scholarly journals Gene expression is a poor predictor of the metabolite abundance in cancer cells

2021 ◽  
Author(s):  
Huaping Li ◽  
Jayne Alexandra Barbour ◽  
Xiaoqiang Zhu ◽  
Jason W. H. Wong
Keyword(s):  
Gene Expression ◽  
Cancer Cell ◽  
Gene Expression Data ◽  
Metabolic Reprogramming ◽  
Metabolic Enzyme ◽  
Global Changes ◽  
Solid Cancer ◽  
Expression Data ◽  
Metabolomic Profiling ◽  
Poor Predictor

Metabolic reprogramming is a hallmark of cancer characterized by global changes in metabolite levels. However, compared with the study of gene expression, profiling of metabolites in cancer samples remains relatively understudied. We obtained metabolomic profiling and gene expression data from 454 human solid cancer cell lines across 24 cancer types from the Cancer Cell Line Encyclopedia (CCLE) database, to evaluate the feasibility of inferring metabolite levels from gene expression data. For each metabolite, we trained multivariable LASSO regression models to identify gene sets that are most predictive of the level of each metabolite profiled. Even when accounting for cell culture conditions or cell lineage in the model, few metabolites could be accurately predicted. In some cases, the inclusion of the upstream and downstream metabolites improved prediction accuracy, suggesting that gene expression is a poor predictor of steady-state metabolite levels. Our analysis uncovered a single robust relationship between the expression of nicotinamide N-methyltransferase (NNMT) and 1-methylnicotinamide (MNA), however, this relationship could only be validated in cancer samples with high purity, as NNMT is not expressed in immune cells. Together, our findings reveal the challenge of inferring metabolite levels from metabolic enzyme levels and suggest that direct metabolomic profiling is necessary to study metabolism in cancer.

scholarly journals Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell

2013 ◽  
Vol 8 (1) ◽  
Author(s):  
Anirban Bhar ◽  
Martin Haubrock ◽  
Anirban Mukhopadhyay ◽  
Ujjwal Maulik ◽  
Sanghamitra Bandyopadhyay ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Time Series ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Gene Expression Data ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Analysis Of Time Series

scholarly journals A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2878
Author(s):  
Hiroaki Imoto ◽  
Suxiang Zhang ◽  
Mariko Okada
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Cell ◽  
Gene Expression Data ◽  
Cancer Cell Line ◽  
Erbb Receptor ◽  
Expression Data ◽  
Rna Seq ◽  
Signaling Dynamics ◽  
Gene Expression Levels

A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate.

scholarly journals GSMA: Gene Set Matrix Analysis, An Automated Method for Rapid Hypothesis Testing of Gene Expression Data

2007 ◽  
Vol 1 ◽  
pp. 117793220700100 ◽  
Author(s):  
Chris Cheadle ◽  
Tonya Watkins ◽  
Jinshui Fan ◽  
Marc A. Williams ◽  
Steven Georas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hypothesis Testing ◽  
Gene Expression Data ◽  
Expression Patterns ◽  
Matrix Analysis ◽  
Global Changes ◽  
Expression Data ◽  
Gene Set ◽  
Automated Method ◽  
Gene Sets

Background Microarray technology has become highly valuable for identifying complex global changes in gene expression patterns. The assignment of functional information to these complex patterns remains a challenging task in effectively interpreting data and correlating results from across experiments, projects and laboratories. Methods which allow the rapid and robust evaluation of multiple functional hypotheses increase the power of individual researchers to data mine gene expression data more efficiently. Results We have developed (gene set matrix analysis) GSMA as a useful method for the rapid testing of group-wise up- or down-regulation of gene expression simultaneously for multiple lists of genes (gene sets) against entire distributions of gene expression changes (datasets) for single or multiple experiments. The utility of GSMA lies in its flexibility to rapidly poll gene sets related by known biological function or as designated solely by the end-user against large numbers of datasets simultaneously. Conclusions GSMA provides a simple and straightforward method for hypothesis testing in which genes are tested by groups across multiple datasets for patterns of expression enrichment.

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.

Sign in / Sign up

Export Citation Format

Share Document