Identification of Candidate Blood mRNA Biomarkers in Intracerebral Hemorrhage Using Integrated Microarray and Weighted Gene Co-expression Network Analysis

Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage

BACKGROUND

Increased oxidative stress (OS) activity following intracerebral hemorrhage (ICH) had significantly impacting patient prognosis. Identifying optimal genes associated with OS could enhance the understanding of OS after ICH.

METHODS

We employed single-cell RNA sequencing (scRNA-seq) to investigate the heterogeneity of OS across various cellular tiers following ICH, aiming to acquire biological insights into ICH. We utilized AUCell, Ucell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, to identify hub genes influencing high OS post-ICH. Furthermore, we employed four machine learning algorithms, eXtreme Gradient Boosting, Boruta, Random Forest, and Least Absolute Shrinkage and Selection Operator, to identify the optimal feature genes. To validate the accuracy of our analysis, we conducted validation in ICH animal experiments.

RESULTS

After analyzing the scRNA-seq dataset using various algorithms, we found that OS activity exhibited heterogeneity across different cellular layers following ICH, with particularly heightened activity observed in monocytes. Further integration of bulk data and machine learning algorithms revealed that ANXA2 and COTL1 were closely associated with high OS after ICH. Our animal experiments demonstrated an increase in OS expression post-ICH. Additionally, the protein expression of ANXA2 and COTL1 was significantly elevated and co-localized with microglia. Pearson correlation coefficient analysis revealed a significant correlation between ANXA2 and OS, indicating strong consistency (r = 0.84, p < 0.05). Similar results were observed for COTL1 and OS (r = 0.69, p < 0.05).

CONCLUSIONS

Following ICH, ANXA2 and COTL1 might penetrate the brain via monocytes, localize within microglia, and enhance OS activity. This might help us better understand OS after ICH.

SLC45A3 Serves as a Potential Therapeutic Biomarker to Attenuate White Matter Injury After Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) is a severe cerebrovascular disease, which impairs patients' white matter even after timely clinical interventions. Indicated by studies in the past decade, ICH-induced white matter injury (WMI) is closely related to neurological deficits; however, its underlying mechanism and pertinent treatment are yet insufficient. We gathered two datasets (GSE24265 and GSE125512), and by taking an intersection among interesting genes identified by weighted gene co-expression networks analysis, we determined target genes after differentially expressing genes in two datasets. Additional single-cell RNA-seq analysis (GSE167593) helped locate the gene in cell types. Furthermore, we established ICH mice models induced by autologous blood or collagenase. Basic medical experiments and diffusion tensor imaging were applied to verify the function of target genes in WMI after ICH. Through intersection and enrichment analysis, gene SLC45A3 was identified as the target one, which plays a key role in the regulation of oligodendrocyte differentiation involving in fatty acid metabolic process, etc. after ICH, and single-cell RNA-seq analysis also shows that it mainly locates in oligodendrocytes. Further experiments verified overexpression of SLC45A3 ameliorated brain injury after ICH. Therefore, SLC45A3 might serve as a candidate therapeutic biomarker for ICH-induced WMI, and overexpression of it may be a potential approach for injury attenuation.

Development and Clinical Validation of a Novel 5 Gene Signature Based on Fatty Acid Metabolism-Related Genes in Oral Squamous Cell Carcinoma

Background/Aim

Lipid metabolism disorders play a crucial role in tumor development and progression. The aim of the study focused on constructing a novel prognostic model of oral squamous cell carcinoma (OSCC) patients using fatty acid metabolism-related genes.

Methods

Microarray test and data from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed genes related to fatty acid metabolism. The quantitative real-time polymerase chain reaction (qRT-PCR) was then used to validate the expression of targeted fatty acid metabolism genes. A risk predictive scoring model of fatty acid metabolism-related genes was generated using a multivariate Cox model. The efficacy of this model was assessed by time-dependent receiver operating characteristic curve (ROC).

Results

14 fatty acid metabolism-related genes were identified by microarray test and TCGA database analysis and then confirmed by PCR. Finally, a 5 gene signature (ACACB, FABP3, PDK4, PPARG, and PLIN5) was constructed and a RiskScore was calculated for each patient. Compared to the high RiskScore group, the low RiskScore group had better overall survival (OS) (p = 0.02). The RiskScore derived from a 5 gene signature was a prognostic factor (HR: 3.73, 95% CI: 1.38, 10.09) for OSCC patients. The predictive classification efficiencies of RiskScore were evaluated and the area under the curve (AUC) values for 1, 3, and 5 years were 0.613, 0.652, and 0.681, respectively. Then we compared the predictive performance of the prognostic model with or without the RiskScore. The 5 gene-derived RiskScore can improve the predictive performance with AUC values of 0.760, 0.803, and 0.830 for 1, 3, and 5 years OS in prognostic model including the RiskScore. While the predicted AUC values of the model without RiskScore for 1, 3, and 5 years OS were 0.699, 0.715, and 0.714, respectively.

Conclusion

We developed a predictive score model using 5 fatty acid metabolism-related genes, which could be a potential prognostic indicator in OSCC.

Identification of TLR2 as a Key Target in Neuroinflammation in Vascular Dementia

Vascular dementia (VaD) is the second most common cause of dementia. At present, precise molecular processes of VaD are unclear. We attempted to discover the VaD relevant candidate genes, enrichment biological processes and pathways, key targets, and the underlying mechanism by microarray bioinformatic analysis. We selected GSE122063 related to the autopsy samples of VaD for analysis. We first took use of Weighted Gene Co-expression Network Analysis (WGCNA) to achieve modules related to VaD and hub genes. Second, we filtered out significant differentially expressed genes (DEGs). Third, significant DEGs then went through Geno Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Fourth, Gene Set Enrichment Analysis (GSEA) was performed. At last, we constructed the protein–protein interaction (PPI) network. The results showed that the yellow module had the strongest correlation with VaD, and we finally identified 21 hub genes. Toll-like receptor 2 (TLR2) was the top hub gene and was strongly correlated with other possible candidate genes. In total, 456 significant DEGs were filtered out and these genes were found to be enriched in the Toll receptor signaling pathway and several other immune-related pathways. In addition, Gene Set Enrichment Analysis results showed that similar pathways were significantly over-represented in TLR2-high samples. In the PPI network, TLR2 was still an important node with high weight and combined scores. We concluded that the TLR2 acts as a key target in neuroinflammation which may participate in the pathophysiological process of VaD.