Analysis Of Microarray Expression Data Research Articles

Advanced network pharmacology and molecular docking-based mechanism study to explore the multi-target pharmacological mechanism of Cymbopogon citratus against Alzheimer's disease

BackgroundAlzheimer's disease (AD) is the most common type of dementia, accounting for at least two-thirds of cases of dementia in people age 65 and older. The limited amount of available data and multiple spectra of pathophysiological mechanisms of AD make it a challenging task and a serious economic load on the community health sector. Cymbopogon citratus is a tall perennial fast-growing grass with tuft of lemon-scented leaves from the annulate and sparingly branched rhizomes. A list of studies evidences the therapeutic efficacy of C. citratus against AD, but the precise molecular mechanism is yet to be discovered. Aim of the studyThis research utilizes network pharmacology and molecular docking approaches to elucidate the multi-target effects of C. Citratus on AD, identifying its active compounds, potential therapeutic targets, and associated signaling pathways. Materials and methodsInitially the active compounds of C. citratus, the target genes related to both the C. citratus and the AD were retrieved from literature as well as databases. The PPI network of these target genes was constructed using STRING database and later the hub genes were screened by importing the network into the Cytoscape. The interactions among the target genes and the compounds were also analyzed using Cytoscape. The Gene Ontology (GO) and KEGG pathways of these hub genes were explored to reveal the genes involved in AD-related pathways. These compound-target, genes-pathway networks were explored which uncovered that bioactive compounds of C. citratus may serve as a magic bullet against AD by influencing the target genes involved in the disease pathogenesis. Further, the microarray data was analyzed to explore the expression level of core target genes. Lastly, docking analysis further strengthened the current findings by validating the effective activity of the bioactive compounds against putative target genes. ResultsNetwork pharmacology based analysis highlighted the pathways related to AD, the ten target genes, and the compounds involved in the AD disease mechanism. Through the analysis of microarray expression data, the two genes SRC and IL6 were found to have differential expression. Moreover, the molecular docking analysis predicted the string binding affinity among these genes and active compounds. ConclusionIn summary, our study proposed that five key compounds catechin, apigenin, 8-prenylnaringenin, quercetin, and luteolin contributed to the development of AD by affecting IL6 and SRC genes. The overall integration of network pharmacology with molecular docking unveiled the multi-target pharmacological mechanisms of C. citratus against AD. This study provides convincing evidence that C. citratus might partially alleviate the AD and ultimately lays a foundation for further experimental research on the anti-AD activity of C. citratus.

Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

Gene Selection (GS) is a strategy method targeted at reducing redundancy, limited expressiveness, and low informativeness in gene expression datasets obtained by DNA Microarray technology. These datasets contain a plethora of diverse and high-dimensional samples and genes, with a significant discrepancy in the number of samples and genes present. The complexities of GS are especially noticeable in the context of microarray expression data analysis, owing to the inherent data imbalance. The main goal of this study is to offer a simplified and computationally effective approach to dealing with the conundrum of attribute selection in microarray gene expression data. We use the Black Widow Optimization algorithm (BWO) in the context of GS to achieve this, using two unique methodologies: the unaltered BWO variation and the hybridized BWO variant combined with the Iterated Greedy algorithm (BWO-IG). By improving the local search capabilities of BWO, this hybridization attempts to promote more efficient gene selection. A series of tests was carried out using nine benchmark datasets that were obtained from the gene expression data repository in the pursuit of empirical validation. The results of these tests conclusively show that the BWO-IG technique performs better than the traditional BWO algorithm. Notably, the hybridized BWO-IG technique excels in the efficiency of local searches, making it easier to identify relevant genes and producing findings with higher levels of reliability in terms of accuracy and the degree of gene pruning. Additionally, a comparison analysis is done against five modern wrapper Feature Selection (FS) methodologies, namely BIMFOHHO, BMFO, BHHO, BCS, and BBA, in order to put the suggested BWO-IG method's effectiveness into context. The comparison that follows highlights BWO-IG's obvious superiority in reducing the number of selected genes while also obtaining remarkably high classification accuracy. The key findings were an average classification accuracy of 94.426, average fitness values of 0.061, and an average number of selected genes of 2933.767.

