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Updated: Aug 16, 2025

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

Published on: February 27, 2015

Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles.

Ania Mesa-Rodríguez^1,2, Augusto Gonzalez^1,3, Ernesto Estevez-Rams⁴

¹The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Sciences and Technology of China, Chengdu 610054, China.

Entropy (Basel, Switzerland)

|December 23, 2022

Summary

This summary is machine-generated.

This study introduces entropic measures for analyzing gene expression data to classify cancer types. These methods effectively distinguish between tumor and normal tissues, aiding in cancer research and diagnosis.

Keywords:

Shannon entropy gene expression information distance tumor discrimination

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Massive gene expression datasets present challenges in data processing and information extraction.
Accurate classification of tumor versus normal tissues is crucial for cancer diagnosis and treatment.

Purpose of the Study:

To apply entropic measures for discriminating between tumor and normal tissue samples using comprehensive gene expression data.
To explore the utility of complexity-entropy diagrams and information distance for cancer classification.

Main Methods:

Utilized entropic measures on whole gene expression datasets to classify samples.
Generated complexity-entropy diagrams by ordering gene expression by pathways.
Applied information distance analysis for further discrimination.

Main Results:

Achieved high success rates in classifying tumor and normal samples across 13 cancer types.
Successfully clustered tumor and normal samples using complexity-entropy diagrams for nine of the thirteen cancer types.
Information distance analysis demonstrated effective discrimination between cancer types, not just tumor vs. normal.

Conclusions:

Developed a novel procedure using entropic measures for tissue classification without prior gene identification or specific cancer models.
The proposed methods show significant potential for broad application in various classification problems beyond cancer research.
This approach offers a powerful tool for analyzing complex biological data and advancing cancer diagnostics.