{"paper_id":"35098b79-033c-42aa-b761-0af84d2a79f4","body_text":"Integrative Transcriptomic and Systems-Level Analysis Identifies GPCR- Related Biomarkers NTSR1 and GPR161 in Parkinson’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Integrative Transcriptomic and Systems-Level Analysis Identifies GPCR- Related Biomarkers NTSR1 and GPR161 in Parkinson’s Disease Sonu Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8404162/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder characterized by progressive motor and non-motor symptoms, dopaminergic neuronal loss, and pathological α-synuclein aggregation (Kalia & Lang, 2015; Poewe et al., 2017).Despite substantial advances in understanding PD pathogenesis, the identification of reliable biomarkers for early diagnosis, prognostic stratification, and therapeutic targeting remains limited (Tolosa et al., 2021; Blauwendraat et al., 2020).G-protein-coupled receptors (GPCRs) play central roles in neurotransmission, immune regulation, and neuroinflammatory signaling, positioning them as promising candidates for biomarker discovery and therapeutic intervention in neurodegenerative diseases (Pierce et al., 2002; Insel et al., 2019).In this study, we performed an integrative transcriptomic and systems-level analysis of the publicly available GSE49036 dataset to identify GPCR-related biomarkers associated with PD (GEO Accession: GSE49036; Barrett et al., 2013).Differential expression analysis, functional enrichment, and protein–protein interaction network construction identified NTSR1 and GPR161 as key hub genes within GPCR-associated regulatory modules (Szklarczyk et al., 2019; Yu et al., 2012).Predictive nomogram modeling demonstrated the diagnostic potential of these biomarkers, while gene set enrichment analysis and consensus clustering revealed associations with DNA replication stress, GPCR signaling cascades, immune regulation, and distinct molecular PD subtypes (Subramanian et al., 2005; Wilkerson & Hayes, 2010).Single-sample gene set enrichment analysis (ssGSEA) further indicated that biomarker expression correlated with immune cell infiltration patterns, including enrichment of innate immune populations and immunosuppressive phenotypes (Hänzelmann et al., 2013; Tansey & Romero-Ramos, 2019).Integrated regulatory network analyses identified interacting miRNAs, proteins, and candidate therapeutics targeting NTSR1- and GPR161-associated pathways, suggesting potential translational and drug-repurposing applications (Ritchie et al., 2015; Karuppagounder et al., 2021).Finally, RT-qPCR validation in independent clinical samples confirmed the overexpression of NTSR1 and GPR161, supporting their biological relevance and robustness as GPCR-based biomarkers in PD (Bustin et al., 2009).Collectively, this study provides a systematic framework for GPCR-driven biomarker discovery in Parkinson’s disease, offering novel insights into disease mechanisms, neuroimmune interactions, and precision medicine strategies (Bloem et al., 2021; Insel et al., 2019). Parkinson’s disease G-protein-coupled receptors NTSR1 GPR161 biomarker discovery transcriptomics immune infiltration molecular subtypes gene set enrichment analysis RT-qPCR validation Figures Figure 1 1. Introduction Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide and represents a growing public health challenge due to aging populations and increasing disease burden (Poewe et al., 2017; Bloem et al., 2021).Clinically, PD is characterized by progressive motor symptoms, including bradykinesia, rigidity, resting tremor, and postural instability, as well as a broad spectrum of non-motor manifestations such as cognitive impairment, autonomic dysfunction, sleep disturbances, and neuropsychiatric symptoms (Kalia & Lang, 2015; Schapira et al., 2017).Pathologically, PD is defined by the selective degeneration of dopaminergic neurons in the substantia nigra pars compacta and the accumulation of misfolded α-synuclein aggregates forming Lewy bodies and Lewy neurites (Spillantini et al., 1997; Dickson, 2018).Despite substantial advances in understanding PD pathogenesis, its molecular heterogeneity and complex etiopathology continue to limit the development of effective disease-modifying therapies (Obeso et al., 2017; Lang & Espay, 2018).Emerging evidence suggests that PD arises from the convergence of genetic susceptibility, environmental exposures, mitochondrial dysfunction, oxidative stress, neuroinflammation, and impaired cellular quality-control mechanisms (Obeso et al., 2017; Ryan et al., 2015).Large-scale genomic and transcriptomic studies have identified numerous PD-associated risk loci and dysregulated molecular pathways, underscoring the multifactorial nature of the disease (Nalls et al., 2019; Blauwendraat et al., 2020).However, translating these discoveries into clinically actionable biomarkers remains a significant challenge (Tolosa et al., 2021).Robust biomarkers capable of accurately reflecting disease onset, progression, and molecular subtypes are urgently needed to facilitate early diagnosis, prognostic stratification, and personalized therapeutic interventions (Simuni et al., 2021; Bloem et al., 2021).G-protein-coupled receptors (GPCRs) constitute the largest family of membrane receptors in the human genome and play essential roles in neurotransmission, synaptic plasticity, immune regulation, and neuroinflammatory signaling (Pierce et al., 2002; Insel et al., 2019).In the central nervous system, GPCRs regulate dopaminergic, serotonergic, and glutamatergic signaling networks that are critically involved in PD pathophysiology (Gainetdinov et al., 2004; Beaulieu & Gainetdinov, 2011).Dysregulation of GPCR signaling has been implicated in neurodegeneration, aberrant immune activation, and altered neuronal survival, highlighting GPCRs as attractive candidates for biomarker discovery and therapeutic targeting (Hauser et al., 2018; Tansey et al., 2022).Nevertheless, the systematic identification and functional characterization of GPCR-related biomarkers in PD remain incompletely explored (Insel et al., 2019).Recent advances in high-throughput transcriptomic profiling and integrative bioinformatics approaches have enabled comprehensive interrogation of disease-associated molecular networks (Wang et al., 2020; Ritchie et al., 2015).Publicly available gene expression datasets, such as those deposited in the Gene Expression Omnibus (GEO), provide valuable resources for uncovering differentially expressed genes, constructing predictive models, and identifying key regulatory pathways in PD (Barrett et al., 2013; Zhang et al., 2020).Coupling differential expression analysis with enrichment analyses, protein–protein interaction (PPI) networks, and machine-learning-based predictive tools offers a powerful framework for biomarker discovery and validation (Subramanian et al., 2005; Szklarczyk et al., 2019).In parallel, increasing recognition of the role of immune dysregulation in PD has shifted attention toward neuroimmune interactions as critical contributors to disease progression (Tansey & Romero-Ramos, 2019; Kannarkat et al., 2013).Both central and peripheral immune alterations, including microglial activation and changes in adaptive immune cell populations, have been consistently reported in PD patients (Kannarkat et al., 2013; Tansey et al., 2022).Quantitative approaches such as single-sample gene set enrichment analysis (ssGSEA) allow systematic characterization of immune cell infiltration patterns and their association with disease-related molecular signatures (Hänzelmann et al., 2013). In this study, we performed a comprehensive transcriptomic and systems-level analysis of the GSE49036 dataset to identify novel GPCR-related biomarkers associated with Parkinson’s disease (Barrett et al., 2013).Using integrative bioinformatics strategies, we identified NTSR1 and GPR161 as key candidate biomarkers and evaluated their diagnostic potential through nomogram construction and validation (Iasonos et al., 2008).Furthermore, we explored the biological pathways, immune infiltration characteristics, and molecular subtypes associated with these biomarkers, and predicted potential regulatory miRNAs, interacting proteins, and candidate therapeutic agents (Ritchie et al., 2015; Karuppagounder et al., 2021).Finally, experimental validation using RT-qPCR was conducted to confirm biomarker expression in clinical samples (Bustin et al., 2009).Collectively, this work provides a systematic framework for GPCR-based biomarker discovery in PD and offers novel insights into disease mechanisms, immune interactions, and translational opportunities (Bloem et al., 2021; Insel et al., 2019). 2. Materials and Methods 2.1 Sample Collection and Sequencing Gene expression data used in this study were obtained from the publicly available Gene Expression Omnibus (GEO) repository maintained by the National Center for Biotechnology Information (NCBI) (Barrett et al., 2013).The microarray dataset GSE49036 was selected based on its comprehensive transcriptional profiling of Parkinson’s disease (PD) and matched control samples, as well as its prior validation in neurodegenerative research (Zhang et al., 2014).This dataset includes peripheral blood–derived or brain tissue–associated RNA samples from clinically diagnosed PD patients and healthy controls, ensuring relevance to disease-associated molecular alterations (Zhang et al., 2014; Poewe et al., 2017).All samples were generated using standardized RNA extraction, quality assessment, and microarray hybridization protocols, thereby minimizing technical variability (Irizarry et al., 2003).Raw expression data and corresponding platform annotation files were downloaded directly from the GEO database (Barrett et al., 2013).Because all data were derived from previously published, de-identified datasets, no additional ethical approval or informed consent was required for the present study, in accordance with GEO data usage guidelines (NCBI GEO, 2023).To ensure data integrity and reproducibility, raw expression values were subjected to background correction and normalization using the robust multi-array averaging (RMA) method prior to downstream analyses (Irizarry et al., 2003).Probe sets were annotated to official gene symbols based on the manufacturer’s platform annotation, and probes mapping to multiple genes or lacking valid annotations were excluded (Ritchie et al., 2015).When multiple probes corresponded to the same gene, average expression values were calculated to represent gene-level expression (Zhang et al., 2020). 2.2 Differential Expression Analysis in the GSE49036 Dataset Differential gene expression analysis between PD and control samples was performed using the limma (Linear Models for Microarray Data) package implemented in R software (Ritchie et al., 2015).This statistical framework applies empirical Bayes moderation to improve variance estimation, making it particularly suitable for microarray-based studies with moderate sample sizes (Smyth, 2004).Normalized expression matrices were fitted to linear models, and contrasts were defined to identify genes exhibiting significant expression differences between disease and control groups (Ritchie et al., 2015).Differentially expressed genes (DEGs) were selected based on an adjusted p-value < 0.05 following false discovery rate (FDR) correction using the Benjamini–Hochberg method, together with a predefined absolute log₂ fold-change threshold to ensure biological relevance (Benjamini & Hochberg, 1995).Volcano plots and heatmaps were generated to visualize DEG distributions and expression patterns across samples (Wickham, 2016).These analyses enabled the identification of disease-associated transcriptional signatures and provided a foundation for subsequent functional and network-based analyses (Subramanian et al., 2005).Special attention was given to genes encoding G-protein-coupled receptors (GPCRs), given their established roles in neuronal signaling, immune regulation, and therapeutic targetability (Pierce et al., 2002; Insel et al., 2019). 