A comprehensive survey on computational learning methods for analysis of gene expression data.

Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

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.

A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining.

The advancement in technology for various scientific experiments and the amount of raw data produced from that is enormous, thus giving rise to various subsets of biologists working with genome, proteome, transcriptome, expression, pathway, and so on. This has led to exponential growth in scientific literature which is becoming beyond the means of manual curation and annotation for extracting information of importance. Microarray data are expression data, analysis of which results in a set of up/downregulated lists of genes that are functionally annotated to ascertain the biological meaning of genes. These genes are represented as vocabularies and/or Gene Ontology terms when associated with pathway enrichment analysis need relational and conceptual understanding to a disease. The chapter deals with a hybrid approach we designed for identifying novel drug-disease targets. Microarray data for muscular dystrophy is explored here as an example and text mining approaches are utilized with an aim to identify promisingly novel drug targets. Our main objective is to give a basic overview from a biologist's perspective for whom text mining approaches of data mining and information retrieval is fairly a new concept. The chapter aims to bridge the gap between biologist and computational text miners and bring about unison for a more informative research in a fast and time efficient manner.

Constitutive Activation of Ras and Inactivation of Ink4a/Arf Allele Induce a Fully Penetrant B-Cell Acute Lymphoblastic Leukemia Phenotype in Mice

Despite recent advances in treatment, precursor-B-cell acute lymphoblastic leukemia (B-ALL) remains a challenging clinical entity. Recent genome-wide studies have uncovered frequent genetic alterations involving activating RAS pathway mutations and loss of the INK4A/ARF locus suggesting their important role in the pathogenesis, relapse, and chemoresistance of B-ALL. Therefore, to better understand the oncogenic mechanisms by which these alterations might promote B-ALL and to develop an in vivo preclinical model of relapsed B-ALL, we engineered mouse strains with induced somatic KrasG12D pathway activation and/or loss of Ink4a/Arf during early stages of B-cell development.Pathologic examination revealed significantly enlarged spleens and increased numbers of mononuclear cells in the spleens of 8-week-old KrasG12D mutated animals with or without Ink4a/Arf inactivation, but obvious histologic and IHC differences in spleens and lymph nodes between the mutant genotypes and CD19Cre/+ controls were not found. However, in agreement with the gross pathologic examination, an increase was noted by flow cytometry in splenic B220+ B-cells of mice with the KrasG12D mutation as compared with CD19Cre/+ and CD19Cre/+;Ink4a/ArfL/+ mice. In keeping with this, a significant increase in number of proliferating (Ki-67+) cells within spleen B-cell areas of CD19Cre/+;KrasG12D/+, and CD19Cre/+;KrasG12D/+;Ink4a/ArfL/+ mice were observed in comparison with CD19Cre/+ controls and CD19Cre/+;Ink4a/ArfL/+ mice. TUNEL staining revealed fewer apoptotic cells within germinal center B-cell areas in CD19Cre/+;KrasG12D/+, CD19Cre/+;Ink4a/ArfL/+, and CD19Cre/+;KrasG12D/+;Ink4a/ArfL/+ mice as compared with CD19Cre/+ controls. Next, to identify the biological processes underlying the observed phenotypical changes, we performed gene-set enrichment analysis of microarray expression data from mutant compared to control mice. In CD19+ B-cells from both CD19Cre/+;KrasG12D/+ and CD19Cre/+;KrasG12D/+;Ink4a/ArfL/+ mice, marked upregulation of gene signatures related to proliferation, Ras-related signaling pathways, apoptosis, rewired cellular metabolism, DNA repair, and pre-B stage of B-cell development were observed in both CD19Cre/+;KrasG12D/+ and CD19Cre/+;KrasG12D/+;Ink4a/ArfL/+ mice. Consistent with our previous observations, the proliferative signature in CD19+ B-cells from CD19Cre/+;Ink4a/ArfL/+ mice was less pronounced; instead, upregulation of several signaling pathways related to independence of external growth signals and retention of “stemness” was noted. Although constitutive activation of KrasG12D in B-cells induced prominent transcriptional changes that resulted in enhanced proliferation, it was insufficient by itself to induce development of a high-grade leukemia/lymphoma. Instead, in 40% of mice these engineered mutations promoted development of a clonal low-grade lymphoproliferative disorder resembling human extranodal marginal-zone lymphoma of mucosa-associated lymphoid tissue or lymphoplasmacytic lymphoma. Interestingly, loss of the Ink4a /Arf locus, apart from reducing the number of apoptotic B-cells broadly attenuated KrasG12D-induced transcriptional signatures. However, combinedKras activation and Ink4a/Arf inactivation cooperated functionally to induce a fully penetrant, highly aggressive B-ALL phenotype. Cross-species bioinformatic analyses consistently demonstrated that B-ALL tumors in the CD19Cre/+;KrasG12D/+;Ink4a/ArfL/+ mice recapitulated gene expression programs found in a poor-prognosis subgroups of human B-ALL that harbor BCR-ABL and CRLF2 rearrangements. Moreover, 90% of examined murine B-ALL tumors showed loss of the wild-type Ink4a/Arf locus without acquisition of common cooperating events, underscoring the role of Ink4a/Arf in restraining Kras-driven oncogenesis in the lymphoid compartment in our model.Taken together, the functional cooperation and the negative impact on survival of concurrent Ras mutation and Ink/Arf deletion in mice suggest that these changes may also have a functional impact in human B-ALL and should be considered in the course of disease classification, as well as for prognosis and choice of therapeutic strategy. DisclosuresSharpless:G1 Therapeutics: Equity Ownership; Pfizer: Other: NA; HealthSpan Diagnostics: Other: NA; Unity Biotechnology: Other: NA.

