The Expression Patterns of Human Cancer-Testis Genes Are Induced through Epigenetic Drugs in Colon Cancer Cells
Bibliographic record
Abstract
Background: The expression of human germline genes is restricted to the germ cells of the gonads, which produce sperm and eggs. The germline genes involved in testis development and potentially activated in cancer cells are known as cancer-testis (CT) genes. These genes are potential therapeutic targets and biomarkers, as well as drivers of the oncogenic process. CT genes can be reactivated by treatment with drugs that demethylate DNA. The majority of the existing literature on CT gene activation focuses on X-chromosome-produced CT genes. We tested the hypothesis that epigenetic landscape changes, such as DNA methylation, can alter several CT gene expression profiles in cancer and germ cells. Methods: Colon cancer (CC) cell lines were treated with the DNA methyltransferase inhibitor (DNMTi) 5-aza-2’-deoxycytidine, or with the histone deacetylase inhibitor (HDACi) trichostatin A (TSA). The effects of these epigenetic treatments on the transcriptional activation of previously published CT genes (CTAG1A, SCP2D1, TKTL2, LYZL6, TEX33, and ACTRT1) and testis-specific genes (NUTM1, ASB17, ZSWIM2, ADAM2, and C10orf82) were investigated. Results: We found that treatment of CC cell lines with 5-aza-2’-deoxycytidine or TSA correlated with activation of X-encoded CT genes and non-X-encoded CT genes in somatic (non-germline) cells. Conclusion: These findings confirm that a subset of CT genes can be regulated by hypomethylating drugs and subsequently provide a potential therapeutic target for cancer.
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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 | 0.000 | 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) | 0.002 | 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".