Analysis Of Microarray Gene Expression Data Research Articles

Abstract 5704: An integrated immune signature predictive of adjuvant immunotherapeutic benefits for high-risk melanoma

Abstract Introduction CTLA4 blockade with ipilimumab was more favorable than interferon-α2b (IFN) in high-risk melanoma in phase adjuvant III trial E1609. Characterization of the pretreatment tumor immune biomarkers and clinical covariates may inform the likelihood of response to ipilimumab and other immune checkpoint inhibitors (ICI), and guide future development of this and other modalities in this patient population. Methods We utilized PATH-SURVEIOR, a bioinformatics framework developed in-house for associating genes and pathway signatures with clinical endpoints, to perform survival analysis of gene expression levels of 31 candidate immune-related biomarkers based on previous preliminary data. We analyzed microarray gene expression data from 471 melanoma patients treated with ipilimumab (ipi) and 248 melanoma patients treated with IFN as part of E1609. We then developed a LASSO Cox regression model and validated our model in 22 patients treated with neoadjuvant ipi in a separate clinical trial. Results Using PATH-SURVEIOR, we evaluated 31 candidate immune biomarkers and their association with patient outcome by including treatment group (ipi and IFN) as a multiplicative covariate interaction in the Cox hazard model. Our analysis identified CXCL9, CD8A, CXCL10, and INPP5D as Tier 1 biomarkers (HR > 1 and P < 0.05) and IDO1, IGKC, and IL2RB as Tier 2 biomarkers (HR > 1 and P < 0.1). Next, we developed an ipilimumab immune-based risk score using LASSO Cox regression (L-IPI7) based on these 7 aggregate biomarkers. We then split our 471 ipi-treated cohort into training (310, 66%) and testing (161, 33%) cohorts and assessed our model for its ability to predict overall survival (OS) and relapse-free survival (RFS). Our risk score was capable of stratifying ipi-treated patients into High-Risk and Low-Risk populations, which correlated with OS. As a negative control, we assessed our risk score in 248 IFN-treated patients and found no significant association with OS. As validation, we applied our L-IPI7 score to a cohort of 22 patients treated with neoadjuvant ipi and determined that the score was able to predict patients with a high risk of relapse. Interestingly, when we developed an interactive Cox-regression model with colitis status (grade 0-1 vs grade 2+), we found that neoadjuvant ipi patients with low-grade colitis were associated with a higher L-IPI7 risk score for disease relapse. In addition, we determined that: i) higher age and higher L-IPI7 risk score identified patients with the worst OS and RFS And ii) female patients with a low L-IPI7 risk scores had a better OS and RFS. Conclusions We developed a broadly applicable model based on LASSO Cox Regression predictive of adjuvant ipi treatment outcomes in melanoma. Our L-IPI7 score based on expression of CXCL9, CD8A, CXCL10, INPP5D, IDO1, IGKC, IL2RB effectively predicts survival, with interactions with age, gender and on-treatment development of colitis. Citation Format: Alyssa N. Obermayer, Timothy I. Shaw, Sandra J. Lee, F. Stephen Hodi, William A. LaFramboise, Walter Storkus, Arivarasan D. Karunamurthy, Patrick Hwu, Howard Streicher, Dung-Tsa Chen, John M. Kirkwood, Ahmad A. Tarhini. An integrated immune signature predictive of adjuvant immunotherapeutic benefits for high-risk melanoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5704.

A computational approach based on weighted gene co-expression network analysis for biomarkers analysis of Parkinson's disease and construction of diagnostic model.

Parkinson's disease (PD) is a common age-related chronic neurodegenerative disease. There is currently no affordable, effective, and less invasive test for PD diagnosis. Metabolite profiling in blood and blood-based gene transcripts is thought to be an ideal method for diagnosing PD. In this study, the objective is to identify the potential diagnostic biomarkers of PD by analyzing microarray gene expression data of samples from PD patients. A computational approach, namely, Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct co-expression gene networks and identify the key modules that were highly correlated with PD from the GSE99039 dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to identify the hub genes in the key modules with strong association with PD. The selected hub genes were then used to construct a diagnostic model based on logistic regression analysis, and the Receiver Operating Characteristic (ROC) curves were used to evaluate the efficacy of the model using the GSE99039 dataset. Finally, Reverse Transcription-Polymerase Chain Reaction (RT-PCR) was used to validate the hub genes. WGCNA identified two key modules associated with inflammation and immune response. Seven hub genes, LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3 were identified from the two modules and used to construct diagnostic models. ROC analysis showed that the diagnostic model had a good diagnostic performance for PD in the training and testing datasets. Results of the RT-PCR experiments showed that there were significant differences in the mRNA expression of LILRB1, LSP1, and MBOAT7 among the seven hub genes. The 7-gene panel (LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3) will serve as a potential diagnostic signature for PD.

