Department of Respiratory and Critical Care Medicine, Jiangsu Taizhou People's Hospital, Taizhou 225300, China.
Comput Math Methods Med. 2022 Jul 6;2022:3918926. doi: 10.1155/2022/3918926. eCollection 2022.
To screen CXC chemokines related to the risk of lung adenocarcinoma (LUAD) using bioinformatics and construct a CXC-based prognostic risk model to improve the diagnosis and treatment of LUAD patients.
The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database were searched to obtain mRNA expression data and clinicopathological information of LUAD patients. CXC genes differentially expressed in LUAD were screened using the R packages. Further, risk factors significantly associated with the survival of LUAD patients were obtained by the univariate Cox proportional hazard regression, LASSO regression, and multivariate Cox proportional hazard regression analysis, following which a risk prediction model was constructed. The performance of the CXCL13-based model in predicting the prognosis of low-risk and high-risk effect LUAD patients was verified, and the association between the model and the degree of tumor immune cell infiltration was investigated.
CXCL13 was significantly highly expressed in the cancer tissues of LUAD patients. The risk of death in patients with highly expressed CXCL13 was about 1.5 times higher than in those with lowly expressed CXCL13 (HR = 1.5153357). CXCL13-based risk scoring showed that the high-risk score of LUAD patients was significantly correlated with poor prognosis, but no relation between the two was found in the GEO validation sets, suggesting that this risk model may not be accurate enough. In addition, activated B cells, CD4+ T cells, CD8+ T cells, and dendritic cells were significantly positively correlated with the high risk of LUAD.
Although we found that a high expression of CXCL13 was associated with a high risk of death and immune cell infiltration and activation in LUAD patients, the CXCL13-based risk model was not accurate enough for predicting the prognosis of LUAD patients.
利用生物信息学筛选与肺腺癌（LUAD）风险相关的 CXC 趋化因子，并构建基于 CXC 的预后风险模型，以提高 LUAD 患者的诊断和治疗水平。
检索癌症基因组图谱（TCGA）数据库和基因表达综合数据库（GEO），获取 LUAD 患者的 mRNA 表达数据和临床病理信息。采用 R 包筛选 LUAD 中差异表达的 CXC 基因。进一步通过单因素 Cox 比例风险回归、LASSO 回归和多因素 Cox 比例风险回归分析，获取与 LUAD 患者生存显著相关的风险因素，构建风险预测模型。验证基于 CXCL13 的模型预测低风险和高风险 LUAD 患者预后的性能，并探讨模型与肿瘤免疫细胞浸润程度的关系。
CXCL13 在 LUAD 患者的癌组织中表达明显升高。CXCL13 高表达患者的死亡风险约为 CXCL13 低表达患者的 1.5 倍（HR=1.5153357）。基于 CXCL13 的风险评分显示，LUAD 患者的高风险评分与预后不良显著相关，但在 GEO 验证集中未发现两者之间存在相关性，提示该风险模型可能不够准确。此外，活化的 B 细胞、CD4+T 细胞、CD8+T 细胞和树突状细胞与 LUAD 的高风险呈显著正相关。
尽管我们发现 CXCL13 高表达与 LUAD 患者死亡风险升高和免疫细胞浸润及激活相关，但基于 CXCL13 的风险模型预测 LUAD 患者预后的准确性不够。
