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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Technical differences between sequencing and microarray platforms impact transcriptomic subtyping of colorectal

Ina A Eilertsen1, Seyed H Moosavi1, Jonas M Strømme2

  • 1Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital, P.O. Box 4953, Nydalen, NO-0424, Oslo, Norway; K. G. Jebsen Colorectal Cancer Research Centre, Oslo University Hospital, P.O. Box 4953, Nydalen, NO-0424, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, P.O. Box 1171 Blindern, NO-0318, Oslo, Norway.

Cancer Letters
|November 4, 2019
PubMed
Summary

Comparing RNA sequencing and microarrays for colorectal cancer (CRC) subtyping revealed platform-specific biases. Assay standardization and careful gene selection are crucial for clinical translation of transcriptomic CRC subtyping.

Keywords:
CRC intrinsic subtypesClassification concordanceColorectal cancerConsensus molecular subtypesMicroarrayRNA sequencing

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Area of Science:

  • Genomics
  • Oncology
  • Bioinformatics

Background:

  • Gene expression profiling is vital for molecular screening in colorectal cancer (CRC).
  • Established frameworks exist for transcriptomic subtyping of CRC.
  • Platform-specific effects can influence transcriptomic analysis outcomes.

Purpose of the Study:

  • To investigate platform-specific effects on transcriptomic subtyping of CRC.
  • To compare gene expression profiles from RNA sequencing and exon-resolution microarrays.
  • To assess the impact of platform differences on established CRC subtyping frameworks.

Main Methods:

  • Comparison of gene expression profiles from 126 primary microsatellite stable CRCs.
  • Analysis of data generated by RNA sequencing and exon-resolution microarrays.
  • Evaluation of classification concordance for consensus molecular subtypes and CRC intrinsic subtypes (CRIS).

Main Results:

  • Strong global gene expression correspondence between platforms, with technical biases noted.
  • Systematic biases affected short, lowly expressed, and highly expressed genes.
  • Classification concordances for subtypes were strong, but subtype distributions differed due to threshold disagreements.
  • Subtypes reliant on low-expression genes (e.g., CRIS-D, tumor-infiltrating lymphocytes) showed weaker inter-platform correspondence.

Conclusions:

  • Subtle platform differences impact the clinical translation of transcriptomic CRC subtyping frameworks.
  • Assay standardization is essential for reliable transcriptomic CRC subtyping.
  • Systematic technical biases necessitate careful selection of classifier genes for accurate subtyping.