Phosphodiesterase type 5 inhibitors enhance chemotherapy in preclinical models of esophageal adenocarcinoma by targeting cancer-associated fibroblasts.

The chemotherapy resistance of esophageal adenocarcinomas (EACs) is underpinned by cancer cell extrinsic mechanisms of the tumor microenvironment (TME). We demonstrate that, by targeting the tumor-promoting functions of the predominant TME cell type, cancer-associated fibroblasts (CAFs) with phosphodiesterase type 5 inhibitors (PDE5i), we can enhance the efficacy of standard-of-care chemotherapy. In ex vivo conditions, PDE5i prevent the transdifferentiation of normal fibroblasts to CAF and abolish the tumor-promoting function of established EAC CAFs. Using shotgun proteomics and single-cell RNA-seq, we reveal PDE5i-specific regulation of pathways related to fibroblast activation and tumor promotion. Finally, we confirm the efficacy of PDE5i in combination with chemotherapy in close-to-patient and in vivo PDX-based model systems. These findings demonstrate that CAFs drive chemotherapy resistance in EACs and can be targeted by repurposing PDE5i, a safe and well-tolerated class of drug administered to millions of patients world-wide to treat erectile dysfunction.

Investigation of gene expression and genetic simultaneous control associated with erectile dysfunction and diabetes.

Diabetes can cause some diseases or abnormalities. One of the disorders caused by diabetes may be erectile dysfunction (ED). ED is sexual dysfunction characterized by the inability to establish or maintain an erect penis during sexual activity and is a common problem of men with chronic type 2 diabetes. These processes, disorders and diseases are highly influenced by the genetics of individuals. In this study, the relationship between genes and diabetes and ED has been explored by a system biology approach. For this purpose, the samples from ten control and diabetic-ED rats were collected. After a search in Gene Expression Omnibus (GEO), series with accession number GSE2457 comprising of 5 normal and 5 diabetic-ED rats were selected. Raw CEL files of these samples were normalized with robust multi-array average (RMA) expression measure method by using the linear models for microarray data (LIMMA) R package. The extracted probe IDs were transformed into 10451 unique and validated official gene symbols. Then, differentially expressed genes (DEGs) were identified between control and normal penile mucosa by employing the LIMMA R package. DEGs were classified by utilizing KEGG to underlying pathways by Enrichr. The expression values of DEGs were used to construct a gene regulatory network (GRN), by the GENEI3 R package. To analyze the topology of constructed GRNs, betweenness centrality was calculated. Genes with higher betweenness centrality scores were then identified, through the CytoNCA. We then took the commonality of DEGs genes and high-top ranking genes from CytoNCA via a predicted interaction network using GeneMANIA as the most likely important genes in erectile dysfunction. Among the 374 DEGs studied, 146 DEGs showed up-regulation and 228 DEGs displayed down-regulation expression in diabetic-ED rats. According to the Volcano plot, the dpp4, LOC102553868, Ndufa412, Oxct1, Atp2b3 and Zfp91 gene down-regulated and Lpl, Retsat, B4galt1 and Pdk4 genes up-regulated in ED and diabetic rats. Furthermore, genes like dpp4 acted as hubs in the inferred GRN.

Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma.

Neoadjuvant therapy followed by surgery is the standard of care for locally advanced esophageal adenocarcinoma (EAC). Unfortunately, response to neoadjuvant chemotherapy (NAC) is poor (20-37%), as is the overall survival benefit at five years (9%). The EAC genome is complex and heterogeneous between patients, and it is not yet understood whether specific mutational patterns may result in chemotherapy sensitivity or resistance. To identify associations between genomic events and response to NAC in EAC, a comparative genomic analysis was performed in 65 patients with extensive clinical and pathological annotation using whole-genome sequencing (WGS). We defined response using Mandard Tumor Regression Grade (TRG), with responders classified as TRG1-2 (n = 27) and non-responders classified as TRG4-5 (n =38). We report a higher non-synonymous mutation burden in responders (median 2.08/Mb vs. 1.70/Mb, p = 0.036) and elevated copy number variation in non-responders (282 vs. 136/patient, p < 0.001). We identified copy number variants unique to each group in our cohort, with cell cycle (CDKN2A, CCND1), c-Myc (MYC), RTK/PIK3 (KRAS, EGFR) and gastrointestinal differentiation (GATA6) pathway genes being specifically altered in non-responders. Of note, NAV3 mutations were exclusively present in the non-responder group with a frequency of 22%. Thus, lower mutation burden, higher chromosomal instability and specific copy number alterations are associated with resistance to NAC.

