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Centrosome amplification-related signature correlated with immune microenvironment and treatment response predicts prognosis and improves diagnosis of hepatocellular carcinoma by integrating machine learning and single-cell analyses

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Abstract

Background

Centrosome amplification is a well-recognized oncogenic driver of tumor initiation and progression across a variety of malignancies and has been linked with tumor aggressiveness, metastasis, and adverse prognosis. Nevertheless, the significance of centrosome amplification in HCC is not well understood.

Methods

The TCGA dataset was downloaded for centrosome amplification-related signature construction using the LASSO-penalized Cox regression algorithm, while the ICGC dataset was obtained for signature validation. Single-cell RNA sequencing from GSE149614 was analyzed to profile gene expression and the liver tumor niche.

Results

A total of 134 centrosome amplification-related prognostic genes in HCC were detected and 6 key prognostic genes (SSX2IP, SPAG4, SAC3D1, NPM1, CSNK1D, and CEP55) among them were screened out to construct a signature with both high sensitivity and specificity in diagnosis and prognosis of HCC patients. The signature, as an independent factor, was associated with frequent recurrences, high mortality rates, advanced clinicopathologic features, and high vascular invasions. Moreover, the signature was intimately associated with cell cycle-related pathways and TP53 mutation profile, suggesting its underlying role in accelerating cell cycle progression and leading to liver cancer development. Meanwhile, the signature was also closely correlated with immunosuppressive cell infiltration and immune checkpoint expression, making it a vital immunosuppressive factor in the tumor microenvironment. Upon single-cell RNA sequencing, SSX2IP and SAC3D1 were found to be specially expressed in liver cancer stem-like cells, where they promoted cell cycle progression and hypoxia.

Conclusions

This study provided a direct molecular link of centrosome amplification with clinical characteristics, tumor microenvironment, and clinical drug-response, highlighting the critical role of centrosome amplification in liver cancer development and therapy resistance, thereby providing valuable insights into prognostic prediction and therapeutic response of HCC.

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Data availability statement

The datasets obtained and explored in this study are available in the TCGA (https://portal.gdc.cancer.gov/) and ICGC (https://icgc.org/). The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

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Acknowledgements

We appreciate each and every individual relevant to development and maintenance of the R packages such as seurat, limma, glmnet, and survival, which will hasten the retrospective statistical analysis of high-throughput sequencing data for cancer.

Funding

This work was supported by grants from National Natural Science Foundation of China, the Natural Science Foundation of China (81172537, 81272900, 81772828), Scientific and Technological Project of Guangdong Province (2016A020216028), the China Postdoctoral Science Fund (2018M643045), the Basic and Applied Basic Research Project of Guangzhou Science and Technology Bureau (202201011003), and the Ph.D. Start-up Fund of Second Affiliated Hospital of Guangzhou Medical University.

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Authors and Affiliations

  1. Guangzhou Key Laboratory for Research and Development of Nano-Biomedical Technology for Diagnosis and Therapy & Guangdong Provincial Education Department Key Laboratory of Nano-Immunoregulation Tumour Microenvironment, Department of Oncology & Translational Medicine Center, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, People’s Republic of China

    Yanli Liu, Min He, **nrong Ke & **gqi Chen

  2. State Key Laboratory of Respiratory Disease, The Second Clinical Medical School, Guangzhou Medical University, Guangzhou, 510180, Guangdong, People’s Republic of China

    Yuting Chen, Jie Zhu & Ziqing Tan

  3. Central Laboratory, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, Guangdong, People’s Republic of China

    Yanli Liu, Min He & **nrong Ke

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  1. Yanli Liu
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Contributions

Conception and design: YLL and JQC. Foundation support: YLL and JQC. Acquisition and analysis of data: YLL, MH, XRK, YTC, JZ, and ZQT. Interpretation of data: YLL, MH, XRK, YTC, JZ, and ZQT. Drafting the manuscript and revising for submission quality: YLL and JQC. Study supervision: JQC.

Corresponding author

Correspondence to **gqi Chen.

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Yanli Liu, Min He, **nrong Ke, Yuting Chen, Jie Zhu, Ziqing Tan and **gqi Chen declare that there are no conflicts of interest.

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Liu, Y., He, M., Ke, X. et al. Centrosome amplification-related signature correlated with immune microenvironment and treatment response predicts prognosis and improves diagnosis of hepatocellular carcinoma by integrating machine learning and single-cell analyses. Hepatol Int 18, 108–130 (2024). https://doi.org/10.1007/s12072-023-10538-5

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