Redox-associated gene and microRNA signatures in degenerative intervertebral disc disease

Abstract

Intervertebral disc degeneration (IVDD) is a major cause of chronic low back pain and disability worldwide. Growing evidence highlights oxidative stress as a key driver of disc degeneration; however, the integrated relationships between gene expression, regulatory microRNAs (miRNAs), and protein-level changes across disease stages remain insufficiently understood. This study aimed to identify oxidative stress-related molecular signatures in IVDD and to explore their miRNA-mediated regulation across degeneration grades. The study included 200 patients with lumbosacral IVDD undergoing microdiscectomy and 100 postmortem control samples without spinal pathology. Degeneration severity was classified using the Pfirrmann scale, and pain intensity was assessed with the Visual Analog Scale (VAS). Gene expression of oxidative stress markers was evaluated using RT-qPCR, while protein levels were quantified by ELISA. Additionally, bioinformatic prediction and RT-qPCR validation were used to analyze mRNA–miRNA interactions. Gene expression analysis revealed progressive downregulation of antioxidant genes CAT (FC ≈ −6.40) and GPX1 (FC ≈ −9.56), alongside upregulation of MAPK8 (FC ≈ 8.18) and IL6 (FC ≈ 8.18), with a moderate increase in NRF1 expression. These values reflect comparisons between advanced degeneration (G5) and controls. In contrast, protein analysis showed an inverse trend, with increasing levels of CAT, GPX1, and NRF1 and decreasing levels of MAPK8 and IL6 as degeneration progressed. miRNA profiling demonstrated significant dysregulation, including downregulation of miR-3163 and miR-196a-1-3p and upregulation of miR-665-3p and miR-4686. Correlation analysis indicated that molecular alterations were more strongly associated with structural degeneration than with pain intensity, as VAS-related differences were generally weak and non-significant. Overall, the results reveal a complex regulatory network in IVDD, characterized by discordant mRNA–protein expression and significant miRNA involvement. Oxidative stress and inflammatory pathways appear tightly regulated at transcriptional and post-transcriptional levels and are more closely linked to structural degeneration than clinical pain.

Keywords

Oxidative stress intervertebral disc degeneration low back pain microRNAs Severity of Illness Index pain

Introduction

Intervertebral disc degeneration (IVDD) is one of the leading causes of chronic low back pain and long-term disability, affecting an increasing proportion of the global population.¹ The intervertebral disc (IVD) is a fibrocartilaginous structure consisting of three main components: the gelatinous nucleus pulposus (NP), the collagen-rich annulus fibrosus (AF), and the cartilaginous endplates (CEPs) that anchor the disc to adjacent vertebral bodies.² The physiological function of the IVD depends on maintaining extracellular matrix (ECM) homeostasis and the delicate balance between anabolic and catabolic activities.³ During degeneration, this equilibrium is disrupted, leading to proteoglycan loss, collagen disorganization, decreased hydration, and compromised biomechanical properties.^4,5

The degree of degeneration is typically classified using Pfirrmann’s grading system, which is based on magnetic resonance imaging (MRI) characteristics, including signal intensity, distinction between NP and AF, disc height, and structural homogeneity. This scale ranges from Grade 1 (G1) – representing a healthy disc – to Grade 5 (G5), indicating severe degeneration with loss of NP-AF distinction, reduced signal intensity, and collapsed disc space. The Pfirrmann scale provides an objective and reproducible method for correlating radiological findings with biochemical and molecular alterations in the disc tissue.^6–8

Among the diverse pathophysiological processes implicated in IVDD, oxidative stress (OS) has emerged as a central driver of degeneration.^9,10 Oxidative stress arises from the excessive production of reactive oxygen species (ROS) and/or impaired antioxidant defense mechanisms.¹¹ Elevated ROS levels lead to mitochondrial dysfunction, lipid peroxidation, DNA damage,¹² and activation of pro-degenerative signaling cascades such as the mitogen-activated protein kinase (MAPK), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) pathways. In IVD cells, oxidative imbalance induces apoptosis, senescence, and autophagy, and amplifies the inflammatory response through the release of cytokines such as interleukin-6 (IL6) and tumor necrosis factor alpha (TNF-α).^9,13,14

In parallel, the activity of endogenous antioxidant systems – including catalase (CAT) and glutathione peroxidase 1 (GPX1) – declines, further aggravating the redox imbalance. Another key regulator of oxidative metabolism is nuclear respiratory factor 1 (NRF1), a transcription factor that modulates mitochondrial biogenesis and the expression of antioxidant enzymes. Collectively, these alterations contribute to a self-perpetuating degenerative cycle in which ROS accumulation accelerates ECM degradation and chronic inflammation, thereby worsening disc structural and functional decline.^15,16

Despite extensive research on the molecular basis of IVDD, the interrelationship between transcriptional, post-transcriptional, and translational regulation of oxidative stress-related genes across different stages of degeneration remains poorly understood.^17–20 Previous studies have often been limited to single molecular markers or restricted to one stage of disc degeneration, without integrating gene expression, microRNA (miRNA) regulation, and protein-level validation.^17–20 Moreover, the identification of molecular signatures that remain consistently altered across multiple Pfirrmann grades could help reveal the fundamental oxidative stress mechanisms common to all stages of disc pathology.^17–20

To address these knowledge gaps, the present study employed an integrated multi-omics approach combining transcriptomic profiling, miRNA expression analysis, and protein quantification using enzyme-linked immunosorbent assay (ELISA). From the initial transcriptomic dataset, five key oxidative stress-related genes – MAPK8, IL6, CAT, GPX1, and NRF1 – were selected, as they were consistently expressed across all degeneration grades (G2–G5) and played pivotal roles in redox regulation, inflammatory signaling, and mitochondrial homeostasis. For these five targets, corresponding regulatory miRNAs were identified, and protein concentrations were measured using ELISA to validate transcriptional findings.

The aim of this study was to identify and validate oxidative stress-related molecular signatures that are common to all grades of IVDD, and to elucidate their regulatory relationships with miRNAs. By integrating transcriptomic, post-transcriptional, and protein-level data, this research provides new insights into the mechanisms of oxidative imbalance in IVDD and highlights potential biomarkers that may serve as diagnostic indicators or therapeutic targets throughout the spectrum of disc degeneration.

Material and methods

Ethical considerations

This study was approved by the Bioethics Committee of the State Academy of Applied Sciences in Przemyśl (approval no. 8/2024, issued on August 1, 2024) and by the Bioethics Committee at the District Medical Chamber in Kraków (approval no. 162/KBL/OIL/2021, issued on December 11 June 2021). All participants provided written informed consent prior to enrollment, confirming their voluntary participation and agreement to the use of anonymized data for analytical and publication purposes. Individuals were fully informed of their right to withdraw from the study at any stage without prejudice. The present investigation extends prior research conducted by our group and other investigators in this field.²¹ This study methodology was built upon the work conducted in our previous papers.^22,23

Study design

To identify genes associated with oxidative stress, an initial screening was performed using the GeneCards database (accessed August 1, 2025). The search for the term “oxidative stress” yielded 14,130 genes, reflecting the broad spectrum of molecular components potentially involved in oxidative stress-related biological processes. From this large dataset, genes were prioritized based on their Relevance Score, which reflects the strength of association between a gene and the searched biological concept, as well as the GeneCards Inferred Functionality Score (GIFtS) indicating the level of functional annotation. Based on these criteria and biological relevance to oxidative stress signaling, a panel of candidate genes was selected for further molecular analysis, including TP53, NOS1, NOS2, NOS3, SOD1, SOD2, SOD3, CAT, GPX1, IL6, TNF, IL1B, MAPK1, MAPK8, MAPK10, JUN, BCL2, TLR4, CXCL8, CYBA, IFNG, MMP9, CRP, ICAM1, SLC2A1, SIRT3, and CAV3. The expression levels of selected oxidative stress-related genes were subsequently evaluated at the transcriptional level using quantitative reverse transcription PCR (RT-qPCR).