Gene networks and expression quantitative trait loci associated with adjuvant chemotherapy response in high-grade serous ovarian cancer

BackgroundA major impediment in the treatment of ovarian cancer is the relapse of chemotherapy-resistant tumors, which occurs in approximately 25% of patients. A better understanding of the biological mechanisms underlying chemotherapy resistance will improve treatment efficacy through genetic testing and novel therapies.MethodsUsing data from high-grade serous ovarian carcinoma (HGSOC) patients in the Cancer Genome Atlas (TCGA), we classified those who remained progression-free for 12 months following platinum-taxane combination chemotherapy as “chemo-sensitive” (N = 160) and those who had recurrence within 6 months as “chemo-resistant” (N = 110). Univariate and multivariate analysis of expression microarray data were used to identify differentially expressed genes and co-expression gene networks associated with chemotherapy response. Moreover, we integrated genomics data to determine expression quantitative trait loci (eQTL).ResultsDifferential expression of the Valosin-containing protein (VCP) gene and five co-expression gene networks were significantly associated with chemotherapy response in HGSOC. VCP and the most significant co-expression network module contribute to protein processing in the endoplasmic reticulum, which has been implicated in chemotherapy response. Both univariate and multivariate analysis findings were successfully replicated in an independent ovarian cancer cohort. Furthermore, we identified 192 cis-eQTLs associated with the expression of network genes and 4 cis-eQTLs associated with BRCA2 expression.ConclusionThis study implicates both known and novel genes as well as biological processes underlying response to platinum-taxane-based chemotherapy among HGSOC patients.

