Background Current cervical cancer risk stratification relies on FIGO stage and lymph node status, which fail to capture molecular heterogeneity and leave patients over- or under-treated. Conventional transcriptomic biomarker discovery relies solely on differential expression, yielding thousands of candidates that often lack disease specificity. To address these limitations, we developed a computational framework that integrates knowledge graph (KG) embeddings with transcriptomic differential expression to identify cervical cancer-specific prognostic biomarkers. Results Among ten embedding models for prediction of gene-disease associations, ComplEx showed the highest cervical cancer specificity (Top 100 mean Z-score = 4.19, range 3.54–6.71). Intersection of KG-predicted candidates (Z ≥ 1.645, P < 0.05) with DESeq2 differentially expressed genes (adjusted P < 0.05, |log2FoldChange|> 1) identified 468 high-confidence candidate genes. Using a nested cross-validated Top-K weighted risk score approach, a 20-gene prognostic signature was finalized. The signature demonstrated an unbiased nested cross-validated C-index of 0.638 ± 0.057 for Top-K selection, while the apparent full-data C-index was 0.760 (bootstrap 95% CI 0.697–0.812) and the mean time-dependent AUC was 0.795. Multivariable Cox regression confirmed the signature as an independent prognostic factor after adjusting for age and FIGO stage (HR = 1.89 per SD, P < 0.001, C-index = 0.803), with the strongest discrimination in Stage I disease (C-index = 0.796). Also, external validation in GSE52903 showed a consistent but non-significant trend toward survival discrimination (log-rank P = 0.055; HR = 1.30). Conclusions This study introduces a Z-score-based specificity framework that integrates knowledge-graph embedding with transcriptomic analysis for cervical cancer biomarker discovery. Unlike conventional KG evaluations that rely solely on link-prediction metrics, the disease-specificity Z-score calibrates each gene against ten control solid tumors to filter pan-cancer noise, thereby enhancing the disease specificity of the transcriptomic analysis. These results support the signature’s hypothesis-generating potential for personalized risk stratification, though prospective validation in independent cohorts with survival endpoints remains essential before clinical application.

A knowledge graph-guided transcriptomic framework identifies a 20-gene prognostic signature for cervical cancer

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2026年7月30日

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Ziyi Deng,
Xuehua Bi,
Hanyuan Zhang

摘要

Background

Current cervical cancer risk stratification relies on FIGO stage and lymph node status, which fail to capture molecular heterogeneity and leave patients over- or under-treated. Conventional transcriptomic biomarker discovery relies solely on differential expression, yielding thousands of candidates that often lack disease specificity. To address these limitations, we developed a computational framework that integrates knowledge graph (KG) embeddings with transcriptomic differential expression to identify cervical cancer-specific prognostic biomarkers.

Results

Among ten embedding models for prediction of gene-disease associations, ComplEx showed the highest cervical cancer specificity (Top 100 mean Z-score = 4.19, range 3.54–6.71). Intersection of KG-predicted candidates (Z ≥ 1.645, P < 0.05) with DESeq2 differentially expressed genes (adjusted P < 0.05, |log₂FoldChange|> 1) identified 468 high-confidence candidate genes. Using a nested cross-validated Top-K weighted risk score approach, a 20-gene prognostic signature was finalized. The signature demonstrated an unbiased nested cross-validated C-index of 0.638 ± 0.057 for Top-K selection, while the apparent full-data C-index was 0.760 (bootstrap 95% CI 0.697–0.812) and the mean time-dependent AUC was 0.795. Multivariable Cox regression confirmed the signature as an independent prognostic factor after adjusting for age and FIGO stage (HR = 1.89 per SD, P < 0.001, C-index = 0.803), with the strongest discrimination in Stage I disease (C-index = 0.796). Also, external validation in GSE52903 showed a consistent but non-significant trend toward survival discrimination (log-rank P = 0.055; HR = 1.30).

Conclusions

This study introduces a Z-score-based specificity framework that integrates knowledge-graph embedding with transcriptomic analysis for cervical cancer biomarker discovery. Unlike conventional KG evaluations that rely solely on link-prediction metrics, the disease-specificity Z-score calibrates each gene against ten control solid tumors to filter pan-cancer noise, thereby enhancing the disease specificity of the transcriptomic analysis. These results support the signature’s hypothesis-generating potential for personalized risk stratification, though prospective validation in independent cohorts with survival endpoints remains essential before clinical application.