Melatonin-Related Genes as Key Players in Alzheimer's Disease: Discovery of Promising Biomarkers for Treatment Targets for Alzheimer's Disease

Melatonin-Related Genes as Key Players in Alzheimer's Disease: Discovery of Promising Biomarkers for Treatment Targets for Alzheimer's Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Melatonin-Related Genes as Key Players in Alzheimer's Disease: Discovery of Promising Biomarkers for Treatment Targets for Alzheimer's Disease Huaxiong Zhang, Dilmurat Hamit, Qing LI, Xiao Hu, San-feng LI, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4772764/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background Melatonin can improve mitophagy, thereby ameliorating cognitive deficits in Alzheimer’s disease (AD) patients. Hence, our research focused on the potential value of melatonin-related genes (MRGs) in AD through bioinformatic analysis. Methods First, the key cells in the single-cell dataset GSE138852 were screened out based on the proportion of annotated cells and Fisher’s test between the AD and control groups. The differentially expressed genes (DEGs) in the key cell and GSE5281 datasets were identified, and the MRGs in GSE5281 were selected via weighted gene coexpression network analysis. After intersecting two sets of DEGs and MRGs, we performed Mendelian randomization analysis to identify the MRGs causally related to AD. The biomarkers GSE5281 and GSE48350 were identified through receiver operating characteristic (ROC) curve and expression analyses. Furthermore, gene set enrichment analysis, immune infiltration analysis and correlation analysis with metabolic pathways were conducted, as well as construction of a regulator network and molecular docking. Results According to the Fisher test, oligodendrocytes were regarded as key cells due to their excellent abundance in the GSE138852 dataset, in which there were 281 DEGs between the AD and control groups. After overlapping with 3,490 DEGs and 550 MRGs in GSE5281, four genes were found to be causally related to AD, namely, GPRC5B , METTL7A , NFKBIA and RASSF4 . Moreover, GPRC5B , NFKBIA and RASSF4 were deemed biomarkers, except for METTL7A , because of their indistinctive expression between the AD and control groups. Biomarkers might be involved in oxidative phosphorylation, adipogenesis and heme metabolism. Moreover, T helper type 17 cells, natural killer cells and CD56dim natural killer cells were significantly correlated with biomarkers. Transcription factors (GATA2, POU2F2, NFKB1, etc.) can regulate the expression of biomarkers. Finally, we discovered that all biomarkers could bind to melatonin with a strong binding energy. Conclusion Our study identified three novel biomarkers related to melatonin for AD, namely, GPRC5B , NFKBIA and RASSF4 , providing a novel approach for the investigation and treatment of AD patients. Biological sciences/Computational biology and bioinformatics Biological sciences/Neuroscience Alzheimer's disease Melatonin Mendelian randomization Biomarker Molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Alzheimer’s disease (AD) is a neurodegenerative disease that is the main cause of dementia and has become one of the most costly, lethal, and burdensome diseases of the 21st century( 1 ). This disorder is characterized by tau-containing intracellular neurofibrillary tangles and amyloid-containing extracellular plaques. The majority of AD patients present difficulties with short-term memory, but they may also have difficulty with expressive speech, visuospatial cognition, and executive function (mental agility)( 2 ). Regrettably, there is currently no effective treatment for AD. As a result, a growing number of studies are focusing on the early diagnosis and treatment of AD. A study on the global epidemiological risk prediction of AD confirmed that in the next 40 years, delaying the onset of AD symptoms by one year could reduce the number of AD patients by more than 9 million( 3 ). It is therefore meaningful to develop preventive intervention strategies for the early stages of the disease. Identifying individuals at high risk for AD means faster diagnosis, better patient classification, higher levels of clinical research, and ultimately obtaining more effective preventive treatment( 4 ). However, the early stages of AD are difficult to diagnose because early symptoms of AD are not typical; therefore, patients are often at an advanced stage of the disease when they seek medical attention. Moreover, it has been estimated that 90–95% of cases of AD occur in people over the age of 65( 5 ). When younger patients lack 'typical' hippocampal volume loss or do not exhibit nonamnestic symptoms, AD dementia may go unnoticed. A cohort of young-onset AD patients with neuropathological confirmation reported a misdiagnosis rate of 53%, compared to 4% for those with typical symptoms( 6 , 7 ). Therefore, it is urgent to identify novel biomarkers with high sensitivity and specificity for the early diagnosis of AD. These findings will serve as a basis for clinical research and as a reference. Melatonin (MLT) is a multifunctional neurohormone secreted primarily by the pineal gland( 8 ) and is mainly involved in the control of circadian rhythms( 9 ). Additionally, MLT is also an antitumor agent, an antioxidant, and a regulator of the immune system( 10 ). It has been shown that MLT secretion decreases with age, and this is especially evident in AD patients( 11 ). Another study revealed that a decrease in MLT appears to be positively correlated with AD progression( 11 , 12 ). In addition, a reduction in CSF MLT levels has been found even at preclinical stages when patients do not display any cognitive impairment (at Braak stages I-II), suggesting that this is an early indicator of AD( 13 , 14 ). Currently, numerous scientific studies have revealed that MLT has therapeutic effects on AD. A review of the literature revealed that MLT might ameliorate the symptoms of AD by affecting the cholinergic system( 15 – 18 ), attenuating tau hyperphosphorylation( 19 – 22 ), regulating the circadian rhythm( 18 ), inhibiting aging and enhancing self-healing ability( 23 , 24 ). In addition, the antioxidant properties of MLT also make it a promising therapeutic candidate for AD, as it is a powerful free radical scavenger( 14 ). However, the biological roles of melatonin-related genes (MRGs) in AD treatment are unclear. Transcriptomic, single-cell sequencing, and Mendelian randomization (MR) studies are now essential research methods. In recent years, transcriptome sequencing has been crucial for analyzing gene expression levels, identifying differentially expressed genes, exploring gene functions, and studying genetic evolution( 25 , 26 ), as well as its high sensitivity and ability to identify low levels of molecules, including nuclear transcription factors (TFs)( 27 ). Moreover, the advent of single-cell sequencing offers remarkable opportunities to explore transcriptomics at the cellular level( 28 ), allowing for the simultaneous study of different cell types, their gene expression profiles, and communication pathways( 29 ). In addition, MR has been extensively utilized in studying causal relationships. This approach employs genetic variants to assess whether an observed link between a risk factor and an outcome is likely causal( 30 ). In MR studies, confounding bias is reduced because genetic variants are randomly assigned at birth, preventing reverse causation, which demonstrates the great potential of MR for inferring causation from observational data. Compared with traditional biochemical methods, all of the abovementioned approaches are fast, inexpensive and efficient alternatives. The integration of these analytical technologies has recently become more common for studying important factors in complex biological pathways. In this study, we explored the relationship between MRGs and AD by identifying important genes linked to AD using transcriptomics, single-cell sequencing, and MR research. Bioinformatics analysis of key genes was subsequently performed to explore immune microenvironment characteristics, regulatory mechanisms, and potential drugs, providing new research ideas for the study and treatment of AD. 2. Materials and Methods 2.1 Dataset access Single-cell mRNA expression profiling of three pairs of brain entorhinal cortexes from aged AD patients (AD1-AD6) and healthy individuals (Ct1-Ct6) in the GSE138852( 31 ) dataset was performed via the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/ ), as well as the expression profiles of the entorhinal cortex in the GSE5281 (N AD =10, N control =13)( 32 ) and GSE48350 (N AD =15, N control =39) datasets ( 33 ). Moreover, a total of 34 melatonin-related regulators were extracted from previous studies( 34 ), as listed in Supplementary Table S1 . The marker genes in each cell type were collected from public literature( 31 ) and the online website of the single-cell atlas of the entorhinal cortex in human AD ( http://adsn.ddnetbio.com/ ) for cell annotation in GSE138852. 2.2 Identification of key cell and differentially expressed genes (DEGs) in the single-cell RNA dataset GSE138852 First, the quality control of the GSE138852 dataset was carried out with 300 ~ 1,500 genes (nFeature_RNA), and the proportion of mitochondrial genes (percent.mt) was less than 5% according to the “Seurat” package (version 4.1.0)( 35 ). After data standardization via the “NormalizeData” function, the top 2,000 highly variable genes were identified through the “FindVariableFeatures” function. Afterwards, principal component analysis (PCA) was performed to determine the principal components (PCs) for follow-up analysis. In the wake of dimensionality reduction, we created a uniform manifold approximation and projection (UMAP) plot to display the cell clusters in the AD and control groups( 36 ) (resolution = 0.1), followed by cell annotation and identification of key cells via Fisher’s test (odds ratio > 1, P < 0.05). Finally, the DEGs in key cells between the AD and control groups were obtained using the “FindMarkers” function (adj. P 0.5, min.pct = 0.1). The extent of cell‒cell communication in the AD and control samples was inferred with the help of the “Celltalker” package (version 0.0.7.900), and the functional analysis of key cells was further explored via the “ReactomeGSA” package (version 1.12.0) ( 37 ). 2.3 Authentication of DEGs and key modular genes related to melatonin in GSE5281 With the help of the “limma” package (version 3.54.0)( 38 ), the DEGs between the AD and control groups in GSE5281 were identified (adj. P 0.5). The results of the differential expression analysis were visualized using volcano plots and heatmaps, which were generated with the “ggplot2” (version 3.4.1) and “ComplexHeatmap” (version 2.14.0) packages( 39 ), respectively. Subsequently, the single sample gene set enrichment analysis (ssGSEA) algorithm in the “GSVA” package (version 1.46.0) ( 40 ) was used to calculate the ssGSEA score in the AD and control samples based on 34 melatonin-related regulators to ascertain the correlation between melatonin-related regulators and disease. By closely following the ssGSEA score, weighted gene coexpression network analysis (WGCNA) was implemented to acquire the modular genes most strongly correlated with the ssGSEA score in GSE5281 using the “WGCNA” package (version 1.71)( 41 ). Briefly, the outlier samples were eliminated through sample clustering analysis, and then a soft threshold was used to construct a coexpression network with a maximum scale-free distribution. All genes were divided into several modules decorated with different colors, and the relevance of the ssGSEA score to these modules was estimated to determine the key modules with the highest correlation. In addition, the key modular genes were further filtered based on gene significance (GS) and module membership (MM) (MM > 0.7, GS > 0.7) and were named melatonin-related genes (MRGs). 