1
|
Tu Z, Li W, Chen Z, Jiang D, Zhou S, Lv S, Cui H. Tumor microenvironment phenotypes and prognostic evaluation tools for osteosarcoma characterized by different prognostic outcomes and immunotherapy responses. J Gene Med 2024; 26:e3572. [PMID: 37525871 DOI: 10.1002/jgm.3572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/28/2023] [Accepted: 07/08/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND The physiological and immunological characteristics of the tumor microenvironment (TME) have a profound impact on the effectiveness of immunotherapy. The present study aimed to define the TME subtype of osteosarcoma according to the signatures representing the global TME of the tumor, as well as create a new prognostic assessment tool to monitor the prognosis, TME activity and immunotherapy response of patients with osteosarcoma. METHODS The enrichment scores of 29 functional gene expression signatures in osteosarcoma samples were calculated by single sample gene set enrichment analysis (ssGSEA). TME classification of osteosarcoma was performed and a prognostic assessment tool was created based on 29 ssGSEA scores to comprehensively correlate them with TME components, immunotherapy efficacy and prognosis of osteosarcoma. RESULTS Three TME subtypes were generated that differed in survival, TME activity and immunotherapeutic response. Four differentially expressed genes between TME subtypes were involved in the development of prognostic assessment tools. The established prognosis assessment tool had strong performance in both training and verification cohorts, could be effectively applied to the survival prediction of samples of different ages, genders and transfer states, and could well distinguish the TME status of different samples. CONCLUSIONS The present study describes three different TME phenotypes in osteosarcoma, provides a risk stratification tool for osteosarcoma prognosis and TME status assessment, and provides additional information for clinical decision-making of immunotherapy.
Collapse
Affiliation(s)
- Zubo Tu
- Orthopedics, Hai'an People's Hospital, Nantong, China
| | - Wang Li
- Orthopedics, Shanghai Zhongye Hospital, Shanghai, China
| | - Zhigang Chen
- Orthopedics, Hai'an People's Hospital, Nantong, China
| | - Dong Jiang
- Orthopedics, Hai'an People's Hospital, Nantong, China
| | - Shiran Zhou
- Orthopedics, Hai'an People's Hospital, Nantong, China
| | - Shujun Lv
- Orthopedics, Hai'an People's Hospital, Nantong, China
| | - Haidong Cui
- Orthopedics, Hai'an People's Hospital, Nantong, China
| |
Collapse
|
2
|
Fu Y, He J, Chen J, Hu J, Guan W, Lou G. EVI2B may be a novel prognostic marker for lung adenocarcinoma. Biomark Med 2023; 17:599-612. [PMID: 37843407 DOI: 10.2217/bmm-2023-0195] [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] [Indexed: 10/17/2023] Open
Abstract
Objective: This study intended to unravel the relationship of EVI2B expression with lung adenocarcinoma (LUAD). Methods: TIMER1.0, Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases, as well as the University of Alabama at Birmingham Cancer website, were used to analyze the expression of EVI2B and its relationship with clinical features. The relationship between survival curve analysis and prognosis was analyzed. The role of EVI2B in LUAD was verified by wet experiments. Results: EVI2B was markedly downregulated in LUAD. There was a relationship between the expression of EVI2B and clinical features. Low EVI2B level was substantially implicated in low survival in LUAD. EVI2B overexpression constrained LUAD cell viability, migration and invasion. Conclusion: EVI2B was related to prognosis and immune microenvironment in LUAD, suggesting that EVI2B may be a novel prognostic marker for LUAD.