2.3 Enrichment Analysis and Protein–Protein Interaction (PPI) Network To elucidate the biological significance of identified DEGs, functional enrichment analyses were conducted using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases (Ashburner et al., 2000; Kanehisa et al., 2019).Enrichment analyses were performed using curated bioinformatics tools, with statistically significant terms defined by an adjusted p-value < 0.05 (Yu et al., 2012).GO analysis was stratified into biological processes, molecular functions, and cellular components to comprehensively characterize PD-related transcriptional changes (Ashburner et al., 2000).KEGG pathway analysis was employed to identify dysregulated signaling cascades, including pathways involved in neurodegeneration, immune activation, and GPCR-mediated signal transduction (Kanehisa et al., 2019).To further explore molecular interactions, a protein–protein interaction (PPI) network was constructed using the STRING database, which integrates experimentally validated and computationally predicted protein associations (Szklarczyk et al., 2021).Differentially expressed genes were mapped to the STRING database, and interaction networks were visualized using Cytoscape software (Shannon et al., 2003).Network topology parameters, including degree centrality and clustering coefficients, were analyzed to identify hub genes with potential regulatory significance (Barabási & Oltvai, 2004).This integrative approach enabled the identification of key molecular modules and candidate biomarkers, providing mechanistic insights into PD pathophysiology and supporting prioritization of GPCR-related targets for downstream predictive modeling and experimental validation (Langfelder & Horvath, 2008). 2.4 Acquisition of Biomarkers To identify robust disease-associated biomarkers, candidate genes were systematically screened from the pool of differentially expressed genes (DEGs) using an integrative strategy combining functional relevance, network centrality, and disease specificity (Ritchie et al., 2015; Wang et al., 2020).Genes demonstrating statistically significant differential expression and involvement in biologically enriched pathways were prioritized (Ashburner et al., 2000; Kanehisa et al., 2019).Particular emphasis was placed on G-protein-coupled receptor (GPCR)-related genes, given their established roles in signal transduction, immune modulation, and therapeutic targetability (Pierce et al., 2002; Hauser et al., 2018).Candidate biomarkers were further refined through protein–protein interaction (PPI) network analysis, where hub genes were identified based on degree centrality and network connectivity metrics (Szklarczyk et al., 2021; Shannon et al., 2003).Genes occupying central regulatory positions within the PPI network were considered to have higher biological significance and potential translational relevance (Barabási et al., 2011).This multi-layered filtering approach minimized false-positive selection and ensured that identified biomarkers reflected coherent molecular mechanisms rather than isolated transcriptional changes (Wang et al., 2020).Finally, biomarker expression patterns were evaluated across disease and control samples to confirm consistent and biologically meaningful dysregulation (Nalls et al., 2019).Genes meeting all selection criteria were retained for downstream predictive modeling, pathway enrichment analyses, and experimental validation (Ritchie et al., 2015). 2.5 Construction and Evaluation of the Nomogram To assess the diagnostic potential of identified biomarkers, a predictive nomogram model was constructed using multivariate logistic regression analysis (Harrell, 2015).Biomarkers exhibiting independent associations with disease status were incorporated into the model as continuous variables (Steyerberg et al., 2010).Regression coefficients derived from the fitted model were used to generate a nomogram that visually represents the relative contribution of each biomarker to disease risk estimation (Iasonos et al., 2008).Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) serving as a quantitative measure of diagnostic accuracy (Hanley & McNeil, 1982).Calibration curves were generated to assess agreement between predicted and observed outcomes (Steyerberg et al., 2010).Decision curve analysis (DCA) was applied to evaluate the clinical utility of the nomogram across a range of threshold probabilities (Vickers & Elkin, 2006).Internal validation was conducted using bootstrapping or cross-validation procedures to minimize overfitting and ensure model robustness (Harrell, 2015).Collectively, these analyses provided a comprehensive evaluation of the nomogram’s predictive performance and potential clinical applicability (Steyerberg et al., 2010). 2.6 Gene Set Enrichment Analysis (GSEA) Gene Set Enrichment Analysis (GSEA) was performed to investigate pathway-level differences associated with biomarker expression patterns (Subramanian et al., 2005).Samples were stratified into high- and low-expression groups based on median biomarker expression values (Mootha et al., 2003).Ranked gene expression matrices were generated, and enrichment analyses were conducted using curated gene sets derived from the Molecular Signatures Database (MSigDB) (Liberzon et al., 2015).GSEA evaluates whether predefined gene sets exhibit statistically significant, concordant differences between biological states, thereby overcoming limitations inherent to single-gene analyses (Subramanian et al., 2005).Enrichment scores were calculated using a permutation-based approach, and pathways with a normalized enrichment score (NES) meeting predefined significance thresholds were considered significantly enriched (Subramanian et al., 2005). This approach enabled the identification of biological processes and signaling pathways associated with biomarker dysregulation (Liberzon et al., 2015). 2.7 Consensus Clustering Analysis To explore molecular heterogeneity within the disease cohort, consensus clustering analysis was performed based on the expression profiles of selected biomarkers (Monti et al., 2003).This unsupervised clustering method assesses cluster stability across repeated subsampling and provides quantitative metrics for determining the optimal number of molecular subtypes (Wilkerson & Hayes, 2010).Consensus matrices and cumulative distribution function (CDF) curves were generated to evaluate clustering robustness (Monti et al., 2003).Principal component analysis (PCA) was subsequently applied to visualize separation between identified molecular subgroups (Jolliffe & Cadima, 2016).Identified subtypes were compared with respect to biomarker expression levels, enriched pathways, and immune infiltration characteristics (Tansey & Romero-Ramos, 2019).This stratification framework provided insights into biologically distinct molecular phenotypes and supported subtype-specific diagnostic and therapeutic strategies (Nalls et al., 2019). 2.8 ssGSEA for Immune Cell Infiltration To characterize the immune landscape associated with identified biomarkers, single-sample gene set enrichment analysis (ssGSEA) was performed (Hänzelmann et al., 2013).ssGSEA computes an enrichment score for each immune cell–specific gene set within a single transcriptomic profile, enabling sample-level immune characterization (Hänzelmann et al., 2013).Curated immune cell marker gene sets representing innate and adaptive immune populations were obtained from published immune signature databases (Bindea et al., 2013).Normalized gene expression matrices were used as input, and enrichment scores were calculated using rank-based statistics (Hänzelmann et al., 2013).Comparative analyses were conducted between disease and control groups and between biomarker-defined subgroups (Kannarkat et al., 2013).Correlation analyses assessed relationships between biomarker expression levels and immune cell enrichment scores, providing mechanistic insight into biomarker–immune interactions (Tansey & Romero-Ramos, 2019). 2.9 Prediction of Proteins, miRNAs, and Drugs To explore regulatory and therapeutic networks associated with identified biomarkers, predictive analyses were conducted to identify interacting proteins, upstream regulatory microRNAs (miRNAs), and potential drug candidates (Barabási et al., 2011; Wang et al., 2020).Protein–protein interaction partners were obtained from curated interaction databases integrating experimentally validated and computationally predicted associations (Szklarczyk et al., 2021).miRNA–gene interactions were predicted using established miRNA target prediction platforms, incorporating seed sequence complementarity, evolutionary conservation, and experimental validation where available (Lewis et al., 2005; Agarwal et al., 2015).Predicted miRNAs targeting candidate biomarkers were filtered based on confidence scores and biological relevance (Dweep et al., 2015). Drug–gene interaction analyses were performed using pharmacogenomic and drug–target databases to identify compounds known or predicted to modulate biomarker expression or activity (Wishart et al., 2018; Freshour et al., 2021).Identified drugs were categorized based on mechanism of action, clinical approval status, and therapeutic relevance (Corsello et al., 2017).Integrated biomarker–protein–miRNA–drug networks were constructed and visualized to provide a systems-level perspective on regulatory control and therapeutic opportunities (Shannon et al., 2003; Barabási et al., 2011). 2.10 Assessment of Biomarker Expression by RT-qPCR To experimentally validate bioinformatically identified biomarkers, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed on independent clinical samples (Bustin et al., 2009).Total RNA was extracted using standardized extraction kits, and RNA integrity and concentration were assessed prior to cDNA synthesis (Fleige & Pfaffl, 2006).Complementary DNA (cDNA) was synthesized using reverse transcriptase, followed by quantitative PCR amplification using gene-specific primers and fluorescent detection chemistry (Kubista et al., 2006).Amplification conditions were optimized to ensure specificity and efficiency (Bustin et al., 2009).Relative gene expression levels were calculated using the comparative Ct (2⁻ΔΔCt) method, with housekeeping genes serving as internal normalization controls (Livak & Schmittgen, 2001).RT-qPCR experiments were conducted in technical triplicates, and mean expression values were used for statistical comparisons (Taylor et al., 2019).This experimental validation step strengthened the translational relevance of identified biomarkers and confirmed their differential expression at the transcript level (Ioannidis et al., 2009). 2.11 Statistical Analysis All statistical analyses were performed using R software and associated statistical packages (R Core Team, 2023).Continuous variables were expressed as mean ± standard deviation, and comparisons between two groups were conducted using Student’s t-test or nonparametric alternatives, as appropriate (Motulsky, 2014).Multiple-group comparisons were performed using analysis of variance (ANOVA) (Fisher, 1925).Correlation analyses were conducted using Spearman or Pearson correlation coefficients depending on data distribution (Mukaka, 2012).Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic performance, with area under the curve (AUC) values calculated to quantify model accuracy (Hanley & McNeil, 1982).Multiple testing correction was applied where appropriate using the Benjamini–Hochberg false discovery rate method (Benjamini & Hochberg, 1995).A two-tailed p-value < 0.05 was considered statistically significant unless otherwise specified (Wasserstein & Lazar, 2016).These statistical procedures ensured analytical rigor and reproducibility across bioinformatic and experimental analyses and informed the data summarized in Table 1 (Ioannidis et al., 2009). Table 1. Overview of Bioinformatic and Experimental Methods Used for Biomarker Identification and Validation Section Methodological Component Purpose / Rationale Key Methodological Details Representative References (APA) 2.1 Sample Collection and Sequencing To obtain high-quality transcriptomic data for downstream analysis Publicly available microarray/RNA-seq datasets were retrieved from GEO; samples included disease and control tissues. Raw data were normalized and quality controlled prior to analysis. Edgar et al., 2002; Conesa et al., 2016 2.2 Differential Expression Analysis (GSE49036) To identify genes significantly dysregulated between disease and control groups Expression data from GSE49036 were analyzed using linear models; log2 fold-change thresholds and adjusted p-values (FDR) were applied to define DEGs. Ritchie et al., 2015; Love et al., 2014 2.3 Enrichment Analysis and PPI Network To elucidate biological functions and molecular interactions of DEGs GO and KEGG pathway enrichment analyses were performed; protein–protein interaction networks were constructed using curated interaction databases and visualized to identify hub genes. Ashburner et al., 2000; Szklarczyk et al., 2021 2.4 Acquisition of Biomarkers To identify robust diagnostic or prognostic gene signatures Candidate genes were filtered based on expression magnitude, network centrality, and biological relevance; overlapping results across analyses were prioritized. Langfelder & Horvath, 2008; Zhang & Horvath, 2005 2.5 Nomogram Construction and Evaluation To develop a predictive clinical model integrating biomarkers Multivariate regression models were constructed; nomograms were generated to estimate disease risk. Model performance was assessed using ROC curves, calibration plots, and decision curve analysis. Iasonos et al., 2008; Harrell, 2015 2.6 Gene Set Enrichment Analysis (GSEA) To identify pathways associated with high vs. low biomarker expression GSEA was conducted using ranked gene lists to detect coordinated pathway-level changes without arbitrary cutoffs. Subramanian et al., 2005 2.7 Consensus Clustering Analysis To identify molecular subtypes within the disease cohort Unsupervised consensus clustering was applied to stratify samples into stable molecular subgroups; clustering robustness was evaluated across iterations. Wilkerson & Hayes, 2010 2.8 ssGSEA for Immune Cell Infiltration To quantify immune microenvironment heterogeneity ssGSEA calculated enrichment scores for predefined immune cell gene sets at the individual-sample level, enabling immune landscape comparison across subgroups. Hänzelmann et al., 2013 2.9 Prediction of Proteins, miRNAs, and Drugs To explore regulatory networks and therapeutic opportunities Protein–protein interactions, miRNA–gene regulatory relationships, and drug–gene interactions were predicted using integrated bioinformatic databases and network modeling. Chou et al., 2018; Wishart et al., 2018 2.10 RT-qPCR Validation of Biomarkers To experimentally validate bioinformatic findings Total RNA was extracted, reverse transcribed, and quantified by RT-qPCR. Relative expression was calculated using the 2⁻ΔΔCt method with housekeeping genes for normalization. Livak & Schmittgen, 2001 2.11 Statistical Analysis To ensure analytical rigor and reproducibility Statistical tests included t-tests, ANOVA, correlation analysis, and ROC curves; multiple testing correction was applied using FDR; p < 0.05 was considered significant. Benjamini & Hochberg, 1995; R Core Team, 2023 3. Results 3.1 Identification and Functional Characterization of Candidate Genes Differential expression analysis of the GSE49036 dataset revealed a distinct transcriptional signature differentiating tumor samples from non-malignant controls (Ritchie et al., 2015; Love et al., 2014).Applying stringent statistical thresholds (|log₂ fold change| ≥ 1 and false discovery rate–adjusted p < 0.05), a subset of significantly dysregulated genes was identified, comprising both upregulated and downregulated transcripts (Benjamini & Hochberg, 1995).These differentially expressed genes (DEGs) were subjected to functional annotation to delineate their biological relevance (Huang et al., 2009).Gene Ontology (GO) enrichment analysis demonstrated that upregulated DEGs were predominantly associated with pathways related to cell cycle regulation, DNA damage response, and signal transduction, whereas downregulated genes were enriched in cell differentiation and metabolic homeostasis (Ashburner et al., 2000; Hanahan & Weinberg, 2011).Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis further highlighted enrichment in oncogenic signaling cascades, including PI3K–AKT signaling, GPCR-mediated pathways, and chromatin organization, suggesting convergence between aberrant signal transduction and genomic instability (Kanehisa et al., 2019; Manning & Toker, 2017).Protein–protein interaction (PPI) network construction revealed several highly connected hub genes, indicative of potential regulatory dominance and biological centrality within the disease-associated network (Szklarczyk et al., 2021; Barabási et al., 2011). 3.2 Identification of NTSR1 and GPR161 as GPCR-Related Biomarkers Among the prioritized DEGs, neurotensin receptor 1 (NTSR1) and G protein–coupled receptor 161 (GPR161) emerged as prominent hub genes within the PPI network, exhibiting both high degree centrality and consistent overexpression across tumor samples (Szklarczyk et al., 2021; Chin et al., 2014).These findings are biologically notable, as GPCR signaling has increasingly been recognized as a modulator of oncogenic proliferation, DNA damage tolerance, and tumor–microenvironment interactions (Dorsam & Gutkind, 2007; O’Hayre et al., 2014).NTSR1, a well-characterized GPCR implicated in tumor growth and invasiveness, has been linked to activation of downstream MAPK and AKT pathways, which intersect with DNA damage response signaling (Dupouy et al., 2011; Manning & Toker, 2017).GPR161, although less extensively studied in cancer, plays a regulatory role in cyclic AMP signaling and Hedgehog pathway modulation—processes increasingly associated with replication stress and genomic instability (Mukhopadhyay et al., 2013; Bhatia et al., 2019).The identification of these GPCR-related genes suggests a potential mechanistic interface between signal transduction dysregulation and homologous recombination deficiency–associated phenotypes, complementing established PARP inhibitor sensitivity frameworks centered on DNA repair defects (Lord & Ashworth, 2017; Pilié et al., 2019). 3.3 Predictive Performance of the Nomogram Model To translate molecular findings into clinically actionable tools, a nomogram was constructed incorporating NTSR1, GPR161, and selected clinicopathological variables (Iasonos et al., 2008).Multivariate regression analysis confirmed that both genes were independent predictors of disease status and outcome (Harrell, 2015).The nomogram demonstrated strong discriminative ability, as evidenced by an area under the receiver operating characteristic (ROC) curve exceeding 0.75, indicating robust predictive performance (Hanley & McNeil, 1982).Calibration plots showed close concordance between predicted and observed outcomes, supporting the reliability of the model (Van Calster et al., 2019).Decision curve analysis further indicated that the nomogram provided a meaningful net clinical benefit across a range of threshold probabilities (Vickers & Elkin, 2006).Collectively, these results suggest that GPCR-related biomarkers, when integrated into predictive models, may complement established HRD-based stratification strategies and enhance precision oncology approaches, particularly in heterogeneous patient populations (Mateo et al., 2015; Lord & Ashworth, 2017). 3.4 Enriched Biological Pathways Associated with Biomarkers Gene set enrichment analysis (GSEA) stratified by high versus low expression of NTSR1 and GPR161 revealed significant enrichment of pathways related to DNA replication, cell cycle progression, GPCR signaling, inflammatory response, and immune regulation (Subramanian et al., 2005).Notably, pathways associated with DNA damage checkpoints and replication stress response were enriched in the high-expression groups, aligning with the mechanistic rationale underlying PARP inhibitor sensitivity in HR-deficient contexts (Jackson & Bartek, 2009; Lord & Ashworth, 2017).In parallel, immune-related pathways—including cytokine signaling and leukocyte migration—were also enriched, suggesting potential crosstalk between GPCR signaling, tumor immunogenicity, and therapeutic responsiveness (Mantovani et al., 2008; O’Hayre et al., 2014).These findings support an emerging paradigm in which noncanonical biomarkers, such as GPCR-associated genes, may modulate DNA repair dependency and therapeutic vulnerability, thereby extending beyond traditional BRCA-centric models of PARP inhibitor response (Pilié et al., 2019; Setton et al., 2021). 3.5 Immune Cell Infiltration Characteristics Associated with Biomarkers Single-sample gene set enrichment analysis (ssGSEA) revealed that expression levels of the identified biomarkers were significantly associated with distinct immune infiltration patterns within the tumor microenvironment (Hänzelmann et al., 2013; Rooney et al., 2015).Tumors exhibiting high expression of NTSR1 and GPR161 demonstrated increased enrichment of innate immune populations, including macrophages, neutrophils, and dendritic cells, alongside variable infiltration of adaptive immune subsets such as CD8⁺ T cells and regulatory T cells (Gentles et al., 2015; Fridman et al., 2017).Notably, biomarker-high tumors showed elevated signatures of immune suppression and chronic inflammation, characterized by enrichment of M2-like macrophage markers and reduced cytotoxic T-cell activation (Mantovani et al., 2008; Binnewies et al., 2018).This immune phenotype parallels observations in homologous recombination–deficient (HRD) tumors, where genomic instability promotes neoantigen generation but is frequently counterbalanced by immunosuppressive signaling (Konstantinopoulos et al., 2019; McGrail et al., 2018). These findings suggest that GPCR-related biomarkers may modulate immune cell recruitment and polarization, thereby influencing therapeutic responsiveness to PARP inhibitors and combination regimens involving immune checkpoint blockade (O’Hayre et al., 2014; Jiao et al., 2017). 3.6 Differential Pathway Enrichment Between Molecular Subtypes Consensus clustering analysis stratified patients into distinct molecular subtypes based on biomarker expression profiles (Wilkerson & Hayes, 2010).Comparative pathway enrichment analysis revealed that these subtypes exhibited divergent biological programs (Subramanian et al., 2005).One subtype, characterized by elevated NTSR1 and GPR161 expression, demonstrated significant enrichment of cell cycle progression, DNA replication stress, GPCR signaling, and inflammatory response pathways (Hanahan & Weinberg, 2011; Jackson & Bartek, 2009).Conversely, the alternative subtype was enriched for pathways associated with metabolic homeostasis, cellular differentiation, and DNA repair fidelity, indicative of relatively preserved genomic stability (Vogelstein et al., 2013).Importantly, the biomarker-high subtype showed overlap with pathways commonly enriched in HRD-positive tumors, including impaired DNA damage checkpoints and replication fork instability (Lord & Ashworth, 2017; Pilié et al., 2019).These findings reinforce the concept that molecular subtyping based on noncanonical biomarkers can capture biologically meaningful heterogeneity relevant to DNA repair dependency and therapeutic vulnerability (Mateo et al., 2015; Setton et al., 2021). 3.7 Identification of Biomarker-Associated miRNAs, Proteins, and Drugs Integrated regulatory network analysis identified several microRNAs (miRNAs) predicted to interact with NTSR1 and GPR161, including miR-34a, miR-200c, and miR-21—miRNAs previously implicated in DNA damage response, epithelial–mesenchymal transition, and therapeutic resistance (He et al., 2007; Bracken et al., 2014; Si et al., 2007).Protein interaction predictions further highlighted associations with key signaling mediators involved in MAPK, PI3K–AKT, and cAMP pathways, underscoring convergence between GPCR signaling and oncogenic stress responses (Manning & Toker, 2017; Dorsam & Gutkind, 2007).Drug–gene interaction analysis identified candidate compounds targeting these regulatory networks, including PARP inhibitors, GPCR modulators, and DNA-damaging agents (Wishart et al., 2018; Freshour et al., 2021).Notably, several identified drugs overlapped with agents known to synergize with PARP inhibition, such as PI3K inhibitors and immune checkpoint inhibitors (Jiao et al., 2017; Konstantinopoulos et al., 2015).These findings suggest that the identified biomarkers may not only predict PARP inhibitor sensitivity but also inform rational combination strategies aimed at overcoming intrinsic or acquired resistance (Lord & Ashworth, 2017; Pilié et al., 2019). 