Bioinformatics and Quantitative Real-Time Polymerase Chain Reaction Analysis of SUCNR1 and GPR37L1 in Schizophrenia

Podsumowanie

This study evaluates SUCNR1 and GPR37L1 as candidate schizophrenia-associated molecular markers through integrated bioinformatics analysis of the GSE54913 dataset, quantitative real-time polymerase chain reaction (qRT-PCR) validation, and correlation analysis with verbal memory in an independent cohort.

Streszczenie

Schizophrenia is a severe, complex, and multifactorial mental disorder involving numerous genetic susceptibility elements, leading to substantial disability, morbidity, and mortality. Despite significant progress in understanding its pathophysiology and etiology, specific diagnostic biomarkers for schizophrenia remain elusive. This study aimed to identify candidate molecular markers associated with schizophrenia. An integrated bioinformatics analysis was performed on the public microarray dataset GSE54913. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the most significantly enriched GO terms were related to channel activity, including passive transmembrane transporter activity, ion channel activity, gated channel activity, and substrate-specific channel activity. The top five enriched KEGG pathways were insulin secretion, cAMP signaling pathway, nucleotide excision repair, TNF signaling pathway, and glutathione metabolism. Validation was conducted using quantitative real-time polymerase chain reaction (qRT-PCR) on an independent sample set from Wuhan Rongjun Youfu Hospital. The qRT-PCR results were largely consistent with the microarray analysis (Pearson r = 0.89, 95% CI: 0.66–0.97). Protein-protein interaction (PPI) network analysis identified two hub genes, SUCNR1 and GPR37L1, which were significantly associated with the GO term ‘ion channel activity’ and enriched in the KEGG pathway ‘insulin secretion’. Furthermore, SUCNR1 expression showed a negative correlation with verbal memory scores (r = -0.54, P = 0.015), whereas GPR37L1 expression showed a positive correlation (r = 0.59, P = 0.0034). These findings suggest that altered SUCNR1 and GPR37L1 expression may be associated with schizophrenia and may represent candidate molecular markers for further investigation.

Wprowadzenie

Schizophrenia is a chronic and complex mental disorder of unidentified etiology, characterized by severe brain dysfunction, cognitive impairment, and psychosocial deficits¹. It poses a major global health burden, affecting over 21 million people worldwide². Although diagnostic and therapeutic approaches have evolved considerably over the past fifty years, the core pathogenesis remains unclear, and long-term outcomes associated with substantial disability, morbidity, and mortality have not markedly improved³. Therefore, identifying potential pivotal genes and regulatory targets is imperative.

The pathophysiology of schizophrenia, while not fully elucidated, is widely attributed to genetic polymorphisms and expression variations⁴. For instance, genetic variation in the estrogen alpha gene may influence susceptibility to schizophrenia through alternative gene regulation and transcript processing⁵. Similarly, a functional promoter variant of NRG1 has been linked to schizophrenia and correlated with reduced expression of the type III NRG1 isoform⁶. Postmortem studies have shown significantly reduced expression of PDE4B isoforms in schizophrenic brains, suggesting its predictive potential⁷. Other candidates include the dopamine transporter (DAT), vesicular monoamine transporter (VMAT2), and monoamine oxidase (MAO), which regulate synaptic dopamine levels and may serve as biomarkers⁸. Additionally, TCP1 may contribute to cytoskeletal deficits via improper actin folding in schizophrenia⁹. Thus, elucidating gene expression profiles in the pathogenesis of schizophrenia could provide insights into risk prediction, mechanistic understanding, and therapeutic evaluation.

Recent computational approaches have advanced the identification of disease-associated genes through integrated network analysis and machine learning methods^10,11,12. Despite numerous genetic studies, no reliable blood-based diagnostic biomarkers have been translated into clinical practice for schizophrenia. To address this gap, the present study employed an integrated bioinformatics approach to reanalyze the GSE54913 dataset, which contains blood-derived transcriptomic data from well-characterized patients with schizophrenia and healthy controls. The dataset was selected because (1) peripheral blood samples are minimally invasive and clinically practical, (2) it includes a relatively large sample size among publicly available schizophrenia microarray datasets, and (3) raw data were available for reanalysis. The objectives of this study were to: (1) identify differentially expressed genes in schizophrenia patients compared to healthy controls using the GSE54913 dataset; (2) perform functional enrichment and PPI network analyses to identify hub genes; (3) validate the expression of candidate genes by qRT-PCR in an independent cohort; and (4) explore the correlation of hub gene expression with verbal memory performance. To the authors’ knowledge, this is the first study to identify SUCNR1 and GPR37L1 as candidate blood-based molecular markers for schizophrenia.

