Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets. In this study, we introduce a novel application (GradWise) that combines rank-based weighted hybrid filter (i.e., mRMR) and embedded (i.e., LASSO) feature selection methods to enhance the performance of feature selection and machine learning models for glioma grading using both clinical and molecular predictors. We utilized publicly available TCGA from the UCI ML Repository and CGGA datasets to identify the most effective scheme that allows for the selection of the minimum number of features with their names. Two popular feature selection methods with a rank-based weighting procedure were employed to conduct comprehensive experiments with the five supervised models. The computational results demonstrate that our proposed method achieves an accuracy rate of 87.007% with 13 features and an accuracy rate of 80.412% with five features on the TCGA and CGGA datasets, respectively. We also obtained four shared biomarkers for the glioma grading that emerged in both datasets and can be employed with transferable value to other datasets and data-based outcome analyses. These findings are a significant step toward highlighting the effectiveness of our approach by offering pioneering results with novel markers with prospects for understanding and targeting the biologic mechanisms of glioma progression to improve patient outcomes.
胶质瘤分级在指导治疗决策、预测患者预后、促进临床试验参与与研究以及制定个体化治疗策略中扮演着关键角色。目前临床胶质瘤分级主要依据切除时获取的组织样本，通过肿瘤形态学特征和分子标志物评估肿瘤侵袭性。随着对分子特征在治疗指导与预后评估中重要性的日益强调，亟需建立整合分子与临床信息的精准化、标准化分级体系。这一需求推动了超越传统预后评估范畴的分子标志物研究，旨在深化对肿瘤生物学的理解，从而识别可药物干预的靶点。 本研究提出一种创新应用方法（GradWise），通过结合基于排序的加权混合过滤法（如mRMR）与嵌入式特征选择方法（如LASSO），提升胶质瘤分级中临床与分子预测因子的特征选择及机器学习模型性能。我们利用UCI机器学习资源库的公开TCGA数据及CGGA数据集，筛选出能够以最少特征数量实现最优性能的方案，并明确特征的具体生物学意义。采用两种基于排序加权的经典特征选择方法，结合五种监督学习模型进行系统性实验验证。 计算结果表明：在TCGA和CGGA数据集中，我们提出的方法分别以13个特征达到87.007%的准确率，以5个特征实现80.412%的准确率。研究同时鉴定出四个在两个数据集中共同出现的胶质瘤分级生物标志物，这些标志物具有可迁移性，可应用于其他数据集及基于数据的预后分析。本研究成果通过提供具有创新性的分子标志物，为揭示胶质瘤进展的生物学机制及开发靶向治疗策略开辟了新途径，在提升患者临床结局方面展现出重要潜力，标志着该研究方法向实际应用迈出了关键一步。
肿瘤(癌症)患者之家