Integrating Gene Expression Profiles Research Articles

Molecular signatures in diabetic foot ulcer by integrated gene expression profiling via bioinformatics analysis

Introduction and Aim: Diabetic foot ulcers (DFUs) are a common and debilitating diabetic consequence leading to lower-limb amputations, long-term disability, and reduced lifespan. There is a lack of clinical diagnosis expertise because of no adequate diagnostic signs for DFU. As a result, the current research aims to find out how differently expressed genes (DEGs) affect the DFU.
 Materials and Methods: Bioinformatics analysis was used to evaluate DEGs using the GSE132187 dataset of the NCBI-GEO database, which contained samples from three hyperglycemic and three normoglycemic macrophage-like cell lines. Following the discovery of DEGs, Gene Ontology (GO) and KEGG pathway enrichment analysis were used to investigate how genes are classified into preset bins based on their functional properties. To discover hub DEGs in DFU, a protein-protein interaction (PPI) network was built and five topological parameters such as degree, stress, Closeness centrality, betweenness centrality, and radiality were evaluated.
 Results: We found 547 DEGs using the GSE132187 dataset, comprising 79 upregulated DEGs and 468 downregulated DEGs. There were 434 nodes and 1724 edges in the PPI network. The giant network uncovered six modules that are significantly enriched in biological processes like positive JNK cascade regulation, positive interferon-gamma production regulation, negative cell proliferation regulation, cellular response to zinc ion, cellular response to lipopolysaccharide, wound healing, and inflammatory response.
 Conclusion: Bioinformatics analysis revealed the major differentially expressed hub-genes implicated in DFUs. These findings suggested that these genes could be used as a DFU prognostic, diagnostic, or therapeutic targets.

Proteotranscriptomics of ocular adnexal B-cell lymphoma reveals an oncogenic role of alternative splicing and identifies a diagnostic marker

BackgroundOcular adnexal B-cell lymphoma (OABL) is a rare subtype of non-Hodgkin lymphoma. The molecular characteristics of OABL remain poorly understood. We performed an integrated study to investigate the proteotranscriptome landscape and identify novel molecular characteristics and biomarkers of OABL.MethodsIntegrated quantitative proteome and transcriptome were performed on 40 OABL 12 idiopathic orbital inflammation, 6 reactive lymphoid hyperplasia, and 13 aesthetic orbital plastic surgery specimens. Complete clinicopathologic and prognostic data of the patients were recorded.ResultsWe identified high global protein-mRNA concordance as a novel characteristic of OABL. High concordance was related to OABL recurrence. By integrated expression profile, motif enrichment and trend analysis, we found that alternative splicing is inflammation-independently dysregulated in OABL. After portraying the aberrant alternative splicing event landscape, we demonstrated the oncogenic role of ADAR, a core splicing regulator that regulates the splicing of Rho GTPase and cell cycle members. We found that ADAR regulates cell proliferation and Rho GTPase inhibitor sensitivity of lymphoma. We identified DNAJC9 as a potential biomarker for OABL in proteomic analyses. Immunohistochemistry and immunofluorescent staining showed the nuclear staining of DNAJC9 was significantly higher in extranodal marginal zone lymphomas compared with inflammation specimens.ConclusionsThese results provide an integrated gene expression profiling and demonstrate that high global protein-mRNA concordance is a prognosis-related molecular characteristic of OABL. We portray the alternative splicing events landscape of OABL, and reveal the oncogenic role of ADAR. We identified strong nuclear staining of DNAJC9 as a promising pathology diagnostic biomarker for extranodal marginal zone lymphomas.

Gene Expression Profiles of the Aging Rat Hippocampus Imply Altered Immunoglobulin Dynamics.

