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  1. Article: Circulating Biomarkers for Cancer Detection: Could Salivary microRNAs Be an Opportunity for Ovarian Cancer Diagnostics?

    Robotti, Marzia / Scebba, Francesca / Angeloni, Debora

    Biomedicines

    2023  Volume 11, Issue 3

    Abstract: MicroRNAs (miRNAs) are small non-coding RNAs with the crucial regulatory functions of gene expression at post-transcriptional level, detectable in cell and tissue extracts, and body fluids. For their stability in body fluids and accessibility to sampling, ...

    Abstract MicroRNAs (miRNAs) are small non-coding RNAs with the crucial regulatory functions of gene expression at post-transcriptional level, detectable in cell and tissue extracts, and body fluids. For their stability in body fluids and accessibility to sampling, circulating miRNAs and changes of their concentration may represent suitable disease biomarkers, with diagnostic and prognostic relevance. A solid literature now describes the profiling of circulating miRNA signatures for several tumor types. Among body fluids, saliva accurately reflects systemic pathophysiological conditions, representing a promising diagnostic resource for the future of low-cost screening procedures for systemic diseases, including cancer. Here, we provide a review of literature about miRNAs as potential disease biomarkers with regard to ovarian cancer (OC), with an excursus about liquid biopsies, and saliva in particular. We also report on salivary miRNAs as biomarkers in oncological conditions other than OC, as well as on OC biomarkers other than miRNAs. While the clinical need for an effective tool for OC screening remains unmet, it would be advisable to combine within a single diagnostic platform, the tools for detecting patterns of both protein and miRNA biomarkers to provide the screening robustness that single molecular species separately were not able to provide so far.
    Language English
    Publishing date 2023-02-21
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2720867-9
    ISSN 2227-9059
    ISSN 2227-9059
    DOI 10.3390/biomedicines11030652
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Top-Down Proteomics of Human Saliva, Analyzed with Logistic Regression and Machine Learning Methods, Reveal Molecular Signatures of Ovarian Cancer.

    Scebba, Francesca / Salvadori, Stefano / Cateni, Silvia / Mantellini, Paola / Carozzi, Francesca / Bisanzi, Simonetta / Sani, Cristina / Robotti, Marzia / Barravecchia, Ivana / Martella, Francesca / Colla, Valentina / Angeloni, Debora

    International journal of molecular sciences

    2023  Volume 24, Issue 21

    Abstract: Ovarian cancer (OC) is the most lethal of all gynecological cancers. Due to vague symptoms, OC is mostly detected at advanced stages, with a 5-year survival rate (SR) of only 30%; diagnosis at stage I increases the 5-year SR to 90%, suggesting that early ...

    Abstract Ovarian cancer (OC) is the most lethal of all gynecological cancers. Due to vague symptoms, OC is mostly detected at advanced stages, with a 5-year survival rate (SR) of only 30%; diagnosis at stage I increases the 5-year SR to 90%, suggesting that early diagnosis is essential to cure OC. Currently, the clinical need for an early, reliable diagnostic test for OC screening remains unmet; indeed, screening is not even recommended for healthy women with no familial history of OC for fear of post-screening adverse events. Salivary diagnostics is considered a major resource for diagnostics of the future. In this work, we searched for OC biomarkers (BMs) by comparing saliva samples of patients with various stages of OC, breast cancer (BC) patients, and healthy subjects using an unbiased, high-throughput proteomics approach. We analyzed the results using both logistic regression (LR) and machine learning (ML) for pattern analysis and variable selection to highlight molecular signatures for OC and BC diagnosis and possibly re-classification. Here, we show that saliva is an informative test fluid for an unbiased proteomic search of candidate BMs for identifying OC patients. Although we were not able to fully exploit the potential of ML methods due to the small sample size of our study, LR and ML provided patterns of candidate BMs that are now available for further validation analysis in the relevant population and for biochemical identification.
    MeSH term(s) Humans ; Female ; Saliva ; Proteomics/methods ; Logistic Models ; Ovarian Neoplasms/diagnosis ; Biomarkers, Tumor ; Machine Learning
    Chemical Substances Biomarkers, Tumor
    Language English
    Publishing date 2023-10-28
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms242115716
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: WITHDRAWN: Oncogenic KRAS

    Lasse-Opsahl, Emily / Baliira, Rachael / Barravecchia, Ivana / McLintock, Elyse / Lee, Jennifer M / Ferris, Sarah F / Espinoza, Carlos E / Hinshaw, Rachael / Cavanaugh, Sophia / Robotti, Marzia / Brown, Kristee / Donahue, Katelyn / Abdelmalak, Kristena Y / Galban, Craig J / Frankel, Timothy L / Zhang, Yaqing / di Magliano, Marina Pasca / Galban, Stefanie

    bioRxiv : the preprint server for biology

    2024  

    Abstract: This manuscript has been withdrawn by the authors due to a dispute over co-first authorship that is currently being arbitrated by the medical school at our institution. Therefore, the authors do not wish this work to be cited as reference for the project. ...

    Abstract This manuscript has been withdrawn by the authors due to a dispute over co-first authorship that is currently being arbitrated by the medical school at our institution. Therefore, the authors do not wish this work to be cited as reference for the project. Upon completion of the arbitration process, we will take steps to revert the current withdrawn status. If you have any questions, please contact the corresponding author.
    Language English
    Publishing date 2024-02-06
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.01.16.568090
    Database MEDical Literature Analysis and Retrieval System OnLINE

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