Identification and validation of PANoptosis-related genes in Alzheimer's disease.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Crohn's disease (CD) is an immune-mediated disorder characterized by immune cell infiltration that induces persistent chronic inflammation of the gastrointestinal tract. Programmed cell death (PCD) plays a critical role in the pathogenesis of CD. This study identified vital PCD-related genes in CD based on immune infiltration using bioinformatic analysis. We obtained two CD datasets from the Gene Expression Omnibus (GEO) database and examined immune cell infiltration to investigate immune cell dysregulation in CD. PCD-related genes were retrieved from the GeneCards database. Based on the differentially expressed genes (DEGs) and PCD gene sets, PCD-related DEGs were identified. Candidate hub genes were identified using a protein-protein interaction (PPI) network, and their diagnostic effectiveness was predicted using receiver operating characteristic (ROC) curve analysis. Functional enrichment and immune infiltration analyses were used to assess the distinct roles of the hub genes. Finally, the miRWalk and ENCORI databases were used to predict which microRNAs (miRNAs) regulated the hub genes and to verify gene expression in CD colonic tissues via transcriptome sequencing. A total of 335 PCD-related DEGs and 3 hub genes (MMP1, SAA1, and PLAU) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses indicated the enrichment of these genes in the immune response. Infiltration analysis of immune cells showed abundant endothelial cells, plasma cells, dendritic cells, and monocytes in the CD samples. Based on the correlation analysis, the three hub genes were positively correlated with monocytes and negatively correlated with CD8 naïve T-cells. MMP1, SAA1, and PLAU correlated with the pathogenicity of CD and had good diagnostic value for CD. The three hub genes were highly expressed in the CD tissues, as confirmed using transcriptome sequencing. This study identified MMP1, SAA1, and PLAU as hub genes involved in PCD in patients with CD. These genes regulate immune cell function and their expression levels are closely related to immune cell infiltration. These findings provide novel insights into the mechanisms underlying CD pathogenesis. The identified PCD genes and regulatory miRNAs are potential biomarkers and therapeutic targets for CD.

BackgroundPapillary thyroid carcinoma (PTC) is a malignant tumor of the endocrine system, and distant metastasis leads to poor prognosis for patients with PTC. The competitive endogenous RNA (ceRNA) network and tumor-infiltrating immune cells might participate in tumor prognosis and distant metastasis. However, few studies have focused on ceRNAs and immune cells in PTC.MethodsWe identified differentially expressed lncRNAs (DELs) using the GEO2R tool of the GEO database. Through comprehensive analysis, we selected lncRNA PROSER2-AS1 and constructed a PROSER2-AS1-mediated ceRNA network. Survival was analyzed with a Kaplan-Meier (KM) curve. Gene set enrichment analysis (GSEA) was performed to determine the function of PROSER2-AS1 in the ceRNA network using TCGA database. Moreover, the relationship between PROSER2-AS1 and immune cell infiltration was analyzed with ssGSEA using the “GSVA” package in R.ResultsComprehensive analysis of the GSE66783 dataset revealed 105 significantly differentially expressed lncRNAs. Univariate and multivariate Cox regression analyses were performed to assess the prognostic significance of the DELs, and we identified lncRNA PROSER2-AS1 as an independent factor for prognosis in PTC (p < 0.05). Considering the online tools LncRNASNP2 and miRWalk3.0, we constructed a PROSER2-AS1-related ceRNA network. Furthermore, the GSEA results suggested that PROSER2-AS1 may be involved in immune cell infiltration and that PROSER2-AS1 was correlated with 14 types of tumor-infiltrating immune cells. PROSER2-AS1 might function through TGFBR3.ConclusionlncRNA PROSER2-AS1 and related mRNAs (TGFBR3) may be potential prognostic biomarkers in PTC and may correlate with immune infiltrates.

