Search Results (148)

Background: Osteoarthritis (OA) is a heterogeneous joint disease characterized by cartilage degeneration. The interplay between extracellular matrix (ECM) remodeling, endoplasmic reticulum (ER) stress, and inflammatory signaling in OA pathogenesis remains incompletely understood. This study aimed to identify robust diagnostic biomarkers and explore the mechanistic convergence of key genes in OA cartilage through an integrated transcriptomic framework. Methods: Three independent cartilage transcriptomic datasets (GSE285234, GSE287861, GSE289464) were integrated after ComBat batch correction. Differentially expressed genes (DEGs) were identified using limma, followed by ORA and GSEA for functional enrichment. LASSO logistic regression identified hub genes for a diagnostic model and nomogram, validated by leave-one-out cross-validation (LOOCV). Consensus clustering stratified OA samples into molecular subtypes. Single-cell RNA-sequencing (scRNA-seq) data (GSE169454, GSE220243) were used to validate cell-type-specific expression. Virtual gene knockout (scTenifoldKnk) and pathway analysis inferred downstream functional consequences. Results: Fifty-eight DEGs (predominantly downregulated) were enriched in ECM and ER protein processing pathways. Six hub genes (EIF2S1, GANAB, STT3A, XBP1, MGP, PMP22) showed robust selection stability. The diagnostic model achieved a LOOCV AUC of 0.769, a well-calibrated nomogram, and superior net benefit. Unsupervised clustering revealed two OA subtypes with divergent unfolded protein response (UPR) and TGF-β pathway activities. scRNA-seq confirmed hub gene expression in chondrocytes and other joint microenvironment cells. Notably, virtual knockout of five hub genes convergently perturbed IL-17, NF-κB, and chemokine signaling pathways. Conclusions: This study identified and validated a six-gene signature reflecting ECM-ER-inflammatory crosstalk in OA cartilage. The convergent perturbation of inflammatory pathways by functionally distinct hub genes reveals a mechanistic core that may serve as a diagnostic panel and a platform for targeted therapeutic investigation in OA. Full article

Background/Objectives: The prognostic significance of mucinous colorectal adenocarcinoma (MAC) is controversial. Some studies report good outcomes relative to conventional colorectal adenocarcinoma (CRC) as is similarly described for MACs in, e.g., the breast, lung, pancreas and prostate. However, other studies refute this, proclaiming either no difference or worse outcomes. Herein, we proffer additional insights into the biology of MAC to explain these conflicting findings. Methods: A literature search was undertaken using keywords pertaining to MAC. Archival cases from our database were analyzed to provide context for our findings. Main Findings: The unifying histologic feature of MACs is their >50% content of extracellular mucin, but they should not be viewed as a monolithic entity, as is commonly portrayed in databases. Instead, MAC is a heterogenous disease as defined by histologic and molecular phenotypes. For example, MACs arising from adenoma-like CRC have relatively good outcomes unlike those from traditional serrated adenomas. Likewise, other factors such as histologic grade (grade 1–3), genomics (e.g., BRAF, KRAS, TP53), microsatellite instability (MSI-H, MSI-L), consensus molecular subtypes (CMS1–CMS4), and mucin types (MUC2, MUC5AC) significantly influence prognosis. These pathophysiologic features, demographics (age and sex) and specific anatomic regions/topography (right/left colon/rectum) can be captured and used to improve prognostic stratification. Conclusions: In contrast to previous studies that largely demarcated MAC as a discrete entity, this paper shows the limitations of this approach by highlighting the various sub-entities comprising MAC. Recognition of this heterogeneity may help to inform future treatment algorithms. Full article

