RNF43 p.G659fs 通过 PI3K/AKT/mTOR 信号通路和 HLA-E 上调导致 MSI-high 结直肠癌中 NK 细胞功能障碍
RNF43 p.G659fs leads to natural killer cell dysfunction in MSI-high colorectal cancer through PI3K/AKT/mTOR signaling and HLA-E up-regulation.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
结直肠癌的诊断基因和免疫浸润分析通过 LASSO 和 SVM 机器学习方法确定：一项生物信息学分析
英文原题：Diagnostic genes and immune infiltration analysis of colorectal cancer determined by LASSO and SVM machine learning methods: a bioinformatics analysis.
查看英文原题
Diagnostic genes and immune infiltration analysis of colorectal cancer determined by LASSO and SVM machine learning methods: a bioinformatics analysis.
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研究概要
ASCL2、BEST4、CFD、DPEPCFD、FOXQ1、TRIB3、KLF4、MMP7、MMP11、PYY 和 PDK4 被确定为结肠癌诊断的关键基因。这些基因有望成为结直肠癌的新型诊断标志物和新药物疗法的靶点。
研究思路结论见上方概要
背景
遗传因素约占结直肠癌风险的35%。既往结直肠癌诊断生物标志物的特异性和敏感性无法满足临床应用的需求。生物数据规模的不断扩大及其固有的复杂性，促使机器学习越来越多地被用于构建具有信息量和预测性的潜在生物学过程模型。本研究的目的是利用机器学习方法识别结直肠癌的诊断基因。
方法
从基因表达综合（GEO）数据库下载了GSE41328和GSE106582数据集。分析了结肠癌与正常组织之间的基因表达差异。通过最小绝对收缩和选择算子（LASSO）和支持向量机（SVM）回归筛选并验证了结直肠癌关键基因。通过CIBERSORT进一步分析了结肠癌患者的免疫细胞浸润及其与关键基因的相关性。
结果
确定了11个关键基因作为结肠癌的生物标志物，即ASCL2、BEST4、CFD、DPEPCFD、FOXQ1、TRIB3、KLF4、MMP7、MMP11、PYY和PDK4。所有11个基因用于结肠癌诊断的受试者工作特征（ROC）曲线下面积（AUC）平均值为0.94，范围为0.91-0.97。在验证集中，11个关键基因在结肠癌和正常受试者之间的表达存在显著差异（P<0.05），平均AUC为0.82，范围为0.70-0.88。免疫细胞浸润分析表明，肿瘤组中浆细胞、T细胞、B细胞、NK细胞、MO、M1、静息树突状细胞、静息肥大细胞、活化肥大细胞和中性粒细胞的相对数量与正常组存在显著差异。
展开英文摘要原文
BACKGROUND
Genetic factors account for approximately 35% of colorectal cancer risk. The specificity and sensitivity of previous diagnostic biomarkers for colorectal cancer could not meet the need of clinical application. The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning to build informative and predictive models of the underlying biological processes. The aim of this study is to identify diagnostic genes of colorectal cancer by using machine learning methods.
METHODS
The GSE41328 and GSE106582 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The gene expression differences between colon cancer and normal tissues were analyzed. The key colorectal cancer genes were screened and validated by Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) regression. Immune cell infiltration and the correlation with the key genes in patients with colon cancer were further analyzed by CIBERSORT.
RESULTS
Eleven key genes were identified as biomarkers for colon cancer, namely ASCL2, BEST4, CFD, DPEPCFD, FOXQ1, TRIB3, KLF4, MMP7, MMP11, PYY, and PDK4 . The mean area under the receiver operating characteristic (ROC) curve (AUC) of all 11 genes for colon cancer diagnosis were 0.94 with a range of 0.91-0.97. In the validation set, the expression of the 11 key genes was significantly different between colon cancer and normal subjects (P<0.05) and the mean AUCs were 0.82 with a range of 0.70-0.88. Immune cell infiltration analyses demonstrated that the relative quantity of plasma cells, T cells, B cells, NK cells, MO, M1, Dendritic cells resting, Mast cells resting, Mast cells activated, and Neutrophils in the tumor group were significantly different to the normal group.
CONCLUSIONS
ASCL2, BEST4, CFD, DPEPCFD, FOXQ1, TRIB3, KLF4, MMP7, MMP11, PYY , and PDK4 were identified as the key genes for colon cancer diagnosis. These genes are expected to become novel diagnostic markers and targets of new pharmacotherapies for colorectal cancer.
论文信息
- 作者
- Li YR、Meng K、Yang G、Liu BH、Li CQ、Zhang JY、Zhang XM
- 第一作者单位
- Department of Gastroenterology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China.China
- 通讯作者单位
- Department of Gastroenterology and Hepatology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.China
- 期刊
- Journal of gastrointestinal oncology2022 Jun
- 原文标识
- PubMed 35837194 · DOI 10.21037/jgo-22-536
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