Human Microarray Datasets Research Articles

#2104 Neutrophil extracellular traps have a pivotal role in tubulointerstitial fibrosis

Abstract Background and Aims Tubulointerstitial fibrosis (TIF) is a typical pathological change in chronic kidney disease (CKD). However, the mechanisms underlying tubulointerstitial fibrosis are still to be fully elucidated. Neutrophil extracellular traps (NETs) participate in the pathophysiological processes of various kidney diseases, but their role in CKD-TIF has not been reported. Here, we investigated the role of NETs in tubulointerstitial fibrosis. Method Immunofluorescence was employed to assess the levels and localization of NETs in the renal tissues of CKD patients. The GSE66494 human CKD microarray dataset was explored to analyze the changes of PAD4 levels. Three CKD mice models, including 14d unilateral ureteral obstruction (UUO), 28d ischemia/reperfusion (I/R) injury, and adenine-induced models were constructed to detect the expression levels of NETs in the kidneys. Then, we established a NETs-deficient (PAD4−/−) CKD mice model and assessed changes in renal function, NETs levels, and renal fibrosis. In vitro, phorbol-12-myristate-13-acetate (the classic inducer of NETs) was used to induce the generation of NETs. Intervention was performed on rat fibroblast NRK49F cells to investigate the mechanism of TIF induced by NETs. Results We first identified NETs levels were significantly elevated in the renal tissues of CKD patients. Additionally, chip data results also indicated elevated PAD4 levels in the renal tissues of CKD patients. NETs levels were elevated in the kidneys of UUO, I/R, and adenine-induced CKD mice, with deposition in the renal interstitium. PAD4-deficient mice did not form neutrophil extracellular traps. Knocking out PAD4 alleviated tubulointerstitial fibrosis in UUO, adenine, and I/R mice after blocking NETs formation. In vitro, NETs intervention in fibroblasts can promote their activation and transformation into myofibroblasts. Conclusion The deletion of NETs by PAD4−/− could significantly improve CKD-TIF. The inhibition of NETs might provide a new molecular target for the prevention and treatment of CKD.

LXRα Promotes Abdominal Aortic Aneurysm Formation Through UHRF1 Epigenetic Modification of miR-26b-3p.

Abdominal aortic aneurysm (AAA) is a severe aortic disease without effective pharmacological approaches. The nuclear hormone receptor LXRα (liver X receptor α), encoded by the NR1H3 gene, serves as a critical transcriptional mediator linked to several vascular pathologies, but its role in AAA remains elusive. Through integrated analyses of human and murine AAA gene expression microarray data sets, we identified NR1H3 as a candidate gene regulating AAA formation. To investigate the role of LXRα in AAA formation, we used global Nr1h3-knockout and vascular smooth muscle cell-specific Nr1h3-knockout mice in 2 AAA mouse models induced with angiotensin II (1000 ng·kg·min; 28 days) or calcium chloride (CaCl2; 0.5 mol/L; 42 days). Upregulated LXRα was observed in the aortas of patients with AAA and in angiotensin II- or CaCl2-treated mice. Global or vascular smooth muscle cell-specific Nr1h3 knockout inhibited AAA formation in 2 mouse models. Loss of LXRα function prevented extracellular matrix degeneration, inflammation, and vascular smooth muscle cell phenotypic switching. Uhrf1, an epigenetic master regulator, was identified as a direct target gene of LXRα by integrated analysis of transcriptome sequencing and chromatin immunoprecipitation sequencing. Susceptibility to AAA development was consistently enhanced by UHRF1 (ubiquitin-like containing PHD and RING finger domains 1) in both angiotensin II- and CaCl2-induced mouse models. We then determined the CpG methylation status and promoter accessibility of UHRF1-mediated genes using CUT&Tag (cleavage under targets and tagmentation), RRBS (reduced representation bisulfite sequencing), and ATAC-seq (assay for transposase-accessible chromatin with sequencing) in vascular smooth muscle cells, which revealed that the recruitment of UHRF1 to the promoter of miR-26b led to DNA hypermethylation accompanied by relatively closed chromatin states, and caused downregulation of miR-26b expression in AAA. Regarding clinical significance, we found that underexpression of miR-26b-3p correlated with high risk in patients with AAA. Maintaining miR-26b-3p expression prevented AAA progression and alleviated the overall pathological process. Our study reveals a pivotal role of the LXRα/UHRF1/miR-26b-3p axis in AAA and provides potential biomarkers and therapeutic targets for AAA.

