An -related lncRNA signature was identified in 179 patients with esophageal squamous cell carcinoma (ESCC).
The low-risk group of patients exhibited significantly better prognosis compared to the high-risk group (-value <0.001).
The model's reliability was confirmed through Brier score and ROC analysis.
Gene set enrichment analysis revealed significant enrichment of cancer- and autophagy-related pathways in the high-risk group.
Metabolism-related pathways were notably enriched in the low-risk group.
Correlation analysis indicated that the model may effectively predict responses to immunotherapy and chemotherapy.
Approximately 35.41% (74/209) of ferroptosis-related genes showed significant correlation with risk scores.
Simplified
BACKGROUND: Considering the significance of and (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 (-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. p
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.
Key numbers
0.755
Prognostic Model Accuracy (1-year OS)
AUC value for the original dataset's one-year survival rate.
0.772
Prognostic Model Accuracy (3-year OS)
AUC value for the original dataset's three-year survival rate.
0.796
Prognostic Model Accuracy (5-year OS)
AUC value for the original dataset's five-year survival rate.
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