Background: Peri-implantitis is a major biological complication for the long-term stability of dental implants but its molecular heterogeneity and mechanism of programmed cell death are unknown. The present study aimed to elucidate the molecular characteristics and alterations in the immune microenvironment of peri-implantitis from the perspective of pyroptosis–ferroptosis crosstalk. Methods: We retrospectively integrated three public GEO transcriptomic datasets (GSE178351, GSE33774, and GSE57631), comprising 29 peri-implant tissue samples, including 16 peri-implantitis samples and 13 healthy controls. Batch effects were corrected, followed by differential expression analysis, GSEA, GSVA, consensus clustering, machine learning-based exploratory feature prioritization, immune-infiltration estimation, and predicted ceRNA network construction. Results: A total of 2450 disease-associated candidate differentially expressed genes were identified, among which 41 genes were associated with both pyroptosis and ferroptosis. Pathway analysis indicated significant upregulation of inflammatory responses, complement activation, TNF-α/NF-κB, IL-6/JAK/STAT3, reactive oxygen species (ROS) pathways and tissue remodeling-related processes in peri-implantitis tissues. Based on these 41 overlap genes, unsupervised clustering suggested two candidate expression clusters, C1 and C2, in the integrated cohort. C1 was predominantly composed of peri-implantitis samples and showed stronger inflammatory and cellular-stress-related pathway activity. BRAF and TRPV1 were prioritized as exploratory candidate genes and showed associations with estimated immune-infiltration patterns. Conclusions: This exploratory analysis suggests that pyroptosis–ferroptosis crosstalk-related signatures may be associated with immune remodeling in peri-implantitis. BRAF and TRPV1 may serve as candidate genes for future validation, while the C1/C2 clusters should be interpreted as preliminary expression patterns rather than established disease subtypes. Full article

Background/Objectives: Prostate cancer (PCa) remains a prevalent malignancy among men, often complicated by recurrence and unfavorable clinical outcomes. Consequently, precise risk stratification and timely clinical intervention are paramount. Initially, we delineated distinct expression profiles of histone modification regulators via unsupervised clustering, identifying PCa subtypes with divergent survival probabilities and biological phenotypes. Subsequently, we sought to develop a prognostic gene signature, derived from the transcriptomic variations among these regulator-defined subtypes, to predict outcomes in PCa patients following radical prostatectomy (RP). Methods: Clinical and transcriptomic data from PCa cohorts were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories for comprehensive analysis. Subtypes driven by histone modification regulators were established using unsupervised consensus clustering, followed by in-depth characterization of their molecular features and associated pathways. A risk-scoring model was then developed to evaluate its prognostic efficacy in this patient population. Results: Stratification based on histone modification regulators yielded four distinct PCa subtypes exhibiting heterogeneous survival outcomes, functional pathways, and genomic mutational landscapes. Following rigorous feature selection, a 21-gene risk signature (HIS_score)—comprising MXD3, CCDC28B, COL11A2, SLC39A5, GPT, DNASE1L2, PIF1, KRTAP5-9, TTLL10, KRTAP5-1, KRTAP5-10, HAGHL, MSLNL, AMH, NKAIN4, CCDC114, SLC9A3, SULT1E1, ALB, SLC6A14, and RPE65—was constructed. Survival analyses demonstrated that patients assigned to the high HIS_score cohort experienced significantly worse clinical outcomes compared to their low-score counterparts. Furthermore, we integrated this signature into a novel clinical nomogram to facilitate individualized prognostic assessments. Conclusions: Derived from transcriptomic disparities between extreme epigenetic subtypes, the HIS_score and its associated nomogram serve as robust prognostic instruments. These tools effectively encapsulate the downstream transcriptional sequelae of histone modification dysregulation, offering clinicians a valuable framework to accurately predict post-RP outcomes and expedite the formulation of personalized therapeutic strategies. Full article

