Integrating bioinformatics and machine learning to uncover lncRNA LINC00269 as a key regulator in Parkinson's disease via pyroptosis pathways

Abstract

Background

Pyroptosis, a specific type of programmed cell death, which has become a significant factor to Parkinson's disease (PD). Concurrently, long non-coding RNAs (lncRNAs) have garnered attention for their regulatory roles in neurodegenerative disorders. This study was designed to ascertain the key lncRNAs in pyroptosis pathways of PD and elucidate their regulatory mechanisms.

Methods

Employing a combination of bioinformatics and machine learning, we analyzed PD data sets GSE133347 and GSE110716. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) recognized different lncRNAs. Through various algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Weighted Gene Co-expression Network Analysis (WGCNA), we recognized LINC01606 and LINC00269, which are key factors during the emergence and development of PD. Furthermore, experimental validation was conducted in PD mouse models to confirm these bioinformatics findings.

Results

The analysis showed that there were a large number of apoptosis-related gene expression changes in Parkinson's syndrome, for example, CASP1 and GSDME were up-regulated, and CASP9 and AIM2 were down-regulated. Among the lncRNAs, LINC01606 and LINC00269 were identified as potential modulators of pyroptosis. Notably, LINC00269 was observed to be significantly downregulated in the brain tissues of a PD mouse model, supporting its involvement in PD. The study also highlighted potential interactions of these lncRNAs with genes like ONECUT2, PRLR, CTNNA3, and LRP2.

Conclusions

This study identifies LINC00269 as a potential contributor to pyroptosis pathways in PD. While further investigation is required to fully elucidate its role, these findings provide new insights into PD pathogenesis and suggest potential avenues for future research on diagnostic and therapeutic targets. The study underscores the importance of integrating bioinformatics with experimental validation in neurodegenerative disease research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-024-02201-y.

Background

Parkinson’s disease (PD) is a hypokinetic disorder marked by a gradual decline in motor function [1]. During the incipient stage of the disease, these motor symptoms may be masked by different non-motor features, such as olfactory disorders, constipation, and mental disorders [2, 3]. PD often presents with a progressive development of malignancy [4]. However, there is no effective long-term treatment that can stop or reverse the neurodegenerative process [5]. Therefore, early diagnosis and intervention represent the best strategies for slowing the progression of the disease [6].

Currently, the specific pathogenesis of PD remains elusive, but it may involve multiple interacting mechanisms. Disease progression may be influenced by both environmental and genetic factors [7, 8]. Various mechanisms, including autophagy dysfunction, apoptosis, and oxidative stress, are likely to intersect and collectively mediate the malignant progression of PD. Pyroptosis is a type of necrosis related to inflammation, and cytolysis of gasdermin D (GSDMD) is an important hallmark during this process [9]. Recent studies have shown that, in addition to caspase-1, -4, -5, and -11, which trigger pyroptosis by cleaving GSDMD, caspase-8 can also activate pyroptosis by cleaving GSDME, revealing the complex role of caspase-8 in regulating cellular pyroptosis [10, 11]. Moreover, inflammasome‐mediated pyroptosis is a major neuronal cell death mechanism [12]. α-Synuclein metabolic disorders and dopaminergic neurodegeneration may be prevented by the inhibition of inflammasomes [13]. Biomarkers associated with the NLRP3 inflammasome have shown an increasing trend in multiple diseases, such as Alzheimer's disease and cancer. However, the mechanism of pyroptosis in PD remains insufficient. Therefore, clarifying the central role of pyroptosis in PD is of vital significance for further understanding the pathological mechanism of the disease and developing potential therapeutic strategies.

Non-coding RNAs occupy an important position in the transcriptome and have an important impact on many neurodegenerative diseases. Current research has shown that lncRNAs are associated with various pathophysiological mechanisms of PD, including α-synuclein aggregation, neuronal apoptosis, autophagy disorders, mitochondrial and lysosomal dysfunction, and neuroinflammation [14]. They are also seen as biomarkers of MPP + -induced pyroptosis and dopaminergic neuronal inflammation [15]. Animal models of PD reveal the association between abnormal changes in α-synuclein, mitochondrial reactive oxygen species (ROS) overactivation, and neuronal cathepsin B release [16]. Therefore, the relationship between epigenetic modifications of lncRNAs and pathological changes of pyroptosis in PD has attracted much attention. In this study, data from the GEO database were combined with bioinformatics and machine learning algorithms to identify lncRNAs connected with pyroptosis. The findings were then validated in a PD mouse model.

