scholarly journals Multi-Omics Analysis of Cancer Cell Lines with High/Low Ferroptosis Scores and Development of a Ferroptosis-Related Model for Multiple Cancer Types

2021 ◽  
Vol 9 ◽  
Author(s):  
Guangyao Shan ◽  
Huan Zhang ◽  
Guoshu Bi ◽  
Yunyi Bian ◽  
Jiaqi Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gene Expression Data ◽  
Cancer Cell Lines ◽  
Expression Data ◽  
Rna Seq ◽  
Multiple Cancer ◽  
Related Model ◽  
Cancer Types

Background: Ferroptosis is a newly identified regulated cell death characterized by iron-dependent lipid peroxidation and subsequent membrane oxidative damage, which has been implicated in multiple types of cancers. The multi-omics differences between cancer cell lines with high/low ferroptosis scores remain to be elucidated.Methods and Materials: We used RNA-seq gene expression, gene mutation, miRNA expression, metabolites, copy number variation, and drug sensitivity data of cancer cell lines from DEPMAP to detect multi-omics differences associated with ferroptosis. Based on the gene expression data of cancer cell lines, we performed LASSO-Logistic regression analysis to build a ferroptosis-related model. Lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), esophageal cancer (ESCA), bladder cancer (BLCA), cervical cancer (CESC), and head and neck cancer (HNSC) patients from the TCGA database were used as validation cohorts to test the efficacy of this model.Results: After stratifying the cancer cell lines into high score (HS) and low score (LS) groups according to the median of ferroptosis scores generated by gene set variation analysis, we found that IC50 of 66 agents such as oxaliplatin (p < 0.001) were significantly different, among which 65 were higher in the HS group. 851 genes such as KEAP1 and NRAS were differentially muted between the two groups. Differentially expressed genes, miRNAs and metabolites were also detected—multiple items such as IL17F (logFC = 6.58, p < 0.001) differed between the two groups. Unlike the TCGA data generated by bulk RNA-seq, the gene expression data in DEPMAP are from pure cancer cells, so it could better reflect the traits of tumors in cancer patients. Thus, we built a 15-signature model (AUC = 0.878) based on the gene expression data of cancer cell lines. The validation cohorts demonstrated a higher mutational rate of NFE2L2 and higher expression levels of 12 ferroptosis-related genes in HS groups.Conclusion: This article systemically analyzed multi-omics differences between cancer cell lines with high/low ferroptosis scores and a ferroptosis-related model was developed for multiple cancer types. Our findings could improve our understanding of the role of ferroptosis in cancer and provide new insight into treatment for malignant tumors.

scholarly journals A Comparative Analysis of Gene-Expression Data of Multiple Cancer Types

PLoS ONE ◽  
2010 ◽  
Vol 5 (10) ◽  
pp. e13696 ◽  
Author(s):  
Kun Xu ◽  
Juan Cui ◽  
Victor Olman ◽  
Qing Yang ◽  
David Puett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Gene Expression Data ◽  
Expression Data ◽  
Multiple Cancer ◽  
Cancer Types

scholarly journals TBIO-26. NON-CANONICAL OPEN READING FRAMES ENCODE FUNCTIONAL PROTEINS ESSENTIAL FOR CANCER CELL SURVIVAL

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii471-iii471
Author(s):  
John Prensner ◽  
Oana Enache ◽  
Victor Luria ◽  
Karsten Krug ◽  
Karl Clauser ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Protein Translation ◽  
Cancer Cell Lines ◽  
Open Reading Frames ◽  
Sequencing Analysis ◽  
Multiple Cancer ◽  
Cancer Types ◽  
Non Coding Rnas ◽  
Reading Frames

