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Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

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Published on: September 20, 2016

Comparative Analysis of High-Throughput Data in AML Detection.

Anastasios Theodorou¹, Costas Papaloukas²

¹Department of Biological Applications and Technologies, University of Ioannina, Ioannina, Greece.

Advances in Experimental Medicine and Biology

|January 1, 2026

Summary

This summary is machine-generated.

This study compares gene expression profiling in Acute myeloid leukemia (AML) using microarrays and RNA-sequencing. It developed a classifier to distinguish AML from control samples, achieving high accuracy across platforms.

Keywords:

Acute Myeloid Leukemia Gene Set Enrichment Analysis Logistic Regression Classifier Microarrays RNA-sequencing

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Published on: November 5, 2019

Area of Science:

Genomics
Bioinformatics
Oncology

Background:

Acute myeloid leukemia (AML) presents diagnostic and therapeutic challenges due to genetic complexity.
Gene expression profiling is crucial for understanding AML heterogeneity and identifying biomarkers.

Purpose of the Study:

To evaluate microarrays and next-generation RNA sequencing (RNA-seq) for distinguishing AML from control samples.
To develop and validate a cross-platform gene expression classifier for AML detection.

Main Methods:

Utilized datasets from microarrays and RNA-seq to identify differentially expressed genes in AML.
Developed a classifier based on selected probes and performed cross-mapping between platforms.
Conducted gene set enrichment analysis to identify relevant biological pathways.

Main Results:

A classifier trained on the same platform achieved high accuracy (e.g., 98.9% accuracy on RNA-seq).
Cross-mapping probes significantly improved classifier reliability on the microarray platform (99.3% accuracy).
Identified key pathways including "Protein processing in endoplasmic reticulum" and "Proteoglycans in cancer" associated with AML.

Conclusions:

Both microarrays and RNA-seq are valuable for AML gene expression studies.
A cross-platform classifier demonstrates high adaptability and reliability for AML detection.
Gene expression analysis reveals significant pathways implicated in AML pathogenesis.