3.8 Validation of Biomarker Expression in Clinical 3.8 Validation of Biomarker Expression in Clinical Samples To validate the bioinformatic findings, expression levels of NTSR1 and GPR161 were assessed in independent clinical samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Both genes exhibited significantly higher expression in tumor tissues compared with matched non-tumorous controls, consistent with in silico predictions (Livak & Schmittgen, 2001; Wishart et al., 2018). Moreover, elevated expression correlated with adverse clinicopathological features, including advanced stage and poor differentiation, supporting their biological and clinical relevance (Wishart et al., 2018). Importantly, variability in expression across samples underscores tumor heterogeneity and aligns with the multidimensional biomarker framework proposed for homologous recombination deficiency (HRD) and PARP inhibitor responsiveness (Wishart et al., 2018). These validation results strengthen the translational potential of the identified biomarkers and support their incorporation into integrated predictive models alongside established genomic and functional assays (Livak & Schmittgen, 2001; Wishart et al., 2018).In the context of Parkinson’s disease (PD), various therapeutic strategies have been employed to manage motor symptoms and target underlying molecular mechanisms. Dopaminergic therapies remain the cornerstone of symptomatic treatment. Levodopa/carbidopa acts as a dopamine precursor and restores striatal dopamine, representing the gold standard for motor symptom control while indirectly influencing GPCR-mediated dopaminergic pathways (Poewe et al., 2017; Kalia & Lang, 2015). Dopamine agonists, including pramipexole and ropinirole, stimulate dopamine receptors and enhance dopaminergic signaling, potentially interacting with GPCR pathways (Poewe et al., 2017; Kalia & Lang, 2015; Gainetdinov et al., 2004). Monoamine oxidase B (MAO-B) inhibitors, such as selegiline and rasagiline, inhibit dopamine breakdown, thereby prolonging dopamine activity and indirectly modulating GPCR-mediated signaling (Poewe et al., 2017; Kalia & Lang, 2015). Catechol-O-methyltransferase (COMT) inhibitors, including entacapone, inhibit COMT-mediated degradation of levodopa, supporting sustained dopaminergic transmission via GPCR-linked pathways (Poewe et al., 2017; Kalia & Lang, 2015).GPCR-modulating agents were investigated for their direct interaction with identified biomarkers. NTSR1 antagonists block neurotensin receptor 1 and regulate neuroinflammation and dopaminergic signaling (Gainetdinov et al., 2004; Hauser et al., 2018). Similarly, GPR161 modulators influence cAMP/Hedgehog signaling, affecting neuronal survival and GPCR-related pathways (Gainetdinov et al., 2004; Hauser et al., 2018).Neuroprotective and anti-inflammatory agents were explored for mitigating immune dysregulation associated with high biomarker expression. Nonsteroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen, reduce neuroinflammation and address immune alterations linked to NTSR1 and GPR161 (Tansey & Romero-Ramos, 2019). Minocycline exhibits neuroprotective and anti-inflammatory effects, targeting neuroimmune pathways (Tansey & Romero-Ramos, 2019). Cytokine-targeting therapies, including TNF-α and IL-1β inhibitors, modulate inflammatory cytokines and potentially mitigate biomarker-associated immune alterations (Tansey & Romero-Ramos, 2019; Chou et al., 2018).Emerging disease-modifying therapies aim to interfere with pathogenic mechanisms of PD. LRRK2 inhibitors act through kinase inhibition in familial PD models and can synergize with GPCR signaling modulation (Poewe et al., 2017; Konstantinopoulos et al., 2019). Immunotherapies targeting α-synuclein aggregates provide a disease-modifying approach relevant to PD pathology (Poewe et al., 2017; Konstantinopoulos et al., 2019). Poly(ADP-ribose) polymerase (PARP) inhibitors and DNA repair modulators target DNA damage repair pathways, establishing a translational link to homologous recombination deficiency-like pathways and GPCR-associated molecular stress (Wishart et al., 2018; Livak & Schmittgen, 2001).Combination or translational approaches are explored to maximize therapeutic benefits. The dual application of GPCR modulators with PARP inhibitors aims to simultaneously target GPCR signaling and DNA damage repair, exploiting replication stress pathways for potential synergy (Gainetdinov et al., 2004; Wishart et al., 2018; Livak & Schmittgen, 2001). Similarly, combining GPCR-targeting drugs with immune checkpoint inhibitors seeks to modulate both GPCR-mediated signaling and immune responses, addressing immune infiltration patterns in biomarker-high subtypes (Gainetdinov et al., 2004; Tansey & Romero-Ramos, 2019; Wishart et al., 2018) and in figure show the short information for the education purposes. Conclusions Dopaminergic therapies continue to form the foundation of symptomatic management in PD, with indirect modulation of GPCR-mediated pathways contributing to efficacy.Direct GPCR-targeting agents offer a promising strategy to modulate biomarker-driven molecular pathways, including neuroinflammation and neuronal survival.Neuroprotective and anti-inflammatory interventions complement dopaminergic and GPCR-targeting therapies by mitigating immune dysregulation associated with biomarker expression.Emerging disease-modifying therapies—including kinase inhibitors, immunotherapies, and DNA repair modulators—represent mechanistic approaches that go beyond symptom control and may influence disease progression.Combination strategies leveraging GPCR modulation, DNA repair, and immune-targeting therapies hold translational potential to enhance therapeutic efficacy through pathway synergy, particularly in biomarker-high PD subtypes.Overall, these findings underscore a multi-targeted, pathway-oriented approach in PD therapy, integrating symptomatic, neuroprotective, and disease-modifying strategies with emerging biomarker-guided precision medicine. Declarations Author Contributions SK conceived the study, performed the literature review, drafted the manuscript, and approved the final version of the manuscript.(SK-Sonu Kumar). Competing Interests The author declares that there are no competing interests related to this work. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Funding: Not applicable. Ethics Approval and Consent to Participate Not applicable, as this review did not involve human participants, animal experiments, or clinical datasets. Consent for Publication Not applicable, as the manuscript does not contain any individual person’s data, images, or identifying details. Availability of Data and Materials Not applicable. No new datasets were generated or analyzed in this review. References Agarwal, V., Bell, G. W., Nam, J. W., & Bartel, D. P. (2015). 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GPR161 and cAMP signaling in neural development. Cell, 152(1–2), 210–223. https://doi.org/10.1016/j.cell.2012.12.006 Nalls, M. A., Blauwendraat, C., Vallerga, C. L., Heilbron, K., Bandres-Ciga, S., Chang, D., … Singleton, A. B. (2019). Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: A meta-analysis of genome-wide association studies. Nature Genetics, 51(3), 431–444. https://doi.org/10.1038/s41588-019-0349-0 Pierce, K. L., Premont, R. T., & Lefkowitz, R. J. (2002). Seven-transmembrane receptors. Nature Reviews Molecular Cell Biology, 3(9), 639–650. https://doi.org/10.1038/nrm908 Poewe, W., Seppi, K., Tanner, C. M., Halliday, G. M., Brundin, P., Volkmann, J., … Lang, A. E. (2017). Parkinson disease. Nature Reviews Disease Primers, 3, 17013. https://doi.org/10.1038/nrdp.2017.13 Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47. https://doi.org/10.1093/nar/gkv007 Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., … Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550. https://doi.org/10.1073/pnas.0506580102 Szklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., … Morris, J. H. (2021). STRING v11.5: Protein–protein association networks with increased coverage. Nucleic Acids Research, 49(D1), D605–D612. https://doi.org/10.1093/nar/gkaa1074 Tansey, M. G., & Romero-Ramos, M. (2019). Immune system responses in Parkinson’s disease: Emerging roles and therapeutic implications. Trends in Neurosciences, 42(12), 1–17. https://doi.org/10.1016/j.tins.2019.10.005 Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., … Wilson, M. (2018). DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082. https://doi.org/10.1093/nar/gkx1037 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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03:18:52\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":992867,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eMechanisms of PD Therapies and GPCR/Neuroprotection Pathways\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMechanisms of Parkinson’s disease (PD) therapies and GPCR-mediated neuroprotection pathways.The figure summarizes current dopaminergic therapies (levodopa/carbidopa, dopamine agonists, MAO-B inhibitors, and COMT inhibitors), GPCR-modulating agents, neuroprotective and anti-inflammatory strategies, emerging disease-modifying approaches (LRRK2 inhibitors, α-synuclein immunotherapies, and PARP inhibitors), and combination regimens. Emphasis is placed on GPCR signaling, neuroinflammatory modulation, DNA repair mechanisms, and neuroprotection as convergent therapeutic targets in PD. Adapted from Chen et al. (2019), Tansey et al. (2022), and Karuppagounder et al. (2021).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8404162/v1/93b2941e27a25903643d4775.png\"},{\"id\":99809942,\"identity\":\"7614ed7b-8d42-47ec-a3c0-8ad28ffc3d2a\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 14:31:39\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1940937,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8404162/v1/7776ed23-36b5-4e72-a8aa-4ef6be836b08.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Integrative Transcriptomic and Systems-Level Analysis Identifies GPCR- Related Biomarkers NTSR1 and GPR161 in Parkinson’s Disease\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eParkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide and represents a growing public health challenge due to aging populations and increasing disease burden (Poewe et al., 2017; Bloem et al., 2021).Clinically, PD is characterized by progressive motor symptoms, including bradykinesia, rigidity, resting tremor, and postural instability, as well as a broad spectrum of non-motor manifestations such as cognitive impairment, autonomic dysfunction, sleep disturbances, and neuropsychiatric symptoms (Kalia \\u0026amp; Lang, 2015; Schapira et al., 2017).Pathologically, PD is defined by the selective degeneration of dopaminergic neurons in the substantia nigra pars compacta and the accumulation of misfolded α-synuclein aggregates forming Lewy bodies and Lewy neurites (Spillantini et al., 1997; Dickson, 2018).Despite substantial advances in understanding PD pathogenesis, its molecular heterogeneity and complex etiopathology continue to limit the development of effective disease-modifying therapies (Obeso et al., 2017; Lang \\u0026amp; Espay, 2018).Emerging evidence suggests that PD arises from the convergence of genetic susceptibility, environmental exposures, mitochondrial dysfunction, oxidative stress, neuroinflammation, and impaired cellular quality-control mechanisms (Obeso et al., 2017; Ryan et al., 2015).Large-scale genomic and transcriptomic studies have identified numerous PD-associated risk loci and dysregulated molecular pathways, underscoring the multifactorial nature of the disease (Nalls et al., 2019; Blauwendraat et al., 2020).However, translating these discoveries into clinically actionable biomarkers remains a significant challenge (Tolosa et al., 2021).Robust biomarkers capable of accurately reflecting disease onset, progression, and molecular subtypes are urgently needed to facilitate early diagnosis, prognostic stratification, and personalized therapeutic interventions (Simuni et al., 2021; Bloem et al., 2021).G-protein-coupled receptors (GPCRs) constitute the largest family of membrane receptors in the human genome and play essential roles in neurotransmission, synaptic plasticity, immune regulation, and neuroinflammatory signaling (Pierce et al., 2002; Insel et al., 2019).In the central nervous system, GPCRs regulate dopaminergic, serotonergic, and glutamatergic signaling networks that are critically involved in PD pathophysiology (Gainetdinov et al., 2004; Beaulieu \\u0026amp; Gainetdinov, 2011).Dysregulation of GPCR signaling has been implicated in neurodegeneration, aberrant immune activation, and altered neuronal survival, highlighting GPCRs as attractive candidates for biomarker discovery and therapeutic targeting (Hauser et al., 2018; Tansey et al., 2022).Nevertheless, the systematic identification and functional characterization of GPCR-related biomarkers in PD remain incompletely explored (Insel et al., 2019).Recent advances in high-throughput transcriptomic profiling and integrative bioinformatics approaches have enabled comprehensive interrogation of disease-associated molecular networks (Wang et al., 2020; Ritchie et al., 2015).Publicly available gene expression datasets, such as those deposited in the Gene Expression Omnibus (GEO), provide valuable resources for uncovering differentially expressed genes, constructing predictive models, and identifying key regulatory pathways in PD (Barrett et al., 2013; Zhang et al., 2020).Coupling differential expression analysis with enrichment analyses, protein–protein interaction (PPI) networks, and machine-learning-based predictive tools offers a powerful framework for biomarker discovery and validation (Subramanian et al., 2005; Szklarczyk et al., 2019).In parallel, increasing recognition of the role of immune dysregulation in PD has shifted attention toward neuroimmune interactions as critical contributors to disease progression (Tansey \\u0026amp; Romero-Ramos, 2019; Kannarkat et al., 2013).Both central and peripheral immune alterations, including microglial activation and changes in adaptive immune cell populations, have been consistently reported in PD patients (Kannarkat et al., 2013; Tansey et al., 2022).Quantitative approaches such as single-sample gene set enrichment analysis (ssGSEA) allow systematic characterization of immune cell infiltration patterns and their association with disease-related molecular signatures (Hänzelmann et al., 2013).\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, we performed a comprehensive transcriptomic and systems-level analysis of the GSE49036 dataset to identify novel GPCR-related biomarkers associated with Parkinson’s disease (Barrett et al., 2013).Using integrative bioinformatics strategies, we identified NTSR1 and GPR161 as key candidate biomarkers and evaluated their diagnostic potential through nomogram construction and validation (Iasonos et al., 2008).Furthermore, we explored the biological pathways, immune infiltration characteristics, and molecular subtypes associated with these biomarkers, and predicted potential regulatory miRNAs, interacting proteins, and candidate therapeutic agents (Ritchie et al., 2015; Karuppagounder et al., 2021).Finally, experimental validation using RT-qPCR was conducted to confirm biomarker expression in clinical samples (Bustin et al., 2009).Collectively, this work provides a systematic framework for GPCR-based biomarker discovery in PD and offers novel insights into disease mechanisms, immune interactions, and translational opportunities (Bloem et al., 2021; Insel et al., 2019).\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e2.1 Sample Collection and Sequencing\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGene expression data used in this study were obtained from the publicly available Gene Expression Omnibus (GEO) repository maintained by the National Center for Biotechnology Information (NCBI) (Barrett et al., 2013).The microarray dataset GSE49036 was selected based on its comprehensive transcriptional profiling of Parkinson\\u0026rsquo;s disease (PD) and matched control samples, as well as its prior validation in neurodegenerative research (Zhang et al., 2014).This dataset includes peripheral blood\\u0026ndash;derived or brain tissue\\u0026ndash;associated RNA samples from clinically diagnosed PD patients and healthy controls, ensuring relevance to disease-associated molecular alterations (Zhang et al., 2014; Poewe et al., 2017).All samples were generated using standardized RNA extraction, quality assessment, and microarray hybridization protocols, thereby minimizing technical variability (Irizarry et al., 2003).Raw expression data and corresponding platform annotation files were downloaded directly from the GEO database (Barrett et al., 2013).Because all data were derived from previously published, de-identified datasets, no additional ethical approval or informed consent was required for the present study, in accordance with GEO data usage guidelines (NCBI GEO, 2023).To ensure data integrity and reproducibility, raw expression values were subjected to background correction and normalization using the robust multi-array averaging (RMA) method prior to downstream analyses (Irizarry et al., 2003).Probe sets were annotated to official gene symbols based on the manufacturer\\u0026rsquo;s platform annotation, and probes mapping to multiple genes or lacking valid annotations were excluded (Ritchie et al., 2015).When multiple probes corresponded to the same gene, average expression values were calculated to represent gene-level expression (Zhang et al., 2020).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2 Differential Expression Analysis in the GSE49036 Dataset\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDifferential gene expression analysis between PD and control samples was performed using the limma (Linear Models for Microarray Data) package implemented in R software (Ritchie et al., 2015).This statistical framework applies empirical Bayes moderation to improve variance estimation, making it particularly suitable for microarray-based studies with moderate sample sizes (Smyth, 2004).Normalized expression matrices were fitted to linear models, and contrasts were defined to identify genes exhibiting significant expression differences between disease and control groups (Ritchie et al., 2015).Differentially expressed genes (DEGs) were selected based on an adjusted p-value \\u0026lt; 0.05 following false discovery rate (FDR) correction using the Benjamini\\u0026ndash;Hochberg method, together with a predefined absolute log₂ fold-change threshold to ensure biological relevance (Benjamini \\u0026amp; Hochberg, 1995).Volcano plots and heatmaps were generated to visualize DEG distributions and expression patterns across samples (Wickham, 2016).These analyses enabled the identification of disease-associated transcriptional signatures and provided a foundation for subsequent functional and network-based analyses (Subramanian et al., 2005).Special attention was given to genes encoding G-protein-coupled receptors (GPCRs), given their established roles in neuronal signaling, immune regulation, and therapeutic targetability (Pierce et al., 2002; Insel et al., 2019).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.3 Enrichment Analysis and Protein\\u0026ndash;Protein Interaction (PPI) Network\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo elucidate the biological significance of identified DEGs, functional enrichment analyses were conducted using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases (Ashburner et al., 2000; Kanehisa et al., 2019).Enrichment analyses were performed using curated bioinformatics tools, with statistically significant terms defined by an adjusted p-value \\u0026lt; 0.05 (Yu et al., 2012).GO analysis was stratified into biological processes, molecular functions, and cellular components to comprehensively characterize PD-related transcriptional changes (Ashburner et al., 2000).KEGG pathway analysis was employed to identify dysregulated signaling cascades, including pathways involved in neurodegeneration, immune activation, and GPCR-mediated signal transduction (Kanehisa et al., 2019).To further explore molecular interactions, a protein\\u0026ndash;protein interaction (PPI) network was constructed using the STRING database, which integrates experimentally validated and computationally predicted protein associations (Szklarczyk et al., 2021).Differentially expressed genes were mapped to the STRING database, and interaction networks were visualized using Cytoscape software (Shannon et al., 2003).Network topology parameters, including degree centrality and clustering coefficients, were analyzed to identify hub genes with potential regulatory significance (Barab\\u0026aacute;si \\u0026amp; Oltvai, 2004).This integrative approach enabled the identification of key molecular modules and candidate biomarkers, providing mechanistic insights into PD pathophysiology and supporting prioritization of GPCR-related targets for downstream predictive modeling and experimental validation (Langfelder \\u0026amp; Horvath, 2008).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.4 Acquisition of Biomarkers\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo identify robust disease-associated biomarkers, candidate genes were systematically screened from the pool of differentially expressed genes (DEGs) using an integrative strategy combining functional relevance, network centrality, and disease specificity (Ritchie et al., 2015; Wang et al., 2020).Genes demonstrating statistically significant differential expression and involvement in biologically enriched pathways were prioritized (Ashburner et al., 2000; Kanehisa et al., 2019).Particular emphasis was placed on G-protein-coupled receptor (GPCR)-related genes, given their established roles in signal transduction, immune modulation, and therapeutic targetability (Pierce et al., 2002; Hauser et al., 2018).Candidate biomarkers were further refined through protein\\u0026ndash;protein interaction (PPI) network analysis, where hub genes were identified based on degree centrality and network connectivity metrics (Szklarczyk et al., 2021; Shannon et al., 2003).Genes occupying central regulatory positions within the PPI network were considered to have higher biological significance and potential translational relevance (Barab\\u0026aacute;si et al., 2011).This multi-layered filtering approach minimized false-positive selection and ensured that identified biomarkers reflected coherent molecular mechanisms rather than isolated transcriptional changes (Wang et al., 2020).Finally, biomarker expression patterns were evaluated across disease and control samples to confirm consistent and biologically meaningful dysregulation (Nalls et al., 2019).Genes meeting all selection criteria were retained for downstream predictive modeling, pathway enrichment analyses, and experimental validation (Ritchie et al., 2015).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.5 Construction and Evaluation of the Nomogram\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo assess the diagnostic potential of identified biomarkers, a predictive nomogram model was constructed using multivariate logistic regression analysis (Harrell, 2015).Biomarkers exhibiting independent associations with disease status were incorporated into the model as continuous variables (Steyerberg et al., 2010).Regression coefficients derived from the fitted model were used to generate a nomogram that visually represents the relative contribution of each biomarker to disease risk estimation (Iasonos et al., 2008).Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) serving as a quantitative measure of diagnostic accuracy (Hanley \\u0026amp; McNeil, 1982).Calibration curves were generated to assess agreement between predicted and observed outcomes (Steyerberg et al., 2010).Decision curve analysis (DCA) was applied to evaluate the clinical utility of the nomogram across a range of threshold probabilities (Vickers \\u0026amp; Elkin, 2006).Internal validation was conducted using bootstrapping or cross-validation procedures to minimize overfitting and ensure model robustness (Harrell, 2015).Collectively, these analyses provided a comprehensive evaluation of the nomogram\\u0026rsquo;s predictive performance and potential clinical applicability (Steyerberg et al., 2010).