Search Results (169)

Background: Brain metastasis is associated with poor prognosis in lung adenocarcinoma (LUAD). Anoikis resistance may contribute to tumor cell survival during metastatic dissemination and brain colonization; however, robust biomarkers for prognostic stratification and brain metastasis-associated classification remain limited. This study aimed to investigate anoikis-related molecular features in LUAD brain metastasis and develop a machine learning-based signature for prognostic assessment and exploratory classification of primary and brain-metastatic LUAD samples. Methods: We integrated single-cell and multi-cohort bulk transcriptomic data. Single-cell analysis was performed to characterize anoikis-related cellular states and intercellular communication in primary and brain-metastatic LUAD samples. In the bulk transcriptomic analysis, TCGA-LUAD was used for prognostic feature selection and risk-model construction, and GSE26939 was used for external prognostic validation. The classification performance of the fixed signature for distinguishing primary LUAD from brain-metastatic LUAD samples was further evaluated in GSE161116 and GSE271259. Immune microenvironment features were assessed, and an LLM-assisted exploratory drug-screening strategy combined with molecular docking was used to prioritize candidate compounds. Results: Single-cell analysis suggested that metastatic epithelial cells exhibited enhanced anoikis-related activity, accompanied by macrophage-associated SPP1-CD44 and MIF-(CD74+CXCR4) communication patterns. Machine learning-based feature selection identified an eight-gene signature consisting of BIRC3, CCL20, CLEC7A, CTSL, GOLM1, ICAM3, MTUS1, and SERPINH1. The signature showed prognostic value in TCGA-LUAD and GSE26939 and demonstrated exploratory classification performance in distinguishing primary LUAD from brain-metastatic LUAD samples. High-risk patients exhibited immune microenvironment alterations and enrichment of tumor progression-related pathways. LLM-assisted compound prioritization and molecular docking highlighted resveratrol and SB431542 as hypothesis-generating candidates with predicted interactions with core targets. Conclusions: This study identified an anoikis-related eight-gene signature for LUAD prognostic stratification and exploratory brain metastasis-associated classification. The findings suggest the potential involvement of anoikis-related tumor–microenvironment interactions in LUAD brain metastasis and provide candidate genes and compounds for further experimental validation. Full article

The Syrian golden hamster (Mesocricetus auratus) is a universally accepted model for visceral leishmaniasis (VL) due to its ability to mimic human disease pathology. Mus musculus (BALB/c) is preferred for evaluating pharmaceutical and immunological responses. This study focuses on the precise role of gene signatures in L. donovani-infected M. auratus and M. musculus, using transcriptomic analysis. Principal component analysis (PCA) revealed distinct clustering among the four groups (uninfected vs. infected spleen samples from M. auratus and M. musculus). After differential expression analysis, 2054 genes in M. auratus and 1108 in M. musculus were found to be differentially expressed, with 153 genes common to both species. Except for 31 genes, most of the commonly dysregulated genes show a similar expression pattern. Although Th1-mediated immune signaling was observed in both cases, the overexpression of LAG3 in both infected groups underscores the important role of T cell exhaustion. Immunological responses against parasite infection in M. auratus appear to be more aggressive, while M. musculus seems more intense. Interestingly, only the M. musculus-infected group shows overexpression of IL-10. Without a definitive role for IL-10, the overexpression of Tgm2, Clec7a, and Adora2b in both species may drive disease outcome. These findings elucidate the immunological mechanisms driving the pathogenesis of VL in rodent models. Full article

