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PubMed2025

Identification of multicohort-based predictive signature for NMIBC recurrence reveals SDCBP as a novel oncogene in bladder cancer.

BACKGROUND: Despite surgical and intravesical chemotherapy interventions, non-muscle invasive bladder cancer (NMIBC) poses a high risk of recurrence, which significantly impacts patient survival. Traditional clinical characteristics alone are inadequate for accurately assessing the risk of NMIBC recurrence, necessitating the development of novel predictive tools. METHODS: We analyzed microarray data of NMIBC samples obtained from the ArrayExpress and GEO databases. LASSO regression was utilized to develop the predictive signature. We combined gene signature and clinicopathological factors to construct a clinical nomogram for estimating NMIBC recurrence in a local cohort. Finally. the biological functions and potential mechanisms of SDCBP in bladder cancer were investigated experimentally in vitro and in vivo. RESULTS: An 8-gene signature was developed, and its efficiency for predicting NMIBC recurrence was evaluated using Kaplan-Meier and time-dependent ROC curves in both training and validation datasets. Immunohistochemical testing revealed elevated levels of ACTN4 and SDCBP in recurrent NMIBC tissues. We integrated the two proteins with clinical factors to develop a nomogram model, which showed superior accuracy compared to individual parameters. Gene Set Variation Analysis and Gene Set Enrichment Analysis unveiled SDCBP exerted cancer-promoting biological processes, such as angiogenesis, EMT, metastasis and proliferation. Experimental procedures demonstrated that silencing SDCBP attenuated cell growth, glucose metabolism and extracellular acidification rate, accompanied by decreased expression of p-AKT, p-ERK1/2, LDHA and Vimentin. CONCLUSIONS: The established 8-gene signature holds promise as a tool for predicting NMIBC recurrence, while targeting SDCBP may represent a potential strategy for delaying disease relapse.

Urinary Bladder Neoplasms↗

PubMed2025

Development and validation of a novel risk stratification signature derived from migrasome and tumor microenvironment-related genes for molecular subtyping and improving clinical outcomes in head and neck squamous cell carcinoma.

BACKGROUND: The tumor microenvironment (TME) and migrasomes released by tumor cells significantly influence carcinogenesis and immune evasion. However, our understanding of the prognostic and therapeutic implications of migrasome and tumor microenvironment-related genes (mtmRGs) in head and neck squamous cell carcinoma (HNSCC) remains limited. METHODS: We explored the relationship between mtmRGs and HNSCC prognosis by utilizing The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Subsequently, we developed an innovative prognostic signature, and assessed its prognostic significance using the Kaplan-Meier method, time-dependent receiver operating characteristic (ROC), and Cox regression analyses. To explore the underlying mechanisms, we conducted gene set variation analysis (GSVA), gene set enrichment analysis (GESA), and immune infiltration analysis. A nomogram was developed to estimate the overall survival (OS) rates for HNSCC patients. Lastly, we chose P4HA1, which was part of the signature, for additional experimental validation in vitro and in vivo. RESULTS: The mtmRGs signature effectively classifies HNSCC patients into two distinct risk subgroups, with the high-risk cohort demonstrating significantly poorer OS. The risk score serves as an independent prognostic factor for HNSCC patients; those with lower risk scores are more likely to exhibit favorable responses to immunotherapy, particularly with CTLA4 inhibitors. Furthermore, a lower risk score is significantly correlated with the sensitivity of HNSCC patients to cyclophosphamide, gemcitabine, and axitinib. CONCLUSION: This study presents an innovative gene signature associated with mtmRGs, which may be utilized both for predicting survival and directing personalized chemotherapy and immunotherapy regiments for patients with HNSCC.

Humans↗

PubMed2026

Serum Proteomic Signatures of Rheumatoid Arthritis Risk and Response: Analysis of a Rheumatoid Arthritis Interception Trial.

