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Abulimiti M, Jia ZY, Wu Y, Yu J, Gong YH, Guan N, Xiong DQ, Ding N, Uddin N, Wang J. Exploring and clinical validation of prognostic significance and therapeutic implications of copper homeostasis-related gene dysregulation in acute myeloid leukemia. Ann Hematol 2024; 103:2797-2826. [PMID: 38879648 DOI: 10.1007/s00277-024-05841-6] [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: 05/09/2024] [Accepted: 06/08/2024] [Indexed: 07/28/2024]
Abstract
The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML.
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Affiliation(s)
| | - Zheng-Yi Jia
- School of Pharmacy, Xinjiang Medical University, Urumqi, 830011, China
| | - Yun Wu
- Department of General Medicine, The First Affiliated Hospital of the Xinjiang Medical University, Urumqi, 830011, China
| | - Jing Yu
- Department of Teaching and Research, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China
| | - Yue-Hong Gong
- Department of Pharmacy, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China
- Xinjiang Key Laboratory of Clinical Drug Research, Urumqi, 830011, China
| | - Na Guan
- Department of Pharmacy, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China
| | - Dai-Qin Xiong
- Department of Pharmacy, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China
- Xinjiang Key Laboratory of Clinical Drug Research, Urumqi, 830011, China
| | - Nan Ding
- Department of Pharmacy, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China
- Xinjiang Key Laboratory of Clinical Drug Research, Urumqi, 830011, China
| | - Nazim Uddin
- Institute of Food Science and Technology, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, 1205, Bangladesh
| | - Jie Wang
- Department of Pharmacy, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China.
- Xinjiang Key Laboratory of Clinical Drug Research, Urumqi, 830011, China.
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Song Y, Yang Z, Gao N, Zhang B. MICAL1 promotes the proliferation in acute myeloid leukemia and is associated with clinical prognosis and immune infiltration. Discov Oncol 2024; 15:279. [PMID: 38995414 PMCID: PMC11245461 DOI: 10.1007/s12672-024-01150-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 07/08/2024] [Indexed: 07/13/2024] Open
Abstract
Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies that has a poor prognosis and a high rate of relapse. Dysregulated metabolism plays an important role in AML progression. This study aimed to conduct a comprehensive analysis of MRGs using TCGA and GEO datasets and further explore the potential function of critical MRGs in AML progression. In this study, we identified 17 survival-related differentially expressed MRGs in AML using TCGA and GEO datasets. The 150 AML samples were divided into three molecular subtypes using 17 MRGs, and we found that three molecular subtypes exhibited a different association with ferroptosis, cuproptosis and m6A related genes. Moreover, a prognostic signature that comprised nine MRGs and had good predictive capacity was established by LASSO-Cox stepwise regression analysis. Among the 17 MRGs, our attention focused on MICAL1 which was highly expressed in many types of tumors, including AML and its overexpression was also confirmed in several AML cell lines. We also found that the expression of MICAL1 was associated with several immune cells. Moreover, functional experiments revealed that knockdown of MICAL1 distinctly suppressed the proliferation of AML cells. Overall, this study not only contributes to a deeper understanding of the molecular mechanisms underlying AML but also provides potential targets and prognostic markers for AML treatment. These findings offer robust support for further research into therapeutic strategies and mechanisms related to AML, with the potential to improve the prognosis and quality of life for AML patients. Nevertheless, further research is needed to validate these findings and explore more in-depth molecular mechanisms.
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Affiliation(s)
- Yinsen Song
- Translational Medicine Research Center (Key Laboratory of Organ Transplantation of Henan Province), The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China
| | - Zhenzhen Yang
- Translational Medicine Research Center (Key Laboratory of Organ Transplantation of Henan Province), The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China
| | - Na Gao
- Translational Medicine Research Center (Key Laboratory of Organ Transplantation of Henan Province), The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China
| | - Bojun Zhang
- Department of Pathogenic Microbiology and Immunology, School of Basic Medical Sciences, Xi'an Jiaotong University, No.76 Yanta West Road, Xi'an, China.
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Wang X, Sun H, Dong Y, Huang J, Bai L, Tang Z, Liu S, Chen S. Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking. Sci Rep 2024; 14:2802. [PMID: 38307903 PMCID: PMC10837443 DOI: 10.1038/s41598-024-53306-7] [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: 11/04/2023] [Accepted: 01/30/2024] [Indexed: 02/04/2024] Open
Abstract
Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.
