Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes

Autophagy and nuclear morphometry are associated with histopathologic features in esophageal squamous cell carcinoma

Less than 15% of patients with esophageal squamous cell carcinoma (ESCC) survive 5 years after diagnosis. A better understanding of the biology of these tumors and the development of clinical biomarkers is needed. Autophagy is a physiological mechanism involved in the turnover of cellular components that plays a key role in cancer. This study evaluated the differential levels of three key regulators of autophagy (SQSTM1, MAP1LC3B, and BECN1) in patients with ESCC, associating autophagy with histopathologic features, including the grade of differentiation, mitotic rate, inflammation score, and the intensity of tumor-infiltrating lymphocytes. Nuclear morphometry of the tumor parenchyma was also assessed, associating it with autophagy and histopathology. All three markers significantly increased in patients with ESCC compared to the control group. Based on the mean expression of each protein in the control group, 57% of patients with ESCC had high levels of all three markers compared to control patients (14%). The most frequent profiles found in ESCC were BECNhigh/MAP1LC3high and BECNhigh/SQSTM1high. According to the TCGA database, we found that the main autophagy genes were upregulated in ESCC. Moreover, high levels of autophagy markers were associated with a poor prognosis. Considering nuclear morphometry, ESCC samples showed a significant reduction in nuclear area, which was strongly negatively correlated with autophagy. Finally, the percentage of normal nuclei was associated with tumor differentiation, while poorly differentiated tumors showed lower SQSTM1 levels. ESCC progression may involve increased autophagy and changes in nuclear structure, associated with clinically relevant histopathological features. KEY MESSAGES: Autophagy markers are co-increased in primary ESCC. Autophagy negatively correlates with nuclear morphometry in ESCC parenchyma. Autophagy and nuclear morphometry are associated with histopathological features. Autophagy is increased in ESCC-TCGA database and associated with poor prognosis.

A clinical prognostic model for patients with esophageal squamous cell carcinoma based on circulating tumor DNA mutation features

Background

Few predictive models have included circulating tumor DNA (ctDNA) indicators to predict prognosis of esophageal squamous cell carcinoma (ESCC) patients. Here, we aimed to explore whether ctDNA can be used as a predictive biomarker in nomogram models to predict the prognosis of patients with ESCC.

Methods

We included 57 patients who underwent surgery and completed a 5-year follow-up. With next-generation sequencing, a 61-gene panel was used to evaluate plasma cell-free DNA and white blood cell genomic DNA from patients with ESCC. We analyzed the relationship between the mutation features of ctDNA and the prognosis of patients with ESCC, identified candidate risk predictors by Cox analysis, and developed nomogram models to predict the 2- and 5-year disease-free survival (DFS) and overall survival (OS). The area under the curve of the receiver operating characteristic (ROC) curve, concordance index (C-index), calibration plot, and integrated discrimination improvement (IDI) were used to evaluate the performance of the nomogram model. The model was compared with the traditional tumor-nodes-metastasis (TNM) staging system.

Results

The ROC curve showed that the average mutant allele frequency (MAF) of ctDNA variants and the number of ctDNA variants were potential biomarkers for predicting the prognosis of patients with ESCC. The predictors included in the models were common candidate predictors of ESCC, such as lymph node stage, angiolymphatic invasion, drinking history, and ctDNA characteristics. The calibration curve demonstrated consistency between the observed and predicted results. Moreover, our nomogram models showed clear prognostic superiority over the traditional TNM staging system (based on C-index, 2-year DFS: 0.82 vs. 0.64; 5-year DFS: 0.78 vs. 0.65; 2-year OS: 0.80 vs. 0.66; 5-year OS: 0.77 vs. 0.66; based on IDI, 2-year DFS: 0.33, p <0.001; 5-year DFS: 0.18, p = 0.04; 2-year OS: 0.28, p <0.001; 5-year OS: 0.15, p = 0.04). The comprehensive scores of the nomogram models could be used to stratify patients with ESCC.

Conclusions

The novel nomogram incorporating ctDNA features may help predict the prognosis of patients with resectable ESCC. This model can potentially be used to guide the postoperative management of ESCC patients in the future, such as adjuvant therapy and follow-up.

A new prognostic risk model based on autophagy-related genes in kidney renal clear cell carcinoma

This study aimed to explore the potential role of autophagy-related genes in kidney renal clear cell carcinoma (KIRC) and develop a new prognostic-related risk model. In our research, we used multiple bioinformatics methods to perform a pan-cancer analysis of the CNV, SNV, mRNA expression, and overall survival of autophagy-related genes, and displayed the results in the form of heat maps. We then performed cluster analysis and LASSO regression analysis on these autophagy-related genes in KIRC. In the cluster analysis, we successfully divided patients with KIRC into five clusters and found that there was a clear correlation between the classification and two clinicopathological features: tumor, and stage. In LASSO regression analysis, we used 13 genes to create a new prognostic-related risk model in KIRC. The model showed that the survival rate of patients with KIRC in the high-risk group was significantly lower than that in the low-risk group, and that there was a correlation between this grouping and the patients’ metastasis, tumor, stage, grade, and fustat. The results of the ROC curve suggested that this model has good prediction accuracy. The results of multivariate Cox analysis show that the risk score of this model can be used as an independent risk factor for patients with KIRC. In summary, we believe that this research provides valuable data supporting future clinical treatment and scientific research.