LMAN2 interacts with HEATR3 to expedite HER2-positive breast cancer advancement and inflammation and Akt/ERK/NF-κB signaling
Bibliographic record
Abstract
The paper aimed to reveal the impacts and the possible mechanism of action of lectin mannose-binding 2 protein (LMAN2) in HER2-positive breast cancer (BC). The expression, prognostic potential of LMAN2, and the correlation between LMAN2 and HEAT repeat containing 3 (HEATR3) in BC were analyzed in TCGA database. Intact, Mentha, and BioGrid databases predicted LMAN2-HEATR3 interactions. Reverse transcription-quantitative PCR and Western blot examined LMAN2 expression. Cell Counting Kit-8, 5-ethynyl-2'-deoxyuridine staining, wound healing, and transwell assays, respectively, detected the aggressive cellular biological behaviors including proliferation, migration, and invasion. Western blot analyzed the expression of matrix metalloproteinases, HEATR3, and protein kinase B (Akt)/extracellular signal-regulated kinase (ERK)/nuclear factor-kappaB (NF-κB) signaling-related proteins. Co-immunoprecipitation assay was used to prove the relationship of LMAN2 with HEATR3. Enzyme-linked immunosorbent assay detected inflammatory cytokine levels. LMAN2 was overexpressed in HER2-positive BC tissues and cells and indicated unfavorable prognosis of BC patients. LMAN2 knockdown suppressed HER2-positive BC cell proliferation, migration, and invasion. LMAN2 interacted with and had a positive correlation with HEATR3. HEATR3 up-regulation reversed the repressive role of LMAN2 interference in the progression of HER2-positive BC, Akt/ERK/NF-κB signaling, and inflammatory response. Altogether, LMAN2 silencing might exert anti-tumor and anti-inflammatory properties and inactivate Akt/ERK/NF-κB signaling in HER2-positive BC via binding to HEATR3.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".