Search Results (295)

Background/Objectives: Human endogenous retroviruses (ERVs) are genomic sequences integrated into the human genome from ancestral exogenous retroviruses and are epigenetically silenced under normal conditions. Growing evidence has shown that they can be reactivated in human diseases such as cancers and autoimmune diseases. However, their clinical implications in colon cancer are yet to be explored. Methods: RNA-seq data were downloaded from RNA Atlas and TCGA for cell lines and tissue samples, respectively. After alignment, ERV expression was quantified against comprehensively compiled ERVs (3220). ERV expression profiles were compared between sequencing protocols, cancer and normal cells, and matched tumor and normal tissue pairs. Unsupervised clustering was used to identify ERV-defined tumor subtypes and their associations with clinical and other molecular features. ERV association with disease-specific survival (DSS) was performed using the Cox regression model. Results: PolyA and total RNA protocols were comparable in ERV expression detection. Cancer cells had significantly increased ERV expression and reactivation. Upregulated ERVs were significantly enriched in viral protein interactions with cytokine and cytokine receptors. ERV expression-defined tumor classes were significantly associated with tumor mutation burden and immuno-phenotypes such as antigen processing and presenting machinery and tumor immune infiltration score. Survival analysis identified 152 ERVs to be independently associated with DSS. Conclusions: ERV abnormal expression is common in colon cancer. The ERV-defined subtypes are associated with tumor immunity, and some ERVs are independently associated with patient outcomes. Full article

Background: Cervicogenic headache (CGH) is a prevalent secondary headache disorder associated with upper cervical spine dysfunction, often involving nociceptive convergence at the trigeminocervical complex. While manual therapy and exercise have demonstrated benefit, autonomic dysregulation may contribute to persistent symptoms. This case report explores the integration of external vagus nerve stimulation (eVNS) into a multimodal physical therapy approach targeting both mechanical and neurophysiological contributors to CGH. Case Description: A 63-year-old female presented with chronic CGH characterized by right-sided suboccipital and supraorbital pain, impaired sleep, and postural dysfunction. Examination revealed a right rotational atlas positional fault, restricted left atlantoaxial (AA) mobility, suboccipital hypertonicity, and reduced deep neck flexor endurance. Initial treatment emphasized manual therapy to restore AA mobility and atlas symmetry, combined with postural correction and neuromuscular training. Intervention: After initial symptom improvement plateaued, eVNS targeting the auricular branch of the vagus nerve was introduced to modulate autonomic tone. The patient used a handheld eVNS device nightly over three weeks. Outcomes: Substantial improvements were observed in the Neck Disability Index (↓77%), Headache Disability Inventory (↓72%), and pain scores (↓100%). Cervical mobility, atlas symmetry, and deep neck flexor endurance improved markedly. The patient reported reduced anxiety, improved sleep, and sustained headache relief at one-month follow-up. Conclusions: This case highlights the potential synergistic benefits of integrating eVNS within a physiotherapy-led CGH management plan. Further research is warranted to explore its role in targeting autonomic imbalance and enhancing conservative treatment outcomes. Full article

Background: RiBi is integral to cell proliferation, and its dysregulation is increasingly recognized as a hallmark of aggressive cancers. We sought to develop and validate a composite “PanRibo-515 score” reflecting RiBi activity across multiple tumor types, assess its prognostic significance, and explore its relationship with immune checkpoint therapy outcomes. Methods: We curated 515 RiBi–associated genes (PanRibo-515) and used a LASSO regression-based strategy on a training dataset (GSE202203) to select the prognostically most relevant subset of 68 genes (OncoRibo-68). Directionality (positive or negative impact on survival) was assigned based on the sign of the LASSO coefficients. We integrated a forward selection approach to identify a refined subset of genes for computing the OncoRibo-68 score. For validation, patients in The Cancer Genome Atlas (TCGA) were stratified into high or low OncoRibo-68 score groups for survival analyses. Additional validation for immunotherapy response was conducted using bioinformatic platforms used for immunotherapy response analysis. Results: A higher OncoRibo-68 score consistently correlated with poorer overall and progression-free survival across multiple cancers. Elevated OncoRibo-68 score was linked to an immunosuppressive tumor microenvironment, but interestingly to increased response to checkpoint inhibitors. Conclusions: Our findings highlight RiBi as an important determinant of tumor aggressiveness and identify the OncoRibo-68 score as a promising biomarker for risk stratification and therapy selection. Future research may evaluate whether targeting RiBi pathways could enhance treatment efficacy, particularly in combination with immunotherapy. Full article