To validate whether transcriptional changes translated into functional protein alterations, the concentrations of selected proteins representing key oxidative stress pathways were measured using ELISA. Proteins analyzed included MAPK8, IL6, CAT, GPX1, and NRF1, which represent distinct components of oxidative stress-related signaling, including inflammatory response (IL6), stress-activated kinase signaling (MAPK8), antioxidant defense mechanisms (CAT and GPX1), and mitochondrial regulatory pathways (NRF1). Protein concentrations were quantified according to the manufacturers’ protocols and expressed as ng/mL or pg/mL depending on the analyte. To investigate potential post-transcriptional regulatory mechanisms, an in silico analysis of miRNA–mRNA interactions was conducted using the miRDB platform (accessed August 1, 2025) and the miRanda prediction algorithm (accessed August 1, 2025). These databases were used to identify miRNAs potentially targeting the analyzed oxidative stress-related genes. Predicted miRNA–mRNA interactions with confidence scores above 90 were considered highly reliable, whereas those with scores below 60 were classified as low-confidence interactions and retained for potential future experimental validation.^24,25 Based on the prediction results, selected candidate miRNAs were subsequently evaluated experimentally. To further explore the functional relationships among the analyzed oxidative stress-related genes, a protein–protein interaction (PPI) network analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (accessed August 1, 2025). A comprehensive schematic representation of the study design, including patient recruitment, sample collection, and molecular analyses, is presented in Figure 1.

Figure 1.

Study design and experimental workflow.

Study group

This section was built upon the work conducted in our previous paper.^22,23 The study included 200 Caucasian individuals (94 females and 106 males) diagnosed with degenerative pathology of the lumbosacral IVD and scheduled for microdiscectomy. The mean age of the cohort was 49.56 ± 15.19 years. Diagnosis was established based on clinical presentation, neurological examination, and MRI findings confirming IVDD. Anthropometric measurements revealed a mean body weight of 83.14 ± 15.83 kg, an average height of 172.39 ± 12.31 cm, and a mean body mass index (BMI) of 26.43 ± 3.38 kg/m². Based on World Health Organization BMI criteria, 43 participants were classified as normal weight (18.5–24.99 kg/m²), 105 as overweight (25.00–29.99 kg/m²), and 52 as obese (≥30.00 kg/m²). All patients received standard pharmacological management appropriate for degenerative spine disorders, including analgesic, anti-inflammatory, and muscle relaxant medications. Participants were eligible if they were adults (≥18 years), provided written informed consent, and had isolated degenerative changes of the lumbosacral IVD confirmed radiologically and clinically, meeting surgical qualification criteria. All patients experienced persistent discogenic lumbar pain unresponsive to conservative treatment for a minimum of 6 weeks. Individuals with symptoms suggestive of radiculopathy or coexisting inflammatory, autoimmune, or structural spinal disorders were excluded. The duration of symptom exacerbation did not exceed 12 weeks. Exclusion criteria comprised age below 18 years, lack of informed consent, radiological evidence of disc herniation with extrusion or sequestration, history of previous lumbar spine surgery, and presence of systemic inflammatory, neoplastic, metabolic, autoimmune, or osteoporotic diseases. Patients with symptom duration shorter than 6 weeks or longer than 12 weeks were also excluded to ensure cohort homogeneity. All neurological examinations were conducted by the same neurosurgeon using a standardized protocol to reduce interobserver variability. The assessment included evaluation of muscle strength and tone in the lower extremities, passive range of motion, tendon reflexes (including patellar, Achilles, plantar, Babinski, and Rossolimo reflexes), sensory function, posture, gait, and spinal mobility, as well as palpation of the lumbosacral region. Pain intensity was quantified using the Visual Analog Scale (VAS), ranging from 0 (no pain) to 10 (maximum pain intensity). These clinical parameters were recorded and incorporated into the study database. Preoperative MRI scans were performed using a Signa Hde 1.5T system (General Electric Medical Systems, Poland). Imaging included spin echo (SE) T1-weighted sequences, fluid-attenuated inversion recovery (FLAIR), fast spin echo (FSE) T2-weighted sequences, and Short Tau Inversion Recovery (STIR), acquired in sagittal and axial planes with slice thicknesses of 3 mm and 4 mm. The degree of IVDD was independently evaluated by two experienced neurosurgeons according to the Pfirrmann classification system.

Control group

The control cohort consisted of 100 Caucasian individuals (53 women and 47 men; mean age 37.3 ± 1.8 years) whose IVDs were collected postmortem from the lumbosacral spine within 48 h of death. Clinical information was obtained from medical documentation and, when necessary, verified through interviews with family members.

Only samples from individuals aged 18–45 years without a history of spinal pathology, metabolic or neoplastic disease, or systemic inflammation were included. Histological evaluation with hematoxylin and eosin (H&E) staining confirmed the absence of degenerative features prior to inclusion. Exclusion criteria comprised samples from individuals outside the age range, those showing histopathological evidence of degeneration, or cases with medical histories suggestive of disorders potentially influencing disc structure or composition.

The control group had a mean body weight of 81.41 ± 13.15 kg, height of 171.76 ± 12.32 cm, and BMI of 28.19 ± 5.34 kg/m². The distribution of BMI categories in the control group was comparable to that observed in the study cohort, with 22 individuals classified as normal weight (BMI 18.5–24.99 kg/m²), 52 as overweight (BMI 25–29.99 kg/m²), and 26 as obese (BMI ≥30 kg/m²).

Table 1 presents the demographic and clinical characteristics of patients IVDD and control individuals.

Table 1.

Baseline characteristics of study participants.

Variable	Study group (patients with IVDD, n = 200)	Control group (postmortem, n = 100)
Age (years, mean ± SD)	49.56 ± 15.19	37.3 ± 1.8
Sex, n (%)	94 women (47.0%), 106 men (53.0%)	53 women (53.0%), 47 men (47.0%)
Body weight (kg, mean ± SD)	83.14 ± 15.83	81.41 ± 13.15
Height (cm, mean ± SD)	172.39 ± 12.31	171.76 ± 12.32
BMI (kg/m², mean ± SD)	26.43 ± 3.38	28.19 ± 5.34
BMI category, n (%)	Normal: 43 (21.5%); overweight: 105 (52.5%); obese: 52 (26.0%)	Normal: 22 (22.0%); overweight: 52 (52.0%); obese: 26 (26.0%)
MRI grading	Pfirrmann classification by two neurosurgeons (1.5 T MRI)	Not applicable; histologically confirmed normal discs
Medications prior to surgery	Analgesics, NSAIDs, and muscle relaxants (standard regimen)	n/a

BMI: body mass index; H&E: hematoxylin and eosin; MRI: magnetic resonance imaging; NSAIDs: nonsteroidal anti-inflammatory drugs; SD: standard deviation; VAS: Visual Analogue Scale for pain.

Collection of biological material for analysis

During microdiscectomy, IVD fragments were excised en bloc. The procedure involved a standard posterior approach with incision through the lumbosacral region, followed by paraspinal muscle dissection and exposure of the affected spinal segment. The degenerated disc fragment was identified and removed. Any blood accumulating in the surgical field was evacuated via a catheter, which was subsequently removed within 24 h postoperatively.

Postmortem IVD tissue collection

This section was built upon the work conducted in our previous paper.^22,23 Control IVD specimens were obtained during forensic autopsies or organ procurement procedures. Tissue collection was performed within 48 h after death to minimize postmortem degradation. Following exposure of the anterior aspect of the lumbosacral spine, disc fragments were carefully excised using sterile surgical instruments. Each sample was immediately transferred into sterile, individually labeled containers and securely sealed to preserve structural and molecular integrity for subsequent analysis.

