Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) is the most common molecular subtype in breast cancer (BC), but drug resistance remains an unsolved problem, particularly in metastatic ones. Cell-cycle related genes play a crucial role in tumorigenesis and progression. However, their relationship with drug resistance and patient prognosis is not yet clear. Here, we introduce a novel and robust HR+/HER2- BC Prognostic Signature (HBPS) based on cell cycle-related gene expression and Cox analysis. 421 h+/HER2- BC patients from the TCGA dataset were used as the training set and 3605 patients from GEO and cBioPortal datasets were used as the validation sets. Subsequently, we explored the underlying biological mechanisms and drug susceptibility associated with the HBPS score. Patients with high HBPS scores exhibited significantly worse prognosis across all sets. The high HBPS score group demonstrated lower levels of immune cell infiltration, downregulation of HALLMARK_KRAS_SIGNALING_DN and HALLMARK_IL2-STAT5 signaling. Moreover, further validation revealed that CDKN2C (one critical gene in the HBPS) deficiency was associated with immuno-cold tumor microenvironment and enhanced HR + HER2- breast cancer cells aggressiveness. Overall, the study's biological insights, crucial for comprehending and tackling drug resistance, hold the potential to inform precise drug treatments in HR+/HER2- BC patients.

In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. MM, an extremely heterogeneous condition, displays often mixed responses to treatment, with a large number of patients showing drug resistance to proteasome inhibitors and immune modulatory agents. However, the methods previously used for genomic and proteomic data analysis techniques are plagued by issues of high dimensionality, imbalanced class distribution and feature redundancy, which work against the accurate predictability and generalizability of such methods. These are compounded by the so-called "curse of dimensionality", with dimensions far outnumbering samples, hence classical model overfitting. In this work, we present QProteoML as an integration of quantum techniques purposefully developed to deal with high-dimensional, imbalanced and redundant data. The framework integrates a combination of Quantum Support Vector Machine (QSVM), Quantum Principal Component Analysis (qPCA), Quantum Annealing (QA) for feature selection and Quantum Generative Adversarial Networks (QGANs) for data augmentation. These quantum algorithms exploit certain quantum phenomena (superposition and entanglement) to perform modelling of nonlinear relationships, dimensionality reduction, and class-imbalance issues. QSVM employs quantum kernels to map data into a higher-dimensional Hilbert space, so that the model can detect complex patterns in MM drug resistance. qPCA reduces dimensionality without loss of important variance, and thus improves computation efficiency. In addition, Quantum Annealing successfully extracts the most informative biomarkers with low redundancy. QProteoML was experimentally tested by comparing accuracy, F1 score and AUC ROC between classical machine learning models such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN). Our results demonstrate that QProteoML performs better than classical models, particularly in identifying the drug resistant minority class of patients. Additionally, the model is interpretable and stresses important biomarkers of drug sensitivity in MM. This research opens the possibility of quantum machine learning in personalised medicine for Multiple Myeloma. It demonstrates that quantum algorithms can perform complex biological data suggesting more reliable and accurate drug sensitivity predictions. Future research will be directed toward clinical validation of the given system with larger and more diverse cohorts of MM patients; the integration of quantum hardware for practical applications.

Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer, presenting significant challenges in treatment and prognostic prediction. Despite advancements in therapeutic approaches in recent years, personalized medicine has not yet achieved a notable breakthrough. Given the poor prognosis of patients, there is an urgent need to enhance the ability for precise prediction. Mitochondria play a crucial role in the metabolism and energy production of cancer cells, yet their specific impact in lung adenocarcinoma warrants further investigation. This study leveraged data from the TCGA and GEO databases to stratify 515 lung adenocarcinoma patients into two distinct subtypes based on mitochondrial-related genes. We systematically evaluated survival outcomes and biological pathway activities between subtypes, characterized their immune infiltration profiles, and developed a prognostic model using subtype-specific differentially expressed genes. Drug sensitivity disparities were further assessed. Single-cell RNA sequencing data were analyzed using an XGBoost classifier to delineate cell-type heterogeneity across subtypes at single-cell resolution. In LUAD, we identified two distinct subtypes. One subtype exhibited active mitochondrial metabolism, which was associated with poor prognosis and higher tumor purity. Moreover, this subtype showed greater sensitivity to Osimertinib. Further single-cell analysis revealed that this subtype was characterized by substantial macrophage infiltration, potentially promoting tumor progression through the NF-κB signaling pathway. Overall, our study identified novel LUAD subtypes and provided new insights into the clinical treatment of LUAD.

