Deciphering the prognostic value and immunotherapeutic strategy of aggrephagy in melanoma: Integrating single-cell sequencing and machine learning.

This study aimed to investigate the role of aggrephagy in cutaneous melanoma (CM) and explore its potential as a biomarker for prognosis and therapeutic targeting. We utilized single-cell sequencing technology and machine learning algorithms to analyze melanoma transcriptome data from the TCGA database and validated our findings using 3 independent datasets from the GEO database. By employing enrichment scoring in single-cell sequencing, we identified characteristic expression patterns of different cell types involved in aggrephagy and constructed an aggrephagy-related signature (ARS).

Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients.

Following complete mesocolic excision (CME), heart failure (HF) emerges as a significant complication, exerting substantial impacts on both short-term and long-term patient prognoses. The primary objective of our investigation was to develop a machine learning model capable of discerning preoperative and intraoperative high-risk factors, facilitating the prediction of HF occurrence subsequent to CME. A cohort comprising 1158 patients diagnosed with colon cancer was enrolled in our study, encompassing 172 individuals who developed postoperative HF.

USP5-Mediated PD-L1 deubiquitination regulates immunotherapy efficacy in melanoma.

Background: The role of post-translational modifications(PTMs) in PD-L1-mediated immune resistance and melanoma progression remains poorly understood.

Methods: We conducted multi-omics analyses and constructed a prognostic model based on PTM-related genes using machine learning to identify key regulators in melanoma. In vitro and in vivo experiments, including cell culture, flow cytometry, and subcutaneous allografts models, were used to investigate USP5's function.

Mechanistic insights into adipose-derived stem cells and exosomes in ischemia-reperfusion injury repair: from shared pathways to organ-specific therapeutics.

Ischemia-reperfusion injury (IRI) has become a significant challenge for clinical treatment due to the complex multi-mechanism pathological cascade response, including oxidative stress, inflammatory bursts, and programmed cell death. Adipose-derived stem cells (ADSCs) and their exosomes (ADSCs-exosomes) are emerging as a breakthrough therapeutic strategy to reverse IRI, owing to their multi-target synergistic effects. This review systematically analyzes the two major repair modes of ADSCs and ADSCs-exosomes: the "common protection" mechanism, which includes anti-inflammatory, anti-oxidative, and anti-apoptotic effects through paracrine regulation of miRNAs targeting the NF-κB/NRF2/β-catenin signaling axis; and precision repair, which is achieved through organ-specific targets, including hepatic mitochondrial dynamics and pyroptosis inhibition, cardiac macrophage polarization and neutrophil clearance, renal anti-fibrosis and erythropoietin (EPO) activation, as well as brain iron death regulation and microglial remodeling.

Corrigendum to 'Tβ4-exosome-loaded hemostatic and antibacterial hydrogel to improve vascular regeneration and modulate macrophage polarization for diabetic wound treatment' [Mater. Today Bio,Volume 31, 2025, 101585].

[This corrects the article DOI: 10.1016/j.mtbio.

Single-Cell and Spatial Transcriptomics Explore Purine Metabolism-Related Prognostic Risk Model and Tumor Immune Microenvironment Modulation in Ovarian Cancer.

Ovarian cancer (OC) ranks as the second leading cause of gynecological cancer-related deaths in women globally. Single-cell and spatial transcriptomics could precisely describe the heterogeneity of OC that affect the clinical treatment. Single-cell sequencing and spatial transcriptomics information were from different public datasets.

CBLB Regulates MAPK-P38 Pathway via MAP3K9 Ubiquitination to Inhibit GBM Cell Invasion and Migration.

Glioma cells exhibit high invasiveness and have the ability to evade surgical resection, radiotherapy, and chemotherapy, which are major factors contributing to the challenges in effective treatment and recurrence. The ubiquitin-proteasome system (UPS) plays a crucial role in posttranslational modification, significantly contributing to the aggressive progression of glioblastoma (GBM). This study identified the E3 ubiquitin ligase CBLB as a crucial and abnormally regulated component of the UPS in GBM, noting its significant downregulation compared to normal brain tissue and its negative correlation with malignant phenotypes and poor prognosis.

iMLGAM: Integrated Machine Learning and Genetic Algorithm-driven Multiomics analysis for pan-cancer immunotherapy response prediction.