Marburg virus (MARV), a highly pathogenic member of the Filoviridae family, causes severe hemorrhagic fever with a high case fatality rate and currently lacks effective therapeutics. The viral entry process, mediated by the interaction between the MARV glycoprotein (GP) and host receptor C-type lectin domain family 4 member M (CLEC4M) (L-SIGN), represents a critical target for early-stage intervention. The active compounds from BindingDB and the decoy from DUDE were used. The RDKit was used for feature engineering. Machine learning models were trained on an initial dataset consisting of 56 active chemicals and 1232 decoys. Among the tested algorithms, the Random Forest model demonstrated superior performance, achieving the highest discriminative ability (AUC = 0.93, MCC = 0.88) on the test set. Virtual screening of 11,032 phytochemicals resulted in 120 predicted actives, of which 42 compounds satisfied drug-likeness criteria. Subsequent molecular docking identified three lead compounds (PubChem IDs: 42608095, 5281601, and 11243993) with moderate-to-promising binding affinities (−6.3 to −6.5 kcal/mol) toward the CLEC4M binding site. ADMET analysis revealed favorable pharmacokinetic and toxicity profiles for the selected lead compounds. DFT calculations of the three compounds highlighted their electronic stability and reactive nature, indicating that PubChem IDs 42608095 and 5281601 possess particularly stable electronic properties conducive to favorable target interactions. Combining machine learning models with molecular docking and Molecular Dynamics (MD) simulations worked well in finding promising phytochemical inhibitors. The MM/GBSA binding free energy calculations further confirmed binding affinities, with values of −10.83 and −11.08 kcal/mol, respectively, suggesting favorable complex stability. These findings provide a pathway for developing new antiviral agents against MARV, pending further experimental validation and optimization. Full article

Background: Breast cancer (BRCA) is a common malignant tumor that seriously threatens women’s health. Studies have shown that histone modifications (HMs) play a vital role in the occurrence and development of BRCA. This study aims to explore the distribution patterns of HMs in the mammary epithelial cell line (HMEC) and breast cancer cell line (MCF-7), and their potential associations with gene expression, patient prognosis, and drug efficacy. Methods: First, the distribution of histone modification (HM) signals in HMEC and MCF-7 cell lines was analyzed. Multiple algorithms were then used to predict the effects of different HMs and their modified regions on gene expression in the two cell lines. Based on four key regions identified from this analysis, 268 HM-related immune-related genes (H_IRGs) were screened, followed by functional enrichment and pathway analysis. Subsequently, Cox and LASSO regression analyses were performed on the H_IRGs to construct a risk scoring model. Results: The random forest algorithm showed the best predictive performance (AUC = 0.92) and identified three key HMs (H3K4me2, H3K27me3, and H3K36me3) and four key regions that strongly influenced gene expression. A risk scoring model was constructed from 11 key IRGs (BCL2A1, PSME2, STC2, ESRRG, CRISP3, IL13RA1, LCN1, EED, CLEC10A, SLURP1, and FGF12). This model effectively predicted patients’ survival in both the training and validation cohorts. Conclusions: In summary, our research results provide a theoretical basis for the occurrence and development of BRCA, and the 11 key IRGs discovered are expected to become potential biomarkers for BRCA prognostic assessment and treatment response prediction. Full article

Background/Objectives: Spaceflight presents a combination of physical and psychosocial stressors that may impact biological aging and health. Understanding how spaceflight influences molecular aging processes is essential as commercial and professional space travel continue to expand. Methods: We analyzed publicly available DNA methylation data to evaluate longitudinal changes in 10 epigenetic aging biomarkers, 6 leukocyte proportion estimates, and 109 DNA methylation-derived protein scores in two astronauts participating in Axiom Space’s AX1 17-day low Earth orbit mission. We calculated mean values for all biomarkers across three timepoints: two weeks before spaceflight (T0), 24 h after spaceflight (T1), and three months after spaceflight (T2). Using the mean values, we next calculated the fold change from baseline for all biomarkers. Because the sample size precluded statistical testing, we identified the top 5% of absolute fold changes to highlight the largest shifts across candidate biomarkers. Results: Across epigenetic clocks, MiAge showed the greatest T0–T1 decrease (−4.26-fold), and DNAmFitAge showed the greatest T0–T2 increase (2.47-fold). NK cells exhibited the largest T0–T1 change, decreasing by 49% (−0.49-fold). B cells exhibited the largest T0–T2 change, decreasing by 11% (−0.11-fold). Proteins meeting a predefined top 5% fold change from baseline criterion at both T1 and T2, included BMP1, CLEC11A, CXCL11, FAP, and LTF. Enrichment analysis indicated involvement of serine-type endopeptidase activity, molecular function activator activity, and cell aggregation pathways. Conclusions: These findings suggest that spaceflight influences methylation-derived biomarkers of aging and immunity even in short-duration missions. These results, though exploratory, contribute to emerging efforts to characterize molecular resilience and vulnerability in human spaceflight. Full article

