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Zhang C, Lin Q, Li C, Qiu Y, Chen J, Zhu X. Comprehensive analysis of the prognostic implication and immune infiltration of CISD2 in diffuse large B-cell lymphoma. Front Immunol 2023; 14:1277695. [PMID: 38155967 PMCID: PMC10754510 DOI: 10.3389/fimmu.2023.1277695] [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: 08/15/2023] [Accepted: 11/15/2023] [Indexed: 12/30/2023] Open
Abstract
Background Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell lymphoma in adults. CDGSH iron sulfur domain 2 (CISD2) is an iron-sulfur protein and plays a critical role of cell proliferation. The aberrant expression of CISD2 is associated with the progression of multiple cancers. However, its role in DLBCL remains unclear. Methods The differential expression of CISD2 was identified via public databases, and quantitative real-time PCR (qRT-PCR) and western blot were used to identifed the expression of CISD2. We estimated the impact of CISD2 on clinical prognosis using the Kaplan-Meier plotter. Meanwhile, the drug sensitivity of CISD2 was assessed using CellMiner database. The 100 CISD2-related genes from STRING obtained and analyzed using the LASSO Cox regression. A CISD2 related signature for risk model (CISD2Risk) was established. The PPI network of CISD2Risk was performed, and functional enrichment was conducted through the DAVID database. The impacts of CISD2Risk on clinical features were analyzed. ESTIMATE, CIBERSORT, and MCP-counter algorithm were used to identify CISD2Risk associated with immune infiltration. Subsequently, Univariate and multivariate Cox regression analysis were applied, and a prognostic nomogram, accompanied by a calibration curve, was constructed to predict 1-, 3-, and 5-years survival probabilities. Results CISD2 was upregulated in DLBCL patients comparing with normal controls via public datasets, similarly, CISD2 was highly expressed in DLBCL cell lines. Overexpression of CISD2 was associated with poor prognosis in DLBCL patients based on the GSE31312, the GSE32918, and GSE93984 datasets (P<0.05). Nine drugs was considered as a potential therapeutic agents for CISD2. By using the LASSO cox regression, twenty seven genes were identified to construct CISD2Risk, and biological functions of these genes might be involved in apoptosis and P53 signaling pathway. The high CISD2Risk value had a worse prognosis and therapeutic effect (P<0.05). The higher stromal score, immune score, and ESTIMATE score were associated with lowe CISD2Risk value, CISD2Risk was negatively correlated with several immune infiltrating cells (macrophages M0 and M1, CD8 T cells, CD4 naïve T cells, NK cell, etc) that might be correlated with better prognosis. Additionally, The high CISD2Risk was identified as an independent prognostic factor for DLBCL patients using both univariate and multivariate Cox regression. The nomogram produced accurate predictions and the calibration curves were in good agreement. Conclusion Our study demonstrates that high expression of CISD2 in DLBCL patients is associated with poor prognosis. We have successfully constructed and validated a good prognostic prediction and efficacy monitoring for CISD2Risk that included 27 genes. Meanwhile, CISD2Risk may be a promising evaluator for immune infiltration and serve as a reference for clinical decision-making in DLBCL patients.