Amplification of transglutaminase 2 enhances tumor-promoting inflammation in gastric cancers

Tumor-promoting inflammation is a hallmark of cancer and is highly associated with tumor progression, angiogenesis, and metastasis. Tumor-associated macrophages (TAMs) are major drivers of tumor-promoting inflammation, but due to the complexity of the tumor microenvironment, the detailed regulatory mechanisms are still under investigation. Here, we investigated a novel role for transglutaminase 2 (TGM2) in the development of tumor-promoting inflammation and recruitment of TAMs to gastric cancer (GC) tissues. When estimated by array comparative genomic hybridization and droplet digital PCR, the copy numbers of the TGM2 gene were amplified in 13.6% (14/103) of GC patients and positively associated with TGM2 expression. Gene set enrichment analysis of expression microarray data for GC samples with high or low TGM2 expression showed that increased TGM2 expression was associated with tumor-promoting inflammation in GC. In addition, the expression of TGM2 was correlated with the expression of markers for macrophages, neutrophils, blood vessels, and lymphatic vessels. Overexpression of TGM2 in GC cells augmented the IL-1β-induced secretion of macrophage-recruiting chemokines and NF-κB activation. TGM2 protein levels were associated with the expression levels of the macrophage marker CD163 in human GC tissue samples. Moreover, GC patients with high expression of TGM2 had a worse prognosis than those with low expression of TGM2. These results suggest TGM2 as a novel regulator of the tumor microenvironment of GC and provide a promising target for constraining tumor-promoting inflammation.

Abstract P4-07-01: Tumor expression and microenvironment in HER2-positive breast cancer before and on HER2-targeted therapy: Analysis of microarray expression data from the TRIO-US B07 trial

Abstract Background: Neoadjuvant HER2-targeted therapy in combination with chemotherapy is a standard treatment approach for early-stage HER2-positive breast cancer. Proposed biomarkers to predict pathologic complete response (pCR), and thereby inform which patients may benefit from de-escalation of therapy, include expression-based subtyping and immune enrichment scores. Little is known about how tumors and their microenvironment may change with HER2-targeted therapy alone, and whether these changes may predict outcome. Methods: The TRIO-US B07 phase II trial randomized 128 participants with stage I-III HER2-positive breast cancer to trastuzumab (N=34), lapatinib (N=36), or the combination (N=58) for three weeks, followed by six cycles of docetaxel and carboplatin with continued HER2-targeted therapy. Fresh-frozen core biopsies of the tumor prior to therapy (N=110) and after 14-21 days of HER2-targeted therapy alone (N=89) were collected, and RNA was extracted and subjected to Agilent Whole Human Genome 44K 2-color chip. The pre-treatment tumor RNA was normalized against a mixed breast tumor reference, and the on-treatment tumor RNA against the matched pre-treatment sample. Absolute intrinsic molecular subtyping was used to determine intrinsic subtype, the iC10 expression-based classifier to determine integrative subtype, gene set enrichment analysis (GSEA) to assess signature changes across treatment, single-sample GSEA to compare individual gene signature scores between tumors, and CIBERSORT to quantify immune cell populations before and on treatment. Results: Primary trial results have been reported previously and showed a pCR rate of 47% with trastuzumab, 25% with lapatinib, and 52% with the combination. Prior to treatment, 56% of tumors classified as the HER2-enriched intrinsic subtype and 78% as the iC5 integrative subtype. HER2-enriched tumors trended toward a higher rate of pCR relative to other intrinsic subtypes (50% vs 33%, P=0.12), as did iC5 tumors relative to other integrative subtypes (48% vs 25%, P=0.08). However, in multivariate analysis, HER2 FISH ratio (P=0.04) and hormone receptor status (P=0.02), each associated themselves with intrinsic and integrative subtype, proved the most valuable in predicting pCR, with little information added by expression-based subtyping. Immune cell signatures correlated with pCR in the trastuzumab-containing arms only. Of 65 gene signatures tested, 47 changed across HER2-targeted therapy with false discovery rate < 0.1, driven by decreasing tumor proliferation, increasing immune cell signatures, and increasing stromal cell/epithelial mesenchymal transition signatures. Quantification of immune cell populations suggested the immune changes were both anti-tumor (CD8+ T cells) and pro-tumor (M2 macrophages). Intrinsic subtype changed in 54% of tumors (79% of these converting to normal-like) and integrative subtype changed in 26%. Change in subtype, proliferation, or immune infiltration with targeted therapy did not correlate with pCR. A higher proportion of tumors treated with trastuzumab alone maintained their proliferation (42%), compared with lapatinib alone (20%; P=0.16) or the combination (16%; P=0.04). Conclusions: In the TRIO-US B07 study, the biomarkers most predictive of response to neoadjuvant HER2-targeted therapy were hormone receptor status in combination with HER2 FISH ratio. Multiple changes in the tumor and its microenvironment occurred with HER2-targeted therapy, but these changes did not predict pCR. Tumors treated with lapatinib tended to decrease proliferation more than tumors treated with trastuzumab, despite trastuzumab being more effective in preventing recurrence, an observation with implications for window of opportunity studies. Citation Format: Jennifer L. Caswell-Jin, Katherine L. McNamara, Judy Dering, Hsiao-Wang Chen, Robert Dichmann, Alejandra Perez, Ravindranath Patel, Eran Kotler, Jason J. Zoeller, Joan S. Brugge, Michael F. Press, Dennis J. Slamon, Christina Curtis, Sara A. Hurvitz. Tumor expression and microenvironment in HER2-positive breast cancer before and on HER2-targeted therapy: Analysis of microarray expression data from the TRIO-US B07 trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-07-01.

