and methods

GSE135862 genetic expression profiles were extracted from the Gene Expression Omnibus (GEO) database. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were carried out. STRING database and Cytoscape software were used to map the protein-protein interaction (PPI) network of the differentially expressed genes (DEGs). After bioinformatics analysis, we applied chemogenetic methods to modulate the activities of GLP-1 neurons in the nucleus tractus solitarius (NTS) and observed the alterations of screened differential genes and their protein expressions in the hypothalamus under different excitatory conditions of GLP-1 neurons.

Results

A total of 49 DEGs were discovered, including 38 downregulated genes and 11 upregulated genes. The two genes with the highest expression scores were biglycan (Bgn) and mitogen-activated protein kinase activated protein kinase 3 (Mapkapk3). The results of GO analysis showed that there were 10 molecular functions of differential genes. Differential genes were mainly localized in seven regions around the cells, and enriched in 10 biology processes. The results of the KEGG signaling pathway enrichment analysis showed that differential genes played an important role in seven pathways. The top 15 genes selected according to the Cytoscape software included Bgn and Mapkapk3. Chemogenetic activation of GLP-1 in NTS induced a decrease in food intake and body mass, while chemogenetic inhibition induced the opposite effect. The gene and protein expression of GLP-1 were upregulated in NTS when activated by chemogenetics. In addition, the expression of Bgn was upregulated and that of Mapkapk3 was downregulated in the hypothalamus.

Conclusion

Our data showed that GLP-1 could modulate the protein expression of Bgn and Mapkapk3. Our findings elucidated the regulatory network in GLP-1 to obesity and might provide a novel diagnostic and therapeutic target for obesity.

Silencing BLNK protects against interleukin-1β-induced chondrocyte injury through the NF-κB signaling pathway

BACKGROUND

Osteoarthritis (OA) is the most common joint disease in the elderly and is characterized by the progressive degeneration of articular cartilage. It is necessary to study the molecular pathology of OA. This study aimed to explore the role and mechanism of BLNK in regulating interleukin-1β (IL-1β)-induced chondrocyte injury and OA progression.

METHODS

GSE1919 (5 normal samples and 5 OA samples) was downloaded from the Gene Expression Omnibus (GEO) database. The limma package in R software was used to identify differentially expressed genes (DEGs) between control and OA-affected cartilage. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the differentially expressed genes were also performed. Apoptosis was assessed by flow cytometry. An OA rat model was established, and the relative expression of BLNK was assessed by real time quantitative PCR (qRT-PCR) and immunohistochemical staining. The expression of collagen II, MMP9, p65 and p-p65 was measured by Western blot analysis. Moreover, inflammatory factors (TNF-α and IL-18) were assessed by ELISA. The NF-κB inhibitor JSH-23 was used to assess the impact of BLNK on the NF-κB signaling pathway.

RESULTS

In total, 1318 DEGs were identified between normal and OA-affected cartilage according to the criteria (P-value <0.05 and |logFC > 1|). These DEGs were mainly enriched in the NF-κB pathway. BLNK was highly expressed in OA cartilage tissue and injured chondrocytes. Silencing BLNK significantly downregulated the IL-1β-induced apoptosis of chondrocytes. Silencing BLNK partially increased collagen II expression and downregulated MMP13 expression. Moreover, silencing BLNK partially decreased TNF-α and IL-18 expression. BLNK silencing inhibited the activation of NF-κB in OA. Silencing BLNK delayed OA progression through the NF-κB signaling pathway.

CONCLUSION

Silencing BLNK delayed OA progression and IL-1β-induced chondrocyte injury by regulating the NF-κB pathway.

Novel diagnostic and prognostic biomarkers of colorectal cancer: Capable to overcome the heterogeneity-specific barrier and valid for global applications

INTRODUCTION

The heterogeneity-specific nature of the available colorectal cancer (CRC) biomarkers is significantly contributing to the cancer-associated high mortality rate worldwide. Hence, this study was initiated to investigate a system of novel CRC biomarkers that could commonly be employed to the CRC patients and helpful to overcome the heterogenetic-specific barrier.

METHODS

Initially, CRC-related hub genes were extracted through PubMed based literature mining. A protein-protein interaction (PPI) network of the extracted hub genes was constructed and analyzed to identify few more closely CRC-related hub genes (real hub genes). Later, a comprehensive bioinformatics approach was applied to uncover the diagnostic and prognostic role of the identified real hub genes in CRC patients of various clinicopathological features.

RESULTS

Out of 210 collected hub genes, in total 6 genes (CXCL12, CXCL8, AGT, GNB1, GNG4, and CXCL1) were identified as the real hub genes. We further revealed that all the six real hub genes were significantly dysregulated in colon adenocarcinoma (COAD) patients of various clinicopathological features including different races, cancer stages, genders, age groups, and body weights. Additionally, the dysregulation of real hub genes has shown different abnormal correlations with many other parameters including promoter methylation, overall survival (OS), genetic alterations and copy number variations (CNVs), and CD8+T immune cells level. Finally, we identified a potential miRNA and various chemotherapeutic drugs via miRNA, and real hub genes drug interaction network that could be used in the treatment of CRC by regulating the expression of real hub genes.

