scholarly journals Integrative Genomics with Mediation Analysis in a Survival Context

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Szilárd Nemes ◽  
Toshima Z. Parris ◽  
Anna Danielsson ◽  
Zakaria Einbeigi ◽  
Gunnar Steineck ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Genomic Analysis ◽  
Mrna Levels ◽  
Integrative Genomics ◽  
Asymptotic Results ◽  
Data Set ◽  
Cancer Data ◽  
Dna And Rna ◽  
Altered Gene

DNA copy number aberrations (DCNA) and subsequent altered gene expression profiles may have a major impact on tumor initiation, on development, and eventually on recurrence and cancer-specific mortality. However, most methods employed in integrative genomic analysis of the two biological levels, DNA and RNA, do not consider survival time. In the present note, we propose the adoption of a survival analysis-based framework for the integrative analysis of DCNA and mRNA levels to reveal their implication on patient clinical outcome with the prerequisite that the effect of DCNA on survival is mediated by mRNA levels. The specific aim of the paper is to offer a feasible framework to test the DCNA-mRNA-survival pathway. We provide statistical inference algorithms for mediation based on asymptotic results. Furthermore, we illustrate the applicability of the method in an integrative genomic analysis setting by using a breast cancer data set consisting of 141 invasive breast tumors. In addition, we provide implementation in R.

scholarly journals Simpler Evaluation of Predictions and Signature Stability for Gene Expression Data

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
Yvonne E. Pittelkow ◽  
Susan R. Wilson
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Forward Selection ◽  
Data Set ◽  
Cancer Data ◽  
Large Gene ◽  
Selection Of

Scientific advances are raising expectations that patient-tailored treatment will soon be available. The development of resulting clinical approaches needs to be based on well-designed experimental and observational procedures that provide data to which proper biostatistical analyses are applied. Gene expression microarray and related technology are rapidly evolving. It is providing extremely large gene expression profiles containing many thousands of measurements. Choosing a subset from these gene expression measurements to include in a gene expression signature is one of the many challenges needing to be met. Choice of this signature depends on many factors, including the selection of patients in the training set. So the reliability and reproducibility of the resultant prognostic gene signature needs to be evaluated, in such a way as to be relevant to the clinical setting. A relatively straightforward approach is based on cross validation, with separate selection of genes at each iteration to avoid selection bias. Within this approach we developed two different methods, one based on forward selection, the other on genes that were statistically significant in all training blocks of data. We demonstrate our approach to gene signature evaluation with a well-known breast cancer data set.

scholarly journals Integrated Genomic Analysis of Sézary Syndrome

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Xin Mao ◽  
Tracy Chaplin ◽  
Bryan D. Young
Keyword(s):  
Copy Number ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Genomic Analysis ◽  
Gene Clusters ◽  
Sézary Syndrome ◽  
Sezary Syndrome ◽  
Snp Microarray ◽  
The Impact ◽  
Chromosome Regions

Sézary syndrome (SS) is a rare variant of primary cutaneous T-cell lymphoma. Little is known about the underlying pathogenesis of S. To address this issue, we used Affymetrix 10K SNP microarray to analyse 13 DNA samples isolated from 8 SS patients and qPCR with ABI TaqMan SNP genotyping assays for the validation of the SNP microarray results. In addition, we tested the impact of SNP loss of heterozygosity (LOH) identified in SS cases on the gene expression profiles of SS cases detected with Affymetrix GeneChip U133A. The results showed: (1) frequent SNP copy number change and LOH involving 1, 2p, 3, 4q, 5q, 6, 7p, 8, 9, 10, 11, 12q, 13, 14, 16q, 17, and 20, (2) reduced SNP copy number at FAT gene (4q35) in 75% of SS cases, and (3) the separation of all SS cases from normal control samples by SNP LOH gene clusters at chromosome regions of 9q31q34, 10p11q26, and 13q11q12. These findings provide some intriguing information for our current understanding of the molecular pathogenesis of this tumour and suggest the possibility of presence of functional SNP LOH in SS tumour cells.

scholarly journals Analysis of ceRNA networks and identification of potential drug targets for drug-resistant leukemia cell K562/ADR

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11429
Author(s):  
Zhaoping Liu ◽  
Yanyan Wang ◽  
Zhenru Xu ◽  
Shunling Yuan ◽  
Yanglin Ou ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Drug Targets ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Leukemia Cell ◽  
Gene Expression Profiles ◽  
Leukemia Cells ◽  
Mrna Levels ◽  
Drug Resistant ◽  
Cerna Network

