1 resultado para Analisi Discriminante, Teoria dei Network, Cross-Validation, Validazione.
em Bioline International
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Resumo:
Purpose: To identify markers for gynecological tumor diagnosis using antibody chip capture. Methods: Marker proteins, including cancer antigen 153 (CA153), CA125, and carcinoembryonic antigen (CEA), were analyzed using antibody chip capture of serum samples. Fifteen agglutinin types that specifically recognized five common glycans (fucose, sialic acid, mannose, N - acetylgalactosamine, and N-acetylglucosamine) were used to detect marker protein glycan levels. The levels of CA153, CA125, and CEA from 49 healthy control samples, 31 breast cancer samples, 24 cervical cancer samples, and 19 ovarian cancer samples were used to measure the glycan levels of these marker proteins. Results: In breast cancer samples, CA153 and CA125 were down-regulated (p < 0.01), while differences in ovarian cancer samples were not statistically significant (p > 0.01). The total accuracy was 85.1 %, with 96.8 % accuracy for breast cancer, 75 % in cervical cancer, and 78.9 % in ovarian cancer. Cross-validation analyses showed that breast cancer had 93.5 % accuracy, cervical cancer was 66.7 %, and ovarian cancer was 68.4 %, leading to 78.4 % total accuracy (58/74). Conclusions: The results indicate that better clinical diagnosis of gynecological tumors can be obtained by monitoring changes in glycan levels of serum proteins and types of proteoglycan changes.