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PubMed2025

Integrated data mining and network pharmacology to explore the prescription patterns from a senior TCM oncologist's clinical practice in treating chemotherapy-induced hand-foot syndrome.

Hand-foot syndrome (HFS) is a common and refractory adverse effect of chemotherapy lacking specific therapeutic strategies currently. Traditional Chinese medicine (TCM) has shown empirical efficacy in clinical HFS management. This study integrated data mining and network pharmacology to systematically elucidate the medication principles and molecular mechanisms underlying Professor Gang Xie's prescriptions for HFS. All medical records from Professor Xie's specialist clinic (January 2020 to March 2025) were retrospectively collected and standardized in Excel. Prescriptions were analyzed through frequency statistics, association and clustering. Active ingredients of core herb pairs and their disease-related targets were identified using TCMSP, HERB, GeneCards, PharmGKB and GEO databases. Protein-protein interaction (PPI) networks, gene ontology (GO), and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed. Molecular docking validated interactions between key bioactive compounds and targets. This study involved 217 prescriptions containing 150 herbs. Core herb combinations comprised Radix Astragali (Huangqi), Poria (Fuling), and Radix Pseudostellariae (Taizishen), predominantly classified as spleen-tonifying agents with warm properties, targeting lung, spleen, and stomach meridians. Network analysis identified 67 bioactive compounds and 899 disease targets. Quercetin, kaempferol, acacetin and luteolin were identified the key ingredients. The core targets (TP53, STAT3, PIK3CA, HSP90AA1, AKT1, CTNNB1, PI3KR1, MAPK1) were enriched in MAPK and PI3K-Akt signaling pathways. Molecular docking confirmed strong binding affinity between key compounds and targets. Professor Xie's therapeutic strategy for HFS emphasizes "spleen fortification, phlegm elimination, and stasis resolution." The core herb combination likely exerts anti-HFS effects via modulation of MAPK and PI3K-Akt pathways, providing a pharmacological basis for TCM-driven HFS management.

Network Pharmacology↗

PubMed2025

Integrating explainable artificial intelligence with multiomics systems biology and electronic health record data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health records data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; 9 tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct subtissues (defined as clusters of samples within a brain tissue that share a specific expression pattern); and gene-gene coexpression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six Food and Drug Administration (FDA)-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large US de-identified insurance-claims database (n = 364 733), exposure to promethazine, one of the candidate drugs, was associated with a 57%-62% lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both P < .001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multiomics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Alzheimer Disease↗

PubMed2025

Integrating explainable AI with multiomics systems biology and EHR data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health record (EHR) data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; nine tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations (SHAP) identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct "subtissues" (clusters of samples); and gene-gene co-expression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six FDA-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large U.S. de-identified insurance-claims database (n = 364733), exposure to promethazine, one of the candidate drugs, was associated with a 57-62 % lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both p < 0.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multi-omics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Computational Biology↗

PubMed2026

SwinePan for pig graph-based pangenome and multiomics data mining.

Pigs are one of the most important livestock species worldwide. Although multiple high-quality reference genomes exist, reliance on a single linear reference limits the detection of structural variants (SVs) and the characterization of population-specific genetic diversity. To address this limitation, we developed SwinePan, a comprehensive and integrated multiomics database for pigs built on a graph-based pangenome framework. SwinePan incorporates a variome derived from the graph-based pangenome, covering 2,598 individuals across 35 breeds, including 185,759 SVs, 117 million SNPs, and 6.8 million indels. The database also integrates transcriptomic data from liver, loin muscle, abdominal fat, and backfat, along with over 150,000 phenotypic records. The online toolkit deployed in SwinePan enables genome-wide association studies (GWAS), expression quantitative trait locus (eQTL) mapping, and colocalization, while interactive modules visualize population structure and multiomics associations, streamlining candidate gene and variant exploration. Additionally, two proof-of-concept analyses demonstrate how SwinePan pinpoints trait-associated loci and deciphers their potential regulatory mechanisms.

Journal Article↗

PubMed2026

Acupoint Selection Patterns and Potential Mechanisms of Acupuncture in Knee Osteoarthritis: A Combined Data Mining and Network Pharmacology Study.

