Gene Expression Profiling Interactive Analysis Databases Research Articles

Prognostic relevance of SMC family gene expression in human sarcoma.

Objective: To explore the prognostic value of the expression of genes encoding structural maintenance of chromosomes (SMCs) in human sarcoma.Results: We found that the levels of SMC1A, SMC2, SMC3, SMC4, SMC5 and SMC6 mRNA were all higher in most tumors compared to normal tissues, and especially in sarcoma. According to the Cancer Cell Line Encyclopedia (CCLE), SMC1A, SMC2, SMC3, SMC4, SMC5 and SMC6 are also highly expressed in sarcoma cell lines. Results of Gene Expression Profiling Interactive Analysis (GEPIA) indicated that high expression of SMC1A was significantly related to poor overall survival (OS) (p<0.05) and disease-free survival (DFS) in sarcoma (p<0.05). Additionally, strong expression of SMC2 was significantly related to poor OS in sarcoma (p<0.05). In contrast, SMC3, SMC4, SMC5, and SMC6 expression had no significant impact on OS or DFS in sarcoma.Conclusions: Expression of SMC family members is significantly different in sarcoma relative to normal tissues, and SMC1A and SMC2 may be useful as prognostic biomarkers.Methods: We performed a detailed comparison of cancer and normal tissues regarding the expression levels of mRNA for SMC family members in various cancers including sarcoma through ONCOMINE and GEPIA (Gene Expression Profile Interactive Analysis) databases.

Expression and prognostic analyses of the insulin-like growth factor 2 mRNA binding protein family in human pancreatic cancer

BackgroundDespite advances in early diagnosis and treatment, cancer remains the leading cause of mortality worldwide. The insulin-like growth factor 2 mRNA binding protein (IGF2BP) family has been reported to be involved in a variety of human malignant tumours. However, little is known about their expression and prognostic value in human pancreatic cancer. Therefore, we performed a detailed cancer versus normal differential analysis.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases were used to analyse the mRNA expression levels of the IGF2BP family in various cancers, including pancreatic cancer. Then, the LinkedOmics and GEPIA databases were used to assess the relation between the expression levels of IGF2BPs and overall survival (OS). Then, univariate and multivariate Cox regression analyses were performed, and subgroups based on grade and stage were analysed. The signalling pathways associated with IGF2BP2 and IGF2BP3 were then investigated via gene set enrichment analysis (GSEA).ResultsIGF2BP2 and IGF2BP3 were associated with each subset of OS based on grade and stage. Further clinical correlation analysis of IGF2BP2 and IGF2BP3 confirmed that IGF2BP2 and IGF2BP3 are fundamental factors in promoting pancreatic cancer progression.ConclusionIGF2BP2 and IGF2BP3 are key factors in promoting the progression of pancreatic cancer and are closely related to overall survival.

Comprehensively analyze the expression and prognostic role for ten-eleven translocations (TETs) in acute myeloid leukemia.

BackgroundThe ten-eleven translocation (TET) family oxidize 5-methylcytosines (5mCs) and promote the locus-specific reversal of DNA. The role of TETs in acute myeloid leukemia (AML) is mostly unknown.MethodsTETs mRNA expression levels were analyzed via Gene Expression Profiling Interactive Analysis (GEPIA). The association TETs expression levels and methylation with prognosis by UALCAN GenomicScape, and METHsurv. We analyzed TETs’ aberration types, located mutations, and structures via cBioPortal. GeneMANIA performed the functional network. Gene ontology (GO) enrichment was analyzed via LinkedOmics. MiWalK identified miRNAs, miTarbase, and TargetScan. Transcription factor (TF) targets were analyzed via ChEA3. GSCAlite analyzed the role of these defined genes in cancer pathways and potential drug targets. Finally, we selected AML patients in our department to investigate the mutated types of TETs.ResultsTETs expression level results showed TET1 (P=0.003) and TET2 (P=0.004) overexpressed in Haferlach leukemia samples, TET3 (P=4.04e-8) downregulation in Andersson leukemia samples. TET2 and TET3 overexpression but TET1 downregulation in the GEPIA database. Overexpression of TET2 leads to positive outcomes (P=0.0091). The upregulation of TET2 led to poor survival for CN-AML patients, but downregulation of TET3 indicated a satisfactory prognosis. The hypermethylation of TETs like cg24705708 (P=0.036), cg05976228 (P=0.022), cg19127638 (P=0.022), cg15254238 (P=0.025), cg07669489 (P=0.037) indicate poor outcomes. Overexpression of GALNS (P=0.024) as an adverse biomarker, downregulation of E2F5 (P=0.037), MAP7 (P=0.019), and NRIP1 (P=0.0013) indicated good prognosis. Regulatory network analysis indicated TETs’ functions, including covalent chromatin modification, histone modification, DNA methylation, or demethylation. Enrichment functions involving. TETs participate in several cancer pathways, including DNA repair response and receptor tyrosine kinase (RTK) signaling pathway. TETs are sensitive to belinostat, ceranib-2, docetaxel, tivantinib, and vincristine.ConclusionPresent study showed that TETs have different expressions in AML, and the expression levels of TETs lead to different outcomes of AML. The TETs cancer pathway analysis will also provide potential therapy methods for AML patients with TETs aberrations.

