Alzheimers disease (AD) is often regarded as one of the primary causes of dementia and frailty. The signs of the illness begin with mild memory issues and proceed to cognitive impairment, dysfunctions in complex daily tasks, and several other domains of cognition. By the time AD is clinically recognized, neuronal loss and neuropathologic abnormalities have devel- oped in several brain locationst1,2. AD is a degenerative and irreversible brain disease that impairs memory, cognition, and, eventually, the ability to perform even the most basic activities. Injury appears to begin in the hippocampus and entorhinal cortex, two areas of the brain critical for memory formation3. Additional brain regions are harmed as more neurons die, and brain tissue is substantially reduced in the latter stages of AD. While numerous variables, like as genetics and lifestyle, impact a persons risk of acquiring AD, age is by far the most important4. The condition is rare before the age of 65, and the recurrence grows in subsequent decades, with a 2433% probability of having the disease by the age of 855.Given the pessimistic projections for the AD population and its associated socioeconomic costs between 2030 and 2050, scientific and clinical research in the field of AD is currently focusing on the early detection of the transitional phase between normal aging, moderate cognitive impairment, and dementia6,7. Throughout the preceding three decades, much has been learnt about the molecular basis of the condition, stressing the potential for developing biomarkers for diagnosis, risk assessment, clinical trials, therapeutic targeting, and discovering novel pharmaceutical targets8,9.

AD is a complex disease that is unlikely to be successfully treated with a single medication or other intervention. Modern pharmacotherapeutic strategies are focused on supporting patients in maintaining mental capacities, regulating behavioral manifestations, and delaying development, hence decreasing the appearance of sickness symptoms10,11. All currently known treatments work by altering the levels of particular neurotransmitters in the brain, principally acetylcholine (ACh) and glutamate. Although this helps with symptoms, it is not a total cure for the condition12. With the growth of bioinformatics, a lot of evidence-based evidence has been gathered. Computation and prediction based on relevant information can give certain alternative suggestions for future clinical diagnosis and therapy and drug research.

Because glutamine (Gln) is the most prevalent amino acid in circulation, cultured tumor cells use it quickly. Gln is commonly used in cellular aerobic glycolysis to maintain TCA flux or as a source of citrate for lipid synthesis in reductive carboxylation13. Furthermore, glutaminolysis enhances proliferative cell survival by decreasing oxidative stress and preserving the integrity of the mitochondrial membrane. Gln serves as an energy source for both tumor and immunological cells14. However, it appears that inflammatory antitumor immune cells, particularly macrophages, do not rely on or even reject Gln metabolism. M2 macrophages are more dependent on Gln than naive macrophages, but decreased Gln metabolism can generate pro-inflammatory M1 macrophages15. As a result, Gln metabolism may be a target for converting tumor-associated macrophages from M2 to M1, hence increasing the anti-tumor inflammatory immune response.

Furthermore, Gln metabolism is important in Th1 cell differentiation and effector T cell activation. These data imply that inhibiting Gln metabolism may be able to restructure TME and boost immunotherapy effectiveness. Some pattern recognition receptors in AD can form huge multiprotein complexes known as inflammasomes. When inflammasomes combine, they create membrane holes and process proinflammatory cytokines, resulting in pyroptosis, a kind of inflammatory cell death16. Innate immune signaling and inflammasome activation are important preventive mechanisms against AD17. Their activation, however, must be strictly managed, since excessive activation can cause neuroinflammation and brain injury. Potential treatment methods for AD have included balancing the hosts innate immune response18. Although targeting Gln metabolism in conjunction with immunotherapy is incredibly promising in AD, the landscape of Gln metabolism in tumor microenvironment (TME) is yet unknown. As a result, we conducted this work to conduct a comprehensive review of GlnMgs and immunotherapy in AD.

In biological research, gene expression analysis is becoming increasingly significant. The Accelerating Medicines Partnership- AD programs availability of high-throughput transcriptome sequencing data and clinical annotation allows us to investigate the altered transcriptional and related molecular pathways implicated in AD. Several research have used gene expression information acquired from the Gene Expression Omnibus (GEO) to investigate the molecular pathways involved in the development of AD19,20. The results of these bioinformatics analyses provide intriguing insights for understanding the pathophysiology and processes of AD from several perspectives. However, no study has used bioinformatics to determine whether GlnMgs are im- portant for AD development. As a result, the goal of this work was to examine the AD-related GEO via the lens of the GlnMgs (Fig.1).

Framework. The data of AD patients were obtained from GEO databases, and then the GlnMgs were matched to carry out difference analysis and risk model construction, respectively.aGSE132903 was used as the main body and GSE63060 was used to verify the model with good grouping, and GlnMgs related to the prognosis of AD patients were obtained.aThen, GO, KEGG and GSEA analyses were performed with multiple databases to obtain the functions related to GlnMgs.aLast, the immune cells, function and RNA changesawere analyzed.

Among the 26 GlnMgs, all were significantly different except for CPS1, GLUD1, CAD, SLC38A1,GMPS (Fig.2a). Some genes cluster in the treat group and some in the control group. Treat: PHGDH, CTPS2, LGSN, GLYATL1, GLUL, ASL, ARHGAP11B. Control: MECP2, NR1H4, NIT2, PFAS, GLS2, GLS, ASNS, PPAT, GFPT1, ASNSD1 (Fig.2b) (Table S2).

