Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes

Xin Yu; Jun Liu; Ruiwen Xie; Mengling Chang; Bichun Xu; Yangqing Zhu; Yuancai Xie; Shengli Yang; Xin Yu; Jun Liu; Ruiwen Xie; Mengling Chang; Bichun Xu; Yangqing Zhu; Yuancai Xie; Shengli Yang

doi:10.3934/mbe.2021333

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 6709-6723. doi: 10.3934/mbe.2021333

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Research article

Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes

1.
Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and PeKing Union Medical College, Shenzhen 518116, China
2.
Department of Chemotherapy, Affiliated Hospital of Nantong University, Nantong 226001, China
3.
Department of Cardiothoracic Surgery, Dongguan People's Hospital, Dongguan, Guangzhou 523000, China
4.
Department of Oncology Radiotherapy, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China
5.
Department of Thoracic Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, China
6.
Department of Thoracic Surgery, Foshan First people's Hospital, Affiliated Hospital of Sun Yat sen University, Foshan 528000, China
^† The authors contributed equally to this work.

Received: 02 June 2021 Accepted: 25 July 2021 Published: 09 August 2021

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.
- N6-methyladenosine,
- autophagy,
- prognostic model,
- lung squamous cell carcinoma
Citation: Xin Yu, Jun Liu, Ruiwen Xie, Mengling Chang, Bichun Xu, Yangqing Zhu, Yuancai Xie, Shengli Yang. Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6709-6723. doi: 10.3934/mbe.2021333

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Abstract

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.

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mbe-18-05-333-Table S1-supplementary.xls
mbe-18-05-333-Table S2-supplementary.xlsx
mbe-18-05-333-Table S3-supplementary.xlsx
mbe-18-05-333-Table S4-supplementary.xlsx

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