The prognostic value and clinical significance of mitophagy-related genes in hepatocellular carcinoma

被引:16
|
作者
Xu, Wei [1 ]
Zhao, Dongxu [2 ]
Huang, Xiaowei [3 ]
Zhang, Man [4 ]
Yin, Minyue [1 ]
Liu, Lu [1 ]
Wu, Hongyu [1 ]
Weng, Zhen [5 ,6 ,7 ]
Xu, Chunfang [1 ]
机构
[1] Soochow Univ, Affiliated Hosp 1, Dept Gastroenterol, Suzhou, Peoples R China
[2] Soochow Univ, Affiliated Hosp 1, Dept Intervent Radiol, Suzhou, Peoples R China
[3] Soochow Univ, Affiliated Hosp 1, Dept Gen Surg, Suzhou, Peoples R China
[4] Xuzhou Med Univ, Affiliated Hosp, Dept Emergency Med, Xuzhou, Peoples R China
[5] Soochow Univ, Cyrus Tang Hematol Ctr, Suzhou, Peoples R China
[6] Soochow Univ, Minist Educ, Engn Ctr Hematol Dis, Suzhou, Peoples R China
[7] Soochow Univ, Collaborat Innovat Ctr Hematol, Suzhou, Peoples R China
关键词
hepatocellular carcinoma; mitophagy; prognosis; tumor microenvironment; chemotherapy; targeted therapy; immune checkpoint; MITOCHONDRIAL DYSFUNCTION; PHASE-III; SIGNALING PATHWAY; SKELETAL-MUSCLE; AUTOPHAGY; PROGRESSION; SORAFENIB; DISEASE; PROTEIN; APOPTOSIS;
D O I
10.3389/fgene.2022.917584
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Background: Mitophagy has been found to play a significant part in the cancer process in a growing number of studies in recent years. However, there is still a lack of study on mitophagy-related genes' (MRGs) prognostic potential and clinical significance in hepatocellular carcinoma (HCC). Methods: We employed bioinformatics and statistical knowledge to examine the transcriptome data of HCC patients in the TCGA and GEO databases, with the goal of constructing a multigene predictive model. Then, we separated the patients into high- and low-risk groups based on the score. The model's dependability was determined using principal components analysis (PCA), survival analysis, independent prognostic analysis, and receiver operating characteristic (ROC) analysis. Following that, we examined the clinical correlations, pharmacological treatment sensitivity, immune checkpoint expression, and immunological correlations between patients in high and low risk groups. Finally, we evaluated the variations in gene expression between high- and low-risk groups and further analyzed the network core genes using protein-protein interaction network analysis. Results: Prognostic models were built using eight genes (OPTN, ATG12, CSNK2A2, MFN1, PGAM5, SQSTM1, TOMM22, TOMM5). During validation, the prognostic model demonstrated high reliability, indicating that it could accurately predict the prognosis of HCC patients. Additionally, we discovered that typical HCC treatment medicines had varying impacts on patients classified as high or low risk, and that individuals classified as high risk are more likely to fail immunotherapy. Additionally, the high-risk group expressed more immunological checkpoints. The immunological status of patients in different risk categories varies as well, and patients with a high-risk score have a diminished ability to fight cancer. Finally, PPI analysis identified ten related genes with potential for research. Conclusion: Our prognostic model had good and reliable predictive ability, as well as clinical diagnosis and treatment guiding significance. Eight prognostic MRGs and ten network core genes merited further investigation.
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页数:20
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