This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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.

The Integrin Subunit Alpha 4 (ITGA4) plays important roles in cancers pathogenesis. However, the expression and association with clinicopathological and survival probability have not been previously assessed in gastric cancer (GC). Protein expression of ITGA4 was assessed in TMA using immunohistochemistry and correlated with clinicopathological factors and survival. The mRNA expression of ITGA4 was also assessed in the HMU-GC cohort. Bioinformatics function analysis was conducted through GSEA. The “CIBERSORT” package was used for immune infiltration analysis. “SvyNom” package is used to construct prognosis model. ITGA4 knock down using shRNA. The evaluation of cell function was performed by CCK-8 and Transwell invasion and migration experiments. ITGA4 was significantly associated with N classification ( P = 0.031 ), tumor location ( P = 0.033 ), WHO classification ( P = 0.007 ), and poor prognosis in mRNA level. GSEA analysis of the validation cohort suggested that ITGA4 was associated with macrophage infiltration. Immunohistochemistry showed that ITGA4 was associated with poor prognosis. Multivariate Cox regression analysis found that ITGA4 ( P = 0.045 ) and lymph node metastasis rate ( P = 0.026 ) were independent prognostic factors and could construct a prognosis model. ITGA4 knockdown cell line significantly reduced the ability of proliferation, invasion, and metastasis. ITGA4 is associated with patient survival in GC and may be an important prognostic biomarker.

Background:SPATA18 (spermatogenesis-associated 18, also called Mieap) encodes a protein that can induce lysosome-like organelles within mitochondria, which plays an important role in tumor growth. We measured the expression of SPATA18 in ccRCC, and assessed its diagnostic and prognostic clinical value in patients with clear cell renal cell carcinoma (ccRCC). Material/Methods:We analyzed SPATA18 expression using data from the TCGA-KIRC cohort, GEO database, and UALCAN database. Immunohistochemistry was carried out to verify the expression in the ccRCC patients. The diagnostic value of SPATA18expression was evaluated by a receiver operating characteristic (ROC) curve. The correlation between clinical characteristics and SPATA18 expression was calculated by chi-square test. The prognostic value of SPATA18 expression was assessed by Kaplan-Meier analysis and Cox analysis. We conducted gene set enrichment analysis (GSEA) using TCGA database. Results:SPATA18 gene exhibited a higher expression in ccRCC tissues than in normal tissues. SPATA18 showed a substantial diagnostic value in ccRCC. SPATA18 expression was correlated with histological grade, clinical stage, T classification, and distant metastasis of ccRCC. Furthermore, high SPATA18 expression was associated with favorable overall survival. Multivariate analysis showed that SPATA18 was an independent risk factor for ccRCC. Gene set enrichment analysis (GSEA) showed that B cell receptors, WNT targets, extracellular matrix, oxidative phosphorylation, calcium metabolism, iron uptake and transport, potassium channels, and insulin receptor were differently enriched in the phenotype that was negatively correlated with SPATA18. Conclusions:Our study indicated that high SPATA18 expression in ccRCC was associated with a good prognosis, and it could be a positive prognostic biomarker for ccRCC.

Background and Objectives:The prognosis is known to differ significantly among advanced gastric cancer (AGC) with Borrmann type III. This study aimed to evaluate the prognosis of these patients more individually. Methods: We selected 542 AGC patients with Borrmann type III. We used the receiver operating characteristic curve to analyze the cutoff values of inflammation indexes, and used Kaplan-Meier and Log rank tests to analyze recurrence-free survival (RFS) and overall survival (OS). The independent risk factors for recurrence and prognosis were analyzed by Cox proportional hazards regression model. The nomogram models were constructed by R studio. Results: Patients with high preoperative fibrinogen (F) and systemic immune-inflammation index (SII) levels had worse RFS and OS and higher risk of postoperative locoregional recurrence, hematogenous metastasis and lymph node metastasis. F and SII can combine with different clinicopathological features (all P<0.05) to construct nomograms to predict 5-year recurrence and prognosis, which both were superior to pTNM stage alone. Conclusion:The nomogram models based on F and SII can evaluate AGC with Borrmann type III postoperative recurrence and prognosis.