Screening of hub genes associated with prognosis in non-small cell lung cancer by integrated bioinformatics analysis.

BackgroundLung cancer is an intractable disease and the second leading cause of cancer-related deaths and morbidity in the world. This study conducted a bioinformatics analysis to identify critical genes associated with poor prognosis in non-small cell lung cancer (NSCLC).MethodsWe downloaded three datasets (GSE33532, GSE27262, and GSE18842) from the gene expression omnibus (GEO), and used the GEO2R online tools to identify the differentially expressed genes (DEGs). We then used the Search Tool for Retrieval of Interacting Genes (STRING) database to establish a protein-protein interaction (PPI) network and used the Cytoscape software to perform a module analysis of the PPI network. A Kaplan-Meier plotter was used to perform the overall survival (OS) analysis, and the Gene Expression Profiling Interactive Analysis (GEPIA) database was used for expression level analysis of hub genes. Further, the UALCAN database was used to validate the relationship between the gene expression level of each hub gene and clinical characteristics.ResultsWe identified 254 DEGs, which were composed of 66 up-regulated genes and 188 down-regulated genes. Out of these, five DEGs were identified as hub genes (CDC20, BUB1, CCNB2, CCNB1, UBE2C) by constructing a PPI network. The use of a Kaplan-Meier plotter to generate patient survival curves suggested a strong relationship between the five hub genes with worse OS. Validation of the above results using the GEPIA database showed that all the hub genes were highly expressed in NSCLC tissues. Using the UALACN data mining platform, we found that the five hub genes are correlated with tumor stage and the status of node metastasis in NSCLC patients.ConclusionsWe identified five hub DEGs that might provide perspectives in the explorations of pathogenesis and treatments for NSCLC.

Exosome-transferred LINC01559 promotes the progression of gastric cancer via PI3K/AKT signaling pathway

Increasing evidence indicates that long non-coding RNAs (lncRNAs) are associated with the progression of human cancers. However, the expression level and function of LINC01559 (long intergenic non-protein coding RNA 1559) in gastric cancer (GC) are rarely reported. Here we found that LINC01559 was upregulated in GC tissues based on GEPIA (Gene Expression Profiling Interactive Analysis) and TCGA (The Cancer Genome Atlas) databases. Also, LINC01559 was expressed at a lower level in GC cells than in mesenchymal stem cells (MSCs). In vitro experiments revealed that silencing LINC01559 remarkably hindered GC cell proliferation, migration and stemness. Then, we identified that LINC01559 was transmitted form MSCs to GC cells via the exosomes. Immunofluorescence staining and electron microscope validated the existence of exosomes in GC cells. Mechanistically, LINC01559 sponged miR-1343-3p to upregulate PGK1 (phosphoglycerate kinase 1), therefore activating PI3K/AKT pathway. Moreover, LINC01559 recruited EZH2 (enhancer of zeste 2 polycomb repressive complex 2 subunit) to PTEN (phosphatase and tensin homolog) promoter, inducing the methylation of PTEN promoter and finally resulting in PTEN repression. Of note, LINC01559 targeted both PGK1 and PTEN to promote GC progression by activating PI3K/AKT pathway. Taken together, our study demonstrated that LINC01559 accelerated GC progression via upregulating PGK1 and downregulating PTEN to trigger phosphatidylinositol 3-kinase/AKT serine/threonine kinase (PI3K/AKT) pathway, indicating LINC01559 as a potential biomarker for GC treatment.

PTPRA facilitates cancer growth and migration via the TNF-α-mediated PTPRA-NF-κB pathway in MCF-7 breast cancer cells.

