Skin Cancer

Development and validation of a prognostic model for predicting survival and immunotherapy benefits in melanoma based on metabolism-relevant genes.

Skin cutaneous melanoma (SKCM) is a fatal form of skin cancer. Metabolism-related genes (MRGs) comprise a group of genes that possess the ability to modulate and regulate metabolic pathways. In this study, the expression levels of MRGs were used to classify SKCM patients into three molecular subtypes. Then the differentially expressed genes (DEGs) among the three MRGs molecular clusters were appli...

SLC3A2 as a key anoikis-related gene for prognosis and tumor microenvironment remodeling in melanoma.

OBJECTIVE: Anoikis, a form of programmed cell death triggered by detachment from the extracellular matrix, plays a crucial role in metastasis and immune escape in melanoma. We aimed to identify anoikis-related prognostic markers using integrated machine learning and single-cell analysis.

Identification of neural crest and melanoma cancer cell invasion and migration genes using high-throughput screening and deep attention networks.

BACKGROUND: Cell migration and invasion are well-coordinated in development and disease but remain poorly understood. We previously showed that the ne...

Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.

Melanoma is the most dangerous type of skin cancer. Although it accounts for only about 1% of all skin cancer cases, it is responsible for the majorit...

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.

Single-cell spatial transcriptomics reveals immunotherapy-driven bone marrow niche remodeling in AML.

Given the graft-versus-leukemia effect observed with allogeneic hematopoietic stem cell transplantation in refractory or relapsed acute myeloid leukem...

Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.

Neoantigens, which are tumor-specific peptides generated by malignant cells, can be presented to T cells to elicit immune responses. Owing to their tu...

Leveraging machine learning models to evaluate immune infiltration in the ovarian cancer microenvironment: a single-cell analysis approach.

BACKGROUND: The prognosis of ovarian cancer is closely related to the degree of immune cell infiltration within the tumor microenvironment. However, c...

Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning.

Baseline genomic data have not demonstrated significant value for predicting the response duration to MAPK inhibitors (MAPKi) in patients with advance...

Analysis of Single-Cell RNA-Seq Data to Investigate Tumor Cell Heterogeneity in Uroepithelial Bladder Cancer and Predict Immunotherapy Response.

BACKGROUND: Numerous studies have suggested a close association between cancer stem cells (CSCs) and the tumor microenvironment (TME), suggesting that...

Metabolic alterations driven by LDHA in CD8 + T cells promote immune evasion and therapy resistance in NSCLC.

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths worldwide. Despite advancements in treatment, prognosis for patients wi...

Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.

Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. Utilizing ...

Corrigendum to 'CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy' [Acad Radiol 32/6 (2025) 3397-3409].

Deep Learning Multi Modal Melanoma Detection: Algorithm Development and Validation.

BACKGROUND: The visual similarity of melanoma and seborrheic keratosis has made it difficult for elderly patients with disabilities to know when to se...

Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex mo...

Exploring the impact of neutrophils on lung adenocarcinoma using Mendelian randomization and transcriptomic study.

Tumor immune microenvironment plays a crucial role in determining the prognosis of lung adenocarcinoma (LUAD), with the interaction of immune cells wi...

Folate metabolism-associated CYP26A1 is a clinico-immune target in colorectal cancer.

Folic acid plays a crucial role in cellular regulation and metabolism, commonly included in dietary supplements. Despite this, its involvement in colo...

Stereotactic radiotherapy or protons for uveal melanoma patients? An artificial intelligence (AI)-based clinical treatment decision-making tool predicting doses to radiotherapy constraints.

PURPOSE: For ocular melanoma, selecting between stereotactic radiotherapy (SRT) and protons requires a lengthy plan comparison process. The purpose of...

DCAF13 Regulates Cell Proliferation and Immune Escape of Hepatocellular Carcinoma Through Activating the NF-κB Pathway.

Hepatocellular carcinoma (HCC), the third leading cause of global cancer deaths, has a high unmet clinical need due to limited therapeutic efficacy. I...

Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers.

PANoptosis is closely associated with tumorigenesis and therapeutic response, yet its role in multiple myeloma (MM) remains unclear. This study analyz...