OBJECTIVE: Our study objective was to identify serum protein signatures associated with progression to rheumatoid arthritis (RA) and response to abatacept in at-risk individuals. METHODS: A total of 440 serum samples from 118 APIPPRA (Arthritis Prevention In the Preclinical Phase of RA with Abatacept) study participants were selected from baseline to RA onset for 46 progressors of RA or to study end for 72 participants who did not develop RA. Samples were analyzed using the SomaScan 7k assay platform. Differential expression analysis was assessed by progression to RA (three pre-RA time intervals to RA, progressors of RA vs nonprogressors, baseline to RA), and by treatment allocation (abatacept vs placebo). Risk and response signatures were identified in the full 7k panel and two prespecified subpanels defined as Inflammatory Mediators and Adaptive Immune Cell panel. RESULTS: We observed significant changes in 80 proteins (68 down-regulated and 12 up-regulated) occurring between RA onset and 6 to 24 months before developing disease. Progression to RA was associated with increased levels of acute-phase reactants SAA1 and SAA2 and reductions in CTLA4, when compared to nonprogressors at the end of treatment. Two up-regulated proteins (CTLA4 and CD86) and seven down-regulated proteins (CXCL13, FCRL4, FCER2, CCL21, LTA|LTB, FDCSP, and IL22RA2) were observed in participants receiving abatacept compared to placebo regardless of RA outcome. CONCLUSION: Protein signatures dominated by acute-phase proteins define progression to RA, whereas changes associated with abatacept therapy highlight potential mechanisms of treatment response. Such signatures provide a better understanding of the immune landscape of the at-risk phase, opening up the possibility of new treatment modalities for RA prevention.

Adult↗

PubMed2026

Sodium Overload-Related Molecular Subtypes and a Four-Gene Prognostic Signature Predict Survival, Immune Landscape, and Therapeutic Response in Acute Myeloid Leukemia.

Sodium overload has recently emerged as a critical metabolic stressor involved in cancer progression; however, its molecular characteristics and clinical relevance in acute myeloid leukemia (AML) remain unexplored. RNA-seq data sets, clinical annotations, and mutational profiles of AML patients were annotations from The Cancer Genome Atlas and integrated with Genotype-Tissue Expression normal samples. Sodium overload-related genes (SORGs) were obtained from GeneCards. Differentially expressed SORGs (DESORGs) screened by applying the limma statistical model, followed by univariate Cox proportional hazards regression, consensus clustering, functional enrichment, immune infiltration analysis, and pathway evaluation. A prognostic signature was developed through least absolute shrinkage and selection operator regression followed by multivariate Cox modeling. The model's performance was further verified in two external GEO data sets (GSE71014 and GSE37642). Nomogram construction, subgroup analysis, tumor mutational burden (TMB) assessment, drug sensitivity prediction, transcription factor (TF) analysis, and competing endogenous RNA (ceRNA) network analyses were also performed. A total of 57 DESORGs were identified, and 2 sodium overload-related molecular subtypes exhibited distinct survival, immune infiltration, and inflammatory pathway activation. A robust four-gene signature (DOCK1, GABRE, HTR7, ACSM1) stratified patients into high- and low-risk categories with significantly different survival across training and validation cohorts. High-risk patients displayed increased immune infiltration, higher TMB, reduced sensitivity to multiple chemotherapeutic drugs, and inferior predicted response to PD-L1 blockade. TF and ceRNA networks revealed multilayered transcriptional and post-transcriptional regulation of the signature genes. This study identifies sodium overload-related molecular heterogeneity in AML and establishes a validated four-gene prognostic signature that integrates genomic, immunologic, and therapeutic features, offering potential utility for personalized risk assessment and treatment optimization.

Humans↗

PubMed2025

Intratumoral B cell and interferon signatures in newly diagnosed glioblastoma are associated with longer survival in patients treated with SurVaxM.