Background: Alopecia areata (AA) is a common autoimmune alopecia disease. Evidence suggests that autophagy-related genes (ARGs) may contribute to its pathophysiology. This study aims to explore and identify potential autophagy-related biomarkers and molecular subtypes in AA. Methods: In this study, autophagy-related differential expression genes (ARDEGs) in AA were identified by comparing the differentially expressed genes (DEGs) in the GSE68801 dataset with the ARGs. Then, we applied three different machine learning methods to identify key hub genes and further verified them on independent datasets. We used the receiver operating characteristic (ROC) curve to evaluate the diagnostic potential of these hub genes and constructed a predictive nomogram. In addition, this study also used the consensus clustering method to define two AA subtypes and explored their immune characteristics and functional pathways through ssGSEA, MCPcounter and enrichment analysis. Experimental validation included qRT-PCR for four hub genes and Western blotting for critical autophagy markers. Results: Our analysis detected 10 ARDEGs in AA. Applying three machine learning algorithms, we identified four candidate hub genes, ATG9B, EIF4EBP1, WIPI1 and CCR2, and verified their expression patterns in independent cohorts. The combined four-gene model and nomogram showed potential diagnostic performance. Consensus cluster analysis divided AA cases into two subtypes, each associated with different immune infiltration and functional pathways. Downregulation of ATG9B and EIF4EBP1 and upregulation of CCR2 were verified by qRT-PCR. Western blotting further suggested altered autophagy-related protein expression in AA lesions, characterized by a reduced LC3B-II/I ratio and Beclin-1 expression and increased SQSTM1 expression. Conclusions: This study identified four candidate autophagy-related genes and two exploratory molecular subtypes in AA and may provide clues for understanding autophagy-related immune dysregulation and support further validation of candidate diagnostic markers. Full article

Background/Objectives: Radiogenomics integrates quantitative imaging features with genomic and molecular data to better characterize tumor biology and support precision oncology. While extensively investigated in solid tumors, its application to hematologic malignancies remains relatively unexplored despite the widespread use of advanced imaging in lymphoma and multiple myeloma. Methods: A systematic review was conducted following PRISMA 2020 guidelines. PubMed, Scopus, and Web of Science were searched up to December 2025 for studies investigating radiogenomic associations in hematologic malignancies. Study quality was assessed using PROBAST and METRICS. Two reviewers independently screened all records and performed data extraction through consensus. Results: Twelve studies were included, covering multiple myeloma and various lymphoma subtypes (aggressive B-cell lymphoma, classical Hodgkin lymphoma, and primary CNS lymphoma). Imaging modalities included PET/CT, MRI and CT. Across studies, radiomic and imaging-derived features were associated with cytogenetic abnormalities, gene expression profiles, and circulating tumor DNA metrics. In multiple myeloma, MRI and CT-based radiomics showed promising ability to predict high-risk cytogenetic abnormalities. In lymphoma, PET-derived volumetric and dissemination features correlated with molecular risk profiles and tumor microenvironment characteristics. Several studies demonstrated improved prognostic performance when imaging features were combined with genomic or clinical variables. Conclusions: Radiogenomic approaches in hematologic malignancies show promising potential for non-invasive risk stratification and improved prognostic assessment. However, current evidence remains limited by small cohorts, heterogeneous methodologies, and a lack of external validation. Prospective multicenter studies and standardized imaging–genomic pipelines will be essential to enable clinical translation. Full article

Colorectal cancer (CRC) is a biologically heterogeneous disease in which single-omics analyses incompletely capture the cross-layer mechanisms underlying tumor progression, immune evasion, and therapeutic resistance. This review critically examines how the integration of genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling is redefining CRC biology and precision oncology. Landmark integrative efforts, including TCGA analyses of 276 colorectal cancer samples, CPTAC proteogenomic profiling of 95 tumors, and recent whole-genome sequencing studies of 2023 CRC cases, have refined molecular subtyping, expanded the driver landscape, and revealed clinically relevant discordance between mRNA abundance and protein activity. Integrative studies further show that oncogenic signaling may be driven by post-transcriptional and post-translational regulation, while spatially resolved profiling and microbiome–metabolite analyses are uncovering previously obscured tumor–microenvironment interactions. We also discuss how artificial intelligence-based approaches, including factor analysis, deep learning, graph-based models, and explainable AI, are improving subtype classification, biomarker discovery, and treatment-response prediction, with particular relevance to microsatellite instability-high and early-onset CRC. Finally, we critically evaluate the principal barriers to clinical translation, including batch effects, cross-platform variability, limited external validation, regulatory constraints, and cost, and outline priorities for building reproducible, clinically deployable multi-omics pipelines for CRC management. Full article

