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Article Open Access

Identification of inflammation‑related biomarkers for osteoarthritis diagnosis and stratification through bulk and single‑cell RNA‑sequencing integration

  • Authors:
    • Jie Xiao
    • Weiqing Wang
    • Xiaotian Li
    • Shiwei Xu
    • Bi Zhang
    • Xin Liao
  • View Affiliations / Copyright

    Affiliations: Department of Orthopedics, The First People's Hospital of Jiande, Jiande, Zhejiang 311600, P.R. China, Department of Neurology, The First People's Hospital of Jiande, Jiande, Zhejiang 311600, P.R. China, Department of Equipment, The First People's Hospital of Jiande, Jiande, Zhejiang 311600, P.R. China
    Copyright: © Xiao et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY 4.0].
  • Article Number: 169
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    Published online on: April 15, 2026
       https://doi.org/10.3892/etm.2026.13164
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Abstract

Osteoarthritis (OA) is a degenerative joint disease, which cannot be cured with present treatment methods. Increasing evidence implicates inflammation in OA pathogenesis, and this has led to investigations into inflammation‑related biomarkers (TNF‑a, IL‑6 and IL‑1b) that may guide diagnosis and targeted interventions. In the present study, a total of six Gene Expression Omnibus transcriptomic datasets, including 60 OA and 39 normal samples, were analyzed. Intersecting differentially expressed genes (DEGs) with an inflammatory response gene set defined inflammation‑related DEGs (IRDGs). Consensus clustering, gene set variation analysis and estimation of stromal and immune cells in malignant tumor tissues using expression data/cell‑type identification by estimating relative subsets of RNA transcripts immune profiling were also carried out. The features were reduced by least absolute shrinkage and selection operator (LASSO) and modeled using generalized linear models, random forests, support vector machines (SVM) and extreme gradient boosting. Single‑cell data were subjected to Seurat clustering, SingleR annotation, Monocle pseudotime and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment. Additionally, following modulation of low‑density lipoprotein receptor (LDLR), adrenomedullin (ADM), MYC or NF‑κB inhibitor‑α (NFKBIA), the viability and apoptosis of ATDC5 cells were assessed. Subsequently, a total of 537 DEGs and 11 IRDGs were identified in the present study. In addition, two OA subtypes, cluster C1 and C2, were identified. Presenilin 1 expression was increased in cluster C2, while the expression of the other IRDGs was upregulated in cluster C1. Only the stromal scores differed significantly. LASSO and machine learning nominated four biomarkers, MYC, ADM, LDLR and NFKBIA, with SVM providing the best overall and robust external validation. Single‑cell analysis of 1,464 chondrocytes revealed broad NFKBIA expression across nine subpopulations. Furthermore, it was demonstrated that downregulation of LDLR, ADM, MYC and NFKBIA reduced cell viability and induced apoptosis in ATDC5 chondrocytes. Integrative bulk single‑cell transcriptomics and machine learning identified MYC, ADM, LDLR and NFKBIA as inflammation‑associated OA biomarkers, revealing subtype‑specific immune heterogeneity. Clinically, this signature may possibly enable earlier diagnosis, patient stratification and targeted interventions to slow cartilage degeneration.
View Figures

Figure 1

Differential gene expression analysis
across Gene Expression Omnibus OA datasets. (A) Volcano plot of
DEGs between patients with OA and controls. (B) Intersection of
DEGs and inflammation-related genes. (C) IRDGs in patients with OA.
(D) Chromosomal localization of IRDGs on 23 human chromosomes. (E)
Protein-protein interaction networks constructed for the 11 IRDGs.
DEGs, differentially expressed genes; IRDG, inflammation-related
differentially expressed genes; OA, osteoarthritis. ADM,
adrenomedullin; BTG2, B-cell translocation gene 2; CDKN1A, cyclin
dependent kinase inhibitor 1A; IL1R1, interleukin 1 receptor type
1; KLF6, kruppel-like factor 6; LDLR, low density lipoprotein
receptor; MYC, MYC proto-oncogene, NAMPT, nicotinamide
phosphoribosyltransferase; NFKBIA, NFKB inhibitor alpha; SELE,
selectin E.