Glioblastoma (GBM) has proved difficult to treat, and there is dire need for more effective therapies. In a single arm phase IIa trial (NCT02455557), treatment of newly diagnosed GBM patients with the peptide vaccine SurVaxM resulted in promising median progression-free and overall survival. To investigate molecular features that associate with GBM responsiveness to SurVaxM, retrospective whole exome and RNA sequencing was performed on patient tumors (n = 34) collected prior to standard of care treatment plus SurVaxM. Differential gene expression and mutational profiles were characterized between patients with short-term (OS < 18 months) or long-term (OS ≥ 18 months) overall survival. Greater expression of interferon, complement, and humoral immunity signatures were associated with long-term survival. Deconvolution of transcriptomes identified enrichment of intratumoral memory B cell populations in long-term survivors that were validated by CD20 staining in matched samples. A five-gene expression signature and a B cell specific signature predicted survival within the SurVaxM-treated cohort, however, these signatures were not associated with improved outcomes in a similarly treated population obtained from The Cancer Genome Atlas (TCGA) that did not receive immunotherapeutic intervention. Although prospective validation is ongoing, the findings in this discovery cohort specify molecular features of GBM associated with better overall survival and potential responsiveness to immunotherapy with SurVaxM.

Humans↗

PubMed2026

PULPO: pipeline of understanding large-scale patterns of oncogenomic signatures.

SUMMARY: PULPO v1.0 is a novel; fully automated pipeline designed for the preprocess and extraction of mutational signatures from raw Optical Genome Mapping (OGM) data. Built using Snakemake and executed within an isolated, Conda-managed environment, PULPO transforms complex cytogenetic alterations, captured at ultra-high resolution, into Catalogue of somatic mutations in cancer mutational signatures (COSMIC). This innovative approach not only enables researchers to work directly from raw OGM inputs but also streamlines the traditionally complex process of signature extraction, making advanced oncogenomic analyses accessible to users with varying levels of bioinformatics expertise. By facilitating the integration of comprehensive structural variants (SVs) and copy number variants (CNVs) data with established signature catalogues, PULPO paves the way for improved diagnostic accuracy and personalized therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The pipeline is open source and freely available under the MIT License at https://github.com/OncologyHNJ/PULPO-v.1.0 and DOI in Zenodo: https://zenodo.org/records/17749097.

Software↗

PubMed2026

hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models.

MOTIVATION: Enrichment analysis is a cornerstone of "omics" data interpretation, enabling researchers to connect analysis results to biological processes and generate testable hypotheses. Enrichment analysis in metabolomics poses distinct challenges for interpretation and multi-omics integration due to the lack of well-defined and consistent connections to well-curated gene-centered biological knowledge repositories. To address these challenges, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis. RESULTS: We validated hypeR-GEM using paired metabolomics-proteomics and metabolomics-transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites' annotation, demonstrating hypeR-GEM's practical utility in a real-world use case. AVAILABILITY AND IMPLEMENTATION: The hypeR-GEM R package, documentation, and workflow examples are freely available at https://github.com/montilab/hypeR-GEM and archived at https://doi.org/10.5281/zenodo.20586748.

Metabolomics↗

PubMed2025

Collateral mutagenesis funnels multiple sources of DNA damage into a ubiquitous mutational signature.

Mutations reflect the net effects of myriad types of damage, replication errors, and repair mechanisms, and thus are expected to differ across cell types with distinct exposures to mutagens, division rates, and cellular programs. Yet when mutations in humans are decomposed into a set of "signatures", one single base substitution signature, SBS5, is present across cell types and tissues, and predominates in post-mitotic neurons as well as male and female germlines [1-3]. The etiology of SBS5 is unknown. By modeling the processes by which mutations arise, we infer that SBS5 is the footprint of errors in DNA synthesis triggered by distinct types of DNA damage. Supporting this hypothesis, we find that SBS5 rates increase with signatures of endogenous and exogenous DNA damage in cancerous and non-cancerous cells and co-vary with repair rates along the genome as expected from model predictions. These analyses indicate that SBS5 captures the output of a "funnel", through which multiple sources of damage result in a similar mutation spectrum. As we further show, SBS5 mutations arise not only from translesion synthesis but also from DNA repair, suggesting that the signature reflects the occasional, shared use of a polymerase.

Journal Article↗

PubMed2025

Phylogenomic signatures of repeat-induced point mutations across the fungal kingdom.