Protein tyrosine phosphatase receptor type A (PTPRA), one of the classic protein tyrosine phosphatases, is crucial for modulating tumorigenesis and metastasis in breast cancer; however, its functional mechanism has not fully elucidated. The present study assessed PTPRA expression and estimated its clinical impact on survival using the Gene Expression Profiling Interactive Analysis database (GEPIA). Growth curves, colony formations and Transwell assays were utilized to examine cell proliferation and migration. Additionally, luciferase reporter assays were used to examine the potential tumor signaling pathways targeted by PTPRA in HEK293T cells. Furthermore, quantitative PCR (qPCR) was utilized to confirm the transcriptional regulation of PTPRA expression. Bioinformatic analyses of data from GEPIA identified PTPRA overexpression in patients with breast cancer. The growth curve, colony formation and transwell experiments demonstrated that PTPRA upregulation significantly promoted the cell proliferation and migration of MCF-7 breast cancer cells. In contrast, PTPRA knockdown significantly attenuated cell proliferation and migration. Mechanistic experiments revealed that the transcriptional activity of NF-κB was higher compared with other classic tumor pathways when they were activated by PTPRA in HEK293T cells. Furthermore, the transcriptional activity of NF-κB was altered in a PTPRA-dose-dependent manner. Additionally, following exposure to TNF-α, PTPRA-deficient MCF-7 cells exhibited lower NF-κB transcriptional activity compared with normal control cells. The results of the present study demonstrate that PTPRA overexpression accelerates inflammatory tumor phenotypes in breast cancer and that the TNF-α-mediated PTPRA-NF-κB pathway may offer novel insight into early diagnosis and optimum treatment for breast cancer.

Identification of Prostate Cancer-Related Circular RNA Through Bioinformatics Analysis.

BackgroundProstate cancer (PCa) is one of the most common malignant tumors worldwide. Accumulating evidence has suggested that circular RNAs (circRNAs) are involved in the development and progression of various cancers, and they show great potential as novel biomarkers. However, the underlying mechanisms and specific functions of most circRNAs in PCa remain unknown. Here, we aimed to identify circRNAs with potential roles in PCa from the PCa expression profile.MethodsWe used data downloaded from the Gene Expression Omnibus to identify circRNAs that were differentially expressed between PCa samples and adjacent non-tumor samples. Relative expression levels of identified circRNAs were validated by quantitative real-time PCR. Micro (mi)RNA response elements were predicted by the CircInteractome database, and miRNA target genes were predicted by miRDB, miRTarBase, and TargetScan databases. Gene ontology (GO) enrichment analysis and pathway analysis revealed the potential biological and functional roles of these target genes. A circRNA–miRNA–mRNA interaction network was constructed by Cytoscape. The interaction between circRNAs and miRNAs in PCa was thoroughly reviewed in the PubMed. Finally, the mRNA expression of these genes was validated by the Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. The expression of proteins encoded by these genes was further validated by the Human protein Atlas (HPA) database.ResultsA total of 60 circRNAs that were differentially expressed between PCa and healthy samples were screened, of which 15 were annotated. Three circRNAs (hsa_circ_0024353, hsa_circ_0085494, hsa_circ_0031408) certified the criteria were studied. The results of quantitative real-time PCR demonstrated that the expression of hsa_circ_0024353 was significantly downregulated in PC-3 cells when compared with RWPE-1 cells, while the expression of hsa_circ_0031408 and hsa_circ_0085494 was significantly upregulated in PC-3 cells when compared with RWPE-1 cells. GO and Kyoto Encyclopedia of Genes and Genomes analyses found that target genes were mainly enriched in metabolic processes and pathways involving phosphoinositide 3-kinase-Akt signaling, hypoxia-inducible factor-1 signaling, p53 signaling, and the cell cycle. A total of 11 miRNA target genes showing differential expression between PCa and healthy samples were selected, and their mRNA and protein expression were validated by GEPIA and HPA databases, respectively. Of these, PDE7B, DMRT2, and TGFBR3 were identified as potentially playing a role in PCa progression. Finally, three circRNA–miRNA–mRNA interaction axes were predicted by bioinformatics: hsa_circ_0024353–hsa-miR-940–PDE7B, hsa_circ_0024353–hsa-miR-1253–DMRT2, and hsa_circ_0085494–hsa-miR-330-3p–TGFBR3.ConclusionThis study identified three circRNA–miRNA–mRNA interaction axes that might provide novel insights into the potential mechanisms underlying PCa development.

Prognostic Value and Immune Infiltrates of ABCA8 and FABP4 in Stomach Adenocarcinoma.

