Construction of stomach adenocarcinoma prognostic signature based on anoikis-related lncRNAs and clinical significance

Predictive Factors of Immunotherapy in Gastric Cancer: A 2024 Update

Many studies on gastric cancer treatment have identified predictors of immunotherapy benefits. This article provides an update on the major developments in research related to predictive factors of immunotherapy for gastric cancer. We used the search term "predictive factors, immunotherapy, gastric cancer" to find the most current publications in the PubMed database related to predictive factors of immunotherapy in gastric cancer. Programmed cell death, genetic, and immunological factors are the main study topics of immunotherapy's predictive factors in gastric cancer. Other preventive factors for immunotherapy in gastric cancer were also found, including clinical factors, tumor microenvironment factors, imaging factors, and extracellular factors. Since there is currently no effective treatment for gastric cancer, we strongly propose that these studies be prioritized.

Exploring the association between inflammatory biomarkers and gastric cancer development: A two-sample mendelian randomization analysis

This study aimed to elucidate the potential causative links between inflammatory biomarkers and gastric cancer risk via a two-sample Mendelian randomization approach. Leveraging genome-wide association study (GWAS) data, we conducted a two-sample Mendelian randomization analysis. Instrumental variable selection for inflammatory markers - namely, tissue factor, monocyte chemotactic protein-1, E-selectin, interleukin 6 receptor, and fatty acid-binding protein 4 - was informed by SNP data from the IEU database. Strongly associated SNPs served as instrumental variables. We applied a suite of statistical methods, including Inverse Variance Weighted (IVW), Weighted Median Estimator (WME), MR-Egger, and mode-based estimates, to compute the odds ratios (ORs) that articulate the impact of these markers on gastric cancer susceptibility. The IVW method revealed that the interleukin 6 receptor was inversely correlated with gastric cancer progression (OR = 0.86, 95% CI = 0.74-0.99, P = .03), whereas fatty acid-binding protein 4 was found to elevate the risk (OR = 1.21, 95% CI = 1.05-1.39, P = .03). Instrumental variables comprised 5, 4, 7, 2, and 3 SNPs respectively. Convergent findings from WME, MR-Egger, and mode-based analyses corroborated these associations. Sensitivity checks, including heterogeneity, horizontal pleiotropy assessments, and leave-one-out diagnostics, affirmed the robustness and reliability of our instruments across diverse gastric malignancy tissues without substantial bias. Our research suggests that the interleukin 6 receptor potentially mitigates, while fatty acid-binding protein 4 may contribute to the pathogenesis of gastric cancer (GC). Unraveling the intricate biological interplay between inflammation and oncogenesis offers valuable insights for preemptive strategies and therapeutic interventions in gastric malignancy management.

Prognostic Implication of Plasma Metabolites in Gastric Cancer

Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients' quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan-Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients.