Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0677930 (primary tumor)
 20,210 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

To study the molecular mechanisms underlying the intensive expression of acetylcholinesterase (AChE) in different tumor types, we characterized levels and composition of its messenger RNA (mRNA) sequences in heterologous tumor cell lines, primary tumor biopsies, and normal fetal and adult tissues and determined their exon-intron origin within the corresponding ACHE gene. Reverse transcription followed by polymerase chain reaction (RT-PCR) revealed three alternatively spliced ACHE mRNAs in NT2/D1 teratocarcinoma, NCI-N-592 small cell lung carcinoma, TE671 medulloblastoma, K-562 erythroleukemia, and 293 transformed embryonal kidney cells. The three ACHE mRNAs include the principal species expressed in brain and muscle and two additional transcripts containing insertions of 751 or 829 residues downstream from the exon 4 domain. The inserted region, which represents an intron in brain and muscle, is expressed in the tumor cell lines either as a "readthrough" form or with 78 residues deleted from its 5' end. A major band of 2.5 kb was labeled with ACHE cDNA in poly(A)+ RNA blots from medulloblastoma cells or brain tissue, whereas a PCR-amplified probe from the inserted domain labeled a 3.4-kb band but not the 2.5-kb band in poly(A)+ RNA from small cell lung carcinoma. The ACHE mRNAs including the alternative insertions were found only in cell lines with levels of the principal ACHE mRNA species equal to or higher than those in brain (1-10 molecules/cell), determined by following the kinetics of mRNA PCR amplification. Genomic DNA sequencing revealed that the inserted domains in the ACHE mRNAs expressed in the tumor cell lines encode C-terminal peptides of 40 and 14 residues. These include a free cysteine, terminate with the consensus HG element, and continue by a 29-residue-long C-terminal hydrophobic cleavable peptide, properties characteristic of precursors to phosphoinositide (PI)-linked proteins. In extension of the reported expression of PI-linked AChE in hemopoietic cells including K-562, our findings demonstrate the existence of ACHE mRNAs with the potential to encode one hydrophilic and two PI-linked forms of AChE in tumor cells from both hemopoietic and nonhemopoietic origins.
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PMID:Expression of three alternative acetylcholinesterase messenger RNAs in human tumor cell lines of different tissue origins. 829 25

Our objective was to predict the outcome of 90Y radioembolization in patients with intrahepatic tumors from pretherapeutic baseline parameters and to identify predictive variables using a machine-learning approach based on random survival forests. Methods: In this retrospective study, 366 patients with primary (n = 92) or secondary (n = 274) liver tumors who had received 90Y radioembolization were analyzed. A random survival forest was trained to predict individual risk from baseline values of cholinesterase, bilirubin, type of primary tumor, age at radioembolization, hepatic tumor burden, presence of extrahepatic disease, and sex. The predictive importance of each baseline parameter was determined using the minimal-depth concept, and the partial dependency of predicted risk on the continuous variables bilirubin level and cholinesterase level was determined. Results: Median overall survival was 11.4 mo (95% confidence interval, 9.7-14.2 mo), with 228 deaths occurring during the observation period. The random-survival-forest analysis identified baseline cholinesterase and bilirubin as the most important variables (forest-averaged lowest minimal depth, 1.2 and 1.5, respectively), followed by the type of primary tumor (1.7), age (2.4), tumor burden (2.8), and presence of extrahepatic disease (3.5). Sex had the highest forest-averaged minimal depth (5.5), indicating little predictive value. Baseline bilirubin levels above 1.5 mg/dL were associated with a steep increase in predicted mortality. Similarly, cholinesterase levels below 7.5 U predicted a strong increase in mortality. The trained random survival forest achieved a concordance index of 0.657, with an SE of 0.02, comparable to the concordance index of 0.652 and SE of 0.02 for a previously published Cox proportional hazards model. Conclusion: Random survival forests are a simple and straightforward machine-learning approach for prediction of overall survival. The predictive performance of the trained model was similar to a previously published Cox regression model. The model has revealed a strong predictive value for baseline cholinesterase and bilirubin levels with a highly nonlinear influence for each parameter.
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PMID:Prediction of 90Y Radioembolization Outcome from Pretherapeutic Factors with Random Survival Forests. 2914 92