2.4 Identification of biomarkers related to melatonin in AD patients By means of overlapping DEGs of key cells in GSE138852, DEGs in GSE5281 and MRGs, the intersecting genes were regarded as candidate genes strongly connected with melatonin in AD. To investigate the biological functions and signaling pathways involved in candidate genes, enrichment analysis was performed using the “clusterProfiler” package (version 4.2.2) ( 42 ) based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases ( P < 0.05). Furthermore, Mendelian randomization (MR) analysis was executed to single out key genes causally associated with AD using the “TwoSampleMR” package (version 0.5.6) ( 43 ) and the inverse variance weighted (IVW) method( 44 ) based on the expression quantitative trait locus (eQTL) data of candidate genes (exposure factors) and genome-wide association study (GWAS) summary data of AD (outcome, ebi-a-GCST90027158) from the IEU OpenGWAS database ( https://gwas.mrcieu.ac.uk/ ). MR‒Egger( 45 ), weighted median( 46 ), simple mode( 43 ) and weighted mode( 47 ) methods were also included in the causal inference analysis. SNPs significantly correlated with exposure factors were screened out as instrumental variables ( P < 5×10 − 8 ), which also lacked linkage disequilibrium (clump = TRUE, r 2 = 0.001, kb = 60) and were irrelevant to outcome (proxies = TRUE, rsq = 0.8). Furthermore, the Steiger test and sensitivity analysis were adopted to evaluate the directionality and reliability of the MR results. In the end, receiver operating characteristic (ROC) curve and expression analyses were performed to identify the biomarkers for AD patients, in which the area under the curve (AUC) of the ROC curve must be greater than 0.7 and the expression trend of biomarkers between the AD and control groups must be in complete agreement coupling with statistical significance in the GSE5281 and GSE48350 datasets. Moreover, an artificial neural network (ANN) was constructed to assess the ability to distinguish patients with AD from control individuals, and ROC curve analysis was performed. 2.5 Gene set enrichment analysis (GSEA) and immune infiltration analysis To explore the signaling pathways affected by biomarkers in AD, GSEA was performed according to the Spearman correlation of biomarkers with all the other genes (adj. P < 0.05). The KEGG gene set was defined as the background gene set, which was obtained from the Molecular Signatures Database ( https://www.gsea-msigdb.org/gsea/msigdb/ ). The five enrichment results with the strongest significance were visualized via the “enrichplot” package (version 1.18.0). Furthermore, the ssGSEA algorithm was also employed to estimate the abundance of 28 immune cells in the AD and control groups in the GSE5281 dataset, as well as the Spearman correlation between differential immune cells and biomarkers via the “psych” package (version 2.2.9), to determine which cells play an important role in the development of AD. 2.6 Correlation analysis between biomarkers and metabolic pathways To explore the relationship between the incidence of AD and body metabolism, the correlations between biomarkers and metabolic pathways were assessed. The ssGSEA algorithm was used to estimate metabolic pathway-related scores based on metabolic pathway-related gene sets, including fatty acid metabolism, oxidative phosphorylation, adipogenesis, bile acid metabolism, hypoxia, heme metabolism, xenobiotic metabolism, cholesterol homeostasis and glycolysis, followed by correlation between biomarkers and differential metabolic pathways. In addition, the AlzData database ( http://www.alzdata.org/ ) was utilized to analyze the expression levels of biomarkers in different brain tissues of AD patients and control individuals. The expression of biomarkers in key cells was also compared between the AD and control groups in the GSE138852 dataset. 2.7 Construction of the regulatory network According to the NetworkAnalyst platform ( https://www.networkanalyst.ca/ ), the transcription factors (TFs) corresponding to biomarkers were predicted based on the JASPER database. The TF-mRNA regulatory network was constructed via Cytoscape software (version 3.8.2)( 48 ). Furthermore, the DGidb ( https://dgidb.org/ ) and DisGeNet ( https://www.disgenet.org/ ) databases were used to predict small molecule drugs and neurological diseases corresponding to biomarkers, respectively. The prediction results were displayed using an mRNA-drug/disease network. 2.8 Molecular docking between biomarkers and melatonin Finally, molecular docking was adopted to research the interactions between biomarkers and melatonin. This analysis was carried out based on the crystal structure of biomarkers and the three-dimensional structure of melatonin, which were obtained from the Protein Data Bank ( http://www.rcsb.org/ ) and PubChem Compound database ( https://pubchem.ncbi.nlm.nih.gov/ ), respectively. The optimal conformation of biomarkers and melatonin was obtained by the CB-Dock online molecular docking tool( 49 ), and then the results were visualized by PyMOL( 50 ). 2.9 Statistical analysis R software (version 4.1.0) was used in this study. Differences among variables were ascertained through the Wilcoxon test. If not specifically stated, P < 0.05 was regarded as statistically significant. 3. Results 3.1 Oligodendrocytes were regarded as key cells in GSE138852 In the wake of quality control, a total of 12,653 cells and 10,850 genes in GSE138852 were screened out (Supplementary Figure S1 A). The top 10 highly variable genes, such as ROBO2 , CEMIP , and CLDN5 , were labeled in the scatter diagram (Supplementary Figure S1 B). With respect to PCA, the top 30 PCs were selected for follow-up analysis (Supplementary Figure S1 C). Afterwards, the resulting nine cell clusters were classified into six cell types, including microglia, astrocytes, neurons, endothelial cells, oligodendrocytes and oligodendrocyte progenitor cells (OPCs) (Fig. 1 A- 1 B). The expression of marker genes in the six annotated cell lines is shown in a bubble diagram (Fig. 1 C). The histogram shows the proportions of cell types in the AD and control samples, and oligodendrocyte cells were the most abundant in both groups (Fig. 1 D). On the basis of the Fisher analysis, we discovered that oligodendrocytes could be key cells for lucubrating (odds ratio = 3.386, p < 0.001) (Supplementary Table S1 ). Importantly, there were 281 DEGs in oligodendrocyte cells between the AD and control groups, including 153 with upregulated expression and 128 with downregulated expression (Fig. 1 E- 1 F). Moreover, the cells in the AD group communicated through ligand‒receptor interactions more closely than did those in the control group (Supplementary Figure S1 D), and the top 10 functional pathways with the greatest differences in oligodendrocyte cells between the two groups, such as MGMT-mediated DNA damage reversal, amino acid conjugation, and agmatine biosynthesis, are displayed in the heatmap (Supplementary Figure S1 E). 3.2 A total of 550 MRGs were acquired from the GSE5281 dataset A total of 3,490 DEGs were identified between the AD and control groups in GSE5281, of which 1,179 were upregulated and 2,311 were downregulated (Fig. 2 A). The expression profiles of the top 10 up- and downregulated DEGs are shown via a heatmap (Fig. 2 B). With regard to the ssGSEA score of melatonin-related regulators in the AD and control groups, we considered the association between MPGs and AD due to the increase in the score ( P < 0.001) (Fig. 2 C). With respect to WGCNA, there was an outlier sample in GSE5281, namely, GSM119619 (Fig. 2 D). When the optimal soft threshold (β) was 10 (R 2 = 0.855), the network bordered a scale-free distribution (Fig. 2 E). Then, eight modules were identified, and the blue (4,648 genes) and yellow (2,088 genes) modules were strongly correlated with AD (|Cor|≥0.79, P < 0.001) (Fig. 2 F- 2 G). After further screening, a total of 550 modular genes were identified as MRGs (Fig. 2 H). 3.3 GPRC5B , NFKBIA and RASSF4 are biomarkers related to melatonin for AD After overlapping 281 DEGs in oligodendrocyte cells in GSE138852, 3,490 DEGs in GSE5281 and 550 MRGs, a total of 21 genes were regarded as candidate genes strongly related to melatonin in AD (Fig. 3 A). These candidate genes were enriched in 286 GO terms (such as myeloid cell homeostasis, neuron projection regeneration and regulation of the canonical Wnt signaling pathway) and 14 KEGG pathways (such as the PI3K-Akt signaling pathway, the Apelin signaling pathway and the Toll-like receptor signaling pathway) (Fig. 3 B- 3 C). According to MR analysis between candidate genes and AD, only four genes ( GPRC5B , METTL7A , NFKBIA and RASSF4 ) were causally related to AD based on the IVW method ( P 1) (Fig. 3 D). The sensitivity analysis and Steiger test confirmed the reliability and directionality of the MR data (Supplementary Table S2 -S4&Figure S2 A). We further identified three biomarkers for AD patients, namely, GPRC5B , NFKBIA and RASSF4 , because of their greater than 0.88 AUC in the GSE5281 dataset (Fig. 3 E) and their significantly increased expression in the AD samples in the GSE5281 and GSE48350 datasets (Fig. 3 F). In addition, the ANN model accurately distinguished AD samples from control samples (Fig. 3 G- 3 H). 3.4 Biomarkers, especially NFKBIA, play important roles in AD development By virtue of GSEA (Fig. 4 A), we discovered that three biomarkers might be connected with Parkinson’s disease, oxidative phosphorylation and Huntington’s disease. GPRC5B and RASSF4 might participate in myocardial contraction, and NFKBIA and RASSF4 might be negatively correlated with AD development. Moreover, there was a significant difference in the enrichment score of 15 immune cells between the AD group and the control group (Fig. 4 B- 4 C). Compared with those in the control group, 14 immune cell types, such as T helper type 17 (Th17) cells, natural killer (NK) cells and immature B cells, were more abundant in the AD group, while CD56dim NK cells were less abundant. Moreover, we evaluated the correlation of these 15 differential cell lines with biomarkers (Fig. 4 D). NFKBIA was most strongly correlated with natural killer cells (Cor = 0.802, P < 0.001), memory B cells (Cor = 0.797, P < 0.001) and T follicular helper cells (Cor = 0.790, P 0.447, P < 0.05). 3.5 Biomarkers associated with brain metabolism are differentially expressed in brain tissues. Additionally, we closely examined the expression of biomarkers in the entorhinal cortex, hippocampus, temporal cortex and frontal cortex of the brain, as well as in oligodendrocyte cells. The expression of GPRC5B, as well as that of NFKBIA , was markedly greater in the entorhinal cortex and frontal cortex of AD patients than in control individuals (Fig. 5 A). Moreover, the expression levels of NFKBIA and RASSF4 in the temporal cortex were clearly greater in the AD group than in the control group. Similarly, the expression of biomarkers in oligodendrocyte cells was also markedly different between the two groups (Fig. 5 B). The correlation between biomarkers and metabolic pathways revealed that three pathways, namely, oxidative phosphorylation, adipogenesis and heme metabolism, were markedly different between the AD and control groups (Fig. 5 C). Oxidative phosphorylation was passively associated with three biomarkers (|Cor|>0.5, P 0.58, P 0.57, P < 0.01) (Fig. 5 D). The TF-mRNA network included three biomarkers and 24 TFs (Fig. 6 A). For instance, GATA2 and POU2F2 might regulate the expression of GPRC5B and RASSF4 , and NFKB1 might participate in the regulation of GPRC5B and NFKBIA expression. In addition, only drugs targeting NFKBIA , such as CHEMBL401565, CHEMBL256967, and DIOSCIN, were obtained. A total of 19 diseases corresponding to GPRC5B or NFKBIA were predicted, such as AD and hyperactive behavior for GPRC5B , cerebral ischemia, brain ischemia and others for NFKBIA . Overall, two biomarkers ( GPRC5B and NFKBIA ), nine drugs and 19 neurological diseases were incorporated into the mRNA-drug/disease network (Fig. 6 B). 3.7 Melatonin is a potential target of three biomarkers Finally, we assessed the interactions and binding modes of three biomarkers with melatonin via molecular docking analysis. GPRC5B can bind to melatonin with a binding energy of -6.2 kcal/mol through the amino acid residues TYR-195 and ARG-125 (Fig. 7 A). NFKBIA might bind to melatonin at a -5.0 kcal/mol affinity via the active residue D100 (Fig. 7 B). RASSF4 also targeted melatonin with a binding energy of -5.8 kcal/mol by interacting with the MET-137 residue (Fig. 7 C). 