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Affiliation(s)
- Xichao Wang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Hao Sun
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Yongfei Dong
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Jie Huang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Lu Bai
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China.
| | - Songbai Liu
- Suzhou Key Laboratory of Medical Biotechnology, Suzhou Vocational Health College, Suzhou, 215009, Jiangsu, China.
| | - Suning Chen
- National Clinical Research Center for Hematologic Diseases, The First Affiliated Hospital of Soochow University, Jiangsu Institute of Hematology, Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China.
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Chen Y, Tang J, Chen L, Chen J. Novel cuproptosis-related lncRNAs can predict the prognosis of patients with multiple myeloma. Transl Cancer Res 2023; 12:3074-3087. [PMID: 38130312 PMCID: PMC10731335 DOI: 10.21037/tcr-23-960] [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: 06/03/2023] [Accepted: 09/28/2023] [Indexed: 12/23/2023]
Abstract
Background Cuproptosis-related long-stranded non-coding RNAs (lncRNAs) have several implications for the prognosis of multiple myeloma (MM). This research aimed to construct a prognostic risk model for MM patients and explore the potential signaling pathways in the risk group. Methods Cuproptosis-related lncRNAs were obtained from the co-expression analysis of cuproptosis-related genes and lncRNAs. Subsequently, twelve cuproptosis-related lncRNAs were selected to construct a prognostic risk model of MM patients by the least absolute shrinkage and selection operator (LASSO) regression. Then, the clinical data of these patients were randomly divided into the training group and the testing group. Next, patients were divided into the low- and high-risk groups according to the median risk score. The Kaplan-Meier survival analysis was performed to clarify the prognostic differences between risk subtypes. Besides, the Cox analysis was conducted to identify whether the risk score can be used as an independent prognostic factor. In addition, the receiver operating characteristic (ROC) curve analysis and the concordance index (C-index) curve analysis were performed to elucidate the value of risk score as a prognostic indicator. Finally, the differential risk analysis and functional enrichment analysis were carried out to identify the potential signaling pathways in the low- and high-risk groups. Results The results demonstrated that the overall survival (OS) of patients in the high-risk group was shorter than that in the low-risk group. There were significant differences in the expression of genes in MM patients between the high- and low-risk groups. The Gene Ontology (GO) analysis results showed that the differentially expressed risk-related genes (DERGs) were mainly concentrated on the collagen-containing extracellular matrix. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results, the DERGs may be related to the neuroactive ligand-receptor interaction and mitogen-activated protein kinase (MAPK) signaling pathway, indicating that they may be involved in the progression of tumors. Conclusions The findings of this study suggest that cuproptosis-related lncRNAs may be effective biomarkers for predicting the prognosis of MM patients, which is anticipated to contribute to the improvement of clinical outcomes.
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Affiliation(s)
- Yuying Chen
- Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jialin Tang
- Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lin Chen
- Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jianbin Chen
- Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Zhu Y, Chang S, Liu J, Wang B. Identification of a novel cuproptosis-related gene signature for multiple myeloma diagnosis. Immun Inflamm Dis 2023; 11:e1058. [PMID: 38018590 PMCID: PMC10629272 DOI: 10.1002/iid3.1058] [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: 04/07/2023] [Revised: 08/19/2023] [Accepted: 10/11/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Multiple myeloma (MM) ranks second among the most prevalent hematological malignancies. Recent studies have unearthed the promise of cuproptosis as a novel therapeutic intervention for cancer. However, no research has unveiled the particular roles of cuproptosis-related genes (CRGs) in the prediction of MM diagnosis. METHODS Microarray data and clinical characteristics of MM patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to identify potential signature genes for MM diagnosis. Predictive performance was further assessed by receiver operating characteristic (ROC) curves, nomogram analysis, and external data sets. Functional enrichment analysis was performed to elucidate the involved mechanisms. Finally, the expression of the identified genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in MM cell samples. RESULTS The optimal gene signature was identified using LASSO and SVM-RFE algorithms based on the differentially expressed CRGs: ATP7A, FDX1, PDHA1, PDHB, MTF1, CDKN2A, and DLST. Our gene signature-based nomogram revealed a high degree of accuracy in predicting MM diagnosis. ROC curves showed the signature had dependable predictive ability across all data sets, with area under the curve values exceeding 0.80. Additionally, functional enrichment analysis suggested significant associations between the signature genes and immune-related pathways. The expression of the genes was validated in MM cells, indicating the robustness of these findings. CONCLUSION We discovered and validated a novel CRG signature with strong predictive capability for diagnosing MM, potentially implicated in MM pathogenesis and progression through immune-related pathways.