OBJECTIVE: To identify the core acupoint prescription and Kellgren-Lawrence (K-L) grade-dependent compatibility patterns of acupuncture for KOA through complex network analysis, and to predict the potential molecular mechanisms underlying the core prescription via network pharmacology. METHODS: Literature was retrieved from PubMed, EMbase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP, and SinoMed (inception to September 3, 2025). Frequency, association rule, complex network, and K-L grade subgroup analyses were applied. Potential targets of the core prescription were identified via network pharmacology and intersected with disease targets from OMIM, Therapeutic Target, GeneCards, and DrugBank. A protein-protein interaction (PPI) network was constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential molecular mechanisms. RESULTS: We included 522 studies, yielding 582 prescriptions involving 123 acupoints. The core prescription comprised 24 acupoints, including Dubi (ST35), Neixiyan (EX-LE4), Liangqiu (ST34), Xuehai (SP10), Zusanli (ST36), Yanglingquan (GB34), Yinlingquan (SP9), among others. K-L subgroup analysis revealed ST35, GB34, SP9, and SP10 as universal core acupoints. The mild-to-moderate subgroup mainly used local acupoints, while the moderate-to-severe subgroup centered on ST35, with increased distal acupoint usage and higher degree values. Network pharmacology analysis identified 77 overlapping targets. Core targets included tumor necrosis factor (TNF), interleukin 6 (IL6), interleukin 1 beta (IL1B), tumor protein p53 (TP53), matrix metallopeptidase 9 (MMP9), signal transducer and activator of transcription 3 (STAT3), transforming growth factor beta 1 (TGFB1), caspase 3 (CASP3), and B-cell lymphoma 2 (BCL2), which were enriched in inflammation and immunity, cartilage metabolism, and tissue repair pathways. CONCLUSION: The core acupoint prescription for KOA features local acupoints combined with distal ones, exhibiting distinct patterns across K-L grades. Our computational findings suggest that core acupoints may potentially delay knee joint degeneration by synergistically regulating inflammation, cartilage metabolism, apoptosis, and tissue repair, although these predictions require experimental validation. These findings provide preliminary evidence and a theoretical basis for standardized clinical point selection and further mechanistic research.

KOA↗

PubMed1990

Assessment of potential biases in the application of MSHA respirable coal mine dust data to an epidemiologic study.

Systematic errors in exposure data will result in biased estimates of the exposure-response relationship derived from epidemiologic analyses. Thus, adjustment of exposure data to account for identified errors may provide for a more accurate assessment of effect. In preparing to apply respirable coal mine dust exposure data collected by the Mine Safety and Health Administration (MSHA) to a study of the pulmonary status of underground coal miners, an assessment of potential systematic errors was undertaken. Potential errors stemming from adjustment of controls during sampling, concentration-dependent sampling, truncation of sampling results, identified sampling equipment problems, and a disproportionate number of low concentration samples in mine operator-collected samples were identified and evaluated. Methods to account for these errors and adjust mean exposures by mine, occupation, and year are given.

Bias↗

PubMedDate not supplied

[The working conditions and health status of miners in Donets Basin coal mines].

Data are reported on working conditions of coal miners considering the main physical (dust, noise, vibration, microclimate) and chemical environmental professional factors and their prognosis up to the year 2005. The authors analyze professional morbidity (pneumoconiosis, dust-induced bronchitis, vibration disease, cochlear neuritis etc.) and diseases with temporary loss of the working capacity invalidity and mortality of miners. The relation between working conditions and health status of miners were analyzed.

Absenteeism↗

PubMed2026

Mechanism of Shaofu Zhuyu decoction in improving diabetic mellitus erectile dysfunction inhibition of ferroptosis based on network pharmacology and experimental validation.