Hematoxylin and eosin (H&E) staining

This section was built upon the work conducted in our previous paper.^22,23

Both surgically obtained and postmortem tissue specimens were subjected to histological analysis using hematoxylin and eosin (H&E) staining. Tissue samples were fixed, embedded in paraffin, and sectioned at a thickness of 5 µm. Staining was performed using a commercially available kit (Abcam, Cambridge, MA, USA) containing modified Lillie–Mayer hematoxylin and eosin Y solutions. The staining procedure included deparaffinization, gradual rehydration through descending ethanol concentrations, nuclear staining with hematoxylin, washing, cytoplasmic counterstaining with eosin, followed by dehydration, xylene clearing, and permanent mounting. Histopathological evaluation was conducted using established and widely accepted grading systems for human IVDD, including the Boos Histological Degeneration Score,²⁶ the classification proposed by Rutges et al.,²⁷ and the consensus criteria developed by the Orthopaedic Research Society (ORS) Spine Section.²⁸ Degenerative changes were characterized by structural disruption of the annulus fibrosus, including fragmentation and fissure formation, increased cellular density with clusters of chondrocyte-like cells within the nucleus pulposus, alterations in extracellular matrix composition such as mucoid degeneration, structural abnormalities of the cartilaginous endplates, and evidence of neovascular infiltration. In contrast, control tissues demonstrated preserved histoarchitectural integrity without features indicative of degeneration. Hematoxylin and eosin staining was employed as the standard histological approach due to its proven effectiveness in assessing tissue morphology, cellular organization, and degenerative structural changes.^26–28 Representative microphotographs illustrating these findings have been reported previously.²²

Extraction of total ribonucleic acid (RNA)

This section was built upon the work conducted in our previous paper.^22,23 Total RNA including miRNAs was extracted from tissue specimens using TRIzol Reagent (Invitrogen Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s protocol. To enhance RNA purity and remove potential contaminants, the isolated RNA was subsequently purified using the RNeasy Mini Kit (QIAGEN, Hilden, Germany). In addition, samples were treated with DNase I (Fermentas International Inc., Burlington, ON, Canada; Cat. No. 18047019) to eliminate residual genomic DNA contamination. RNA integrity was initially assessed by electrophoresis on a 1% agarose gel containing ethidium bromide, confirming the presence of clearly defined 28S and 18S rRNA bands. RNA concentration and purity were determined spectrophotometrically by measuring absorbance at 260 and 280 nm. Only samples with an A260/A280 ratio ranging from 1.8 to 2.0 were considered acceptable for downstream analyses. To obtain a more objective evaluation of RNA quality, RNA integrity was further assessed using the RNA Integrity Number (RIN) determined by capillary electrophoresis with the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RIN values were evaluated for both the study and control groups to ensure comparable RNA quality and to minimize potential bias related to RNA degradation. Only RNA samples with RIN values ≥7.0 were included in subsequent RT-qPCR analyses.

RT-qPCR analysis of mRNA and miRNA expression

RT-qPCR was performed to validate the expression of genes related to oxidative stress and selected miRNAs. Total RNA extracted from tissue samples was used for both mRNA and miRNA analyses. For mRNA expression analysis, RT-qPCR was conducted using the SensiFAST SYBR No-ROX One-Step kit (Bioline, London, UK) according to the manufacturer’s protocol. Each reaction contained total RNA as a template and gene-specific primers listed in Table 2. The amplification was carried out using the following thermal cycling conditions: reverse transcription at 45°C for 10 min, polymerase activation at 95°C for 2 min, followed by 40 amplification cycles consisting of denaturation at 95°C for 5 s, annealing at 60°C for 10 s, and extension at 72°C for 5 s. Relative mRNA expression levels were calculated using the comparative 2^−ΔΔCt method. β-actin (ACTB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as endogenous reference genes for normalization.

Table 2.

Nucleotide sequences of primers used in RT-qPCR analysis.

mRNA	Oligonucleotide sequence (5′–3′)
CAT	Forward: TTGTTGAGGTTGAATAGATAGTTTT
CAT	Reverse: ACCCTAATTATCCTACATACACATC
SOD1	Forward: TTTGGGTAATGTGATTGTTGATAAA
SOD1	Reverse: AAAACCTCAAACTACATCCAAAAAA
NOS1	Forward: TTGGGGGATTTAGTTAATGTGTAGT
NOS1	Reverse: AAAACTTCCAACACCAACTCTAAAA
NOS2	Forward: GATTGTTTTGGTAATGGAGAGAAAT
NOS2	Reverse: AAACTAAAAAACCCCCAAATAAAAC
NOS3	Forward: AGAAGATTTTTAAAGAAGTGGTTAA
NOS3	Reverse: TATACTAATTACCACCAACACCAAC
TP53	Forward: GTTTAGGGAGTAGGTAGTTGTTGG
TP53	Reverse: AATATTTCCTAACTCAAAAAAAACTC
TNF	Forward: GTTTTTGTTAGAGGGAGATTTTAGAG
TNF	Reverse: ACAATAATCCCAAAATAAACCTACC
SOD2	Forward: GTTAAGGGTAGGTTTTAGGTTGAG
SOD2	Reverse: TAACAAAAATATATTCAAACCCATC
MAPK8 (JNK1)	Forward: ATTGTATTTTGAAGATTTTTGATTT
MAPK8 (JNK1)	Reverse: AATTTTATAACAAACCATTTCTCCC
TLR4	Forward: GGATGATGTTAGGATGATGTTTGT
TLR4	Reverse: CCTCAAAAAATTAAAACTCAAATCC
IL6	Forward: TTTAGGAGAAGATTTTAAAGATGTAGT
IL6	Reverse: TCTCCTTTCTCAAAACTAAAATACC
IL1B	Forward: TTTAGGATTTGGATTTTTGTTTTTT
IL1B	Reverse: ACATCTTCCTCAACTTATCCATAAC
GPX1	Forward: ATTCCCTCAAGTACGTCCGG
GPX1	Reverse: GGAGACCAGGTGATGAGCTT
JUN	Forward: TTTCAGGAGGCTGGAGGAAG
JUN	Reverse: CTGCCACCAATTCCTGCTTT
CXCL8 (IL8)	Forward: TAAATATGATTTTTAAGTTGGTCGT
CXCL8 (IL8)	Reverse: TTACAATAATTTCTATATTAACGCA
CRP	Forward: GGTTTTTGTATTATTGGTAGTAGGA
CRP	Reverse: CACAATAAAAACTTTAAAAAACTTC
BCL2	Forward: GTTTTCGTTTTCGGTTTTTTTATC
BCL2	Reverse: CCTATAATATTTTCCCCTTCTCGAC
IFNG	Forward: GGTAATTGATTTGAATGTTTAACGT
IFNG	Reverse: CAACCATTACTAAAATACTCTTCGAC
MAPK10 (JNK3)	Forward: GGTAAATATTGTCGGATTAGGATC
MAPK10 (JNK3)	Reverse: TAATTTCTCATCTATAAAAAACGAA
SOD3	Forward: GTGTTTTTGTTTGTTTTTGGTAGTC
SOD3	Reverse: GTAACCTTAACGTACATATCTCGAA
CAV3	Forward: GTAAGGAGATTGATTTGGTGAATC
CAV3	Reverse: AATAATATAACTCACCTTCCACACG
SLC2A1 (GLUT1)	Forward: TTAATGTTTTTTAGAAGGTGATCGA
SLC2A1 (GLUT1)	Reverse: AACCACTAAAAAAAACCAAAACGTA
MMP9	Forward: TATTTTGTTTGTATTATCGACGGTC
MMP9	Reverse: CGTCCTAAATATAAAATCTCTCGCT
NRF1	Forward: TTTTTTTCGAGGATATTTTTTACGA
NRF1	Reverse: CTATTACCACAACAACAACAACGAC
ICAM1	Forward: GAAGTTAGAGGTTTTAGAAGGGATC
ICAM1	Reverse: AATTACAAAACAAAAAAAACTACGC
SIRT3	Forward: AAGATTTTTTTTAGGGGAGGATATT
SIRT3	Reverse: TCCCAAAAATAAACAACAAATCTAC
GAPDH	Forward: GGTGAAGGTCGGAGTCAACGGA
GAPDH	Reverse: GAGGGATCTCGCTCCTGGAAGA
ACTB	Forward: TCACCCACACTGTGCCCATCTACGA
ACTB	Reverse: CAGCGGAACCGCTCATTGCCAATGG
hsa-miR-3163	Forward: AACGGCTATAAAATGAGGGCAGT
hsa-miR-3163	Reverse: CAGTGCAGGGTCCGAGGT
hsa-miR-665	Forward: GCCGAGACCAGGAGGCTGA
hsa-miR-665	Reverse: CTCAACTGGTGTCGTGGA
hsa-miR-196a-1-3p	Forward: TAGGTAGTTTCATGTTGTTGG
hsa-miR-196a-1-3p	Reverse: GAACATGTCTGCGTATCTC
hsa-miR-4686	Forward: AACACGTGTATCTGCTGGGCT
hsa-miR-4686	Reverse: CAGTGCAGGGTCCGAGGT
RNU6	Forward: GCTTCGGCAGCACATATACTAAAAT
RNU6	Reverse: CGCTTCACGAATTTGCGTGTCAT