Background Stomach adenocarcinoma (STAD) is a common malignancy worldwide with poor prognosis. Therefore, it is important to identify a valuable prognostic biomarker for STAD. The aim of present study was to identify novel prognostic biomarkers for STAD and evaluate the potential role of hub genes in STAD. Methods Gene Expression Profiling Interactive Analysis (GEPIA) and Cancer RNA-Seq Nexus were performed to identify differentially expressed genes (DEGs). Subsequently, hub genes were selected by a Venn diagram, and the expression of key genes was confirmed by UALCAN database. Furthermore, survival analysis of these hub genes was performed using Oncolnc and Human Protein Atlas (HPA) database. Gene alteration status of hub genes was assessed by cBioPortal. Finally, we investigated the association between hub genes and immune cell infiltration in STAD through the Tumor Immune Estimation Resource (TIMER) and GEPIA database. Results Three common hub genes were obtained, including 2 downregulated DEGs (ABCA8 and FABP4) and one upregulated DEG (SLC52A3). Furthermore, increased expression of ABCA8 and FABP4 and decreased expression of SLC52A3 were correlated with poor prognosis. Meanwhile, three hub genes showed genetic alterations in various datasets of STAD. Finally, our results showed that ABCA8 and FABP4 displayed a positive correlation with immune infiltration, especially in M2 macrophages. Conclusions The results of this study suggest that ABCA8 and FABP4 may be used as prognostic biomarkers and correlated with immune infiltration in STAD.

High expression of ghrelin and obestatin prepropeptide in tumor tissues predicted adverse overall survival in gastric carcinoma patients.

Background:Gastric cancer (GC) is the most prevailing digestive tract malignant tumor worldwide with high mortality and recurrence rates. However, its potential molecular mechanism and prognostic biomarkers are still not fully understood. We aim to screen novel prognostic biomarkers related to GC prognosis using comprehensive bioinformatic tools.Methods:Four gene expression microarray data were downloaded from the Gene Expression Omnibus (GEO) database (GSE26942, GSE33335, GSE63089, and GSE79973). Differentially expressed genes (DEGs) between gastric carcinoma and normal gastric tissue samples were identified by an integrated bioinformatic analysis. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using statistical software R. STRING and Cytoscape software were employed to construct protein–protein interaction (PPI) networks. Hub genes with a high score of connectivity identified from the PPI network were identified. Prognostic values of hub genes were evaluated in GSE15459 dataset. Hub genes related to GC overall survival were further validated in GEPIA (Gene Expression Profiling Interactive Analysis) online tool.Results:A total of 12 upregulated DEGs and 59 downregulated DEGs were identified when the 4 microarray data overlapped. Among them, 10 hub genes with a high score of connectivity were identified. High expression of ghrelin and obestatin prepropeptide (GHRL), BGN, TIMP metallopeptidase inhibitor 1, thrombospondin 2, secreted phosphoprotein 1, and low expression of CHGA were associated with a poor overall survival of gastric cancer (all log rank P < .05). After validation in GEPIA database, only GHRL was confirmed associated with a poor overall survival of gastric cancer (log rank P = .04).Conclusions:GHRL could be used as a novel biomarker for the prediction of a poor overall survival of gastric cancer, and could be a novel therapeutic target for gastric cancer treatment. However, future experimental studies are still required to validate these findings.

TSPYL5 activates endoplasmic reticulum stress to inhibit cell proliferation, migration and invasion in colorectal cancer.

Testis-specific protein Y-encoded-like 5 (TSPYL5), a member of the nucleosome assembly protein (NAP) superfamily, functions as a tumor suppressor in ovarian and lung cancer, yet its clinical significance and molecular mechanism in colorectal cancer (CRC) remain unclear. TSPYL5 expression was analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database. CRC cell lines HCT116 and HT29 were forced to overexpress TSPYL5 by transfection with pcDNA3.1-TSPYL5. Cell proliferation, apoptosis, migration, and invasion were examined by EdU proliferation assays, flow cytometry, and Transwell assays, respectively. Endoplasmic reticulum stress (ERS) was examined by transmission electron microscopy. Western blot analyses were performed to assess the expression of ERS-associated proteins. GEPIA database analysis showed that CRC patients had lower levels of TSPYL5 expression in their tumor tissues when compared with their para-carcinoma tissues. In vitro experiments indicated that TSPYL5 overexpression significantly suppressed cell proliferation, migration, and invasion, and induced apoptosis and ERS in HCT116 and HT29 cells. Furthermore, the levels of caspase-1, caspase-3, Bax, ATF4, and CHOP protein expression were upregulated after TSPYL5 was overexpressed. In conclusion, our data suggest that TSPYL5 can activate an ERS response that suppresses the proliferation, migration, and invasion of tumor cells. This mechanism may represent a promising therapeutic strategy for CRC.