OBJECTIVE: To explore the medication patterns and mechanisms of action of Shaofu Zhuyu decoction (, SFZYD) in inhibiting ferroptosis through the nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase 1 (HO-1)/glutathione peroxidase 4 (GPX4) pathway to improve diabetes mellitus-induced erectile dysfunction (DMED). METHODS: Firstly, data mining was employed to identify the medication patterns of Traditional Chinese Medicine (TCM) in treating DMED. Secondly, network pharmacology combined with a ferroptosis database was used to predict the targets. Subsequently, cell counting kit-8, 4',6-diamidino-2-phenylindole staining, reverse transcription-polymerase chain reaction (RT-PCR), and reagent kits were utilized to assess the repair effects of SFZYD on corpus cavernosum endothelial cells (CCECs) induced by high glucose (HG). Metabolic indicators, hematoxylin-eosin staining, and Masson staining were performed to observe the restorative effects of SFZYD on erectile function and penile tissue in diabetic rats. Finally, using Nrf2 inhibitors, the expression of related proteins and mRNAs was detected through Western blotting and RT-PCR. Reactive oxygen species levels and mitochondrial membrane potential were detected by flow cytometry. RESULTS: Data mining revealed that the prescription rules for blood stasis-type DMED coincide with the treatment principles of SFZYD. Network pharmacology identified 48 ferroptosis-related targets, primarily heme oxygenase 1 (HMOX1) and GPX4. Kyoto Encyclopedia of Genes and Genomes enrichment analysis associated these targets with the ferroptosis pathway. SFZYD repaired HG-induced CCECs damage and restored HMOX1 and GPX4 mRNA levels. in vivo, SFZYD effectively alleviated erectile dysfunction and repaired blood sinuses and fibrosis in diabetic rats. Following Nrf2 inhibition, the expression of Nrf2, HMOX1, and GPX4 decreased, while SFZYD intervention reversed these effects, improving ferroptosis and oxidative stress indicators. CONCLUSION: This study explored the potential mechanisms and efficacy of the TCM prescription SFZYD in treating DMED through data mining, network pharmacology analysis, cellular experiments, and animal experiments. It verified its effectiveness in repairing HG-induced CCECs damage, improving the pathological state of penile tissue in diabetic rats, and restoring erectile function by regulating the Nrf2/HO-1/GPX4 signaling pathway. This provides new insights and scientific evidence for treating DMED with TCM.

Male↗

PubMed2026

Systematic functional evaluation of CNGA1 missense variants associated with retinitis pigmentosa.

BACKGROUND: Missense variants are frequently classified as variants of uncertain significance (VUS) according to the guidelines of the American College of Medical Genetics and Genomics and the Association of Molecular Pathology (ACMG/AMP). Consequently, disease relevance remains elusive, impeding molecular genetic diagnostics, patients` and family genetic counseling, and identification of patients eligible for clinical trials. Functional studies are critical for resolving the clinical significance of VUS. CNGA1 encodes the main subunit of the rod cyclic nucleotide-gated (CNG) channel, a vital component of the phototransduction cascade. Variants in CNGA1 are a rare cause of autosomal recessive retinitis pigmentosa and a phase I/II gene augmentation trial (NCT06291935) is currently ongoing highlighting the necessity to differentiate benign from pathogenic variants. METHODS: CNGA1 missense variants compiled from retinal disease patient cohorts, public databases and literature were functionally investigated using a medium-throughput aequorin-based assay and in vitro minigene splice assays for predicted exonic spliceogenic variants. Functional data were correlated with the in silico prediction of five variant effect predictors (VEPs) and applied to support or revise variants' ACMG/AMP classification. RESULTS: Data mining revealed 86 missense CNGA1 variants - including three novel - most of them lacking functional data; 65.1% of the variants were initially classified as VUS. The aequorin-based assay showed that 72.1% of tested variants significantly impaired CNG channel function and were classified as functionally abnormal, while 23.3% were functionally normal and 5% remained functionally uncertain. Correlation of the functional data with in silico predictions identified AlphaMissense and CPT-1 to be the most suitable tools for assessing CNGA1 missense variants. Using in vitro minigene splice assays, two putative missense variants were shown to induce missplicing. Based on the functional findings, 62.1% of the variants initially classified as VUS were re-categorized as likely pathogenic or likely benign. Furthermore, 93.3% of the variants initially classified as likely pathogenic showed an effect on CNGA1 channel function, confirming their disease relevance and supporting their reclassification as pathogenic. CONCLUSION: This study represents the first comprehensive functional assessment of disease-associated CNGA1 missense variants, thus significantly advancing the understanding of their disease relevance and improving molecular genetic diagnostics in patients.

Humans↗

PubMed2025

Logan: Planetary-Scale Genome Assembly Surveys Life's Diversity.

The breadth of life's diversity is unfathomable, but public nucleic acid sequencing data offers a window into the dispersion and evolution of genetic diversity across Earth. However the rapid growth and accumulation of sequence data have outpaced efficient analysis capabilities. The largest collection of freely available sequencing data is the Sequence Read Archive (SRA), comprising 27.3 million datasets or 5 × 1016 basepairs. To realize the potential of the SRA, we constructed Logan, a massive sequence assembly transforming short reads into long contigs and compressing the data over 100-fold, enabling highly efficient petabase-scale analysis. We created Logan-Search, a k-mer index of Logan for free planetary-scale sequence search, returning matches in minutes. We used Logan contigs to identify >200 million plastic-degrading enzyme homologs, and validate novel enzymes with catalytic activities exceeding current reference standards. Further, we vastly expand the known diversity of proteins (30-fold over UniRef50), plasmids (22-fold over PLSDB), P4 satellites (4.5-fold), and the recently described Obelisk RNA elements (3.7-fold). Logan also enables ecological and biomedical data mining, such as global tracking of antimicrobial resistance genes and the characterization of viral reactivation across millions of human BioSamples. By transforming the SRA, Logan democratizes access to the world's public genetic data and opens frontiers in biotechnology, molecular ecology, and global health.