CONCLUSION

In conclusion, we have identified six real hub genes as potential biomarkers of CRC patients that could help to overcome the heterogenetic-specific barrier across different clinicopathological features.

BoxCar increases the depth and reproducibility of diabetic urinary proteome analysis

PURPOSE

Mass spectrometry-based proteomics performs well in high throughput detection of urinary proteins. Nonetheless, protein identification depth and reproducibility remain the challenges in diabetic urinary proteome with high complexity and broad dynamic range, especially for low-abundant proteins. As a new data acquisition strategy, the BoxCar method was reported to benefit for low-abundant protein identification. Whether it is propitious to diabetic samples with high dynamic range proteomes has not been discussed yet. We aimed to apply BoxCar method to diabetic urine sample analysis, and to compare it with standard data dependent acquisition (DDA) method on protein identification in detail.

EXPERIMENTAL DESIGN

We performed seven technical replicates analysis on two urine samples from healthy individuals and diabetic patients to evaluate protein detection of BoxCar and standard DDA methods on single sample. Further comparison of two methods was made on multiple diabetic urine samples.

RESULTS

BoxCar could increase over 20% of identified proteins and performed better quantitative reproducibility than standard DDA method either in single or multiple diabetic urinary samples. BoxCar also improved the detection of low-abundant proteins. Functional enrichment analysis of normal albuminuria or microalbuminuria samples indicated that BoxCar acquired more diabetes-related biological information.

CONCLUSIONS AND CLINICAL RELEVANCE

The study demonstrates that BoxCar could enhance the depth and reproducibility in diabetic urinary proteome analysis, which provides reference for mass spectrometry approach selection in clinical urinary proteomic research.

Novel ι-Carrageenan Tetrasaccharide Alleviates Liver Lipid Accumulation via the Bile Acid-FXR-SHP/PXR Pathway to Regulate Cholesterol Conversion and Fatty Acid Metabolism in Insulin-Resistant Mice

ι-Carrageenan tetrasaccharide (ιCTs), a novel oligosaccharide, was hydrolyzed from ι-carrageenan with targeting marine tool-enzyme Cgi82A. Previously, we have found ιCTs exhibited a hypoglycemic effect, whether it could regulate lipid metabolism remains unknown. In this study, the insulin-resistant mice induced by high-fat-high-sucrose diet were orally administrated with ιCTs (30 mg/kg·bw) for 20 weeks. The results showed that the contents of triglyceride and cholesterol in both serum and liver were reduced by ιCTs, and their excretion in feces were promoted, suggesting lipid accumulation was inhibited. Intriguingly, the overall levels of bile acid in serum, liver, and feces were all raised by ιCTs. Given that bile acids are the essential signal factors for regulating lipid metabolism via the farnesoid-X-receptor (FXR), we conducted serum bile acid profile analysis and found that the levels of high-affinity agonists deoxycholic acid and lithocholic acid were decreased in the ιCTs group, showing that ιCTs failed to activate FXR. Western blot analysis showed that ιCTs downregulated hepatic FXR and small heterodimer partner (SHP) expression and increased downstream CYP7A1 expression via regulating the FXR-SHP signal to accelerate liver cholesterol conversion. Meanwhile, ιCTs decreased the expression of PXR and SREBP1c and elevated the expression of PPARα and CPT1α via regulating the FXR-PXR-SREBP1c/PPARα signal to inhibit fatty acid synthesis and promote fatty acid β-oxidation. To the best of our knowledge, this study for the first time reported that ιCTs alleviated liver lipid accumulation via the bile acid-FXR-SHP/PXR signal to regulate cholesterol conversion and fatty acid metabolism, which highlighted a new idea for ameliorating insulin resistance.

Identification of significant genes and therapeutic agents for breast cancer by integrated genomics

Breast cancer is the most commonly diagnosed malignancy in women; thus, more cancer prevention research is urgently needed. The aim of this study was to predict potential therapeutic agents for breast cancer and determine their molecular mechanisms using integrated bioinformatics. Summary data from a large genome-wide association study of breast cancer was derived from the UK Biobank. The gene expression profile of breast cancer was from the Oncomine database. We performed a network-wide association study and gene set enrichment analysis to identify the significant genes in breast cancer. Then, we performed Gene Ontology analysis using the STRING database and conducted Kyoto Encyclopedia of Genes and Genomes pathway analysis using Cytoscape software. We verified our results using the Gene Expression Profile Interactive Analysis, PROgeneV2, and Human Protein Atlas databases. Connectivity map analysis was used to identify small-molecule compounds that are potential therapeutic agents for breast cancer. We identified 10 significant genes in breast cancer based on the gene expression profile and genome-wide association study. A total of 65 small-molecule compounds were found to be potential therapeutic agents for breast cancer.