Bioinformatics analysis of key biomarkers and potential molecular mechanisms in hepatocellular carcinoma induced by hepatitis B virus.

Background:Hepatocellular carcinoma (HCC) accounts for up to 90% of all primary hepatic malignancies; it is the sixth most common cancer and the second most common cause of cancer mortality worldwide. Numerous studies have shown that hepatitis B virus and its products, HBV integration, and mutation can induce HCC. However, the molecular mechanisms underpinning the regulation of HCC induced by HBV remain unclear.Methods:We downloaded 2 gene expression profiling datasets, of HBV and of HCC induced by HBV, from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between HCC and HBV were identified to explore any predisposing changes in gene expression associated with HCC. DEGs between HCC and adjacent healthy tissues were investigated to identify genes that may play a key role in HCC. Any overlapping genes among these DEGs were included in our bioinformatics analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlapping genes were performed using the Metascape online database; the protein–protein interaction (PPI) network was analyzed using the STRING online database; and we obtained the hub genes of the PPI network using Cytoscape software. An overall survival (OS) analysis of hub genes was performed using km-plotter and the gene expression profiling interactive analysis (GEPIA) online database. The expression levels of hub genes were determined using the TCGA and GEPIA databases. Finally, the relationships between hub genes and tumors were analyzed using the comparative toxicogenomics database (CTD).Results:We identified 113 overlapping genes from the 2 datasets. Using functional and pathway analyses, we found that the overlapping genes were mainly related to the AMPK signaling pathway and cellular responses to cadmium ions. C8A, SPP2, KLKB1, PROZ, C6, FETUB, MBL2, HGFAC, C8B, and ANGPTL3 were identified as hub genes and C8A, SPP2, PROZ, C6, HGFAC, and C8B were found to be significant for survival.Conclusion:The DEGs re-analyzed between HCC and hepatitis B enable a systematic understanding of the molecular mechanisms of HCC reliant on hepatitis B virus.

CD38 Predicts Favorable Prognosis by Enhancing Immune Infiltration and Antitumor Immunity in the Epithelial Ovarian Cancer Microenvironment.

The identification of predictive biomarkers and novel targets to optimize immunotherapy strategies for epithelial ovarian cancer (EOC) is urgently needed. CD38 is a multifunctional glycoprotein that acts as an ectoenzyme and immune receptor. However, the underlying immunological mechanisms and prognostic value of CD38 in EOC remain unclear. CD38 gene expression in EOC was evaluated by using Gene Expression Profiling Interactive Analysis (GEPIA) and TISIDB database. The prognostic value was calculated using GEPIA and Kaplan–Meier plotter. Gene set enrichment analysis was conducted to study the roles of CD38 in the EOC microenvironment. Furthermore, the relationship between CD38 expression level and immune cell infiltration was analyzed by the Tumor Immune Estimation Resource and TISIDB. The GEPIA and TISIDB databases showed that CD38 expression in EOC was higher than that in normal tissue and was highest in the immunoreactive subtype among the four molecular types. A total of 424 cases from GEPIA revealed that high levels of CD38 were associated with longer disease-free survival [hazard ratio (HR) = 0.66, P = 0.00089] and increased overall survival rate (HR = 0.67, P = 0.0016). Kaplan–Meier plotter also confirmed the prognostic value of CD38 in EOC. Data from The Cancer Genome Atlas database demonstrated that gene signatures in many categories, such as immune response and adaptive immune response, were enriched in EOC samples with high CD38 expression. In addition, CD38 was positively correlated with immune cell infiltration, especially infiltration of activated CD8+ T cells, CD4+ T cells, and B cells. CD38 is positively correlated with prognosis and immune cell infiltration in the EOC microenvironment and contributes to the regulation of antitumor immunity. CD38 could be used as a prognostic biomarker and potential immunotherapy target.

Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis.

The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC.

Upregulation of desmoglein 2 and its clinical value in lung adenocarcinoma: a comprehensive analysis by multiple bioinformatics methods.

