Abstract：Objective To construct pyroptosis-related long non-coding RNA(lncRNA) prognostic model for thyroid cancer and explore its prognostic value. Methods The genes related to pyroptosis were obtained from MSigDB database; the sequencing data and clinical data of thyroid cancer patients were obtained from the Cancer Genome Atlas database and they were divided into experimental group and validation group at a ratio of 1∶1; the co-expression network of pyroptosis-related genes and lncRNAs was constructed, and the correction coefficient |r|＞0.5 and P＜0.001 lncRNAs were screened. Univariate Cox regression analysis identified lncRNAs associated with prognosis. Lasso-Cox regression analysis was used to construct a prognostic model, and the risk scores of experimental group and validation group were calculated. Thyroid cancer patients were divided into high-risk and low-risk groups according to the optimal cut-off value of the receiver operating characteristics (ROC) curve of the risk score. Survival analysis was performed using Kaplan-Meier method. Univariate and multivariate Cox regression analyses were used to evaluated the independent prognostic value of risk scores. Immune infiltration within the tumor was analyzed by CIBERSORT and the correlation between risk score and immune checkpoint was analyzed. Results In this study, a total of 1 035 lncRNAs associated with pyroptosis were obtained, and 10 lncRNAs were finally screened. Risk score =1.197×AC092809.3+0.285×MIR3945HG+1.886×AL450326.1+1.457×AL360181.2+0.539×LMNTD2-AS1+1.090×AC069360.1-0.709×DOCK9-DT +0.841×GAPLINC+1.013×PARAL1+0.870×SLC12A5-AS1, the optimal cut-off value obtained by ROC curve was 0.050. The survival rate of high-risk group was lower than that of low-risk group (P＜0.01). Univariate and multivariate Cox regression analysis showed that the HR values of risk scores were 2.718 (95%CI: 2.039- 3.624, P＜0.01) and 4.832 (95%CI: 1.562- 14.945, P＜0.01), respectively. In high-risk group, monocytes, memory B cells, and eosinophils increased, and mast cells and regulatory T cells decreased. Risk scores were associated with expression of CD80, CD160, and ADORA2A at immune checkpoints. Conclusion The prognostic-related lncRNAs prognostic model constructed in this study has good predictive value for the prognosis of thyroid cancer.
许绍廷 张赟建▲. 基于细胞焦亡相关lncRNA构建甲状腺癌的预后模型研究[J]. 中国医药导报, 2023, 20(15): 12-17.
XU Shaoting ZHANG Yunjian▲. Study of prognostic model for thyroid cancer based on pyroptosis-related lncRNA#br#. 中国医药导报, 2023, 20(15): 12-17.
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