Gene-gene Interaction Network Research Articles

The expression of salivary EGF, VEGF, endothelin, and transferrin in waterpipe and cigarette smokers.

The aim of this study was to evaluate the effects of two forms of tobacco smoking, cigarettes and water pipe smoking (WPS), on the expression of a panel of salivary proteins in healthy adults. Three groups of age and gender-matched participants were enrolled in the study: never-smokers, cigarette smokers and WPS (N = 55 per group). Expression of epidermal growth factor (EGF), vascular endothelial growth factor (VEGF), endothelin and transferrin in unstimulated whole saliva was estimated using enzyme-linked immunosorbent assays. Statistical analysis consisted of one-way ANOVA and Tukey's post hoc tests, in addition to bioinformatics analysis. VEGF expression was the least in WPS (51.1 ± 14.5 pg/ml) compared to both controls (150.1 ± 13.8 pg/ml) and cigarette smokers (93 ± 9.9 pg/ml), with a significant difference in WPS (p < 0.001) and cigarette smokers (p < 0.01) compared to controls. Furthermore, transferrin showed the weakest expression in the WPS group (1238 ± 261.4 pg/ml) compared to controls (2205.6 ± 298.6 pg/ml) (p = 0.05) and cigarette smokers (1805.4 ± 244 pg/ml). Neither EGF nor endothelin expression showed any statistical difference between the groups (p > 0.05). Gene-gene interaction network demonstrated that FLT1, TFRC, KDR, VEGFB and PGF genes had the highest potential for interaction with the studied proteins. Further functional annotations on the identified markers in the interaction network were performed to identify HIF-1 pathways among the most relevant pathways. In conclusion, smoking habits alter the expression of salivary VEGF and transferrin, which may correspond to early sub-clinical changes in the oral mucosa. The clinical relevance of these salivary changes requires further research.

Gene-gene interaction network analysis indicates CNTN2 is a candidate gene for idiopathic generalized epilepsy.

Twin and family studies have established the genetic contribution to idiopathic generalized epilepsy (IGE). The genetic architecture of IGE is generally complex and heterogeneous, and the majority of the genetic burden in IGE remains unsolved. We hypothesize that gene-gene interactions contribute to the complex inheritance of IGE. CNTN2 (OMIM* 615,400) variants have been identified in cases with familial adult myoclonic epilepsy and other epilepsies. To explore the gene-gene interaction network in IGE, we took the CNTN2 gene as an example and investigated its co-occurrent genetic variants in IGE cases. We performed whole-exome sequencing in 114 unrelated IGE cases and 296 healthy controls. Variants were qualified with sequencing quality, minor allele frequency, in silico prediction, genetic phenotype, and recurrent case numbers. The STRING_TOP25 gene interaction network analysis was introduced with the bait gene CNTN2 (denoted as A). The gene-gene interaction pair mode was presumed to be A + c, A + d, A + e, with a leading gene A, or A + B + f, A + B + g, A + B + h, with a double-gene A + B, or other combinations. We compared the number of gene interaction pairs between the case and control groups. We identified three pairs in the case group, CNTN2 + PTPN18, CNTN2 + CNTN1 + ANK2 + ANK3 + SNTG2, and CNTN2 + PTPRZ1, while we did not discover any pairs in the control group. The number of gene interaction pairs in the case group was much more than in the control group (p = 0.021). Taking together the genetic bioinformatics, reported epilepsy cases, and statistical evidence in the study, we supposed CNTN2 as a candidate pathogenic gene for IGE. The gene interaction network analysis might help screen candidate genes for IGE or other complex genetic disorders.

