Evidence map›Paper›PMID 40555979›Full record

ArticleCancer cell international2025

A novel mesothelioma molecular classification based on malignant cell differentiation.

Jun Liu, Yifan Liu, Yuwei Lu, Wei Zhang, Jiale Yan, Bingnan Lu, Yuntao Yao, Shuyuan Xian, Donghao Lyu, Jiaying Shi and 5 more

Abstract read
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In one paragraph

Article in Cancer cell international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Jun Liu *Department of Anesthesiology, Shanghai Pulmonary Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200000, China.
Yifan Liu *Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No.1665 Kongjiang Road, Shanghai, 200092, China.
Yuwei Lu *Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Wei Zhang *Department of Burn Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, 200433, China.
Jiale YanDepartment of Gynecology, Shanghai First Maternity and Infant Hospital Affiliated to Tongji University School of Medicine, 2699 Gaoke West Road, Shanghai, 201204, China.
Bingnan LuDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No.1665 Kongjiang Road, Shanghai, 200092, China.
Yuntao YaoDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No.1665 Kongjiang Road, Shanghai, 200092, China.
Shuyuan XianDepartment of Burn Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, 200433, China.
Donghao LyuDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No.1665 Kongjiang Road, Shanghai, 200092, China.
Jiaying ShiShanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Yuanan LiShanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Xinru WuShanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Chenguang BaiDepartment of Pathology, The First Affiliated Hospital of Naval Medical University, Shanghai, 200433, China. bcg709@126.com.
Jie ZhangDepartment of Gynecology, Shanghai First Maternity and Infant Hospital Affiliated to Tongji University School of Medicine, 2699 Gaoke West Road, Shanghai, 201204, China. jiezhang@tongji.edu.cn.
Yuan ZhangDepartment of Pulmonary and Critical Care Medicine, Shanghai Pulmonary Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200000, China. zhs0905@126.com.

Funding

Cincal Research Youth Project of Shanghai Health Commission 20204Y0383National Natural Science Foundation of China 82270064Shanghai Municipal Health Commission 201940306
6 · The paper itself

Abstract

backgroundThe high heterogeneity and multi-directional poor differentiation of tumor cells in mesothelioma (MESO) contributes to tumor growth and malignant biological behaviors. However, a molecular classification based on differentiated states of tumor cells remains void.

methodsWe performed dimensionality reduction analysis on the single-cell RNA sequencing profiles available from the GEO database, to visualize the cell types in MESO. Multi-omics analysis was done to supplement the plausibility of classification. We also constructed regulatory networks to detect the function of important tumor cell differential genes (TCDGs) in the MESO.

resultsFollowing twice dimensionality reduction analysis and clustering, eight malignant cell subtypes in the MESO were visualized. According to the expression of TCDGs, MESO was classified into three subtypes (Malignant differentiation-related MESO, Benign differentiation-related MESO, and Neutral differentiation-related MESO) with prognostic differences. The prediction model was built by 12 key TCDGs (ALDH2, HP, CASP1, RTP4, PDZK1IP1, TOP2A, LOXL2, CKS2, SPARC, TLCD3A, C6orf99, and SERPINH1) and validated with high accuracy. In the regulatory networks of MESO subtypes, RTP4, CASP1, MYO1B, SLC7A5, LOXL2, and GHR were labeled as key genes. A total of 14 potential inhibitors were predicted. Clinical specimens validated the reliability of the clinical subtyping of MESO patients.

conclusionThe novel molecular classification system and the prognostic prediction model might benefit the management of MESO patients.

Indexed as

Gene classificationMesothelioma (MESO)Prognostic prediction modelSingle-cell RNA sequencing (scRNA-seq)Tumor heterogeneity

Identifiers

PMID40555979
PMCPMC12188665

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