Integrating Gene Expression Profiles Research Articles

Enhanced fatty acid oxidation provides glioblastoma cells metabolic plasticity to accommodate to its dynamic nutrient microenvironment

Despite advances in molecularly characterizing glioblastoma (GBM), metabolic alterations driving its aggressive phenotype are only beginning to be recognized. Integrative cross-platform analysis coupling global metabolomic and gene expression profiling on patient-derived glioma identified fatty acid β-oxidation (FAO) as a metabolic node in GBM. We determined that the biologic consequence of enhanced FAO is directly dependent upon tumor microenvironment. FAO serves as a metabolic cue to drive proliferation in a β-HB/GPR109A dependent autocrine manner in nutrient favorable conditions, while providing an efficient, alternate source of ATP only in nutrient unfavorable conditions. Rational combinatorial strategies designed to target these dynamic roles FAO plays in gliomagenesis resulted in necroptosis-mediated metabolic synthetic lethality in GBM. In summary, we identified FAO as a dominant metabolic node in GBM that provides metabolic plasticity, allowing these cells to adapt to their dynamic microenvironment. Combinatorial strategies designed to target these diverse roles FAO plays in gliomagenesis offers therapeutic potential in GBM.

Statin Use Is Associated with Lower Risk of PTEN-Null and Lethal Prostate Cancer.

Statins are associated with lower risk of aggressive prostate cancer, but lethal prostate cancer is understudied and contributing mechanisms are unclear. We prospectively examined statins and lethal prostate cancer risk in the Health Professionals Follow-up Study (HPFS), tested associations with molecular subtypes, and integrated gene expression profiling to identify putative mechanisms. Our study included 44,126 men cancer-free in 1990, followed for prostate cancer incidence through 2014, with statin use recorded on biennial questionnaires. We used multivariable Cox regression to examine associations between statins and prostate cancer risk overall, by measures of clinically significant disease, and by ERG and PTEN status. In an exploratory analysis, age-adjusted gene set enrichment analysis identified statin-associated pathways enriched in tumor and adjacent normal prostate tissue. During 24 years of follow-up, 6,305 prostate cancers were diagnosed and 801 (13%) were lethal (metastatic at diagnosis or metastatic/fatal during follow-up). Relative to never/past use, current statin use was inversely associated with risk of lethal prostate cancer [HR, 0.76; 95% confidence interval (CI), 0.60-0.96] but not overall disease. We found a strong inverse association for risk of PTEN-null cancers (HR, 0.40; 95% CI, 0.19-0.87) but not PTEN-intact cancers (HR, 1.18; 95% CI, 0.95-1.48; P heterogeneity = 0.01). Associations did not differ by ERG. Inflammation and immune pathways were enriched in normal prostate tissue of statin ever (n = 10) versus never users (n = 103). Molecular tumor classification identified PTEN and inflammation/immune activation as potential mechanisms linking statins with lower lethal prostate cancer risk. These findings support a potential causal association and could inform selection of relevant biomarkers for statin clinical trials.

Integrated Gene Expression Profiling Analysis Reveals Probable Molecular Mechanism and Candidate Biomarker in Anti-TNFα Non-Response IBD Patients.

PurposeTo explore the molecular mechanism and search for candidate biomarkers in the gene expression profile of IBD patients associated with the response to anti-TNFα agents.MethodsDifferentially expressed genes (DEGs) of response vs non-response IBD patients in datasets GSE12251, GSE16879, and GSE23597 were integrated using NetworkAnalyst. We conducted functional enrichment analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and extracted hub genes from the protein–protein interaction network. The proportion of immune cell types was estimated via CIBERSORT. ROC curve analysis and binomial Lasso regression were applied to assess the expression level of hub genes in datasets GSE12251, GSE16879, and GSE23597, and another two datasets GSE107865 and GSE42296.ResultsA total of 287 DEGs were obtained from the integrated dataset. They were enriched in 14 Gene Ontology terms and 11 KEGG pathways. Polarization from M2 to M1 macrophages was relatively high in non-response individuals. We found nine hub genes (TLR4, TLR1, TLR8, CCR1, CD86, CCL4, HCK, and FCGR2A), mainly related to the interaction between Toll-like Receptor (TLR) pathway and FcγR signaling in non-response anti-TNFα individuals. FCGR2A, HCK, TLR1, TLR4, TLR8, and CCL4 show great value for prediction in intestinal tissue. Besides, FCGR2A, HCK, and TLR8 might be candidate blood biomarkers of anti-TNFα non-response IBD patients.ConclusionOver-activated interaction between FcγR-TLR axis in the innate immune cells of IBD patients might be used to identify non-response individuals and increased our understanding of resistance to anti-TNFα therapy.

