Molecular Biomedicine

Effects of HOX family regulator-mediated modification patterns and immunity characteristics on tumor-associated cell type in endometrial cancer

AbstractEndometrial cancer (UCEC) is one of three major malignant tumors in women. The HOX gene regulates tumor development. However, the potential roles of HOX in the expression mechanism of multiple cell types and in the development and progression of tumor microenvironment (TME) cell infiltration in UCEC remain unknown. In this study, we utilized both the The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database to analyze transcriptome data of 529 patients with UCEC based on 39 HOX genes, combing clinical information, we discovered HOX gene were a pivotal factor in the development and progression of UCEC and in the formation of TME diversity and complexity. Here, a new scoring system was developed to quantify individual HOX patterns in UCEC. Our study found that patients in the low HOX score group had abundant anti-tumor immune cell infiltration, good tumor differentiation, and better prognoses. In contrast, a high HOX score was associated with blockade of immune checkpoints, which enhances the response to immunotherapy. The Real-Time quantitative PCR (RT-qPCR) and Immunohistochemistry (IHC) exhibited a higher expression of the HOX gene in the tumor patients. We revealed that the significant upregulation of the HOX gene in the epithelial cells can activate signaling pathway associated with tumour invasion and metastasis through single-cell RNA sequencing (scRNA-seq), such as nucleotide metabolic proce and so on. Finally, a risk prognostic model established by the positive relationship between HOX scores and cancer-associated fibroblasts (CAFs) can predict the prognosis of individual patients by scRNA-seq and transcriptome data sets. In sum, HOX gene may serve as a potential biomarker for the diagnosis and prediction of UCEC and to develop more effective therapeutic strategies.

PARPs and PARP inhibitors: molecular mechanisms and clinical applications

Abstract Poly (ADP-ribose) polymerases (PARPs) are a diverse family of enzymes that regulate genome stability, cell death, and stress responses through ADP-ribosylation. Among them, PARP1, PARP2, and PARP3 are central to cellular DNA repair, while tankyrases, and their isoforms, contribute to telomere maintenance, transcriptional regulation, immune signaling, and metabolism. Dysregulated PARP activity drives genomic instability, apoptosis, parthanatos, and tumor microenvironment remodeling, thereby linking PARPs to oncogenesis, immune escape, and therapy resistance. Clinically, PARP inhibitors (PARPi), such as olaparib, niraparib, rucaparib, and talazoparib, exploit synthetic lethality in homologous recombination–deficient tumors and are increasingly applied in ovarian, breast, prostate, and pancreatic cancers. Beyond oncology, preclinical studies demonstrate antiviral efficacy of PARPi against hepatitis B virus, human immunodeficiency virus, and coronaviruses, and also therapeutic potential in neurodegeneration, cardiovascular disease, fibrosis, and metabolic disorders. However, PARPi resistance arises through restoration of DNA repair, replication fork protection, epigenetic changes, and drug-target dynamics, while adverse events—including hematologic toxicity, gastrointestinal disturbance, and organ-specific effects—limit a broader use. Next-generation PARPi with improved isoform selectivity, PROteolysis-TArgeting Chimera (PROTAC) degraders, and rational combinations with ATR/CHK1 inhibitors, immune checkpoint blockade, or epigenetic modulators offer strategies to enhance efficacy and overcome resistance. Emerging biomarker-driven approaches, including liquid biopsies and functional assays, may further personalize therapy. By integrating canonical DNA repair roles with non-canonical signaling and host–virus interactions, PARPs represent pivotal regulators. Similarly, the versatile therapeutics of PARPi have implications that extend beyond oncology into a broader and diverse range of other human diseases.

Clinical-grade AI model for molecular subtyping of endometrial cancer: a multi-center cohort study in China