Hyperhomocysteinemia may aggravate abdominal aortic aneurysm formation by up-regulating RASSF2

Abdominal aortic aneurysm (AAA) is a fatal cardiovascular disorder with high mortality and morbidity rates. To date, no drug has shown to significantly alleviate the risk of AAA. Previous studies have indicated that hyperhomocysteinemia (HHcy) significantly increases the incidence of AAA by disrupting endothelial cell homeostasis; however, the potential molecular mechanisms require clarification. Herein, we aimed to integrate transcriptomics analysis and molecular biology experiments to explore the potential molecular targets by which HHcy may increase the incidence of AAA. We integrated two AAA data profiles (GSE57691 and GSE7084) based on previously published microarray ribonucleic acid sequencing (RNAseq) data from the GEO database. Additionally, 500 μM homocysteine-treated human aorta endothelium cells microarray dataset (GSE175748) was downloaded and processed. Subsequently, single-cell RNA-seq profiles of the aortic aneurysms (GSE155468) were downloaded, scaled, and processed for further analysis. The microarray profiles analysis demonstrated that the Ras association domain family member 2 (RASSF2) and interleukin (IL)-1β are potentially the target genes involved in the HHcy-mediated aggravation of AAA formation. Single-cell RNAseq analysis revealed that RASSF2 might impair endothelial cell function by increasing inflammatory cell infiltration to participate in AAA formation. Finally, we conducted reverse transcription quantitative polymerase chain reaction and immunofluorescence analysis to validate the up-regulated mRNA expression of RASSF2 (p = 0.008) and IL-1β (p = 0.002) in AAA tissue compared to control tissue. Immunofluorescence staining revealed overexpression of RASSF2 protein in AAA tissue sections compared to control tissue (p = 0.037). Co-localization of RASSF2 and the aortic endothelium cell marker, CD31, was observed in tissue sections, indicating the potential involvement of RASSF2 in aortic endothelial cells. To summarise, our preliminary study revealed that HHcy may worsen AAA formation by up-regulating the expression of RASSF2 and IL-1β in aortic endothelium cells.

The use of gene expression datasets in feature selection research: 20 years of inherent bias?

AbstractFeature selection algorithms are frequently employed in preprocessing machine learning pipelines applied to biological data to identify relevant features. The use of feature selection in gene expression studies began at the end of the 1990s with the analysis of human cancer microarray datasets. Since then, gene expression technology has been perfected, the Human Genome Project has been completed, new microarray platforms have been created and discontinued, and RNA‐seq has gradually replaced microarrays. However, most feature selection methods in the last two decades were designed, evaluated, and validated on the same datasets from the microarray technology's infancy. In this review of over 1200 publications regarding feature selection and gene expression, published between 2010 and 2020, we found that 57% of the publications used at least one outdated dataset, 23% used only outdated data, and 32% did not cite data sources. Other issues include referencing databases that are no longer available, the slow adoption of RNA‐seq datasets, and bias toward human cancer data, even for methods designed for a broader scope. In the most popular datasets, some being 23 years old, mislabeled samples, experimental biases, distribution shifts, and the absence of classification challenges are common. These problems are more predominant in publications with computer science backgrounds compared to publications from biology and can lead to inaccurate and misleading biological results.This article is categorized under: Algorithmic Development > Biological Data Mining Technologies > Machine Learning

Glucose-mediated insulin secretion is improved in FHL2-deficient mice and elevated FHL2 expression in humans is associated with type 2 diabetes

Aims/hypothesisThe general population is ageing, involving an enhanced incidence of chronic diseases such as type 2 diabetes. With ageing, DNA methylation of FHL2 increases, as well as expression of the four and a half LIM domains 2 (FHL2) protein in human pancreatic islets. We hypothesised that FHL2 is actively involved in glucose metabolism.MethodsPublicly available microarray datasets from human pancreatic islets were analysed for FHL2 expression. In FHL2-deficient mice, we studied glucose clearance and insulin secretion. Gene expression analysis and glucose-stimulated insulin secretion (GSIS) were determined in isolated murine FHL2-deficient islets to evaluate insulin-secretory capacity. Moreover, knockdown and overexpression of FHL2 were accomplished in MIN6 cells to delineate the underlying mechanism of FHL2 function.ResultsTranscriptomics of human pancreatic islets revealed that individuals with elevated levels of HbA1c displayed increased FHL2 expression, which correlated negatively with insulin secretion pathways. In line with this observation, FHL2-deficient mice cleared glucose more efficiently than wild-type littermates through increased plasma insulin levels. Insulin sensitivity was comparable between these genotypes. Interestingly, pancreatic islets isolated from FHL2-deficient mice secreted more insulin in GSIS assays than wild-type mouse islets even though insulin content and islet size was similar. To support this observation, we demonstrated increased expression of the transcription factor crucial in insulin secretion, MAF BZIP transcription factor A (MafA), higher expression of GLUT2 and reduced expression of the adverse factor c-Jun in FHL2-deficient islets. The underlying mechanism of FHL2 was further delineated in MIN6 cells. FHL2-knockdown led to enhanced activation of forkhead box protein O1 (FOXO1) and its downstream genes such as Mafa and Pdx1 (encoding pancreatic and duodenal homeobox 1), as well as increased glucose uptake. On the other hand, FHL2 overexpression in MIN6 cells blocked GSIS, increased the formation of reactive oxygen species and increased c-Jun activity.Conclusions/interpretationOur data demonstrate that FHL2 deficiency improves insulin secretion from beta cells and improves glucose tolerance in mice. Given that FHL2 expression in humans increases with age and that high expression levels of FHL2 are associated with beta cell dysfunction, we propose that enhanced FHL2 expression in elderly individuals contributes to glucose intolerance and the development of type 2 diabetes.Data availabilityThe human islet microarray datasets used are publicly available and can be found on https://www.ncbi.nlm.nih.gov/geo/.Graphical abstract

