Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning

PROS1 is a crucial gene in the macrophage efferocytosis of diabetic foot ulcers: a concerted analytical approach through the prisms of computer analysis

BACKGROUND

Diabetic foot ulcers (DFUs) pose a serious long-term threat because of elevated mortality and disability risks. Research on its biomarkers is still, however, very limited. In this paper, we have effectively identified biomarkers linked with macrophage excretion in diabetic foot ulcers through the application of bioinformatics and machine learning methodologies. These findings were subsequently validated using external datasets and animal experiments. Such discoveries are anticipated to offer novel insights and approaches for the early diagnosis and treatment of DFU.

METHODS

In this work, we used the Gene Expression Omnibus (GEO) database's datasets GSE68183 and GSE80178 as the training dataset to build a gene model using machine learning methods. After that, we used the training and validation sets to validate the model (GSE134431). On the model genes, we performed enrichment analysis using both gene set variant analysis (GSVA) and gene set enrichment analysis (GSEA). Additionally, the model genes were subjected to immunological association and immune function analyses.

RESULTS

In this study, PROS1 was identified as a potential key target associated with macrophage efflux in DFU by machine learning and bioinformatics approaches. Subsequently, the key biomarker status of PROS1 in DFU was also confirmed by external datasets. In addition, PROS1 also plays a key role in macrophage exudation in DFU. This gene may be associated with macrophage M1, CD4 memory T cells, naïve B cells, and macrophage M2, and affects IL-17, Rap1, hedgehog, and JAK-STAT signaling pathways.

CONCLUSIONS

PROS1 was identified and validated as a biomarker for DFU. This finding has the potential to provide a target for macrophage clearance of DFU.

[Identification of Osteoarthritis Inflamm-Aging Biomarkers by Integrating Bioinformatic Analysis and Machine Learning Strategies and the Clinical Validation]

Objective

To identify inflamm-aging related biomarkers in osteoarthritis (OA).

Methods

Microarray gene profiles of young and aging OA patients were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) were obtained from the Human Aging Genome Resource (HAGR) database. The differentially expressed genes of young OA and older OA patients were screened and then intersected with ARGs to obtain the aging-related genes of OA. Enrichment analysis was performed to reveal the potential mechanisms of aging-related markers in OA. Three machine learning methods were used to identify core senescence markers of OA and the receiver operating characteristic (ROC) curve was used to assess their diagnostic performance. Peripheral blood mononuclear cells were collected from clinical OA patients to verify the expression of senescence-associated secretory phenotype (SASP) factors and senescence markers.

Results

A total of 45 senescence-related markers were obtained, which were mainly involved in the regulation of cellular senescence, the cell cycle, inflammatory response, etc. Through the screening with the three machine learning methods, 5 core senescence biomarkers, including FOXO3, MCL1, SIRT3, STAG1, and S100A13, were obtained. A total of 20 cases of normal controls and 40 cases of OA patients, including 20 cases in the young patient group and 20 in the elderly patient group, were enrolled. Compared with those of the young patient group, C-reactive protein (CRP), interleukin (IL)-6, and IL-1β levels increased and IL-4 levels decreased in the elderly OA patient group (P<0.01); FOXO3, MCL1, and SIRT3 mRNA expression decreased and STAG1 and S100A13 mRNA expression increased (P<0.01). Pearson correlation analysis demonstrated that the selected markers were associated with some indicators, including erythrocyte sedimentation rate (ESR), IL-1β, IL-4, CRP, and IL-6. The area under the ROC curve of the 5 core aging genes was always greater than 0.8 and the C-index of the calibration curve in the nomogram prediction model was 0.755, which suggested the good calibration ability of the model.

Conclusion

FOXO3, MCL1, SIRT3, STAG1, and S100A13 may serve as novel diagnostic biomolecular markers and potential therapeutic targets for OA inflamm-aging.

Identifying and Validating GSTM5 as an Immunogenic Gene in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning

Background

A diabetic foot ulcer (DFU) is a serious, long-term condition associated with a significant risk of disability and mortality. However, research on its biomarkers is still limited. This study utilizes bioinformatics and machine learning methods to identify immune-related biomarkers for DFU and validates them through external datasets and animal experiments.

Methods

This study used bioinformatics and machine learning to analyze microarray data from the Gene Expression Omnibus (GEO) database to identify key genes associated with DFU. Animal experiments were conducted to validate these findings. This research employs the datasets GSE68183 and GSE80178 retrieved from the GEO database as the training dataset for building a gene machine learning model, and after conducting differential analysis on the data, this study used package glmnet and package e1071 to construct LASSO and SVM-RFE machine learning models, respectively. Subsequently, we validated the model using the training set and validation set (GSE134431). We conducted enrichment analysis, including GSEA and GSVA, on the model genes. We also performed immune functional analysis and immune-related analysis on the model genes. Finally, we conducted immunohistochemistry (IHC) validation on the model genes.

Results

This study identifies GSTM5 as a potential immune-related key target in DFU using machine learning and bioinformatics methods. Subsequent validation through external datasets and IHC experiments also confirms GSTM5 as a critical biomarker for DFU. The gene may be associated with T cells regulatory (Tregs) and T cells follicular helper, and it influences the NF-κB, GnRH, and MAPK signaling pathway.