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Affiliation(s)
- ChaoFeng Zhang
- Department of Haematology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
- Department of Hematology and Rheumatology, The Affiliated Hospital of Putian University, Putian, China
- The School of Basic Medicine, Putian University, Putian, China
| | - Qi Lin
- Department of Pharmacy, The Affiliated Hospital of Putian University, Putian, China
| | - ChunTuan Li
- Department of Haematology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
| | - Yang Qiu
- The School of Basic Medicine, Putian University, Putian, China
| | - JingYu Chen
- The School of Basic Medicine, Putian University, Putian, China
| | - XiongPeng Zhu
- Department of Haematology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
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Wu H, Zhang J, Fu L, Wu R, Gu Z, Yin C, He K. Identification and Development of a 4-Gene Ferroptosis Signature Predicting Overall Survival for Diffuse Large B-Cell Lymphoma. Technol Cancer Res Treat 2023; 22:15330338221147772. [PMID: 36762399 PMCID: PMC9926004 DOI: 10.1177/15330338221147772] [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] [Indexed: 02/11/2023] Open
Abstract
Background: Diffuse large B-cell lymphoma (DLBCL) is a well-differentiated disease, which makes the diagnosis and therapeutic strategy a difficult problem. While ferroptosis, as an iron-dependent form of regulated cell death, it plays an important role in causing several types of cancer. This study is aimed at exploring the prognostic value of ferroptosis-related genes in DLBCL. Methods: In our study, mRNA expression and matching clinical data of DLBCL patients were derived from Gene Expression Omnibus (GEO) database. First, multivariate cox regression model and nomogram which can predict the DLBCL patients' prognosis were built and validated. The multigene signature was constructed and optimized by the least absolute shrinkage and selection operator (LASSO) cox regression model. Also, ferroptosis-related subtypes were developed by consistent cluster. Last but not least, we explored the association between categories of infiltrating immune cells and model genes' expression. Results: Our results showed that 27 gene expressions were correlated with overall survival (OS) in the univariate cox regression analysis. A 4-gene signature was constructed through these genes to stratify patients into high-low risk groups using risk score derived from model (model 1:gene expression model). The OS of patients in the high-risk group was shorter than that of patients in the low-risk group in the TNM stage and clinically distinct subtypes (activated B cell [ABC], germinal center B cell [GCB]) (P < .001). Furthermore, it was shown that the risk score was an independent factor in clinical cox regression model for OS (model 2:clinical model) (HR>1, P < .010). Besides, in consistent cluster analysis, ferroptosis prognosis status was different among 3 subtypes. Moreover, the correlation analysis between 4-gene with immune cells showed dendritic cells may be significantly associated with DLBCL. Conclusion: This research constructed an innovative ferroptosis-related gene signature for prognostic estimation of DLBCL patients. Solutions targeting ferroptosis could be an important therapeutic intervention for DLBCL.
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Affiliation(s)
- Huitao Wu
- Medical Big Data Research Center, Medical Innovation Research
Division of PLA General Hospital, Beijing, P. R. China,Intelligent Healthcare Team, Baidu Inc., Beijing, China
| | - Junyan Zhang
- Medical Big Data Research Center, Medical Innovation Research
Division of PLA General Hospital, Beijing, P. R. China,National Engineering Laboratory for Medical Big Data Application
Technology, Chinese PLA
General Hospital, Beijing, China
| | - Li Fu
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province,
College of Materials and Environmental Engineering,
Hangzhou
Dianzi University, Hangzhou, China
| | - Rilige Wu
- Medical Big Data Research Center, Medical Innovation Research
Division of PLA General Hospital, Beijing, P. R. China,National Engineering Laboratory for Medical Big Data Application
Technology, Chinese PLA
General Hospital, Beijing, China
| | - Zhenyang Gu
- The Fifth Medical Center of PLA General Hospital, Beijing,
China
| | - Chengliang Yin
- Medical Big Data Research Center, Medical Innovation Research
Division of PLA General Hospital, Beijing, P. R. China,National Engineering Laboratory for Medical Big Data Application
Technology, Chinese PLA
General Hospital, Beijing, China,Chengliang Yin, Medical Big Data Research
Center, Medical Innovation Research Division of PLA General Hospital, Beijing
100853, P. R. China.
| | - Kunlun He
- Medical Big Data Research Center, Medical Innovation Research
Division of PLA General Hospital, Beijing, P. R. China,National Engineering Laboratory for Medical Big Data Application
Technology, Chinese PLA
General Hospital, Beijing, China,Beijing Key Laboratory of Chronic Heart Failure Precision Medicine,
Medical Innovation Research Division of Chinese PLA General Hospital, Beijing,
China,Military Translational Medicine Lab, Medical Innovation Research
Division of Chinese PLA General Hospital, Beijing, China,Key Laboratory of Biomedical Engineering and Translational Medicine,
Ministry of Industry and Information Technology, Medical Innovation Research
Division of Chinese PLA General Hospital, Beijing, China,Kunlun He, Medical Big Data Research
Center, Medical Innovation Research Division of PLA General Hospital, Beijing
100853, P. R. China.