Methods

Data collection and discrepancy analysis

The Homo sapiens PD data sets GSE133347 [17] and GSE110716 [18] were collected from the GEO database. The GSE133347 data set comprises chromatin immunoprecipitation (ChIP) data including 10 samples (five disease groups and five healthy control groups) and the data platform is GPL21047. mRNA and lncRNAs were distinguished according to annotation information, and the data set was normalized. The GSE110716 data set comprises sequencing data including 16 samples (eight disease groups and eight healthy control groups) and the data platform is GPL15433.

To analyze the effect of pyroptosis on gene expression in PD, we examined 18 pyroptosis-related regulatory genes reported in the literature: CASP family proteins, NLRP1, NLRP3, NLRP6, GSDM family proteins, MEFV, PYCARD, IL1B and AIM2 [19, 20]. Based on sample grouping information, we used the "limma" and "DEseq2" R packages to conduct differential analysis of mRNA and lncRNAs in GSE133347 and GSE110716 [21]. Then We visualized the results by the R packages pheatmap and ggplot2.

Acquisition LncRNAs related to pyroptosis in PD

We performed Spearman correlation analysis on 590 genes associated with pyroptosis and successfully identified 18 lncRNAs that exhibit differential expression in Parkinson's disease (PD). The data threshold was defined as P < 0.05 and related coefficients | r |> 0.2.

Analysis of differentially expressed gene (DEG) function and pathway enrichment

In this study, we use the R 'clusterProfiler'[22] software package on DEGs enrichment analysis in PD with statistical significance defined as P < 0.05.

Gene set enrichment analysis (GSEA)

GSEA is a knowledge-based algorithm designed to explore the enrichment of gene probes in genome expression profiles [23]. In this study, two symbol gene sets in the MSigDB database [24], the R package "c2.kegg.v7.4" and "H.ARI.V7.4" [25] were applied in GSEA analysis, with statistical significance determined by P < 0.05.

Screening for pyroptosis-related LncRNAs in PD

To screen for key biomarkers for PD from pyroptosis-related lncRNAs, we used three algorithms—RF, LASSO regression [26], and WGCNA [27]. We screened the pyroptosis-related lncRNAs using the random forest method to identify biomarkers that could be used to diagnose PD; 129 lncRNAs were obtained at a threshold value of > 0.005. We applied LASSO regression with the R-package “glmnet” while completing feature selection and dimensionality reduction and generating two lncRNAs. We applied WGCNA to analyze the lncRNAs modules that were correlated with PD [27]. Overlapping genes were subsequently selected from the above three taxonomic models for further analysis.

Immune infiltration analysis

We utilized ssGSEA to quantify specific immune cells and their response activity. ssGSEA calculates an enrichment score that indicates the total enrichment level of gene sets in each sample. The list of immune infiltration-related genes was based on a previous study [28] and the ImmPort database [29]. We performed immune infiltration analysis using the CIBERSORTx algorithm [30] to identify enriched immune cells with differences between the PD and normal group; Spearman-related analysis determined the correlation of key biomarkers to immune cell ratios.

LncRNA target gene prediction

We used the Rtool database [31] to predict and search for mRNAs that could bind to key lncRNAs, and SumEnergy was used as the sequencing criterion to identify the first 500 mRNAs.

PCR analysis in PD mouse model

Subsequent procedures involved extracting total RNA from the midbrain substantia nigra region. For the qPCR analysis, we utilized TransScript RNA extraction kits, Uni All-in-One First-Strand cDNA Synthesis SuperMix for qPCR (One-Step gDNA Removal), and TransStart Top Green qPCR SuperMix (Kit codes: ER101-01, AU341-02-V2, TG-AQ601-04).

The specific primers used for LINC00269 were LINC00269-F: TTGTAAGCTTCAGCCCCTCC and LINC00269-R: GGACGTGGCTGTTCACTGAT, and for LINC01606 were LINC01606-F: CTCACGCGATTTGCAGAAGG and LINC01606-R: TCTGCCCAGTGTGCAATCTT, as detailed in the study by Chandra et al. [32].

Parkinson's disease mouse model

In this study, we employed an experimental animal model to investigate Parkinson's Disease (PD). The selected model was carefully evaluated and officially provided by the Ethics Committee for Experimental Animals at the First Affiliated Hospital of Harbin Medical University (Ethics approval number: 2022065), ensuring full compliance with ethical standards. We selected healthy male C57BL/6 mice, which were healthy, aged between 6 and 8 weeks, and had an approximate weight of 20 g, maintained in a controlled, barrier-enforced environment by professional caretakers.