BCL2: B-cell lymphoma 2; CAT: catalase; CAV3: caveolin-3; CRP: C-reactive protein; CXCL8: C-X-C motif chemokine ligand 8 (interleukin-8); CYBA: cytochrome b-245 α chain (p22phox); GPX1: glutathione peroxidase 1; ICAM1: intercellular adhesion molecule 1; IFNG: interferon γ; IL1B: interleukin 1 β; IL6: interleukin 6; JUN: Jun proto-oncogene (AP-1 transcription factor subunit); MAPK1: mitogen-activated protein kinase 1 (ERK2); MAPK10: mitogen-activated protein kinase 10 (JNK3); MAPK8: mitogen-activated protein kinase 8 (JNK1); MMP9: matrix metallopeptidase 9 (gelatinase B); NOS1: nitric oxide synthase 1 (neuronal NOS); NOS2: nitric oxide synthase 2 (inducible NOS); NOS3: nitric oxide synthase 3 (endothelial NOS); NRF1: nuclear respiratory factor 1; SIRT3: sirtuin 3 (NAD-dependent deacetylase); SLC2A1: solute carrier family 2 member 1 (glucose transporter 1, GLUT1); SOD1: superoxide dismutase 1 (Cu/Zn SOD); SOD2: superoxide dismutase 2 (Mn SOD, mitochondrial); SOD3: superoxide dismutase 3 (extracellular SOD); TLR4: toll-like receptor 4; TNF: tumor necrosis factor α; TP53: tumor protein p53.

For miRNA expression analysis, 500 ng of total RNA containing the small RNA fraction was subjected to reverse transcription using the miScript II RT Kit (Qiagen, Hilden, Germany), following the manufacturer’s instructions. During reverse transcription, mature miRNAs were polyadenylated by poly(A) polymerase and subsequently converted into complementary DNA (cDNA) using oligo(dT) primers containing a universal adapter sequence. This strategy enables selective amplification of mature miRNAs during the subsequent real-time PCR step. Quantitative PCR for miRNAs was performed using SYBR Green chemistry. Quantitative PCR for miRNA detection was performed using the cycling conditions recommended for the miScript SYBR Green PCR system: initial activation at 95°C for 15 min followed by 40 cycles of 94°C for 15 s, 55°C for 30 s, and 70°C for 30 s. Reactions were carried out using miRNA-specific forward primers and corresponding reverse primers listed in Table 2. RNU6 small nuclear RNA (RNU6) was used as the endogenous reference control for normalization of miRNA expression levels. Each reaction was performed in triplicate, and no-template controls were included to verify the absence of contamination. Relative expression levels of both mRNAs and miRNAs were determined using the comparative 2^−ΔΔCt method. All primer sequences used in the RT-qPCR assays are summarized in Table 2.

ELISA for protein quantification

To complement the transcriptomic findings, ELISA were conducted to quantify selected proteins associated with the oxidative stress pathway. The analyses were performed according to the manufacturers’ protocols using commercially available kits: Mitogen-Activated Protein Kinase 8 (MAPK8; Cat. No. MBS1603534), Interleukin 6 (IL6; Cat. No. MBS2021124), Catalase (CAT; Cat. No. MBS2021346), and Glutathione Peroxidase 1 (GPX1; Cat. No. MBS765363) from MyBioSource (San Diego, CA, USA). Additionally, the Nuclear Respiratory Factor 1 (NRF1) protein was measured using a Human NRF1 ELISA Kit (Cat. No. EH10654; FineTest^®, Wuhan Fine Biotech Co., Ltd., China).

Absorbance was recorded at 450 nm with a Varioskan Flash Multimode Microplate Reader (Thermo Fisher Scientific, Waltham, MA, USA), and protein concentrations were calculated using standard calibration curves. All assays were performed in triplicate to ensure precision, reproducibility, and analytical reliability.

STRING network and functional enrichment analysis

PPI mapping and functional enrichment analyses were conducted using the STRING database (version 12.0; accessed on 1 August 2025). Networks were generated using a high-confidence interaction threshold (score > 0.7), integrating evidence derived from experimental studies, co-expression data, and curated biological pathway repositories.

The enrichment strength parameter was expressed as log₁₀(observed/expected), indicating the ratio between the number of observed and expected protein–protein associations. Statistical significance was determined using the false discovery rate (FDR), with values of p < 0.05 regarded as significant.

To extend the functional characterization, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied to identify overrepresented biological processes and molecular pathways among the differentially expressed genes. The results were visualized as bubble plots, where bubble size reflected the number of genes associated with each term and color intensity corresponded to FDR significance levels.

Statistical analysis

All statistical analyses were performed using StatPlus Professional (AnalystSoft Inc., Walnut, CA, USA). Data distribution normality was assessed using the Shapiro–Wilk test. Continuous variables are presented as mean ± standard deviation (SD).

For comparisons between two independent groups, the Student’s t-test was applied when the data followed a normal distribution. Comparisons among three or more groups were conducted using one-way analysis of variance (ANOVA), followed by Scheffé’s post hoc test to identify pairwise differences between groups.

Associations between categorical variables, such as the relationship between pain intensity (VAS) and Pfirrmann degeneration grade, were analyzed using the chi-square (χ²) test, and the strength of association was evaluated using Cramér’s V coefficient.

To examine relationships between molecular markers and clinical variables, Spearman’s rank correlation coefficient (ρ) was used due to the potential non-normal distribution of biological data. Furthermore, multiple linear regression analysis was performed to determine the independent effects of VAS score and Pfirrmann degeneration grade on the expression levels of analyzed mRNAs, miRNAs, and proteins.

To estimate the magnitude of observed differences, effect sizes (Cohen’s d) were calculated for major between-group comparisons.

All statistical tests were two-tailed, and results were considered statistically significant at p < 0.05.

Sample size determination

Sample size estimation was conducted using the G*Power software, version 3.1.²⁹ Calculations were based on effect sizes obtained from prior studies investigating elemental and biochemical imbalances in degenerative spinal conditions. To ensure adequate statistical power, the analysis targeted an 80% probability of detecting a true effect (β = 0.20) at a significance level of α = 0.05. This determined that a minimum of 150 patients and 100 control subjects would be required to achieve reliable comparisons between groups. The final study population – comprising 200 patients and 100 controls – exceeded these minimum thresholds, thereby increasing statistical robustness and reducing the risk of Type II error. Additionally, effect size coefficients (Cohen’s d) were computed for major between-group comparisons to assess the magnitude of observed differences and their potential clinical relevance beyond mere statistical significance.

Results

Assessment of pain intensity and the degree of IVD degeneration in the study group

The distribution of patients according to pain intensity assessed using the VAS and the degree of IVDD classified according to the Pfirrmann grading system is presented in Table 3.

Table 3.

Distribution of patients according to pain intensity (VAS) and Pfirrmann degeneration grade.

VAS	Pfirrmann 2	Pfirrmann 3	Pfirrmann 4	Pfirrmann 5	Total
3	8	10	36	0	54
4	4	5	19	0	28
5	5	5	31	0	41
6	6	11	13	2	32
7	3	5	0	0	8
8	4	5	11	0	20
9	2	4	0	0	6
10	0	0	7	4	11
Total	32	45	117	6	200

A total of 200 patients were included in the analysis. The distribution of IVDD according to the Pfirrmann classification showed that the majority of individuals presented grade 4 degeneration (n = 117, 58.5%), followed by grade 3 (n = 45, 22.5%), grade 2 (n = 32, 16.0%), and grade 5 (n = 6, 3.0%).

Pain intensity assessed using the VAS scale ranged from 3 to 10 points, with the largest subgroup observed at VAS 3 (n = 54) and VAS 5 (n = 41). Higher Pfirrmann grades tended to occur more frequently among patients with higher VAS scores.

