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Background Lymph node metastasis (LNM) is a major prognostic determinant in early-stage cervical squamous cell carcinoma (SCC); however, conventional preoperative imaging has demonstrated sensitivities below 60% for sub-centimeter metastases, resulting in treatment misallocation for approximately 20–30% of patients. Purpose This study aimed to develop and validate a minimal gene expression signature that can be performed on routine biopsy specimens to predict preoperative LNM in cervical SCC. Methods This retrospective biomarker discovery and validation study had a two-phase design. In the discovery phase, we analyzed transcriptomic data from 116 The Cancer Genome Atlas (TCGA) cervical SCC samples and identified differentially expressed genes (DEGs) using Benjamini–Hochberg (BH) false discovery rate correction (FDR 0.10). Then, we refined them using the least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis. The locked signature was independently validated in a prospectively collected cohort of 202 patients (101 LNM-positive patients and 101 LNM-negative patients) from the Fudan University Shanghai Cancer Center using quantitative reverse transcription–polymerase chain reaction (qRT-PCR), with histopathological lymphadenectomy confirmation as the reference standard. Performance was assessed based on area under the curve (AUC), sensitivity, specificity, predictive values, and bootstrap internal validation. Decision curve analysis and a combined molecular-clinical model were also evaluated. Results Of the 231 DE genes, a three-gene signature (LOC494141, GLOD5, and GML) was identified by sequential filtering. In the independent validation cohort, the signature achieved an AUC of 0.745 (95% CI: 0.676–0.814), with a sensitivity of 62.38%, a specificity of 64.36%, a positive predictive value of 63.64%, and a negative predictive value of 63.11%. Bootstrap validation confirmed model robustness (optimism-corrected AUC: 0.722; calibration slope: 0.913; Hosmer–Lemeshow p = 0.387). A combined model integrating the signature with tumor size and lymphovascular space invasion achieved an AUC of 0.789 (95% CI: 0.724–0.854), with a significant incremental value net reclassification improvement (NRI) = 0.42, p = 0.001; integrated discrimination improvement (IDI) = 0.065, p 0.001. Decision curve analysis demonstrated net clinical benefit across threshold probabilities of 20–70%. At a 20% population prevalence, the adjusted negative predictive value reached 87.3%. Conclusion This three-gene expression signature provides clinically informative preoperative risk stratification for LNM in cervical SCC. Intended as a complementary tool within integrated clinical assessment frameworks rather than a standalone diagnostic tool, this affordable qRT-PCR-based assay holds particular promise for resource-limited settings, pending prospective multicenter validation.
Duan et al. (Wed,) studied this question.