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Li R, Yan X, Zhong W, Zheng J, Li X, Liang J, Hu Z, Liu H, Chen G, Yang Y, Zhang J, Qu E, Liu W. Stratifin promotes the malignant progression of HCC via binding and hyperactivating AKT signaling. Cancer Lett 2024; 592:216761. [PMID: 38490326 DOI: 10.1016/j.canlet.2024.216761] [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: 09/24/2023] [Revised: 02/06/2024] [Accepted: 02/22/2024] [Indexed: 03/17/2024]
Abstract
Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor with limited treatment options and poor prognosis. In this study, we reveal the pivotal role of Stratifin (SFN), also recognized as 14-3-3σ, in driving HCC progression. Our investigation underscores a substantial upregulation of SFN within HCC tissues, manifesting a significant association with worse prognostic outcomes among HCC patients. In vitro and in vivo experiments reveal that SFN overexpression significantly amplifies proliferation, mitigates sorafenib-induced effects on HCC cells, and enhances tumorigenesis. While SFN silencing exerts converse effects on HCC progression. Additionally, we unveil a critical interaction between SFN and AKT, where SFN boosts AKT kinase activity by disrupting the binding of PHLPP2 and AKT, thereby intensifying the malignant progression of HCC cells. In conclusion, this study identifies the oncogenic role of SFN and elucidates the regulatory mechanism of the SFN/AKT axis in HCC, which may provide valuable insights into the mechanisms of HCC progression and potential targets for therapeutic intervention.
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Affiliation(s)
- Rong Li
- Guangdong Provincial Key Laboratory of Liver Disease Research, Guangzhou, 510630, China; Guangdong Province Engineering Laboratory for Transplantation Medicine, Organ Transplantation Research Center of Guangdong Province, Guangzhou, 510630, China
| | - Xijing Yan
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Wenhui Zhong
- Department of Pancreatic and Gastric Surgery, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jun Zheng
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Xuejiao Li
- Guangdong Provincial Key Laboratory of Liver Disease Research, Guangzhou, 510630, China
| | - Jinliang Liang
- Guangdong Provincial Key Laboratory of Liver Disease Research, Guangzhou, 510630, China
| | - Zhongying Hu
- Guangdong Provincial Key Laboratory of Liver Disease Research, Guangzhou, 510630, China
| | - Huanyi Liu
- Guangdong Provincial Key Laboratory of Liver Disease Research, Guangzhou, 510630, China
| | - Guihua Chen
- Guangdong Provincial Key Laboratory of Liver Disease Research, Guangzhou, 510630, China; Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Yang Yang
- Guangdong Province Engineering Laboratory for Transplantation Medicine, Organ Transplantation Research Center of Guangdong Province, Guangzhou, 510630, China; Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China.
| | - Jianwei Zhang
- Department of Pancreatic and Gastric Surgery, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Enze Qu
- Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China.
| | - Wei Liu
- Guangdong Provincial Key Laboratory of Liver Disease Research, Guangzhou, 510630, China; Guangdong Province Engineering Laboratory for Transplantation Medicine, Organ Transplantation Research Center of Guangdong Province, Guangzhou, 510630, China.
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Chen Y, Deng X, Li Y, Han Y, Peng Y, Wu W, Wang X, Ma J, Hu E, Zhou X, Shen E, Zeng S, Cai C, Qin Y, Shen H. Comprehensive molecular classification predicted microenvironment profiles and therapy response for HCC. Hepatology 2024:01515467-990000000-00822. [PMID: 38537130 DOI: 10.1097/hep.0000000000000869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 02/07/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND AND AIMS Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for patients with HCC. The clinical applications of documented molecular subtypes are constrained by several issues. APPROACH AND RESULTS We integrated 3 single-cell data sets to describe the TME landscape and identified 6 prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry, and multiplex immunofluorescence. The prognosis-related score was constructed based on a machine-learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The 5 transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes, and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while classes 2 and 4 were characterized by a lack of T-cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that classes 5 and 3 were sensitive to immune checkpoint blockade and targeted therapy, whereas classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistance to all conventional HCC therapies. Four potential therapeutic agents and 4 targets were further identified for high prognosis-related score patients with HCC. CONCLUSIONS Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC.
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Affiliation(s)
- Yihong Chen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiangying Deng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yin Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ying Han
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yinghui Peng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wantao Wu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xinwen Wang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jiayao Ma
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Erya Hu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xin Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Edward Shen
- Department of Life Science, McMaster University, Hamilton, Ontario, Canada
| | - Shan Zeng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Changjing Cai
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yiming Qin
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hong Shen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Zhan W, Hu H, Hao B, Zhu H, Yan T, Zhang J, Wang S, Liu S, Zhang T. Development of machine learning-based malignant pericardial effusion-related model in breast cancer: Implications for clinical significance, tumor immune and drug-therapy. Heliyon 2024; 10:e27507. [PMID: 38463870 PMCID: PMC10923851 DOI: 10.1016/j.heliyon.2024.e27507] [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: 09/25/2023] [Revised: 01/30/2024] [Accepted: 02/29/2024] [Indexed: 03/12/2024] Open
Abstract
Background Malignant pericardial effusion (MPE) is a common complication of advanced breast cancer (BRCA) and plays an important role in BRCA. This study is aims to construct a prognostic model based on MPE-related genes for predicting the prognosis of breast cancer. Methods The BRCA samples are analyzed based on the expression of MPE-related genes by using an unsupervised cluster analysis method. This study processes the data by least absolute shrinkage and selection operator and multivariate Cox analysis, and uses machine learning algorithms to construct BRCA prognostic model and develop web tool. Results BRCA patients are classified into three clusters and a BRCA prognostic model is constructed containing 9 MPE-related genes. There are significant differences in signature pathways, immune infiltration, immunotherapy response and drug sensitivity testing between the high and low-risk groups. Of note, a web-based tool (http://wys.helyly.top/cox.html) is developed to predict overall survival as well as drug-therapy response of BRCA patients quickly and conveniently, which can provide a basis for clinicians to formulate individualized treatment plans. Conclusion The MPE-related prognostic model developed in this study can be used as an effective tool for predicting the prognosis of BRCA and provides new insights for the diagnosis and treatment of BRCA patients.
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Affiliation(s)
- Wendi Zhan
- School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, Hunan, 421001, China
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Haihong Hu
- School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, Hunan, 421001, China
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Bo Hao
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Hongxia Zhu
- School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, Hunan, 421001, China
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Ting Yan
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Jingdi Zhang
- School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, Hunan, 421001, China
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Siyu Wang
- Department of Medical Oncology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Saiyang Liu
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Taolan Zhang
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Phase I Clinical Trial Center, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
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He W, Huang Y, Shi X, Wang Q, Wu M, Li H, Liu Q, Zhang X, Huang C, Li X. Identifying a distinct fibrosis subset of NAFLD via molecular profiling and the involvement of profibrotic macrophages. J Transl Med 2023; 21:448. [PMID: 37415134 PMCID: PMC10326954 DOI: 10.1186/s12967-023-04300-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 06/23/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND There are emerging studies suggesting that non-alcoholic fatty liver disease (NAFLD) is a heterogeneous disease with multiple etiologies and molecular phenotypes. Fibrosis is the key process in NAFLD progression. In this study, we aimed to explore molecular phenotypes of NAFLD with a particular focus on the fibrosis phenotype and also aimed to explore the changes of macrophage subsets in the fibrosis subset of NAFLD. METHODS To assess the transcriptomic alterations of key factors in NAFLD and fibrosis progression, we included 14 different transcriptomic datasets of liver tissues. In addition, two single-cell RNA sequencing (scRNA-seq) datasets were included to construct transcriptomic signatures that could represent specific cells. To explore the molecular subsets of fibrosis in NAFLD based on the transcriptomic features, we used a high-quality RNA-sequencing (RNA-seq) dataset of liver tissues from patients with NAFLD. Non-negative matrix factorization (NMF) was used to analyze the molecular subsets of NAFLD based on the gene set variation analysis (GSVA) enrichment scores of key molecule features in liver tissues. RESULTS The key transcriptomic signatures on NAFLD including non-alcoholic steatohepatitis (NASH) signature, fibrosis signature, non-alcoholic fatty liver (NAFL) signature, liver aging signature and TGF-β signature were constructed by liver transcriptome datasets. We analyzed two liver scRNA-seq datasets and constructed cell type-specific transcriptomic signatures based on the genes that were highly expressed in each cell subset. We analyzed the molecular subsets of NAFLD by NMF and categorized four main subsets of NAFLD. Cluster 4 subset is mainly characterized by liver fibrosis. Patients with Cluster 4 subset have more advanced liver fibrosis than patients with other subsets, or may have a high risk of liver fibrosis progression. Furthermore, we identified two key monocyte-macrophage subsets which were both significantly correlated with the progression of liver fibrosis in NAFLD patients. CONCLUSION Our study revealed the molecular subtypes of NAFLD by integrating key information from transcriptomic expression profiling and liver microenvironment, and identified a novel and distinct fibrosis subset of NAFLD. The fibrosis subset is significantly correlated with the profibrotic macrophages and M2 macrophage subset. These two liver macrophage subsets may be important players in the progression of liver fibrosis of NAFLD patients.
