已发表论文

基于生物信息学分析的肺鳞癌患者自噬相关长非编码 RNA 预后特征

 

Authors Liu B, Zhao Y, Yang S

Received 30 July 2021

Accepted for publication 13 September 2021

Published 12 October 2021 Volume 2021:14 Pages 6621—6637

DOI https://doi.org/10.2147/IJGM.S331327

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Scott Fraser

Purpose: Lung cancer is the most common and deadly cancer type affecting humans. Although huge progress has been made on early diagnosis and precision treatment, the overall 5 year survival rate remains low. In this study, we constructed an autophagy-related long non-coding RNA (lncRNA) prognostic signature for guiding clinical practice.
Methods: From The Cancer Genome Atlas, we retrieved mRNA and lncRNA expression matrices of patients with lung squamous carcinoma. We then established a prognostic risk model using Lasso regression and multivariate Cox regression. The model generated a risk score to differentiate high- and low-risk groups. An ROC curve and nomogram were used to visualize the predictive ability of the current signatures. Finally, we used Gene Set Enrichment Analysis to determine gene ontology and pathway enrichment.
Results: After screening 1248 autophagy-related lncRNAs, we selected seven lncRNAs (LUCAT1, AC022150.2, AL035425.3, AC138976.2, AC106786.1, GPRC5D-AS1 and AP006545.2) for our signature. Univariate (hazard ratio [HR] = 2.147, 95% confidence interval [CI]: 1.681– 2.743, < 0.001) and multivariate (HR = 2.096, 95% CI: 1.652– 2.658, < 0.001) Cox regression analyses revealed that the risk score is an independent predictive factor for LUSC patients. Further, areas under the receiver operating characteristic curve were 0.622, 0.699, and 0.721, respectively, for the 1 year, 3 year, and 5 year risk scores—indicating a reliable model. Selected lncRNAs were primarily enriched in autophagy, metabolism, MAPK pathway, and JAK/STAT pathway. Further drug sensitivity analysis revealed that low-risk patients were more sensitive to Cisplatin, Docetaxel, Vinblastine, and Vinorelbine. Finally, a multi-omics analysis found that lncRNA-linked proteins IKBKB and SQSTM1 were expressed at low levels and significantly correlated in tumor samples, compared with normal tissue.
Conclusion: Our prognostic model successfully predicted patient prognosis in lung cancer.
Keywords: non-small cell lung cancer, cancer therapy, non-coding RNA, cancer prognosis, The Cancer Genome Atlas