Insights into dietary phytochemicals targeting Parkinson's disease key genes and pathways: A network pharmacology approach

Parkinson's disease (PD) is a complex neurological disease associated with the degeneration of dopaminergic neurons. Oxidative stress is a key player in instigating apoptosis in dopaminergic neurons. To improve the survival of neurons many dietary phytochemicals have gathered significant attention recently. Thus, the present study implements a comprehensive network pharmacology approach to unravel the mechanisms of action of dietary phytochemicals that benefit disease management. A literature search was performed to identify ligands (i.e., comprising dietary phytochemicals and Food and Drug Administration pre-approved PD drugs) in the PubMed database. Targets associated with selected ligands were extracted from the search tool for interactions of chemicals (STITCH) database. Then, the construction of a gene-gene interaction (GGI) network, analysis of hub-gene, functional and pathway enrichment, associated transcription factors, miRNAs, ligand-target interaction network, docking were performed using various bioinformatics tools together with molecular dynamics (MD) simulations. The database search resulted in 69 ligands and 144 unique targets. GGI and subsequent topological measures indicate histone acetyltransferase p300 (EP300), mitogen-activated protein kinase 1 (MAPK1) or extracellular signal-regulated kinase (ERK)2, and CREB-binding protein (CREBBP) as hub genes. Neurodegeneration, MAPK signaling, apoptosis, and zinc binding are key pathways and gene ontology terms. hsa-miR-5692a and SCNA gene-associated transcription factors interact with all the 3 hub genes. Ligand-target interaction (LTI) network analysis suggest rasagiline and baicalein as candidate ligands targeting MAPK1. Rasagiline and baicalein form stable complexes with the Y205, K330, and V173 residues of MAPK1. Computational molecular insights suggest that baicalein and rasagiline are promising preclinical candidates for PD management.

Intestinal mRNA expression analysis of polarity-related genes identified the discriminatory ability of CRB3 as a diagnostic marker for celiac disease.

Celiac disease (CD) is a chronic autoimmune disorder characterized by an abnormal immune response to gluten, a protein found in wheat, barley, and rye. It is well established that the integrity of epithelial tight junctions (TJs) and adherens junctions (AJs) plays a crucial role in the pathogenesis of CD. These junctional complexes contribute to the apical-basal polarity of the intestinal epithelial cells, which is crucial for their proper functioning. Sixty CD subjects, and 50 controls were enrolled in the current study. Mucosal samples were obtained from the distal duodenum, total RNA was extracted and complementary DNA was synthesized. The relative expression levels of the desired genes were evaluated by quantitative real-time polymerase chain reaction based on ΔΔCt method. The gene-gene interaction network was also constructed using GeneMANIA. CRB3 (p = .0005), LKB1 (p < .0001), and SCRIB (p = .0005) had lower expression in CD patients compared to controls, while PRKCZ expression did not differ between groups (p > .05). CRB3 represented a significant diagnostic value for differentiating CD patients from the control group (p = .02). The aim of the current study was to evaluate the changes in the mRNA expression levels of SCRIB, PRKCZ, LKB1, and CRB3 genes in the small intestinal biopsy samples of CD patients in comparison to the healthy control subjects. Our data uncover the importance of polarity-related genes (especially CRB3) in CD pahtomechanism, that may facilitate the planning of the future studies looking for finding innovative diagnostic and therapeutic strategies for CD.

Characterization of the Heat Shock Transcription Factor Family in Lycoris radiata and Its Potential Roles in Response to Abiotic Stresses.

Heat shock transcription factors (HSFs) are an essential plant-specific transcription factor family that regulates the developmental and growth stages of plants, their signal transduction, and their response to different abiotic and biotic stresses. The HSF gene family has been characterized and systematically observed in various species; however, research on its association with Lycoris radiata is limited. This study identified 22 HSF genes (LrHSFs) in the transcriptome-sequencing data of L. radiata and categorized them into three classes including HSFA, HSFB, and HSFC, comprising 10, 8, and 4 genes, respectively. This research comprises basic bioinformatics analyses, such as protein sequence length, molecular weight, and the identification of its conserved motifs. According to the subcellular localization assessment, most LrHSFs were present in the nucleus. Furthermore, the LrHSF gene expression in various tissues, flower developmental stages, two hormones stress, and under four different abiotic stresses were characterized. The data indicated that LrHSF genes, especially LrHSF5, were essentially involved in L. radiata development and its response to different abiotic and hormone stresses. The gene-gene interaction network analysis revealed the presence of synergistic effects between various LrHSF genes' responses against abiotic stresses. In conclusion, these results provided crucial data for further functional analyses of LrHSF genes, which could help successful molecular breeding in L. radiata.

