ArticleTranslational cancer research2022
Construction and validation of prognostic prediction established on N6-methyladenosine related genes in cervical squamous cell carcinoma.
Article in Translational cancer research, 2022. 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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6 citing papers in PubMed, 7 citations in OpenAlex.
- Differences in the expression profiles of m6A-related genes and the prognostic role in immunotherapy for lung adenocarcinoma patients.Frontiers in oncology · 2026Article
- Epitranscriptomics and cervical cancer: the emerging role of mExpert reviews in molecular medicine · 2024Review
- A comprehensive review of mEpigenomics · 2024Review
- Elucidating prognosis in cervical squamous cell carcinoma and endocervical adenocarcinoma: a novel anoikis-related gene signature model.Frontiers in oncology · 2024Article
- Review
- The m6A reader IGF2BP1 manipulates BUB1B expression to affect malignant behaviors, stem cell properties, and immune resistance of non-small-cell lung cancer stem cells.Cytotechnology · 2023Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cervical cancer (CESC) is the second most common cancer death in middle-aged women. The N6-methyladenosine (m6A) plays an essential role in the epitranscriptomics of cancer and affects immune cell infiltration. Our study used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data to construct and validate prognostic prediction established on m6A-related genes in CESC. Methods: We gained gene expression and clinical characteristics from TCGA and GEO. After differentially expression analysis of the m6A-related genes, we identified eight genes of CESC development. Next, we executed consensus clustering to analyze CESC types established on the differential expression of the m6A-related genes and found different subtypes significantly correlate with survival prognosis, immune microenvironment, and PD-L1 expression. Then, based on Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, a five-gene ( Results: The CESC could be divided into subtypes according to eight expression m6A-related genes. Different subtypes are related to various immune cells, immune scores, and the expression of the PD-L1. We develop a risk prediction model: risk score = (0.023558929) * Exp IGF2BP1 + (0.021148829) * Exp IGF2BP2 + (0.045035491) * Exp HNRNPA2B1 + (-0.106566550) * Exp YTHDF1 + (-0.001037932) * Exp RBM15. Moreover, different m6A-related genes significantly correlated with immune cells. Conclusions: The m6A-related genes risk prediction model plays an essential role in predicting CESC patients. The m6A-related genes affected the immune cell infiltration in CESC. These results suggest that the expression of m6A-related genes may influence the immune therapy of CESC and be the potential therapeutic target.
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