Abstract This study aims to screen for common differentially expressed genes (DEGs) related to Parkinson’s disease (PD) and circadian rhythm (CR) through bioinformatics methods, and further analyze their potential molecular mechanisms and traditional Chinese medicine-targeted components, providing new targets and drug development ideas for the treatment of PD. This study first obtained PD-related microarray data from the GEO database to screen for differentially expressed genes. Using the WGCNA algorithm to construct a gene co-expression network and filter key module genes. Combine GeneCards and Msigdb databases to obtain CR-related genes, and use the Venny2.1 tool to screen for common DEGs between PD and CR. Further, the String database and Cytoscape software were used to construct a protein-protein interaction network, and the DAVID platform was utilized for KEGG and GO enrichment analysis. Using the Cytohubba plugin to screen hub genes and evaluate their diagnostic value with ROC curves. In addition, the mRNA-miRNA regulatory network was constructed using the Mirwalk database, and potential targeted traditional Chinese medicines and their core components were predicted using the Coremine and HERB databases. Finally, molecular docking was used to verify the binding activity of key components with the targets. This study identified a total of 659 PD-related DEGs and 2555 CR-related genes. Through WGCNA analysis, 3586 key module genes were obtained, and finally, 62 common key genes for PD and CR were screened and a PPI network was constructed. GO and KEGG enrichment analyses showed that these genes are mainly enriched in biological processes such as dopamine biosynthesis and neurodegenerative disease pathways. Through Cytohubba, two hub genes, SNCA and DRD2, were identified, and their expression levels were significantly lower in the PD group compared to the control group. ROC curve analysis showed that DRD2 and SNCA had high diagnostic value (AUC of 0.87 and 0.80, respectively). The further constructed mRNA-miRNA network shows that SNCA and DRD2 are associated with 669 and 404 miRNAs, respectively, and there are 143 common miRNAs. Three core traditional Chinese medicines (Gastrodia Elata, Malt, Papaya) and their five core components (ent-Epicatechin, HMF, Protocatechuic Acid, Succinic Acid, and Vanillin) were screened through the Coremine database and the HERB database. The molecular docking results show that ent-Epicatechin, vanillin, and protocatechuic acid have binding energies with the target protein below − 5.5 kcal/mol, indicating stable binding. This study identified the hub genes SNCA and DRD2 related to PD and CR through bioinformatics analysis, revealing their potential molecular mechanisms and targeted traditional Chinese medicine components. These findings provide new biomarkers and candidate molecules for drug development in the diagnosis and treatment of PD. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-16854-0. Keywords: Circadian rhythm (CR); Parkinson’s disease (PD); Bioinformatics; Hub genes, medicine food homologous herb Subject terms: Computational biology and bioinformatics, Biomarkers, Circadian rhythms and sleep, Diseases of the nervous system Introduction Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease. It is characterized by the progressive loss of dopaminergic neurons in the substantia nigra of the midbrain, leading to motor symptoms such as bradykinesia, rigidity, resting tremor, and gait disturbances^[28]1. Although current treatment methods can alleviate symptoms, they cannot cure or halt the progression of the disease. Effectively preventing and treating PD remains a significant challenge for humanity.In fact, the non-motor symptoms of PD often appear before the motor symptoms, severely affecting the patient’s quality of life. Common non-motor symptoms include sleep disorders, olfactory reduction, depression, and constipation, among which sleep disorders are the most prevalent in PD patients, and in some patients, the onset of sleep disorders precedes other symptoms^[29]2–[30]4. This suggests that early identification and intervention of non-motor symptoms may be a potentially effective strategy for preventing and treating the occurrence and development of PD. The circadian rhythms generated by the endogenous biological clock within organisms are one of the research hotspots in life sciences. Specifically, circadian rhythms are a fundamental physiological regulatory mechanism within organisms, regulating various physiological processes over a 24-hour period to adapt to daily environmental changes, including sleep, wakefulness, eating, and metabolism^[31]5. Circadian rhythm disruption affects neurological diseases by causing pathological changes such as neurotransmitter imbalance, increased oxidative stress, and exacerbated neuroinflammation^[32]6. Circadian rhythm disruption is a common non-motor symptom in PD, and there is a complex interrelationship between the two^[33]7. On one hand, the main pathological feature of PD is the accumulation of α-synuclein (α-Syn) in Lewy bodies, which triggers neuronal inflammation and cell death. The impact of the damaged neuronal cells is not limited to the substantia nigra but also extends to subcortical nuclei related to circadian rhythms, including the locus coeruleus, pontine