Aging is a process that leads to the deterioration in physiological functioning of the brain. Prior research has proposed that hippocampal aging is accompanied by genetic alterations in neural, synaptic, and immune functions. Nevertheless, interactome-based interrogations of gene alterations in hippocampal aging, remain scarce. Our study integrated gene expression profiles of the hippocampus from young and aged rats and functionally classified network-mapped genes based on their interactome. Hippocampal differentially expressed genes (DEGs) between young (5–8 months) and aged (21–26 months) male rats (Rattus norvegicus) were retrieved from five publicly available datasets (GSE14505, GSE20219, GSE14723, GSE14724, and GSE14725; 38 young and 29 aged samples). Encoded hippocampal proteins of age-related DEGs and their interactome were predicted. Clustered network DEGs were identified and the highest-ranked was functionally annotated. A single cluster of 19 age-related hippocampal DEGs was revealed, which was linked with immune response (biological process, P = 1.71E-17), immunoglobulin G binding (molecular function, P = 1.92E-08), and intrinsic component of plasma membrane (cellular component, P = 1.25E-06). Our findings revealed dysregulated hippocampal immunoglobulin dynamics in the aging rat brain. Whether a consequence of neurovascular perturbations and dysregulated blood-brain barrier permeability, the role of hippocampal immunoregulation in the pathobiology of aging warrants further investigation.

Integrated gene expression profiling analysis reveals SERPINA3, FCN3, FREM1, MNS1 as candidate biomarkers in heart failure and their correlation with immune infiltration.

BackgroundThe purpose of this study was to identify possible diagnostic indicators for heart failure (HF) and to investigate the function of immune cell infiltration in this pathophysiology.MethodsHF datasets from the Gene Expression Metascape database were utilized. R software was used to the identify differentially-expressed genes (DEGs) and perform functional correlation analysis. Least absolute shrinkage and selection operator (LASSO) and Boruta algorithms elimination algorithms were then employed to screen and validate the HF diagnostic variables. Finally, Single-sample Gene Set Enrichment Analysis (ssGSEA) was utilized to assess immune cell infiltration in HF tissues, and the Spearman association between gene expression and immune cell concentration was investigated.ResultsA total of 239 DEGs were screened in this study. SERPINA3 (area under the curve, AUC =0.958), FCN3 (AUC =0.972), FREM1 (AUC =0.954), and MNS1 (AUC =0.948) were identified as diagnostic factors of HF. The gene set differentiation analysis (GSVA) (R package “GSVA”) results showed that the high expression of FREM1 and MNS1 genes was involved in bile acid, fatty acid, and heme metabolism, suggesting that the core gene affects the progression of HF by regulating metabolism. Meanwhile, the high expression of FCN3 and SERPINA3 was related to xenobiotic metabolism, inflammatory response, and adipogenesis.ConclusionsGiven the importance of immune cell infiltration in the genesis and progression of HF, SERPINA3, FCN3, FREM1, and MNS1 may be used as diagnostic variables for HF.

Identification of TAZ as the essential molecular switch in orchestrating SCLC phenotypic transition and metastasis.

Small-cell lung cancer (SCLC) is a recalcitrant cancer characterized by high metastasis. However, the exact cell type contributing to metastasis remains elusive. Using a Rb1L/L/Trp53L/L mouse model, we identify the NCAMhiCD44lo/– subpopulation as the SCLC metastasizing cell (SMC), which is progressively transitioned from the non-metastasizing NCAMloCD44hi cell (non-SMC). Integrative chromatin accessibility and gene expression profiling studies reveal the important role of the SWI/SNF complex, and knockout of its central component, Brg1, significantly inhibits such phenotypic transition and metastasis. Mechanistically, TAZ is silenced by the SWI/SNF complex during SCLC malignant progression, and its knockdown promotes SMC transition and metastasis. Importantly, ectopic TAZ expression reversely drives SMC-to-non-SMC transition and alleviates metastasis. Single-cell RNA-sequencing analyses identify SMC as the dominant subpopulation in human SCLC metastasis, and immunostaining data show a positive correlation between TAZ and patient prognosis. These data uncover high SCLC plasticity and identify TAZ as the key molecular switch in orchestrating SCLC phenotypic transition and metastasis.