A comprehensive analysis of mRNA expression profiles of Esophageal Squamous Cell Carcinoma reveals downregulation of Desmoglein 1and crucial genomic targets.

Esophageal Squamous Cell Carcinoma (ESCC) is a histological subtype of esophageal cancer that begins in the squamous cells in the esophagus. In only 19% of the ESCC-diagnosed patients, a five-year survival rate has been seen. This necessitates the identification of high-confidence biomarkers for early diagnosis, prognosis, and potential therapeutic targets for the mitigation of ESCC. We performed a meta-analysis of 10 mRNA datasets and identified consistently perturbed genes across the studies. Then, integrated with ESCC ATLAS to segregate 'core' genes to identify consequences of primary gene perturbation events leading to gene-gene interactions and dysregulated molecular signaling pathways. Further, by integrating with toxicogenomics data, inferences were drawn for gene interaction with environmental exposures, trace elements, chemical carcinogens, and drug chemicals. We also deduce the clinical outcomes of candidate genes based on survival analysis using the ESCC related dataset in The Cancer Genome Atlas. We identified 237 known and 18 novel perturbed candidate genes. Desmoglein 1 (DSG1) is one such gene that we found significantly downregulated (Fold Change =-1.89, p-value = 8.2e-06) in ESCC across six different datasets. Further, we identified 31 'core' genes (that either harbor genetic variants or are regulated by epigenetic modifications) and found regulating key biological pathways via adjoining genes in gene-gene interaction networks. Functional enrichment analysis showed dysregulated biological processes and pathways including "Extracellular matrix", "Collagen trimmer" and "HPV infection" are significantly overrepresented in our candidate genes. Based on the toxicogenomic inferences from Comparative Toxicogenomics Database we report the key genes that interacted with risk factors such as tobacco smoking, zinc, nitroso benzylmethylamine, and drug chemicals such as cisplatin, Fluorouracil, and Mitomycin in relation to ESCC. We also point to the STC2 gene that shows a high risk for mortality in ESCC patients. We identified novel perturbed genes in relation to ESCC and explored their interaction network. DSG1 is one such gene, its association with microbiota and a clinical presentation seen commonly with ESCC hints that it is a good candidate for early diagnostic marker. Besides, in this study we highlight candidate genes and their molecular connections to risk factors, biological pathways, drug chemicals, and the survival probability of ESCC patients.

An approach for searching genes in signaling pathways or gene-gene interaction networks related to Hypertension in the Mexican population

IntroductionThe selection of single nucleotide polymorphisms (SNPs) to evaluate the genetic susceptibility in complex traits is often conducted in isolation, without considering the entire set of genes. Incorporating signaling pathways or gene-gene interaction search may provide a more comprehensive approach to selecting SNP candidates for further study. ObjectiveTo propose a systematic procedure for identifying SNPs candidates with complex traits such as hypertension and blood pressure. MethodsSequential stages to SNPs selection: 1) literature review to identify SNPs, following the PRISMA methodology, 2) identification and selection of signaling pathways and selection of gene-gene interaction networks using the STRING software, and 3) application of specific criteria for SNPs candidates, including: a) SNPs with minor allele frequency > 5% in the target population, b) SNPs located within genes involved in three or more signaling pathways, and c) SNPs that are not in linkage disequilibrium, with a D’or r2 value < 0.8. ResultsStage 1) A total of 44 publications were selected, providing information on 230 genes evaluated with blood pressure. Stage 2) Using the STRING software, we selected 7 signaling pathways with a false discovery rate < 0.0001 and strength ≥ 0.8; and we identified 16 genes belonging to gene-gene interaction networks, six of them share ≥ 3 signaling pathways. Stage 3) Finally, 7 SNPs were selected for genotyping in the Health Workers Cohort Study. We observed a positive association between SNPs with hypertension incidence in males (rs1130214, rs3807989) and females (rs5051, rs2493123). ConclusionOur methodological proposal may be a reliable way for selecting SNPs candidates to study complex traits.

