Identification of Potential Key Genes Associated With the Pathogenesis and Prognosis of Gastric Cancer Based on Integrated Bioinformatics Analysis

A Novel Panel of 80 RNA Biomarkers with Differential Expression in Multiple Human Solid Tumors against Healthy Blood Samples

The aim of this study was to identify genes with higher expression in solid tumor cells by comparing human tumor biopsies with healthy blood samples using both in silico statistical analysis and experimental validations. This approach resulted in a novel panel of 80 RNA biomarkers with high discrimination power to detect circulating tumor cells in blood samples. To identify the 80 RNA biomarkers, Affymetrix HG-U133 plus 2.0 microarrays datasets were used to compare breast tumor tissue biopsies and breast cancer cell lines with blood samples from patients with conditions other than cancer. A total of 859 samples were analyzed at the discovery stage, consisting of 417 mammary tumors, 41 breast lines, and 401 control samples. To confirm this discovery, external datasets of eight types of tumors were used, and experimental validation studies (NanoString n-counter gene expression assay) were performed, totaling 5028 samples analyzed. In these analyses, the 80 biomarkers showed higher expression in all solid tumors analyzed relative to healthy blood samples. Experimental validation studies using NanoString assay confirmed the results were not dependent of the gene expression platform. A panel of 80 RNA biomarkers was described here, with the potential to detect solid tumor cells present in the blood of multiple tumor types.

A four-gene transcript score to predict metastatic-lethal progression in men treated for localized prostate cancer: Development and validation studies

BACKGROUND

Molecular studies have tried to address the unmet need for prognostic biomarkers in prostate cancer (PCa). Some gene expression tests improve upon clinical factors for prediction of outcomes, but additional tools for accurate prediction of tumor aggressiveness are needed.

METHODS

Based on a previously published panel of 23 gene transcripts that distinguished patients with metastatic progression, we constructed a prediction model using independent training and testing datasets. Using the validated messenger RNAs and Gleason score (GS), we performed model selection in the training set to define a final locked model to classify patients who developed metastatic-lethal events from those who remained recurrence-free. In an independent testing dataset, we compared our locked model to established clinical prognostic factors and utilized Kaplan-Meier curves and receiver operating characteristic analyses to evaluate the model's performance.

RESULTS

Thirteen of 23 previously identified gene transcripts that stratified patients with aggressive PCa were validated in the training dataset. These biomarkers plus GS were used to develop a four-gene (CST2, FBLN1, TNFRSF19, and ZNF704) transcript (4GT) score that was significantly higher in patients who progressed to metastatic-lethal events compared to those without recurrence in the testing dataset (P = 5.7 × 10^-11 ). The 4GT score provided higher prediction accuracy (area under the ROC curve [AUC] = 0.76; 95% confidence interval [CI] = 0.69-0.83; partial area under the ROC curve [pAUC] = 0.008) than GS alone (AUC = 0.63; 95% CI = 0.56-0.70; pAUC = 0.002), and it improved risk stratification in subgroups defined by a combination of clinicopathological features (ie, Cancer of the Prostate Risk Assessment-Surgery).

CONCLUSION

Our validated 4GT score has prognostic value for metastatic-lethal progression in men treated for localized PCa and warrants further evaluation for its clinical utility.