Journal Article↗

PubMed2025

Causal relationship between educational attainment and the occurrence of venous thromboembolism.

BACKGROUND: The association between educational attainment (EA) and arterial thrombotic disease has been reported, but the causal relationship between EA and venous thromboembolism (VTE) is not clear. We aimed to assess the causal effect of EA on VTE using the two-sample mendelian randomization (MR) method. METHODS: Data mining was conducted on the genome wide association studies (GWAS), with exposure factor EA and outcome factor VTE. Two-sample Mendelian Randomization (TSMR) analysis was conducted, with the results obtained from the random effects inverse variance weighted method (IVW). Use the MR-Egger method for pleiotropy analysis and leave one method for sensitivity analysis to verify the reliability of the data. RESULTS: Genetically predicted decreased EA was associated with a decreased risk of VTE in both the FinnGen consortium and UK Biobank (FinnGen-VTE: OR = 0.848; 95% CI 0.776-0.927; P = 2.84 × 10-4; UKB-VTE OR = 0.996; 95% CI 0.994-0.999; P = 0.008) under a multiplicative random-effects IVW model. Results were consistent in all sensitivity analyses and no horizontal pleiotropy was detected. CONCLUSIONS: The MR technique instructed a potential inverse causative relationship between EA and occurrence of VTE. Therefore, patients with low EA should be more vigilant about the occurrence of VTE.

Venous Thromboembolism↗

PubMed2026

T-SMmOTE: tweaked synthetic majority minority oversampling technique for data scarcity issue in multi omics studies.

MOTIVATION: Multiomics data offer a rich data mine for modeling complex as well as day-to-day diseases, but their practical deployment is constrained by the limited sample availability. To this end, generating synthetic samples is a viable remedy. Extant schemes operating along this line, however, are mostly limited to augmenting the minority class in imbalanced datasets and often produce synthetic samples that lack sufficient diversity and fail to faithfully capture the underlying data distribution. As a result, the full potential of synthetic augmentation in multi-omics learning remains underexplored. The aim is to address the data scarcity problem in multi-omics domain. We propose a synthetic oversampling framework, which is dedicated to addressing overall data scarcity in multi-omics datasets and the lack of diversity in synthetic samples. Contrary to conventional methods that restrict augmentation to minority classes and rely on interpolation of two neighbors, our method generates diverse yet distribution-aligned synthetic samples by interpolating three neighbors and extends this augmentation paradigm to the majority class. The framework first balances the dataset by generating synthetic minority samples, and subsequently augments the balanced dataset by oversampling both majority and minority classes. RESULTS: Empirical evaluation on multi-omics data obtained from three heterogeneous health scenarios-inflammatory bowel disease, multi-organ dysfunction syndrome, and colorectal cancer-substantiates the utility of the proposed scheme in improving the predictive performance. The models trained on T-SMmOTE-augmented data achieve higher Matthews correlation coefficient values, along with improvedscores for both majority and minority classes. Notably, oversampling of the majority class improves the cognition of the minority class as well. We also explore the consistency of the class distributions between the original and augmented class-specific datasets. These findings confirm the capability of our scheme to learn from small, high-dimensional multi-omics datasets and highlight its potential for non-invasive disease detection. AVAILABILITY AND IMPLEMENTATION: https://github.com/payelu/TSMm.

Journal Article↗

PubMed1992

[Improved medical care for miners with ischemic heart disease].

The article is devoted to coronary disease in miners of deep Donbass mines. Data of its prevalence, chemical and functional features are given. Rapid progress of the disease was found to correlate with unfavourable factors of occupational environment. Mechanisms of dangerous heart rythm disorders formation during the work are shown. The main points of the programme improving the health care of miners suffering from coronary heart disease are described.

Adult↗

PubMed2026

DDX21 Enhances Radiosensitivity in Head and Neck Squamous Cell Carcinoma by Suppressing MK2-Mediated DNA Damage Response.

