We selected patients with periprosthetic AL after primary THA who underwent rTHA from May 2023 to May 2024 at the Department of Orthopedics, Henan Provincial People’s Hospital, as the study group. The diagnosis of AL was consistent with the diagnostic criteria described in previous literature. Seven patients with a PFF after THA from our hospital were recruited as the control group, with the diagnosis of PFF conforming to the Unified Classification System (UCS) (Duncan and Haddad, 2014). Informed consent was obtained from all participants or their families. This study was conducted in accordance with the principles of the Declaration of Helsinki (1975) and was approved by the Medical Ethics Committee of Henan Provincial People’s Hospital (IRB ID: 2022-68). The interface membrane from the AL group was obtained from the surrounding tissues of the liner–ball–prosthetic neck, whereas the control samples were obtained from the hyperplastic synovial tissue within the hip joint adjacent to the prosthesis, matching the AL group’s anatomical origin. Demographic data such as gender, age, body mass index (BMI), smoking history, and drinking history were recorded for both groups, and in addition, data on the prosthetic survival period after primary THA, diagnosis, site of prosthetic loosening, and type of implant used for primary THA were also recorded for the AL group.

Sample preparation: the interface membrane from the AL group and the synovial tissues from the control group were fully ground using a homogenizer. Total RNA is extracted from the samples using TRIzol (Invitrogen). The mRNA is then isolated from the total RNA using Oligo-dT magnetic beads (Invitrogen).

Fragmentation: the purified mRNA is fragmented into smaller pieces of ∼300 bp.

cDNA synthesis: the fragmented mRNA is reverse-transcribed into cDNA using cDNA synthesis reagents (Yeasen Biotechnology).

Adapter ligation: adapters are ligated to both ends of the cDNA fragments.

cDNA purification and fragment sorting: beads are utilized to selectively bind and isolate the 200–300 bp of DNA fragments (Yeasen Biotechnology).

Amplification: the adapter-ligated cDNA fragments are amplified using PCR to generate sufficient material for sequencing (Thermo Fisher Scientific).

Sequencing: the amplified cDNA library is loaded onto a flow cell and sequenced using the Illumina NovaSeq X Plus platform (Illumina, United States).

Sangerbox tools (http://www.sangerbox.com/tool) is a user-friendly interface that supports differential analysis and provides interactive customizable analysis tools, including PCA, various types of correlation analyses, and enrichment analyses, as well as some other common tools and functions (Shen et al., 2022). The gene expression profile data obtained from RNA sequencing were analyzed using the “stats” package in R software. Specifically, the z-score was first performed on the expression profile, and the “prcomp” function was further used for dimensionality reduction analysis to obtain the matrix after dimensionality reduction.

The gene expression profile data were analyzed using the “limma” package in R software to identify DEGs; |log2FC| ≥ 1 and adjusted p-value <0.05 were considered statistically significant. The expression patterns of DEGs were visualized in the form of volcano plots and heatmaps.

The DEGs were imported into Sangerbox tools, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and Gene Ontology (GO) enrichment analyses, including biological process (BP), molecular function (MF), and cellular component (CC), were completed based on the latest subset gene annotation. An adjusted p-value <0.05 and FDR < 0.05 were considered indicative of significant signaling pathways. The enrichment analysis results were visualized using bubble plots or Circos plots.

The top 250 DEGs were selected using the STRING database (https://string-db.org) for protein–protein interaction (PPI) network analysis, and the data were visualized using Cytoscape software and its Molecular Complex Detection (MCODE) plugin (version 3.8.2, San Diego, United States). The MCODE function in Cytoscape was further utilized to screen gene clusters, which are ranked according to their scores.

Gene expression profile data were analyzed using Sangerbox tools immunoinfiltration analysis function. The R software “CIBERSORT” package was used to calculate the proportion of 22 types of immune cells in each sample with the Wilcoxon test (Newman et al., 2015); a p-value <0.05 was considered statistically significant, and stacked histogram visualization was performed. Spearman’s correlation coefficient was employed to analyze the correlation between the proportion of infiltrating immune cells and immune-related DEGs; a p-value <0.05 or −log (p-value) >1.3 was considered statistically significant, and a heatmap was used for visualization.