Accounting for 15–30% of colorectal cancer cases, the serrated pathway remains poorly characterized compared to the adenoma–carcinoma sequence. It involves sessile serrated lesions as precursors and is characterized by BRAF mutations (BRAF^V600E), CpG island hypermethylation, and microsatellite instability (MSI). Using label-free proteomics, we compared normal tissue margins from patients with diverticular disease, sessile serrated lesions, low-grade adenomas, and high-grade adenomas. We identified S100A14 as significantly overexpressed in sessile serrated lesions compared to low-grade adenomas, high-grade adenomas, and normal tissues. This overexpression was confirmed by immunohistochemical scoring in an independent cohort. Gene expression analyses of public datasets showed higher S100A14 expression in BRAF^V600E-mutated and MSI-H colorectal cancers compared to microsatellite stable BRAF^wt tumors. This finding was confirmed by immunohistochemical scoring in an independent colorectal cancer cohort. Furthermore, single-cell RNA sequencing analysis from the Human Colon Cancer Atlas revealed that S100A14 expression in tumor cells positively correlated with the abundance of tumoral CD8⁺ cytotoxic T cells, particularly the CD8⁺ CXCL13⁺ subset, known for its association with a favorable response to immunotherapy. Collectively, our results demonstrate for the first time that S100A14 is a potential biomarker of serrated neoplasia and further suggests its potential role in predicting immunotherapy responses in colorectal cancer. Full article

The European plum (Prunus domestica L.) is a hexaploid species that is grown worldwide for its tasty fruits. Many pomological forms and varieties exist, and thus it is important for genebank curators, breeders, growers, and/or control authorities to distinguish them with certainty. The purpose of this study was to select and verify a set of simple sequence repeat (SSR) markers for reliable genotyping, and to optimize their use in a one-reaction format for easy routine practice. After testing 78 SSR markers from different diploid Prunus species, 8 SSR markers were selected, multiplexed, and successfully verified as being able to distinguish all 242 unique genotypes tested. The selected markers were relatively easily scored and highly heterogenic, giving more than 35 alleles/genotype on average. The allele atlas was created to become a valuable tool for allele calling that should lead to standardized and reliable genotyping results between laboratories. The population analysis confirmed high diversity of the Czech germplasm collection used. The kit was also successfully tested for diploid “plums” of various origins and interspecies hybrids, as these are sometimes phenotypically indistinguishable from hexaploid European plums. The one-tube approach substantially simplified the plum genotyping laboratory workflow, minimizes errors, and saves labor, time, and money. Full article

Background: Glioblastoma (GBM) is one of the most challenging malignancies in all of neoplasms. These malignancies are associated with unfavorable clinical outcomes and significantly compromised patient wellbeing. The immunological landscape within the tumor microenvironment (TME) plays a critical role in determining GBM prognosis. By mining data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and correlating them with immune responses in the TME, genes associated with the immune microenvironment with potential prognostic value were obtained. Method: We selected GSE16011 as the training set. Gene expression profiles were substrates scored by both ESTIMATE and xCell, and immune cell subpopulations in GBM were analyzed by CIBERSORT. Gene expression profiles associated with low immune scores were performed by lasso regression, Cox analysis and random forest (RF) to identify a prognostic model for the multiple genes associated with immune infiltration in GBM. Then we constructed a nomogram to optimize the prognostic model using GSE7696 and TCGA-GBM as validation sets and evaluated these data for gene mutation and gene enrichment analysis. Result: The prognostic correlation between the six genes (MEOX2, PHYHIP, RBBP8, ST18, TCF12, and THRB) and GBM was finally found by lasso regression, Cox regression, and RF, and the online database obtained that all six genes were differentially expressed in GBM. Therefore, a prognostic correlation model was constructed based on the six genes. Kaplan–Meier (KM) survival analysis showed that this prognostic model had excellent prognostic ability. Conclusions: Prognostic models based on tumor microenvironment and immune score stratification and the construction of related genes have potential applications for prognostic analysis of GBM patients. Full article

