Parkinson’s disease (PD) is the second most common neurodegenerative disease and although many studies have been done on this disease, the underlying mechanisms are still poorly understood and further studies are warranted. Therefore, this study identified circRNA expression profiles in the cerebral cortex (CC), hippocampus (HP), striatum (ST), and cerebellum (CB) regions of the 1-methyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mouse model using RNA sequencing (RNA-seq), and differentially expressed circRNA were validated using reverse transcription quantitative real-time PCR (qRT-PCR). Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and competing endogenous RNA (ceRNA) network analyses were also performed to explore the potential function of circRNAs. The results show that, compared with the control group, 24, 66, 71, and 121 differentially expressed circRNAs (DE-circRNAs) were found in the CC, HP, ST, and CB, respectively. PDST vs. PDCB, PDST vs. PDHP, and PDCB vs. PDHP groups have 578, 110, and 749 DE-circRNAs, respectively. Then, seven DE-cirRNAs were selected for qRT-PCR verification, where the expressions were consistent with the sequencing analysis. The GO and KEGG pathway analyses revealed that these DE-circRNAs participate in several biological functions and signaling pathways, including glutamic synapse, neuron to neuron synapse, cell morphogenesis involved in neuron differentiation, Parkinson’s disease, axon guidance, cGMP-PKG signaling pathway, and PI3K-Akt signaling pathway. Furthermore, the KEGG analysis of the target genes predicted by DE-circRNAs indicated that the target genes predicted by mmu_circRNA_0003292, mmu_circRNA_0001320, mmu_circRNA_0005976, and mmu_circRNA_0005388 were involved in the PD-related pathway. Overall, this is the first study on the expression profile of circRNAs in the different brain regions of PD mouse model. These results might facilitate our understanding of the potential roles of circRNAs in the pathogenesis of PD. Moreover, the results also indicate that the mmu_circRNA_0003292-miRNA-132-Nr4a2 pathway might be involved in the regulation of the molecular mechanism of Parkinson’s disease.

Over the past decade, there has been increasing attention on the interaction between microbiota and bile acid metabolism. Bile acids are not only involved

In this work, we focus on the detection methods of cfDNA methylation based on NGS and the latest progress.

RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points.

Introduction: Osimertinib shows remarkable efficacy as a first‐line therapy for lung adenocarcinoma (LUAD), yet acquired resistance remains a major challenge. Ferroptosis dysregulation has emerged as a key contributor to drug resistance, highlighting ferroptosis‐associated genes as potential biomarkers and therapeutic targets. Methods: Ferroptosis‐related osimertinib resistance genes were identified through integrated TCGA and GEO analyses, and a prognostic model was constructed using Cox and LASSO regression. Drug‐response profiles, immune infiltration patterns, and clinical associations were systematically evaluated. Functional experiments were conducted to elucidate the role of SLC2A1 in osimertinib resistance. Results: A three‐gene ferroptosis‐associated signature (SLC2A1, LIFR, and GDF15) was established with high prognostic accuracy and independence. Low‐risk patients showed greater chemotherapy sensitivity, whereas high‐risk patients exhibited increased M0/M1 macrophage and CD8 ⁺ T‐cell infiltration. High SLC2A1 expression was associated with reduced infiltration of antitumor lymphocytes—including naïve CD8 ⁺ T cells, Th17, Tfh, MAIT cells, B cells, γδT cells, and CD4 ⁺ /CD8 ⁺ T cells—and increased levels of exhausted T cells, regulatory T cells, Th1/Tem cells, and myeloid‐derived populations (DCs, monocytes, macrophages, and neutrophils). Clinical cohort validation confirmed the prognostic relevance of these genes. Mechanistically, SLC2A1 enhanced osimertinib resistance by activating the SLC7A11/GPX4 antioxidant axis and suppressing lipid peroxidation, thereby promoting ferroptosis evasion and reshaping an immunosuppressive tumor microenvironment. Conclusion: This study reveals the clinical and biological significance of ferroptosis‐related resistance genes in LUAD and identifies a previously unrecognized mechanism through which SLC2A1 drives osimertinib resistance by promoting ferroptosis escape and reshaping the immunosuppressive tumor microenvironment. These findings highlight SLC2A1 as a promising therapeutic target for overcoming resistance and improving precision treatment strategies in LUAD.