Aberrant expression and demethylation of γ‐synuclein in colorectal cancer, correlated with progression of the disease
Bibliographic record
Abstract
Recent evidence suggests that gamma-synuclein is abnormally expressed in a high percentage of tumor tissues of diversified cancer types, but rarely expressed in tumor-matched non-neoplastic adjacent tissues (NNAT). The molecular mechanism of CpG island demethylation may underlie aberrant gamma-synuclein expression. To fully understand the roles of aberrant gamma-synuclein expression and demethylation in the development of colorectal cancer (CRC), we examined the expression and methylation status of gamma-synuclein in 67 CRC samples, 30 NNAT samples, and five CRC cell lines as well. By using reverse transcription-polymerase chain reaction (RT-PCR), western blot, and immunohistochemistry analyses, gamma-synuclein expression was detected in both HT-29 and HCT116 cells, and was much higher in CRC samples than in NNAT samples (P < 0.05). The demethylating agent, 5-aza-2 cent-deoxycytidine, can induce re-expression of gamma-synuclein in COLO205, LoVo, and SW480 cells. Unmethylated gamma-synuclein alleles were detected in HT-29, HCT116, and LoVo cells by nested methylation-specific PCR, and the demethylated status of gamma-synuclein was much higher in CRC samples than in NNAT samples by real-time quantitative methylation-specific PCR (P < 0.05). The results of genomic bisulfite DNA sequencing further confirmed that the aberrant gamma-synuclein expression in CRC was primarily attributed to the demethylation of CpG island. The protein expression and demethylation status of gamma-synuclein in 67 CRC samples correlated with clinical stage, lymph node involvement, and distant metastasis. These findings suggest an involvement of aberrant gamma-synuclein expression and demethylation in progression of CRC, especially in advanced stages.
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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 | 0.001 | 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 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".