Yang Long, Tian Ye, Cao Xiaofei, Wang Jiawei, Luo Baoyang
Department of Gastrointestinal Surgery, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou, 225300, China.
Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, China.
Discov Oncol. 2024 Oct 10;15(1):542. doi: 10.1007/s12672-024-01398-y.
Liver metastasis is one of the primary causes of poor prognosis in colon adenocarcinoma (COAD) patients, but there are few studies on its biomarkers.
The Cancer Genome Atlas (TCGA)-COAD, GSE41258, and GSE49355 datasets were acquired from the public database. Differentially expressed genes (DEGs) between liver metastasis and primary tumor samples in COAD were identified by limma, and functional enrichment analysis were performed. MuTect2 and maftools were used to measure somatic mutation rates, while ADTEx was used to measure copy number variations (CNVs). The intersection of three machine learning methods, support vector machine (SVM), Random Forest, and least absolute shrinkage and selection operator (LASSO), is utilized to screen biomarkers, and their diagnostic performance is subsequently validated. The correlation between biomarkers and immune cells infiltration was analyzed by Spearman method.
47 DEGs between liver metastasis and primary tumor samples in COAD were obtained, which were mainly enriched in the complement and coagulation, extracellular matrix (ECM), and peptidase regulator activity, etc. 38 out of 47 DEGs had mutations and exhibited a high frequency of CNV amplification or deletion. Furthermore, 3 biomarkers (MMP3, MAB21L2, and COLEC11) were screened, which showed good diagnostic performance. The proportion of multiple immune cells, such as B cells naive, T cells CD4 naive, Monocytes, and Dendritic cells resting, was higher in liver metastasis samples than that in primary tumor samples. Meanwhile, MMP3, MAB21L2, and COLEC11 exhibited an outstanding correlation with immune cells infiltration.
In short, 3 biomarkers with good diagnostic efficacy were identified, providing a new perspective of therapeutic targets for liver metastasis in COAD.
肝转移是结肠腺癌（COAD）患者预后不良的主要原因之一，但其生物标志物的研究较少。
从公共数据库获取癌症基因组图谱（TCGA）-COAD、GSE41258和GSE49355数据集。使用limma识别COAD中肝转移样本和原发肿瘤样本之间的差异表达基因（DEG），并进行功能富集分析。使用MuTect2和maftools测量体细胞突变率，而ADTEx用于测量拷贝数变异（CNV）。利用支持向量机（SVM）、随机森林和最小绝对收缩和选择算子（LASSO）这三种机器学习方法的交集来筛选生物标志物，随后验证其诊断性能。通过Spearman方法分析生物标志物与免疫细胞浸润之间的相关性。
获得了COAD中肝转移样本和原发肿瘤样本之间的47个DEG，主要富集于补体和凝血、细胞外基质（ECM）和肽酶调节活性等方面。47个DEG中有38个发生了突变，并表现出较高频率的CNV扩增或缺失。此外，筛选出3个生物标志物（MMP3、MAB21L2和COLEC11），其显示出良好的诊断性能。肝转移样本中多种免疫细胞的比例，如幼稚B细胞、幼稚CD4 T细胞、单核细胞和静息树突状细胞，高于原发肿瘤样本。同时，MMP3、MAB21L2和COLEC11与免疫细胞浸润表现出显著相关性。
简而言之，鉴定出了3个具有良好诊断效能的生物标志物，为COAD肝转移的治疗靶点提供了新的视角。