Chronic obstructive pulmonary disease (COPD) is a leading global cause of mortality. Its molecular pathogenesis, especially systemic immune dysregulation, remains unclear. Public transcriptomic datasets underwent machine learning to identify biomarkers, which were validated in external cohorts and single-cell RNA-seq. In vitro and ex vivo validations included: qRT-PCR and ELISA in CSE/LPS-stimulated macrophages to assess drug efficacy (Celecoxib/Lovastatin); RORC overexpression; Western blotting in patient-derived primary T cells and TCF7-deficient Jurkat cells with genetic rescue; and immunofluorescence of human lungs. A three-gene signature (RORC, TCF7, and CLEC4D) consistently discriminated COPD. CLEC4D localized to myeloid cells, while RORC/TCF7 mapped to lymphoid lineages. Celecoxib and Lovastatin attenuated macrophage inflammation, partially via RORC preservation. Crucially, the TCF7 protein was depleted in COPD primary T cells. TCF7 knockdown downregulated pro-caspase-8, which was fully reversed by TCF7 re-expression. Immunofluorescence confirmed disrupted TCF7/caspase-8 spatial patterns in COPD lungs. This signature highlights innate hyperactivation and adaptive T-cell alterations in COPD, providing novel mechanistic insights into immune dysregulation and potential pharmacological targets. Full article

Spinocerebellar Ataxia type 2 (SCA2) and Amyotrophic Lateral Sclerosis type 13 (ALS13) are triggered by polyglutamine expansion in Ataxin-2 (ATXN2). To understand these neurodegenerative disorders at the molecular level, the brains of 10-month-old Atxn2-CAG100-knockin mice were analyzed as microglial, astroglial and neuronal fractions via global RNA sequencing. Data were validated by comparison with the spinal cord oligonucleotide microarray profile or filtered by RNA-seq consistency. Here, we show that the mutation causes a massive inflammatory response in microglia and a reciprocal loss of neuronal transcripts in glial fractions, suggesting severe synapse loss. Beyond these general neurodegenerative signs, we identify pathognomonic changes in the machinery for protein translation and RNA splicing. Glial fractions showed upregulation of Gpnmb (to 2082%), Cst7, Clec7a, Axl, Csf1, Lgals3, Lgals3bp, Slc11a1, and Usp18 as an unspecific neuroinflammatory signature, versus downregulation of axonal Nefh (to <19%), and synaptic Scn4b, Camk2b, Rab15, and Grin1 mRNAs correlating with circuit disconnection. In all fractions, reductions in Kif5a, Rph3a, and Cplx1 were noted versus disease-specific inductions of ribosomal subunits, presumably mirroring the partial loss-of-function of ATXN2 as RNA translation modulator. Selective accumulations of embryonic factors Rnu1b2 and Eef1a1 versus downregulation of adult Eef1a2 specify the mutation impact on splicing and translation elongation. As a potential underpinning of toxic gain-of-function, the proteostasis transcript Rnf213 appeared increased in astroglial and microglial fractions. These transcriptome data suggest altered ribosomal and spliceosome machinery, with massive microgliosis versus mild astrogliosis, at the core of SCA2 and ALS13. Full article