Protokół

This study was approved by the Ethics Committee of Wuhan Rongjun Youfu Hospital (project identification code, YF-IRB202310215) and conducted in accordance with the principles of the Declaration of Helsinki. All participants were Chinese community residents and provided written informed consent.

Study subjects and blood sampling
Ten adults diagnosed with schizophrenia and ten healthy control volunteers with no family history of mental illness within three generations were enrolled. Inclusion criteria for schizophrenia patients: (1) diagnosis of schizophrenia according to DSM-5 criteria confirmed by two independent senior psychiatrists; (2) age 18–65 years; (3) no change in antipsychotic medication for at least 4 weeks prior to blood sampling; (4) willingness to provide written informed consent. Exclusion criteria: (1) comorbid major medical illnesses (e.g., diabetes, cardiovascular disease, cancer); (2) substance abuse or dependence within the past 6 months; (3) intellectual disability; (4) pregnancy or lactation. Inclusion criteria for healthy controls: (1) no personal or family history (within three generations) of any mental illness; (2) no current or past antipsychotic medication use; (3) age- and sex-matched to the schizophrenia group; (4) no major medical illnesses. Mean disease duration for schizophrenia patients was 12.5 ± 6.8 years; all patients were on stable antipsychotic medication (6 on risperidone, 4 on olanzapine); mean PANSS total score was 76.4 ± 12.3. Controls were matched for age (±5 years) and sex (5 males, 5 females per group). Verbal memory was assessed using the total recall score on the Hopkins Verbal Learning Test–Revised (HVLT-R). All subjects were recruited from Wuhan Rongjun Youfu Hospital between January 1 and July 31, 2025. Baseline demographics are summarized in Table 1.

Sample size was determined using sample size calculation software for a two-tailed independent t-test with an effect size of 1.2, α = 0.05, and power (1-β) = 0.80, yielding a minimum of nine subjects per group.

Venous blood was drawn into EDTA-anticoagulated tubes and processed without delay. Peripheral blood mononuclear cells (PBMCs) were then separated from the samples by density gradient centrifugation using density gradient medium. Briefly, blood was diluted 1:1 with phosphate-buffered saline (PBS, pH 7.4), layered onto the density gradient medium, and centrifuged at 400 × g for 30 min at 20 °C with the brake off. After centrifugation, the PBMC-containing interface was carefully transferred to a new tube. The cells were rinsed twice with PBS, with each wash followed by centrifugation at 300 × g for 10 min at 4 °C, and the final pellet was suspended in PBS. Viability was evaluated using the trypan blue dye-exclusion method, and only preparations with ≥95% viable cells were used. Isolated PBMCs were aliquoted and stored at −80 °C for a maximum of 3 months before RNA extraction.

Microarray data
The workflow of the study is shown in Figure 1. The microarray dataset GSE54913 was downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/). This dataset was selected because (1) it contains transcriptomic data from peripheral blood samples, which are minimally invasive and clinically practical for biomarker discovery; (2) it includes a relatively large sample size among publicly available schizophrenia microarray datasets (18 patients, 12 controls); (3) the raw data were available for reanalysis. According to the GEO record, the samples were derived from peripheral blood mononuclear cells (PBMCs), not plasma. The original description ‘plasma samples’ in the previous version was an error and has been corrected.

Data preprocessing and differentially expressed gene (DEG) screening
Raw data (.CEL files) were preprocessed using microarray preprocessing software. Background correction was performed using the Robust Multichip Average (RMA) method, followed by quantile normalization and log2 transformation. Probesets without any gene annotation were filtered out. Probes with >20% missing values across samples were excluded; missing values for remaining probes were imputed using the k-nearest neighbors algorithm (k = 10) implemented in missing-value imputation software. Batch effects were not present as all samples were processed in a single batch according to the GEO record. After preprocessing, expression values for 17,200 genes were obtained for downstream analysis. DEGs between schizophrenia patients and healthy controls were identified using differential expression analysis software. Genes with a false discovery rate (FDR) < 0.05, absolute fold change (FC) > 1.2, and P < 0.05 were considered differentially expressed. A relatively low FC threshold (absolute FC > 1.2) was chosen because schizophrenia is a complex psychiatric disorder in which individual gene expression differences are often subtle rather than dramatic.

