Tissue-Specific Molecular Biomarker Signatures of Type 2 Diabetes: An Integrative Analysis of Transcriptomics and Protein-Protein Interaction Data

System biology approaches for drug repurposing

Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.

Comparative analysis of diisononyl phthalate and di(isononyl)cyclohexane-1,2 dicarboxylate plasticizers in regulation of lipid metabolism in 3T3-L1 cells

Diisononyl phthalate (DINP) and di(isononyl)cyclohexane-1,2-dicarboxylate (DINCH) are plasticizers introduced to replace previously used phthalate plasticizers in polymeric products. Exposure to DINP and DINCH has been shown to impact lipid metabolism. However, there are limited studies that address the mechanisms of toxicity of these two plasticizers. Here, a comparative toxicity analysis has been performed to evaluate the impacts of DINP and DINCH on 3T3-L1 cells. The preadipocyte 3T3-L1 cells were exposed to 1, 10, and 100 μM of DINP or DINCH for 10 days and assessed for lipid accumulation, gene expression, and protein analysis. Lipid staining showed that higher concentrations of DINP and DINCH can induce adipogenesis. The gene expression analysis demonstrated that both DINP and DINCH could alter the expression of lipid-related genes involved in adipogenesis. DINP and DINCH upregulated Pparγ, Pparα, C/EBPα Fabp4, and Fabp5, while both compounds significantly downregulated Fasn and Gata2. Protein analysis showed that both DINP and DINCH repressed the expression of FASN. Additionally, we analyzed an independent transcriptome dataset encompassing temporal data on lipid differentiation within 3T3-L1 cells. Subsequently, we derived a gene set that accurately portrays significant pathways involved in lipid differentiation, which we subsequently subjected to experimental validation through quantitative polymerase chain reaction. In addition, we extended our analysis to encompass a thorough assessment of the expression profiles of this identical gene set across 40 discrete transcriptome datasets that have linked to diverse pathological conditions to foreseen any potential association with DINP and DINCH exposure. Comparative analysis indicated that DINP could be more effective in regulating lipid metabolism.

Identifying the mitochondrial metabolism network by integration of machine learning and explainable artificial intelligence in skeletal muscle in type 2 diabetes

Imbalance in glucose metabolism and insulin resistance are two primary features of type 2 diabetes/diabetes mellitus. Its etiology is linked to mitochondrial dysfunction in skeletal muscle tissue. The mitochondria are vital organelles involved in ATP synthesis and metabolism. The underlying biological pathways leading to mitochondrial dysfunction in type 2 diabetes can help us understand the pathophysiology of the disease. In this study, the mitochondrial gene expression dataset were retrieved from the GSE22309, GSE25462, and GSE18732 using Mitocarta 3.0, focusing specifically on genes that are associated with mitochondrial function in type 2 disease. Feature selection on the expression dataset of skeletal muscle tissue from 107 control patients and 70 type 2 diabetes patients using the XGBoost algorithm having the highest accuracy. For interpretation and analysis of results linked to the disease by examining the feature importance deduced from the model was done using SHAP (SHapley Additive exPlanations). Next, to comprehend the biological connections, study of protein-protien and mRNA-miRNA networks was conducted using String and Mienturnet respectively. The analysis revealed BDH1, YARS2, AKAP10, RARS2, MRPS31, were potential mitochondrial target genes among the other twenty genes. These genes are mainly involved in the transport and organization of mitochondria, regulation of its membrane potential, and intrinsic apoptotic signaling etc. mRNA-miRNA interaction network revealed a significant role of miR-375; miR-30a-5p; miR-16-5p; miR-129-5p; miR-1229-3p; and miR-1224-3p; in the regulation of mitochondrial function exhibited strong associations with type 2 diabetes. These results might aid in the creation of novel targets for therapy and type 2 diabetes biomarkers.

Immune cell-specific and common molecular signatures in rheumatoid arthritis through molecular network approaches

Rheumatoid arthritis (RA) is an autoimmune disorder and common symptom of RA is chronic synovial inflammation. The pathogenesis of RA is not fully understood. Therefore, we aimed to identify underlying common and distinct molecular signatures and pathways among ten types of tissue and cells obtained from patients with RA. In this study, transcriptomic data including synovial tissues, macrophages, blood, T cells, CD4+T cells, CD8+T cells, natural killer T (NKT), cells natural killer (NK) cells, neutrophils, and monocyte cells were analyzed with an integrative and comparative network biology perspective. Each dataset yielded a list of differentially expressed genes as well as a reconstruction of the tissue-specific protein-protein interaction (PPI) network. Molecular signatures were identified by a statistical test using the hypergeometric probability density function by employing the interactions of transcriptional regulators and PPI. Reporter metabolites of each dataset were determined by using genome-scale metabolic networks. It was defined as the common hub proteins, novel molecular signatures, and metabolites in two or more tissue types while immune cell-specific molecular signatures were identified, too. Importantly, miR-155-5p is found as a common miRNA in all tissues. Moreover, NCOA3, PRKDC and miR-3160 might be novel molecular signatures for RA. Our results establish a novel approach for identifying immune cell-specific molecular signatures of RA and provide insights into the role of common tissue-specific genes, miRNAs, TFs, receptors, and reporter metabolites. Experimental research should be used to validate the corresponding genes, miRNAs, and metabolites.

In silico based analysis to explore genetic linkage between atherosclerosis and its potential risk factors

Atherosclerosis (ATH) is a chronic cardiovascular disease characterized by plaque formation in arteries, and it is a major cause of illness and death. Although therapeutic advances have significantly improved the prognosis of ATH, missing therapeutic targets pose a significant residual threat. This research used a systems biology approach to identify the molecular biomarkers involved in the onset and progression of ATH, analysing microarray gene expression datasets from ATH and tissues impacted by risk factors such as high cholesterol, adipose tissue, smoking, obesity, sedentary lifestyle, stress, alcohol consumption, hypertension, hyperlipidaemia, high fat, diabetes to find the differentially expressed genes (DEGs). Bioinformatic analyses of Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted on differentially expressed genes, revealing metabolic and signaling pathways (the chemokine signaling pathway, cytokine-cytokine receptor interaction, the cytosolic DNA-sensing pathway, the peroxisome proliferator-activated receptors signaling pathway, and the nuclear factor-kappa B signaling pathway), ten hubs proteins (CCL5, CCR1, TLR1, CCR2, FCGR2A, IL1B, CD163, AIF1, CXCL-1 and TNF), five transcription factors (YY1, FOXL1, FOXC1, SRF, and GATA2), and five miRNAs (mir-27a-3p, mir-124-3p, mir-16-5p, mir-129-2-3p, mir-1-3p). These findings identify potential biomarkers that may increase knowledge of the mechanisms underlying ATH and their connection to risk factors, aiding in the development of new therapies.