Collapse
Affiliation(s)
- Yin Fu
- Department of Cardiothoracic Surgery, Yiwu Central Hospital, Yiwu City, Zhejiang Province, 322000, China
| | - Junming He
- Department of Cardiothoracic Surgery, Yiwu Central Hospital, Yiwu City, Zhejiang Province, 322000, China
| | - Jian Chen
- Department of Cardiothoracic Surgery, Yiwu Central Hospital, Yiwu City, Zhejiang Province, 322000, China
| | - Jiangwei Hu
- Department of Cardiothoracic Surgery, Yiwu Central Hospital, Yiwu City, Zhejiang Province, 322000, China
| | - Wei Guan
- Department of Cardiothoracic Surgery, Yiwu Central Hospital, Yiwu City, Zhejiang Province, 322000, China
| | - Guoliang Lou
- Department of Cardiothoracic Surgery, Yiwu Central Hospital, Yiwu City, Zhejiang Province, 322000, China
| |
Collapse
|
3
|
Ding R, Zhao C, Jing Y, Chen R, Meng Q. Basement membrane-related regulators for prediction of prognoses and responses to diverse therapies in hepatocellular carcinoma. BMC Med Genomics 2023; 16:81. [PMID: 37081465 PMCID: PMC10116671 DOI: 10.1186/s12920-023-01504-z] [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: 01/03/2023] [Accepted: 03/28/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) remains a global health threat. Finding a novel biomarker for assessing the prognosis and new therapeutic targets is vital to treating this patient population. Our study aimed to explore the contribution of basement membrane-related regulators (BMR) to prognostic assessment and therapeutic response prediction in HCC. MATERIAL AND METHODS The RNA sequencing and clinical information of HCC were downloaded from TCGA-LIHC, ICGC-JP, GSE14520, GSE104580, and CCLE datasets. The BMR signature was created by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and used to separate HCC patients into low- and high-risk groups. We conducted analyses using various R 4.1.3 software packages to compare prognoses and responses to immunotherapy, transcatheter arterial chemoembolization (TACE), and chemotherapeutic drugs between the groups. Additionally, stemness indices, molecular functions, and somatic mutation analyses were further explored in these subgroups. RESULTS The BMR signature included 3 basement membrane-related genes (CTSA, P3H1, and ADAM9). We revealed that BMR signature was an independent risk contributor to poor prognosis in HCC, and high-risk group patients presented shorter overall survival. We discovered that patients in the high-risk group might be responsive to immunotherapy, while patients in the low-risk group may be susceptible to TACE therapy. Over 300 agents were screened to identify effective drugs for the two subgroups. CONCLUSION Overall, basement membrane-related regulators represent novel biomarkers in HCC for assessing prognosis, response to immunotherapy, the effectiveness of TACE therapy, and drug susceptibility.
Collapse
Affiliation(s)
- Ruili Ding
- Department of Anesthesiology, Renmin Hospital of Wuhan University, No.238, Jiefang Road, Wuhan, 430061, Hubei Province, China
| | - Chuanbing Zhao
- Department of Pancreatic Surgery, Renmin Hospital of Wuhan University, No.238, Jiefang Road, Wuhan, 430061, Hubei Province, China
| | - Yixin Jing
- Department of Anesthesiology, Renmin Hospital of Wuhan University, No.238, Jiefang Road, Wuhan, 430061, Hubei Province, China
| | - Rong Chen
- Department of Anesthesiology, Renmin Hospital of Wuhan University, No.238, Jiefang Road, Wuhan, 430061, Hubei Province, China
| | - Qingtao Meng
- Department of Anesthesiology, Renmin Hospital of Wuhan University, No.238, Jiefang Road, Wuhan, 430061, Hubei Province, China.
| |
Collapse
|
4
|
Ma Y, Zheng S, Xu M, Chen C, He H. Establishing and Validating an Aging-Related Prognostic Signature in Osteosarcoma. Stem Cells Int 2023; 2023:6245160. [PMID: 37964984 PMCID: PMC10643040 DOI: 10.1155/2023/6245160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/22/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2023] Open
Abstract
Aging is an inevitable process that biological changes accumulate with time and results in increased susceptibility to different tumors. But currently, aging-related genes (ARGs) in osteosarcoma were not clear. We investigated the potential prognostic role of ARGs and established an ARG-based prognostic signature for osteosarcoma. The transcriptome data and corresponding clinicopathological information of patients with osteosarcoma were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Molecular subtypes were generated based on prognosis-related ARGs obtained from univariate Cox analysis. With ARGs, a risk signature was built by univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Differences in clinicopathological features, immune infiltration, immune checkpoints, responsiveness to immunotherapy and chemotherapy, and biological pathways were assessed according to molecular subtypes and the risk signature. Based on risk signature and clinicopathological variables, a nomogram was established and validated. Three molecular subtypes with distinct clinical outcomes were classified based on 36 prognostic ARGs for osteosarcoma. A nine-ARG-based signature in the TCGA cohort, including BMP8A, CORT, SLC17A9, VEGFA, GAL, SSX1, RASGRP2, SDC3, and EVI2B, has been created and developed and could well perform patient stratification into the high- and low-risk groups. There were significant differences in clinicopathological features, immune checkpoints and infiltration, responsiveness to immunotherapy and chemotherapy, cancer stem cell, and biological pathways among the molecular subtypes. The risk signature and metastatic status were identified as independent prognostic factors for osteosarcoma. A nomogram combining ARG-based risk signature and metastatic status was established, showing great prediction accuracy and clinical benefit for osteosarcoma OS. We characterized three ARG-based molecular subtypes with distinct characteristics and built an ARG-based risk signature for osteosarcoma prognosis, which could facilitate prognosis prediction and making personalized treatment in osteosarcoma.