Background Drug resistance is the main obstacle in the treatment of leukemia. As a member of the competitive endogenous RNA (ceRNA) mechanism, underlying roles of lncRNA are rarely reported in drug-resistant leukemia cells. Methods The gene expression profiles of lncRNAs and mRNAs in doxorubicin-resistant K562/ADR and sensitive K562 cells were established by RNA sequencing (RNA-seq). Expression of differentially expressed lncRNAs (DElncRNAs) and DEmRNAs was validated by qRT-PCR. The potential biological functions of DElncRNAs targets were identified by GO and KEGG pathway enrichment analyses, and the lncRNA-miRNA-mRNA ceRNA network was further constructed. K562/ADR cells were transfected with CCDC26 and LINC01515 siRNAs to detect the mRNA levels of GLRX5 and DICER1, respectively. The cell survival rate after transfection was detected by CCK-8 assay. Results The ceRNA network was composed of 409 lncRNA-miRNA pairs and 306 miRNA-mRNA pairs based on 67 DElncRNAs, 58 DEmiRNAs and 192 DEmRNAs. Knockdown of CCDC26 and LINC01515 increased the sensitivity of K562/ADR cells to doxorubicin and significantly reduced the half-maximal inhibitory concentration (IC50) of doxorubicin. Furthermore, knockdown of GLRX5 and DICER1 increased the sensitivity of K562/ADR cells to doxorubicin and significantly reduced the IC50 of doxorubicin. Conclusions The ceRNA regulatory networks may play important roles in drug resistance of leukemia cells. CCDC26/miR-140-5p/GLRX5 and LINC01515/miR-425-5p/DICER1 may be potential targets for drug resistance in K562/ADR cells. This study provides a promising strategy to overcome drug resistance and deepens the understanding of the ceRNA regulatory mechanism related to drug resistance in CML cells.

scholarly journals Aripiprazole as a candidate treatment of COVID-19 identified through genomic analysis

2020 ◽  
Author(s):  
Benedicto Crespo-Facorro ◽  
Miguel Ruiz-Veguilla ◽  
Javier Vazquez-Bourgon ◽  
Ana C. Sanchez-Hidalgo ◽  
Nathalia Garrido-Torres ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Phase 1 ◽  
Gene Expression Profiles ◽  
Genomic Analysis ◽  
Cell Receptor ◽  
Therapeutic Drug ◽  
P Value ◽  
Immunological Parameters ◽  
Altered Expression ◽  
Exact Test

Background: Antipsychotics suppress expression of inflammatory cytokines and inducible inflammatory enzymes. Elopiprazole (a phenylpiperazine antipsychotic drug in phase 1) has been characterized as a therapeutic drug to treat SARS-CoV-2 infection in a repurposing study. We aim to investigate the potential effects of aripiprazole (an FDA approved phenylpiperazine) on COVID19-related immunological parameters. Methods: Differential gene expression profiles of non-COVID versus COVID RNA-Seq samples (CRA002390 project in GSA database) and drug-naive patients with psychosis at baseline and after three months of aripiprazole treatment was identified. An integrative analysis between COVID and aripiprazole immunomodulatory antagonist effects was performed. Findings: 82 out the 377 genes (21.7%) with expression significantly altered by aripiprazole have also their expression altered in COVID-19 patients and in 93.9% of these genes their expression is reverted by aripiprazole. The number of common genes with expression altered in both analyses is significantly higher than expected (Fisher's Exact Test, two tail; P value=3.2e-11). 11 KEGG pathways were significantly enriched with genes with altered expression both in COVID-19 patients and aripiprazole medicated schizophrenia patients (P adj<0.05). The most significant pathways were associated to the immune system such as the inflammatory bowel disease (IBD) (the most significant pathway with a P adj of 0.00021), Th1 and Th2 cell differentiation and B cell receptor signaling pathway, all three related to the defense against infections. Interpretation: This exploratory investigation may provide further support to the notion that protective effect is exerted by phenylpiperazine by modulating the immunological dysregulation associated to COVID-19. Along with many ongoing studies and clinical trials, repurposing available medications could be of use in countering SARS-CoV-2 infection, but require further studies and trials.

Improved Feature Selection by Incorporating Gene Similarity into the LASSO

2012 ◽  
Vol 3 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Christopher E. Gillies ◽  
Xiaoli Gao ◽  
Nilesh V. Patel ◽  
Mohammad-Reza Siadat ◽  
George D. Wilson
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Personalized Medicine ◽  
Objective Function ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Genetic Profile ◽  
Data Set ◽  
Coordinate Descent Algorithm ◽  
Gene Similarity

Personalized medicine is customizing treatments to a patient’s genetic profile and has the potential to revolutionize medical practice. An important process used in personalized medicine is gene expression profiling. Analyzing gene expression profiles is difficult, because there are usually few patients and thousands of genes, leading to the curse of dimensionality. To combat this problem, researchers suggest using prior knowledge to enhance feature selection for supervised learning algorithms. The authors propose an enhancement to the LASSO, a shrinkage and selection technique that induces parameter sparsity by penalizing a model’s objective function. Their enhancement gives preference to the selection of genes that are involved in similar biological processes. The authors’ modified LASSO selects similar genes by penalizing interaction terms between genes. They devise a coordinate descent algorithm to minimize the corresponding objective function. To evaluate their method, the authors created simulation data where they compared their model to the standard LASSO model and an interaction LASSO model. The authors’ model outperformed both the standard and interaction LASSO models in terms of detecting important genes and gene interactions for a reasonable number of training samples. They also demonstrated the performance of their method on a real gene expression data set from lung cancer cell lines.

