Identification of potential biomarkers of gout through competitive endogenous RNA network analysis

Challenges for sustainable development goal of land degradation neutrality in drylands: Evidence from the Northern Slope of the Tianshan Mountains, China

The SDG 15.3.1 target of Land Degradation Neutrality (LDN) only has 15 years from conception (in 2015) to realization (in 2030). Therefore, investigating the effectiveness and challenges of LDN has become a priority, especially in drylands, where fragile ecosystems intersect with multiple disturbances. In this study, solutions are proposed and validated based on the challenges of LDN. We chose the Northern Slope of the Tianshan Mountains as a case study and set baselines in 2005 and 2010. The region and degree of land change (including degraded, stable, and improved) were depicted at the pixel scale (100 × 100 m), and LDN realization was assessed at the regional scale (including administrative districts and 5000 × 5000 m grids). The results showed a significant disparity between the two baselines. The number of areas that realized the LDN target was rare, regardless of the scale of the administrative districts or grids. Chord plots, Spearman's correlation, and curve estimation were employed to reveal the relationship between LDN and seven natural or socioeconomic factors. We found that substantial degradation was closely related to the expansion of unused, urban, and mining land and reduction in water, glaciers, and forests. Further evidence suggests that agricultural development both positively and negatively affects LDN, whereas urbanization and mining activities are undesirable for LDN. Notably, the adverse effects of glacier melting require additional attention. Therefore, we consider the easy-to-achieve and hard-to-achieve baselines as the mandatory and desirable targets of LDN, respectively, and focus further efforts in three aspects: preventing agricultural exploitation from occupying ecological resources, defining reasonable zones for urbanization and mining, and reducing greenhouse gas emissions to mitigate warming. Overall, this study is expected to be a beneficial addition to existing LDN theoretical systems and serve as a case validation of the challenges of LDN in drylands.

Identifying potential ferroptosis key genes for diagnosis and treatment of postmenopausal osteoporosis through competitive endogenous RNA network analysis

Objective

Postmenopausal osteoporosis (PMOP) is a common systemic metabolic bone disorder that is owing to the reduced estrogen secretion and imbalance of bone absorption and bone formation in postmenopausal women. Ferroptosis has been identified as a novel modulatory mechanism of osteoporosis. Nevertheless, the particular modulatory mechanism between ferroptosis and PMOP is still unclear. The objective of the current investigation was to detect potential biomarkers connected to ferroptosis in PMOP and discover its probable mechanism through bioinformatics.

Methods

We downloaded PMOP-related microarray datasets from the database of Gene Expression Omnibus (GEO) and obtained the differentially expressed genes (DEGs). Utilizing bioinformatics analysis, the DEGs were intersected with the ferroptosis dataset to obtain ferroptosis-connected mRNAs. Enrichment analysis employing KOBAS 3.0 was conducted to comprehend the biological functions and enrichment pathways of the DEGs. The generation of the protein-protein interaction (PPI) network was conducted with the aim of identifying central genes. Lastly, the coexpression and competitive endogenous RNA (ceRNA) networks were built using Cytoscape. With the help of external datasets GSE56815 to verify the reliability of the hub genes by plotting ROC curves.

Results

We identified 178 DE microRNAs (miRNAs), 138 DE circular RNAs (circRNAs), and 86 ferroptosis-related mRNAs. Enrichment analysis exhibited that mRNAs were primarily connected with the signaling pathways of PI3K/Akt, metabolism, mTOR, FoxO, HIF-1, AMPK, MAPK, ferroptosis, VEGF, and NOD-like receptors. Generation of the PPI network detected eight hub genes. The circRNA/miR-23b-3p/PTEN axis may relieve PMOP by inhibiting ferroptosis through targeting the pathway of PI3K/Akt signaling, which is a vital modulatory pathway for PMOP progression. Moreover, the ROC curves ultimately indicates that the four hub genes have greater diagnostic importance in PMOP samples in contrast to the normal group samples, which may be possible markers for PMOP diagnosis.

Conclusions

Bioinformatics analysis identified four hub genes, namely, PTEN, SIRT1, VEGFA, and KRAS, as potential biomarkers for PMOP diagnosis and management. Moreover, the circRNA/miR-23b-3p/PTEN axis may relieve PMOP by suppressing ferroptosis through targeting the pathway of PI3K/Akt signaling, providing a new avenue to explore the pathogenesis of PMOP.