Network Analysis of Common Genes and Transcriptional Factors between Celiac Disease and Inflammatory Bowel Diseases.

BACKGROUND Understanding the associations among different disorders remarkably improves their diagnosis and treatments. Celiac disease is the most complicated and prevalent form of immune-mediated diseases. On the other hand, inflammatory bowel diseases lead to inflammation of the intestine with an unknown cause. Although inflammatory bowel diseases have been often thought of as an autoimmune disorder, they can be triggered by whatever that can lead to the inflammation in the whole bowel. Henceforth, both aforementioned diseases are related to autoimmune attacks and cause a sort of inflammatory event, which exploring trade-off among them supposedly will lead to discovering important genes and, in turn, to the possible common therapeutic protocols. In the current study, we aimed to determine the correlation between the common genes in celiac disease and inflammatory bowel diseases. METHODS 314 and 851 genes correlated with celiac disease and inflammatory bowel diseases respectively extracted from DisGeNET were subjected to an in-silico data analysis framework to mine prognosticates genes and the associated pathways. RESULTS 149 shared genes between these diseases regulated by highlighted transcription factors NFKB1, IRF1, STAT1, HSF1, GATA3 were characterized as discriminating molecules, which by further screening were enriched in pathways mostly involved in apoptosis, T cell activation, and cytokine, chemokine, and interleukin signaling. CONCLUSION We observed that the identified common genes were associated with a wide range of pathogenic mechanisms underlying these diseases.

Differential Connectivity in Colorectal Cancer Gene Expression Network

Background: Colorectal cancer (CRC) is one of the challenging types of cancers; thus, exploring effective biomarkers related to colorectal could lead to significant progresses toward the treatment of this disease. Methods: In the present study, CRC gene expression datasets have been reanalyzed. Mutual differentially expressed genes across 294 normal mucosa and adjacent tumoral samples were then utilized in order to build two independent transcriptional regulatory networks. By analyzing the networks topologically, genes with differential global connectivity related to cancer state were determined for which the potential transcriptional regulators including transcription factors were identified. Results: The majority of differentially connected genes (DCGs) were up-regulated in colorectal transcriptome experiments. Moreover, a number of these genes have been experimentally validated as cancer or CRC-associated genes. The DCGs, including GART, TGFB1, ITGA2, SLC16A5, SOX9, and MMP7, were investigated across 12 cancer types. Functional enrichment analysis followed by detailed data mining exhibited that these candidate genes could be related to CRC by mediating in metastatic cascade in addition to shared pathways with 12 cancer types by triggering the inflammatory events. Discussion: Our study uncovered correlated alterations in gene expression related to CRC susceptibility and progression that the potent candidate biomarkers could provide a link to disease.

Exploring Potential Biomarkers Underlying Pathogenesis of Alzheimer's Disease by Differential Co-expression Analysis.

BACKGROUND: Alzheimer's Disease (AD) is the most common form of dementia in the elderly. Due to the facts that biological causes of AD are complex in addition to increasing rates of AD worldwide, a deeper understanding of AD etiology is required for AD treatment and diagnosis. METHODS: To identify molecular pathological alterations in AD brains, GSE36980 series containing microarray data samples from temporal cortex, frontal cortex and hippocampus were downloaded from Gene Expression Omnibus (GEO) database and valid gene symbols were subjected to building a gene co-expression network by a bioinformatics tool known as differential regulation from differential co-expression (DCGL) software package. Then, a network-driven integrative analysis was performed to find significant genes and underlying biological terms. RESULTS: A total of 17088 unique genes were parsed into three independent differential co-expression networks. As a result, a small number of differentially co-regulated genes mostly in frontal and hippocampus lobs were detected as potential biomarkers related to AD brains. Ultimately differentially co-regulated genes were enriched in biological terms including response to lipid and fatty acid and pathways mainly signaling pathway such as G-protein signaling pathway and glutamate receptor groups II and III. By conducting co-expression analysis, our study identified multiple genes that may play an important role in the pathogenesis of AD. CONCLUSION: The study aimed to provide a systematic understanding of the potential relationships among these genes and it is hoped that it could aid in AD biomarker discovery.

