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Lou QM, Lai FF, Li JW, Mao KJ, Wan HT, He Y. Mechanisms of cuproptosis and its relevance to distinct diseases. Apoptosis 2024; 29:981-1006. [PMID: 38824478 DOI: 10.1007/s10495-024-01983-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 06/03/2024]
Abstract
Copper is a trace element required by the organism, but once the level of copper exceeds the threshold, it becomes toxic and even causes death. The underlying mechanisms of copper-induced death are inconclusive, with different studies showing different opinions on the mechanism of copper-induced death. Multiple investigations have shown that copper induces oxidative stress, endoplasmic reticulum stress, nucleolar stress, and proteasome inhibition, all of which can result in cell death. The latest research elucidates a copper-dependent death and denominates it as cuproptosis. Cuproptosis takes place through the combination of copper and lipoylated proteins of the tricarboxylic acid cycle, triggering agglomeration of lipoylated proteins and loss of iron-sulfur cluster proteins, leading to proteotoxic stress and ultimately death. Given the toxicity and necessity of copper, abnormal levels of copper lead to diseases such as neurological diseases and cancer. The development of cancer has a high demand for copper, neurological diseases involve the change of copper contents and the binding of copper to proteins. There is a close relationship between these two kinds of diseases and copper. Here, we summarize the mechanisms of copper-related death, and the association between copper and diseases, to better figure out the influence of copper in cell death and diseases, thus advancing the clinical remedy of these diseases.
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Affiliation(s)
- Qiao-Mei Lou
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Fei-Fan Lai
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jing-Wei Li
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Kun-Jun Mao
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Hai-Tong Wan
- School of Basic Medicine Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
| | - Yu He
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
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Wu Y, Li L, Wang L, Zhang S, Zeng Z, Lu J, Wang Z, Zhang Y, Zhang S, Li H, Chen T. m 1A regulator-mediated methylation modification patterns correlated with autophagy to predict the prognosis of hepatocellular carcinoma. BMC Cancer 2024; 24:506. [PMID: 38649860 PMCID: PMC11034060 DOI: 10.1186/s12885-024-12235-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/08/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND N1-methyladenosine (m1A), among the most common internal modifications on RNAs, has a crucial role to play in cancer development. The purpose of this study were systematically investigate the modification characteristics of m1A in hepatocellular carcinoma (HCC) to unveil its potential as an anticancer target and to develop a model related to m1A modification characteristics with biological functions. This model could predict the prognosis for patients with HCC. METHODS An integrated analysis of the TCGA-LIHC database was performed to explore the gene signatures and clinical relevance of 10 m1A regulators. Furthermore, the biological pathways regulated by m1A modification patterns were investigated. The risk model was established using the genes that showed differential expression (DEGs) between various m1A modification patterns and autophagy clusters. These in vitro experiments were subsequently designed to validate the role of m1A in HCC cell growth and autophagy. Immunohistochemistry was employed to assess m1A levels and the expression of DEGs from the risk model in HCC tissues and paracancer tissues using tissue microarray. RESULTS The risk model, constructed from five DEGs (CDK5R2, TRIM36, DCAF8L, CYP26B, and PAGE1), exhibited significant prognostic value in predicting survival rates among individuals with HCC. Moreover, HCC tissues showed decreased levels of m1A compared to paracancer tissues. Furthermore, the low m1A level group indicated a poorer clinical outcome for patients with HCC. Additionally, m1A modification may positively influence autophagy regulation, thereby inhibiting HCC cells proliferation under nutrient deficiency conditions. CONCLUSIONS The risk model, comprising m1A regulators correlated with autophagy and constructed from five DEGs, could be instrumental in predicting HCC prognosis. The reduced level of m1A may represent a potential target for anti-HCC strategies.
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Affiliation(s)
- Yingmin Wu
- Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, 561113, Guiyang, China.
- Department of Surgery, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China.
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Guizhou Medical University, Guiyang, China.