4. Discussion AD is the primary cause of dementia in older adults( 51 ). As a chronic, multifaceted and multifactorial neurodegenerative disorder( 52 ), there is currently no cure or effective drug treatment for AD( 53 , 54 ). Within 5 to 12 years after the first symptoms appear, AD progresses slowly and irreversibly, eventually leading to death( 55 ) and imposing a significant burden on patients, their families and society. Therefore, a novel, effective, and safe therapeutic strategy for AD is urgently needed. This study revealed that AD is mostly associated with the MRGs GPRC5B, NFKBIA and RASSF4. The results revealed that MLT might alleviate the pathology of patients with AD through these targets, and the key genes related to small molecule drugs revealed by network pharmacology analysis might be candidates for AD treatment. In this study, we identified three promising biomarkers (GPRC5B, NFKBIA, and RASSF4) for targeting AD treatment. Additionally, molecular docking studies showed that these biomarkers bound well to MLT. G protein-coupled receptor, family C, group 5, member B (GPRC5B), an orphan receptor belonging to the G protein-coupled receptor (GPCR) family, contributes to neurogenesis( 56 ). Its neuronal enrichment is controversial at the mRNA level( 57 , 58 ) but consistent at the protein level, with the highest levels in the neocortex and hippocampus( 59 ). Murine GPRC5B impacts synaptic formation and neurogenesis( 60 ), while rat GPRC5B may be related to microglial activation( 61 ). A previous study also revealed that RNA editing of GPRC5B was associated with AD dementia, neuropathological measures and longitudinal cognitive decline( 62 ). However, research on the effect of GPRC5B on AD has rarely been reported, necessitating further investigation. NF-κB inhibitor alpha (NF-κBIα), another MRG member, interacts with REL dimers to inhibit the NF-κB/REL complex involved in inflammation. Mutations upstream of NF-κB have previously been reported to affect NF-κB activity in AD patients, leading to anatomical defects such as shrinkage of the entorhinal cortex and early AD limbic system( 63 ). NFKBIA degradation followed by phosphorylation is critical for the activation of NF-κB( 64 ). In addition, NFKBIA was reported to be upregulated in AD patients( 65 , 66 ), suggesting that it is a potential target for studying and treating AD. RAS association domain family 4 (RASSF4) is a member of the RASSF protein family( 67 ). The function of RASSF4 remains elusive but may play a role in tumor suppression( 68 ), early myogenic differentiation( 69 ) and cell proliferation( 70 ). However, its relationship with AD has scarcely been reported. This study suggests the potential relevance of RASSF4 to AD for the first time and warrants further investigation into the detailed mechanism involved. Through GSEA, we found that these three key MRGs were mainly enriched in Parkinson's disease (PD), the oxidative phosphorylation pathway, the transforming growth factor β (TGF-β) signaling pathway, the pathways involved in cancer, cardiac muscle contraction, etc.. As a neurodegenerative disease, PD ranks second only to AD. A study investigating the comorbidity of AD and PD revealed a strong association between these two disorders( 71 ), and several studies have shown that AD and PD share common pathogenic mechanisms, such as the NO pathway, p62 dysfunction, neuroexcitotoxicity and oxidative stress( 72 – 75 ). Oxidative phosphorylation is another pathway enriched in MRGs in AD. The brain is a highly energy-consuming organ, and neurons rely mainly on the mitochondrial oxidative phosphorylation system for energy( 76 , 77 ). In mild AD and amnestic cognitive impairment, decreased glucose metabolism due to oxidative damage impairs cognitive function and leads to synaptic dysfunction and neuronal death( 78 ). In addition, a primary pathological event in AD is mitochondrial dysfunction, characterized by alterations in mitochondrial morphology, the generation of reactive oxygen species and impaired oxidative phosphorylation( 79 – 81 ). In AD neurodegeneration, both amyloid-beta (Aβ) and tau work together to inhibit the production and activity of mitochondrial respiratory complexes, leading to a dysfunctional oxidative phosphorylation system( 82 ), thus aggravating AD. There was also an enriched pathway of MRGs in AD involving the TGF-β signaling pathway. TGF-β is a pleiotropic cytokine that is crucial for embryonic development, physiological tissue homeostasis, and several pathological behaviors( 83 ). TGF-β1 is one of three isoforms of the TGF-β superfamily and has been reported to possess immunomodulatory and neuroprotective properties( 84 ). A lack of TGF-β1 not only promotes the buildup of Aβ but also contributes to the formation of neurofibrillary tangles( 85 ), underscoring its significance in AD( 86 ). Furthermore, the roles of other enriched pathways, including pathways involved in cancer, cardiac muscle contraction, etc., in AD are still unclear. Hence, further research is warranted. According to our cell clustering and annotation analyses, we identified oligodendrocytes as key players in AD pathology, which was consistent with previous study results( 87 – 89 ). In the central nervous system (CNS), the main function of oligodendrocytes is to synthesize the myelin sheath to speed up nerve impulses and protect axons( 90 ). However, it is known that oligodendrocytes are susceptible to ischemic and proinflammatory changes in their environment, and their death provokes further demyelination far beyond the initial injury site, leading to sensory, motor, cognitive, and autonomic nerve impairment( 91 ). Previous research has indicated that myelin damage and oligodendrocyte loss in AD are not only a result of neuronal degeneration but also contribute to the early stages of the disease, highlighting the importance of oligodendrocytes in the onset and progression of the disease( 92 ). In our study, we found the upregulation of 3 key MRGs in oligodendrocytes. Regrettably, few studies have investigated the possible relationships between these MRGs and oligodendrocytes and AD. Therefore, further research is required to fully understand this issue. Regarding the immune infiltration analysis, significant disparities were found in the infiltration of fourteen types of immune cells between AD patients and controls, with 13 upregulated and 1 downregulated. Among these upregulated immune cells, Th17 cells have been implicated as crucial mediators of AD development( 93 ). Their derived proinflammatory cytokines are expressed and upregulated in AD and other neurological diseases( 94 – 96 ). The neutralization of Th17 cells drives IL-17, leading to a decrease in amyloid-β‐induced neuroinflammation and AD-like behavior( 93 ). As expected, these results were consistent with the results of our study. In addition, we observed greater infiltration of NK cells in the AD group than in the control group. This contradicts previous studies that reported reduced blood NK cells in AD patients compared to controls( 97 ). In addition, other studies have revealed decreased cytotoxic function and lower functional potential of NK cells in AD patients than in normal controls( 98 – 100 ). Moreover, reduced NK cell levels improved neuroinflammation and cognitive decline in a mouse model of AD( 101 ). These inconsistencies likely stem from the different materials used in the studies. While our study focused on the entorhinal cortex dataset, previous studies obtained results from the peripheral blood of AD patients. In relation to the inconsistent conclusions, further research and strong evidence are still needed. Furthermore, our study identified CD56dim NK cells as the sole downregulated immune cell subset infiltrating AD patients. These cytotoxic effector cells primarily produce IFN-γ when stimulated( 97 ). Although the specific impact of CD56dim NK cells on AD pathogenesis remains uncertain, prior research has demonstrated decreased levels of certain markers, such as IFN-γ, in NK cells from individuals with AD( 97 ), suggesting that the presence and activity of these immune cells might be diminished in AD patients. To construct the integrated regulatory network, TF prediction was performed on 3 MRGs, resulting in the identification of 24 related TFs in the TF regulatory network. Almost all of these TFs have been confirmed to be correlated with AD. For instance, AP-2γ, encoded by Tfap2c, in hippocampal progenitors promotes neuron proliferation and differentiation. Its ablation in the adult brain has been linked to reduced neurogenesis, disrupted neural coherence, and cognitive deficits( 102 ). Furthermore, AP-2γ may suppress NFKBIA expression( 103 ) and enhance Th17 and Th1 cell activation( 104 ), suggesting that AP-2γ potentially influences the neuroinflammatory process of AD by affecting NFKBIA. However, further research is needed to verify this point. GATA2 is essential for activating neuroglobin, a protein that protects neuronal cells in AD( 105 ). Neuroglobin levels increase in the early and moderate stages of AD but decrease in the advanced stages, possibly due to reduced GATA2 levels. Further research is needed to confirm this relationship( 106 , 107 ). Nuclear respiratory factor 1 (NRF1), a TF that activates genes important for mitochondrial function and biogenesis, could play a role in neurodegenerative diseases such as AD by affecting both mitochondrial and nonmitochondrial functions( 108 ). In this study, we report that NRF1 is a regulator of RASSF4, although the distinct connection between them has not been examined. A previous study revealed that RASSF4 overexpression could downregulate the mitochondrial membrane potential in colorectal cancer( 109 ), suggesting that NRF1 might impact mitochondrial function through the regulation of RASSF4, consequently influencing AD. Undoubtedly, additional tests are necessary to confirm this assertion. Upstream Stimulatory Factor-1 (USF1) controls genes related to lipid metabolism, including APOE and the Aβ precursor protein. Women with the USF1 haplotype GCGCAC are at greater risk for AD-related neuropathological lesions, particularly neuritic and late-stage penile plaques. Younger carriers of the CCGCAC haplotype are more likely to have nonneuritic and diffuse senile plaques( 110 ). Therefore, USF1 could be a potential target for the treatment of AD. The expression of EGR1 and β-secretase 1 (BACE1) plays a crucial role in the deposition of Aβ, with the inhibition of EGR1 binding to the BACE1 promoter resulting in the suppression of BACE1 expression. This subsequently leads to a reduction in Aβ deposition and an enhancement of memory function in rats with AD. Additionally, most of the remaining TFs were also demonstrated to be associated with AD via bioinformatic analysis( 109 , 111 – 114 ). Overall, the aforementioned 24 TFs have the potential to be targeted for AD therapy, but further research is needed to confirm how these TFs regulate MRGs in AD. As is well known, commonly used drugs for AD, such as donepezil hydrochloride and galantamine, have limited effectiveness and cannot cure the disease. To explore new treatment options, the key MRG targets were subsequently submitted to the DGIdb for predicting drugs for AD, and 9 candidate drugs were identified. Among them, dioscin, wedelolactone and gefitinib have been demonstrated to potentially have therapeutic efficacy for AD( 115 – 117 ). Dioscin and wedelolactone are plant extracts known for their safety, low toxicity, and beneficial effects on various diseases, and they improve AD symptoms mainly by adjusting oxidative stress, inflammation and neurotoxicity( 115 , 118 , 119 ). Gefitinib, an EGFR inhibitor, has also been confirmed to be effective in alleviating AD by attenuating Aβ pathology and improving cognitive function( 117 , 120 ). These findings indicated that these drugs could be potential candidates for AD treatment. However, the efficacy of several other small molecule drugs in the treatment of AD is still unclear, necessitating additional studies. Additionally, we predicted diseases associated with these biomarkers, including hyperactive behavior for GPRC5B and various diseases such as cerebrovascular diseases and neurodegenerative disorders for NFKBIA. Many of these diseases might involve a common pathogenic mechanism of AD( 121 – 123 ), suggesting that GPRC5B and NFKBIA are potential therapeutic targets for these conditions. Conclusion In this study, we employed transcriptome data analysis in conjunction with MR analysis for the first time to identify three key significantly associated with AD. Notably, GPRC5B and RASSF4 have been rarely reported in previous studies on AD. Based on these findings, we conducted a systematic analysis of the biological processes and important signaling pathways enriched by MRGs. Furthermore, we investigated changes in immune cell infiltration and metabolic pathways in both AD patients and control subjects, revealing associations between differential immune infiltrating cells and key MRGs. Additionally, through single-cell analysis, we discovered the crucial role of oligodendrocytes in AD pathogenesis and performed an analysis of intercellular communication while visualizing key cellular functions. Finally, by constructing regulatory networks involving MRGs and their corresponding TFs, we predicted potential drugs for AD along with their related targets using network pharmacology. Our findings not only enhance our understanding of the mechanisms of melatonin therapy for AD but also provide valuable insights into key MRGs, cells involved in disease progression, potential therapeutic drugs as well as related biological pathways that may open up new avenues for treating AD. However, it is important to note that our study is based on existing reports, therefore, further clinical trials and basic research are warranted to validate these results. Declarations Ethics approval and consent to participate No ethical approval was required for this analysis. Consent for publication: Consent for publication Availability of data and materials ： All data generated or analysed during this study are included in this published article [and its supplementary information files]. Competing interests ： The authors declare that they have no competing interests Funding: This study was funded by Tianchi Talent Program. Author Contributions: This research was designed by ZH, LH, and LQ. ZH and LH had complete access to all the data in the study and took full responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the statistical analysis, critically reviewed the manuscript during the writing process, and approved the final version for publication. ZH and LH are the study guarantors. Acknowledgments: The work was supported by an Tianchi Talent Program, which awarded to Dr. ZHANG Hua-xiong. References Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, et al. Alzheimer's disease. Lancet (London, England). 