Co-expression of tumor suppressor p53 (TP53) and cancer testis antigens (CTAs) as the possible indicator of “cancer-free” status
Bibliographic record
Abstract
Objective: Biomarkers are biological substances that can be measured and objectively evaluated as indicators of concrete processes at different levels. Advances in biomedicine facilitated the use and importance of biomarkers for healthcare purposes. Several biomarkers that are used in the field of oncology are already identified and used in clinical practice, although their sensitivity is not sufficient. To contribute to this issue, we aimed to determine the expression of total cancer-testis antigens (CTAs) in correlation with the expression levels of tumor suppressor proteins p53 (TP53) and p63 (TP63) as well as BRCA1 in a healthy cohort. Materials and Methods: We analyzed samples of 90 blood donors (28, 31.1% – females, 62, 68.9% – males) as they can be considered as an appropriate group for recruiting health cohorts. The age distribution of the subjects was between 20 and 60 years. The enzyme linked immunosorbent assay analysis was used for the determination of CTAs, TP53, TP63, and BRCA1 expression levels. Results: A strong correlation between CTAs and TP53 expression levels has been revealed. The expression variables of targeted biomarkers are not equally distributed. The data specific to CTAs, TP53, and TP63 expression levels are skewed to the left. In the case of BRCA1, the data may indicate the presence of 2 subgroups for study subjects. Conclusions: The co-expression of CTAs and TP53 may be considered as the indicator of “cancer-free” status. This parameter may be piloted for cancer screening and early diagnosis purposes. However, the role of CTAs for cellular process regulation and especially regulation of tumor suppressor gene p53 shall be investigated further.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".