Nodal T-follicular helper cell lymphomas comprise a biologically similar but morphologically diverse family of T-cell neoplasms, including angioimmunoblastic T-cell lymphoma, nodal T-follicular helper cell lymphoma, follicular-type, and nodal TFH lymphoma, not otherwise specified. Despite recurrent molecular alterations involving RHOA, IDH2, TET2, and DNMT3A, the diagnosis of TFH lymphomas remains challenging because of their mimicry of other lymphoid neoplasms and reactive lymphadenopathy. A key pitfall is confusion with classical Hodgkin lymphoma, as admixed Epstein–Barr virus-positive large B-cells with Reed–Sternberg cell-like morphology and immunophenotype can be found in TFH lymphomas. Similarly, follicular-type TFH lymphoma is often misclassified as follicular B-cell lymphoma unless T-cell lineage is investigated by immunophenotyping and the absence of BCL2 or BCL6 rearrangement is established. The ‘not otherwise specified’ category should be reserved for cases with proven T-follicular helper immunophenotype but lacks definitive angioimmunoblastic or follicular architecture. Comparing current frameworks, 5th edition of the World Health Organization classification permits rare CD4/CD8 double negative cases, while International Consensus Classification requires CD4 positivity. Some of these distinctions may appear taxonomic as all T-follicular helper T-cell lymphoma subtypes share molecular alterations, prognosis, and treatment approach. However, these classifications are meaningful from the perspective of a histopathologic diagnosis as a wrong diagnosis may lead to ineffective treatment approach. Accurate recognition of these lymphomas prevents misclassification, avoids inappropriate regimens, and ensures eligibility for proper clinical trials. A structured approach integrating morphology, multiparameter immunohistochemistry, flow cytometry, and molecular testing provides the best safeguard against diagnostic pitfalls and refines classification across subtypes. Full article

Background: Cellular senescence is a key regulatory mechanism in tumor initiation and progression, influencing cancer development through modulation of the cell cycle, the immune microenvironment, and inflammatory responses. However, the molecular characteristics and potential clinical value of senescence-related genes in lung adenocarcinoma (LUAD) have not been systematically elucidated. This study aimed to comprehensively characterize the expression patterns, molecular subtypes, and prognostic significance of cellular senescence-related genes in LUAD, and to identify key regulatory determinants. Methods: Transcriptomic data of cellular senescence-related genes were obtained from The Cancer Genome Atlas (TCGA) cohort, and integrated analyses were performed to characterize their mutational landscape, copy number variations, and differential expression profiles. Senescence-related molecular subtypes were established using consensus clustering, followed by gene set variation analysis (GSVA) for pathway enrichment and immune infiltration analyses. A prognostic risk model was subsequently constructed using LASSO-penalized Cox regression, and its predictive performance was systematically evaluated. Candidate key regulators were further prioritized through bioinformatic screening, identifying FOLR1 as a hub gene. The biological function of FOLR1 was validated by qRT–PCR, Western blotting, assessment in clinical specimens, and a subcutaneous xenograft tumor model in mice. Results: Cellular senescence-related genes in LUAD exhibited a high frequency of somatic mutations and copy number alterations, accompanied by marked transcriptional dysregulation. Based on the expression profiles of these genes, LUAD patients could be stratified into three distinct molecular subtypes with significantly different clinical outcomes. These subtypes displayed pronounced heterogeneity in pathway enrichment patterns and immune cell infiltration. The subsequently developed prognostic signature demonstrated robust predictive performance in both the training and validation cohorts. Functional assays showed that FOLR1 was significantly downregulated in LUAD tissues and cell lines; FOLR1 knockdown promoted tumor cell proliferation, whereas restoration of its expression or pharmacological intervention markedly suppressed tumor progression. Consistently, in vivo xenograft experiments further corroborated the tumor-suppressive role of FOLR1 in lung adenocarcinoma. Conclusions: This study systematically delineated the molecular landscape of cellular senescence-related genes in LUAD and elucidated their associations with the tumor immune microenvironment and patient prognosis. Moreover, FOLR1 was identified as a potential tumor suppressor and therapeutic target. These findings provide a theoretical basis for senescence-informed molecular stratification and the development of precision treatment strategies in lung adenocarcinoma. Full article

