Kong Lingbo, Wang Chenfei, Lu Xiaohui, Zhu Qianqi, Song Yihua, Feng Xingmei
Wuxi Stomatological Hospital, Wuxi, Jiangsu, China.
Department of Stomatology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, China.
Discov Oncol. 2025 May 11;16(1):723. doi: 10.1007/s12672-025-02578-0.
A significant role in many cancers is played by cuproptosis, a new term for the copper-dependent regulatory cell death pattern. However, as a new research hotspot, the cuproptosis-related lncRNAs (CRLs) associated with regulation in oral squamous cell carcinoma (OSCC) patients are currently not well understood.
Long noncoding RNA (lncRNA) data were downloaded from the Cancer Genome Atlas database (TCGA). The 'LIMMA' package in R software was used to screen for differential expression of CRLs. LASSO regression and COX regression models were used to construct prognostic signature based on 4 prognostic CRLs. Finally, the relationship of risk characteristics with immune correlation analysis, somatic mutations, PCA, biological molecular pathways and drug sensitivity was investigated.
A cuproptosis-related lncRNAs prognostic signature was developed by us. Based on the risk scores, the OSCC samples were split into high- and low-risk groups using this signature. The two risk groups differed significantly in immune functions, drug sensitivity, and overall survival. The risk model showed better prognostic predictive power compared to the traditional clinicopathological signature. By qPCR trial, we also verified the expression of STARD4-AS1 in OSCC cell lines and tissues was in line with our results from this experimental screen. Through cell experiments, we have confirmed that knocking down STARD4-AS1 promotes the proliferation and migration ability of OSCC cells.
The CRLs signature contributes to new understandings of the treatment of OSCC and is a rubost biomarker for predicting the prognosis of patients with OSCC.
铜死亡是一种新的依赖铜的调节性细胞死亡模式，在许多癌症中发挥着重要作用。然而，作为一个新的研究热点，目前对于口腔鳞状细胞癌（OSCC）患者中与调节相关的铜死亡相关长链非编码RNA（CRLs）了解甚少。
从癌症基因组图谱数据库（TCGA）下载长链非编码RNA（lncRNA）数据。使用R软件中的“LIMMA”包筛选CRLs的差异表达。基于4个预后CRLs，使用LASSO回归和COX回归模型构建预后特征。最后，研究风险特征与免疫相关性分析、体细胞突变、主成分分析、生物分子途径和药物敏感性之间的关系。
我们开发了一种铜死亡相关lncRNAs预后特征。基于风险评分，使用该特征将OSCC样本分为高风险组和低风险组。两个风险组在免疫功能、药物敏感性和总生存期方面存在显著差异。与传统的临床病理特征相比，风险模型显示出更好的预后预测能力。通过qPCR试验，我们还验证了STARD4-AS1在OSCC细胞系和组织中的表达与我们从该实验筛选结果一致。通过细胞实验，我们证实敲低STARD4-AS1可促进OSCC细胞的增殖和迁移能力。
CRLs特征有助于对OSCC治疗的新认识，是预测OSCC患者预后的可靠生物标志物。
