Metabolic and immune‐related gene signatures: Predictive stratification and prognostic implications in gastric cancer

Abstract

BackgroundGastric cancer, marked by its heterogeneous nature, showcases various molecular subtypes and clinical trajectories. This research delves into the significance of metabolic and immune‐driven pathways in gastric cancer, constructing a prognostic signature derived from differentially expressed metabolic and immune‐correlated genes (DE‐MIGs).MethodsMetabolic and immune‐associated gene were sourced from the GeneCards database. Differential expression analysis on the TCGA‐STAD dataset was executed using the limma package, unveiling 51 DE‐MIGs that underwent functional enrichment scrutiny. The LASSO Cox regression methodology guided the creation of the prognostic signature, and individual patient risk scores were determined. Assessment tools like CIBERSORT, ESTIMATE and ssGSEA were deployed to study the immune microenvironment, while mutation profiles, genomic stability, resistance to chemotherapy and immunotherapy responsiveness were scrutinized across distinct signature categorizations.ResultsAmong the identified DE‐MIGs, 26 were significantly tied to the overall survival of gastric cancer patients. The developed prognostic signature proficiently differentiated patients into high‐risk and low‐risk cohorts, with the latter showing markedly better outcomes. The study underscored the centrality of the immune microenvironment in influencing gastric cancer outcomes. Key pathways such as TGF‐Beta, TP53 and NRF2 dominated the high‐risk group, whereas the LRTK−RAS and WNT pathways characterized the low‐risk group. Interestingly, the low‐risk segment also manifested a heightened tumor mutation burden and enhanced susceptibility to immunotherapy.ConclusionsOur findings introduce a pivotal prognostic signature, rooted in DE‐MIGs, that effectively segregates gastric cancer patients into distinct risk‐based segments. Insights into the influential role of the immune microenvironment in gastric cancer progression pave the way for more refined therapeutic interventions.

Predictive Factors of Immunotherapy in Gastric Cancer: A 2024 Update

“…Important pathways, such as the TGF-beta, TP53, and NRF2 pathways, dominated the high-risk group, whereas the LRTK-RAS and WNT pathways represented the low-risk group. Patients with GC were effectively divided into high-risk and low-risk cohorts based on a signature of differentially expressed metabolic and immune-correlated genes (DE-MIGs), with the latter group exhibiting noticeably improved results [ 36 ].…”

“…--STK40 is the most important gene linked to senescence that influences the development of GC [27] -CS might represent the therapeutic impact of immunotherapy [28] -MFAP2+ CAFs create an immune-evading TME with immune effector cells that are debilitated [29] the immunotherapy characteristics of the TME were strongly correlated with two genes (MSH4 and RPL22L1) and four methylation probes (EPM2AIP1|MLH1:cg27331401, LNP1:cg05428436, and TSC22D2:cg15048832) [30] a lncRNA signature model associated with DNA methylation regulators is a novel method for predicting the treatment response and stratification of patients with GC [31] -N6-methyladenosine (m6A) regulator-mediated genes in the glycolytic pathway predict the prognosis and immunotherapy response of GC patients [32] the adenosine pathway-based signature is a potentially useful risk stratification tool that might inform personalized prognostication and immunotherapy for GC patients [33] the TIDE score and the IPS indicate that decreased immunotherapy benefit is associated with elevated expression of CHSY3 [34] -FIRGs have significant therapeutic and predictive potential for GC and have a role in the onset and progression of the disease [35] -TGF-beta, TP53, and NRF2 pathways dominated the high-risk group, whereas the LRTK-RAS and WNT pathways represented the low-risk group [36] the Anoikis-related lncRNA genes predictive model has been shown to have high predictive accuracy and may be used as a guide for prognostic evaluation and clinical treatment of patients with STAD [37] five methylation-related genes, namely, CHAF1A, CPNE8, PHLDA3, SPARC, and EHF were found to potentially be significant for patient prognosis [38] -Hedgehog signaling, a highly conserved system that controls cell division and growth, is crucial in the development of STAD [39] -SPP1 has been identified as a stand-alone predictor of STAD prognosis and may control the disease course [40] the T-cell-related genes CD5, ABCA8, SERPINE2, ESM1, SERPINA5, and NMU have been suggested as prospective targets for immunotherapy in patients with STAD and can aid in prognosis estimation [41] -ICB treatment for EBVaGC may be better guided by the identification of SMARCA4, TMB, and CTLA-4 mutations as possible predictive markers of ICB success [42] Table 3. Major developments in immunological predictive factors research.…”