- Guizhou Provincial Key Laboratory of Pathogenesis and Drug Research on Common Chronic Diseases, Guizhou Medical University, 561113, Guiyang, China.
| | - Lian Li
- Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, 561113, Guiyang, China
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Guizhou Medical University, Guiyang, China
- Guizhou Provincial Key Laboratory of Pathogenesis and Drug Research on Common Chronic Diseases, Guizhou Medical University, 561113, Guiyang, China
| | - Long Wang
- Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, 561113, Guiyang, China
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Guizhou Medical University, Guiyang, China
- Guizhou Provincial Key Laboratory of Pathogenesis and Drug Research on Common Chronic Diseases, Guizhou Medical University, 561113, Guiyang, China
| | - Shenjie Zhang
- Department of Surgery, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China
| | - Zhirui Zeng
- Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, 561113, Guiyang, China
- Department of Surgery, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Guizhou Medical University, Guiyang, China
- Guizhou Provincial Key Laboratory of Pathogenesis and Drug Research on Common Chronic Diseases, Guizhou Medical University, 561113, Guiyang, China
| | - Jieyu Lu
- Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, 561113, Guiyang, China
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Guizhou Medical University, Guiyang, China
- Guizhou Provincial Key Laboratory of Pathogenesis and Drug Research on Common Chronic Diseases, Guizhou Medical University, 561113, Guiyang, China
| | - Zhi Wang
- Department of Surgery, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China
| | - Yewei Zhang
- Department of Surgery, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China
| | - Shilong Zhang
- Department of Surgery, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China
| | - Haiyang Li
- Department of Surgery, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China.
- Guizhou Institute of Precision Medicine, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China.
| | - Tengxiang Chen
- Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, 561113, Guiyang, China.
- Department of Surgery, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China.
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Guizhou Medical University, Guiyang, China.
- Guizhou Provincial Key Laboratory of Pathogenesis and Drug Research on Common Chronic Diseases, Guizhou Medical University, 561113, Guiyang, China.
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Chen E, Zou Z, Wang R, Liu J, Peng Z, Gan Z, Lin Z, Liu J. Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies. Front Immunol 2024; 15:1244392. [PMID: 38694506 PMCID: PMC11061862 DOI: 10.3389/fimmu.2024.1244392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 03/25/2024] [Indexed: 05/04/2024] Open
Abstract
Objective Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy. Methods We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the "one-class logistic regression (OCLR)" algorithm to obtain the mRNAsi of patients with HCC. We performed "unsupervised consensus clustering" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC. Results We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines. Conclusion The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.
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Affiliation(s)
- Erbao Chen
- Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Zhilin Zou
- Department of Ophthalmology, Affiliated Eye Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Rongyue Wang
- Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Jie Liu
- Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Zhen Peng
- Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Zhe Gan
- Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Zewei Lin
- Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Jikui Liu
- Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
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Yang BF, Ma Q, Hui Y, Gao XC, Ma DY, Li JX, Pei ZX, Huang BR. Identification of cuproptosis and ferroptosis-related subgroups and development of a signature for predicting prognosis and tumor microenvironment landscape in hepatocellular carcinoma. Transl Cancer Res 2023; 12:3327-3345. [PMID: 38192999 PMCID: PMC10774034 DOI: 10.21037/tcr-23-685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 11/08/2023] [Indexed: 01/10/2024]
Abstract
