Self-expandable metal stent of esophagogastric junction versus pyloric area obstruction in advanced gastric cancer patients: Retrospective, comparative, single-center study

Gut microbiome and nutrition-related predictors of response to immunotherapy in cancer: making sense of the puzzle

The gut microbiome is emerging as an important predictor of response to immune checkpoint inhibitor (ICI) therapy for patients with cancer. However, several nutrition-related patient characteristics, which are themselves associated with changes in gut microbiome, are also prognostic markers for ICI treatment response and survival. Thus, increased abundance of Akkermansia muciniphila, Phascolarctobacterium, Bifidobacterium and Rothia in stool are consistently associated with better response to ICI treatment. A. muciniphila is also more abundant in stool in patients with higher muscle mass, and muscle mass is a strong positive prognostic marker in cancer, including after ICI treatment. This review explores the complex inter-relations between the gut microbiome, diet and patient nutritional status and the correlations with response to ICI treatment. Different multivariate approaches, including archetypal analysis, are discussed to help identify the combinations of features which may select patients most likely to respond to ICI treatment.

Prognostic importance of the preoperative New-Naples prognostic score for patients with gastric cancer

BACKGROUND

The wide applicability of the Naples prognostic score (NPS) is still worthy of further study in gastric cancer (GC). This study aimed to construct a New-NPS based on the differences in immunity and nutrition in patients with upper and lower gastrointestinal tumors to help obtain an individualized prediction of prognosis.

METHODS

This study retrospectively analyzed patients who underwent radical gastrectomy from April 2014 to September 2016. The cutoff values of the preoperative neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), serum albumin (Alb), and total cholesterol (TC) were calculated by ROC curve analysis. ROC and t-ROC were used to evaluate the accuracy of the prognostic markers. The Kaplan-Meier method and log-rank test were used to analyze the overall survival probability. Univariate and multivariate analyses based on Cox risk regression were used to show the independent predictors. The nomogram was made by R studio. The predictive accuracy of nomogram was assessed using a calibration plot, concordance index (C-index), and decision curve.

RESULTS

A total of 737 patients were included in training cohort, 411 patients were included in validation cohort. ROC showed that the New-NPS was more suitable for predicting the prognosis of GC patients. NPS = 2 indicated a poor prognosis. Multivariate analysis showed that CEA (P = 0.026), Borrmann type (P = 0.001), pTNM (P < 0.001), New-NPS (P < 0.001), and nerve infiltration (P = 0.035) were independent risk factors for prognosis.

CONCLUSION

The New-NPS based on the cutoff values of NLR, LMR, Alb, and TC is not only suitable for predicting prognosis but can also be combined with clinicopathological characteristics to construct a nomogram model for GC patients.