Deconstructing cervical cancer heterogeneity: squamous cell carcinoma subtypes and CCM2 vulnerability in adenocarcinoma

Cervical cancer, a leading female malignancy with marked histological heterogeneity, encompassing squamous cell carcinoma (SCC), adenocarcinoma (AC), adenosquamous carcinoma (ASC), and neuroendocrine carcinoma (NEC), which differ in prognosis and treatment response. However, the molecular features of these histotypes, particularly the rare ASC and NEC subtypes, remain incompletely understood, and a dedicated molecular classification for SCC is lacking, limiting the development of subtype-specific therapeutic strategies. We performed deep proteomic profiling of 404 cervical cancer tissue samples, comprising 298 SCC, 74 AC, 22 ASC, and 10 NEC cases. Comparative proteomic, pathway enrichment, tumor microenvironment, survival, and machine-learning analyses were conducted to characterize subtype-specific molecular features, identify candidate therapeutic targets, and develop a prognostically relevant molecular classification system for SCC. Distinct proteomic signatures were observed between SCC and AC. AC was characterized by activation of epithelial-mesenchymal transition and glycoprotein metabolic pathways, and CCM2 was functionally validated as a candidate mediator of AC cell invasion and migration. SCC was classified into three cervical squamous molecular subtypes (CSMSs): CSMS1 (immunosuppressive), CSMS2 (immune-enriched), and CSMS3 (oxidative stress response). This classification significantly stratified patient survival in an independent external cohort (log-rank p = 0.022). Clinical-molecular subtype analysis showed the CSMS1 subtype had the poorest prognosis in specific subgroups (age > 45, BMI < 25 kg/m 2 , moderate differentiation, FIGO III/IV), especially with non-surgical treatment, supporting “clinical + molecular” stratification for high-risk identification. This large-scale proteomic study provides a comprehensive molecular landscape of the major histotypes of cervical cancer, identifies CCM2 as a potential therapeutic target associated with the aggressive phenotype of AC, and establishes a clinically relevant molecular classification system for SCC. Integrating the CSMS classification with conventional clinical characteristics may improve the identification of high-risk patients and facilitate precision management of cervical cancer.

Authors

Institutions

Publication Details

Journal
Genome Medicine
Published
2026-10-05
DOI
https://doi.org/10.1186/s13073-026-01784-x
Primary Topic
Cervical Cancer and HPV Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deconstructing cervical cancer heterogeneity: squamous cell carcinoma subtypes and CCM2 vulnerability in adenocarcinoma

Wei Zhu, Jingkui Tian, Zhao Wang, Hanmei Lou et al.
Genome Medicine
Cervical Cancer and HPV Research
article

Deconstructing cervical cancer heterogeneity: squamous cell carcinoma subtypes and CCM2 vulnerability in adenocarcinoma

Wei Zhu, Jingkui Tian, Zhao Wang, Hanmei Lou, Xiaojuan Lv, Guanting Pang, Xiaojing Zhang, Luyu Ma, Xiaoyong Zhang, Hui Ye, Yue Feng, Jing Tang
article en

Abstract

Cervical cancer, a leading female malignancy with marked histological heterogeneity, encompassing squamous cell carcinoma (SCC), adenocarcinoma (AC), adenosquamous carcinoma (ASC), and neuroendocrine carcinoma (NEC), which differ in prognosis and treatment response. However, the molecular features of these histotypes, particularly the rare ASC and NEC subtypes, remain incompletely understood, and a dedicated molecular classification for SCC is lacking, limiting the development of subtype-specific therapeutic strategies. We performed deep proteomic profiling of 404 cervical cancer tissue samples, comprising 298 SCC, 74 AC, 22 ASC, and 10 NEC cases. Comparative proteomic, pathway enrichment, tumor microenvironment, survival, and machine-learning analyses were conducted to characterize subtype-specific molecular features, identify candidate therapeutic targets, and develop a prognostically relevant molecular classification system for SCC. Distinct proteomic signatures were observed between SCC and AC. AC was characterized by activation of epithelial-mesenchymal transition and glycoprotein metabolic pathways, and CCM2 was functionally validated as a candidate mediator of AC cell invasion and migration. SCC was classified into three cervical squamous molecular subtypes (CSMSs): CSMS1 (immunosuppressive), CSMS2 (immune-enriched), and CSMS3 (oxidative stress response). This classification significantly stratified patient survival in an independent external cohort (log-rank p = 0.022). Clinical-molecular subtype analysis showed the CSMS1 subtype had the poorest prognosis in specific subgroups (age > 45, BMI < 25 kg/m 2 , moderate differentiation, FIGO III/IV), especially with non-surgical treatment, supporting “clinical + molecular” stratification for high-risk identification. This large-scale proteomic study provides a comprehensive molecular landscape of the major histotypes of cervical cancer, identifies CCM2 as a potential therapeutic target associated with the aggressive phenotype of AC, and establishes a clinically relevant molecular classification system for SCC. Integrating the CSMS classification with conventional clinical characteristics may improve the identification of high-risk patients and facilitate precision management of cervical cancer.

Genome Medicine
Chinese Academy of Sciences (CN), Tongde Hospital of Zhejiang Province (CN), Zhejiang Cancer Hospital (CN), Shandong First Medical University (CN)
Openalex Percentile: Top 11%
Cervical Cancer and HPV Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.