Lv Shaohua, Qian Zhipeng, Li Jianhao, Piao Songlin, Li Jichen
Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150081, China.
Stomatology School, Harbin Medical University, 143 Yiman Street, Nangang District, Harbin, Heilongjiang, China.
J Oncol. 2022 Feb 7;2022:5286251. doi: 10.1155/2022/5286251. eCollection 2022.
Oral squamous cell carcinoma (OSCC) is a commonly encountered head and neck malignancy. Increasing evidence shows that there are abnormal immune response and chronic cell hypoxia in the development of OSCC. However, there is a lack of a reliable hypoxia-immune-based gene signature that may serve to accurately prognosticate OSCC.
The mRNA expression data of OSCC patients were extracted from the TCGA and GEO databases. Hypoxia status was identified using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Both ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used for further evaluation of immune status. The DEGs in different hypoxia and immune status were determined, and univariate Cox regression was used to identify significantly prognostic genes. A machine learning method, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, allowed us to construct prognostic gene signature to predict the overall survival (OS) of OSCC patients.
A total of 773 DEGs were identified between hypoxia high and low groups. According to immune cell infiltration, patients were divided into immune high, medium, and low groups and immune-associated DEGs were identified. A total of 193 overlapped DEGs in both immune and hypoxia status were identified. With the univariate and LASSO Cox regression model, eight signature mRNAs (FAM122C, RNF157, RANBP17, SOWAHA, KIAA1211, RIPPLY2, INSL3, and DNAH1) were selected for further calculation of their respective risk scores. The risk score showed a significant association with age and perineural and lymphovascular invasion. In the GEO validation cohort, a better OS was observed in patients from the low-risk group in comparison with those in the high-risk group. High-risk patients also demonstrated different immune infiltration characteristics from the low-risk group and the low-risk group showed potentially better immunotherapy efficacy in contrast to high-risk ones.
The hypoxia-immune-based gene signature has prognostic potential in OSCC.
口腔鳞状细胞癌（OSCC）是一种常见的头颈部恶性肿瘤。越来越多的证据表明，在OSCC的发生发展过程中存在异常免疫反应和慢性细胞缺氧。然而，缺乏一种可靠的基于缺氧-免疫的基因特征来准确预测OSCC的预后。
从TCGA和GEO数据库中提取OSCC患者的mRNA表达数据。使用t分布随机邻域嵌入（t-SNE）算法确定缺氧状态。采用ESTIMATE和单样本基因集富集分析（ssGSEA）进一步评估免疫状态。确定不同缺氧和免疫状态下的差异表达基因（DEGs），并使用单变量Cox回归来识别具有显著预后意义的基因。一种机器学习方法，即最小绝对收缩和选择算子（LASSO）Cox回归分析，使我们能够构建预后基因特征来预测OSCC患者的总生存期（OS）。
在缺氧高分组和低分组之间共鉴定出773个DEGs。根据免疫细胞浸润情况，将患者分为免疫高、中、低三组，并鉴定出免疫相关的DEGs。在免疫和缺氧状态下共鉴定出193个重叠的DEGs。通过单变量和LASSO Cox回归模型，选择了8个特征mRNA（FAM122C、RNF157、RANBP17、SOWAHA、KIAA1211、RIPPLY2、INSL3和DNAH1）进一步计算它们各自的风险评分。风险评分与年龄、神经周围和淋巴管侵犯显著相关。在GEO验证队列中，低风险组患者的OS明显优于高风险组患者。高风险患者与低风险组也表现出不同的免疫浸润特征，与高风险患者相比，低风险组显示出潜在更好的免疫治疗效果。
基于缺氧-免疫的基因特征在OSCC中具有预后潜力。