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Affiliation(s)
- Yidong Zhu
- Department of Traditional Chinese Medicine, Shanghai Tenth People's HospitalTongji University School of MedicineShanghaiChina
| | - Shuaikang Chang
- Department of Hematology, Shanghai East HospitalTongji University School of MedicineShanghaiChina
| | - Jun Liu
- Department of Traditional Chinese Medicine, Shanghai Tenth People's HospitalTongji University School of MedicineShanghaiChina
| | - Bo Wang
- Department of Endocrinology, Yangpu HospitalTongji University School of MedicineShanghaiChina
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Song J, Sun X, Wang T, Yan L, Su P, Yuan L. Construction and validation of a cuproptosis-related lncRNA prognosis signature in bladder carcinoma. J Cancer Res Clin Oncol 2023; 149:11207-11221. [PMID: 37354222 DOI: 10.1007/s00432-023-05013-5] [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: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND Bladder cancer (BLCA) is a prevalent urological tumor with high morbidity and mortality. However, BLCA treatment remains challenging due to a lack of effective biomarkers. Long non-coding RNAs (lncRNAs), as active participants in tumor progression are involved in multiple biological regulatory mechanisms, and cuproptosis-related genes participate in the development of cancer. It is important to discover cuproptosis- related lncRNAs for BLCA diagnosis and treatment. METHODS A predictive signature was constructed based on least absolute shrinkage and selection operator regression (LASSO) and Cox regression analyses of the 9 cuproptosis-related lncRNAs. Samples were divided into high-risk group and low-risk group based on their median risk scores to explore their prognosis. RESULTS This signature is well predictive, as evidenced by the receiver operating characteristic curves (ROC curves) and K-M curves. Based on the nomogram, we were able to visually forecast the survival rates of patients with BLCA at 1-, 3-, and 5-year, and the calibration plots displayed that the actual results were well matched with the predicted 1-, 3-, and 5-year survival rates. Furthermore, BLCA patients in the high-risk group had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score and lower TMB. Finally, we investigated the response of antitumor drugs for BLCA patients in different risk groups, and a statistically significant difference was observed in the sensitivity of those drugs between low- and the high-risk groups. CONCLUSION According to the 9 cuproptosis-related lncRNAs, we constructed a signature which can be served as a promising prognostic biomarker for BLCA patients.
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Affiliation(s)
- Jinbo Song
- Department of Urology Surgery, Honghui Hospital, Xi'an Jiaotong University, Beilin District, Xi'an, 710054, Shaanxi, China.
| | - Xiaoke Sun
- Department of Urology Surgery, Honghui Hospital, Xi'an Jiaotong University, Beilin District, Xi'an, 710054, Shaanxi, China
| | - Ting Wang
- Department of Urology Surgery, Honghui Hospital, Xi'an Jiaotong University, Beilin District, Xi'an, 710054, Shaanxi, China
| | - Li Yan
- Department of Urology Surgery, Honghui Hospital, Xi'an Jiaotong University, Beilin District, Xi'an, 710054, Shaanxi, China
| | - Pengxiao Su
- Department of Urology Surgery, Honghui Hospital, Xi'an Jiaotong University, Beilin District, Xi'an, 710054, Shaanxi, China
| | - Leihong Yuan
- Department of Urology Surgery, Honghui Hospital, Xi'an Jiaotong University, Beilin District, Xi'an, 710054, Shaanxi, China
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Shi H, Gao L, Zhang W, Jiang M. Long non-coding RNAs regulate treatment outcome in leukemia: What have we learnt recently? Cancer Med 2023. [PMID: 37148556 DOI: 10.1002/cam4.6027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/03/2023] [Accepted: 04/21/2023] [Indexed: 05/08/2023] Open
Abstract
Leukemia is a group of highly heterogeneous and life-threatening blood cancers that originate from abnormal hematopoietic stem cells. Multiple treatments are approved for leukemia, including chemotherapy, targeted therapy, hematopoietic stem cell transplantation, radiation therapy, and immunotherapy. Unfortunately, therapeutic resistance occurs in a substantial proportion of patients and greatly compromises the treatment efficacy of leukemia, resulting in relapse and mortality. The abnormal activity of receptor tyrosine kinases, cell membrane transporters, intracellular signal transducers, transcription factors, and anti-apoptotic proteins have been shown to contribute to the emergence of therapeutic resistance. Despite these findings, the exact mechanisms of treatment resistance are still not fully understood, which limits the development of effective measures to overcome it. Long non-coding RNAs (lncRNA) are a class of regulatory molecules that are gaining increasing attention, and lncRNA-mediated regulation of therapeutic resistance against multiple drugs for leukemia is being revealed. These dysregulated lncRNAs not only serve as potential targets to reduce resistance but also might improve treatment response prediction and individualized treatment decision. Here, we summarize the recent findings on lncRNA-mediated regulation of therapeutic resistance in leukemia and discuss future perspectives on how to make use of the dysregulated lncRNAs in leukemia to improve treatment outcome.
