Gene expression profile analysis of pancreatic cancer based on microarray data

Identification of GNG7 as a novel biomarker and potential therapeutic target for gastric cancer via bioinformatic analysis and in vitro experiments

Gastric cancer (GC) is one of the most common malignancies with unfavorable prognoses. The present study aimed to identify novel biomarkers or potential therapeutic targets in GC via bioinformatic analysis and in vitro experiments. The Gene Expression Omnibus and The Cancer Genome Atlas databases were used to screen the differentially expressed genes (DEGs). After protein-protein interaction network construction, both module and prognostic analyses were performed to identify prognosis-related genes in GC. The expression patterns and functions of G protein γ subunit 7 (GNG7) in GC were then visualized in multiple databases and further verified using in vitro experiments. A total of 897 overlapping DEGs were detected and 20 hub genes were identified via systematic analysis. After accessing the prognostic value of the hub genes using the online server Kaplan-Meier plotter, a six-gene prognostic signature was identified, which was also significantly correlated with the process of immune infiltration in GC. The results of open-access database analyses suggested that GNG7 is downregulated in GC; this downregulation was associated with tumor progression. Furthermore, the functional enrichment analysis unveiled that the GNG7-coexpressed genes or gene sets were closely correlated with the proliferation and cell cycle processes of GC cells. Finally, in vitro experiments further confirmed that GNG7 overexpression inhibited GC cell proliferation, colony formation, and cell cycle progression and induced apoptosis. As a tumor suppressor gene, GNG7 suppressed the growth of GC cells via cell cycle blockade and apoptosis induction and thus may be used as a potential biomarker and therapeutic target for GC.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

Bioinformatic and experimental analyses of key biomarkers in pancreatic cancer

The present study aimed to screen the key genes in pancreatic cancer and to explore the pathogenesis of pancreatic cancer. A total of three expression profiling datasets (GSE28735, GSE16515 and GSE15471) associated with pancreatic cancer were retrieved from the public gene chip database. The differentially expressed genes (DEGs) were screened by GEO2R and subjected to Gene Ontology (GO) and signaling pathway enrichment analysis. Furthermore, a protein interaction network was constructed. The GEPIA online database was used to screen for genes that affect the prognosis of pancreatic cancer. Finally, cell functional experiments were performed on the selected key genes. A total of 72 DEGs were identified, including 52 upregulated and 20 downregulated genes. Enrichment analysis revealed roles of the DEGs in endodermal cell differentiation, cell adhesion, extracellular matrix-receptor interaction and PI3K-Akt signaling pathway. In total, 10 key nodal genes were identified, including integrin subunit α 2 (ITGA2), ITGB6 and collagen α 1 chain 1. Through survival analysis, two genes with an impact on the prognosis of pancreatic cancer were identified, namely ITGA2 and ITGB6. Silencing of ITGB6 in a pancreatic cancer cell line significantly suppressed cell proliferation and induced cell cycle arrest at G2/M phase. The identified key genes and signaling pathways may help to deepen the understanding of the molecular mechanisms involved in pancreatic cancer and provide a theoretical basis to develop novel therapies.

Identification of hub genes in gastric cancer by integrated bioinformatics analysis

BACKGROUND

Gastric cancer (GC) is one of the most common cancer worldwide. With the high rates of metastasis and recurrence, its overall survival remains poor at the present time. Hence, seeking new potential therapeutic targets of GC is important and urgent.

METHODS

We retrieved the gene expression profiles and clinical data from The Cancer Genome Atlas (TCGA) datasets. After screening differentially expressed genes (DEGs), we carried out the survival analysis for overall survival to pick out robust DEGs. To explore the role of these robust DEGs, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Subsequently, protein interactions network was constructed utilizing the Search Tool for the Retrieval of Interacting Genes (STRING) database. We then presented the module analysis and filtered out hub genes by the Cytoscape software. Finally, Kaplan-Meier analysis was utilized to demonstrate the prognostic role of these hub genes.

RESULTS

According to the gene expression profiles of TCGA and the survival analysis, 238 robust DEGs were filtered out, consisting of 140 up-regulated and 98 down-regulated genes. The up-regulated DEGs were mainly enriched in systemic lupus erythematosus, cytokine activity, and alcoholism, while down-regulated DEGs were mainly enriched in steroid hormone receptor activity, immune response, and metabolism. Through the construction of the protein-protein interaction (PPI) network, eight hub genes were finally screened out, including CCR8, HIST1H3B, HIST1H2AH, HIST1H2AJ, NPY, HIST2H2BF, GNG7, and CCL25.