Explainable Multilayer Graph Neural Network for Cancer Gene Prediction.

The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene-gene interactions or provide limited explanations for their predictions. These methods are restricted to a single biological network, which cannot capture the full complexity of tumorigenesis. Models trained on different biological networks often yield different and even opposite cancer gene predictions, hindering their trustworthy adaptation. Here, we introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes by leveraging multiple gene-gene interaction networks and pan-cancer multi-omics data. Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction. Our method consistently outperforms all existing methods, with an average 7.15% improvement in area under the precision-recall curve (AUPR) over the current state-of-the-art method. Importantly, EMGNN integrated multiple graphs to prioritize newly predicted cancer genes with conflicting predictions from single biological networks. For each prediction, EMGNN provided valuable biological insights via both model-level feature importance explanations and molecular-level gene set enrichment analysis. Overall, EMGNN offers a powerful new paradigm of graph learning through modeling the multilayered topological gene relationships and provides a valuable tool for cancer genomics research. Our code is publicly available at https://github.com/zhanglab-aim/EMGNN.

Immunological function and prognostic value of lymphoid-specific helicase in liver hepatocellular carcinoma.

Lymphoid-specific helicase (HELLS), a SNF2-like chromatin-remodeling enzyme, plays a key role in tumor progression via its DNA methylation function. However, the effects of HELLS on immune infiltration and prognosis in liver hepatocellular carcinoma (LIHC) remain uncertain. The Tumor Immune Estimation Resource (TIMER) database was employed to explore the pan-cancer mRNA expression of HELLS and its correlation with immunity. GEPIA2 was used to verify the correlation between HELLS expression and survival. The role of HELLS in cancer was explored via gene set enrichment analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) and the construction of gene-gene and protein-protein interaction networks (PPI). Additionally, correlations between DNA methylation, HELLS expression, and immune-related genes were explored in LIHC. HELLS expression in LIHC clinical samples was determined using qRT-PCR and western blotting. The effects of downregulated HELLS expression in hepatocellular carcinoma cells was explored via transfection experiments in vitro. High HELLS mRNA expression was identified in several cancers and was significantly associated with poorer prognosis in LIHC. Furthermore, HELLS expression was positively correlated with tumor-infiltrating lymphocytes and immune checkpoint genes in LIHC. Bioinformatics analysis suggested that DNA methylation of HELLS may be associated with the immune response. Results from the TCGA-LIHC dataset, clinical samples, and functional analysis indicated that HELLS contributed to tumor progression in LIHC. The study findings demonstrate that HELLS is an important factor in promoting LIHC malignancy and might serve as a potential biomarker for LIHC.

Association of HLA-B27 Gene and Rheumatoid Arthritis: Analysis of Potential Role as a Predictive Biomarker

Rheumatoid arthritis (RA) is a predominant inflammatory arthritis in human. The function of HLA-B27 gene in other types of arthritis has been studied, however, its function in RA is unclear. This study investigated the relative expression of HLA-B27 gene in RA patients compared to normal control and assesses its suitability as a biomarker for early detection of RA. Bioinformatics analysis was used to determine the profile of HLA-B27 gene in different human tumors to demonstrate the possible involvement of HLA-B27 in both RA and cancer; and also, to discover its functional association with other human genes. Samples of human blood from RA patients and healthy individuals were collected, and RNA extraction, cDNA synthesis and qPCR were carried out to detect relative expression of HLA-B27. ROC analysis was undertaken to investigate HLA-B27 diagnostic performance; GENT2 platform was used to compare HLA-B27 expression levels in different human tumors, and gene-gene interaction network was generated using GeneMANIA to identify correlation of HLA-B27 with other human genes. The qPCR analysis demonstrated an increase in the HLA-B27 expression by 1.65 fold in RA compared to normal control. ROC analysis indicated that HLA-B27 expression could efficiently differentiate RA from normal, supporting its potential use as diagnostic molecular biomarkers. The GENT2 revealed that HLA-B27 expression levels vary across different tumor types, most notably in heart tissue. The gene-gene interaction network revealed that KIR3DL1, KIR3DS1, LILRB1, B2M and LILRA1 were the leading genes showing the highest correlations with the HLA-B27. Our results indicate that HLA-B27 gene is involved in the RA pathogenesis and it can be used as a molecular biomarker for the diagnosis of RA. Our findings could lead to the discovery of novel diagnostic, preventive and therapeutic strategies.

Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis.

With the development of biotechnology, a large amount of multi-omics data have been collected for precision medicine. There exists multiple graph-based prior biological knowledge about omics data, such as gene-gene interaction networks. Recently, there has been an increasing interest in introducing graph neural networks (GNNs) into multi-omics learning. However, existing methods have not fully exploited these graphical priors since none have been able to integrate knowledge from multiple sources simultaneously. To solve this problem, we propose a multi-omics data analysis framework by incorporating multiple prior knowledge into graph neural network (MPK-GNN). To the best of our knowledge, this is the first attempt to introduce multiple prior graphs into multi-omics data analysis. Specifically, the proposed method contains four parts: (1) a feature-level learning module to aggregate information from prior graphs; (2) a projection module to maximize the agreement among prior networks by optimizing a contrastive loss; (3) a sample-level module to learn a global representation from input multi-omics features; (4) a task-specific module to flexibly extend MPK-GNN for various downstream multi-omics analysis tasks. Finally, we verify the effectiveness of the proposed multi-omics learning algorithm on the cancer molecular subtype classification task. Experimental results show that MPK-GNN outperforms other state-of-the-art algorithms, including multi-view learning methods and multi-omics integrative approaches.

Bioinformatics analysis and prediction of Alzheimer's disease and alcohol dependence based on Ferroptosis-related genes.

Alzheimer's disease (AD) is a neurodegenerative disease whose origins have not been universally accepted. Numerous studies have demonstrated the relationship between AD and alcohol dependence; however, few studies have combined the origins of AD, alcohol dependence, and programmed cell death (PCD) to analyze the mechanistic relationship between the development of this pair of diseases. We demonstrated in previous studies the relationship between psychiatric disorders and PCD, and in the same concerning neurodegeneration-related AD, we found an interesting link with the Ferroptosis pathway. In the present study, we explored the bioinformatic interactions between AD, alcohol dependence, and Ferroptosis and tried to elucidate and predict the development of AD from this aspect. We selected the Alzheimer's disease dataset GSE118553 and alcohol dependence dataset GSE44456 from the Gene Expression Omnibus (GEO) database. Ferroptosis-related genes were gathered through Gene Set Enrichment Analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and relevant literature, resulting in a total of 88 related genes. For the AD and alcohol dependence datasets, we conducted Limma analysis to identify differentially expressed genes (DEGs) and performed functional enrichment analysis on the intersection set. Furthermore, we used ferroptosis-related genes and the DEGs to perform machine learning crossover analysis, employing Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify candidate immune-related central genes. This analysis was also used to construct protein-protein interaction networks (PPI) and artificial neural networks (ANN), as well as to plot receiver operating characteristic (ROC) curves for diagnosing AD and alcohol dependence. We analyzed immune cell infiltration to explore the role of immune cell dysregulation in AD. Subsequently, we conducted consensus clustering analysis of AD using three relevant candidate gene models and examined the immune microenvironment and functional pathways between different subgroups. Finally, we generated a network of gene-gene interactions and miRNA-gene interactions using Networkanalyst. The crossover of AD and alcohol dependence DEG contains 278 genes, and functional enrichment analysis showed that both AD and alcohol dependence were strongly correlated with Ferroptosis, and then crossed them with Ferroptosis-related genes to obtain seven genes. Three candidate genes were finally identified by machine learning to build a diagnostic prediction model. After validation by ANN and PPI analysis, ROC curves were plotted to assess the diagnostic value of AD and alcohol dependence. The results showed a high diagnostic value of the predictive model. In the immune infiltration analysis, functional metabolism and immune microenvironment of AD patients were significantly associated with Ferroptosis. Finally, analysis of target genes and miRNA-gene interaction networks showed that hsa-mir-34a-5p and has-mir-106b-5p could simultaneously regulate the expression of both CYBB and ACSL4. We obtained a diagnostic prediction model with good effect by comprehensive analysis, and validation of ROC in AD and alcohol dependence data sets showed good diagnostic, predictive value for both AD (AUC 0. 75, CI 0.91-0.60), and alcohol dependence (AUC 0.81, CI 0.95-0.68). In the consensus clustering grouping, we identified variability in the metabolic and immune microenvironment between subgroups as a likely cause of the different prognosis, which was all related to Ferroptosis function. Finally, we discovered that hsa-mir-34a-5p and has-mir-106b-5p could simultaneously regulate the expression of both CYBB and ACSL4.