Collapse
Affiliation(s)
- Yibo Ma
- Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China 116044
| | - Shuo Zheng
- The Second Ward of Department of Orthopedics, The Second Hospital of Dalian Medical University, Dalian, China 116000
| | - Mingjun Xu
- The Second Hospital of Dalian Medical University, Dalian Medical University, Dalian, China 116000
| | - Changjian Chen
- The First Ward of Department of Orthopedics, The Second Hospital of Dalian Medical University, Dalian, China 116000
| | - Hongtao He
- The Third Ward of Department of Orthopedics, The Second Hospital of Dalian Medical University, Dalian, China 116000
| |
Collapse
|
5
|
Chen D, Liu Z, Wang J, Yang C, Pan C, Tang Y, Zhang P, Liu N, Li G, Li Y, Wu Z, Xia F, Zhang C, Nie H, Tang Z. Integrative genomic analysis facilitates precision strategies for glioblastoma treatment. iScience 2022; 25:105276. [PMID: 36300002 PMCID: PMC9589211 DOI: 10.1016/j.isci.2022.105276] [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: 05/09/2022] [Revised: 08/29/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP−BEZ235, GDC−0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM. A multiomics-based classification of GBM was established Single-cell transcriptomic profiling of GBM subclasses was revealed using Scissor A robust prognostic risk model was developed for GBM by machine learning method Prediction of potential agents based on molecular and prognostic risk stratification
Collapse
Affiliation(s)
- Danyang Chen
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhicheng Liu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jingxuan Wang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chen Yang
- State Key Laboratory of Oncogenes and Related Genes, Department of Liver Surgery and Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200032, China
| | - Chao Pan
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yingxin Tang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ping Zhang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Na Liu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Gaigai Li
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yan Li
- State Key Laboratory of Oncogenes and Related Genes, Department of Liver Surgery and Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200032, China,Department of Immunology, Sun Yat-Sen University, Zhongshan School of Medicine, Guangzhou, Guangdong 510080, China
| | - Zhuojin Wu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Feng Xia
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Cuntai Zhang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Hao Nie
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China,Corresponding author
| | - Zhouping Tang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China,Corresponding author
| |
Collapse
|
6
|
Characterization of the Tumor Microenvironment in Osteosarcoma Identifies Prognostic- and Immunotherapy-Relevant Gene Signatures. J Immunol Res 2022; 2022:6568278. [PMID: 36065454 PMCID: PMC9440849 DOI: 10.1155/2022/6568278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 05/06/2022] [Accepted: 06/27/2022] [Indexed: 11/17/2022] Open
Abstract
The osteosarcoma (OS) microenvironment is composed of tumor cells, immune cells, and stromal tissue and is emerging as a pivotal player in OS development and progression. Thus, microenvironment-targeted strategies are urgently needed to improve OS treatment outcomes. Using principal component analysis (PCA), we systematically examined the tumor microenvironment (TME) and immune cell infiltration of 88 OS cases and constructed a TME scoring system based on the TMEscore high and TMEscore low phenotypes. Our analysis revealed that TMEscore high correlates with longer survival in OS patients, elevated immune cell infiltration, increased immune checkpoints, and increased sensitivity to chemotherapy. TMEscore low strongly correlated with immune exclusion. These observations were externally validated using a GEO dataset (GSE21257) from 53 OS patients. Our laboratory data also proved our findings. This finding enhances our understanding of the immunological landscape in OS and may uncover novel targeted therapeutic strategies.
Collapse
|