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Affiliation(s)
- Weiwei He
- Department of Endocrinology and Diabetes, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xaimen, China
- Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
| | - Yinxiang Huang
- Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
| | - Xiulin Shi
- Department of Endocrinology and Diabetes, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xaimen, China
- Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
| | - Qingxuan Wang
- Department of Endocrinology and Diabetes, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xaimen, China
- Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
| | - Menghua Wu
- Department of Endocrinology and Diabetes, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xaimen, China
- Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
| | - Han Li
- Department of Endocrinology and Diabetes, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xaimen, China
- Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
| | - Qiuhong Liu
- Department of Endocrinology and Diabetes, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xaimen, China
- Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
| | - Xiaofang Zhang
- Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
| | - Caoxin Huang
- Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China.
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China.
| | - Xuejun Li
- Department of Endocrinology and Diabetes, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xaimen, China.
- Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China.
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China.
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Huang P, Hu YD, Liu YJ, Li JP, Zhang YH. An Analysis Regarding the Association Between the Nuclear Pore Complex (NPC) and Hepatocellular Carcinoma (HCC). J Hepatocell Carcinoma 2023; 10:959-978. [PMID: 37377841 PMCID: PMC10292625 DOI: 10.2147/jhc.s417501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
Background The nuclear pore complex (NPC) is the main mediator of nuclear and cytoplasmic communication, and delaying or blocking nuclear RNA export and protein shuttling can inhibit cell proliferation and induce apoptosis. Although NPC is a research hotspot in structural biology, relevant studies in hepatocellular carcinoma are scarce, especially in terms of translation into clinical practice. Methods This study used a bioinformatics approach combining validation experiments to investigate the biological mechanisms that may be related with NPC. A series of experiments performed to explore the function of the Targeting protein for Xenopus kinesin-like protein 2 (TPX2) in HCC. Results Patients with HCC can be divided into two NPC clusters. Patients with high NPC levels (C1) had a shorter survival time than those with low NPC levels (C2) and are characterised by high levels of proliferative signals. We demonstrated that TPX2 regulates HCC growth and inhibits apoptosis in an NPC-dependent manner and contributes to the maintenance of HCC stemness. We developed the NPCScore to predict the prognosis and degree of differentiation in HCC patients. Conclusion NPC plays an important role in the malignant proliferation of HCC. Assessing NPC expression patterns could help enhance our understanding of tumor cell proliferation and could guide more effective chemotherapeutic strategies.
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Affiliation(s)
- Pan Huang
- Department of Oncology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, Jiangsu, 215600, People’s Republic of China
| | - Yi-dou Hu
- Department of Oncology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, Jiangsu, 215600, People’s Republic of China
| | - Yuan-jie Liu
- No. 1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, People’s Republic of China
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, 210029, People’s Republic of China
| | - Jie-pin Li
- Department of Oncology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, Jiangsu, 215600, People’s Republic of China
- No. 1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, People’s Republic of China
- Key Laboratory of Tumor System Biology of Traditional Chinese Medicine, Nanjing, Jiangsu, 210029, People’s Republic of China
| | - Yong-hua Zhang
- Department of Oncology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, Jiangsu, 215600, People’s Republic of China
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Kang Z, Sun JB, Lin F, Huang XY, Huang Q, Chen DN, Zheng QS, Xue XY, Xu N, Wei Y. Subtype and prognostic analysis of immunogenic cell death-related gene signature in prostate cancer. Front Oncol 2023; 13:1160972. [PMID: 37346077 PMCID: PMC10279955 DOI: 10.3389/fonc.2023.1160972] [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: 02/07/2023] [Accepted: 05/09/2023] [Indexed: 06/23/2023] Open
Abstract
Background Immunogenic cell death (ICD) plays a vital role in tumor progression and immune response. However, the integrative role of ICD-related genes and subtypes in the tumor microenvironment (TME) in prostate cancer (PCa) remains unknown. Materials and methods The sample data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Memorial Sloan Kettering Cancer Center (MSKCC) prostate cancer-related databases. We first divided the subtypes based on ICD genes from 901 PCa patients and then identified the prognosis- related genes (PRGs) between different ICD subtypes. Subsequently, all the patients were randomly split into the training and test groups. We developed a risk signature in the training set by least absolute shrinkage and selection operator (LASSO)-Cox regression. Following this, we verified this prognostic signature in both the training test and external test sets. The relationships between the different subgroups and clinical pathological characteristics, immune infiltration characteristics, and mutation status of the TME were examined. Finally, the artificial neural network (ANN) and fundamental experiment study were constructed to verify the accuracy of the prognostic signature. Results We identified two ICD clusters with immunological features and three gene clusters composed of PRGs. Additionally, we demonstrated that the risk signature can be used to evaluate tumor immune cell infiltration, prognostic status, and an immune checkpoint inhibitor. The low-risk group, which has a high overlap with group C of the gene cluster, is characterized by high ICD levels, immunocompetence, and favorable survival probability. Furthermore, the tumor progression genes selected by the ANN also exhibit potential associations with risk signature genes. Conclusion This study identified individuals with high ICD levels in prostate cancer who may have more abundant immune infiltration and revealed the potential effects of risk signature on the TME, immune checkpoint inhibitor, and prognosis of PCa.
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Affiliation(s)
- Zhen Kang
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Jiang-Bo Sun
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Fei Lin
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Xu-Yun Huang
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Qi Huang
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Dong-Ning Chen
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Qing-Shui Zheng
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Xue-Yi Xue
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Ning Xu
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Yong Wei
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
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Su Q, Hua F, Xiao W, Liu B, Wang D, Qin X. Investigation of Hippo pathway-related prognostic lncRNAs and molecular subtypes in liver hepatocellular carcinoma. Sci Rep 2023; 13:4521. [PMID: 36941336 PMCID: PMC10027880 DOI: 10.1038/s41598-023-31754-x] [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: 11/17/2022] [Accepted: 03/16/2023] [Indexed: 03/23/2023] Open
Abstract
This study aimed to investigate Hippo pathway-related prognostic long noncoding RNAs (lncRNAs) and their prognostic value in liver hepatocellular carcinoma (LIHC). Expression and clinical data regarding LIHC were acquired from The Cancer Genome Atlas and European Bioinformatics Institute array databases. Hippo pathway-related lncRNAs and their prognostic value were revealed, followed by molecular subtype investigations. Differences in survival, clinical characteristics, immune cell infiltration, and checkpoint expression between the subtypes were explored. LASSO regression was used to determine the most valuable prognostic lncRNAs, followed by the establishment of a prognostic model. Survival and differential expression analyses were conducted between two groups (high- and low-risk). A total of 313 Hippo pathway-related lncRNAs were identified from LIHC, of which 88 were associated with prognosis, and two molecular subtypes were identified based on their expression patterns. These two subtypes showed significant differences in overall survival, pathological stage and grade, vascular invasion, infiltration abundance of seven immune cells, and expression of several checkpoints, such as CTLA-4 and PD-1/L1 (P < 0.05). LASSO regression identified the six most valuable independent prognostic lncRNAs for establishing a prognosis risk model. Risk scores calculated by the risk model assigned patients into two risk groups with an AUC of 0.913 and 0.731, respectively, indicating that the high-risk group had poor survival. The risk score had an independent prognostic value with an HR of 2.198. In total, 3007 genes were dysregulated between the two risk groups, and the expression of most genes was elevated in the high-risk group, involving the cell cycle and pathways in cancers. Hippo pathway-related lncRNAs could stratify patients for personalized treatment and predict the prognosis of patients with LIHC.
