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Technical and Biological Biases in Bulk Transcriptomic Data Mining for Cancer Research
Hengrui Liu1,2, Yiying Li3, Miray Karsidag4
1Cancer Research Institute, Jinan University, Guangzhou, China.
Journal of Cancer
|January 2, 2025
Summary
Transcriptomics, including RNA sequencing, advances cancer research by analyzing gene expression. However, researchers must address technical and biological biases for accurate interpretation and effective cancer therapies.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Transcriptomic data, particularly from RNA sequencing (RNA-seq), has revolutionized cancer research.
- Large-scale datasets like The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) offer insights into cancer biology.
- Analysis of transcriptomic data is essential for understanding gene expression patterns and disease mechanisms.
Purpose of the Study:
- To review the impact of transcriptomics on cancer research.
- To highlight the challenges and biases in transcriptomic data analysis.
- To emphasize the need for bias mitigation for reliable clinical outcomes.
Main Methods:
- Review of existing literature on transcriptomics in cancer research.
- Focus on large-scale datasets (TCGA, GTEx).
- Discussion of technical (sequencing methods) and biological (heterogeneity, purity) biases.
Main Results:
- Transcriptomic data provides critical insights into cancer biology.
- Technical biases stem from microarray, RNA-seq, and nanopore sequencing.
- Biological biases include tumor heterogeneity and sample purity issues.
- Misinterpretation of correlational data and bulk data attribution are common pitfalls.
Conclusions:
- Understanding and mitigating biases in transcriptomic data is crucial for accurate interpretation.
- Addressing these challenges enhances the robustness of cancer research.
- Improved application of transcriptomic data can lead to better cancer therapies and diagnostics.
