Chen Jun, Wu Tingting, Yang Yongwen
Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, China.
National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Cancer Cell Int. 2024 Sep 11;24(1):314. doi: 10.1186/s12935-024-03486-z.
Sialylation in uterine corpus endometrial carcinoma (UCEC) differs significantly from apoptotic and ferroptosis pathways. It plays a crucial role in cancer progression and immune response modulation. Exploring how sialylation affects tumor behavior and its link with long non-coding RNAs (lncRNAs) may provide new insights into UCEC prognosis and treatment.
We obtained RNA transcriptome, clinical, and mutation data of UCEC samples from the TCGA database. Our approach involved developing a risk model based on the co-expression patterns of sialylation genes and lncRNAs. Prognostic lncRNAs were identified through Cox regression and further refined using LASSO analysis. To understand the biological functions and pathways of model-associated differentially expressed genes (MADEGs), we conducted enrichment analyses. We also assessed the immune infiltration status of MADEGs using eight different algorithms, which helped in evaluating the potential for immunotherapy. Additionally, we validated the expression of these lncRNAs in UCEC using cell lines and clinical samples.
We developed a UCEC risk model using five sialylation-related lncRNAs (AC004884.2, AC026202.2, LINC01579, LINC00942, SLC16A1-AS1). This model, confirmed through Cox analysis and clinical evaluation, effectively predicted patient outcomes. Survival data analysis across entire cohort, as well as within training and test groups, indicated better survival in low-risk UCEC patients. Enrichment analyses linked MADEGs to sialylation functions and cancer pathways. High-risk patients showed increased responsiveness to immune checkpoint inhibitors (ICIs), as indicated by immunological assessments. Subgroup C2 patients showed superior outcomes and a robust response to immunotherapy and chemotherapy. Notably, LINC01579, LINC00942, and SLC16A1-AS1 were significantly overexpressed in UCEC clinical tumor samples as well as in Ishikawa and HEC-1-B cell lines, compared to the normal groups.
This lncRNA signature associated with sialylation could guide prognosis, enhance the understanding of molecular mechanisms, and inform treatment strategies in UCEC. It highlights the potential for the use of ICIs and chemotherapy.
子宫体子宫内膜癌（UCEC）中的唾液酸化与凋亡和铁死亡途径存在显著差异。它在癌症进展和免疫反应调节中起着关键作用。探索唾液酸化如何影响肿瘤行为及其与长链非编码RNA（lncRNA）的联系，可能为UCEC的预后和治疗提供新的见解。
我们从TCGA数据库中获取了UCEC样本的RNA转录组、临床和突变数据。我们的方法包括基于唾液酸化基因和lncRNA的共表达模式建立风险模型。通过Cox回归确定预后lncRNA，并使用LASSO分析进一步优化。为了解模型相关差异表达基因（MADEG）的生物学功能和途径，我们进行了富集分析。我们还使用八种不同算法评估了MADEG的免疫浸润状态，这有助于评估免疫治疗的潜力。此外，我们使用细胞系和临床样本验证了这些lncRNA在UCEC中的表达。
我们使用五个与唾液酸化相关的lncRNA（AC004884.2、AC026202.2、LINC01579、LINC00942、SLC16A1-AS1）建立了UCEC风险模型。通过Cox分析和临床评估证实，该模型有效地预测了患者的预后。对整个队列以及训练组和测试组的生存数据分析表明，低风险UCEC患者的生存率更高。富集分析将MADEG与唾液酸化功能和癌症途径联系起来。免疫评估表明，高风险患者对免疫检查点抑制剂（ICI）的反应性增加。C2亚组患者显示出更好的预后以及对免疫治疗和化疗的强烈反应。值得注意的是，与正常组相比，LINC01579、LINC00942和SLC16A1-AS1在UCEC临床肿瘤样本以及Ishikawa和HEC-1-B细胞系中显著过表达。
这种与唾液酸化相关的lncRNA特征可以指导UCEC的预后，加深对分子机制的理解，并为治疗策略提供参考。它突出了使用ICI和化疗的潜力。