Grammatical error correction (GEC) for Chinese learner English is still dominated by sentence-level modeling, which limits discourse-level consistency and weakens adaptation to learner-specific error profiles. From an instructional perspective, these limitations also reduce the value of automated feedback as a basis for spaced-repetition instructional structures sensitive to individual differences. This study proposes a symmetry-aware dual-encoder architecture for context-aware GEC in Chinese learner English. A context encoder captures preceding-sentence information, while a source encoder integrates BERT-based semantic representations with Bi-GRU-based syntactic features for the current sentence. A gated decoder performs asymmetric fusion of local and contextual evidence. To better reflect corpus-level tendencies in Chinese learner English, a CLEC-informed augmentation strategy generates synthetic errors using empirical category frequencies as a coarse sampling prior. Experiments on CoNLL-2014, JFLEG, and CLEC show consistent improvements over strong neural baselines in F_0.5 and GLEU under the current desktop-oriented implementation setting. Nevertheless, the integration of BERT, dual encoders, and gated decoding introduces non-negligible computational overhead, and the present system is therefore better suited to desktop writing-support scenarios than to strict real-time or large-scale online deployment. The proposed framework thus provides a practical technical basis for personalized grammar feedback and for future spaced-repetition instructional designs in ESL writing support. Full article

Background/Objectives: CLEC-2 (C-type lectin-like receptor 2), the newest discovered platelet receptor, is involved in platelet activation and aggregation, the inflammatory response, tumor growth, metastasis, and angiogenesis. These unique features make CLEC-2 a promising candidate for a new biomarker and therapeutic target. The aim of our study was to evaluate the diagnostic value of CLEC-2 in patients with colorectal cancer (CRC). Methods: The serum CLEC-2 concentration was determined using ELISA methods in 64 CRC patients and 25 healthy subjects. Results: Our results indicate that the concentration of the serum CLEC-2 was significantly higher in CRC patients than in healthy subjects. Furthermore, the CLEC-2 levels were significantly higher in G3- than G2-grade CRC, and in patients with more advanced CRC, such as those with lymph node and distant metastases, than in patients without any metastases. CLEC-2 showed a positive correlation with platelet indices (PLT and MPV). The area under the ROC curve confirmed CLEC-2’s excellent diagnostic power in distinguishing between patients with CRC. Conclusions: Our results indicate that CLEC-2 may be associated with CRC development and suggest that the identification of this biomarker could be useful for determining CRC progression. Full article

Background: Hypertension is an important target for primordial prevention of complex, noncommunicable diseases, and its prevalence remains high across populations. The urban population in India is at a high risk of hypertension, but the genetic basis of hypertension in this population remains poorly understood. Methods: We conducted a pooled whole-blood genome-wide association study of 28 pools representing 1402 participants of the Diabetes In Sindhi Families In Nagpur (DISFIN) study, which enrolled families of probands with type 2 diabetes (T2D). Genotyping was done using Illumina’s Global Screening Array. Results: From a total of 608,550 single-nucleotide variants, 191 were found to be significantly associated with hypertension even after adjusting for metabolic comorbidities, batch effects, pooling error, kinship status, and pooling variation. These variants mapped to 180 well-characterized genes comprising 55 (31%) genes, and encode long noncoding RNAs (lncRNAs). Many of the genes significantly associated with hypertension (including 35% of the lncRNAs) have also been reported by other studies. However, we identified novel genes (SBF2, ARHGAP12, EPAS1, CLEC16A, and LRPPRC) to be associated with hypertension. The most significantly associated lncRNA gene was FLYWCH-AS1. Bioinformatic analyses indicated that these novel genes are likely to have functional importance in hypertension. Conclusions: Our study thus points to the potential candidate genes associated with hypertension in endogamous Sindhi families with T2D patients. The replicable and functional role of these candidate genes should be investigated in future studies. Full article