Background Ferroptosis and cuproptosis play a crucial role in the progression and dissemination of hepatocellular carcinoma (HCC). The primary objective of this study was to develop a unique scoring system for predicting the prognosis and immunological landscape of HCC based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs). Methods As the training cohort, we assembled a novel HCC cohort by merging gene expression data and clinical data from The Cancer Genome Atlas (TCGA) database, and Gene Expression Omnibus (GEO) database. The validation cohort consisted of 230 HCC cases taken from the International Cancer Genome Consortium (ICGC) database. Multiple genomic characteristics, such as tumor mutation burden (TMB), and copy number variations were analyzed concurrently. On the basis of the expression of CRGs and FRGs, patients were classified into cuproptosis and ferroptosis subtypes. Then, we constructed a risk model using least absolute shrinkage and selection operator (LASSO) analysis and Cox regression analysis based on ferroptosis and cuproptosis-related differentially expressed genes (DEGs). Patients were separated into two groups according to median risk score. We compared the immunophenotype, tumor microenvironment (TME), cancer stem cell index, and treatment sensitivity of two groups. Results Three subtypes of ferroptosis and two subtypes of cuproptosis were identified among the patients. A greater likelihood of survival (P<0.05) was expected for patients in FRGcluster B and CRGcluster B. After that, a confirmed risk signature for ferroptosis and cuproptosis was developed and tested. Patients in the low-risk group had significantly higher survival rates than those in the high-risk group, according to our study (P<0.001). There was also a strong correlation between the signature and other variables including immunophenoscore, TMB, cancer stem cell index, immunological checkpoint genes, and sensitivity to chemotherapeutics. Conclusions Through this comprehensive research, we identified a unique risk signature associated with HCC patients' treatment status and prognosis. Our findings highlight FRGs' and CRGs' significance in clinical practice and imply ferroptosis and cuproptosis may be therapeutic targets for HCC patients.
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Affiliation(s)
- Bin-Feng Yang
- Department of Oncology, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, China
| | - Qi Ma
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Yuan Hui
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Xiang-Chun Gao
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Da-You Ma
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Jing-Xian Li
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Zheng-Xue Pei
- Department of Integrative Medicine, Gansu Provincial Cancer Hospital, Lanzhou, China
| | - Bang-Rong Huang
- Department of Oncology, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, China
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Liu C, Xiao Z, Wu S, Yang Z, Ji G, Duan J, Zhou T, Cao J, Liu X, Xu F. Multi-cohort validation study of a four-gene signature for risk stratification and treatment response prediction in hepatocellular carcinoma. Comput Biol Med 2023; 167:107694. [PMID: 37956625 DOI: 10.1016/j.compbiomed.2023.107694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 10/25/2023] [Accepted: 11/06/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND The intricate molecular landscape of hepatocellular carcinoma (HCC) presents a significant challenge to achieving precise risk stratification through clinical genetic testing. At present, there is a paucity of robust gene signatures that could assist clinicians in making clinical decisions for patients with HCC. METHODS We obtained gene expression profiles of patients with HCC from 20 independent cohorts available in public databases. A gene signature was developed by employing two machine learning algorithms. In addition to validating the signature with high-throughput data in public cohorts, we external validated the signature in 64 HCC cases by RT-PCR method. We compared genomic, transcriptomic and proteomic features between different subgroups. We also compared our signature to 130 gene signatures that have already been published. RESULTS We developed a novel four-gene signature, designated as HCC4, that demonstrates significant potential for the prediction of survival outcomes in more than 1300 patients with HCC. The HCC4 also has potential for predicting recurrence and tumor volume doubling time, assessing transcatheter arterial chemoembolization and immunotherapy responses, and non-invasive detection of HCC. The high HCC4 score group shows a higher frequency of mutations in genes TP53, RB1 and TSC1/2, as well as increased activity of cell-cycle, glycolysis and hypoxia signaling pathways, higher cancer stemness score, and lower lipid metabolism activity. In seven HCC cohorts, HCC4 exhibited a higher average C-index in predicting overall survival compared to the 130 signatures previously published. Drug screening indicated that patients with high HCC4 scores were more sensitive to agents targeting AURKA, TUBB, JMJD6 and KIFC1. CONCLUSIONS Our findings demonstrated that HCC4 is a powerful tool for improving risk stratification and for identifying HCC patients who are most likely to benefit from TACE treatment, immunotherapy, and other experimental therapies.
