Development of a model to predict the prognosis of esophageal carcinoma based on autophagy-related genes

Future Oncol. 2022 Feb;18(6):701-717. doi: 10.2217/fon-2021-0070. Epub 2022 Jan 20.

Abstract

Aim: To identify a potential prognostic signature of esophageal carcinoma based on autophagy-related genes (ARGs). Methods: RNA sequencing and clinical data were downloaded from the Cancer Genome Atlas. Significantly different ARGs were identified by Wilcoxon signed-rank test. A prognostic model was established employing Cox regression analysis. The model was evaluated by receiver operating characteristic and Kaplan-Meier curve. Results: A total of 28 significantly different ARGs were identified. Seven ARGs were screened to construct the prognostic model. The efficacy of the model was verified. A nomogram also validated the role of risk score in predicting prognosis. Enrichment analyses showed the possible underlying mechanisms. Conclusion: The seven-ARGs prognostic model was validated to be promising for predicting the prognosis of patients with esophageal carcinoma.

Keywords: autophagy; esophageal carcinoma; prognosis; risk score; survival.

Plain language summary

Plain language summary Autophagy is an important metabolic process in cells. Also known as type II cell death, it is a process in which cells degrade their damaged organelles and macromolecular substances by lysosomes. Autophagy is reported to be involved in the development of multiple tumor types, including esophageal carcinoma (ESCA). Autophagy-related genes (ARGs) are those genes proved to be closely related to, or in control of, autophagy. We aimed to identify a model for predicting prognosis based on ARGs, using gene expression profiles and clinical data of ESCA patients from an online database. We identified 28 ARGs as having significantly different activity in ESCA cells compared with normal cells. Among them, four genes were less active, and 24 genes were more active in cancer. Seven ARGs were screened to construct the prognostic model. The effectiveness of the model in predicting the prognosis of ESCA patients was confirmed by standard statistical methods. This study is valuable for finding possible therapeutic targets and predicting prognosis in ESCA patients based on ARGs.

MeSH terms

  • Aged
  • Autophagy / genetics*
  • Biomarkers, Tumor / genetics*
  • Esophageal Neoplasms / genetics*
  • Esophageal Neoplasms / pathology*
  • Female
  • Gene Expression Profiling*
  • Humans
  • Kaplan-Meier Estimate
  • Male
  • Middle Aged
  • Nomograms
  • Proportional Hazards Models
  • Risk Assessment / methods*
  • Risk Factors

Substances

  • Biomarkers, Tumor