Skip to main content

Main menu

  • Home
  • Current Issue
  • Archive
  • Info for
    • Authors
    • Editorial Policies
    • Advertisers
    • Editorial Board
    • Special Issues
  • Journal Metrics
  • Other Publications
    • Anticancer Research
    • Cancer Genomics & Proteomics
    • Cancer Diagnosis & Prognosis
  • More
    • IIAR
    • Conferences
  • About Us
    • General Policy
    • Contact
  • Other Publications
    • In Vivo
    • Anticancer Research
    • Cancer Genomics & Proteomics

User menu

  • Register
  • Subscribe
  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
In Vivo
  • Other Publications
    • In Vivo
    • Anticancer Research
    • Cancer Genomics & Proteomics
  • Register
  • Subscribe
  • My alerts
  • Log in
  • My Cart
In Vivo

Advanced Search

  • Home
  • Current Issue
  • Archive
  • Info for
    • Authors
    • Editorial Policies
    • Advertisers
    • Editorial Board
    • Special Issues
  • Journal Metrics
  • Other Publications
    • Anticancer Research
    • Cancer Genomics & Proteomics
    • Cancer Diagnosis & Prognosis
  • More
    • IIAR
    • Conferences
  • About Us
    • General Policy
    • Contact
  • Visit iiar on Facebook
  • Follow us on Linkedin
Research ArticleClinical Studies
Open Access

Stage-dependent Expression of Autophagy-related Genes in Patients With Knee Osteoarthritis

ZEYNEP DÜNDAR ÖK, MUHAMMED ERDİ GÜRBÜZ, NUSRET ÖK, GERGANA LENGEROVA, MARTINA BOZHKOVA, STELIYAN PETROV and AYLİN KÖSELER
In Vivo May 2026, 40 (3) 1776-1787; DOI: https://doi.org/10.21873/invivo.14330
ZEYNEP DÜNDAR ÖK
1Department of Internal Medicine, Denizli State Hospital, Denizli, Türkiye;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
MUHAMMED ERDİ GÜRBÜZ
2Department of Orthopedics and Traumatology, Faculty of Medicine, Pamukkale University, Denizli, Türkiye;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
NUSRET ÖK
2Department of Orthopedics and Traumatology, Faculty of Medicine, Pamukkale University, Denizli, Türkiye;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
GERGANA LENGEROVA
3Prof. Dr. Elissay Yanev Department of Medical Microbiology and Immunology, Medical University of Plovdiv, Plovdiv, Bulgaria;
4Research Institute, Medical University of Plovdiv, Plovdiv, Bulgaria;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
MARTINA BOZHKOVA
3Prof. Dr. Elissay Yanev Department of Medical Microbiology and Immunology, Medical University of Plovdiv, Plovdiv, Bulgaria;
4Research Institute, Medical University of Plovdiv, Plovdiv, Bulgaria;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
STELIYAN PETROV
3Prof. Dr. Elissay Yanev Department of Medical Microbiology and Immunology, Medical University of Plovdiv, Plovdiv, Bulgaria;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
AYLİN KÖSELER
5Department of Biophysics, Faculty of Medicine, Pamukkale University, Denizli, Türkiye
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: akoseler{at}pau.edu.tr
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

Background/Aim: Autophagy plays a crucial role in maintaining cellular homeostasis and has been implicated in the pathogenesis of knee osteoarthritis (OA). However, data on radiographic stage-dependent transcriptional variation of autophagy-related genes in patients with knee OA, particularly using peripheral blood samples, remain limited. The aim of this study was to evaluate whether disease severity was associated with stage-dependent changes in the expression of selected autophagy-related genes within a patient cohort.

Patients and Methods: A total of 200 patients diagnosed with knee OA were included in the study. Disease severity was classified according to the Kellgren-Lawrence radiographic grading system. Peripheral blood samples were collected, and the expression levels of selected autophagy-related genes were analyzed using quantitative real-time polymerase chain reaction [autophagy-related 5 (ATG5), ATG7, unc-51-like kinase 1 (ULK1), microtubule-associated protein 1 light chain 3 beta (LC3B), WD repeat domain phosphoinositide-interacting protein 1 (WIPI1), neighbor of BRCA1 gene 1 (NBR1), forkhead box O3 (FOXO3), transcription factor EB (TFEB)]. Relative gene expression was calculated using the ΔCt method, and comparisons were performed across radiographic stages. Associations between gene expression levels and systemic inflammatory markers were also assessed.

Results: Significant stage-dependent differences were observed in the expression of ULK1, TFEB, WIPI1, and NBR1 (p<0.05), with higher ΔCt values (reduced relative expression) in advanced radiographic stages compared with early-stage disease. In contrast, ATG5, ATG7, LC3B, and FOXO3 expression remained stable across radiographic stages. Furthermore, no significant associations were observed between expression of autophagy-related genes and systemic inflammatory status, as assessed by C-reactive protein levels.

Conclusion: In patients with knee OA, regulatory and early autophagy-related genes exhibit radiographic stage-associated transcriptional alterations in peripheral blood, while expression of core autophagy machinery genes remain relatively stable. These findings suggest that disease severity is associated with selective transcriptional changes in autophagy-related pathways within the OA patient population and support further investigation of stage-dependent molecular patterns in knee OA.

Keywords:
  • Knee osteoarthritis
  • autophagy
  • ULK1
  • TFEB
  • WIPI1
  • NBR1

Introduction

Knee osteoarthritis (OA) is a long-term and progressive condition of the joint where the cartilage gradually deteriorates. At the same time, the subchondral bone remodels, there is inflammation of the synovial lining, and the patient experiences pain, eventually leading to loss of function and a decrease in their quality of life (1). The number of people suffering from knee OA increases with age and it is one of the main reasons for disability globally (2). Even though knee OA is clinically very problematic, the molecular pathways that cause the disease and its progression are still not fully understood, and the current treatment options mainly focus on relieving the symptoms rather than changing the course of the (3).