Potential Signature Therapeutic Biomarkers TOP2A, MAD2L1, and CDK1 in Colorectal Cancer: A Systems Biomedicine-Based Approach

Colorectal cancer is the third deadliest and fourth most diagnosed cancer. It is heterogeneously driven by varied mutations and mutagens, and thus, it is challenging for targeted therapy. The rapid advancement of high-throughput technology presents considerable opportunities for discovering new colon cancer biomarkers. In the present study, we have explored and identified the biomarkers based on molecular interactions. We curated cancer datasets that were not micro-dissected and performed gene expression analysis. The protein-protein interactions were curated, and a network was constructed for the up-regulated genes. The hub genes were analyzed using 12 different topological parameters. The correlation analysis selected TOP2A, CDK1, CCNB1, AURKA, and MAD2L1 as hub genes. Further, survival analysis was performed to determine the effectiveness of the hub gene on the patient's survival rate. Our findings explore various transcription factors such as E2F4, FOXM1, E2F6, MAX, and SIN3A, along with kinases CSNK2A1, MAPK14, CDK1, CDK4, and CDK2, as potential molecular signatures and aid researchers in understanding the pathophysiological mechanisms underlying CRC development and thus providing novel therapeutic and diagnostic recourse. Furthermore, investigating miRNAs, we focused on hsa-miR-215-5p, hsa-miR-192-5p, and hsa-miR-193b-3p due to their observed impact on a diverse set of colorectal cancer genes. Thereby, the current approach brings into light CRC- related genes at the RNA and protein levels that can potentially act as novel biomarkers opening doors to diagnostic and treatment purposes.

Transcriptomic Profiling of Rectus Abdominis Muscle in Women with Gestational Diabetes-Induced Myopathy: Characterization of Pathophysiology and Potential Muscle Biomarkers of Pregnancy-Specific Urinary Incontinence

Gestational diabetes mellitus (GDM) is recognized as a "window of opportunity" for the future prediction of such complications as type 2 diabetes mellitus and pelvic floor muscle disorders, including urinary incontinence and genitourinary dysfunction. Translational studies have reported that pelvic floor muscle disorders are due to a GDM-induced-myopathy (GDiM) of the pelvic floor muscle and rectus abdominis muscle (RAM). We now describe the transcriptome profiling of the RAM obtained by Cesarean section from GDM and non-GDM women with and without pregnancy-specific urinary incontinence (PSUI). We identified 650 genes in total, and the differentially expressed genes were defined by comparing three control groups to the GDM with PSUI group (GDiM). Enrichment analysis showed that GDM with PSUI was associated with decreased gene expression related to muscle structure and muscle protein synthesis, the reduced ability of muscle fibers to ameliorate muscle damage, and the altered the maintenance and generation of energy through glycogenesis. Potential genetic muscle biomarkers were validated by RT-PCR, and their relationship to the pathophysiology of the disease was verified. These findings help elucidate the molecular mechanisms of GDiM and will promote the development of innovative interventions to prevent and treat complications such as post-GDM urinary incontinence.

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: a bioinformatics analytic workflow

Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged.

Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis

Non-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein–protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low- and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment.

Elucidating the metabolic characteristics of pancreatic β-cells from patients with type 2 diabetes (T2D) using a genome-scale metabolic modeling

Diabetes is a global health problem caused primarily by the inability of pancreatic β-cells to secrete adequate insulin. Despite extensive research, the identity of factors contributing to the dysregulated metabolism-secretion coupling in the β-cells remains elusive. The present study attempts to capture some of these factors responsible for the impaired β-cell metabolism-secretion coupling that contributes to diabetes pathogenesis. The metabolic-flux profiles of pancreatic β-cells were predicted using genome-scale metabolic modeling for ten diabetic patients and ten control subjects. Analysis of these flux states shows reduction in the mitochondrial fatty acid oxidation and mitochondrial oxidative phosphorylation pathways, that leads to decreased insulin secretion in diabetes. We also observed elevated reactive oxygen species (ROS) generation through peroxisomal fatty acid β-oxidation. In addition, cellular antioxidant defense systems were found to be attenuated in diabetes. Our analysis also uncovered the possible changes in the plasma metabolites in diabetes due to the β-cells failure. These efforts subsequently led to the identification of seven metabolites associated with cardiovascular disease (CVD) pathogenesis, thus establishing its link as a secondary complication of diabetes.

Systems biomarkers for papillary thyroid cancer prognosis and treatment through multi-omics networks

The identification of biomolecules associated with papillary thyroid cancer (PTC) has upmost importance for the elucidation of the disease mechanism and the development of effective diagnostic and treatment strategies. Despite particular findings in this regard, a holistic analysis encompassing molecular data from different biological levels has been lacking. In the present study, a meta-analysis of four transcriptome datasets was performed to identify gene expression signatures in PTC, and reporter molecules were determined by mapping gene expression data onto three major cellular networks, i.e., transcriptional regulatory, protein-protein interaction, and metabolic networks. We identified 282 common genes that were differentially expressed in all PTC datasets. In addition, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA), two Erb-B2 receptors (ERBB2 and ERBB4), two cyclin-dependent receptors (CDK1 and CDK2), and three histone deacetylase receptors (HDAC1, HDAC2, and HDAC3) came into prominence as proteomic signatures in addition to several metabolites including lactaldehyde and proline at the metabolome level. Significant associations with calcium and MAPK signaling pathways and transcriptional and post-transcriptional activities of 12 TFs and 110 miRNAs were also observed at the regulatory level. Among them, six miRNAs (miR-30b-3p, miR-15b-5p, let-7a-5p, miR-130b-3p, miR-424-5p, and miR-193b-3p) were associated with PTC for the first time in the literature, and the expression levels of miR-30b-3p, miR-15b-5p, and let-7a-5p were found to be predictive of disease prognosis. Drug repositioning and molecular docking simulations revealed that 5 drugs (prochlorperazine, meclizine, rottlerin, cephaeline, and tretinoin) may be useful in the treatment of PTC. Consequently, we report here biomolecule candidates that may be considered as prognostic biomarkers or potential therapeutic targets for further experimental and clinical trials for PTC.

miRNAs as Predictive Factors in Early Diagnosis of Gestational Diabetes Mellitus

INTRODUCTION

Circulating miRNAs are important mediators in epigenetic changes. These non-coding molecules regulate post-transcriptional gene expression by binding to mRNA. As a result, they influence the development of many diseases, such as gestational diabetes mellitus (GDM). Therefore, this study investigates the changes in the miRNA profile in GDM patients before hyperglycemia appears.