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Affiliation(s)
- Qiongfei Su
- Department of Oncology, The First Affiliated Hospital of Guangdong, Pharmaceutical University, Guangzhou, China
| | - Fengyang Hua
- Department of Oncology, The First Affiliated Hospital of Guangdong, Pharmaceutical University, Guangzhou, China
| | - Wanying Xiao
- Department of Oncology, The First Affiliated Hospital of Guangdong, Pharmaceutical University, Guangzhou, China
| | - Baoqiu Liu
- Department of Oncology, The First Affiliated Hospital of Guangdong, Pharmaceutical University, Guangzhou, China
| | - Dongxia Wang
- Department of Radiation Oncology, Affiliated Dongguan People's Hospital, Southern Medical University, Dongguan, China.
| | - Xintian Qin
- Department of Oncology, The First Affiliated Hospital of Guangdong, Pharmaceutical University, Guangzhou, China.
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Cheng Z, Li L, Zhang Y, Ren Y, Gu J, Wang X, Zhao H, Lu H. HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol. Brief Bioinform 2023; 24:7025463. [PMID: 36736372 DOI: 10.1093/bib/bbac601] [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: 07/01/2022] [Revised: 11/01/2022] [Accepted: 11/29/2022] [Indexed: 02/05/2023] Open
Abstract
Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.
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Affiliation(s)
- Zhiwei Cheng
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
| | - Leijie Li
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
| | - Yuening Zhang
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
| | - Yongyong Ren
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
| | - Jianlei Gu
- Department of Biostatistics, Yale University, New Haven, CT, United States
| | - Xinbo Wang
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, United States
| | - Hui Lu
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
- Department of General Surgery, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Identification of the Subtypes of Renal Ischemia-Reperfusion Injury Based on Pyroptosis-Related Genes. Biomolecules 2023; 13:biom13020275. [PMID: 36830644 PMCID: PMC9952921 DOI: 10.3390/biom13020275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 12/29/2022] [Accepted: 01/19/2023] [Indexed: 02/05/2023] Open
Abstract
Ischemia-reperfusion injury (IRI) often occurs in the process of kidney transplantation, which significantly impacts the subsequent treatment and prognosis of patients. The prognosis of patients with different subtypes of IRI is quite different. Therefore, in this paper, the gene expression data of multiple IRI samples were downloaded from the GEO database, and a double Laplacian orthogonal non-negative matrix factorization (DL-ONMF) algorithm was proposed to classify them. In this algorithm, various regularization constraints are added based on the non-negative matrix factorization algorithm, and the prior information is fused into the algorithm from different perspectives. The connectivity information between different samples and features is added to the algorithm by Laplacian regularization constraints on samples and features. In addition, orthogonality constraints on the basis matrix and coefficient matrix obtained by the algorithm decomposition are added to reduce the influence of redundant samples and redundant features on the results. Based on the DL-ONMF algorithm for clustering, two PRGs-related IRI isoforms were obtained in this paper. The results of immunoassays showed that the immune microenvironment was different among PRGS-related IRI types. Based on the differentially expressed PRGs between subtypes, we used LASSO and SVM-RFE algorithms to construct a diagnostic model related to renal transplantation. ROC analysis showed that the diagnostic model could predict the outcome of renal transplant patients with high accuracy. In conclusion, this paper presents an algorithm, DL-ONMF, which can identify subtypes with different disease characteristics. Comprehensive bioinformatic analysis showed that pyroptosis might affect the outcome of kidney transplantation by participating in the immune response of IRI.
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Wang Q, Li X, Chen Y, Gong J, Hu B. Classification and survival prediction in early-stage cirrhosis by gene expression profiling. J Viral Hepat 2023; 30:116-128. [PMID: 36355440 DOI: 10.1111/jvh.13769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/21/2022] [Accepted: 10/29/2022] [Indexed: 11/12/2022]
Abstract
Liver cirrhosis has been increasingly diagnosed at an early stage owing to the non-invasive diagnostic techniques. However, it is difficult to identify patients at high risk of disease progression. Screening cirrhotic patients with poor prognosis who are most in need of surveillance is still challenging. Gene expression data GSE15654 and GSE14520 were downloaded for performing unsupervised clustering analysis. The prognostic differences between the different clusters were explored by Cox regression. Integrative analysis of gene expression signature, immune cell enrichments and clinical characterization was performed for different clusters. Two distinctive subclasses were identified in HCV-related GSE15654, and Kaplan-Meier analysis indicated that subtype 2 had lower survival rates than subtype 1 (p = 0.0399). Further analysis revealed subtype 2 had a higher density of follicular T helper cells, resting natural killer cells and M0, M2 macrophages while subtype 1 with a higher fraction of naive B cells, memory B cells, resting memory CD 4 T cells, activated natural killer cells and monocytes. 226 differentially expressed genes were identified between the two subtypes, and Reactome analysis showed the mainly enriched pathways were biological oxidations and fatty acid metabolism. Five hub genes (AKT1, RPS16, CDC42, CCND1 and PCBP2) and three significant modules were extracted from the PPI network. The results were validated in HBV-related GSE14520 cohort. We identified two subtypes of patients with different prognosis for hepatitis C-related early-stage liver cirrhosis. Bioinformatics analysis of the gene expression and immune cell profile may provide fresh insight into understanding the prognosis difference.
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Affiliation(s)
- Qingliang Wang
- Department of General Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaojie Li
- Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yaqiong Chen
- Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiao Gong
- Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bo Hu
- Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Lin P, Wen DY, Pang JS, Liao W, Chen YJ, He Y, Yang H. Proteomics profiling of nontumor liver tissues identifies prognostic biomarkers in hepatitis B-related hepatocellular carcinoma. J Med Virol 2023; 95:e27732. [PMID: 35315116 DOI: 10.1002/jmv.27732] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/07/2022] [Accepted: 03/19/2022] [Indexed: 01/27/2023]
Abstract
Hepatocellular carcinoma (HCC) often occurs following chronic hepatitis B virus (HBV) infection, leading to high recurrence and a low 5-year survival rate. We developed an overall survival (OS) prediction model based on protein expression profiles in HBV-infected nontumor liver tissues. We aimed to demonstrate the feasibility of using protein expression profiles in nontumor liver tissues for survival prediction. A univariate Cox and differential expression analysis were performed to identify candidate prognostic factors. A multivariate Cox analysis was performed to develop the liver gene prognostic index (LGPI). The survival differences between the different risk groups in the training and validation cohorts were also estimated. A total of 363 patients, 159 in the training cohort, and 204 in the validation cohort were included. Of the 6478 proteins extracted from nontumor liver tissues, we identified 1275 proteins altered between HCC and nontumor liver tissues. A total of 1090 out of 6478 proteins were significantly related to OS. The prognostic values of the proteins in nontumor tissues were mostly positively related to those in the tumor tissues. Protective proteins were mainly enriched in the metabolism-related pathways. From the differentially expressed proteins, the top 10 most significant prognosis-related proteins were submitted for LGPI construction. In the training and validation cohorts, this LGPI showed a great ability for distinguishing patients' OS risk stratifications. After adjusting for clinicopathological features, the LGPI was an independent prognostic factor in the training and validation cohorts. We demonstrated the prognostic value of protein expression profiling in nontumor liver tissues. The proposed LGPI was a promising predictive model for estimating OS in HBV-related HCC.