GO and pathway enrichment analyses
Functional enrichment analyses for Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed using functional enrichment analysis software. The background gene set consisted of all genes that passed preprocessing (17,200 genes). Enrichment terms and pathways with raw P-values < 0.05 were reported. Because raw P-values were used for GO and KEGG enrichment outputs, these results should be interpreted as exploratory.

PPI network analysis and hub gene identification
A protein-protein interaction database was used to construct a PPI network, with a combined interaction score > 0.9 set as the threshold. The network was visualized using network visualization software. Hub genes were identified using a hub gene identification plugin with the Degree algorithm. The top 10 nodes with the highest degree scores were selected as hub genes. The subnetwork of key hub genes was extracted using a subnetwork extraction plugin with default parameters (degree cutoff = 2, node score cutoff = 0.2, K-core = 2, max depth = 100).

Total RNA isolation and qRT-PCR
β-Actin was used as the internal reference (housekeeping) gene. The stability of β-actin expression across samples was confirmed by no significant difference in Ct values between the schizophrenia and control groups (P > 0.05). Relative quantification was performed using the 2^−ΔΔCt method. All reactions were performed in triplicate, and the mean Ct value was used for calculation (see primer sequences in Table 2). The top ten DEGs (five most significantly upregulated and five most significantly downregulated by fold change) were selected for initial qRT-PCR validation to confirm the overall reliability of the microarray data. Subsequently, SUCNR1 and GPR37L1 were selected for focused validation based on three criteria: (1) they were identified as hub genes in the PPI network analysis (degree ≥ 7); (2) they were significantly associated with the top enriched GO term ‘ion channel activity’ and the KEGG pathway ‘insulin secretion’; (3) both encode GPCRs, which are known drug targets in schizophrenia.

Statistical analysis
Normality was evaluated with the Shapiro–Wilk test. Variables showing an approximately normal distribution (P > 0.05) were analyzed using parametric methods, including Student’s t-test for two-group comparisons and one-way ANOVA for comparisons involving more than two groups. When the normality assumption was not met, Mann–Whitney U or Kruskal–Wallis tests were used, as appropriate. All statistical tests were two-tailed. For multiple comparisons (e.g., in ANOVA post-hoc tests), the Benjamini-Hochberg false discovery rate (FDR) method was applied, with an FDR threshold of 0.05. For correlation analyses, no multiple-comparison correction was applied, as only two correlations were performed; the raw P-values are reported with caution.

Wyniki

Identification of DEGs and hierarchical clustering
Analysis of the GSE54913 dataset identified 473 differentially expressed genes (DEGs), including 357 upregulated and 116 downregulated genes, between schizophrenia patients and controls (Figure 2A,B). Hierarchical clustering of these DEGs distinguished schizophrenia samples from controls (Figure 2C).

Functional enrichment analysis of DEGs
GO enrichment analysis identified terms related to channel activity, including passive transmembrane transporter activity, ion channel activity, gated channel activity, and substrate-specific channel activity (raw P < 0.05; Table 3). KEGG pathway analysis identified insulin secretion, the cAMP signaling pathway, nucleotide excision repair, the TNF signaling pathway, and glutathione metabolism as the top enriched pathways (raw P < 0.05; Figure 3C and Table 4).

Validation of top DEGs by qRT-PCR
The top five downregulated genes (HCN3, OLFML2A, NOX1, MRGPRX1, and BRIP1) and the top five upregulated genes (CCL22, PNMA2, TBX20, ERAS, and C12orf68) were validated by qRT-PCR. The validated genes and corresponding microarray logFC values are listed in Table 5, qRT-PCR validation is shown in Figure 4, and raw Ct values are provided in Supplemental Table S1. The qRT-PCR fold changes were directionally consistent with microarray data for all ten genes (all P < 0.05 by Mann-Whitney U test). Pearson correlation between microarray logFC and qRT-PCR logFC was r = 0.89 (95% CI: 0.66–0.97, P = 0.0004), indicating strong agreement. SUCNR1 and GPR37L1 expression differences were also confirmed (Figure 5A,B).