OSov: An Interactive Web Server to Evaluate Prognostic Biomarkers for Ovarian Cancer.

Simple SummaryThe OSov web server incorporates gene expression profiles with clinical risk factors to estimate the ovarian cancers patients’ survival, and provides a tool for multiple analysis, such as forest-plot, uni/multi-variate survival analysis, Kaplan-Meier plot and nomogram construction.Ovarian cancer is one of the most aggressive and highly lethal gynecological cancers. The purpose of our study is to build a free prognostic web server to help researchers discover potential prognostic biomarkers by integrating gene expression profiling data and clinical follow-up information of ovarian cancer. We construct a prognostic web server OSov (Online consensus Survival analysis for Ovarian cancer) based on RNA expression profiles. OSov is a user-friendly web server which could present a Kaplan–Meier plot, forest plot, nomogram and survival summary table of queried genes in each individual cohort to evaluate the prognostic potency of each queried gene. To assess the performance of OSov web server, 163 previously published prognostic biomarkers of ovarian cancer were tested and 72% of them had their prognostic values confirmed in OSov. It is a free and valuable prognostic web server to screen and assess survival-associated biomarkers for ovarian cancer.

Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients.

Objective: To explore the molecular mechanism and search for the candidate differentially expressed genes (DEGs) with the predictive and prognostic potentiality that is detectable in the whole blood of patients with ST-segment elevation (STEMI) and those with post-STEMI HF.Methods: In this study, we downloaded GSE60993, GSE61144, GSE66360, and GSE59867 datasets from the NCBI-GEO database. DEGs of the datasets were investigated using R. Gene ontology (GO) and pathway enrichment were performed via ClueGO, CluePedia, and DAVID database. A protein interaction network was constructed via STRING. Enriched hub genes were analyzed by Cytoscape software. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics analyses were performed to build machine learning models for predicting STEMI. Hub genes for further validated in patients with post-STEMI HF from GSE59867.Results: We identified 90 upregulated DEGs and nine downregulated DEGs convergence in the three datasets (|log2FC| ≥ 0.8 and adjusted p < 0.05). They were mainly enriched in GO terms relating to cytokine secretion, pattern recognition receptors signaling pathway, and immune cells activation. A cluster of eight genes including ITGAM, CLEC4D, SLC2A3, BST1, MCEMP1, PLAUR, GPR97, and MMP25 was found to be significant. A machine learning model built by SLC2A3, CLEC4D, GPR97, PLAUR, and BST1 exerted great value for STEMI prediction. Besides, ITGAM and BST1 might be candidate prognostic DEGs for post-STEMI HF.Conclusions: We reanalyzed the integrated transcriptomic signature of patients with STEMI showing predictive potentiality and revealed new insights and specific prospective DEGs for STEMI risk stratification and HF development.

Screening of characteristic genes in ulcerative colitis by integrating gene expression profiles

BackgroundThis study aimed to screen the feature modules and characteristic genes related to ulcerative colitis (UC) and construct a support vector machine (SVM) classifier to distinguish UC patients.MethodsFour datasets that contained UC and control samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) with consistency were screened via the MetaDE method. The weighted gene coexpression network (WGCNA) was used to distinguish significant modules based on the four datasets. The protein–protein interaction network was established based on intersection genes. Enrichment analysis of Gene Ontology (GO) biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were established based on DAVID. An SVM combined with recursive feature elimination was also applied to construct a disease classifier for the disease diagnosis of UC patients. The efficacy of the SVM classifier was evaluated through receiver operating characteristic curves.ResultsTwelve highly preserved modules were obtained using the WGCNA, and 2009 DEGs with significant consistency were selected using the MetaDE method. Sixteen significantly related GO BPs and 12 KEGG pathways were obtained, such as cytokine-cytokine receptor interaction, cell adhesion molecules, and leukocyte transendothelial migration. Subsequently, 41 genes were used to construct an SVM classifier, such as CXCL1, CCR2, IL1B, and IL1A. The area under the curve (AUC) was 0.999 in the training dataset, whereas the AUC was 0.886, 0.790, and 0.819 in the validation set (GSE65114, GSE37283, and GSE36807, respectively).ConclusionsAn SVM classifier based on feature genes might correctly identify healthy people or UC patients.