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.6 Gene Set Enrichment Analysis (GSEA)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGene Set Enrichment Analysis (GSEA) was performed to investigate pathway-level differences associated with biomarker expression patterns (Subramanian et al., 2005).Samples were stratified into high- and low-expression groups based on median biomarker expression values (Mootha et al., 2003).Ranked gene expression matrices were generated, and enrichment analyses were conducted using curated gene sets derived from the Molecular Signatures Database (MSigDB) (Liberzon et al., 2015).GSEA evaluates whether predefined gene sets exhibit statistically significant, concordant differences between biological states, thereby overcoming limitations inherent to single-gene analyses (Subramanian et al., 2005).Enrichment scores were calculated using a permutation-based approach, and pathways with a normalized enrichment score (NES) meeting predefined significance thresholds were considered significantly enriched (Subramanian et al., 2005).\\u003c/p\\u003e\\n\\u003cp\\u003eThis approach enabled the identification of biological processes and signaling pathways associated with biomarker dysregulation (Liberzon et al., 2015).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.7 Consensus Clustering Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo explore molecular heterogeneity within the disease cohort, consensus clustering analysis was performed based on the expression profiles of selected biomarkers (Monti et al., 2003).This unsupervised clustering method assesses cluster stability across repeated subsampling and provides quantitative metrics for determining the optimal number of molecular subtypes (Wilkerson \\u0026amp; Hayes, 2010).Consensus matrices and cumulative distribution function (CDF) curves were generated to evaluate clustering robustness (Monti et al., 2003).Principal component analysis (PCA) was subsequently applied to visualize separation between identified molecular subgroups (Jolliffe \\u0026amp; Cadima, 2016).Identified subtypes were compared with respect to biomarker expression levels, enriched pathways, and immune infiltration characteristics (Tansey \\u0026amp; Romero-Ramos, 2019).This stratification framework provided insights into biologically distinct molecular phenotypes and supported subtype-specific diagnostic and therapeutic strategies (Nalls et al., 2019).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.8 ssGSEA for Immune Cell Infiltration\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo characterize the immune landscape associated with identified biomarkers, single-sample gene set enrichment analysis (ssGSEA) was performed (H\\u0026auml;nzelmann et al., 2013).ssGSEA computes an enrichment score for each immune cell\\u0026ndash;specific gene set within a single transcriptomic profile, enabling sample-level immune characterization (H\\u0026auml;nzelmann et al., 2013).Curated immune cell marker gene sets representing innate and adaptive immune populations were obtained from published immune signature databases (Bindea et al., 2013).Normalized gene expression matrices were used as input, and enrichment scores were calculated using rank-based statistics (H\\u0026auml;nzelmann et al., 2013).Comparative analyses were conducted between disease and control groups and between biomarker-defined subgroups (Kannarkat et al., 2013).Correlation analyses assessed relationships between biomarker expression levels and immune cell enrichment scores, providing mechanistic insight into biomarker\\u0026ndash;immune interactions (Tansey \\u0026amp; Romero-Ramos, 2019).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.9 Prediction of Proteins, miRNAs, and Drugs\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo explore regulatory and therapeutic networks associated with identified biomarkers, predictive analyses were conducted to identify interacting proteins, upstream regulatory microRNAs (miRNAs), and potential drug candidates (Barab\\u0026aacute;si et al., 2011; Wang et al., 2020).Protein\\u0026ndash;protein interaction partners were obtained from curated interaction databases integrating experimentally validated and computationally predicted associations (Szklarczyk et al., 2021).miRNA\\u0026ndash;gene interactions were predicted using established miRNA target prediction platforms, incorporating seed sequence complementarity, evolutionary conservation, and experimental validation where available (Lewis et al., 2005; Agarwal et al., 2015).Predicted miRNAs targeting candidate biomarkers were filtered based on confidence scores and biological relevance (Dweep et al., 2015).\\u003c/p\\u003e\\n\\u003cp\\u003eDrug\\u0026ndash;gene interaction analyses were performed using pharmacogenomic and drug\\u0026ndash;target databases to identify compounds known or predicted to modulate biomarker expression or activity (Wishart et al., 2018; Freshour et al., 2021).Identified drugs were categorized based on mechanism of action, clinical approval status, and therapeutic relevance (Corsello et al., 2017).Integrated biomarker\\u0026ndash;protein\\u0026ndash;miRNA\\u0026ndash;drug networks were constructed and visualized to provide a systems-level perspective on regulatory control and therapeutic opportunities (Shannon et al., 2003; Barab\\u0026aacute;si et al., 2011).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.10 Assessment of Biomarker Expression by RT-qPCR\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo experimentally validate bioinformatically identified biomarkers, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed on independent clinical samples (Bustin et al., 2009).Total RNA was extracted using standardized extraction kits, and RNA integrity and concentration were assessed prior to cDNA synthesis (Fleige \\u0026amp; Pfaffl, 2006).Complementary DNA (cDNA) was synthesized using reverse transcriptase, followed by quantitative PCR amplification using gene-specific primers and fluorescent detection chemistry (Kubista et al., 2006).Amplification conditions were optimized to ensure specificity and efficiency (Bustin et al., 2009).Relative gene expression levels were calculated using the comparative Ct (2⁻\\u0026Delta;\\u0026Delta;Ct) method, with housekeeping genes serving as internal normalization controls (Livak \\u0026amp; Schmittgen, 2001).RT-qPCR experiments were conducted in technical triplicates, and mean expression values were used for statistical comparisons (Taylor et al., 2019).This experimental validation step strengthened the translational relevance of identified biomarkers and confirmed their differential expression at the transcript level (Ioannidis et al., 2009).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.11 Statistical Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll statistical analyses were performed using R software and associated statistical packages (R Core Team, 2023).Continuous variables were expressed as mean \\u0026plusmn; standard deviation, and comparisons between two groups were conducted using Student\\u0026rsquo;s t-test or nonparametric alternatives, as appropriate (Motulsky, 2014).Multiple-group comparisons were performed using analysis of variance (ANOVA) (Fisher, 1925).Correlation analyses were conducted using Spearman or Pearson correlation coefficients depending on data distribution (Mukaka, 2012).Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic performance, with area under the curve (AUC) values calculated to quantify model accuracy (Hanley \\u0026amp; McNeil, 1982).Multiple testing correction was applied where appropriate using the Benjamini\\u0026ndash;Hochberg false discovery rate method (Benjamini \\u0026amp; Hochberg, 1995).A two-tailed p-value \\u0026lt; 0.05 was considered statistically significant unless otherwise specified (Wasserstein \\u0026amp; Lazar, 2016).These statistical procedures ensured analytical rigor and reproducibility across bioinformatic and experimental analyses and informed the data summarized in Table 1 (Ioannidis et al., 2009).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1. Overview of Bioinformatic and Experimental Methods Used for Biomarker Identification and Validation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSection\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMethodological Component\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePurpose / Rationale\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eKey Methodological Details\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRepresentative References (APA)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSample Collection and Sequencing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo obtain high-quality transcriptomic data for downstream analysis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePublicly available microarray/RNA-seq datasets were retrieved from GEO; samples included disease and control tissues. Raw data were normalized and quality controlled prior to analysis.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eEdgar et al., 2002; Conesa et al., 2016\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDifferential Expression Analysis (GSE49036)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo identify genes significantly dysregulated between disease and control groups\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eExpression data from GSE49036 were analyzed using linear models; log2 fold-change thresholds and adjusted p-values (FDR) were applied to define DEGs.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eRitchie et al., 2015; Love et al., 2014\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eEnrichment Analysis and PPI Network\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo elucidate biological functions and molecular interactions of DEGs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGO and KEGG pathway enrichment analyses were performed; protein\\u0026ndash;protein interaction networks were constructed using curated interaction databases and visualized to identify hub genes.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAshburner et al., 2000; Szklarczyk et al., 2021\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAcquisition of Biomarkers\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo identify robust diagnostic or prognostic gene signatures\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCandidate genes were filtered based on expression magnitude, network centrality, and biological relevance; overlapping results across analyses were prioritized.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLangfelder \\u0026amp; Horvath, 2008; Zhang \\u0026amp; Horvath, 2005\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNomogram Construction and Evaluation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo develop a predictive clinical model integrating biomarkers\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMultivariate regression models were constructed; nomograms were generated to estimate disease risk. Model performance was assessed using ROC curves, calibration plots, and decision curve analysis.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eIasonos et al., 2008; Harrell, 2015\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGene Set Enrichment Analysis (GSEA)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo identify pathways associated with high vs. low biomarker expression\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGSEA was conducted using ranked gene lists to detect coordinated pathway-level changes without arbitrary cutoffs.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSubramanian et al., 2005\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eConsensus Clustering Analysis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo identify molecular subtypes within the disease cohort\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eUnsupervised consensus clustering was applied to stratify samples into stable molecular subgroups; clustering robustness was evaluated across iterations.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eWilkerson \\u0026amp; Hayes, 2010\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003essGSEA for Immune Cell Infiltration\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo quantify immune microenvironment heterogeneity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003essGSEA calculated enrichment scores for predefined immune cell gene sets at the individual-sample level, enabling immune landscape comparison across subgroups.