114027
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
利用单细胞和批量 RNA 测序数据进行整合转录组分析以揭示肝细胞癌中与细胞焦亡相关的预后特征
Authors Li J, Di Y, Kang X, Song Z, Sun Z
Received 29 July 2025
Accepted for publication 16 December 2025
Published 24 December 2025 Volume 2025:12 Pages 2971—2988
DOI https://doi.org/10.2147/JHC.S557035
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr David Gerber
Jiangbo Li,1,* Yupeng Di,2,* Xiaoli Kang,2,* Zhuo Song,2 Zhijia Sun2
1Bioinformatics Center of AMMS, Beijing, People’s Republic of China; 2Department of Radiation Oncology, Air Force Characteristic Medical Center, Air Force Medical University, Beijing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Zhijia Sun, Department of Radiation Oncology, Air Force Characteristic Medical Center, Air Force Medical University, Beijing, 100142, People’s Republic of China, Email sunzhijia@fmmu.edu.cn
Background: The influence of pyroptosis on tumors is complex and diverse. However, its specific impact on hepatocellular carcinoma (HCC) is still not well understood. Therefore, the objective of this study was to develop a prognostic signature for HCC based on pyroptosis-related genes.
Methods: The single-cell RNA sequencing (scRNA-seq) data, mRNA expression files and corresponding clinical information of HCC were obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. Python was used to process scRNA-seq data and calculated the enrichment score of pyroptosis-related genes (PRGs). Weight Co-Expression Network Analysis was used to identify pyroptosis-related hub genes. By overlapping the PRGs from scRNA-seq analysis and bulk RNA-seq analysis, respectively. Then, Univariate cox and LASSO regression were used to construct the pyroptosis prognostic model. Multivariate cox was used to identify independent factors for HCC and then developed a nomogram. The biological functions, survival analysis, immune characteristics, therapy response, and m6A modification status were analyzed.
Results: Based on the scRNA-seq analysis and bulk RNA-seq analysis, hub PRGs were identified in HCC. Of those genes, five PRGs (ADGRE2, FCER1G, SLC9A9, CYBB, SLAMF6) were selected as a prognostic signature. The risk score established from the prognostic signature was an independent prognostic factor for HCC. The high-risk score group is associated with a poor prognosis, characterized by immunosuppressive features.
Conclusion: This study uniquely integrates single-cell and bulk transcriptomic data to systematically identify pyroptosis-related prognostic biomarkers, pinpointing their cellular origin within the tumor microenvironment.
Keywords: hepatocellular carcinoma, single-cell RNA sequencing, pyroptosis, prognosis, HNRNPA2B1
1Bioinformatics Center of AMMS, Beijing, People’s Republic of China; 2Department of Radiation Oncology, Air Force Characteristic Medical Center, Air Force Medical University, Beijing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Zhijia Sun, Department of Radiation Oncology, Air Force Characteristic Medical Center, Air Force Medical University, Beijing, 100142, People’s Republic of China, Email sunzhijia@fmmu.edu.cn
Background: The influence of pyroptosis on tumors is complex and diverse. However, its specific impact on hepatocellular carcinoma (HCC) is still not well understood. Therefore, the objective of this study was to develop a prognostic signature for HCC based on pyroptosis-related genes.
Methods: The single-cell RNA sequencing (scRNA-seq) data, mRNA expression files and corresponding clinical information of HCC were obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. Python was used to process scRNA-seq data and calculated the enrichment score of pyroptosis-related genes (PRGs). Weight Co-Expression Network Analysis was used to identify pyroptosis-related hub genes. By overlapping the PRGs from scRNA-seq analysis and bulk RNA-seq analysis, respectively. Then, Univariate cox and LASSO regression were used to construct the pyroptosis prognostic model. Multivariate cox was used to identify independent factors for HCC and then developed a nomogram. The biological functions, survival analysis, immune characteristics, therapy response, and m6A modification status were analyzed.
Results: Based on the scRNA-seq analysis and bulk RNA-seq analysis, hub PRGs were identified in HCC. Of those genes, five PRGs (ADGRE2, FCER1G, SLC9A9, CYBB, SLAMF6) were selected as a prognostic signature. The risk score established from the prognostic signature was an independent prognostic factor for HCC. The high-risk score group is associated with a poor prognosis, characterized by immunosuppressive features.
Conclusion: This study uniquely integrates single-cell and bulk transcriptomic data to systematically identify pyroptosis-related prognostic biomarkers, pinpointing their cellular origin within the tumor microenvironment.
Keywords: hepatocellular carcinoma, single-cell RNA sequencing, pyroptosis, prognosis, HNRNPA2B1