Comprehensive Analysis Reveals the Potential Diagnostic Value of Biomarkers Associated With Aging and Circadian Rhythm in Knee Osteoarthritis

Abstract

Objective: Knee osteoarthritis (KOA) is characterized by structural changes. Aging is a major risk factor for KOA. Therefore, the objective of this study was to examine the role of genes related to aging and circadian rhythms in KOA.

Methods: This study identified differentially expressed genes (DEGs) by comparing gene expression levels between normal and KOA samples from the GEO database. Subsequently, we intersected the DEGs with aging-related circadian rhythm genes to obtain a set of aging-associated circadian rhythm genes differentially expressed in KOA. Next, we conducted Mendelian randomization (MR) analysis, using the differentially expressed aging-related circadian rhythm genes in KOA as the exposure factors, their SNPs as instrumental variables, and KOA as the outcome event, to explore the causal relationship between these genes and KOA. We then performed Gene Set Enrichment Analysis (GSEA) to investigate the pathways associated with the selected biomarkers, conducted immune infiltration analysis, built a competing endogenous RNA (ceRNA) network, and performed molecular docking studies. Additionally, the findings and functional roles of the biomarkers were further validated through experiments on human cartilage tissue and cell models.

Results: A total of 75 differentially expressed aging-circadian rhythm related genes between the normal group and the KOA group were identified by difference analysis, primarily enriched in the circadian rhythm pathway. Two biomarkers (PFKFB4 and DDIT4) were screened by MR analysis. Then, immune infiltration analysis showed significant differences in three types of immune cells (resting dendritic cells, resting mast cells, and M2 macrophages), between the normal and KOA groups. Drug prediction and molecular docking results indicated stable binding of PFKFB4 to estradiol and bisphenol_A, while DDIT4 binds stably to nortriptyline and trimipramine. Finally, cell lines with stable expression of the biomarkers were established by lentiviral infection and resistance screening, Gene expression was significantly elevated in overexpressing cells of PFKFB4 and DDIT4 and reversed the proliferation and migration ability of cells after IL-1β treatment.

Conclusions: Two diagnostic and therapeutic biomarkers associated with aging-circadian rhythm in KOA were identified. Functional analysis, molecular mechanism exploration, and experimental validation further elucidated their roles in KOA, offering novel perspectives for the prevention and treatment of the disease.

Keywords: DDIT4; PFKFB4; aging; circadian rhythm; knee osteoarthritis; mendelian randomization.

FIGURE 1

Screening of the differentially expressed aging‐circadian rhythm related genes (DE‐ARGs_CIRGs) in knee osteoarthritis (KOA). (A) Volcano map of differentially expressed genes (DEGs) in the GSE114007 dataset. (B) Heatmap illustrated the expression levels of DEGs. Genes with high expression are represented in red, while those with low expression are depicted in blue. (C) Principal component analysis (PCA) analysis of normal and KOA samples. (D) Heatmap of correlations between 30 aging‐related genes and 30 circadian rhythm‐related genes. (E) Venn diagram of DEGs and aging‐related_circadian rhythm‐related genes.

FIGURE 2

Functional enrichment analysis and Gene Set Variation Analysis (GSVA). (A) Bubble chart of Gene Ontology (GO) enrichment analysis. BP, biological process; CC, cellular component; MF, molecular function. (B) The circos diagram of GO enrichment analysis. (C) Bubble chart of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. (D) GSVA in “Hallmark” background. (E) GSVA in “KEGG” background.

FIGURE 3

Forest plots combined with individual single nucleotide polymorphism (SNP) estimates risk effects related to biomarkers and KOA. (A) The forest map illustrated the causal effects of SNPs associated with CHPF2 on KOA. (B) The forest map illustrated the causal effects of SNPs associated with PFKFB4 on KOA. (C) The forest map illustrated the causal effects of SNPs associated with PER2 on KOA. (D) The forest map illustrated the causal effects of SNPs associated with DDIT4 on KOA. (E) The forest map illustrated the causal effects of SNPs associated with BCL3 on KOA.

FIGURE 4

Leave‐one‐out test for Mendelian randomization (MR) of biomarkers. (A–E) Leave‐one‐out forest map for CHPF2, BCL3, PFKFB4, PER2, and DDIT4 on KOA, respectively. Each line in the figure represented the effect of the model when that specific line of SNPs was excluded.

FIGURE 5

Diagnostic ability assessment, expression level verification and chromosome localization of biomarkers. (A) Receiver operating characteristic (ROC) curves for biomarkers in training set GSE114007 and validation set GSE51588, respectively. (B) Expression level box plots for biomarkers in training set GSE114007 and validation set GSE51588 between KOA and normal groups, respectively. (C) Differential mRNA expression of PFKFB4 and DDIT4 in normal cartilage tissues and osteoarthritis cartilage tissues. (D) The circle diagram of biomarkers localization on chromosomes.

FIGURE 6

Gene Set Enrichment Analysis (GSEA) and competing endogenous RNA (ceRNA) network. (A) GSEA of PFKFB4. (B) GSEA of DDIT4. (C) ceRNA of biomarkers. Pink denoted biomarkers, green signified microRNAs (miRNAs), and yellow indicated long non‐coding RNAs (lncRNAs).

FIGURE 7

Immune infiltration analysis. (A) Stack bar diagram of 22 immune cells score. (B) Box plot of 22 immune cells proportion between different groups in normal and KOA groups. “*” represents p < 0.05, “**” represents p < 0.01. (C) Pearson correlation heat map between immune cells. (D) Heat map of correlation between biomarkers and differential immune cells. “*” represents p < 0.05, “**” represents p < 0.01.

FIGURE 8

Results of molecular docking of biomarkers and drugs. (A) PFKFB4 and estradiol. (B) PFKFB4 and bisphenol_A. (C) DDIT4 and nortriptyline. (D) DDIT4 and trimipramine.

FIGURE 9

Effects on proliferation and migration of PFKFB4 overexpression were observed in vitro. (A) RT‐PCR was used to detect the overexpression efficiency of PFKFB4. (B, C) CCK‐8 and EDU staining were used to measure the proliferation activity. (D, E) Scratch wound test and Transwell were uesd to assay the migration ability.

FIGURE 10

Effects on proliferation and migration of DDIT4 overexpression were observed in vitro. (A) RT‐PCR was used to detect the overexpression efficiency of DDIT4. (B, C) CCK‐8 and EDU staining were used to measure the proliferation activity. (D, E) Scratch wound test and Transwell were uesd to assay the migration ability.