Penalized logistic regression with prior information for microarray gene expression classification.

Cancer classification and gene selection are important applications in DNA microarray gene expression data analysis. Since DNA microarray data suffers from the high-dimensionality problem, automatic gene selection methods are used to enhance the classification performance of expert classifier systems. In this paper, a new penalized logistic regression method that performs simultaneous gene coefficient estimation and variable selection in DNA microarray data is discussed. The method employs prior information about the gene coefficients to improve the classification accuracy of the underlying model. The coordinate descent algorithm with screening rules is given to obtain the gene coefficient estimates of the proposed method efficiently. The performance of the method is examined on five high-dimensional cancer classification datasets using the area under the curve, the number of selected genes, misclassification rate and F-score measures. The real data analysis results indicate that the proposed method achieves a good cancer classification performance with a small misclassification rate, large area under the curve and F-score by trading off some sparsity level of the underlying model. Hence, the proposed method can be seen as a reliable penalized logistic regression method in the scope of high-dimensional cancer classification.

A hybrid feature selection approach for Microarray datasets using graph theoretic-based method

The feature selection process plays an important role in different fields, particularly in bioinformatics and microarray gene expression data analysis, for choosing discriminative genes from high-dimensional datasets and selecting a subset of highly relevant features with low redundancy that may lead to build improved prediction models. Consequently, this study proposes a new feature selection method that integrates Preordonnances theory in terms of new Relevance and Complementarity criteria introduced here and also connectivity in undirected Weighted Graphs (PCRWG). The method can handle high-dimensional data. PCRWG retains the relevant and complementary features to select effective features in large scale gene datasets. The proposed algorithm operates in two phases: filtering and wrapping. The strength of the first phase is that it is preceded by a step that further reduces the number of predictors by removing those in disagreement with the target based on the new proposed relevance criterion. Then, the proposed heuristic uses the relevance-complementarity ratio between preordonnances to automatically update the compromise rule between relevance and complementarity. In the wrapping phase, the suggested graph-based approach using maximal clique is based on a powerful relevance-complementarity matrix to consolidate edges, two connected interdependent features are complementary to each other, and it is possible to have high discriminative power when they serve as a group. We highlight the fact that existing graph-based feature selection algorithms do not consider relevance and complementarity simultaneously. The experiments were carried out on three simulated scenarios and the thirteen most popular cancer microarray gene datasets. Formally, they are eight binary and five multi-class microarray data. A 10-fold cross validation was used to evaluate the Support Vector Machine (SVM), Naive Bayes (NB) and artificial Neural Network (NN) classifiers. The empirical results demonstrate the high performance of the proposed hybrid approach when compared to the most recently published articles.

Molecular mechanism of the effect of gegen qinlian decoction on type 2 diabetes mellitus based on network pharmacology and molecular docking