During the experiment, we used 20 8-week-old male C57BL/6J mice, divided into a control group and an MPTP group of 10 mice each. Due to the impact of the pandemic lockdown measures on experimental conditions, only 7 mice from each of the control and experimental groups survived and were ultimately included in the analysis. Although the sample size was limited, the survival data from these mice are statistically significant and provide a reliable experimental foundation for this study. Both groups were housed in a 12-h light–dark cycle, with the temperature maintained at 22 ± 1°C and humidity at 45–55%, with free access to food and water. After 1 week of acclimatization, we established the model. For the experimental procedure, the mice received MPTP with an intraperitoneal (i.p.) injection at a dosage of 30 mg/kg daily for 7 days following a protocol established by other studies [33, 34]. Control groups received saline injections. One-week post-model induction, we used sodium pentobarbital (50 mg/kg, i.p.) to anesthetize the mice and then performed blood extraction and cardiac perfusion. Next, the experiment will continue in the midbrain substantia nigra region.

Preparation of mouse brain samples for dissection and analysis

Mice were rapidly euthanized by decapitation, and the brain was carefully removed in its entirety. The brain was then placed in a -80°C freezer for 5 min to achieve quick freezing. After the brain was frozen, it was transferred to an ice tray. A coronal sections (3–4 mm in thickness) was taken from the brain, starting from the region just anterior to the anterior fontanelle, which is aligned with the anterior margin of the optic chiasm in the ventral direction. On the coronal section, the substantia nigra region is distinguishable from other brain areas due to its unique color and clear boundaries. The substantia nigra was carefully dissected using forceps or other appropriate tools.

Mouse behavioral testing

On day 8 (24 h after the last MPTP injection), behavioral tests were conducted, followed by euthanasia of the mice and collection of brain tissues.

1. Open field test (OFT): Each mouse was placed in the center of an activity monitoring chamber and allowed to acclimate for 15 min. On the test day, mice were allowed to move freely in the open field for 10 min, during which their movement trajectories, walking distance, and average speed were recorded. A video tracking system was used to monitor the mice's activities and analyze the time spent in the central and peripheral zones to assess exploration behavior and anxiety levels.

2. Hanging test: Mice were placed and secured on a horizontal grid until they grasped it firmly. The grid was then inverted vertically, causing the mice to hang. The hanging time was recorded for each mouse during three trials, with the average duration taken as the final result. This test was used to evaluate muscle strength and endurance.

3. Gait analysis: Gait analysis was employed to reveal changes in stride length and width in Parkinson-like mice. After a 1-h acclimation in an undisturbed dark testing room, each mouse was positioned at the left starting end and trained to voluntarily run along a glass track to the opposite side. Removable paper towels were placed at the bottom of the track, and non-toxic paint of different colors was applied to the mice's fore and hind paws to record footprints. A video capture system and gait analysis software were used to record and analyze parameters such as stride length, stride width, stride frequency, and gait symmetry to assess motor coordination and gait stability.

Statistical analysis

The R programming environment was utilized for performing all data calculations and statistical analyses. To compare differences between two groups of continuous variables, the independent Student's t test was employed for normally distributed data, while the Mann–Whitney U test was utilized for data that deviated from normal distribution. Statistical significance was established with a significance level of P < 0.05, and P values were assessed as two-tailed.

Results

Pyroptosis-associated gene expression in PD

The methodology employed in our study, which serves as a foundation for the results presented, is illustrated in Fig. 1. In our analysis of PD and healthy control samples, we initially focused on analyzing the expression levels of genes associated with pyroptosis (Fig. 2). We performed batch effect removal and data standardization on the data set, and the histograms illustrating the data distribution before and after these processes are presented (Fig. 2a, b). This visual comparison underscores the achieved consistency and reliability of the data set, essential for the precision of our further analysis (Fig. 2a, b). To analyze the effects in PD and normal tissue, we performed differential analysis of mRNAs and lncRNAs, using limma packages, to obtain DEGs and differentially expressed lncRNAs in PD. The results revealed notable differences in lncRNA and mRNA expression comparing the PD group and the control group (Fig. 2c–e). To further explore pyroptosis-associated gene expression in PD, a box diagram was plotted (Fig. 2f). This analysis revealed elevated expression levels of CASP1 and GSDME in PD samples, while CASP9 and AIM2 exhibited lower expression levels (Fig. 2f).

Fig. 1.

Overview of the analytical process utilized in this research

Fig. 2.