Autophagy is a highly conserved intracellular degradation pathway that facilitates the clearance and reuse of damaged organelles and macromolecules via lysosomal processing. The latter is vital for maintaining cellular homeostasis and is especially crucial under stress scenarios such as nutrient scarcity, oxidative stress, and inflammation (4). In the case of joint tissues, autophagy is one of the mechanisms that the body uses to maintain cartilage and cell survival. Dysregulation of autophagy has been associated with the initiation and the worsening of OA (5, 6). Both experimental and clinical research has demonstrated that autophagic activity is likely to be maintained at normal levels or even upregulated in the early stages of OA. On the contrary, a gradual reduction of autophagy in advanced disease has been reported, which might lead to cell dysfunction and tissue (7).

Autophagy is a multistep process that involves different molecular components that regulate initiation, vesicle nucleation, elongation, cargo recognition, and lysosomal fusion (8). The unc-51-like kinase 1 (ULK1) complex serves as a main initiator of autophagy upon cellular energy and nutrient signaling, whereas WD repeat domain phosphoinositide-interacting protein 1 (WIPI1) and autophagy-related proteins take part in autophagosome formation and membrane dynamics (9, 10). Transcriptional regulators such as transcription factor EB (TFEB) coordinate lysosomal biogenesis and expression of autophagy-related genes, thus linking the environmental stress response to the long-term regulation of autophagic capacity (11). Furthermore, selective autophagy receptors, such as neighbor of BRCA DNA repair-associated 1 gene 1 (NBR1), mediate the specific degradation of certain cellular components and thus indicate that different steps of autophagy may be differentially affected during disease (12).

Previous studies have explored how autophagy affects OA by measuring autophagy-related markers in cartilage tissue, synovium, or experimental models (5, 6, 13). These studies have indeed shed light on the role of autophagy in OA pathophysiology; however, the results reported differ based on disease stage, tissue type, and experimental setting (5). Although changes in autophagy-related gene expression have been reported, a comprehensive understanding of stage-dependent transcriptional regulation in human knee OA remains limited, particularly in studies using minimally invasive and clinically applicable sample sources (14).

Many studies have compared autophagy-related markers between patients with OA and healthy individuals, but such case-control studies do not really investigate whether molecular changes are directly related to the severity of the disease shown by an X-ray. Thus, without a healthy control group, the current study aimed to assess the changes in expression of some autophagy-related genes in an OA depending on the stage of the disease according to Kellgren-Lawrence (K-L) grade (15).

Hence, to investigate the stage-dependent expression patterns of certain genes related to autophagy in patients with knee OA, the study utilized peripheral blood samples. The study targeted genes participating in autophagy initiation and regulation, and in selective autophagy, as well as the essential structural components of the autophagy machinery, aiming to detect changes in gene transcription linked to the severity of the disease. A better knowledge of these molecular patterns can help explain the role of autophagy regulation in the progression of knee OA and may also pave the way for the development of molecular indicators that would complement clinical and radiological.

Patients and Methods

Study population. A total of 200 patients diagnosed with knee OA were included in the study. Patients were recruited from the outpatient clinics of Pamukkale University Hospital based on clinical and radiographic diagnostic criteria. Inclusion criteria comprised age ≥40 years and radiographic evidence of knee OA according to the K-L grading system. Exclusion criteria included inflammatory rheumatic diseases, previous knee surgery, malignancy, active infection, chronic autoimmune disorders, and current use of systemic corticosteroids or immunosuppressive therapy. The present study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Pamukkale University Faculty of Medicine (approval number: E-60116787-020-805062). Written informed consent was obtained from all participants prior to enrollment.

The diagnosis of OA was established based on clinical evaluation and radiographic findings. Disease severity was classified according to the K-L grading system (stages 1-4) using standard anteroposterior knee radiographs and this was used to stratify patients for stage-dependent analyses. Grade 0 represents a normal joint structure; grade 1 indicates doubtful joint space narrowing and possible osteophytic lipping; grade 2 shows definite osteophyte formation with possible joint space narrowing; grade 3 is characterized by moderate multiple osteophytes, definite joint space narrowing, and subchondral sclerosis; grade 4 demonstrates severe joint space narrowing with large osteophytes, marked sclerosis, and bony deformity (Figure 1).

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Kellgren-Lawrence radiographic grading of knee osteoarthritis.

Clinical and laboratory assessment. Demographic characteristics (age and sex) and routine laboratory parameters were recorded for all participants. Peripheral blood samples were collected to assess systemic inflammatory status and gene expression profiles. Complete blood counts were measured using an automated hematology analyzer (Sysmex XN-1000; Sysmex Corporation, Kobe, Japan). C-Reactive protein (CRP) levels were determined by an immunoturbidimetric method using an Abbott Architect c16000 analyzer (Abbott Diagnostics, Abbott Park, IL, USA). Erythrocyte sedimentation rate was measured with an automated system (Alifax TEST1; Alifax S.p.A., Padua, Italy). All laboratory analyses were conducted at the Central Laboratory of Pamukkale University Hospital in accordance with the manufacturer’s instructions and international quality control standards. The laboratory data obtained were used in statistical analyses along with the stage of knee OA and gene expression levels related to autophagy. The overall workflow of the study, including patient selection, clinical evaluation, blood sample collection, and gene expression analysis, is summarized in Figure 2.

Figure 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2.

Overview of patient evaluation, molecular analysis, and key outcomes in patients with knee osteoarthritis (OA). ATG5: Autophagy-related 5; ATG7: autophagy-related 7; FOXO3: Forkhead box O3; LC3B: microtubule-associated protein 1 light chain 3 beta; NBR1: neighbor of BRCA DNA repair-associated 1 gene 1; TFEB: transcription factor EB; ULK1: unc-51-like kinase 1; WIPI1: WD repeat domain phosphoinositide-interacting protein 1.

Blood sample collection and RNA isolation. Peripheral venous blood samples were obtained between 08:00 and 10:00 a.m. under fasting conditions when possible. Samples intended for gene expression analysis were collected into EDTA-containing tubes and transported to the laboratory within 2 h of collection. Total RNA was isolated from peripheral blood using the QIAamp RNA Blood Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. RNA concentration and purity were assessed spectrophotometrically (A260/A280 ratio), and only samples meeting quality criteria were included. RNA samples were stored at −80°C until complementary DNA (cDNA) synthesis. cDNA synthesis was performed using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA) following the manufacturer’s protocol. Equal amounts of total RNA were used for each reverse transcription reaction to ensure consistency across samples.

