Novel cancer biomarkers discoveries are driven by the application of omics technologies. The vast quantity of highly dimensional data necessitates the implementation of feature selection. The mathematical basis of different selection methods varies considerably, which may influence subsequent inferences. In the study, feature selection and classification methods were employed to identify six signature gene sets of grade 2 and 3 astrocytoma samples from the Rembrandt repository. Subsequently, the impact of these variables on classification and further discovery of biological patterns was analysed. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and hierarchical clustering revealed that the data set (10,096 genes) exhibited a high degree of noise, feature redundancy, and lack of distinct patterns. The application of feature selection methods resulted in a reduction in the number of genes to between 28 and 128. Notably, no single gene was selected by all of the methods tested. Selection led to an increase in classification accuracy and noise reduction. Significant differences in the Gene Ontology terms were discovered, with only 13 terms overlapping. One selection method did not result in any enriched terms. KEGG pathway analysis revealed only one pathway in common (cell cycle), while the two methods did not yield any enriched pathways. The results demonstrated a significant difference in outcomes when classification-type algorithms were utilised in comparison to mixed types (selection and classification). This may result in the inadvertent omission of biological phenomena, while simultaneously achieving enhanced classification outcomes.
新型癌症生物标志物的发现得益于组学技术的应用。海量的高维度数据使得特征筛选的实施成为必要。不同筛选方法的数学基础存在显著差异，这可能影响后续的推断分析。本研究采用特征筛选与分类方法，从Rembrandt数据库中鉴定出6组2级和3级星形细胞瘤样本的特征基因集。随后分析了这些变量对分类及生物学模式进一步发现的影响。主成分分析（PCA）、均匀流形逼近与投影（UMAP）以及层次聚类分析显示，该数据集（10,096个基因）存在高度噪声、特征冗余且缺乏明显模式特征。应用特征筛选方法后，基因数量减少至28至128个。值得注意的是，所有测试方法均未共同筛选出任何一个基因。筛选过程提高了分类准确性并降低了噪声干扰。基因本体（GO）术语分析显示出显著差异，仅13个术语存在重叠。其中一种筛选方法未产生任何富集术语。KEGG通路分析仅发现一个共同通路（细胞周期），而两种方法未产生任何富集通路。研究结果表明，与混合类型方法（筛选与分类结合）相比，使用分类型算法会产生显著不同的结果。这可能导致生物学现象被无意忽略，但同时也能获得更好的分类效果。
Signature Genes Selection and Functional Analysis of Astrocytoma Phenotypes: A Comparative Study
肿瘤(癌症)患者之家