Identification of Tissue-Specific Expressed Hub Genes and Potential Drugs in Rheumatoid Arthritis Using Bioinformatics Analysis

Background: Rheumatoid arthritis (RA) is a common autoimmune disease characterized by progressive, destructive polyarthritis. However, the cause and underlying molecular events of RA are not clear. Here, we applied integrated bioinformatics to identify tissue-specific expressed hub genes involved in RA and reveal potential targeted drugs. Methods: Three expression profiles of human microarray datasets involving fibroblast-like synoviocytes (FLS) were downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed mRNAs (DEGs), miRNAs (DEMs), and lncRNAs (DELs) between normal and RA synovial samples were screened using GEO2R tool. BioGPS was used to identified tissue-specific expressed genes. Functional and pathway enrichment analyses were performed for common DEGs using the DAVID database, and the protein-protein interaction (PPI) network of common DEGs was constructed to recognize hub genes by the STRING database. Based on receiver operating characteristic (ROC) curve, we further investigated the prognostic values of tissue-specific expressed hub genes in RA patients. Connectivity Map (CMap) was run to identify novel anti-RA potential drugs. The DEM–DEG pairs and ceRNA network containing key DEMs were established by Cytoscape. Results: We obtain a total of 418 DEGs, 23 DEMs and 49 DELs. 64 DEGs were verified as tissue-specific expressed genes, most derive from the hematologic/immune system (20/64, 31.25%). GO term and KEGG pathway enrichment analysis showed that DEGs focused primarily on immune-related biological process and NF-κB pathway. 10 hub genes were generated via using MCODE plugin. Among them, SPAG5, CUX2, and THEMIS2 were identified as tissue-specific expressed hub genes, these 3 tissue-specific expressed hub genes have superior diagnostic value in the RA samples compared with osteoarthritis (OA) samples. 5 compounds (troleandomycin, levodopa, trichostatin A, LY-294002, and levamisole) rank among the top five in connectivity score. In addition, 5 miRNAs were identified to be key DEMs, the lncRNA–miRNA–mRNA network with five key DEMs was formed. The networks containing tissue-specific expressed hub genes are as follows: ARAP1-AS2/miR-20b-3p/TRIM3, ARAP1-AS2/miR-30c-3p/FRZB. Conclusion: This study indicates that screening for identify tissue-specific expressed hub genes and ceRNA network in RA using integrated bioinformatics analyses could help us understand the mechanism of development of RA. Besides, SPAG5 and THEMIS2 might be candidate biomarkers for diagnosis of RA. LY-294002, trichostatin A, and troleandomycin may be potential drugs for RA.

A Broad m6A Modification Landscape in Inflammatory Bowel Disease.

