CD142 plays a key role in the carcinogenesis of gastric adenocarcinoma by inhibiting <i>BCL2</i>-dependent autophagy
Bibliographic record
Abstract
CD142 is expressed on the surface of multiple malignant tumors and contributes to carcinogenesis. However, the role of CD142 in the pathogenesis of gastric adenocarcinoma (GAC) remains unclear. This study aimed to investigate the role of CD142 in GAC carcinogenesis. Our results showed that CD142 expression was significantly increased in GAC cancer tissues, especially in those with significant invasion or metastasis. The invasion and migration of CD142-positive SNU16 cells was significantly increased compared to that of CD142-negative cells. Moreover, CD142 overexpression promoted the invasion and migration of SGC083 cells, but CD142 silencing had the opposite effect. In addition, there was a positive correlation between CD142 expression in cancer tissues and serum Interleukin-8 (IL-8) levels. CD142 overexpression promoted IL-8 production in SGC083 cells. In vivo analysis showed that the implantation of CD142-positive SNU16 cells promoted the growth of xenograft tumor and the production of IL-8. Mechanistically, CD142 silencing not only inhibited the expression of BCL2 and the interaction between BCL2 and Beclin1, but also promoted the autophagic response in SGC083. Furthermore, CD142 silencing-induced IL-8 degradation was recovered by treatment with the autophagy inhibitor, 3-MA. CD142 can inhibit autophagic cell death and autophagic degradation of IL-8 in GAC, which exerts an effect on GAC carcinogenesis.
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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) | 0.001 | 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".