The chi-square test demonstrated a statistically significant association between VAS score and Pfirrmann grade (χ² = 83.80, df = 21, p < 0.000001). The strength of this relationship, assessed using Cramér’s V coefficient (V = 0.374), indicated a moderate association between pain severity and the degree of disc degeneration.

RT-qPCR analysis of gene expression profiles across disease severity groups

Figure 2 illustrates the relative mRNA expression levels (fold change) of oxidative stress–related genes across degeneration grades (G2–G5) compared to the control group. A distinct and consistent pattern of gene regulation was observed with increasing Pfirrmann grade.

Figure 2.

Oxidative stress-related gene expression analyzed by RT-qPCR across Pfirrmann grades Bar graphs showing relative expression levels (fold change, mean ± SD) of selected genes measured by RT-qPCR. Positive values indicate upregulation and negative values indicate downregulation in degenerated IVD tissue compared with control samples.

Genes associated with antioxidant defense, including CAT, SOD1, GPX1, and SIRT3, demonstrated progressive downregulation across all degeneration stages, with the most pronounced decrease observed in G5. In contrast, genes involved in inflammatory signaling and oxidative stress responses, such as NOS1–3, TNF, IL6, IL1B, MAPK family members (MAPK1, MAPK8, MAPK10), and CXCL8, exhibited a gradual increase in expression with advancing degeneration severity.

Additionally, genes related to extracellular matrix remodeling and immune activation, including MMP9, ICAM1, and TLR4, showed elevated expression levels, particularly in higher Pfirrmann grades. Anti-apoptotic and regulatory genes such as BCL2 also displayed altered expression patterns consistent with disease progression.

Detailed quantitative values, including mean expression levels and statistical analysis, are provided in Supplemental Table 1.

Gene expression analysis demonstrated a consistent pattern of dysregulation associated with increasing pain intensity (Figure 3). Pro-inflammatory and stress-related genes (e.g., MAPK8, IL6, TNF, CXCL8, JUN) showed a tendency toward higher expression at higher VAS scores, particularly at VAS 10. In contrast, antioxidant-related genes (CAT, GPX1, SOD1, SOD3) and anti-apoptotic BCL2 remained downregulated across all VAS levels, with the most pronounced decrease observed at the highest pain intensity. Although a general trend toward greater molecular dysregulation with increasing VAS was observed, differences between intermediate VAS levels were less consistent. Detailed quantitative data, including fold changes, standard deviations, and statistical analysis, are presented in Supplemental Table 2.

Figure 3.

Expression profile of selected genes across VAS pain scores Bar graphs showing fold-change values (mean ± SD) of selected genes measured by RT-qPCR across VAS categories. Positive values indicate upregulation and negative values indicate downregulation in degenerated IVD in comparison to control samples.

Results of ELISA assay

Protein analysis revealed significant differences in the concentrations of all evaluated oxidative stress-related markers across Pfirrmann grades (Table 4; ANOVA, p < 0.001 for all proteins). MAPK8 and IL6 levels showed a progressive decrease with increasing disc degeneration severity, with the highest concentrations observed in the control group and the lowest in grade G5. In contrast, antioxidant-related proteins, including CAT, GPX1, and NRF1, demonstrated a gradual increase across advancing Pfirrmann grades. Post hoc analysis (Scheffé test) confirmed that all degenerative groups (G2–G5) differed significantly from the control group for each analyzed protein, indicating a clear shift in oxidative stress-associated molecular profiles with disease progression.

Table 4.

Protein concentrations (ELISA) of oxidative stress-related markers across Pfirrmann grades.

Protein	C	G2	G3	G4	G5	ANOVA p-value	Post hoc (Scheffé)
MAPK8 (ng/mL)	8.5 ± 1.0	6.2 ± 0.9	5.1 ± 0.8	4.0 ± 0.72	3.6 ± 0.7	<0.001	C vs G2–G5
IL6 (pg/mL)	35.98 ± 6.12	25.12 ± 5.13	21.90 ± 5.20	18.12 ± 4.55	16.12 ± 4.12	<0.001	C vs G2–G5
CAT (ng/mL)	2.81 ± 0.52	3.53 ± 0.51	4.20 ± 0.60	4.84 ± 0.61	5.32 ± 0.74	<0.001	C vs G2–G5
GPX1 (ng/mL)	1.92 ± 0.41	2.51 ± 0.45	3.00 ± 0.50	3.33 ± 0.54	3.69 ± 0.63	<0.001	C vs G2–G5
NRF1 (ng/mL)	0.72 ± 0.12	0.95 ± 0.15	1.15 ± 0.18	1.35 ± 0.20	1.59 ± 0.22	<0.001	C vs G2–G5

C: control; CAT: catalase; G2–G5: grade of disc degeneration according to Pfirrmann’s scale; GPX1: glutathione peroxidase 1; IL6: interleukin 6; MAPK8: mitogen-activated protein kinase 8 (JNK1); NRF1: nuclear respiratory factor 1.

Data are presented as mean ± standard deviation.

Analysis of protein concentrations across VAS-defined subgroups did not reveal statistically significant differences for any of the evaluated markers (Table 5; ANOVA, p > 0.05 for all proteins). However, borderline significance was observed for MAPK8 (p = 0.056) and IL6 (p = 0.052), suggesting a possible trend toward variation with pain intensity. Post hoc analysis identified limited pairwise differences, including IL6 between VAS 5 and VAS 7, and GPX1 between VAS 7 and VAS 10.

Table 5.

Protein concentrations (ELISA) of oxidative stress-related markers across VAS score.

Protein	VAS 3	VAS 4	VAS 5	VAS 6	VAS 7	VAS 8	VAS 9	VAS 10	ANOVA p-value
MAPK8 (ng/mL)	4.41 ± 1.11	4.43 ± 0.73	4.15 ± 0.84	4.61 ± 1.23	5.66 ± 1.16	4.67 ± 1.29	5.35 ± 0.98	4.07 ± 0.89	0.056
IL6 (pg/mL)	18.95 ± 4.96	19.25 ± 4.28	18.96 ± 5.67	19.15 ± 6.06	22.46 ± 5.09	19.83 ± 4.77	20.56 ± 4.69	20.05 ± 4.71	0.052
CAT (ng/mL)	4.63 ± 0.66	4.54 ± 0.68	4.71 ± 0.71	4.63 ± 0.74	4.14 ± 0.69	4.40 ± 0.61	3.92 ± 0.42	4.94 ± 0.63	0.504
GPX1 (ng/mL)	3.18 ± 0.59	3.29 ± 0.45	3.22 ± 0.56	3.09 ± 0.63	2.68 ± 0.27	2.98 ± 0.62	2.98 ± 0.69	3.52 ± 0.83	0.122
NRF1 (ng/mL)	1.24 ± 0.23	1.26 ± 0.18	1.30 ± 0.22	1.21 ± 0.27	0.90 ± 0.20	1.18 ± 0.24	1.14 ± 0.06	1.46 ± 0.19	0.288

CAT: catalase; GPX1: glutathione peroxidase 1; IL6: interleukin 6; MAPK8: mitogen-activated protein kinase 8 (JNK1).

Data are presented as mean ± standard deviation.

Differential expression of oxidative stress-related miRNAs

Expression analysis of selected miRNAs revealed grade-dependent alterations across Pfirrmann stages (Figure 4). hsa-miR-3163 and hsa-miR-196a-1-3p were consistently downregulated in all degeneration grades compared to control samples, with the lowest expression levels observed in G4. In contrast, hsa-miR-665-3p and hsa-miR-4686 were upregulated across all grades, with peak expression detected in G3–G5.Detailed quantitative results, including fold change values (mean ± SD) and statistical analysis, are provided in Supplemental Table 3.

Figure 4.

Expression levels of oxidative stress-related miRNAs across Pfirrmann grades (G2–G5 vs control) Bar graph showing fold-change values (mean ± SD) for miRNAs validated by RT-qPCR. Positive values indicate upregulation and negative values indicate downregulation in degenerated IVD in comparison to control samples.