Background and Aims: N6-Methyladenosine (m6A) is the most common post-transcriptional modification on eukaryotic mRNA, affecting the mRNA’s fate. The role of m6A regulation in inflammatory bowel disease is unclear. Here, we investigated the m6A landscape in inflammatory bowel diseases (IBD). Methods: Eleven human IBD microarray datasets were recruited from the Gene Expression Omnibus database and four were selected as discovery cohorts. An RNA-seq dataset from the Inflammatory Bowel Disease Multi’omics Database was used as a validation cohort. m6A regulators were measured in volunteers’ colonic samples. Consensus clustering and immune scoring were used to estimate the characteristics of m6A regulation in IBD. m6A-related characteristics of different sub-phenotypes, sample sources, and biological therapeutic responses were determined using seven independent datasets. Results: m6A modification involves methyltransferases (writers), demethylases (erasers), and methylation-reading proteins (readers). A wide interaction exists between m6A regulators and IBD risk genes. The IBD risk loci can also be modified by m6A modifications in the public m6A sequencing data. Furthermore, m6A regulators displayed extensive differential expression in four independent discovery cohorts that share common differential genes (IGF2BP2, HNRNPA2B1, ZCCHC4, and EIF3I). In the validated cohort and enrolled volunteers’ colonic biopsy samples, the differential m6A regulators were reconfirmed. Two clusters of consensus clustering exhibit different immune phenotypes. m6A-modified positions exist in the core IBD immune cytokines. Another set of IBD datasets revealed m6A-related differences across clinical phenotypes, biological samples, and therapeutic response subgroups in IBD patients. Conclusion: Regulation of m6A methylation is widely involved in IBD occurrence and development. m6A modifications in risk variants, core cytokines, immune cells, and other proteins may deeply influence the pathophysiology and clinical phenotypes. Further studies are needed to determine its role in IBD.

LncRNA PCAT19 induced by SP1 and acted as oncogene in gastric cancer competitively binding to miR429 and upregulating DHX9.

Increasing evidence suggests that long non-coding RNAs (lncRNAs) are crucial in cancer biological processes. To investigate if lncRNA contributes to gastric cancer (GC), we conducted a bioinformatics analysis in human microarray datasets, and the results showed that lncRNA prostate cancer-associated transcript 19 (PCAT19) was upregulated in GC. Quantitative reverse-transcriptase PCR and in situ hybridization assays also revealed that PCAT19 was upregulated in GC tissues. The PCAT19 expression in GC was significantly related to tumor size, lymph node metastasis, and pathological stage. Moreover, patients with higher PCAT19 expression levels were more likely to have a poor prognosis for overall survival. The knockdown of PCAT19 by siRNA significantly suppressed the proliferation and invasion of GC cells. The cell distribution of PCAT19 in GC cells was examined by fluorescence in situ hybridization assay, and the results showed that it was mainly located in the cytoplasm. Mechanistically, PCAT19 sponges miR-429 and promotes DHX9 expression. In addition, the transcription factor SP1 is involved in PCAT19 activation. Our results demonstrate that lncRNA PCAT19 is induced by SP1 and acts as an oncogene in GC that competitively binds to miR429 and upregulates DHX9.

Differentiation of pancreatic ductal adenocarcinoma and chronic pancreatitis using graph neural networks on histopathology and collagen fiber features

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. However, the symptoms and radiographic appearance of chronic pancreatitis (CP) mimics that of PDAC, and sometimes the 2 entities can also be difficult to differentiate microscopically. The need for accurate differentiation of PDAC and CP has become a major topic in pancreatic pathology. These 2 diseases can present similar histomorphological features, such as excessive deposition of fibrotic stroma in the tissue microenvironment and inflammatory cell infiltration. In this paper, we present a quantitative analysis pipeline empowered by graph neural networks (GNN) capable of automatic detection and differentiation of PDAC and CP in human histological specimens. Modeling histological images as graphs and deploying graph convolutions can enable the capture of histomorphological features at different scales, ranging from nuclear size to the organization of ducts. The analysis pipeline combines image features computed from co-registered hematoxylin and eosin (H&E) images and Second-Harmonic Generation (SHG) microscopy images, with the SHG images enabling the extraction of collagen fiber morphological features. Evaluating the analysis pipeline on a human tissue micro-array dataset consisting of 786 cores and a tissue region dataset consisting of 268 images, it attained 86.4% accuracy with an average area under the curve (AUC) of 0.954 and 88.9% accuracy with an average AUC of 0.957, respectively. Moreover, incorporating topological features of collagen fibers computed from SHG images into the model further increases the classification accuracy on the tissue region dataset to 91.3% with an average AUC of 0.962, suggesting that collagen characteristics are diagnostic features in PDAC and CP detection and differentiation.

Identification and validation of novel biomarkers and therapeutics for pulpitis using connectivity mapping.

