Knockdown of Mgat5 inhibits CD133<sup>+</sup> human pulmonary adenocarcinoma cell growth in vitro and in vivo
Bibliographic record
Abstract
PURPOSE: In spite of many therapeutic advances, the prognosis of lung cancer remains poor. Therefore, understanding the molecular mechanisms underlying cancer progression, invasion and metastasis is needed. Accumulating evidence indicate that N-acetylglucosaminyltransferase V (Mgat5 or GnT-V) is involved in cancer development. The purpose of this study was to characterize the expression and function of Mgat5 in CD133+ pulmonary adenocarcinoma cells. METHODS: CD133+ pulmonary adenocarcinoma cells were separated by magnetic activated cell sorting (MACS) from excised pulmonary adenocarcinoma specimens from 10 patients. Expression of Mgat5 in CD133+ cells was detected by fluorescent quantitative RT-PCR (FQRT-PCR) and Western blot. Subsequently, CD133+ cells were transfected with specific siRNA of Mgat5 to evaluate the effects of Mgat5 inhibition on cancer cell growth in vivo and in vitro. RESULTS: Expression of Mgat5 was 1.2-fold and 1.4-fold higher in CD133+cells than in CD133- cells detected by FQRT-PCR and Western Blot, respectively (p < 0.05). The L-PHA binding assay also showed higher reactivity in CD133+ cells than in CD133- cells. In addition, Mgat5-specific siRNA efficiently knocked down the expression of Mgat5 in CD133+ cells. Interestingly, downregulation of Mgat5 resulted in significant inhibition of cancer cell growth in vitro and in vivo. CONCLUSION: Mgat5 is expressed at a relatively high level in CD133+ lung adenocarcinoma cells, and knockdown of Mgat5 in CD133+ cells inhibits cancer cell growth both in vitro and in vivo. These findings suggest Mgat5 may play an important role during oncogenesis, identifying a potential therapeutic target for pulmonary adenocarcinoma.
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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".