Gleason score (GS) is one of the best predictors of prostate cancer (PCa) aggressiveness; however, its biological features need to be elucidated. This study aimed to explore the biological characteristics of localized/locally advanced PCa stratified using in silico GS analysis. Biological features were analyzed using gene set variation analysis and the xCell algorithm with mRNA expression in two independent public databases: The Cancer Genome Atlas (TCGA) (n = 493; radical prostatectomy cohort) and GSE116918 (n = 248; radiation therapy cohort). GS levels were positively correlated with the activity levels of cell proliferation-related gene sets, including E2F targets, the G2M checkpoint, the mitotic spindle, and MYC targets v1 and v2 in both cohorts. Furthermore, GS levels were positively associated with the activity levels of immune-related gene sets and infiltrating fractions of immune cells, including CD4+ memory T cells, dendritic cells, M1 macrophages, and Th2 cells, in both cohorts. Notably, GS levels were positively associated with the score levels of homologous recombination defects, intratumor heterogeneity, fraction genome alteration, neoantigens, and mutation rates in the TCGA cohort. In conclusion, PCa with high GS levels was associated with cancer cell proliferation, immune cell infiltration, and high mutation rates, which may reflect worse clinical outcomes. Full article

Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of kidney cancer and is often diagnosed at advanced stages. PIEZO1, a mechanosensitive ion channel, has been implicated in cancer progression, but its prognostic relevance in ccRCC remains unclear. This study aimed to evaluate the expression pattern of PIEZO1 in ccRCC and its association with clinicopathological characteristics and patient survival. Immunohistochemical analysis was performed on formalin-fixed, paraffin-embedded tumor tissues from 111 patients with ccRCC, along with 23 matched peritumoral non-cancerous tissues. Protein expression was quantified using the H-score system. Associations with tumor grade, staging, and overall survival (OS) were analyzed. mRNA expression data were retrieved from The Cancer Genome Atlas (TCGA) to validate the protein-level findings. Functional enrichment and pathway analyses were conducted to explore the biological context of PIEZO1-related gene expression. PIEZO1 showed predominantly cytoplasmic localization, with significantly lower expression in tumor tissues compared to adjacent non-malignant tissue (p < 0.0001). High PIEZO1 expression was correlated with higher tumor grade (p = 0.0147) and shorter OS (p = 0.0047). These findings were confirmed at the mRNA level in the TCGA cohort. Multivariate Cox regression analysis identified PIEZO1 as an independent prognostic factor for OS. In conclusion, PIEZO1 may serve as a clinically relevant biomarker in ccRCC. Its overexpression is associated with more aggressive tumor characteristics and poor prognosis, underscoring the need for further investigation into its functional role and potential as a therapeutic target. Full article