Identification of KNOP1 as a prognostic marker in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignancy with a poor prognosis. This study aimed to evaluate the role and molecular mechanism of lysine-rich nucleolar protein 1 (KNOP1) in HCC. Data from The Cancer Genome Atlas (TCGA), genotype-tissue expression (GTEx), and Gene Expression Omnibus (GEO) databases were used to compare KNOP1 expression in normal and HCC tissues. The Human Protein Atlas (HPA) database was used to verify KNOP1 protein expression. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment, protein-protein and gene-gene interaction network, DNA methylation, genetic alteration, and immune cell infiltration analyses were used to analyze the function and pathway enrichment of KNOP1. Finally, receiver operating characteristic (ROC) curves, Kaplan-Meier (KM) analysis, univariate/multivariate Cox regression analyses, and nomograms were used to predict the clinical and prognostic significance of KNOP1. KNOP1 expression was higher in HCC tissue samples than in normal specimens. Additionally, high KNOP1 expression was positively correlated with T helper 2 (Th2) cells and immune checkpoints. KM analysis, Cox regression analysis, and nomogram prognostic model prediction suggested that high KNOP1 expression is a risk factor for poor HCC prognosis. KNOP1 overexpression is associated with poor HCC prognosis and increased proportions of immune cell infiltration and checkpoints. KNOP1 is a potential biomarker for evaluating HCC prognosis.

A novel prognostic biomarker: GINS3 is correlated with methylation and immune escape in liver hepatocellular carcinoma.

Liver cancer remains one of the tricky malignancies nowadays. GINS complex subunit 3 (GINS3), part of the GINS tetrameric complex, is significantly upregulated in many cancers, including liver hepatocellular carcinoma (LIHC). With the development of liver cancer treatment, immune and molecular targeted therapy gradually becomes a promising treatment. However, the key target for liver cancer is still indistinct. Herein, the underneath mechanism of GINS3 was investigated to verify its role as a biomarker in LIHC. Genomic expression, genetic alteration, and methylation analyses were obtained from The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), The University of Alabama at Birmingham CANcer (UALCN), and Human Protein Atlas (HPA), cBioPortal, and MethSurv databases. Subsequently, the diagnostic and prognostic role of GINS3 in LIHC were analyzed based on data from receiver operating characteristic (ROC), Kaplan-Meier plotter (KM-plotter), and univariate and multivariate cox regression analyses. The functional analyses were conducted with GeneMANIA and STRING databases, gene-gene, and protein-protein interaction (PPI) networks, Gene Ontology (GO) term, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Tumor Immune Estimation Resource (TIMER), Tumor-Immune System Interaction Database (TISIDB), and Gene Expression Profiling Interactive Analysis (GEPIA) were utilized to explore the internal connection with the immune escape. Through the analyses of genomic expression, GINS3 was significantly upregulated in LIHC and positively correlated with higher T classification. ROC analysis indicated GINS3 as a potential biomarker in the diagnosis of LIHC. KM-plotter, univariate and multivariate cox regression analyses both associated GINS3 with poor prognosis in LIHC patients. GINS3 genetic alteration, gene-gene interaction, PPI networks, and enrichment analysis further revealed that GINS3 played a pivotal role in the progression of LIHC. Furthermore, hypermethylation of GINS3 at different cytosine-guanine (CpG) sites was correlated with better or worse overall survival (OS) in LIHC and GINS3 was also closely correlated with m6A modification. Moreover, results supported that GINS3 could influence the tumor microenvironment and relate to the immune checkpoints. Taken together, comprehensive analyses from this study supported GINS3 as a novel targeted biomarker in LIHC.