Background: Atypical teratoid/rhabdoid tumors (AT/RT) are rare, highly aggressive pediatric central nervous system (CNS) malignancies. AT/RT of the lateral ventricle is an exceptionally rare subgroup, with only 11 reported cases. SMARCB1 inactivation is the primary molecular feature of AT/RT. Current consensus is to classify AT/RT based on methylation and molecular profiles into the following subgroups: AT/RT-TYR, AT/RT-SHH, AT/RT-MYC, and a potentially distinct SMARCA4-deficient subtype. AT/RT-MYC exhibits high levels of CD8⁺ tumor-infiltrating lymphocytes, indicating immunogenic potential. Case presentation: We report three pediatric cases presenting with intracranial hypertension and seizures. Diagnosis was confirmed via histopathology and molecular profiling. Interventions included gross total resection, chemotherapy, radiotherapy, and combined immune checkpoint inhibitors (pembrolizumab and ipilimumab). Outcomes varied from rapid progression to 3-year recurrence-free survival. A cohort of 14 pediatric patients with lateral ventricle AT/RT, comprising 3 institutional cases and 11 cases identified from the PubMed database, was evaluated through a narrative synthesis. Conclusions: These advancements highlight the crucial role of molecular subtyping in tailoring personalized treatments, including epigenetic modifiers and immune-based regimens. However, clinical validation is essential to establish standardized protocols. Integrating genomic, epigenetic, and immune microenvironment profiling may enhance risk assessment and treatment precision, ultimately improving survival and quality of life in pediatric patients. Full article

Background/Objectives: AP-2δ, encoded by TFAP2D, is one of the least characterized members of the AP-2 transcription factor family, although available evidence suggests biologically relevant roles in lung cancer that have not yet been thoroughly examined. The aim of the present study was to provide an integrated characterization of AP-2δ/TFAP2D in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Methods: LUAD and LUSC data were obtained from The Cancer Genome Atlas. The analysis comprised the expression profiling of AP-2δ target genes, survival-guided TFAP2D stratification, clinical profiling, differential expression and intersection analyses, methylation-derived chromatin compartment profiling, TFAP2D-associated cofactor rewiring, and genome-wide enrichment of AP-2δ targets. In parallel, pocket prioritization was performed using an AlphaFold model of AP-2δ with cross-tool consensus mapping. Results: TFAP2D stratification delineated biologically-distinct states in both histological subtypes (LUAD and LUSC). AP-2δ target genes showed subtype-specific expression patterns and functional organization. The consistent survival association was observed for progression-free interval rather than uniformly across all endpoints. Clinical profiling was more closely associated with molecular subtype composition than broad clinicopathological differences. Differential expression analyses identified both shared and histology-dependent programs associated with TFAP2D. In the chromatin-compartment analysis, LUSC showed a broader and more coherent footprint, whereas LUAD displayed more selective cofactor rewiring. Structure-based analysis prioritized a small set of reproducible candidate pockets concentrated within ordered regions of the TF_AP-2 domain. Conclusions: AP-2δ marks biologically meaningful but histologically non-uniform regulatory states in lung cancer. These findings provide an integrated framework for understanding TFAP2D-dependent regulation in LUAD and LUSC, highlighting AP-2δ as a candidate for future mechanistic and translational investigation. Full article

