■尽管唾液酸结合Ig样凝集素(SIGLEC)在整个癌症的内吞作用和免疫调节中具有公认的作用,它们在结肠腺癌(COAD)中的分子复杂性未得到充分开发。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。
■我们研究了SIGLEC与各种属性之间的相关性,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。为了充分了解多个SIGLEC共同进化的行为并减去其主导效应,我们还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,和两个监督学习算法,最小绝对收缩和选择算子(LASSO)和神经网络(NN)。
■我们发现COAD样本中的表达水平明显降低,系统增强了不同SIGLEC之间的相关性。我们证明SIGLEC14根据Hazzard比率显着影响总生存率(OS),而使用PCA进一步提高了对OS和无病间隔(DFI)的敏感性。我们发现任何单个SIGLEC与癌症分期无关,这可以通过使用PCA得到显著改善。我们通过差异表达基因(DEG)进一步鉴定了SIGLEC-1,15和CD22作为COAD中的hub基因,考虑到与癌症状态和免疫细胞丰度的相关性,这与我们的PCA识别的关键成分PC-1,2,5一致。作为延伸,我们使用SOM对SIGLEC进行可视化,并显示COAD患者之间的异同.SOM还可以帮助我们根据SIGLEC状态定义子样本,免疫细胞和癌症T分期都有相应的变化,例如。
■我们得出结论,SIGLEC-1,15和CD22是SIGLEC家族中治疗COAD最有前途的中心基因。PCA在预后和临床分析方面提供了显着增强,在使用SOM的同时,进一步揭示了COAD的过渡阶段或潜在亚型。
UNASSIGNED: Despite 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.
UNASSIGNED: We 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).
UNASSIGNED: We 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.
UNASSIGNED: We 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.