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机器学习揭示了具有阴性淋巴结转移的结直肠癌的分子分类。

Machine learning revealed molecular classification of colorectal cancer with negative lymph node metastasis.

机构信息

College of Data Science, Jiaxing University, Jiaxing, Zhejiang, China.

School of Economics and Management, Huaibei Normal University, Huaibei, Anhui, China.

出版信息

Biomarkers. 2022 Feb;27(1):86-94. doi: 10.1080/1354750X.2021.2016971. Epub 2021 Dec 25.

Abstract

Accurate preoperative staging directly affects the treatment decision of patients with rectal cancer. However, our understanding of the immune subclasses of CRC without lymph node metastasis is still incomplete. Here, we first analyzed the subclasses of CRC without lymph node metastasis on the Cancer Genome Atlas (TCGA) and verified its stability in the GSE39582 dataset. Four immune subclasses (C1-C4) were identified and verified by non-negative matrix factorization (NMF) of gene expression profiles. Then, ICI scores of six genes were constructed to characterize subclasses. There were significant differences in metabolic and progression-associated signatures, immune characteristics, and clinical characteristics among subclasses. C3 represented a good prognosis with high TMB. C4 showed unique immune characteristics. We believe that C3 is the initial stage of CRC. After the C1 and C2 stages, it progresses to the C4 stage, and finally, lymph node metastasis occurs. This work may help to provide a basis for immunotherapy decision-making in early CRC and may guide personalized methods of cancer immunotherapy.

摘要

准确的术前分期直接影响直肠癌患者的治疗决策。然而,我们对无淋巴结转移的 CRC 的免疫亚类的了解仍不完整。在这里,我们首先在癌症基因组图谱(TCGA)上分析了无淋巴结转移的 CRC 的亚类,并在 GSE39582 数据集上验证了其稳定性。通过基因表达谱的非负矩阵分解(NMF)鉴定并验证了四个免疫亚类(C1-C4)。然后,构建了六个基因的 ICI 评分来描述亚类。亚类之间在代谢和进展相关特征、免疫特征和临床特征方面存在显著差异。C3 代表预后良好,TMB 较高。C4 表现出独特的免疫特征。我们认为 C3 是 CRC 的初始阶段。在 C1 和 C2 阶段之后,它进展到 C4 阶段,最后发生淋巴结转移。这项工作可能有助于为早期 CRC 的免疫治疗决策提供依据,并可能指导癌症免疫治疗的个性化方法。

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