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Background <p>The 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. </p> Methods <p>We 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.</p> Results <p>Following 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.</p> Conclusion <p>The novel molecular classification system and the prognostic prediction model might benefit the management of MESO patients.</p>

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A novel mesothelioma molecular classification based on malignant cell differentiation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月24日

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  • Jun Liu,
  • Yifan Liu,
  • Yuwei Lu,
  • Wei Zhang,
  • Jiale Yan,
  • Bingnan Lu,
  • Yuntao Yao,
  • Shuyuan Xian,
  • Donghao Lyu,
  • Jiaying Shi,
  • Yuanan Li,
  • Xinru Wu,
  • Chenguang Bai,
  • Jie Zhang,
  • Yuan Zhang

摘要

Background

The 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.

Methods

We 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.

Results

Following 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.

Conclusion

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