To create an irreversible pulpitis gene signature from microarray data of healthy and inflamed dental pulps, followed by a bioinformatics approach using connectivity mapping to identify therapeutic compounds that could potentially treat pulpitis. The Gene Expression Omnibus (GEO) database, an international public repository of genomics data sets, was searched for human microarray datasets assessing pulpitis. An irreversible pulpitis gene expression signature was generated by differential expression analysis. The statistically significant connectivity map (ssCMap) method was used to identify compounds with a highly correlating gene expression pattern. qPCR was used to validate novel pulpitis genes. An ex vivo pulpitis model was used to test the effects of the compounds identified, and the level of inflammatory cytokines was measured with qPCR, ELISA and multiplex array. Means were compared using the t-test or ANOVA with the level of significance set at p≤.05. Pulpitis gene signatures were created using differential gene expression analysis at cutoff points p=.0001 and .000018. Top upregulated genes were selected as potential pulpitis biomarkers. Among these, IL8, IL6 and MMP9 were previously identified as pulpitis biomarkers. Novel upregulated genes, chemokine (C-C motif) ligand 21 (CCL21), metallothionein 1H (MT1H) and aquaporin 9 (AQP9) were validated in the pulp tissue of teeth clinically diagnosed with irreversible pulpitis using qPCR. ssCMap analysis identified fluvastatin (Statin) and dequalinium chloride (Quaternary ammonium) as compounds with the strongest correlation to the gene signatures (p=.0001). Fluvastatin reduced IL8, IL6, CCL21, AQP9 (p<.001) and MMP9 (p<.05) in the ex vivo pulpitis model, while dequalinium chloride reduced AQP9 (p<.001) but had no significant effect on the other biomarkers. AQP9, MT1H and CCL21 were identified and validated as novel biomarkers for pulpitis. Fluvastatin and dequalinium chloride identified by the ssCMap as potential therapeutics for pulpitis reduced selected pulpitis biomarkers in an ex vivo pulpitis model. In vivo testing of these licenced drugs is warranted.

In Silico, In Vitro, and In Vivo Analysis Identifies Endometrial Circadian Clock Genes in Recurrent Implantation Failure.

Previous work has demonstrated the role of the circadian clock in ovarian steroid hormone synthesis and attributed embryo implantation failure associated with arrhythmic circadian clock genes to insufficient ovarian-derived progesterone synthesis. Research on expression of core circadian clock genes in the endometrium itself and possible roles in compromised endometrial receptivity and recurrent implantation failure (RIF) are limited. We aimed to assess the core circadian clock gene profiling in human endometrium across the menstrual cycle and the possible gene interaction networks in the endometrial receptivity of window of implantation (WOI) as well as RIF. The study was initially an in silico study, with confirmatory lab-based data from primary human endometrial stromal cells (hESCs) as well as endometrial biopsies obtained from 60 women undergoing gynecological surgery in a clinical research center. The study included 30 RIF women and 30 age-matched and body mass index-matched controls. Initial data mining and bioinformatics analysis of human endometrial microarray datasets across the menstrual cycle and between RIF women versus controls demonstrated the varied expression of core circadian clock genes across menstrual cycle, including the key role of PER2 in WOI and RIF. A PER2-centered network was investigated in the regulation of endometrial receptivity. We also confirmed the evidently increased mRNA expression of SHTN1, RXFP1, KLF5, and STEAP4 in the endometrium of RIF women, displaying the same trend as PER2 did, without any changes in MT1E and FKBP5. Treatment of PER2 siRNA in hESCs verified the positive regulation of PER2 to SHTN1, KLF5, and STEAP4. Aberrant expression of endometrial PER2 might contribute to impaired endometrial receptivity and development of RIF via regulating SHTN1, KLF5, and STEAP4.

Bioinformatics Analysis Reveals MicroRNA-193a-3p Regulates ACTG2 to Control Phenotype Switch in Human Vascular Smooth Muscle Cells.

Aortic dissection (AD) is among the most fatal cardiovascular diseases. However, the pathogenesis of AD remains poorly understood. This study aims to integrate the microRNAs (miRNA) and mRNA profiles and use bioinformatics analyses with techniques in molecular biology to delineate the potential mechanisms involved in the development of AD. We used the human miRNA and mRNA microarray datasets GSE98770, GSE52093, and GEO2R, Venn diagram analysis, gene ontology, and protein–protein interaction networks to identify target miRNAs and mRNAs involved in AD. RNA interference, western blotting, and luciferase reporter assays were performed to validate the candidate miRNAs and mRNAs in AD tissues and human vascular smooth muscle cells (VSMCs). Furthermore, we studied vascular smooth muscle contraction in AD. In silico analyses revealed that miR-193a-3p and ACTG2 were key players in the pathogenesis of AD. miR-193a-3p was upregulated in the AD tissues. We also found that biomarkers for the contractile phenotype in VSMCs were downregulated in AD tissues. Overexpression and depletion of miR-193a-3p enhanced and suppressed VSMC proliferation and migration, respectively. Dual luciferase reporter assays confirmed that ACTG2 was a target of miR-193a-3p. ACTG2 was also downregulated in human AD tissues and VMSCs overexpressing miR-193a-3p. Taken together, miR-193a-3p may be a novel regulator of phenotypic switching in VSMCs and the miR-193a-3p/ACTG2 axis may serve as a promising diagnostic biomarker and therapeutic candidate for AD.