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Affiliation(s)
- Cuicui Liu
- Department of Clinical Laboratory, Shanghai University of Medicine & Health Sciences Affiliated Sixth People's Hospital South Campus, No.6600 Nanfeng Hwy, Shanghai, 201499, China.
| | - Zhijun Xiao
- Department of Pharmacy, Shanghai University of Medicine & Health Sciences Affiliated Sixth People's Hospital South Campus, No.6600 Nanfeng Hwy, Shanghai, 201499, China.
| | - Shenghong Wu
- Department of Oncology, Shanghai University of Medicine & Health Sciences Affiliated Sixth People's Hospital South Campus, No.6600 Nanfeng Hwy, Shanghai, 201499, China.
| | - Zhen Yang
- Department of Central Laboratory, Shanghai University of Medicine & Health Sciences Affiliated Sixth People's Hospital South Campus, No.6600 Nanfeng Hwy, Shanghai, 201499, China.
| | - Guowen Ji
- Department of Respiratory Medicine, Shanghai University of Medicine & Health Sciences Affiliated Sixth People's Hospital South Campus, No.6600 Nanfeng Hwy, Shanghai, 201499, China.
| | - Jingjing Duan
- Department of Pharmacy, Shanghai University of Medicine & Health Sciences Affiliated Sixth People's Hospital South Campus, No.6600 Nanfeng Hwy, Shanghai, 201499, China.
| | - Ting Zhou
- Department of Pharmacy, Shanghai University of Medicine & Health Sciences Affiliated Sixth People's Hospital South Campus, No.6600 Nanfeng Hwy, Shanghai, 201499, China.
| | - Jinming Cao
- Department of Pharmacy, Shanghai University of Medicine & Health Sciences Affiliated Sixth People's Hospital South Campus, No.6600 Nanfeng Hwy, Shanghai, 201499, China.
| | - Xiufeng Liu
- Department of Pharmacy, Shanghai University of Medicine & Health Sciences Affiliated Sixth People's Hospital South Campus, No.6600 Nanfeng Hwy, Shanghai, 201499, China.
| | - Feng Xu
- Department of Pharmacy, Shanghai University of Medicine & Health Sciences Affiliated Sixth People's Hospital South Campus, No.6600 Nanfeng Hwy, Shanghai, 201499, China.
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Li W, Zhao B, Wang Q, Lu J, Wu X, Chen X. Integrated analysis of tumour-derived exosome-related immune genes to predict progression and immune status of hepatocellular carcinoma. Clin Immunol 2023; 256:109774. [PMID: 37774907 DOI: 10.1016/j.clim.2023.109774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/01/2023] [Accepted: 09/12/2023] [Indexed: 10/01/2023]
Abstract
Tumour-derived exosomes (TDEs) play an important role in tumourigenesis and progression by regulating components in the tumour microenvironment (TME), however, the role of TDE-related immune genes in hepatocellular carcinoma is not fully known. We systematically analysed TDE genes from ExoCarta and immune genes from Immport,Machine learning ultimately identified eight TDE-related prognostic immune genes and used them as the basis for constructing a risk model, which was constructed to better predict patients with hepatocellular carcinoma (HCC) compared with published prognostic models. There were significant differences between the high and low risk groups in terms of biological functioning. Low-risk group were more sensitive to immunotherapy, the sensitivity to oxaliplatin and cisplatin differed between the high- and low-risk groups, and knockout of the core gene RAC1 limited the malignant biological behaviour of hepatocellular carcinoma cells. In conclusion, TIRGs are effective in predicting the prognosis of patients with hepatocellular carcinoma and provide a new perspective on immunotherapy and chemotherapy for patients.
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Affiliation(s)
- Wenhua Li
- Shihezi University School of Medicine, Shihezi 832000, China; Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi 832000, China
| | - Bin Zhao
- Shihezi University School of Medicine, Shihezi 832000, China; Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi 832000, China
| | - Qianwen Wang
- Shihezi University School of Medicine, Shihezi 832000, China; Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi 832000, China
| | - Junxia Lu
- Shihezi University School of Medicine, Shihezi 832000, China; Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi 832000, China
| | - Xiangwei Wu
- Shihezi University School of Medicine, Shihezi 832000, China; The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi 832000, China; Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi 832000, China.
| | - Xueling Chen
- Shihezi University School of Medicine, Shihezi 832000, China; Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi 832000, China.