nucleus, raphe nucleus, suprachiasmatic nucleus, and hypothalamus^[34]8. The degenerative lesions in these areas lead to the occurrence of non-motor symptoms of Parkinson’s disease. On the other hand, sleep disorders may promote neurodegeneration through mechanisms such as affecting the cerebrospinal fluid circulation system and disrupting N3 sleep^[35]9. Moreover, the expression rhythm of the circadian factor Bmal1 and other clock genes in peripheral blood mononuclear cells of PD patients is significantly weakened, further reflecting the complexity of the association between the two^[36]10. Circadian rhythm disturbances and PD interact to form a vicious cycle, accelerating the progression of the disease. Therefore, delving deeply into the intrinsic molecular mechanisms of both is of crucial significance in revealing the pathogenesis of PD and developing new therapeutic strategies. The concept of “Homology of medicine and food” is one of the core concepts of “medicine in food” in traditional Chinese medicine. It emphasizes that food and medicine are of the same origin^[37]11, which can not only satisfy hunger, but also regulate diseases. It is a characteristic theory of traditional Chinese medicine in preventing and treating diseases. However, current related research is mostly limited to the level of experience summary or active screening of single medicinal materials, and lacks the systematic integration of “disease-syndrome-target-food”, especially in the systematic work of incorporating circadian rhythm disorder, a key non-motor symptom in the early stage of Parkinson’s disease (PD), into the research framework of homologous medicine and food, the research progress is seriously insufficient. Although circadian rhythm disorder has been recognized as one of the prodromal phenotypes of Parkinson’s disease, the molecular targets and signaling pathways shared by the two have not been systematically analyzed, resulting in the inability to provide accurate guidance for food and drug compatibility at the mechanism level. In addition, there are no reports on the “food-medicine” combination and its core active ingredients screened out based on the common targets of circadian rhythm and Parkinson’s disease in the food-medicine homologous directory published by the National Health Commission, and there is a lack of candidate schemes that can be directly transformed into functional foods or early intervention products. With the increasing awareness of health, the development of traditional Chinese medicine with the same origin of medicine and food is expected to provide people with a safe, effective and economical way of self-care. Based on this, this study used bioinformatics technology to identify the common differentially expressed genes of Parkinson’s disease and circadian rhythm, and further targeted and predicted the homologous Chinese medicine and potential core chemical components of medicine and food according to the core targets, hoping to provide a new treatment idea and strategy for the prevention and treatment of Parkinson’s disease from the perspective of circadian rhythm regulation, fill the current research gap, and promote the research in this field to a higher level. Methods Acquisition of genes Differential expression genes in PD The PD microarray data ([38]GSE20163^[39]12, [40]GSE20141^[41]13, [42]GSE7621^[43]14 were obtained from the GEO database ([44]https://www.ncbi.nlm.nih.gov/geo/)^[45]15. All datasets are of the species Homo sapiens, with samples derived from the substantia nigra of the brain. Of these, the [46]GSE20163 microarray dataset comprises 17 samples: A total of 8 control samples and 9 PD samples were analysed. The [47]GSE20141 microarray dataset comprised a total of 18 samples: A total of 8 control samples and 10 PD samples were analysed. The [48]GSE7621 microarray dataset comprised a total of 25 samples: The study comprised a total of 9 control samples and 16 PD samples. The three expression datasets were batch-normalised using the Sangerbox tool([49]http://sangerbox.com/home.html#), and significantly differentially expressed genes were identified using the criteria of P-value < 0.05 and |logFC| > 1. Construction of Co-Expression network and identificaion of key modules Firstly, gene expression profiles were utilised to calculate the median absolute deviation (MAD) for each gene. The subsequent step entailed the removal of the bottom 50% of genes, which were identified as those with the smallest MAD values. The implementation of the goodSamplesGenes method within the WGCNA R package facilitated the elimination of outlier genes and samples. In the subsequent stage of the analysis, WGCNA was utilised to construct a scale-free co-expression network, with a sensitivity setting of 3. In order to perform a more detailed analysis of the module, the following steps were taken: firstly, the dissimilarity of module eigengenes was calculated; secondly, a cutoff value for the module dendrogram was selected; and thirdly, some modules were merged. Furthermore, we merged modules with distances less than 0.25, thereby obtaining 20 co-expression modules. It is important to note that the grey module is regarded as a set of genes that cannot be allocated to any module. Targets of CR CR-related genes were retrieved from the GeneCard database (Updated: Jul 16, 