Quantitative real-time polymerase chain reaction (qPCR). Gene expression analyses were conducted using qPCR with PowerUp™ SYBR™ Green Master Mix (Applied Biosystems) on an Applied Biosystems 7500 Fast Real-Time PCR System. The autophagy-related genes analyzed were autophagy-related 5 (ATG5), ATG7, ULK1, WIPI1, microtubule-associated protein 1 light chain 3 beta (MAP1LC3B ; hereafter referred to as LC3B) NBR1, forkhead box O3 (FOXO3), and TFEB. Primer sequences are provided in Table I. Primer specificity was confirmed by melting-curve analysis. PCR cycling conditions were as follows: initial denaturation at 95 °C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s and annealing/extension at 60°C for 60 s. Gene expression levels were normalized using glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and β-actin (ACTB) as reference genes. Relative expression was calculated using the ΔCt method, as no healthy control group was available to serve as a calibrator for ΔΔCt analysis.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table I.

Primer sequences for qPCR analysis.

Statistical analysis. Statistical analyses were performed using IBM SPSS Statistics (version 26.0; IBM Corp., Armonk, NY, USA). Data distribution was assessed using histograms, Q-Q plots, and the Shapiro-Wilk test. Continuous variables were expressed as the mean±standard deviation, or median (interquartile range), depending on their distribution. Comparisons of gene expression levels across OA stages were performed using one-way analysis of variance or the Kruskal-Wallis test, as appropriate. Post hoc comparisons were conducted with Tukey HSD or Mann-Whitney U-tests, with Holm-Bonferroni correction applied for multiple comparisons.

Correlations between gene expression and laboratory parameters were evaluated using Pearson or Spearman correlation analyses. Receiver operating characteristic (ROC) curve analysis was performed to assess the ability of selected genes to discriminate early-stage (K-L1-2) from advanced-stage (K-L 3-4) OA. A value of p<0.05 was considered statistically significant.

Results

A total of 200 patients diagnosed with knee OA were included in the study. After classifying patients into four groups according to the K-L radiographic grading system, the distribution of patients across disease stages was uneven, with fewer individuals in stage 1. Demographic characteristics and routine laboratory parameters, including inflammatory markers, are summarized in Table II.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table II.

Demographic and clinical characteristics of the study population.

Stage-dependent expression of autophagy-related genes. The expression levels of selected autophagy-related genes were analyzed in peripheral blood samples using quantitative real-time PCR, and ΔCt values were compared across K-L stages 1-4. The distribution of ΔCt values for all analyzed autophagy-related genes according to K-L stage is presented in Figure 3. Eight genes were evaluated: ATG5, ATG7, ULK1, LC3B, TFEB, FOXO3, WIPI1, and NBR1. Statistically significant stage-dependent differences were observed for ULK1, TFEB, WIPI1, and NBR1 expression (Kruskal-Wallis test, p<0.05). Post hoc analyses demonstrated that these differences were primarily driven by comparisons between early-stage OA (K-L stages 1-2) and advanced-stage OA (K-L stage 4). Specifically, ULK1 and TFEB ΔCt values were significantly higher in advanced stages compared with early stages, indicating lower relative mRNA expression in patients with more severe disease. Similarly, WIPI1 and NBR1 expression levels exhibited significant stage-dependent alterations, with reduced expression observed predominantly in advanced-stage OA.

Figure 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 3.

Distribution of ΔCt values for the expression of autophagy-related genes according to the severity of knee osteoarthritis based on Kellgren-Lawrence (K-L) grade. Box plots show expression levels of (A) autophagy-related 5 (ATG5), (B) autophagy-related 7 (ATG7), (C) unc-51-like kinase 1 (ULK1), (D) microtubule-associated protein 1 light chain 3 beta (LC3B), (E) transcription factor EB (TFEB), (F) forkhead box O3 (FOXO3), (G) WD repeat domain phosphoinositide-interacting protein 1 (WIPI1), and (H) neighbor of BRCA DNA repair-associated 1 gene 1 (NBR1). The boxes represent the median and interquartile range; whiskers indicate minimum and maximum values; circles denote outliers. Comparisons between K-L stages were performed using the Kruskal-Wallis test, followed by post hoc pairwise comparisons with Holm-Bonferroni correction where appropriate. Statistically significant stage-dependent differences were observed for ULK1, TFEB, WIPI1, and NBR1. Significantly different at *p<0.05, **p<0.01.

In contrast, no statistically significant differences were detected in the expression levels of ATG5, ATG7, LC3B, or FOXO3 across OA stages (p>0.05), suggesting relative stability of these genes throughout disease progression.

Correlation between autophagy-related gene expression and inflammatory markers. The relationship between autophagy-related gene expression and systemic inflammation was assessed using Spearman correlation analysis with CRP levels. No statistically significant correlations were identified between CRP levels and the expression of any of the analyzed autophagy-related genes (all p>0.05). Although weak correlation tendencies were observed for certain gene-CRP pairs, these did not reach statistical significance and were not indicative of a consistent association. Given the lack of significant findings, correlation results are summarized descriptively, and detailed graphical representations were omitted to maintain clarity and focus on the primary stage-dependent analyses.

ROC curve analysis. ROC curve analysis was conducted to assess the ability of selected autophagy-related genes to discriminate early-stage (K-L grades 1-2) from advanced-stage (K-L grades 3-4) knee OA. Individually, ULK1, TFEB, WIPI1, and NBR1 showed modest discriminatory capacity. A multigene model incorporating these four genes demonstrated improved discrimination compared with single-gene analyses. The corresponding ROC curves and areas under the curve are shown in Figure 4.

Figure 4.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 4.

