HMGB3 characterization in gastric cancer
Bibliographic record
Abstract
Gastric cancer is a major health problem worldwide; it is the second most common cause of cancer death in the world. Recent studies indicate that the high-mobility group (HMG) of chromosomal proteins is associated with cancer progression. However, HMGB3 has been little studied. We analyzed the co-expression network between HMGB3 and differentially-expressed genes in the GSE17187 database, identifying the relevant transcription factors, and the conserved domain of HMGB3 to understand the underlying regulation mechanisms involved in gastric cancer. Thirty-one relationships between 11 differentially-expressed genes were included in a co-expression network; many of these genes have been identified as related to cancer, including TBX5 and TFR2. Further analysis identified nine transcription factors, these being GATA3, MZF1, GATA1, GATA2, SRY, REL, NFYB, NFYC, and NFYA, which could interact with HMGB3 to regulate target gene expression and consequently regulate gastric cancer cell proliferation, migration and invasion. The HMG-box domain was very similar in various species, with only a few amino acid changes, indicating conserved functions in HMG-box. This information helps to provide insight into the molecular mechanisms of HMGB3 in human gastric cancer.
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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 | 0.002 | 0.001 |
| 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".