International Journal of General Medicine (Nov 2021)

A Novel Autophagy-Related Long Non-Coding RNA Signature to Predict Prognosis and Therapeutic Response in Esophageal Squamous Cell Carcinoma

  • Shi X,
  • Liu X,
  • Pan S,
  • Ke Y,
  • Li Y,
  • Guo W,
  • Wang Y,
  • Ruan Q,
  • Zhang X,
  • Ma H

Journal volume & issue
Vol. Volume 14
pp. 8325 – 8339

Abstract

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Xiaobo Shi,1 Xiaoxiao Liu,1 Shupei Pan,1 Yue Ke,1 Yuxing Li,1 Wei Guo,1 Yuchen Wang,1 Qinli Ruan,1 Xiaozhi Zhang,2 Hongbing Ma1 1Department of Radiation Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, People’s Republic of China; 2Department of Radiation Oncology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, People’s Republic of ChinaCorrespondence: Hongbing MaDepartment of Radiation Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157, Xi Wu Road, Xi’an, 710004, Shaanxi, People’s Republic of ChinaTel +86 13991845066Email [email protected]: Considering the significance of autophagy and long non-coding RNAs (lncRNAs) in the biology of esophageal squamous cell carcinoma (ESCC), the present study aimed to identify a new autophagy-related lncRNA signature to forecast the clinical outcomes of ESCC patients and to guide individualized treatment.Methods: The expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. We extracted autophagy-related genes from the Human Autophagy Database and identified autophagy-related lncRNAs through Spearman correlation analysis. Univariate, least absolute shrinkage and selection operator and multivariate Cox regression analyses were performed on GSE53625 to construct an autophagy-related lncRNAs prognostic signature. The model was subjected to bootstrap internal validation, and the expression levels of lncRNAs were verified by TCGA database. The potential molecular mechanism of the model was explored by gene set enrichment analysis (GSEA). Spearman correlation coefficient examined the correlation between risk score and ferroptosis-associated genes as well as the response to immunotherapy and chemotherapy.Results: We identified and validated an autophagy-related lncRNAs prognostic signature in 179 patients with ESCC. The prognosis of patients in the low-risk group was significantly better than that in the high-risk group (p-value < 0.001). The reliability of the model was verified by Brier score and ROC. GSEA results showed significant enrichment of cancer- and autophagy-related signaling pathways in the high-risk group and metabolism-related pathways in the low-risk group. Correlation analysis indicated that the model can effectively forecast the effect of immunotherapy and chemotherapy. About 35.41% (74/209) ferroptosis-related genes were significantly correlated with risk scores.Conclusion: In brief, we constructed a novel autophagy-related lncRNAs signature (LINC02024, LINC01711, LINC01419, LCAL1, FENDRR, ADAMTS9-AS1, AC025244.1, AC015908.6 and AC011997.1), which could improve the prediction of clinical outcomes and guide individualized treatment of ESCC patients.Keywords: autophagy, esophageal squamous cell carcinoma, lncRNA, prognosis, therapeutic response

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