Gene expression associated with the mechanism of pyroptosis in Parkinson's disease in GSE133347 data sets. a Distribution of gene expression levels across individual samples before normalization, with pink indicating normal group samples and black representing disease group samples. b Distribution of gene expression levels across individual samples following normalization. This highlights the consistent quality of samples post-normalization process, with color coding maintained as pink for normal and black for disease samples to facilitate clear differentiation. c Volcano plot visualizing differentially expressed lncRNAs between PD and HC. LncRNAs with significant fold changes are labeled. The x-axis represents the log2 fold change, and the y-axis represents the negative logarithm to the base 10 of the p value. d Volcano plot demonstrating differentially expressed genes. Noteworthy genes with substantial upregulation and downregulation are labeled, emphasizing potential key players in pyroptosis in PD. e Heatmap showcasing the differential expression patterns of genes in PD versus HC. The color spectrum, ranging from blue to yellow, indicates expression levels from low to high, respectively. f Comparative analysis of selected pyroptosis-related genes between PD and HC groups presented as a box plot. Gene expression levels are plotted on the y-axis. The statistical significance between the groups for each gene is annotated above. Legend for statistical significance: ns (not significant, p > 0.05), * (p < 0.05), ** (p < 0.01)

Functional enrichment analysis

To analyze the relationship between DEGs in PD with various biological aspects, we performed functional enrichment analysis on these PD-related DEGs. The evidence indicated that the DEGs were engaged in specific processes in biology such as animal organ regeneration, vitamin transport, and erythrocyte differentiation regulation. Identified cellular components were mainly microtubule-associated complexes, microtubules, and kinesin complexes. Molecular functions were characterized by activities like microtubule motor function, minus-end-directed microtubule motor action, and cytoskeletal motor activity (Fig. 3a, Table 1). KEGG analysis provided evidence that the DEGs were engaged in the IL-17 signaling pathway and antifolate resistance, ABC transporters, rheumatoid arthritis, TGF-beta signaling pathway, apoptosis-multiple species, and legionellosis (Fig. 3b, Table 2). To validate these findings, GSEA was conducted using both Hallmark and KEGG gene sets (Fig. 3c, d). The results indicate that the Hallmark gene collection associated with PD was mainly enriched in, hedgehog signaling, INF-α and INF-γ response pathways. In general, we found a high correlation between DEGs related to PD and immunity. The KEGG gene collection was mainly enriched in olfactory transduction, taste transduction, phenylalanine metabolism, and starch and sucrose metabolism pathways (Fig. 3c, d).

Fig. 3.

Enrichment analysis of differentially differentiated genes in GSE133347 data sets. a Gene ontology enrichment histogram illustrating the major cellular processes and molecular functions impacted. The x-axis denotes the number of genes associated with each pathway, while the y-axis lists specific pathway names. The length of each column signifies the count of genes enriched in the corresponding pathway, and the shade of red indicates the significance of the enrichment, with darker shades representing lower P values. (BP, Biological Process.CC, Cellular Component. MF, Molecular Function.) b KEGG enrichment dot plot presents pathways influenced by the differentially expressed genes. The position of each dot along the x-axis corresponds to the gene ratio in each pathway, while the y-axis enumerates the pathways. The size of the dots reflects the number of genes associated with each pathway, and the color signifies the significance, with darker hues indicating lower P values. c GSEA for hallmark gene sets showcases the enrichment score across the ranked data set. The enriched gene sets are color-coded based on their P value. d GSEA for KEGG gene sets, again visualizing the enrichment score across the data set. Enriched pathways are differentiated by color according to their associated P values

Table 1.

GO enrichment analysis results

ONTOLOGY	ID	Description	p value
BP	GO:0031100	Animal organ regeneration	0.000197
BP	GO:0051180	Vitamin transport	0.000345
BP	GO:0045646	Regulation of erythrocyte differentiation	0.000439
CC	GO:0005875	Microtubule-associated complex	0.000049
CC	GO:0005874	Microtubule	0.000065
CC	GO:0005871	Kinesin complex	0.004366
MF	GO:0003777	Microtubule motor activity	0.000121
MF	GO:0008569	Minus-end-directed microtubule motor activity	0.000527
MF	GO:0003774	Cytoskeletal motor activity	0.000857

Table 2.

