Novel insight into ferroptosis-related genes, molecular subtypes, and immune characteristics in intracranial aneurysms

LncRNA HOXA11-AS intercepts the POU2F2-mediated downregulation of SLC3A2 in osteoarthritis to suppress ferroptosis

BACKGROUND

Osteoarthritis (OA) is a prevalent ailment characterized by the gradual degradation of joints, resulting in discomfort and restricted movement. The recently proposed mechanism of ferroptosis is intricately associated with the initiation and progression of OA. Our study found that the long non-coding RNA HOXA11-AS reduces ferroptosis by increasing the expression of SLC3A2 through the transcription factor POU2F2.

MATERIALS AND METHODS

HOXA11-AS was identified through lncRNA microarray analysis, and its impact on chondrocytes and extracellular matrix was assessed using real-time quantitative PCR, western blotting, and CCK8 assays. Subsequently, overexpression of HOXA11-AS in the knee joints of mice confirmed its protective efficacy on chondrocyte phenotype in the OA model. The involvement of HOXA11-AS in regulating ferroptosis via SLC3A2 was further validated through RNA sequencing analysis of mouse cartilage and the assessment of malondialdehyde levels and glutathione peroxidase activity. Finally, a combination of RNA sequencing, pull-down assays, mass spectrometry (MS), and chromatin immunoprecipitation (ChIP) techniques was employed to identify POU2F2 as the crucial transcription factor responsible for repressing the expression of SLC3A2, which can be effectively inhibited by HOXA11-AS.

RESULTS

Our study demonstrated that HOXA11-AS effectively enhanced the metabolic homeostasis of chondrocytes, and alleviated the progression of OA in vitro and in vivo experiments. Furthermore, HOXA11-AS was found to enhance SLC3A2 expression, a key regulator of ferroptosis, by interacting with the transcriptional repressor POU2F2.

CONCLUSIONS

HOXA11-AS promotes SLC3A2 expression and inhibits chondrocyte ferroptosis, by binding to the transcriptional repressor POU2F2, offering a promising and innovative therapeutic approach for OA.

Machine Learning Models for Brain Arteriovenous Malformations Presenting with Hemorrhage Based on Clinical and Angioarchitectural Characteristics

RATIONALE AND OBJECTIVES

This study aims to develop the best diagnostic model for brain arteriovenous malformations (bAVMs) rupture by using machine learning (ML) algorithms.

MATERIALS AND METHODS

We retrospectively included 353 adult patients with ruptured and unruptured bAVMs. The clinical and radiological data on patients were collected. The significant variables were selected using univariable logistic regression. We constructed and compared the predictive models using five supervised ML algorithms, multivariable logistic regression, and R2eDAVM scoring system. A complementary systematic review and meta-analysis of studies was aggregated to explore the potential predictors for bAVMs rupture.

RESULTS

We found that a small nidus size of <3 cm, deep and infratentorial location, longer filling time, and deep and single venous drainage were associated with a higher risk of bAVMs rupture. The multilayer perceptron model showed the best performance with an area under the curve value of 0.736 (95% CI 0.67-0.801) and 0.713 (95% CI 0.647-0.779) in the training and test dataset, respectively. In our pooled analysis, we also found that the male sex, a single feeding artery, hypertension, non-White race, low Spetzler-Martin grade, and coexisting aneurysms are risk factors for bAVMs rupture.

CONCLUSION

This study further demonstrated the clinical and angioarchitectural characteristics in predicting bAVMs hemorrhage.

Decoding the biology and clinical implication of neutrophils in intracranial aneurysm

OBJECTIVE

Abundant neutrophils have been identified in both ruptured and unruptured intracranial aneurysm (IA) domes, with their function and clinical implication being poorly characterized.

MATERIALS AND METHODS

We employed single-cell RNA sequencing (scRNA-Seq) datasets of both human and murine model, and external bulk mRNA sequencing datasets to thoroughly explore the features and functional heterogeneous of neutrophils infiltrating the IA dome.

RESULTS

We found that both unruptured and ruptured IA dome contain a substantial population of neutrophils, characterized by FCGR3B, G0S2, CSF3R, and CXCR2. These cells exhibited heterogeneity in terms of function and differentiation. Despite similar transcriptional activation, neutrophils in IA dome expressed a repertoire of gene programs that mimicked transcriptomic alterations observed from bone marrow to peripheral blood, showing self-similarity. In addition, the recruitment of neutrophils in unruptured IA was primarily mediated by monocytes/macrophages, and once ruptured, both neutrophils, and a specific subset of inflammatory smooth muscle cells (SMCs) were involved in the process. The receiver operator characteristic curve (ROC) analysis indicated that distinct neutrophil subclusters were associated with IA formation and rupture, respectively. By reviewing current studies, we found that neutrophils play a detrimental role to IA wall integrity through secreting specific ligands, ferroptosis driven by ALOX5AP and PTGS2, and the formation of neutrophil extracellular traps (NETs) mediated by PADI4.