Background: The N⁶-methyladenosine (m⁶A) modification of eukaryotic mRNA is the most prevalent of such epigenetic modifications and has recently been identified as a potential player in the pathogenesis and progression of hepatocellular carcinoma (HCC). With the increasing emergence of immunotherapy in the treatment of HCC, we have evaluated the potential of m⁶A-related genes in predicting overall survival and the therapeutic efficacy of immunotherapy in HCC patients. Methods: We employed transcriptomic data from TCGA-LIHC and GSE76427, comprising a total of 485 HCC patients, as the training set. Based on 23 recognized m⁶A regulators, we performed clustering analysis on HCC patients. The intersecting differentially expressed genes (DEGs) among subtypes were used in least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression analyses to construct the risk model. For the quantification of a risk model of HCC patients, a risk score was developed and correlated with clinical and immunological parameters. Furthermore, a single-cell transcriptomic atlas was used to analyze the relationship between model genes and immune cell subpopulations. Mechanistic studies included in vitro assays to validate the association between the m⁶A-related gene ANLN and the progression of HCC. Results: Internal (TCGA and GEO) and external validation (ICGC) suggested that an 8-gene risk score provides an accurate and stable prognostic assessment for HCC. Furthermore, the high-risk score, characterized by elevated TP53 mutation frequency, tumor mutation burden (TMB), and tumor stem cell characteristics indicated a poor prognosis. The prognostic signature was associated with immune cell infiltration in HCC. Those patients with a high-risk score had lower immune tolerance with a better prediction of the efficacy of immunotherapy. The risk model helps to assess and predict the response and prognosis of HCC patients to immune checkpoint inhibitors (ICIs). Additionally, single-cell RNA sequencing data revealed that the high-risk group had a higher proportion of T cells and fewer immunosuppressive T cells, potentially correlating with a better response to immunotherapy. Finally, in vitro experiments showed that ANLN, an m⁶A-related gene, promoted the proliferation and migration of HCC cells. Conclusions: In this study, we identified and validated an m⁶A gene signature consisting of eight genes that can be used to predict prognosis and immunotherapy efficacy in HCC patients. Full article

Background: Gliomas represent the most prevalent and aggressive primary brain tumors, requiring precise classification to guide treatment strategies and improve patient outcomes. Purpose: This study aimed to develop and evaluate a machine learning-driven approach for glioma classification by identifying the most relevant genetic and clinical biomarkers while demonstrating clinical utility. Methods: A dataset from The Cancer Genome Atlas (TCGA) containing 23 features was analyzed using an integrative approach combining Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), and SHapley Additive exPlanations (SHAP) for feature selection. The refined feature set was used to train four machine learning models: Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. Comprehensive evaluation included class distribution analysis, calibration assessment, and decision curve analysis. Results: The feature selection approach identified 13 key predictors, including IDH1, TP53, ATRX, PTEN, NF1, EGFR, NOTCH1, PIK3R1, MUC16, CIC mutations, along with Age at Diagnosis and race. XGBoost achieved the highest AUC (0.93), while Logistic Regression recorded the highest testing accuracy (88.09%). Class distribution analysis revealed excellent GBM detection (Average Precision 0.840–0.880) with minimal false negatives (5–7 cases). Calibration analysis demonstrated reliable probability estimates (Brier scores 0.103–0.124), and decision curve analysis confirmed substantial clinical utility with net benefit values of 0.36–0.39 across clinically relevant thresholds. Conclusions: The integration of feature selection techniques with machine learning models enhances diagnostic precision, interpretability, and clinical utility in glioma classification, providing a clinically ready framework that bridges computational predictions with evidence-based medical decision-making. Full article

Background/Objectives: Glioblastoma multiforme (GBM) is an aggressive and heterogeneous brain tumor with poor prognosis, emphasizing the need for reliable molecular biomarkers to improve patient stratification and treatment planning. This study aimed to identify key genes associated with overall survival in GBM by employing and comparing machine learning (ML) and deep learning (DL) approaches using RNA-Seq gene expression data. Methods: RNA-Seq expression and clinical data for primary GBM tumors were obtained from The Cancer Genome Atlas (TCGA). A univariate Cox proportional hazards regression was used to identify survival-associated genes. For survival prediction, ML-based feature selection techniques—RF, GB, SVM-RFE, RF-RFE, and PCA—were used to construct multivariate Cox models. Separately, DeepSurv, a DL-based survival model, was trained using the significant genes from the univariate analysis. Gradient-based importance scoring was applied to determine key genes from the DeepSurv model. Results: Univariate analysis yielded 694 survival-associated genes. The best ML-based Cox model (RF-RFE with 90% training data) achieved a c-index of 0.725. In comparison, DeepSurv demonstrated superior performance with a c-index of 0.822. The top 10 genes were identified from the DeepSurv analysis, including CMTR1, GMPR, and PPY. Kaplan–Meier survival curves confirmed their prognostic significance, and network analysis highlighted their roles in processes such as purine metabolism, RNA processing, and neuroendocrine signaling. Conclusions: This study demonstrates the effectiveness of combining ML and DL models to identify prognostic gene expression biomarkers in GBM, with DeepSurv providing higher predictive accuracy. The findings offer valuable insights into GBM biology and highlight candidate biomarkers for further validation and therapeutic development. Full article