Receiver operating characteristic (ROC) curves evaluating the ability of autophagy-related genes to discriminate early-stage (Kellgren-Lawrence grades 1-2) from advanced-stage (Kellgren-Lawrence grades 3-4) knee osteoarthritis. ROC curves are shown for autophagy-related 5 (ATG5), ATG7, forkhead box O3 (FOXO3), microtubule-associated protein 1 light chain 3 beta (LC3B), neighbor of BRCA DNA repair-associated 1 gene 1 (NBR1), transcription factor EB (TFEB), unc-51-like kinase 1 (ULK1), WD repeat domain phosphoinositide-interacting protein 1 (WIPI1), along with a combined multigene model. The corresponding areas under the curve (AUC) and 95% confidence intervals are given. The diagonal line represents the line of no discrimination. Singly, ULK1, TFEB, WIPI1, and NBR1 showed modest discriminatory capacity. A model incorporating all four of these genes demonstrated improved discrimination compared with single-gene analyses.

Figure 5 summarizes the integrative interpretation of the results without implying a direct mechanistic pathway.

Figure 5.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 5.

Integrative interpretation of the results without implying a direct mechanistic pathway.

Discussion

In this work, we carried out a study on transcriptional changes related to autophagy at different stages in patients with knee OA by using blood samples. The main result is that ULK1, TFEB, WIPI1 and NBR1 genes showed significant stage-dependent changes, concomitant with OA progression, while ATG5, ATG7, LC3B, and FOXO3 did not change significantly at the mRNA expression level with OA stage. This finding indicates that the transcriptional changes observed along OA progression might mainly be concerned with regulation and early-stage components of the autophagy pathway rather than affecting all autophagy-related genes evenly.

Autophagy is a complex, tightly controlled cellular pathway with several stages, and loss of control at different points in this pathway can result in various biological consequences (26). ULK1 is the main autophagy-initiating protein that responds to cellular stress signals, whereas TFEB is the predominant transcription factor that regulates the expression of autophagy- and lysosome-related genes (27, 28). WIPI1 functions in the initial steps of autophagosome formation and membrane remodeling, and it is identified as a component of the core early autophagy machinery (29). On the other hand, NBR1 is a selective autophagy adaptor protein that plays a role in cargo recognition and delivery of ubiquitinated proteins to autophagosomes (30). The finding that the expression of these regulatory and early-process genes varies at different stages and that their transcriptional differences are dependent on the stage supports the idea that the molecular changes associated with OA may mainly impact the upstream regulatory control of autophagy (31, 32).

Our results largely align with previous studies investigating the role of autophagy in OA (31, 32). Cartilage-based and experimental investigations have demonstrated that autophagic activity and the expression of regulatory autophagy-related genes, including ULK1-associated signaling pathways, decline with aging and OA progression (16, 31, 32). In contrast, certain core components of the autophagy machinery appear to remain relatively stable at the transcriptional level. Our findings showing no significant stage-dependent changes in ATG5, ATG7, and LC3B mRNA expression are consistent with this observation. Collectively, both previous experimental data and our results suggest that OA progression may preferentially affect upstream regulatory nodes of the autophagy pathway rather than uniformly altering downstream structural components (26, 31, 32).

TFEB, a major transcription factor that integrates cellular stress responses with metabolic and proteostatic adaptations, regulates transcriptions of genes involved in lysosomal biogenesis and autophagy (11). Through the coordinated lysosomal expression and regulation network, TFEB coordinates numerous autophagy and lysosome genes, playing a central role in the long-term maintenance of cellular homeostasis (33). In the context of OA, studies on experimental OA models and human osteoarthritic cartilage tissues showed that the expression of TFEB and/or nuclear translocation was suppressed (11, 28, 31-33). A decrease in TFEB activity has been linked to lysosomal dysfunction, insufficient autophagic flux, and an increased sensitivity of chondrocytes to oxidative stress, which ultimately leads to the degradation of the cartilage matrix and the loss of cells, thereby accelerating joint degeneration (11, 32).

Recent experimental studies have demonstrated that pharmacological or genetic activation of TFEB enhances autophagic flux, improves lysosomal function, and attenuates inflammatory signaling pathways in cellular and animal models (11, 17, 28). In OA models, TFEB activation has been associated with improved chondrocyte survival and reduced cartilage degeneration (31, 32). These findings suggest that TFEB may represent not only a downstream consequence of degenerative processes but also a potential disease-modifying therapeutic target. Within this context, our study detected stage-dependent TFEB transcriptional changes in peripheral blood, which, as OA severity increases, support systemic impairment of autophagy upper regulatory mechanisms (31). This situation is consistent with the idea that OA progression might be associated with a gradual impact on the regulatory nodal points which are responsible for the initiation and maintenance of autophagy (31-33).

However, it is not realistic to consider the transcriptional profiles obtained from peripheral blood to be a direct reflection of the molecular changes specific to joint tissue. Peripheral TFEB expression is less about intra-articular chondrocytes and more about the combined response of circulating immune cells exposed to systemic inflammatory and metabolic signals (34). Thus, while our findings are consistent with tissue-based studies showing a decrease in TFEB activity in late-stage OA (32), these results should not be considered as direct evidence of intra-articular TFEB dysfunction but rather as an indication of systemic regulatory changes associated with disease stage (24). In the future, holistic studies in which peripheral biomarkers are considered together with joint tissue expression, protein level analyses, and functional autophagy assessments will clarify the real role of TFEB in OA.

WIPI1 is a key regulator of the early stages of autophagy and is recruited to phosphatidylinositol 3-phosphate-enriched membranes during phagophore formation, where it contributes to membrane expansion (29, 35). In recent years, gene expression studies have shown that genes involved in early phases of autophagy can exhibit transcriptional changes in degenerative and aging-related diseases whereas the structural markers representing the late stages of autophagosome (e.g., LC3B) may remain more stable (20-22, 26, 31, 36). This suggests that rather than a complete loss of autophagy, regulatory and initiating mechanisms are selectively.

In this context, the stage-dependent transcriptional changes of WIPI1 identified in our study suggest that the genes involved in the early stages of autophagy are differently regulated during OA progression. However, these findings do not provide direct evidence whether autophagy is functionally upregulated or downregulated; instead of demonstrating functional autophagy activation or suppression, these results indicate that disease severity is associated with transcriptional modulation of genes regulating early autophagy.