Bioinformatics analysis and experimental validation study. To investigate the role and expression patterns of disulfidptosis-related genes in spinal cord injury (SCI), identify potential pivotal genes, and explore possible therapeutic targets. Shanghai, China. Data acquisition and pre-processing: Screened 27 disulfidptosis-related genes based on literature and downloaded RNA-sequencing data of ASCI patients from GEO database (GSE151371); Identification of differentially expressed genes (DEGs): Used R package "limma" for differential gene expression analysis between ASCI samples and normal controls; Evaluating immune cell infiltration: Employed ssGSEA algorithm and CIBERSORT to determine immune cell abundance; Identification and functional verification of key genes: Intersected disulfidptosis-related genes with DEGs, and used machine learning techniques (Random Forest, Lasso, Support Vector Machine) to identify hub genes. Validated hub genes expression by real-time PCR; Construction of a diagnostic model: Developed a backpropagation neural network clinical prediction model based on hub genes and clinical features, and evaluated its performance using ROC curve. 6. Subcluster analysis: Performed consensus cluster analysis of ASCI samples and hub genes, and used GSVA to elucidate functional differences between subgroups. Identified 7764 DEGs in ASCI, with GO and KEGG enrichment in inflammation and autophagy-related pathways; Found differences in immune cell infiltration between ASCI and control groups, and correlation between immune cells and DRGs; Determined seven hub genes (MYL6, NUBPL, CYFIP1, IQGAP1, FLNB, SLC7A11, CD2AP) through machine learning; Validated the expression of hub genes by qRT-PCR; Constructed a clinical diagnostic model with good predictive accuracy (overall dataset accuracy of 83.3%); Identified two subtypes of ASCI based on hub genes, with different immune infiltration and pathway activity. Disulfidptosis is closely related to spinal cord injury. The identified hub genes and subtypes provide new insights for biomarker and therapeutic target research. The diagnostic model has potential for clinical application, but further studies are needed due to limitations such as small sample size. This study was supported in part by the project of Youth Scientific and Technological Talents of PLA (2020QN06125), Changhong Talent Project in First affiliated hospital of Navy Medical University (Wei Xianzhao) and Basic Medical Research Project in First affiliated hospital of Navy Medical University (2023PY17). I want to reiterate that there is no prior publication of figures or tables and no conflict of interest in the submission of this manuscript. The graphical abstract is divided into two parts. The upper section sequentially illustrates the occurrence of disulfidptosis and changes in the immune microenvironment in the human body after SCI. The lower section displays the construction of a diagnostic model for SCI through the detection of changes in disulfidptosis-related genes, combined with patient clinical information.

BackgroundIntervertebral disc degeneration (IDD) has become a serious public health problem, the mechanism of which is complex and still unclear. We aimed to construct a ceRNA network related to IDD to explore its pathogenesis.MethodsWe downloaded the GSE67566, GSE63492, GSE116726 and GSE124272 datasets from GEO database, and obtained the differentially expressed RNAs. Then, we constructed a ceRNA network and the KEGG and GO enrichment analysis were performed. Finally, we performed immune cell infiltration analysis on the GSE124272 dataset and analysed the correlation between immune cell abundance and hub genes expression levels.ResultsThe ceRNA network included three down-regulated circRNAs: hsa_circ_0074817, hsa_circ_0002702, hsa_circ_0003600, three up-regulated miRNAs: hsa-miR-4741, hsa-miR-3158-5p, hsa-miR-508-5p, and 57 down-regulated mRNAs, including six hub genes: IGF1, CHEK1, CCNB1, OIP5, BIRC5, AR. GO and KEGG analysis revealed that the network is involved in various biological functions. Immune infiltration analysis showed that IDD was closely related to immune cell infiltration, and hub genes could further affect the development of IDD by affecting immune cell infiltration.ConclusionThis study identified the hsa_circ_0074817-hsa-miR-508-5p-IGF1/CHEK1/CCNB1, the hsa_circ_0003600-hsa-miR-4741-BIRC5/OIP5/AR and the hsa_circ_0002702-hsa-miR-3158-5p-IGF1/AR as important regulatory axis of IDD, which will help us gain further insight into the pathogenesis of IDD and determine potential therapeutic targets.