Expression analysis of selected miRNAs across VAS subgroups revealed relatively stable patterns with no clear linear trend in relation to pain intensity (Figure 5). hsa-miR-3163 and hsa-miR-196a-1-3p remained consistently downregulated across all VAS levels, while hsa-miR-665-3p and hsa-miR-4686 were persistently upregulated. Although minor fluctuations were observed between intermediate VAS groups, the most pronounced changes were noted at VAS 10, particularly for hsa-miR-3163 and hsa-miR-196a-1-3p. Detailed quantitative data, including fold change values (mean ± SD) and statistical analysis, are presented in Supplemental Table 4.

Figure 5.

Expression levels of selected miRNAs across VAS scores Bar graph showing fold-change values (mean ± SD) for miRNAs validated by RT-qPCR. Positive values indicate upregulation and negative values indicate downregulation in degenerated IVD in comparison to control samples.

Association of mRNA, miRNA, and protein expression with pain intensity and degeneration severity

Spearman correlation analysis revealed generally weak and predominantly non-significant associations between mRNA expression levels and pain intensity (VAS), indicating that transcriptional changes in oxidative stress–related pathways were not directly linked to subjective pain severity. Correlation coefficients for VAS were low and clustered close to zero across most analyzed transcripts. Only isolated statistically significant correlations were observed for IL6 (ρ = −0.141, p = 0.047) and IL1B (ρ = −0.161, p = 0.023), although these associations remained weak.

In contrast, strong and statistically significant correlations were consistently observed between gene expression levels and the Pfirrmann degeneration grade. Genes involved in nitric oxide signaling and inflammatory pathways, including NOS1, NOS2, NOS3, TNF, and IL6, demonstrated strong positive correlations with Pfirrmann grade (ρ ≈ 0.78–0.83, p < 0.001). Similarly, transcriptional regulators such as TP53, MAPK8, and JUN were significantly positively associated with increasing degeneration severity.

Among antioxidant-related genes, an opposite pattern was observed. CAT, SOD1, and GPX1 mRNA levels showed strong negative correlations with Pfirrmann grade (ρ ≈ −0.79 to −0.85, p < 0.001), suggesting a progressive decline in antioxidant defense mechanisms with advancing disc degeneration.

Regression analysis confirmed these findings, demonstrating significant negative β coefficients for CAT, SOD1, GPX1, and other protective genes, whereas inflammatory and nitric oxide-related genes exhibited positive β coefficients in relation to Pfirrmann grade (Table 6).

Table 6.

Correlation and multiple regression analysis evaluating associations between mRNAs expression, pain intensity (VAS), and Pfirrmann degeneration grade.

Marker	VAS (ρ)	VAS p	β (VAS)	β (VAS) p	Pfirrmann (ρ)	Pfirrmann p	β (Pfirrmann)	β (Pfirrmann) p
CAT	0.081	0.252	0	0.991	−0.79	<0.001	−0.999	<0.001
SOD1	0.014	0.843	−0.023	0.517	−0.809	<0.001	−1.151	<0.001
NOS1	−0.003	0.967	0.015	0.648	0.787	<0.001	1.014	<0.001
NOS2	−0.027	0.706	0.008	0.836	0.811	<0.001	1.171	<0.001
NOS3	−0.133	0.060	−0.021	0.571	0.813	<0.001	1.194	<0.001
TP53	−0.128	0.071	−0.03	0.480	0.833	<0.001	1.389	<0.001
TNF	−0.034	0.631	0.027	0.466	0.804	<0.001	1.166	<0.001
SOD2	−0.11	0.122	−0.016	0.712	0.843	<0.001	1.41	<0.001
MAPK8	−0.103	0.146	−0.006	0.879	0.829	<0.001	1.277	<0.001
CYBA	0.042	0.558	0.032	0.331	0.789	<0.001	1.018	<0.001
TLR4	−0.042	0.554	0.02	0.576	0.819	<0.001	1.131	<0.001
IL6	−0.141	0.047	−0.028	0.476	0.826	<0.001	1.277	<0.001
MAPK1	−0.021	0.767	0.014	0.669	0.787	<0.001	0.999	<0.001
IL1B	−0.161	0.023	−0.02	0.589	0.831	<0.001	1.209	<0.001
GPX1	0.065	0.363	−0.012	0.795	−0.853	<0.001	−1.492	<0.001
JUN	−0.015	0.834	0.029	0.358	0.781	<0.001	0.971	<0.001
CXCL8	−0.05	0.485	0.009	0.833	0.824	<0.001	1.307	<0.001
CRP	−0.022	0.758	0.017	0.644	0.786	<0.001	1.121	<0.001
BCL2	−0.02	0.782	−0.033	0.434	−0.849	<0.001	−1.4	<0.001
IFNG	−0.027	0.699	0.024	0.497	0.798	<0.001	1.102	<0.001
MAPK10	−0.061	0.391	0.013	0.723	0.827	<0.001	1.194	<0.001
SOD3	0.055	0.435	0	0.989	−0.8	<0.001	−1.102	<0.001
CAV3	0.075	0.293	−0.007	0.854	−0.826	<0.001	−1.199	<0.001
SLC2A1	−0.089	0.210	−0.006	0.872	0.83	<0.001	1.307	<0.001
MMP9	−0.012	0.861	0.026	0.517	0.812	<0.001	1.277	<0.001
NRF1	−0.114	0.109	−0.009	0.778	0.795	<0.001	1.01	<0.001
ICAM1	−0.095	0.183	−0.004	0.910	0.793	<0.001	1.112	<0.001
SIRT3	0.062	0.382	−0.004	0.920	−0.85	<0.001	−1.4	<0.001

Spearman correlation analysis revealed generally weak and predominantly non-significant associations between miRNA expression levels and pain intensity (VAS). A weak but statistically significant positive correlation with VAS was observed only for hsa-miR-196a-1-3p (ρ = 0.147, p = 0.038), while the remaining miRNAs did not demonstrate significant relationships with pain severity. Regression analysis identified a modest but significant association between hsa-miR-665-3p expression and VAS (β = −0.021, p = 0.048), suggesting a limited and inconsistent link between miRNA expression and subjective pain. In contrast, stronger and statistically significant associations were observed between selected miRNAs and Pfirrmann degeneration grade. hsa-miR-4686 showed a strong positive correlation with degeneration severity (ρ = 0.632, p < 0.001), whereas hsa-miR-3163 and hsa-miR-196a-1-3p demonstrated moderate negative correlations (ρ = −0.383 and −0.444, respectively; p < 0.001). These findings were further supported by regression analysis, which confirmed significant associations for hsa-miR-3163, hsa-miR-196a-1-3p, and hsa-miR-4686 with Pfirrmann grade (Table 7).

Table 7.

Correlation and multiple regression analysis evaluating associations between miRNAs expression, pain intensity (VAS), and Pfirrmann degeneration grade.

Marker	VAS (ρ)	VAS p	β (VAS)	β (VAS) p	Pfirrmann (ρ)	Pfirrmann p	β (Pfirrmann)	β (Pfirrmann) p
hsa-miR-3163	0.117	0.100	0.016	0.413	−0.383	<0.001	−0.262	<0.001
hsa-miR-665-3p	−0.12	0.091	−0.021	0.048	0.079	0.264	0.056	0.035
hsa-miR-196a-1-3p	0.147	0.038	0.041	0.075	−0.444	<0.001	−0.298	<0.001
hsa-miR-4686	−0.036	0.612	−0.004	0.609	0.632	<0.001	0.187	<0.001

Spearman correlation analysis demonstrated predominantly weak and non-significant associations between protein concentrations and pain intensity (VAS). A weak but statistically significant negative correlation was observed only for CAT (ρ = −0.171, p = 0.015), while the remaining proteins did not show significant relationships with pain severity. Regression analysis confirmed this finding, with CAT showing borderline significance in relation to VAS (β = −0.051, p = 0.050), whereas other proteins remained non-significant. In contrast, all analyzed proteins exhibited statistically significant correlations with Pfirrmann degeneration grade (p < 0.001). MAPK8 and IL6 showed moderate negative correlations (ρ = −0.724 and −0.470, respectively), indicating decreasing levels with advancing degeneration. Conversely, antioxidant-related proteins, including CAT, GPX1, and NRF1, demonstrated moderate to strong positive correlations with Pfirrmann grade (ρ = 0.476–0.650), suggesting increased expression in more advanced stages of disc degeneration. These relationships were further supported by regression analysis, which revealed significant β coefficients for all proteins in relation to Pfirrmann grade (Table 8).

