AI Medical Compendium Topic:
Biomarkers, Tumor

Clear Filters Showing 831 to 840 of 1021 articles

Characterization of m6A-Related Genes in Tumor-Associated Macrophages for Prognosis, Immunotherapy, and Drug Prediction in Lung Adenocarcinomas Based on Machine Learning Algorithms.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Tumor-associated macrophages (TAMs) are a vital immune component within the tumor microenvironment (TME) of lung adenocarcinoma (LUAD), exerting significant influence on tumor growth, metastasis, and drug resistance. N6-methyladenosine (m6A) modifica...

Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.

Journal of cellular and molecular medicine
Oral cancer is among the most prevalent malignant tumours worldwide; prognosis can be affected by several factors, including molecular subtypes, immune microenvironment and clinical characteristics. In this study, we aimed to apply machine learning m...

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer.

Seminars in cancer biology
Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although ur...

Identification of molecular subtypes and a prognostic signature based on machine learning and purine metabolism-related genes in breast cancer.

Medicine
Breast cancer (BC), one of the most prevalent malignant tumors worldwide, lacks efficacious diagnostic biomarkers and therapeutic targets. This study harnesses multi-omics data to identify novel purine metabolism-related genes (PMRG) as potential bio...

Machine learning-assisted washing-free detection of extracellular vesicles by target recycling amplification based fluorescent aptasensor for accurate diagnosis of gastric cancer.

Talanta
Extracellular vesicles (EVs) are promising non-invasive biomarkers for cancer diagnosis. EVs proteins play a critical role in tumor progress and metastasis. However, accurately and reliably diagnosing cancers is greatly limited by single protein mark...

Unsupervised machine learning-based stratification and immune deconvolution of liver hepatocellular carcinoma.

BMC cancer
BACKGROUND: Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer and a leading cause of cancer-related deaths globally. The tumour microenvironment (TME) influences treatment response and prognosis, yet its heterogeneity remains ...

MANIFEST: Multiomic Platform for Cancer Immunotherapy.

Cancer discovery
Immunotherapy has revolutionized survival outcomes for many patients diagnosed with cancer. However, biomarkers that can reliably distinguish treatment responders from nonresponders, predict potential life-threatening and life-changing drug-induced t...

Identifying GAP43, NMU, and TEX29 as Potential Prognostic Biomarkers for COPD Combined With Lung Cancer Patients Using Machine Learning.

The journal of gene medicine
Chronic obstructive pulmonary disease (COPD) and lung cancer, frequently comorbid conditions intricately linked through smoking, represent significant global health challenges. COPD is a common comorbidity in nonsmall cell lung cancer (NSCLC) patient...

Identification of a Biomarker Panel in Extracellular Vesicles Derived From Non-Small Cell Lung Cancer (NSCLC) Through Proteomic Analysis and Machine Learning.

Journal of extracellular vesicles
Antigen fingerprint profiling of tumour-derived extracellular vesicles (TDEVs) in the body fluids is a promising strategy for identifying tumour biomarkers. In this study, proteomic and immunological assays reveal significantly higher CD155 levels in...