MATERIALS AND METHODS

The study group consisted of 24 patients with GDM, and the control group was 24 normoglycemic pregnant women who were matched for body mass index (BMI), age, and gestational age. GDM was diagnosed with an oral glucose tolerance test between the 24th and 26th weeks of pregnancy. The study had a prospective design, and serum for analysis was obtained in the first trimester of pregnancy. Circulating miRNAs were measured using the NanoString quantitative assay platform. Validation with real time-polymerase chain reaction (RT-PCR) was performed on the same group of patients. Mann-Whitney U-test and Spearman correlation were done to assess the significance of the results.

RESULTS

Among the 800 miRNAs, 221 miRNAs were not detected, and 439 were close to background noise. The remaining miRNAs were carefully investigated for their average counts, fold changes, p-values, and false discovery rate (FDR) scores. We selected four miRNAs for further validation: miR-16-5p, miR-142-3p, miR-144-3p, and miR-320e, which showed the most prominent changes between the studied groups. The validation showed up-regulation of miR-16-5p (p<0.0001), miR-142-3p (p=0.001), and miR-144-3p (p=0.003).

CONCLUSION

We present changes in miRNA profile in the serum of GDM women, which may indicate significance in the pathophysiology of GDM. These findings emphasize the role of miRNAs as a predictive factor that could potentially be useful in early diagnosis.

System and network biology-based computational approaches for drug repositioning

Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.

Systematic review of transcriptome and microRNAome associations with gestational diabetes mellitus

PURPOSE

Gestational diabetes (GDM) is associated with increased risk for preterm birth and related complications for both the pregnant person and newborn. Changes in gene expression have the potential to characterize complex interactions between genetic and behavioral/environmental risk factors for GDM. Our goal was to summarize the state of the science about changes in gene expression and GDM.

DESIGN

The systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

METHODS

PubMed articles about humans, in English, from any date were included if they described mRNA transcriptome or microRNA findings from blood samples in adults with GDM compared with adults without GDM.

RESULTS

Sixteen articles were found representing 1355 adults (n=674 with GDM, n=681 controls) from 12 countries. Three studies reported transcriptome results and thirteen reported microRNA findings. Identified pathways described various aspects of diabetes pathogenesis, including glucose and insulin signaling, regulation, and transport; natural killer cell mediated cytotoxicity; and fatty acid biosynthesis and metabolism. Studies described 135 unique miRNAs that were associated with GDM, of which eight (miR-16-5p, miR-17-5p, miR-20a-5p, miR-29a-3p, miR-195-5p, miR-222-3p, miR-210-3p, and miR-342-3p) were described in 2 or more studies. Findings suggest that miRNA levels vary based on the time in pregnancy when GDM develops, the time point at which they were measured, sex assigned at birth of the offspring, and both the pre-pregnancy and gestational body mass index of the pregnant person.

CONCLUSIONS

The mRNA, miRNA, gene targets, and pathways identified in this review contribute to our understanding of GDM pathogenesis; however, further research is warranted to validate previous findings. In particular, longitudinal repeated-measures designs are needed that control for participant characteristics (e.g., weight), use standardized data collection methods and analysis tools, and are sufficiently powered to detect differences between subgroups. Findings may be used to improve early diagnosis, prevention, medication choice and/or clinical treatment of patients with GDM.

Genetic association between major depressive disorder and type 2 diabetes mellitus: Shared pathways and protein networks

BACKGROUND

Major depressive disorder (MDD) and type 2 diabetes mellitus (T2DM) are common public health disorders that often co-occur. This study aims to determine whether gene expression profiles from individuals with MDD or T2DM overlap and if there are any functional interconnectivity between identified genes using protein-protein interaction (PPI).

METHODS

The DNA microarray datasets were extracted from the Gene Expression Omnibus. Gene expression dataset GSE98793 from a case-control study of MDD (64 healthy control subjects, 128 patients) and dataset GSE15653 from a case-control study of T2DM (nine controls, nine individuals with T2DM) were used for this secondary and post-hoc analysis. GO enrichment analyses and Reactome pathway enrichment analysis were performed for functional enrichment analyses with the shared genes. PPI networks, PPI clusters and hub genes were performed to detect the potential relationships among differentially expressed genes (DEG) -encoding proteins in both MDD and T2DM.

RESULTS

A total of 3640 DEGs were identified in the MDD group when compared to the control group, whereas 3700 DEGs were identified in the T2DM group when compared to the control groups, among which 244 DEGs were overlap genes. The identified DEGs were enriched for Interleukin-4 and Interleukin-13 signaling, neutrophil degranulation, as well as other select species of the innate immune system. The biological processes of neurofibrillary tangle assembly regulation, tau-protein kinase activity regulation, amyloid-beta clearance regulation, amyloid-beta formation regulation and neuron apoptotic processes were also identified. Molecular function analysis indicated that identified genes were mainly enriched for amyloid-beta binding. 925 out of 1006 protein-protein interactions and six sub-networks were identified reflecting the disparate biological domains of overlapping genes. Ten hub genes further highlight the putative importance of tau-protein kinase activity, inflammatory response and neuron apoptotic regulatory processes across MDD and T2DM.

CONCLUSIONS

Our results indicate that an overlapping genetic architecture subserves MDD and T2DM. Genes relevant to the innate immune system, tau protein formation, and cellular aging were identified. Results indicate that the common, often comorbid, conditions of MDD and T2DM have a pathoetiologic nexus.

Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Differential Interactome Based Drug Repositioning Unraveled Abacavir, Exemestane, Nortriptyline Hydrochloride, and Tolcapone as Potential Therapeutics for Colorectal Cancers

There is a critical requirement for alternative strategies to provide the better treatment in colorectal cancer (CRC). Hence, our goal was to propose novel biomarkers as well as drug candidates for its treatment through differential interactome based drug repositioning. Differentially interacting proteins and their modules were identified, and their prognostic power were estimated through survival analyses. Drug repositioning was carried out for significant target proteins, and candidate drugs were analyzed via in silico molecular docking prior to in vitro cell viability assays in CRC cell lines. Six modules (mAPEX1, mCCT7, mHSD17B10, mMYC, mPSMB5, mRAN) were highlighted considering their prognostic performance. Drug repositioning resulted in eight drugs (abacavir, ribociclib, exemestane, voriconazole, nortriptyline hydrochloride, theophylline, bromocriptine mesylate, and tolcapone). Moreover, significant in vitro inhibition profiles were obtained in abacavir, nortriptyline hydrochloride, exemestane, tolcapone, and theophylline (positive control). Our findings may provide new and complementary strategies for the treatment of CRC.