Table 8.

Correlation and multiple regression analysis evaluating associations between proteins’ concentration, pain intensity (VAS), and Pfirrmann degeneration grade.

Marker	VAS (ρ)	VAS p	β (VAS)	β (VAS) p	Pfirrmann (ρ)	Pfirrmann p	β (Pfirrmann)	β (Pfirrmann) p
MAPK8 (ng/mL)	0.036	0.617	0.022	0.572	−0.724	<0.001	−1.049	<0.001
IL6 (pg/mL)	−0.004	0.955	−0.1	0.590	−0.47	<0.001	−3.44	<0.001
CAT (ng/mL)	−0.171	0.015	−0.051	0.050	0.65	<0.001	0.64	<0.001
GPX1 (ng/mL)	−0.12	0.089	−0.024	0.263	0.476	<0.001	0.393	<0.001
NRF1 (ng/mL)	−0.036	0.610	−0.001	0.933	0.636	<0.001	0.203	<0.001

CAT: catalase; GPX1: glutathione peroxidase 1; IL6: interleukin 6; MAPK8: mitogen-activated protein kinase 8 (JNK1).

STRING analysis

PPI network analysis of the differentially expressed genes identified a highly interconnected network comprising 22 nodes and 131 edges, significantly exceeding the expected number of edges (54). The network demonstrated an average node degree of 11.9 and a high average local clustering coefficient of 0.84, indicating strong functional connectivity among the analyzed proteins. The PPI enrichment p-value (<1.0 × 10⁻¹⁶) confirmed that the observed interactions were significantly greater than expected by chance, suggesting that these genes participate in closely related biological pathways. Functional enrichment analysis of GO Biological Processes revealed that the analyzed genes were predominantly associated with processes related to inflammatory response, immune system regulation, nitric oxide biosynthesis, oxidative stress response, and regulation of cytokine production. Additional enriched processes included cellular responses to stress, regulation of apoptotic signaling, and extracellular matrix remodeling, indicating that the identified gene set is functionally linked to mechanisms involved in inflammation-driven tissue damage and oxidative imbalance. Within the PPI network, several proteins, including IL6, TNF, NOS2, NOS3, TLR4, MMP9, and SOD family members, formed central hubs connecting pathways involved in immune activation, oxidative stress regulation, and cellular stress-response signaling (Figure 6).

Figure 6.

GO biological process enrichment and PPI network analysis of differentially expressed genes.

Discussion

The present study provides an integrated multi-omics characterization of oxidative stress-related molecular alterations underlying lumbosacral IVDD. By combining transcriptomic, miRNA, and protein-level analyses across Pfirrmann grades II to V, we identified five genes – MAPK8, IL6, CAT, GPX1, and NRF1 – that were consistently dysregulated throughout all stages of degeneration. Focusing on genes that remain altered across the entire spectrum of disc pathology offers a distinct advantage. It enables the identification of molecular signatures that are not stage-specific but instead reflect fundamental mechanisms sustaining disease progression. Such grade-independent markers likely represent a stable molecular core of chronic oxidative and inflammatory dysregulation and may serve as reliable biomarkers or therapeutic targets applicable across diverse clinical presentations of IVDD.

Furthermore, the concurrent evaluation of regulatory miRNAs enabled the exploration of post-transcriptional control, a key mechanism modulating protein synthesis and turnover in metabolically stressed disc cells. This integrative approach not only confirmed transcriptional alterations but also demonstrated how miRNA-mediated regulation contributes to the complex balance between adaptive and degenerative cellular responses.

Oxidative stress plays a central role in the pathogenesis of IVDD and represents a key mechanism linking molecular alterations with structural degeneration. Excessive production of reactive oxygen species (ROS) leads to mitochondrial dysfunction, DNA damage, and lipid peroxidation.¹¹ At the cellular level, ROS accumulation promotes apoptosis and senescence of nucleus pulposus cells, thereby reducing regenerative capacity. In parallel, oxidative stress activates pro-inflammatory signaling pathways, including NF-κB and MAPK cascades, which enhance the production of cytokines such as IL6 and TNF-α.^9,13,14 These processes stimulate matrix-degrading enzymes, including matrix metalloproteinases, ultimately leading to extracellular matrix breakdown and loss of disc integrity. Impaired antioxidant defense mechanisms, such as reduced CAT and GPX1 expression observed in this study, further exacerbate this imbalance and contribute to a self-perpetuating degenerative cycle.^15,16

Among the analyzed genes, MAPK8 emerged as a central component of the oxidative stress signaling network. MAPK8 is a critical mediator of stress-activated protein kinase pathways and regulates apoptosis, inflammatory signaling, and autophagy.^30,31 In the present study, MAPK8 mRNA levels were consistently elevated across all Pfirrmann grades, indicating sustained activation of redox-sensitive kinase signaling during disc degeneration.³² In contrast, ELISA measurements revealed a gradual decrease in MAPK8 protein concentration with increasing degeneration severity. This discrepancy suggests the involvement of post-transcriptional and post-translational regulatory mechanisms. Although the predicted regulatory miRNA hsa-miR-3163 was significantly downregulated across all grades – which would theoretically favor increased MAPK8 translation – the observed reduction in protein levels indicates that additional mechanisms may be involved. These may include enhanced proteasomal degradation, altered protein stability, or negative feedback regulation under chronic oxidative stress conditions.³³ Sustained activation of the JNK signaling pathway has previously been associated with oxidative damage and apoptosis in degenerative tissues, including IVDs.³⁴

Moreover, activation of JNK and related MAPK pathways contributes to extracellular matrix degradation, ROS accumulation, cellular senescence, and apoptosis in nucleus pulposus cells.³⁰ Prolonged endoplasmic reticulum stress may further enhance degradation of misfolded proteins via the ubiquitin–proteasome system, a process closely linked to MAPK pathway activation.³⁵

Taken together, these findings suggest a dynamic regulatory balance between increased transcriptional activation of MAPK8 and enhanced degradation or turnover of its protein product in chronically stressed IVD tissue.

A similar regulatory pattern was observed for IL6, a multifunctional cytokine linking oxidative stress with inflammatory amplification.³⁶ IL6 mRNA expression was significantly elevated in degenerated discs, supporting its well-established role in promoting inflammatory signaling, matrix metalloproteinase activation, and extracellular matrix breakdown.³⁷ In contrast, IL6 protein levels progressively decreased with increasing degeneration severity. This discrepancy may reflect accelerated cytokine secretion into the extracellular microenvironment or increased local consumption within inflammatory cascades.³⁸ Although direct evidence in disc tissue remains limited, the downregulation of miR-196a-1-3p observed in this study may relieve transcriptional repression of IL6, thereby contributing to increased mRNA expression. Simultaneously, post-transcriptional processes such as rapid secretion or translational inefficiency may reduce intracellular protein levels. Such decoupling between transcript and protein expression has been reported in other chronic inflammatory conditions and reflects the complexity of cytokine regulation in degenerative tissues. The identified miRNA–mRNA interactions further emphasize the importance of post-transcriptional regulation in IVDD. The inverse relationship between MAPK8 and hsa-miR-3163, as well as IL6 and hsa-miR-196a-1-3p, suggests that reduced miRNA expression may contribute to increased transcriptional activity of pro-inflammatory genes. Conversely, the upregulation of miRNAs targeting antioxidant genes, such as hsa-miR-665-3p (GPX1) and hsa-miR-4686 (CAT), may represent a compensatory or potentially maladaptive feedback mechanism. These findings indicate that miRNA-mediated regulation actively shapes the balance between oxidative damage and antioxidant defense, influencing disease progression. Importantly, these regulatory networks may serve as potential biomarkers or therapeutic targets in IVDD.^17–20

The observed changes in antioxidant enzyme expression further support the central role of oxidative stress in IVDD pathogenesis. While CAT and GPX1 transcripts were significantly reduced, their corresponding protein concentrations increased with disease severity. This pattern suggests activation of compensatory antioxidant responses under chronic oxidative stress conditions. Catalase and glutathione peroxidase are essential components of cellular defense against ROS accumulation and oxidative damage.³⁹ Experimental studies have shown that increased GPX1 activity may preserve mitochondrial integrity and protect nucleus pulposus cells from oxidative injury.⁴⁰ Additionally, cellular responses to sustained oxidative or mechanical stress may involve translational upregulation or stabilization of antioxidant proteins, even in the absence of increased mRNA expression.⁴¹ Therefore, the elevated CAT and GPX1 protein levels observed in advanced degeneration likely represent an adaptive response aimed at limiting oxidative damage.