Prediction of synergistic drug combinations using PCA-initialized deep learning

BackgroundCancer is one of the main causes of death worldwide. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. However, the immense number of possible drug combinations and large synergistic space makes it infeasible to screen all effective drug pairs experimentally. Therefore, it is crucial to develop computational approaches to predict drug synergy and guide experimental design for the discovery of rational combinations for therapy.ResultsWe present a new deep learning approach to predict synergistic drug combinations by integrating gene expression profiles from cell lines and chemical structure data. Specifically, we use principal component analysis (PCA) to reduce the dimensionality of the chemical descriptor data and gene expression data. We then propagate the low-dimensional data through a neural network to predict drug synergy values. We apply our method to O’Neil’s high-throughput drug combination screening data as well as a dataset from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. We compare the neural network approach with and without dimension reduction. Additionally, we demonstrate the effectiveness of our deep learning approach and compare its performance with three state-of-the-art machine learning methods: Random Forests, XGBoost, and elastic net, with and without PCA-based dimensionality reduction.ConclusionsOur developed approach outperforms other machine learning methods, and the use of dimension reduction dramatically decreases the computation time without sacrificing accuracy.

Clinical Considerations for Integrating Gene Expression Profiling into Cutaneous Squamous Cell Carcinoma Management.

Gene expression profile (GEP) testing is now commercially available for metastatic risk prediction in patients with cutaneous squamous cell carcinoma (CSCC) and one or more high-risk factors. The purpose of this article is to provide an early framework for healthcare providers looking to integrate patient-specific tumor biology into their clinical practice using GEP testing. To develop a framework for clinical use, an expert panel was convened to identify CSCC management decision points where GEP testing may be immediately incorporated into practice until the definitive results of prospective trials become available. Based on their discussion, the expert panel focused on the areas of nodal evaluation, adjuvant radiation therapy, and follow-up and surveillance. The panel emphasized that GEP prognostic test results should not currently be used as a surrogate for standard of care treatment but as an additional data point when determining individualized management for patients with high-risk CSCC. Whenever possible, decisions on management plans for these patients should be developed with multidisciplinary input. J Drugs Dermatol. 2021;20:6(Suppl):s5-11. doi:10.36849/JDD.6068.

Integrative Analysis of DNA Methylation and Transcriptome Identifies a Predictive Epigenetic Signature Associated With Immune Infiltration in Gliomas.

Glioma is the most common primary brain tumor with poor prognosis and high mortality. The purpose of this study was to use the epigenetic signature to predict prognosis and evaluate the degree of immune infiltration in gliomas. We integrated gene expression profiles and DNA methylation data of lower-grade glioma and glioblastoma to explore epigenetic differences and associated differences in biological function. Cox regression and lasso analysis were used to develop an epigenetic signature based on eight DNA methylation sites to predict prognosis of glioma patients. Kaplan–Meier analysis showed that the overall survival time of high- and low-risk groups was significantly separated, and ROC analysis verified that the model had great predictive ability. In addition, we constructed a nomogram based on age, sex, 1p/19q status, glioma type, and risk score. The epigenetic signature was obviously associated with tumor purity, immune checkpoints, and tumor-immune infiltrating cells (CD8+ T cells, gamma delta T cells, M0 macrophages, M1 macrophages, M2 macrophages, activated NK cells, monocytes, and activated mast cells) and thus, it may find application as a guide for the evaluation of immune infiltration or in treatment decisions in immunotherapy.

