PDF(Pubmed)
Abstract：
Uterine pathologies pose a challenge to women\'s health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.
摘要：
子宫病变在全球范围内对妇女的健康构成挑战。尽管进行了广泛的研究,一些常见疾病的原因和起源尚未明确。这项研究提出了从不同的数据集转录组数据的综合分析，包括相关的子宫病理学，如子宫内膜异位症，子宫内膜癌和子宫平滑肌瘤。利用Shapley值比较分析(CASH)技术，我们证明了其在改善经典差异表达分析的结果方面的功效，这些结果来自微阵列实验的转录组数据。CASH集成了微阵列游戏算法与Bootstrap重采样，提供一个强大的统计框架，以减轻表达数据中潜在异常值的影响。我们的发现揭示了这些妇科疾病背后的分子特征的新见解，强调CASH是在复杂的生物学环境中提高转录组学分析精度的有价值的工具。这项研究有助于更深入地了解与这些病理相关的基因表达模式和潜在的生物标志物。为未来的诊断和治疗策略提供启示。