The chronic respiratory condition known as chronic obstructive pulmonary disease (COPD) was one of the main causes of death and disability worldwide. This study aimed to explore and elucidate new targets and molecular mechanisms of COPD by constructing competitive endogenous RNA (ceRNA) networks. GSE38974 and GSE106986 were used to select DEGs in COPD samples and normal samples. Cytoscape software was used to construct and present protein-protein interaction (PPI) network, mRNA-miRNA co-expression network and ceRNA network. The CIBERSORT algorithm and the Lasso model were used to screen the immune infiltrating cells and hub genes associated with COPD, and the correlation between them was analyzed. COPD cell models were constructed in vitro and the expression level of ceRNA network factors mediated by hub gene was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). In this study, 852 differentially expressed genes were screened in the GSE38974 dataset, including 439 upregulated genes and 413 downregulated genes. Gene clustering analysis of PPI network results was performed using the Minimum Common Tumor Data Element (MCODE) in Cytoscape, and seven hub genes were screened using five algorithms in cytoHubba. CCL20 was verified as an important hub gene based on mRNA-miRNA co-expression network, GSE106986 database validation and the analysis of ROC curve results. Finally, we successfully constructed the circDTL-hsa-miR-330-3p-CCL20 network by Cytoscape. Immune infiltration analysis suggested that CCL20 can co-regulate immune cell migration and infiltration through chemokines CCL7 and CXCL3. In vitro experiments, the expression of circDTL and CCL20 was increased, while the expression of hsa-miR-330-3p was decreased in the COPD cell model. By constructing the circDTL-hsa-miR-330-3p-CCL20 network, this study contributes to a better understanding of the molecular mechanism of COPD development, which also provides important clues for the development of new therapeutic strategies and drug targets.

Identification of mitochondria-related genes as diagnostic biomarkers for diabetic nephropathy and their correlation with immune infiltration: New insights from bioinformatics analysis

BackgroundFerroptosis is a newly defined form of programmed cell death and plays an important role in Alzheimer’s disease (AD) pathology. This study aimed to integrate bioinformatics techniques to explore biomarkers to support the correlation between ferroptosis and AD. In addition, further investigation of ferroptosis-related biomarkers was conducted on the transcriptome characteristics in the asymptomatic AD (AsymAD).MethodsThe microarray datasets GSE118553, GSE132903, GSE33000, and GSE157239 on AD were downloaded from the GEO database. The list of ferroptosis-related genes was extracted from the FerrDb website. Differentially expressed genes (DEGs) were identified by R “limma” package and used to screen ferroptosis-related hub genes. The random forest algorithm was used to construct the diagnostic model through hub genes. The immune cell infiltration was also analyzed by CIBERSORTx. The miRNet and DGIdb database were used to identify microRNAs (miRNAs) and drugs which targeting hub genes.ResultsWe identified 18 ferroptosis-related hub genes anomalously expressed in AD, and consistent expression trends had been observed in both AsymAD The random forest diagnosis model had good prediction results in both training set (AUC = 0.824) and validation set (AUC = 0.734). Immune cell infiltration was analyzed and the results showed that CD4+ T cells resting memory, macrophages M2 and neutrophils were significantly higher in AD. A significant correlation of hub genes with immune infiltration was observed, such as DDIT4 showed strong positive correlation with CD4+ T cells memory resting and AKR1C2 had positive correlation with Macrophages M2. Additionally, the microRNAs (miRNAs) and drugs which targeting hub genes were screened.ConclusionThese results suggest that ferroptosis-related hub genes we screened played a part in the pathological progression of AD. We explored the potential of these genes as diagnostic markers and their relevance to immune cells which will help in understanding the development of AD. Targeting miRNAs and drugs provides new research clues for preventing the development of AD.