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Yao C, Xu R, Li Q, Xiao S, Hu M, Xu L, Zhuang Q. Identification and validation of an immunological microenvironment signature and prediction model for epstein-barr virus positive lymphoma: Implications for immunotherapy. Front Oncol 2022; 12:970544. [PMID: 36249005 PMCID: PMC9559214 DOI: 10.3389/fonc.2022.970544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/09/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundEpstein-Barr virus (EBV) is considered a carcinogenic virus, which is associated with high risk for poor prognosis in lymphoma patients, and there has been especially no satisfying and effective treatment for EBV+ lymphoma. We aimed to identify the immunological microenvironment molecular signatures which lead to the poor prognosis of EBV+ lymphoma patients.MethodsDifferential genes were screened with microarray data from the GEO database (GSE38885, GSE34143 and GSE13996). The data of lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) from the TCGA database and GSE4475 were used to identify the prognostic genes. The data of GSE38885, GSE34143, GSE132929, GSE58445 and GSE13996 were used to eluate the immune cell infiltration. Formalin-fixed, paraffin-embedded tissue was collected for Real Time Quantitative PCR from 30 clinical samples, including 15 EBV+ and 15 EBV- lymphoma patients.ResultsFour differential genes between EBV+ and EBV- lymphoma patients were screened out with the significance of the survival and prognosis of lymphoma, including CHIT1, SIGLEC15, PLA2G2D and TMEM163. Using CIBERSORT to evaluate immune cell infiltration, we found the infiltration level of macrophages was significantly different between EBV+ and EBV- groups and was closely related to different genes. Preliminary clinical specimen verification identified that the expression levels of CHIT1 and TMEM163 were different between EBV+ and EBV- groups.ConclusionsOur data suggest that differences in expression levels of CHIT1 and TMEM163 and macrophage infiltration levels may be important drivers of poor prognosis of EBV+ lymphoma patients. These hub genes may provide new insights into the prognosis and therapeutic target for EBV+ lymphoma.
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Affiliation(s)
- Chenjiao Yao
- Department of General medicine, The 3rd Xiangya Hospital, Central South University, Changsha, China
| | - Ruoyao Xu
- Transplantation Center, The 3rd Xiangya Hospital, Central South University, Changsha, China
| | - Qianyuan Li
- Department of General medicine, The 3rd Xiangya Hospital, Central South University, Changsha, China
| | - Sheng Xiao
- Department of Pathology, The 3rd Xiangya Hospital, Central South University, Changsha, China
| | - Min Hu
- Department of Hematology, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Linyong Xu
- School of Life Science, Central South University, Changsha, China
| | - Quan Zhuang
- Transplantation Center, The 3rd Xiangya Hospital, Central South University, Changsha, China
- Research Center of National Health Ministry on Transplantation Medicine, Changsha, China
- *Correspondence: Quan Zhuang,
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Zheng Y, Wu R, Wang X, Yin C. Identification of a Four-Gene Metabolic Signature to Evaluate the Prognosis of Colon Adenocarcinoma Patients. Front Public Health 2022; 10:860381. [PMID: 35462848 PMCID: PMC9021388 DOI: 10.3389/fpubh.2022.860381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 03/14/2022] [Indexed: 11/24/2022] Open
Abstract
Background Colon adenocarcinoma (COAD) is a highly heterogeneous disease, thus making prognostic predictions uniquely challenging. Metabolic reprogramming is emerging as a novel cancer hallmark that may serve as the basis for more effective prognosis strategies. Methods The mRNA expression profiles and relevant clinical information of COAD patients were downloaded from public resources. The least absolute shrinkage and selection operator (LASSO) Cox regression model was exploited to establish a prognostic model, which was performed to gain risk scores for multiple genes in The Cancer Genome Atlas (TCGA) COAD patients and validated in GSE39582 cohort. A forest plot and nomogram were constructed to visualize the data. The clinical nomogram was calibrated using a calibration curve coupled with decision curve analysis (DCA). The association between the model genes' expression and six types of infiltrating immunocytes was evaluated. Apoptosis, cell cycle assays and cell transfection experiments were performed. Results Univariate Cox regression analysis results indicated that ten differentially expressed genes (DEGs) were related with disease-free survival (DFS) (P-value< 0.01). A four-gene signature was developed to classify patients into high- and low-risk groups. And patients with high-risk exhibited obviously lower DFS in the training and validation cohorts (P < 0.05). The risk score was an independent parameter of the multivariate Cox regression analyses of DFS in the training cohort (HR > 1, P-value< 0.001). The same findings for overall survival (OS) were obtained GO enrichment analysis revealed several metabolic pathways with significant DEGs enrichment, G1/S transition of mitotic cell cycle, CD8+ T-cells and B-cells may be significantly associated with COAD in DFS and OS. These findings demonstrate that si-FUT1 inhibited cell migration and facilitated apoptosis in COAD. Conclusion This research reveals that a novel metabolic gene signature could be used to evaluate the prognosis of COAD, and targeting metabolic pathways may serve as a therapeutic alternative.
