Background: The role of pyroptosis and lncRNAs in breast cancer remains controversial. This study aimed to explore the pyroptosis-related lncRNAs in breast cancer.
Methods: All the data used for bioinformatics analysis were downloaded from The Cancer Genome Atlas database. Limma package was used to perform difference analysis, and distinguish mRNA and lncRNA. Survival package was used to conduct prognosis analysis. LASSO algorithm, univariate cox analysis and multivariate cox analysis were used to construct the prognosis model. P value < 0.05 was regarded as statistically significant.
Results: Based on the seven pyroptosis-related lncRNAs tightly associated with patients’ prognosis, a prognostic prediction model was finally developed, which showed powerful effectiveness (Training cohort, one-year AUC = 0.82, 95% Cl = 0.69– 0.95, three-year AUC = 0.77, 95% Cl = 0.68– 0.85, five-year AUC = 0.74, 95% Cl = 0.66– 0.82; Validation cohort, one-year AUC = 0.68, 95% Cl = 0.53– 0.84, three-year AUC = 0.72, 95% Cl = 0.64– 0.81, five-year AUC = 0.67, 95% Cl = 0.57– 0.77). GSEA analysis demonstrated that the protein secretion, angiogenesis, TGF-β signaling and MTORC1 signaling might be involved in the high-risk patients. Moreover, immune infiltration analysis showed that the risk score was positively correlated with Tgd and Th2 cells, yet negatively correlated with CD8+ T cells, cytotoxic cells and T helper cells, which might partly explain the poor prognosis of high-risk patients. Finally, the expression level of seven model lncRNAs in the real world was validated by qRT-PCR using four cancer cell lines (MCF-7, T47D, MDA-MB-231, MDA-MB-469).
Conclusion: In conclusion, our study identified lncRNAs that are remarkably correlated with patients’ survival and might participate in the pyroptosis process, which might be underlying tumor biomarker and therapeutic targets. This study may provide direction for future research.



中文翻译：

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通过交互分析鉴定与乳腺癌预后相关的新型焦亡相关 lncRNA

背景：细胞焦亡和 lncRNA 在乳腺癌中的作用仍存在争议。本研究旨在探讨乳腺癌中与细胞焦亡相关的lncRNA。
方法：用于生物信息学分析的所有数据均从癌症基因组图谱数据库下载。Limma包用于进行差异分析，区分mRNA和lncRNA。生存包用于进行预后分析。采用LASSO算法、单变量cox分析和多变量cox分析构建预后模型。P值<0.05被认为具有统计学意义。
结果：基于与患者预后密切相关的 7 个细胞焦亡相关 lncRNA，最终开发了一个预后预测模型，该模型显示出强大的有效性（训练队列，一年 AUC = 0.82，95% Cl = 0.69–0.95，三年 AUC = 0.77，95% Cl = 0.68–0.85，五年 AUC = 0.74，95% Cl = 0.66–0.82；验证队列，一年 AUC = 0.68，95% Cl = 0.53–0.84，三年 AUC = 0.72 , 95% Cl = 0.64–0.81, 五年 AUC = 0.67, 95% Cl = 0.57–0.77)。GSEA分析表明蛋白质分泌、血管生成、TGF-β信号和MTORC1信号可能与高危患者有关。此外，免疫浸润分析显示风险评分与 Tgd 和 Th2 细胞呈正相关，而与 CD8+ T 细胞、细胞毒性细胞和 T 辅助细胞呈负相关，这可能部分解释了高危患者的不良预后。最后，使用四种癌细胞系（MCF-7、T47D、MDA-MB-231、MDA-MB-469）通过qRT-PCR验证了现实世界中七种模型lncRNA的表达水平。
结论：总之，我们的研究确定了与患者生存率显着相关并可能参与细胞焦亡过程的 lncRNA，这可能是潜在的肿瘤生物标志物和治疗靶点。本研究可为今后的研究提供方向。