Hence, our WIPI1 data do not reveal the presence of a selective autophagy type or a specific autophagic activation state, but rather show that early autophagy control points are transcriptionally affected in OA.

On the other hand, NBR1 is a selective autophagy adaptor protein that directly binds ubiquitinated cargo and facilitates its delivery to autophagosomes for degradation (19, 30). Experimental studies have demonstrated that NBR1 functions in cooperation with LC3 to mediate selective autophagic clearance of protein aggregates and other ubiquitinated substrates (19). NBR1 is particularly involved in protein quality control and cellular homeostasis under chronic stress conditions (37). There are only a few studies directly investigating the role of NBR1 in OA. However, the changes in expression of selective autophagy adaptor proteins have been shown to be associated with chronic inflammation, oxidative stress, and degenerative processes in various disease models (37, 38).

In this context, in our work, the changes in NBR1 mRNA levels depending on the disease stage suggest that the molecular pathways associated with selective autophagy could be involved in the adaptation process in the progression of OA (38). Nevertheless, it is not possible to say that these changes indicate a specific selective autophagy subtype (e.g., mitophagy) or a functional autophagy output directly. Instead, our data demonstrate that NBR1 might be regulated as part of the cellular stress response in OA, but the functional significance of this regulation needs to be uncovered by advanced mechanistic and tissue-based.

Remarkably, no substantial correlations were found between the expression of autophagy-related genes and the systemic inflammatory marker CRP. This suggests that the changes in gene transcription identified in this study were not simply the result of systemic inflammation as measured by CRP. On the other hand, since correlation analyses were carried out on all OA stages combined and immune cell subpopulations were not examined, more comprehensive stage-specific and cell-type-specific studies are.

At the mRNA level, the stage-dependent expression patterns of ULK1, TFEB, WIPI1, and NBR1 observed within this patient cohort suggest that regulatory autophagy-related genes may have potential as complementary molecular indicators of disease severity, particularly when interpreted alongside clinical and radiographic assessments. This interpretation is supported by the modest discriminatory performance observed in ROC analysis (Figure 4). Nevertheless, the absence of a healthy control group, the cross-sectional study design, and the lack of protein-level or functional validation preclude classification of these genes as diagnostic biomarkers. Rather, they should be considered exploratory severity-associated molecular markers requiring further validation.

One of the major features of the present work is the within-cohort design. Since there was no healthy control group, the transcriptional differences found should be seen as severity-associated changes in K-L stages rather than dysregulation compared to a normal baseline. Therefore, this method focused on determining if the gradual development of the disease was related to changes in autophagy gene expression in the OA population. By focusing exclusively on intra-cohort comparisons, this approach minimizes inter-group variability and potential confounding effects that may occur when external control groups are included.

Several limitations should be acknowledged. Firstly, the cross-sectional design of this study allows assessment of gene expression at a single time point and does not provide information about longitudinal molecular changes during OA progression. Secondly, only blood mRNA was analyzed without autophagy estimations in tissues or protein level or functional studies. Further work including repeated sampling over time, analysis of joint tissues, and use of functional tests will shed more light on autophagy regulation in knee OA.

In conclusion, this study has revealed that the genes primarily responsible for the initiation, regulation, and formation of the autophagosome early stages are differentially expressed depending on the stage of the disease in knee OA. At the same time, the genes essential in structural autophagy hardly changed at the mRNA level in peripheral blood. Our findings are consistent with previous reports indicating that dysregulation of upstream regulatory components of the autophagy pathway is associated with OA progression and cartilage degeneration (16, 31, 32). Experimental and mechanistic studies have further emphasized that impairment of key regulatory nodes, including ULK1- and TFEB-mediated pathways, may contribute to disrupted autophagic homeostasis in degenerative conditions (11, 28, 33). Together, these data support the concept that disease severity in OA may be linked to alterations at the regulatory level of the autophagy network rather than to uniform disruption of core structural machinery (26, 31, 32). This offers a platform for future integrative and mechanistic.

Acknowledgements

This study was supported by Strategic Research and Innovation Programme for the Development of the Medical University–Plovdiv (SRIPD-MUP)” Contract No BG-RRP-2.004-0007-C01.

Footnotes

  • Authors’ Contributions

    Conceptualization, Z.Ö. and A.K.; methodology, Z.Ö. and A.K.; software, A.K.; validation, A.K., Z.Ö., M.G. and N.Ö.; formal analysis, A.K.; investigation, Z.Ö., M.G., N.Ö., and A.K.; resources, Z.Ö., and A.K.; data curation, A.K.; writing–original draft preparation, A.K.; writing–review and editing, G.L., M.B., and S.P.; visualization, A.K.; supervision, A.K.; project administration, A.K.; funding acquisition, A.K. All Authors read and agreed to the published version of the manuscript.

  • Conflicts of Interest

    The Authors declare that they have no conflicts of interest.

  • Artificial Intelligence (AI) Disclosure

    No artificial intelligence (AI) tools, including large language models or machine learning software, were used in the preparation, analysis, or presentation of this manuscript.

  • Received January 16, 2026.
  • Revision received February 28, 2026.
  • Accepted March 6, 2026.
  • Copyright © 2026 The Author(s). Published by the International Institute of Anticancer Research.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