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Affiliation(s)
- Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dong-Yue Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jin-Shu Pang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Wei Liao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yu-Ji Chen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Huang J, Zhao C, Zhang X, Zhao Q, Zhang Y, Chen L, Dai G. Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicine. Front Pharmacol 2022; 13:1079566. [PMID: 36569318 PMCID: PMC9780394 DOI: 10.3389/fphar.2022.1079566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/30/2022] [Indexed: 12/14/2022] Open
Abstract
Background: Although immune microenvironment-related chemokines, extracellular matrix (ECM), and intrahepatic immune cells are reported to be highly involved in hepatitis B virus (HBV)-related diseases, their roles in diagnosis, prognosis, and drug sensitivity evaluation remain unclear. Here, we aimed to study their clinical use to provide a basis for precision medicine in hepatocellular carcinoma (HCC) via the amalgamation of artificial intelligence. Methods: High-throughput liver transcriptomes from Gene Expression Omnibus (GEO), NODE (https://www.bio.sino.org/node), the Cancer Genome Atlas (TCGA), and our in-house hepatocellular carcinoma patients were collected in this study. Core immunosignals that participated in the entire diseases course of hepatitis B were explored using the "Gene set variation analysis" R package. Using ROC curve analysis, the impact of core immunosignals and amino acid utilization related gene on hepatocellular carcinoma patient's clinical outcome were calculated. The utility of core immunosignals as a classifier for hepatocellular carcinoma tumor tissue was evaluated using explainable machine-learning methods. A novel deep residual neural network model based on immunosignals was constructed for the long-term overall survival (LS) analysis. In vivo drug sensitivity was calculated by the "oncoPredict" R package. Results: We identified nine genes comprising chemokines and ECM related to hepatitis B virus-induced inflammation and fibrosis as CLST signals. Moreover, CLST was co-enriched with activated CD4+ T cells bearing harmful factors (aCD4) during all stages of hepatitis B virus pathogenesis, which was also verified by our hepatocellular carcinoma data. Unexpectedly, we found that hepatitis B virus-hepatocellular carcinoma patients in the CLSThighaCD4high subgroup had the shortest overall survival (OS) and were characterized by a risk gene signature associated with amino acids utilization. Importantly, characteristic genes specific to CLST/aCD4 showed promising clinical relevance in identifying patients with early-stage hepatocellular carcinoma via explainable machine learning. In addition, the 5-year long-term overall survival of hepatocellular carcinoma patients can be effectively classified by CLST/aCD4 based GeneSet-ResNet model. Subgroups defined by CLST and aCD4 were significantly involved in the sensitivity of hepatitis B virus-hepatocellular carcinoma patients to chemotherapy treatments. Conclusion: CLST and aCD4 are hepatitis B virus pathogenesis-relevant immunosignals that are highly involved in hepatitis B virus-induced inflammation, fibrosis, and hepatocellular carcinoma. Gene set variation analysis derived immunogenomic signatures enabled efficient diagnostic and prognostic model construction. The clinical application of CLST and aCD4 as indicators would be beneficial for the precision management of hepatocellular carcinoma.
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Affiliation(s)
- Jun Huang
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China,*Correspondence: Jun Huang, ; Liping Chen, ; Guifu Dai,
| | - Chunbei Zhao
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Xinhe Zhang
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Qiaohui Zhao
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Yanting Zhang
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Liping Chen
- Key Laboratory of Gastroenterology and Hepatology, State Key Laboratory for Oncogenes and Related Genes, Department of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China,Shanghai Public Health Clinical Center, Fudan University, Shanghai, China,*Correspondence: Jun Huang, ; Liping Chen, ; Guifu Dai,
| | - Guifu Dai
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China,*Correspondence: Jun Huang, ; Liping Chen, ; Guifu Dai,
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Zhang L, Xu J, Chu X, Zhang H, Yao X, Zhang J, Guo Y. Identification of the cuproptosis-related molecular subtypes and an immunotherapy prognostic model in hepatocellular carcinoma. BMC Bioinformatics 2022; 23:485. [PMID: 36384423 PMCID: PMC9667659 DOI: 10.1186/s12859-022-04997-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/20/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Cuproptosis, a newly discovered mode of cell death, has been less studied in hepatocellular carcinoma (HCC). Exploring the molecular characteristics of different subtypes of HCC based on cuproptosis-related genes (CRGs) is meaningful to HCC. In addition, immunotherapy plays a pivotal role in treating HCC. Exploring the sensitivity of immunotherapy and building predictive models are critical for HCC. METHODS The 357 HCC samples from the TCGA database were classified into three subtypes, Cluster 1, Cluster 2, and Cluster 3, based on the expression levels of ten CRGs genes using consensus clustering. Six machine learning algorithms were used to build models that identified the three subtypes. The molecular features of the three subtypes were analyzed and compared from some perspectives. Moreover, based on the differentially expressed genes (DEGs) between Cluster 1 and Cluster 3, a prognostic scoring model was constructed using LASSO regression and Cox regression, and the scoring model was used to predict the efficacy of immunotherapy in the IMvigor210 cohort. RESULTS Cluster 3 had the worst overall survival compared to Cluster 1 and Cluster 2 (P = 0.0048). The AUC of the Catboost model used to identify Cluster 3 was 0.959. Cluster 3 was significantly different from the other two subtypes in gene mutation, tumor mutation burden, tumor microenvironment, the expression of immune checkpoint inhibitor genes and N6-methyladenosine regulatory genes, and the sensitivity to sorafenib. We believe Cluster 3 is more sensitive to immunotherapy from the above analysis results. Therefore, based on the DEGs between Cluster 1 and Cluster 3, we obtained a 7-gene scoring prognostic model, which achieved meaningful results in predicting immunotherapy efficacy in the IMvigor210 cohort (P = 0.013). CONCLUSIONS Our study provides new ideas for molecular characterization and immunotherapy of HCC from machine learning and bioinformatics. Moreover, we successfully constructed a prognostic model of immunotherapy.
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Affiliation(s)
- Li Zhang
- grid.460069.dDepartment of Oncology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingwei Xu
- grid.460069.dDepartment of Oncology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiufeng Chu
- grid.460069.dDepartment of Oncology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hongqiao Zhang
- grid.460069.dDepartment of Oncology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xueyuan Yao
- grid.460069.dDepartment of Oncology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian Zhang
- grid.460069.dDepartment of Oncology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanwei Guo
- Department of Oncology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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A Novel circRNA hsa_circRNA_002178 as a Diagnostic Marker in Hepatocellular Carcinoma Enhances Cell Proliferation, Invasion, and Tumor Growth by Stabilizing SRSF1 Expression. JOURNAL OF ONCOLOGY 2022; 2022:4184034. [PMID: 36065311 PMCID: PMC9440807 DOI: 10.1155/2022/4184034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/15/2022] [Accepted: 07/26/2022] [Indexed: 12/14/2022]
Abstract
Background. Previous research studies have shown that the elevation of circular RNA (circRNA), hsa_circRNA_002178, was associated with the poor prognosis of breast cancer and colorectal cancer, while its molecular mechanisms underlying the effects on hepatocellular carcinoma (HCC) are still elusive. Methods. The microarray dataset GSE97332 was obtained from the Gene Expression Omnibus (GEO) database and calculated by using the GEO2R tool to identify differentially expressed circRNAs. Differentially expressed hsa_circRNA_002178, in 7 HCC tissue samples and paracancerous tissues, as well as in HCC cell lines and normal hepatocytes, was checked by RT-qPCR. Cell proliferation, invasion, migration, and epithelial-to-mesenchymal transition (EMT)-related proteins were tested in hsa_circRNA_002178-overexpressed or hsa_circRNA_002178-knocked down HCC cells. Subsequently, we identified whether hsa_circRNA_002178 binds to serine- and arginine-rich splicing factor 1 (SRSF1) and then analyzed their function in regulating HCC cell behavior. The effect on HCC cell xenograft tumor growth was observed by the knockdown of hsa_circRNA_002178 in vivo. Results. GEO2R-based analysis displayed that hsa_circRNA_002178 was upregulated in HCC tissues. Overexpression or knockdown of hsa_circRNA_002178 encouraged or impeded HCC cell proliferation, migration, invasion, and EMT program. Mechanically, hsa_circRNA_002178 bound to SRSF1 3′-untranslated region (UTR) and stabilized its expression. SRSF1 weakening eliminated the effects of pcDNA-hsa_circRNA_002178 on cell malignant behavior. Finally, the knockdown of hsa_circRNA_002178 was confirmed to prevent xenograft tumor growth. Conclusions. hsa_circRNA_002178 overexpression encouraged the stability of SRSF1 mRNA expression, and it may serve as an upstream factor of SRSF1 for the diagnosis of HCC.