Differential Protein Interactome in Esophageal Squamous Cell Carcinoma Offers Novel Systems Biomarker Candidates with High Diagnostic and Prognostic Performance

Esophageal squamous cell carcinoma (ESCC) is among the most dangerous cancers with high mortality and lack of robust diagnostics and personalized/precision therapeutics. To achieve a systems-level understanding of tumorigenesis, unraveling of variations in the protein interactome and determination of key proteins exhibiting significant alterations in their interaction patterns during tumorigenesis are crucial. To this end, we have described differential protein-protein interactions and differentially interacting proteins (DIPs) in ESCC by utilizing the human protein interactome and transcriptome. Furthermore, DIP-centered modules were analyzed according to their potential in elucidation of disease mechanisms and improvement of efficient diagnostic, prognostic, and treatment strategies. Seven modules were presented as potential diagnostic, and 16 modules were presented as potential prognostic biomarker candidates. Importantly, our findings also suggest that 30 out of the 53 repurposed drugs were noncancer drugs, which could be used in the treatment of ESCC. Interestingly, 25 of these, proposed as novel drug candidates here, have not been previously associated in a context of esophageal cancer. In this context, risperidone and clozapine were validated for their growth inhibitory potential in three ESCC lines. Our findings offer a high potential for the development of innovative diagnostic, prognostic, and therapeutic strategies for further experimental studies in line with predictive diagnostics, targeted prevention, and personalization of medical services in ESCC specifically, and personalized cancer care broadly.

Cancer Stem Cell Transcriptome Profiling Reveals Seed Genes of Tumorigenesis: New Avenues for Cancer Precision Medicine

Cancer stem-like cells (CSCs) possess the ability to self-renew and differentiate, and they are among the major factors driving tumorigenesis, metastasis, and resistance to chemotherapy. Therefore, it is critical to understand the molecular substrates of CSC biology so as to discover novel molecular biosignatures that distinguish CSCs and tumor cells. Here, we report new findings and insights by employing four transcriptome datasets associated with CSCs, with CSC and tumor samples from breast, lung, oral, and ovarian tissues. The CSC samples were analyzed to identify differentially expressed genes between CSC and tumor phenotypes. Through comparative profiling of expression levels in different cancer types, we identified 17 "seed genes" that showed a mutual differential expression pattern. We showed that these seed genes were strongly associated with cancer-associated signaling pathways and biological processes, the immune system, and the key cancer hallmarks. Further, the seed genes presented significant changes in their expression profiles in different cancer types and diverse mutation rates, and they also demonstrated high potential as diagnostic and prognostic biomarkers in various cancers. We report a number of seed genes that represent significant potential as "systems biomarkers" for understanding the pathobiology of tumorigenesis. Seed genes offer a new innovation avenue for potential applications toward cancer precision medicine in a broad range of cancers in oncology in the future.

Targeting the tumor stroma: integrative analysis reveal GATA2 and TORYAIP1 as novel prognostic targets in breast and ovarian cancer

Tumor stroma interaction is known to take a crucial role in cancer growth and progression. In the present study, it was performed gene expression analysis of stroma samples with ovarian and breast cancer through an integrative analysis framework to identify common critical biomolecules at multiomics levels. Gene expression datasets were statistically analyzed to identify common differentially expressed genes (DEGs) by comparing tumor stroma and normal stroma samples. The integrative analyses of DEGs indicated that there were 59 common core genes, which might be feasible to be potential marks for cancer stroma targeted strategies. Reporter molecules (i.e. receptor, transcription factors and miRNAs) were determined through a statistical test employing the hypergeometric probability density function. Afterward, the tumor microenvironment protein-protein interaction and the generic network were reconstructed by using identified reporter molecules and common core DEGs. Through a systems medicine approach, it was determined that hub biomolecules, AR, GATA2, miR-124, TOR1AIP1, ESR1, EGFR, STAT1, miR-192, GATA3, COL1A1, in tumor microenvironment generic network. These molecules were also identified as prognostic signatures in breast and ovarian tumor samples via survival analysis. According to literature searching, GATA2 and TORYAIP1 might represent potential biomarkers and candidate drug targets for the stroma targeted cancer therapy applications.

Molecular pathways behind acquired obesity: Adipose tissue and skeletal muscle multiomics in monozygotic twin pairs discordant for BMI

Tissue-specific mechanisms prompting obesity-related development complications in humans remain unclear. We apply multiomics analyses of subcutaneous adipose tissue and skeletal muscle to examine the effects of acquired obesity among 49 BMI-discordant monozygotic twin pairs. Overall, adipose tissue appears to be more affected by excess body weight than skeletal muscle. In heavier co-twins, we observe a transcriptional pattern of downregulated mitochondrial pathways in both tissues and upregulated inflammatory pathways in adipose tissue. In adipose tissue, heavier co-twins exhibit lower creatine levels; in skeletal muscle, glycolysis- and redox stress-related protein and metabolite levels remain higher. Furthermore, metabolomics analyses in both tissues reveal that several proinflammatory lipids are higher and six of the same lipid derivatives are lower in acquired obesity. Finally, in adipose tissue, but not in skeletal muscle, mitochondrial downregulation and upregulated inflammation are associated with a fatty liver, insulin resistance, and dyslipidemia, suggesting that adipose tissue dominates in acquired obesity.

Multiomics analyses of adipose tissue and skeletal muscle in BMI-discordant twins

Excess body weight downregulates mitochondrial pathways in both tissues

Excess body weight upregulates proinflammatory pathways in both tissues

Adipose tissue alterations are associated with metabolic health in acquired obesity

Cancer Signaling Transcriptome Is Upregulated in Type 2 Diabetes Mellitus

We aimed to explore the differences in the whole transcriptome of peripheral blood mononuclear cells between elderly individuals with and without type 2 diabetes (T2D). We conducted a microarray-based transcriptome analysis of 19 individuals with T2D and 15 without. Differentially expressed genes according to linear models were submitted to the Ingenuity Pathway Analysis system to conduct a functional enrichment analysis. We established that diseases, biological functions, and canonical signaling pathways were significantly associated with T2D patients when their logarithms of Benjamini–Hochberg-adjusted p-value were >1.30 and their absolute z-scores were >2.0 (≥2.0 meant “upregulation” and ≤ −2.0 “downregulation”). Cancer signaling pathways were the most upregulated ones in T2D (z-score = 2.63, −log(p-value) = 32.3; 88.5% (n = 906) of the total differentially expressed genes located in these pathways). In particular, integrin (z-score = 2.52, −log(p-value) = 2.03) and paxillin (z-score = 2.33, −log(p-value) = 1.46) signaling pathways were predicted to be upregulated, whereas the Rho guanosine diphosphate (Rho-GDP) dissociation inhibitor signaling pathway was predicted to be downregulated in T2D individuals (z-score = −2.14, −log(p-value) = 2.41). Our results suggest that, at transcriptional expression level, elderly individuals with T2D present an increased activation of signaling pathways related to neoplastic processes, T-cell activation and migration, and inflammation.

