Identification of Predictive Biomarkers for Lymph Node Involvement in Obese Women With Endometrial Cancer
Bibliographic record
Abstract
Obesity, an established risk factor for endometrial cancer (EC), is also associated to increased risks of intraoperative and postoperative complications. A reliable tool to identify patients at low risk for lymph node (LN) metastasis may allow minimizing the surgical staging and omit lymphadenectomy in obese patients. To identify molecular biomarkers that could predict LN involvement in obese patients with EC we performed gene expression analysis in 549 EC patients using publicly available transcriptomic datasets. Patients were filtrated according to cancer subtype, weight (>30 kg/m2) and LN status. While in the LN+ group, NEB, ANK1, AMIGO2, LZTS1, FKBP5, CHGA, USP32P1, CLIC6, CEMIP, HMCN1 and TNFRSF10C genes were highly expressed; in the LN- group CXCL14, FCN1, EPHX3, DDX11L2, TMEM254, RNF207, LTK, RPL36A, HGAL, B4GALNT4, KLRG1 genes were up-regulated. As a second step, we investigated these genes in our patient cohort of 35 patients (15 LN+ and 20 LN-) and found the same correlation with the in-silico analysis. In addition, immunohistochemical expression was confirmed in the tumor tissue. Altogether, our findings propose a novel panel of genes able to predict LN involvement in obese patients with endometrial 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".