The interface membrane and synovial tissues of the same quality were cut into 1-mm³ pieces using eye scissors, fully cracked at 4°C for 30 min according to the ratio of 20 mg tissue:200 μL lysate, and completely shaken once every 10 min. The cracked sample was centrifuged at 14,000g at 4°C for 10 min, the supernatant retained, the protein concentration determined, and the protein concentration of different samples adjusted to be consistent. Western blot analysis was performed after the loading buffer was added. The following primary antibodies were used: anti-CD68 (1:4,000, Proteintech), anti-CD163 (1:500, Proteintech), anti-OPN (1:2,000, Proteintech), and anti-β-actin (1:1,000, PTM). The intensity of each band was quantified using AlphaEaseFC software, and the expression was calculated relative to the β-actin level.

The interface membrane and synovial samples were fixed with 10% neutral formalin, and paraffin sections were performed. After dehydration, hematoxylin–eosin (HE) staining was carried out to evaluate the inflammatory infiltration of synovial tissue by the synovitis score (Krenn et al., 2006). For immunohistochemical (IHC) analysis, after dehydration, the paraffin sections were repaired with citric acid, and endogenous peroxidase blocker was added to seal the sections with bovine serum albumin. The following primary antibodies were used: anti-CD68 (1:2,000, Proteintech), anti-CD163 (1:1,000, Proteintech), and anti-OPN (1:250, Proteintech). The average optical density (AOD) of CD68, CD163, and OPN was statistically analyzed using ImageJ (version 1.8.0). AOD (%area) = integrated option density (IOD)/area.

The top 150 upregulated DEGs were screened using the criteria of |log₂FC| > 1.5 and adjusted p-value <0.05 and then were analyzed using the CMap database (https://clue.io) to search for potential small-molecule compounds. The top 10 small-molecule compounds with the highest negative connectivity scores (CSs) were identified as small-molecule compounds with potential therapeutic effects, and the results were visualized in the form of heatmaps and Sankey maps.

All data between the two groups were analyzed statistically and plotted using GraphPad Prism software (version 8.0, San Diego). The categorical variables were assessed using Fisher’s test. Descriptive statistics are expressed as mean with standard deviation. The normality of the data distribution was evaluated using the Kolmogorov–Smirnov test. Levene’s test was used to assess the homogeneity of variance. Independent samples t-tests were applied to analyze normally distributed values. Data with non-Gaussian distribution were analyzed using the non-parametric Mann–Whitney U test. Two-tailed values of p < 0.05 were considered statistically significant.

A total of five patients with AL of hip prostheses and five patients with a PFF after THA were studied. Three patients in the AL group had the diagnosis of avascular necrosis of the femoral head (ANFH) at primary THA, and two patients had a fracture of the femoral neck. The control group is the same as the AL group. The frictional interface of the primary THA prostheses in both AL and control groups includes two cases of ceramic–polyethylene and three cases of ceramic–ceramic bearings. The mean survival time of the prosthesis in the AL group was 6.8 ± 2.59 years when rTHA was performed, including one case of simple acetabular prosthesis loosening, two cases of simple femoral stalk prosthesis loosening, and two cases of both acetabular and femoral prosthesis loosening. The mean survival time of the prosthesis in the control group was 4.6 ± 3.13 years. There were no significant differences in age, sex, BMI, operative site, friction interface, and drinking and smoking history between the two groups. The demographics of the two groups are shown in Table 1.

A total of 2,184 DEGs were identified in AL samples with an adjusted p-value <0.05, including 2,050 upregulated genes and 134 downregulated genes (Figure 1A). The PCA results showed significant differences between the two samples within the group (Figure 1B). The volcano plot was used to describe the expression pattern of DEGs in the AL samples and controls (Figure 1C), and the heatmap showed the top 50 upregulated DEGs and the top 50 downregulated DEGs (Figure 1D).