Differentially Expressed Genes and Enriched Signaling Pathways in the Adipose Tissue of Obese People

As the prevalence of obesity increases, so does the occurrence of obesity-related complications, such as cardiovascular and cerebrovascular diseases, diabetes, and some cancers. Increased adipose tissue is the main cause of harm in obesity. To better understand obesity and its related complications, we analyzed the mRNA expression profiles of adipose tissues from 126 patients with obesity and 275 non-obese controls. Using an integrated bioinformatics method, we explored the functions of 113 differentially expressed genes (DEGs) between them. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses revealed that upregulated DEGs were enriched in immune cell chemotaxis, complement-related cascade activation, and various inflammatory signaling pathways, while downregulated DEGs enriched in nutrient metabolism. The CIBERSORT algorithm indicated that an increase in macrophages may be the main cause of adipose tissue inflammation, while decreased γδ T cells reduce sympathetic action, leading to dysregulation of adipocyte thermogenesis. A protein-protein interaction network was constructed using the STRING database, and the top 10 hub genes were identified using the cytoHubba plug-in in Cytoscape. All were confirmed to be obesity-related using a separate dataset. In addition, we identified chemicals related to these hub genes that may contribute to obesity. In conclusion, we have successfully identified several hub genes in the development of obesity, which provide insights into the possible mechanisms controlling obesity and its related complications, as well as potential biomarkers and therapeutic targets for further research.

Underlying Mechanism of Insulin Resistance: A Bioinformatics Analysis Based on Validated Related-Genes from Public Disease Databases

Background

The underlying mechanism of insulin resistance is complex; bioinformatics analysis is used to explore the mechanism based differential expression genes (DEGs) obtained from omics analysis. However, the expression and role of most DEGs involved in bioinformatics analysis are invalidated. This study aimed to disclose the mechanism of insulin resistance via bioinformatics analysis based on validated insulin resistance-related genes (IRRGs) collected from public disease-gene databases.

Material/Methods

IRRGs were collected from 4 disease databases including NCBI-Gene, CTD, RGD, and Phenopedia. GO and KEGG analysis of IRRGs were performed by DAVID. Then, the STRING database was employed to construct a protein–protein interaction (PPI) network of IRRGs. The module analysis and hub genes identification were carried out by MCODE and cytoHubba plugin of Cytoscape based on the primary PPI network, respectively.

Results

A total of 1195 IRRGs were identified. Response to drug, hypoxia, insulin, positive regulation of transcription from RNA polymerase II promoter, cell proliferation, inflammatory response, negative regulation of apoptotic process, glucose homeostasis, cellular response to insulin stimulus, and aging were proposed as the crucial functions related to insulin resistance. Ten insulin resistance-related pathways included the pathways of insulin resistance, pathways in cancer, adipocytokine, prostate cancer, PI3K-Akt, insulin, AMPK, HIF-1, prolactin, and pancreatic cancer signaling pathway were revealed. INS, AKT1, IL-6, TP53, TNF, VEGFA, MAPK3, EGFR, EGF, and SRC were identified as the top 10 hub genes.

Conclusions

The current study presented a landscape view of possible underlying mechanism of insulin resistance by bioinformatics analysis based on validated IRRGs.

Systems Genetics for Mechanistic Discovery in Heart Diseases

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

Identification of potential hub genes via bioinformatics analysis combined with experimental verification in colorectal cancer

Colorectal cancer (CRC) is a kind of malignant cancer with high morbidity and mortality. The purpose of this study was to explore potential regulated key genes involved in CRC through bioinformatics analysis and experimental verification. The gene expression profile data were downloaded from the Gene Expression Omnibus, and the differential expression genes were detected in cancerous and paracancerous samples of CRC patients, respectively. Then functional enrichment analysis, such as the Kyoto Encyclopedia of Genes and Genomes pathway analysis as well as the protein-protein interaction network were constructed, and the highly related genes were clustered by Molecular COmplex DEtection algorithm to find out the core interaction in different genes' crosstalk. The genes affecting CRC prognosis were screened by the Human Protein Atlas database. In addition, the expression level of core genes was detected by GEPIA database, and the core genes' changes in large-scale cancer genome data set were directly analyzed by cBioPortal database. The expression of the predicted hub genes DSN1, AHCY, and ERCC6L was verified by reverse-transcription quantitative polymerase chain reaction in CRC cells. The gene function of DSN1 was analyzed by wound healing and colony formation assays. The results showed that silencing of DSN1 could significantly reduce the migration and proliferation of CRC cells. Further, BUB1B, the potential interacting protein of DSN1, was also predicted via bioinformatics analysis. Above all, this study shows that bioinformatics analysis combined with experimental method verification provide more potential vital genes for the prevention and therapy of CRC.