Objectives: Innate lymphoid cells (ILCs) and natural killer (NK) cells represent a diverse group of innate immune populations that modulate immune responses and tissue equilibrium across various diseases, including cancer. In the present study, we analyzed single-cell RNA sequencing (scRNA-seq) data to explore the landscape and functional status of ILC subsets in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The GSE164690 dataset, which includes preprocessed scRNA-seq and clinical data, was acquired from the Gene Expression Omnibus database. The Cancer Genome Atlas database was used to develop the survival prediction model. Results: A total of 95,809 immune cells were clustered into 16 immune cell clusters, among which 7278 NK cells were further subdivided into 11 clusters. Among the 11 clusters, eight NK cell clusters, two intraepithelial ILC1 (ieILC1) clusters, and one ieILC1–NK-intermediate (ieILC1-NK-int) cluster were identified. Among the ieILC1/NK clusters, ieILC1-1 exhibited the highest immunological activity and was mainly derived from human papillomavirus-positive samples. Further, ieILC1s showed higher enrichment of pathways related to inflammation and effector functions—such as inflammatory response, interferon-gamma response, and interferon-alpha response—compared to the other clusters. Moreover, we developed prognostic prediction models based on differentially expressed genes in the ieILC1/NK clusters. Risk scores of the ieILC1-1, ieILC1-NK-int, and NK clusters were identified as independent prognostic factors for shorter overall survival (OS) and progression-free survival (PFS). Recursive partitioning revealed that combining ieILC1-1 and the NK clusters strongly predicted shorter OS and PFS. Conclusions: Our findings highlight the diverse landscape and prognostic significance of ieILC1/NK cells in patients with HNSCC. Full article

Phosphomannomutase 2 (PMM2) catalyzes the interconversion of mannose-6-phosphate and mannose-1-phosphate, a key step in the biosynthesis of GDP-mannose for N-glycosylation. Its deficiency is the most common cause of congenital disorders of glycosylation (CDGs), accounting for the subtype known as PMM2-CDG. PMM2-CDG is a rare autosomal recessive disease characterized by multisystemic dysfunction, including cerebellar atrophy, peripheral neuropathy, developmental delay, and coagulation abnormalities. The disease is associated with a spectrum of pathogenic missense mutations, particularly at residues involved in dimerization and catalytic function (i.e., p.Phe119Leu and p.Arg141His). The dimerization of PMM2 is considered essential for enzymatic activity, although it remains unclear whether this supports structural stability alone, or whether both subunits are catalytically active—a distinction that may affect how mutations in each monomer contribute to overall enzyme function and disease phenotype. PMM2 has a paralog, phosphomannomutase 1 (PMM1), which shares substantial structural similarity—including obligate dimerization—and displays mutase activity in vitro, but does not compensate for PMM2 deficiency in vivo. To investigate potential heterodimerization between PMM1 and PMM2 and the effect of interface mutations over PMM2 dimer stability, we first assessed the likelihood of their co-expression using data from GTEx and the Human Protein Atlas. Building on this expression evidence, we modeled all possible dimeric combinations between the two paralogs using AlphaFold3. Models of the PMM2 and PMM1 homodimers were used as internal controls and aligned closely with their respective reference biological assemblies (RMSD < 1 Å). In contrast, the PMM2/PMM1 heterodimer model, the primary result of interest, showed high overall confidence (pLDDT > 90), a low inter-chain predicted alignment error (PAE∼1 Å), and robust interface confidence scores (iPTM = 0.80). Then, we applied PISA, PRODIGY, and mmCSM-PPI to assess interface energetics and evaluate the impact of missense variants specifically at the dimerization interface. Structural modeling suggested that PMM2/PMM1 heterodimers were energetically viable, although slightly less stable than PMM2 homodimers. Interface mutations were predicted to reduce dimer stability, potentially contributing to the destabilizing effects of disease-associated variants. These findings offer a structural framework for understanding PMM2 dimerization, highlighting the role of interface stability, paralogs co-expression, and sensitivity to disease-associated mutations. Full article