Drug Repositioning for P-Glycoprotein Mediated Co-Expression Networks in Colorectal Cancer

Colorectal cancer (CRC) is one of the most fatal types of cancers that is seen in both men and women. CRC is the third most common type of cancer worldwide. Over the years, several drugs are developed for the treatment of CRC; however, patients with advanced CRC can be resistant to some drugs. P-glycoprotein (P-gp) (also known as Multidrug Resistance 1, MDR1) is a well-identified membrane transporter protein expressed by ABCB1 gene. The high expression of MDR1 protein found in several cancer types causes chemotherapy failure owing to efflux drug molecules out of the cancer cell, decreases the drug concentration, and causes drug resistance. As same as other cancers, drug-resistant CRC is one of the major obstacles for effective therapy and novel therapeutic strategies are urgently needed. Network-based approaches can be used to determine specific biomarkers, potential drug targets, or repurposing approved drugs in drug-resistant cancers. Drug repositioning is the approach for using existing drugs for a new therapeutic purpose; it is a highly efficient and low-cost process. To improve current understanding of the MDR-1-related drug resistance in CRC, we explored gene co-expression networks around ABCB1 gene with different network sizes (50, 100, 150, 200 edges) and repurposed candidate drugs targeting the ABCB1 gene and its co-expression network by using drug repositioning approach for the treatment of CRC. The candidate drugs were also assessed by using molecular docking for determining the potential of physical interactions between the drug and MDR1 protein as a drug target. We also evaluated these four networks whether they are diagnostic or prognostic features in CRC besides biological function determined by functional enrichment analysis. Lastly, differentially expressed genes of drug-resistant (i.e., oxaliplatin, methotrexate, SN38) HT29 cell lines were found and used for repurposing drugs with reversal gene expressions. As a result, it is shown that all networks exhibited high diagnostic and prognostic performance besides the identification of various drug candidates for drug-resistant patients with CRC. All these results can shed light on the development of effective diagnosis, prognosis, and treatment strategies for drug resistance in CRC.

Novel molecular signatures and potential therapeutics in renal cell carcinomas: Insights from a comparative analysis of subtypes

Renal cell carcinomas (RCCs) are among the highest causes of cancer mortality. Although transcriptome profiling studies in the last decade have made significant molecular findings on RCCs, effective diagnosis and treatment strategies have yet to be achieved due to lack of adequate screening and comparative profiling of RCC subtypes. In this study, a comparative analysis was performed on RNA-seq based transcriptome data from each RCC subtype, namely clear cell RCC (KIRC), papillary RCC (KIRP) and kidney chromophobe (KICH), and mutual or subtype-specific reporter biomolecules were identified at RNA, protein, and metabolite levels by the integration of expression profiles with genome-scale biomolecular networks. This approach revealed already-known biomarkers in RCCs as well as novel biomarker candidates and potential therapeutic targets. Our findings also pointed out the incorporation of the molecular mechanisms of KIRC and KIRP, whereas KICH was shown to have distinct molecular signatures. Furthermore, considering the Dipeptidyl Peptidase 4 (DPP4) receptor as a potential therapeutic target specific to KICH, several drug candidates such as ZINC6745464 were identified through virtual screening of ZINC molecules. In this study, we reported valuable data for further experimental and clinical efforts, since the proposed molecules have significant potential for screening and therapeutic purposes in RCCs.

CMap analysis identifies Atractyloside as a potential drug candidate for type 2 diabetes based on integration of metabolomics and transcriptomics

Background

This research aimed at exploring the mechanisms of alterations of metabolites and pathways in T2D from the perspective of metabolomics and transcriptomics, as well as uncovering novel drug candidate for T2D treatment.

Methods

Metabolites in human plasma from 42 T2D patients and 45 non‐diabetic volunteers were detected by liquid chromatography‐mass spectrometer (LC‐MS). Microarray dataset of the transcriptome was obtained from Gene Expression Omnibus (GEO) database. Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to conduct pathway enrichment analysis. Connectivity Map (CMap) was employed to select potential drugs for T2D therapy. In vivo assay was performed to verify above findings. The protein expression levels of ME1, ME2 and MDH1 were detected by Western blot to determine the status of NAD/NADH cofactor system.

Results

In our study, differentially expressed metabolites were selected out between healthy samples and T2D samples with selection criteria P value < .05, |Fold Change| > 2, including N‐acetylglutamate and Malate. Genes set enrichment analysis (GSEA) revealed that 34 pathways were significantly enriched in T2D. Based on CMap analysis and animal experiments, Atractyloside was identified as a potential novel drug for T2D treatment via targeting ME1, ME2 and MDH1 and regulating the NAD/NADH cofactor system.

Conclusion

The present research revealed differentially expressed metabolites and genes, as well as significantly altered pathways in T2D via an integration of metabolomics, transcriptomics and CMap analysis. It was also demonstrated that comprehensive analysis based on metabolomics and transcriptomics was an effective approach for identification and verification of metabolic biomarkers and alternated pathways.

Network-based computational approach to identify genetic links between cardiomyopathy and its risk factors

Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory CMP development and progression using a systems biology approach. The authors analysed microarray gene expression datasets from CMP and tissues affected by risk factors including smoking, ageing factors, high body fat, clinical depression status, insulin resistance, high dietary red meat intake, chronic alcohol consumption, obesity, high-calorie diet and high-fat diet. The authors identified differentially expressed genes (DEGs) from each dataset and compared those from CMP and risk factor datasets to identify common DEGs. Gene set enrichment analyses identified metabolic and signalling pathways, including MAPK, RAS signalling and cardiomyopathy pathways. Protein-protein interaction (PPI) network analysis identified protein subnetworks and ten hub proteins (CDK2, ATM, CDT1, NCOR2, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E and HIST1H4L). Five transcription factors (FOXC1, GATA2, FOXL1, YY1, CREB1) and five miRNAs were also identified in CMP. Thus the authors' approach reveals candidate biomarkers that may enhance understanding of mechanisms underlying CMP and their link to risk factors. Such biomarkers may also be useful to develop new therapeutics for CMP.

Systems biology based drug repositioning for development of cancer therapy

Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.