CONCLUSIONS

Our study picked out eight hub genes, which might be potential prognostic biomarkers for GC and even be treatment targets for clinical implication in the future.

An Integrated Data Analysis of mRNA, miRNA and Signaling Pathways in Pancreatic Cancer

Although many genes and miRNAs have been reported for various cancers, pancreatic cancer's specific genes or miRNAs have not been studied precisely yet. Therefore, we have analyzed the gene and miRNA expression profile of pancreatic cancer data in the gene expression omnibus (GEO) database. The microarray-derived miRNAs and mRNAs were annotated by gene ontology (GO) and signaling pathway analysis. We also recognized mRNAs that were targeted by miRNA through the mirDIP database. An integrated analysis of the microarray revealed that only 6 out of 43 common miRNAs had significant differences in their expression profiles between the tumor and normal groups (P value < 0.05 and |log Fold Changes (logFC)|> 1). The hsa-miR-210 had upregulation, whereas hsa-miR-375, hsa-miR-216a, hsa-miR-217, hsa-miR-216b and hsa-miR-634 had downregulation in pancreatic cancer (PC). The analysis results also revealed 109 common mRNAs by microarray and mirDIP 4.1 databases. Pathway analysis showed that amoebiasis, axon guidance, PI3K-Akt signaling pathway, absorption and focal adhesion, adherens junction, platelet activation, protein digestion, human papillomavirus infection, extracellular matrix (ECM) receptor interaction, and riboflavin metabolism played important roles in pancreatic cancer. GO analysis revealed the significant enrichment in the three terms of biological process, cellular component, and molecular function, which were identified as the most important processes associated strongly with pancreatic cancer. In conclusion, DTL, CDH11, COL5A1, ITGA2, KIF14, SMC4, VCAN, hsa-mir-210, hsa-mir-217, hsa-mir-216a, hsa-mir-216b, hsa-mir-375 and hsa-mir-634 can be reported as the novel diagnostic or even therapeutic markers for the future studies. Also, the hsa-mir-107 and hsa-mir-125a-5p with COL5A1, CDH11 and TGFBR1 genes can be introduced as major miRNA and genes on the miRNA-drug-mRNA network. The new regulatory network created in our study could give a deeper knowledge of the pancreatic cancer.

Identification of Differentially Expressed Genes and miRNAs Associated with Esophageal Squamous Cell Carcinoma by Integrated Analysis of Microarray Data

To identify candidate key genes and miRNAs associated with esophageal squamous cell carcinoma (ESCC) development and prognosis, the gene expression profiles and miRNA microarray data including GSE20347, GSE38129, GSE23400, and GSE55856 were downloaded from the Gene Expression Omnibus (GEO) database. Clinical and survival data were retrieved from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs) was analyzed via DAVID, while the DEG-associated protein-protein interaction network (PPI) was constructed using the STRING database. Additionally, the miRNA target gene regulatory network and miRNA coregulatory network were constructed, using the Cytoscape software. Survival analysis and prognostic model construction were performed via the survival (version 2.42-6) and rbsurv R packages, respectively. The results showed a total of 2575, 2111, and 1205 DEGs, and 226 differentially expressed miRNAs (DEMs) were identified. Pathway enrichment analyses revealed that DEGs were mainly enriched in 36 pathways, such as the proteasome, p53, and beta-alanine metabolism pathways. Furthermore, 448 nodes and 1144 interactions were identified in the PPI network, with MYC having the highest random walk score. In addition, 7 DEMs in the microarray data, including miR-196a, miR-21, miR-205, miR-194, miR-103, miR-223, and miR-375, were found in the regulatory network. Moreover, several reported disease-related miRNAs, including miR-198a, miR-103, miR-223, miR-21, miR-194, and miR-375, were found to have common target genes with other DEMs. Survival analysis revealed that 85 DEMs were related to prognosis, among which hsa-miR-1248, hsa-miR-1291, hsa-miR-421, and hsa-miR-7-5p were used for a prognostic survival model. Taken together, this study revealed the important roles of DEGs and DEMs in ESCC development, as well as DEMs in the prognosis of ESCC. This will provide potential therapeutic targets and prognostic predictors for ESCC.