Identification of potential biomarkers and pathways in ulcerative colitis with combined public mRNA and miRNA expression microarray data analysis.

Ulcerative colitis (UC) is a chronic, relapsing and non-specific inflammatory disease, involving various genes and pathways in their pathogenesis. Increasing evidences have showed that microRNAs (miRNAs) act as key post-transcriptional regulators of gene expression in UC. This current study aimed to identify key miRNAs, potential target genes, and relevant pathways involved in UC to uncover their underlying molecular mechanisms by using bioinformatics analysis. The mRNA and miRNA expression profiles were retrieved and downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by using the R software package. A total of 79 DEGs and 47 DEMIs were obtained. And a panel of miRNAs and their target mRNAs were identified. It showed that miR-1231 may be a key regulator for DUOX2 and TFF1. CCL11 may be potentially targeted by miR-625. MMP1 may play vital roles in the development of UC by regulating the miR-1228/PPAR signaling pathway. In addition, we validated the most significantly up/down-expressed miRNAs (miR-92b, miR-625) and two of their corresponding target mRNAs (AQP8 and TAGAP, CCL11 and CHI3L1) in colon tissues of UC models preliminarily. The results were consistent with the microarray analysis. These findings may provide new insights into representing key mechanisms associated with the development of UC.

Identification of potential biomarkers in cervical cancer with combined public mRNA and miRNA expression microarray data analysis.

Cervical cancer is the fourth most prevalent malignancy in females worldwide. Early diagnosis is key to improving survival rates. Molecular biomarkers are an important method for diagnosing a number of types of cancer, including cervical cancer. The present study utilized public data from three mRNA microarray datasets and one microRNA dataset to analyze the key genes involved in cervical cancer. The mRNA and microRNA expression profile datasets (GSE9750, GSE46857, GSE67522 and GSE30656) were downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) and microRNAs (DEMs) were screened using the online tool GEO2R. By using the DEGs consistent across the three mRNA datasets, a functional and pathway enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed and module analysis performed using the Search Tool for the Retrieval of Interacting Genes. Validated target genes of the DEMs were identified using the miRecords website. Using the identified target genes of the DEMs, a survival analysis was performed using the OncoLnc online tool. A total of 73 DEGs and 19 DEMs were screened from the microarray expression profile datasets. ‘Integrin-mediated’, ‘proteolysis’ and ‘phosphoinositide 3 kinase-protein kinase 3’ signaling pathways were the most enriched in the DEGs. Three of the DEGs, including Ras homolog family member B (RhoB), stathmin 1 (STMN1) and cyclin D1 (CCNB1) were validated DEM target genes. The OncoLnc survival analysis identified that RhoB was associated with a significantly longer overall survival, whereas STMN1 was associated with a significantly reduced overall survival time in patients with cervical cancer. Finally, data from The Cancer Genome Atlas revealed an association between the mRNA expression levels of RhoB and STMN1, and the overall survival time for patients with cervical cancer. In conclusion, RhoB and STMN1 were identified as key genes that may provide potential targets for cervical cancer diagnosis and treatment.