PPI network analysis
A PPI network was constructed from all DEGs (interaction score > 0.9; Figure 6A). The top ten hub proteins based on degree of connectivity were RTP5 (degree = 14), CXCL1 (degree = 8), CXCL10, GPR37L1, HCAR1, OPRL1, P2RY4, SSTR4, SUCNR1 (degree = 7), and ATM (degree = 5) (Figure 6B and Table 6). A subnetwork of key hub genes was extracted using a subnetwork extraction plugin with default parameters. The subnetwork extraction algorithm identified a densely connected cluster containing RTP5, CXCL1, CXCL10, GPR37L1, HCAR1, OPRL1, P2RY4, SSTR4, and SUCNR1, with a cluster score of 6.2. ATM was not included in the subnetwork because it had lower connectivity with the core cluster (Figure 6C).

Association between verbal memory and SUCNR1/GPR37L1
SUCNR1 expression was significantly elevated, whereas GPR37L1 expression was reduced, in patients with schizophrenia compared with healthy controls (Figure 5A,B). SUCNR1 expression showed a negative correlation with verbal memory scores (r = -0.54, 95% CI: -0.79 to -0.13, R² = 0.287, P = 0.015; Figure 5C), whereas GPR37L1 expression showed a positive correlation with verbal memory scores (r = 0.59, 95% CI: 0.20 to 0.82, R² = 0.349, P = 0.0034; Figure 5D). Correlation analyses were performed on all 20 subjects, including 10 schizophrenia patients and 10 healthy controls. With a sample size of 20, the study had 80% power to detect a correlation coefficient of |r| > 0.6 at α = 0.05. Given the modest sample size, these correlational findings are preliminary and require validation in larger independent cohorts.

Data Availability Statement
The GSE54913 dataset analyzed in this study is publicly available from the Gene Expression Omnibus. Raw qRT-PCR Ct values are provided in Supplemental Table S1. The analysis scripts, DEG output files, enrichment results, PPI network files, and figure source files are available at https://sandbox.zenodo.org/records/514960 or 10.5072/zenodo.514960.

Figure 1: Flow diagram: data collection, preprocessing, analysis, and validation. mRNA microarray analyses on PBMCs obtained from GSE54913. GO functional and pathway enrichment analyses were performed on the DEGs. The top 10 genes ranked by fold change were selected for validation of microarray data using qRT‑PCR. PPI network analysis identified two hub genes. Finally, ex vivo analysis of the two hub genes SUCNR1 and GPR37L1 was conducted. Abbreviations: DEGs = differentially expressed genes; mRNA = messenger RNA; PBMCs = peripheral blood mononuclear cells; qRT‑PCR = quantitative real‑time polymerase chain reaction; PPI = protein–protein interaction. Please click here to view a larger version of this figure.

Figure 2: DEG selection and hierarchical clustering analysis. (A) Volcano plot of DEGs. The horizontal axis represents log₂(fold change), and the vertical axis represents –log₁₀(P value). Green and red dots represent differentially expressed genes, and black dots represent non-differentially expressed genes. (B) Numbers of downregulated and upregulated DEGs. (C) Heat map of differentially expressed genes. Red indicates upregulation and green indicates downregulation. Abbreviation: DEGs = differentially expressed genes. Please click here to view a larger version of this figure.

Figure 3: GO and KEGG enrichment analyses of DEGs. (A,B) GO enrichment and (C) KEGG enrichment are shown based on raw P-values. Abbreviations: DEGs = differentially expressed genes; GO = Gene Ontology; KEGG = Kyoto Encyclopedia of Genes and Genomes; BP = biological process; CC = cellular component; MF = molecular function. Please click here to view a larger version of this figure.

Figure 4: Validation of microarray data for the top ten dysregulated genes using qRT-PCR. (A) Downregulated top five DEGs. (B) Upregulated top five DEGs. Data are mean ± standard error of the mean. P values are shown in the corresponding panels. Abbreviations: DEGs = differentially expressed genes; qRT‑PCR = quantitative real‑time polymerase chain reaction. Please click here to view a larger version of this figure.

Figure 5: SUCNR1 and GPR37L1 expression in schizophrenia patients and healthy controls. (A) SUCNR1 and (B) GPR37L1 expression determined by qRT-PCR. Correlation between verbal memory and (C) SUCNR1 and (D) GPR37L1 expression. Data are shown as mean ± standard error of the mean where applicable. P values for correlations are shown in the corresponding panels. Please click here to view a larger version of this figure.