Radioresistance remains a significant challenge in the radiotherapy (RT) of head and neck squamous cell carcinoma (HNSCC). However, the biological factors that govern sensitivity to this therapy are not well-understood. The DEAD-box family is known for its role in genome stability, and inextricably linked to the radiotherapy resistance of tumors. This study found the role of the RNA helicase DDX21 in regulating radiosensitivity through extensive data mining. High DDX21 expression predicted improved survival after postoperative radiotherapy. Overexpression of DDX21 increased radiosensitivity in vitro and in vivo, whereas depletion promoted radioresistance. In vitro, DDX21 enhanced radiation-induced DNA damage, genomic instability, and apoptosis by binding MK2 and suppressing MK2 phosphorylation independently of p38 activity. Meanwhile MK2 inhibition restored and further augmented radiosensitivity in DDX21-deficient cells and xenografts by increasing DNA damage and apoptosis. Overall, DDX21 regulates radiosensitivity in HNSCC by suppressing MK2 signaling and modulating the radiation-induced DNA damage response. Its expression may serve as a potential biomarker associated with radiosensitivity, and MK2 inhibition offers a promising approach to overcome radioresistance in tumors with low DDX21 expression.

DDX21↗

PubMed2025

MED12-STAT1-TAP2 axis regulates CD8 + T cell cytotoxicity and mediates immunotherapy outcome in non-small cell lung cancer.

Although immunotherapy for late-stage non-small cell lung carcinoma (NSCLC) has been clinically utilized, its prognosis remains highly heterogeneous, prompting us to investigate novel predictive immunotherapy biomarkers for NSCLC. We analyzed the correlations between MED12 nonsynonymous mutations and survival, clinical, genomic, transcriptomic information, and immune infiltration information through data mining across multiple datasets. We also investigated the mechanism of MED12 using luciferase assay, Western blot, ChIP-PCR, and siRNA. MED12 is significantly associated with survival in completely independent immunotherapy datasets, including MSKCC (N = 350), Naiyer2015 (N = 34), our own (N = 295) and the pan-cancer dataset, but not in the TCGA dataset, where patients received non-immunotherapy regimens. Mutations in MED12 showed no significant correlation with known metrics (TMB, IPS/CTLA4/PD1 status, PD-1/PD-L1 expression, and TCR/BCR status) or DNA Damage Repair (DDR) pathway mutations, yet they carried independent prognostic information according to the Cox multivariate regression. On the other hand, MED12 mutation is significantly associated with multiple immune-related pathways and immune infiltration of CD8 + T cells and activated NK cells. Lactate dehydrogenase assay revealed that knockdown of TAP2 restored the upregulation of CD8 + T cell cytotoxicity triggered by MED12 knockdown. ChIP-PCR, luciferase assay and siRNA knock down assay indicate that MED12 binds to the promoter region of STAT1 to suppress its transcription, while the transcription factor STAT1 promotes the transcription of TAP2, thus inhibiting the antigen processing and presentation. Collectively, MED12 mutation is an independent and valuable biomarker for predicting the response to immune checkpoint inhibitor (ICI)therapy in NSCLC by modulating CD8 + T cell cytotoxicity via the STAT1/TAP2 axis.

Humans↗

PubMed2024

A novel molecular pathway of lipid accumulation in human hepatocytes caused by PFOA and PFOS.

Exposed to ubiquitously perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) has been associated with non-alcoholic fatty liver disease (NAFLD), yet the underlying molecular mechanism remains elusive. The extrapolation of empirical studies correlating per- and polyfluoroalkyl substance (PFAS) exposure with NAFLD occurrence to real-life exposure was hindered by the limited availability of mechanistic data at environmentally relevant concentrations. Herein, a novel pathway mediating hepatocyte lipid accumulation by PFOA and PFOS at human-relevant dose (<10 μM) was identified by integrating CRISPR-Cas9 genome screening, concentration-dependent transcriptional assay in HepG2 cell and epidemiological data mining. 1) At genetic level, nudt7 showed the highest enriched potency among 569 NAFLD-related genes, and the transcription of nudt7 was significantly downregulated by PFOA and PFOS exposure (<7 μM). 2) At molecular pathway, upon exposure to ≤10-4 μM PFOA and PFOS, the downregulation of nudt7 transcriptional expression triggered the reduction of Ace-CoA hydrolase activity. 3) At cellular level, increased lipids were measured in HepG2 cells with PFOA and PFOS (<2 μM). Overall, we identified a novel mechanism mediated by transcriptional downregulation of nudt7 gene in hepatocellular lipid increase treated with PFOA and PFOS, which could potentially explain the NAFLD occurrence associated with exposure to PFASs in humans.

