Technical and Biological Biases in Bulk Transcriptomic Data Mining for Cancer Research

Cancer research has been significantly advanced by the integration of transcriptomic data through high-throughput sequencing technologies like RNA sequencing (RNA-seq). This paper reviews the transformative impact of transcriptomics on understanding cancer biology, focusing on the use of extensive datasets such as The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). While transcriptomic data provides crucial insights into gene expression patterns and disease mechanisms, the analysis is fraught with technical and biological biases. Technical biases include issues related to microarray, RNA-seq, and nanopore sequencing methods, while biological biases arise from factors like tumor heterogeneity…

• NN
  National Natural Science Foundation of China

  Award: 81603342
• KC
  K. C. Wong Education Foundation
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2022A1515012641