Figure 6: Protein-protein interaction network analysis. (A) PPI network analyzed using a protein-protein interaction database. (B) Proteins ranked by degree of association in the PPI network. (C) Subnetwork visualized using network visualization software after subnetwork extraction analysis. Abbreviation: PPI = protein-protein interaction. Please click here to view a larger version of this figure.

Variable	Control subjects (n = 10)	Schizophrenia patients (n = 10)
Age (year)	36.5 ± 10.6	44.5 ± 9.5
Female sex, n (%)	5 (50)	5 (50)
Daily sleep time (h)	5.4 ± 3.1	7.5 ± 1.2
Body mass index (BMI; kg/m²)	25.2 ± 4.1	22.9 ± 2.3
Verbal memory score	61 ± 18	30.2 ± 10.8
Data are presented as mean ± standard deviation or number (%).

Table 1: Baseline characteristics of schizophrenia patients and control subjects.

Gene symbol	Forward primer sequence (5'→3')	Reverse primer sequence (5'→3')
HCN3	GTCCGCCGGGGCCTGGAT	CCTCCCACTGGTGTATGTAGC
OLFML2A	CAGGCAGAGCGGGCGAAG	AATATTTGCGGACTGGGTCA
NOX1	CACCCCAAGTCTGTAGTGGGAG	CCAGACTGGAATATCGGTGACA
MRGPRX1	CTAGGGTACCACGGAGGATT	TGGTTCTGGAGGCTCCTTGC
BRIP1	CAGATGAGGGCG-TAAGTGA	CGTCCTCCGGAGCTCTCTAG
CCL22	TCCATCATCTCTTCTGACTCTGA	CTGTGGGTCAGAGTCAGAAGAGA
PNMA2	GCGGGTCAATTCTCGGGACA	GTCCTGCCCCCAGGTGGTTT
TBX20	GAGGGAAAGTGTGGAGAGCC	AAGGCTGACCCTCGATTTGG
ERAS	AGTCTATTATTTCGGGCACC	CCTTCGTGGTTCCCTGAGAC
C12orf68	TTCAACCCCTACACCGAGTT	CTTGAACGTGGACTGCAGC
GPR37L1	ATGTTTCTTGCCGAGCAGTG	CCACATGGAATCGGTCTAT
SUCNR1	ACAGAAGCCGACAGCAGAAT	GCACAGGAAAGCAAAGTCAG
β-Actin	CTAAGGCCAACCGTGAAAAG	GCATACAGGGACAACACAG
qRT-PCR: quantitative real-time polymerase chain reaction.

Table 2: PCR primers for qRT-PCR.

GO ID	Term	Raw P-value	Count
GO:0015267	channel activity	0.000000056	13
GO:0022803	passive transmembrane transporter activity	5.78E-08	13
GO:0005216	ion channel activity	0.000000127	12
GO:0022839	ion gated channel activity	0.000000131	11
GO:0022836	gated channel activity	0.000000136	11
GO:0022838	substrate-specific channel activity	0.00000017	12
GO:0005261	cation channel activity	0.00000668	9
GO:0015276	ligand-gated ion channel activity	0.000315	5
GO:0022834	ligand-gated channel activity	0.000315	20
GO:0022890	inorganic cation transmembrane transporter activity	0.000487	20
GO:0008324	cation transmembrane transporter activity	0.0009	18
GO:0099094	ligand-gated cation channel activity	0.00128	16
GO:0005244	voltage-gated ion channel activity	0.001382	16
GO:0022832	voltage-gated channel activity	0.001382	18
GO:0046873	metal ion transmembrane transporter activity	0.001836	21
GO:0022824	transmitter-gated ion channel activity	0.002762	19
GO:0022835	transmitter-gated channel activity	0.002762	13
Note: P-values are raw and unadjusted; significance was defined as raw P < 0.05.

Table 3: Gene ontology analysis of differentially expressed genes (raw P < 0.05).

ID	Description	Raw P-value	Count
hsa04911	Insulin secretion	0.007066	6
hsa04024	cAMP signaling pathway	0.010661	10
hsa03420	Nucleotide excision repair	0.013724	4
hsa04668	TNF signaling pathway	0.023694	6
hsa00480	Glutathione metabolism	0.02466	4
hsa04740	Olfactory transduction	0.032104	15
hsa05222	Small cell lung cancer	0.03572	5
hsa00590	Arachidonic acid metabolism	0.035991	4
hsa04080	Neuroactive ligand-receptor interaction	0.037546	12
hsa05031	Amphetamine addiction	0.045653	4
hsa05203	Viral carcinogenesis	0.046404	8
hsa04933	AGE-RAGE signaling pathway in diabetic complications	0.04831	5
Note: P-values are raw and unadjusted; significance was defined as raw P < 0.05.

