Bingxuan Ren

Background: Cancer‐associated fibroblasts (CAFs), as a key component of the tumor microenvironment, have not been systematically elucidated in glioblastoma (GBM). Our study aims to develop a prognostic model integrating CAFs‐related features, with the goal of providing new insights for precise stratification and optimized treatment strategies for GBM patients. Methods: Utilizing GBM‐related data from reputable public databases, we utilized the Seurat package in R to analyze single‐cell RNA sequencing (scRNA‐seq) data for the characterization of CAFs in GBM. We identified CAFs phenotypes and screened for key CAFs‐related genes significantly associated with patient prognosis. Using regression analysis, we constructed a CAFs‐based risk score, which was subsequently validated in multiple independent cohorts. A nomogram integrating the risk score and clinicopathological features was also developed. Furthermore, we systematically evaluated the prognostic and therapeutic relevance of the model in GBM patients through multi‐dimensional analyses, including gene mutation profiling, pathway enrichment analysis, immune infiltration, immunotherapy response, and drug sensitivity analysis. Results: A total of six CAFs‐related genes (FAM241B, LSM2, IGFBP2, LOXL1, OSMR, and STOX1) were identified as significantly associated with GBM prognosis. We used it to construct the CAFs‐based risk score model, which demonstrated robust prognostic performance across multiple cohorts and served as an independent predictor of overall survival in GBM patients, efficiently categorizing groups into high and low risk. By integrating clinical features, the nomogram model significantly increased predictive accuracy and reliability. Analytical results indicated a statistically significant association between the computed risk score and the level of immune cell infiltration. Furthermore, the established prognostic model exhibited robust efficacy in predicting patient outcomes following conventional targeted treatments as well as immunotherapeutic interventions. Conclusions: This study introduces a GBM risk profiling framework and accompanying nomogram, offering exceptional accuracy in prognostic prediction for GBM. The framework and nomogram provide valuable insights into the roles of CAFs and key genes in GBM progression and immunity, and extend beyond classification by offering promising avenues for deciphering tumor mutations, mapping immune landscapes, refining drug predictions, and forecasting the efficacy of immunotherapeutic interventions. These findings have the potential to significantly improve personalized treatment strategies and patient outcomes.

Endemic cretinism (EC) is one of the most severe iodine deficiency disorders, leading to typical symptoms such as neurodevelopmental impairments or mental deficits. In addition to environmental factors, the pathogenesis of its genetic contribution remains unclear. The study revealed the differential expression profiles of long non-coding RNA(lncRNA) and messenger RNA(mRNA) based on high-throughput RNA-seq. GO and KEGG analyses were used to annotate the function and pathway of differentially expressed (DE) mRNA and co-expressed mRNA. The protein–protein interaction(PPI) network was established. The expression levels of three lncRNAs and six mRNAs were validated by quantitative real-time PCR analysis (qRT-PCR) and subjected to correlation analysis. Compared to controls, a total of 864 lncRNAs and 393 mRNAs were differentially expressed. The PPI network had 149 nodes and 238 edges, and three key protein-coding genes were observed. Levels of LINC01220 and target mRNA IDO1 were statistically elevated in EC patients. Differentially expressed lncRNA may be a new potential player in EC. LINC01220 and IDO1 might interact with each other to participate in EC. The biological process of regulation of postsynaptic membrane potential and the Rap1 signaling pathway might exert a regulating role in the pathophysiological process of EC. Our findings could provide more theoretical and experimental evidence for investigating the pathophysiological mechanisms of EC.

Purpose Autoimmune thyroiditis (AIT) is one of the most common autoimmune endocrine diseases. The currently recognized causes are genetic susceptibility, environmental factors and immune disorders. It is important to clarify the pathogenesis for the prevention, diagnosis, treatment of AIT and scientific iodine supplementation. This study analyzed the DNA methylation levels of PRKAA2, ITGA6, PRL and THEM4 genes related to PI3K-AKT signaling pathway, compared the DNA methylation levels between cases and controls from different water iodine levels in Shandong Province of China, and evaluated the contribution of PI3K-AKT signaling pathway-related genes in AIT. Methods A total of 176 adult AIT patients were included from three different water iodine areas, and 176 healthy controls were included according to gender, age and BMI. According to the results of the Illumina Methylation 850 K BeadChip in our previous research, the significant methylation differences of genes on the PI3K-AKT signaling pathway related to AIT were determined. The MethylTarget™ assay was used to detect the methylation levels of the target genes, and real-time PCR experiments were used to verify the mRNA expression levels. Results Compared with the control group, PRKAA2_3 and 15 CpG sites were hyper-methylated. ITGA6 gene and 2 CpG sites were hypo-methylated in AIT cases. The mRNA expression of ITGA6 gene was negatively correlated with the DNA methylation levels of ITGA6 gene and 2 CpG sites. Compared with cases and controls in areas with different water iodine levels, methylation differences were mainly in PRKAA2 and ITGA6 genes. The methylation levels of PRKAA2_1 and PRKAA2_3 were positively correlated with age. The methylation levels of PRL and THEM4 genes were negatively correlated with age. The methylation level of PRKAA2_3 was positively correlated with FT₄. Conclusion In summary, we identified aberrant DNA methylation levels of PRKAA2 and ITGA6 genes related to PI3K-AKT signaling pathway in the blood of AIT patients. Both iodine supplementation after long-term iodine deficiency and iodine excess can affect the DNA methylation levels of PRKAA2 and ITGA6 genes, and the former affects more obviously. In ITGA6 gene, this aberrant epigenetic modification is associated with the increased mRNA expression.