Background: Advanced liver fibrosis (LF) is a major determinant of prognosis across chronic liver diseases. Current biomarkers are often etiology-specific and lack cross-cohort robustness. Shared molecular drivers across etiologies remain incompletely defined, and effective anti-fibrotic therapies are limited. Methods: We developed a multi-algorithm consensus machine-learning framework to derive a robust LF progression signature. In the training non-alcoholic fatty liver disease (NAFLD) cohort GSE213621 (n = 368), samples were formulated as a binary classification task (mild fibrosis, F0–F2; advanced fibrosis, F3–F4). Candidate genes were screened in parallel using Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme Gradient Boosting (XGBoost). Genes selected by at least two algorithms were defined as a high-consensus pool, and genes consistently selected by all four algorithms were prioritized to construct a core signature. Model performance was evaluated by stratified cross-validation in the training cohort and externally validated in four independent cohorts of different etiologies (GSE49541, GSE84044, GSE130970, and GSE276114). Cellular sources of signature genes were characterized using single-cell RNA sequencing (scRNA-seq) datasets GSE136103 (human) and GSE172492 (mouse). For therapeutic discovery, the high-consensus expression profile was queried against the Connectivity Map (CMap) to prioritize compounds predicted to reverse the fibrotic transcriptional program. Withaferin A (WFA) was selected for experimental validation in a carbon tetrachloride (CCl₄)-induced mouse LF model and in the transforming growth factor-β1 (TGF-β1)-stimulated human hepatic stellate cell line LX-2. Bulk liver RNA-seq profiling was performed to interrogate WFA-associated molecular changes in vivo. Results: We identified a six-gene signature (CLEC4M, COL25A1, ITGBL1, NALCN, PAPPA, and PEG3) that discriminated advanced from mild fibrosis, achieving a mean AUC of 0.890 in internal cross-validation and an average AUC of 0.864 across external validation cohorts. scRNA-seq analysis revealed cell-type-specific expression with prominent enrichment in fibroblast populations. In vivo, WFA markedly attenuated CCl₄-induced fibrosis (p < 0.05) and reversed 1314 fibrosis-associated differentially expressed genes (adjusted p < 0.05), which were enriched in fatty acid metabolism and PPAR signaling, as well as extracellular matrix (ECM)–receptor interaction and focal adhesion (adjusted p < 0.05). In vitro, WFA suppressed TGF-β1-induced LX-2 activation, reducing α-SMA and Fibronectin expression (p < 0.05). Conclusions: We report a six-gene signature that robustly predicts advanced LF across etiologies, define its cellular context using single-cell atlases, and validate the anti-fibrotic activity of WFA in both in vivo and in vitro models. Bulk liver RNA-seq and cellular evidence further suggest that WFA-associated effects are linked to lipid metabolic programs, ECM remodeling, and attenuation of hepatic stellate cell activation. Full article

In glioblastoma, the strong immunosuppression of the tumor immune microenvironment fosters tumor aggressiveness and decreases the effectiveness of therapeutic interventions, including immunotherapies. An intricate network of connections among tumor cells, stroma and infiltrating immune cells sustains immunosuppression. Lectins are immunoregulatory glycan-binding receptors contributing to immunosuppression. Their targeting is proposed as an appealing strategy for anti-cancer therapy. In this work, network-based approaches were exploited to identify a lectin profile that could dissect the complexity of tumor-immunity interactions in glioblastoma. Differential co-expression analysis, employing TCGA, CGGA and GTEx databases (145, 133 and 255 samples, respectively), identified a cluster of novel C-type lectins, with ASGR2 and CLEC12A as principal hubs. Furthermore, TIMER2.0 analysis revealed that their expression was significantly associated with immunosuppressive cells. ASGR2 and CLEC12A expression was also validated by cytofluorimetric analysis on both tumor and liquid biopsies from 20 glioblastoma patients. We report that ASGR2 and CLEC12A C-type lectins are associated with tumor-infiltrating immunosuppressive myeloid subsets and discriminate patients’ poor prognosis. These results suggest that C-type lectins may contribute to the immunosuppressive network sustained by infiltrating myeloid immune cells in GB, resulting in exploitable targets for therapeutic interventions. Full article

