Skin Cancer

Advancing prussian blue nanoparticle-mediated photothermal therapy through machine learning and multiomics integration.

Prussian blue nanoparticles (PBNPs) are a versatile platform for administering photothermal therapy (PTT) in cancer therapy applications. PBNPs combine biocompatibility, safety, and clinical translational potential with durable treatment outcomes in preclinical cancer models. In this perspective, we focus on aspects critical to the workflow of implementing PBNP-PTT in cancer treatment, drawing ins...

Predictive modeling of molecular activity underlying physical cell-cell interactions.

Interactions between cells are central to tissue organization and function in health and disease. Labeling immune partnerships by sortagging intercellular contacts (LIPSTIC) quantitatively measures direct physical cell-cell interactions. Combined with single-cell RNA sequencing (scRNA-seq), it jointly profiles cell interaction intensities and intracellular transcriptomes. Here, we present group la...

Integrative bioinformatics and experiments identify RIBC2 as a key regulator in the esophageal cancer.

Early detection of esophageal cancer (EC) remains a major challenge due to the limited understanding of its initial molecular alterations. Therefore, ...

Integrated LIBS-Raman spectroscopy coupled with explainable machine learning for biochemical characterization of melanoma.

Melanoma remains one of the most aggressive and lethal cutaneous malignancies, underscoring the need for objective and label-free diagnostic methods t...

Integrative analysis of immune and microbial subtypes predicts immunotherapy response in stomach adenocarcinoma.

The tumor immune microenvironment and intratumoral microbiota play critical roles in cancer progression and immunotherapy response, yet their integrat...

Machine learning-driven computational drug repurposing to identify new tubulin inhibitors against cancer.

Tubulin is a validated anticancer target, yet the clinical translation of colchicine-binding site inhibitors remains limited by toxicity and resistanc...

Integrating Spatial Proteogenomics in Cancer Research.

BACKGROUND: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial pro...

Low resource federated learning for classification of nail disease by deploying cross-silo and heterogeneously dataset distributions.

Nail diseases, including such common conditions as fungus, and more serious issues like melanoma, may be important clues to the overall health and req...

Multidisciplinary Team Diagnosis and Treatment of well-differentiated thyroid carcinoma: Current Landscape and Future Prospects.

BACKGROUND: The incidence of thyroid cancer has increased markedly in recent years, largely driven by well-differentiated thyroid carcinoma (WDTC). WD...

CT-based radiomics for predicting PD-L1 expression status in non-small cell lung cancer using a hybrid machine learning model.

PURPOSE: Programmed cell death ligand-1 (PD-L1) is a key prognostic and predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). ...

Stochasticity in cancer immunotherapy stems from rare but functionally critical Spark T cells.

Cancer immunotherapies trigger highly variable responses in patients and in genetically identical mouse models. To assess the intrinsic stochasticity ...

Learning the Principles of T Cell Antigen Discernment.

T cells are central to the adaptive immune response, capable of detecting pathogenic antigens while ignoring healthy tissues with remarkable specifici...

Exploring a specialized lactylation pattern to predict the prognosis and sensitivity of immunotherapy in breast cancer via machine learning.

Applying biotechnology to overcome cancer drug resistance and improve public health outcomes.

This review examines how biotechnology advances (CRISPR/Cas9, next-generation targeted therapies, nanotechnology-based drug delivery, and immunotherap...

Advances in multi-omics for esophageal squamous cell carcinoma: Diagnostic, prognostic, and therapeutic perspectives.

Esophageal squamous cell carcinoma (ESCC) remains a major health burden, particularly in Asia, with poor patient prognosis despite advancements in rad...

Machine learning-based prognostic model of stemness and angiogenesis-related genes for predicting prognosis and immune infiltration in patients with HCC.

Hepatocellular carcinoma (HCC), the predominant form of primary liver cancer, ranks as the fourth leading cause of cancer-associated mortality globall...

AlphaMissense pathogenicity scores predict response to immunotherapy and enhances the predictive capability of tumor mutation burden.

Tumor Mutational Burden (TMB) is a widely used biomarker for selecting cancer patients for immune checkpoint inhibitor (ICI) therapy. However, TMB alo...

Metabolic characterization of tumor-immune interactions by multiplexed immunofluorescence reveals spatial mechanisms of immunotherapy response in non-small cell lung carcinoma (NSCLC).

Immune checkpoint inhibitors (ICI) have improved clinical outcomes for some patients with advanced NSCLC, however a substantial proportion of patients...

Integrated multi-dataset screening to predict prognosis and identify immunotherapy gene targets in hepatocellular carcinoma patients.

This study systematically predicts prognosis and key gene targets for immunotherapy in hepatocellular carcinoma (HCC) patients based on the joint scre...

Noninvasive skin imaging of melanocytic and nonmelanocytic tumours: recent findings.

PURPOSE OF REVIEW: Early diagnosis of skin cancer is essential for patient care. Noninvasive skin imaging devices have become increasingly used for th...