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Affiliation(s)
- Yang Zheng
- Graduate School, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Acute Abdomen Disease Associated Organ Injury and ITCWM Repair, Institute of Integrative Medicine for Acute Abdominal Diseases, Tianjin Nankai Hospital, Tianjin, China
| | - Rilige Wu
- College of Science, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ximo Wang
- Graduate School, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Acute Abdomen Disease Associated Organ Injury and ITCWM Repair, Institute of Integrative Medicine for Acute Abdominal Diseases, Tianjin Nankai Hospital, Tianjin, China
- Tianjin Haihe Hospital, Tianjin, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
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Ma SY, Tian XP, Cai J, Su N, Fang Y, Zhang YC, Wang JN, Peter Gale R, Cai QQ. A prognostic immune risk score for diffuse large B-cell lymphoma. Br J Haematol 2021; 194:111-119. [PMID: 33942291 DOI: 10.1111/bjh.17478] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/17/2021] [Accepted: 03/22/2021] [Indexed: 11/28/2022]
Abstract
We constructed a prognostic score for persons with diffuse large B-cell lymphoma (DLBCL) based on infiltrating immune cells. Data of 956 consecutive subjects were retrieved from the Gene Expression Omnibus database and assigned to training (GSE10846, n = 305) or validation (GSE87371 n = 206 and GSE117556 n = 445 combined) cohorts. Proportions of non-lymphoma cells in the sample were inferred using the ESTIMATE algorithm. An immune risk score was constructed comprised of eight types of non-lymphoma immune cells calculated using the CIBERSORT algorithm. Five-year survival of subjects with an immune risk score ≤ 0·45 in the training cohort was better than that of subjects with a score > 0·45 (hazard ratio [HR] = 3·99; 95% confidence interval [CI] = 2·74, 5·82; P < 0·001). HR in the validation cohort was HR = 2·17 (1·47, 3·21; P < 0·001). Enrichment analyses indicated correlations with genes controlling immune-related biological processes and pathways. A nomogram comprised of the immune risk score and most covariates including age, lactate dehydrogenase concentration (LDH), lymphoma-type (germinal centre B cell [GCB] versus non-GCB), Eastern Cooperative Oncology Group performance status (ECOG-PS) and rituximab therapy had a C-statistic of 0·76 compared with C-statistics of 0·69 and 0·69 for the International Prognostic Index (IPI) and Revised International Prognostic Index (R-IPI). These data indicate the immune risk score is an accurate, independent survival predictor in persons with DLBCL.
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Affiliation(s)
- Shu-Yun Ma
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Xiao-Peng Tian
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Jun Cai
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Ning Su
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Yu Fang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Yu-Chen Zhang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Jin-Ni Wang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Robert Peter Gale
- Department of Immunology and Inflammation, Centre of Haematology, Imperial College London, London, UK
| | - Qing-Qing Cai
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
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