To address the substantial variability in immune checkpoint blockade (ICB) therapy effectiveness, we developed an innovative R package called integrated Machine Learning and Genetic Algorithm-driven Multiomics analysis (iMLGAM), which establishes a comprehensive scoring system for predicting treatment outcomes through advanced multi-omics data integration. Our research demonstrates that iMLGAM scores exhibit superior predictive performance across independent cohorts, with lower scores correlating significantly with enhanced therapeutic responses and outperforming existing clinical biomarkers. Detailed analysis revealed that tumors with low iMLGAM scores display distinctive immune microenvironment characteristics, including increased immune cell infiltration and amplified antitumor immune responses.

Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma.

Background: Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melanoma prognosis by employing multi-omics analysis and machine learning techniques, ultimately leading to the development of novel therapeutic strategies.

Methods: We obtained and processed transcriptomic data, including RNA expression profiles, methylation microarray data, gene mutation data, and clinical information, from the TCGA dataset using multi-omics analysis and machine learning techniques.

Deubiquitination by USP7 Stabilizes JunD and Activates AIFM2 (FSP1) to Inhibit Ferroptosis in Melanoma.

Ferroptosis resistance in melanoma cells is a key factor in melanoma progression, influenced by the tumor microenvironment. This study investigates the regulatory mechanisms of the USP7-JunD-AIFM2 pathway, which contributes to ferroptosis resistance in melanoma cells. We identified USP7 as a critical deubiquitinase that stabilizes the transcription factor JunD.

Single-cell RNA sequencing reveals a new mechanism of endothelial cell heterogeneity and healing in diabetic foot ulcers.

Diabetic foot ulcers (DFU) are a common and severe complication among diabetic patients, posing a significant burden on patients' quality of life and healthcare systems due to their high incidence, amputation rates, and mortality. This study utilized single-cell RNA sequencing technology to deeply analyze the cellular heterogeneity of the skin on the feet ofDFU patients and the transcriptomic characteristics of endothelial cells, aiming to identify key cell populations and genes associated with the healing and progression of DFU. The study found that endothelial cells from DFU patients exhibited significant transcriptomic differences under various conditions, particularly in signaling pathways related to inflammatory responses and angiogenesis.

Tβ4-exosome-loaded hemostatic and antibacterial hydrogel to improve vascular regeneration and modulate macrophage polarization for diabetic wound treatment.

Diabetic wounds often exhibit delayed healing due to compromised vascular function and intensified inflammation. In this study, we overexpressed Thymosin β4 (Tβ4) in Adipose-Derived Stem Cells (ADSCs) to produce Exosomes (Exos) rich in Tβ4. We then utilized a dual photopolymerizable hydrogel composed of Hyaluronic Acid Methacryloyl (HAMA) and Poly-L-lysine Methacryloyl (PLMA) for the sustained release of Tβ4-Exos on diabetic wounds.

The promise of mitochondria in the treatment of glioblastoma: a brief review.

Glioblastoma (GBM) is a prevalent and refractory type of brain tumor. Over the past two decades, there have been minimal advancements in GBM therapy. The current standard treatment involves surgical excision followed by radiation and chemotherapy.

New Anti-Fibrotic Strategies for Keloids: Insights From Single-Cell Multi-Omics.

Keloids are complex pathological skin scars characterised by excessive growth of fibrous tissue and abnormal accumulation of extracellular matrix (ECM). Despite various treatment options available, the treatment of keloids remains a major clinical challenge due to high recurrence rates and inconsistent therapeutic outcomes. By collecting three keloid tissues and three normal skin samples and utilising single-cell RNA sequencing (scRNA-seq), we delved into the cellular heterogeneity and molecular mechanisms of keloids.

Predicting the risk of gastroparesis in critically ill patients after CME using an interpretable machine learning algorithm - a 10-year multicenter retrospective study.