scholarly journals PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kota Fujisawa ◽  
Mamoru Shimo ◽  
Y.-H. Taguchi ◽  
Shinya Ikematsu ◽  
Ryota Miyata
Keyword(s):  
Gene Expression ◽  
Feature Extraction ◽  
Target Genes ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Principal Component ◽  
Data Set ◽  
Immune Related Genes ◽  
Unsupervised Feature Extraction

AbstractCoronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-κB) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-κB activity was suppressed by H3K36me3 in COVID-19 patient blood.

scholarly journals The effects of a globin blocker on the resolution of 3’mRNA sequencing data in porcine blood

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Kyu-Sang Lim ◽  
Qian Dong ◽  
Pamela Moll ◽  
Jana Vitkovska ◽  
Gregor Wiktorin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Data Sets ◽  
Globin Genes ◽  
Sequencing Data ◽  
Globin Mrna ◽  
Data Set ◽  
Mrna Sequencing ◽  
Porcine Blood

Abstract Background Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of globin mRNA in porcine blood. These limitations can be overcome by the use of QuantSeq 3’mRNA sequencing (QuantSeq) combined with a method to deplete or block the processing of globin mRNA prior to or during library construction. Here, we validated the effectiveness of QuantSeq using a novel specific globin blocker (GB) that is included in the library preparation step of QuantSeq. Results In data set 1, four concentrations of the GB were applied to RNA samples from two pigs. The GB significantly reduced the proportion of globin reads compared to non-GB (NGB) samples (P = 0.005) and increased the number of detectable non-globin genes. The highest evaluated concentration (C1) of the GB resulted in the largest reduction of globin reads compared to the NGB (from 56.4 to 10.1%). The second highest concentration C2, which showed very similar globin depletion rates (12%) as C1 but a better correlation of the expression of non-globin genes between NGB and GB (r = 0.98), allowed the expression of an additional 1295 non-globin genes to be detected, although 40 genes that were detected in the NGB sample (at a low level) were not present in the GB library. Concentration C2 was applied in the rest of the study. In data set 2, the distribution of the percentage of globin reads for NGB (n = 184) and GB (n = 189) samples clearly showed the effects of the GB on reducing globin reads, in particular for HBB, similar to results from data set 1. Data set 3 (n = 84) revealed that the proportion of globin reads that remained in GB samples was significantly and positively correlated with the reticulocyte count in the original blood sample (P < 0.001). Conclusions The effect of the GB on reducing the proportion of globin reads in porcine blood QuantSeq was demonstrated in three data sets. In addition to increasing the efficiency of sequencing non-globin mRNA, the GB for QuantSeq has an advantage that it does not require an additional step prior to or during library creation. Therefore, the GB is a useful tool in the quantification of whole gene expression profiles in porcine blood.

scholarly journals NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution

2019 ◽  
Author(s):  
Daiwei Tang ◽  
Seyoung Park ◽  
Hongyu Zhao
Keyword(s):  
Tumor Microenvironment ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Expression Profiles ◽  
Mrna Level ◽  
Nonnegative Matrix ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Supplementary Information ◽  
Mrna Levels

Abstract Motivation A number of computational methods have been proposed recently to profile tumor microenvironment (TME) from bulk RNA data, and they have proved useful for understanding microenvironment differences among therapeutic response groups. However, these methods are not able to account for tumor proportion nor variable mRNA levels across cell types. Results In this article, we propose a Nonnegative Matrix Factorization-based Immune-TUmor MIcroenvironment Deconvolution (NITUMID) framework for TME profiling that addresses these limitations. It is designed to provide robust estimates of tumor and immune cells proportions simultaneously, while accommodating mRNA level differences across cell types. Through comprehensive simulations and real data analyses, we demonstrate that NITUMID not only can accurately estimate tumor fractions and cell types’ mRNA levels, which are currently unavailable in other methods; it also outperforms most existing deconvolution methods in regular cell type profiling accuracy. Moreover, we show that NITUMID can more effectively detect clinical and prognostic signals from gene expression profiles in tumor than other methods. Availability and implementation The algorithm is implemented in R. The source code can be downloaded at https://github.com/tdw1221/NITUMID. Supplementary information Supplementary data are available at Bioinformatics online.

A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks

2004 ◽  
Vol 3 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Markus Ruschhaupt ◽  
Wolfgang Huber ◽  
Annemarie Poustka ◽  
Ulrich Mansmann
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Statistical Processing ◽  
Primary Data ◽  
High Dimensional ◽  
Microarray Gene Expression ◽  
Data Set ◽  
Dimensional Classification ◽  
Biological Interpretation

We demonstrate a concept and implementation of a compendium for the classification of high-dimensional data from microarray gene expression profiles. A compendium is an interactive document that bundles primary data, statistical processing methods, figures, and derived data together with the textual documentation and conclusions. Interactivity allows the reader to modify and extend these components. We address the following questions: how much does the discriminatory power of a classifier depend on the choice of the algorithm that was used to identify it; what alternative classifiers could be used just as well; how robust is the result. The answers to these questions are essential prerequisites for validation and biological interpretation of the classifiers. We show how to use this approach by looking at these questions for a specific breast cancer microarray data set that first has been studied by Huang et al. (2003).

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