Increased expression of plakoglobin is associated with upregulated MAPK and PI3K/AKT signalling pathways in early resectable pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types, and it is associated with a 5-year survival rate of <10% due to limited early detection methods and ineffective therapeutic options. Thus, an improved understanding of the mechanisms involved in the early stages of PDAC tumorigenesis is crucial in order to identify potential novel diagnostic and therapeutic targets. The most common signalling aberrations in PDAC occur in the Wnt/Notch signalling pathway, as well as within the epidermal growth factor receptor (EGFR) pathway and its associated ligands, EGF and transforming growth factor-β. In addition, the RAS family of oncogenes, which act downstream of EGFR, are found mutated in most pancreatic cancer samples. Plakoglobin, a component of the EGFR signalling pathway, serves an important role in normal cell adhesion; however, its role in PDAC is largely unknown. The present study used transcriptome sequencing and focussed proteome microarrays to identify dysregulated genes and proteins in PDAC. The presence of upregulated plakoglobin expression levels was identified as a distinguishing feature between the PDAC microenvironment and normal pancreatic tissue. Furthermore, plakoglobin was demonstrated to be associated with the differential upregulation of the PI3K/AKT and MAPK signalling pathways in the tumour microenvironment, which suggested that it may serve an important role in PDAC tumourigenesis.

Identification of Key Genes and Pathways in Pancreatic Cancer Gene Expression Profile by Integrative Analysis

Background: Pancreatic cancer is one of the malignant tumors that threaten human health. Methods: The gene expression profiles of GSE15471, GSE19650, GSE32676 and GSE71989 were downloaded from the gene expression omnibus database including pancreatic cancer and normal samples. The differentially expressed genes between the two types of samples were identified with the Limma package using R language. The gene ontology functional and pathway enrichment analyses of differentially-expressed genes were performed by the DAVID software followed by the construction of a protein–protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in cytoscape software, and the reliability and survival analysis of hub genes was carried out in The Cancer Genome Atlas gene expression data. Results: The 138 differentially expressed genes were significantly enriched in biological processes including cell migration, cell adhesion and several pathways, mainly associated with extracellular matrix-receptor interaction and focal adhesion pathway in pancreatic cancer. The top hub genes, namely thrombospondin 1, DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were identified from the protein–protein interaction network. The expression levels of hub genes were consistent with data obtained in The Cancer Genome Atlas. DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were significantly linked with poor survival in pancreatic adenocarcinoma. Conclusions: These hub genes may be used as potential targets for pancreatic cancer diagnosis and treatment.

G Protein γ subunit 7 loss contributes to progression of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common urinary neoplasm, looking for useful candidates to establish scientific foundation for the therapy of ccRCC is urgent. We downloaded genomic profiles of GSE781, GSE6244, GSE53757, and GSE66271 from the Gene Expression Omnibus (GEO) database. GEO2R was used to analyze the derivative genes, while hub genes were screened by protein-protein interactions and cytoscape. Further, overall survival, gene methylation, gene mutation, and gene expression were all analyzed using bioinformatics tools. Colony formation and cell-cycle assay were used to detect the biological function of GNG7 in vitro. We found that GNG7 was downregulated in ccRCC tissues and negatively associated with overall survival in ccRCC patients. We also found that promoter methylation and frequent gene mutation were responsible for GNG7 gene suppression. GNG7 low expression was related to upregulation of enhancer of zeste homolog 2 and downregulation of disabled homolog 2-interacting protein. Further, Gene Set Enrichment Analysis results showed that mTOR1, E2F, G2M, and MYC pathways were all significantly altered in response to GNG7 low expression. In vitro, A498 and 786-O cells in which GNG7 expression was silenced, exhibited a lower G1 phase when compared to the negative control cells. Taken together, our findings suggest that GNG7 is a tumor suppressor gene in ccRCC progression and represents a novel candidate for ccRCC treatment.

