The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
CENPA Was a Cell Cycle Biomarker in Cancers With Diagnostic Prognostic, and Therapeutic Value
Open accessPreprint — published as 10.1371/journal.pone.0314745 · Paper page
Abstract
Background Cancer is one of the most concerning public health issues in the world. One of cancer hallmarks wildly accepted is sustaining proliferative signaling, which involved most of the cell cycle biological activities. Centromeric histone, Centromere Protein A (CENPA), a variant of canonical histone H3, plays an essential role in selective chromosome segregation in the cell cycle. However, so far, a systematic pan-cancer bioinformatic analysis has not been done yet. Methods We accessed genome, transcriptome, and clinical information from open databases. The genetic alteration, mRNA expression, functional enrichment, stemness, mutation association, expression in cell populations and cellular locations, cell cycle association, survival association of CENPA, and immune association were analyzed. A prognostic model for glioma patients was constructed as an example application of CENPA as a biomarker. Drugs targeting CENPA in cancer cells were also screened and predicted by the CENPA correlation of drug sensitivity and protein-ligand docking. Results CENPA had low gene mutation in cancers. CENPA was overexpressed in almost all cancer types in TCGA compared to their normal control. CENPA was highly expressed in the nucleus of malignant cells. CENPA was associated with the cell cycle of cancer cells. CENPA is a biomarker for the cell cycle G2 phase in cancer cells. CENPA was a diagnostic and prognostic biomarker across multiple cancer types. The prognosis of glioma with CENPA was reliable and can be applied with other prognostic factors. CENPA was associated with the immune microenvironment. Drugs CD-437, 3-Cl-AHPC, Trametinib, BI-2536, and GSK461364 were predicted to target CENPA in cancer cells. Conclusion CENPA was a cell cycle biomarker in cancers with diagnostic and prognostic value.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Flagged first, then by citations
Metadata
- Authors
- Liu, H., & Tang, T.
- Affiliations
- Jinan University (CN)
- Sun Yat-sen University Cancer Center (CN)
- Corresponding author
- Tang, Tao
- Venue
- PLoS ONE
- Publisher
- MDPI AG
- Published
- 2023-03-06
- Volume
- 20
- Issue
- 1
- Pages
- e0314745-e0314745
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC11737691
- OpenAlex
- W4406459740
- Cited by
- 48
- References
- 1
- Citations by year
- 2026: 7 · 2025: 41
- FWCI
- 21.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (81603342)
- K. C. Wong Education Foundation
- Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515012948, 2022A1515012641, 2022A151501264, 2022A151501264, 2022A1515012641, 2024A1515012948)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- CENP-A, histone H3 variant, tumor markers, prognostic factors, therapeutic target, cell cycle, G2 phase, glioma, tumor immune microenvironment, drug response prediction, protein-ligand docking, pan-cancer analysis, single-cell RNA sequencing, bulk RNA-seq
- MeSH terms
- Centromere Protein A, Chromosomal Proteins, Non-Histone, Humans, Mutation, Neoplasms, Prognosis, Biomarkers, Tumor, Gene Expression Regulation, Neoplastic, Sequence Analysis, RNA, Single-Cell Analysis, Tumor Microenvironment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
