Abnormalities in autophagy-related genes (ARGs) are closely related to the occurrence and development of thyroid carcinoma (THCA). However, the effect of ARGs on the prognosis of THCA remains unclear. Here, by analyzing data from TCGA, 26 differentially expressed ARGs were screened. Cox regression and Lasso regression were utilized to analyze the prognosis of the training group, and a risk model was constructed. Our results show that low-risk patients had better overall survival (OS) than high-risk patients, and the area under the ROC curve in the training and testing groups was significant (3-year AUC, 0.735 vs 0.796; 5-year AUC, 0.821 vs 0.804). In addition, a comprehensive analysis of the 5 identified ARGs demonstrated that most of them were related to OS in THCA patients, and two of them (CX3CL1 and CDKN2A) were differentially expressed in THCA and normal thyroid tissues at the protein level. GSEA suggested that the inactivation of the cell defense system and the activation of some classical tumor signaling pathways are important driving forces for the progression of THCA. This study demonstrated that the 5 ARGs in the survival model are promising multidimensional biomarkers for the diagnosis, prognosis, and treatment of THCA.

Emerging evidence suggests that the dysregulation of autophagy-related genes (ARGs) is coupled with the carcinogenesis and progression of breast cancer (BRCA). We constructed three subtype-specific risk models using differentially expressed ARGs. In Luminal, Her-2, and Basal-like BRCA, four- (BIRC5, PARP1, ATG9B, and TP63), three- (ITPR1, CCL2, and GAPDH), and five-gene (PRKN, FOS, BAX, IFNG, and EIF4EBP1) risk models were identified, which all have a receiver operating characteristic > 0.65 in the training and testing dataset. Multivariable Cox analysis showed that those risk models can accurately and independently predict the overall survival of BRCA patients. Comprehensive analysis showed that the 12 identified ARGs were correlated with the overall survival of BRCA patients; six of the ARGs (PARP1, TP63, CCL2, GAPDH, FOS, and EIF4EBP1) were differentially expressed between BRCA and normal breast tissue at the protein level. In addition, the 12 identified ARGs were highly interconnected and displayed high frequency of copy number variation in BRCA samples. Gene set enrichment analysis suggested that the deactivation of the immune system was the important driving force for the progression of Basal-like BRCA. This study demonstrated that the 12 ARG signatures were potential multi-dimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA.