Prognostic score model based on six m6A‐related autophagy genes for predicting survival in esophageal squamous cell carcinoma. Issue 7 (25th May 2022) - Chen, Funan; Gong, Erxiu; Ma, Jun; Lin, Jiehuan; Wu, Canxing; Chen, Shanshan; Hu, Shuqiao
Abstract: Background: Prognostic signatures based on autophagy genes have been proposed for esophageal squamous cell carcinoma (ESCC). Autophagy genes are closely associated with m6A genes. Our purpose is to identify m6A‐related autophagy genes in ESCC and develop a survival prediction model. Methods: Differential expression analyses for m6A genes and autophagy genes were performed based on TCGA and HADd databases followed by constructing a co‐expression network. Uni‐variable Cox regression analysis was performed for m6A‐related autophagy genes. Using the optimal combination of feature genes by LASSO Cox regression model, a prognostic score (PS) model was developed and subsequently validated in an independent dataset. Results: The differential expression of 13 m6A genes and 107 autophagy genes was observed between ESCC and normal samples. The co‐expression network contained 13 m6A genes and 96 autophagy genes. Of the 12 m6A‐related autophagy genes that were significantly related to survival, DAPK2, DIRAS3, EIF2AK3, ITPR1, MAP1LC3C, and TP53 were used to construct a PS model, which split the training set into two risk groups with significant different survival ratios ( p  = 0.015, 1‐year, 3‐year, and 5‐year AUC = 0.873, 0.840, and 0.829). Consistent results of GSE53625 dataset confirmed predictive ability of the model ( p  = 0.024, 1‐year, 3‐year, and 5‐year AUC = 0.793, 0.751, and 0.744). The six‐gene PS score was an independent prognostic factor from clinical factors (HR, 2.362; 95% CI, 1.390–7.064; p ‐value = 0.012). Conclusion: Our study recommends 6 m6A‐related autophagy genes as promising prognostic biomarkers and develops a PS model to predict survival in ESCC. Abstract : This study aimed to explore m6A‐related autophagy genes in ESCC and develop a survival prediction model. Differential expression analysis for m6A genes and autophagy genes was performed followed by constructing a co‐expression network. Uni‐variable Cox regression analysis was performed for m6A‐related autophagy genes. Using the optimal combination of feature genes by LASSO Cox regression model, a prognostic score model was developed and subsequently validated in an independent dataset.