2025) ([50]https://www.genecards.org/)^[51]16 and the Msigdb database(Updated Jun 6, 2025) ([52]https://www.gsea-msigdb.org/)^[53]17 using the search term “circadian rhythm”. The CR gene database was obtained by combining and removing duplicate values. Common targets for CR and PD The utilisation of Venny2.1 ([54]http://bioinformatics.psb.ugent.be/webtools/Venn/) is imperative for the cross-referencing of the PD differentially expressed genes obtained in 2.1.1, the important modular genes obtained in 2.1.2, and the CR-related genes obtained in 2.1.3, with the objective of identifying the common DEGs of PD and CR. Functional enrichment analysis PPI network construction of common DEGs The DEGs obtained in 2.1.3 were imported into the String database 12.0 (Updated July 26, 2023)^[55]18 ([56]https://cn.string-db.org/) to obtain protein interaction information. The protein information that had been obtained was imported into Cytoscape 3.7.2 software in order to construct a protein-protein interaction network and calculate the node degree values. The nodes in the network were then adjusted according to the degree values to generate a protein-protein interaction (PPI) network. KEGG and GO enrichment analysis The common DEGs obtained in 2.1.4 were uploaded to the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (Updated December 22, 2024) platform^[57]19, and the identifier was selected as “OFFICIAL_GENE_SYMBOL”, the species was selected as “Homo sapiens”, and the list type was selected as Gene list. The designated identifier is ‘GENE_SYMBOL’, the species is ‘Homo sapiens’, and the list type is designated ‘Gene list’. The Kyoto Encyclopedia of Genes and Genomes (KEGG)^[58]20 signalling pathway enrichment and Gene Ontology (GO) (molecular function, biological process and cellular component) results were obtained (P < 0.05). The enrichment results are visualized using bubble charts and bar charts. Hub gene analysis Screening of HUB targets and construction of PPI networks The results obtained from the String database in section “[59]PPI network construction of common DEGs” are to be imported into Cytoscape 3.7.2^[60]21. The five analysis algorithms – degree, edge percolation component, density of maximum neighborhood component, Radality, and Stress – are to be used in the Cytohubba^[61]22 plugin to calculate the top 10 nodes for each algorithm. Subsequently, the Venny2.1 tool should be utilised to screen the results from the aforementioned five algorithms, with the objective of identifying hub genes. Differential expression of hub genes in PD The [62]GSE49036 dataset^[63]23 was utilised as an external validation dataset. Hub gene expression was retrieved from the GEO database for each sample in the PD and control groups in the [64]GSE49036 dataset. The analysis employed histograms to illustrate the expression levels of the hub genes. Functional enrichment analysis of hub genes The PPI network construction method for hub genes is consistent with the approach outlined in 2.2.1; the enrichment method for KEGG signalling pathways and GO analysis is the same as described in 2.2.2. Diagnostic value of hub genes The diagnostic value of hub genes was assessed by ROC curves. The external dataset [65]GSE49036 was utilised to assess the efficacy of the prediction model in differentiating between Parkinson’s disease and normal samples. Biomarker-based mRNA-miRNA regulatory networks The Mirwalk database (Updated Aug, 2024)([66]http://mirwalk.umm.uni-heidelberg.de/)^[67]24 should be used to obtain information regarding microRNAs (miRNAs) that are associated with hub genes. This website constitutes a comprehensive database of interactions between microRNAs (miRNAs) and their target genes, encompassing data from multiple species. The construction of a hub gene-miRNA network can be facilitated by employing the Cytoscape tool. Prediction and analysis of potential targeted traditional Chinese medicines and components of hub gene The hub gene was imported into the CoremineMedical database ([68]http://www.Coremine.com/medical/). A P-value of less than 0.05 was utilised to screen potential target traditional Chinese medicines, which were then compared with the list of traditional Chinese medicines that are also food ingredients, as published by the National Health Commission, and subsequent supplements. The objective of this comparison was to identify potential traditional Chinese medicines that are also food ingredients for further analysis. The core traditional Chinese medicines were imported into the HERB database (HERB 2.0, [69]http://herb.ac.cn) with a view to querying their chemical components. The Network Analyzer was utilised to calculate topological parameters for analysis, and components with a centrality (betweenness centrality > 0) were preliminarily screened to determine core components. The core components must be imported into the Swiss ADME database (Updated June 26, 2024) ([70]http://www.swissadme.ch) for the purpose of conducting a pharmacokinetic assessment. Components with “high” intestinal permeability and at least two “yes” responses to the five pharmacokinetic principles are selected for the purpose of determining the core components. Molecular docking The subsequent step involves performing molecular docking between the screened key components and the target. The 3D