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Multi-omic profiling reveals potential biomarkers of hepatocellular carcinoma prognosis and therapy response among mitochondria-associated cell death genes in the context of 3P medicine

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Abstract

Background

Cancer cell growth, metastasis, and drug resistance are major challenges in treating liver hepatocellular carcinoma (LIHC). However, the lack of comprehensive and reliable models hamper the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) strategy in managing LIHC.

Methods

Leveraging seven distinct patterns of mitochondrial cell death (MCD), we conducted a multi-omic screening of MCD-related genes. A novel machine learning framework was developed, integrating 10 machine learning algorithms with 67 different combinations to establish a consensus mitochondrial cell death index (MCDI). This index underwent rigorous evaluation across training, validation, and in-house clinical cohorts. A comprehensive multi-omics analysis encompassing bulk, single-cell, and spatial transcriptomics was employed to achieve a deeper insight into the constructed signature. The response of risk subgroups to immunotherapy and targeted therapy was evaluated and validated. RT-qPCR, western blotting, and immunohistochemical staining were utilized for findings validation.

Results

Nine critical differentially expressed MCD-related genes were identified in LIHC. A consensus MCDI was constructed based on a 67-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. MCDI correlated with immune infiltration, Tumor Immune Dysfunction and Exclusion (TIDE) score and sorafenib sensitivity. Findings were validated experimentally. Moreover, we identified PAK1IP1 as the most important gene for predicting LIHC prognosis and validated its potential as an indicator of prognosis and sorafenib response in our in-house clinical cohorts.

Conclusion

This study developed a novel predictive model for LIHC, namely MCDI. Incorporating MCDI into the PPPM framework will enhance clinical decision-making processes and optimize individualized treatment strategies for LIHC patients.

Graphical Abstract

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Data availability

All the data used in this study were collected in this article and supplemental materials.

Availability of data and materials

TCGA datasets enrolled in this study are openly available in the National Cancer Institute GDC Data Portal (https://portal.gdc.cancer.gov/). TCGA data are displayed under the Project IDs “TCGA-LIHC.” GEO datasets are publicly available in the National Center for Biotechnology Information Portal (https://www.ncbi.nlm.nih.gov/geo/), including GSE14520 and GSE156625. ICGC-LIRI dataset is openly available in ICGC (https://dcc.icgc.org/releases/current/Projects). The spatial transcriptomics of LIHC cohorts can be obtained from Mendeley Data (skrx2fz79n). IMvigor210 bladder cancer cohort can be accessed from http://research-pub.gene.com/IMvigor210CoreBiologies.

Code availability

Analyses were conducted using R (version 4.2.1) and GraphPad (GraphPad Prism 8.0). The codes used to support the findings of this study is available from the corresponding author on reasonable request.

Abbreviations

AUC:

Area under the curve

BCLC:

Barcelona Clinic Liver Cancer

CC:

Consensus clustering

CI:

Confidence interval

CNV:

Copy number variation

DCA:

Decision curve analysis

EPO:

Erythropoietin

G6PD:

Glucose-6-phosphate dehydrogenase

GDSC:

Genomics of Drug Sensitivity in Cancer

GEO:

Gene Expression Omnibus

GINS1:

GINS complex subunit 1 (Sld5 homolog)

GO:

Gene ontology

GSVA:

Gene set variation analysis

HR:

Hazard ratio

IHC:

Immunohistochemistry

ICGC:

International Cancer Genome Consortium

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LASSO:

Least absolute shrinkage and selection operator

LOOCV:

Leave-one-out cross-validation

LIHC:

Liver hepatocellular carcinoma

MCD:

Mitochondria-associated cell death

MCD-DEGs:

Mitochondria-associated cell death differentially expressed genes

MCDI:

Mitochondrial cell death index

MCDS:

Mitochondria-associated cell death signature genes

NC:

Normal controls

OS:

Overall survival

PAK1IP1:

P21-activated kinase 1 interacting protein 1

PCA:

Principal component analysis

PPPM:

Predictive, preventive, and personalized medicine

RFS:

Recurrence-free survival

RT-qPCR:

Reverse transcription-quantitative polymerase chain reaction

ROC:

Receiver operating characteristic

RSF:

Random survival forest

TIDE:

Tumor Immune Dysfunction and Exclusion

TIMER:

Tumor Immune Estimation Resource

UMAP:

Uniform Manifold Approximation and Projection

SD:

Standard deviation

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Funding

This work was supported by a grant from the National Natural Science Foundation of China (82002588, 32300731), the Natural Science Foundation project of Shanghai (23ZR1477200), the State Key Laboratory of the Cancer Biology Project (CBSKL2022ZDKF05) and the Shanghai Key Laboratory of Cell Engineering (14DZ2272300).

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Authors and Affiliations

  1. Clinical Cancer Institute, Center for Translational Medicine, Naval Medical University, 800 Xiangyin Road, Shanghai, 200433, China

    Dingtao Hu, Xu Shen, Peng Gao, Yuan Chen & Jin Ding

  2. Department of Emergency, Shanghai Tenth People’s Hospital, Tongji University, School of Medicine, 301 Yanchang Middle Road, Shanghai, 200072, China

    Tiantian Mao, Xiaofeng Li & Yugang Zhuang

  3. University of Shanghai for Science and Technology, Shanghai, 200093, China

    Yuan Chen

  4. The Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China

    Weifeng Shen

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  1. Dingtao Hu
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Contributions

All authors searched the literature, designed the study, interpreted the findings, and revised the manuscript. DTH, XS, and PG carried out data management and statistical analysis and drafted the manuscript. DTH, XS, PG, TTM, YC, WFS, YGZ, and JD helped with cohort identification and data management. YGZ and JD contributed to the critical revision of the manuscript.

Corresponding authors

Correspondence to Yugang Zhuang or Jin Ding.

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All participants were enrolled from the Eastern Hepatobiliary Surgery Hospital (EHBH), and the sample collection procedure was approved by the ethics committee of EHBH.

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The authors declare no competing interests.

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Dingtao Hu, Xu Shen, and Peng Gao share co-first authorship.

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Hu, D., Shen, X., Gao, P. et al. Multi-omic profiling reveals potential biomarkers of hepatocellular carcinoma prognosis and therapy response among mitochondria-associated cell death genes in the context of 3P medicine. EPMA Journal (2024). https://doi.org/10.1007/s13167-024-00362-8

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