Medical Oncology Department of Gastrointestinal Cancer, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Liaoning Province, China.
Shenyang Tenth People's Hospital (Shenyang Chest Hospital), Shenyang, Liaoning, P. R. China.
IET Syst Biol. 2022 Apr;16(2):72-83. doi: 10.1049/syb2.12041.
Colon cancer (CC) is one of the most commonly diagnosed tumours worldwide. Single-cell RNA sequencing (scRNA-seq) can accurately reflect the heterogeneity within and between tumour cells and identify important genes associated with cancer development and growth. In this study, scRNA-seq was used to identify reliable prognostic biomarkers in CC. ScRNA-seq data of CC before and after 5-fluorouracil treatment were first downloaded from the Gene Expression Omnibus database. The data were pre-processed, and dimensionality reduction was performed using principal component analysis and t-distributed stochastic neighbour embedding algorithms. Additionally, the transcriptome data, somatic variant data, and clinical reports of patients with CC were obtained from The Cancer Genome Atlas database. Seven key genes were identified using Cox regression analysis and the least absolute shrinkage and selection operator method to establish signatures associated with CC prognoses. The identified signatures were validated on independent datasets, and somatic mutations and potential oncogenic pathways were further explored. Based on these features, gene signatures, and other clinical variables, a more effective predictive model nomogram for patients with CC was constructed, and a decision curve analysis was performed to assess the utility of the nomogram. A prognostic signature consisting of seven prognostic-related genes, including CAV2, EREG, NGFRAP1, WBSCR22, SPINT2, CCDC28A, and BCL10, was constructed and validated. The proficiency and credibility of the signature were verified in both internal and external datasets, and the results showed that the seven-gene signature could effectively predict the prognosis of patients with CC under various clinical conditions. A nomogram was then constructed based on features such as the RiskScore, patients' age, neoplasm stage, and tumor (T), nodes (N), and metastases (M) classification, and the nomogram had good clinical utility. Higher RiskScores were associated with a higher tumour mutational burden, which was confirmed to be a prognostic risk factor. Gene set enrichment analysis showed that high-score groups were enriched in 'cytoplasmic DNA sensing', 'Extracellular matrix receptor interactions', and 'focal adhesion', and low-score groups were enriched in 'natural killer cell-mediated cytotoxicity', and 'T-cell receptor signalling pathways', among other pathways. A robust seven-gene marker for CC was identified based on scRNA-seq data and was validated in multiple independent cohort studies. These findings provide a new potential marker to predict the prognosis of patients with CC.
结直肠癌（CC）是全球最常见的肿瘤之一。单细胞 RNA 测序（scRNA-seq）可以准确反映肿瘤细胞内和细胞间的异质性，并鉴定与癌症发展和生长相关的重要基因。本研究利用 scRNA-seq 鉴定 CC 可靠的预后生物标志物。首先从基因表达综合数据库中下载 CC 患者氟尿嘧啶治疗前后的 scRNA-seq 数据。对数据进行预处理，采用主成分分析和 t 分布随机邻域嵌入算法进行降维处理。此外，从癌症基因组图谱数据库中获取 CC 患者的转录组数据、体细胞变异数据和临床报告。利用 Cox 回归分析和最小绝对收缩和选择算子方法，确定 7 个关键基因，建立与 CC 预后相关的标志物。在独立数据集上验证所鉴定的标志物，并进一步探讨体细胞突变和潜在的致癌途径。基于这些特征、基因标志物和其他临床变量，构建了一种更有效的 CC 患者预测模型列线图，并进行决策曲线分析评估列线图的实用性。构建了一个由 7 个预后相关基因组成的基因特征，包括 CAV2、EREG、NGFRAP1、WBSCR22、SPINT2、CCDC28A 和 BCL10。在内部和外部数据集上验证了该特征的准确性和可信度，结果表明，该 7 基因标志物可以在各种临床情况下有效预测 CC 患者的预后。然后基于特征（如 RiskScore、患者年龄、肿瘤分期、肿瘤（T）、淋巴结（N）和转移（M）分类）构建了列线图，该列线图具有良好的临床实用性。较高的 RiskScore 与较高的肿瘤突变负担相关，这被证实是一个预后危险因素。基因集富集分析表明，高评分组在“细胞质 DNA 感应”、“细胞外基质受体相互作用”和“焦点粘附”等途径中富集，低评分组在“自然杀伤细胞介导的细胞毒性”和“T 细胞受体信号通路”等途径中富集。基于 scRNA-seq 数据鉴定了一个稳健的 CC 七基因标志物，并在多个独立的队列研究中得到验证。这些发现为预测 CC 患者的预后提供了一个新的潜在标志物。
