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<p>The altered gene expression caused by arsenic exposure in human is a challenging area for research since no relevant animal model is available. Despite the fact that numerous laboratory-based research has been employed microarrays to profile gene expression, the effect of this contaminant on the human population can only be accurately deduced from population-based studies due to substantial variance in dose–response sensitivity in various species and cell lines. This work seeks to establish a comprehensive knowledge of the signature profile of arsenic-induced altered gene expression and its statistical significance, which eventually leads to the development of skin lesions and other arsenicosis related manifestations. Furthermore, supplementary feature extraction strategies are used to spotlight the broad observations made over the cohort in order to determine the relationship between the identified gene expression and the exposure level and its relevance.</p>

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Integration of gene expression profiling and mathematical modelling to unravel significant biomarkers in population exposed to arsenic

  • 期刊论文

  • 发表日期：

    2025年1月29日

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  • Anirban Dey,
  • Tamalika Sanyal,
  • Pritha Bhattacharjee,
  • Kaushik Das Sharma,
  • Pritha Bhattacharjee Sr.

摘要

The altered gene expression caused by arsenic exposure in human is a challenging area for research since no relevant animal model is available. Despite the fact that numerous laboratory-based research has been employed microarrays to profile gene expression, the effect of this contaminant on the human population can only be accurately deduced from population-based studies due to substantial variance in dose–response sensitivity in various species and cell lines. This work seeks to establish a comprehensive knowledge of the signature profile of arsenic-induced altered gene expression and its statistical significance, which eventually leads to the development of skin lesions and other arsenicosis related manifestations. Furthermore, supplementary feature extraction strategies are used to spotlight the broad observations made over the cohort in order to determine the relationship between the identified gene expression and the exposure level and its relevance.