Department of Pathology, Case Western Reserve University School of Medicine, 2103 Cornell Rd., Wolstein Research Bldg. Rm 3501, Cleveland, OH, 44106, USA.
School of Medicine, University of Michigan, Ann Arbor, MI, USA.
BMC Bioinformatics. 2024 Sep 27;25(1):314. doi: 10.1186/s12859-024-05932-1.
The interpretation of large datasets, such as The Cancer Genome Atlas (TCGA), for scientific and research purposes, remains challenging despite their public availability. In this study, we focused on identifying gene expression profiles most relevant to patient prognosis and aimed to develop a method and database to address this issue. To achieve this, we introduced Luo's Optimization Categorization Curve (LOCC), an innovative tool for visualizing and scoring continuous variables against dichotomous outcomes. To demonstrate the efficacy of LOCC using real-world data, we analyzed gene expression profiles and patient data from TCGA hepatocellular carcinoma samples.
To showcase LOCC, we demonstrate an optimal cutoff for E2F1 expression in hepatocellular carcinoma, which was subsequently validated in an independent cohort. Compared to ROC curves and their AUC, LOCC offered a superior description of the predictive value of E2F1 expression across various cancer types. The LOCC score, comprised of factors representing significance, range, and impact of the biomarker, facilitated the ranking of all gene expression profiles in hepatocellular carcinoma, aiding in the evaluation and understanding of previously published prognostic gene signatures. We also demonstrate that LOCC does not have the same assumptions required of Cox proportional hazards modeling for accurate analysis. Repeated sampling demonstrated that LOCC scores outperformed ROC's AUC in discriminating predictors from non-predictors. Additionally, gene set enrichment analysis revealed significant associations between certain genes and prognosis, such as E2F target genes and G2M checkpoint with poor prognosis, and bile acid metabolism and oxidative phosphorylation with good prognosis.
In summary, we present LOCC as a novel visualization tool for the analysis of gene expression in cancer, particularly for understanding and selecting cutoffs. Our findings suggest that LOCC scores, which effectively rank genes based on their prognostic potential, represent a more suitable approach than ROC curves and Cox proportional hazard for prognostic modeling and understanding in cancer gene expression analysis. LOCC holds promise as an invaluable tool for advancing precision medicine and furthering biomarker research. Further research regarding multivariable integration and validation will help LOCC reach its full potential and establish its utility across diverse cancer types and clinical settings.
尽管大型数据集（如癌症基因组图谱（TCGA））已经公开，但要将其用于科学和研究目的进行解释仍然具有挑战性。在这项研究中，我们专注于确定与患者预后最相关的基因表达谱，并旨在开发一种方法和数据库来解决这个问题。为了实现这一目标，我们引入了 Luo 的优化分类曲线（LOCC），这是一种用于可视化和对二分类结果进行评分的创新工具。为了使用真实世界的数据展示 LOCC 的功效，我们分析了 TCGA 肝细胞癌样本的基因表达谱和患者数据。
为了展示 LOCC，我们展示了肝细胞癌中 E2F1 表达的最佳截断值，随后在独立队列中进行了验证。与 ROC 曲线及其 AUC 相比，LOCC 更能描述 E2F1 表达在各种癌症类型中的预测价值。LOCC 评分由代表标志物的显著性、范围和影响的因素组成，有助于对肝细胞癌中所有基因表达谱进行排名，有助于评估和理解以前发表的预后基因特征。我们还证明，LOCC 不需要 Cox 比例风险模型进行准确分析所需的相同假设。重复采样表明，LOCC 评分在区分预测因子和非预测因子方面优于 ROC 的 AUC。此外，基因集富集分析显示，某些基因与预后之间存在显著关联，例如 E2F 靶基因和 G2M 检查点与预后不良相关，而胆汁酸代谢和氧化磷酸化与预后良好相关。
总之，我们提出 LOCC 作为一种新的可视化工具，用于分析癌症中的基因表达，特别是用于理解和选择截断值。我们的研究结果表明，LOCC 评分根据基因的预后潜力对基因进行有效排名，与 ROC 曲线和 Cox 比例风险相比，更适合用于癌症基因表达分析中的预后建模和理解。LOCC 有望成为推进精准医学和推进生物标志物研究的宝贵工具。进一步的多变量整合和验证研究将有助于 LOCC 充分发挥其潜力，并在不同的癌症类型和临床环境中确立其效用。