Pooled CRISPR screening in pancreatic cancer cells implicates co-repressor complexes as a cause of multiple drug resistance via regulation of epithelial-to-mesenchymal transition

BackgroundPancreatic ductal adenocarcinoma (PDAC) patients suffer poor outcomes, including a five-year survival of below 10%. Poor outcomes result in part from therapeutic resistance that limits the impact of cytotoxic first-line therapy. Novel therapeutic approaches are needed, but currently no targeted therapies exist to treat PDAC.MethodsTo assess cellular resistance mechanisms common to four cytotoxic chemotherapies (gemcitabine, 5-fluorouracil, irinotecan, and oxaliplatin) used to treat PDAC patients, we performed four genome-wide CRISPR activation (CRISPRact) and CRISPR knock-out (CRISPRko) screens in two common PDAC cell lines (Panc-1 and BxPC3). We used pathway analysis to identify gene sets enriched among our hits and conducted RNA-sequencing and chromatin immunoprecipitation-sequencing (ChIP-seq) to characterize top hits from our screen. We used scratch assays to assess changes in cellular migration with HDAC1 overexpression.ResultsOur data revealed activation of ABCG2, a well-described efflux pump, as the most consistent mediator of resistance in each of our screens. CRISPR-mediated activation of genes involved in transcriptional co-repressor complexes also conferred resistance to multiple drugs. Expression of many of these genes, including HDAC1, is associated with reduced survival in PDAC patients. Up-regulation of HDAC1 in vitro increased promoter occupancy and expression of several genes involved in the epithelial-to-mesenchymal transition (EMT). These cells also displayed phenotypic changes in cellular migration consistent with activation of the EMT pathway. The expression changes resulting from HDAC1 activation were also observed with activation of several other co-repressor complex members. Finally, we developed a publicly available analysis tool, PancDS, which integrates gene expression profiles with our screen results to predict drug sensitivity in resected PDAC tumors and cell lines.ConclusionOur results provide a comprehensive resource for identifying cellular mechanisms of drug resistance in PDAC, mechanistically implicate HDAC1, and co-repressor complex members broadly, in multi-drug resistance, and provide an analytical tool for predicting treatment response in PDAC tumors and cell lines.

TNPO1-mediated nuclear import of ARID1B promotes tumor growth in ARID1A-deficient gynecologic cancer.

Karyopherin-β proteins are critically involved in cancer progression and have been reported as potential biomarkers and therapeutic targets for tumor treatment. However, TNPO1, as an important karyopherin-β family member, underlying functional roles in cancers remain largely unclear. In this study, under integrated gene-expression profiling screen of karyopherin-β in gynecologic cancer, we identify TNPO1 as a pivotal contributor to the gynecologic cancer progression. Remarkably, ARID1A-deficient gynecologic cancer cells are specifically vulnerable to the genetic perturbations of TNPO1 in vitro and in vivo. Mechanistically, TNPO1 is selectively responsible for nuclear import of ARID1B, which is a synthetic lethal target in ARID1A-inactivating mutation cancers. Furthermore, TNPO1 or ARID1B knockdown changes chromatin accessibility that results in loss of H3K4me1 and H3K27ac marker, diminishing activated transcription factor of the AP-1 family, and inactivating the PI3K/AKT signaling pathway by reducing growth pathway genes expression including PIK3CA and FGFR2. Together, this work indicates that the oncogenic function of TNPO1 and maybe represent a novel therapeutic strategy to treat ARID1A-deficient gynecologic cancer.

Multimodal regularized linear models with flux balance analysis for mechanistic integration of omics data.

High-throughput biological data, thanks to technological advances, have become cheaper to collect, leading to the availability of vast amounts of omic data of different types. In parallel, the in silico reconstruction and modeling of metabolic systems is now acknowledged as a key tool to complement experimental data on a large scale. The integration of these model- and data-driven information is therefore emerging as a new challenge in systems biology, with no clear guidance on how to better take advantage of the inherent multisource and multiomic nature of these data types while preserving mechanistic interpretation. Here, we investigate different regularization techniques for high-dimensional data derived from the integration of gene expression profiles with metabolic flux data, extracted from strain-specific metabolic models, to improve cellular growth rate predictions. To this end, we propose ad-hoc extensions of previous regularization frameworks including group, view-specific and principal component regularization and experimentally compare them using data from 1143 Saccharomyces cerevisiae strains. We observe a divergence between methods in terms of regression accuracy and integration effectiveness based on the type of regularization employed. In multiomic regression tasks, when learning from experimental and model-generated omic data, our results demonstrate the competitiveness and ease of interpretation of multimodal regularized linear models compared to data-hungry methods based on neural networks. All data, models and code produced in this work are available on GitHub at https://github.com/Angione-Lab/HybridGroupIPFLasso_pc2Lasso. Supplementary data are available at Bioinformatics online.