KEGG enrichment analysis results

ID	Description	p value
hsa01523	Antifolate resistance	0.000566
hsa04657	IL-17 signaling pathway	0.006047
hsa02010	ABC transporters	0.019918
hsa05323	Rheumatoid arthritis	0.020731
hsa04350	TGF-beta signaling pathway	0.021732
hsa04215	Apoptosis-multiple species	0.037209
hsa05134	Legionellosis	0.042704

Screening of pyroptosis-associated lncRNAs for diagnostic markers

Given pyroptosis's significant role in PD pathology, we identified lncRNAs associated with pyroptosis, potentially serving as diagnostic markers for PD. We defined lncRNAs by applying a correlation coefficient cutoff exceeding 0.2 and a significance threshold with a P value below 0.05 as pyroptosis-associated, identifying 590 such lncRNAs related to PD. Employing a random forest approach, we narrowed down our focus and identified 129 pyroptosis-related lncRNAs with the potential for PD diagnosis (Fig. 4a, b). We used LASSO regression and obtained two pyroptosis-related lncRNAs that can be used as classifiers for PD (Fig. 4c, d).

Fig. 4.

Identification of key lncRNAs associated with pyroptosis using Random Forest and LASSO regression approaches. a Random forest error rate plotted against the number of trees, highlighting the robustness and stability of the model in predicting pyroptosis-related lncRNAs in PD in GSE133347. b Importance plot showing the most influential lncRNAs, ranked based on their contribution to the model. The x-axis represents the Inchoate Purity metric, suggesting the impact of each lncRNA in the random forest model. c LASSO coefficient profiles of the lncRNAs. Each curve corresponds to a lncRNA, and the optimal value of the regularization parameter (λ) is indicated by the dotted vertical lines, suggesting a robust selection of lncRNAs associated with pyroptosis. d Pathway of coefficient progression in the LASSO model. As the log(λ) value decreases, more lncRNAs become significant predictors, underlining their potential role in pyroptosis processes

Then, we applied WGCNA to recognize the PD-related lncRNA modules (Fig. 5a–d). Based on the WGCNA analysis, a total of 20 significant co-expression modules were created. Module–trait correlation analysis demonstrated that among these various modules associated with PD, one module (magenta, Fig. 5c) emerged as the most important one, showing t the most significant correlation with PD (0.8) and P = 0.005; the module included a total of 93 lncRNAs. Module conservatism analysis showed that the magenta module had a certain degree of conservatism (Fig. 5e, f). Integrating the results from the three analytical methods, we identified two key lncRNAs: LINC01606 and LINC00269. The results are displayed using the Venn diagram (Fig. 6a). Subsequently, we analyzed the correlation between these two lncRNAs and genes associated with pyroptosis. As illustrated in the network diagram (Fig. 6b), LINC01606 exhibits a notable negative correlation with GSDMB and GSDMA, while showing a notable positive relationship with MEFV. LINC00269 exhibits a notable negative relationship with GSDMD, but positively correlated with CASP9, AIM2, and PYCARD. We investigated the expression levels of LINC01606 and LINC00269 in PD patients as opposed to normal controls using box plots and then found that the abundance of LINC00269 was significantly low and that of LINC01606 was significantly high in PD (Fig. 6c). The receiver operating characteristic (ROC) curve shows that PD samples can be well distinguished from normal controls based on the expression of LINC01606 (Fig. 6d) and LINC00269 (Fig. 6e). We thus conclude that LINC01606 and LINC00269 could serve as promising new biomarkers for diagnosing.

Fig. 5.

The data set was used to GSE133347 construct the WGCNA module. a Analysis of scale-free topology model fit (y-axis) for various soft-thresholding powers (x-axis). The left inset showcases a detailed view of the fit indices for different powers. b Mean network connectivity (y-axis) plotted against varying soft-thresholding powers (x-axis). The inset provides a closer view of the average connectivity for different powers. c Heatmap portraying the relationships between consensus modules and traits. Each module is represented by a unique color, and the correlation strength is color-coded based on the scale. d Dendrogram illustrating the hierarchical clustering of differentially expressed lncRNAs associated with PD. The color row beneath the dendrogram specifies the module assignment determined by the dynamic tree cut. e Scatter plot representing the median rank-based module preservation (y-axis) versus module size (x-axis). Each dot, colored according to the module it represents, offers insights into the preservation and prominence of the module across data sets. f Module preservation Z-summary statistics (y-axis) plotted against the module size (x-axis). Each colored dot represents a different module, with positioning giving an indication of module preservation across different data sets

Fig. 6.