Among the investigated genes, NRF1 displayed the most consistent regulatory pattern, with progressive upregulation at both mRNA and protein levels across all degeneration grades. NRF1 is a key transcription factor regulating mitochondrial biogenesis, oxidative phosphorylation, and antioxidant defense.¹⁶ Its increased expression suggests that mitochondrial adaptation is a major cellular response to chronic oxidative stress in degenerating discs. The positive association between NRF1 expression and elevated CAT and GPX1 protein levels further supports coordinated regulation of antioxidant defenses via mitochondrial pathways.⁴² Previous studies have demonstrated that activation of NRF1 and PGC-1α signaling enhances cellular metabolism and maintains redox homeostasis in degenerative tissues.^43,44 Thus, increased NRF1 activity likely reflects a compensatory mechanism aimed at counteracting mitochondrial dysfunction and oxidative damage.

The STRING-based protein interaction analysis further supports the concept that oxidative stress–related genes function within an interconnected regulatory network. The PPI network revealed strong interactions between oxidative stress regulators, inflammatory mediators, and apoptotic signaling components. Key nodes included TNF, TP53, NOS3, and MAPK family members, linking oxidative stress with cytokine signaling and cellular stress responses.^19,45 Functional enrichment analysis confirmed overrepresentation of biological processes related to oxidative stress response, cytokine signaling, nitric oxide metabolism, and apoptosis regulation.⁴⁶ These findings indicate that IVDD progression is driven by complex interactions between oxidative imbalance, inflammation, and compensatory antioxidant responses. In early stages, pro-inflammatory pathways dominated by MAPK8 and IL6 activation may promote cellular stress and extracellular matrix degradation. In later stages, adaptive mechanisms involving NRF1 activation and increased antioxidant enzyme activity become more prominent. This shift reflects the dynamic and biphasic nature of redox regulation in disc degeneration.^47,48

Importantly, the present study demonstrated a weak or absent association between molecular alterations and pain intensity (VAS). This finding is consistent with the well-recognized discordance between structural degeneration and clinical symptoms in IVDD. Pain perception is a complex and multifactorial process influenced not only by local tissue pathology but also by nerve ingrowth, inflammatory sensitization, central nervous system processing, and psychosocial factors. Therefore, molecular changes within the disc may reflect biological disease progression rather than subjective symptom severity. This dissociation highlights the limitations of pain-based clinical assessment and underscores the potential value of molecular biomarkers as objective indicators of degeneration.

Despite these insights, several limitations should be considered when interpreting the present findings. First, the cross-sectional design of the study limits the ability to determine causal relationships between oxidative stress and degenerative progression. Second, although postmortem control IVDs were histologically normal, potential metabolic differences associated with postmortem tissue collection may influence baseline molecular profiles. Early postmortem processes, including autolysis and alterations in water–electrolyte balance, may affect gene expression and protein stability, thereby introducing a degree of variability. To minimize these effects, control samples were collected within 48 h of death, histologically verified to exclude degenerative changes, and preserved under low-temperature conditions to maintain molecular integrity. Importantly, postmortem tissue remains the most physiologically relevant and ethically feasible source of non-degenerated human IVDs, and similar approaches have been widely adopted in previous studies.^49,50 Third, although bioinformatic predictions combined with miRNA expression profiling suggest potential regulatory relationships, functional validation – such as luciferase reporter assays or miRNA gain- and loss-of-function studies – is required to confirm direct miRNA–mRNA interactions. Furthermore, the measurement of cytokines in tissue lysates may not accurately reflect their extracellular activity in vivo, where paracrine and autocrine signaling play critical roles in cellular communication. Future studies employing single-cell transcriptomics and spatial gene expression profiling may provide deeper insight into cell-type–specific molecular alterations within degenerating intervertebral discs. In addition, longitudinal clinical investigations are needed to determine whether the identified oxidative stress-related molecular signatures can serve as reliable biomarkers for disease progression or therapeutic response. Finally, the exploration of pharmacological strategies targeting mitochondrial biogenesis and antioxidant regulatory pathways may offer promising therapeutic avenues for the management of intervertebral disc degeneration.

Conclusion

In conclusion, this study identifies a consistent oxidative stress-related molecular network composed of MAPK8, IL6, CAT, GPX1, and NRF1 that remains dysregulated across all stages of lumbosacral IVDD. The integration of transcriptomic, miRNA, and protein analyses highlights complex interactions between transcriptional and post-transcriptional regulatory mechanisms that contribute to persistent oxidative imbalance in degenerating disc tissue. The present study demonstrates that selected oxidative stress-associated genes and their regulatory microRNAs are significantly dysregulated in degenerative intervertebral disc tissue. The observed miRNA–mRNA interactions and their association with Pfirrmann classification and VAS pain scores indicate that redox-related regulatory networks may play a role in the molecular mechanisms underlying disc degeneration and symptomatic disease progression.

Supplemental Material

sj-docx-1-mpx-10.1177_17448069261444495 – Supplemental material for Redox-associated gene and microRNA signatures in degenerative intervertebral disc disease

Supplemental material, sj-docx-1-mpx-10.1177_17448069261444495 for Redox-associated gene and microRNA signatures in degenerative intervertebral disc disease by Damian Strojny, Karol Szwej, Rafał Staszkiewicz, Dawid Sobański, Paweł Gogol, Mária Lehotská, Bozena Majchrowicz and Beniamin Oskar Grabarek in Molecular Pain

Footnotes

Author contributions

Conceptualization, Damian Strojny, B.O.G.; methodology, Damian Strojny, Dawid Sobański. P.G. and R.S.; investigation, Damian Strojny, P.G.; R.S.; Dawid Sobański.; formal analysis, Damian Strojny and B.O.G.; resources, M.L. and B.M.; data curation, Damian Strojny; B.O.G.; writing – original draft preparation, Damian Strojny, R.S.; Dawid Sobański, P.G.; and B.O.G.; writing – review and editing, Damian Strojny and B.O.G, supervision, B.O.G; visualization, K.S. project administration, B.O.G. All authors have read and agreed to the published version of the manuscript.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Ethical considerations

This study was approved by the Bioethics Committee of the State Academy of Applied Sciences in Przemyśl (Approval No. 8/2024, issued on August 1, 2024) and by the Bioethics Committee at the District Medical Chamber in Kraków (Approval No. 162/KBL/OIL/2021, issued on December 11 June 2021). All procedures were performed in accordance with the recommendations contained in the 2013 Helsinki Declaration, and patient data were pseudo-anonymized. For the control group, clinical material was obtained postmortem in accordance with the Act of July 1, 2005, on the Collection, Storage, and Transplantation of Cells, Tissues, and Organs (Journal of Laws of 2020, item 2134). Article 5 of this Act operates on an opt-out basis for organ donation.

Consent to participate

Written, voluntary consent was obtained from each participant before their inclusion in the study group. This ensured that participants were fully informed about the nature, purpose, and procedures of the study, as well as their rights to withdraw at any time without consequence.

Consent for publication

Written, voluntary consent was obtained from each participant for the use of anonymized data collected during the study in academic publications. Participants were assured that no personal or identifying information would be disclosed.

ORCID iDs

Damian Strojny

Rafał Staszkiewicz

Dawid Sobański

Paweł Gogol

Beniamin Oskar Grabarek

Data availability statement

The data used to support the findings of this study are included in the article.

Supplemental material

Supplemental material for this article is available online.

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