Fungal genome sizes exhibit more than a 100-fold variation, largely driven by the expansion of repetitive sequences such as transposable elements (TEs). Silencing mechanisms targeting TEs at the epigenetic or transcript level have independently evolved in many lineages. In fungi, repeat-induced point mutation (RIP) targets TEs by recognizing repetitive sequences and inducing mutagenesis. However, the prevalence of RIP across the fungal kingdom and the fidelity of the canonical C-to-T mutation signatures remain unclear. In this study, we address these gaps by tracking shifts in genome architecture across the fungal kingdom. We find that a striking approximately 30-fold increase in genome size within a clade of leotiomycetes is associated with the absence of several RIP-related genes, suggesting a relaxation of genome defense mechanisms during this expansion. To track the impact of genome defenses, we designed a quantitative screen for RIP-like mutation signatures. The phylum of ascomycetes was unique in showing enrichment in mutation signatures in non-coding and repetitive sequences, consistent with a phylogenetically restricted occurrence of RIP-like genome defense systems. Then, we performed a phylogeny-aware association study to identify gene functions associated with RIP-like mutation signatures. We identified a zinc-finger protein as the strongest candidate underpinning a novel mechanism of genome defenses. Our findings reveal the multifaceted drivers of genome defense systems and their close ties to genome size evolution in fungi, particularly in lineages with evidence for recent RIP activity, highlighting how proximate molecular mechanisms can shape genome evolution on deep phylogenetic scales.

Genome, Fungal↗

PubMed2024

Genome-wide scan for selection signatures in Mexican Sardo Negro Zebu cattle.

The Sardo Negro cattle (SN) is the only zebu cattle breed developed in Mexico. Since its development, the selection could have led to an increase in the homozygosity level in some regions of the genome and made differentiation with other cattle populations. We aimed to identify and characterize selection signatures in SN using medium-density SNP data using four approaches: 1) Runs of homozygosity (ROH) 2) Nucleotide Diversity 3) Tajima's D and 4) the Wright's fixation index (FST). A sample of 555 SN animals genotyped for 65k SNPs was used to obtain ROH segments considered regions under selection. The FST values were estimated by comparing the sample of genotyped SN animals with samples of genotyped animals from the Gir, Brahman, and Ongole breeds. Only one region mapped to 35.78-42.51 Mb on BTA6 was considered a selection signature by the ROH method. This selection signature overlapped with the lowest diversity, negative values of Tajima's D and a diversification region between SN and the other Zebu breeds by FST. We found several candidate genes (LCORL, NCAPG, and SLIT2) related to growth and other economically important productive traits in this common region. Using the FST method, different regions, such as regions on BTA8 (8:93.4-93.9 Mb), BTA11 (11:99.2-99.7), and BTA14 (14: 26.1-26.8) related to growth and milk traits also were defined as candidate selection signatures. The selective signals identified in this study reflected the direction of the selection pressure that primarily involves the increase of live weight traits in the Sardo Negro cattle breeding program.

Animals↗

PubMed2025

Gut microbial and functional alterations lead to metagenomic signatures for midgut neuroendocrine tumor patients and for carcinoid syndrome.

Midgut neuroendocrine tumors (NET) derive from enterochromaffin cells, which have a close interrelationship with intestinal microbiota. Recently, we have utilized 16S rRNA sequencing to uncover that midgut NET patients have a depleted gut microbiome and a specific fecal microbial signature. This study aims to validate these findings and to further characterize the role of microbes and microbial metabolic pathways in midgut NET patients with and without carcinoid syndrome (CS). Fecal samples from 60 midgut NET patients and 20 household-matched controls were subjected to whole metagenome sequencing. The gut microbial community composition of midgut NET patients differed from that of controls, with 2 genera, 17 species and 9 microbial pathways showing differential abundance (P < 0.001). No differences in the microbial composition were observed between midgut NET patients with and without CS (P > 0.05). However, we did observe changes in inter-genus correlations of Bacteroides, Odoribacter, Parasutterella, Klebsiella, Ruminococcus and Proteobacteria when comparing these two patient groups. A signature of 16 microbial species (area under the receiver operating characteristics (AUROC) curve 0.892) or 18 microbial pathways (AUROC 0.909) accurately predicted the presence of a midgut NET. Furthermore, a microbial signature consisting of 14 functional microbial pathways distinguished CS patients from non-CS patients (AUROC 0.807). Thus, this study confirms that the gut microbiome of midgut NET patients is altered at the metagenomic level, which is not related to the presence of CS. A fecal microbial signature could constitute a novel biomarker for the diagnosis of midgut NET or CS.

