Prognostic score model based on six m6A‐related autophagy genes for predicting survival in esophageal squamous cell carcinoma

Abstract

Background: Prognostic signatures based on autophagy genes have been proposedfor 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 m6Arelated 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.

Identification of Nine M6A-Related Long Noncoding RNAs as Prognostic Signatures Associated with Oxidative Stress in Oral Cancer Based on Data from The Cancer Genome Atlas

“…TCGA-acquired data also included expression data for 23 M6A-related genes, including eight writer genes (METTL16, METTL3, METTL14, RBM15B, WTAP, RBM15, VIRMA, and ZC3H13), 13 reader genes (FMR1, HNRNPC, HNRNPA2B1, IGF2BP3, IGF2BP2, IGF2BP1, LRPRRC, RBMX, YTHDF3, YTHDF2, YTHDC2, YTHDC1, and YTHDF1), and two eraser genes (ALKBH5 and FTO) A . The anatomic stage was determined based on the criteria published by the American Joint Committee on Cancer (AJCC) Staging Manual, 8th edition (2017) [15].…”

Identification of m6A-associated autophagy genes in non-alcoholic fatty liver

“…The training set and validation set were downloaded via the Gene Expression Omnibus (GEO) ( https://www.ncbi.nlm.nih.gov/gds ) database, in which the contained 33 NAFLD samples and 34 normal samples (samples type: liver wedge biopsy) ( Xanthakos et al, 2015 ), and validation set contained 72 NAFLD samples and three normal samples (samples type: liver biopsy) ( Hoang et al, 2019 ). Additionally, 232 autophagy-related genes (ARGs) were extracted from the human autophagy database (HADb) ( Chen et al, 2022 ) ( http://www.autophagy.lu/ ), and 37 m6A were obtained from the literature ( Li et al, 2022 ).…”

Novel Insights into The Roles of N6-methyladenosine (m6A) Modification and Autophagy in Human Diseases

“…Previous studies indicated that YTH domaincontaining proteins act as potential diagnostic biomarkers in patients with cancer [90][91][92][93]. Chen et al [94] examined differentially expressed m 6 A and autophagy genes in esophageal squamous cell carcinoma (ESCC). They developed and validated a predictive model based on six characteristic autophagy genes for predicting the survival of patients with ESCC.…”