Correlation between renin-angiotensin system (RAS) related genes, type 2 diabetes, and cancer: Insights from metanalysis of transcriptomics data

Although studies have provided significant evidence about the role of RAS in mediating cancer risk in type 2 diabetes mellitus (DM), conclusions about the central molecular mechanisms underlying this disease remain to be reached, because this type of information requires an integrative multi-omics approach. In the current study, meta-analysis was performed on type 2 diabetes and breast, bladder, liver, pancreas, colon and rectum cancer-associated transcriptome data, and reporter biomolecules were identified at RNA, protein, and metabolite levels using the integration of gene expression profiles with genome-scale biomolecular networks in diabetes samples. This approach revealed that RAS biomarkers could be associated with cancer initiation and progression, which include metabolites (particularly, aminoacyl-tRNA biosynthesis and ABC transporters) as novel biomarker candidates and potential therapeutic targets. We detected downregulation and upregulation of differentially expressed genes (DEGs) in blood, pancreatic islets, liver and skeletal muscle from normal and diabetic patients. DEGs were combined with 211 renin-angiotensin-system related genes. Upregulated genes were enriched using Pathway analysis of cancer in pancreatic islets, blood and skeletal muscle samples. It seems that the changes in mRNA are contributing to the phenotypic changes in carcinogenesis, or that they are as a result of the phenotypic changes associated with the malignant transformation. Our analyses showed that Ctsg and Ednrb are downregulated in cancer samples. However, by immunohistochemistry experiments we observed that EDNRB protein showed increased expression in tumor samples. It is true that alterations in mRNA expression do not always reflect alterations in protein expression, since post-translational changes can occur in proteins. In this study, we report valuable data for further experimental and clinical analysis, because the proposed biomolecules have significant potential as systems biomarkers for screening or for therapeutic purposes in type 2 diabetes and cancer-associated pathways.

Use of big data in drug development for precision medicine: an update

INTRODUCTION

Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios.

AREAS COVERED

Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery.

EXPERT OPINION

In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.

Identification of Novel Components of Target-of-Rapamycin Signaling Pathway by Network-Based Multi-Omics Integrative Analysis

Target of rapamycin (TOR) is a major signaling pathway and regulator of cell growth. TOR serves as a hub of many signaling routes, and is implicated in the pathophysiology of numerous human diseases, including cancer, diabetes, and neurodegeneration. Therefore, elucidation of unknown components of TOR signaling that could serve as potential biomarkers and drug targets has a great clinical importance. In this study, our aim is to integrate transcriptomics, interactomics, and regulomics data in Saccharomyces cerevisiae using a network-based multiomics approach to enlighten previously unidentified, potential components of TOR signaling. We constructed the TOR-signaling protein interaction network, which was used as a template to search for TOR-mediated rapamycin and caffeine signaling paths. We scored the paths passing from at least one component of TOR Complex 1 or 2 (TORC1/TORC2) using the co-expression levels of the genes in the transcriptome data of the cells grown in the presence of rapamycin or caffeine. The resultant network revealed seven hitherto unannotated proteins, namely, Atg14p, Rim20p, Ret2p, Spt21p, Ylr257wp, Ymr295cp, and Ygr017wp, as potential components of TOR-mediated rapamycin and caffeine signaling in yeast. Among these proteins, we suggest further deciphering of the role of Ylr257wp will be particularly informative in the future because it was the only protein whose removal from the constructed network hindered the signal transduction to the TORC1 effector kinase Npr1p. In conclusion, this study underlines the value of network-based multiomics integrative data analysis in discovering previously unidentified components of the signaling networks by revealing potential components of TOR signaling for future experimental validation.

Drug Targeting and Biomarkers in Head and Neck Cancers: Insights from Systems Biology Analyses

The head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers in the world, but robust biomarkers and diagnostics are still not available. This study provides in-depth insights from systems biology analyses to identify molecular biomarker signatures to inform systematic drug targeting in HNSCC. Gene expression profiles from tumors and normal tissues of 22 patients with histological confirmation of nonmetastatic HNSCC were subjected to integrative analyses with genome-scale biomolecular networks (i.e., protein-protein interaction and transcriptional and post-transcriptional regulatory networks). We aimed to discover molecular signatures at RNA and protein levels, which could serve as potential drug targets for therapeutic innovation in the future. Eleven proteins, 5 transcription factors, and 20 microRNAs (miRNAs) came into prominence as potential drug targets. The differential expression profiles of these reporter biomolecules were cross-validated by independent RNA-Seq and miRNA-Seq datasets, and risk discrimination performance of the reporter biomolecules, BLNK, CCL2, E4F1, FOSL1, ISG15, MMP9, MYCN, MYH11, miR-1252, miR-29b, miR-29c, miR-3610, miR-431, and miR-523, was also evaluated. Using the transcriptome guided drug repositioning tool, geneXpharma, several candidate drugs were repurposed, including antineoplastic agents (e.g., gemcitabine and irinotecan), antidiabetics (e.g., rosiglitazone), dermatological agents (e.g., clocortolone and acitretin), and antipsychotics (e.g., risperidone), and binding affinities of the drugs to their potential targets were assessed using molecular docking analyses. The molecular signatures and repurposed drugs presented in this study warrant further attention for experimental studies since they offer significant potential as biomarkers and candidate therapeutics for precision medicine approaches to clinical management of HNSCC.

Type 2 Diabetes Mellitus: Integrative Analysis of Multiomics Data for Biomarker Discovery

Increased fasting plasma glucose (FPG) is an independent risk factor for type 2 diabetes mellitus (T2DM). The development of systems biology technologies for integration of multiomics data is crucial for predicting increased FPG levels. In this case-control study, immunoglobulin (Ig) G glycosylation profiling and genome-wide association analyses were performed on 511 participants, and among them 76 had increased FPG (aged 47.6 ± 6.14 years), and 435 had decreased or fluctuant FPG (aged 47.9 ± 6.08 years). We identified nine single nucleotide polymorphisms (SNPs) in five genes (RPL7AP27, SNX30, SLC39A12, BACE2, and IGFL2) that were significantly associated with increased FPG (odds ratios 1.937-2.393). Moreover, of the 24 glycan peaks (GPs), GPs 3, 8, and 11 presented positive trends with increased FPG levels, whereas GPs 4 and 14 presented negative trends. A significant improvement of predictive power was observed when adding 24 IgG GPs to 9 SNPs with the area under the curve increased from 0.75 to 0.81. This report shows that the combination of candidate SNPs with IgG glycomics offers biomarker potentials for T2DM. The substantial predictive power obtained from integrating genomics and glycomics biomarkers suggests the feasibility of applying such multiomics strategies to enable predictive, preventive, and personalized medicine for T2DM.