structures of the key components can be downloaded from PubChem ([71]http://pubchem.ncbi.nlm.nih.gov/)^[72]25. The molecular structures of the core targets are obtained from the Protein Data Bank (PDB) database^[73]26, and water molecules and native ligands are removed using PyMoL software^[74]27. The docking process employs AutoDock Tools 1.5.6^[75]28 software to add hydrogen atoms to the protein and calculate charges. Subsequently, AutoDock Vina is utilised for molecular docking. Subsequently, molecular docking was performed using Auto Dock Vina, and the best-fitting combinations were visualized. Statistical analysis In all the above sections, the results of the study are considered to be statistically significant by satisfying P < 0.05. Result Screening of common targets Identification of DEGs Differential gene expression analysis was performed on three Parkinson’s disease datasets ([76]GSE20141, [77]GSE20163, and [78]GSE7621) in the GEO database, using P-values < 0.05 and |logFC| > 1 as screening criteria. A total of 659 differentially expressed genes (DEGs) were identified, including 355 upregulated genes and 304 downregulated genes (Figs. [79]1 and [80]2). Fig. 1. [81]Fig. 1 [82]Open in a new tab Analysis of sequencing data within the Parkinson’s disease group and the healthy control group. (A) Principal Component Analysis (PCA) of the two groups. The PCA plot shows that PC1 and PC2 account for 14.26% and 6.60% of the total variance, respectively. (B) The volcano plot shows the differentially expressed genes between the two groups. Normalized fold change and P values are used to construct the volcano plot. The horizontal and vertical lines represent the P-value and Fold Change changes, respectively. Red and green dots represent genes that are significantly upregulated and downregulated, respectively. Black dots indicate genes that do not show statistically significant changes. (C) Heatmap of gene sequencing expression data for Parkinson’s disease group (n = 35) and non-Parkinson’s disease group (n = 25) samples. Red indicates upregulation, and blue indicates downregulation. Fig. 2. [83]Fig. 2 [84]Open in a new tab WGCNA analysis of the Parkinson’s disease dataset and identification of key modules. (A) Sample clustering dendrogram, with different colors reflecting different co-expression modules. (B,C) Selection of soft threshold power. (D) Correlation analysis between modules and clinical status of Parkinson’s disease. Red indicates a negative correlation, and blue indicates a positive correlation. The numbers within the boxes indicate the level of statistical significance. (E) Different module clustering A total of 2555 CR-related genes were obtained from the GeneCard database (19) and the Msigdb database(Fig. [85]3A). Fig. 3. [86]Fig. 3 [87]Open in a new tab Identification of DEGs and PPI network construction and GO and KEGG analysis. (A) Intersection Venn diagram of differentially expressed genes, characteristic module genes, and circadian rhythm-related genes. (B) Protein interaction network constructed based on 62 DGEs. (C) Protein interaction network plotted in Cytoscape software. (D) Graph of KEGG analysis results. (E) Graph of GO analysis results. Construction of WGCNA and gene modules screening The WGCNA algorithm was utilised to identify key gene modules with a strong association to PD (Fig. [88]2A). Following the construction of a clustering tree diagram and the implementation of dynamic tree cutting, a total of 20 gene modules were obtained and displayed in a heat map. At a soft threshold of approximately 3.093, the network exhibited optimal scale-free properties (highest R2), while at a soft threshold of approximately 3.114, the network exhibited optimal sparsity (lowest average connectivity) (Fig. [89]2B,C). The results demonstrated that the turquoise module (cor = −0.43; P = 5.4e−4), orange module (cor = 0.39; P = 1.8e−3), green module (cor = 0.33; P = 0.01), brown module (cor = 0.29; P = 0.02), and darkturquoise module (cor = 0.27; P = 0.04) exhibited the strongest positive and negative associations with PD, respectively. The five modules under consideration encompass a total of 3586 genes (Fig. [90]2D,E). Screening of common targets and construction of PPI networks The 659 differentially expressed genes obtained in Step 4.1, the 3586 key module genes obtained after WGCNA analysis in Step 4.2 and the intersection with the 2555 CR-related genes were used to identify the 62 final key genes for CR and PD (Fig. [91]3A). The String database was utilised to obtain PPI data for the key genes of interest (Fig. [92]3B). The data was subsequently imported into Cytoscape 3.7.2 software to construct a PPI network, which contained 62 nodes and 110 edges (Fig. [93]3C). A PPI enrichment P-value of 3.91e−13 was obtained, thereby highlighting the potential important collaborative relationships between these genes in biological processes (Fig. [94]4). Fig. 4. [95]Fig. 4 [96]Open in a new tab Identification of hub genes. (A) Venn diagram of the intersection of hub genes identified by different methods. (B) Expression of hub genes in the [97]GSE49036 dataset. (C) Protein-protein interaction network constructed based on hub genes. (D) Graph of KEGG analysis results. (E) Graph of GO analysis results. (F) ROC curve of SNCA. (G) ROC curve of DRD2. Gene enrichment