Figure 2

OA subtype classification based on
differentially expressed inflammatory genes. (A) Consensus
clustering matrix heatmap defining two clusters (k=2) and their
correlation areas. (B) Consensus CDF curve demonstrating optimal
clustering. (C) δ-area curve of consensus clustering in OA,
indicating the relative change in the area under the CDF curve for
each category number k compared with k-1. The horizontal axis
represents the category number k, whereas the vertical axis
represents the relative change in the area under the CDF curve. (D)
Heatmap of the expression profiles of 11 inflammation-related
differentially expressed genes in the two clusters. (E) PC analysis
illustrating distinct immune subtypes in clusters C1 and C2. (F)
GSVA pathway analysis comparing the top upregulated (red) and
downregulated (blue) pathways between two distinct clusters. OA,
osteoarthritis; CDF, cumulative distribution function; GSVA, gene
set variation analysis; KEGG, Kyoto Encyclopedia of Genes and
Genomes; PC, principal component. ADM, adrenomedullin; BTG2, B-cell
translocation gene 2; CDKN1A, cyclin dependent kinase inhibitor 1A;
IL1R1, interleukin 1 receptor type 1; JAK, Janus kinase; KLF6,
kruppel-like factor 6; LDLR, low density lipoprotein receptor; MYC,
MYC proto-oncogene, NAMPT, nicotinamide phosphoribosyltransferase;
NFKBIA, NFKB inhibitor alpha; STAT, signal transducer and activator
of transcription; SELE, selectin E.

Figure 3

Immune characteristics of different
OA subtypes. (A) Defining two clusters (k=2) matrix score boxplots.
(B) Correlation matrix of 22 immune cell types, stromal scores and
immune scores in OA. Red indicates a positive correlation and blue
indicates a negative correlation. (C) Differentially expressed
immune cells between clusters C1 and C2 in OA. (D) Differentially
expressed immune functions in clusters C1 and C2. (E) Expression
levels of immune checkpoints in clusters C1 and C2.
*P<0.05, **P<0.01 and
***P<0.001. OA, osteoarthritis; ns, not significant;
NK, natural killer; HLA, Human leukocyte antigen; CCR, CC chemokine
receptor; MHC, major histocompatibility complex; APC,
antigen-presenting cell.

Figure 4

Machine-learning model construction.
(A) LASSO regression analysis for preliminary screening of OA
signature genes. (B) Selection of disease-related signature genes
through LASSO regression. (C) Residual box line diagram for the
GLM, RF, SVM and XGB models. The red dot indicates the root mean
square of residuals. (D) Reverse cumulative distribution of
residuals for different machine learning models. (E) Receiver
operating curve analysis verifying model accuracy. (F) Calibration
curve for assessing the accuracy of the nomogram. (G) Nomogram
predicting OA incidence based on four signature genes. LASSO, least
absolute shrinkage and selection operator; OA, osteoarthritis; GLM,
generalized linear models; RF, random forests; SVM, support vector
machines; XGB, extreme gradient boosting; ADM, adrenomedullin;
LDLR, low-density lipoprotein receptor; NFKBIA, NF-κB inhibitor-α;
KLF6, Krüppel-like factor 6; NAMPT, nicotinamide
phosphoribosyltransferase; PSEN1, presenilin-1; SELE, selectin
E.

Figure 5

ROC curves of the four osteoarthritis
disease signature genes in the training and test sets. ROC curves
of ADM in the (A) training, (B) GSE82107, (C) GSE117999 and (D)
GSE169077 datasets. ROC curves of LDLR in the (E) training, (F)
GSE82107, (G) GSE117999 and (H) GSE169077 datasets. ROC curves of
MYC in the (I) training, (J) GSE82107, (K) GSE117999 and (L)
GSE169077 datasets. ROC curves of NFKBIA in the (M) training, (N)
GSE82107, (O) GSE117999 and (P) GSE169077 datasets. ROC, receiver
operating curve; ADM, adrenomedullin; LDLR, low-density lipoprotein
receptor; NFKBIA, NF-κB inhibitor-α; AUC, area under the curve.