Aged↗

PubMed2026

Identification of a novel signature for prognostic stratification and integrative analyses in lung adenocarcinoma.

BACKGROUND: Recently, research has revealed that the Golgi apparatus is involved in the development process of cancer; however, the specific effect of Golgi apparatus-related genes (GAGs) in lung adenocarcinoma (LUAD) remains unclear. This study aims to construct a more concise and practical risk model in LUAD using GAG. METHODS: The gene expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and GAGs were downloaded from the Gene Set Enrichment Analysis (GSEA) database. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were performed to identify the prognostic GAG signature. Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. The correlation between the risk model and the immune landscape was examined using CIBERSORT and TIDE analyses. Also, the genes in the signature were assessed by single-cell RNA sequencing (scRNA-seq). RESULTS: A prognostic signature comprising 5 GAG genes (GNPNAT1, RGS20, CAV3, NTSR1, and FURIN) was established after LASSO and multi-Cox analyses. Both the Kaplan-Meier analysis and the ROC curves supported the strong predictive utility of the risk model. Specifically, the former yielded significant stratification in all three validation datasets (P=1.2001e-05, P=0.006, and P=0.04), while the latter provided further evidence of its predictive precision through the area under the curve. In addition, we found that the low-risk group responded better to immunotherapy than the high-risk group (P<0.0001). scRNA-seq analysis revealed the distribution patterns of the 5 GAG genes in cells. Finally, we assessed the situation of tumor mutation burden (TMB) and performed functional analysis based on the risk model of GAGs. CONCLUSIONS: The risk model based on GAGs can effectively stratify the prognosis of patients and predict immunotherapy responses in LUAD.

Golgi apparatus↗

PubMed2026

Integrated Genome-Wide Association Studies and Selection Signature Analysis Reveal the Genetic Architecture of the Wattle Trait in Goats.

Wattles are finger-like appendages on the ventral neck of goats, serving as a distinctive morphological marker for breed identification that serves potential implications for production performance. However, their genetic basis remains incompletely characterized. Here, we integrated a genome-wide association study (GWAS) and selection signature analysis to identify candidate genes and genomic regions associated with the wattle trait in goats. Using a linear mixed model, GWAS on 463 goats (23 wattled and 440 non-wattled) identified 385 quantitative trait loci (QTLs) at a 5% false discovery rate, yielding 346 candidate genes. The most significant association signal was observed on chromosome 10 (72.61-73.48 Mb), where the lead SNP (rs636481767) is located within a region containing GJD2, GREM1, and FMN1, showing strong linkage disequilibrium (r2 > 0.6) with surrounding loci. Subsequent selection signature analysis (23 wattled and 23 non-wattled) identified 83 genomic regions harboring 119 candidate genes. The strongest signals were detected at MFSD14B on chromosome 8 (FST = 0.154, log2π-ratio = 2.611) and PDLIM7 on chromosome 7 (FST = 0.144, log2π-ratio = 0.806). KEGG pathway enrichment analysis revealed that GWAS-associated genes were involved in glycosylation and immune responses, whereas selection-signature genes were enriched in DNA repair and the Hippo, Notch, and Wnt pathways. Furthermore, cross-species PheWAS revealed that human FMN1 is associated with dermatological, skeletal, and metabolic phenotypes, while porcine FMN1 is associated with backfat thickness and loin muscle depth. Overall, this study provides molecular markers of potential value for goat breeding and pinpoints key candidate genes for future functional validation of wattle development.

Animals↗

PubMed2026

Multi-Omics Biomarker Signatures for Precision Diagnosis and Prognosis in Primary Liver Cancer: A Literature Review.