2021;397(10284):1577–90. Knopman DS, Amieva H, Petersen RC, Chételat G, Holtzman DM, Hyman BT, et al. Alzheimer disease. Nature reviews Disease primers. 2021;7(1):33. 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Chemical biology & drug design. 2014;83(1):81–8. Zhang Z, Han K, Wang C, Sun C, Jia N. Dioscin Protects against Aβ1–42 Oligomers-Induced Neurotoxicity via the Function of SIRT3 and Autophagy. Chemical & pharmaceutical bulletin. 2020;68(8):717–25. Zhong X, Liu M, Yao W, Du K, He M, Jin X, et al. Epigallocatechin-3-Gallate Attenuates Microglial Inflammation and Neurotoxicity by Suppressing the Activation of Canonical and Noncanonical Inflammasome via TLR4/NF-κB Pathway. Molecular nutrition & food research. 2019;63(21):e1801230. Choi HJ, Jeong YJ, Kim J, Hoe HS. EGFR is a potential dual molecular target for cancer and Alzheimer's disease. Frontiers in pharmacology. 2023;14:1238639. Luczynski P, Laule C, Hsiung GR, Moore GRW, Tremlett H. Coexistence of Multiple Sclerosis and Alzheimer's disease: A review. Multiple sclerosis and related disorders. 2019;27:232–8. Ye C, Kong L, Wang Y, Zheng J, Xu M, Xu Y, et al. Causal associations of sarcopenia-related traits with cardiometabolic disease and Alzheimer's disease and the mediating role of insulin resistance: A Mendelian randomization study. Aging cell. 2023;22(9):e13923. Han Z, Tian R, Ren P, Zhou W, Wang P, Luo M, et al. Parkinson's disease and Alzheimer's disease: a Mendelian randomization study. BMC medical genetics. 2018;19(Suppl 1):215. Additional Declarations No competing interests reported. Supplementary Files 2.Table.zip FigureS1d1.tif FigureS2d1.tif Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Sep, 2024 Reviews received at journal 27 Aug, 2024 Reviews received at journal 17 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviewers invited by journal 05 Aug, 2024 Editor assigned by journal 05 Aug, 2024 Editor invited by journal 05 Aug, 2024 Submission checks completed at journal 30 Jul, 2024 First submitted to journal 20 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4772764","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":341573227,"identity":"4849e36f-ba81-4225-ac24-2d4667562674","order_by":0,"name":"Huaxiong Zhang","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Huaxiong","middleName":"","lastName":"Zhang","suffix":""},{"id":341573228,"identity":"a66207f2-765e-4230-abda-40b15d5c9cc1","order_by":1,"name":"Dilmurat Hamit","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Dilmurat","middleName":"","lastName":"Hamit","suffix":""},{"id":341573229,"identity":"49222a52-20c9-4088-8bd2-8bb113cafaf4","order_by":2,"name":"Qing LI","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"LI","suffix":""},{"id":341573230,"identity":"1625a37c-dffa-4b9a-83d0-69758a605dca","order_by":3,"name":"Xiao Hu","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Hu","suffix":""},{"id":341573231,"identity":"b048b97e-e284-483b-99d4-f586362b21d0","order_by":4,"name":"San-feng LI","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"San-feng","middleName":"","lastName":"LI","suffix":""},{"id":341573232,"identity":"6d5e11d3-5b84-44da-b4e8-564021fbe64e","order_by":5,"name":"Fu XU","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Fu","middleName":"","lastName":"XU","suffix":""},{"id":341573233,"identity":"5e891575-a450-4ce0-80b0-a7a0ed97d4b1","order_by":6,"name":"Ming-yuan WANG","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Ming-yuan","middleName":"","lastName":"WANG","suffix":""},{"id":341573234,"identity":"7b36960b-5d0d-45b0-9ea8-d2173d392e46","order_by":7,"name":"Guo-qing BAO","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Guo-qing","middleName":"","lastName":"BAO","suffix":""},{"id":341573235,"identity":"a9979398-9ffd-4f5a-ad11-abceb8f3c106","order_by":8,"name":"Hong-yan LI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACPgkwZcPABiQPfPhBhBY2iJY0kBbGgzN7iNdyGEQwH+ZgI0aLdI+ZNG/b+cQ+6QagRh4GeX6xAwS0yJwxNpzZdjuxTeYAw+ECCwbDmbMTCDksx/DBx7bbxmwSCQyHZ/AwJBjcJqzF4EBi2zmIFh424rSAbDkgR4qWtGLDGeeS5dhkDjYAA1mCsF/4JZK3SfOU2fHIz24+/OHDDxt5fmkCWhgYOAwYGEHRIcHYACIJKQcB9gcMDH+IVTwKRsEoGAUjEgAAEYI9ldliutgAAAAASUVORK5CYII=","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":true,"prefix":"","firstName":"Hong-yan","middleName":"","lastName":"LI","suffix":""}],"badges":[],"createdAt":"2024-07-20 10:53:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4772764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4772764/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-80755-x","type":"published","date":"2025-02-04T15:56:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63809739,"identity":"26793391-7ddd-4b6f-9dce-e7b9cfbdaded","added_by":"auto","created_at":"2024-09-02 13:52:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":544387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOligodendrocyte cell was regarded as key cell in GSE138852\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-B）\u003c/strong\u003e: The nine cell clusters were classified into six cell types. \u003cstrong\u003e（C）\u003c/strong\u003e: Bubble diagram of the expression of marker genes in six annotated cells, with bubble size indicating gene proportion and color gradient (from blue to yellow) indicating gene expression levels. \u003cstrong\u003e（D）\u003c/strong\u003e: Histogram showing the proportion of cell types in AD and control samples. \u003cstrong\u003e（E）\u003c/strong\u003e: Volcano plot displaying the differentially expressed genes (DEGs)between the AD and control groups. \u003cstrong\u003e（F）\u003c/strong\u003e: Bubble diagram of oligodendrocytes in the AD and control groups.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/25abf31b1b3e62c290da560a.jpg"},{"id":63809735,"identity":"dc1b24d1-b449-4747-9cfb-38a10972943c","added_by":"auto","created_at":"2024-09-02 13:52:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1014315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMRGs obtained from GSE5281\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A）: Volcano plot of differentially expressed genes (DEGs) in AD and control samples. (B): Heatmap of differentially expressed genes in AD and control samples.（C）: The ssGSEA scores of melatonin-related regulatory factors in the AD group and the control group.（D）: Sample clustering dendrogram. （E）: Soft thresholding selection, scale-free fitting index (left), average connectivity (right),the red line indicating the selected soft threshold. （F）: Hierarchical clustering tree. （G）: Heatmap showing the correlation between modules and traits. （H）: Scatter plot illustrating the correlation strength between GS and MM of key modules.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/7855b4fe238350897d7bc238.jpg"},{"id":63809734,"identity":"9a01e03c-ea5e-49da-bca5-7f713e10e763","added_by":"auto","created_at":"2024-09-02 13:52:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1156596,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of melatonin-related AD candidate genes and biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e（A）：Venn diagram of overlapping genes in three algorithms. （B）: GO annotation plot. (C): KEGG enrichment plot. (D): Forest plot of candidate genes and AD. (E):ROC curves of 4 candidate genes (GPRC5B , METTL7A, NFKBIA and RASSF)..( F):Differences in gene expression of four candidate genes between AD patient and control groups in two datasets .(G): ANN Modeling of 3 Biomarkers Between AD Patient Groups and Controls.(H): ROC curve for ANN Models in Two Data Sets.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/0341695b52a3099ff860e5c4.jpg"},{"id":63809737,"identity":"9255ebfc-cb97-4e96-82b4-463ac0736bcf","added_by":"auto","created_at":"2024-09-02 13:52:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1101842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe role of biomarkers in the development of AD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A): GSEA plot of biomarkers. ( B): Enrichment scores of 15 immune cells. (C): Expression plot of AD group compared to control group. (D): Correlation of 15 different cells with biomarkers.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/3cecba770028cd8bdb24109e.jpg"},{"id":63810943,"identity":"355409d4-2ecd-49aa-a2cf-8bcad905093c","added_by":"auto","created_at":"2024-09-02 14:00:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":933967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of biomarkers in brain tissue\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A): Cross-platform normalized expression level of GPRC5B and NFKBIA. (B): Expression of biomarkers in two groups of oligodendrocytes. (C): Three metabolic pathways between AD group and control group. (D): Relationship between lipogenesis and biomarkers.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/7efcacc28c6ab041fd66a8c9.jpg"},{"id":63809740,"identity":"3dad972a-8cce-41b6-9510-453b1ce3f7fd","added_by":"auto","created_at":"2024-09-02 13:52:28","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":479334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTF-mRNA network and mRNA-drug/disease network based on public databases.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A): Relationships between 3 biomarkers and 24 TFs in the TF-mRNA network. (B): Network diagram of 2 biomarkers, 9 drugs, and 19 neurological disorders in the mRNA-drug/disease network.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/a61aa502545111276c96c43c.jpg"},{"id":63810944,"identity":"fa3a2688-991f-4e85-8afe-169bf618dd7a","added_by":"auto","created_at":"2024-09-02 14:00:28","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":798983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteractions and binding models of three biomarkers with melatonin.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A): Binding of GPRC5B with melatonin. (B): Binding of NFKBIA with melatonin. (C): Binding of RASSF4 with melatonin.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/1496c777062bb6a323102262.jpg"},{"id":75931335,"identity":"6952a8b0-ec9d-4bac-abce-ae7c3ca16d88","added_by":"auto","created_at":"2025-02-10 16:14:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7331723,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/818da8cf-d612-45b5-a868-ee2a0f649a94.pdf"},{"id":63809742,"identity":"c7daa623-4f83-4be4-9d59-8cc923268db1","added_by":"auto","created_at":"2024-09-02 13:52:28","extension":"zip","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":73599,"visible":true,"origin":"","legend":"","description":"","filename":"2.Table.zip","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/12e5ba847fbfbf7edbcca041.zip"},{"id":63810945,"identity":"41ec8fc2-99cb-477f-8e0c-ad07c24e68fe","added_by":"auto","created_at":"2024-09-02 14:00:28","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":1640276,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1d1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/302ac2161e131da3505ff6d5.tif"},{"id":63809744,"identity":"ce17f78f-4168-45c8-b07c-ed39206c40f4","added_by":"auto","created_at":"2024-09-02 13:52:28","extension":"tif","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":1489920,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2d1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4772764/v1/d7d7138b9024493e9d50564b.