Gegen qinlian decoction (GGQLD) is a Traditional Chinese Medicine formula comprising four herbal medicines. It is commonly used to treat bacterial dysentery in the past. Modern research has repositioned this decoction and applied it to treat type 2 diabetes mellitus (T2DM) and has achieved good results. However, the molecular mechanism of GGQLD in treating T2DM is still not fully known. Hence, this study was designed to explore the effective components and potential mechanism of GGQLD in the treatment of T2DM by using network pharmacology and molecular docking technology. The herb-compound-target interaction network was constructed by retrieving and screening of active components in different herbs in GGQLD and corresponding T2DM-related target genes across multiple databases. Subsequently, STRING and Cytoscape were applied to analyze and construct PPI network. Then, GO and KEGG pathway enrichment analysis were performed to investigate biological processes and pathways involved in GGQLD. The differentially expressed analysis was used to verify whether the expression of key target genes in T2DM and non-T2DM samples was statistically significant. Finally, the binding capacity between active components and key targets was validated by molecular docking using AutoDock4. The results shows that there are 60 active components involved in 100 T2DM-related targets that are identified in GGQLD formula. PPI networks were constructed and 10 key targets (IL1B, EGFR, VEGFA, IL6, JUN, TP53, MAPK3, AKT1, CASP3 and TNF) were obtained after topological analysis. Further, GO and KEGG analysis showed that GGQLD may play an important role in treating T2DM and its complications by synergistically regulating many biological processes and pathways involved in inflammatory response, hypoxia reaction, angiogenesis, cell growth, apoptosis, senescence, and biological processes mediated by PI3K-AKT, HIF-1, EGFR signaling pathways. Gene expression microarray data analysis showed that the expression of IL1B, IL6, EGFR, TP53, VEGFA, and JUN in T2DM samples were significantly different from those in normal controls. 4 high-affinity active components of GGQLD binding with the 10 key targets, including kaempferol, quercetin, naringenin and beta-sitosterol have been identified by molecular docking. Furthermore, the molecular dynamics simulation analysis by GROMACS showed the stability of the compound-protein binding complex. Our research preliminarily revealed the material basis and relevant mechanisms of GGQLD in treating T2DM and its complications. The prediction results might facilitate the development of GGQLD or its active compounds as alternative therapy for T2DM, and provide support for further study and experimental validation.

The dysregulation of microarray gene expression in cervical cancer is associated with overexpression of a unique messenger RNA signature.

Background and Objectives:Human papillomavirus (HPV) is the fourth most common cause of cervical cancer (CC). The aim of the present study was to investigate gene expression levels of previously identified transcriptional signatures for malignant and non-malignant CC.Materials and Methods:To validate of previously analyzed microarray gene expression data, we selected two hub genes (CDK1 and PLK1) and four differentially expressed mRNAs that were common in pre-malignant-normal and malignant-pre-malignant networks (SMS, NNT, UHMK1 and DEPDC1). To this purpose, the study included women diagnosed histologically with malignant CC (n=15), pre-malignant (n=15), and normal subjects (n=15). The expression of six host genes and viral E6/E7 genes were measured by quantitative Real-Time PCR.Results:The results showed higher expression of CDK1/PLK1 hub genes and SMS, NNT and UHMK1 genes in malignant CC group than non-malignant CC group and normal group. A positive correlation was observed between gene expression of viral E6/E7 oncogenes and UHMK1 gene.Conclusion:Dysregulation of several mRNA signatures are a common feature of CC and can be potentially used as diagnostic and prognostic biomarkers as well as can be applied to therapeutic targets for CC treatment.

Analysis of Micro Array Gene Expression Data Using Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSO-FWC) Technique

In the rapidly advancing field of genomics, microarray technologies have turned into a ground-breaking system on simultaneous monitoring the expression patterns of multiple genes under various arrangements of constraints. A fundamental errand is to propose diagnostic techniques to distinguish cluster of genes comparative expression designs and are initiated by comparative conditions. And furthermore, the relating investigation has issue is to cluster multi-condition gene expression data. To overcome these issues, the vast measure of data obtained by this technology, resort to clustering methods that distinguish clusters of genes of share similar expression profiles. The motivation of this work is to introduce a clustering method in microarray gene expression data analysis. Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSOFWC) algorithm is proposed to analyze gene expression data. In high dimensionality, a quick heuristic based pre-processing technique is employed to diminish some of the basic domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is implemented in MATLAB tool used for finding further feature subsets. The investigative are directed to distinguish the execution of the proposed work with existing clustering approaches.

A systems biology approach to identifying genetic factors affected by aging, lifestyle factors, and type 2 diabetes that influences Parkinson's disease progression