Multifaceted Roles of Asporin in Cancer: Current Understanding

The small leucine-rich proteoglycan (SLRP) family consists of 18 members categorized into five distinct classes, the traditional classes I–III, and the non-canonical classes IV–V. Unlike the other class I SLRPs (decorin and biglycan), asporin contains a unique and conserved stretch of aspartate (D) residues in its N terminus, and germline polymorphisms in the D-repeat-length are associated with osteoarthritis and prostate cancer progression. Since the first discovery of asporin in 2001, previous studies have focused mainly on its roles in bone and joint diseases, including osteoarthritis, intervertebral disc degeneration and periodontal ligament mineralization. Recently, asporin gene expression was also reported to be dysregulated in tumor tissues of different types of cancer, and to act as oncogene in pancreatic, colorectal, gastric, and prostate cancers, and some types of breast cancer, though it is also reported to function as a tumor suppressor gene in triple-negative breast cancer. Furthermore, asporin is also positively or negatively correlated with tumor proliferation, migration, invasion, and patient prognosis through its regulation of different signaling pathways, including the TGF-β, EGFR, and CD44 pathways. In this review, we seek to elucidate the signaling pathways and functions regulated by asporin in different types of cancer and to highlight some important issues that require investigation in future research.

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein-protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.

Identification of AUNIP as a candidate diagnostic and prognostic biomarker for oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) is one of the most common malignant tumors worldwide. Patients with poorly differentiated OSCC often exhibit a poor prognosis. AUNIP (Aurora Kinase A and Ninein Interacting Protein), also known as AIBp, plays a key role in cell cycle and DNA damage repair. However, the function of AUNIP in OSCC remains elusive.

Methods

The differentially expressed genes (DEGs) were obtained using R language. Receiver operating characteristic curve analysis was performed to identify diagnostic markers for OSCC. The effectiveness of AUNIP in diagnosing OSCC was evaluated by machine learning. AUNIP expression was analyzed in publicly available databases and clinical specimens. Bioinformatics analysis and in vitro experiments were conducted to explore biological functions and prognostic value of AUNIP in OSCC.

Findings

The gene integration analysis revealed 90 upregulated DEGs. One candidate biomarker, AUNIP, for the diagnosis of OSCC was detected, and its expression gradually increased along with malignant differentiation of OSCC. Bioinformatics analysis demonstrated that AUNIP could be associated with tumor microenvironment, human papillomavirus infection, and cell cycle in OSCC. The suppression of AUNIP inhibited OSCC cell proliferation and resulted in G0/G1 phase arrest in OSCC cells. The survival analysis showed that AUNIP overexpression predicted poor prognosis of OSCC patients.

Interpretation: AUNIP could serve as a candidate diagnostic and prognostic biomarker for OSCC and suppression of AUNIP may be a potential approach to preventing and treating OSCC.

Fund

Taishan Scholars Project in Shandong Province (ts201511106) and the National Natural Science Foundation of China (Nos. 61603218).

Global transcriptomic analysis identifies SERPINE1 as a prognostic biomarker associated with epithelial-to-mesenchymal transition in gastric cancer

Background

The plasminogen activation system plays a pivotal role in regulating tumorigenesis. In this work, we aim to identify key regulators of plasminogen activation associated with tumorigenesis and explore potential mechanisms in gastric cancer (GC).

Methods

Gene profiling datasets were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened for and obtained by the GEO2R tool. The Database for Annotation, Visualization and Integrated Discovery was used for GO and KEGG enrichment analysis. Gene set enrichment analysis (GSEA) was performed to verify molecular signatures and pathways among The Cancer Genome Atlas or GEO datasets. Correlations between SERPINE1 and markers of epithelial-to-mesenchymal transition (EMT) were analyzed using the GEPIA database and quantitative real-time PCR (qRT-PCR). Interactive networks of selected genes were built by STRING and Cytoscape software. Finally, selected genes were verified with the Kaplan–Meier (KM) plotter database.

Results

A total of 104 overlapped upregulated and 61 downregulated DEGs were obtained. Multiple GO and KEGG terms associated with the extracellular matrix were enriched among the DEGs. SERPINE1 was identified as the only regulator of angiogenesis and the plasminogen activator system among the DEGs. A high level of SERPINE1 was associated with a poor prognosis in GC. GSEA analysis showed a strong correlation between SERPINE1 and EMT, which was also confirmed with the GEPIA database and qRT-PCR validation. FN1, TIMP1, MMP2, and SPARC were correlated with SERPINE1.The KM plotter database showed that an overexpression of these genes correlated with a shorter survival time in GC patients.