Alzheimer's disease (AD) is the most common form of dementia characterized by a prominent cognitive deterioration of sufficient magnitude to impair daily living. Increasing studies indicate that non-coding RNAs (ncRNAs) are involved in ferroptosis and AD progression. However, the role of ferroptosis-related ncRNAs in AD remains unexplored. We obtained the intersection of differentially expressed genes in GSE5281 (brain tissue expression profile of patients with AD) from the GEO database and ferroptosis-related genes (FRGs) from the ferrDb database. Least absolute shrinkage and selection operator model along with weighted gene co-expression network analysis screened for FRGs highly associated with AD. A total of five FRGs were identified and further validated in GSE29378 (area under the curve = 0.877, 95% confidence interval = 0.794-0.960). A competing endogenous RNA (ceRNA) network of ferroptosis-related hub genes (EPT1, KLHL24, LRRFIP1, CXCL2 and CD44) was subsequently constructed to explore the regulatory mechanism between hub genes, lncRNAs and miRNAs. Finally, CIBERSORT algorithms were used to unravel the immune cell infiltration landscape in AD and normal samples. M1 macrophages and mast cells were more infiltrated whereas memory B cells were less infiltrated in AD samples than in normal samples. Spearman's correlation analysis revealed that LRRFIP1 was positively correlated with M1 macrophages (r = -0.340, P < 0.001) whereas ferroptosis-related lncRNAs were negatively correlated with immune cells, wherein miR7-3HG correlated with M1 macrophages and NIFK-AS1, EMX2OS and VAC14-AS1 correlated with memory B cells (|r| > 0.3, P < 0.001). We constructed a novel ferroptosis-related signature model including mRNAs, miRNAs and lncRNAs, and characterized its association with immune infiltration in AD. The model provides novel ideas for the pathologic mechanism elucidation and targeted therapy development of AD.

Background There is still no clear understanding of the pathogenesis of atrial fibrillation (AF). For this purpose, we used integrated analysis to uncover immune infiltration characteristics and investigated their relationship with competing endogenous RNA (ceRNA) network in AF. Methods Three AF mRNA data sets (GSE14975, GSE79768, and GSE41177) were integrated using the SVA method from Gene Expression Omnibus (GEO). Together with AF circRNA data set (GSE129409) and miRNA data set (GSE70887) from GEO database, we built a ceRNA network. Then hub genes were screened by the Cytoscape plug-in cytoHubba from a protein-protein interaction (PPI) network. As well, CIBERSORT was employed to investigate immune infiltration, followed by Pearson correlation coefficients to unravel the correlation between AF-related infiltrating immune cells and hub genes. Ulteriorly, circRNA-miRNA-mRNA regulatory axises that could be immunologically related to AF were obtained. Results Ten hub genes were identified from the constructing PPI network. The immune infiltration analysis revealed that the number of monocytes and neutrophils was higher, as well as the number of dendritic cells activated and T cells regulatory (Tregs) was lower in AF. Seven hub genes (C5AR1, CXCR4, HCK, LAPTM5, MPEG1, TLR8, and TNFSF13B) were associated with those 4 immune cells (P < 0.05). We found that the circ_0005299–miR-1246–C5AR1 and circRNA_0079284-miR-623-HCK/CXCR4 regulatory axises may be associated with the immune mechanism of AF. Conclusion The findings of our study provide insights into immuno-related ceRNA networks as potential molecular regulators of AF progression.

Vitiligo is an autoimmune disorder characterized by pigment loss, and current treatment options remain inadequate. This study aims to identify oxidative stress-related biomarkers and hub genes associated with vitiligo diagnosis through genomic analysis and to examine the role of immune cell infiltration in the pathogenesis of vitiligo. The mRNA expression profile dataset GSE75819 was retrieved from the GEO database. Differential expression of oxidative stress-related genes in vitiligo was analyzed using R software. Protein-protein interaction (PPI) analysis, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on the differentially expressed genes (DEGs). Immune cell infiltration between vitiligo and normal control groups was assessed using the CIBERSORT algorithm. Additionally, two machine learning algorithms were employed to identify hub genes, perform enrichment analyses, and evaluate their correlation with immune infiltration. A total of 415 Oxidative Stress-DEGs were identified in vitiligo, including 317 up-regulated and 98 down-regulated genes. PPI analysis highlighted the significance of certain ribosomal protein genes. KEGG enrichment analysis suggested an association between vitiligo and various neurodegenerative conditions, particularly through pathways such as oxidative phosphorylation and ribosome biogenesis. GO enrichment analysis indicated that the hub genes were significantly enriched in mitochondrial-related activities. Significant differences in immune infiltration patterns were observed between vitiligo patients and normal controls. Machine learning algorithms identified oxidative stress-related key genes associated with vitiligo, notably the DCT gene, whose expression was strongly linked to the activity of specific immune cell subsets and melanin biosynthetic pathways. Oxidative stress-related DEGs, ribosomal proteins, immune infiltration, and hub genes related to melanin biosynthesis, particularly DCT, are closely associated with the pathogenesis of vitiligo. These findings enhance our understanding of vitiligo and may aid in identifying therapeutic targets for the disease.

