Data Sheet 1_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx

IntroductionAbnormalities in ubiquitination-related pathways or systems are closely associated with various cancers, including cervical cancer (CC). However, the biological function and clinical value of ubiquitination-related genes (UbLGs) in CC remain unclear. This study aimed to explore key UbLGs associated with CC, construct a prognostic model, and investigate their potential clinical and immunological significance.
MethodsDifferentially expressed genes (DEGs) between CC (tumor) and standard samples in self-sequencing and TCGA-GTEx-CESC datasets were identified using differential analysis. We identified overlaps between DEGs in both datasets and UbLGs, revealing key crossover genes. Subsequently, biological markers were identified via univariate Cox regression analysis and least absolute shrinkage and selection operator algorithms. After conducting independent prognostic analysis, immune infiltration analysis was performed to investigate the immune cells that differed between the two risk subgroups. Differences in immune checkpoint expression between the subgroups were analyzed. Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) was performed to confirm the expression trends of the biomarkers.
ResultsDifferentially expressed genes related to ubiquitination were screened from the Self-seq and TCGAGTEx-CESC datasets, and five key biomarkers (MMP1, RNF2, TFRC, SPP1, and CXCL8) were identified. The risk score model constructed based on these biomarkers could effectively predict the survival rate of cervical cancer patients (AUC >0.6 for 1/3/5 years). Immune microenvironment analysis showed that 12 types of immune cells, including memory B cells and M0 macrophages, as well as four immune checkpoints, exhibited significant differences between the high-risk and low-risk groups. RT-qPCR confirmed that MMP1, TFRC, and CXCL8 were upregulated in tumor tissues.
DiscussionOur study identified five ubiquitination-related biomarkers, namely, MMP1, RNF2, TFRC, SPP1, and CXCL8, which were significantly associated with CC. The validated risk model demonstrates strong predictive value for patient survival. These findings provide crucial insights into the role of ubiquitination in CC pathogenesis and offer valuable targets for advancing future research and therapeutic strategies.

引言：泛素化相关通路或系统异常与包括宫颈癌（Cervical Cancer, CC）在内的多种恶性肿瘤密切相关。然而，泛素化相关基因（Ubiquitination-related Genes, UbLGs）在宫颈癌中的生物学功能与临床价值仍有待阐明。本研究旨在筛选与宫颈癌相关的关键泛素化相关基因，构建预后模型，并探讨其潜在的临床与免疫学意义。
方法：本研究通过差异分析，在自主测序数据集与TCGA-GTEx-CESC数据集的宫颈癌组织与正常对照样本中筛选差异表达基因（Differentially Expressed Genes, DEGs）。取两个数据集中共有的差异表达基因与泛素化相关基因的交集，得到核心交叉基因。随后，通过单变量Cox回归分析与最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）算法筛选预后生物标志物。在完成独立预后分析后，开展免疫浸润分析，以明确不同风险亚组间的免疫细胞差异；同时分析两组间免疫检查点的表达差异。最后通过实时定量聚合酶链反应（Real-Time Quantitative Polymerase Chain Reaction, RT-qPCR）验证候选生物标志物的表达趋势。
结果：从自主测序数据集与TCGA-GTEx-CESC数据集中筛选得到泛素化相关差异表达基因，并最终确定5个核心生物标志物：MMP1、RNF2、TFRC、SPP1及CXCL8。基于上述标志物构建的风险评分模型可有效预测宫颈癌患者的生存率（1年、3年、5年受试者工作特征曲线下面积（Area Under the Curve, AUC）均大于0.6）。免疫微环境分析显示，记忆B细胞、M0巨噬细胞等12种免疫细胞，以及4个免疫检查点的表达在高风险组与低风险组间存在显著差异。RT-qPCR验证结果表明，MMP1、TFRC与CXCL8在宫颈癌组织中呈高表达。
讨论：本研究筛选得到5个与宫颈癌密切相关的泛素化相关生物标志物：MMP1、RNF2、TFRC、SPP1及CXCL8。经验证的风险评分模型对患者生存率具有良好的预测效能。本研究结果为阐明泛素化在宫颈癌发病机制中的作用提供了重要依据，同时为未来宫颈癌的相关研究与治疗策略开发提供了潜在的靶向靶点。