OSpaad: An online tool to perform survival analysis by integrating gene expression profiling and long‐term follow‐up data of 1319 pancreatic carcinoma patients

Pancreatic carcinoma (PC) is a type of highly lethal malignant tumor that has unfavorable outcomes. One major challenge in improving clinical outcomes is to identify novel biomarkers for prognosis. In this study, we developed an online consensus survival tool for pancreatic adenocarcinoma (OSpaad), which allows researchers and clinicians to analyze the prognostic value of selected genes in PC. OSpaad contains 1319 unique PC cases that have both gene expression data and correspondent clinical data from seven individual cohorts and provides four survival terms including overall survival, disease-specific survival, disease-free interval, progression-free interval for prognosis evaluation. To meet the different research needs, OSpaad allows users to limit survival analysis in subgroups by selecting different terms of clinical confounding factors such as TNM stage, sex, smoking time, lymph invasion, and race. Moreover, we showed that 97% (116 out of 120) previously reported prognostic biomarkers, including ERBB2, TP53, EGFR and so forth, were validated and confirmed their prognostic significance in OSpaad, demonstrating the well performance of survival analysis in OSpaad. OSpaad is a user-friendly online tool with a straightforward interface allowing clinicians and basic research scientists with even a limited bioinformatics background to easily screen and evaluate the prognostic value of genes in a large PC cohort. This online tool can be accessed at http://bioinfo.henu.edu.cn/PAAD/PAADList.jsp.

μCB-seq: microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells.

Single-cell RNA sequencing (scRNA-seq) enables the investigation of complex biological processes in multicellular organisms with high resolution. However, many phenotypic features that are critical to understanding the functional role of cells in a heterogeneous tissue or organ are not directly encoded in the genome and therefore cannot be profiled with scRNA-seq. Quantitative optical microscopy has long been a powerful approach for characterizing diverse cellular phenotypes including cell morphology, protein localization, and chemical composition. Combining scRNA-seq with optical imaging has the potential to provide comprehensive single-cell analysis, allowing for functional integration of gene expression profiling and cell-state characterization. However, it is difficult to track single cells through both measurements; therefore, coupling current scRNA-seq protocols with optical measurements remains a challenge. Here, we report microfluidic cell barcoding and sequencing (μCB-seq), a microfluidic platform that combines high-resolution imaging and sequencing of single cells. μCB-seq is enabled by a novel fabrication method that preloads primers with known barcode sequences inside addressable reaction chambers of a microfluidic device. In addition to enabling multi-modal single-cell analysis, μCB-seq improves gene detection sensitivity, providing a scalable and accurate method for information-rich characterization of single cells.

Integrative Gene Expression Profiling Analysis to Investigate Potential Prognostic Biomarkers for Colorectal Cancer.

BackgroundDespite noteworthy advancements in the multidisciplinary treatment of colorectal cancer (CRC) and deeper understanding in the molecular mechanisms of CRC, many of CRC patients with histologically identical tumors present different treatment response and prognosis. Thus, more evidence on novel predictive and prognostic biomarkers for CRC remains urgently needed.This study aims to identify potential prognostic biomarkers for CRC with integrative gene expression profiling analysis.Material/MethodsDifferential expression analysis of paired CRC and adjacent normal tissue samples in 6 microarray datasets was independently performed, and the 6 datasets were integrated by the robust rank aggregation method to detect consistent differentially expressed genes (DEGs). Aberrant expression patterns of these genes were further validated in RNA sequencing data. Then, gene set enrichment analysis (GSEA) was performed to investigate significantly dysregulated biological functions in CRC. Finally, univariate, LASSO and multivariate Cox regression models were built to identify key prognostic genes in CRC patients.ResultsA total of 990 DEGs (495 downregulated and 495 upregulated genes) were acquired after integratedly analyzing the 6 microarray datasets, and 4131 DEGs (2050 downregulated and 2081 upregulated genes) were obtained from the RNA sequencing dataset. Subsequently, these DEGs were intersected and 885 consistent DEGs were finally identified, including 458 downregulated and 427 upregulated genes. Two risky prognostic genes (TIMP1 and LZTS3) and 5 protective prognostic genes (AXIN2, CXCL1, ITLN1, CPT2 and CLDN23) were identified, which were significantly associated with the prognosis of CRC.ConclusionsThe 7 genes that we identified would provide more evidence for further applying novel diagnostic and prognostic biomarkers in clinical practice to facilitate personalized treatment of CRC.

Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis

The emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators – such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others – as predictive features of cancer outcome. However, identification of “driver genes” associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively. iEDGE first identifies the cis and trans gene expression signatures associated with the presence/absence of a particular epi-DNA alteration across samples. It then applies tests of statistical mediation to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks. We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible at https://montilab.bu.edu/iEDGE. In summary, integrative analysis of SCNAs and gene expression using iEDGE successfully identified known cancer driver genes and putative cancer therapeutic targets across 19 cancer types in the TCGA. The proposed method can easily be applied to the integration of gene expression profiles with other epi-DNA assays in a variety of disease contexts.

Combining Gene Expression Profiling and Artificial Intelligence to Diagnose B-Cell Non-Hodgkin Lymphoma

Combining Gene Expression Profiling and Artificial Intelligence to Diagnose B-Cell Non-Hodgkin Lymphoma

PKR-like Endoplasmic Reticulum Kinase Mediates Apoptosis Induced By Pharmacological AMP-Activated Protein Kinase Activation in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a myeloid progenitor-derived neoplasm of poor prognosis, particularly among the elderly, in whom age and comorbidities preclude the use of intensive therapies. Novel therapeutic approaches for AML are therefore critically needed. Adenosine monophosphate (AMP) activated protein kinase (AMPK) is a pleiotropic serine/threonine kinase promoting catabolism that represses anabolism and enhances autophagy in response to stress1. AMPK heterotrimers comprise catalytic α- and regulatory β- and γ-subunits, the latter harboring binding sites for AMP. Targets of AMPK include a host of metabolic pathway enzymes mediating carbohydrate, lipid and protein synthesis and metabolism. Accumulating evidence implicates AMPK in cancer biology, primarily as a tumor suppressor, although minimal AMPK activity may also be required for cancer cell growth under stress conditions2,3. Pharmacological activation of AMPK thus represents an attractive new strategy for targeting AML. We previously used the selective small molecule AMPK activator GSK621 to show that AMPK activation induces cytotoxicity in AML but not in normal hematopoietic cells, contingent on concomitant activation of the mammalian target of rapamycin complex 1 (mTORC1)4. However, the precise mechanisms of AMPK-induced AML cytotoxicity have remained unclear. We integrated gene expression profiling and bioinformatics proteomic analysis to identify the serine/threonine kinase PERK - one of the key effectors of the endoplasmic reticulum stress response - as a potential novel target of AMPK. We showed that PERK was directly phosphorylated by AMPK on at least two conserved residues (serine 439 and threonine 680) and that AMPK activators elicited a PERK/eIF2A signaling cascade independent of the endoplasmic reticulum stress response in AML cells. CRISPR/Cas9 depletion and complementation assays illuminated a critical role for PERK in apoptotic cell death induced by pharmacological AMPK activation. Indeed, GSK621 induced mitochondrial membrane depolarization and apoptosis in AML cells, an effect that was mitigated when cells were depleted of PERK or expressed PERK with a loss of function AMPK phosphorylation site mutation. We identified the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2) as a downstream target of the AMPK/PERK pathway, as its expression was enhanced in PERK knockdown AML cells. Moreover, selective pharmacologic activation of ALDH2 by the small molecule ALDA-1 recapitulated the protective effects of PERK depletion in the face of pharmacological AMPK activation. Corroborating the impact of the AMPK/PERK axis on mitochondrial apoptotic function, BH3 profiling showed marked Bcl-2 dependency in AML cells treated with GSK621. This dependency was abrogated in PERK-depleted cells, suggesting a role for PERK in mitochondrial priming to cell death. In vitro drug combination studies further demonstrated synergy between the clinical grade Bcl-2 inhibitor venetoclax (ABT-199) and each of four AMPK activators (GSK621, MK-8722, PF-06409577 and compound 991) in multiple AML cell lines. Finally, the addition of GSK621 to venetoclax enhanced anti-leukemic activity in primary AML patient samples ex vivo and in humanized mouse models in vivo. These findings together clarify the mechanisms of cytotoxicity induced by AMPK activation and suggest that combining pharmacologic AMPK activators with venetoclax may hold therapeutic promise in AML.