Glioblastoma (GBM) is a highly aggressive brain tumor with a complex tumor microenvironment (TME) that includes immune cell infiltration, notably macrophages. The role of macrophages in GBM progression is influenced by their polarization state, which can be either pro-inflammatory (M1) or immunosuppressive (M2). This study investigates the macrophage polarization in GBM, identifying key macrophage-related genes and their impact on tumor progression. Analysis of TCGA-GBM data revealed that macrophage infiltration correlates with poor prognosis, with 41 risk-associated genes identified. DSP dataset analysis highlighted 378 differentially expressed genes between CD68⁺ macrophages and GFAP⁺ controls, including immune-related genes like SPP1, CD74, and C3. Cross-validation with single-cell RNA-seq confirmed the expression of 9 key genes, with 7 genes being macrophage-specific. In vitro experiments using conditioned media from GBM cell lines demonstrated that GBM cells promote macrophage polarization towards an M2-like phenotype. Overexpression of CD74, CLEC7A, and IFI30 in macrophages further enhanced M2 polarization, which was associated with increased tumor-promoting functions, including enhanced invasion and reduced apoptosis in GBM cells. Together, these findings highlight the role of M2 macrophage polarization in promoting GBM progression and suggest that targeting macrophage polarization pathways may offer therapeutic potential. Full article

The lack of reliable diagnostic tools and relapse monitoring for latent tuberculosis infection (LTBI) constitutes a major obstacle to global tuberculosis (TB) control. This highlights an urgent need for robust animal models and predictive biomarkers. To address this, we report the successful establishment of a rapid murine model of recapitulating the active, latent, and relapse phases of TB within a compressed ten-week timeframe—hence termed the rapid multi-stage TB murine model. In this model, mice were first intravenously infected with Mycobacterium tuberculosis, followed by a four-week isoniazid (INH) regimen starting at two weeks post-infection. By week six, pulmonary bacterial loads in most mice dropped below the detection limit, signifying the establishment of latency. Reactivation was subsequently triggered by a four-week administration of anti-TNF-α (Tumor Necrosis Factor-α) monoclonal antibody. Leveraging this reproducible and time-efficient model, we performed transcriptomic profiling of peripheral blood and identified a distinct sixteen-gene signature (including Ets2, Fam111a, Fosl2, Gadd45b, Nfkbid, Rgs1, Bhlhe40, Il1r2, Clec2d, Kmo, Lynx1, Papd4, Trim34a, Wrb, Nlrp12, Spns1) that dynamically tracks disease progression. Collectively, these findings not only provide a valuable and efficient preclinical tool but also deliver transformable candidate biomarkers with immediate potential to guide the development of novel diagnostic strategies for LTBI surveillance and management. Full article

Background/Objectives: Messenger RNA (mRNA) vaccine technology has shown great potential in the prevention of infectious diseases and treatment of cancers, but its full potential is limited by non-specific delivery mediated by the current lipid nanoparticle (LNP) platform. Methods: Here, we developed a dendritic cell (DC)-targeting LNP incorporated with an ultra-high-affinity CLEC9A-specific nanobody that facilitates enhanced DC uptake but reduced liver accumulation. We assessed the therapeutic efficacy of nanobody-functionalized lipid nanoparticles (Nb-LNPs) in a mouse Lewis lung carcinoma (LLC) model, alongside an evaluation of T cell-mediated immune responses and dendritic cell activation, facilitated by the delivery of mRNA-based neoantigen vaccines. Results: Compared with the use of an unfunctionalized LNP, personalized mRNA cancer vaccines encapsulated with this Nb-LNP demonstrated not only superior anti-tumor effects but also a favorable bio-safety profile in a mouse Lewis lung carcinoma model. The mRNA Nb-LNP neoantigen vaccines also induced substantially higher levels of DC maturation and more potent antigen-specific T cell responses, in particular CD4⁺ T cell responses, which are critical for initiation of anti-tumor immunity and immune memory. Conclusions: Taken together, these results suggest that precision-engineered LNPs conjugated with a CLEC9A-specific antibody or nanobody could be a promising platform for delivering mRNA vaccines specifically to dendritic cells, improving their prophylactic or therapeutic effects. Full article