Background: This study investigates the role of N6-methyladenosine (m6A) regulators in osteoporosis (OP) and their interplay with the immune microenvironment, aiming to identify potential m6A-related biomarkers for OP risk assessment and treatment. Methods: Transcriptomic data from GEO datasets were analyzed for differential expression of 22 m6A regulators and immune infiltration patterns. Consensus clustering and m6Ascore grouping defined molecular subtypes, while machine learning algorithms identified potential biomarkers, leading to the construction and validation of a nomogram. Experimental validation involved peripheral blood monocytes (PBMCs) transcriptome sequencing and Western blot of bone tissue. Results: FTO, HNRNPC, and METTL4 were upregulated, while CBLL1 and YTHDF2 were downregulated in OP, with two distinct m6A modification patterns and immune phenotypes identified. METTL4, HIRA, MATN4, and YTHDF2 were selected as potential biomarkers, and the nomogram demonstrated favorable predictive performance in training and external datasets. Single-cell RNA sequencing confirmed the cellular distribution of these biomarkers. HIRA heterogeneity in Marrow Mesenchymal Stem Cells (BMSCs) was associated with distinct cell–cell communication patterns. Transcriptome sequencing confirmed HIRA RNA downregulation in OP PBMCs, and Western blot verified decreased HIRA protein in OP bone tissue. Conclusions: This study establishes a potential m6A-related biomarker signature for OP and provides multi-level experimental evidence that HIRA is a consistently downregulated biomarker, linking epigenetic modification to immune dysregulation in osteoporosis. Full article

Osteosarcoma (OS), the most prevalent primary malignant bone tumor with a dismal prognosis, exhibits significant heterogeneity in programmed cell death (PCD) pathways, but its subtype-specific functional mechanisms remain poorly characterized. This study integrated PCD-related gene signatures to delineate molecular subtypes in OS via consensus clustering, successfully defining four distinct subtypes with divergent prognostic outcomes and immune microenvironments. Differential expression, functional enrichment, and protein–protein interaction (PPI) network analyses revealed subtype-specific PCD pathway associations (e.g., lysosome-dependent cell death, apoptosis, pyroptosis and anoikis), while comparative immune profiling and clinical characterization further refined subgroup identities. A robust prognostic risk model incorporating five pivotal genes (SERPINE2, CBS, SQLE, UBE2D4, and S100A13) and metastasis status demonstrated superior predictive performance in both training and external validation cohorts. These findings not only elucidate the functional architecture of PCD across OS molecular subtypes but also establish a clinically actionable model for precision risk stratification and tailored therapeutic strategies. Full article

Colorectal cancer (CRC) is a heterogeneous disease driven by complex genetic, epigenetic, and microenvironmental alterations. Members of the PR domain-containing (PRDM) protein family have emerged as context-dependent regulators of CRC initiation, progression, tumor cell plasticity, immune modulation, and therapeutic response. Accumulating evidence highlights divergent roles for PRDM proteins as tumor suppressors, oncogenes, or isoform-dependent dual-function regulators. Collectively, PRDM family members represent a central node of transcriptional/epigenetic control in CRC biology, with significant potential as biomarkers for early detection, prognosis, and treatment stratification, as well as promising candidates for epigenetic and pathway-directed therapeutic strategies. Full article

Colorectal cancer (CRC) remains a leading cause of cancer morbidity and mortality worldwide, with nearly one quarter of patients presenting with metastatic disease at diagnosis. The epidermal growth factor receptor (EGFR) plays a central role in CRC pathogenesis through activation of downstream RAS/RAF/MAPK and PI3K/AKT/mTOR signaling pathways, and has become a major therapeutic target. Anti-EGFR monoclonal antibodies, cetuximab and panitumumab, have demonstrated survival benefit in selected patients, particularly those with left-sided, RAS wild-type tumors. However, primary and acquired resistance limit their efficacy, underscoring the need for predictive biomarkers and novel strategies. This review synthesizes current knowledge of EGFR biology, therapeutic integration, and biomarker development, including RAS and BRAF mutations, MSI status, HER2 amplification, EGFR ligands (AREG/EREG), consensus molecular subtypes, and liquid biopsy applications. We also discuss mechanisms of resistance such as pathway reactivation, receptor mutations, and epithelial-to-mesenchymal transition, alongside emerging approaches, including combination regimens, ctDNA-guided rechallenge, and genotype-specific inhibitors. Collectively, these insights highlight the evolving landscape of precision oncology in CRC and the importance of molecular stratification to optimize EGFR-targeted therapy and overcome resistance. Full article