Screening marker genes of type 2 diabetes mellitus in mouse lacrimal gland by LASSO regression

Exploring the risk factors and clustering patterns of periodontitis in patients with different subtypes of diabetes through machine learning and cluster analysis

AIM

To analyse the risk factors contributing to the prevalence of periodontitis among clusters of patients with diabetes and to examine the clustering patterns of clinical blood biochemical indicators.

MATERIALS AND METHODS

Data regarding clinical blood biochemical indicators and periodontitis prevalence among 1804 patients with diabetes were sourced from the National Health and Nutrition Examination Survey (NHANES) database spanning 2009 to 2014. A clinical prediction model for periodontitis risk in patients with diabetes was constructed via the XGBoost machine learning method. Furthermore, the relationships between diabetes patient clusters and periodontitis prevalence were investigated through consistent consensus clustering analysis.

RESULTS

Seventeen clinical blood biochemical indicators emerged as superior predictors of periodontitis in patients with diabetes. Patients with diabetes were subsequently categorized into two subtypes: Cluster A presented a slightly lower periodontitis prevalence (74.80%), whereas Cluster B presented a higher prevalence risk (83.68%). Differences between the two groups were considered statistically significant at a p value of ≤0.05. There was marked variability in the associations of different cluster characteristics with periodontitis prevalence.

CONCLUSIONS

Machine learning combined with consensus clustering analysis revealed a greater prevalence of periodontitis among patients with diabetes mellitus in Cluster B. This cluster was characterized by a smoking habit, a lower education level, a higher income-to-poverty ratio, and higher levels of albumin (ALB g/L) and alanine aminotransferase (ALT U/L).

The GLCCI1/STAT3 pathway: a novel pathway involved in diabetic cognitive dysfunction and the therapeutic effect of salidroside

Diabetic cognitive dysfunction (DCD) is a complication of diabetes that seriously affects quality of life. Glucocorticoid-induced transcript 1 (GLCCI1) has been found to be involved in inflammation, apoptosis and autophagy in various diseases. However, the distribution of GLCCI1 in the brain and its role in DCD have not yet been revealed. In addition, the potential therapeutics effects of salidroside (SAL), a phenyl propyl glycoside compound known for its neuroprotective effects in treating DCD are unknow. In the present study, we found that GLCCI1 was localized in hippocampal neurons. C57BL/6 J mice with DCD presented downregulation of GLCCI1 and Bcl-2 and upregulation of p-STAT3/STAT3, Bax, Cleaved Caspase-3/Caspase-3. Overexpression of GLCCI1 or SAL administration relieved DCD, reversed the changes in the expression of these cytokines, and alleviated morphological alterations in hippocampal neurons. Interestingly, SAL alleviated DCD and attenuated the expression of GLCCI1 and p-STAT3, showing similar effects as GLCCI1 overexpression. These findings suggest that the GLCCI1/STAT3 axis plays a crucial role in DCD and is involved in SAL-mediated attenuation of DCD.

Predictability comparison of sizing parameters for postoperative vault after implantable Collamer lens implantation

PURPOSE

This study aims to assess the accuracy of three parameters (white-to-white distance [WTW], angle-to-angle [ATA], and sulcus-to-sulcus [STS]) in predicting postoperative vault and to formulate an optimized predictive model.

METHODS

In this retrospective study, a cohort of 465 patients (comprising 769 eyes) who underwent the implantation of the V4c implantable Collamer lens with a central port (ICL) for myopia correction was examined. Least absolute shrinkage and selection operator (LASSO) regression and classification models were used to predict postoperative vault. The influences of WTW, ATA, and STS on predicting the postoperative vault and ICL size were analyzed and compared.

RESULTS

The dataset was randomly divided into training (80%) and test (20%) sets, with no significant differences observed between them. The screened variables included only seven variables which conferred the largest signal in the model, namely, lens thickness (LT, estimated coefficients for logistic least absolute shrinkage of -0.20), STS (-0.04), size (0.08), flat K (-0.006), anterior chamber depth (0.15), spherical error (-0.006), and cylindrical error (-0.0008). The optimal prediction model depended on STS (R2=0.419, RMSE=0.139), whereas the least effective prediction model relied on WTW (R2=0.395, RMSE=0.142). In the classified prediction models of the vault, classification prediction of the vault based on STS exhibited superior accuracy compared to ATA or WTW.

CONCLUSIONS

This study compared the capabilities of WTW, ATA, and STS in predicting postoperative vault, demonstrating that STS exhibits a stronger correlation than the other two parameters.

Screening mitochondria-related biomarkers in skin and plasma of atopic dermatitis patients by bioinformatics analysis and machine learning

Background

There is a significant imbalance of mitochondrial activity and oxidative stress (OS) status in patients with atopic dermatitis (AD). This study aims to screen skin and peripheral mitochondria-related biomarkers, providing insights into the underlying mechanisms of mitochondrial dysfunction in AD.

