Ovarian cancer, a major gynecological malignancy, often remains undetected until advanced stages, necessitating more effective early screening methods. Existing biomarker based on differential genes often suffers from variations in clinical practice. To overcome the limitations of absolute gene expression values including batch effects and biological heterogeneity, we introduced a pairwise biosignature leveraging intra-sample differentially ranked genes (DRGs) and machine learning for ovarian cancer detection across diverse cohorts. We analyzed ten cohorts comprising 872 samples with 796 ovarian cancer and 76 normal. Our method, DRGpair, involves three stages: intra-sample ranking differential analysis, reversed gene pair analysis, and iterative LASSO regression. We identified four DRG pairs, demonstrating superior diagnostic performance compared to current state-of-the-art biomarkers and differentially expressed genes in seven independent cohorts. This rank-based approach not only reduced computational complexity but also enhanced the specificity and effectiveness of biomarkers, revealing DRGs as promising candidates for ovarian cancer detection and offering a scalable model adaptable to varying cohort characteristics.

中文翻译：

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基于差异排序基因的样本内反向对揭示了卵巢癌的生物特征

卵巢癌是一种主要的妇科恶性肿瘤，通常直到晚期才被发现，因此需要更有效的早期筛查方法。现有的基于差异基因的生物标志物在临床实践中经常会出现变化。为了克服绝对基因表达值的局限性，包括批次效应和生物异质性，我们引入了一种成对生物印记，利用样本内差异排序基因（DRG）和机器学习来检测不同群体的卵巢癌。我们分析了 10 个队列，包括 872 个样本，其中 796 例为卵巢癌，76 例为正常。我们的方法 DRGpair 涉及三个阶段：样本内排名差异分析、反向基因对分析和迭代 LASSO 回归。我们确定了四个 DRG 对，与当前最先进的生物标志物和七个独立队列中的差异表达基因相比，展示了卓越的诊断性能。这种基于排名的方法不仅降低了计算复杂性，而且增强了生物标志物的特异性和有效性，揭示了 DRG 作为卵巢癌检测的有希望的候选者，并提供了适应不同队列特征的可扩展模型。