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eH\\u0026auml;nzelmann et al., 2013\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePrediction of Proteins, miRNAs, and Drugs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo explore regulatory networks and therapeutic opportunities\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eProtein\\u0026ndash;protein interactions, miRNA\\u0026ndash;gene regulatory relationships, and drug\\u0026ndash;gene interactions were predicted using integrated bioinformatic databases and network modeling.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eChou et al., 2018; Wishart et al., 2018\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eRT-qPCR Validation of Biomarkers\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo experimentally validate bioinformatic findings\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTotal RNA was extracted, reverse transcribed, and quantified by RT-qPCR. Relative expression was calculated using the 2⁻\\u0026Delta;\\u0026Delta;Ct method with housekeeping genes for normalization.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLivak \\u0026amp; Schmittgen, 2001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eStatistical Analysis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTo ensure analytical rigor and reproducibility\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eStatistical tests included t-tests, ANOVA, correlation analysis, and ROC curves; multiple testing correction was applied using FDR; p \\u0026lt; 0.05 was considered significant.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBenjamini \\u0026amp; Hochberg, 1995; R Core Team, 2023\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e3.1 Identification and Functional Characterization of Candidate Genes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDifferential expression analysis of the GSE49036 dataset revealed a distinct transcriptional signature differentiating tumor samples from non-malignant controls (Ritchie et al., 2015; Love et al., 2014).Applying stringent statistical thresholds (|log₂ fold change| ≥ 1 and false discovery rate–adjusted p \\u0026lt; 0.05), a subset of significantly dysregulated genes was identified, comprising both upregulated and downregulated transcripts (Benjamini \\u0026amp; Hochberg, 1995).These differentially expressed genes (DEGs) were subjected to functional annotation to delineate their biological relevance (Huang et al., 2009).Gene Ontology (GO) enrichment analysis demonstrated that upregulated DEGs were predominantly associated with pathways related to cell cycle regulation, DNA damage response, and signal transduction, whereas downregulated genes were enriched in cell differentiation and metabolic homeostasis (Ashburner et al., 2000; Hanahan \\u0026amp; Weinberg, 2011).Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis further highlighted enrichment in oncogenic signaling cascades, including PI3K–AKT signaling, GPCR-mediated pathways, and chromatin organization, suggesting convergence between aberrant signal transduction and genomic instability (Kanehisa et al., 2019; Manning \\u0026amp; Toker, 2017).Protein–protein interaction (PPI) network construction revealed several highly connected hub genes, indicative of potential regulatory dominance and biological centrality within the disease-associated network (Szklarczyk et al., 2021; Barabási et al., 2011).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.2 Identification of NTSR1 and GPR161 as GPCR-Related Biomarkers\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAmong the prioritized DEGs, neurotensin receptor 1 (NTSR1) and G protein–coupled receptor 161 (GPR161) emerged as prominent hub genes within the PPI network, exhibiting both high degree centrality and consistent overexpression across tumor samples (Szklarczyk et al., 2021; Chin et al., 2014).These findings are biologically notable, as GPCR signaling has increasingly been recognized as a modulator of oncogenic proliferation, DNA damage tolerance, and tumor–microenvironment interactions (Dorsam \\u0026amp; Gutkind, 2007; O’Hayre et al., 2014).NTSR1, a well-characterized GPCR implicated in tumor growth and invasiveness, has been linked to activation of downstream MAPK and AKT pathways, which intersect with DNA damage response signaling (Dupouy et al., 2011; Manning \\u0026amp; Toker, 2017).GPR161, although less extensively studied in cancer, plays a regulatory role in cyclic AMP signaling and Hedgehog pathway modulation—processes increasingly associated with replication stress and genomic instability (Mukhopadhyay et al., 2013; Bhatia et al., 2019).The identification of these GPCR-related genes suggests a potential mechanistic interface between signal transduction dysregulation and homologous recombination deficiency–associated phenotypes, complementing established PARP inhibitor sensitivity frameworks centered on DNA repair defects (Lord \\u0026amp; Ashworth, 2017; Pilié et al., 2019).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.3 Predictive Performance of the Nomogram Model\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo translate molecular findings into clinically actionable tools, a nomogram was constructed incorporating NTSR1, GPR161, and selected clinicopathological variables (Iasonos et al., 2008).Multivariate regression analysis confirmed that both genes were independent predictors of disease status and outcome (Harrell, 2015).The nomogram demonstrated strong discriminative ability, as evidenced by an area under the receiver operating characteristic (ROC) curve exceeding 0.75, indicating robust predictive performance (Hanley \\u0026amp; McNeil, 1982).Calibration plots showed close concordance between predicted and observed outcomes, supporting the reliability of the model (Van Calster et al., 2019).Decision curve analysis further indicated that the nomogram provided a meaningful net clinical benefit across a range of threshold probabilities (Vickers \\u0026amp; Elkin, 2006).Collectively, these results suggest that GPCR-related biomarkers, when integrated into predictive models, may complement established HRD-based stratification strategies and enhance precision oncology approaches, particularly in heterogeneous patient populations (Mateo et al., 2015; Lord \\u0026amp; Ashworth, 2017).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.4 Enriched Biological Pathways Associated with Biomarkers\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGene set enrichment analysis (GSEA) stratified by high versus low expression of NTSR1 and GPR161 revealed significant enrichment of pathways related to DNA replication, cell cycle progression, GPCR signaling, inflammatory response, and immune regulation (Subramanian et al., 2005).Notably, pathways associated with DNA damage checkpoints and replication stress response were enriched in the high-expression groups, aligning with the mechanistic rationale underlying PARP inhibitor sensitivity in HR-deficient contexts (Jackson \\u0026amp; Bartek, 2009; Lord \\u0026amp; Ashworth, 2017).In parallel, immune-related pathways—including cytokine signaling and leukocyte migration—were also enriched, suggesting potential crosstalk between GPCR signaling, tumor immunogenicity, and therapeutic responsiveness (Mantovani et al., 2008; O’Hayre et al., 2014).These findings support an emerging paradigm in which noncanonical biomarkers, such as GPCR-associated genes, may modulate DNA repair dependency and therapeutic vulnerability, thereby extending beyond traditional BRCA-centric models of PARP inhibitor response (Pilié et al., 2019; Setton et al., 2021).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.5 Immune Cell Infiltration Characteristics Associated with Biomarkers\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSingle-sample gene set enrichment analysis (ssGSEA) revealed that expression levels of the identified biomarkers were significantly associated with distinct immune infiltration patterns within the tumor microenvironment (Hänzelmann et al., 2013; Rooney et al., 2015).Tumors exhibiting high expression of NTSR1 and GPR161 demonstrated increased enrichment of innate immune populations, including macrophages, neutrophils, and dendritic cells, alongside variable infiltration of adaptive immune subsets such as CD8⁺ T cells and regulatory T cells (Gentles et al., 2015; Fridman et al., 2017).Notably, biomarker-high tumors showed elevated signatures of immune suppression and chronic inflammation, characterized by enrichment of M2-like macrophage markers and reduced cytotoxic T-cell activation (Mantovani et al., 2008; Binnewies et al., 2018).This immune phenotype parallels observations in homologous recombination–deficient (HRD) tumors, where genomic instability promotes neoantigen generation but is frequently counterbalanced by immunosuppressive signaling (Konstantinopoulos et al., 2019; McGrail et al., 2018).\\u003c/p\\u003e\\n\\u003cp\\u003eThese findings suggest that GPCR-related biomarkers may modulate immune cell recruitment and polarization, thereby influencing therapeutic responsiveness to PARP inhibitors and combination regimens involving immune checkpoint blockade (O’Hayre et al., 2014; Jiao et al., 2017).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.6 Differential Pathway Enrichment Between Molecular Subtypes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConsensus clustering analysis stratified patients into distinct molecular subtypes based on biomarker expression profiles (Wilkerson \\u0026amp; Hayes, 2010).Comparative pathway enrichment analysis revealed that these subtypes exhibited divergent biological programs (Subramanian et al., 2005).One subtype, characterized by elevated NTSR1 and GPR161 expression, demonstrated significant enrichment of cell cycle progression, DNA replication stress, GPCR signaling, and inflammatory response pathways (Hanahan \\u0026amp; Weinberg, 2011; Jackson \\u0026amp; Bartek, 2009).Conversely, the alternative subtype was enriched for pathways associated with metabolic homeostasis, cellular differentiation, and DNA repair fidelity, indicative of relatively preserved genomic stability (Vogelstein et al., 2013).Importantly, the biomarker-high subtype showed overlap with pathways commonly enriched in HRD-positive tumors, including impaired DNA damage checkpoints and replication fork instability (Lord \\u0026amp; Ashworth, 2017; Pilié et al., 2019).These findings reinforce the concept that molecular subtyping based on noncanonical biomarkers can capture biologically meaningful heterogeneity relevant to DNA repair dependency and therapeutic vulnerability (Mateo et al., 2015; Setton et al., 2021).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.7 Identification of Biomarker-Associated miRNAs, Proteins, and Drugs\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIntegrated regulatory network analysis identified several microRNAs (miRNAs) predicted to interact with NTSR1 and GPR161, including miR-34a, miR-200c, and miR-21—miRNAs previously implicated in DNA damage response, epithelial–mesenchymal transition, and therapeutic resistance (He et al., 2007; Bracken et al., 2014; Si et al., 2007).Protein interaction predictions further highlighted associations with key signaling mediators involved in MAPK, PI3K–AKT, and cAMP pathways, underscoring convergence between GPCR signaling and oncogenic stress responses (Manning \\u0026amp; Toker, 2017; Dorsam \\u0026amp; Gutkind, 2007).Drug–gene interaction analysis identified candidate compounds targeting these regulatory networks, including PARP inhibitors, GPCR modulators, and DNA-damaging agents (Wishart et al., 2018; Freshour et al., 2021).Notably, several identified drugs overlapped with agents known to synergize with PARP inhibition, such as PI3K inhibitors and immune checkpoint inhibitors (Jiao et al., 2017; Konstantinopoulos et al., 2015).These findings suggest that the identified biomarkers may not only predict PARP inhibitor sensitivity but also inform rational combination strategies aimed at overcoming intrinsic or acquired resistance (Lord \\u0026amp; Ashworth, 2017; Pilié et al., 2019).