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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".