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Related Experiment Video

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Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection

Zishuang Zhang1, Zhi-Ping Liu2,3

  • 1Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, Shandong, China.

BMC Medical Genomics
|August 26, 2021
PubMed
Summary

Robust biomarker discovery for hepatocellular carcinoma (HCC) is crucial. This study identifies reliable HCC gene biomarkers by analyzing TCGA data using multiple feature selection methods, enhancing diagnostic accuracy.

Keywords:
Akaike information criterionBiomarker discoveryFeature selectionHepatocellular carcinomaOmics data

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Hepatocellular carcinoma (HCC) is a prevalent cancer, necessitating reliable biomarkers for diagnosis and prognosis.
  • High-throughput omics data from The Cancer Genome Atlas (TCGA) offers a valuable resource for identifying HCC biomarker genes.
  • Existing biomarker discovery methods lack investigation into the robustness of identification across different feature selection techniques.

Purpose of the Study:

  • To develop a robust method for discovering hepatocellular carcinoma (HCC) biomarker genes from gene expression data.
  • To identify reliable HCC biomarkers by finding consensus gene signatures across multiple feature selection techniques.
  • To provide a statistical interpretation for the feature selection process using Akaike Information Criterion (AIC).

Main Methods:

  • Utilized six different recursive feature elimination (RFE) methods with cross-validation (RFE-CV) on TCGA liver cancer data.
  • Identified robust biomarkers as genes consistently selected across the six different RFE subsets.
  • Employed Akaike Information Criterion (AIC) to interpret the feature selection optimization process and validate selected biomarkers.

Main Results:

  • Overlapping gene subsets from six RFE-CV classifiers represent robust HCC biomarkers.
  • Akaike Information Criterion (AIC) values provided a statistical foundation for the machine learning-based feature selection.
  • Genes within more optimally selected subsets demonstrated stronger biological relevance and implications.

Conclusions:

  • The intersection of biomarkers selected by different classifiers enhances feature selection quality.
  • The proposed method offers a robust approach for screening biomarkers in complex diseases using high-throughput data.
  • The interpretable and robust biomarker discovery method proved superior in classification tasks.