Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success.
Matheus Correia CasottiDébora Dummer MeiraAléxia Stefani Siqueira ZetumBruno Cancian de AraújoDanielle Ribeiro Campos da SilvaEldamária de Vargas Wolfgramm Dos SantosFernanda Mariano GarciaFlavia de PaulaGabriel Mendonça SantanaLuana Santos LouroLyvia Neves Rebello AlvesRaquel Furlani Rocon BragaRaquel Silva Dos Reis TrabachSara Santos BernardesThomas Erik Santos LouroEduardo Cremonese Filippi ChielaGuido LenzElizeu Fagundes de CarvalhoIuri Drumond LouroPublished in: Genes (2023)
Precision and organization govern the cell cycle, ensuring normal proliferation. However, some cells may undergo abnormal cell divisions (neosis) or variations of mitotic cycles (endopolyploidy). Consequently, the formation of polyploid giant cancer cells (PGCCs), critical for tumor survival, resistance, and immortalization, can occur. Newly formed cells end up accessing numerous multicellular and unicellular programs that enable metastasis, drug resistance, tumor recurrence, and self-renewal or diverse clone formation. An integrative literature review was carried out, searching articles in several sites, including: PUBMED, NCBI-PMC, and Google Academic, published in English, indexed in referenced databases and without a publication time filter, but prioritizing articles from the last 3 years, to answer the following questions: (i) "What is the current knowledge about polyploidy in tumors?"; (ii) "What are the applications of computational studies for the understanding of cancer polyploidy?"; and (iii) "How do PGCCs contribute to tumorigenesis?"
Keyphrases
- cell cycle
- induced apoptosis
- cell cycle arrest
- cell proliferation
- healthcare
- stem cells
- papillary thyroid
- endoplasmic reticulum stress
- cell death
- single cell
- machine learning
- big data
- free survival
- case report
- squamous cell carcinoma
- oxidative stress
- randomized controlled trial
- mesenchymal stem cells
- bone marrow
- deep learning
- meta analyses