BackgroundThe occurrence and progression of hepatic fibrosis (HF) is accompanied by inflammatory damage. Immune genes play a pivotal role in fibrogenesis and inflammatory damage in HF by regulating immune cell infiltration. However, the immune mechanisms of HF are inadequately studied. Therefore, this research aims to identify the immune genes and biological pathway which involved in fibrosis formation and inflammatory damage in HF and explore immune target-based therapeutics for HF.MethodsThe expression dataset GSE84044 of HF was downloaded from the GEO database. The crucial module genes for HF were screened according to weighted gene co-expression network analysis (WGCNA). The crucial module genes were mapped to immune-related genes obtained from the ImmPort database to obtain the hepatic fibrosis immune genes (HFIGs). In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed on HFIGs. Then, the protein-protein interaction (PPI) network was conducted on HFIGs and hub genes were identified from the PPI network. Moreover, immune infiltration analysis was performed to identified correlation between hub gene and immune cell infiltration. To verify the reliability of the GSE84044 expression profile data analysis, a rat model of CCl4-induced HF was established, followed by transcriptome sequencing and immunofluorescence analysis and quantitative reverse transcription (q-PCR) experiments were performed in HF rats and normal rat liver tissues. Finally, CMAP platform was used to explore immune target-based therapeutics for HF.ResultsIn the bioinformatics analysis of GSE84044 data, 98 HFIGs were screened. These genes were mainly involved in inflammation-related biological pathways such as NOD-like receptor signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway and PI3K-Akt signaling pathway. From the PPI network, 10 hub genes were identified, including CXCL8, IL18, CXCL10, CD8A, IL7, PTPRC, CCL5, IL7R, CXCL9 and CCL2. Immune infiltration analysis showed that immune cells like neutrophils, natural killer (NK) cells, macrophages M1 and macrophages M2 were significantly correlated with the hepatic fibrosis process and hub gene expression was significantly correlated with these immune cells. Notably, most of the biological pathways HFIGs riched and all the hub gene expression except CXCL8 were validated in subsequent transcriptome and qRCR experiments. Finally, 15 small molecule compounds with the potential to reverse the high expression of hub genes were screen out as potential therapeutic agents for HF.ConclusionThe immune genes CXCL8, IL18, CXCL10, CD8A, IL7, PTPRC, CCL5, IL7R, CXCL9 and CCL2 may play an essential role in the fibrosis formation and inflammatory damage in HF. The outcomes of this research provide a basis for the study of the immune mechanisms of HF and contribute to the diagnosis and prevention and treatment of HF in clinical practice.

BackgroundHead and neck squamous cell carcinoma (HNSCC) is the sixth most common tumor in human. Research has shown that HPV status HNSCC is a unique prognosis factor, which may due to its immune infiltration landscape. But the underlying mechanism is unclear.MethodsIn this study, we used a combination of several bioinformatics tools, including WCGNA, ssGSEA, CIBERSORT, TIDE,etc., to explore significant genes both related to HPV infection status and immune cell infiltration in HNSCC patients.ResultsCombined with several bioinformatics algorithms, eight hub genes were identified, including LTB, CD19, CD3D, SKAP1, KLRB1, CCL19, TBC1D10C and ARHGAP4. In HNSCC population, the hub genes had a stable co-expression, which was related to immune cell infiltration, especially CD8+ T cells, and the infiltrative immune cells were in a dysfunctional status. Samples with high hub genes expression presented with better response to immune check point block (ICB) therapy and sensitivity to bleomycin and methotrexate.ConclusionsThe eight hub genes we found presented with a stable co-expression in immune cell infiltration of HPV + ve HNSCC population. The co-expression of hub genes related to an immune microenvironment featuring an increase in immune cells but high degree of immune dysfunction status. Patients with high hub gene expression had a better response to ICB treatment, bleomycin and methotrexate. The co-expression of hub genes may be related to immune infiltration status in patients. The concrete molecular mechanism of hub genes function demands further exploration.