Internal enhancement of DNA damage by a novel bispecific antibody-drug conjugate-like therapeutics via blockage of mTOR and PD-L1 signal pathways in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a refractory malignant tumor with poor prognosis, limited chemotherapeutic efficacy, and only about 5% of 5-year survival rate. We generated a dual-targeting ligand-based lidamycin (DTLL) to investigate its efficacy against pancreatic cancer after preparing its precursor, DTLP. DTLP was shown specifically binding to EGFR and HER2 on cell surface, followed by endocytosis into cytoplasm of pancreatic cancer cells. DTLL significantly promoted apoptosis and cell cycle arrest at G2/M stages and inhibited cell proliferation. Pancreatic tumors of either MIA-paca-2 cell line-derived (CDX) or patient-derived xenograft (PDX) mouse models were significantly regressed in response to DTLL. It suggested that DTLL might be a highly potent bispecific antibody-drug conjugate (ADC)-like agent for pancreatic cancer therapy. LDM is known to function as an antitumor cytotoxic agent by its induction of DNA damage in cancer cells, therefore, DTLL, as its derivative, also showed similar cytotoxicity. However, we found that DTLL might reverse the AKT/mTOR feedback activation induced by LDM at the first time. The results from both in vitro and in vivo experiments suggested that DTLL enhanced DNA damage via EGFR/HER2-dependent blockage of PI3K/AKT/mTOR and PD-L1 signaling pathways in cancer cells, leading to the inhibition of cell proliferation and immunosurveillance escape from pancreatic tumor. Our studies on DTLL functional characterization revealed its novel mechanisms on internal enhancement of DNA damage and implied that DTLL might provide a promising targeted therapeutic strategy for pancreatic cancer.

Identification of hub genes and analysis of prognostic values in pancreatic ductal adenocarcinoma by integrated bioinformatics methods

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the world, and more molecular mechanisms should be illuminated to meet the urgent need of developing novel detection and therapeutic strategies. We analyzed the related microarray data to find the possible hub genes and analyzed their prognostic values using bioinformatics methods. The mRNA microarray datasets GSE62452, GSE15471, GSE102238, GSE16515, and GSE62165 were finally chosen and analyzed using GEO2R. The overlapping genes were found by Venn Diagrams, functional and pathway enrichment analyses were performed using the DAVID database, and the protein-protein interaction (PPI) network was constructed by STRING and Cytoscape. OncoLnc, which was linked to TCGA survival data, was used to investigate the prognostic values. In total, 179 differentially expressed genes (DEGs) were found in PDAC, among which, 130 were up-regulated genes and 49 were down-regulated. DAVID showed that the up-regulated genes were significantly enriched in extracellular matrix and structure organization, collagen catabolic and metabolic process, while the down-regulated genes were mainly involved in proteolysis, reactive oxygen species metabolic process, homeostatic process and cellular response to starvation. From the PPI network, the 21 nodes with the highest degree were screened as hub genes. Based on Molecular Complex Detection (MCODE) plug-in, the top module was formed by ALB, TGM, PLAT, PLAU, EGF, MMP7, MMP1, LAMC2, LAMA3, LAMB3, COLA1, FAP, CDH11, COL3A1, ITGA2, and VCAN. OncoLnc survival analysis showed that, high expression of ITGA2, MMP7, ITGB4, ITGA3, VCAN and PLAU may predict poor survival results in PDAC. The present study identified hub genes and pathways in PDAC, which may be potential targets for its diagnosis, treatment, and prognostic prediction.

The evolution into personalized therapies in pancreatic ductal adenocarcinoma: challenges and opportunities

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer related mortality in the United States in 2030, with a 5-year overall survival of less than 10% despite decades of extensive research. Pancreatic cancer is marked by the accumulation of complex molecular changes, complex tumor-stroma interaction, and an immunosuppressive tumor microenvironment. PDAC has proven to be resistant to many cytotoxic, targeted and immunologic treatment approaches. Areas covered: In this paper, we review the major areas of research in PDAC, with highlights on the challenges and areas of opportunity for personalized treatment approaches. Expert commentary: The focus of research in pancreatic cancer has moved away from developing conventional cytotoxic combinations. The marked advances in understanding the molecular biology of this disease especially in the areas of the microenvironment, metabolism, and DNA repair have opened new opportunities for developing novel treatment strategies. Improved understanding of molecular abnormalities allows the development of personalized treatment approaches.

