Construction and validation of a novel senescence-related risk score can help predict the prognosis and tumor microenvironment of gastric cancer patients and determine that STK40 can affect the ROS accumulation and proliferation ability of gastric cancer cells

Abstract

BackgroundIn recent years, significant molecules have been found in gastric cancer research. However, their precise roles in the disease’s development and progression remain unclear. Given gastric cancer’s heterogeneity, prognosis prediction is challenging. This study aims to assess patient prognosis and immune therapy efficacy using multiple key molecules.MethodThe WGCNA algorithm was employed to identify modules of genes closely related to immunity. A prognostic model was established using the Lasso-Cox method to predict patients’ prognosis. Single-sample gene set enrichment analysis (ssGSEA) was conducted to quantify the relative abundance of 16 immune cell types and 13 immune functions. The relationship between risk score and TMB, MSI, immune checkpoints, and DNA repair genes was examined to predict the effectiveness of immune therapy. GO and KEGG analyses were performed to explore potential pathways and mechanisms associated with the genes of interest. Single-cell RNA sequencing was utilized to investigate the expression patterns of key genes in different cell types.ResultsThrough the WGCNA algorithm and Lasso-Cox algorithm selected KL, SERPINE1, and STK40 as key genes for constructing the prognostic model. The SSGSEA algorithm was employed to evaluate the infiltration of immune cells and immune functions in different patients, and their association with the risk score was investigated. The high-risk group exhibited lower TMB and MSI compared to the low-risk group. MMR and immune checkpoint analysis revealed a significant correlation between the risk score and multiple molecules. Finally, we also believe that STK40 is the most critical senescence-related gene affecting the progression of gastric cancer. In vitro experiments showed that ROS accumulation and cell proliferation ability of gastric cancer cells were impaired when STK40 was knocked down.ConclusionIn summary, we’ve constructed a prognostic model utilizing key genes for gastric cancer prognosis, while also showcasing its efficacy in predicting patient response to immunotherapy.

Predictive Factors of Immunotherapy in Gastric Cancer: A 2024 Update

“…Thirteen immune activities and sixteen immune cell types were quantified in terms of relative abundance using single-sample gene set enrichment analysis (ssGSEA). The most important gene linked to senescence that influences the development of GC is serine/threonine kinase 40 (STK40) [ 27 ]. The ssGSEA collagen score (CS) was used to assess the expression level of genes associated with collagen.…”

“…--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.…”