We developed a predictive model associated with ferroptosis to provide a more comprehensive view of esophageal squamous cell carcinoma (ESCC) immunotherapy. Gene expression data and corresponding clinical outcomes were obtained from the GEO and The Cancer Genome Atlas (TCGA) databases, and a ferroptosis-related gene set was obtained from the FerrDb database. We identified 45 ferroptosis-related genes that were differentially expressed, including enrichment in genes involved in the immune system process. We established a ferroptosis-related gene-based prognostic model based on the results of univariate Cox regression and multivariate Cox regression analyses, with an area under the curve (AUC) of 0.76 (3 years). We found that the patients with low-risk scores showed a higher proportion of CD8 + T cells, CD4 + memory activated T cells, etc. Finally, a predictive ferroptosis-related prognostic nomogram, which included the predictive values of age, gender, grade, TNM stage, and risk score, was established to predict overall survival. In sum, we developed a ferroptosis-related gene-based prognostic model that provides novel insights into the prediction of ESCC prognosis and identifies the relevance of the immune microenvironment for patient outcomes.

BackgroundEsophageal cancer is one of the most common cancer types, with its most common distant metastatic site being the lung. Currently, population-based data regarding the proportion and prognosis of patients with esophageal cancer with lung metastases (ECLM) at the time of diagnosis is insufficient. Therefore, we aimed to determine the proportion of patients with ECLM at diagnosis, as well as to investigate the prognostic parameters of ECLM.MethodsThis population-based observational study obtained data from the Surveillance, Epidemiology, and End Results (SEER) database registered between 2010 and 2016. Multivariable logistic regression was performed to identify predictors of the presence of ECLM at diagnosis. Multivariable Cox regression and competing risk analysis were used to assess prognostic factors in patients with ECLM. Median survival was estimated using Kaplan–Meier curves.ResultsOf 10,965 patients diagnosed with esophageal cancer between 2010 and 2016, 713 (6.50%) presented with initial lung metastasis at diagnosis. Lung metastasis represented 27.15% of all cases with metastatic disease to any distant site. Considering all patients with esophageal cancer, multivariable logistic regression indicated that pathology grade, pathology type, T staging, N staging, race, and number of extrapulmonary metastatic sites were predictive factors for the occurrence of lung metastases at diagnosis. The median survival time of patients with ECLM was 4.0 months. Patients receiving chemotherapy or chemoradiotherapy had the longest median overall survival, 7.0 months. Multivariable Cox regression indicated that age, histology type, T2 staging, number of extrapulmonary metastatic sites, and treatment (chemotherapy, radiotherapy, or chemoradiotherapy) were independent predictors for overall survival (OS). Multivariable competing risk analysis determined that age, number of extrapulmonary metastatic sites, and treatment were independent predictors for esophageal cancer-specific survival (CSS).ConclusionThe findings of this study may provide important information for the early diagnosis of ECLM, as well as aid physicians in choosing appropriate treatment regimens for these patients.

Objective The study objective was to investigate the use of peripheral blood biomarkers as predictors of patient survival. The aim of this study was to identify the baseline peripheral blood biomarkers associated with clinical outcome in patients with early lung cancer (stage I-II) treated with surgery. Methods We included and analysed data from 376 patients with early-stage lung cancer who underwent a standard lobectomy. Univariate and multivariate Cox regression analyses were performed on all patients to assess the relationships between progression-free survival (PFS) and overall survival (OS) and the peripheral blood biomarker metrics measured before surgical treatment. The peripheral blood parameters included monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and dimeric fibrin fragment D (D-dimer). Results After univariate Cox regression analysis, low MLR, low NLR, low PLR and low D-dimer values were significantly associated with both better OS and PFS (all p < 0.05). In multivariate Cox regression analysis, a low MLR was significantly and independently associated with both better overall survival and better progression-free survival (both p <0.05). A low D-dimer level was significantly and independently associated with better overall survival (p <0.05). Furthermore, the categorization of patients according to the number of factors with favourable results revealed that those without favourable results had significantly worse outcomes than that of those patients with at least one. Conclusion A baseline signature of low MLR, low NLR, low PLR, and low D-dimer values was associated with a better survival outcome for patients treated with surgery. Patients with more favourable results had better survival outcomes.

BackgroundThe hepatic metastasis pattern of esophageal cancer (EC) has not been fully explored. The primary objective of this study was to explore the predictors of esophageal cancer with hepatic metastasis (ECHM) at the time of diagnosis. In addition, we also analyzed the factors affecting ECHM prognosis.MethodsWe used the Surveillance, Epidemiology and End Result (SEER) database to identify ECHM patients at the time of initial diagnosis. The ECHM predictors were identified using multivariate logistic regression. Multivariate Cox regression and competing survival risk analyses were performed to identify factors associated with all-cause mortality and EC-specific mortality of ECHM, respectively.ResultsA total of 10,965 eligible EC patients were identified in the SEER database between 2010 and 2016, of which 1,197 were ECHM patients, accounting for 10.9% of the entire cohort. In the whole cohort, eight ECHM predictors (age, primary site, grade, histology type, T staging, N staging, insurance status, and number of extrahepatic metastatic sites) were determined using multivariate logistic regression analysis. Multivariate Cox regression and multivariate competing survival risks models confirmed that the male sex, advanced age, squamous cancer, and multiple extrahepatic metastasis increased the risk of both all-cause and EC-specific mortality, whereas chemotherapy and chemotherapy plus radiotherapy significantly reduced the risk of both.ConclusionsThis study explored population-level predictors of hepatic metastasis at the time of EC diagnosis and analyzed the clinical characteristics affecting the prognosis in ECHM patients. These findings may provide clinicians with a reference for the screening and treatment of hepatic metastasis in EC.

Glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1) is a key enzyme associated with glucose metabolism and uridine diphosphate-N-acetylglucosamine biosynthesis. Abnormal GNPNAT1 expression might be associated with carcinogenesis. We analyzed multiple lung adenocarcinoma (LUAD) gene expression databases and verified GNPNAT1 higher expression in LUAD tumor tissues than in normal tissues. Moreover, we analyzed the survival relationship between LUAD patients’ clinical status and GNPNAT1 expression, and found higher GNPNAT1 expression in LUAD patients with unfavorable prognosis. We built GNPNAT1 gene co-expression networks and further annotated the co-expressed genes’ Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and various associated regulatory factors. These co-expression genes’ functional networks mainly participate in chromosome segregation, RNA metabolic process, and RNA transport. We analyzed GNPNAT1 genetic alterations and co-occurrence networks, and the functional networks of these genes showed that GNPNAT1 participates in multiple steps of cell cycle transition and in the development of some cancers. We assessed the correlation between GNPNAT1 expression and cancer immune infiltrates and showed that GNPNAT1 expression is correlated with several immune cells, chemokines, and immunomodulators in LUAD. We found that GNPNAT1 correlates with LUAD development and prognosis, laying a foundation for further research, especially in immunotherapy.