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Melatonin-Related Genes as Key Players in Alzheimer's Disease: Discovery of Promising Biomarkers for Treatment Targets for Alzheimer's Disease","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a neurodegenerative disease that is the main cause of dementia and has become one of the most costly, lethal, and burdensome diseases of the 21st century(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This disorder is characterized by tau-containing intracellular neurofibrillary tangles and amyloid-containing extracellular plaques. The majority of AD patients present difficulties with short-term memory, but they may also have difficulty with expressive speech, visuospatial cognition, and executive function (mental agility)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Regrettably, there is currently no effective treatment for AD. As a result, a growing number of studies are focusing on the early diagnosis and treatment of AD. A study on the global epidemiological risk prediction of AD confirmed that in the next 40 years, delaying the onset of AD symptoms by one year could reduce the number of AD patients by more than 9\u0026nbsp;million(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It is therefore meaningful to develop preventive intervention strategies for the early stages of the disease. Identifying individuals at high risk for AD means faster diagnosis, better patient classification, higher levels of clinical research, and ultimately obtaining more effective preventive treatment(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, the early stages of AD are difficult to diagnose because early symptoms of AD are not typical; therefore, patients are often at an advanced stage of the disease when they seek medical attention. Moreover, it has been estimated that 90\u0026ndash;95% of cases of AD occur in people over the age of 65(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). When younger patients lack 'typical' hippocampal volume loss or do not exhibit nonamnestic symptoms, AD dementia may go unnoticed. A cohort of young-onset AD patients with neuropathological confirmation reported a misdiagnosis rate of 53%, compared to 4% for those with typical symptoms(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, it is urgent to identify novel biomarkers with high sensitivity and specificity for the early diagnosis of AD. These findings will serve as a basis for clinical research and as a reference.\u003c/p\u003e \u003cp\u003eMelatonin (MLT) is a multifunctional neurohormone secreted primarily by the pineal gland(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and is mainly involved in the control of circadian rhythms(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Additionally, MLT is also an antitumor agent, an antioxidant, and a regulator of the immune system(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). It has been shown that MLT secretion decreases with age, and this is especially evident in AD patients(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Another study revealed that a decrease in MLT appears to be positively correlated with AD progression(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In addition, a reduction in CSF MLT levels has been found even at preclinical stages when patients do not display any cognitive impairment (at Braak stages I-II), suggesting that this is an early indicator of AD(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Currently, numerous scientific studies have revealed that MLT has therapeutic effects on AD. A review of the literature revealed that MLT might ameliorate the symptoms of AD by affecting the cholinergic system(\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), attenuating tau hyperphosphorylation(\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), regulating the circadian rhythm(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), inhibiting aging and enhancing self-healing ability(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In addition, the antioxidant properties of MLT also make it a promising therapeutic candidate for AD, as it is a powerful free radical scavenger(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, the biological roles of melatonin-related genes (MRGs) in AD treatment are unclear.\u003c/p\u003e \u003cp\u003eTranscriptomic, single-cell sequencing, and Mendelian randomization (MR) studies are now essential research methods. In recent years, transcriptome sequencing has been crucial for analyzing gene expression levels, identifying differentially expressed genes, exploring gene functions, and studying genetic evolution(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), as well as its high sensitivity and ability to identify low levels of molecules, including nuclear transcription factors (TFs)(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Moreover, the advent of single-cell sequencing offers remarkable opportunities to explore transcriptomics at the cellular level(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), allowing for the simultaneous study of different cell types, their gene expression profiles, and communication pathways(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In addition, MR has been extensively utilized in studying causal relationships. This approach employs genetic variants to assess whether an observed link between a risk factor and an outcome is likely causal(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In MR studies, confounding bias is reduced because genetic variants are randomly assigned at birth, preventing reverse causation, which demonstrates the great potential of MR for inferring causation from observational data. Compared with traditional biochemical methods, all of the abovementioned approaches are fast, inexpensive and efficient alternatives. The integration of these analytical technologies has recently become more common for studying important factors in complex biological pathways.\u003c/p\u003e \u003cp\u003eIn this study, we explored the relationship between MRGs and AD by identifying important genes linked to AD using transcriptomics, single-cell sequencing, and MR research. Bioinformatics analysis of key genes was subsequently performed to explore immune microenvironment characteristics, regulatory mechanisms, and potential drugs, providing new research ideas for the study and treatment of AD.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Dataset access\u003c/h2\u003e \u003cp\u003eSingle-cell mRNA expression profiling of three pairs of brain entorhinal cortexes from aged AD patients (AD1-AD6) and healthy individuals (Ct1-Ct6) in the GSE138852(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) dataset was performed via the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), as well as the expression profiles of the entorhinal cortex in the GSE5281 (N\u003csub\u003eAD\u003c/sub\u003e=10, N\u003csub\u003econtrol\u003c/sub\u003e=13)(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and GSE48350 (N\u003csub\u003eAD\u003c/sub\u003e=15, N\u003csub\u003econtrol\u003c/sub\u003e=39) datasets (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Moreover, a total of 34 melatonin-related regulators were extracted from previous studies(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), as listed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The marker genes in each cell type were collected from public literature(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and the online website of the single-cell atlas of the entorhinal cortex in human AD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://adsn.ddnetbio.com/\u003c/span\u003e\u003cspan address=\"http://adsn.ddnetbio.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for cell annotation in GSE138852.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Identification of key cell and differentially expressed genes (DEGs) in the single-cell RNA dataset GSE138852\u003c/h2\u003e \u003cp\u003eFirst, the quality control of the GSE138852 dataset was carried out with 300\u0026thinsp;~\u0026thinsp;1,500 genes (nFeature_RNA), and the proportion of mitochondrial genes (percent.mt) was less than 5% according to the \u0026ldquo;Seurat\u0026rdquo; package (version 4.1.0)(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). After data standardization via the \u0026ldquo;NormalizeData\u0026rdquo; function, the top 2,000 highly variable genes were identified through the \u0026ldquo;FindVariableFeatures\u0026rdquo; function. Afterwards, principal component analysis (PCA) was performed to determine the principal components (PCs) for follow-up analysis. In the wake of dimensionality reduction, we created a uniform manifold approximation and projection (UMAP) plot to display the cell clusters in the AD and control groups(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) (resolution\u0026thinsp;=\u0026thinsp;0.1), followed by cell annotation and identification of key cells via Fisher\u0026rsquo;s test (odds ratio\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Finally, the DEGs in key cells between the AD and control groups were obtained using the \u0026ldquo;FindMarkers\u0026rdquo; function (adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |avg_log\u003csub\u003e2\u003c/sub\u003eFoldChange|\u0026gt;0.5, min.pct\u0026thinsp;=\u0026thinsp;0.1). The extent of cell‒cell communication in the AD and control samples was inferred with the help of the \u0026ldquo;Celltalker\u0026rdquo; package (version 0.0.7.900), and the functional analysis of key cells was further explored via the \u0026ldquo;ReactomeGSA\u0026rdquo; package (version 1.12.0) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Authentication of DEGs and key modular genes related to melatonin in GSE5281\u003c/h2\u003e \u003cp\u003eWith the help of the \u0026ldquo;limma\u0026rdquo; package (version 3.54.0)(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), the DEGs between the AD and control groups in GSE5281 were identified (adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log\u003csub\u003e2\u003c/sub\u003eFoldChange|\u0026gt;0.5). The results of the differential expression analysis were visualized using volcano plots and heatmaps, which were generated with the \u0026ldquo;ggplot2\u0026rdquo; (version 3.4.1) and \u0026ldquo;ComplexHeatmap\u0026rdquo; (version 2.14.0) packages(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), respectively. Subsequently, the single sample gene set enrichment analysis (ssGSEA) algorithm in the \u0026ldquo;GSVA\u0026rdquo; package (version 1.46.0) (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) was used to calculate the ssGSEA score in the AD and control samples based on 34 melatonin-related regulators to ascertain the correlation between melatonin-related regulators and disease. By closely following the ssGSEA score, weighted gene coexpression network analysis (WGCNA) was implemented to acquire the modular genes most strongly correlated with the ssGSEA score in GSE5281 using the \u0026ldquo;WGCNA\u0026rdquo; package (version 1.71)(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Briefly, the outlier samples were eliminated through sample clustering analysis, and then a soft threshold was used to construct a coexpression network with a maximum scale-free distribution. All genes were divided into several modules decorated with different colors, and the relevance of the ssGSEA score to these modules was estimated to determine the key modules with the highest correlation. In addition, the key modular genes were further filtered based on gene significance (GS) and module membership (MM) (MM\u0026thinsp;\u0026gt;\u0026thinsp;0.7, GS\u0026thinsp;\u0026gt;\u0026thinsp;0.7) and were named melatonin-related genes (MRGs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Identification of biomarkers related to melatonin in AD patients\u003c/h2\u003e \u003cp\u003eBy means of overlapping DEGs of key cells in GSE138852, DEGs in GSE5281 and MRGs, the intersecting genes were regarded as candidate genes strongly connected with melatonin in AD. To investigate the biological functions and signaling pathways involved in candidate genes, enrichment analysis was performed using the \u0026ldquo;clusterProfiler\u0026rdquo; package (version 4.2.2) (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, Mendelian randomization (MR) analysis was executed to single out key genes causally associated with AD using the \u0026ldquo;TwoSampleMR\u0026rdquo; package (version 0.5.6) (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and the inverse variance weighted (IVW) method(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) based on the expression quantitative trait locus (eQTL) data of candidate genes (exposure factors) and genome-wide association study (GWAS) summary data of AD (outcome, ebi-a-GCST90027158) from the IEU OpenGWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). MR‒Egger(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), weighted median(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), simple mode(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and weighted mode(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) methods were also included in the causal inference analysis. SNPs significantly correlated with exposure factors were screened out as instrumental variables (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), which also lacked linkage disequilibrium (clump\u0026thinsp;=\u0026thinsp;TRUE, r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001, kb\u0026thinsp;=\u0026thinsp;60) and were irrelevant to outcome (proxies\u0026thinsp;=\u0026thinsp;TRUE, rsq\u0026thinsp;=\u0026thinsp;0.8). Furthermore, the Steiger test and sensitivity analysis were adopted to evaluate the directionality and reliability of the MR results. In the end, receiver operating characteristic (ROC) curve and expression analyses were performed to identify the biomarkers for AD patients, in which the area under the curve (AUC) of the ROC curve must be greater than 0.7 and the expression trend of biomarkers between the AD and control groups must be in complete agreement coupling with statistical significance in the GSE5281 and GSE48350 datasets. Moreover, an artificial neural network (ANN) was constructed to assess the ability to distinguish patients with AD from control individuals, and ROC curve analysis was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Gene set enrichment analysis (GSEA) and immune infiltration analysis\u003c/h2\u003e \u003cp\u003eTo explore the signaling pathways affected by biomarkers in AD, GSEA was performed according to the Spearman correlation of biomarkers with all the other genes (adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The KEGG gene set was defined as the background gene set, which was obtained from the Molecular Signatures Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The five enrichment results with the strongest significance were visualized via the \u0026ldquo;enrichplot\u0026rdquo; package (version 1.18.0). Furthermore, the ssGSEA algorithm was also employed to estimate the abundance of 28 immune cells in the AD and control groups in the GSE5281 dataset, as well as the Spearman correlation between differential immune cells and biomarkers via the \u0026ldquo;psych\u0026rdquo; package (version 2.2.9), to determine which cells play an important role in the development of AD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Correlation analysis between biomarkers and metabolic pathways\u003c/h2\u003e \u003cp\u003eTo explore the relationship between the incidence of AD and body metabolism, the correlations between biomarkers and metabolic pathways were assessed. The ssGSEA algorithm was used to estimate metabolic pathway-related scores based on metabolic pathway-related gene sets, including fatty acid metabolism, oxidative phosphorylation, adipogenesis, bile acid metabolism, hypoxia, heme metabolism, xenobiotic metabolism, cholesterol homeostasis and glycolysis, followed by correlation between biomarkers and differential metabolic pathways. In addition, the AlzData database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.alzdata.org/\u003c/span\u003e\u003cspan address=\"http://www.alzdata.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to analyze the expression levels of biomarkers in different brain tissues of AD patients and control individuals. The expression of biomarkers in key cells was also compared between the AD and control groups in the GSE138852 dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Construction of the regulatory network\u003c/h2\u003e \u003cp\u003eAccording to the NetworkAnalyst platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the transcription factors (TFs) corresponding to biomarkers were predicted based on the JASPER database. The TF-mRNA regulatory network was constructed via Cytoscape software (version 3.8.2)(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Furthermore, the DGidb (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dgidb.org/\u003c/span\u003e\u003cspan address=\"https://dgidb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and DisGeNet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.disgenet.org/\u003c/span\u003e\u003cspan address=\"https://www.disgenet.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases were used to predict small molecule drugs and neurological diseases corresponding to biomarkers, respectively. The prediction results were displayed using an mRNA-drug/disease network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Molecular docking between biomarkers and melatonin\u003c/h2\u003e \u003cp\u003eFinally, molecular docking was adopted to research the interactions between biomarkers and melatonin. This analysis was carried out based on the crystal structure of biomarkers and the three-dimensional structure of melatonin, which were obtained from the Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and PubChem Compound database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), respectively. The optimal conformation of biomarkers and melatonin was obtained by the CB-Dock online molecular docking tool(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), and then the results were visualized by PyMOL(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eR software (version 4.1.0) was used in this study. Differences among variables were ascertained through the Wilcoxon test. If not specifically stated, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Oligodendrocytes were regarded as key cells in GSE138852\u003c/h2\u003e \u003cp\u003eIn the wake of quality control, a total of 12,653 cells and 10,850 genes in GSE138852 were screened out (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). The top 10 highly variable genes, such as \u003cem\u003eROBO2\u003c/em\u003e, \u003cem\u003eCEMIP\u003c/em\u003e, and \u003cem\u003eCLDN5\u003c/em\u003e, were labeled in the scatter diagram (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). With respect to PCA, the top 30 PCs were selected for follow-up analysis (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). Afterwards, the resulting nine cell clusters were classified into six cell types, including microglia, astrocytes, neurons, endothelial cells, oligodendrocytes and oligodendrocyte progenitor cells (OPCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The expression of marker genes in the six annotated cell lines is shown in a bubble diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The histogram shows the proportions of cell types in the AD and control samples, and oligodendrocyte cells were the most abundant in both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). On the basis of the Fisher analysis, we discovered that oligodendrocytes could be key cells for lucubrating (odds ratio\u0026thinsp;=\u0026thinsp;3.386, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Importantly, there were 281 DEGs in oligodendrocyte cells between the AD and control groups, including 153 with upregulated expression and 128 with downregulated expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Moreover, the cells in the AD group communicated through ligand‒receptor interactions more closely than did those in the control group (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD), and the top 10 functional pathways with the greatest differences in oligodendrocyte cells between the two groups, such as MGMT-mediated DNA damage reversal, amino acid conjugation, and agmatine biosynthesis, are displayed in the heatmap (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 A total of 550 MRGs were acquired from the GSE5281 dataset\u003c/h2\u003e \u003cp\u003eA total of 3,490 DEGs were identified between the AD and control groups in GSE5281, of which 1,179 were upregulated and 2,311 were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The expression profiles of the top 10 up- and downregulated DEGs are shown via a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). With regard to the ssGSEA score of melatonin-related regulators in the AD and control groups, we considered the association between MPGs and AD due to the increase in the score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). With respect to WGCNA, there was an outlier sample in GSE5281, namely, GSM119619 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). When the optimal soft threshold (β) was 10 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.855), the network bordered a scale-free distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Then, eight modules were identified, and the blue (4,648 genes) and yellow (2,088 genes) modules were strongly correlated with AD (|Cor|\u0026ge;0.79, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). After further screening, a total of 550 modular genes were identified as MRGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cem\u003eGPRC5B\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e are biomarkers related to melatonin for AD\u003c/h2\u003e \u003cp\u003eAfter overlapping 281 DEGs in oligodendrocyte cells in GSE138852, 3,490 DEGs in GSE5281 and 550 MRGs, a total of 21 genes were regarded as candidate genes strongly related to melatonin in AD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). These candidate genes were enriched in 286 GO terms (such as myeloid cell homeostasis, neuron projection regeneration and regulation of the canonical Wnt signaling pathway) and 14 KEGG pathways (such as the PI3K-Akt signaling pathway, the Apelin signaling pathway and the Toll-like receptor signaling pathway) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). According to MR analysis between candidate genes and AD, only four genes (\u003cem\u003eGPRC5B\u003c/em\u003e, \u003cem\u003eMETTL7A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e) were causally related to AD based on the IVW method (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and these genes were risk factors for AD (hazard ratio\u0026thinsp;\u0026gt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The sensitivity analysis and Steiger test confirmed the reliability and directionality of the MR data (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e-S4\u0026amp;Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). We further identified three biomarkers for AD patients, namely, \u003cem\u003eGPRC5B\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e, because of their greater than 0.88 AUC in the GSE5281 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) and their significantly increased expression in the AD samples in the GSE5281 and GSE48350 datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). In addition, the ANN model accurately distinguished AD samples from control samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Biomarkers, especially NFKBIA, play important roles in AD development\u003c/h2\u003e \u003cp\u003eBy virtue of GSEA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), we discovered that three biomarkers might be connected with Parkinson\u0026rsquo;s disease, oxidative phosphorylation and Huntington\u0026rsquo;s disease. \u003cem\u003eGPRC5B\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e might participate in myocardial contraction, and \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e might be negatively correlated with AD development. Moreover, there was a significant difference in the enrichment score of 15 immune cells between the AD group and the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Compared with those in the control group, 14 immune cell types, such as T helper type 17 (Th17) cells, natural killer (NK) cells and immature B cells, were more abundant in the AD group, while CD56dim NK cells were less abundant. Moreover, we evaluated the correlation of these 15 differential cell lines with biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). \u003cem\u003eNFKBIA\u003c/em\u003e was most strongly correlated with natural killer cells (Cor\u0026thinsp;=\u0026thinsp;0.802, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), memory B cells (Cor\u0026thinsp;=\u0026thinsp;0.797, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and T follicular helper cells (Cor\u0026thinsp;=\u0026thinsp;0.790, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, CD56dim NK cells were significantly negatively correlated with three biomarkers (|Cor|\u0026gt;0.447, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Biomarkers associated with brain metabolism are differentially expressed in brain tissues.