Laryngeal cancer (LC) is a malignant tumor that occurs in the head and neck. Laryngeal cancer is one of the most common cancers of the neck and head, and its prognosis has always been poor. The incidence of LC increased gradually and showed an early rising trend. Laryngeal cancer is rarely studied in relation to immunity, Malignant tumors will change the state of the human body in various ways to adapt to their own survival and avoid the immune system. This study aims to explore the immune molecular mechanism of laryngeal cancer through bioinformatics analysis. The gene expression data was downloaded for 3 microarray datasets: GSE27020, GSE59102, and GSE51985. CIBERSORT algorithm was performed to evaluate immune cell infiltration in tissues between LC and healthy control (HC). Differentially expressed genes (DEGs) were screened. Functional correlation of DEGs were analyzed by Gene Ontology, Gene Set Enrichment Analysis and Kyoto encyclopedia of genes and genomes. Candidate biomarkers were identified by cytoHubba of Cytoscape. Spearman correlations between the above biomarkers and infiltrating immune cells were explored using R software analysis. The immune cell types of LC and HC were significantly different. Twenty-one DEGs were obtained by cross-screening. The function of DEGs is closely related to the number of immune cells. Five central genes (TNNT3, TNNI2, Desmin, matrix metallopeptidase 9 and cytotoxic T lymphocyte antigen 4) were screened. The HUB gene was demonstrated to have the ability to diagnose LC and HC with good specificity and sensitivity. The correlation between immune cells and biomarkers showed that hub gene was positively correlated with macrophages and dendritic cells, and negatively correlated with CD4 + T cell. TNNT3, TNNI2, Desmin, matrix metallopeptidase 9 and cytotoxic T lymphocyte antigen 4 can be used as diagnostic biomarker for LC. Macrophages, dendritic cells and CD4 + T cell may participate in the occurrence and development of LC.

BackgroundParkinson’s disease (PD) is a common neurodegenerative disease in the elderly population. However, there are no reliable diagnostic biomarkers for PD, and the pathogenesis of PD still needs further study. The aim of the current study was to identify potential biomarkers and explore the pathogenesis of PD. MethodsWe conducted an integrative analysis of messenger RNA (mRNA), microRNA (miRNA), and long noncoding RNA (lncRNA) expression profiles of PD using data from the Gene Expression Omnibus (GEO). The GSE110720, GSE110719 and GSE133347 data sets were selected and analysed. Gene ontology (GO) enrichment and gene set variation analysis (GSVA) were performed for annotation, visualization, and integrated discovery. Protein–protein interaction (PPI) and competing endogenous RNA (ceRNA) networks were constructed, and hub genes were identified. Meanwhile, the immune infiltration analysis of hub genes was analysed. Moreover, receiver operating characteristic (ROC) curves were generated to verify the diagnostic value of the differentially expressed genes (DEGs). Finally, the genes with high area under the curve (AUC) values were verified by human samples. ResultsWe identified 464 DEGs closely related to PD, including 154 mRNAs, 134 miRNAs, and 176 lncRNAs. The GO analyses indicated that changes in PD were mainly enriched in receptor ligand activity and cytokine receptor binding. The KEGG enrichment analysis showed that these DEGs were significantly involved in cytokine-cytokine receptor interactions, signalling pathways regulating the pluripotency of stem cells and Th17 cell differentiation. GSVA suggested that growth factor binding, IL2-stat5 signalling, and IL6-jak-stat3 signalling were crucial in the development of PD. A total of five hub genes (NPBWR2, CXCL10, CXCL5, S1PR5, and GALR1) were selected via the PPI network. A ceRNA network of the CXCL5, CXCL10 and S1PR5 genes was constructed, and target genes of the three genes were screened. The immune infiltration analysis showed that there were significant differences in a variety of immune cells between the hub genes. The expression of DEGs was validated in clinical human samples by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The expression levels of hsa-miR6895–5p, hsa-miR6791–5p, hsa-miR518f-5p, hsa-miR455–3p and TEKT4P2 were decreased, while the levels of TPTE2P6 were increased in human samples. These findings are consistent with the bioinformatics analysis results. ConclusionWe found that the immune inflammatory response and immune cell regulation were involved in the pathogenesis of PD. Five hub genes involved in the immune infiltration biological processes of PD based on bioinformatics. We verified the DEGs with significant differences by qRT-PCR. These findings might provide new insight into the pathogenesis of PD and the development of diagnostic and therapeutic strategies for PD.