Exon Array Analysis to Identify Diethyl-nitrosamine Differentially Regulated and Alternately Spliced Genes in Early Liver Carcinogenesis in the Transgenic Mouse ATT-myc Model

Objectives: To identify the regulated genes or the spliced genes of diethylnitorsamine (NDEA) in ATT-myc mice versus control group. Methods: We analysed the 9 hybridizations on the MouseExon10ST array of NDEA treatments and control non- transgenic by application of a mixed model analysis of variance. Results: The 907 genes had regulated significantly between the groups and 916 genes had regulated with a significant exon-group interaction among of them 150 genes had regulated with both gene and possible splicing differences (p<0.01). The 7,618 genes had tested for the alternative gene up-regulation and splicing and compared to the gene-classifications. The genes functions, pathways and gene-classifications in the current study had presented in the contingency table analysis of the set of the regulated genes and alternatively spliced that regulated significantly in the ATT-myc mice treated by diethylnitorsamine versus control non-transgenic. The GOMolFn of gene-classification had 321 groups that had significantly regulated in the set of the regulated genes or differentially spliced. While the GOProcess of gene-classification had 330 groups that had significantly regulated in the set of differentially regulated genes or spliced. Additionally, the CELlLoc of gene-classification had 70 groups that had significantly regulated in the set of differentially regulated genes spliced. Finally, the Pathway gene-classification had 8 groups that had significantly regulated in the set of differentially regulated genes or spliced (p<0.01) in diethylnitorsamine when compared to control group. Conclusion: we summarized the toxicogenomics induced by diethylnitrosamine in early liver carcinogenesis in ATT-myc transgenic mice of liver cancer. Doi: 10.28991/SciMedJ-2021-0302-6 Full Text: PDF

A large-scale comparative study of isoform expressions measured on four platforms

Background Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles, and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 404 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes, and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array, and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion In the comparisons of the platforms for measuring the expressions at both isoform and gene levels, we found that (1) the agreement on isoform expressions is lower than the agreement on gene expressions across the four platforms; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array than NanoString in isoform quantification; (4) different RNA-seq isoform quantification methods show varying estimation results, and among the methods, Net-RSTQ and eXpress are more consistent across the platforms; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

A large-scale comparative study of isoform expressions measured on four platforms

Background: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles, and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA-sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with {\color{red}404} custom-designed probes for measuring the expressions of 478 isoforms in 155 genes, and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array, and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion: In the comparisons of the platforms for measuring the expressions at both isoform and gene levels, we found that (1) the agreement on isoform expressions is lower than the agreement on gene expressions across the four platforms; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array than NanoString in isoform quantification; (4) different RNA-seq isoform quantification methods show varying estimation results, and among the methods, Net-RSTQ and eXpress are more consistent across the platforms; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

A large-scale comparative study of isoform expressions measured on four platforms

Background: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA-sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 403 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion: In the comparisons of the platforms for evaluating expressions at both isoform and gene levels, we found that (1) the degree of agreement across platforms on quantifying isoform expressions is lower than gene expressions; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array quantification results on isoform-level compared to NanoString; (4) different RNA-seq isoform quantification algorithms showed inconsistent results, and two isoform quantification methods Net-RSTQ and eXpress are more consistent across the platforms in the comparison; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma

PURPOSE Molecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression–based classification system for molecular subtyping of patients with high-grade serous ovarian carcinoma (HGSOC). METHODS Unprocessed exon array (569 tumor and nine normal) and RNA sequencing (RNA-seq; 376 tumor) HGSOC data sets, with clinical annotations, were downloaded from the Genomic Data Commons portal. Sample clustering was performed by non-negative matrix factorization by using isoform-level expression estimates. The association between the subtypes and overall survival was evaluated by Cox proportional hazards regression model after adjusting for the covariates. A novel classification system was developed for HGSOC molecular subtyping. Robustness and generalizability of the gene signatures were validated using independent microarray and RNA-seq data sets. RESULTS Sample clustering recaptured the four known The Cancer Genome Atlas molecular subtypes but switched the subtype for 22% of the cases, which resulted in significant ( P = .006) survival differences among the refined subgroups. After adjusting for covariate effects, the mesenchymal subgroup was found to be at an increased hazard for death compared with the immunoreactive subgroup. Both gene- and isoform-level signatures achieved more than 92% prediction accuracy when tested on independent samples profiled on the exon array platform. When the classifier was applied to RNA-seq data, the subtyping calls agreed with the predictions made from exon array data for 95% of the 279 samples profiled by both platforms. CONCLUSION Isoform-level expression analysis successfully stratifies patients with HGSOC into groups with differing prognosis and has led to the development of robust, platform-independent gene signatures for HGSOC molecular subtyping. The association of the refined The Cancer Genome Atlas HGSOC subtypes with overall survival, independent of covariates, enhances the clinical annotation of the HGSOC cohort.

A large-scale comparative study of isoform expressions measured on four platforms

Background: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles and thus, it is necessary to accurately measure isoform expressions as well as the genes'. While previous studies have demonstrated the strong agreement between mRNA-sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based and RT-qPCR platforms using 46 cancer cell lines across different cancer types to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 403 custom-designed probes for measuring the expressions of 405 isoforms in 155 genes and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines, and then combined the data with the matched RNA-seq, Exon-array and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion: In the comparisons of the platforms for evaluating expressions at both isoform and gene levels, we found that (1) the degree of agreement across platforms on quantifying isoform expressions is lower than gene expressions; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq quantification results on isoform-level compared to NanoString and Exon-array; (4) different RNA-seq isoform quantification algorithms showed inconsistent results, and two isoform quantification methods Net-RSTQ and eXpress are more consistent across the platforms in the comparison; (5) RNA-seq has the best overall consistent with the other platforms on gene expression quantification.

Mechanotransduction signaling in podocytes from fluid flow shear stress

Recently, we and others have found that hyperfiltration-associated increase in biomechanical forces, namely, tensile stress and fluid flow shear stress (FFSS), can directly and distinctly alter podocyte structure and function. The ultrafiltrate flow over the major processes and cell body generates FFSS to podocytes. Our previous work suggests that the cyclooxygenase-2 (COX-2)-PGE2-PGE2 receptor 2 (EP2) axis plays an important role in mechanoperception of FFSS in podocytes. To address mechanotransduction of the perceived stimulus through EP2, cultured podocytes were exposed to FFSS (2 dyn/cm2) for 2 h. Total RNA from cells at the end of FFSS treatment, 2-h post-FFSS, and 24-h post-FFSS was used for whole exon array analysis. Differentially regulated genes ( P < 0.01) were analyzed using bioinformatics tools Enrichr and Ingenuity Pathway Analysis to predict pathways/molecules. Candidate pathways were validated using Western blot analysis and then further confirmed to be resulting from a direct effect of PGE2 on podocytes. Results show that FFSS-induced mechanotransduction as well as exogenous PGE2 activate the Akt-GSK3β-β-catenin (Ser552) and MAPK/ERK but not the cAMP-PKA signal transduction cascades. These pathways are reportedly associated with FFSS-induced and EP2-mediated signaling in other epithelial cells as well. The current regimen for treating hyperfiltration-mediated injury largely depends on targeting the renin-angiotensin-aldosterone system. The present study identifies specific transduction mechanisms and provides novel information on the direct effect of FFSS on podocytes. These results suggest that targeting EP2-mediated signaling pathways holds therapeutic significance for delaying progression of chronic kidney disease secondary to hyperfiltration.