In many malignancies, an increased number of tumor-infiltrating lymphocytes (TILs) is recognized as a favorable prognostic factor, with exceptions such as renal cell carcinoma. However, the clinical significance of TIL size remains unclear. T-cell activation by mitogens increases cell size, partly via c-myc expression, suggesting that larger T cells may be more activated. We hypothesized that TIL size might be prognostically relevant in cancer patients. Here, we examined the relationship between the size and number of tumor-infiltrating CD8 + T cells and patient prognosis in 96 cases of esophageal squamous cell carcinoma (ESCC). We employed artificial intelligence (AI) analysis to quantify the mean size of intratumoral CD8+ T cells in each sample. Patients were then divided into "Large" and "Small" CD8+ T cell groups according to the median T-cell size. Similarly, we classified cases into "High" and "Low" groups based on CD8 + T-cell numbers. We found that patients in the Large CD8+ T cell group had significantly better overall survival than those in the Small CD8+ T cell group by a univariate analysis (p = 0.039), but the difference did not reach statistical significance in a multivariate analysis (p = 0.054). Patients in the High CD8 + T cell group had better outcomes than those in the Low CD8+ T cell group. There was no significant correlation between CD8+ T cell size and count, and their combination (Large/High) identified a subgroup of patients with the most favorable prognosis. Our findings suggest that CD8+ T cell size could serve as an independent prognostic marker in ESCC.

The mechanisms underlying clear cell renal cell carcinoma (ccRCC) metastasis remain largely unexplored. We demonstrate that Deleted in Split hand/Split foot protein 1 (DSS1), a critical cofactor of BRCA2 in DNA repair, is upregulated in metastatic ccRCC and promotes both tumor growth and distant metastasis. Mechanistically, DSS1 interacts with LC3 and promotes its degradation via TRIM25-mediated Lys63 (K63)-linked polyubiquitination at LC3B-K51. This impairs (macro) autophagic flux and leads to p62 accumulation, thereby stabilizing TWIST1 and facilitating its nuclear translocation, ultimately activating epithelial-mesenchymal transition (EMT). DSS1 highly expressed (DSS1^hi) tumor cells are enriched in late-stage tumors and are associated with microvascular invasion within a vascularized invasive niche at the tumor-stromal interface, mediated by SPP1-ITGB1 interactions. Clinically, DSS1^hi tumor cells correlate with therapeutic resistance and poorer patient outcomes. Collectively, these findings provide new insights into the mechanisms of ccRCC metastasis and suggest potential avenues for therapeutic intervention.

Understanding the mechanisms underlying chemoresistance is critical for improving cancer therapies. SIN1 plays a pivotal role in maintaining mTORC2 integrity and activation, which regulates key cellular processes. In this study, we demonstrate that elevated SIN1 expression in pancreatic ductal adenocarcinoma (PDAC) correlates with poor patient survival outcomes. Conversely, SIN1 deletion reduces tumor growth and enhances PDAC sensitivity to chemotherapy. We identify USP33 as a bona fide deubiquitanase of SIN1, essential for its stabilization in PDAC. This stabilization promotes chemoresistance by activating the mTORC2-AKT pathway. Additionally, we show that CDK1 directly phosphorylates USP33, enhancing its deubiquitinase activity toward SIN1 and driving PDAC progression. Inhibition or genetic ablation of CDK1 significantly diminishes these malignant phenotypes. Furthermore, we observe a strong positive correlation between CDK1, USP33, and SIN1 expressions in PDAC tissues. Our results provide compelling preclinical evidence that targeting the CDK1-USP33 axis may offer a promising therapeutic strategy to destabilize SIN1 and overcome chemoresistance in PDAC and potentially other aggressive cancers.

Monoclonal antibodies (mAb) are key therapeutic agents in cancer immunotherapy and exert their effects through Fc receptor-dependent and -independent mechanisms. However, the nanoscale receptor reorganization resulting from mAb binding and its implications for the therapeutic mode of action remain poorly understood. Here, we present a multi-target 3D RESI super-resolution microscopy technique that directly visualizes the structural organization of CD20 receptors and the Type I (e.g., Rituximab) and Type II (e.g., Obinutuzumab) anti-CD20 therapeutic antibodies and quantitatively analyze these interactions at single-protein resolution in situ. We discover that, while Type I mAbs promote higher-order CD20 oligomerization, Type II mAbs induce limited clustering, leading to differences in therapeutic function. Correlating RESI with functional studies for Type II antibodies with different hinge region flexibilities, we show that the oligomeric CD20 arrangement determines the Type I or Type II function. Thus, the nanoscale characterization of CD20-mAb complexes enhances our understanding of the structure-function relationships of therapeutic antibodies and offers insights into the design of next-generation mAb therapies.