Correlation between circadian rhythm related genes, type 2 diabetes, and cancer: Insights from metanalysis of transcriptomics data

Clock genes work as an auto-regulated transcription-translational loop of circadian genes that drives the circadian rhythms in each cell and they are essential to physiological requests. Since metabolism is a dynamic process, it involves several physiological variables that circadian cycling. The clock genes alterations can affect multiple systems concomitantly, because they constitute the promoter factors for relevant metabolic pathways. Considering the intertwined structure of signaling, regulatory, and metabolic processes within a cell, we employed a genome-scale biomolecular network. Accordingly, a meta-analysis of diabetic-associated transcriptomic datasets was performed, and the core information on differentially expressed genes (DEGs) was obtained by statistical analyses. In the current study, meta-analysis was performed on type 2 diabetes, circadian rhythm-related genes, and breast, bladder, liver, pancreas, colon and rectum cancer-associated transcriptome data using the integration of gene expression profiles with genome-scale biomolecular networks in diabetes samples. First, we detected downregulated and upregulated DEGs in mouse cortex and hypothalamus samples of mice with sleep deprivation. In summary, upregulated genes active genes associated with oxidative phosphorylation, cancer and diabetes, mainly in hypothalamus specimens. In cortex, we observed mainly downregulation of immune system. DEGs were combined with 214 circadian rhythm related genes to type 2 DM and cancer samples. We observed that several common genes deregulated in both diseases. Klf10, Ntkr3, Igf1, Usp2, Ezh2 were both downregulated in type 2 DM and cancer samples, while Arntl2 and Agrp were upregulated. It seems that the changes in mRNA are contributing to the phenotypic changes in type 2 DM, resulting in phenotypic changes associated with the malignant transformation. Taking those genes to perform a survival analysis, we found only Igf1, Usp2 and Arntl2 genes associated with patient outcomes. While Igf1 and Usp2 downregulation had a negative impact, Arntl2 upregulation was associated with poor survival both in BLCA and BRCA cancer samples. Our data stimulate efforts in news studies to achieve the experimental and clinical validation about these biomolecules.

Microglia facilitate repair of demyelinated lesions via post-squalene sterol synthesis.

The repair of inflamed, demyelinated lesions as in multiple sclerosis necessitates the clearance of cholesterol-rich myelin debris by microglia/macrophages and the switch from a pro-inflammatory to an anti-inflammatory lesion environment. Subsequently, oligodendrocytes increase cholesterol levels as a prerequisite for synthesizing new myelin membranes. We hypothesized that lesion resolution is regulated by the fate of cholesterol from damaged myelin combined with oligodendroglial sterol synthesis. By integrating expression profiling, genetics, and comprehensive phenotyping, we found that paradoxically sterol synthesis in myelin-phagocytosing microglia/macrophages determines repair of acutely demyelinated lesions. Rather than producing cholesterol, microglia/macrophages synthesized desmosterol, the immediate cholesterol precursor. Desmosterol activated LXR-signaling to resolve inflammation, creating a permissive environment for oligodendrocyte differentiation. Moreover, LXR-target gene products facilitated the efflux of lipid/cholesterol from lipid-laden microglia/macrophages to support remyelination by oligodendrocytes. Consequently, pharmacological stimulation of sterol synthesis boosted repair of demyelinated lesions, suggesting novel therapeutic strategies for myelin repair in multiple sclerosis.