Expressions of Peptidoglycan Recognition Protein 1, Neuron Towards Axon Guidance Factor-1 and miR-142-3p and Their Correlations in Patients with Rheumatoid Arthritis

Purpose

We aimed to explore the expressions of peptidoglycan recognition protein 1 (PGLYRP-1), neuron towards axon guidance factor-1 (Netrin-1) and miR-142-3p and their correlations in patients with rheumatoid arthritis.

Patients and Methods

Sixty patients with rheumatoid arthritis treated from January 2022 to January 2023 were enrolled as a rheumatoid group, 30 patients with osteoarthritis were selected as an osteoarthritis group, and 30 healthy volunteers were recruited as a control group. The enzyme-linked immunosorbent assay, Western blotting and reverse transcriptase-polymerase chain reaction were employed to measure the expressions of PGLYRP-1, Netrin-1 and miR-142-3p, respectively. The correlations among PGLYRP-1, Netrin-1 and miR-142-3p expressions in patients with rheumatoid arthritis were analyzed.

Results

In patients with rheumatoid arthritis, PGLYRP-1 expression was negatively correlated with Netrin-1 expression (r=-0.570, P=0.001) but positively correlated with miR-142-3p expression (r=0.599, P=0.001), and a negative correlation was found between Netrin-1 and miR-142-3p expressions (r=-0.468, P=0.001). The combined detection of PGLYRP-1, Netrin-1 and miR-142-3p was more sensitive and less specific for predicting the prognosis of patients with rheumatoid arthritis than the measurement of a single marker (P<0.05).

Conclusion

The combined measurement of PGLYRP-1, Netrin-1 and miR-142-3p has a predictive value for the prognosis of patients with rheumatoid arthritis.

Identification of lncRNA-miRNA-mRNA networks in circulating exosomes as potential biomarkers for systemic sclerosis

Objective

This study aimed to analyze potential biomarkers for systemic sclerosis (SSc) by constructing lncRNA-miRNA-mRNA networks in circulating exosomes (cirexos).

Materials and methods

Differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in SSc cirexos were screened using high-throughput sequencing and detected with real-time quantitative PCR (RT-qPCR). Differentially expressed genes (DEGs) were analyzed using the DisGeNET, GeneCards, GSEA4.2.3, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Receiver operating characteristic (ROC) curves, correlation analyses, and a double-luciferase reporter gene detection assay were used to analyze competing endogenous RNA (ceRNA) networks and clinical data.

Results

In this study, 286 DEmRNAs and 192 DElncRNAs were screened, of which 18 DEGs were the same as the SSc-related genes. The main SSc-related pathways included extracellular matrix (ECM) receptor interaction, local adhesion, platelet activation, and IgA production by the intestinal immune network. A hub gene, COL1A1, was obtained by a protein-protein interaction (PPI) network. Four ceRNA networks were predicted through Cytoscape. The relative expression levels of COL1A1, ENST0000313807, and NON-HSAT194388.1 were significantly higher in SSc, while the relative expression levels of hsa-miR-29a-3p, hsa-miR-29b-3p, and hsa-miR-29c-3p were significantly lower in SSc (P < 0.05). The ROC curve showed that the ENST00000313807-hsa-miR-29a-3p-COL1A1 network as a combined biomarker of SSc is more valuable than independent diagnosis, and that it is correlated with high-resolution CT (HRCT), Scl-70, C-reactive protein (CRP), Ro-52, IL-10, IgM, lymphocyte percentage, neutrophil percentage, albumin divided by globulin, urea, and RDW-SD (P < 0.05). Double-luciferase reporter gene detection showed that ENST00000313807 interacts with hsa-miR-29a-3p, which interacts with COL1A1.

Conclusion

The ENST00000313807-hsa-miR-29a-3p-COL1A1 network in plasma cirexos represents a potential combined biomarker for the clinical diagnosis and treatment of SSc.