Identification of Hub Genes and Pathways in a Rat Model of Renal Ischemia-Reperfusion Injury Using Bioinformatics Analysis of the Gene Expression Omnibus (GEO) Dataset and Integration of Gene Expression Profiles.

BackgroundThis study aimed to identify hub genes and pathways in a rat model of renal ischemia-reperfusion injury (IRI) using bioinformatics analysis of the Gene Expression Omnibus (GEO) microarray dataset and integration of gene expression profiles.Material/MethodsGEO software and the GEO2R calculation method were used to analyze two mRNA profiles, including GSE 39548 and GSE 108195. The co-expression of differentially expressed genes (DEGs) were identified and searched in the DAVID and STRING databases for pathway and protein-protein interaction (PPI) analysis. Cytoscape was used to draw the PPI network. DEGs were also analyzed using the Molecular Complex Detection (MCODE) algorithm. Cytoscape and cytoHubba were used to analyze the hub genes and visualize the molecular interaction networks. Rats (n=20) included the IRI model group (n=10) and a control group (n=10). Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to measure and compare the expression of the identified genes in rat renal tissue in the IRI model and the control group.ResultsTen hub genes were identified, STAT3, CD44, ITGAM, CCL2, TIMP1, MYC, THBS1, IGF1, SOCS3, and CD14. Apart from IGF1, qRT-PCR showed that expression of these genes was significantly increased in renal tissue in the rat model of IRI. The HIF-1α signaling pathway was involved in IRI in the rat model, which was supported by MCODE analysis.ConclusionsIn a rat model of renal IRI, bioinformatics analysis of the GEO dataset and integration of gene expression profiles identified involvement of HIF-1α signaling and the STAT3 hub gene.

Mechanistic basis of neonatal heart regeneration revealed by transcriptome and histone modification profiling

The adult mammalian heart has limited capacity for regeneration following injury, whereas the neonatal heart can readily regenerate within a short period after birth. To uncover the molecular mechanisms underlying neonatal heart regeneration, we compared the transcriptomes and epigenomes of regenerative and nonregenerative mouse hearts over a 7-d time period following myocardial infarction injury. By integrating gene expression profiles with histone marks associated with active or repressed chromatin, we identified transcriptional programs underlying neonatal heart regeneration, and the blockade to regeneration in later life. Our results reveal a unique immune response in regenerative hearts and a retained embryonic cardiogenic gene program that is active during neonatal heart regeneration. Among the unique immune factors and embryonic genes associated with cardiac regeneration, we identified Ccl24, which encodes a cytokine, and Igf2bp3, which encodes an RNA-binding protein, as previously unrecognized regulators of cardiomyocyte proliferation. Our data provide insights into the molecular basis of neonatal heart regeneration and identify genes that can be modulated to promote heart regeneration.

Integrative gene expression profiling reveals that dysregulated triple microRNAs confer paclitaxel resistance in non-small cell lung cancer via co-targeting MAPT.

