Proteogenomic characterization of cervical cancer identifies molecular subtypes predictive of clinical outcomes and subtype-specific targets
Xun Tian, Mansheng Li, Zhi Wang, Tian Fang, Yi Liu, Jin Fang, Lejing Wang, Zhichao Jiang, Xingyu Zhao, Chen Cao, Zhiqiang Yu, Meiying Yang, Songfeng Wu, Yifan Wu, Rui Tian, Hui Wang, Yunping Zhu, Zheng Hu
Xun Tian, Mansheng Li, Zhi Wang, Tian Fang, Yi Liu, Jin Fang, Lejing Wang, Zhichao Jiang, Xingyu Zhao, Chen Cao, Zhiqiang Yu, Meiying Yang, Songfeng Wu, Yifan Wu, Rui Tian, Hui Wang, Yunping Zhu, Zheng Hu
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Clinical Research and Public Health Genetics Oncology Virology

Proteogenomic characterization of cervical cancer identifies molecular subtypes predictive of clinical outcomes and subtype-specific targets

Abstract

BACKGROUND Cervical cancer (CC) remains the fourth leading cause of cancer-related deaths in women globally, with poor prognosis for metastatic and recurrent cases. Although genomic alterations have been extensively characterized, global proteogenomic landscape of the disease is largely under explored.METHODS Here, we present the first genome-wide proteogenomic characterization of CC, analyzing 139 tumor-normal tissue pairs using whole-genome sequencing, transcriptomics, proteomics, and phosphoproteomics.RESULTS We identified 4 distinct molecular subtypes with unique clinical outcomes: epithelial-mesenchymal transition (EMT, C1), proliferation (C2), immune response (C3), and epithelial differentiation (C4). A 4-protein classifier (CDH13, TP53BP1, NNMT, HSPB1) was developed with strong prognostic and predictive value, particularly for immunotherapy response in subtype C3. Phosphoproteomic profiling uncovered subtype-specific kinase activity, identifying actionable therapeutic targets.CONCLUSION Our findings further revealed previously uncharacterized somatic copy number alterations, extrachromosomal DNA landscape, and human-HPV fusion peptides, with implications for genetic heterogeneity and therapeutic targets. This study enhances the understanding of cervical cancer through deeper proteogenomic insights and facilitates the development of personalized therapeutic strategies to improve patient outcomes.FUNDING Noncommunicable Chronic Diseases-National Science and Technology Major Project (2025ZD0544102);The National Natural Science Foundation of China (82172584); Key Technology R&D Program of Hubei (2024BCB057 and 2025BCB053); National Natural Science Foundation of China (82373260); the “4+X” clinical trial programs of Women’s Hospital, School of Medicine, Zhejiang University (LY2022004); and the programs of Zhejiang Traditional Chinese Medicine Innovation Team (CZ2024009); and Guangxi Natural Science Foundation (2024GXNSFBA010045).

Authors

Xun Tian, Mansheng Li, Zhi Wang, Tian Fang, Yi Liu, Jin Fang, Lejing Wang, Zhichao Jiang, Xingyu Zhao, Chen Cao, Zhiqiang Yu, Meiying Yang, Songfeng Wu, Yifan Wu, Rui Tian, Hui Wang, Yunping Zhu, Zheng Hu

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Figure 2

The impacts of SCNAs on mRNA and proteins.

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The impacts of SCNAs on mRNA and proteins. (A and B) Significant arm-lev...
(A and B) Significant arm-level and focal-level SCNA events (Q-value < 0.25) in patients with CC. (C) Heatmaps show significant positive (red) and negative (blue) Spearman’s correlations (Benjamini-Hochberg Padj < 0.001) between CNAs and mRNA (left) or protein (right) levels. (D) Number of CNAs with significant cis effects (P < 0.01, Spearman’s correlation) on mRNA alone, or protein alone, or both. (E) Scatterplot showing proteins significantly associated with patient overall survival (OS) (P < 0.05, Log-rank test) and upregulated in tumors (Benjamini-Hochberg Padj < 0.01, fold change > 1.5, modified t test). (F) Spearman’s correlations between RABIF CNA and mRNA (left) or protein (right) abundances. (G) Box plot showing RABIF mRNA and protein levels in copy number gain CC samples versus paired NATs (2-sided Student’s t test). Centers indicate the medians, the upper and lower boundaries of the boxes indicate the 75th and 25th percentile, whiskers extend to 1.5× IQR. (H and I) Western blot analysis of RABIF knockout efficiency in SiHa (H) and HeLa (I) cells (2 biological replicates). (J and K) Colony formation assays in SiHa (J) and HeLa (K) cells following RABIF knockout. Data represent means ± SEM (n = 3 biological replicates, 1-way ANOVA with Tukey’s multiple comparisons test). (L and M) Tumor growth curves in xenograft models of SiHa (L) and HeLa (M) cells with RABIF knockout. Data represent mean ± SEM (n = 5 mice per group, two-way analysis of variance). (N) Representative IHC images of RABIF-high (top-left) and RABIF-low (bottom-left) expression in an external patient cohort (n = 102). Scale bars: 200 μm. Kaplan-Meier curves for PFS and OS are shown. Statistical significances were determined by Log-rank test. *P < 0.05, **P < 0.01, ***P < 0.001.