Leucine-rich glioma inactivated 3: Integrative analyses reveal its potential prognostic role in cancer.

Leucine-rich glioma inactivated 3 (LGI3) is a secreted protein in vertebrates, which belongs to the LGI family. In our previous study, LGI3 was found to be expressed in brain, adipose tissues and the skin, where it functions as a multifunctional cytokine. In the present study, we used bioinformatic tools to perform data mining, phylogenetics and prognostic association analysis to investigate the prognostic role of LGI3 in cancers. The sequences of LGI3 orthologues were analyzed from various species, and it was found that LGI3 was highly conserved in mammals and that the subsets of amino acid residues were phylogenetically coevolved in four major clusters. Single nucleotide polymorphisms (SNPs) of the human LGI3 gene included 228 functionally relevant variants (missense, nonsense and frameshift) in a total of 1,042 SNPs. Four missense SNPs had a global minor allele frequency ≥0.001. Somatic mutations in cancer with functional relevance were found in various types of cancer, including uterine, stomach and lung cancer. In addition, five amino acid residues with cancer mutations were shown to be coevolved in the vertebrate phylogeny, suggesting their importance in protein dysfunctions in cancer. One conserved amino acid and three SNPs were found to be mutated in stomach cancer and melanoma. Analysis of expression microarray data demonstrated that the expression of LGI3 was significantly associated with the prognosis of brain, colorectal and lung cancer. Taken together, these results suggested that the genetic variations and expression levels of LGI3 have potential value in cancer prognosis.

Leucine-rich glioma inactivated 3: integrative analyses support its prognostic role in glioma.

BackgroundLeucine-rich glioma inactivated 3 (LGI3) is a secreted protein member of LGI family. We previously reported that LGI3 was expressed in brain, adipose tissues and skin, where it played roles as a multifunctional cytokine. We postulated that LGI3 may be involved in cytokine network in cancers.AimThis study aimed to analyze differentially expressed genes in glioma tissues and glioma cohort data to investigate the prognostic role of LGI3 and its receptors.Materials and methodsExpression microarray data from Gene Expression Omnibus and glioma cohort data were analyzed using bioinformatic tools for statistical analysis, protein–protein interactions, functional enrichment and pathway analyses and prognostic association analysis.ResultsWe found that LGI3 and its receptors, ADAM22 and ADAM23, were significantly downregulated in glioma tissues. Eleven upregulated genes and two downregulated genes in glioma tissues were found to be the previously reported LGI3-regulated genes. Protein–protein interaction network analysis showed that 85% of the LGI3-regulated and glioma-altered genes formed a cluster of interaction network. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed the association of these genes with hypoxia responses, p53 and Akt signaling and various cancer-related pathways including glioma. Analysis of expression microarray data of glioma cohorts demonstrated that low expression levels of LGI3, ADAM22 and ADAM23 were significantly associated with poor prognosis of glioma.ConclusionThese results propose that LGI3 and its receptors may play a prognostic role in glioma.

Frontotemporal dementia: insights into the biological underpinnings of disease through gene co-expression network analysis.