The underlying causes of Parkinson's disease (PD) remain unclear, making it difficult to determine how particular aspects of a patient's health may affect PD development and their treatment response. One alternative approach of investigating interactions between PD and co-morbidities (or other health factors) is to compare the dysregulated cellular or gene pathways they share with affected tissues. To achieve this, we developed a quantitative framework to reveal significant interactions between PD and other significant health factors that may be impacted. In this study, we analyzed gene expression microarray data from tissues affected by PD, type II diabetes (T2D), aging (AG), sedentary lifestyle (SL), high dietary fat (HFD), hypercholesterolemia (HC), high body fat (HBF), high red meat diet (RMD), high alcohol consumption (AC), smoking (SM) and control datasets. We have developed genetic associations of these factors with PD, based on the neighborhood-based benchmarking and multilayer network topology. We identified 1323 differentially expressed genes (DEGs) in the PD patients compared to healthy controls, 779 genes with down-regulated expression and 544 genes up-regulated. 69 dysregulated genes were common to PD and AC datasets; PD datasets also shared, respectively, 51, 45, 43 and 42 DEGs with the T2D, AG, HFD and HBF datasets. Ontological and pathway analyses identified significant gene ontology and molecular pathways with the potential to enhance our understanding of the fundamental molecular factors associated with PD progression. Moreover, we employed a validation gene expression dataset along with Mendelian Inheritance in Man (OMIM) and dbGaP as gold benchmark databases to validate the identified PD associated genes and molecular pathways. Our formulated methodologies used a network-based approach and identified a number of significant genes and pathways that may particularly affect PD, signifying how PD incidence and development may be influenced by the risk factors that may indicate avenues to new therapeutic approaches.

Systems biology and bioinformatics approach to identify gene signatures, pathways and therapeutic targets of Alzheimer's disease

Abstract Alzheimer's disease (AD) develops relentlessly in affected individuals and its occurrence is increasing. A clinical test to diagnose early-stage AD could be an important means of enabling interventions to slow its progression. However, available neuroimaging and cerebrospinal fluid-based diagnoses are very costly. Therefore, detecting AD from blood transcripts that mirror the expression of brain transcripts in the AD could improve the diagnosis. To achieve this goal, we employed a transcriptional analysis of affected tissues and integrated them with cis-eQTL data. In this study, we analyzed microarray gene expression data of brain and blood cells from AD patients and control individuals. Differentially expressed genes (DEGs) common to both brain tissue and blood cells were identified. Potential common genes and molecular pathways were identified using overlapping DEGs through the pathway and gene ontology enrichment analysis. We identified 18 significantly dysregulated genes shared by both brain and blood cells in AD affected individuals. We validated these candidates as disease-associated genes using gold-standard benchmarking databases (gene SNP-disease linkage). Significant molecular pathway and gene ontology indicating AD progression were identified. This study also identified regulatory factors, including transcription factors (TFs), microRNAs and candidate drugs. In sum, we identified new putative links between pathological processes in brain tissue and blood cells in AD that may allow assessment of AD status using blood samples. Thus, our formulated methodologies demonstrate the power of gene and gene expression analysis for brain-related pathologies transcriptomics, cis-eQTL, and epigenetics data from brain and blood cells.

Identifying the Most Appropriate Pattern for Identification of Gene Expression Changes in Ovarian Cancer Using Microarray

Background: Microarray technology is an accurate method for recognition of disease association gene alterations. However, there still is not an effective approach for the evaluation of gene expression in ovarian cancer. Objectives: A reliable approach is described to identify genes associated with ovarian cancer. Methods: Microarray gene expression data analysis was applied to correct systematic differences through four different normalization methods; LOESS, 3D LOESS, and neural network (NN3, NN4). Then, three different clustering methods of K-means, fuzzy C-means, and hierarchical methods were examined on corrected gene expression values. The proposed approach was tested on a reliable source of genes’ information, where the entropy of genes in samples and Euclidean distance were used for gene selection. Results: Our findings revealed that a neural-network-based normalization method could better control the effects of non-biological variations from microarray data. Moreover, the hierarchical clustering was more effective compared to other methods, and resulted in the identification of three genes, including BC029410, DUSP2, and ILDR1, as candidates for disease-association genes. Conclusions: According to the finding of the present study, hierarchical clustering with nonlinear-based normalization could have the ability to prioritize genes for ovarian cancer.

Performance Improvement of Gene Selection Methods using Outlier Modification Rule

