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/m 2 ) 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".