Predicting Outcomes in Esophageal Squamous Cell Carcinoma Using scRNA-Seq and Bulk RNA-Seq: A Model Development and Validation Study.

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2025-01-01 DOI:10.1002/cam4.70617

Jiaqi Zhang, Shunzhe Song, Yuqing Li, Aixia Gong

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Abstract

Background: Altered glucose metabolism is a critical characteristic from the beginning stage of esophageal squamous cell carcinoma (ESCC), and the phenomenon is presented as a pink-color sign under endoscopy after iodine staining. Therefore, calculating the metabolic score based on the glucose metabolic gene sets may bring some novel insights, enabling the prediction of prognosis and the identification of treatment choices for ESCC.

Methods: A total of 8, 99, and 140 individuals from The Gene Expression Omnibus database, The Cancer Genome Atlas database, and the Memorial Sloan Kettering Cancer Center, respectively, were encompassed in the investigation. Patients diagnosed with ESCC after surgery were enrolled for further validation.

Results: A total of 13 kinds of cell clusters were screened, and the squamous epithelium was identified with the highest score. And 558 differential genes were selected from the single-cell RNA sequencing (scRNA-seq) dataset. Four glucose metabolism-related genes, namely, SERP1, CTSC, RAP2B, and SSR4, were identified as hub genes to develop a risk prognostic model. The model was validated in another external cohort. According to the risk score (RS) determined by the model, the patients were categorized into low- and high-risk groups (LRG and HRG). Compared with LRG, HRG indicated poor survival and decreased drug sensitivity. Additionally, the immune microenvironment and pathway enrichment were different between the two groups. Immunohistochemical staining revealed that hub genes were expressed differently in ESCC tissues, high- and low-grade intraepithelial neoplasia, and adjacent normal tissues.

Conclusion: Four hub genes (SERP1, CTSC, RAP2B, and SSR4) screened based on glucose metabolism developed a predictive model in ESCC patients. The RS was established as an independent risk factor for predicting prognosis. These findings may enhance understanding of ESCC's molecular profile and serve as a new prognostic tool for better patient stratification and treatment planning in clinical practice.

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使用scRNA-Seq和Bulk RNA-Seq预测食管鳞状细胞癌的预后：一项模型开发和验证研究

背景：糖代谢改变是食管鳞状细胞癌（ESCC）早期的一个关键特征，碘染色后在内镜下表现为粉红色征象。因此，基于葡萄糖代谢基因集计算代谢评分可能会带来一些新的见解，从而能够预测ESCC的预后和确定治疗选择。方法：研究对象分别来自The Gene Expression Omnibus数据库、The Cancer Genome Atlas数据库和Memorial Sloan Kettering Cancer Center的8、99和140名个体。手术后诊断为ESCC的患者入组进一步验证。结果：共筛选到13种细胞簇，以鳞状上皮细胞鉴定得分最高。从单细胞RNA测序（scRNA-seq）数据集中筛选出558个差异基因。4个葡萄糖代谢相关基因，即SERP1、CTSC、RAP2B和SSR4，被确定为枢纽基因，以建立风险预后模型。该模型在另一个外部队列中得到了验证。根据模型确定的风险评分（RS）将患者分为低危组（LRG）和高危组（HRG）。与LRG相比，HRG的生存期较差，药物敏感性降低。此外，两组之间的免疫微环境和途径富集也不同。免疫组化染色显示，枢纽基因在ESCC组织、高、低级别上皮内瘤变及邻近正常组织中表达不同。结论：基于糖代谢筛选的4个中心基因（SERP1、CTSC、RAP2B和SSR4）在ESCC患者中建立了预测模型。RS是预测预后的独立危险因素。这些发现可能会增强对ESCC分子特征的理解，并在临床实践中为更好的患者分层和治疗计划提供新的预后工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.