Exploring conserved mRNA-miRNA interactions in colon and lung cancers.

AIM: The main goal of this analysis was prioritization of co-expressed genes and miRNAs that are thought to have important influences in the pathogenesis of colon and lung cancers. BACKGROUND: MicroRNAs (miRNAs) as small and endogenous noncoding RNAs which regulate gene expression by repressing mRNA translation or decreasing stability of mRNAs; they have proven pivotal roles in different types of cancers. Accumulating evidence indicates the role of miRNAs in a wide range of biological processes from oncogenesis and tumor suppressors to contribution to tumor progression. Colon and lung cancers are frequently encountered challenging types of cancers; therefore, exploring trade-off among underlying biological units such as miRNA with mRNAs will probably lead to identification of promising biomarkers involved in these malignancies. METHODS: Colon cancer and lung cancer expression data were downloaded from Firehose and TCGA databases and varied genes extracted by DCGL software were subjected to build two gene regulatory networks by parmigene R package. Afterwards, a network-driven integrative analysis was performed to explore prognosticates genes, miRNAs and underlying pathways. RESULTS: A total of 192 differentially expressed miRNAs and their target genes within gene regulatory networks were derived by ARACNE algorithm. BTF3, TP53, MYC, CALR, NEM2, miR-29b-3p and miR-145 were identified as bottleneck nodes and enriched via biological gene ontology (GO) terms and pathways chiefly in biosynthesis and signaling pathways by further screening. CONCLUSION: Our study uncovered correlated alterations in gene expression that may relate with colon and lung cancers and highlighted the potent common biomarker candidates for the two diseases.

Celiac disease and hepatitis C relationships in transcriptional regulatory networks.

AIM: we mainly aimed to elucidate potential comorbidities between celiac disease and hepatitis c by means of data and network analysis approaches. BACKGROUND: understanding the association among the disorders evidently has important impact on the diagnosis and therapeutic approaches. Celiac disease is the most challenging, common types of autoimmune disorders. On the other hand, hepatitis c virus genome products like some proteins are supposed to be resemble to gliadin types that in turn activates gluten intolerance in people with inclined to gluten susceptibilities. Moreover, a firm support of association between chronic hepatitis and celiac disease remains largely unclear. Henceforth exploring cross-talk among these diseases will apparently lead to the promising discoveries concerning important genes and regulators. METHODS: 321 and 1032 genes associated with celiac disease and hepatitis c retrieved from DisGeNET were subjected to build a gene regulatory network. Afterward a network-driven integrative analysis was performed to exploring prognosticates genes and related pathways. RESULTS: 105 common genes between these diseases included 11 transcription factors were identified as hallmark molecules where by further screening enriched in biological GO terms and pathways chiefly in immune systems and signaling pathways such as chemokines, cytokines and interleukins. CONCLUSION: in silico data analysis approaches indicated that the identified selected combinations of genes covered a wide range of known functions triggering the inflammation implicated in these diseases.

A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets.

A Gene Regulatory Network (GRN) is a collection of interactions between molecular regulators and their targets in cells governing gene expression level. Omics data explosion generated from high-throughput genomic assays such as microarray and RNA-Seq technologies and the emergence of a number of pre-processing methods demands suitable guidelines to determine the impact of transcript data platforms and normalization procedures on describing associations in GRNs. In this study exploiting publically available microarray and RNA-Seq datasets and a gold standard of transcriptional interactions in Arabidopsis, we performed a comparison between six GRNs derived by RNA-Seq and microarray data and different normalization procedures. As a result we observed that compared algorithms were highly data-specific and Networks reconstructed by RNA-Seq data revealed a considerable accuracy against corresponding networks captured by microarrays. Topological analysis showed that GRNs inferred from two platforms were similar in several of topological features although we observed more connectivity in RNA-Seq derived genes network. Taken together transcriptional regulatory networks obtained by Robust Multiarray Averaging (RMA) and Variance-Stabilizing Transformed (VST) normalized data demonstrated predicting higher rate of true edges over the rest of methods used in this comparison.