Methods

Public data were obtained from MitoCarta 3.0 and GEO database. We screened mitochondria-related differentially expressed genes (MitoDEGs) using R language and then performed GO and KEGG pathway analysis on MitoDEGs. PPI and machine learning algorithms were also used to select hub MitoDEGs. Meanwhile, the expression of hub MitoDEGs in clinical samples were verified. Using ROC curve analysis, the diagnostic performance of risk model constructed from these hub MitoDEGs was evaluated in the training and validation sets. Further computer-aided algorithm analyses included gene set enrichment analysis (GSEA), immune infiltration and mitochondrial metabolism, centered on these hub MitoDEGs. We also used real-time PCR and Spearman method to evaluate the relationship between plasma circulating cell-free mitochondrial DNA (ccf-mtDNA) levels and disease severity in AD patients.

Results

MitoDEGs in AD were significantly enriched in pathways involved in mitochondrial respiration, mitochondrial metabolism, and mitochondrial membrane transport. Four hub genes (BAX, IDH3A, MRPS6, and GPT2) were selected to take part in the creation of a novel mitochondrial-based risk model for AD prediction. The risk score demonstrated excellent diagnostic performance in both the training cohort (AUC = 1.000) and the validation cohort (AUC = 0.810). Four hub MitoDEGs were also clearly associated with the innate immune cells' infiltration and the molecular modifications of mitochondrial hypermetabolism in AD. We further discovered that AD patients had considerably greater plasma ccf-mtDNA levels than controls (U = 92.0, p< 0.001). Besides, there was a significant relationship between the up-regulation of plasma mtDNA and the severity of AD symptoms.

Conclusions

The study highlights BAX, IDH3A, MRPS6 and GPT2 as crucial MitoDEGs and demonstrates their efficiency in identifying AD. Moderate to severe AD is associated with increased markers of mitochondrial damage and cellular stress (ccf=mtDNA). Our study provides data support for the variation in mitochondria-related functional characteristics of AD patients.

Integrated bioinformatics analysis of SEMA3C in tongue squamous cell carcinoma using machine-learning strategies

Tongue squamous cell carcinoma (TSCC) is an aggressive oral cancer with a high incidence of metastasis and poor prognosis. We aim to identify and verify potential biomarkers for TSCC using bioinformatics analysis. To begin with, we examined clinical and RNA expression information of individuals with TSCC from the Gene Expression Omnibus (GEO) database. Differential expression analysis and functional analysis were conducted. Multiple machine-learning strategies were next employed to screen and determine the hub gene, and receiver operating characteristic (ROC) analysis was used to assess diagnostic value. Semaphorin3C (SEMA3C) was identified as a critical biomarker, presenting high diagnostic accuracy for TSCC. In the validation cohorts, SEMA3C exhibited high expression levels in TSCC. The high expression of SEMA3C was a poor prognostic factor in TSCC by the Kaplan-Meier curve. Based on the Gene Ontology (GO) analysis, SEMA3C was mapped in terms related to cell adhesion, positive regulation of JAK-STAT, positive regulation of stem cell maintenance, and positive regulation of NF-κB activity. Single-cell RNA sequencing (ScRNA-seq) analysis showed cells expressing SEMA3C were predominantly tumor cells. Then, we further verified that SEMA3C had high expression in TSCC clinical samples. In addition, the knockdown of SEMA3C suppressed the proliferation, migration, and invasion of TSCC cells in vitro. This study is the first to report the involvement of SEMA3C in TSCC, suggesting that upregulated SEMA3C could be a novel and critical potential biomarker for future predictive diagnostics, prevention, prognostic assessment, and personalized medical services in TSCC.

A comprehensive review of the family of very-long-chain fatty acid elongases: structure, function, and implications in physiology and pathology

BACKGROUND

The very-long-chain fatty acid elongase (ELOVL) family plays essential roles in lipid metabolism and cellular functions. This comprehensive review explores the structural characteristics, functional properties, and physiological significance of individual ELOVL isoforms, providing insights into lipid biosynthesis, cell membrane dynamics, and signaling pathways.

AIM OF REVIEW

This review aims to highlight the significance of the ELOVL family in normal physiology and disease development. By synthesizing current knowledge, we underscore the relevance of ELOVLs as potential therapeutic targets.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We emphasize the association between dysregulated ELOVL expression and diseases, including metabolic disorders, skin diseases, neurodegenerative conditions, and cancer. The intricate involvement of ELOVLs in cancer biology, from tumor initiation to metastasis, highlights their potential as targets for anticancer therapies. Additionally, we discuss the prospects of using isoform-specific inhibitors and activators for metabolic disorders and cancer treatment. The identification of ELOVL-based biomarkers may advance diagnostics and personalized medicine.

CONCLUSION

The ELOVL family's multifaceted roles in lipid metabolism and cellular physiology underscore its importance in health and disease. Understanding their functions offers potential therapeutic avenues and personalized treatments.