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Gastric Cancer Subtypes in Tumour and Nontumour Tissues by Immunologic and Hallmark Gene Sets. JOURNAL OF ONCOLOGY 2022; 2022:7887711. [PMID: 36065314 PMCID: PMC9440817 DOI: 10.1155/2022/7887711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 11/17/2022]
Abstract
A previous research study on differentiating gastric cancer (GC) into distinct subtypes or prognostic models was mostly based on GC tissues, which neglected the role of nontumour tissues in GC subtypes. The purpose of the research was to identify GC subtypes on the basis of tumour and adjacent nontumour tissues to assess the prognosis of GC patients. We characterized three GC subtypes on the basis of the immunologic and hallmark gene sets in GC and adjacent nontumour tissues: among them, the GC patients with subtype I had the longest survival time compared to patients with other subtypes. The classification was closely associated with T stage and pathological stage of GC patients. A prognostic model containing two gene sets was constructed by LASSO analysis. Kaplan–Meier analysis showed that patients in the high-risk group survived longer than those in the low-risk group and the two prognostic genes sets in the model were strongly correlated with survival status. Then, GO and KEGG analyses and PPI network show that nontumour and tumour tissues are influencing the prognosis of GC patients in separate manners. In summary, we emphasized the prognostic value of nontumour tissue in GC patients and proposed a novel insight that both changes in tumour and nontumour tissues should be taken into account when selecting a treatment strategy for GC.
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Sun Z, Zhao Y, Wei Y, Ding X, Tan C, Wang C. Identification and validation of an anoikis-associated gene signature to predict clinical character, stemness, IDH mutation, and immune filtration in glioblastoma. Front Immunol 2022; 13:939523. [PMID: 36091049 PMCID: PMC9452727 DOI: 10.3389/fimmu.2022.939523] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundGlioblastoma (GBM) is the most prominent and aggressive primary brain tumor in adults. Anoikis is a specific form of programmed cell death that plays a key role in tumor invasion and metastasis. The presence of anti-anoikis factors is associated with tumor aggressiveness and drug resistance.MethodsThe non-negative matrix factorization algorithm was used for effective dimension reduction for integrated datasets. Differences in the tumor microenvironment (TME), stemness indices, and clinical characteristics between the two clusters were analyzed. Difference analysis, weighted gene coexpression network analysis (WGCNA), univariate Cox regression, and least absolute shrinkage and selection operator regression were leveraged to screen prognosis-related genes and construct a risk score model. Immunohistochemistry was performed to evaluate the expression of representative genes in clinical specimens. The relationship between the risk score and the TME, stemness, clinical traits, and immunotherapy response was assessed in GBM and pancancer.ResultsTwo definite clusters were identified on the basis of anoikis-related gene expression. Patients with GBM assigned to C1 were characterized by shortened overall survival, higher suppressive immune infiltration levels, and lower stemness indices. We further constructed a risk scoring model to quantify the regulatory patterns of anoikis-related genes. The higher risk score group was characterized by a poor prognosis, the infiltration of suppressive immune cells and a differentiated phenotype, whereas the lower risk score group exhibited the opposite effects. In addition, patients in the lower risk score group exhibited a higher frequency of isocitrate dehydrogenase (IDH) mutations and a more sensitive response to immunotherapy. Drug sensitivity analysis was performed, revealing that the higher risk group may benefit more from drugs targeting the PI3K/mTOR signaling pathway.ConclusionWe revealed potential relationships between anoikis-related genes and clinical features, TME, stemness, IDH mutation, and immunotherapy and elucidated their therapeutic value.
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Affiliation(s)
- Zhongzheng Sun
- Department of Neurosurgery, The Second Hospital of Shandong University, Jinan, China
| | - Yongquan Zhao
- Department of Neurosurgery, Dongying City District People’s Hospital, Dongying, China
| | - Yan Wei
- Department of Neurology, The Second Hospital of Shandong University, Jinan, China
| | - Xuan Ding
- Department of Neurosurgery, The Second Hospital of Shandong University, Jinan, China
| | - Chenyang Tan
- Department of Neurosurgery, The Second Hospital of Shandong University, Jinan, China
| | - Chengwei Wang
- Department of Neurosurgery, The Second Hospital of Shandong University, Jinan, China
- *Correspondence: Chengwei Wang,
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Xie H, Shi M, Liu Y, Cheng C, Song L, Ding Z, Jin H, Cui X, Wang Y, Yao D, Wang P, Yao M, Zhang H. Identification of m6A- and ferroptosis-related lncRNA signature for predicting immune efficacy in hepatocellular carcinoma. Front Immunol 2022; 13:914977. [PMID: 36032107 PMCID: PMC9402990 DOI: 10.3389/fimmu.2022.914977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 07/13/2022] [Indexed: 12/16/2022] Open
Abstract
Background N6-methyladenosine (m6A) methylation and ferroptosis assist long noncoding RNAs (lncRNAs) in promoting immune escape in hepatocellular carcinoma (HCC). However, the predictive value of m6A- and ferroptosis-related lncRNAs (mfrlncRNAs) in terms of immune efficacy remains unknown. Method A total of 365 HCC patients with complete data from The Cancer Genome Atlas (TCGA) database were used as the training cohort, and half of them were randomly selected as the validation cohort. A total of 161 HCC patients from the International Cancer Genome Consortium (ICGC) database were used as external validation (ICGC cohort). Results We first identified a group of specific lncRNAs associated with both m6A regulators and ferroptosis-related genes and then constructed prognosis-related mfrlncRNA pairs. Based on this, the mfrlncRNA signature was constructed using the least absolute shrinkage and selection operator (LASSO) analysis and Cox regression. Notably, the risk score of patients was proven to be an independent prognostic factor and was better than the TNM stage and tumor grade. Moreover, patients with high-risk scores had lower survival rates, higher infiltration of immunosuppressive cells (macrophages and Tregs), lower infiltration of cytotoxic immune cells (natural killer cells), poorer immune efficacy (both immunophenoscore and score of tumor immune dysfunction and exclusion), higher IC50, and enrichment of the induced Treg pathway, which confirmed that the mfrlncRNA signature contributed to survival prediction and risk stratification of patients with HCC. Conclusions The mfrlncRNA signature, which has great prognostic value, provides new clues for identifying “cold” and “hot” tumors and might have crucial implications for individualized therapy to improve the survival rate of patients with HCC.