Primary liver cancer (PLC) is a biologically heterogeneous group of malignancies dominated by hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and a smaller subset of combined hepatocellular-cholangiocarcinoma (cHCC-CCA), and its clinical burden remains high because current diagnostic and prognostic tools do not adequately capture molecular diversity. Conventional imaging, serum markers, and histopathological assessment remain insufficient for precise early diagnosis, subtype-resolved classification, and outcome stratification, while tissue and liquid biopsy approaches have expanded the range of analytes available for clinical assessment. Recent studies have identified candidate biomarker signatures across genomic, epigenomic, transcriptomic, proteomic, metabolomic, and circulating layers, suggesting that integrated multi-omics profiling may better represent tumor lineage, clonal evolution, immune context, and therapeutic vulnerability than isolated molecular readouts. However, these layers are not equally mature for clinical use: genomic testing is closest to routine therapeutic application in iCCA, plasma methylation assays are advancing for HCC surveillance augmentation, and many proteomic or metabolomic panels remain validation-stage tools. Their clinical value remains constrained by sampling bias, biospecimen-dependent signal loss, assay standardization, cost, and the need for prospective validation across clinically diverse populations. This narrative review critically synthesizes current evidence on multi-omics biomarker signatures for precision diagnosis and prognosis in primary liver cancer and argues that clinically useful signatures should be question-specific, stage-aware, and specimen-aware rather than universal multi-analyte panels.

Humans↗

PubMed2026

A Risk Score for Polycystic Ovary Syndrome Based on Meta-Analysis and Machine Learning of Gut Microbiota Signatures.

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine and metabolic disorder among reproductive-age women, in which emerging evidence suggests a substantial role played by the gut microbiota. To comprehensively evaluate gut microbiota alterations in PCOS and identify microbial biomarkers through integrated analysis, a systematic search of PubMed, Web of Science, and Embase was conducted for studies employing 16S rRNA gene sequencing of fecal samples from PCOS cohorts. Ten eligible PCOS cohorts, comprising 858 individuals, were included in the study, from which a risk score was derived using a 20-gene gut microbial signature associated with PCOS. Meta-analysis at the genus level identified that Subdoligranulum, NK4A214_group, and Collinsella significantly decreased, and Bacteroides increased in PCOS across multiple cohorts. Machine learning analysis identified a 20-genus microbial signature using the least absolute shrinkage and selection operator (LASSO) method, which was used to construct a risk score with an AUC of 0.835 in diagnosis prediction. Network analysis further identified Negativibacillus and Lachnospiraceae_UCG_010 as potential driver microbes in PCOS. The analysis in this study highlights key alterations in the gut microbiota across PCOS cohorts. The identified gut microbial signature and derived LASSO-based risk model offer novel insights and a potential tool for PCOS diagnosis.

Polycystic Ovary Syndrome↗

PubMed2025

Whole-genome sequencing identifies genetic diversity and adaptive signatures of hypoxia and ultraviolet radiation in Chinese chickens.

INTRODUCTION: Domestic chickens primarily descended from the wild red junglefowl, play a crucial role in global egg and meat production. China hosts diverse indigenous chicken populations that have adapted to various environmental conditions, including high-altitude with hypoxic and ultraviolet radiation stress. METHOD: We analyzed whole-genome sequences of 118 birds from five Indigenous Chinese chicken populations and 295 chicken genomes from publicly available databases to identify genomic diversity, admixture, and selection signatures of chickens adapted to high-altitude environments. Selection signatures were identified using nucleotide diversity (π), Tajima's D, XPEHH, and XP-CLR, selection scan methods. RESULTS: We observed a reduction in genetic diversity and historical declines in effective population size in high-altitude chicken, suggesting ongoing selection pressures shaping these populations. Selection scans identified nine genomic regions under strong positive selection, enriched for genes associated with hypoxia and ultraviolet radiation. Notably, five genes (TPK1, BAZ2B, MARCHF7, LLGL2, and RCAN3) were repeatedly detected across multiple selection signature analyses. RNA-seq analysis further confirmed the differential expression of these genes in the lung and heart tissues of chickens adapted to high and low altitudes, reinforcing their role in physiological adaptation to hypoxic environments. Altitude adaptation is driven by the selection of genes involved in oxygen metabolism, cellular stress response, and energy regulation. CONCLUSION: Our study provides compelling genetic evidence for differentiation between high and low and high-altitude Chinese chicken populations. These findings also ensure our understanding of local adaptation in poultry and establish a genomic framework for breeding strategies to improve environmental resilience to altitude-related stressors.