Evaluation of LINC01606 & LINC00269 in PD diagnostics. a Venn diagram illustrating the overlap in gene identification among three analysis methods: WGCNA, RF, and LASSO. b Interaction network showcasing the associations of genes LINC01606 & LINC00269 with pyroptosis-related genes. The thickness of the lines indicates the strength of correlations, with color coding indicating positive (red) and negative (blue) correlations. c Box plot depicting differential expression levels of LINC01606 and LINC00269 in PD patients compared to healthy controls (HC) in the data set GSE133347. The individual data points represent outliers in the expression distribution. d Receiver operating characteristic (ROC) curve for LINC01606, highlighting its diagnostic potential for PD with an area under the curve (AUC) value of 0.92 in the data set GSE133347. e ROC curve for LINC00269, demonstrating its perfect diagnostic capability for PD with an AUC value of 1 in the data set GSE133347

Immune infiltration analysis of PD

To assess immune infiltration differences in PD and normal tissues, we utilized the ssGSEA algorithm for 28 immune cell types and CIBERSORTx algorithm for 22 immune cells. The ssGSEA analysis, via the Wilcoxon test, revealed five immune cell types with significant expression differences between PD and normal groups: activated CD4 T cells, NK cells, eosinophils, and neutrophils (Fig. 7a). Employing the CIBERSORTx algorithm identified four immune cell types significantly differing between PD and normal tissues: M0-type macrophages, CD8 T cells, plasma cells, and γδ T cells (Fig. 7b).

Fig. 7.

Analysis and visualization of immune cell infiltration in GSE133347 data sets. a Analysis of immune cell infiltration differences between PD and HC samples using the ssGSEA algorithm. Each dot represents individual samples, showcasing varying infiltration levels across different immune cell types. b Comparative examination of immune cell distribution based on the CIBERSORTx algorithm, highlighting distinct patterns between PD and HC. c Heatmap illustrating correlation patterns among 28 immune cell subtypes as determined by the ssGSEA algorithm. Red indicates positive correlations, while blue denotes negative correlations, with the intensity of the color indicating the strength of the association. d Heatmap showing the correlation among 22 immune cell categories using the CIBERSORTx algorithm. Interactions between different immune cells are represented through color gradients, with deeper shades indicating stronger correlations

Correlation heat maps of immune cells indicated significant positive a correlation between monocytes and memory CD8 T cells, an inverse correlation between eosinophils and mast cells, and strong correlations among various immune cell infiltration levels (Fig. 7c, d).

Correlation analysis of key biomarkers with infiltrative immune cells

We examined the connection between the expression levels of LINC00269 and LINC01606 with those of 22 distinct immune cell types. Our findings indicated that LINC00269 was positively associated with M2-type macrophages, neutrophils, and γδ T cells, while it exhibited a negative association with CD4 T memory resting cells (Fig. 8a). In contrast, LINC01606 was positively associated with M0 macrophages, while it demonstrated a negative correlation with both γδ T cells and CD8 T cells (Fig. 8b). Hence, these two biomarkers, instrumental in PD diagnosis, may also serve as indicators of immune infiltration in PD.

Fig. 8.

Correlation between lncRNA expression and immune cell infiltration. a Analysis of the relationship between the expression of LINC00269 and the infiltration levels of 22 distinct immune cell subtypes. Each dot signifies a specific immune cell type, and its position on the x-axis represents the correlation strength. The color and size of the dots indicate the correlation's direction and magnitude, respectively. b Examination of the correlation between LINC01606 expression and the infiltration patterns of the 22 immune cell categories. The positioning and attributes of the dots provide insights into the nature and strength of the association between the LncRNA expression and immune cell infiltration

Identification of target genes for LINC00269 and LINC01606

Utilizing the SumEnergy criterion in the Rtools database, we identified mRNAs potentially binding to LINC00269 and LINC01606, selecting the top 500. The results are displayed in Supplementary Material 1. 350 mRNAs could be combined with both LINC00269 and LINC01606 (Fig. 9a). We employed a different approach to analyze the data of GSE110716. We obtained 84 differential genes with P < 0.05 and |logFC|> 0.5 (Fig. 9b). DEGs were intersected with genes that could bind to LINC00269 and LINC01606, and four genes were identified: ONECUT2, PRLR, CTNNA3, and LRP2 (Fig. 9c, d). The ROC curves indicate that the expression of these four genes effectively distinguishes PD from normal controls, suggesting a regulatory role of LINC00269 and LINC01606 in PD through their interaction with these genes (Fig. 9e–h).

Fig. 9.