BackgroundDesmoglein-2 (DSG2), a desmosomal adhesion molecule, is found to be closely related to tumorigenesis in recent years. However, the clinical value of DSG2 in lung adenocarcinoma remains unclear.MethodsReal-time reverse transcription-quantitative polymerase chain reaction (qRT-PCR) was utilized to detect the expression of DSG2 in 40 paired lung adenocarcinoma tissues and corresponding non-cancerous tissues. Data from The Cancer Genome Atlas (TCGA) and Oncomine datasets were also downloaded and analyzed. The correlation between DSG2 and clinicopathological features was investigated. The expression of DSG2 protein by immunohistochemical was also detected from tissue microarray and the Human Protein Atlas database. Integrated meta-analysis combining the three sources (qRT-PCR data, TCGA data and Oncomine datasets) was performed to evaluate the clinical value of DSG2. Univariate and multivariate Cox regression analyses were used to explore the prognostic value of DSG2. Then, co-expressed genes were calculated by Pearson correlation analysis. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to investigate the underlying molecular mechanism. The expression level in lung adenocarcinoma and prognostic significance of the top ten co-expressed genes were searched from Gene Expression Profiling Interactive Analysis (GEPIA) online database.ResultsDSG2 was highly expressed in lung adenocarcinoma tissues based on qRT-PCR, TCGA and Oncomine datasets. The protein expression of DSG2 was also higher in lung adenocarcinoma. According to qRT-PCR and TCGA, high DSG2 expression was positively associated with tumor size (p = 0.027, p = 0.001), lymph node metastasis (p = 0.014, p < 0.001) and TNM stage (p = 0.023, p < 0.001). The combined standard mean difference values of DSG2 expression based on the three sources were 1.30 (95% confidence interval (CI): 1.08–1.52) using random effect model. The sensitivity and specificity were 0.73 (95% CI [0.69–0.76]) and 0.96 (95% CI [0.89–0.98]). The area under the curve based on summarized receiver operating characteristic (SROC) curve was 0.79 (95% CI [0.75–0.82]). Survival analysis revealed that high DSG2 expression was associated with a short overall survival (hazard ratio [HR] = 1.638; 95% CI [1.214–2.209], p = 0.001) and poor progression-free survival (HR = 1.475; 95% CI [1.102–1.974], p < 0.001). A total of 215 co-expressed genes were identified. According to GO and KEGG analyses, these co-expressed genes may be involved in “cell division”, “cytosol”, “ATP binding” and “cell cycle”. Based on GEPIA database, seven of the top ten co-expressed genes were highly expressed in lung adenocarcinoma (DSC2, SLC2A1, ARNTL2, ERO1L, ECT2, ANLN and LAMC2). High expression of these genes had shorter overall survival.ConclusionsThe expression of DSG2 is related to the tumor size, lymph node metastasis and TNM stage. Also, DSG2 predicts poor prognosis in lung adenocarcinoma.

Inhibition of cyclinB1 Suppressed the Proliferation, Invasion, and Epithelial Mesenchymal Transition of Hepatocellular Carcinoma Cells and Enhanced the Sensitivity to TRAIL-Induced Apoptosis.

BackgroundCyclinB1 is highly expressed in various tumor tissues and plays an important role in tumor progression. However, its role in hepatocellular carcinoma (HCC) remains unclear. Therefore, the aim of this study was to explore the role of cyclinB1 in the development and progression of HCC.MethodsThe expression of cyclinB1 was analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database, and detected in HCC tissues and HCC cell lines through quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blotting. CyclinB1-short hairpin RNA (Sh-cyclinB1) was transfected into HCC cells to knockdown cyclinB1, and the effect of cyclinB1 knockdown on HCC was examined via the MTT assay, colony formation assay, wound healing assay, scratch assay, cell cycle analysis in vitro, and xenograft model in nude mice. In addition, the role of cyclinB1 on tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-induced apoptosis was measured using flow cytometry and Western blotting.ResultsThe GEPIA database analysis showed that cyclinB1 was highly expressed in HCC tissues. The results of qRT-PCR and Western blotting proved that the expression of cyclinB1 was significantly increased in HCC tissues and cell lines. The data of the MTT assay, colony formation assay, and cell cycle analysis indicated that cyclinB1 knockdown inhibited the proliferation of HCC cells. In addition, cell migration, invasion, and epithelial mesenchymal transition were also impaired by cyclinB1 knockdown. Furthermore, the xenograft model in nude mice demonstrated that inhibition of cyclinB1 suppressed tumor growth and metastasis in vivo. Finally, the results of flow cytometry and Western blotting indicated that inhibition of cyclinB1 enhanced the sensitivity of HCC cells to TRAIL-induced apoptosis.ConclusionOverall, these data provide reasonable evidence that cyclinB1 may serve as a proto-oncogene during the progression of HCC.