Background: DNA microarray technology allows researchers to measure the expression levels of thousands of genes simultaneously. The main objective of microarray gene expression (GE) data analysis is to detect biomarker genes that are Differentially Expressed (DE) between two or more experimental groups/conditions. Objective: There are some popular statistical methods in the literature for the selection of biomarker genes. However, most of them often produce misleading results in presence of outliers. Therefore, in this study, we introduce a robust approach to overcome the problems of classical methods. Methods: We use median and median absolute deviation (MAD) for our robust procedure. In this procedure, a gene was considered as outlying gene if at least one of the expressions of this gene does not belong to a certain interval of the proposed outlier detection rule. Otherwise, this gene was considered as a non-outlying gene. Results: We investigate the performance of the proposed method in a comparison of the traditional method using both simulated and real gene expression data analysis. From a real colon cancer gene expression data analysis, the proposed method detected an additional fourteen (14) DE genes that were not detected by the traditional methods. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, we observed that these additional 14 DE genes are involved in three important metabolic pathways of cancer disease. The proposed method also detected nine (9) additional DE genes from another head-and-neck cancer gene expression data analysis; those involved in top ten metabolic pathways obtain from the KEGG pathway database. Conclusion: The simulation as well as real cancer gene expression datasets results show better performance with our proposed procedure. Therefore, the additional genes detected by the proposed procedure require further wet lab validation.

Ambient particulate matter attenuates Sirtuin1 and augments SREBP1-PIR axis to induce human pulmonary fibroblast inflammation: molecular mechanism of microenvironment associated with COPD.

Evidences have shown a strong link between particulate matter (PM) and increased risk in human mortality and morbidity, including asthma, chronic obstructive pulmonary disease (COPD), respiratory infection, and lung cancer. However, the underlying toxicologic mechanisms remain largely unknown. Utilizing PM-treated human pulmonary fibroblasts (HPF) models, we analyzed gene expression microarray data and Ingenuity Pathway Analysis (IPA) to identify that the transcription factor sterol regulatory element-binding protein 1 (SREBP1) was the main downstream regulator of Sirtuin1 (SIRT1). Quantitative PCR and western blot results showed that SIRT1 inhibited SREBP1 and further downregulated Pirin (PIR) and Nod-like receptor protein 3 (NLRP3) inflammasome after PM exposure. Inhibitors of SIRT1, SREBP1, and PIR could reverse PM-induced inflammation. An in silico analysis revealed that PIR correlated with smoke exposure and early COPD. Immunohistochemical analysis of tissue microarrays from PM-fed mouse models was used to determine the association of PIR with PM. These data demonstrate that the SIRT1-SREBP1-PIR/ NLRP3 inflammasome axis may be associated with PM-induced adverse health issues. SIRT1 functions as a protector from PM exposure, whereas PIR acts as a predictor of PM-induced pulmonary disease. The SIRT1-SREBP1-PIR/ NLRP3 inflammasome axis may present several potential therapeutic targets for PM-related adverse health events.

PF399 GENE EXPRESSION PROFILING OF CD26+ LEUKEMIC STEM CELL POPULATION FROM CML PATIENTS

Background:Nowadays, chronic myeloid leukemia (CML) has become a well manageable disease and majority of the patients achieve remission on tyrosine kinase inhibitor (TKI) treatment. However, the disease usually shows a low‐level persistence during therapy that arises from putative leukemic stem cells (LSC), which are, despite BCR‐ABL1 positivity, resistant to TKI treatment. LSC can be identified and isolated based on a surface expression of specific surface markers of which CD26 is perhaps the best described with high positive correlation with the occurrence of BCR‐ABL1.Aims:Our aim was to compare gene expression (GE) profiles of CD26+ LSC, CD26‐ HSC and CD38+ progenitor cells (PC) from CML patients, and identify potential novel CML LSC markers or disrupted pathways.Methods:Bone marrow (BM) cells were obtained from 18 CML patients and stained for the following surface markers – CD45, CD34, CD38 and CD26. Cells were FACS sorted into 3 populations – LSC (CD34+CD38‐CD26+), HSC (CD34+CD38‐CD26‐) and PC (CD34+CD38+). cDNA samples from 5 patients were pre‐amplified with Ovation Pico WTA System V2 whole transcriptome amplification kit (NuGEN) and analysed on SurePrint G3 human GE 8x60k microarray (Agilent). Gene expression data validation was performed on the same cell populations collected from a novel extended cohort of 13 CML patients on Wafergen SmartChip real‐time PCR system.Results:Analysis of GE microarray data identified over 272 differentially expressed genes (160 upregulated and 112 downregulated) between CD26‐ HSC and CD26+ LSC, and over 1330 differentially expressed genes (531 upregulated and 799 downregulated) between CD26‐ HSC and CD38+ progenitors. (P ≤ 0.05; fold change ≥ 1). The subset of the most deregulated genes between populations was validated on an extended cohort of different CML patients using SmartChip real‐time PCR, showing high correlation between both systems (R2 LSC = 0.808; R2 PC = 0.913) and supporting the results of GE microarray analysis. Validated candidate genes (n = 15), consisting of those involved in important pathways (e.g. IL1, mTOR signalling ‐ TAB2, PELI2, MYCBP2) or associated with oncogenic transformation (e.g. MYB, RAD51), will be further studied in functional assays. Of highest interest are the top deregulated genes histidine decarboxylase (HDC) and immunoglobulin J chain (IGJ), whose expression was previously reported deregulated in BCR‐ABL1 positive cells or CML stem cells. Both HDC and IGJ exhibited opposite expression pattern in CD26+ LSC and CD38+ PC subpopulations compared to CD26‐ HSC (FCHDC = −2.8 vs 4.3; FC IGJ = 2.8 vs −2.8; FC ‐ fold diference in expression). Further, we identified CD69 (early lymphocyte activation antigen) as a gene specifically upregulated in CD26 + LSC, whose expression was also shown to be elevated in cell lines expressing BCR‐ABL1. Currently, we are analysing its potential as a specific surface marker.Summary/Conclusion:In summary, we identified consistent changes of gene expression in CD26+ LSC as well as CD38+ progenitor populations, with only 3% gene overlap showing specificity of these patterns for selected populations. These results were successfully verified using high‐throughput real‐time PCR. The involvement of identified candidate genes and pathways, which could provide important insights into the CML stem cell biology, are currently evaluated.Supported by MUNI/A/1105/2018, by Ministry of Health of the Czech Republic, grant nr. 17–30397A, and by institutional support of Faculty of Medicine, MU.