analysis for the DEGs GO and KEGG pathway enrichment analyses were used to assess the function of these 62 differentially expressed genes (Fig. [98]3D,E). Among the KEGG pathway enrichment results, many pathways associated with neurological disorders and neurodegenerative diseases were enriched, such as Cocaine addiction(hsa05030), Parkinson disease(hsa05012), Pathways of neurodegeneration - multiple diseases(hsa05022), Dopaminergic synapse(hsa04728), Alzheimer disease(hsa05010), Amphetamine addiction(hsa05031), Folate biosynthesis(hsa00790), Alcoholism(hsa05034), NOD-like receptor signaling pathway(hsa04621), HIF-1 signaling pathway(hsa04066), Diabetic cardiomyopathy(hsa05415), Fluid shear stress and atherosclerosis(hsa05418), Spinocerebellar ataxia(hsa05017). The top 10 BP (biological process), CC (cellular component) and MF (molecular function) terms with the lowest P-values in each category were selected and visualized using Horizontal bar plots (Fig. [99]5B). The DEGs were primarily enriched in BP terms such as dopamine biosynthetic process(GO:0042416), dopamine uptake involved in synaptic transmission(GO:0051583), locomotory behavior(GO:0007626), response to amphetamine(GO:0001975), hyaloid vascular plexus regression(GO:1990384), regulation of sodium ion transport(GO:0002028), excitatory postsynaptic potential(GO:0060079), rhythmic process(GO:0048511), DNA metabolic process(GO:0006259), neuron migration(GO:0001764). Genes were also enriched in CC terms like cytosol(GO:0005829), axon(GO:0030424), synaptic vesicle membrane(GO:0030672), cytoplasm(GO:0005737), dendrite(GO:0030425), nucleus(GO:0005634), growth cone(GO:0030426), dopaminergic synapse(GO:0098691), extracellular exosome(GO:0070062), neuronal dense core vesicle(GO:0098992). Additionally, the DEGs were enriched in MF terms including protein binding(GO:0005515), identical protein binding(GO:0042802), magnesium ion binding(GO:0000287), dopamine binding(GO:0035240), GTP binding(GO:0005525), GTPase activity(GO:0003924), ubiquitin protein ligase binding(GO:0031625), DNA-binding transcription factor binding(GO:0140297), protein kinase binding(GO:0019901), lipid binding (GO:0008289). Hub genetic analysis Acquisition of hub genes The top 10 nodes were calculated using the degree, edge percolation component, density of maximum neighborhood component, Radality, and Stress 5 algorithms in the Cytohubba plugin of Cytoscape 3.7.2. Subsequently, VENNY2.1 was utilised to identify hub genes from the results of these five algorithms. Following a comprehensive review of the relevant literature, SNCA and DRD2 were identified as the two hub genes (Fig. [100]4A; Table [101]1). Table 1. The gene lists obtained by the five algorithms respectively. Compound name GI absorption Lipinski Ghose Veber Egan Muegge ent-Epicatechin High Yes Yes Yes Yes Yes HMF High Yes No Yes Yes No protocatechuic acid High Yes No Yes Yes No succinic acid High Yes No Yes Yes No vanillin High Yes No Yes Yes No [102]Open in a new tab Biomarker expression validation and PPI network construction The external dataset [103]GSE49036, obtained from the GEO database, was utilised for the validation of the expression levels of hub genes. The expression levels of SNCA and DRD2 in the PD group were lower than those in the control group, and these differences were found to be statistically significant (Fig. [104]4B). The PPI network was constructed using the String database and Cytoscape 3.7.2 software (Fig. [105]4C). KEGG signaling pathway and gene ontology Enrichment analysis using the DAVID database revealed that one signalling pathway was enriched: Parkinson’s disease (hsa05012) (p < 0.05) (Fig. [106]5). GO analysis revealed a total of ten items (p < 0.05). The DEGs were primarily enriched in BP terms such as dopamine uptake involved in synaptic transmission (GO:0051583), regulation of dopamine secretion (GO:0014059), regulation of long-term neuronal synaptic plasticity (GO:0048169), excitatory postsynaptic potential (GO:0060079), response to xenobiotic stimulus (GO:0009410). Genes were also enriched in CC terms like axon terminus (GO:0043679), synaptic vesicle membrane (GO:0030672), axon (GO:0030424), synapse (GO:0045202). Additionally, the DEGs were enriched in MF terms including identical protein binding (GO:0042802) (Fig. [107]4D,E) Diagnostic value of hub genes The model’s performance was evaluated using the area under the ROC curve (AUC) (Fig. [108]4F,G). Notably, the AUC for the DRD2 dataset was 0.87 and for the SNCA dataset it was 0.80, indicating high accuracy in categorising gene expression data. Construction of mRNA-miRNA networks To further investigate the upstream regulatory networks and potential molecular mechanisms of the two biomarkers SNCA and DRD2, this study queried the upstream miRNAs of these two biomarker genes using the Mirwalk online database. The results showed that the SNCA gene was associated with 669 miRNAs, and the DRD2 gene was associated with 404 miRNAs. Using a Venn diagram, 143 miRNAs with common predictive relationships between the two biomarker genes were screened, and the mRNA-miRNA network was constructed using Cytoscape 3.7.2 software (Fig. [109]5A,B) Fig. 5. [110]Fig. 5 [111]Open in a new tab The miRNA regulatory network of hub gene and drug and active ingredient screening. (A) Venn diagram of miRNAs related to SNCA and