Identification of CDC25C as a Potential Biomarker in Hepatocellular Carcinoma Using Bioinformatics Analysis.

Hepatocellular carcinoma (HCC) is the most aggressive type of gastrointestinal tumor, with a high rate of mortality. However, identifying biomarkers for the treatment of HCC remains to be developed. We aimed to determine whether cell division cycle 25C (CDC25C) could be used as a novel diagnostic and therapeutic biomarker in HCC. Expression of CDC25C in HCC was analyzed by using GEPIA (Gene Expression Profiling Interactive Analysis) and UALCAN databases. GEPIA and CBioPortal databases were applied to analyze patients’survival and CDC25C mutations, respectively. PPI (Protein-Protein Interaction) network was further built by STRING (Search Tool for the Retrieval of Interacting Genes) and Metascape Web portals. To the best of our knowledge, the novel observations identified in the present study reveal that the expression of CDC25C in HCC was significantly enhanced when compare to that in normal liver tissues (P < 0.001). A higher CDC25C expression resulted in a remarkably shorter disease free survival as well as overall survival. Moreover, the expression of CDC25C in HCC was related to HCC patients’grade and race, but not gender. The expression levels of CDC25C elevated gradually from stage 1 to 3 but decreased in stage 4. The specific gene mutations V41A, L87 H, N222 K and X309-splice of CDC25C occurred in HCC samples and these unique mutations were not detected in any other tumor tissues. Finally, PPI networks and GO enrichment analysis suggested that CDC25C might be associated with cell cycle and p53 signaling pathway. Taken together, bioinformatics analysis revealed that CDC25C might be a potential diagnostic predictor for HCC.

Novel candidate biomarkers of origin recognition complex 1, 5 and 6 for survival surveillance in patients with hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) has high morbidity and mortality and lacks effective biomarkers for early diagnosis and survival surveillance. Origin recognition complex (ORC), consisting of ORC1-6 isoforms, was examined to assess the potential significance of ORC isoforms for HCC prognosis.Methods: Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) databases were used to examine differential isoform expression, stage-specific expression, calculate Pearson correlations and perform survival analysis. A human protein atlas database was utilized to evaluate the protein expression of ORCs in liver tissue. The cBioPortal database was used to assess isoform mutations and the survival significance of ORCs in HCC. Cytoscape software was employed to construct gene ontologies, metabolic pathways and gene-gene interaction networks.Results: Differential expression analysis indicated that ORC1 and ORC3-6 were highly expressed in tumor tissues in the Oncomine and GEPIA databases, while ORC2 was not. All the ORCs were showed positive and statistically significant correlations with each other (all P<0.001). ORC1-2 and ORC4-6 expressions were associated with disease stages I-IV (all P<0.05), but ORC3 was not. Survival analysis found that ORC1 and ORC4-6 expressions were associated with overall survival (OS), and ORC1-3 and ORC5-6 expression were associated with recurrence-free survival (RFS; all P<0.05). In addition, low expression of these ORC genes consistently indicated better prognosis compared with high expression. Protein expression analysis revealed that ORC1 and ORC3-6 were expressed in normal liver tissues, whereas ORC2 was not. Enrichment analysis indicated that ORCs were associated with DNA metabolic process, sequence-specific DNA binding and were involved in DNA replication, cell cycle, E2F-enabled inhibition of pre-replication complex formation and G1/S transition.Conclusions: Differentially expressed ORC1, 5 and 6 are candidate biomarkers for survival prediction and recurrence surveillance in HCC.

PLK1 Is Implicated in the Poor Prognosis of Hepatocellular Carcinoma

We aimed to identify if PLK1 could be used as a new diagnostic and therapeutic biomarker in hepatocellular carcinoma (HCC) patient. Expression of PLK1 in HCC was analyzed by using GEPIA (Gene Expression Profiling Interactive Analysis) and UALCAN databases. GEPIA and CBioPortal tools were applied to determine patients’ survival and PLK1 mutations, respectively. PPI (Protein-Protein Interaction) networks were further built by STRING (Search Tool for the Retrieval of Interacting Genes) and Metascape Web portals. The data demonstrated that the expression of PLK1 in HCC was significantly enhanced when compared to normal liver tissues (P < 0.001). A higher PLK1 expression resulted in a remarkably shorter disease-free survival as well as overall survival. Moreover, the expression of PLK1 in HCC was related to HCC patients’ grade and race, but not gender. The data also suggested that expression of PLK1 elevated gradually from stage 1 to 3 but decreased in stage 4. Three specific gene mutations K146R, S335Afs*120 and D429H of PLK1 occurred in HCC and these unique mutations were not seen in any other tumor tissues. Finally, PPI networks and GO enrichment analysis suggested that PLK1 might be associated with cell cycle and p53 signaling pathway etc. Taken together, our novel findings suggest that PLK1 is implicated in the poor prognosis of hepatocellular carcinoma.

