利用单细胞RNA测序（ScRNA-seq）和批量RNA分析对HPV阴性口腔鳞状细胞癌T细胞进行异质性分析和预后模型构建。

Heterogeneity analysis and prognostic model construction of HPV negative oral squamous cell carcinoma T cells using ScRNA-seq and bulk-RNA analysis.

作者信息

Li Chunyan, Lv Zengbo, Li Chongxin, Yang Shixuan, Liu Feineng, Zhang Tengfei, Wang Lin, Zhang Wen, Deng Ruoyu, Xu Guoyu, Luo Huan, Zhao Yinhong, Lv Jialing, Zhang Chao

机构信息

Department of Oncology, the First People's Hospital of Qujing City/the Qujing Affiliated Hospital of Kunming Medical University, 1 Yuanlin Road, Qujing, Yunnan, China.

出版信息

Funct Integr Genomics. 2025 Jan 23;25(1):25. doi: 10.1007/s10142-024-01525-6.

DOI:10.1007/s10142-024-01525-6

PMID:39849233

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11759468/

Abstract

BACKGROUND

T cells are involved in every stage of tumor development and significantly influence the tumor microenvironment (TME). Our objective was to assess T-cell marker gene expression profiles, develop a predictive risk model for human papilloma virus (HPV)-negative oral squamous cell carcinoma (OSCC) utilizing these genes, and examine the correlation between the risk score and the immunotherapy response.

METHODS

We acquired scRNA-seq data for HPV-negative OSCC from the GEO datasets. We performed cell‒cell communication, trajectory, and pathway enrichment analyses of T-cell-associated genes. In addition, we constructed and validated a T-cell-associated gene prognostic model for HPV-negative OSCC patients using TCGA and GEO data and assessed the immune infiltration status of HPV-negative OSCC patients .qRT-PCR was used to detect the expression level of prognosis-related genes in different risk groups.

RESULTS

ScRNA-seq was conducted on 28,000 cells derived from 14 HPV-negative OSCC samples and 6 normal samples. We identified 4,635 T cells from these cells and identified 774 differentially expressed genes(DEGs) associated with T cells across five distinct T-cell subtypes. Through the integration of bulk-RNAseq data, we established a prognostic model based on DEGs related to T cells. By separating patients into high-risk and low-risk groups according to these prognostic related genes, we can accurately predict their survival rates and the immune infiltration status of the TME.qRT-PCR results showed that compared with the patients of low risk group, the expression of PMEPA1, SH2D2A, SMS and PRDX4 were significantly up-regulated in high risk group.

CONCLUSION

This study provides a resource for understanding the heterogeneity of T cells in HPV-negative OSCC patients and associated prognostic risk models. It provides new insights for predicting survival and level of immune infiltration in patients with HPV-negative OSCC.

摘要

背景

T细胞参与肿瘤发展的各个阶段，并对肿瘤微环境（TME）产生显著影响。我们的目的是评估T细胞标志物基因表达谱，利用这些基因建立人乳头瘤病毒（HPV）阴性口腔鳞状细胞癌（OSCC）的预测风险模型，并研究风险评分与免疫治疗反应之间的相关性。

方法

我们从GEO数据集中获取了HPV阴性OSCC的单细胞RNA测序（scRNA-seq）数据。我们对T细胞相关基因进行了细胞间通讯、轨迹和通路富集分析。此外，我们使用TCGA和GEO数据构建并验证了HPV阴性OSCC患者的T细胞相关基因预后模型，并评估了HPV阴性OSCC患者的免疫浸润状态。采用qRT-PCR检测不同风险组中预后相关基因的表达水平。

结果

对来自14个HPV阴性OSCC样本和6个正常样本的28000个细胞进行了scRNA-seq。我们从这些细胞中鉴定出4635个T细胞，并在五种不同的T细胞亚型中鉴定出774个与T细胞相关的差异表达基因（DEG）。通过整合批量RNA测序数据，我们建立了一个基于与T细胞相关的DEG的预后模型。根据这些预后相关基因将患者分为高风险组和低风险组，我们可以准确预测他们的生存率和TME的免疫浸润状态。qRT-PCR结果显示，与低风险组患者相比，高风险组中PMEPA1、SH2D2A、SMS和PRDX4的表达显著上调。