利用单细胞测序和转录组测序建立结直肠癌免疫细胞的9基因预后模型

doi:10.19401/j.cnki.1007-3639.2024.06.003

摘要/Abstract

摘要：

背景与目的：结直肠癌（colorectal carcinoma，CRC）是一种常见的恶性肿瘤，其发病率和死亡率仅次于胃癌和食管癌，在消化系统恶性肿瘤中居第二位。越来越多的证据表明，免疫细胞在CRC的发生、发展中发挥着重要作用。本研究旨在构建一个与免疫细胞相关的基因的预后模型，以预测CRC患者的预后并进行精准管理。方法：从基因表达综合（Gene Expression Omnibus，GEO）数据库和癌症基因图谱（The Cancer Genome Atlas，TCGA）数据库下载结直肠癌的单细胞RNA测序（single-cell RNA sequencing，scRNA-seq）和普通转录组测序（RNA sequencing，RNA-seq）数据以及临床信息，提取免疫细胞亚型的差异基因，通过Cox回归和LASSO回归分析在TCGA数据中筛选出与预后相关的基因，同时使用GSE39582和GSE41258进行外部验证。基于预后模型进行化疗药物敏感性分析、免疫治疗效果分析、风险评分相关的通路分析以及临床相关性分析。采用实时荧光定量聚合酶链反应（real-time fluorescence quantitative polymerase chain reaction，RTFQ-PCR）和免疫组织化学检测验证模型基因在10例经术后病理学检查的新鲜冷冻结直肠癌样本和细胞系中的表达水平，样本采集均已获得复旦大学附属肿瘤医院伦理委员会的批准（伦理编号：050432-4-2108*）。结果：本研究根据scRNA-seq数据集（GSE161277）定义了16个细胞群，并通过R包celldex将这些群标记为不同的细胞类型。然后对免疫细胞亚型进行差异分析，共获得了374个差异表达基因。通过单因素Cox和LASSO回归分析，本研究在CRC中构建了一个9基因风险预后模型。该风险模型表现出对预后的可靠预测效果，对预测抗肿瘤药物敏感性、免疫治疗效果、潜在分子机制及临床特征等方面起重要作用，高风险分数的患者从免疫疗法中受益概率较低。结论：我们基于CRC肿瘤微环境中免疫细胞的异质性构建了一个包含9个基因的风险预后模型，并据此预测了CRC患者的生存和治疗效果。

关键词: 结直肠癌, 预后模型, 肿瘤免疫与免疫治疗, 药物筛选, scRNA-seq

Abstract:

Background and purpose: Colorectal carcinoma (CRC) is a common malignant tumor with incidence and mortality rates second only to gastric cancer and esophageal cancer among digestive system malignancies. Increasing evidence suggests that immune cells play a significant role in the occurrence and development of CRC. The aim of this study was to construct a prognostic model related to immune cell-associated genes to predict the prognosis of CRC patients and enable precise management. Methods: Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data, along with clinical information for colorectal cancer, were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Differential genes of immune cell subtypes were extracted, and genes related to prognosis were screened in TCGA data using Cox regression and LASSO regression, with external validation using GSE39582 and GSE41258. The prognostic model was used to analyze chemotherapy drug sensitivity, assess immunotherapy efficacy, analyze pathways related to risk scores, and perform clinical correlation analysis. Finally, the expression levels of model genes were validated in 10 fresh frozen CRC tissues with post-operative pathological examination and cell lines using real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) and immunohistochemistry. All samples were approved by the Fudan University Shanghai Cancer Center Ethics Committee (No. 050432-4-2108*). Results: We defined 16 cell clusters based on the scRNA-seq dataset (GSE161277) and labeled these clusters as different cell types using the R package celldex. Differential analysis of immune cell subtypes yielded 374 differentially expressed genes. Using univariate Cox and LASSO analyses, we constructed a 9-gene risk prognostic model in CRC. This risk model exhibited reliable prognostic prediction performance and played an important role in predicting anti-tumor drug sensitivity, immunotherapy efficacy, potential molecular mechanisms, and clinical characteristics. Patients with high-risk scores had a lower probability of benefiting from immunotherapy. Conclusion: We constructed a 9-gene risk prognostic model based on the heterogeneity of immune cells in the CRC tumor microenvironment, which predicted the survival and treatment outcomes of CRC patients.

Key words: Colorectal carcinoma, Prognostic model, Cancer immunity and immunotherapy, Drug screening, scRNA-seq

中图分类号:

R735.3+4

唐楠, 黄慧霞, 刘晓健. 利用单细胞测序和转录组测序建立结直肠癌免疫细胞的9基因预后模型[J]. 中国癌症杂志, 2024, 34(6): 548-560.

TANG Nan, HUANG Huixia, LIU Xiaojian. Integrated single-cell sequencing and transcriptome sequencing to reveal a 9-gene prognostic signature of immune cells in colorectal cancer[J]. China Oncology, 2024, 34(6): 548-560.

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Primer	Sequence (5‘-3’)
CD36-1-F	GGCTGTGACCGGAACTGTG
CD36-1-R	AGGTCTCCAACTGGCATTAGAA
CTSD-F	ATTCAGGGCGAGTACATGATCC
CTSD-R	CGACACCTTGAGCGTGTAG
G0S2-F	TCGGCCTGATGGAGACTGT
G0S2-R	TTCTGGAGAGCCTGTCGCT
TIMP1-F	AGAGTGTCTGCGGATACTTCC
TIMP1-R	CCAACAGTGTAGGTCTTGGTG
HSPA1A-F	AGCTGGAGCAGGTGTGTAAC
HSPA1A-R	TACCTCCTCAATGGTGGGGC
VSIG4-1-F	GGGGCACCTAACAGTGGAC
VSIG4-1-R	GTCTGAGCCACGTTGTACCAG
CLEC4A-F	GACTGTGCTAGAATGGAGGCT
CLEC4A-R	GTCGCTGACCTTCTGGATCTG
TKT-1-F	TCATCGAGTGCTACATTGCTG
TKT-1-R	GCCATGCGAATCTGGTCAAAG
ASAH1-1-F	AGATGTCATGTGGATAGGGTTCC
ASAH1-1-R	GGGGCCAATATCTTGGTCTTG
β-actin-F	TTGTTACAGGAAGTCCCTTGCC
β-actin-R	ATGCTATCACCTCCCCTGTGTG