DRD2. (B) Hub gene-miRNA network. (C) Target- Medicine Food Homologous Herb-component-key component network diagram. (D) Binding energy of key components with target molecules. (E) Docking diagram of the molecule with the highest binding energy. Predictive analysis of potential target Chinese herbal medicines and identification of core components The SNCA and DRD2 data sets were imported into the Coremine database, and the potential target Chinese herbal medicines were screened using a P-value cut-off of 0.05. A total of four Chinese herbal medicines were matched by SNCA: gou teng, mu gua, bai man tuo luo zi, and yang jin hua. Meanwhile, DRD2 matched 13 Chinese herbal medicines, including mai ya, xia tian wu, chui xu shang lu, and tian ma. In accordance with the list of herbal medicines with both medicinal and food uses, as published by the National Health Commission and its subsequent supplements, three core herbal medicines were ultimately selected. Tian ma, mai ya, and mu gua. The core herbs were entered into the HERB database to query their chemical components, initially yielding 395 compounds. Following the screening process, which involved the utilisation of parameter standards and the Swiss ADME database, a total of five core components were identified: ent-Epicatechin, HMF, protocatechuic acid, succinic acid, and vanillin (Fig. [112]5C; Table [113]2). Table 2. Screening conditions for key compounds. EPC DMNC degree RC SC DDC SLC18A2 CCNA2 DRD2 SNCA DRD2 DDC DRD2 ENO2 CCNA2 ENO2 SLC6A3 ENO2 HMOX1 TH GCH1 DRD2 HMOX1 PSMC5 PSMC5 PSMC5 GCH1 PSMC5 PSMD2 DRD2 PSMD2 CCNA2 PSMD2 RBX1 RELA SLC18A2 COPS6 RBX1 SLC18A2 HMOX1 SNCA SNCA SNCA SNCA PSMD2 TH CCT2 TH TH YWHAZ TXN RAN TXN TXN TXN [114]Open in a new tab Molecular docking The molecular docking results were then subjected to a ranking based on a binding energy criterion of <-5.5 kJ/mol. The results demonstrated that the key compounds exhibited favourable binding activity with the target. Among the identified binding energies, those of ent-Epicatechin, vanillin, and protocatechuic acid were all found to be below − 5.5 kcal/mol, thereby indicating that their binding to the target protein is stable. In the present study, ent-Epicatechin was found to exhibit the most significant binding energy on DRD2. These compounds have been demonstrated to exert beneficial regulatory effects on both PD and CR, thus providing potential candidate molecules for drug development (Fig. [115]5D,E). Discussion With the development of an aging society, the incidence of PD has shown a trend of annual increase, becoming a major neurodegenerative disease threatening the health of the elderly alongside Alzheimer’s disease. Currently, the clinical diagnosis of Parkinson’s disease mainly relies on the appearance of motor symptoms. However, non-motor symptoms already exist in the early stages of the disease, even up to twenty years before a formal diagnosis^[116]29,[117]30. Highly suggestive, non-motor symptoms have early predictive value for the onset of PD^[118]31. Circadian rhythm disturbances are recognized as prodromal symptoms of PD and develop with the progression of the disease. The molecular mechanisms linking the two are currently unclear, and there are no ideal drugs in clinical practice that can simultaneously improve circadian rhythm disorders and PD. Therefore, this study delves deeply into the molecular markers and associated signaling pathways related to circadian rhythms and PD, and selectively screens natural medicines and active ingredients based on key targets, which is of great significance for the early prediction and treatment of PD. In this study, we screened 2222 differentially expressed genes from three PD microarray datasets ([119]GSE20163, [120]GSE20141, and [121]GSE7621) in the GEO database. Using the WGCNA method, we screened a large number of genes to identify gene modules closely related to PD clinical characteristics, which contained 3586 genes. Additionally, we obtained circadian rhythm-related genes from the GeneCards and Msigdb databases and performed an intersection analysis with the differentially expressed genes and feature module genes, ultimately identifying 62 common differentially expressed genes between PD and CR. Through PPI network analysis, KEGG pathway enrichment analysis, and GO functional annotation analysis of the intersecting differentially expressed genes, we found that these genes were primarily enriched in pathways related to neurodegenerative diseases, such as the Parkinson’s disease pathway (hsa05012) and the dopaminergic synapse pathway (hsa04728). In α-synuclein overexpression (ASO) model mice, researchers found that these mice exhibited significant circadian rhythm defects in motor activity, specifically reduced nighttime activity and increased fragmentation of wheel-running activity. Additionally, Per2 expression remained unchanged, but the firing rate of SCN neurons decreased, indicating that weakened circadian rhythm output is a key feature of PD^[122]32. Hong’s research further demonstrated that chronic inflammation in rats leads to significant neuroinflammation and dopamine neuron loss, with the mechanism associated with reduced expression of circadian rhythm genes such as Bmal1 and Clock^[123]33. This study further revealed the intrinsic biological association between CR and