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Updated: Jul 10, 2026
06:52
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
Published on: July 22, 2020
Common human cancer genes discovered by integrated gene-expression analysis
Yan Lu1, Yijun Yi, Pengyuan Liu
1Department of Surgery and the Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri, United States of America.
Plos One
|November 9, 2007
Summary
Researchers identified a 187-gene signature, refined to 28 genes, that accurately detects various cancers. This cancer-independent gene signature aids in understanding common cancer mechanisms and improving diagnostics.
Area of Science:
- Genomics
- Oncology
- Molecular Biology
Background:
- Microarray technology offers objective cancer diagnosis and prognosis assessment.
- Cancer-specific gene studies often lack broad validation in large patient cohorts.
- Identifying commonly regulated genes across cancers can illuminate shared molecular mechanisms.
Purpose of the Study:
- To identify and validate a cancer type-independent gene signature.
- To assess the commonality of gene expression across diverse human malignancies.
- To discover genes and pathways universally dysregulated in cancer.
Main Methods:
- Integrated gene-expression analysis of 2,186 samples from 39 studies.
- Validation using quantitative reverse transcription polymerase chain reaction (QRT-PCR) and tissue microarrays.
- Assessment of gene signature discriminative power in training and independent datasets.
Main Results:
- Identified 187 genes dysregulated in most cancer samples, forming a signature with 92.6% accuracy.
- Refined signature to 28 genes, achieving 80% accuracy in classifying mixed cancer types.
- Signature accurately predicted novel cancer types and identified dysregulated pathways like glycolysis and cell cycle.
Conclusions:
- The gene signature captures essential transcriptional features of neoplastic transformation.
- Findings provide insights into common molecular mechanisms of cancer.
- The signature offers potential for improved cancer diagnostics, prognostics, and therapy.
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