Alzheimer’s disease (AD) is a severe neurodegenerative disorder for which identification of differentially expressed genes is one way to find new therapeutic targets. Here, we conducted analysis to identify age-independent, AD-specific genes. We found that the MET, WIF1, and NPTX2 genes are downregulated in AD. WIF1 and MET are implicated in Wnt and MET signaling and regulate GSK3β activity and are thus linked with AD. Importantly, we found that the GMPR gene exhibited a gradual increase in AD progression. A logistic model based on GMPR has good ability to classify AD cases. GMPR’s product GMPR1 is in the AMPK and adenosine receptor pathways and is thus associated with Tau phosphorylation in AD. This allows GMPR1 to be a therapeutic target. Therefore, we screened five possible inhibitors to GMPR1 by docking GMPR1 with 1,174 approved drugs. Among them, lumacaftor is ideal. We then tested the effects of lumacaftor on AD model mice. After 20 days of oral administration, we observed that β-Amyloid accumulation was slowed down, and phosphorylation of Tau was almost eliminated in the treated mice. We highlight the elevated expression level of GMPR in AD and propose a therapeutic strategy of inhibiting GMPR1 with lumacaftor.

Nucleosome plays a role in transcriptional regulation through occluding the binding of proteins to DNA sites. Nucleosome occupancy varies among different cell types. Identification of such variation will help to understand regulation mechanism. The previous researches focused on the methods for two-sample comparison. However, a multiple-sample comparison (n ≥ 3) is necessary, especially in studying development and cancer. Here, we proposed a Chi-squared test-based approach, named as Dimnp, to identify differential nucleosome regions (DNRs) in multiple samples. Dimnp is designed for sequenced reads data and includes the modules of both calling nucleosome occupancy and identifying DNRs. We validated Dimnp on dataset of the mutant strains in which the modifiable histone residues are mutated into alanine in Saccharomyces cerevisiae. Dimnp shows a good capacity (area under the curve > 0.87) compared with the manually identified DNRs. Just by one time, Dimnp is able to identify all the DNRs identified by two-sample method Danpos. Under a deviation of 40 bp, the matched DNRs are above 60% between Dimnp and Danpos. With Dimnp, we found that promoters and telomeres are highly dynamic upon mutating the modifiable histone residues. We developed a tool of identifying the DNRs in multiple samples and cell types. The tool can be applied in studying nucleosome variation in gradual change in development and cancer.

Glioblastoma multiforme (GBM) exhibits considerable heterogeneity and associates with genome-wide alterations of the repressed chromatin marks DNA methylation and H3 lysine 27 trimethylation (H3K27me3). Tri-methylation on lysine 4 of histone H3 (H3K4me3) is an activating epigenetic mark that is enriched at promoter and promotes expression. It will be helpful in GBM diagnosis and treatment to identify the alteration of H3K4me3 between human GBM and GBM-surrounding tissues. Here, we performed an analysis using next-generation sequencing techniques to identify H3K4me3 modification in a case of GBM and the GBM-surrounding tissues. The results revealed a global decrease in H3K4me3 in GBM, especially at promoters and CpG islands. In GBM, homeobox genes gain H3K4me3, whereas the cell–cell adhesion-related cadherin genes lose H3K4me3. The products of the homeobox genes are highly connected with Ras-signalling and PI3K-Akt signalling pathways. Using The Cancer Genome Atlas (TCGA) data, we inferred the homeobox-regulated genes’ expression is higher in 548 GBM cases than in 27 lower grade glioma cases giving that OLIG2 expression can be a reference. The results suggested that the H3K4me3 alteration is related to the formation and migration of GBM cells. We also found an extremely high reads count at epidermal growth factor receptor (EGFR) promoter, probably due to an amplification of copy number. Our analysis provides a case study about the change of H3K4me3 during shift to GBM.

Previous studies suggested that nucleosomes are enriched with single nucleotide polymorphisms (SNPs) in humans and that the occurrence of mutations is closely associated with CpG dinucleotides. We aimed to determine if the chromatin organization is genomic locus specific around SNPs, and if newly occurring mutations are associated with SNPs.

Gliomas are heterogeneous primary central nervous system tumors with diverse molecular subtypes and variable prognosis. A paradigm shift in histopathology is underway as samples are digitized into whole-slide images (WSIs). Deep learning (DL) shows great potential for glioma diagnosis, subtyping, grading, and prognosis. This review summarizes recent advances in WSI-based DL models, covering data preprocessing, model architectures, and translational performance. Model evolution has progressed from CNNs (e.g. ResNet, capturing local features via convolution) to Transformers (e.g. Vision transformer, modeling global dependencies via self‐attention), hybrid architectures, and large language model. Typical pipelines include quality control, stain normalization, patch extraction, feature integration, and prediction. Public datasets such as The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium serve as key resources. On various datasets (public and institutional), a glioma‐specific model ResNet‐50 achieved AUC 0.983 for subtype classification; a Vision Transformer model reached AUC 0.960 for molecular typing; a hybrid model, ROAM, attained AUC 0.990; and the pan‐cancer hybrid model called CHIEF achieved AUC 0.9397 for diagnosis. Attention heatmaps and Shapley Additive exPlanations provide interpretability by linking model outputs to histologic regions. Novel multimodal fusion integrates genomic, proteomic, radiologic, and clinical data to enhance prediction and uncover biological relevance. Future research may focus on enhancing model generalizability and predictive accuracy via advanced architectures, developing lightweight models for resource-limited settings to facilitate clinical translation.