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Zhu Y, Tan JK, Goon JA. Cuproptosis- and m6A-Related lncRNAs for Prognosis of Hepatocellular Carcinoma. BIOLOGY 2023; 12:1101. [PMID: 37626987 PMCID: PMC10451969 DOI: 10.3390/biology12081101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/01/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023]
Abstract
Cuproptosis and N6-methyladenosine (m6A) have potential as prognostic predictors in cancer patients, but their roles in hepatocellular carcinoma (HCC) are unclear. This study aimed to screen a total of 375 HCC samples were retrieved from the TCGA database, and lncRNAs related to cuproptosis and m6A were obtained through correlation analysis. To construct a risk assessment model, univariate Cox regression analysis and LASSO Cox regression were employed. Analyze the regulatory effect of relevant risk assessment models on tumor mutation load (TMB) and immune microenvironment. A total of five lncRNAs (AC007405.3, AL031985.3, TMCC1-AS1, MIR210HG, TMEM220-AS1) with independent overall survival-related risk models were obtained by LASSO survival regression. TP53 and CTNNB1 were the three genes found to have the most mutations in high-risk group patients. The high-risk group with low TMB had the worst survival, whereas the low-risk group with high TMB had the best survival. KEGG pathway analysis revealed that the high-risk group was enriched with cell cycle, oocyte meiosis, cell senescence, and glycolysis/glucose production pathways. We constructed a reliable cuproptosis- and m6A-related lncRNA model for the prognosis of HCC. The model may provide new insights into managing HCC patients, but further research is needed to validate it.
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Affiliation(s)
| | | | - Jo Aan Goon
- Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
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Li W, Wang Q, Lu J, Zhao B, Geng Y, Wu X, Chen X. Machine learning-based prognostic modeling of lysosome-related genes for predicting prognosis and immune status of patients with hepatocellular carcinoma. Front Immunol 2023; 14:1169256. [PMID: 37275878 PMCID: PMC10237352 DOI: 10.3389/fimmu.2023.1169256] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/10/2023] [Indexed: 06/07/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. Lysosomes are organelles that play an important role in cancer progression by breaking down biomolecules. However, the molecular mechanisms of lysosome-related genes in HCC are not fully understood. Methods We downloaded HCC datasets from TCGA and GEO as well as lysosome-related gene sets from AIMGO. After univariate Cox screening of the set of lysosome-associated genes differentially expressed in HCC and normal tissues, risk models were built by machine learning. Model effects were assessed using the concordance index (C-index), Kaplan-Meier (K-M) and receiver operating characteristic curves (ROC). Additionally, we explored the biological function and immune microenvironment between the high- and low-risk groups, and analyzed the response of the high- and low-risk groups to immunotherapy responsiveness and chemotherapeutic agents. Finally, we explored the function of a key gene (RAMP3) at the cellular level. Results Univariate Cox yielded 46 differentially and prognostically significant lysosome-related genes, and risk models were constructed using eight genes (RAMP3, GPLD1, FABP5, CD68, CSPG4, SORT1, CSPG5, CSF3R) derived from machine learning. The risk model was a better predictor of clinical outcomes, with the higher risk group having worse clinical outcomes. There were significant differences in biological function, immune microenvironment, and responsiveness to immunotherapy and drug sensitivity between the high and low-risk groups. Finally, we found that RAMP3 inhibited the proliferation, migration, and invasion of HCC cells and correlated with the sensitivity of HCC cells to Idarubicin. Conclusion Lysosome-associated gene risk models built by machine learning can effectively predict patient prognosis and offer new prospects for chemotherapy and immunotherapy in HCC. In addition, cellular-level experiments suggest that RAMP3 may be a new target for the treatment of HCC.