To compare the efficacy and safety of thermal ablation (TA) in the treatment of benign thyroid nodules (BTN) and follicular thyroid neoplasms (FTN).

This retrospective study included 1900 patients with BTN or FTN who underwent TA between January 2016 and August 2024. Patients were categorized according to the Bethesda category, and propensity score matching (PSM) was employed to control for confounding factors. Kaplan-Meier curves were used to analyze disease progression and tumor disappearance.

After PSM (1:1), 106 patients (median age 45 years [IQR 36-56]; 90 women) were included in the BTN group, and 106 patients (median age 46 years [IQR 34-58]; 87 women) were included in the FTN group. The median follow-up durations were 36 months (IQR, 12-60) for the BTN group and 35 months (IQR, 18-49) for the FTN group. Technical success rates were 100% in both groups. The median volume reduction rates (VRR) at 12 months were 86.7% (IQR: 62.5%-95.1%) in the BTN group and 91.5% (IQR: 64.3%-100%) in the FTN group. No significant differences were observed between the BTN and FTN groups in disease progression (4.7% vs. 5.7%, P > 0.99), progression-free survival rates (1-year:95.8% vs. 99.1%, P = 0.449; 3-year: 96.2% vs. 84.5%, P = 0.883; 5-year: 93.1% vs. 96.2%, P = 0.594; overall: 84.5% vs. 93.1%, P = 0.7), complications (2.8% vs. 3.8%, P > 0.99), or tumor disappearance (50.1% vs. 32.5%, P = 0.089). Transient hoarseness was the only major complication.

TA could achieve comparable safety and efficacy outcomes for both BTN and FTN.

To investigate the effects of methylenetetrahydrofolate reductase (MTHFR) rs1801133 and γ-glutamyl hydrolase (GGH) rs11545078 gene polymorphisms on plasma concentrations and toxicity following high-dose methotrexate (MTX) therapy in children with acute lymphoblastic leukemia (ALL).

Children with ALL treated at the Xuzhou Children's Hospital of Xuzhou Medical University from January 2021 to April 2024 were selected for this study. Genotypes of MTHFR rs1801133 and GGH rs11545078 were determined using multiplex polymerase chain reaction. MTX plasma concentrations were measured by enzyme-multiplied immunoassay technique, and toxicity was graded according to the Common Terminology Criteria for Adverse Events version 5.0. The relationships between MTHFR rs1801133 and GGH rs11545078 genotypes and both MTX plasma concentrations and associated toxicities were analyzed.

In the low-risk ALL group, the MTHFR rs1801133 genotype was associated with increased MTX plasma concentrations at 72 hours (P<0.05). In the intermediate- to high-risk group, the MTHFR rs1801133 genotype was associated with increased MTX plasma concentrations at 48 hours (P<0.05), and the GGH rs11545078 genotype was associated with increased MTX plasma concentrations at 48 hours (P<0.05). In the intermediate- to high-risk group, the MTHFR rs1801133 genotype was associated with the occurrence of reduced hemoglobin (P<0.05), and the GGH rs11545078 genotype was associated with the occurrence of thrombocytopenia (P<0.05).

Detection of MTHFR rs1801133 and GGH rs11545078 genotypes can be used to predict increased MTX plasma concentrations and the occurrence of toxic reactions in high-dose MTX treatment of ALL, enabling timely interventions to enhance safety.

Neurofibromatosis type 1 (NF1) is a genetic disorder characterised by neurofibromas and skeletal anomalies, including lumbar kyphoscoliosis and dysmorphic sacral vertebrae. We report a rare case of a male in his late 30s with NF1 presenting with a left-sided hip dislocation and an unusual response to spinal anaesthesia. Spinal anaesthesia was attempted using 3 mL of 0.5% hyperbaric bupivacaine (dextrose 8%), resulting in a unilateral block. Pulmonary function tests did not favour general anaesthesia; hence, an MRI was performed that showed extensive abnormalities in the spine. The next day, ultrasound-guided epidural anaesthesia at T10-T11 was performed successfully with placement of an epidural catheter in situ for postoperative pain control. This is a rare phenomenon underlining the complexity of anaesthetic management in patients with NF1 and significant spinal abnormalities.