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Affiliation(s)
- Hongjun Xie
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, and Medical School of Nantong University, Nantong, China
| | - Muqi Shi
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, and Medical School of Nantong University, Nantong, China
| | - Yifei Liu
- Department of Pathology, Affiliated Hospital of Nantong University, Nantong, China
| | - Changhong Cheng
- Department of Clinical Laboratory, People’s Hospital of Ganyu District, Lianyungang, China
| | - Lining Song
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, and Medical School of Nantong University, Nantong, China
| | - Zihan Ding
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, and Medical School of Nantong University, Nantong, China
| | - Huanzhi Jin
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, and Medical School of Nantong University, Nantong, China
| | - Xiaohong Cui
- Department of General Surgery, Shanghai Electric Power Hospital, Shanghai, China
| | - Yan Wang
- Department of Emergency, Affiliated Hospital of Nantong University, Nantong, China
| | - Dengfu Yao
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, and Medical School of Nantong University, Nantong, China
| | - Peng Wang
- Department of General Surgery, Affiliated Hospital of Nantong University, Nantong, China
| | - Min Yao
- Department of Immunology, Medical School of Nantong University, Nantong, China
- *Correspondence: Haijian Zhang, ; Min Yao,
| | - Haijian Zhang
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, and Medical School of Nantong University, Nantong, China
- *Correspondence: Haijian Zhang, ; Min Yao,
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Guo C, Tang Y, Yang Z, Li G, Zhang Y. Hallmark-guided subtypes of hepatocellular carcinoma for the identification of immune-related gene classifiers in the prediction of prognosis, treatment efficacy, and drug candidates. Front Immunol 2022; 13:958161. [PMID: 36032071 PMCID: PMC9399518 DOI: 10.3389/fimmu.2022.958161] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Hepatocellular carcinoma (HCC), accounting for ~90% of all primary liver cancer, is a prevalent malignancy worldwide. The intratumor heterogeneity of its causative etiology, histology, molecular landscape, and immune phenotype makes it difficult to precisely recognize individuals with high mortality risk or tumor-intrinsic treatment resistance, especially immunotherapy. Herein, we comprehensively evaluated the activities of cancer hallmark gene sets and their correlations with the prognosis of HCC patients using gene set variation analysis (GSVA) and identified two HCC subtypes with distinct prognostic outcomes. Based on these subtypes, seven immune-related genes (TMPRSS6, SPP1, S100A9, EPO, BIRC5, PLXNA1, and CDK4) were used to construct a novel prognostic gene signature [hallmark-guided subtypes-based immunologic signature (HGSIS)] via multiple statistical approaches. The HGSIS-integrated nomogram suggested an enhanced predictive performance. Interestingly, oncogenic hallmark pathways were significantly enriched in the high-risk group and positively associated with the risk score. Distinct mutational landscapes and immune profiles were observed between different risk groups. Moreover, immunophenoscore (IPS) and tumor immune dysfunction and exclusion (TIDE) analysis showed different sensitivities of HGSIS risk groups for immune therapy efficacy, and the pRRophetic algorithm indicated distinguishable responses for targeted/chemotherapies in different groups. KIF2C was picked out as the key target concerning HGSIS, and the top 10 small molecules were predicted to bind to the active site of KIF2C via molecular docking, which might be further used for candidate drug discovery of HCC. Taken together, our study offers novel insights for clinically significant subtype recognition, and the proposed signature may be a helpful guide for clinicians to improve the treatment regimens.
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Affiliation(s)
- Chengbin Guo
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Yuqin Tang
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhao Yang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Gen Li
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China
- *Correspondence: Yongqiang Zhang, ; Gen Li,
| | - Yongqiang Zhang
- Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China
- *Correspondence: Yongqiang Zhang, ; Gen Li,
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19
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Wang Z, Zhao Z, Xia Y, Cai Z, Wang C, Shen Y, Liu R, Qin H, Jia J, Yuan G. Potential biomarkers in the fibrosis progression of nonalcoholic steatohepatitis (NASH). J Endocrinol Invest 2022; 45:1379-1392. [PMID: 35226336 DOI: 10.1007/s40618-022-01773-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 02/17/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Fibrosis is the only histological feature reflecting the severity and prognosis of nonalcoholic steatohepatitis (NASH). We aim to explore novel genes associated with fibrosis progression in NASH. METHODS Two human RNA-seq datasets were downloaded from the public database. Weighted gene co-expression network analysis (WGCNA) was used to identify their co-expressed modules and further bioinformatics analysis was performed to identify hub genes within the modules. Finally, based on two single-cell RNA-seq datasets from mice and one microarray dataset from human, we further observed the expression of hub genes in different cell clusters and liver tissues. RESULTS 7 hub genes (SPP1, PROM1, SOX9, EPCAM, THY1, CD34 and MCAM) associated with fibrosis progression were identified. Single-cell RNA-seq analysis revealed that those hub genes were expressed by different cell clusters such as cholangiocytes, natural killer (NK) cells, and hepatic stellate cells (HSCs). We also found that SPP1 and CD34 serve as markers of different HSCs clusters, which are associated with inflammatory response and fibrogenesis, respectively. Further study suggested that SPP1, SOX9, MCAM and THY1 might be related to NASH-associated hepatocellular carcinoma (HCC). Receiver operating characteristic (ROC) analysis showed that the high expression of these genes could well predict the occurrence of HCC. At the same time, there were significant differences in metabolism-related pathway changes between different HCC subtypes, and SOX9 may be involved in these changes. CONCLUSIONS The present study identified novel genes associated with NASH fibrosis and explored their effects on fibrosis from a single-cell perspective that might provide new ideas for the early diagnosis, monitoring, evaluation, and prediction of fibrosis progression in NASH.
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Affiliation(s)
- Z Wang
- Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China
| | - Z Zhao
- Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China
| | - Y Xia
- Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China
| | - Z Cai
- Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China
| | - C Wang
- Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China
| | - Y Shen
- Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China
| | - R Liu
- Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China
| | - H Qin
- Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China
| | - J Jia
- Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China.
| | - G Yuan
- Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China.
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20
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Wu J, He J, Zhang J, Ji H, Wang N, Ma S, Yan X, Gao X, Du J, Liu Z, Hu S. Identification of EMT-Related Genes and Prognostic Signature With Significant Implications on Biological Properties and Oncology Treatment of Lower Grade Gliomas. Front Cell Dev Biol 2022; 10:887693. [PMID: 35656554 PMCID: PMC9152435 DOI: 10.3389/fcell.2022.887693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/14/2022] [Indexed: 12/13/2022] Open
Abstract
The epithelial-mesenchymal transition (EMT) is an important process that drives progression, metastasis, and oncology treatment resistance in cancers. Also, the adjacent non-tumor tissue may affect the biological properties of cancers and have potential prognostic implications. Our study aimed to identify EMT-related genes in LGG samples, explore their impact on the biological properties of lower grade gliomas (LGG) through the multi-omics analysis, and reveal the potential mechanism by which adjacent non-tumor tissue participated in the malignant progression of LGG. Based on the 121 differentially expressed EMT-related genes between normal samples from the GTEx database and LGG samples in the TCGA cohort, we identified two subtypes and constructed EMTsig. Because of the genetic, epigenetic, and transcriptomic heterogeneity, malignant features including clinical traits, molecular traits, metabolism, anti-tumor immunity, and stemness features were different between samples with C1 and C2. In addition, EMTsig could also quantify the EMT levels, variation in prognosis, and oncology treatment sensitivity of LGG patients. Therefore, EMTsig could assist us in developing objective diagnostic tools and in optimizing therapeutic strategies for LGG patients. Notably, with the GSVA, we found that adjacent non-tumor tissue might participate in the progression, metastasis, and formation of the tumor microenvironment in LGG. Therefore, the potential prognostic implications of adjacent non-tumor tissue should be considered when performing clinical interventions for LGG patients. Overall, our study investigated and validated the effects of EMT-related genes on the biological properties from multiple perspectives, and provided new insights into the function of adjacent non-tumor tissue in the malignant progression of LGG.