Systematic metabolic analysis of potential target, therapeutic drug, diagnostic method and animal model applicability in three neurodegenerative diseases.

Considerable evidence suggests that metabolic abnormalities are associated with neurodegenerative diseases. This study aimed to conduct a systematic metabolic analysis of Alzheimer’s disease (AD), Parkinson’s disease (PD) and Huntington’s disease (HD). Human and mouse model microarray datasets were downloaded from the Gene Expression Omnibus database. The metabolic genes and pathways were collected from the Recon 3D human metabolic model. Drug and target information was obtained from the DrugBank database. This study identified ATP1A1, ATP6V1G2, GOT1, HPRT1, MAP2K1, PCMT1 and PLK2 as key metabolic genes that were downregulated in AD, PD and HD. We screened 57 drugs that target these genes, such as digoxin, ouabain and diazoxide. This study constructed multigene diagnostic models for AD, PD and HD by using metabolic gene expression profiles in blood, all models showed high accuracy (AUC > 0.8) both in the experimental and validation sets. Furthermore, analysis of animal models showed that there was almost no consistency among the metabolic changes between mouse models and human diseases. This study systematically revealed the metabolic damage among AD, PD, and HD and uncovered the differences between animal models and human diseases. This information may be helpful for understanding the metabolic mechanisms and drug development for neurodegenerative diseases.

The Molecular Processes in the Trabecular Meshwork After Exposure to Corticosteroids and in Corticosteroid-Induced Ocular Hypertension.

PurposeTo identify processes that contribute to corticosteroid-induced ocular hypertension and candidate target genes for treatment.MethodsA systematic search identified five human microarray datasets investigating the effect of dexamethasone versus a control medium on trabecular meshwork (TM) tissue. After thorough quality control, samples of low quality were removed, and the datasets were integrated. Additionally, a bovine RNA-sequencing dataset allowed to investigate differences in gene expression profiling between cows with and without corticosteroid-induced ocular hypertension (responders vs. nonresponders). The obtained datasets were used as input for parallel pathway analyses. Significantly changed pathways were clustered into functional categories and the results were further investigated. A network visualizing the differences between the responders and nonresponders was created.ResultsSeven functional pathway clusters were found to be significantly changed in TM cells exposed to dexamethasone versus a control medium and in TM cells of responders versus nonresponders: collagen, extracellular matrix, adhesion, WNT-signaling, inflammation, adipogenesis, and glucose metabolism. In addition, cell cycle and senescence were only significantly changed in responders versus nonresponders. The network of the differential gene expression between responders and nonresponders shows many connections between the identified processes via shared genes.ConclusionsNine functional pathway clusters synthesize the molecular response to dexamethasone exposure in TM cells and are likely to be involved in the pathogenesis of corticosteroid-induced ocular hypertension.

Identification of potential radiation-responsive biomarkers based on human orthologous genes with possible roles in DNA repair pathways by comparison between Arabidopsis thaliana and homo sapiens

Rapid and reliable ionization radiation (IR) exposure estimation has become increasingly important in environment due to the urgent requirement of medical evaluation and treatment in the event of nuclear accident emergency. Human DNA repair genes can be identified as important candidate biomarkers to assess IR exposure, while how to find the enough sensitive and specific biomarkers in the DNA repair networks is still challenged and not fully determined. The conserved features of DNA repair pathways may facilitate interdisciplinary studies that cross the traditional boundaries between animal and plant biology, with the aim of identifying undiscovered human DNA repair genes for potential radiation-responsive biomarkers. In this work, an in silico method of homologous comparison was performed to identify the human orthologues of A. thaliana DNA repair genes, and thereby to explore the sensitive and specific human radiation-responsive genes to evaluate the IR exposure levels. The results showed that a total of 16 putative candidate genes were involved in the human DNA repair pathways of homologous recombination (HR) and non-homologous end joining (NHEJ), and most of them were confirmed by previous experiments. Additionally, we analyzed the gene expression patterns of these 16 candidate genes in several human transcript microarray datasets with different IR treatments. The results indicated that most of the gene expression levels for these candidate genes were significantly changed under different radiation treatments. Based on these results, we integrated these putative human DNA repair genes into the DNA repair pathways to propose new insights of the HR and NHEJ pathways, which can also provide the potential targets for the development of radiation biomarkers. Notably, two putative DNA repair genes, named ERCC1 and ESCO2, were identified and were considered to be the sensitive and specific biomarkers in response to γ-ray exposures.