References

  1. ↵
    1. Geng R,
    2. Li J,
    3. Yu C,
    4. Zhang C,
    5. Chen F,
    6. Chen J,
    7. Ni H,
    8. Wang J,
    9. Kang K,
    10. Wei Z,
    11. Xu Y,
    12. Jin T
    : Knee osteoarthritis: Current status and research progress in treatment (Review). Exp Ther Med 26(4): 481, 2023. DOI: 10.3892/etm.2023.12180
    OpenUrlCrossRef
  2. ↵
    1. Hu Y,
    2. Chen X,
    3. Wang S,
    4. Jing Y,
    5. Su J
    : Subchondral bone microenvironment in osteoarthritis and pain. Bone Res 9(1): 20, 2021. DOI: 10.1038/s41413-021-00147-z
    OpenUrlCrossRefPubMed
  3. ↵
    1. Rodriguez-Merchan EC
    : The current role of disease-modifying osteoarthritis drugs. Arch Bone Jt Surg 11(1): 11-22, 2023. DOI: 10.22038/ABJS.2021.56530.2807
    OpenUrlCrossRefPubMed
  4. ↵
    1. Lu G,
    2. Wang Y,
    3. Shi Y,
    4. Zhang Z,
    5. Huang C,
    6. He W,
    7. Wang C,
    8. Shen HM
    : Autophagy in health and disease: From molecular mechanisms to therapeutic target. MedComm (2020) 3(3): e150, 2022. DOI: 10.1002/mco2.150
    OpenUrlCrossRef
  5. ↵
    1. Lee DY,
    2. Bahar ME,
    3. Kim CW,
    4. Seo MS,
    5. Song MG,
    6. Song SY,
    7. Kim SY,
    8. Kim DR,
    9. Kim DH
    : Autophagy in osteoarthritis: a double-edged sword in cartilage aging and mechanical stress response: a systematic review. J Clin Med 13(10): 3005, 2024. DOI: 10.3390/jcm13103005
    OpenUrlCrossRefPubMed
  6. ↵
    1. Lv X,
    2. Zhao T,
    3. Dai Y,
    4. Shi M,
    5. Huang X,
    6. Wei Y,
    7. Shen J,
    8. Zhang X,
    9. Xie Z,
    10. Wang Q,
    11. Li Z,
    12. Qin D
    : New insights into the interplay between autophagy and cartilage degeneration in osteoarthritis. Front Cell Dev Biol 10: 1089668, 2022. DOI: 10.3389/fcell.2022.1089668
    OpenUrlCrossRefPubMed
  7. ↵
    1. Li M,
    2. Wei CB,
    3. Li HF,
    4. He K,
    5. Bai RJ,
    6. Zhang FJ
    : Osteopontin inhibits autophagy via CD44 and avβ3 integrin and promotes cell proliferation in osteoarthritic fibroblast-like synoviocytes. BMC Musculoskelet Disord 26(1): 274, 2025. DOI: 10.1186/s12891-025-08509-y
    OpenUrlCrossRefPubMed
  8. ↵
    1. Li H,
    2. Lismont C,
    3. Revenco I,
    4. Hussein MAF,
    5. Costa CF,
    6. Fransen M
    : The peroxisome-autophagy redox connection: a double-edged sword? Front Cell Dev Biol 9: 814047, 2021. DOI: 10.3389/fcell.2021.814047
    OpenUrlCrossRef
  9. ↵
    1. Zou L,
    2. Liao M,
    3. Zhen Y,
    4. Zhu S,
    5. Chen X,
    6. Zhang J,
    7. Hao Y,
    8. Liu B
    : Autophagy and beyond: Unraveling the complexity of UNC-51-like kinase 1 (ULK1) from biological functions to therapeutic implications. Acta Pharm Sin B 12(10): 3743-3782, 2022. DOI: 10.1016/j.apsb.2022.06.004
    OpenUrlCrossRefPubMed
  10. ↵
    1. Strong LM,
    2. Chang C,
    3. Riley JF,
    4. Boecker CA,
    5. Flower TG,
    6. Buffalo CZ,
    7. Ren X,
    8. Stavoe AK,
    9. Holzbaur EL,
    10. Hurley JH
    : Structural basis for membrane recruitment of ATG16L1 by WIPI2 in autophagy. Elife 10: e70372, 2021. DOI: 10.7554/eLife.70372
    OpenUrlCrossRefPubMed
  11. ↵
    1. Chen H,
    2. Gong S,
    3. Zhang H,
    4. Chen Y,
    5. Liu Y,
    6. Hao J,
    7. Liu H,
    8. Li X
    : From the regulatory mechanism of TFEB to its therapeutic implications. Cell Death Discov 10(1): 84, 2024. DOI: 10.1038/s41420-024-01850-6
    OpenUrlCrossRefPubMed
  12. ↵
    1. Vargas JNS,
    2. Hamasaki M,
    3. Kawabata T,
    4. Youle RJ,
    5. Yoshimori T
    : The mechanisms and roles of selective autophagy in mammals. Nat Rev Mol Cell Biol 24(3): 167-185, 2023. DOI: 10.1038/s41580-022-00542-2
    OpenUrlCrossRefPubMed
  13. ↵
    1. Bai RJ,
    2. Liu D,
    3. Li YS,
    4. Tian J,
    5. Yu DJ,
    6. Li HZ,
    7. Zhang FJ
    : OPN inhibits autophagy through CD44, integrin and the MAPK pathway in osteoarthritic chondrocytes. Front Endocrinol (Lausanne) 13: 919366, 2022. DOI: 10.3389/fendo.2022.919366
    OpenUrlCrossRefPubMed
  14. ↵
    1. Li Z,
    2. Li D,
    3. Su H,
    4. Xue H,
    5. Tan G,
    6. Xu Z
    : Autophagy: An important target for natural products in the treatment of bone metabolic diseases. Front Pharmacol 13: 999017, 2022. DOI: 10.3389/fphar.2022.999017
    OpenUrlCrossRefPubMed
  15. ↵
    1. Kellgren JH,
    2. Lawrence JS
    : Radiological assessment of osteo-arthrosis. Ann Rheum Dis 16(4): 494-502, 1957. DOI: 10.1136/ard.16.4.494
    OpenUrlFREE Full Text
  16. ↵
    1. Egan DF,
    2. Shackelford DB,
    3. Mihaylova MM,
    4. Gelino S,
    5. Kohnz RA,
    6. Mair W,
    7. Vasquez DS,
    8. Joshi A,
    9. Gwinn DM,
    10. Taylor R,
    11. Asara JM,
    12. Fitzpatrick J,
    13. Dillin A,
    14. Viollet B,
    15. Kundu M,
    16. Hansen M,
    17. Shaw RJ
    : Phosphorylation of ULK1 (hATG1) by AMP-activated protein kinase connects energy sensing to mitophagy. Science 331(6016): 456-461, 2011. DOI: 10.1126/science.1196371
    OpenUrlAbstract/FREE Full Text
  17. ↵
    1. Settembre C,
    2. Di Malta C,
    3. Polito VA,
    4. Garcia Arencibia M,
    5. Vetrini F,
    6. Erdin S,
    7. Erdin SU,
    8. Huynh T,
    9. Medina D,
    10. Colella P,
    11. Sardiello M,
    12. Rubinsztein DC,
    13. Ballabio A
    : TFEB links autophagy to lysosomal biogenesis. Science 332(6036): 1429-1433, 2011. DOI: 10.1126/science.1204592
    OpenUrlAbstract/FREE Full Text
    1. Polson HE,
    2. De Lartigue J,
    3. Rigden DJ,
    4. Reedijk M,
    5. Urbé S,
    6. Clague MJ,
    7. Tooze SA
    : Mammalian Atg18 (WIPI2) localizes to omegasome-anchored phagophores and positively regulates LC3 lipidation. Autophagy 6(4): 506-522, 2010. DOI: 10.4161/auto.6.4.11863
    OpenUrlCrossRefPubMed
  18. ↵
    1. Kirkin V,
    2. Lamark T,
    3. Sou YS,
    4. Bjørkøy G,
    5. Nunn JL,
    6. Bruun JA,
    7. Shvets E,
    8. McEwan DG,
    9. Clausen TH,
    10. Wild P,
    11. Bilusic I,
    12. Theurillat JP,
    13. Øvervatn A,
    14. Ishii T,
    15. Elazar Z,
    16. Komatsu M,
    17. Dikic I,
    18. Johansen T
    : A role for NBR1 in autophagosomal degradation of ubiquitinated substrates. Mol Cell 33(4): 505-516, 2009. DOI: 10.1016/j.molcel.2009.01.020