Animals↗

PubMed2025

A novel glycogene-related signature for prognostic prediction and immune microenvironment assessment in kidney renal clear cell carcinoma.

BACKGROUND: Kidney Renal Clear Cell Carcinoma (KIRC) is a prevalent urinary malignancies worldwide. Glycosylation is a key post-translational modification that is essential in cancer progression. However, its relationship with prognosis, tumour microenvironment (TME), and treatment response in KIRC remains unclear. METHOD: Expression profiles and clinical data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering, Cox regression, and LASSO regression analyses were conducted to develop an optimal glycogene-related signature. The prognostic relevance of this molecular signature was rigorously analyzed, along with its connections to tumour microenvironment (TME), tumour mutation burden, immune checkpoint activity, cancer-immunity cycle regulation, immunomodulatory gene expression patterns, and therapeutic response profiles. Validation was performed using real-world clinical specimens, quantitative PCR (qPCR), and immunohistochemistry (IHC), supported by cohort analyses from the Human Protein Atlas (HPA) database. RESULTS: A glycogene-associated prognostic scoring system was established to categorize patients into risk-stratified subgroups. Patients in the high-risk cohort exhibited significantly poorer survival outcomes (p < 0.001). By incorporating clinicopathological variables into this framework, we established a predictive nomogram demonstrating strong calibration and a concordance index (C-index) of 0.78. The high-risk subgroup displayed elevated immune infiltration scores (p < 0.001), upregulated expression of immune checkpoint-related genes (p < 0.05), and an increased frequency of somatic mutations (p = 0.043). The risk score positively correlated with cancer-immunity cycle activation and immunotherapy-related signals. The high-risk groups also showed associations with T cell exhaustion, immune-activating genes, chemokines, and receptors. Drug sensitivity analysis revealed that low-risk patients were more sensitive to sorafenib, pazopanib, and erlotinib, whereas high-risk individuals responded better to temsirolimus (p < 0.01). qPCR and IHC analyses consistently revealed distinct expression patterns of MX2 and other key genes across the risk groups, further corroborated by the HPA findings. CONCLUSION: This glycogene-based signature provides a robust tool for predicting prognosis, TME characteristics, and therapeutic responses in KIRC, offering potential clinical utility in patient management.

Humans↗

PubMed2026

Mutational signatures in blood-brain barrier: mechanisms, computational insights, and clinical applications in precision oncology.

The blood - brain barrier (BBB) plays a central role in maintaining central nervous system (CNS) homeostasis, and its disruption is a defining feature of malignant brain tumors such as glioblastoma. Emerging evidence indicates that BBB dysfunction not only alters the tumor microenvironment but also shapes the mutational processes that drive genomic instability in CNS malignancies. This review synthesizes current understanding of the biological mechanisms linking BBB breakdown with distinct mutational signatures, including those arising from oxidative stress, hypoxia-induced replication stress, lipid peroxidation, inflammation, and metabolic reprogramming. Advances in next-generation sequencing, coupled with computational tools such as non-negative matrix factorization, Bayesian modeling, and deep learning, have enabled precise extraction of these signatures and their integration with multi-omics data. Clinically, BBB-associated mutational signatures offer significant promise for therapeutic stratification, prediction of treatment response, and noninvasive monitoring through cerebrospinal fluid - derived circulating tumor DNA. Despite these advances, challenges persist due to limited tissue accessibility, low-yield CSF samples, incomplete mechanistic models, and the lack of CNS-specific analytical frameworks. A deeper understanding of BBB-driven mutational processes, supported by improved computational approaches and integrative datasets, holds potential to advance precision oncology in neuro-oncology.

Humans↗