PD, particularly in dopamine metabolism and neuronal signaling pathways. Next, we used the Cytohubba plugin in Cytoscape software, employing five computational methods (degree, edge percolation component, density of maximum neighborhood component, Radality, Stress) to analyze the protein-protein interaction network of 62 DEGs, ultimately identifying SNCA (α-synuclein) and DRD2 (dopamine receptor D2) as the two key target genes. For these two key targets, we not only conducted enrichment analysis and PPI network construction but also evaluated their diagnostic efficacy in PD by plotting ROC curves. In addition, we also validated the expression of the two genes using the independent validation dataset [124]GSE49036. The results indicate that the expression of SNCA and DRD2 in PD patients differs significantly from that in the control group, and ROC curve analysis shows that they have high diagnostic value. α-synuclein (SNCA) is a protein primarily found in the presynaptic membrane of neurons. This protein forms insoluble amyloid fibrils, which are difficult to degrade once deposited in neurons, ultimately leading to neuronal death. Liu et al. observed excessive daytime sleepiness and impaired predictive ability in a fly model with α-synuclein overexpression, suggesting that abnormal expression of α-synuclein may cause PD-related sleep pattern disturbances^[125]34. As a member of the G protein-coupled receptor family, DRD2 plays an extremely critical role in the regulation of physiological functions. In recent years, numerous studies have focused on the complex mechanisms of action of DRD2 in the fields of CR and PD, revealing its great potential as a potential therapeutic target. In terms of circadian rhythm regulation, compared to PD patients without sleep disorders, PD patients with sleep disorders show a significant decrease in DRD2 levels in the left parahippocampal gyrus of the brain, and this decrease is closely related to the progression of the disease. Animal experiments further corroborated the critical role of DRD2 in the sleep-wake cycle: Compared to wild-type (WT) mice, DRD2 knockout mice showed significantly reduced wakefulness, with increased durations of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. When the dopamine transporter inhibitor [126]GBR12909 was used for intervention, the arousal effect in DRD2 knockout mice was only one-third of that in wild-type mice^[127]35 In the field of PD treatment, the DRD2-mediated epidermal growth factor receptor (EGFR) signaling pathway is essential for maintaining the integrity of midbrain dopaminergic neurons and supporting adult neurogenesis, making it a potential therapeutic target for promoting endogenous regenerative responses in PD^[128]36. Based on this, DRD2 plays a crucial role in both CR and PD, and the study of its associated mechanisms not only helps to deepen the understanding of the pathophysiology of PD with sleep disorders but also provides strong evidence for the early prediction and intervention of the disease. This study further provides evidence for SNCA and DRD2 as intrinsic targets associated with CR and PD. After identifying the key target genes, we imported SNCA and DRD2 into the Coremine database, using P < 0.05 as the screening criterion, and successfully matched potential targeted traditional Chinese medicines. Furthermore, based on the list of food-medicine homologous Chinese herbal medicines issued by the National Health Commission, three core Chinese herbal medicines were selected: Tianma, Maiya, and Mu gua. These three traditional Chinese medicines have long been widely used in the treatment of Parkinson’s disease-related symptoms in traditional Chinese medicine theory. To deeply explore the pharmacological basis of these core traditional Chinese medicines, we imported them into the HERB database, initially screening out 395 compounds. After rigorous parameter standard screening and pharmacokinetic property evaluation using the Swiss ADME database, we ultimately identified six key chemical components: ent-Epicatechin, HMF, oleic acid, protocatechuic acid, succinic acid, and vanillin. Molecular docking results indicate that the selected key compounds can spontaneously bind with the targets, further validating the potential therapeutic effects of the herbs in PD. “Food therapy” interventions have been widely applied in the fields of health and medicine. Tianma, Maiya, and Mu gua, these three core traditional Chinese medicines, can be used both as medicinal ingredients and as food ingredients, and have been included in the list of food-medicine homology herbs published by the National Health Commission. Gastrodia elata has neuroprotective effects. Studies have shown that one of the main active components of Gastrodia elata, gastrodin, can effectively alleviate the appearance of Parkinson-like phenotypes in Pink1B9 mutant fruit flies, including extending lifespan, restoring crawling ability, rescuing the progressive loss of dopaminergic neuron clusters in the posterior lateral part of the forebrain, and increasing dopamine levels in the brain^[129]37. At the same time, gastrodin can significantly reduce the spontaneous activity of mice, shorten the time required to fall asleep, and increase the number of sleep episodes, thereby exerting sedative and hypnotic effects^[130]38 Barley plays