    OpenUrlCrossRefPubMed
  19. ↵
    1. Mizushima N,
    2. Levine B,
    3. Cuervo AM,
    4. Klionsky DJ
    : Autophagy fights disease through cellular self-digestion. Nature 451(7182): 1069-1075, 2008. DOI: 10.1038/nature06639
    OpenUrlCrossRefPubMed
    1. Komatsu M,
    2. Waguri S,
    3. Ueno T,
    4. Iwata J,
    5. Murata S,
    6. Tanida I,
    7. Ezaki J,
    8. Mizushima N,
    9. Ohsumi Y,
    10. Uchiyama Y,
    11. Kominami E,
    12. Tanaka K,
    13. Chiba T
    : Impairment of starvation-induced and constitutive autophagy in Atg7-deficient mice. J Cell Biol 169(3): 425-434, 2005. DOI: 10.1083/jcb.200412022
    OpenUrlAbstract/FREE Full Text
  20. ↵
    1. Kabeya Y,
    2. Mizushima N,
    3. Ueno T,
    4. Yamamoto A,
    5. Kirisako T,
    6. Noda T,
    7. Kominami E,
    8. Ohsumi Y,
    9. Yoshimori T
    : LC3, a mammalian homologue of yeast Apg8p, is localized in autophagosome membranes after processing. EMBO J 19(21): 5720-5728, 2000. DOI: 10.1093/emboj/19.21.5720
    OpenUrlAbstract
    1. Warr MR,
    2. Binnewies M,
    3. Flach J,
    4. Reynaud D,
    5. Garg T,
    6. Malhotra R,
    7. Debnath J,
    8. Passegué E
    : FOXO3A directs a protective autophagy program in haematopoietic stem cells. Nature 494(7437): 323-327, 2013. DOI: 10.1038/nature11895
    OpenUrlCrossRefPubMed
  21. ↵
    1. Barber RD,
    2. Harmer DW,
    3. Coleman RA,
    4. Clark BJ
    : GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol Genomics 21(3): 389-395, 2005. DOI: 10.1152/physiolgenomics.00025.2005
    OpenUrlCrossRefPubMed
    1. Vandesompele J,
    2. De Preter K,
    3. Pattyn F,
    4. Poppe B,
    5. Van Roy N,
    6. De Paepe A,
    7. Speleman F
    : Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3(7): RESEARCH0034, 2002. DOI: 10.1186/gb-2002-3-7-research0034
    OpenUrlCrossRefPubMed
  22. ↵
    1. Kapuy O,
    2. Holczer M,
    3. Márton M,
    4. Korcsmáros T
    : Autophagy-dependent survival is controlled with a unique regulatory network upon various cellular stress events. Cell Death Dis 12(4): 309, 2021. DOI: 10.1038/s41419-021-03599-7
    OpenUrlCrossRefPubMed
  23. ↵
    1. Mercer TJ,
    2. Tooze SA
    : The ingenious ULKs: expanding the repertoire of the ULK complex with phosphoproteomics. Autophagy 17(12): 4491-4493, 2021. DOI: 10.1080/15548627.2021.1968615
    OpenUrlCrossRefPubMed
  24. ↵
    1. Tan A,
    2. Prasad R,
    3. Lee C,
    4. Jho EH
    : Past, present, and future perspectives of transcription factor EB (TFEB): mechanisms of regulation and association with disease. Cell Death Differ 29(8): 1433-1449, 2022. DOI: 10.1038/s41418-022-01028-6
    OpenUrlCrossRefPubMed
  25. ↵
    1. Sporbeck K,
    2. Haas ML,
    3. Pastor-Maldonado CJ,
    4. Schüssele DS,
    5. Hunter C,
    6. Takacs Z,
    7. Diogo de Oliveira AL,
    8. Franz-Wachtel M,
    9. Charsou C,
    10. Pfisterer SG,
    11. Gubas A,
    12. Haller PK,
    13. Knorr RL,
    14. Kaulich M,
    15. Macek B,
    16. Eskelinen EL,
    17. Simonsen A,
    18. Proikas-Cezanne T
    : The ABL-MYC axis controls WIPI1-enhanced autophagy in lifespan extension. Commun Biol 6(1): 872, 2023. DOI: 10.1038/s42003-023-05236-9
    OpenUrlCrossRefPubMed
  26. ↵
    1. North BJ,
    2. Ohnstad AE,
    3. Ragusa MJ,
    4. Shoemaker CJ
    : The LC3-interacting region of NBR1 is a protein interaction hub enabling optimal flux. J Cell Biol 224(4): e202407105, 2025. DOI: 10.1083/jcb.202407105
    OpenUrlCrossRefPubMed
  27. ↵
    1. Qin J,
    2. Zhang J,
    3. Wu JJ,
    4. Ru X,
    5. Zhong QL,
    6. Zhao JM,
    7. Lan NH
    : Identification of autophagy-related genes in osteoarthritis articular cartilage and their roles in immune infiltration. Front Immunol 14: 1263988, 2023. DOI: 10.3389/fimmu.2023.1263988
    OpenUrlCrossRefPubMed
  28. ↵
    1. Valenti MT,
    2. Dalle Carbonare L,
    3. Zipeto D,
    4. Mottes M
    : Control of the autophagy pathway in osteoarthritis: key regulators, therapeutic targets and therapeutic strategies. Int J Mol Sci 22(5): 2700, 2021. DOI: 10.3390/ijms22052700
    OpenUrlCrossRefPubMed
  29. ↵
    1. Franco-Juárez B,
    2. Coronel-Cruz C,
    3. Hernández-Ochoa B,
    4. Gómez-Manzo S,
    5. Cárdenas-Rodríguez N,
    6. Arreguin-Espinosa R,
    7. Bandala C,
    8. Canseco-Ávila LM,
    9. Ortega-Cuellar D
    : TFEB; beyond its role as an autophagy and lysosomes regulator. Cells 11(19): 3153, 2022. DOI: 10.3390/cells11193153
    OpenUrlCrossRef
  30. ↵
    1. Hunter DJ,
    2. Collins JE,
    3. Deveza L,
    4. Hoffmann SC,
    5. Kraus VB
    : Biomarkers in osteoarthritis: current status and outlook - the FNIH Biomarkers Consortium PROGRESS OA study. Skeletal Radiol 52(11): 2323-2339, 2023. DOI: 10.1007/s00256-023-04284-w
    OpenUrlCrossRefPubMed
  31. ↵
    1. De Leo MG,
    2. Berger P,
    3. Mayer A
    : WIPI1 promotes fission of endosomal transport carriers and formation of autophagosomes through distinct mechanisms. Autophagy 17(11): 3644-3670, 2021. DOI: 10.1080/15548627.2021.1886830
    OpenUrlCrossRefPubMed
  32. ↵
    1. Sepúlveda D,
    2. Grunenwald F,
    3. Vidal A,
    4. Troncoso-Escudero P,
    5. Cisternas-Olmedo M,
    6. Villagra R,
    7. Vergara P,
    8. Aguilera C,
    9. Nassif M,
    10. Vidal RL
    : Insulin-like growth factor 2 and autophagy gene expression alteration arise as potential biomarkers in Parkinson’s disease. Sci Rep 12(1): 2038, 2022. DOI: 10.1038/s41598-022-05941-1
    OpenUrlCrossRef
  33. ↵
    1. Rasmussen NL,
    2. Kournoutis A,
    3. Lamark T,
    4. Johansen T
    : NBR1: The archetypal selective autophagy receptor. J Cell Biol 221(11): e202208092, 2022. DOI: 10.1083/jcb.202208092
    OpenUrlCrossRefPubMed
  34. ↵
    1. Song H,
    2. Zhu Y,
    3. Hu C,
    4. Liu Q,
    5. Jin Y,
    6. Tang P,
    7. Xia J,
    8. Xie D,
    9. Jiang S,
    10. Yao G,
    11. Liu Z,
    12. Hu Z
    : Selective autophagy receptor NBR1 retards nucleus pulposus cell senescence by directing the clearance of SRBD1. Int J Biol Sci 20(2): 701-717, 2024. DOI: 10.7150/ijbs.90186
    OpenUrlCrossRefPubMed
PreviousNext
Back to top