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Affiliation(s)
- Wenhua Li
- Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China
- Department of Immunology, Shihezi University School of Medicine, Shihezi, China
| | - Qianwen Wang
- Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China
- Department of Immunology, Shihezi University School of Medicine, Shihezi, China
| | - Junxia Lu
- Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China
- Department of Immunology, Shihezi University School of Medicine, Shihezi, China
| | - Bin Zhao
- Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China
- Department of Immunology, Shihezi University School of Medicine, Shihezi, China
| | - Yuqing Geng
- Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China
- Department of Immunology, Shihezi University School of Medicine, Shihezi, China
| | - Xiangwei Wu
- Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China
- Department of Immunology, Shihezi University School of Medicine, Shihezi, China
- The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, China
| | - Xueling Chen
- Key Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China
- Department of Immunology, Shihezi University School of Medicine, Shihezi, China
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Ma Q, Hui Y, Huang BR, Yang BF, Li JX, Fan TT, Gao XC, Ma DY, Chen WF, Pei ZX. Ferroptosis and cuproptosis prognostic signature for prediction of prognosis, immunotherapy and drug sensitivity in hepatocellular carcinoma: development and validation based on TCGA and ICGC databases. Transl Cancer Res 2023; 12:46-64. [PMID: 36760376 PMCID: PMC9906058 DOI: 10.21037/tcr-22-2203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022]
Abstract
Background Hepatocellular carcinoma (HCC) is a common malignancy. Ferroptosis and cuproptosis promote HCC spread and proliferation. While fewer studies have combined ferroptosis and cuproptosis to construct prognostic signature of HCC. This work attempts to establish a novel scoring system for predicting HCC prognosis, immunotherapy, and medication sensitivity based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs). Methods FerrDb and previous literature were used to identify FRGs. CRGs came from original research. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases included the HCC transcriptional profile and clinical information [survival time, survival status, age, gender, Tumor Node Metastasis (TNM) stage, etc.]. Correlation, Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses were used to narrow down prognostic genes and develop an HCC risk model. Using "caret", R separated TCGA-HCC samples into a training risk set and an internal test risk set. As external validation, we used ICGC samples. We employed Kaplan-Meier analysis and receiver operating characteristic (ROC) curve to evaluate the model's clinical efficacy. CIBERSORT and TIMER measured immunocytic infiltration in high- and low-risk populations. Results TXNRD1 [hazard ratio (HR) =1.477, P<0.001], FTL (HR =1.373, P=0.001), GPX4 (HR =1.650, P=0.004), PRDX1 (HR =1.576, P=0.002), VDAC2 (HR =1.728, P=0.008), OTUB1 (HR =1.826, P=0.002), NRAS (HR =1.596, P=0.005), SLC38A1 (HR =1.290, P=0.002), and SLC1A5 (HR =1.306, P<0.001) were distinguished to build predictive model. In both the model cohort (P<0.001) and the validation cohort (P<0.05), low-risk patients had superior overall survival (OS). The areas under the curve (AUCs) of the ROC curves in the training cohort (1-, 3-, and 5-year AUCs: 0.751, 0.727, and 0.743), internal validation cohort (1-, 3-, and 5-year AUCs: 0.826, 0.624, and 0.589), and ICGC cohort (1-, 3-, and 5-year AUCs: 0.699, 0.702, and 0.568) were calculated. Infiltration of immune cells and immunological checkpoints were also connected with our signature. Treatments with BI.2536, Epothilone.B, Gemcitabine, Mitomycin.C, Obatoclax. Mesylate, and Sunitinib may profit high-risk patients. Conclusions We analyzed FRGs and CRGs profiles in HCC and established a unique risk model for treatment and prognosis. Our data highlight FRGs and CRGs in clinical practice and suggest ferroptosis and cuproptosis may be therapeutic targets for HCC patients. To validate the model's clinical efficacy, more HCC cases and prospective clinical assessments are needed.
