ERCC6 plays a promoting role in the progression of non-small cell lung cancer
Bibliographic record
Abstract
Although excision repair cross-complementing group 6 (ERCC6) has been reported to be associated with lung cancer risk, the specific roles of ERCC6 in non-small cell lung cancer (NSCLC) progression are inadequately studied. Thus, this study aimed to examine the potential functions of ERCC6 in NSCLC. The expression of ERCC6 in NSCLC was analyzed by immunohistochemical staining and quantitative PCR. Celigo cell count, colony formation, flow cytometry, wound-healing, and transwell assays were used to evaluate the effects of ERCC6 knockdown on the proliferation, apoptosis, and migration of NSCLC cells. The effect of ERCC6 knockdown on tumor-forming ability of NSCLC cells was estimated by establishing xenograft model. ERCC6 was highly expressed in NSCLC tumor tissues and cell lines, and high ERCC6 expression was significantly associated with poor overall survival. Additionally, ERCC6 knockdown significantly suppressed cell proliferation, colony formation and migration, while accelerated cell apoptosis of NSCLC cells in vitro. Moreover, ERCC6 knockdown inhibited tumor growth in vivo. Further studies verified that ERCC6 knockdown attenuated the expression levels of Bcl-w, CCND1, and c-Myc. Altogether, these data unveil a major role of ERCC6 in the progression of NSCLC, and ERCC6 is expected to become a novel therapeutic target for NSCLC treatment.
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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) | 0.002 | 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".