In this issue

In Vivo: 40 (3)
In Vivo
Vol. 40, Issue 3
May-June 2026
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
  • Ed Board (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on In Vivo.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Stage-dependent Expression of Autophagy-related Genes in Patients With Knee Osteoarthritis
(Your Name) has sent you a message from In Vivo
(Your Name) thought you would like to see the In Vivo web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
2 + 5 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Citation Tools
Stage-dependent Expression of Autophagy-related Genes in Patients With Knee Osteoarthritis
ZEYNEP DÜNDAR ÖK, MUHAMMED ERDİ GÜRBÜZ, NUSRET ÖK, GERGANA LENGEROVA, MARTINA BOZHKOVA, STELIYAN PETROV, AYLİN KÖSELER
In Vivo May 2026, 40 (3) 1776-1787; DOI: 10.21873/invivo.14330

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Reprints and Permissions
Share
Stage-dependent Expression of Autophagy-related Genes in Patients With Knee Osteoarthritis
ZEYNEP DÜNDAR ÖK, MUHAMMED ERDİ GÜRBÜZ, NUSRET ÖK, GERGANA LENGEROVA, MARTINA BOZHKOVA, STELIYAN PETROV, AYLİN KÖSELER
In Vivo May 2026, 40 (3) 1776-1787; DOI: 10.21873/invivo.14330
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Patients and Methods
    • Results
    • Discussion
    • Acknowledgements
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF

Related Articles

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

  • Association Between Dipeptidyl Peptidase-4 Inhibitor Use and Acute Kidney Injury in Patients With Diabetes Mellitus: A Disproportionality Analysis Based on the FAERS
  • Older Age and Outcomes of Intravesical Bacillus Calmette-Guérin for Non-muscle-invasive Bladder Cancer
  • Expression Patterns of T-cell immunoreceptor With Ig and ITIM domains (TIGIT) in Classical Hodgkin Lymphoma: A Clinicopathological Study
Show more Clinical Studies

Keywords

  • Knee osteoarthritis
  • autophagy
  • ULK1
  • TFEB
  • WIPI1
  • NBR1
In Vivo

© 2026 In Vivo

Powered by HighWire