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Updated: May 27, 2026
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
Published on: November 3, 2010
Deciphering causal and statistical relations of molecular aberrations and gene expressions in NCI-60 cell lines
Shyh-Dar Li1, Tatsuaki Tagami, Ying-Fu Ho
1Ontario Institute for Cancer Research, Toronto, Canada.
Background:
Cancer cells harbor a large number of molecular alterations such as mutations, amplifications and deletions on DNA sequences and epigenetic changes on DNA methylations. These aberrations may dysregulate gene expressions, which in turn drive the malignancy of tumors. Deciphering the causal and statistical relations of molecular aberrations and gene expressions is critical for understanding the molecular mechanisms of clinical phenotypes.
Results:
In this work, we proposed a computational method to reconstruct association modules containing driver aberrations, passenger mRNA or microRNA expressions, and putative regulators that mediate the effects from drivers to passengers. By applying the module-finding algorithm to the integrated datasets of NCI-60 cancer cell lines, we found that gene expressions were driven by diverse molecular aberrations including chromosomal segments' copy number variations, gene mutations and DNA methylations, microRNA expressions, and the expressions of transcription factors. In-silico validation indicated that passenger genes were enriched with the regulator binding motifs, functional categories or pathways where the drivers were involved, and co-citations with the driver/regulator genes. Moreover, 6 of 11 predicted MYB targets were down-regulated in an MYB-siRNA treated leukemia cell line. In addition, microRNA expressions were driven by distinct mechanisms from mRNA expressions.
Conclusions:
The results provide rich mechanistic information regarding molecular aberrations and gene expressions in cancer genomes. This kind of integrative analysis will become an important tool for the diagnosis and treatment of cancer in the era of personalized medicine.
Insights
This study introduces a computational method to link cancer-driving molecular aberrations with gene expression changes. The findings reveal diverse mechanisms driving gene expression and offer insights for personalized cancer medicine.
Area of Science:
- Genomics
- Computational Biology
- Cancer Research
Background:
- Cancer cells exhibit numerous molecular alterations (mutations, epigenetic changes) that dysregulate gene expression and drive tumor malignancy.
- Understanding the relationships between molecular aberrations and gene expression is crucial for deciphering cancer's molecular mechanisms.
Purpose of the Study:
- To develop a computational method for reconstructing association modules linking driver aberrations to gene expression.
- To investigate the diverse molecular aberrations that influence gene expression in cancer.
Main Methods:
- Proposed a computational method to reconstruct association modules.
- Applied a module-finding algorithm to integrated NCI-60 cancer cell line datasets.
- Performed in-silico validation of predicted associations.
Main Results:
- Gene expression is driven by various molecular aberrations, including copy number variations, mutations, DNA methylations, microRNA, and transcription factor expressions.
- In-silico validation confirmed enrichment of functional categories and pathways related to drivers in passenger genes.
- MicroRNA and mRNA expressions are regulated by distinct mechanisms.
Conclusions:
- The study provides mechanistic insights into molecular aberrations and gene expression in cancer genomes.
- Integrative analysis of molecular data is a valuable tool for cancer diagnosis and treatment in personalized medicine.
