肿瘤细胞治疗研究
英文原题：Exploration of the Immune-Related Long Noncoding RNA Prognostic Signature and Inflammatory Microenvironment for Cervical Cancer.
目的：我们的研究开发了用于宫颈癌（CC）风险分层的免疫相关长链非编码RNA（lncRNA），并探讨了预后因素、炎症微环境浸润和化疗治疗。
目的：我们的研究开发了用于宫颈癌（CC）风险分层的免疫相关长链非编码RNA（lncRNAs），并探讨了预后因素、炎症微环境浸润以及化疗治疗。方法：从TCGA TARGET GTEx数据库和TCGA数据库收集CC的RNA-seq数据和临床信息。lncRNAs和免疫相关特征分别从GENCODE数据库和ImPort数据库获取。我们通过单因素Cox、LASSO和多因素Cox回归方法筛选出免疫相关lncRNA特征。我们建立了一个核心免疫相关lncRNAs的免疫相关风险模型，以评估风险评分是否为独立预后预测因子。采用xCell和CIBERSORTx算法评估风险评分与肿瘤浸润免疫细胞丰度竞争的价值。基于IC50预测器，还进行了通过TIDE算法对肿瘤免疫治疗反应的评估，以及针对免疫相关风险模型预测创新推荐药物。结果：我们成功建立了六个免疫相关lncRNAs（AC006126.4、EGFR-AS1、RP4-647J21.1、LINC00925、EMX2OS和BZRAP1-AS1）用于CC的预后预测。构建了免疫相关风险模型，其中我们观察到高风险组与较差的生存结局密切相关。风险评分随临床病理参数和肿瘤分期而变化，并且是影响CC预后的独立危险因素。xCell算法揭示核心免疫相关特征与免疫细胞相关，尤其是肥大细胞、DCs、巨核细胞、记忆B细胞、NK细胞和Th1细胞。CIBERSORTx算法揭示了一个炎症性微环境，其中高风险组中naive B细胞（p < 0.01）、活化树突状细胞（p < 0.05）、活化肥大细胞（p < 0.0001）、CD8 + T细胞（p < 0.001）和调节性T细胞（p < 0.01）显著降低，而巨噬细胞M0（p < 0.001）、巨噬细胞M2（p < 0.05）、静息肥大细胞（p < 0.0001）和中性粒细胞（p < 0.01）高度富集。TIDE的结果表明，低风险组（124/137）的免疫治疗应答者数量显著增加（p = 0.00000022），相比高风险组（94/137），提示CC患者的免疫治疗应答与风险评分完全负相关。最后，我们比较了高风险组和低风险组中差异IC50预测值，鉴定出12种化合物可作为CC患者未来的治疗方法。结论：在本研究中，六种免疫相关lncRNAs被提出可用于预测CC的结局，这有利于免疫治疗的制定。
Purpose: Our research developed immune-related long noncoding RNAs (lncRNAs) for risk stratification in cervical cancer (CC) and explored factors of prognosis, inflammatory microenvironment infiltrates, and chemotherapeutic therapies. Methods: The RNA-seq data and clinical information of CC were collected from the TCGA TARGET GTEx database and the TCGA database. lncRNAs and immune-related signatures were obtained from the GENCODE database and the ImPort database, respectively. We screened out immune-related lncRNA signatures through univariate Cox, LASSO, and multivariate Cox regression methods. We established an immune-related risk model of hub immune-related lncRNAs to evaluate whether the risk score was an independent prognostic predictor. The xCell and CIBERSORTx algorithms were employed to appraise the value of risk scores which are in competition with tumor-infiltrating immune cell abundances. The estimation of tumor immunotherapy response through the TIDE algorithm and prediction of innovative recommended medications on the target to immune-related risk model were also performed on the basis of the IC50 predictor. Results: We successfully established six immune-related lncRNAs (AC006126.4, EGFR-AS1, RP4-647J21.1, LINC00925, EMX2OS, and BZRAP1-AS1) to carry out prognostic prediction of CC. The immune-related risk model was constructed in which we observed that high-risk groups were strongly linked with poor survival outcomes. Risk scores varied with clinicopathological parameters and the tumor stage and were an independent hazard factor that affect prognosis of CC. The xCell algorithm revealed that hub immune-related signatures were relevant to immune cells, especially mast cells, DCs, megakaryocytes, memory B cells, NK cells, and Th1 cells. The CIBERSORTx algorithm revealed an inflammatory microenvironment where naive B cells ( p < 0.01), activated dendritic cells ( p < 0.05), activated mast cells ( p < 0.0001), CD8 + T cells ( p < 0.001), and regulatory T cells ( p < 0.01) were significantly lower in the high-risk group, while macrophages M0 ( p < 0.001), macrophages M2 ( p < 0.05), resting mast cells ( p < 0.0001), and neutrophils ( p < 0.01) were highly conferred. The result of TIDE indicated that the number of immunotherapy responders in the low-risk group (124/137) increased significantly ( p = 0.00000022) compared to the high-risk group (94/137), suggesting that the immunotherapy response of CC patients was completely negatively correlated with the risk scores. Last, we compared differential IC50 predictive values in high- and low-risk groups, and 12 compounds were identified as future treatments for CC patients. Conclusion: In this study, six immune-related lncRNAs were suggested to predict the outcome of CC, which is beneficial to the formulation of immunotherapy.
MEMBER ACCOUNT
登录成功会直接打开下一页。