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Affiliation(s)
- Huiping Shi
- The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People's Republic of China
| | - Liang Gao
- Institutes of Biology and Medical Sciences, Soochow University, Suzhou, Jiangsu, People's Republic of China
| | - Weili Zhang
- Department of Gastroenterology, Xiangcheng People's Hospital, Suzhou, Jiangsu, People's Republic of China
| | - Min Jiang
- Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People's Republic of China
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Zhu Y, He J, Li Z, Yang W. Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia. BMC Bioinformatics 2023; 24:37. [PMID: 36737692 PMCID: PMC9896718 DOI: 10.1186/s12859-023-05148-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 01/13/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood. METHODS The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan-Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples. RESULTS A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples. CONCLUSION Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.
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Affiliation(s)
- Yidong Zhu
- grid.412538.90000 0004 0527 0050Department of Traditional Chinese Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072 China
| | - Jun He
- grid.412538.90000 0004 0527 0050Department of Traditional Chinese Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072 China ,grid.412538.90000 0004 0527 0050Department of Hematology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072 China
| | - Zihua Li
- grid.412538.90000 0004 0527 0050Department of Traditional Chinese Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072 China ,grid.412538.90000 0004 0527 0050Department of Orthopedics, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072 China
| | - Wenzhong Yang
- Department of Hematology, Shanghai Punan Hosptial of Pudong New District, Shanghai, 200125, China.
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Tu H, Zhang Q, Xue L, Bao J. Cuproptosis-Related lncRNA Gene Signature Establishes a Prognostic Model of Gastric Adenocarcinoma and Evaluate the Effect of Antineoplastic Drugs. Genes (Basel) 2022; 13:genes13122214. [PMID: 36553481 PMCID: PMC9777654 DOI: 10.3390/genes13122214] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/14/2022] [Accepted: 11/23/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND One of the most frequent malignancies of the digestive system is stomach adenocarcinoma (STAD). Recent research has demonstrated how cuproptosis (copper-dependent cell death) differs from other cell death mechanisms that were previously understood. Cuproptosis regulation in tumor cells could be a brand-new treatment strategy. Our goal was to create a cuproptosis-related lncRNA signature. Additionally, in order to evaluate the possible immunotherapeutic advantages and drug sensitivity, we attempted to study the association between these lncRNAs and the tumor immune microenvironment of STAD tumors. METHODS The TCGA database was accessed to download the RNA sequencing data, genetic mutations, and clinical profiles for TCGA STAD. To locate lncRNAs related to cuproptosis and build risk-prognosis models, three techniques were used: co-expression network analysis, Cox-regression techniques, and LASSO techniques. Additionally, an integrated methodology was used to validate the models' predictive capabilities. Then, using GO and KEGG analysis, we discovered the variations in biological functions between each group. The link between the risk score and various medications for STAD treatment was estimated using the tumor mutational load (TMB) and tumor immune dysfunction and rejection (TIDE) scores. RESULT We gathered 22 genes linked to cuproptosis based on the prior literature. Six lncRNAs related to cuproptosis were used to create a prognostic marker (AC016394.2, AC023511.1, AC147067.2, AL590705.3, HAGLR, and LINC01094). After that, the patients were split into high-risk and low-risk groups. A statistically significant difference in overall survival between the two groups was visible in the survival curves. The risk score was demonstrated to be an independent factor affecting the prognosis by both univariate and multivariate Cox regression analysis. Different risk scores were substantially related to the various immunological states of STAD patients, as further evidenced by immune cell infiltration and ssGSEA analysis. The two groups had differing burdens of tumor mutations. In addition, immunotherapy was more effective for STAD patients in the high-risk group than in the low-risk group, and risk scores for STAD were substantially connected with medication sensitivity. CONCLUSIONS We discovered a marker for six cuproptosis-associated lncRNAs linked to STAD as prognostic predictors, which may be useful biomarkers for risk stratification, evaluation of possible immunotherapy, and assessment of treatment sensitivity for STAD.
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Affiliation(s)
- Hengjia Tu
- Nanshan School, Guangzhou Medical University, Guangzhou 511436, China
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
- Correspondence: ; Tel.: +86-19860075568
| | - Qingling Zhang
- Nanshan School, Guangzhou Medical University, Guangzhou 511436, China
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
| | - Lingna Xue
- Nanshan School, Guangzhou Medical University, Guangzhou 511436, China
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
| | - Junrong Bao
- Faculty of Big Data and Computing, Guangdong Baiyun University, No.1 Xueyuan Road, Guangzhou 510450, China
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