LncRNA ZFAS1 promotes proliferation and migration and inhibits apoptosis in nasopharyngeal carcinoma via the PI3K/AKT pathway in vitro.

Increasing evidence shows that long non-coding RNAs (lncRNAs) play a key role in the development of various cancers. Zinc finger antisense 1 (ZFAS1) is a novel lncRNA with previously demonstrated associations with several types of cancer. Here we examined the expression and potential function of the ZFAS1 in nasopharyngeal carcinoma (NPC). We detected ZFAS1 expression in GSE12452, a human microarray dataset, and NPC cell lines. Small interfering RNA against ZFAS1 was used to elucidate the cellular functions of ZFAS1 using MTT, colony formation, cell cycle, cell apoptosis, transwell invasion and migration and western blot assays. An activator of the PI3K/AKT signaling pathway (740Y-P) was used to determine the contribution of PI3K/AKT. ZFAS1 was significantly upregulated in NPC tissues and cell lines. Silencing ZFAS1 significantly inhibited cell proliferation and invasion, arrested cell cycle progression and promoted cell apoptosis, as well as reduced epithelial-mesenchymal transition. Moreover, 740Y-P could rescue the effects of ZFAS1 knockdown on proliferation, apoptosis and invasion in 5-8F cells. ZFAS1 might play an oncogenic role in NPC and facilitate cell proliferation and invasion via the PI3K/AKT signaling pathway in NPC cells.

Substantia Nigra Dysregulated Gene Spectra and Blood Biomarkers for Parkinson's Disease

Background: Parkinson's disease (PD) is a common and complex neurodegenerative disease with an elusive etiologies. Both molecular mechanisms and biomarkers remain to be further uncovered in PD. Methods: Qualified human microarray datasets of the substantia nigra and blood were acquired from PD case-control studies. A discovery and replication two-phase method was used to identify dysregulated genes with functional enrichment analyses. An integrated analysis was used to screen PD biomarkers followed by validations in blood samples of 50 PD patients and 50 controls. Findings: Eight datasets of substantia nigra and 3 datasets of blood were retrieved, including 21 million data points from 146 substantia nigra and 571 blood samples. A total of 5 up-regulated genes and 91 down-regulated genes (fold-change > 1.5 and P 1.2 and P < 0.05. TUBB2A in reduced expression decently predicted PD as a blood biomarker (sensitivity, 0.72; specificity, 0.70; AUC, 0.730 [95% CI, 0.632-0.828]). Interpretation: Our study provides novel insight into elucidating mechanisms underlying PD pathophysiology and the development of promising PD biomarkers. Funding: This work was supported by funding from Zhejiang Provincial Natural Science Foundation (LD19H090001 and LZ19H090002), National Natural Science Foundation of China (81571087, 81771380, and 81771510), and Wenzhou Municipal Science and Technology Bureau (C20170003). Declaration of Interest: The authors declare that there is no actual or potential conflict of interest. Ethical Approval: All participants provided written informed consent. The study was approved by the ethics committee of the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University.

Abstract 2147: Galectin-1: A marker for claudin-low breast cancer

Abstract Metastasis and mortality among patients with triple negative breast cancer (TNBC) of the claudin-low subtype remain high because these tumors are clinically aggressive, chemoresistant, and lack targeted therapies. We have previously used proteomic approaches to analyze orthotopic and metastatic tumors from the syngeneic murine T11 tumor model, which displays gene expression profiles that mirror human claudin-low TNBC. These studies identified Galectin-1 (Gal-1), an N-acetyllactosamine-binding protein, as an overexpressed protein in cultured T11 cells, primary tumors, and lung metastases compared to normal lung and mammary fat pad tissue from healthy control mice. These results were confirmed by Western immunoblot and immunohistochemistry and were further supported by analysis of murine tumor microarray data sets, which revealed Gal-1 overexpression in the claudin-low subtype compared to other breast cancer subtypes. Here we extend our murine studies to investigate Gal-1 expression in breast cancer patients, with specific inclusion of claudin-low tumors. To compare differential Gal-1 expression across breast cancer subtypes, we initially analyzed published human microarray data sets from normal breast and primary breast tumors (total n=423, of which 51 are the claudin-low subtype): the highest Gal-1 expression was observed in claudin-low breast cancer. In order to validate these findings, we are examining breast cancer specimens from a unique cohort of ethnically diverse patients from rural Eastern North Carolina with a median follow-up of over 8 years. RNASeq data from a subset of this cohort (n=121) was used to assign subtypes (using PAM50 and the claudin-low classifier). Further studies are in progress to compare Gal-1 expression between subtypes and correlate with clinicopathologic factors and outcome. Immunohistochemical Gal-1 staining of primary tumor tissue blocks is underway to confirm expression in each subtype at the protein level. Immunohistochemistry will also define Gal-1 expression in the cell types comprising the heterogeneous tumor microenvironment (tumor, stromal, immune cells) and its subcellular localization (nuclear, cytoplasmic, membrane-associated). Through this ongoing work evaluating Gal-1 across breast cancer subtypes, we will help corroborate initial findings from the T11 claudin-low mouse model and yield further insights into the role of Gal-1 in breast cancer progression. Citation Format: Kassondra Balestrieri, Moses McDaniel, Christiana Shoopman, Nasreen Vohra, Kathryn Verbanac. Galectin-1: A marker for claudin-low breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2147.

