Comprehensive characterization of pyroptosis phenotypes with distinct tumor immune profiles in gastric cancer to aid immunotherapy.

OBJECTIVE: Pyroptosis is a form of programmed cell death that is essential for immunity. Herein, this study was conducted to uncover the implication of pyroptosis in immunomodulation and tumor microenvironment (TME) in gastric cancer. METHODS: Prognostic pyroptosis-related genes were extracted to identify different pyroptosis phenotypes and pyroptosis genomic phenotypes via unsupervised clustering analysis in the gastric cancer meta-cohort cohort (GSE15459, GSE62254, GSE84437, GSE26253 and TCGA-STAD). The activation of hallmark gene sets was quantified by GSVA and immune cell infiltration was estimated via ssGSEA and CIBERSORT. Through PCA algorithm, pyroptosis score was conducted. The predictors of immune response (TMB and IPS) and genetic mutations were evaluated. The efficacy of pyroptosis score in predicting immune response was verified in two anti-PD-1 therapy cohorts. RESULTS: Three different pyroptosis phenotypes with different prognosis, biological pathways and tumor immune microenvironment were established among 1275 gastric cancer patients, corresponding to three immune phenotypes: immune-inflamed, immune-desert, and immune-excluded. According to the pyroptosis score, patients were separated into high and low pyroptosis score groups. Low pyroptosis score indicated favorable survival outcomes, enhanced immune responses, and increased mutation frequency. Moreover, low pyroptosis score patients displayed more clinical benefits from anti-PD-1 and prolonged survival time. CONCLUSION: Our findings uncovered a nonnegligible role of pyroptosis in immunomodulation and TME multiformity and complicacy in gastric cancer. Quantifying the pyroptosis score in individual tumors may tailor more effective immunotherapeutic strategies.

A Deep Automated Skeletal Bone Age Assessment Model with Heterogeneous Features Learning.

Skeletal bone age assessment is a widely used standard procedure in both disease detection and growth prediction for children in endocrinology. Conventional manual assessment methods mainly rely on personal experience in observing X-ray images of left hand and wrist to calculate bone age, which show some intrinsic limitations from low efficiency to unstable accuracy. To address these problems, some automated methods based on image processing or machine learning have been proposed, while their performances are not satisfying enough yet in assessment accuracy. Motivated by the remarkable success of deep learning (DL) techniques in the fields of image classification and speech recognition, we develop a deep automated skeletal bone age assessment model based on convolutional neural networks (CNNs) and support vector regression (SVR) using multiple kernel learning (MKL) algorithm to process heterogeneous features in this paper. This deep framework has been constructed, not only exploring the X-ray images of hand and twist but also some other heterogeneous information like race and gender. The experiment results prove its better performance with higher bone age assessment accuracy on two different data sets compared with the state of the art, indicating that the fused heterogeneous features provide a better description of the degree of bones' maturation.

An ultrasensitive aptasensor for Ochratoxin A using hexagonal core/shell upconversion nanoparticles as luminophores.

We developed an ultrasensitive luminescence resonance energy transfer (LRET) aptasensor for Ochratoxin A (OTA) detection, using core/shell upconversion nanoparticles (CS-UCNPs) as luminophores. The OTA aptamer was tagged to CS-UCNPs as energy donor and graphene oxide (GO) acted as energy acceptor. The π-π stacking interaction between the aptamer and GO brought CS-UCNPs and GO in close proximity hence initiated the LRET process resulting in quenching of CS-UCNPs luminescence. A linear calibration was obtained between the luminescence intensity and the logarithm of OTA concentration in the range from 0.001ngmL-1 to 250ngmL-1, with a detection limit of 0.001ngmL-1. The aptasensor showed good specificity towards OTA in beer samples. The ultrahigh sensitivity and pronounced robustness in beer sample matrix suggested promising prospect of the aptasensor inpractical applications.

Proton pump inhibitor pantoprazole inhibits the proliferation, self­renewal and chemoresistance of gastric cancer stem cells via the EMT/ß­catenin pathways.

The cancer stem cell (CSC) model suggests that a small subset of cancer cells possess stem cell properties and plays a crucial role in tumor initiation, metastasis and resistance to anticancer therapy. Exploration of the specific therapies targeting at CSCs has been a crucial issue in antitumor research. Gastric cancer (GC) cells often exist in an ischemic microenvironment with acidic conditions in vivo, thus maintenance of cellular pH homeostasis is important for the survival and function of GC cells. Proton pump inhibitors (PPIs) may prevent intracellular proton extrusions which consequently reduce cancer cell survival under acidic conditions. The effects of PPIs on the suppression of the viability and invasiveness of GC cells have been reported, but the functional role of pantoprazole (PPZ) in GC cells remains unknown. In this study, we found that when cells were treated with PPZ, the 5­fluorouracil (5­FU) chemosensitivity was upregulated, meanwhile the sphere formation ability and the relative expression levels of stem cell markers CD44, CD24, ABCG2, EpCAM and Lgr5 were significantly decreased. It was hypothesized that PPZ inhibits the GC CSCs. Successively a sphere formation culture was performed to establish CSC models and the effect of PPZ on GC CSCs from SGC-7901 and HGC­27 cells was explored. The addition of PPZ reduced the relative expression of CSC markers and anti­drug markers accompanied by a decrease in proliferation, 5­FU chemoresistance and self­renewal capacity via epithelial­mesenchymal transition (EMT)/ß­catenin pathways. The study suggests that PPZ could be a promising novel specific therapeutic strategy for targeting GC CSCs.