Conclusions

In conclusion, SERPINE1 is a potent biomarker associated with EMT and a poor prognosis in GC. Furthermore, FN1, TIMP1, MMP2, and SPARC are correlated with SERPINE1 and may serve as therapeutic targets in reversing EMT in GC.

Identification of a prognostic 28-gene expression signature for gastric cancer with lymphatic metastasis

Gastric cancer (GC) patients have high mortality due to late-stage diagnosis, which is closely associated with lymph node metastasis. Exploring the molecular mechanisms of lymphatic metastasis may inform the research into early diagnostics of GC. In the present study, we obtained RNA-Seq data from The Cancer Genome Altas and used Limma package to identify differentially expressed genes (DEGs) between lymphatic metastases and non-lymphatic metastases in GC tissues. Then, we used an elastic net-regularized COX proportional hazard model for gene selection from the DEGs and constructed a regression model composed of 28-gene signatures. Furthermore, we assessed the prognostic performance of the 28-gene signature by analyzing the receive operating characteristic curves. In addition, we selected the gene PELI2 amongst 28 genes and assessed the roles of this gene in GC cells. The good prognostic performance of the 28-gene signature was confirmed in the testing set, which was also validated by GSE66229 dataset. In addition, the biological experiments showed that PELI2 could promote the growth and metastasis of GC cells by regulating vascular endothelial growth factor C. Our study indicates that the identified 28-gene signature could be considered as a sensitive predictive tool for lymphatic metastasis in GC.

ITGBL1 promotes EMT, invasion and migration by activating NF-κB signaling pathway in prostate cancer

Background: Integrin beta-like 1 (ITGBL1) was extensively demonstrated to contribute the metastasis and progression in a variety of cancers. However, its role of ITGBL1 in prostate cancer (PCa) is still not reported.

Methods: Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blot were performed to detect ITGBL1 expression in PCa tissues and cell lines. Immunohistochemical (IHC) staining of ITGBL1 in 174 PCa tissues was performed. The influence of ITGL1 expression in PCa cells epithelial-mesenchymal transition (EMT), migration and invasion was investigated. Notably, the possible mechanisms underlying the action of ITGBL1 in vivo and vitro assays were explored.

Results: We analyzed PCa dataset from The Cancer Genome Atlas (TCGA) and found that ITGBL1 was upregulated in PCa tissues. Overexpression of ITGBL1 is positively associated with the progression and lymph node metastasis in PCa patients. Furthermore, upregulating ITGBL1 enhanced the invasion, migration abilities and EMT in PCa cells. Conversely, downregulating ITGBL1 exhibited an opposite effect. Our findings further demonstrated that ITGBL1 promoted invasion and migration via activating NF-κB signaling in PCa cells.

Conclusion: Therefore, our results identify a novel metastasis-related gene in PCa, which will help to develop a novel therapeutic strategy in metastatic PCa.

ANXA2 Silencing Inhibits Proliferation, Invasion, and Migration in Gastric Cancer Cells

Annexin A2 (ANXA2) has been well known to associate with the progress of malignant tumor. However, the biological behavior of ANXA2 in gastric cancer (GC) remains unclear. We made a hypothesis in transcriptome level from TCGA datasets. Then, we used immunohistochemical staining to quantify the expression level of ANXA2 protein in GC tissues compared with adjacent tissues. Quantitative real-time PCR and western blot were used for analyzing ANXA2 expression in human GC (SGC-7901, MKN-45, BGC-823, and AGS) cell lines. We investigated the effect of a lentivirus-mediated knock-down of ANXA2 on the proliferation, invasion and migration of gastric cancer AGS cells. Cell proliferation was examined by MTT and colony formation tests. Cell apoptosis and cycle were measured by flow cytometry. Migration and invasion were detected by transwell assay. We found that high expression of ANXA2 can increase the mobility of cancer cells from TCGA datasets. ANXA2 was upregulated in GC tissues compared with adjacent tissues. AGS cell line displayed significantly higher expression of ANXA2 among the four GC cell lines. In addition, ANXA2 silencing led to a weakened ability of proliferation, invasion, and migration in GC cells; targeting of ANXA2 may be a potential therapeutic strategy for GC patients.

Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer

Background and Objective

Endometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes.

Methods and Results

An mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20, and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1, and TRPM4 were constructed. The signature performed well for prognosis prediction (p < 0.001) and time-dependent receiver-operating characteristic (ROC) analysis displayed an area under the curve (AUC) of 0.797, 0.734, 0.729, and 0.647 for 1, 3, 5, and 10-year overall survival (OS) prediction, respectively.

Conclusion

This study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction.

A systematic simulation-based meta-analytical framework for prediction of physiological biomarkers in alopecia

Background

Alopecia or hair loss is a complex polygenetic and psychologically devastating disease affecting millions of men and women globally. Since the gene annotation and environmental knowledge is limited for alopecia, a systematic analysis for the identification of candidate biomarkers is required that could provide potential therapeutic targets for hair loss therapy.

Results

We designed an interactive framework to perform a meta-analytical study based on differential expression analysis, systems biology, and functional proteomic investigations. We analyzed eight publicly available microarray datasets and found 12 potential candidate biomarkers including three extracellular proteins from the list of differentially expressed genes with a p-value < 0.05. After expression profiling and functional analysis, we studied protein–protein interactions and observed functional associations of source proteins including WIF1, SPON1, LYZ, GPRC5B, PTPRE, ZFP36L2, HBB, PHF15, LMCD1, KRT35 and VAV3 with target proteins including APCDD1, WNT1, WNT3A, SHH, ESRI, TGFB1, and APP. Pathway analysis of these molecules revealed their role in major physiological reactions including protein metabolism, signal transduction, WNT, BMP, EDA, NOTCH and SHH pathways. These pathways regulate hair growth, hair follicle differentiation, pigmentation, and morphogenesis. We studied the regulatory role of β-catenin, Nf-kappa B, cytokines and retinoic acid in the development of hair growth. Therefore, the differential expression of these significant proteins would affect the normal level and could cause aberrations in hair growth.

Conclusion

Our integrative approach helps to prioritize the biomarkers that ultimately lessen the economic burden of experimental studies. It will also be valuable to discover mutants in genomic data in order to increase the identification of new biomarkers for similar problems.

Electronic supplementary material

The online version of this article (10.1186/s40709-019-0094-x) contains supplementary material, which is available to authorized users.

Integrated bioinformatics analysis of As, Au, Cd, Pb and Cu heavy metal responsive marker genes through Arabidopsis thaliana GEO datasets

BACKGROUND

Current environmental pollution factors, particularly the distribution and diffusion of heavy metals in soil and water, are a high risk to local environments and humans. Despite striking advances in methods to detect contaminants by a variety of chemical and physical solutions, these methods have inherent limitations such as small dimensions and very low coverage. Therefore, identifying novel contaminant biomarkers are urgently needed.

METHODS

To better track heavy metal contaminations in soil and water, integrated bioinformatics analysis to identify biomarkers of relevant heavy metal, such as As, Cd, Pb and Cu, is a suitable method for long-term and large-scale surveys of such heavy metal pollutants. Subsequently, the accuracy and stability of the results screened were experimentally validated by quantitative PCR experiment.

