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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
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Pathological imaging-assisted cancer gene-environment interaction analysis.

Kuangnan Fang1, Jingmao Li1, Qingzhao Zhang1,2

  • 1Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, China.

Biometrics
|May 3, 2023
PubMed
Summary

This study introduces a novel method using pathological imaging data to improve gene-environment interaction analysis for cancer. This approach enhances prediction accuracy and stability in understanding cancer outcomes.

Keywords:
assisted analysiscancer G-E interaction analysishigh-dimensional penalizationpathological imaging

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Area of Science:

  • Genomics and Bioinformatics
  • Cancer Research
  • Medical Imaging Analysis

Background:

  • Gene-environment (G-E) interactions are crucial for understanding cancer outcomes and phenotypes.
  • G-E interaction analysis faces challenges like high dimensionality and weak signals, unlike main-effect analyses.
  • Existing methods for G-E interaction analysis often lack sufficient information.

Purpose of the Study:

  • To develop an assisted estimation and variable selection approach for gene-environment interaction analysis in cancer.
  • To leverage pathological imaging data as an additional information source for G-E interaction analysis.
  • To improve the analysis of gene expression and overall survival in lung adenocarcinoma (LUAD).

Main Methods:

  • Developed a penalization-based approach to integrate pathological imaging data into G-E interaction analysis.
  • Employed an assisted estimation and variable selection strategy.
  • Applied the method to The Cancer Genome Atlas (TCGA) data for lung adenocarcinoma (LUAD).

Main Results:

  • The proposed method demonstrates competitive performance in simulation studies.
  • Analysis of TCGA LUAD data using pathological imaging data yielded distinct findings compared to traditional methods.
  • The assisted G-E interaction analysis showed competitive prediction performance and improved stability.

Conclusions:

  • Pathological imaging data can serve as a valuable, readily available information source to enhance G-E interaction analysis in cancer.
  • The developed penalization-based approach offers an intuitive and effective strategy for assisted G-E interaction analysis.
  • This novel approach provides improved insights into cancer outcomes by integrating diverse data types.