General Practice Department, The First People's Hospital of Jiashan, Jiaxing, Zhejiang, People's Republic of China.
Integrated TCM and Western Medicine Department, Cancer Hospital of The University of Chinese Academy of Sciences, Hangzhou, Zhejiang, People's Republic of China.
BMC Womens Health. 2022 Sep 3;22(1):365. doi: 10.1186/s12905-022-01942-4.
As heterogeneity of cervical squamous cell carcinoma (CSCC), prognosis assessment for CSCC patients remain challenging. To develop novel prognostic strategies for CSCC patients, associated biomarkers are urgently needed. This study aimed to cluster CSCC samples from a molecular perspective. CSCC expression data sets were obtained from The Cancer Genome Atlas and based on the accessed expression profile, a co-expression network was constructed with weighted gene co-expression network analysis to form different gene modules. Tumor microenvironment was evaluated using ESTIMATE algorithm, observing that the brown module was highly associated with tumor immunity. CSCC samples were clustered into three subtypes by consensus clustering based on gene expression profiles in the module. Gene set variation analysis showed differences in immune-related pathways among the three subtypes. CIBERSORT and single-sample gene set enrichment analysis analyses showed the difference in immune cell infiltration among subtype groups. Also, Human leukocyte antigen protein expression varied considerably among subtypes. Subsequently, univariate, Lasso and multivariate Cox regression analyses were performed on the genes in the brown module and an 8-gene prognostic model was constructed. Kaplan-Meier analysis illuminated that the low-risk group manifested a favorable prognosis, and receiver operating characteristic curve showed that the model has good predictive performance. qRT-PCR was used to examine the expression status of the prognosis-associated genes. In conclusion, this study identified three types of CSCC from a molecular perspective and established an effective prognostic model for CSCC, which will provide guidance for clinical subtype identification of CSCC and treatment of patients.
由于宫颈鳞状细胞癌（CSCC）存在异质性，因此 CSCC 患者的预后评估仍然具有挑战性。为了为 CSCC 患者开发新的预后策略，迫切需要相关的生物标志物。本研究旨在从分子角度对 CSCC 样本进行聚类。从癌症基因组图谱（The Cancer Genome Atlas）中获取 CSCC 表达数据集，并根据访问的表达谱，使用加权基因共表达网络分析构建共表达网络，以形成不同的基因模块。使用 ESTIMATE 算法评估肿瘤微环境，观察到棕色模块与肿瘤免疫高度相关。根据模块中的基因表达谱，通过共识聚类将 CSCC 样本聚类为三个亚型。基因集变异分析显示三个亚型之间存在免疫相关途径的差异。CIBERSORT 和单样本基因集富集分析显示亚组间免疫细胞浸润存在差异。此外，人类白细胞抗原蛋白在亚型之间的表达差异也很大。随后，对棕色模块中的基因进行单变量、Lasso 和多变量 Cox 回归分析，并构建了一个 8 基因预后模型。Kaplan-Meier 分析表明，低风险组表现出良好的预后，ROC 曲线表明该模型具有良好的预测性能。使用 qRT-PCR 检测与预后相关基因的表达状态。总之，本研究从分子角度鉴定了三种 CSCC，并为 CSCC 建立了有效的预后模型，这将为 CSCC 的临床亚型识别和患者治疗提供指导。
