Skin Cancer

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 specificity and sensitivity. Quantitatively understanding how T cell receptors discern among antigens requires biophysical models and theoretical analyses of signaling networks. Here, we review current theoretical frameworks of antigen recognition in the cont...

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...

Overcoming Resistance in EGFR-Mutant Cancers: A Comprehensive Review of Inhibitor Evolution and SAR-Based Design.

The epidermal growth factor receptor (EGFR) is a key target in cancer therapy, mainly in non-small cell lung cancer (NSCLC). Though, the efficacy of E...

Fifty Years of Natural Killer Cells: Milestones and Future Horizons.

The discovery of natural killer (NK) cells stands among the fundamental milestones in modern immunology. To mark the 50-year anniversary of the first ...

Intelligent multi omics and industry 6.0 and 7.0 enabled technologies for deciphering the tumor immune microenvironment and advancing cancer immunotherapy.

Recent advancements in cancer immunotherapy have transformed clinical oncology, with monoclonal antibodies (mAbs), immune checkpoint inhibitors, adopt...

A novel signature of palmitoylation for predicting prognosis and therapeutic response of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) represents an extremely complex and heterogeneous malignant tumor. Protein palmitoylation, a highly conserved and pivot...

RNA modifications in cancer and their detection: a review.

Ribonucleic acid (RNA) modifications, once viewed as static structural features, are now recognized as dynamic regulators of the 'epitranscriptome' th...

Deep learning for early diagnosis of uveal melanoma: a systematic review and meta-analysis.

BACKGROUND: Uveal melanoma (UM) is a rare cancer with an estimated annual incidence of 6 incidences per million people. About half of UM patients deve...

A deep state-space analysis framework for cancer patient latent state estimation and classification from EHR time-series data.

Advancements in deep learning technologies and an increase in medical data have enhanced the accuracy of disease diagnosis and treatment strategies. N...

Response to venom immunotherapy: Exploratory retrospective machine learning clustering analysis.

BACKGROUND: Hymenoptera venom immunotherapy is an established treatment for severe allergic reactions, aiming to modulate the immune response and redu...

Multi-omics analysis reveals features improving outcomes and immunotherapy resistance in MSS colorectal cancer.

In contrast to the MSI-H subtype, most microsatellite-stable (MSS) colorectal cancer (CRC) patients exhibit poor prognosis and limited responsiveness ...

Unraveling heterogeneity in LUAD via multi-omics integration: molecular classification and therapeutic implications.

Lung adenocarcinoma (LUAD) displays significant morphological and molecular heterogeneity both within and between tumors. This heterogeneity, coupled ...

Construction of a risk model based on exosome-related genes predict clinical prognosis and therapeutic response and revealing TIMP1 as a promising target in colorectal cancer.

Various exosome-derived proteins have been reported to play essential roles in regulating colorectal cancer progression and affecting the prognosis of...