Genetic effects of welding fumes on the development of respiratory system diseases

BackgroundThe welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WF exposure may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood. MethodsWe developed a quantitative framework to identify gene expression effects of WF exposure that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (diseasome) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology. ResultsWe observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEG analysis, using disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC. ConclusionsOur network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure.

The influence of depression on ovarian cancer: Discovering molecular pathways that identify novel biomarkers and therapeutic targets

Abstract Depressive illness is a significant risk factor for ovarian cancer development (OC). The underlying mechanism is unclear (perhaps involving altered neuroendocrine factors), but identifying associated alterations in gene expression of OC in depression sufferers may uncover novel factors that affect OC progression. We thus analyzed microarray gene expression data from OC tissue taken from patients diagnosed with and without depression. We identified 34 differentially expressed genes (DEGs) of depression from OC patients. Gene ontology (GO) and KEGG pathway analyses indicated several molecular pathways including complement and coagulation cascades, the hippo signaling pathway, ether lipid metabolism, the MAPK signaling pathway, and antigen processing and presentation were overrepresented among DEGs. Subsequent, protein-protein interaction (PPI) analysis revealed pathway hub proteins (FOS, EGR1, JUNB, HSPA1B, FGFR3, TRIB1, CTSB, SERPINE1), regulatory transcription factors (TFs; FOS, EGR1, JUNB) and one miRNA, miR-101-5p from TFs-miRNAs coregulatory networks. The prognostic survival analysis of the DEGs revealed CXCL12, ARL4C, NQO2 associated with worse OC survival outcomes. Protein-metabolite interaction network analysis showed that upregulated protein CHPT1 (a choline phosphotransferase) interacts with four important phospholipid synthesis and signaling metabolites. Protein-drug interaction analysis revealed SERPINE1 and PLAT proteins interact with compounds that include thrombolytic drugs, plasmin inhibitors, thiazolidinediones, NSAIDs, and hypertension treatments. Thus, we identified evidence for factors and pathways altered in OC tissue when patient depression is evident, and characterized important aspects of these OC-depression links that may be instrumental in developing new treatments for OC patients.