BackgroundPaclitaxel has shown significant anti-tumor activity against non-small cell lung cancer (NSCLC); however, resistance to paclitaxel frequently occurs and represents a significant clinical problem and its underlying molecular mechanism remains elusive.MethodsLong-term treatment of culture cell with paclitaxel was carried out to mimic the development of acquired drug resistance in NSCLC. Cell proliferation and clonogenic assay and apoptosis evaluation were carried out to determine the efficacy of paclitaxel on NSCLC cells. Western blot analyses were performed to determine the expression and activation of proteins. Apoptosis enzyme-linked immunosorbent assay was used to quantify cytoplasmic histone-associated DNA fragments. Microarray analyses were applied to explore both mRNA and miRNA expression profiles in NSCLC cells followed by integrative analysis. qRT-PCR was carried out to verify the differentially expressed mRNAs and miRNAs.ResultsThe expression of 652 genes was shown to be changed at least 2-fold in paclitaxel-resistant NSCLC (H460_TaxR) cells with 511 upregulated and 141 downregulated as compared with that in parental H460 cells. The differentially expressed genes were functionally enriched in regulating the cell proliferation, cell death, and response to endogenous stimulus, and clustered in pathways such as cancer and signaling by the G protein-coupled receptor (GPCR). Moreover, 43 miRNAs were shown to be differentially expressed in H460_TaxR cells with 15 upregulated and 28 downregulated as compared with parental H460 cells. A total of 289 pairs of miRNA-potential target gene were revealed in H460_TaxR cells by bioinformatics analysis. Furthermore, integrative analysis of miRNAs and gene expression profiles revealed that dysregulated miR-362-3p, miR-766-3p, and miR-6507-3p might confer paclitaxel resistance in NSCLC via targeting MAPT simultaneously.ConclusionOur findings suggested that specific manipulation of MAPT-targeting miRNAs may be a novel strategy to overcome paclitaxel resistance in patients with NSCLC especially large-cell lung carcinoma.

Identification of critical genes associated with human osteosarcoma metastasis based on integrated gene expression profiling.

Osteosarcoma is the most common type of malignant bone cancer, which often affects teenagers and young adults. The present study aimed to screen for critical genes and microRNAs (miRNAs/miRs) involved in osteosarcoma. A total of four microarray datasets (accession numbers GSE32981, GSE21257, GSE14827 and GSE14359) were downloaded from the Gene Expression Omnibus database. Following data preprocessing, module analysis was performed to identify the stable modules using the weighted gene co-expression network analysis (WGCNA) package. The differentially expressed genes (DEGs) between metastatic samples and non-metastatic samples were screened, followed by gene co-expression network construction, and Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Subsequently, prognosis-associated genes were screened and a miRNA-target gene regulatory network was constructed. Finally, the data for critical genes were validated. WGCNA analysis identified six modules; blue and yellow modules were significantly positively associated with osteosarcoma metastasis. A total of 1,613 DEGs were screened between primary tissue samples and metastatic samples. Following comparison of the genes in the two (blue and yellow) modules, a total of 166 DEGs were identified (metastatic samples vs. non-metastatic samples). Functional enrichment analysis demonstrated that these DEGs were mainly involved in ‘defense response’, ‘p53 signaling pathway’ and ‘lysosome’. By utilizing the clinical information in GSE21257, 10 critical genes associated with osteosarcoma prognosis were obtained, including CTP synthase 2 (CTPS2), tumor protein p53 inducible protein 3 (TP53I3) and solute carrier family 1 member 1 (SLC1A1). In addition, hsa-miR-422a and hsa-miR-194 were highlighted in the miRNA-target gene network. Finally, matrix metallopeptidase 3 (MMP3) and vascular endothelial growth factor B (VEGFB) were predicted as critical genes in osteosarcoma metastasis. CTPS2, TP53I3 and SLC1A1 may serve major roles in osteosarcoma development, and hsa-miR-422a, hsa-miR-194, MMP3 and VEGFB may be associated with osteosarcoma metastasis.

Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

BackgroundLong non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes in diseases such as cancer, although the functions of most remain poorly understood. To address this, here we apply a novel strategy to integrate gene expression profiles across 32 cancer types, and cluster human lncRNAs based on their pan-cancer protein-coding gene associations. By doing so, we derive 16 lncRNA modules whose unique properties allow simultaneous inference of function, disease specificity and regulation for over 800 lncRNAs.ResultsRemarkably, modules could be grouped into just four functional themes: transcription regulation, immunological, extracellular, and neurological, with module generation frequently driven by lncRNA tissue specificity. Notably, three modules associated with the extracellular matrix represented potential networks of lncRNAs regulating key events in tumour progression. These included a tumour-specific signature of 33 lncRNAs that may play a role in inducing epithelial-mesenchymal transition through modulation of TGFβ signalling, and two stromal-specific modules comprising 26 lncRNAs linked to a tumour suppressive microenvironment and 12 lncRNAs related to cancer-associated fibroblasts. One member of the 12-lncRNA signature was experimentally supported by siRNA knockdown, which resulted in attenuated differentiation of quiescent fibroblasts to a cancer-associated phenotype.ConclusionsOverall, the study provides a unique pan-cancer perspective on the lncRNA functional landscape, acting as a global source of novel hypotheses on lncRNA contribution to tumour progression.