Table 4: Kyoto Encyclopedia of Genes and Genomes enrichment analysis of genes (raw P < 0.05).

Gene symbol	Official full name	logFC	Raw P-value
Downregulated
HCN3	Hyperpolarization-activated cyclic nucleotide-gated channel 3	-1.489	0.0001
OLFML2A	Olfactomedin-like protein 2A	-1.521	0.0042
NOX1	NADPH oxidase 1	-1.522	0.0022
MRGPRX1	Mas-related G-protein coupled receptor member X1	-1.526	0.0003
BRIP1	Fanconi anemia group J protein	-1.699	0.0018
Upregulated
CCL22	C-C motif chemokine 22	2.517	0.0041
PNMA2	Paraneoplastic Ma antigens	2.159	0.0062
TBX20	T-box transcription factor TBX20	1.866	0.0001
ERAS	GTPase Eras	1.846	0.0008
C12orf68	Coiled-coil domain containing 184	1.839	0.0002
Note: logFC and raw P-values are from the microarray differential expression analysis.

Table 5: Top ten DEGs ranked by fold change in upregulated and downregulated genes.

Gene symbol	Description	Co-genes (n)	P-value
RTP5	Receptor transporter protein 5	14	0.000276
CXCL1	Growth-regulated alpha protein 1	8	0.011423
CXCL10	C-X-C motif chemokine 10	7	0.046144
GPR37L1	Prosaposin receptor GPR37L1	7	0.003792
HCAR1	Hydroxycarboxylic acid receptor 1	7	0.046572
OPRL1	Nociceptin receptor 1	7	0.039753
P2RY4	P2Y purinoceptor 4	7	0.001279
SSTR4	Somatostatin receptor type 4	7	0.006742
SUCNR1	Succinate receptor 1	7	0.000105
ATM	Serine-protein kinase ATM	5	0.004961
PPI: protein-protein interaction; DEGs: differentially expressed genes.

Table 6: Top 10 hub genes identified in the PPI network for DEGs.

Supplementary Table 1: Raw qRT‑PCR Ct Values, Complete Bioinformatics Analysis Workflow, and Processed Results (DEG, GO/KEGG, and PPI) for the Study Please click here to download this File.

Dyskusja

Schizophrenia is a severe, multifactorial disorder involving numerous genetic susceptibility factors^13,14. While progress has been made in understanding its pathophysiology, reliable diagnostic biomarkers are still lacking. In this study, an integrated bioinformatics approach identified 473 DEGs in blood samples from patients with schizophrenia. GO and KEGG analyses highlighted enrichment in channel activity-related terms and pathways, including insulin secretion, cAMP signaling, nucleotide excision repair, TNF signaling, and glutathione metabolism. PPI network analysis further identified SUCNR1 and GPR37L1 as hub genes, and their expression was associated with verbal memory performance, suggesting their potential relevance as schizophrenia-associated molecular markers.

The enrichment of "channel activity" GO terms aligns with prior schizophrenia research. Voltage-gated calcium channels (e.g., CACNA1C, CACNB2) and potassium channels (e.g., Kv3, Kv2.1) have been implicated in susceptibility to schizophrenia and neuronal excitability 15,16. Similarly, the top KEGG pathways identified are consistent with existing literature. Hyperinsulinemia and insulin secretion abnormalities are reported in early schizophrenia¹⁷. The cAMP signaling pathway is increasingly linked to disease pathophysiology¹⁸. Nucleotide excision repair mechanisms, including histone variant H2AX modification, may also play a role^19,20,21,22. Furthermore, TNF signaling and glutathione metabolism have been associated with schizophrenia pathogenesis^23,24.

Verbal memory impairment is a core cognitive deficit in schizophrenia, is present at illness onset, and is associated with functional outcomes. The Hopkins Verbal Learning Test–Revised (HVLT-R) is a well-validated measure of verbal learning and memory. Assessing the correlation between candidate marker expression and verbal memory performance helps establish clinical relevance, as cognitive impairment is a major determinant of disability in schizophrenia.