Genetic effects of welding fumes on the progression of neurodegenerative diseases

BackgroundWelding involves exposure to fumes, gases and radiant energy that can be hazardous to human health. Welding fumes (WFs) comprise a complex mixture of metallic oxides, silicates and fluorides that may result in different health effects. Inhalation of WFs in large quantities over a long periods may pose a risk of developing neurodegenerative diseases (NDGDs), but the nature of this risk is poorly understood. To address this we performed transcriptomic analysis to identify links between WF exposure and NDGDs. MethodsWe developed quantitative frameworks to identify the gene expression relationships of WF exposure and NDGDs. We analyzed gene expression microarray data from fume-exposed tissues and NDGDs including Parkinson's disease (PD), Alzheimer's disease (AD), Lou Gehrig's disease (LGD), Epilepsy disease (ED) and multiple sclerosis disease (MSD) datasets. We constructed disease–gene relationship networks and identified dysregulated pathways, ontological pathways and protein–protein interaction sub-network using multilayer network topology and neighborhood-based benchmarking. ResultsWe observed that WF associated genes share 18, 16, 13, 19 and 19 differentially expressed genes with PD, AD, LGD, ED and MSD respectively. Gene expression dysregulation along with relationship networks, pathways and ontologic analysis indicate that WFs may be linked to the progression of these NDGDs. ConclusionsOur developed network-based approach to analysis and investigate the genetic effects of welding fumes on PD, AD, LGD, ED and MSD neurodegenerative diseases could be helpful to understand the causal influences of WF exposure for the progression of the NDGDs.

Targeting BCL-2, BCL-XL, BCL-W and MDM2 in B-Cell Acute Lymphoblastic Leukemia Is Highly Effective Ex Vivo

Despite the advances in the treatment of acute lymphoblastic leukemia (ALL), a major fraction of adult patients still succumb to leukemia- or treatment-related events. In particular, the outcome of elderly ALL patients remains dismal. Our aim was to discover new or repurposed drugs for B-cell ALL in a clinically relevant ex vivo drug sensitivity testing platform.We analyzed 19 primary B-ALL samples using a well-established drug sensitivity and resistance testing platform and a drug panel including 65 drugs in five different concentrations. The main drug classes were glucocorticoids, MDM2 antagonists, and inhibitors of BCR-ABL1, VEGFR, BCL-2, BCL-XL, BET, MEK, JAK, Aurora kinase, PI3K, MTOR, IGF1R, ERK, STAT3, STAT5, HSP90 and NAMPT proteins. The samples were viably frozen bone marrow (BM) mononuclear cells collected at diagnosis. The cohort included both Philadelphia-positive (Ph+) (n=10) and Ph-negative (Ph-) (n=9) patients with a median age of 43 years (range 22-68). Cell viability (CellTiter-Glo) was measured after plating and after a three-day incubation with the drugs. A drug sensitivity score (DSS) was calculated from the viability readouts, which takes into account the area under the dose response curve, measuring both drug efficacy and potency. DSS values >10 are considered effective and >20 highly effective.As an overall view of drug sensitivity, a heatmap and dendrograms from DSS values are shown in Figure 1A. As expected, most patients were sensitive to glucocorticoids and tyrosine kinase inhibitors (TKIs) showed efficacy in Ph+ ALL. In addition, two Ph-negative patients were sensitive to TKIs, suggesting a Philadelphia-like disease. Drugs that showed pan-ALL efficacy included BCL-2 family inhibitors, idasanutlin (MDM2 inhibitor), luminespib (HSP90 inhibitor), daporinad (NMPRT inhibitor) and plicamycin (antineoplastic antibiotic). For the other drugs, only individual patients showed sensitivity, in line with the diverse molecular background of ALL.Strikingly, 17/19 (89%) of patients in our cohort were highly sensitive (DSS>20) to navitoclax (a BCL-2, BCL-XL and BCL-W inhibitor), whereas the BCL-2-specific inhibitor venetoclax was effective only in a distinct subset of patients (Figure 1B). 6/19 (32%) of patients were highly sensitive (DSS>20) to venetoclax and represented all risk classes based on age, white blood cell counts and karyotype, but interestingly, all were Ph-negative. Overall, response to venetoclax correlated with response to navitoclax (Spearman, r=0.85; P<0.0001).To examine differential gene expression of anti-apoptotic proteins between Ph+ and Ph- patients, we analyzed microarray gene expression data from ArrayExpress public database (www.ebi.ac.uk/arrayexpress, E-MTAB-5035). The analyzed cohort included 96 Ph- and 41 Ph+ adult B-ALL patients. Ph-negative samples were characterized with higher BCL-2 expression, whereas Ph-positive samples showed higher BCL-W expression and a trend to higher BCL-XL expression (Figure 1C). Thus, lack of venetoclax efficacy ex vivo in Ph-positive ALL indicated dependence on BCL-W and BCL-XL, as also reflected in the gene expression analyses.Inhibitors of BCL-2, such as navitoclax and venetoclax, potently induce apoptosis in a variety of cancer cells. Both inhibitors showed promising efficacy in our B-ALL samples. Dose-limiting thrombocytopenia has limited the use of navitoclax in solid tumors. However, in our assay navitoclax showed more uniform potency, particularly in Ph+ samples suggesting a rational combination with tyrosine kinase inhibitors. Similar to conventional cytotoxic agents used in ALL, a therapeutic window may exist for safe use of navitoclax in acute leukemia. In conclusion, targeting the multidomain anti-apoptotic proteins (BCL-2, BCL-XL, BCL-W, MCL-1) and TP53 with MDM2, possibly in combination, is a promising strategy for improving outcome of adult B-ALL. [Display omitted] DisclosuresHohtari:Incyte: Research Funding. Heckman:Novartis: Research Funding; Celgene: Research Funding; Orion Pharma: Research Funding. Wennerberg:Novartis: Research Funding. Mustjoki:Ariad: Research Funding; Pfizer: Honoraria, Research Funding; Celgene: Honoraria; Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding. Porkka:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.