Expression analysis of IL-2, TBX21 and SOCS1 in peripheral blood cells of celiac disease patients reveals the diagnostic potential of IL-2.

Celiac disease (CD) is a chronic immune-mediated enteropathy and a cytokine network is involved in its pathogenesis. Interleukin-2 (IL-2) has a key role in the adaptive immune pathogenesis of CD and has been reported to be one of the earliest cytokines to be elicited after gluten exposure by CD patients. This study aimed at investigating the expression level of IL-2 and functionally related genes SOCS1 and TBX21 in active and treated CD patients compared to controls. Peripheral blood (PB) samples were collected from 40 active CD (ACD), 100 treated CD, and 100 healthy subjects. RNA was extracted, cDNA was synthesized and mRNA expression levels of the desired genes were investigated by Real-time PCR. The gene-gene interaction network was also constructed by GeneMANIA. Our results showed a higher PB mRNA expression of IL-2 in ACD patients compared to controls (p = 0.001) and treated CD patients (p˂0.0001). The mRNA expression level of TBX21 was also significantly up-regulated in ACD patients compared to controls (P = 0.03). SOCS1 mRNA level did not differ between active and treated CD patients and controls (p˃0.05) but showed a significant correlation with the patient's aphthous stomatitis symptom (r = 0.37, p = 0.01). ROC curve analysis suggested that the use of IL-2 levels can reach a high specificity and sensitivity in discriminating active CD patients. The PB level of IL-2 has the potential to be introduced as a diagnostic biomarker for CD. Larger cohort studies, including pediatric patients, are needed to achieve more insights in this regard.

Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma.

Osteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease. In our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks. Our study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc. To develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.

Single-cell pair-wise relationships untangled by composite embedding model

In multicellular organisms, cell identity and functions are primed and refined through interactions with other surrounding cells. Here, we propose a scalable machine learning method, termed SPRUCE, which is designed to systematically ascertain common cell-cell communication patterns embedded in single-cell RNA-seq data. We applied our approach to investigate tumor microenvironments consolidating multiple breast cancer datasets and found seven frequently observed interaction signatures and underlying gene-gene interaction networks. Our results implicate that a part of tumor heterogeneity, especially within the same subtype, is better understood by differential interaction patterns rather than the static expression of known marker genes.

Comprehensive analysis of PTPN family expression and prognosis in acute myeloid leukemia.