Exploration of target genes associated with LINC00269 & LINC01606. a Venn diagram illustrating the overlap of mRNAs associated with LINC00269 and LINC01606. The overlapping region represents common mRNAs interacting with both lncRNAs. b Volcano plot from GSE110716 data set, showcasing differentially expressed genes. Red dots indicate up-regulated genes, while blue dots denote down-regulated genes. Key genes of interest are highlighted. c Venn diagram capturing the convergence of genes that bind to the key biomarkers and those that are differentially expressed in the GSE110716 data set. d Box plots delineating the expression patterns of ONECUT2, PRLR, CTNNA3, and LRP2 between healthy controls (HC) and Parkinson's Disease (PD) patients. Asterisks denote levels of statistical significance. e–h, Receiver operating characteristic (ROC) curves for ONECUT2, PRLR, CTNNA3, and LRP2. The area under curve (AUC) values offer insights into the potential diagnostic utility of these genes

PCR analysis in PD mouse model

C57BL/6 mice received intraperitoneal injections of MPTP to establish a PD animal model. Substantia nigra-containing tissue blocks were harvested, followed by qPCR analysis of the collected brain tissues. The analysis showed that LINC00269 was markedly reduced in the PD relative to the normal controls (P = 0.016) (Fig. 10a). Meanwhile, LINC01606 expression in PD mouse model exhibited an increasing trend, but there was a lack of statistical significance (P = 0.69) (Fig. 10b).

Fig. 10.

Differential expression of lncRNAs in control vs Parkinson's Disease model mice. a Relative expression of lncRNA LINC00269 in control mice and those treated to induce a Parkinson's disease model. b Relative expression of lncRNA LINC01606 in the paired groups. The methodology employed was real-time qPCR. Error bars denote standard deviation. Distinct p values highlight the statistical disparity between the control and the PD groups

Discussion

As global aging intensifies, Parkinson's disease (PD) has become an increasingly severe public health issue, with mortality rates rising year by year [35]. Therefore, in-depth research into the pathogenesis of Parkinson's disease is particularly urgent. Recent studies have shown that long non-coding RNAs (lncRNAs) play an important role in the pathogenesis of Parkinson's disease and are closely related to pyroptosis in disease progression[36]. Pyroptosis, a distinct form of programmed cell death, has been confirmed to play a significant role in various neurodegenerative diseases, and its role in Parkinson's disease is receiving growing attention[37]. Previous research has revealed a complex relationship between lncRNAs and pyroptosis in various chronic diseases, such as cancer [38–40]. Increasing evidence now supports that lncRNAs participate in the progression of Parkinson's disease by regulating pyroptosis-related signaling pathways. Specifically, lncRNAs may influence the expression of pyroptosis-associated proteins (such as GSDMD and GSDME), regulate the activation of microglia and astrocytes, and thereby trigger neuroinflammation and neuronal death [41]. In particular, the loss of dopaminergic neurons is a key factor in causing motor dysfunction in Parkinson's disease, and pyroptosis may play an important role in this process. Existing studies suggest that pyroptosis may exacerbate local neuroinflammatory responses, leading to neuronal death, particularly in the damage to dopaminergic neurons [42]. Therefore, investigating the interactions between lncRNAs and pyroptosis in Parkinson's disease will not only help to elucidate the disease's pathological mechanisms but also provide potential new targets for future diagnostic and therapeutic strategies.

In this study, we analyzed mRNA and lncRNA differences in two GEO data sets using limma software package and identified differences in lncRNA and mRNA expression in PD samples. In addition, we observed differential expressions of pyroptosis-related genes in PD, with higher CASP1 and GSDME, alongside decreased expression of CASP9 and AIM2. This pattern suggests that both cell pyroptosis and inflammatory processes contribute to the development of PD [43].

DEGs enrichment analysis, GO enrichment analysis of molecular functions such as microfilament microtubule movement, and KEGG analysis showed that DEGs in PD were particularly relevant to immunity. Through previous studies [19, 20], we confirmed the pathogenesis of PD immune response. Two lncRNAs associated with pyroptosis were analyzed and further studied: LINC01606 and LINC00269. LINC01606 was inversely correlated with GSDMB and GSDMA and positively correlated with MEFV. On the other hand, LINC00269 showed a negative relationship with GSDMD whereas it was positively related to CASP9, AIM2, and PYCARD. These related genes can mediate inflammation or are involved in the process of apoptosis. Our analysis revealed a notable reduction in LINC00269 expression and a notable rise in LINC01606 expression in PD samples compared to normal samples. Interestingly, oncology studies have demonstrated that LINC01606 is elevated in colon cancer and is an indicator of poor prognosis [43]. We subsequently verified these results using qPCR, based on our findings, we propose LINC00269 and LINC01606 as novel biomarkers for PD diagnosis [44–46].

