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A novel mitochondrial unfolded protein response-related risk signature to predict prognosis, immunotherapy and sorafenib sensitivity in hepatocellular carcinoma

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Abstract

Hepatocellular carcinoma (HCC) is a common cause of cancer-associated death worldwide. The mitochondrial unfolded protein response (UPRmt) not only maintains mitochondrial integrity but also regulates cancer progression and drug resistance. However, no study has used the UPRmt to construct a prognostic signature for HCC. This work aimed to establish a novel signature for predicting patient prognosis, immune cell infiltration, immunotherapy, and chemotherapy response based on UPRmt-related genes (MRGs). Transcriptional profiles and clinical information were obtained from the TCGA and ICGC databases. Cox regression and LASSO regression analyses were applied to select prognostic genes and develop a risk model. The TIMER algorithm was used to investigate immunocytic infiltration in the high- and low-risk subgroups. Here, two distinct clusters were identified with different prognoses, immune cell infiltration statuses, drug sensitivities, and response to immunotherapy. A risk score consisting of seven MRGs (HSPD1, LONP1, SSBP1, MRPS5, YME1L1, HDAC1 and HDAC2) was developed to accurately and independently predict the prognosis of HCC patients. Additionally, the expression of core MRGs was confirmed by immunohistochemistry (IHC) staining, single-cell RNA sequencing, and spatial transcriptome analyses. Notably, the expression of prognostic MRGs was significantly correlated with sorafenib sensitivity in HCC and markedly downregulated in sorafenib-treated HepG2 and Huh7 cells. Furthermore, the knockdown of LONP1 decreased the proliferation, invasion, and migration of HepG2 cells, suggesting that upregulated LONP1 expression contributed to the malignant behaviors of HCC cells. To our knowledge, this is the first study to investigate the consensus clustering algorithm, prognostic potential, immune microenvironment infiltration and drug sensitivity based on the expression of MRGs in HCC. In summary, the UPRmt-related classification and prognostic signature could assist in determining the prognosis and personalized therapy of HCC patients from the perspectives of predictive, preventative and personalized medicine.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The authors would like to thank the researchers and staff of the above software and databases.

Funding

This study was supported by the Guiding Funds of the Central Government for Supporting the Development of the Local Science and Technology (Grant No. 236Z3003G), the Hebei Province Higher Education Scientific Research Special Task Project (Grant No. JZX2024020), the Hebei Administration of Traditional Chinese Medicine (Grant No. 2023072) and the Innovation and Entrepreneurship Training Program for Undergraduates (Grant No. 202310094004).

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Author notes

  1. Sidi Zhang, Hanyao Guo, Hongyu Wang and Xiaopeng Liu have contributed equally to this work.

Authors and Affiliations

  1. Ministry of Education Key Laboratory of Molecular and Cellular Biology, Hebei Collaborative Innovation Center for Eco-Environment, Hebei Research Center of the Basic Discipline of Cell Biology, Hebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, Hebei, China

    Sidi Zhang, Hanyao Guo, Hongyu Wang, Xiaopeng Liu, Meixia Wang, Xiaoyu Liu, Yumei Fan & Ke Tan

  2. Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China

    Xiaopeng Liu

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  1. Sidi Zhang
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Contributions

Study concept and design: KT, SZ, HG, HW and XL. Acquisition of data: KT, SZ, HG and HW. Analysis and interpretation of the data: KT, SZ, HG, HW, XL and KT. RT-PCR analysis: SZ and MW. Statistical analysis: KT, HG, SZ and HW. Drafting of the article: KT. Critical revision and final approval of the article: HG, XL, YF and KT. Obtained funding: KT and HW. Study supervision: KT. All the authors contributed to the article and approved the submitted version.

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Correspondence to Ke Tan.

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Zhang, S., Guo, H., Wang, H. et al. A novel mitochondrial unfolded protein response-related risk signature to predict prognosis, immunotherapy and sorafenib sensitivity in hepatocellular carcinoma. Apoptosis (2024). https://doi.org/10.1007/s10495-024-01945-6

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