AIMC Topic: Biomarkers, Tumor

Clear Filters Showing 41 to 50 of 1403 articles

Macrophage mitophagy-related genes predict prognosis and therapeutic response in lung adenocarcinoma.

Scientific reports
Mitochondrial autophagy (mitophagy) in macrophages is crucial yet poorly understood within the lung adenocarcinoma (LUAD) tumor microenvironment. This study aimed to identify key macrophage mitophagy-related genes and develop a robust prognostic mode...

Regulators of homologous recombination deficiency identified by machine learning using somatic multi-omics data.

Life science alliance
Homologous recombination deficiency (HRD) is a critical biomarker for guiding targeted therapies, yet the full range of somatic alterations driving HRD across cancers remains incompletely characterized. Here, we present a tumor-agnostic machine learn...

Construction and validation of gene signature for prognosis and drug sensitivity in cholangiocarcinoma based on cellular senescence related genes.

Scientific reports
Cholangiocarcinoma is a very deadly epithelial cell cancer with poor clinical outcome. Cellular senescence plays a vital role in the oncogenesis and the aggressiveness of cholangiocarcinoma. Integrative machine learning procedure including 10 methods...

An assisted diagnostic and prognostic model for endometrial cancer using 36 serological markers and clinical variables from 562 patients.

Scientific reports
Endometrial carcinoma (EC) has demonstrated a concerning epidemiological trajectory. Current evaluation systems for EC are limited to postoperative analysis, necessitating the development of a preoperative risk stratification model. Researchers aimed...

Multimodal AI and tumour microenvironment integration predicts metastasis in cutaneous melanoma.

Nature communications
Accurate prognostication is essential to guide clinical management in localised cutaneous melanoma (CM), the form of skin cancer with the highest mortality. While the tumour microenvironment (TME) plays a key role in disease progression, current stag...

Automatically quantifying spatial heterogeneity of immune and tumor hypoxia environment and predicting disease-free survival for patients with rectal cancer.

Cancer immunology, immunotherapy : CII
Immunohistochemistry (IHC) remains the gold standard for evaluating protein expression in tumor microenvironment analysis. This approach hinders robust correlation analyses between spatial heterogeneity in the tumor microenvironment and clinical outc...

Integrated machine learning and single-cell analysis identify chromatin-remodeling gene signature for diagnosis and prognosis in nasopharyngeal carcinoma.

Clinical and experimental medicine
This study examines the function of chromatin-remodeling genes (CRGs) in nasopharyngeal carcinoma (NPC), with an emphasis on their potential as prognostic and diagnostic biomarkers. We examined gene expression information collected from multiple data...

Automated quantification of Ki-67 expression in breast cancer from H&E-stained slides using a transformer-based regression model.

Breast cancer research : BCR
BACKGROUND: Accurate quantification of the Ki-67 proliferation index is essential for breast cancer prognosis and treatment planning. Current automated methods, including classical and deep learning approaches based on cell detection or segmentation,...

Machine learning-based screening and validation of pyroptosis-associated prognostic genes and potential drugs in cervical cancer.

BMC medical genomics
Pyroptosis is a newly discovered form of programmed cell death, but its mechanism in the development of cervical cancer has not been elucidated. Cervical cancer differentially expressed pyroptosis-related genes were identified via bioinformatic analy...

Development of a consensus molecular classifier for pancreatic ductal adenocarcinoma.

Genome medicine
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) presents a significant challenge, with a 5-year survival rate of approximately 10%. Tumor heterogeneity contributes to the limited effectiveness of treatments. Several tumor and stroma molecular cla...