Background: Tyrosyl phosphorylation is carried out by a group of enzymes known as non-receptor protein tyrosine phosphatases (PTPNs). In the current investigation, it is hoped to shed light on the relationships between the expression patterns of PTPN family members and the prognosis of acute myeloid leukemia (AML). Methods: PTPN expression was examined using GEPIA and GEO databases. To investigate the connection between PTPN expression and survival in AML patients, we downloaded data from the Broad TCGA Firehose and Clinical Proteomic Tumor Analysis (CPTAC) of the Cancer Genome Atlas (TCGA). We used quantitative real-time PCR (qRT-PCR) to confirm that essential genes were performed in clinical samples and cell lines. We then used western blot to verify that the genes expressed in the above databases were positive in normal tissues, AML patient samples, and AML cell lines. Next, we investigated associations between genome-wide expression profiles and PTPN6 expression using the GEO datasets. We investigated the interactive exploration of multidimensional cancer genomics using the cBioPortal datasets. Using the DAVID database, a study of gene ontology enrichment was performed. The protein-protein interaction (PPI) network was created using the STRING portal, and the gene-gene interaction network was performed using GeneMANIA. Results: Data from GEO and GEPIA revealed that most PTPN family members were linked to AML. Patients with leukemia have elevated levels of several PTPN members. All of the AML patients' poor overall survival (OS, p < .05) was significantly linked with higher expression of PTPN1, PTPN6, and PTPN7. Additionally, clinical samples showed that the expression of PTPN 6, PTPN 7, PTPN 13, and PTPN 14 was higher than normal in AML patients (p = .0116, p = .0034, p = .0092, and p = .0057, respectively) and AML cell lines (p = .0004, p = .0035, p = .0357, and p = .0177, respectively). Western blotting results showed that the expression of PTPN6 in AML samples and AML cell lines was significantly higher than that in normal control samples. Conclusion: Differentially expressed PTPN family members were found in AML. The prognosis of patients and PTPN gene expression were shown to be correlated. PTPN6 is one of these members and may be used as an AML diagnostic and prognostic marker.

LncRNA UCA1 inhibits mitochondrial dysfunction of skeletal muscle in type 2 diabetes mellitus by sequestering miR-143-3p to release FGF21.

Increasing evidence suggests that insulin resistance in type 2 diabetes mellitus (T2DM) is associated with mitochondrial dysfunction in skeletal muscle, while the underlying molecular mechanisms remain elusive. This study aims to construct a ceRNA regulatory network that is involved in mitochondrial dysfunction of skeletal muscle in T2DM. Based on GEO database analysis, differentially expressed lncRNA and mRNA profiles were identified in skeletal muscle tissues of T2DM. Next, LASSO regression analysis was conducted to predict the key lncRNAs related to T2DM, which was validated by receiver operating characteristic (ROC) analysis. Moreover, the miRNAs related to skeletal muscle in T2DM were identified by WGCNA, followed by construction of gene-gene interaction network and GO and KEGG enrichment analyses. It was found that 12 lncRNAs and 6 miRNAs were related to skeletal muscle in T2DM. Moreover, the lncRNA-miRNA-mRNA ceRNA network involving UCA1, miR-143-3p, and FGF21 was constructed. UCA1, and FGF21 were downregulated, while miR-143-3p was upregulated in skeletal muscle cells (SkMCs) exposed to palmitic acid. Additionally, ectopic expression experiments were performed in SkMCs to confirm the effects of UCA1/miR-143-3p/FGF21 on mitochondrial dysfunction by determining mitochondrial ROS, oxygen consumption rate (OCR), membrane potential, and ATP level. Overexpression of miR-143-3p increased ROS accumulation and reduced the OCR, fluorescence ratio of JC-1, and ATP level, which were reversed by upregulation of UCA1 or FGF21. Collectively, lncRNA UCA1 inhibited mitochondrial dysfunction of skeletal muscle in T2DM by sequestering miR-143-3p away from FGF21, therefore providing a potential therapeutic target for alleviating mitochondrial dysfunction of skeletal muscle in T2DM.

Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data.

System-level network-based approaches are an emerging field in the biomedical domain since biological networks can be used to analyze complicated biological processes and complex human disorders more efficiently. Network biomarkers are groups of interconnected molecular components causing perturbations in the entire network topology that can be used as indicators of pathogenic biological processes when studying a given disease. Although in the last years computational systems-based approaches have gained ground on the path to discovering new network biomarkers, in complex diseases like Alzheimer's disease (AD), this approach has still much to offer. Especially the adoption of single-cell RNA sequencing (scRNA-seq) has now become the dominant technology for the study of stochastic gene expression. Toward this orientation, we propose an R workflow that extracts disease-perturbed subpathways within a pathway network. We construct a gene-gene interaction network integrated with scRNA-seq expression profiles, and after network processing and pruning, the most active subnetworks are isolated from the entire network topology. The proposed methodology was applied on a real AD-based scRNA-seq data, providing already existing and new potential AD biomarkers in gene network context.