ArticleFrontiers in cell and developmental biology2021
Fanconi Anemia Pathway Genes Advance Cervical Cancer
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- The Fanconi Anemia Pathway Inhibits mTOR Signaling and Prevents Accelerated Translation in Head and Neck Cancer Cells.Cancers · 2025Article
- Identification of a disulfidptosis-related genes signature for diagnostic and immune infiltration characteristics in cervical cancer.PloS one · 2025Article
- p53 in the Molecular Circuitry of Bone Marrow Failure Syndromes.International journal of molecular sciences · 2023Review
- A systematic approach identifies p53-DREAM pathway target genes associated with blood or brain abnormalities.Disease models & mechanisms · 2023Article
- Gene signature of mScientific reports · 2022Article
- Pan-cancer analysis of the prognostic and immunological role of Fanconi anemia complementation group E.Frontiers in genetics · 2022Article
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fanconi anemia (FA) pathway is a typical and multienzyme-regulated DNA damage repairer that influences the occurrence and development of disease including cancers. Few comprehensive analyses were reported about the role of FA-related genes (FARGs) and their prognostic values in cancers. In this study, a comprehensive pan-cancer analysis on 79 FARGs was performed. According to the correlation analyses between HPV integration sites and FARGs, we found that FARGs played specific and critical roles in HPV-related cancers, especially in cervical cancer (CC). Based on this, a FARGs-associated prognostic risk score (FPS) model was constructed, and subsequently a nomogram model containing the FPS was developed with a good accuracy for CC overall survival (OS) and recurrence-free survival (RFS) outcome prediction. We also used the similar expression pattern of FARGs by consensus clustering analysis to separate the patients into three subgroups that exhibited significant differential OS but not RFS. Moreover, differential expressed genes (DEGs) between the two risk groups or three clusters were identified and immune pathways as well as cell adhesion processes were determined by functional enrichment analysis. Results indicated that FARGs might promote occurrence and development of CC by regulating the immune cells' infiltration and cell adhesion. In addition, through the machine learning models containing decision tree, random forest, naïve bayes, and support vector machine models, screening of important variables on CC prognosis, we finally determined that
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