This study was conducted to elucidate the link between adjuvant radiotherapy and survival in pathologic node-negative (pN0) esophageal cancer patients with upfront esophagectomy.

N6-Methyladenosine (m6A) has attracted growing interest among scholars as an important regulator of mRNA expression. Although the significant role of m6A in multiple biological processes (like proliferation and growth of cancers) has been comprehensively described, an analysis of its possible role in stomach adenocarcinoma (STAD) of tumor immune microenvironment (TIME) remains lacking. The data for RNA expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) were downloaded from The Cancer Genome Atlas (TCGA). Subsequently, 23 m6A regulators were curated, with patients being clustered into three m6A subtypes and m6A-related gene subtypes. Furthermore, they were compared based on overall survival (OS). This study also evaluates the association between m6A regulators and immune as well as response to the treatment. According to the TCGA-STAD cohort, three m6A clusters conformed to three phenotypes, immune-inflamed, immune-dessert, and immune-excluded, respectively. Patients who displayed lower m6A scores presented better overall survival outcomes. The GEO cohort demonstrated that those with a low m6A score had obvious general survival benefits and clinical advantages. Low m6A scores can carry the enhanced neoantigen loads, triggering an immune response. Meanwhile, three anti-PD-1 cohorts have confirmed the value of predicting survival outcomes. The results of this study indicate that m6A regulators are associated with TIME, and the m6A score is an efficient prognostic biomarker and predictive indicator for immunotherapy and chemotherapeutics. Moreover, comprehensive evaluations of m6A regulators in tumors will broaden our comprehension of TIME, efficiently guiding enhancing explorations on immunotherapy and chemotherapy strategies for STAD.

Mucin 16 (MUC16) mutation ranks third among all common mutations in lung adenocarcinoma (LUAD), and it has a certain effect on LUAD development and prognostic outcome. This research aimed to analyze the effects of MUC16 mutation on LUAD immunophenotype regulation and determine the prognostic outcome using an immune prognostic model (IPM) built with immune-related genes. The MUC16 mutation status and mRNA expression profiles were analyzed using diverse platforms and among several LUAD patients (n = 691). An IPM was then constructed using differentially expressed immune-related genes (DEIRGs) in MUC16MUT LUAD cases, and the data were compared with those of MUC16WT LUAD cases. The IPM's performance in distinguishing high-risk cases from low-risk ones among 691 LUAD cases was verified. Additionally, a nomogram was built and applied in the clinical setting. Furthermore, a comprehensive IPM-based analysis of how MUC16 mutation affected the tumor immune microenvironment (TIME) of LUAD was performed. MUC16 mutation decreased the immune response in LUAD. As revealed by functional annotation, the DEIRGs in the IPM were most significantly enriched in the humoral immune response function and the immune system disease pathway. Moreover, high-risk cases were associated with increased proportions of immature dendritic cells, neutrophils, and B-cells; enhanced type I interferon T-cell response; and increased expression of PD-1, CTLA-4, TIM-3, and LAG3 when compared with low-risk cases. MUC16 mutation shows potent association with TIME of LUAD. The as-constructed IPM displays high sensitivity to MUC16 mutation status and can be applied to discriminate high-risk LUAD cases from low-risk ones.

Advanced esophageal squamous cell carcinoma (ESCC) still has a dismal prognostic outcome. However, the current approaches are unable to evaluate patient survival. Pyroptosis represents a novel programmed cell death type which widely investigated in various disorders and can influence tumor growth, migration, and invasion. Furthermore, few existing studies have used pyroptosis-related genes (PRGs) to construct a model for predicting ESCC survival. Therefore, the present study utilized bioinformatics approaches for analyzing ESCC patient data obtained from the TCGA database to construct the prognostic risk model and applied it to the GSE53625 dataset for validation. There were 12 differentially expressed PRGs in healthy and ESCC tissue samples, among which eight were selected through univariate and LASSO cox regression for constructing the prognostic risk model. According to K-M and ROC curve analyses, our eight-gene model might be useful in predicting ESCC prognostic outcomes. Based on the cell validation analysis, C2, CD14, RTP4, FCER3A, and SLC7A7 were expressed higher in KYSE410 and KYSE510 than in normal cells (HET-1A). Hence, ESCC patient prognostic outcomes can be assessed by our PRGs-based risk model. Further, these PRGs may also serve as therapeutic targets.

The significance of long non-coding RNAs (lncRNAs) as pivotal mediators of histone acetylation and their influential role in predicting the prognosis of lung adenocarcinoma (LUAD) has been increasingly recognized. However, there remains uncertainty regarding the potential utility of acetylation-related lncRNAs (ARLs) in prognosticating the overall survival (OS) of LUAD specimens.