GO-BP enrichment analysis showed enrichment of these DEGs in immune-related signaling pathways such as “immune system process,” “immune response,” “leukocyte activation,” “immune effector process,” “cell activation involved in immune response,” and “leukocyte activation involved in immune response” (Figure 2A). In terms of GO-CC analysis, these DEGs were mainly enriched in “bounding membrane of organelle,” “cytoplasmic vesicle,” and “intracellular vesicle” processes (Figure 2B). GO-MF enrichment analysis suggested that these DEGs were mainly enriched in “enzyme binding,” “Ras GTPase binding,” and “small GTPase binding” processes (Figure 2C).

To further uncover the potential enrichment pathways of DEGs, PPI network analysis was performed. Cluster analysis was further carried out via the MCODE function in Cytoscape software based on the PPI network (Figure 2D; Table 2). The top gene cluster included many immune-related genes, such as CD68, CD163, CD4, CD80, and CD86, other immune cell markers, and inflammation-related genes such as CCL3, CCL4, CCR1, and CCR5 (Figure 2E). GO-BP enrichment analysis showed that the genes included in the cluster with the highest score were mainly enriched in immune-related processes such as “immune response,” “positive regulation of immune system process,” “defense response,” “immune system process,” “regulation of immune system process,” and “positive regulation of immune response” and inflammation-related signaling pathways such as “cellular response to cytokine stimulus” and “response to cytokine” (Figures 2F, G). KEGG enrichment analysis showed that these genes were mainly enriched in “Toll-like receptor signaling pathway,” “rheumatoid arthritis,” “cell adhesion molecules,” “cytokine–cytokine receptor interaction,” and “NF-κB signaling pathway” (Figures 2H, I).

The enrichment analyses suggested that DEGs in AL samples were closely related to immune-related signaling pathways. Therefore, the CIBERSORT algorithm was employed to obtain the immune cell characteristics to explore the immune regulation and the correlation between DEGs and infiltrating immune cells in AL. Immunoinfiltration analysis displayed the proportion of 22 types of immune cells in each sample (Figure 3A). Compared with the control group, the proportion of M1 macrophages increased in the AL samples, whereas M2 macrophages tended to increase, but there was no statistical difference (Figure 3B). Immune-related genes in the top 20 upregulated genes were screened, and correlation analysis was performed with 22 types of immune-infiltrating cells. The correlation analysis showed that M1 macrophages were significantly positively correlated with CD177, SIGLEC1, HLA-DQA1, ADA2, SPP1, CD163, and CD68. M2 macrophages were significantly positively correlated with CD177, HLA-DQA1, SIGLEC1, ADA2, and NCKAP1L. Plasma cells were significantly negatively correlated with CD163, MSR1, FCGR3A, and CD68 (Figure 3C).

As bioinformatics analysis and immunoinfiltration analysis suggested significant abnormal immune and inflammatory responses in the AL group, HE staining was used to evaluate the inflammatory infiltration of synovial tissue by the synovitis scores of the two groups. The results showed that the synovitis score of the AL group was significantly higher than that of the control group (average 6.2 vs. 3.8; AL group vs. control group) (Figure 4A). In order to further explore the role of M1 macrophages in the AL group interface membrane, we screened the genes closely related to M1 macrophages in the above correlation analysis, which revealed three key genes CD68, CD163, and SPP1 [encoding protein osteopontin (OPN)]. IHC results showed that the expression of CD68, CD163, and OPN in the AL group was significantly higher than that in the control group (AOD; CD68, average 30.03% vs. 5.85%; CD163, average 29.05% vs. 10.27%; OPN, average 31.28% vs. 9.41%; AL group vs. control group) (Figures 4B–D). Western blot analysis revealed that the expression of CD68, CD163, and OPN significantly increased in the AL samples compared with that in the controls (Figure 4E).

To predict the potential small-molecule compounds that might play a role in therapeutic intervention of AL patients, the top 150 DEGs (all upregulated genes) were analyzed via the CMap database. The top 10 compounds with the highest negative scores were predicted to be potential therapeutic drugs for the treatment of AL: desoxypeganine, metyrapone, MRS-1220, valproic acid, actarit, warfarin, fluoropyruvate, caffeic acid, talampicillin, and tetrabenazine (Figure 5A). The Sankey diagram showed the targeted pathways of these 10 compounds (Figure 5B), and the chemical structure is visualized in Figure 5C. These compounds represent computational predictions, and their biological relevance to AL needs further in vitro/in vivo validation.