Conclusion

This study identified and validated GSTM5 as a biomarker for DFU. This finding may potentially provide a target for immune therapy for DFU.

Synovium is a sensitive tissue for mapping the negative effects of systemic iron overload in osteoarthritis: identification and validation of two potential targets

BACKGROUND

The prevention and treatment of osteoarthritis (OA) pose a major challenge in its research. The synovium is a critical tissue in the systematic treatment of OA. The present study aimed to investigate potential target genes and their correlation with iron overload in OA patients.

METHODS

The internal datasets for analysis included the microarray datasets GSE46750, GSE55457, and GSE56409, while the external datasets for validation included GSE12021 and GSE55235. The GSE176308 dataset was used to generate single-cell RNA sequencing profiles. To investigate the expression of the target genes in synovial samples, quantitative reverse transcription-PCR, western blotting, and immunohistochemical assay were conducted. ELISA was used to detect the levels of ferritin and Fe2+ in both serum and synovium.

RESULTS

JUN and ZFP36 were screened from the differentially expressed genes, and their mRNA were significantly reduced in the OA synovium compared to that in normal synovium. Subsequently, complex and dynamically evolving cellular components were observed in the OA synovium. The mRNA level of JUN and ZFP36 differed across various cell clusters of OA synovium and correlated with immune cell infiltration. Moreover, ferritin and Fe2+ were significantly increased in the serum and synovium of OA patients. Further, we found that JUN elevated and ZFP36 decreased at protein level.

CONCLUSIONS

The synovium is a sensitive tissue for mapping the adverse effects of systemic iron overload in OA. JUN and ZFP36 represent potential target genes for attenuating iron overload during OA treatment. Some discrepancies between the transcription and protein levels of JUN suggest that post-transcriptional modifications may be implicated. Future studies should also focus on the roles of JUN and ZFP36 in inducing changes in cellular components in the synovium during OA pathogenesis.

Total Flavonoids of Rhizoma Drynariae Treat Osteoarthritis by Inhibiting Arachidonic Acid Metabolites Through AMPK/NFκB Pathway

Objective

Previous clinical studies have found that total flavonoids of Rhizoma Drynariae (TFRD) have a good therapeutic effect on osteoarthritis (OA), but its therapeutic mechanism needs further research.

Methods

OA rat model was established by Hulth method and was intervened by TFRD. Pathological assessments were conducted to assess the protective effect of TFRD on cartilage. Serum metabolomics and network pharmacology were detected to predict the mechanism of TFRD treating OA. In further experiments, molecular biology experiment was carried out to confirm the predicted mechanisms in vivo and in vitro.

Results

TFRD can effectively reduce chondrocyte apoptosis and cartilage degeneration in OA model rats. Serum metabolomics revealed that the intervention effect may be closely related to arachidonic acid metabolism pathway. Network pharmacologic prediction showed that COX-2 was the key target of TFRD in treating OA, and its mechanism might be related with NFκB, apoptosis, AMPK and arachidonic acid metabolism pathway. In vivo experiments indicated that TFRD can inhibit the abnormal expression of COX-2 mRNA in OA model rats. In the in vitro studies, the expression of COX-2 mRNA and protein increased, AMPK phosphorylation was inhibited, and NFκB signaling pathway was activated in IL-1β-induced chondrocytes, and these changes can be reversed by TFRD. After the activation of AMPK signaling pathway or the block-down of NFκB signaling pathway, the effect of TFRD on COX-2 mRNA expression was significantly weakened.

Conclusion

TFRD can inhibit COX-2-mediated arachidonic acid metabolites, and its mechanism is closely related to AMPK/NFκB pathway, which may be a key mechanism in the treatment of OA.

Erythritol Can Inhibit the Expression of Senescence Molecules in Mouse Gingival Tissues and Human Gingival Fibroblasts

Oral aging causes conditions including periodontal disease. We investigated how the sugar alcohol erythritol, which has anti-caries effects, impacts aging periodontal tissues and gingival fibroblasts in mice and humans in vivo and in vitro. Mice were classified into three groups: control groups of six-week-old (YC) and eighteen-month-old mice (AC) and a group receiving 5% w/w erythritol water for 6 months (AE). After rearing, RNA was extracted from the gingiva, and the levels of aging-related molecules were measured using PCR. Immunostaining was performed for the aging markers p21, γH2AX, and NF-κB p65. p16, p21, γH2AX, IL-1β, and TNFα mRNA expression levels were higher in the gingiva of the AC group than in the YC group, while this enhanced expression was significantly suppressed in AE gingiva. NF-κB p65 expression was high in the AC group but was strongly suppressed in the AE group. We induced senescence in cultured human gingival fibroblasts using H2O2 and lipopolysaccharide before erythritol treatment, which reduced elevated senescence-related marker (p16, p21, SA-β-gal, IL-1β, and TNFα) expression levels. Knockdown of PFK or PGAM promoted p16 and p21 mRNA expression, but erythritol subsequently rescued pyruvate production. Overall, intraoral erythritol administration may prevent age-related oral mucosal diseases.