This study delivers a thorough analysis of long non-coding RNAs (lncRNAs) in regulating programmed cell death (PCD), vital for neurodegenerative diseases like Alzheimer’s disease (AD) and Parkinson’s disease (PD). We propose a new framework PCDLnc, and identified 20 significant lncRNAs, including HEIH, SNHG15, and SNHG5, associated with PCD gene sets, which were known for roles in proliferation and apoptosis in neurodegenerative diseases. By using GREAT software, we identified regulatory functions of top lncRNAs in different neurodegenerative diseases. Moreover, lncRNAs cis-regulated mRNAs linked to neurodegeneration, including JAK2, AKT1, EGFR, CDC42, SNCA, and ADIPOQ, highlighting their therapeutic potential in neurodegenerative diseases. A further exploration into the differential expression of mRNA identified by PCDLnc revealed a role in apoptosis, ferroptosis and autophagy. Additionally, protein-protein interaction (PPI) network analysis exposed abnormal interactions among key genes, despite their consistent expression levels between disease and normal samples. The randomforest model effectively distinguished between disease samples, indicating a high level of accuracy. Shared gene subsets in AD and PD might serve as potential biomarkers, along with disease-specific gene sets. Besides, we also found the strong relationship between AD and immune infiltration. This research highlights the role of lncRNAs and their associated genes in PCD in neurodegenerative diseases, offering potential therapeutic targets and diagnostic markers for future study and clinical application.

Background: MicroRNAs are key biomarkers for human diseases; however, experimentally identifying miRNA-disease associations was costly and inefficient. To improve the robustness and interpretability of computational models, we developed a multi-order adaptive graph-based miRNA-disease association model (MAGMDA). Results: MAGMDA introduced an adaptive moment-order selection mechanism and a dynamic threshold network, which replaced the fixed moment orders and manually defined thresholds commonly used in traditional high-order statistical modeling with learnable components. By integrating numerically stable high-order moment computation with cross-order attention aggregation, MAGMDA enhanced model robustness and interpretability while maintaining computational efficiency. Five-fold cross-validation on the HMDD v2.0 dataset showed that MAGMDA achieved an AUC of 93.58% and an AUPR of 93.48%. Across multiple evaluation metrics, MAGMDA outperformed representative existing methods, exhibiting particularly consistent advantages on ranking-related metrics such as the area under the receiver operating characteristic curve (AUC) and the area under the precision–recall curve (AUPR), while maintaining smaller performance variations across different cross-validation folds, indicating stable predictive ability under different data partitions. Mechanistic diagnostic analysis further verified the stable utilization of the adaptive modules across different training folds. To demonstrate biological utility, we applied MAGMDA to hepatocellular carcinoma research. Based on the model predictions and analysis of the TCGA-LIHC cohort, we constructed a seven-gene prognostic signature, which was successfully validated in independent datasets and showed strong prognostic stratification ability, highlighting its potential for clinical translation. Conclusions: MAGMDA effectively improved miRNA-disease association prediction through an adaptive multi-order moment modeling framework and, supported by systematic biological validation, provided a powerful tool for translational research from computational discovery to clinical application. The MAGMDA framework and data resources are publicly available at https://github.com/zhangclbio/MAGMDA .

Background/Objectives: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor prognosis. T cell exhaustion (TEX) is a key factor in tumor immune evasion and therapeutic resistance. In this study, we integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data to characterize TEX-related transcriptional features in HCC. Methods: We first computed TEX scores for each sample using a curated 65-gene signature and classified them into high-TEX and low-TEX groups by the median score. Differentially expressed genes were identified separately in scRNA-seq and bulk RNA-seq data, then intersected to retain shared candidates. A 26-gene prognostic signature was derived from these candidates via univariate Cox and LASSO regression analysis. Results: The high-TEX group exhibited increased expression of immune checkpoint molecules and antigen presentation molecules, suggesting a tumor microenvironment that is more immunosuppressive but potentially more responsive to immunotherapy. Functional enrichment analysis and protein–protein interaction (PPI) network construction further validated the roles of these genes in immune regulation and tumor progression. Conclusions: This study provides a comprehensive characterization of the TEX landscape in HCC and identifies a robust gene signature associated with prognosis and immune infiltration. These findings highlight the potential of targeting TEX-related genes for personalized immunotherapeutic strategies in HCC.