An integrated approach to biomarker discovery reveals gene signatures highly predictive of cancer progression

Current cancer biomarkers present variability in their predictive power and demonstrate limited clinical efficacy, possibly due to the lack of functional relevance of biomarker genes to cancer progression. To address this challenge, a biomarker discovery pipeline was developed to integrate gene expression profiles from The Cancer Genome Atlas and essential survival gene datasets from The Cancer Dependency Map, the latter of which catalogs genes driving cancer progression. By applying this pipeline to lung adenocarcinoma, lung squamous cell carcinoma, and glioblastoma, genes highly associated with cancer progression were identified and designated as progression gene signatures (PGSs). Analysis of area under the receiver operating characteristics curve revealed that PGSs predicted patient survival more accurately than previously identified cancer biomarkers. Moreover, PGSs stratified patients with high risk for progressive disease indicated by worse prognostic outcomes, increased frequency of cancer progression, and poor responses to chemotherapy. The robust performance of these PGSs were recapitulated in four independent microarray datasets from Gene Expression Omnibus and were further verified in six freshly dissected tumors from glioblastoma patients. Our results demonstrate the power of an integrated approach to cancer biomarker discovery and the possibility of implementing PGSs into clinical biomarker tests.

DPPN-SVM: Computational Identification of Mis-Localized Proteins in Cancers by Integrating Differential Gene Expressions With Dynamic Protein-Protein Interaction Networks.

Eukaryotic cells contain numerous components, which are known as subcellular compartments or subcellular organelles. Proteins must be sorted to proper subcellular compartments to carry out their molecular functions. Mis-localized proteins are related to various cancers. Identifying mis-localized proteins is important in understanding the pathology of cancers and in developing therapies. However, experimental methods, which are used to determine protein subcellular locations, are always costly and time-consuming. We tried to identify cancer-related mis-localized proteins in three different cancers using computational approaches. By integrating gene expression profiles and dynamic protein-protein interaction networks, we established DPPN-SVM (Dynamic Protein-Protein Network with Support Vector Machine), a predictive model using the SVM classifier with diffusion kernels. With this predictive model, we identified a number of mis-localized proteins. Since we introduced the dynamic protein-protein network, which has never been considered in existing works, our model is capable of identifying more mis-localized proteins than existing studies. As far as we know, this is the first study to incorporate dynamic protein-protein interaction network in identifying mis-localized proteins in cancers.

Multiple Bioinformatics Analyses of Integrated Gene Expression Profiling Data and Verification of Hub Genes Associated with Diabetic Retinopathy.

BackgroundDiabetic retinopathy (DR) is a serious complication of diabetes that can lead to blindness. This study aimed to identify the core genes and molecular functions involved in DR through multiple bioinformatics analyses.Material/MethodsThe mRNA gene profiles of human DR tissues from the GSE60436 and GSE53257 datasets were assessed with R software and integrated to identify the co-expressed differentially expressed genes (DEGs). Multiple bioinformatics analyses were used: Gene Ontology (GO) analysis, signaling pathway analysis, and hub gene prediction. Quantitative reverse transcription-PCR (qRT-PCR) was used to verify the hub genes.ResultsThe Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool suggested that the biological processes of the DEGs focused on mitochondrial transport, the cellular components focused on mitochondria, and molecular functions focused on catalytic activity. The results provided by DAVID were consistent with those provided by STRING and the GeneMANIA online database. All the DEGs function in metabolic pathways, consistent with the g: Profiler online analysis results. The protein-protein interaction (PPI) networks forecasted by STRING and GeneMANIA were entered into Cytoscape for cytoHubba degree analysis. The hub genes predicted by cytoHubba suggested that fumarate hydratase (FH) might be relevant to DR. qRT-PCR suggested that the expression of FH was higher in DR retinal tissues than in normal control tissues.ConclusionsMultiple bioinformatics analyses verified that FH could be used as a potential diagnostic marker and new therapeutic target of DR.

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism.

Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.