Background Gastric cancer (GC) is the third most common cause of cancer deaths worldwide. In the present study, we aimed to identify novel GC biomarkers by integrating isobaric tags of relative and absolute quantitation (iTRAQ) for aberrantly expressed proteins in GC patients. Methods Using stable isotope tags, we labeled an initial discovery group comprising four paired gastric cancer and adjacent gastric tissue samples, and subjected them to LC‐ESI‐MS/MS. We used a validation set comprising 129 paired gastric cancer and adjacent gastric tissues from patients and benign healthy controls to validate the candidate targets. Results We identified two proteins, NAD(P)‐dependent steroid dehydrogenase‐like (NSDHL) and neutral cholesterol ester hydrolase 1 (NCEH1), that were significantly overexpressed in GC tissues. The sensitivity and specificity of NSDHL were 80.6% and 74.4%, respectively, in GC compared with a sensitivity of 25.6% in adjacent tissues and 24% in benign healthy controls. The area under the ROC curve (AUC) for NSDHL was 0.810 for GC detection. Overexpression of NSDHL in GC was significantly correlated with local tumor invasion. The sensitivity and specificity of NCEH1 were 77.5% and 73.6%, respectively, in GC compared with a sensitivity of 26.4% in adjacent tissues and 20% in benign controls. The AUC for NSDHL was 0.792. Overexpression of NCEH1 was significantly associated with tumor histological classification and local invasion. Moreover, a combined analysis of NSDHL and NCEH1 achieved a sensitivity and specificity of 85.7% and 83%, respectively, and the AUC was 0.872. The combined analysis of NSDHL and NCEH1 was significantly correlated with histological grade and TNM Ⅱ‐Ⅳ staging. Conclusions iTRAQ‐labeled quantitative proteomics represents a powerful method to identify novel cancer biomarkers. The present study identified NSDHL and NCEH1 as useful biomarkers for screening, diagnosis, and prognosis of patients with gastric cancer.
Background and aims Observational studies have suggested a complex relationship between obesity and multiple sclerosis (MS). However, the role of genetic factors in the comorbidity and whether obesity exist consistent shared genetic relationships with MS, remains unclear. Our study aims to investigate the extent of shared genetic architecture underlying obesity and MS. Methods Based on genome-wide association studies (GWAS) summary statistics, we investigate the genetic correlation by the linkage disequilibrium score regression (LDSC) and genetic covariance analyzer (GNOVA). The casualty was identified by using bidirectional Mendelian randomization. Linkage disequilibrium score regression in specifically expressed genes (LDSC-SEG) and multi-marker analysis of GenoMic annotation (MAGMA) were utilized to investigate single-nucleotide polymorphisms (SNP) enrichment in the tissue and cell-type levels. We then identified shared risk SNPs using cross-trait meta-analyses and Heritability Estimation from Summary Statistics (ρ-HESS). We further explore the potential functional genes for BMI and MS using summary-data-based Mendelian randomization (SMR). Result We found significantly positive genetic correlation and 18 novel shared genetic SNPs were identified in cross-trait meta-analyses. We found the causality of BMI on MS using Mendelian randomization, but slight inconsistent evidence for the causality of MS on BMI. We observed tissue-specific level SNP heritability enrichment for BMI in 9 tissues and MS in 4 tissues, and in cell-type-specific level SNP heritability enrichment 12 consistent cell types were identified for BMI and MS in brain, spleen, lung and whole blood. Conclusion Our study identifies the genetical correlation and shared risk SNPs between BMI and MS. These findings could provide new insights into the etiology of comorbidity and have implications for future therapeutic trials.
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