ObjectiveThe aim of the study was to propose a signature based on genes associated with antigen processing and presentation (APscore) to predict prognosis and response to immune checkpoint inhibitors (ICIs) in advanced gastric cancer (aGC).BackgroundHow antigen presentation-related genes affected the immunotherapy response and whether they could predict the clinical outcomes of the immune checkpoint inhibitor (ICI) in aGC remain largely unknown.MethodsIn this study, an aGC cohort (Kim cohort, RNAseq, N=45) treated by ICIs, and 467 aGC patients from seven cohorts were conducted to investigate the value of the APscore predicting the prognosis and response to ICIs. Subsequently, the associations of the APscore with the tumor microenvironment (TME), molecular characteristics, clinical features, and somatic mutation variants in aGC were assessed. The area under the receiver operating characteristic curve (AUROC) of the APscore was analyzed to estimate response to ICIs. Cox regression or Log-rank test was used to estimate the prognosis of aGC patients.ResultsThe APscore constructed by principal component analysis algorithms was an effective predictive biomarker of the response to ICIs in the Kim cohort and 467 aGC patients (Kim: AUC =0.85, 95% CI: 0.69–1.00; 467 aGC: AUC =0.69, 95% CI: 0.63–0.74). The APscore also was a prognostic biomarker in 467 aGC patients (HR=1.73, 95% CI: 1.21−2.46). Inhibitory immunity, decreased TMB and low stromal scores were observed in the high APscore group, while activation of immunity, increased TMB, and high stromal scores were observed in the low APscore group. Next, we evaluated the value of several central genes in predicting the prognosis and response to ICIs in aGC patients, and verified them using immunogenic, transcriptomic, genomic, and multi-omics methods. Lastly, a predictive model built successfully discriminated patients with vs. without immunotherapy response and predicted the survival of aGC patients.ConclusionsThe APscore was a new biomarker for identifying high-risk aGC patients and patients with responses to ICIs. Exploration of the APscore and hub genes in multi-omics GC data may guide treatment decisions.
Glycoprotein (GP) Ibα ectodomain shedding has important implications for thrombosis and hemostasis. A disintegrin and metalloproteinase 17 (ADAM17) was identified to play an essential role in agonist induced GPIbα shedding. The relationship of GPIbα shedding and ADAM17 in the acute stage of atherosclerotic ischemic stroke (AIS) patients has not been thoroughly studied. A total of 306 patients and 230 controls matched for age, sex, race, history of hypertension and diabetes mellitus were enrolled in the study. GPIbα, ADAM17, glycocalicin were detected by flow cytometry, Western blotting, and enzyme-linked immunosorbent assay (ELISA) respectively. Compared with the control group, the expression of GPIbα in patients with acute ischemic stroke was significantly lower (P=0.000, P<0.01). Plasma glycocalicin and ADAM17 in AIS group were higher than those in control group (P=0.699, P=0.000). Pearson's analysis showed glycocalicin bore no correlation with GPIbα in AIS patients (r=0.095, P>0.05). GPIbα and National Institute of Health Stroke Scale (NIHSS) had negative correlation (r=-0.514, P<0.01). Our findings indicate that ADAM17 may be a risk factor for ischemic stroke in Chinese and the expression of GPIbα can serve as a measure for stroke severity.
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