Wei Hao-Tang, Xie Li-Ye, Liu Yong-Gang, Deng Ya, Chen Feng, Lv Feng, Tang Li-Ping, Hu Bang-Li
Department of Gastrointestinal Surgery, Third Affiliated Hospital of Guangxi Medical University, Nanning, China.
Department of Research, Guangxi Medical University Cancer Hospital, Nanning, China.
Front Oncol. 2024 Jun 19;14:1413273. doi: 10.3389/fonc.2024.1413273. eCollection 2024.
Angiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis.
CRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay.
Two distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls.
This study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.
血管生成在结直肠癌（CRC）中起关键作用，但其潜在机制仍需进一步探索。本研究旨在通过全面的多组学分析阐明血管生成相关基因（ARGs）在CRC中的意义。
根据ARGs表达对CRC患者进行分类，以形成血管生成相关簇（ARCs）。我们研究了ARCs与患者生存、临床特征、共识分子亚型（CMS）、癌症干细胞（CSC）指数、肿瘤微环境（TME）、基因突变及免疫治疗反应之间的相关性。利用三种机器学习算法（LASSO、Xgboost和决策树），我们筛选出与ARCs相关的关键ARGs，并在独立队列中进一步验证。基于关键ARGs开发了一种预后特征，并在单细胞RNA测序（scRNA-seq）水平进行分析。通过逆转录聚合酶链反应（RT-PCR）检测在外部队列、临床组织和血液样本中验证基因表达。
识别出两种不同的ARC亚型，它们与患者生存、临床特征、CMS、CSC指数和TME显著相关，但与基因突变无关。四个基因（S100A4、COL3A1、TIMP1和APP）被确定为关键ARCs，能够区分ARC亚型。基于这些基因的预后特征有效地将患者分为高风险或低风险类别。scRNA-seq分析表明，这些基因主要在免疫细胞而非癌细胞中表达。在两个外部队列和临床样本中的验证证实了CRC与对照之间存在显著的表达差异。
本研究在CRC中识别出两种ARG亚型，并突出了与这些亚型相关的四个关键基因，为个性化CRC治疗策略提供了新见解。