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Affiliation(s)
- Jiasheng Wu
- Department of Neurosurgery, Emergency Medicine Center, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, China.,Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jinru He
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jiheng Zhang
- Department of Neurosurgery, Emergency Medicine Center, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, China.,Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hang Ji
- Department of Neurosurgery, Emergency Medicine Center, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, China.,Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Nan Wang
- Department of Neurosurgery, Emergency Medicine Center, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, China.,Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shuai Ma
- Department of Neurosurgery, Emergency Medicine Center, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, China.,Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiuwei Yan
- Department of Neurosurgery, Emergency Medicine Center, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, China.,Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xin Gao
- Department of Neurosurgery, Emergency Medicine Center, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, China.,Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jianyang Du
- Department of Neurosurgery, Emergency Medicine Center, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, China.,School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.,Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Zhihui Liu
- Department of Neurosurgery, Emergency Medicine Center, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, China.,Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shaoshan Hu
- Department of Neurosurgery, Emergency Medicine Center, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, China.,Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
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21
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Huo J, Cai J, Wu L. Comprehensive analysis of metabolic pathway activity subtypes derived prognostic signature in hepatocellular carcinoma. Cancer Med 2022; 12:898-912. [PMID: 35651292 PMCID: PMC9844627 DOI: 10.1002/cam4.4858] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/20/2022] [Accepted: 05/15/2022] [Indexed: 01/26/2023] Open
Abstract
OBJECTIVE Metabolic reprogramming is one of the hallmarks of cancer, but metabolic pathway activity-related subtypes of hepatocellular carcinoma (HCC) have not been identified. METHODS Based on the quantification results of 41 metabolic pathway activities by gene set variation analysis, the training cohort (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) with the nonnegative matrix factorization method. Totally 1371 differentially expressed genes among C1, C2, and C3 were identified, and an 8-gene risk score was established by univariable Cox regression analysis, least absolute shrinkage and selection operator method, and multivariable Cox regression analysis. RESULTS C1 had the strongest metabolic activity, good prognosis, the highest CTNNB1 mutation rate, with massive infiltration of eosinophils and natural killer cells. C2 had the weakest metabolic activity, poor prognosis, was younger, was inclined to vascular invasion and advanced stage, had the highest TP53 mutation rate, exhibited a higher expression level of immune checkpoints, accompanied by massive infiltration of regulatory T cells. C3 had moderate metabolic activity and prognosis, the highest LRP1B mutation rate, and a higher infiltration level of neutrophils and macrophages. Internal cohorts (TCGA, n = 370; GSE14520, n = 239), external cohorts (ICGC, n = 231; GSE116174, n = 64), and clinical subgroup validation showed that the risk score was applicable for patients with diverse clinical features and was effective in predicting the prognosis and malignant progression of patients with HCC. Compared with the low-risk group, the high-risk group had a poor prognosis, enhanced cancer stem cell characteristics, activated DNA damage repair, weakened metabolic activity, cytolytic activity, and interferon response. CONCLUSION We identified HCC subtypes from the perspective of metabolism-related pathway activity and proposed a robust prognostic signature for HCC.
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Affiliation(s)
- Junyu Huo
- Liver Disease CenterThe Affiliated Hospital of Qingdao UniversityQingdaoChina
| | - Jinzhen Cai
- Liver Disease CenterThe Affiliated Hospital of Qingdao UniversityQingdaoChina
| | - Liqun Wu
- Liver Disease CenterThe Affiliated Hospital of Qingdao UniversityQingdaoChina
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22
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Liu Q, Wang Y, Gao H, Sun F, Wang X, Zhang H, Wang J. An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer. Front Genet 2022; 13:837301. [PMID: 35422849 PMCID: PMC9002098 DOI: 10.3389/fgene.2022.837301] [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: 12/16/2021] [Accepted: 03/02/2022] [Indexed: 12/24/2022] Open
Abstract
Background: The tumor immune microenvironment (TIME) plays an important role in the development and prognosis of bladder cancer. It is essential to conduct a risk model to explore the prognostic value of the immunologic genes and establish an individualized prognostic signature for predicting the survival of patients with bladder cancer. Method: The differentially expressed immunologic genes (DEGs) are identified in The Cancer Genome Atlas (TCGA). The nonnegative matrix factorization (NMF) was used to stratify the DEGs in TCGA. We used the least absolute shrinkage and selection operator (LASSO) Cox regression and univariate Cox analysis to establish a prognostic risk model. A nomogram was used to establish an individualized prognostic signature for predicting survival. The potential pathways underlying the model were explored. Results: A total of 1,018 DEGs were screened. All samples were divided into two clusters (C1 and C2) by NMF with different immune cell infiltration, and the C2 subtype had poor prognosis. We constructed a 15-gene prognostic risk model from TCGA cohort. The patients from the high-risk group had a poor overall survival rate compared with the low-risk group. Time-dependent ROC curves demonstrated good predictive ability of the signature (0.827, 0.802, and 0.812 for 1-, 3-, and 5-year survival, respectively). Univariate and multivariate Cox regression analyses showed that the immunologic prognostic risk model was an independent factor. The decision curve demonstrated a relatively good performance of the risk model and individualized prognostic signature, showing the best net benefit for 1-, 3-, and 5-year OS. Gene aggregation analysis showed that the high-risk group was mainly concentrated in tumorigenesis and migration and immune signaling pathways. Conclusion: We established a risk model and an individualized prognostic signature, and these may be useful biomarkers for prognostic prediction of patients with bladder cancer.
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Affiliation(s)
- Qing Liu
- Department of Medical Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yunchao Wang
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Huayu Gao
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Fahai Sun
- Department of Urology, Fifth Peoples Hospital Jinan, Jinan, China
| | - Xuan Wang
- Department of Urology, Fifth Peoples Hospital Jinan, Jinan, China
| | - Huawei Zhang
- Department of Medical Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Jianning Wang
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
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23
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Yin J, He X, Xia H, He L, Li D, Hu L, Zheng S, Huang Y, Li S, Hu W. Head and Neck Squamous Cell Carcinoma Subtypes Based on Immunologic and Hallmark Gene Sets in Tumor and Non-tumor Tissues. Front Surg 2022; 9:821600. [PMID: 35187059 PMCID: PMC8850349 DOI: 10.3389/fsurg.2022.821600] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 01/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Non-tumor tissue has a significant impact on the prognosis of head and neck squamous cell carcinoma (HNSCC). Previous studies for HNSCC have mainly focused on tumor tissue, greatly neglecting the role of non-tumor tissue. This study aimed to identify HNSCC subtypes and prognostic gene sets based on activity changes of immunologic and hallmark gene sets in tumor and adjacent non-tumor tissues to improve patient prognosis. Methods In the study, we used gene set variation analysis (GSVA) to estimate the relative enrichment of gene sets over the sample population, and identified relevant subtypes of HNSCC by Cox regression analysis and the non-negative matrix factorization (NMF) method. The representative gene sets were identified by calculating the differential enrichment score of gene sets between each of the two subgroups, intersecting them, and screening them using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen out potential prognostic gene sets and establish a risk model. Finally, genes encompassed in each prognostic gene set were obtained and subjected to enrichment analysis and protein–protein interaction (PPI) in tumor and non-tumor tissues. Results We identified three subtypes of HNSCC based on gene sets in tumor and non-tumor tissues, and patients with subtype 1 had a higher survival rate than subtypes 2 and 3. The subtypes were related to the survival status, pathological stage, and T stage of HNSCC patients. In total 450 differentially gene sets and 39 representative gene sets were obtained by calculating the differential enrichment score of gene sets between each of the two subgroups, intersecting them, and screening them using univariate Cox regression analysis. The prognostic model was constructed by LASSO regression analysis, including five prognostic gene sets. Kaplan-Meier analysis indicated that different risk groups and the five prognostic gene sets were associated with survival status in the model. Finally, enrichment analysis and PPI indicated that non-tumor and tumor tissues affect the prognosis of HNSCC patients in different ways. Conclusion In conclusion, we provide a novel insight for rational treatment strategies and precise prognostic assessments based on tumor and adjacent non-tumor tissues, suggesting that more emphasis should be placed on changes in adjacent non-tumor and tumor tissues, rather than just the tumor itself.