an important role in insomnia. After analyzing clinical prescriptions for treating insomnia, JIA and others found that malt is one of the frequently used drugs. HMF, oleic acid, and protocatechuic acid are active components screened from malt. Among them, protocatechuic acid affects the expression levels of D2DR, iNOS, and TH in the striatum and midbrain of PD model mice^[131]39; whereas oleic acid can increase the amplitude of clock genes and lipid metabolism-related genes, thereby influencing circadian rhythms^[132]40. Succinic acid, vanillin, and ent-Epicatechin are the main active components of papaya. Mondal et al. found that vanillin alleviates neurobehavioral disorders, oxidative stress, and abnormal TH protein expression in mouse models, thereby regulating dopaminergic dysregulation mechanisms and treating PD^[133]41; Sonia et al. discovered that the expression of the circadian factor BMAL1 is regulated by the succinic acid/SUCRNR1 axis^[134]42, suggesting that succinic acid is closely related to circadian rhythms. Among all the molecular docking results, the target DRD2 has the highest affinity with the active component of papaya, ent-Epicatechin, reflecting its inherent high bioactivity. The aforementioned drugs may become dual therapeutic agents targeting both PD and circadian rhythm disorders, providing new directions for the development of ideal candidate drugs in the future. Limitations In this study, the molecular mechanisms associated with CR and PD were deeply excavated through bioinformatics, network pharmacology and molecular docking, and potentially effective pharmacophore drugs were hypothesized. However, the study still suffers from the following limitations: 1) Although this study screened key genes (e.g., SNCA and DRD2) associated with PD and CR and predicted potential pharmacophore drugs and their core components by various bioinformatics methods, in vivo or in vitro experiments have not yet been conducted to validate the biological functions and therapeutic effects of these genes and drugs. Future studies will further validate the biological relevance of these key genes and drugs through in vitro cellular experiments (e.g., qPCR, Western blot) and in vivo animal model experiments (e.g., behavioral tests)0.2) Limited sample size: Although three datasets, [135]GSE20163, [136]GSE20141, and [137]GSE7621, were analyzed in the present study, and external datasets were used [138]GSE49036 for external validation, the sample size is still small, which may affect the stability and generalizability of the results. In subsequent studies, we will incorporate more clinical datasets or conduct multicenter clinical trials to deeply analyze the intrinsic link between CR and PD.3) Although the molecular docking results showed that some of the core components (e.g., ent-Epicatechin, vanillin, and protocatechuic acid) had a stable binding ability with the target proteins, the molecular docking results can only provide theoretical binding affinity information and cannot fully reflect the actual effect of the drug in the organism. Follow-up experiments will be conducted to further verify the biological activity and therapeutic potential of these drug components through in vitro cellular experiments and in vivo animal model experiments. Despite these limitations, the bioinformatics analysis in this study provides new potential biomarkers and drug candidates for the diagnosis and treatment of PD, and lays the foundation for subsequent experimental studies and clinical applications. Future studies will focus on addressing these limitations to further improve the results and promote research progress in related fields. Conclusion This study, through a rigorous bioinformatics analysis process, delves deeply into the complex relationship between PD and CR, from differential gene screening, clinical characteristic genes, circadian rhythm target association analysis, to key target screening and the prediction of traditional Chinese medicine and active ingredients. It successfully uncovers potential drug targets and active ingredients of traditional Chinese medicine, providing molecular markers based on circadian rhythms for the early diagnosis of PD, and target-oriented precision screening for traditional Chinese medicine. This study not only expands our understanding of the mechanisms linking PD and CR but also highlights its translational medical value by providing a target map for preclinical drug screening. It offers an innovative solution with Chinese characteristics to address neurodegenerative diseases in the context of an aging society, potentially reshaping the clinical practice model for early intervention and chronic disease management in PD. In the future, we will further validate the mechanisms of action of these key target genes and traditional Chinese medicine components through in vitro cell experiments and in vivo animal models, deeply exploring their potential application value in disease treatment. At the same time, we will continue to focus on the interaction between circadian rhythms and neurodegenerative diseases, providing strong support for the diagnosis, treatment, and prevention of more diseases. Supplementary Information Below is the link to the electronic supplementary material. [139]Supplementary Material 1^ (6.8MB, zip) Acknowledgements