Humans↗

PubMed2026

Elevated Triggering Receptor Expressed on Myeloid Cells 2 Expression in Tumor-Associated Macrophages Suppresses Cytotoxic T Cell Infiltration and Facilitates Immune Escape in Colorectal Cancer.

BACKGROUND & AIMS: Emerging evidence supports a crucial role for tumor-associated macrophages in shaping the immunosuppressive tumor microenvironment. Furthermore, research has identified that the triggering receptor expressed on myeloid cells 2 has immunomodulatory functions. The present investigated the potential effect of triggering receptor expressed on myeloid cells 2 expression in tumor-associated macrophages on facilitating immune evasion in colorectal cancer. METHODS: Immunohistochemical analysis of clinical specimens, complemented by extensive data mining from The Cancer Genome Atlas, revealed a significant upregulation of triggering receptor expressed on myeloid cells 2 in colorectal cancer-associated tumor-associated macrophages, with this upregulation exhibiting a correlation with poor patient prognosis. RESULTS: Mechanistically, triggering receptor expressed on myeloid cells 2+ tumor-associated macrophages were found to drive fibroblast activation through transforming growth factor-β signaling, inducing fibroblast-activated protein-positive cancer-associated fibroblasts that secrete collagen I/III to establish dense peritumoral barriers. Spatial profiling revealed that these fibrous structures physically impede CD8+ T-cell infiltration, restricting cytotoxic lymphocytes to stromal compartments. Intriguingly, triggering receptor expressed on myeloid cells 2 deficiency enhanced the secretion of matrix metalloproteinase 13 by macrophages, thereby promoting extracellular matrix degradation and improving T-cell penetration. In vivo, Trem2-knockout mice showed a reduction in tumor growth with enhanced intratumoral CD8+ T-cell infiltration compared with wild-type controls. CONCLUSIONS: Our findings establish triggering receptor expressed on myeloid cells 2+ tumor-associated macrophages as central regulators of stromal remodeling and suggest that therapeutic targeting of the triggering receptor expressed on myeloid cells 2/transforming growth factor-β/fibroblast-activated protein pathway may overcome immune resistance in patients with colorectal cancer.

Colorectal Neoplasms↗

PubMed2024

Discovery and characterization of complete genomes of 38 head-tailed proviruses in four predominant phyla of archaea.

Archaea play a significant role in natural ecosystems and the human body. Archaeal viruses exert a considerable influence on the structure and composition of archaeal communities and their associated ecological environments. The present study revealed the complete genomes of 38 archaeal head-tailed proviruses through comprehensive data mining. The hosts of these proviruses were identified as belonging to the following four dominant phyla: Halobacteriota, Thermoplasmatota, Thermoproteota, and Nanoarchaeota. In addition to the 14 proviruses of halophilic archaea related to the Graaviviridae family, the remaining proviruses exhibited limited genetic similarities to known (pro)viruses, suggesting the existence of 14 potential novel families. Of the 38 archaeal proviruses, 30 have the potential to lyse host cells. Eleven proviruses contain genes linked to antiviral defense mechanisms, including those involved in restriction modification (RM), clustered regularly interspaced short palindromic repeat (CRISPR)-associated (CRISPR-Cas) nucleases, defense island system associated with restriction-modification (DISARM), and DNA degradation (Dnd). Moreover, auxiliary metabolic genes were identified in the proviruses of Bathyarchaeia and Halobacteriota archaea, including those involved in carbohydrate and amino acid metabolism. Our findings indicate the diversity of archaeal viruses, their interactions with archaeal hosts, and their roles in the adaptation of the host.IMPORTANCEThe field of archaeal virology has seen a rapid expansion through the use of metagenomics, yet the diversity of these viruses remains largely uncharted. In this study, the complete genomes of 38 novel archaeal proviruses were identified for the following four dominant phyla: Halobacteriota, Thermoplasmatota, Thermoproteota, and Nanoarchaeota. Two families and six genera of Archaea were the first to be identified as hosts for viruses. The proviruses were found to contain diverse genes that were involved in distinct adaptation strategies of viruses to hosts. Our findings contribute to the expansion of the lineages of archaeal viruses and highlight their intricate interactions and essential roles in enabling host survival and adaptation to diverse environmental conditions.

Archaea↗