\u003c/h2\u003e \u003cp\u003eAdditionally, we closely examined the expression of biomarkers in the entorhinal cortex, hippocampus, temporal cortex and frontal cortex of the brain, as well as in oligodendrocyte cells. The expression of \u003cem\u003eGPRC5B, as well as that of NFKBIA\u003c/em\u003e, was markedly greater in the entorhinal cortex and frontal cortex of AD patients than in control individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Moreover, the expression levels of \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e in the temporal cortex were clearly greater in the AD group than in the control group. Similarly, the expression of biomarkers in oligodendrocyte cells was also markedly different between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The correlation between biomarkers and metabolic pathways revealed that three pathways, namely, oxidative phosphorylation, adipogenesis and heme metabolism, were markedly different between the AD and control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Oxidative phosphorylation was passively associated with three biomarkers (|Cor|\u0026gt;0.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Adipogenesis was positively correlated with biomarkers (Cor\u0026thinsp;\u0026gt;\u0026thinsp;0.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and heme metabolism was positively correlated with \u003cem\u003eGPRC5B\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e (Cor\u0026thinsp;\u0026gt;\u0026thinsp;0.57, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe TF-mRNA network included three biomarkers and 24 TFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). For instance, GATA2 and POU2F2 might regulate the expression of \u003cem\u003eGPRC5B\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e, and NFKB1 might participate in the regulation of \u003cem\u003eGPRC5B\u003c/em\u003e and \u003cem\u003eNFKBIA\u003c/em\u003e expression. In addition, only drugs targeting \u003cem\u003eNFKBIA\u003c/em\u003e, such as CHEMBL401565, CHEMBL256967, and DIOSCIN, were obtained. A total of 19 diseases corresponding to \u003cem\u003eGPRC5B\u003c/em\u003e or \u003cem\u003eNFKBIA\u003c/em\u003e were predicted, such as AD and hyperactive behavior for \u003cem\u003eGPRC5B\u003c/em\u003e, cerebral ischemia, brain ischemia and others for \u003cem\u003eNFKBIA\u003c/em\u003e. Overall, two biomarkers (\u003cem\u003eGPRC5B\u003c/em\u003e and \u003cem\u003eNFKBIA\u003c/em\u003e), nine drugs and 19 neurological diseases were incorporated into the mRNA-drug/disease network (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Melatonin is a potential target of three biomarkers\u003c/h2\u003e \u003cp\u003eFinally, we assessed the interactions and binding modes of three biomarkers with melatonin via molecular docking analysis. \u003cem\u003eGPRC5B\u003c/em\u003e can bind to melatonin with a binding energy of -6.2 kcal/mol through the amino acid residues TYR-195 and ARG-125 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). \u003cem\u003eNFKBIA\u003c/em\u003e might bind to melatonin at a -5.0 kcal/mol affinity via the active residue D100 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). \u003cem\u003eRASSF4\u003c/em\u003e also targeted melatonin with a binding energy of -5.8 kcal/mol by interacting with the MET-137 residue (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAD is the primary cause of dementia in older adults(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). As a chronic, multifaceted and multifactorial neurodegenerative disorder(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), there is currently no cure or effective drug treatment for AD(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Within 5 to 12 years after the first symptoms appear, AD progresses slowly and irreversibly, eventually leading to death(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) and imposing a significant burden on patients, their families and society. Therefore, a novel, effective, and safe therapeutic strategy for AD is urgently needed. This study revealed that AD is mostly associated with the MRGs GPRC5B, NFKBIA and RASSF4. The results revealed that MLT might alleviate the pathology of patients with AD through these targets, and the key genes related to small molecule drugs revealed by network pharmacology analysis might be candidates for AD treatment.\u003c/p\u003e \u003cp\u003eIn this study, we identified three promising biomarkers (GPRC5B, NFKBIA, and RASSF4) for targeting AD treatment. Additionally, molecular docking studies showed that these biomarkers bound well to MLT. G protein-coupled receptor, family C, group 5, member B (GPRC5B), an orphan receptor belonging to the G protein-coupled receptor (GPCR) family, contributes to neurogenesis(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Its neuronal enrichment is controversial at the mRNA level(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) but consistent at the protein level, with the highest levels in the neocortex and hippocampus(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Murine GPRC5B impacts synaptic formation and neurogenesis(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), while rat GPRC5B may be related to microglial activation(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). A previous study also revealed that RNA editing of GPRC5B was associated with AD dementia, neuropathological measures and longitudinal cognitive decline(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). However, research on the effect of GPRC5B on AD has rarely been reported, necessitating further investigation. NF-κB inhibitor alpha (NF-κBIα), another MRG member, interacts with REL dimers to inhibit the NF-κB/REL complex involved in inflammation. Mutations upstream of NF-κB have previously been reported to affect NF-κB activity in AD patients, leading to anatomical defects such as shrinkage of the entorhinal cortex and early AD limbic system(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). NFKBIA degradation followed by phosphorylation is critical for the activation of NF-κB(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). In addition, NFKBIA was reported to be upregulated in AD patients(\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), suggesting that it is a potential target for studying and treating AD. RAS association domain family 4 (RASSF4) is a member of the RASSF protein family(\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). The function of RASSF4 remains elusive but may play a role in tumor suppression(\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), early myogenic differentiation(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e) and cell proliferation(\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). However, its relationship with AD has scarcely been reported. This study suggests the potential relevance of RASSF4 to AD for the first time and warrants further investigation into the detailed mechanism involved.\u003c/p\u003e \u003cp\u003eThrough GSEA, we found that these three key MRGs were mainly enriched in Parkinson's disease (PD), the oxidative phosphorylation pathway, the transforming growth factor β (TGF-β) signaling pathway, the pathways involved in cancer, cardiac muscle contraction, etc.. As a neurodegenerative disease, PD ranks second only to AD. A study investigating the comorbidity of AD and PD revealed a strong association between these two disorders(\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e), and several studies have shown that AD and PD share common pathogenic mechanisms, such as the NO pathway, p62 dysfunction, neuroexcitotoxicity and oxidative stress(\u003cspan additionalcitationids=\"CR73 CR74\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Oxidative phosphorylation is another pathway enriched in MRGs in AD. The brain is a highly energy-consuming organ, and neurons rely mainly on the mitochondrial oxidative phosphorylation system for energy(\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). In mild AD and amnestic cognitive impairment, decreased glucose metabolism due to oxidative damage impairs cognitive function and leads to synaptic dysfunction and neuronal death(\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). In addition, a primary pathological event in AD is mitochondrial dysfunction, characterized by alterations in mitochondrial morphology, the generation of reactive oxygen species and impaired oxidative phosphorylation(\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). In AD neurodegeneration, both amyloid-beta (Aβ) and tau work together to inhibit the production and activity of mitochondrial respiratory complexes, leading to a dysfunctional oxidative phosphorylation system(\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e), thus aggravating AD. There was also an enriched pathway of MRGs in AD involving the TGF-β signaling pathway. TGF-β is a pleiotropic cytokine that is crucial for embryonic development, physiological tissue homeostasis, and several pathological behaviors(\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). TGF-β1 is one of three isoforms of the TGF-β superfamily and has been reported to possess immunomodulatory and neuroprotective properties(\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e). A lack of TGF-β1 not only promotes the buildup of Aβ but also contributes to the formation of neurofibrillary tangles(\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e), underscoring its significance in AD(\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e). Furthermore, the roles of other enriched pathways, including pathways involved in cancer, cardiac muscle contraction, etc., in AD are still unclear. Hence, further research is warranted.\u003c/p\u003e \u003cp\u003eAccording to our cell clustering and annotation analyses, we identified oligodendrocytes as key players in AD pathology, which was consistent with previous study results(\u003cspan additionalcitationids=\"CR88\" citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e). In the central nervous system (CNS), the main function of oligodendrocytes is to synthesize the myelin sheath to speed up nerve impulses and protect axons(\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e). However, it is known that oligodendrocytes are susceptible to ischemic and proinflammatory changes in their environment, and their death provokes further demyelination far beyond the initial injury site, leading to sensory, motor, cognitive, and autonomic nerve impairment(\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e). Previous research has indicated that myelin damage and oligodendrocyte loss in AD are not only a result of neuronal degeneration but also contribute to the early stages of the disease, highlighting the importance of oligodendrocytes in the onset and progression of the disease(\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e). In our study, we found the upregulation of 3 key MRGs in oligodendrocytes. Regrettably, few studies have investigated the possible relationships between these MRGs and oligodendrocytes and AD. Therefore, further research is required to fully understand this issue.\u003c/p\u003e \u003cp\u003eRegarding the immune infiltration analysis, significant disparities were found in the infiltration of fourteen types of immune cells between AD patients and controls, with 13 upregulated and 1 downregulated. Among these upregulated immune cells, Th17 cells have been implicated as crucial mediators of AD development(\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). Their derived proinflammatory cytokines are expressed and upregulated in AD and other neurological diseases(\u003cspan additionalcitationids=\"CR95\" citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e). The neutralization of Th17 cells drives IL-17, leading to a decrease in amyloid-β‐induced neuroinflammation and AD-like behavior(\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). As expected, these results were consistent with the results of our study. In addition, we observed greater infiltration of NK cells in the AD group than in the control group. This contradicts previous studies that reported reduced blood NK cells in AD patients compared to controls(\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). In addition, other studies have revealed decreased cytotoxic function and lower functional potential of NK cells in AD patients than in normal controls(\u003cspan additionalcitationids=\"CR99\" citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e). Moreover, reduced NK cell levels improved neuroinflammation and cognitive decline in a mouse model of AD(\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e). These inconsistencies likely stem from the different materials used in the studies. While our study focused on the entorhinal cortex dataset, previous studies obtained results from the peripheral blood of AD patients. In relation to the inconsistent conclusions, further research and strong evidence are still needed. Furthermore, our study identified CD56dim NK cells as the sole downregulated immune cell subset infiltrating AD patients. These cytotoxic effector cells primarily produce IFN-γ when stimulated(\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). Although the specific impact of CD56dim NK cells on AD pathogenesis remains uncertain, prior research has demonstrated decreased levels of certain markers, such as IFN-γ, in NK cells from individuals with AD(\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e), suggesting that the presence and activity of these immune cells might be diminished in AD patients.