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24
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Identification of Novel Subtypes in Lung Adenocarcinoma: Evidence from Gene Set Variation Analysis in Tumor and Adjacent Nontumor Samples. DISEASE MARKERS 2022; 2022:2602812. [PMID: 35096200 PMCID: PMC8793346 DOI: 10.1155/2022/2602812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 12/20/2021] [Indexed: 11/24/2022]
Abstract
In patients with lung adenocarcinoma (LUAD), the prognostic role of adjacent nontumor tissues is still unknown. Alterations in the activity of immunologic and hallmark gene sets in adjacent nontumor tissues may have a potential influence on cell proliferation of normal lung cell after pulmonary lobectomy. We sought to discover LUAD subgroups and prognostic gene sets based on changes in gene set activity in tumor and adjacent nontumor tissues. Firstly, we used gene set variation analysis (GSVA) to characterize the activity changes of 4922 gene sets in LUAD and nontumor samples. Luckily, we identified three novel LUAD subtypes using the nonnegative matrix factorization (NMF) algorithm. In detailed, patients with subtype-3 had a favorable prognosis, but subtypes 1 and 2 had a bad prognosis. In addition, patients with subtype-3 in the validation cohort also lived longer. Meanwhile, using the LASSO-Cox algorithm, we discovered 15 prognostic gene sets in tumors (T gene sets) and two prognostic gene sets in adjacent nontumors (N gene sets). Interestingly, genes from N gene sets were related with immune response in nontumor tissues, but genes from T gene sets were correlated with DNA damaging and repairing in tumor tissues. These findings highlighted the possibility of a stronger immune response in nearby nontumor tissues. In conclusion, our study established a theoretical foundation for selecting therapy strategy for LUAD patients that should be guided by changes in activity in tumor and adjacent nontumor tissues, particularly after pulmonary lobectomy.
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25
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Chen S. Glioma Subtypes Based on the Activity Changes of Immunologic and Hallmark Gene Sets in Cancer. Front Endocrinol (Lausanne) 2022; 13:879233. [PMID: 35774141 PMCID: PMC9236851 DOI: 10.3389/fendo.2022.879233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 04/25/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Glioma is the most common primary cranial brain tumor that arises from the cancelation of glial cells (which can be in the brain or spinal cord). It is due to innate genetic risk factors or induced by a carcinogenic environment. If left untreated, the disease has a poor prognosis. METHODS In this study, we downloaded glioma data from TCGA database and GEO (GSE4412). The GSEA database was used to screen tumor microenvironment-related gene sets. Cancer subtypes were classified by GSVA enrichment method. RESULTS By GSVA enrichment analysis, we obtain three Gliomas cancer subtypes. After further survival prognosis analysis and biological function analysis, we obtained 13 tumor microenvironment gene sets and 14 core genes that affect patients' survival prognosis, and these genes have the potential to become targets for targeted therapies and disease detection. CONCLUSION We screened a total of 13 gene sets through a series of enrichment analyses, statistical and prognostic analyses, etc. Among them, 14 core genes were identified, namely: TOP2A, TPX2, BUB1, AURKB, AURKA, CDK1, BUB1B, CCNA2, CCNB2, CDCA8, CDC20, KIF11, KIF20A and KIF2C.
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Affiliation(s)
- Sihan Chen
- Taikang (Ningbo) Hospital Co., Ltd. Yinzhou, Ningbo, China
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26
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Pang Y, Tan G, Yang X, Lin Y, Chen Y, Zhang J, Xie T, Zhou H, Fang J, Zhao Q, Ren X, Li J, Lyu J, Wang Z. Iron-sulphur cluster biogenesis factor LYRM4 is a novel prognostic biomarker associated with immune infiltrates in hepatocellular carcinoma. Cancer Cell Int 2021; 21:463. [PMID: 34488769 PMCID: PMC8419973 DOI: 10.1186/s12935-021-02131-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 07/31/2021] [Indexed: 12/11/2022] Open
Abstract
Background LYRM4 is necessary to maintain the stability and activity of the human cysteine desulfurase complex NFS1-LYRM4-ACP. The existing experimental results indicate that cancer cells rely on the high expression of NFS1. However, the role of LYRM4 in liver hepatocellular carcinoma (LIHC) remains unclear. Methods In this study, we combined bioinformatics analysis and clinical specimens to evaluate the mRNA, protein expression, and gene regulatory network of LYRM4 in LIHC. Furthermore, we detected the activity of several classical iron-sulphur proteins in LIHC cell lines through UV-vis spectrophotometry. Results The mRNA and protein levels of LYRM4 were upregulated in LIHC. Subsequent analysis revealed that the LYRM4 mRNA expression was related to various clinical stratifications, prognosis, and survival of LIHC patients. In addition, the mRNA expression of LYRM4 was significantly associated with ALT, tumour thrombus, and encapsulation of HBV-related LIHC patients. IHC results confirmed that LYRM4 was highly expressed in LIHC tissues and showed that the expression of LYRM4 protein in LIHC was significantly correlated with age and serum low-density lipoprotein (LDL) and triglyceride (TG) content. In particular, the mRNA expression of key iron- sulphur proteins POLD1 and PRIM2 was significantly overexpressed and correlated with poor prognosis in LIHC patients. Compared with hepatocytes, the activities of mitochondrial complex I and aconitate hydratase (ACO2) in LIHC cell lines were significantly increased. These results indicated that the iron-sulphur cluster (ISC) biosynthesis was significantly elevated in LIHC, leading to ISC-dependent metabolic reprogramming. Changes in the activity of ISC-dependent proteins may also occur in paracancerous tissues. Further analysis of the biological interaction and gene regulation networks of LYRM4 suggested that these genes were mainly involved in the citric acid cycle and oxidative phosphorylation. Finally, LYRM4 expression in LIHC was significantly positively correlated with the infiltrating levels of six immune cell types, and both factors were strongly associated with prognosis. Conclusion LYRM4 could be a novel prognostic biomarker and molecular target for LIHC therapy. In particular, the potential regulatory networks of LYRM4 overexpression in LIHC provide a scientific basis for future research on the role of the ISC assembly mechanism and LYRM4-mediated sulphur transfer routes in carcinogenesis. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-02131-3.
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Affiliation(s)
- Yilin Pang
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410128, Hunan, China.,Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Guoqiang Tan
- Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Xunjun Yang
- Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.,Department of Laboratory Medicine, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Yuanshan Lin
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410128, Hunan, China
| | - Yao Chen
- Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jinping Zhang
- Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ting Xie
- Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Huaibin Zhou
- Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun Fang
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410128, Hunan, China
| | - Qiongya Zhao
- Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Xiaojun Ren
- Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jianghui Li
- Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jianxin Lyu
- Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China. .,People's Hospital of Hangzhou Medical College, Hangzhou, 310014, China.
| | - Zheng Wang
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410128, Hunan, China.
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27
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Zheng Q, Yang Q, Zhou J, Gu X, Zhou H, Dong X, Zhu H, Chen Z. Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions. Cancer Cell Int 2021; 21:330. [PMID: 34193146 PMCID: PMC8243542 DOI: 10.1186/s12935-021-02033-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/19/2021] [Indexed: 02/07/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical outcomes. The HCC microenvironment is important for both tumour carcinogenesis and immunogenicity, but a classification system based on immune signatures has not yet been comprehensively described. Methods HCC datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) were used in this study. Gene set enrichment analysis (GSEA) and the ConsensusClusterPlus algorithm were used for clustering assessments. We scored immune cell infiltration and used linear discriminant analysis (LDA) to improve HCC classification accuracy. Pearson's correlation analyses were performed to assess relationships between immune signature indices and immunotherapies. In addition, weighted gene co-expression network analysis (WGCNA) was applied to identify candidate modules closely associated with immune signature indices. Results Based on 152 immune signatures from HCC samples, we identified four distinct immune subtypes (IS1, IS2, IS3, and IS4). Subtypes IS1 and IS4 had more favourable prognoses than subtypes IS2 and IS3. These four subtypes also had different immune system characteristics. The IS1 subtype had the highest scores for IFNγ, cytolysis, angiogenesis, and immune cell infiltration among all subtypes. We also identified 11 potential genes, namely, TSPAN15, TSPO, METTL9, CD276, TP53I11, SPINT1, TSPO, TRABD2B, WARS2, C9ORF116, and LBH, that may represent potential immunological biomarkers for HCC. Furthermore, real-time PCR revealed that SPINT1, CD276, TSPO, TSPAN15, METTL9, and WARS2 expression was increased in HCC cells. Conclusions The present gene-based immune signature classification and indexing may provide novel perspectives for both HCC immunotherapy management and prognosis prediction. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-02033-4.
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Affiliation(s)
- Qiuxian Zheng
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Qin Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Jiaming Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Xinyu Gu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Haibo Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Xuejun Dong
- Department of Clinical Laboratory Center, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
| | - Haihong Zhu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China.
| | - Zhi Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China.
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