Abstract Accurate molecular subtyping is essential for guiding precision treatment and prognostic stratification in endometrial cancer (EC). However, current methods, based on Sanger sequencing and immunohistochemistry (IHC), are costly, time-intensive, and difficult to implement widely in routine clinical practice, particularly in resource-limited settings. To overcome these challenges, we developed a deep-learning pipeline that directly infers EC molecular subtypes from routine hematoxylin-and-eosin (H&E) whole-slide images (WSIs). The framework integrates super-resolution enhancement (SRResGAN), transformer-based lesion segmentation (MedSAM), and a ResNet-101 classifier for molecular subtype prediction, with an LSTM module for survival modeling. This retrospective study included 393 Chinese patients diagnosed between 2010 and 2018, all with ≥ 5 years of follow-up. Molecular subtypes—POLE mut , mismatch repair-deficient (MMRd), p53abnormal (p53abn), and no specific molecular profile (NSMP)—were confirmed by Sanger sequencing and immunohistochemistry. The model achieved high classification accuracies (92% for POLE mut and MMRd, 91% for p53abn, and 90% for NSMP), with a strong correlation between predicted and observed survival (R2 = 0.9692; MAE = 123 days). External validation on two independent cohorts ( N  = 35 and N  = 83) confirmed robust generalizability across institutions. This study represents the first large-scale, multicenter, AI-based digital pathology model for EC molecular classification in China. The proposed workflow provides an automated, interpretable, and cost-efficient alternative to conventional molecular testing, supporting precision oncology, fertility-preserving management, and clinical decision-making in real-world practice.

Cervicovaginal lavages uncover growth factors as key biomarkers for early diagnosis and prognosis of endometrial cancer

AbstractEndometrial cancer (EC) rates are continuing to rise and it remains the most common gynecologic cancer in the US. Existing diagnostic methods are invasive and can cause pain and anxiety. Hence, there is a need for less invasive diagnostics for early EC detection. The study objective was to evaluate the utility of growth factors collected through minimally invasive cervicovaginal lavage (CVL) sampling as diagnostic and prognostic biomarkers for EC. CVL samples from 192 individuals undergoing hysterectomy for benign or malignant conditions were collected and used to quantify the concentrations of 19 growth and angiogenic factors using multiplex immunoassays. Patients were categorized based on disease groups: benign conditions (n = 108), endometrial hyperplasia (n = 18), and EC (n = 66). EC group was stratified into grade 1/2 endometrial endometrioid cancer (n = 53) and other EC subtypes (n = 13). Statistical associations were assessed using receiver operating characteristics, Spearman correlations and hierarchical clustering. Growth and angiogenic factors: angiopoietin-2, endoglin, fibroblast activation protein (FAP), melanoma inhibitory activity, and vascular endothelial growth factor-A (VEGF-A) were significantly (p < 0.0001) elevated in EC patients. A multivariate model combining 11 proteins with patient age and body mass index exhibited excellent discriminatory potential (area under curve = 0.918) for EC, with a specificity of 90.7% and a sensitivity of 87.8%. Moreover, angiopoietin-2, FAP and VEGF-A significantly (p < 0.05–0.001) associated with tumor grade, size, myometrial invasion, and mismatch repair status. Our results highlight the innovative use of growth and angiogenic factors collected through CVL sampling for the detecting endometrial cancer, showcasing not only their diagnostic potential but also their prognostic value.

Decoding the role of mesothelin in tumor dynamics and targeted treatment innovations

Abstract Mesothelin (MSLN) is among the most studied cancer-related antigens, and it is extensively studied as a therapeutic target for the treatment of various malignancies, including pleural mesothelioma, pancreatic ductal adenocarcinoma, and ovarian cancer. However, despite the development of many MSLN-targeting strategies, such as antibody–drug conjugates (ADC), bispecific antibodies, and CAR-T cells, clinical responses have remained limited, underscoring the need for a deeper understanding of MSLN biology. Over the past decades, many studies have highlighted a link between MSLN and cancer progression and its association with specific features within the tumor microenvironment (TME). More recently, mechanistic evidence has emerged showing the involvement of MSLN in the establishment of key malignant features, such as the epithelial-to-mesenchymal transition (EMT) and matrix metalloproteinase 7-mediated remodeling of the extracellular matrix (ECM). Furthermore, these studies also show a direct role for MSLN in the immunosuppressive polarization of the TME through the interaction with CD206 macrophage receptors (leading to an M2-like polarization) and by promoting the transition of mesothelial cells into specific cancer-associated fibroblasts (CAFs). This review synthesizes current evidence on MSLN transcriptional regulation and its functional implications in invasion, metastasis, and immune evasion. We also summarize ongoing therapeutic strategies targeting MSLN and discuss how TME-driven resistance mechanisms are shaping the next generation of MSLN-directed therapies. By integrating molecular insights with translational perspectives, this work provides a comprehensive overview of MSLN biology and its emerging therapeutic relevance in cancer.