Integrated Proteomics and Metabolomics Profiling Unveils Biomarkers and Immune Characteristics for Pelvic Lymph Node Metastasis in Cervical Cancer
Bibliographic record
Abstract
Pelvic lymph node metastasis (PLNM) significantly affects the prognosis of cervical cancer (CC). However, current imaging examinations and serum squamous cell carcinoma antigen (SCCA) testing are inadequate for assessing the pelvic lymph node status in CC. To identify accurate noninvasive biomarkers for diagnosing PLNM and minimizing unnecessary postoperative lymphadenectomy and its associated complications, we performed a comprehensive proteomic and metabolomic analysis of plasma from 124 patients with CC, along with a proteomic analysis of 60 paired tissue samples. Through machine learning methods, we identified potential plasma biomarkers (TTR, MASP2, APOD, and 7α-hydroxy-cholestene-3-one) and constructed a diagnostic model. In the validation cohort, the diagnostic model combined with SCCA exhibited a higher sensitivity (72.4%) than SCCA (64.3%) and imaging examination (14.3%). The plasma protein biomarkers were consistently validated in paired tissue samples. Additionally, immune infiltration analysis demonstrated that CD4 and CD8 T cells were highly infiltrated in the PLNM group, suggesting a potentially enhanced response to immunotherapy. Here, we established a biomarker panel for PLNM and highlighted the altered immune characteristics associated with PLNM, offering valuable insights for the development of immunotherapy strategies for patients with PLNM.
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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 | 0.001 | 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 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".