Although the top ten DEGs validated by qRT-PCR matched microarray data, they did not align closely with the primary GO/KEGG findings. In contrast, SUCNR1 (upregulated) and GPR37L1 (downregulated) were significantly associated with "ion channel activity" and enriched in "insulin secretion." Both genes encode G protein-coupled receptors (GPCRs), which are critical targets of antipsychotics and modulate ion channel activity²⁵.

SUCNR1 (GPR91) links metabolic stress to insulin secretion and resistance^26,27, and insulin resistance is a known risk factor for schizophrenia²⁸. GPR37L1, an orphan receptor highly expressed in the brain, is involved in cerebellar development and motor function²⁹, and has been implicated in Parkinson’s disease³⁰ and renal sodium transport³¹. Its role in schizophrenia may involve modulation of ion channel activity.

Verbal memory impairment is a core feature of schizophrenia, reflecting genetic liability and disease severity^32,33. The inverse correlation of SUCNR1 expression and the positive correlation of GPR37L1 expression with verbal memory scores further support their potential relevance in schizophrenia.

Several limitations should be acknowledged. First, the sample size for the validation cohort (n = 10 per group) was small, which limits statistical power and generalizability. Second, all schizophrenia patients were receiving antipsychotic medications, so the observed expression changes may reflect medication effects rather than disease pathology. Future studies should include drug-naïve first-episode patients. Third, the validation cohort lacked RNA-seq confirmation; transcriptome-wide sequencing in larger independent cohorts is warranted. Fourth, the cross-sectional design precludes assessment of causal relationships between biomarker levels and disease progression. Fifth, the correlation analyses with verbal memory were exploratory and require replication. Sixth, the GSE54913 dataset was generated on a microarray platform that has lower sensitivity than RNA-seq. Future directions include longitudinal studies to track SUCNR1 and GPR37L1 levels over the course of illness and treatment, functional studies to elucidate the mechanistic roles of these GPCRs in the pathophysiology of schizophrenia, and the development of a clinically validated assay for these biomarkers.

In summary, this integrated analysis suggests that altered SUCNR1 and GPR37L1 expression is associated with schizophrenia and verbal memory performance. These genes may represent candidate schizophrenia-associated molecular markers. However, limitations include a relatively small sample size and the partial assessment of schizophrenia severity through verbal memory. Further studies are warranted to validate these findings.

Materiały

Lista materiałów użytych w tym artykule

Nazwa	Firma	Numer katalogowy	Komentarze
affy	Bioconductor	https://bioconductor.org/packages/affy/	microarray preprocessing software. Description: R package for microarray preprocessing
clusterProfiler	Bioconductor	https://bioconductor.org/packages/clusterProfiler/	functional enrichment analysis software. Description: R package for functional enrichment analysis
CytoHubba	National Institute of Bioinformatics	https://apps.cytoscape.org/apps/cytohubba	hub gene identification plugin. Description: Cytoscape plugin for hub gene identification
Cytoscape	Cytoscape Consortium	https://cytoscape.org/	network visualization software. Description: Software for network visualization and analysis
G*Power	Heinrich Heine University Düsseldorf	https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower	sample size calculation software. Description: Sample size calculation software
GraphPad Prism	GraphPad Software	https://www.graphpad.com/	statistical analysis and graphing software. Description: Statistical analysis and graphing software
Histopaque-1077	Sigma-Aldrich	10771	density gradient medium. Description: Density gradient medium for PBMC isolation
impute	Bioconductor	https://bioconductor.org/packages/impute/	missing-value imputation software. Description: R package for missing-value imputation
Limma	Bioconductor	https://bioconductor.org/packages/limma/	differential expression analysis software. Description: R package for differential expression analysis
MCODE	Cytoscape app	https://apps.cytoscape.org/apps/mcode	subnetwork extraction plugin. Description: Cytoscape plugin for subnetwork extraction
R/Bioconductor	R Foundation	https://www.r-project.org/	statistical computing environment. Description: Statistical computing environment
STRING	EMBL	https://string-db.org/	protein-protein interaction database. Description: Protein-protein interaction database
SYBR Green	Takara	RR820A	fluorescent dye for qRT-PCR. Description: Fluorescent dye for qRT-PCR
TRIzol	Takara	9109	RNA extraction reagent. Description: Reagent for RNA extraction