A prognostic model based on tumor microenvironment-related lncRNAs predicts therapy response in pancreatic cancer
A prognostic model based on tumor microenvironment-related lncRNAs predicts therapy response in pancreatic cancer
复制标题
DOI：
10.1007/s10142-023-00964-x
复制
发表时间：
2023-03-01
影响因子：
2.9
通讯作者：
Yu，Xiaoqing
中科院分区：
文献类型：
--
作者：
Lu，Jianzhong;Tan，Jinhua;Yu，Xiaoqing
胰腺癌是一种侵袭性恶性肿瘤，死亡率高，生存率低。浸润在肿瘤微环境（TME）中的免疫细胞和基质细胞显著影响免疫治疗和药物反应。因此，我们鉴定tme相关的lncrna，建立预测胰腺癌患者治疗效果的预后模型。首先，我们对差异表达基因（deg）进行加权基因共表达网络分析（WGCNA），以确定tme相关模块特征基因。根据模块特征基因，通过单因素Cox、最小绝对收缩和选择算子（LASSO）和多因素Cox分析筛选tme相关预后lncrna，构建预后风险评分（RS）模型。然后，采用随时间变化的受试者工作特征（ROC）曲线和Kaplan-Meier分析对该模型的预测能力进行评估。此外，还进行了功能富集、免疫细胞浸润和体细胞突变分析。最后，应用肿瘤免疫功能障碍和排斥（TIDE）评分和药物敏感性分析预测治疗反应。在本研究中，鉴定了11个与tme相关的预后lncrna，以建立预后RS模型。根据RS，与高危患者相比，低危患者预后更好，体细胞突变率更低，TIDE评分更低，对吉西他滨和紫杉醇的敏感性更高。上述发现提示低危患者可能从免疫治疗中获益更多，高危患者可能从化疗中获益更多。在本研究中，我们基于11个tme相关lncrna建立了预后RS模型，可能有助于改善临床决策。
Pancreatic cancer is an aggressive malignant tumor with high mortality and a low survival rate. The immune and stromal cells that infiltrate in the tumor microenvironment (TME) significantly impact immunotherapy and drug responses. Therefore, we identify the TME-related lncRNAs to develop a prognostic model for predicting the therapy efficacy in pancreatic cancer patients. Firstly, we identified differentially expressed genes (DEGs) for weighted gene co-expression network analysis (WGCNA) to identify the TME-related module eigengenes. According to the module eigengenes, the TME-related prognostic lncRNAs were screened through the univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses to construct a prognostic risk score (RS) model. Next, the predictive power of this model was evaluated by the time-dependent receiver operating characteristic (ROC) curve and Kaplan-Meier analyses. In addition, functional enrichment, immune cell infiltration, and somatic mutation analyses were performed. Finally, tumor immune dysfunction and exclusion (TIDE) score and drug sensitivity analyses were applied to predict therapy response. In this study, 11 TME-related prognostic lncRNAs were identified to develop the prognostic RS model. According to the RS, the low-risk patients had a better prognosis, lower rates of somatic mutation, lower TIDE scores, and higher sensitivity to gemcitabine and paclitaxel compared to high-risk patients. The findings above suggested that low-risk patients may benefit more from immunotherapy, and high-risk patients may benefit more from chemotherapy. Within this study, we established a prognostic RS model based on 11 TME-related lncRNAs, which may help improve clinical decision-making.