Elevated expression of the centromere protein‐A(CENP‐A)‐encoding gene as a prognostic and predictive biomarker in human cancers
Bibliographic record
Abstract
Centromere protein-A (CENP-A), a histone-H3 variant, plays an essential role in cell division by ensuring proper formation and function of centromeres and kinetochores. Elevated CENP-A expression has been associated with cancer development. This study aimed to establish whether elevated CENP-A expression can be used as a prognostic and predictive cancer biomarker. Molecular profiling of CENP-A in human cancers was investigated using genomic, transcriptomic and patient information from databases, including COSMIC, Oncomine, Kaplan-Meier plotter and cBioPortal. A network of CENP-A co-expressed genes was derived from cBioPortal and analyzed using Ingenuity Pathway Analysis (IPA) and Oncomine protocols to explore the function of CENP-A and its predictive potential. Transcriptional and post-transcriptional regulation of CENP-A expression was analyzed in silico. It was found that CENP-A expression was elevated in 20 types of solid cancer compared with normal counterparts. Elevated CENP-A expression highly correlated with cancer progression and poor patient outcome. Genomic analysis indicated that the elevated CENP-A expression was not due to alterations in the sequence or copy number of the CENP-A gene. Furthermore, CENP-A can be regulated by key oncogenic proteins and tumor-suppressive microRNAs. CENP-A co-expression network analysis indicated that CENP-A function is associated with cell cycle progression. Oncomine analysis showed a strong correlation between elevated CENP-A expression and oncolytic response of breast cancer patients to taxane-based chemotherapy. In conclusion, elevated CENP-A expression is coupled to malignant progression of numerous types of cancer. It may be useful as a biomarker of poor patient prognosis and as a predictive biomarker for taxane-based chemotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".