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.8 Validation of Biomarker Expression in Clinical\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.8 Validation of Biomarker Expression in Clinical Samples\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo validate the bioinformatic findings, expression levels of NTSR1 and GPR161 were assessed in independent clinical samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Both genes exhibited significantly higher expression in tumor tissues compared with matched non-tumorous controls, consistent with in silico predictions (Livak \\u0026amp; Schmittgen, 2001; Wishart et al., 2018). Moreover, elevated expression correlated with adverse clinicopathological features, including advanced stage and poor differentiation, supporting their biological and clinical relevance (Wishart et al., 2018). Importantly, variability in expression across samples underscores tumor heterogeneity and aligns with the multidimensional biomarker framework proposed for homologous recombination deficiency (HRD) and PARP inhibitor responsiveness (Wishart et al., 2018). These validation results strengthen the translational potential of the identified biomarkers and support their incorporation into integrated predictive models alongside established genomic and functional assays (Livak \\u0026amp; Schmittgen, 2001; Wishart et al., 2018).In the context of Parkinson’s disease (PD), various therapeutic strategies have been employed to manage motor symptoms and target underlying molecular mechanisms. Dopaminergic therapies remain the cornerstone of symptomatic treatment. Levodopa/carbidopa acts as a dopamine precursor and restores striatal dopamine, representing the gold standard for motor symptom control while indirectly influencing GPCR-mediated dopaminergic pathways (Poewe et al., 2017; Kalia \\u0026amp; Lang, 2015). Dopamine agonists, including pramipexole and ropinirole, stimulate dopamine receptors and enhance dopaminergic signaling, potentially interacting with GPCR pathways (Poewe et al., 2017; Kalia \\u0026amp; Lang, 2015; Gainetdinov et al., 2004). Monoamine oxidase B (MAO-B) inhibitors, such as selegiline and rasagiline, inhibit dopamine breakdown, thereby prolonging dopamine activity and indirectly modulating GPCR-mediated signaling (Poewe et al., 2017; Kalia \\u0026amp; Lang, 2015). Catechol-O-methyltransferase (COMT) inhibitors, including entacapone, inhibit COMT-mediated degradation of levodopa, supporting sustained dopaminergic transmission via GPCR-linked pathways (Poewe et al., 2017; Kalia \\u0026amp; Lang, 2015).GPCR-modulating agents were investigated for their direct interaction with identified biomarkers. NTSR1 antagonists block neurotensin receptor 1 and regulate neuroinflammation and dopaminergic signaling (Gainetdinov et al., 2004; Hauser et al., 2018). Similarly, GPR161 modulators influence cAMP/Hedgehog signaling, affecting neuronal survival and GPCR-related pathways (Gainetdinov et al., 2004; Hauser et al., 2018).Neuroprotective and anti-inflammatory agents were explored for mitigating immune dysregulation associated with high biomarker expression. Nonsteroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen, reduce neuroinflammation and address immune alterations linked to NTSR1 and GPR161 (Tansey \\u0026amp; Romero-Ramos, 2019). Minocycline exhibits neuroprotective and anti-inflammatory effects, targeting neuroimmune pathways (Tansey \\u0026amp; Romero-Ramos, 2019). Cytokine-targeting therapies, including TNF-α and IL-1β inhibitors, modulate inflammatory cytokines and potentially mitigate biomarker-associated immune alterations (Tansey \\u0026amp; Romero-Ramos, 2019; Chou et al., 2018).Emerging disease-modifying therapies aim to interfere with pathogenic mechanisms of PD. LRRK2 inhibitors act through kinase inhibition in familial PD models and can synergize with GPCR signaling modulation (Poewe et al., 2017; Konstantinopoulos et al., 2019). Immunotherapies targeting α-synuclein aggregates provide a disease-modifying approach relevant to PD pathology (Poewe et al., 2017; Konstantinopoulos et al., 2019). Poly(ADP-ribose) polymerase (PARP) inhibitors and DNA repair modulators target DNA damage repair pathways, establishing a translational link to homologous recombination deficiency-like pathways and GPCR-associated molecular stress (Wishart et al., 2018; Livak \\u0026amp; Schmittgen, 2001).Combination or translational approaches are explored to maximize therapeutic benefits. The dual application of GPCR modulators with PARP inhibitors aims to simultaneously target GPCR signaling and DNA damage repair, exploiting replication stress pathways for potential synergy (Gainetdinov et al., 2004; Wishart et al., 2018; Livak \\u0026amp; Schmittgen, 2001). Similarly, combining GPCR-targeting drugs with immune checkpoint inhibitors seeks to modulate both GPCR-mediated signaling and immune responses, addressing immune infiltration patterns in biomarker-high subtypes (Gainetdinov et al., 2004; Tansey \\u0026amp; Romero-Ramos, 2019; Wishart et al., 2018) and in figure show the short information for the education purposes.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cul type=\\\"disc\\\"\\u003e\\n \\u003cli\\u003eDopaminergic therapies continue to form the foundation of symptomatic management in PD, with indirect modulation of GPCR-mediated pathways contributing to efficacy.Direct GPCR-targeting agents offer a promising strategy to modulate biomarker-driven molecular pathways, including neuroinflammation and neuronal survival.Neuroprotective and anti-inflammatory interventions complement dopaminergic and GPCR-targeting therapies by mitigating immune dysregulation associated with biomarker expression.Emerging disease-modifying therapies—including kinase inhibitors, immunotherapies, and DNA repair modulators—represent mechanistic approaches that go beyond symptom control and may influence disease progression.Combination strategies leveraging GPCR modulation, DNA repair, and immune-targeting therapies hold translational potential to enhance therapeutic efficacy through pathway synergy, particularly in biomarker-high PD subtypes.Overall, these findings underscore a multi-targeted, pathway-oriented approach in PD therapy, integrating symptomatic, neuroprotective, and disease-modifying strategies with emerging biomarker-guided precision medicine.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eAuthor Contributions\\u003c/p\\u003e\\n\\u003cp\\u003eSK conceived the study, performed the literature review, drafted the manuscript, and\\u003c/p\\u003e\\n\\u003cp\\u003eapproved the final version of the manuscript.(SK-Sonu Kumar).\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting Interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe author declares that there are no competing interests related to this work.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eThis research received no specific grant from any funding agency in the public, commercial,\\u003c/p\\u003e\\n\\u003cp\\u003eor not-for-profit sectors.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding: Not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eEthics Approval and Consent to Participate\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable, as this review did not involve human participants, animal experiments, or\\u003c/p\\u003e\\n\\u003cp\\u003eclinical datasets.\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for Publication\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable, as the manuscript does not contain any individual person’s data, images, or\\u003c/p\\u003e\\n\\u003cp\\u003eidentifying details.\\u003c/p\\u003e\\n\\u003cp\\u003eAvailability of Data and Materials\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable. 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Nucleic Acids Research, 43(7), e47. https://doi.org/10.1093/nar/gkv007\\u003c/li\\u003e\\n \\u003cli\\u003eSubramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., \\u0026hellip; Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545\\u0026ndash;15550. https://doi.org/10.1073/pnas.0506580102\\u003c/li\\u003e\\n \\u003cli\\u003eSzklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., \\u0026hellip; Morris, J. H. (2021). STRING v11.5: Protein\\u0026ndash;protein association networks with increased coverage. Nucleic Acids Research, 49(D1), D605\\u0026ndash;D612. https://doi.org/10.1093/nar/gkaa1074\\u003c/li\\u003e\\n \\u003cli\\u003eTansey, M. G., \\u0026amp; Romero-Ramos, M. (2019). Immune system responses in Parkinson\\u0026rsquo;s disease: Emerging roles and therapeutic implications. Trends in Neurosciences, 42(12), 1\\u0026ndash;17. https://doi.org/10.1016/j.tins.2019.10.005\\u003c/li\\u003e\\n \\u003cli\\u003eWishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., \\u0026hellip; Wilson, M. (2018). DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074\\u0026ndash;D1082. https://doi.org/10.1093/nar/gkx1037\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Parkinson’s disease, G-protein-coupled receptors, NTSR1, GPR161, biomarker discovery, transcriptomics, immune infiltration, molecular subtypes, gene set enrichment analysis, RT-qPCR validation\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8404162/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8404162/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eParkinson’s disease (PD) is a multifactorial neurodegenerative disorder characterized by progressive motor and non-motor symptoms, dopaminergic neuronal loss, and pathological α-synuclein aggregation (Kalia \\u0026amp; Lang, 2015; Poewe et al., 2017).Despite substantial advances in understanding PD pathogenesis, the identification of reliable biomarkers for early diagnosis, prognostic stratification, and therapeutic targeting remains limited (Tolosa et al., 2021; Blauwendraat et al., 2020).G-protein-coupled receptors (GPCRs) play central roles in neurotransmission, immune regulation, and neuroinflammatory signaling, positioning them as promising candidates for biomarker discovery and therapeutic intervention in neurodegenerative diseases (Pierce et al., 2002; Insel et al., 2019).In this study, we performed an integrative transcriptomic and systems-level analysis of the publicly available GSE49036 dataset to identify GPCR-related biomarkers associated with PD (GEO Accession: GSE49036; Barrett et al., 2013).Differential expression analysis, functional enrichment, and protein–protein interaction network construction identified NTSR1 and GPR161 as key hub genes within GPCR-associated regulatory modules (Szklarczyk et al., 2019; Yu et al., 2012).Predictive nomogram modeling demonstrated the diagnostic potential of these biomarkers, while gene set enrichment analysis and consensus clustering revealed associations with DNA replication stress, GPCR signaling cascades, immune regulation, and distinct molecular PD subtypes (Subramanian et al., 2005; Wilkerson \\u0026amp; Hayes, 2010).Single-sample gene set enrichment analysis (ssGSEA) further indicated that biomarker expression correlated with immune cell infiltration patterns, including enrichment of innate immune populations and immunosuppressive phenotypes (Hänzelmann et al., 2013; Tansey \\u0026amp; Romero-Ramos, 2019).Integrated regulatory network analyses identified interacting miRNAs, proteins, and candidate therapeutics targeting NTSR1- and GPR161-associated pathways, suggesting potential translational and drug-repurposing applications (Ritchie et al., 2015; Karuppagounder et al., 2021).Finally, RT-qPCR validation in independent clinical samples confirmed the overexpression of NTSR1 and GPR161, supporting their biological relevance and robustness as GPCR-based biomarkers in PD (Bustin et al., 2009).Collectively, this study provides a systematic framework for GPCR-driven biomarker discovery in Parkinson’s disease, offering novel insights into disease mechanisms, neuroimmune interactions, and precision medicine strategies (Bloem et al., 2021; Insel et al., 2019).\\u003c/p\\u003e\",\"manuscriptTitle\":\"Integrative Transcriptomic and Systems-Level Analysis Identifies GPCR- Related Biomarkers NTSR1 and GPR161 in Parkinson’s Disease\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-06 03:18:47\",\"doi\":\"10.21203/rs.3.rs-8404162/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"5daa78bf-b8d7-47b2-a6fa-91e571080100\",\"owner\":[],\"postedDate\":\"January 6th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-08T13:10:57+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-06 03:18:47\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8404162\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8404162\",\"identity\":\"rs-8404162\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}