Abstract The brain is the foremost non-gonadal tissue for expression of non-coding RNAs of unclear function. Yet, whether such transcripts are truly non-coding or rather the source of non-canonical protein translation is unknown. Here, we used functional genomic screens to establish the cellular bioactivity of non-canonical proteins located in putative non-coding RNAs or untranslated regions of protein-coding genes. We experimentally interrogated 553 open reading frames (ORFs) identified by ribosome profiling for three major phenotypes: 257 (46%) demonstrated protein translation when ectopically expressed in HEK293T cells, 401 (73%) induced gene expression changes following ectopic expression across 4 cancer cell types, and 57 (10%) induced a viability defect when the endogenous ORF was knocked out using CRISPR/Cas9 in 8 human cancer cell lines. CRISPR tiling and start codon mutagenesis indicated that the biological impact of these non-canonical ORFs required their translation as opposed to RNA-mediated effects. We functionally characterized one of these ORFs, G029442—renamed GREP1 (Glycine-Rich Extracellular Protein-1)—as a cancer-implicated gene with high expression in multiple cancer types, such as gliomas. GREP1 knockout in >200 cancer cell lines reduced cell viability in multiple cancer types, including glioblastoma, in a cell-autonomous manner and produced cell cycle arrest via single-cell RNA sequencing. Analysis of the secretome of GREP1-expressing cells showed increased abundance of the oncogenic cytokine GDF15, and GDF15 supplementation mitigated the growth inhibitory effect of GREP1 knock-out. Taken together, these experiments suggest that the non-canonical ORFeome is surprisingly rich in biologically active proteins and potential cancer therapeutic targets deserving of further study.

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

2019 ◽  
Author(s):  
Wei Zhang ◽  
Raphael Petegrosso ◽  
Jae Woong Chang ◽  
Jiao Sun ◽  
Jeongsik Yong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Study ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Exon Array ◽  
Cancer Cell Lines ◽  
Rna Seq ◽  
Gene Expressions ◽  
Isoform Quantification

Abstract 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.

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

2019 ◽  
Author(s):  
Wei Zhang ◽  
Raphael Petegrosso ◽  
Jae Woong Chang ◽  
Jeongsik Yong ◽  
Jeremy Chien ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Study ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Exon Array ◽  
Cancer Cell Lines ◽  
Rna Seq ◽  
Eukaryotic Genes ◽  
Isoform Quantification

Abstract 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.

scholarly journals SVExpress: identifying gene features altered recurrently in expression with nearby structural variant breakpoints

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiqun Zhang ◽  
Fengju Chen ◽  
Chad J. Creighton
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Integrative Analysis ◽  
Human Tumors ◽  
Gene Fusions ◽  
Expression Data ◽  
Altered Expression ◽  
Altered Gene

Abstract Background Combined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements. Somatic structural variants (SVs), as identified by WGS, can involve altered gene cis-regulation, gene fusions, copy number alterations, or gene disruption. The absence of computational tools to streamline integrative analysis steps may represent a barrier in identifying genes recurrently altered by genomic rearrangement. Results Here, we introduce SVExpress, a set of tools for carrying out integrative analysis of SV and gene expression data. SVExpress enables systematic cataloging of genes that consistently show increased or decreased expression in conjunction with the presence of nearby SV breakpoints. SVExpress can evaluate breakpoints in proximity to genes for potential enhancer translocation events or disruption of topologically associated domains, two mechanisms by which SVs may deregulate genes. The output from any commonly used SV calling algorithm may be easily adapted for use with SVExpress. SVExpress can readily analyze genomic datasets involving hundreds of cancer sample profiles. Here, we used SVExpress to analyze SV and expression data across 327 cancer cell lines with combined SV and expression data in the Cancer Cell Line Encyclopedia (CCLE). In the CCLE dataset, hundreds of genes showed altered gene expression in relation to nearby SV breakpoints. Altered genes involved TAD disruption, enhancer hijacking, and gene fusions. When comparing the top set of SV-altered genes from cancer cell lines with the top SV-altered genes previously reported for human tumors from The Cancer Genome Atlas and the Pan-Cancer Analysis of Whole Genomes datasets, a significant number of genes overlapped in the same direction for both cell lines and tumors, while some genes were significant for cell lines but not for human tumors and vice versa. Conclusion Our SVExpress tools allow computational biologists with a working knowledge of R to integrate gene expression with SV breakpoint data to identify recurrently altered genes. SVExpress is freely available for academic or commercial use at https://github.com/chadcreighton/SVExpress. SVExpress is implemented as a set of Excel macros and R code. All source code (R and Visual Basic for Applications) is available.

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