Abstract
In cancer research, transcriptional aberrations are often deduced from mRNA-based gene expression profiling (GEP). Although transcriptome sequencing (RNA-seq) has gained ground in the recent past, mRNA-based microarrays remain a useful asset for high-throughput experiments in many laboratories. Possible reasons are the lower per-sample costs and the opportunity to analyze obtained GEP data in association with published data sets. There are established and widely used methods for the analysis of microarray data, which increase the comparability of different GEP data sets and facilitate data-mining approaches. However, analytic pitfalls, such as batch effects and issues of sample purity, e.g. by complex tissue composition, are often not properly addressed by these standard approaches. Moreover, most of these tools do not capitalize on the full range of public data sources or do not take advantage of the analytic possibilities for functional interpretation or of comprehensive meta-analyses. We present an overview of the most critical steps in the analysis of microarray-based GEP data. We discuss software and database query solutions that may be useful foreach step and for generally overcoming analytic challenges. Aside from machine-learning applications to classify and cluster samples, we describe clinical applications of GEP, including a novel exploratory algorithm to identify potential biomarkers of prognosis in small sample cohorts as demonstrated by exemplary data from lymphatic leukemias. Overall, this review and the attached source code provide guidance to both molecular biologists and bioinformaticians / biostatisticians to properly conduct GEP analyses as well as to evaluate the clinical / biological relevance of obtained results.
Highlights
Gene expression analysis includes reverse transcription of mRNA into cDNA and probing of gene transcripts of interest by specific primers designed for target PCR amplification, followed by quantitative, semi-quantitative, or electrophoresis (e.g. Southern blotting) detection methods
Two public databases are commonly used for the comparison of own microarray data with independent data sets, for example in a meta-analysis, namely the GEO database [21] and the ArrayExpress database [22], with GEO featuring a larger number of integrated samples
For gene expression profiling (GEP) studies in such scenarios, we propose an alternative algorithm for the identification of prognostic gene expression signatures, which we demonstrate by the example of GEP data generated from peripheral blood tumor samples of patients with T-cell prolymphocytic leukemia (T-PLL) and chronic lymphocytic leukemia (CLL)
Summary
Gene expression analysis includes reverse transcription of mRNA into cDNA and probing of gene transcripts of interest by specific primers designed for target PCR amplification (gold standard), followed by quantitative, semi-quantitative (e.g. qRT-PCR), or electrophoresis (e.g. Southern blotting) detection methods. RNA-seq does not require the prior design of specific probes, rendering it a highly versatile approach for gene expression profiling (GEP). Published reports using RNA-seq in cancer often lack statistical power for comprehensive gene expression analyses due to a limited sample size. MRNA-based microarrays have remained the initial method of choice for high-throughput analyses of gene expression in many laboratories. Reasons for this include the associated lower per-sample costs as well as the availability of already published microarray-derived GEP data in AIMS Medical Science. To illustrate the proposed analytic steps, we present analyses on exemplary data of previously published and own GEP data, all obtained in patients with B- and T-cell leukemias or lymphomas
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