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.

Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model.

Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients, which can not only effectively avoid overtreatment and medical resources waste, but also provide scientific basis to help medical staff and patients family members to make right medical decisions. As well known, cancer is a partly inherited disease with various important biological markers, especially the gene expression profile data and clinical data. Therefore, the accuracy of prediction model can be improved by integrating gene expression profile data and clinical data. In this paper, we proposed an end-to-end model, Attention-based Multi-NMF DNN (AMND), which combines clinical data and gene expression data extracted by Multiple Nonnegative Matrix Factorization algorithms (Multi-NMF) for the prognostic prediction of breast cancer. The innovation of this method is highlighted through using clinical data and combining multiple feature selection methods with the help of Attention mechanism. The results of comprehensive performance evaluation show that the proposed model reports better predictive performances than either models only using data of single modality, e.g., gene or clinical, or models based on any single NMF improved methods which only use one of the NMF algorithms to extract features. The performance of our model is competitive or even better than other previously reported models. Meanwhile, AMND can be extended to the survival prediction of other cancer diseases, providing a new strategy for breast cancer prognostic prediction.

Detecting Diagnostic Biomarkers of Alzheimer's Disease by Integrating Gene Expression Data in Six Brain Regions.

Alzheimer's disease (AD) is a neurodegenerative and progressive disease, which often causes irreversible damages to the cerebrum. The pathogenesis of AD is far from being fully understood, while there are some popular hypotheses. So far, the diagnosis of AD relies only on clinical screening in the form of imaging techniques or cerebrospinal fluid analysis, which may lead to inaccurate evaluation and then cause the delay of suitable treatments. While molecular biomarkers provide promising alternatives of establishing correct relationships between genotypes and phenotypes of clinical symptoms. In this paper, we propose a machine-learning-based method of identifying potential diagnostic biomarkers of AD based on gene coexpression network by integrating gene expression profiles in six brain regions. After building an integrated gene coexpression network of multiple brain regions, we decompose the differential network into some subnetwork modules. The module candidates from these coexpressed gene communities are then identified by screening their discriminative powers in control from disease samples. The potential biomarkers are then validated by multiple cross-validations and functional enrichment analyses. If the biomarkers successfully pass clinical significance tests, they can be used as a reference for clinical diagnosis after wet-experimental validations.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review.

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.

High expression of CHML predicts poor prognosis of multiple myeloma.

Multiple myeloma is a hematological tumor with a malignant proliferation of myeloma cells. Although the survival time after treatment has improved, the recurrence rate of MM is still high. Choroideremia-like (CHML) protein is essential for the prenylation modification of various Rab proteins and it exerts biological effects on vesicle trafficking and signal transduction. However, little is identified about the relationship between CHML gene and MM. We integrated gene expression profiles of 1907 MM patients (1959 MM samples) from the 7 datasets. The relationship between CHML gene expression level and event-free survival (EFS), overall survival (OS), ISS stage, molecular subtype, relapse, therapy was analyzed. The differential gene exression profile of CHML-high MM group and CHML-low MM group and possible pathway related to CHML were conducted. Our data showed that EFS (P < 0.0001) and OS (P < 0.0001) in MM patients with high expression of CHML were lower than those with low CHML expression. The gene expression level of CHML was increased in subtypes of MM with poor prognosis, especially in proliferation subtype (P < 0.001). Cell division pathway (P < 0.01) was high enriched of the differential expressed genes of CHML-high group vs CHML-low group. CHML gene can be considered as an independent factor to evaluate the prognosis of MM. High expression of CHML is associated with poor survival, which is related to cell proliferation and cell division of myeloma cells.

Network integration of multi-tumour omics data suggests novel targeting strategies

We characterize different tumour types in search for multi-tumour drug targets, in particular aiming for drug repurposing and novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allows us to define three cluster-specific signatures, with genes belonging to NF-κB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we define new pharmacological strategies validated by in vitro experiments that show inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways that could possibly be used, singularly or in combination, for the design of novel treatment strategies.