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Data from Common Cancer Biomarkers

Posted on 2023-03-30 - 17:03
Abstract

There is an increasing interest in complementing conventional histopathologic evaluation with molecular tools that could increase the sensitivity and specificity of cancer staging for diagnostic and prognostic purposes. This study strove to identify cancer-specific markers for the molecular detection of a broad range of cancer types. We used 373 archival samples inclusive of normal tissues of various lineages and benign or malignant tumors (predominantly colon, melanoma, ovarian, and esophageal cancers). All samples were processed identically and cohybridized with an identical reference RNA source to a custom-made cDNA array platform. The database was split into training (n = 201) and comparable prediction (n = 172) sets. Leave-one-out cross-validation and gene pairing analysis identified putative cancer biomarkers overexpressed by malignant lesions independent of tissue of derivation. In particular, seven gene pairs were identified with high predictive power (87%) in segregating malignant from benign lesions. Receiver operator characteristic curves based on the same genes could segregate malignant from benign tissues with 94% accuracy. The relevance of this study rests on the identification of a restricted number of biomarkers ubiquitously expressed by cancers of distinct histology. This has not been done before. These biomarkers could be used broadly to increase the sensitivity and accuracy of cancer staging and early detection of locoregional or systemic recurrence. Their selective expression by cancerous compared with paired normal tissues suggests an association with the oncogenic process resulting in stable expression during disease progression when the presently used differentiation markers are unreliable. (Cancer Res 2006; 66(6): 2953-61)

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Cancer Research

AUTHORS (15)

  • Christopher F. Basil
    Yingdong Zhao
    Katia Zavaglia
    Ping Jin
    Monica C. Panelli
    Sonia Voiculescu
    Susanna Mandruzzato
    Hueling M. Lee
    Barbara Seliger
    Ralph S. Freedman
    Phil R. Taylor
    Nan Hu
    Paola Zanovello
    Francesco M. Marincola
    Ena Wang

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