Cancer proteomics, current status, challenges, and future outlook

Afzal, Insha; Muzaffer, Umar; Ali, Sofi Imtiyaz; Sheikh, Wajid Mohammad; Rakhshan, Rabia; Nabi, Showkat Ul; Ahmad, Sheikh Bilal; Kulam, Mohd Afsahul; Paul, V.I.; Bashir, Showkeen Muzamil

doi:10.1016/b978-0-323-95072-5.00011-0

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Bioinformatics1

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2024

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“…Machine learning (ML), a subset of artificial intelligence (AI), can be used to predict useful diagnostic and prognostic biomarkers. ML or AI methods can help in combining and integrating a large amount of cancer proteome data as well as overcoming challenges in data collection and analysis ( 17 ). Recently, AI approaches have been included in various researches to detect cancer stages and discover biomarkers.…”

Section: Bioinformaticsmentioning

confidence: 99%

Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells

Jafari,

Farahani,

Abdollahpour-Alitappeh

et al. 2024

Front. Oncol.

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Cervical cancer (CC) is a major global health problem and leading cause of cancer deaths among women worldwide. Early detection through screening programs has reduced mortality; however, screening compliance remains low. Identifying non-invasive biomarkers through proteomics for diagnosis and monitoring response to treatment could improve patient outcomes. Here we review recent proteomics studies which have uncovered biomarkers and potential drug targets for CC. Additionally, we explore into the role of cervical cancer stem cells and their potential implications in driving CC progression and therapy resistance. Although challenges remain, proteomics has the potential to revolutionize the field of cervical cancer research and improve patient outcomes.

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Section: Bioinformaticsmentioning

confidence: 99%