Construction and Assessment of a Prognostic Risk Model for Cervical Cancer Based on Lactate Metabolism-Related lncRNAs

被引:1
|
作者
Gao, Ya [1 ]
Liu, Hongyang [1 ]
Wan, Junhu [2 ]
Chang, Fenghua [1 ]
Zhang, Lindong [1 ]
Wang, Wenjuan [1 ]
Zhang, Qinshan [1 ]
Feng, Quanling [1 ]
机构
[1] Zhengzhou Univ, Dept Obstet & Gynecol, Affiliated Hosp 3, Zhengzhou, Henan, Peoples R China
[2] Zhengzhou Univ, Dept Clin Lab, Key Clin Lab Henan Prov, Affiliated Hosp 1, Zhengzhou, Henan, Peoples R China
来源
INTERNATIONAL JOURNAL OF GENERAL MEDICINE | 2023年 / 16卷
关键词
cervical cancer; lactate metabolism; long non-coding RNA; bioinformatics; prognostic model;
D O I
10.2147/IJGM.S411511
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Purpose: Cervical cancer (CC) has the fourth highest incidence and mortality rate among female cancers. Lactate is a key regulator promoting tumor progression. Long non-coding RNAs (lncRNAs) are closely associated with cervical cancer (CC). The study was aimed to develop a prognostic risk model for cervical cancer based on lactate metabolism-associated lncRNAs and to determine their clinical prognostic value.Patients and Methods: In this study, CESC transcriptome data were obtained from the TCGA database. 262 lactate metabolism-associated genes were extracted from MsigDB (Molecular Characterization Database). Then, correlation analysis was used to identify LRLs. Univariate Cox regression analysis was performed afterwards, followed by least absolute shrinkage and selection operator (LASSO) regression analysis and multiple Cox regression analysis. 10 lncRNAs were finally identified to construct a risk score model. They were divided into two groups of high risk and low risk according to the median of risk scores. The predictive performance of the models was assessed by Kaplan-Meier (K-M) analysis, subject work characteristics (ROC) analysis, and univariate and multivariate Cox analyses. To assess the clinical utility of the prognostic model, we performed functional enrichment analysis, immune micro-environment analysis, mutation analysis, and column line graph generation.Results: We constructed a prognostic model consisting of 10 LRLs at CC. We observed that high-risk populations were strongly associated with poor survival outcomes. Risk score was an independent risk factor for CC prognosis and was strongly associated with immune microenvironment analysis and tumor mutational load.Conclusion: We developed a risk model of lncRNAs associated with lactate metabolism and used it to predict prognosis of CC, which could guide and facilitate the progress of new treatment strategies and disease monitoring in CC patients.
引用
收藏
页码:2943 / 2960
页数:18
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