BackgroundIn frontotemporal dementia (FTD) there is a critical lack in the understanding of biological and molecular mechanisms involved in disease pathogenesis. The heterogeneous genetic features associated with FTD suggest that multiple disease-mechanisms are likely to contribute to the development of this neurodegenerative condition.We here present a systems biology approach with the scope of i) shedding light on the biological processes potentially implicated in the pathogenesis of FTD and ii) identifying novel potential risk factors for FTD. We performed a gene co-expression network analysis of microarray expression data from 101 individuals without neurodegenerative diseases to explore regional-specific co-expression patterns in the frontal and temporal cortices for 12 genes (MAPT, GRN, CHMP2B, CTSC, HLA-DRA, TMEM106B, C9orf72, VCP, UBQLN2, OPTN, TARDBP and FUS) associated with FTD and we then carried out gene set enrichment and pathway analyses, and investigated known protein-protein interactors (PPIs) of FTD-genes products.ResultsGene co-expression networks revealed that several FTD-genes (such as MAPT and GRN, CTSC and HLA-DRA, TMEM106B, and C9orf72, VCP, UBQLN2 and OPTN) were clustering in modules of relevance in the frontal and temporal cortices. Functional annotation and pathway analyses of such modules indicated enrichment for: i) DNA metabolism, i.e. transcription regulation, DNA protection and chromatin remodelling (MAPT and GRN modules); ii) immune and lysosomal processes (CTSC and HLA-DRA modules), and; iii) protein meta/catabolism (C9orf72, VCP, UBQLN2 and OPTN, and TMEM106B modules). PPI analysis supported the results of the functional annotation and pathway analyses.ConclusionsThis work further characterizes known FTD-genes and elaborates on their biological relevance to disease: not only do we indicate likely impacted regional-specific biological processes driven by FTD-genes containing modules, but also do we suggest novel potential risk factors among the FTD-genes interactors as targets for further mechanistic characterization in hypothesis driven cell biology work.Electronic supplementary materialThe online version of this article (doi:10.1186/s13024-016-0085-4) contains supplementary material, which is available to authorized users.

Extending meta-learning framework for clustering gene expression data with component-based algorithm design and internal evaluation measures

Class retrieval in gene expression microarray data analysis is highly challenging task. Because of high class imbalance, highly dimensional feature space and small number of samples most of the algorithms fail to capture real complex structures in data 'golden standard'. Therefore, one of the major problems in this area is selection of the best suited algorithm for data at hand. We address this problem by proposing an extended meta-learning framework for ranking and selection of algorithms for clustering gene expression microarray data. Proposed framework introduces several improvements compared to the original one: extended algorithm space, extended meta-feature space and introduction of cutting edge techniques for meta-feature selection and parameter optimisation of meta-algorithms. System was evaluated on large algorithm and problem space 504 algorithms and 30 datasets and showed very promising results in prediction of algorithm performance for specific problems.

An integrative approach to analyze microarray datasets for prioritization of genes relevant to lens biology and disease.

Microarray-based profiling represents an effective method to analyze cellular or tissue-specific gene expression on the genome-level. However, in comparative analyses between control and mutant samples, microarrays often identify a large number of differentially expressed genes, in turn making it challenging to isolate the select “high-priority candidates” that are most relevant to an observed mutant phenotype. Here, we describe an integrative approach for mouse mutant lens microarray gene expression analysis using publically accessible systems-level information such as wild-type mouse lens expression data in iSyTE (integrated Systems Tool for Eye gene discovery), protein–protein interaction data in public databases, gene ontology enrichment data, and transcription factor binding profile data. This strategy, when applied to small Maf Mafg −/−:Mafk +/− mouse lens microarray datasets (deposited in NCBI Gene Expression Omnibus database with accession number GSE65500) in Agrawal et al. 2015 [1], led to the effective prioritization of candidate genes linked to lens defects in these mutants. Indeed, from the original list of genes that are differentially expressed at ± 1.5-fold and p < 0.05 in Mafg −/−:Mafk +/− mutant lenses, this analysis led to the identification of thirty-six high-priority candidates, in turn reducing the number of genes for further study by approximately 1/3 of the total. Moreover, eight of these genes are linked to mammalian cataract in the published literature, validating the efficacy of this approach. Additionally, these high-priority candidates contribute valuable information for the assembly of a gene regulatory network in the lens. In sum, the pipeline outlined in this report represents an effective approach for initial as well as downstream microarray expression data analysis to identify genes important for lens biology and cataracts. We anticipate that this integrative strategy can be extended to prioritize phenotypically relevant candidate genes from microarray data in other cells and tissues.