A High-Performance Genome-Wide Association Study Algorithm based on Analysis of Pairs of Individuals

An extremely simple and high-performance genome-wide association study (GWAS) algorithm for estimating the main and epistatic effects of markers or single nucleotide polymorphisms (SNPs) is proposed. The main idea underlying the algorithm is based on comparison of genotypes of pairs of individuals and comparison of the corresponding phenotype values. It is used the intuitive assumption that changes of alleles corresponding to important SNPs in a pair of individuals lead to a large difference of phenotype values of these individuals. In other words, the algorithm is based on considering pairs of individuals instead of SNPs or pairs of SNPs. The main advantage of the algorithm is that it weakly depends on the number of SNPs in a genotype matrix. It mainly depends on the number of individuals, which is typically very small in comparison with the number of SNPs. Another important advantage of the algorithm is that it can detect the epistatic effect viewed as gene-gene interaction without additional computations. The algorithm can also be used when the phenotype takes only two values (the case-control study). Moreover, it can be simply extended from the analysis of binary genotype matrices to the microarray gene expression data analysis. Numerical experiments with real data sets consisting of populations of double haploid lines of barley illustrate the outperformance of the proposed algorithm in comparison with standard GWAS algorithms from the computation point of view especially for detecting the gene-gene interactions. The ways for improving the proposed algorithm are discussed in the paper.

Elephant search optimization combined with deep neural network for microarray data analysis

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, Elephant search (ESA) based optimization is proposed to select best gene expressions from the large volume of microarray data. Firefly search (FFS) is also used to understand the effectiveness of the Elephant search method in feature selection process. Stochastic gradient descent based Deep Neural Network as Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on ten most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning approach are compared with the most recent published article for its suitability in future Bioinformatics research. Finally, Statistical significance test by one-way ANOVA with post hoc Tukey’s test is conducted to deduce a number of insights on the selection of the best classification model.

The prognostic value of a seven-microRNA classifier as a novel biomarker for the prediction and detection of recurrence in glioma patients.

Glioma is often diagnosed at a later stage, and the high risk of recurrence remains a major challenge. We hypothesized that the microRNA expression profile may serve as a biomarker for the prognosis and prediction of glioblastoma recurrence. We defined microRNAs that were associated with good and poor prognosis in 300 specimens of glioblastoma from the Cancer Genome Atlas. By analyzing microarray gene expression data and clinical information from three random groups, we identified 7 microRNAs that have prognostic and prognostic accuracy: microRNA-124a, microRNA-129, microRNA-139, microRNA-15b, microRNA-21, microRNA-218 and microRNA-7. The differential expression of these miRNAs was verified using an independent set of glioma samples from the Affiliated People's Hospital of Jiangsu University. We used the log-rank test and the Kaplan-Meier method to estimate correlations between the miRNA signature and disease-free survival/overall survival. Using the LASSO model, we observed a uniform significant difference in disease-free survival and overall survival between patients with high-risk and low-risk miRNA signature scores. Furthermore, the prognostic capability of the seven-miRNA signature was demonstrated by receiver operator characteristic curve analysis. A Circos plot was generated to examine the network of genes and pathways predicted to be targeted by the seven-miRNA signature. The seven-miRNA-based classifier should be useful in the stratification and individualized management of patients with glioma.