Skin Cancer

Metabolic determinants of cancer immunotherapy outcomes identified by plasma profiling.

Immune-checkpoint inhibitors benefit a subset of patients with advanced cancer, and the metabolic determinants of response remain unclear. Here, using targeted metabolomics and metagenomics, we profiled 4,336 plasma samples from 1,714 patients across five tumor types and 16 cohorts spanning Europe and North America, longitudinally sampled during five immune-checkpoint inhibitor-based treatment mod...

Mapping the research landscape of myelodysplastic syndromes: a scientometric analysis (2014-2025).

This scientometric study critically evaluates the global evolution of myelodysplastic syndromes (MDS) research from 2014 to 2025, aiming to integrate fragmented knowledge and identify emerging trends. A scientometric analysis was conducted using data retrieved from the Web of Science Core Collection on August 2, 2025. The search was restricted to "article" and "review" in English containing "Myelo...

Identification of T-cell exhaustion-related biomarkers in aortic dissection via integrated multi-omics analysis and mendelian randomization.

BACKGROUND: Aortic dissection (AD) is a life-threatening cardiovascular emergency characterized by high acute mortality. While immune dysregulation is...

Factors to consider in a primary care setting when using convolutional neural network tools for melanoma diagnostics: a retrospective analysis of images and patient characteristics.

Primary care physicians typically perform the initial assessment of skin lesions. Artificial intelligence tools, using techniques such as convolutiona...

MLMarker: a machine learning framework for tissue inference and biomarker discovery.

MLMarker is a machine learning tool that computes continuous tissue similarity scores for proteomics data, addressing the challenge of interpreting co...

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Purpose To develop and systematically evaluate an iterative training approach, termed the expert-guided annotation loop, for efficient reference stand...

FFixR: A Machine Learning Framework for Accurate Somatic Mutation Calling from FFPE RNA-Seq Data in Cancer.

MOTIVATION: Formalin-fixed paraffin-embedded (FFPE) tissues are widely used in clinical and research settings, yet their use for detecting somatic mut...

Molecular characteristics and modifications of glioblastoma affecting the efficacy of immunotherapy.

Glioblastoma is a highly aggressive brain tumor characterized by complex genetic, molecular, and epigenetic features that present significant challeng...

Genetically engineered outer membrane vesicles as next-generation bionanomaterials for therapeutic and diagnostic applications.

Outer membrane vesicles (OMVs), naturally released by Gram-negative bacteria, represent a unique class of bionanomaterials with inherent immunogenicit...

Machine learning-based integration develops a novel lysosome-related prognostic signature associated with prognosis and immune infiltration landscape in acute myeloid leukemia.

BACKGROUND: Lysosomes are essential for intracellular degradation and recycling, and changes in their function significantly contribute to tumor growt...

Scenario-based multi-parametric QC for quality control of complex algorithms used in clinical care.

Complex algorithms and prediction models are increasingly being developed, investigated and applied for clinical use in medical laboratories and beyon...

Expression of the High Affinity Neurotensin Receptor Defines an Immune‑cold, Poor‑prognosis Colorectal Cancer Subtype.

OBJECTIVE: We investigated neurotensin receptor 1 (NTSR1) as a potential mediator of the mechanical immune barrier that contributes to T-cell exclusio...

Rational design of active pharmaceutical ingredient-based co-assembled nanoparticles for cancer immunotherapy.

Combination strategy is crucial for enhancing cancer therapeutic efficacy, but co-delivery of multiple active pharmaceutical ingredients (APIs) remain...

Single-cell and multi-omics integration delineates the landscape of gene-wise intratumor heterogeneity and identifies prognostic biomarkers for immunotherapy in non-small cell lung cancer.

BACKGROUND: Gene-wise intratumor heterogeneity (ITH), defined as spatial variability in the expression of individual genes across tumor regions, remai...

2-Hydroxyisobutyrylation and Phosphorylation Crosstalk Guides Metastasis Prediction and Immunotherapy in Esophageal Squamous Cell Carcinoma.

Posttranslational modification (PTM) is pivotal in cancer progression. However, the mechanisms, biological function, and clinical significance underly...

Exploring the immune landscape and immunotherapy relevance of ER stress-related lncRNAs in melanoma through multi-omics and explainable machine learning.

BACKGROUND: Melanoma is a highly aggressive malignancy with poor clinical outcomes, and endoplasmic reticulum (ER) stress plays an important role in t...

Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.

BACKGROUND: Immunotherapy has shown limited efficacy in a substantial subset of CRC patients, yet the mechanisms underlying therapeutic resistance rem...

Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.

OBJECTIVE: Adipose tissue is a highly heterogeneous and plastic endocrine and immune organ whose characteristics have been linked to prognosis in NSCL...

Data-driven system identification in cancer systems biology: A multiscale modeling approach to melanoma.

BACKGROUND AND OBJECTIVE: Developing systems biology models of cancer is critical for improving understanding of complex biological interactions and a...

Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.

T-cell receptor (TCR) cross-reactivity, whereby a single TCR recognizes multiple peptide-MHC (pMHC) ligands, is essential for immune surveillance but ...