High gene expression levels of VEGFA and CXCL8 in the peritumoral brain zone are associated with the recurrence of glioblastoma: A bioinformatics analysis.

The present study aimed to identify differentially regulated genes between the peritumoral brain zone (PBZ) and tumor core (TC) of glioblastoma (GBM), to elucidate the underlying molecular mechanisms and provide a target for the treatment of tumors. The GSE13276 and GSE116520 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) for the PBZ and TC were obtained using the GEO2R tool. The bioinformatics and evolutionary genomics online tool Venn was used to identify common DEGs between the two datasets. The Database for Annotation, Visualization, and Integrated Discovery online tool was used to analyze enriched pathways of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The Search Tool for the Retrieval of Interacting Genes/Proteins online tool was used to construct a protein-protein interaction (PPI) network of DEGs. Hub genes were identified using Cytohubba, a plug-in for Cytoscape. The Gene Expression Profiling Interactive Analysis (GEPIA) database was utilized to perform survival analysis. In total, 75 DEGs, including 12 upregulated and 63 downregulated genes, were identified. In the GO term analysis, these DEGs were mainly enriched in ‘regulation of angiogenesis’ and ‘central nervous system development’. Furthermore, in the KEGG pathway analysis, the DEGs were mainly enriched in ‘bladder cancer’ and ‘endocytosis’. When filtering the results of the PPI network analysis using Cytohubba, a total of 10 hub genes, including proteolipid protein 1, myelin associated oligodendrocyte basic protein, contactin 2, myelin oligodendrocyte glycoprotein, myelin basic protein, myelin associated glycoprotein, SRY-box transcription factor 10, C-X-C motif chemokine ligand 8 (CXCL8), vascular endothelial growth factor A (VEGFA) and plasmolipin, were identified. These hub genes were further subjected to GO term and KEGG pathway analysis, and were revealed to be enriched in ‘central nervous system development’, ‘bladder cancer’ and ‘rheumatoid arthritis’. These hub genes were used to perform survival analysis using the GEPIA database, and it was determined that VEGFA and CXCL8 were significantly associated with a reduction in the overall survival of patients with GBM. In conclusion, the results suggest that the recurrence of GBM is associated with high gene expression levels VEGFA and CXCL8, and the development of the central nervous system.

AKR1C1-3, notably AKR1C3, are distinct biomarkers for liver cancer diagnosis and prognosis: Database mining in malignancies.

Aldo-keto reductases, known as AKR1C1-AKR1C4 enzymes, are pivotal to NADPH-dependent reduction, and their expression is highly associated with the progression of malignant cancers. However, the expression and distinct prognostic value of the AKR1C family members in liver cancer are not well established. In the current study, the expression of AKR1C isoforms was studied using the Oncomine online databases. In addition, their expression profiles were analyzed in cancer cell lines using data from the Cancer Cell lines Encyclopedia (CCLE) database. Furthermore, the mRNA expression levels of AKR1C family members between liver cancer and normal liver samples were assessed by the Gene Expression Profiling Interactive Analysis (GEPIA) database. The AKR1C1-3 prognostic value was further investigated by the Kaplan-Meier plotter database in liver cancer patients. It was found that the expression levels of AKR1C3 were elevated significantly in liver cancer tissues and cells as demonstrated by the Oncomine, CCLE and GEPIA databases. The expression levels of AKR1C1 and AKR1C2 in liver cancer tissues did not increase significantly in the Oncomine database while expression was significantly high in CCLE and GEPIA databases. However, the expression levels of the AKR1C4 gene as determined by the CCLE, GEPIA and Oncomine databases were not consistent. Therefore, the Kaplan-Meier survival curves of liver cancer patients with the expression of AKR1C1-3 genes were next analyzed. The data indicated that high expression levels of AKR1C1-3 were correlated with lower overall survival in liver cancer patients. Using the co-expression and PPI network, AKR1C1-3 genes were identified that were involved in the same pathway displaying 44 total unique interactors. These results suggested that the increased AKR1C1-3, notably AKR1C3 expression levels served as possible diagnostic biomarkers and essential prognostic factors for liver cancer patients. The roles of AKR1C4 in liver cancer require further examination.