Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0007095 (carcinoid)
 6,990 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Sixty-six human lung neoplasms of different histological types and normal bronchial epithelial cells of newborn babies and adults were studied histochemically using ConA and PSA and the result was compared with that of CEA. Normal mucosal epithelium could bind to ConA, and the location of ConA receptors was related to the maturation of mucosal epithelial cells. Normal mucosal epithelium in adult bronchi failed to be stained with PSA and anti-CEA, and most of lung neoplasms could bind to PSA and positive for CEA, indicating that new glycoconjugate and CEA-glycoprotein could be synthesized after malignant transformation of mucosal epithelium. The binding of ConA, PSA and anti-CEA to cell membrane and nucleus membrane was characteristic of squamous cell lung cancer while lung adenocarcinoma mainly showed cytoplasmic staining. The weak staining of ConA, PSA and anti-CEA in small cell carcinoma and negative staining in carcinoid and malignant melanoma help testify that their origin may differ from that of squamous cell carcinoma and adenocarcinoma.
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PMID:[Histochemical localization of glyco-conjugate and CEA-glycoprotein in human lung neoplasms]. 224 89

Lung cancer is one of the leading causes of death worldwide. There are three major types of lung cancers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and carcinoid. NSCLC is further classified into lung adenocarcinoma (LADC), squamous cell lung cancer (SQCLC) as well as large cell lung cancer. Many previous studies demonstrated that DNA methylation has emerged as potential lung cancer-specific biomarkers. However, whether there exists a set of DNA methylation markers simultaneously distinguishing such three types of lung cancers remains elusive. In the present study, ROC (Receiving Operating Curve), RFs (Random Forests) and mRMR (Maximum Relevancy and Minimum Redundancy) were proposed to capture the unbiased, informative as well as compact molecular signatures followed by machine learning methods to classify LADC, SQCLC and SCLC. As a result, a panel of 16 DNA methylation markers exhibits an ideal classification power with an accuracy of 86.54%, 84.6% and a recall 84.37%, 85.5% in the leave-one-out cross-validation (LOOCV) and independent data set test experiments, respectively. Besides, comparison results indicate that ensemble-based feature selection methods outperform individual ones when combined with the incremental feature selection (IFS) strategy in terms of the informative and compact property of features. Taken together, results obtained suggest the effectiveness of the ensemble-based feature selection approach and the possible existence of a common panel of DNA methylation markers among such three types of lung cancer tissue, which would facilitate clinical diagnosis and treatment.
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PMID:Classification of lung cancer using ensemble-based feature selection and machine learning methods. 2551 21