Waveguiding and SERS Simplified Raman Spectroscopy on Biological Samples

Biomarkers detection at an ultra-low concentration in biofluids (blood, serum, saliva, etc.) is a key point for the early diagnosis success and the development of personalized therapies. However, it remains a challenge due to limiting factors like (i) the complexity of analyzed media, and (ii) the aspecificity detection and the poor sensitivity of the conventional methods. In addition, several applications require the integration of the primary sensors with other devices (microfluidic devices, capillaries, flasks, vials, etc.) where transducing the signal might be difficult, reducing performances and applicability. In the present work, we demonstrate a new class of optical biosensor we have developed integrating an optical waveguide (OWG) with specific plasmonic surfaces. Exploiting the plasmonic resonance, the devices give consistent results in surface enhanced Raman spectroscopy (SERS) for continuous and label-free detection of biological compounds. The OWG allows driving optical signals in the proximity of SERS surfaces (detection area) overcoming spatial constraints, in order to reach places previously optically inaccessible. A rutile prism couples the remote laser source to the OWG, while a Raman spectrometer collects the SERS far field scattering. The present biosensors were implemented by a simple fabrication process, which includes photolithography and nanofabrication. By using such devices, it was possible to detect cell metabolites like Phenylalanine (Phe), Adenosine 5-triphosphate sodium hydrate (ATP), Sodium Lactate, Human Interleukin 6 (IL6), and relate them to possible metabolic pathway variation.

Identification of Prognostic Biomarker Signatures and Candidate Drugs in Colorectal Cancer: Insights from Systems Biology Analysis

Background and objectives: Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, but early diagnosis ameliorates the survival of CRC. This report aimed to identify molecular biomarker signatures in CRC. Materials and Methods: We analyzed two microarray datasets (GSE35279 and GSE21815) from the Gene Expression Omnibus (GEO) to identify mutual differentially expressed genes (DEGs). We integrated DEGs with protein–protein interaction and transcriptional/post-transcriptional regulatory networks to identify reporter signaling and regulatory molecules; utilized functional overrepresentation and pathway enrichment analyses to elucidate their roles in biological processes and molecular pathways; performed survival analyses to evaluate their prognostic performance; and applied drug repositioning analyses through Connectivity Map (CMap) and geneXpharma tools to hypothesize possible drug candidates targeting reporter molecules. Results: A total of 727 upregulated and 99 downregulated DEGs were detected. The PI3K/Akt signaling, Wnt signaling, extracellular matrix (ECM) interaction, and cell cycle were identified as significantly enriched pathways. Ten hub proteins (ADNP, CCND1, CD44, CDK4, CEBPB, CENPA, CENPH, CENPN, MYC, and RFC2), 10 transcription factors (ETS1, ESR1, GATA1, GATA2, GATA3, AR, YBX1, FOXP3, E2F4, and PRDM14) and two microRNAs (miRNAs) (miR-193b-3p and miR-615-3p) were detected as reporter molecules. The survival analyses through Kaplan–Meier curves indicated remarkable performance of reporter molecules in the estimation of survival probability in CRC patients. In addition, several drug candidates including anti-neoplastic and immunomodulating agents were repositioned. Conclusions: This study presents biomarker signatures at protein and RNA levels with prognostic capability in CRC. We think that the molecular signatures and candidate drugs presented in this study might be useful in future studies indenting the development of accurate diagnostic and/or prognostic biomarker screens and efficient therapeutic strategies in CRC.

Network-based approach to identify molecular signatures and therapeutic agents in Alzheimer's disease

Alzheimer's disease (AD) is a dynamic degeneration of the brain with progressive dementia. Considering the uncertainties in its molecular mechanism, in the present study, we employed network-based integrative analyses, and aimed to explore the key molecules and their associations with small drugs to identify potential biomarkers and therapeutic agents for the AD. First of all, we studied a transcriptome dataset and identified 1521 differentially expressed genes (DEGs). Integration of transcriptome data with protein-protein and transcriptional regulatory interactions resulted with central (hub) proteins (UBA52, RAC1, CREBBP, AR, RPS11, SMAD3, RPS6, RPL12, RPL15, and UBC), regulatory transcription factors (FOXC1, GATA2, YY1, FOXL1, NFIC, E2F1, USF2, SRF, PPARG, and JUN) and microRNAs (mir-335-5p, mir-26b-5p, mir-93-5p, mir-124-3p, mir-17-5p, mir-16-5p, mir-20a-5p, mir-92a-3p, mir-106b-5p, and mir-192-5p) as key signaling and regulatory molecules associated with transcriptional changes for the AD. Considering these key molecules as potential therapeutic targets and Connectivity Map (CMap) architecture, candidate small molecular agents (such as STOCK1N-35696) were identified as novel potential therapeutics for the AD. This study presents molecular signatures at RNA and protein levels which might be useful in increasing discernment of the molecular mechanisms, and potential drug targets and therapeutics to design effective treatment strategies for the AD.

A proteomic signature that reflects pancreatic beta-cell function

AIM

Proteomics has the potential to enhance early identification of beta-cell dysfunction, in conjunction with monitoring the various stages of type 2 diabetes onset. The most routine method of assessing pancreatic beta-cell function is an oral glucose tolerance test, however this method is time consuming and carries a participant burden. The objectives of this research were to identify protein signatures and pathways related to pancreatic beta-cell function in fasting blood samples.

METHODS

Beta-cell function measures were calculated for MECHE study participants who completed an oral glucose tolerance test and had proteomic data (n = 100). Information on 1,129 protein levels was obtained using the SOMAscan assay. Receiver operating characteristic curves were used to assess discriminatory ability of proteins of interest. Subsequent in vitro experiments were performed using the BRIN-BD11 pancreatic beta-cell line. Replication of findings were achieved in a second human cohort where possible.

RESULTS

Twenty-two proteins measured by aptamer technology were significantly associated with beta-cell function/HOMA-IR while 17 proteins were significantly associated with the disposition index (p ≤ 0.01). Receiver operator characteristic curves determined the protein panels to have excellent discrimination between low and high beta-cell function. Linear regression analysis determined that beta-endorphin and IL-17F have strong associations with beta-cell function/HOMA-IR, β = 0.039 (p = 0.005) and β = -0.027 (p = 0.013) respectively. Calcineurin and CRTAM were strongly associated with the disposition index (β = 0.005 and β = 0.005 respectively, p = 0.012). In vitro experiments confirmed that IL-17F modulated insulin secretion in the BRIN-BD11 cell line, with the lower concentration of 10 ng/mL significantly increasing glucose stimulated insulin secretion (p = 0.043).

CONCLUSIONS

Early detection of compromised beta-cell function could allow for implementation of nutritional and lifestyle interventions before progression to type 2 diabetes.