RESULTS

We obtained 168 differentially expressed genes (DEGs) which contained 59 up-regulated genes and 109 down-regulated genes through comparative bioinformatics analyses. Subsequently, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of these DEGs were performed, respectively. GO analyses found that these DEGs were mainly related to responses to chemicals, responses to stimulus, responses to stress, responses to abiotic stimulus, and so on. KEGG pathway analyses of DEGs were mainly involved in the protein degradation process and other biologic process, such as the phenylpropanoid biosynthesis pathways and nitrogen metabolism. Moreover, we also speculated that nine candidate core biomarker genes (namely, NILR1, PGPS1, WRKY33, BCS1, AR781, CYP81D8, NR1, EAP1 and MYB15) might be tightly correlated with the response or transport of heavy metals. Finally, experimental results displayed that these genes had the same expression trend response to different stresses as mentioned above (Cd, Pb and Cu) and no mentioned above (Zn and Cr).

CONCLUSION

In general, the identified biomarker genes could help us understand the potential molecular mechanisms or signaling pathways responsive to heavy metal stress in plants, and could be applied as marker genes to track heavy metal pollution in soil and water through detecting their expression in plants growing in those environments.

Integrated Bioinformatics Analysis the Function of RNA Binding Proteins (RBPs) and Their Prognostic Value in Breast Cancer

Background and Purpose: Breast cancer is one of the leading causes of death among women. RNA binding proteins (RBPs) play a vital role in the progression of many cancers. Functional investigation of RBPs may contribute to elucidating the mechanisms underlying tumor initiation, progression, and invasion, therefore providing novel insights into future diagnosis, treatment, and prognosis.

Methods: We downloaded RNA sequencing data from the cancer genome atlas (TCGA) by UCSC Xena and identified relevant RBPs through an integrated bioinformatics analysis. We then analyzed biological processes of differentially expressed genes (DEGs) by DAVID, and established their interaction networks and performed pathway analysis through the STRING database to uncover potential biological effects of these RBPs. We also explored the relationship between these RBPs and the prognosis of breast cancer patients.

Results: In the present study, we obtained 1092 breast tumor samples and 113 normal controls. After data analysis, we identified 90 upregulated and 115 downregulated RBPs in breast cancer. GO and KEGG pathway analysis indicated that these significantly changed genes were mainly involved in RNA processing, splicing, localization and RNA silencing, DNA transposition regulation and methylation, alkylation, mitochondrial gene expression, and transcription regulation. In addition, some RBPs were related to histone H3K27 methylation, estrogen response, inflammatory mediators, and translation regulation. Our study also identified five RBPs associated with breast cancer prognosis. Survival analysis found that overexpression of DCAF13, EZR, and MRPL13 showed worse survival, but overexpression of APOBEC3C and EIF4E3 showed better survival.

Conclusion: In conclusion, we identified key RBPs of breast cancer through comprehensive bioinformatics analysis. These RBPs were involved in a variety of biological and molecular pathways in breast cancer. Furthermore, we identified five RBPs as a potential prognostic biomarker of breast cancer. Our study provided novel insights to understand breast cancer at a molecular level.

Application of next-generation sequencing technology to precision medicine in cancer: joint consensus of the Tumor Biomarker Committee of the Chinese Society of Clinical Oncology

Next-generation sequencing (NGS) technology is capable of sequencing millions or billions of DNA molecules simultaneously. Therefore, it represents a promising tool for the analysis of molecular targets for the initial diagnosis of disease, monitoring of disease progression, and identifying the mechanism of drug resistance. On behalf of the Tumor Biomarker Committee of the Chinese Society of Clinical Oncology (CSCO) and the China Actionable Genome Consortium (CAGC), the present expert group hereby proposes advisory guidelines on clinical applications of NGS technology for the analysis of cancer driver genes for precision cancer therapy. This group comprises an assembly of laboratory cancer geneticists, clinical oncologists, bioinformaticians, pathologists, and other professionals. After multiple rounds of discussions and revisions, the expert group has reached a preliminary consensus on the need of NGS in clinical diagnosis, its regulation, and compliance standards in clinical sample collection. Moreover, it has prepared NGS criteria, the sequencing standard operation procedure (SOP), data analysis, report, and NGS platform certification and validation.

