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. 2023 Apr 27;13(1):6895.
doi: 10.1038/s41598-023-33277-x.

A novel Alzheimer's disease prognostic signature: identification and analysis of glutamine metabolism genes in immunogenicity and immunotherapy efficacy

Zixuan Wu #  1 , Ping Liu #  1 , Baisheng Huang  2 , Sisi Deng  1 , Zhenyan Song  1 , Xindi Huang  1 , Jing Yang  3 , Shaowu Cheng  4
Affiliations

A novel Alzheimer's disease prognostic signature: identification and analysis of glutamine metabolism genes in immunogenicity and immunotherapy efficacy

Zixuan Wu et al. Sci Rep. .

Abstract

Alzheimer's disease (AD) is characterized as a distinct onset and progression of cognitive and functional decline associated with age, as well as a specific neuropathology. It has been discovered that glutamine (Gln) metabolism plays a crucial role in cancer. However, a full investigation of its role in Alzheimer's disease is still missing. This study intended to find and confirm potential Gln-related genes associated with AD using bioinformatics analysis. The discovery of GlnMgs was made possible by the intersection of the WGCNA test and 26 Gln-metabolism genes (GlnMgs). GlnMgs' putative biological functions and pathways were identified using GSVA. The LASSO method was then used to identify the hub genes as well as the diagnostic efficiency of the four GlnMgs in identifying AD. The association between hub GlnMgs and clinical characteristics was also studied. Finally, the GSE63060 was utilized to confirm the levels of expression of the four GlnMgs. Four GlnMgs were discovered (ATP5H, NDUFAB1, PFN2, and SPHKAP). For biological function analysis, cell fate specification, atrioventricular canal development, and neuron fate specification were emphasized. The diagnostic ability of the four GlnMgs in differentiating AD exhibited a good value. This study discovered four GlnMgs that are linked to AD. They shed light on potential new biomarkers for AD and tracking its progression.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 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.a˘GSE132903 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.a˘Then, GO, KEGG and GSEA analyses were performed with multiple databases to obtain the functions related to GlnMgs.a˘Last, the immune cells, function and RNA changesa˘were analyzed.
Figure 2
Figure 2
Principal component analysis. (a) GlnMgs. (b) Expression of GlnMgs in clusters.
Figure 3
Figure 3
Expression of GlnMgs. (a) Expression of GlnMgs on sequences. (b,c) The correlation between GlnMgs and related genes.
Figure 4
Figure 4
Expression of Immune cells. (a,b) Expression of immune cells in different clusters. (c) Correlation between GlnMgs and immune cells.
Figure 5
Figure 5
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.
Figure 6
Figure 6
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).
Figure 7
Figure 7
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.
Figure 8
Figure 8
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.
Figure 9
Figure 9
(a) Identification of GlnMgs with a venn diagram. (b,c) Residuala˘expression patterns. (d) AUC of train group. (e) AUC of test group.
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