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Affiliation(s)
- Qi Ma
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Yuan Hui
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Bang-Rong Huang
- Department of Oncology, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, China
| | - Bin-Feng Yang
- Department of Oncology, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, China
| | - Jing-Xian Li
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Ting-Ting Fan
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Xiang-Chun Gao
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Da-You Ma
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Wei-Fu Chen
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Zheng-Xue Pei
- Department of Integrative Medicine, Gansu Cancer Hospital, Lanzhou, China
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Ke C, Dai S, Xu F, Yuan J, Fan S, Chen Y, Yang L, Li Y. Cuproptosis regulatory genes greatly contribute to clinical assessments of hepatocellular carcinoma. BMC Cancer 2023; 23:25. [PMID: 36611155 PMCID: PMC9824945 DOI: 10.1186/s12885-022-10461-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/19/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a common abdominal cancer with dissatisfactory therapeutic effects. The discovery of cuproptosis lights on new approach for cancer treatment and assessment. So far, there is extremely limited research investigating the roles of cuproptosis-related (CR) genes in cancers. METHODS A novel CR risk signature was constructed using the Lasso regression analysis. Its prognostic value was assessed via a series of survival analyses and validated in three GEO cohorts. The effects of CR risk signature on tumor immune microenvironment (TIM) were explored through CIBERSORT, ESTIMATE, and ssGSEA algorithms. Using GESA, we investigated its impacts on various metabolism process. The somatic mutation features of CR signature genes were also explored via cBioPortal database. Using tumor mutation burden, expressions of immune checkpoints, TIDE score, IMvigor 210 cohort, and GSE109211 dataset, we explored the potential associations of CR risk score with the efficacy of immune checkpoint inhibitors (ICIs) and sorafenib. Finally, the biofunctions of DLAT in HCC cells were ascertained through qPCR, immunohistochemistry, colony formation, and Transwell assays. RESULTS FDX1, DLAT, CDKN2A and GLS constituted the CR risk signature. CR risk signature possessed high prognostic value and was also applicable to three validation cohorts. Meanwhile, it could improve the accuracy and clinical making-decision benefit of traditional prognostic model. Moreover, high CR risk was indicative of unfavorable anti-tumor immune response and active metabolisms of glycolysis and nucleotide. As for therapeutic correlation, CR risk score was a potential biomarker for predicting the efficacy of ICIs and sorafenib. Through qPCR and immunohistochemistry detection in clinical samples, we reconfirmed DLAT was significantly upregulated in HCC samples. Overexpression of DLAT could promote the proliferation, migration, and invasion of HepG2 and HuH-7 cells. CONCLUSIONS The novel CR risk signature greatly contributed to the clinical assessment of HCC. Cuproptosis regulatory gene DLAT possessed cancer-promoting capacities and was expected to be a promising therapeutic target for HCC.
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Affiliation(s)
- Changwei Ke
- grid.452672.00000 0004 1757 5804Department of Emergency, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province China
| | - Shejiao Dai
- grid.452672.00000 0004 1757 5804Department of Gastroenterology, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157, West Five Road, Xi’an, 710004 Shaanxi China
| | - Fangshi Xu
- grid.43169.390000 0001 0599 1243Department of Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi Province China
| | - Jia Yuan
- grid.452672.00000 0004 1757 5804Department of Gastroenterology, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157, West Five Road, Xi’an, 710004 Shaanxi China
| | - Shuting Fan
- grid.452672.00000 0004 1757 5804Department of Gastroenterology, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157, West Five Road, Xi’an, 710004 Shaanxi China
| | - Yang Chen
- grid.43169.390000 0001 0599 1243Department of Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi Province China
| | - Longbao Yang
- Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, West Five Road, Xi'an, 710004, Shaanxi, China. .,Department of Outpatient, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China.
| | - Yong Li
- Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, West Five Road, Xi'an, 710004, Shaanxi, China. .,Department of Outpatient, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China.
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