Identification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis

BACKGROUND

The current study aimed to identify potential diagnostic and prognostic gene biomarkers for colorectal cancer (CRC) based on the Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) dataset.

METHODS

Microarray data of gene expression profiles of CRC from GEO and RNA-sequencing dataset of CRC from TCGA were downloaded. After screening overlapping differentially expressed genes (DEGs) by R software, functional enrichment analyses of the DEGs were performed using the DAVID database. Then, the STRING database and Cytoscape were used to construct a protein-protein interaction (PPI) network and identify hub genes. The receiver operating characteristic (ROC) curves were conducted to assess the diagnostic values of the hub genes. Cox proportional hazards regression was performed to screen the potential prognostic genes. Kaplan-Meier curve and the time-dependent ROC curve were used to assess the prognostic values of the potential prognostic genes for CRC patients.

RESULTS

Integrated analysis of GEO and TCGA databases revealed 207 common DEGs in CRC. A PPI network consisted of 70 nodes and 170 edges were constructed and top 10 hub genes were identified. The area under curve (AUC) of the ROC curves of the hub genes were 0.900, 0.927, 0.869, 0.863, 0.980, 0.682, 0.903, 0.790, 0.995, and 0.989 for CCL19, CXCL1, CXCL5, CXCL11, CXCL12, GNG4, INSL5, NMU, PYY, and SST, respectively. A prognostic gene signature consisted of 9 genes including SLC4A4, NFE2L3, GLDN, PCOLCE2, TIMP1, CCL28, SCGB2A1, AXIN2, and MMP1 was constructed with a good performance in predicting overall survivals of CRC patients. The AUC of the time-dependent ROC curve was 0.741 for 5-year survival.

CONCLUSION

The results in this study might provide some directive significance for further exploring the potential biomarkers for diagnosis and prognosis prediction of CRC patients.

In silico analyses for potential key genes associated with gastric cancer

BACKGROUND

Understanding hub genes involved in gastric cancer (GC) metastasis could lead to effective approaches to diagnose and treat cancer. In this study, we aim to identify the hub genes and investigate the underlying molecular mechanisms of GC.

METHODS

To explore potential therapeutic targets for GC,three expression profiles (GSE54129, GSE33651, GSE81948) of the genes were extracted from the Gene Expression Omnibus (GEO) database. The GEO2R online tool was applied to screen out differentially expressed genes (DEGs) between GC and normal gastric samples. Database for Annotation, Visualization and Integrated Discovery was applied to perform Gene Ontology (GO) and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network of these DEGs was constructed using a STRING online software. The hub genes were identified by the CytoHubba plugin of Cytoscape software. Then, the prognostic value of these identified genes was verified by gastric cancer database derived from Kaplan-Meier plotter platform.

RESULTS

A total of 85 overlapped upregulated genes and 44 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, endodermal cell differentiation, and endoderm formation. Moreover, five KEGG pathways were significantly enriched, including ECM-receptor interaction, amoebiasis, AGE-RAGE signaling pathway in diabetic complications, focal adhesion, protein digestion and absorption. By combining the results of PPI network and CytoHubba, a total of nine hub genes including COL1A1, THBS1, MMP2, CXCL8, FN1, TIMP1, SPARC, COL4A1, and ITGA5 were selected. The Kaplan-Meier plotter database confirmed that overexpression levels of these genes were associated with reduced overall survival, except for THBS1 and CXCL8.

CONCLUSIONS

Our study suggests that COL1A1, MMP2, FN1, TIMP1, SPARC, COL4A1, and ITGA5 may be potential biomarkers and therapeutic targets for GC. Further study is needed to assess the effect of THBS1 and CXCL8 on GC.

Identification of key candidate genes and biological pathways in bladder cancer

Background

Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms.

Methods

The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer.

Results

In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA.

Conclusions

This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.