Alzheimer's disease (AD) and Huntington's disease (HD) share neuroinflammatory mechanisms, yet their specific immune microenvironments remain poorly understood. Integrating transcriptomic profiles of peripheral blood and frontal cortex tissues with 2,160 immune-related genes, we analysed their shared immunopathology. Differential analysis identified 64 peripheral and 159 central consistently dysregulated immune genes, intersecting to isolate 10 co-expressed all-immune genes. Functional enrichment highlighted neutrophil and monocyte activation, alongside IL-17 and T-cell receptor signalling pathways. Machine learning (LASSO and Boruta) robustly pinpointed MMP9 as the core shared immune hub gene. External validation revealed MMP9 exhibited modest diagnostic performance in peripheral blood (AD AUC = 0.616; HD AUC = 0.619) but stronger predictive accuracy in brain tissues (AD AUC = 0.825; HD AUC = 0.876). Furthermore, MMP9 expression positively correlated with neutrophil and M0 macrophage infiltration. While modest peripheral accuracy limits its standalone diagnostic utility, this cross-tissue analysis establishes MMP9 as a consistently upregulated candidate molecular indicator of shared neuroinflammation, offering a valuable target for future mechanistic research.

Background: Tumor hypoxia in non-small cell lung cancer (NSCLC) promotes malignant progression and treatment resistance by enhancing abnormal vasculature, invasiveness, and metastasis. However, the molecular mechanisms underlying hypoxia-driven tumor progression remain incompletely understood. Methods: In this study, patient samples, cell lines, single-cell transcriptomic data, and spatial transcriptomic data were comprehensively analyzed to investigate hypoxia-associated molecular alterations in NSCLC. Results: A global trend toward shortened 3’ untranslated regions (3’UTRs) was observed in hypoxic tumors. Analysis of hypoxia-related alternative polyadenylation (APA) events revealed preferential usage of proximal polyadenylation sites (poly(A) sites, PASs) in CARM1. Shortening of the CARM1 3’UTR was associated with hypoxia and may serve as a candidate biomarker. This APA event may reduce putative microRNA (miRNA) binding sites and contribute to increased CARM1 expression, while potentially influencing the expression of hypoxia-related genes such as SELENBP1. Drug sensitivity analysis further suggested that patients with shorter CARM1 3’UTRs may exhibit differential responses to cisplatin chemotherapy. Moreover, single-cell and spatial transcriptomic analyses demonstrated enhanced interactions between hypoxic tumor cells and fibroblasts, highlighting a potential role for APA in remodeling the hypoxic tumor microenvironment. Conclusions: Our findings identify hypoxia-related APA features and characterize hypoxia-associated alterations within the NSCLC tumor microenvironmen, providing new insights into the molecular landscape of hypoxia-associated tumor progression.

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by profound molecular heterogeneity and high relapse rates, posing significant clinical challenges. Programmed cell death (PCD), encompassing diverse regulated modalities such as apoptosis, necroptosis, and ferroptosis, plays a key role in leukemogenesis and therapeutic response; however, a comprehensive prognostic framework integrating multi-modal PCD pathways in AML remains elusive. In this study, we performed a systematic transcriptomic analysis of 1624 genes associated with 13 distinct PCD forms. A novel computational pipeline combining a variational autoencoder (VAE) for dimensionality reduction and a multilayer perceptron (MLP) for classification was employed to identify robust PCD-related biomarkers, interpreted via SHapley Additive exPlanations (SHAP) analysis. This approach identified 48 candidate genes with discriminative potential between AML and normal bone marrow. Unsupervised consensus clustering based on these genes delineated two molecular subtypes exhibiting divergent clinical outcomes and immune microenvironment profiles. The subtype demonstrated an immunosuppressive phenotype, characterized by enriched regulatory T cells, M2 macrophages, and elevated expression of inhibitory immune checkpoints, correlating with inferior survival. We developed an 8-gene prognostic signature (SORL1, PIK3R5, RIPK3, ELANE, GPX1, VNN1, CD74, and IL3RA) that effectively categorized patients into high- and low-risk groups with notable survival differences, validated across independent cohorts. A prognostic nomogram combining the risk score, age, and cytogenetic risk enhanced the prediction accuracy for overall survival. Our study presents an integrative model that connects multi-modal PCD pathways to AML prognosis, offering a new molecular subtyping system and a clinically applicable risk assessment tool for improved prognostication and personalized treatment strategies.