Global transcriptome‑wide analysis of the function of GDDR in acute gastric lesions

A Novel Six-Gene-Based Prognostic Model Predicts Survival and Clinical Risk Score for Gastric Cancer

Background: Autophagy plays a vital role in cancer initiation, malignant progression, and resistance to treatment. However, autophagy-related genes (ARGs) have rarely been analyzed in gastric cancer (GC). The purpose of this study was to analyze ARGs in GC using bioinformatic analysis and to identify new biomarkers for predicting the overall survival (OS) of patients with GC.

Methods: The gene expression profiles and clinical data of patients with GC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and ARGs were obtained from two other datasets (the Human Autophagy Database and Molecular Signatures Database). Lasso, univariate, and multivariate Cox regression analyses were performed to identify the OS-related ARGs. Finally, a six-ARG model was identified as a prognostic indicator using the risk-score model, and survival and prognostic performance were analyzed based on the Kaplan-Meier test and ROC curve. Estimate calculations were used to assess the immune status of this model, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed for investigating the functions and terms associated with the model-related genes in GC.

Results: The six ARGs, DYNLL1, PGK2, HPR, PLOD2, PHYHIP, and CXCR4, were identified using Lasso and Cox regression analyses. Survival analysis revealed that the OS of GC patients in the high-risk group was significantly lower than that of the low-risk group (p < 0.05). The ROC curves revealed that the risk score model exhibited better prognostic performance with respect to OS. Multivariate Cox regression analysis indicated that the model was an independent predictor of OS and was not affected by most of the clinical traits (p < 0.05). The model-related genes were associated with immune suppression and several biological process terms, such as extracellular structure organization and matrix organization. Moreover, the genes were associated with the P13K-Akt signaling pathway, focal adhesion, and MAPK signaling pathway.

Conclusions: This study presents potential prognostic biomarkers for GC patients that would aid in determining the best patient-specific course of treatment.

Stromal score as a prognostic factor in primary gastric cancer and close association with tumor immune microenvironment

Background

Gastric cancer remains one of the major causes for tumor‐related deaths worldwide. Our study aimed to provide an understanding of primary gastric cancer and prompt its clinical diagnosis and treatment.

Methods

We integrated the expression profiles and overall survival information of primary gastric cancer in TCGA and GEO database and estimated the stromal score of each sample by the estimate R package. Stromal score and clinicopathologic characteristics associated with overall survival were analyzed by using Cox regression and the Kaplan‐Meier method. Gene set enrichment analysis (GSEA) and KEGG analysis were performed to explore the potential molecular mechanism in TCGA dataset. The relationship between immunotherapy‐associated markers or immune cell types and stromal score was explored by using Pearson correlation analysis.

Results

A total of 796 samples were collected for the analysis. Patients with stromal score‐high showed poor overall survival (P < .01, HR: 1.407, 95% CI: 1.144‐1.731) and identified as an independent prognostic factor. KEGG analysis revealed that stromal score actively involved in diverse tumor‐associated pathways. GSEA analysis also revealed stromal score associated with diverse immune‐related biological processes. Furthermore, stromal score was related with immunotherapy‐associated markers and multiple immune cells.

Conclusion

Our results showed that stromal score could serve as a potential prognostic biomarker in primary gastric cancer and play an important role in the recognition, surveillance, and prognosis of gastric cancer.

Inhibition of GKN2 Attenuates Acute Gastric Lesions Through the NLRP3 Inflammasome

Objective: Acute gastric lesions are commonly seen in critically ill patients in the intensive care unit and can result in significant upper gastrointestinal bleeding. However, the signaling mechanisms that regulate this severe disease are still unclear. In this study, we explored the involvement of gastrokine 2 (GKN2) in the development of acute gastric lesions in mice. Approach: We measured the degree of injury using the water immersion restraint stress mouse model. Inflammatory cells and factors were analyzed after gastric lesion induction. The luciferase reporter assay was used to detect the transcription activity of nuclear receptor subfamily 3 group C member 1 (NR3C1) in regulation of GKN2. We also detected the downstream pathway of GKN2 in gastric lesions. Results: The results showed that GKN2 could aggravate stress-induced gastric lesions and gastric mucosal cell death. Moreover, the gastric lesion promoted by GKN2 was gastric acid independent. GKN2 could recruit neutrophils and promote the release of inflammatory factors to contribute to inflammation. NR3C1, activated by cortisol under stress, could act as a transcription factor to upregulate the expression of GKN2. Innovation: This study elucidates the process of gastric lesion at a molecular level and explores the possible contender biomarkers for diagnosis and drug targets in wound healing of gastric lesions. Conclusion: In conclusion, GKN2, which was upregulated by cortisol, aggravated the gastric lesion through activation of the inflammasome and inflammatory reaction.