The combination of molecular docking and network pharmacology reveals the molecular mechanism of Danggui Niantong decoction in treating gout

BACKGROUND

Due to unhealthy diet and living habits, the incidence of gout is on the rise and has become a common disease with a high incidence. Danggui Niantong decoction (DGNTD), as a classic formula composed of 15 common herbs, has been widely used in clinical practice since ancient times to prevent and treat gout. However, the pharmacological mechanism and target of DGNTD are not clear.

METHODS

The potential active compounds and targets of DGNTD were obtained by traditional Chinese medicine systems pharmacology (TCMSP) database, and the differential genes of gout patients and controls were analyzed in gene expression omnibus (GEO) database. GSEA analysis of differential genes with GSEA 4.1.0 software and then the differential genes were intersected with the gout-related disease targets searched by GeneCard, CTD and OMIM disease database to obtain the final disease target. The "Traditional Chinese medicine-Active compounds-Targets" network was constructed by Cytoscape3.7.2 software. The R packet is used for enrichment analysis. The molecular docking between the active compound of DGNTD and the core target was verified by AutoDockTools software.

RESULTS

Two hundred eighty six and 244 targets of DGNTD-related active components and 652 targets of gout were obtained, of which 13 targets were potential targets of DGNTD in the treatment of gout. GSEA analysis showed that the differential genes were mainly involved in apoptosis, inflammatory reaction, and receptor metabolism and so on. Gene ontology (GO) functional enrichment analysis shows that DGNTD regulates many biological processes, such as the response to purine-containing compound and response to lipopolysaccharide, positive regulation of acute inflammatory response and other cellular components. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis shows that DGNTD treatment of gout is mainly related to interleukin-17 (IL-17), Toll-like receptor, rheumatoid arthritis, tumor necrosis factor (TNF) and so on. The results of molecular docking showed that the five active compounds in DGNTD had strong binding activity to core protein receptors.

CONCLUSIONS

The active compounds of DGNTD may achieve the purpose of treating gout by acting on the core target (CASP8, CXCL8, FOS, IL1B, IL6, JUN, PTGS2, STAT1, MMP1, TNF) to regulate cell metabolism, proliferation and apoptosis, and improve inflammatory response, which is the result of multi-component, multi-target and multi-pathway interaction. It provides an idea for the development of new combined drugs for gout.

Identification of the common differentially expressed genes and pathogenesis between neuropathic pain and aging

Background

Neuropathic pain is a debilitating disease caused by damage or diseases of the somatosensory nervous system. Previous research has indicated potential associations between neuropathic pain and aging. However, the mechanisms by which they are interconnected remain unclear. In this study, we aim to identify the common differentially expressed genes (co-DEGs) between neuropathic pain and aging through integrated bioinformatics methods and further explore the underlying molecular mechanisms.

Methods

The microarray datasets GSE24982, GSE63442, and GSE63651 were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and co-DEGs were first identified. Functional enrichment analyses, protein-protein Interaction (PPI) network, module construction and hub genes identification were performed. Immune infiltration analysis was conducted. Targeted transcription factors (TFs), microRNAs (miRNAs) and potential effective drug compounds for hub genes were also predicted.

Results

A total of 563 and 1,250 DEGs of neuropathic pain and aging were screened, respectively. 16 genes were further identified as co-DEGs. The functional analysis emphasizes the vital roles of the humoral immune response and complement and coagulation cascades in these two diseases. Cxcl14, Fblim1, RT1-Da, Serping1, Cfd, and Fcgr2b were identified as hub genes. Activated B cell, mast cell, activated dendritic cell, CD56 bright natural killer cell, effector memory CD8 + T cell, and type 2 T helper cell were significantly up-regulated in the pain and aging condition. Importantly, hub genes were found to correlate with the activated B cell, activated dendritic cell, Gamma delta T cell, central memory CD4 + T cell and mast cell in pain and aging diseases. Finally, Spic, miR-883-5p, and miR-363-5p et al. were predicted as the potential vital regulators for hub genes. Aldesleukin, Valziflocept, MGD-010, Cinryze, and Rhucin were the potential effective drugs in neuropathic pain and aging.

Conclusion

This study identified co-DEGs, revealed molecular mechanisms, demonstrated the immune microenvironment, and predicted the possible TFs, miRNAs regulation networks and new drug targets for neuropathic pain and aging, providing novel insights into further research.