Potential biomarkers and therapeutic targets in cervical cancer: Insights from the meta-analysis of transcriptomics data within network biomedicine perspective

The malignant neoplasm of the cervix, cervical cancer, has effects on the reproductive tract. Although infection with oncogenic human papillomavirus is essential for cervical cancer development, it alone is insufficient to explain the development of cervical cancer. Therefore, other risk factors such as host genetic factors should be identified, and their importance in cervical cancer induction should be determined. Although gene expression profiling studies in the last decade have made significant molecular findings about cervical cancer, adequate screening and effective treatment strategies have yet to be achieved. In the current study, meta-analysis was performed on cervical cancer-associated transcriptome data and reporter biomolecules were identified at RNA (mRNA, miRNA), protein (receptor, transcription factor, etc.), and metabolite levels by the integration of gene expression profiles with genome-scale biomolecular networks. This approach revealed already-known biomarkers, tumor suppressors and oncogenes in cervical cancer as well as various receptors (e.g. ephrin receptors EPHA4, EPHA5, and EPHB2; endothelin receptors EDNRA and EDNRB; nuclear receptors NCOA3, NR2C1, and NR2C2), miRNAs (e.g., miR-192-5p, miR-193b-3p, and miR-215-5p), transcription factors (particularly E2F4, ETS1, and CUTL1), other proteins (e.g., KAT2B, PARP1, CDK1, GSK3B, WNK1, and CRYAB), and metabolites (particularly, arachidonic acids) as novel biomarker candidates and potential therapeutic targets. The differential expression profiles of all reporter biomolecules were cross-validated in independent RNA-Seq and miRNA-Seq datasets, and the prognostic power of several reporter biomolecules, including KAT2B, PCNA, CD86, miR-192-5p and miR-215-5p was also demonstrated. In this study, we reported valuable data for further experimental and clinical efforts, because the proposed biomolecules have significant potential as systems biomarkers for screening or therapeutic purposes in cervical carcinoma.

Molecular genetic contributions to self-rated health

Background: Poorer self-rated health (SRH) predicts worse health outcomes, even when adjusted for objective measures of disease at time of rating. Twin studies indicate SRH has a heritability of up to 60% and that its genetic architecture may overlap with that of personality and cognition.

Methods: We carried out a genome-wide association study (GWAS) of SRH on 111 749 members of the UK Biobank sample. Univariate genome-wide complex trait analysis (GCTA)-GREML analyses were used to estimate the proportion of variance explained by all common autosomal single nucleotide polymorphisms (SNPs) for SRH. Linkage disequilibrium (LD) score regression and polygenic risk scoring, two complementary methods, were used to investigate pleiotropy between SRH in the UK Biobank and up to 21 health-related and personality and cognitive traits from published GWAS consortia.

Results: The GWAS identified 13 independent signals associated with SRH, including several in regions previously associated with diseases or disease-related traits. The strongest signal was on chromosome 2 (rs2360675, P = 1.77 x 10^-10) close to KLF7. A second strong peak was identified on chromosome 6 in the major histocompatibility region (rs76380179, P = 6.15 x 10^-10). The proportion of variance in SRH that was explained by all common genetic variants was 13%. Polygenic scores for the following traits and disorders were associated with SRH: cognitive ability, education, neuroticism, body mass index (BMI), longevity, attention-deficit hyperactivity disorder (ADHD), major depressive disorder, schizophrenia, lung function, blood pressure, coronary artery disease, large vessel disease stroke and type 2 diabetes.

Conclusions: Individual differences in how people respond to a single item on SRH are partly explained by their genetic propensity to many common psychiatric and physical disorders and psychological traits.

Differentially expressed circulating microRNAs in the development of acute diabetic Charcot foot

Aim:

Charcot foot (CF) is a rare complication of Type 2 diabetes (T2D).

Materials & methods:

We assessed circulating miRNAs in 17 patients with T2D and acute CF (G1), 17 patients with T2D (G2) and equivalent neuropathy and 17 patients with T2D without neuropathy (G3) using the high-throughput miRNA expression profiling.

Results:

51 significantly deregulated miRNAs were identified in G1 versus G2, 37 in G1 versus G3 and 64 in G2 versus G3. Furthermore, we demonstrated that 16 miRNAs differentially expressed between G1 versus G2 could be involved in osteoclastic differentiation. Among them, eight are key factors involved in CF pathophysiology.

Conclusion:

Our data reveal that CF patients exhibit an altered expression profile of circulating miRNAs.

Circulating MicroRNAs as Biomarkers of Gestational Diabetes Mellitus: Updates and Perspectives

Gestational diabetes mellitus (GDM) is defined as any degree of carbohydrate intolerance, with onset or first recognition during second or third trimester of gestation. It is estimated that approximately 7% of all pregnancies are complicated by GDM and that its prevalence is rising all over the world. Thus, the screening for abnormal glucose levels is generally recommended as a routine component of care for pregnant women. However, additional biomarkers are needed in order to predict the onset or accurately monitor the status of gestational diabetes. Recently, microRNAs, a class of small noncoding RNAs demonstrated to modulate gene expression, have been proven to be secreted by cells of origin and can be found in many biological fluids such as serum or plasma. Such feature renders microRNAs as optimal biomarkers and sensors of in situ tissue alterations. Furthermore, secretion of microRNAs via exosomes has been reported to contribute to tissue cross talk, thus potentially represents, if disrupted, a mechanistic cause of tissue/cell dysfunction in a specific disease. In this review, we summarized the recent findings on circulating microRNAs and gestational diabetes mellitus with particular focus on the potential use of microRNAs as putative biomarkers of disease as well as a potential cause of GDM complications and β cell dysfunction.

Multiomics Analysis of Tumor Microenvironment Reveals Gata2 and miRNA-124-3p as Potential Novel Biomarkers in Ovarian Cancer

Ovarian cancer is a common and, yet, one of the most deadly human cancers due to its insidious onset and the current lack of robust early diagnostic tests. Tumors are complex tissues comprised of not only malignant cells but also genetically stable stromal cells. Understanding the molecular mechanisms behind epithelial-stromal crosstalk in ovarian cancer is a great challenge in particular. In the present study, we performed comparative analyses of transcriptome data from laser microdissected epithelial, stromal, and ovarian tumor tissues, and identified common and tissue-specific reporter biomolecules-genes, receptors, membrane proteins, transcription factors (TFs), microRNAs (miRNAs), and metabolites-by integration of transcriptome data with genome-scale biomolecular networks. Tissue-specific response maps included common differentially expressed genes (DEGs) and reporter biomolecules were reconstructed and topological analyses were performed. We found that CDK2, EP300, and SRC as receptor-related functions or membrane proteins; Ets1, Ar, Gata2, and Foxp3 as TFs; and miR-16-5p and miR-124-3p as putative biomarkers and warrant further validation research. In addition, we report in this study that Gata2 and miR-124-3p are potential novel reporter biomolecules for ovarian cancer. The study of tissue-specific reporter biomolecules in epithelial cells, stroma, and tumor tissues as exemplified in the present study offers promise in biomarker discovery and diagnostics innovation for common complex human diseases such as ovarian cancer.