Background: Gastroparesis following complete mesocolic excision (CME) can precipitate a cascade of severe complications, which may significantly hinder postoperative recovery and diminish the patient's quality of life. In the present study, four advanced machine learning algorithms-Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and -nearest neighbor (KNN)-were employed to develop predictive models. The clinical data of critically ill patients transferred to the intensive care unit (ICU) post-CME were meticulously analyzed to identify key risk factors associated with the development of gastroparesis.

Overexpressed CD73 attenuates GSDMD-mediated astrocyte pyroptosis induced by cerebral ischemia-reperfusion injury through the A2B/NF-κB pathway.

Ischemic stroke, resulting from the blockage or narrowing of cerebral vessels, causes brain tissue damage due to ischemia and hypoxia. Although reperfusion therapy is essential to restore blood flow, it may also result in reperfusion injury, causing secondary damage through mechanisms like oxidative stress, inflammation, and excitotoxicity. These effects significantly impact astrocytes, neurons, and endothelial cells, aggravating brain injury and disrupting the blood-brain barrier.

Transcriptional patterns of brain structural abnormalities in CSVD-related cognitive impairment.

Background: Brain structural abnormalities have been associated with cognitive impairment in individuals with small cerebral vascular disease (CSVD). However, the molecular and cellular factors making the different brain structural regions more vulnerable to CSVD-related cognitive impairment remain largely unknown.

Materials And Methods: Voxel-based morphology (VBM) was performed on the structural magnetic resonance imaging data of 46 CSVD-related cognitive impairment and 73 healthy controls to analyze and compare the gray matter volume (GMV) between the 2 groups.

Identification of intraoperative hypoxemia and hypoproteinemia as prognostic indicators in anastomotic leakage post-radical gastrectomy: an 8-year multicenter study utilizing machine learning techniques.

Background: Complications and mortality rates following gastrectomy for gastric cancer have improved over recent years; however, complications such as anastomotic leakage (AL) continue to significantly impact both immediate and long-term prognoses. This study aimed to develop a machine learning model to identify preoperative and intraoperative high-risk factors and predict mortality in patients with AL after radical gastrectomy.

Methods: For this investigation, 906 patients diagnosed with gastric cancer were enrolled and evaluated, with a comprehensive set of 36 feature variables collected.

Bridging Chronic Inflammation and Digestive Cancer: The Critical Role of Innate Lymphoid Cells in Tumor Microenvironments.

The incidence and mortality of digestive system-related cancers have always been high and attributed to the heterogeneity and complexity of the immune microenvironment of the digestive system. Furthermore, several studies have shown that chronic inflammation in the digestive system is responsible for cancer incidence; therefore, controlling inflammation is a potential strategy to stop the development of cancer. Innate Lymphoid Cells (ILC) represent a heterogeneous group of lymphocytes that exist in contrast to T cells.

Utilizing machine learning algorithms for predicting risk factors for bone metastasis from right-sided colon carcinoma after complete mesocolic excision: a 10-year retrospective multicenter study.

Background: Bone metastasis (BM) occurs when colon cancer cells disseminate from the primary tumor site to the skeletal system via the bloodstream or lymphatic system. The emergence of such bone metastases typically heralds a significantly poor prognosis for the patient. This study's primary aim is to develop a machine learning model to identify patients at elevated risk of bone metastasis among those with right-sided colon cancer undergoing complete mesocolonectomy (CME).

Identification of Disulfidptosis-Related Genes in Ischemic Stroke by Combining Single-Cell Sequencing, Machine Learning Algorithms, and In Vitro Experiments.

Background: Ischemic stroke (IS) is a severe neurological disorder with a pathogenesis that remains incompletely understood. Recently, a novel form of cell death known as disulfidptosis has garnered significant attention in the field of ischemic stroke research. This study aims to investigate the mechanistic roles of disulfidptosis-related genes (DRGs) in the context of IS and to examine their correlation with immunopathological features.

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package.

Application value of intraoperative electrophysiological monitoring in cerebral eloquent area glioma surgery: a retrospective cohort study.

Introduction: Surgery for gliomas involving eloquent areas is a very challenging microsurgical procedure. Maximizing both the extent of resection (EOR) and preservation of neurological function have always been the focus of attention. Intraoperative neurophysiological monitoring (IONM) is widely used in this kind of surgery.