Prostate cancer (PCa) is the second most common cancer and the second leading cause of cancer-related mortality among men. Gene expression analysis has been crucial in understanding tumor biology and providing disease progression markers. Cell surface glycoproteins and those in the extracellular matrix play significant roles in the PCa microenvironment by promoting migration, invasion, and metastasis. The molecular and histopathological heterogeneity of prostate tumors necessitates a new marker discovery to better stratify patients at risk for poor prognosis. In this study, our objectives were to investigate and characterize the localization and expression of SLIT/ROBO in PCa samples from transgenic mice and human tumor samples, aiming to identify novel prognostic markers and potential therapeutic targets. We conducted histopathological, immunohistochemical, and bioinformatics analyses on prostate tumors from two knockout mice models (Pb-Cre4/Pten^f/f and Pb-Cre4/Trp53^f/f;Rb1^f/f) and human prostate tumors. Transcriptomic analyses revealed special changes in the expression of genes related to the SLIT/ROBO neural signaling pathway. We further characterized the gene and protein expression of the SLIT/ROBO pathway in knockout animal samples, and protein expression in the PCa samples of patients with different Gleason scores. Public datasets with clinical data from patients (The Human Protein Atlas, cBioPortal, SurvExpress and CamcAPP) were used to validate the gene and protein expression of SLIT1, SLIT2, ROBO1, and ROBO4, correlating these alterations with the prognosis of subgroups of patients. Our findings highlight potential biomarkers of the SLIT/ROBO pathway with prognostic and predictive value, as well as promising therapeutic targets for PCa. Full article

Introduction: Bone-age assessment using posteroanterior left hand–wrist radiographs is indispensable in pediatric endocrinology and forensic age determination. Traditional methods—Greulich–Pyle atlas and Tanner–Whitehouse scoring—are time-consuming, operator-dependent, and prone to inter- and intra-observer variability. Aim: To systematically review the performance of AI-based models for automated bone-age estimation from left PA hand–wrist radiographs. Materials and Methods: A systematic review was carried out and previously registered in PROSPERO (CRD42024619808) in MEDLINE (PubMed), Google Scholar, ELSEVIER (Scopus), EBSCOhost, Cochrane Library, Web of Science (WoS), IEEE Xplore, and ProQuest for original studies published between 2019 and 2024. Two independent reviewers extracted study characteristics and outcomes, assessed methodological quality via the Newcastle–Ottawa Scale, and evaluated bias using ROBINS-E. Results: Seventy-seven studies met inclusion criteria, encompassing convolutional neural networks, ensemble and hybrid models, and transfer-learning approaches. Commercial systems (e.g., BoneXpert^®, Physis^®, VUNO Med^®-BoneAge) achieved mean absolute errors of 2–31.8 months—significantly surpassing Greulich–Pyle and Tanner–Whitehouse benchmarks—and reduced reading times by up to 87%. Common limitations included demographic bias, heterogeneous imaging protocols, and scarce external validation. Conclusions: AI-based approaches have substantially advanced automated bone-age estimation, delivering clinical-grade speed and mean absolute errors below 6 months. To ensure equitable, generalizable performance, future work must prioritize demographically diverse training cohorts, implement bias-mitigation strategies, and perform local calibration against region-specific standards. Full article