Integrated bulk and single-cell transcriptomes reveal pyroptotic signature in prognosis and therapeutic options of hepatocellular carcinoma by combining deep learning

Author:

Liu Yang1,Li Hanlin1,Zeng Tianyu1,Wang Yang1,Zhang Hongqi23,Wan Ying1,Shi Zheng45ORCID,Cao Renzhi6ORCID,Tang Hua178ORCID

Affiliation:

1. School of Basic Medical Sciences, Southwest Medical University , Luzhou 646000 , China

2. School of Life Science and Technology , Center for Informational Biology, , Chengdu 610054 , China

3. University of Electronic Science and Technology of China , Center for Informational Biology, , Chengdu 610054 , China

4. Clinical Genetics Laboratory , Clinical Medical College & Affiliated Hospital, , Chengdu 610106 , China

5. Chengdu University , Clinical Medical College & Affiliated Hospital, , Chengdu 610106 , China

6. Department of Computer Science, Pacific Lutheran University , Tacoma, Washington 98447 , USA

7. Basic Medicine Research Innovation Center for Cardiometabolic Diseases,Ministry of Education , Luzhou 646000 , China

8. Medical Engineering & Medical Informatics Integration and Transformational Medicine Key Laboratory of Luzhou City , Luzhou 646000 , China

Abstract

Abstract Although some pyroptosis-related (PR) prognostic models for cancers have been reported, pyroptosis-based features have not been fully discovered at the single-cell level in hepatocellular carcinoma (HCC). In this study, by deeply integrating single-cell and bulk transcriptome data, we systematically investigated significance of the shared pyroptotic signature at both single-cell and bulk levels in HCC prognosis. Based on the pyroptotic signature, a robust PR risk system was constructed to quantify the prognostic risk of individual patient. To further verify capacity of the pyroptotic signature on predicting patients’ prognosis, an attention mechanism-based deep neural network classification model was constructed. The mechanisms of prognostic difference in the patients with distinct PR risk were dissected on tumor stemness, cancer pathways, transcriptional regulation, immune infiltration and cell communications. A nomogram model combining PR risk with clinicopathologic data was constructed to evaluate the prognosis of individual patients in clinic. The PR risk could also evaluate therapeutic response to neoadjuvant therapies in HCC patients. In conclusion, the constructed PR risk system enables a comprehensive assessment of tumor microenvironment characteristics, accurate prognosis prediction and rational therapeutic options in HCC.

Funder

National Natural Science Foundation of China

Sichuan Science and Technology Program

Publisher

Oxford University Press (OUP)

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