\u003c/p\u003e \u003cp\u003eTo construct the integrated regulatory network, TF prediction was performed on 3 MRGs, resulting in the identification of 24 related TFs in the TF regulatory network. Almost all of these TFs have been confirmed to be correlated with AD. For instance, AP-2γ, encoded by Tfap2c, in hippocampal progenitors promotes neuron proliferation and differentiation. Its ablation in the adult brain has been linked to reduced neurogenesis, disrupted neural coherence, and cognitive deficits(\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e). Furthermore, AP-2γ may suppress NFKBIA expression(\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e) and enhance Th17 and Th1 cell activation(\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e), suggesting that AP-2γ potentially influences the neuroinflammatory process of AD by affecting NFKBIA. However, further research is needed to verify this point. GATA2 is essential for activating neuroglobin, a protein that protects neuronal cells in AD(\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e). Neuroglobin levels increase in the early and moderate stages of AD but decrease in the advanced stages, possibly due to reduced GATA2 levels. Further research is needed to confirm this relationship(\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e). Nuclear respiratory factor 1 (NRF1), a TF that activates genes important for mitochondrial function and biogenesis, could play a role in neurodegenerative diseases such as AD by affecting both mitochondrial and nonmitochondrial functions(\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e). In this study, we report that NRF1 is a regulator of RASSF4, although the distinct connection between them has not been examined. A previous study revealed that RASSF4 overexpression could downregulate the mitochondrial membrane potential in colorectal cancer(\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e), suggesting that NRF1 might impact mitochondrial function through the regulation of RASSF4, consequently influencing AD. Undoubtedly, additional tests are necessary to confirm this assertion. Upstream Stimulatory Factor-1 (USF1) controls genes related to lipid metabolism, including APOE and the Aβ precursor protein. Women with the USF1 haplotype GCGCAC are at greater risk for AD-related neuropathological lesions, particularly neuritic and late-stage penile plaques. Younger carriers of the CCGCAC haplotype are more likely to have nonneuritic and diffuse senile plaques(\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e). Therefore, USF1 could be a potential target for the treatment of AD. The expression of EGR1 and β-secretase 1 (BACE1) plays a crucial role in the deposition of Aβ, with the inhibition of EGR1 binding to the BACE1 promoter resulting in the suppression of BACE1 expression. This subsequently leads to a reduction in Aβ deposition and an enhancement of memory function in rats with AD. Additionally, most of the remaining TFs were also demonstrated to be associated with AD via bioinformatic analysis(\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e, \u003cspan additionalcitationids=\"CR112 CR113\" citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e). Overall, the aforementioned 24 TFs have the potential to be targeted for AD therapy, but further research is needed to confirm how these TFs regulate MRGs in AD.\u003c/p\u003e \u003cp\u003eAs is well known, commonly used drugs for AD, such as donepezil hydrochloride and galantamine, have limited effectiveness and cannot cure the disease. To explore new treatment options, the key MRG targets were subsequently submitted to the DGIdb for predicting drugs for AD, and 9 candidate drugs were identified. Among them, dioscin, wedelolactone and gefitinib have been demonstrated to potentially have therapeutic efficacy for AD(\u003cspan additionalcitationids=\"CR116\" citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e). Dioscin and wedelolactone are plant extracts known for their safety, low toxicity, and beneficial effects on various diseases, and they improve AD symptoms mainly by adjusting oxidative stress, inflammation and neurotoxicity(\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e). Gefitinib, an EGFR inhibitor, has also been confirmed to be effective in alleviating AD by attenuating Aβ pathology and improving cognitive function(\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e). These findings indicated that these drugs could be potential candidates for AD treatment. However, the efficacy of several other small molecule drugs in the treatment of AD is still unclear, necessitating additional studies. Additionally, we predicted diseases associated with these biomarkers, including hyperactive behavior for GPRC5B and various diseases such as cerebrovascular diseases and neurodegenerative disorders for NFKBIA. Many of these diseases might involve a common pathogenic mechanism of AD(\u003cspan additionalcitationids=\"CR122\" citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e), suggesting that GPRC5B and NFKBIA are potential therapeutic targets for these conditions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we employed transcriptome data analysis in conjunction with MR analysis for the first time to identify three key significantly associated with AD. Notably, GPRC5B and RASSF4 have been rarely reported in previous studies on AD. Based on these findings, we conducted a systematic analysis of the biological processes and important signaling pathways enriched by MRGs. Furthermore, we investigated changes in immune cell infiltration and metabolic pathways in both AD patients and control subjects, revealing associations between differential immune infiltrating cells and key MRGs. Additionally, through single-cell analysis, we discovered the crucial role of oligodendrocytes in AD pathogenesis and performed an analysis of intercellular communication while visualizing key cellular functions. Finally, by constructing regulatory networks involving MRGs and their corresponding TFs, we predicted potential drugs for AD along with their related targets using network pharmacology. Our findings not only enhance our understanding of the mechanisms of melatonin therapy for AD but also provide valuable insights into key MRGs, cells involved in disease progression, potential therapeutic drugs as well as related biological pathways that may open up new avenues for treating AD. However, it is important to note that our study is based on existing reports, therefore, further clinical trials and basic research are warranted to validate these results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo ethical approval was required for this analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Tianchi Talent Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was designed by ZH, LH, and LQ. ZH and LH had complete access to all the data in the study and took full responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the statistical analysis, critically reviewed the manuscript during the writing process, and approved the final version for publication. ZH and LH are the study guarantors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported by an Tianchi Talent Program, which awarded to Dr. ZHANG Hua-xiong.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eScheltens P, De Strooper B, Kivipelto M, Holstege H, Ch\u0026eacute;telat G, Teunissen CE, et al. Alzheimer's disease. Lancet (London, England). 2021;397(10284):1577\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnopman DS, Amieva H, Petersen RC, Ch\u0026eacute;telat G, Holtzman DM, Hyman BT, et al. 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BMC medical genetics. 2018;19(Suppl 1):215.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer's disease, Melatonin, Mendelian randomization, Biomarker, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-4772764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4772764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMelatonin can improve mitophagy, thereby ameliorating cognitive deficits in Alzheimer\u0026rsquo;s disease (AD) patients. Hence, our research focused on the potential value of melatonin-related genes (MRGs) in AD through bioinformatic analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFirst, the key cells in the single-cell dataset GSE138852 were screened out based on the proportion of annotated cells and Fisher\u0026rsquo;s test between the AD and control groups. The differentially expressed genes (DEGs) in the key cell and GSE5281 datasets were identified, and the MRGs in GSE5281 were selected via weighted gene coexpression network analysis. After intersecting two sets of DEGs and MRGs, we performed Mendelian randomization analysis to identify the MRGs causally related to AD. The biomarkers GSE5281 and GSE48350 were identified through receiver operating characteristic (ROC) curve and expression analyses. Furthermore, gene set enrichment analysis, immune infiltration analysis and correlation analysis with metabolic pathways were conducted, as well as construction of a regulator network and molecular docking.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAccording to the Fisher test, oligodendrocytes were regarded as key cells due to their excellent abundance in the GSE138852 dataset, in which there were 281 DEGs between the AD and control groups. After overlapping with 3,490 DEGs and 550 MRGs in GSE5281, four genes were found to be causally related to AD, namely, \u003cem\u003eGPRC5B\u003c/em\u003e, \u003cem\u003eMETTL7A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e. Moreover, \u003cem\u003eGPRC5B\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e were deemed biomarkers, except for \u003cem\u003eMETTL7A\u003c/em\u003e, because of their indistinctive expression between the AD and control groups. Biomarkers might be involved in oxidative phosphorylation, adipogenesis and heme metabolism. Moreover, T helper type 17 cells, natural killer cells and CD56dim natural killer cells were significantly correlated with biomarkers. Transcription factors (GATA2, POU2F2, NFKB1, etc.) can regulate the expression of biomarkers. Finally, we discovered that all biomarkers could bind to melatonin with a strong binding energy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study identified three novel biomarkers related to melatonin for AD, namely, \u003cem\u003eGPRC5B\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eRASSF4\u003c/em\u003e, providing a novel approach for the investigation and treatment of AD patients.\u003c/p\u003e","manuscriptTitle":"Melatonin-Related Genes as Key Players in Alzheimer's Disease: Discovery of Promising Biomarkers for Treatment Targets for Alzheimer's Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-02 13:52:23","doi":"10.21203/rs.3.rs-4772764/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-04T06:05:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-27T06:19:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-17T22:38:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196168614311790822245815179631117598095","date":"2024-08-16T05:36:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261318485565956223979697730755311591664","date":"2024-08-07T22:53:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-05T12:43:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-05T12:34:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-05T09:12:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-30T05:01:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-20T10:52:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"686b3c2a-4b51-49bf-b744-1928885d59a1","owner":[],"postedDate":"September 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36192707,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":36192708,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-02-10T16:11:13+00:00","versionOfRecord":{"articleIdentity":"rs-4772764","link":"https://doi.org/10.1038/s41598-024-80755-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-04 15:56:58","publishedOnDateReadable":"February 4th, 2025"},"versionCreatedAt":"2024-09-02 13:52:23","video":"","vorDoi":"10.1038/s41598-024-80755-x","vorDoiUrl":"https://doi.org/10.1038/s41598-024-80755-x","workflowStages":[]},"version":"v1","identity":"rs-4772764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4772764","identity":"rs-4772764","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}