Microarray Analysis of the Expression Profile of Long Non-Coding RNAs Indicates lncRNA RP11-263F15.1 as a Biomarker for Diagnosis and Prognostic Prediction of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy with poor prognostic outcomes. Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) play an important role in the development and progression of carcinogenesis. Nevertheless, little is known about the role of lncRNAs in PDAC. The aim of the current study was to find differentially expressed lncRNAs and related mRNAs in human PDAC tissues and adjacent normal tissues by microarray analysis, and investigate the relationship between lncRNA RP11-263F15.1 levels and the clinicaopathological features of PDAC patients. It was found that 4364 lncRNAs and 4862 related mRNAs were significantly dysregulated in PDAC tissues as compared with adjacent normal tissues with a fold change ≥2.0 (P<0.05). GO and pathway analyses showed that the up-regulated gene profiles were related to several pathways associated with carcinogenesis, while the down-regulated gene profiles were closely correlated with nutrient metabolism. RP11-263F15.1 levels were associated with histologic differentiation (P=0.001). Besides, Kaplan-Meier analysis showed that high expression of RP11-263F15.1 was associated with poor outcomes, but multivariate analysis suggested that RP11-263F15.1 was not an independent factor for predicting prognosis of PDAC. In conclusion, these data indicate that differentially expressed lncRNAs and mRNAs were involved in the carcinogenesis of PDAC, and RP11-263F15.1 may prove to be a potential biomarker for the diagnosis and prognostic prediction of PDAC.

CDK1-mediated mitotic phosphorylation of PBK is involved in cytokinesis and inhibits its oncogenic activity

PDZ-binding kinase (PBK) plays a major role in proliferation and in safeguarding mitotic fidelity in cancer cells. Frequently upregulated in many cancers, PBK drives tumorigenesis and metastasis. PBK has been shown to be phosphorylated in mitosis by cyclin-dependent kinase 1 (CDK1)/cyclin B, however, no studies have been done examining PBK mitotic phosphorylation in oncogenesis. Additionally to the previously identified Threonine-9 phosphorylation, we found that Threonine-24, Serine-32, and Serine-59 of PBK are also phosphorylated. PBK is phosphorylated in vitro and in cells by CDK1 during antimitotic drug-induced mitotic arrest and in normal mitosis. We demonstrated that mitotic phosphorylation of Threonine-9 is involved in cytokinesis. The non-phosphorylatable mutant PBK-T9A augments tumorigenesis to a greater extent than wild type PBK in breast cancer cells, suggesting that PBK mitotic phosphorylation inhibits its tumor promoting activity. The PBK-T9A mutant also transforms and increases the proliferation of immortalized breast epithelial cells. Collectively, this study reveals that CDK1-mediated mitotic phosphorylation of PBK is involved in cytokinesis and inhibits its oncogenic activity.

Human basonuclin 2 up-regulates a cascade set of interferon-stimulated genes with anti-cancerous properties in a lung cancer model

BACKGROUND

Human basonuclin 2 (BNC2) acts as a tumor suppressor in multiple cancers in an as yet unidentified manner. The role and expression of the BNC2 gene in lung cancer has not yet been investigated.

METHODS

BNC2 expression was studied in the A549 and BEAS-2B cell lines, as well as in lung cancer tissue. Illumina array analysis and a viability assay were used to study the effects of transient transfection of BNC2 in A549 cells. Ingenuity pathway analysis and g:Profiler were applied to identify affected pathways and networks. RT-qPCR was used to validate the array results.

RESULTS

We showed the reduced mRNA expression of BNC2 in non-small cell lung cancer tissue and lung cancer cell line A549 compared to non-cancerous lung tissue and BEAS-2B cells, respectively. Further array analysis demonstrated that the transfection of BNC2 into A549 cells resulted in the increased expression of 139 genes and the down-regulation of 13 genes. Pathway analysis revealed that half of the up-regulated genes were from the interferon/signal transducer and activator of transcription signaling pathways. The differential expression of selected sets of genes, including interferon-stimulated and tumor suppressor genes of the XAF1 and OAS families, was confirmed by RT-qPCR. In addition, we showed that the over-expression of BNC2 inhibited the proliferation of A549 cells.

CONCLUSION

Our data suggest that human BNC2 is an activator of a subset of IFN-regulated genes and might thereby act as a tumor suppressor.