BackgroundDespite the recognized roles of Sialic acid-binding Ig-like lectins (SIGLECs) in endocytosis and immune regulation across cancers, their molecular intricacies in colon adenocarcinoma (COAD) are underexplored. Meanwhile, the complicated interactions between different SIGLECs are also crucial but open questions.MethodsWe investigate the correlation between SIGLECs and various properties, including cancer status, prognosis, clinical features, functional enrichment, immune cell abundances, immune checkpoints, pathways, etc. To fully understand the behavior of multiple SIGLECs’ co-evolution and subtract its leading effect, we additionally apply three unsupervised machine learning algorithms, namely, Principal Component Analysis (PCA), Self-Organizing Maps (SOM), K-means, and two supervised learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) and neural network (NN).ResultsWe find significantly lower expression levels in COAD samples, together with a systematic enhancement in the correlations between distinct SIGLECs. We demonstrate SIGLEC14 significantly affects the Overall Survival (OS) according to the Hazzard ratio, while using PCA further enhances the sensitivity to both OS and Disease Free Interval (DFI). We find any single SIGLEC is uncorrelated to the cancer stages, which can be significantly improved by using PCA. We further identify SIGLEC-1,15 and CD22 as hub genes in COAD through Differentially Expressed Genes (DEGs), which is consistent with our PCA-identified key components PC-1,2,5 considering both the correlation with cancer status and immune cell abundance. As an extension, we use SOM for the visualization of the SIGLECs and show the similarities and differences between COAD patients. SOM can also help us define subsamples according to the SIGLECs status, with corresponding changes in both immune cells and cancer T-stage, for instance.ConclusionWe conclude SIGLEC-1,15 and CD22 as the most promising hub genes in the SIGLECs family in treating COAD. PCA offers significant enhancement in the prognosis and clinical analyses, while using SOM further unveils the transition phases or potential subtypes of COAD.

中文翻译：

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通过机器学习揭示结肠腺癌 SIGLEC 家族的中心基因

背景尽管唾液酸结合 Ig 样凝集素 (SIGLEC) 在癌症的内吞作用和免疫调节中的作用已得到公认，但其在结肠腺癌 (COAD) 中的分子复杂性尚未得到充分研究。同时，不同SIGLEC之间复杂的相互作用也是至关重要但悬而未决的问题。方法我们研究SIGLEC与各种特性之间的相关性，包括癌症状态、预后、临床特征、功能富集、免疫细胞丰度、免疫检查点、通路等。为了理解多个SIGLEC的共同进化行为并减去其主导效应，我们另外应用了三种无监督机器学习算法，即主成分分析（PCA）、自组织映射（SOM）、K-means和两种监督学习结果我们发现 COAD 样本中的表达水平显着降低，并且不同 SIGLEC 之间的相关性系统增强。我们证明 SIGLEC14 根据风险比显着影响总生存期 (OS)，而使用 PCA 进一步增强了对 OS 和无病间隔 (DFI) 的敏感性。我们发现任何单个 SIGLEC 与癌症分期不相关，通过使用 PCA 可以显着改善这一点。我们通过差异表达基因（DEG）进一步将 SIGLEC-1,15 和 CD22 确定为 COAD 中的中心基因，考虑到与癌症状态和免疫细胞的相关性，这与我们 PCA 确定的关键成分 PC-1,2,5 一致丰富。作为扩展，我们使用 SOM 来可视化 SIGLEC，并显示 COAD 患者之间的相似点和差异。 SOM 还可以帮助我们根据 SIGLEC 状态定义子样本，例如免疫细胞和癌症 T 阶段的相应变化。结论我们得出 SIGLEC-1,15 和 CD22 是 SIGLEC 家族中最有希望治疗的中心基因COAD。 PCA 显着增强了预后和临床分析，同时使用 SOM 进一步揭示了 COAD 的过渡阶段或潜在亚型。