INTERPRETATION

This study delineated the biology and potential clinical implications of neutrophils in IA dome and provided a reliable basis for future researches.

Renal interferon-inducible protein 16 expression is associated with disease activity and prognosis in lupus nephritis

BACKGROUND

Lupus nephritis (LN) is one of the most severe complications of systemic lupus erythematosus (SLE). However, the current management of LN remains unsatisfactory due to sneaky symptoms during early stages and lack of reliable predictors of disease progression.

METHODS

Bioinformatics and machine learning algorithms were initially used to explore the potential biomarkers for LN development. Identified biomarker expression was evaluated by immunohistochemistry (IHC) and multiplex immunofluorescence (IF) in 104 LN patients, 12 diabetic kidney disease (DKD) patients, 12 minimal change disease (MCD) patients, 12 IgA nephropathy (IgAN) patients and 14 normal controls (NC). The association of biomarker expression with clinicopathologic indices and prognosis was analyzed. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were utilized to explore potential mechanisms.

RESULTS

Interferon-inducible protein 16 (IFI16) was identified as a potential biomarker for LN. IFI16 was highly expressed in the kidneys of LN patients compared to those with MCD, DKD, IgAN or NC. IFI16 co-localized with certain renal and inflammatory cells. Glomerular IFI16 expression was correlated with pathological activity indices of LN, while tubulointerstitial IFI16 expression was correlated with pathological chronicity indices. Renal IFI16 expression was positively associated with systemic lupus erythematosus disease activity index (SLEDAI) and serum creatinine while negatively related to baseline eGFR and serum complement C3. Additionally, higher IFI16 expression was closely related to poorer prognosis of LN patients. GSEA and GSVA suggested that IFI16 expression was involved in adaptive immune-related processes of LN.

CONCLUSION

Renal IFI16 expression is a potential biomarker for disease activity and clinical prognosis in LN patients. Renal IFI16 levels may be used to shed light on predicting the renal response and develop precise therapy for LN.

Transcriptomic Studies on Intracranial Aneurysms

Intracranial aneurysm (IA) is a relatively common vascular malformation of an intracranial artery. In most cases, its presence is asymptomatic, but IA rupture causing subarachnoid hemorrhage is a life-threating condition with very high mortality and disability rates. Despite intensive studies, molecular mechanisms underlying the pathophysiology of IA formation, growth, and rupture remain poorly understood. There are no specific biomarkers of IA presence or rupture. Analysis of expression of mRNA and other RNA types offers a deeper insight into IA pathobiology. Here, we present results of published human studies on IA-focused transcriptomics.

Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in moyamoya disease

Background: Growing evidence suggests the links between moyamoya disease (MMD) and autoimmune diseases. However, the molecular mechanism from genetic perspective remains unclear. This study aims to clarify the potential roles of autoimmune-related genes (ARGs) in the pathogenesis of MMD. Methods: Two transcription profiles (GSE157628 and GSE141025) of MMD were downloaded from GEO databases. ARGs were obtained from the Gene and Autoimmune Disease Association Database (GAAD) and DisGeNET databases. Differentially expressed ARGs (DEARGs) were identified using "limma" R packages. GO, KEGG, GSVA, and GSEA analyses were conducted to elucidate the underlying molecular function. There machine learning methods (LASSO logistic regression, random forest (RF), support vector machine-recursive feature elimination (SVM-RFE)) were used to screen out important genes. An artificial neural network was applied to construct an autoimmune-related signature predictive model of MMD. The immune characteristics, including immune cell infiltration, immune responses, and HLA gene expression in MMD, were explored using ssGSEA. The miRNA-gene regulatory network and the potential therapeutic drugs for hub genes were predicted. Results: A total of 260 DEARGs were identified in GSE157628 dataset. These genes were involved in immune-related pathways, infectious diseases, and autoimmune diseases. We identified six diagnostic genes by overlapping the three machine learning algorithms: CD38, PTPN11, NOTCH1, TLR7, KAT2B, and ISG15. A predictive neural network model was constructed based on the six genes and presented with great diagnostic ability with area under the curve (AUC) = 1 in the GSE157628 dataset and further validated by GSE141025 dataset. Immune infiltration analysis showed that the abundance of eosinophils, natural killer T (NKT) cells, Th2 cells were significant different between MMD and controls. The expression levels of HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DRB6, HLA-F, and HLA-G were significantly upregulated in MMD. Four miRNAs (mir-26a-5p, mir-1343-3p, mir-129-2-3p, and mir-124-3p) were identified because of their interaction at least with four hub DEARGs. Conclusion: Machine learning was used to develop a reliable predictive model for the diagnosis of MMD based on ARGs. The uncovered immune infiltration and gene-miRNA and gene-drugs regulatory network may provide new insight into the pathogenesis and treatment of MMD.

Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis

Objective

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.

Materials and methods

Gene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.

Results

A total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.

Conclusion

DUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.