Open AccessConstruction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes
Author(s) -
Xin Yu,
Jun Li,
Ruiwen Xie,
Mengling Chang,
Bichun Xu,
Yan Zhu,
Yu Xie,
Shuai Yang
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2021333
Subject(s) - proportional hazards model , gene , autophagy , biology , oncology , univariate analysis , medicine , multivariate analysis , genetics , apoptosis
Objective We aimed to construct a novel prognostic model based on N6-methyladenosine (m6A)-related autophagy genes for predicting the prognosis of lung squamous cell carcinoma (LUSC). Methods Gene expression profiles and clinical information of Patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) database. In addition, m6A- and autophagy-related gene profiles were obtained from TCGA and Human Autophagy Database, respectively. Pearson correlation analysis was performed to identify the m6A-related autophagy genes, and univariate Cox regression analysis was conducted to screen for genes associated with prognosis. Based on these genes, LASSO Cox regression analysis was used to construct a prognostic model. The corresponding prognostic score (PS) was calculated, and patients with LUSC were assigned to low- and high-risk groups according to the median PS value. An independent dataset (GSE37745) was used to validate the prognostic ability of the model. CIBERSORT was used to calculate the differences in immune cell infiltration between the high- and low-risk groups. Results Seven m6A-related autophagy genes were screened to construct a prognostic model: CASP4 , CDKN1A , DLC1 , ITGB1 , PINK1 , TP63 , and EIF4EBP1 . In the training and validation sets, patients in the high-risk group had worse survival times than those in the low-risk group; the areas under the receiver operating characteristic curves were 0.958 and 0.759, respectively. There were differences in m6A levels and immune cell infiltration between the high- and low-risk groups. Conclusions Our prognostic model of the seven m6A-related autophagy genes had significant predictive value for LUSC; thus, these genes may serve as autophagy-related therapeutic targets in clinical practice.