LEM domain containing 1 promotes proliferation via activating the PI3K/Akt signaling pathway in gastric cancer.

Gastric cancer (GC) is one of the most common cancers worldwide and has especially high morbidity and mortality in China. LEM domain containing 1 (LEMD1), an important cancer-testis antigen, has been reported to be overexpressed in various cancers and promotes the progression of cancers. However, the biological characteristics of LEMD1 remain to be explored in GC. The connection between LEMD1 expression and GC progression was analyzed by using The Cancer Genome Atlas datasets and our human microarray datasets. A Kaplan-Meier plot was used to analyze the relationship between LEMD1 expression and prognosis. The expression of LEMD1 was analyzed by quantitative real-time polymerase chain reaction and Western blot, and the proliferation ability of GC cells was analyzed by cell proliferation and colony formation assays and 5-ethynyl-2'-deoxyuridine analysis. The cell cycle and apoptosis were analyzed by flow cytometry. Furthermore, subcutaneously implanted tumor models in nude mice were used to demonstrate the role of LEMD1 in promoting tumor proliferation in vivo. In this study, we demonstrated that the LEMD1 expression level was increased in GC tissues and cells compared with normal tissues and GES-1. The in vivo and in vitro assays showed that LEMD1 promoted GC cell proliferation by regulating the cell cycle and apoptosis. Moreover, we showed that LEMD1 regulated cell proliferation by activating the phosphatidylinositol 3 kinase (PI3K) / protein kinase B (AKT) signaling pathway. Overall, the results of our study suggest that LEMD1 contributes to GC proliferation by regulating the cell cycle and apoptosis via activation of the PI3K/AKT signaling pathway. LEMD1 may act as a potential target for GC treatment.

Identification of Key Genes and Candidated Pathways in Human Autosomal Dominant Polycystic Kidney Disease by Bioinformatics Analysis

Background/Aims: Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic form of kidney disease. High-throughput microarray analysis has been applied for elucidating key genes and pathways associated with ADPKD. Most genetic profiling data from ADPKD patients have been uploaded to public databases but not thoroughly analyzed. This study integrated 2 human microarray profile datasets to elucidate the potential pathways and protein-protein interactions (PPIs) involved in ADPKD via bioinformatics analysis in order to identify possible therapeutic targets. Methods: The kidney tissue microarray data of ADPKD patients and normal individuals were searched and obtained from NCBI Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, and enriched pathways and central node genes were elucidated using related websites and software according to bioinformatics analysis protocols. Seven DEGs were validated between polycystic kidney disease and control kidney samples by quantitative real-time polymerase chain reaction. Results: Two original human microarray datasets, GSE7869 and GSE35831, were integrated and thoroughly analyzed. In total, 6,422 and 1,152 DEGs were extracted from GSE7869 and GSE35831, respectively, and of these, 561 DEGs were consistent between the databases (291 upregulated genes and 270 downregulated genes). From 421 nodes, 34 central node genes were obtained from a PPI network complex of DEGs. Two significant modules were selected from the PPI network complex by using Cytotype MCODE. Most of the identified genes are involved in protein binding, extracellular region or space, platelet degranulation, mitochondrion, and metabolic pathways. Conclusions: The DEGs and related enriched pathways in ADPKD identified through this integrated bioinformatics analysis provide insights into the molecular mechanisms of ADPKD and potential therapeutic strategies. Specifically, abnormal decorin expression in different stages of ADPKD may represent a new therapeutic target in ADPKD, and regulation of metabolism and mitochondrial function in ADPKD may become a focus of future research.