CD8 and CD4 T cell infiltration has been confirmed by studies [46–49]. Our analysis comparing immune infiltration between PD and control groups showed a substantial positive association between monocytes and effector memory CD8 T cells, which exhibited a strong inverse correlation in eosinophils and mast cells, and a strong correlation between cell types. Therefore, we conclude that LINC01606 and LINC00269 are closely related to cell pyroptosis genes. The strong link of LINC01606 and LINC00269 with immune infiltration indirectly confirms that pyroptosis-associated lncRNAs are strongly associated with the aggressive progression of PD [16, 43]. Moreover, we identified mRNAs that may bind LINC00269 and LINC01606 to obtain ONECUT2, PRLR, CTNNA3, and LRP2. These genes have been associated with cell differentiation, cell-to-cell adhesion, intercellular signaling, ligand reuptake, and endocrine and paracrine. Such as repressing genes ONECUT2, which can suppress lateral differentiation pathways in the midbrain floor plate. The midbrain floor plate during the embryonic stage contains the grandmother cells of neurons, which mainly produce dopamine [50]. The CTNNA3 is essential for the mediation of cell–cell adhesion in muscle cells and has been implicated in neurodegenerative diseases according to existing literature [51]. In addition, increased expression of LRP2 has been observed in nigral DA neurons [52, 53]. By binding these four genes, LINC00269 and LINC01606 may modulate the expression of upstream genes and contribute to immune regulation in PD.

Research on LINC01606 has focused on gastrointestinal tumors, such as colon and gastric cancer, and its elevated levels lead to disease exacerbation and indicate a poor prognosis [43]. The bioinformatics analysis in this study suggests that the expression of LINC01606 in patients with PD is also elevated. Therefore, LINC01606 could be utilized as a biomarker to assist in clinical diagnosis. A study of LINC00269 in relation to osteosarcoma has noted that the expression of LINC00269 within the experimental group was potentially elevated in comparison with the control group and that result was associated with the autophagy phenotype [54]. It has also been pointed out that LINC00269 belongs to class II genes with extensive polymorphisms. Class II gene expression products are predominantly found on the exterior of specialized antigen-presenting cells and active T cells; thus, LINC00269 is linked to the immune response associated with the disease and indirectly involved in pyroptosis [55]. These findings are further corroborated by PCR analysis conducted in PD patients and within the substantia nigra of a rodent model for the disease. LINC00269 can establish a competitive endogenous RNA network through interactions with various miRNAs to regulate the occurrence and progression of disease, and the overexpression of LINC00269 impairs migration and invasion in animal models of ovarian cancer[56]. Therefore, it appears that alterations in class II genes may modify the course of the disease.

We expect that additional data will support our results in the future. In this paper, PCR detection was performed on samples of substantia nigra of mouse midbrain with PD to verify changes in LINC00269 levels. The regulatory relationship of upstream pyroptosis-related genes still needs to be further demonstrated in future work, and the changes in protein levels related to the immune and pyroptosis phenotypes and their molecular mechanisms require clarification. To this end, an increasing number of targeted biomarkers to assist in PD diagnosis at the gene, cellular, and molecular levels are screened.

Although this study has been carefully designed and accurately analyzed, it still has a few shortcomings. First, there are few sequencing databases of PD, and repeated verification between databases cannot be achieved. Second, the sample size is relatively small, because, due to the epidemic impact, a return to face-to-face visits has been limited to varying degrees.

Conclusions

In this study, we used bioinformatics methods to conduct an in-depth analysis of lncRNAs associated with PD, and the results showed that LINC00269 and LINC01606 are strongly linked to the mechanisms underlying PD. Meanwhile, we plan to explore further the relevant upstream and downstream regulatory mechanisms to reveal their specific role in diseases. This research approach is anticipated to offer new strategies and tools for diagnosis and intervention of PD.

Author contributions

All authors have made substantial, direct, and intellectual contributions to the work and approved it for publication. LLM was responsible for all of the design of the work and Visualization. Y L was contributed to the part of acquisition and analysis of data. YJR and NM were contributed to the part of interpretation of data and statistical analyses in the work. J F was contributed to the project administration. R B was contributed to the writing—original draft preparation. XZ X was contributed to the Translation of articles. YT was responsible for review and funding acquisition of the work.

Funding

The work was supported by the National Natural Science Foundation of China (No. 81771508), the Department of Education, Heilongjiang Province (No. 12521291), and the Natural Science Foundation of Heilongjiang Province (No. D201235).

Availability of data and materials

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

The study was carried out in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University (humans protocol code: IRB-AF/SC-04/02.0 and date of approval: August 15, 2022; animals protocol code: 2022065 and date of approval: July 21, 2022).

Competing interests

The authors declare no competing interests.