Development of a prognostic genetic signature to predict the metastatic risk associated with cutaneous melanoma.

The development of a genetic signature for the identification of high-risk cutaneous melanoma tumors would provide a valuable prognostic tool with value for stage I and II patients who represent a remarkably heterogeneous group with a 3% to 55% chance of disease progression and death 5 years from diagnosis. A prognostic 28-gene signature was identified by analysis of microarray expression data. Primary cutaneous melanoma tumor tissue was evaluated by RT-PCR for expression of the signature, and radial basis machine (RBM) modeling was performed to predict risk of metastasis. RBM analysis of cutaneous melanoma tumor gene expression reports low risk (class 1) or high risk (class 2) of metastasis. Metastatic risk was predicted with high accuracy in development (ROC = 0.93) and validation (ROC = 0.91) cohorts of primary cutaneous melanoma tumor tissue. Kaplan-Meier analysis indicated that the 5-year disease-free survival (DFS) rates in the development set were 100% and 38% for predicted classes 1 and 2 cases, respectively (P < 0.0001). DFS rates for the validation set were 97% and 31% for predicted classes 1 and 2 cases, respectively (P < 0.0001). Gene expression profile (GEP), American Joint Committee on Cancer stage, Breslow thickness, ulceration, and age were independent predictors of metastatic risk according to Cox regression analysis. The GEP signature accurately predicts metastasis risk in a multicenter cohort of primary cutaneous melanoma tumors. Preliminary Cox regression analysis indicates that the signature is an independent predictor of metastasis risk in the cohort presented.

Prediction of core cancer genes using a hybrid of feature selection and machine learning methods.

Machine learning techniques are of great importance in the analysis of microarray expression data, and provide a systematic and promising way to predict core cancer genes. In this study, a hybrid strategy was introduced based on machine learning techniques to select a small set of informative genes, which will lead to improving classification accuracy. First feature filtering algorithms were applied to select a set of top-ranked genes, and then hierarchical clustering and collapsing dense clusters were used to select core cancer genes. Through empirical study, our approach is capable of selecting relatively few core cancer genes while making high-accuracy predictions. The biological significance of these genes was evaluated using systems biology analysis. Extensive functional pathway and network analyses have confirmed findings in previous studies and can bring new insights into common cancer mechanisms.

A novel random forests-based feature selection method for microarray expression data analysis.

High-dimensional data and a large number of redundancy features in bioinformatics research have created an urgent need for feature selection. In this paper, a novel random forests-based feature selection method is proposed that adopts the idea of stratifying feature space and combines generalised sequence backward searching and generalised sequence forward searching strategies. A random forest variable importance score is used to rank features, and different classifiers are used as a feature subset evaluating function. The proposed method is examined on five microarray expression datasets, including leukaemia, prostate, breast, nervous and DLBCL, and the average accuracies of the SVM classifier in these datasets are 100%, 95.24%, 85%, 91.67%, and 91.67%, respectively. The results show that the proposed method could not only improve the classification accuracy but also greatly reduce the computation time of the feature selection process.

A novel filter feature selection method for paired microarray expression data analysis.

In recent years, a large amount of microarray data sets are produced with tens of thousands of genes. Feature selection has become a very sharp tool to select the informative genes. However, few feature selection methods consider the effect of paired samples, which are much more considered in the experiments of these years. Here, we propose a new feature selection method for paired microarray data sets analysis. It uses the fold change instead of the subtraction in the original approach, measures the statistical significant using the q-value of False Discovery Rate (FDR) and also decreases the influence of redundant genes. We compare the proposed method with another six existing methods in predict performance, stability of gene lists, functional stability and functional enrichment analysis using six kinds of paired cancer data sets. Comparison results show that our proposed method achieves better effectiveness, stability and consistency when it is applied to paired data sets.