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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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RNA-seq03:21

RNA-seq

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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Comparison Of Rna-seq And Microarray In The Prediction Of Protein Expression And Survival Prediction.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Comparison Of Rna-seq And Microarray In The Prediction Of Protein Expression And Survival Prediction.

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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

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Comparison of RNA-Seq and microarray in the prediction of protein expression and survival prediction.

Won-Ji Kim1, Bo Ram Choi2, Joseph J Noh1

  • 1Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Frontiers in Genetics
|March 11, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

RNA-sequencing (RNA-seq) and microarray gene expression profiling show similar performance in predicting cancer protein levels and clinical outcomes. However, specific gene differences and varied survival model accuracy across cancer types warrant further investigation.

Keywords:
RNA-seqRPPATCGAbiomarkers

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Area of Science:

  • Genomics
  • Cancer Research
  • Biomarker Discovery

Background:

  • Gene expression profiling is crucial for identifying cancer biomarkers.
  • RNA-sequencing (RNA-seq) and microarray are common technologies for this purpose.
  • Comparing their performance is essential for reliable clinical endpoint prediction.

Purpose of the Study:

  • To compare RNA-seq and microarray performance in predicting protein expression and clinical endpoints.
  • To assess gene expression correlation with protein levels using reverse phase protein array (RPPA).
  • To evaluate survival prediction model accuracy across different platforms and cancer types.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) datasets for lung, colorectal, renal, breast, endometrial, and ovarian cancers.
microarray
protein expression
  • Calculated correlation coefficients between RNA-seq/microarray gene expression and RPPA protein expression.
  • Developed and compared random forest survival prediction models using top 103 survival-related genes.

    • Main Results:

    • High correlation between mRNA levels and protein expression measured by RPPA was observed.
    • Most genes showed similar expression correlation between RNA-seq and microarray.
    • Significant differences in 16 genes were noted; BAX and PIK3CA showed recurrent associations in specific cancers.
    • Survival model performance varied, with microarray outperforming RNA-seq in some cancers (colorectal, renal, lung) and vice versa in others (ovarian, endometrial).

    Conclusions:

    • Both RNA-seq and microarray are valuable for gene expression profiling with good correlation to protein levels.
    • Identified specific genes with differential expression patterns between the two methods.
    • Survival prediction model performance is platform- and cancer-type dependent, requiring careful consideration for clinical application.