Principal component analysis. (a) GlnMgs. (b) Expression of GlnMgs in clusters.

We calculated the chromosomal positions of GlnMgs and visualized them in circles (Fig.3a) (Table S3).aThen, in order to clarify the expression of these genes, we conducted correlation analysis of these genes (Fig.3b,c).

Expression of GlnMgs. (a) Expression of GlnMgs on sequences. (b,c) The correlation between GlnMgs and related genes.

The immune environment is extremely essential in the onset and progression of AD. CIBERSORT was used to examine the immune cell components in adipose tissue. We built barplot and corplot to show the results of immune cells (Fig.4a,b). Then, in order to clarify the expression of these genes, we conducted correlation analysis of these genes and immune cells (Fig.4c).

Expression of Immune cells. (a,b) Expression of immune cells in different clusters. (c) Correlation between GlnMgs and immune cells.

When the clustering variable (k) was set to 2, the intragroup correlations were the strongest and the intergroup correlations were the smallest, indicating that AD patients could be divided into two groups based on GlnMgs (Fig.5a). Based on this cluster, we also discussed the expression of the GlnMgs in different clusters. CTPS2, ARHGAP11B, and NR1H4 were not significantly different between the two groups (Fig.5b,c). According to the PCA results, patients with varying risks were divided into two groups (Fig.5d). Based on the previous results, we also analyzed the results of immune cell infiltration according to different clusters (Fig.5e,f).

Cluster analysis. (a) Consensus clustering matrix. (b,c) Expression of the GlnMgs in different clusters. (d) PCA. (e,f) Immune cell infiltration of different clusters.

The GlnMgs were used to conduct the enrichment analysis. The MF mainly involves atp dependent dna dna annealing activity, beta galactoside cmp alpha-2-3-sialyltransferase activity, gomf phosphatidylinositol-3-4-5-trisphosphate binding. The BP mainly involves cell fate specification, atrioventricular canal development, neuron fate specification (Fig.6a). The pathways analysis showed that the notch signaling pathway, primary immunodeficiency, renin angiotensin system were enriched (Fig.6b).

Enrichment analysis for DEGs. (a) GO. (b) KEGG. (a) Barplot graph for GO enrichment (the longer bar means the more genes enriched; q-value: the adjusted p-value). (b) Barplot graph for KEGG pathways (the longer bar means the more genes enriched).

To establish an approximation scale-free topology for the network, a soft-thresholding power was applied (Fig.7a). The genes with the highest variance were grouped and integrated into nine co-expression modules (Fig.7b). The relationship between module eigengene and clinical characteristics was investigated using Pearsons correlation analysis (Fig.7c). The turquoise module was shown to be highly connected with the Group attribute (i.e. AD and ND) and to have the greatest association (Fig.7d) (Table S4).

Co-expression module construction. (a) Soft threshold power mean connection and scale-free fitting index anal- ysis. (b) Clustering of dendrograms According to dynamic tree cutting, the genes were sorted into distinct modules using hierarchical clustering with a threshold of 0.25. Each color represents a separate module. (c) Heatmap of correlations between module eigengenes and clinical characteristics. (d) Gene scatterplot in the turquoise module.

To establish an approximation scale-free topology for the network, a soft-thresholding power was applied (Fig.8a). The co-expression modules were formed by clustering the variance genes (Fig.8b). Pearsons correlation analysis was used to investigate the relationship between module eigengene and clinical characteristics (Fig.8c). The module was shown to be strongly linked with the Group characteristic (i.e. AD and ND) and to have the greatest association (Fig.8d) (Table S5).

Cluster construction of co-expression modules (a) Soft threshold power mean connection and scale-free fitting index analysis. (b) Dendrogram clustering (c) Heatmap of correlations between module eigengenes and clinical characteristics. (d) Gene scatterplot in the grey module.

DEGs, grey module genes (WGCNA), and GlnMgs overlapping as well. A total of 34 genes were crossed (Fig.9a) (Table S6). The Boxplots depicted the residual expression patterns of these genes in AD (Fig.9b).aThere are some differences in the proportions of the four different modes (Fig.9c). As seen in Fig.6e, the GlnMgs diagnostic capacity in distinguishing AD from control samples revealed a satisfactory diagnostic value, with an Areas under the curve (AUC)aof RF: 0.784, SVM: 0.759, XGB: 0.788, and GLM: 0.666 (Fig.9d). An AUC of 0.784 (95% CI 0.6550.896) in GSE132903, an AUC of 0.815 (95% CI 0.7340.895) in GSE63060 (Fig.9e) (Table S7).

(a) Identification of GlnMgs with a venn diagram. (b,c) Residualaexpression patterns. (d) AUC of train group. (e) AUC of test group.

The hub gene in the XGB model predicted three drugs. These include ME-344, METFORMIN HYDROCHLORIDE, NV-128(Table 1). In addition, we predicted all interacting genes for drug and gene relationships (Table 2).

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