Figure 6

Expression profile analysis of four
osteoarthritis signature genes in the single cell RNA-sequencing
data. (A) Bubble map displaying the expression levels of ADM, LDLR,
MYC and NFKBIA genes in the nine clusters. A darker blue color
indicates a higher expression level. t-SNE scatter plots
illustrating the expression of (B) ADM, (C) LDLR, (D) MYC and (E)
NFKBIA in chondrocytes. Red represents upregulated genes and green
indicates downregulated genes. (F) Bubble map visualizing the
expression levels of four genes in chondrocytes. Violin diagram
showing the differential expression of (G) ADM, (H) LDLR, (I) MYC
and (J) NFKBIA in chondrocytes. ADM, adrenomedullin; LDLR,
low-density lipoprotein receptor; NFKBIA, NF-κB inhibitor-α; t-SNE,
t-distributed stochastic neighbor embedding.

Figure 7

LDLR increases cell viability and
suppresses apoptosis in ATDC5 chondrocytes. (A) Reverse
transcription-quantitative PCR was carried out to measure the mRNA
expression of LDLR in ATDC5 cells after LDLR modulation. (B)
Western blotting was conducted to determine protein levels of LDLR
in ATDC5 cells after LDLR modulation. (C) A Cell-Counting Kit 8
assay was carried out to evaluate ATDC5 cell viability after LDLR
modulation. (D) Annexin V/PI flow cytometry was used to measure
apoptosis in ATDC5 cells after LDLR modulation. (E) Quantitative
results for apoptosis. (F) Western blotting was conducted to
measure protein expression of MMP13 in ATDC5 cells after LDLR
modulation. **P<0.01 and ***P<0.001.
LDLR, low-density lipoprotein receptor; sh, short hairpin; EV,
empty vector; OE, overexpression; OD, optical density.
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Copy and paste a formatted citation
Spandidos Publications style
Xiao J, Wang W, Li X, Xu S, Zhang B and Liao X: Identification of inflammation‑related biomarkers for osteoarthritis diagnosis and stratification through bulk and single‑cell RNA‑sequencing integration. Exp Ther Med 31: 169, 2026.
APA
Xiao, J., Wang, W., Li, X., Xu, S., Zhang, B., & Liao, X. (2026). Identification of inflammation‑related biomarkers for osteoarthritis diagnosis and stratification through bulk and single‑cell RNA‑sequencing integration. Experimental and Therapeutic Medicine, 31, 169. https://doi.org/10.3892/etm.2026.13164
MLA
Xiao, J., Wang, W., Li, X., Xu, S., Zhang, B., Liao, X."Identification of inflammation‑related biomarkers for osteoarthritis diagnosis and stratification through bulk and single‑cell RNA‑sequencing integration". Experimental and Therapeutic Medicine 31.6 (2026): 169.
Chicago
Xiao, J., Wang, W., Li, X., Xu, S., Zhang, B., Liao, X."Identification of inflammation‑related biomarkers for osteoarthritis diagnosis and stratification through bulk and single‑cell RNA‑sequencing integration". Experimental and Therapeutic Medicine 31, no. 6 (2026): 169. https://doi.org/10.3892/etm.2026.13164
Copy and paste a formatted citation
x
Spandidos Publications style
Xiao J, Wang W, Li X, Xu S, Zhang B and Liao X: Identification of inflammation‑related biomarkers for osteoarthritis diagnosis and stratification through bulk and single‑cell RNA‑sequencing integration. Exp Ther Med 31: 169, 2026.
APA
Xiao, J., Wang, W., Li, X., Xu, S., Zhang, B., & Liao, X. (2026). Identification of inflammation‑related biomarkers for osteoarthritis diagnosis and stratification through bulk and single‑cell RNA‑sequencing integration. Experimental and Therapeutic Medicine, 31, 169. https://doi.org/10.3892/etm.2026.13164
MLA
Xiao, J., Wang, W., Li, X., Xu, S., Zhang, B., Liao, X."Identification of inflammation‑related biomarkers for osteoarthritis diagnosis and stratification through bulk and single‑cell RNA‑sequencing integration". Experimental and Therapeutic Medicine 31.6 (2026): 169.
Chicago
Xiao, J., Wang, W., Li, X., Xu, S., Zhang, B., Liao, X."Identification of inflammation‑related biomarkers for osteoarthritis diagnosis and stratification through bulk and single‑cell RNA‑sequencing integration". Experimental and Therapeutic Medicine 31, no. 6 (2026): 169. https://doi.org/10.3892/etm.2026.13164
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