1
|
Li S, Zheng Z, Wang B. Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma. Sci Rep 2024; 14:12934. [PMID: 38839983 PMCID: PMC11153634 DOI: 10.1038/s41598-024-63736-y] [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: 07/27/2023] [Accepted: 05/31/2024] [Indexed: 06/07/2024] Open
Abstract
Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been identified as a critical characteristic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabolism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts-TARGET-OS, GSE21257, GSE39058, and GSE16091-were amalgamated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selection processes, encompassing analyses of differentially expressed genes between subtypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarkers related to lipid metabolism in TARGET-OS. We selected the most effective algorithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (C-index) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lipid metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with significantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid metabolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TARGET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability. Our results offer a promising tool to enhance the clinical management of osteosarcoma, potentially leading to improved clinical outcomes.
Collapse
Affiliation(s)
- Shuai Li
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Renmin Middle Road 139, Changsha, 410011, Hunan, China
| | - Zhenzhong Zheng
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Renmin Middle Road 139, Changsha, 410011, Hunan, China
| | - Bing Wang
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Renmin Middle Road 139, Changsha, 410011, Hunan, China.
| |
Collapse
|
2
|
Liu G, Wang S, Liu J, Zhang J, Pan X, Fan X, Shao T, Sun Y. Using machine learning methods to study the tumour microenvironment and its biomarkers in osteosarcoma metastasis. Heliyon 2024; 10:e29322. [PMID: 38623240 PMCID: PMC11016722 DOI: 10.1016/j.heliyon.2024.e29322] [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: 08/24/2023] [Revised: 04/04/2024] [Accepted: 04/04/2024] [Indexed: 04/17/2024] Open
Abstract
Background The long-term prognosis for patients with osteosarcoma (OS) metastasis remains unfavourable, highlighting the urgent need for research that explores potential biomarkers using innovative methodologies. Methods This study explored potential biomarkers for OS metastasis by analysing data from the Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases. The synthetic minority oversampling technique (SMOTE) was employed to tackle class imbalances, while genes were selected using four feature selection algorithms (Monte Carlo feature selection [MCFS], Borota, minimum-redundancy maximum-relevance [mRMR], and light gradient-boosting machine [LightGBM]) based on the gene expression matrix. Four machine learning (ML) algorithms (support vector machine [SVM], extreme gradient boosting [XGBoost], random forest [RF], and k-nearest neighbours [kNN]) were utilized to determine the optimal number of genes for building the model. Interpretable machine learning (IML) was applied to construct prediction networks, revealing potential relationships among the selected genes. Additionally, enrichment analysis, survival analysis, and immune infiltration were performed on the featured genes. Results In DS1, DS2, and DS3, the IML algorithm identified 53, 45, and 46 features, respectively. Using the merged gene set, we obtained a total of 79 interpretable prediction rules for OS metastasis. We subsequently conducted an in-depth investigation on 39 crucial molecules associated with predicting OS metastasis, elucidating their roles within the tumour microenvironment. Importantly, we found that certain genes act as both predictors and differentially expressed genes. Finally, our study unveiled statistically significant differences in survival between the high and low expression groups of TRIP4, S100A9, SELL and SLC11A1, and there was a certain correlation between these genes and 22 various immune cells. Conclusions The biomarkers discovered in this study hold significant implications for personalized therapies, potentially enhancing the clinical prognosis of patients with OS.
Collapse
Affiliation(s)
- Guangyuan Liu
- The First Department of Orthopedic Surgery, Third Hospital of Shijiazhuang, Tiyu South Avenue No.15, Shijiazhuang, Hebei Province, China
| | - Shaochun Wang
- Department of Oncology, Shijiazhuang People's Hospital, No.365, Jian Hua Nan Da Jie, Shijiazhuang, Hebei Province, China
| | - Jinhui Liu
- The First Department of Orthopedic Surgery, Third Hospital of Shijiazhuang, Tiyu South Avenue No.15, Shijiazhuang, Hebei Province, China
| | - Jiangli Zhang
- The First Department of Orthopedic Surgery, Third Hospital of Shijiazhuang, Tiyu South Avenue No.15, Shijiazhuang, Hebei Province, China
| | - Xiqing Pan
- The First Department of Orthopedic Surgery, Third Hospital of Shijiazhuang, Tiyu South Avenue No.15, Shijiazhuang, Hebei Province, China
| | - Xiao Fan
- The First Department of Orthopedic Surgery, Third Hospital of Shijiazhuang, Tiyu South Avenue No.15, Shijiazhuang, Hebei Province, China
| | - Tingting Shao
- Department of Pediatrics, Peking University First Hospital, 8 Xishku Street, Xicheng District, Beijing, China
| | - Yi Sun
- Department of Surgery, Shijiazhuang People's Hospital, No.365, Jian Hua Nan Da Jie, Shijiazhuang, Hebei Province, China
| |
Collapse
|
3
|
Yang L, Long Y, Xiao S. Osteosarcoma-Associated Immune Genes as Potential Immunotherapy and Prognosis Biomarkers. Biochem Genet 2024; 62:798-813. [PMID: 37452172 DOI: 10.1007/s10528-023-10444-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 06/29/2023] [Indexed: 07/18/2023]
Abstract
Immune-modulating therapies exhibit abundant promise compared to traditional treatment for osteosarcoma. We aim to establish an immune-related prognostic signatures in osteosarcoma. We identified the differentially expressed genes in osteosarcoma compared with normal controls using the GEO dataset. The intersection with immune-related genes was considered as differentially expressed immune genes. Potential prognosis-related differentially expressed genes were first analyzed with the multifactor Cox regression and then the step function performed the iteration. The best model was finally chosen as the immunological risk score signature model. And finally, we evaluated the correlation of genes in the prognostic model with immune cells, common immune checkpoints, and immune checkpoint blockade responses. We identified 1527 significantly upregulated and 2407 significantly downregulated genes in osteosarcoma compared to normal samples. In the 258 differentially expressed immune genes, 20 genes with independent prognostic significance were included in the step function. Finally, we constructed a prognostic signature for overall survival based on five immune genes (JAG2, PLXNB1, CMKLR1, NUDT6, and PSMC4) in osteosarcoma. These five genes are closely related to immune infiltration. Osteosarcoma with high JAG2 expression or low CMKLR1 expression may be associated with better immune checkpoint blockade response. JAG2 overexpression or CMKLR1 inhibition induced sensitivity to PD-1 antibody in osteosarcoma cells. We constructed a prognosis prediction signature containing five immune-related genes (JAG2, PLXNB1, CMKLR1, NUDT6, and PSMC4) with a significant prognostic value in osteosarcoma. Significant differences in immune infiltration and immunotherapy responses were identified between groups with different levels of these genes.
Collapse
Affiliation(s)
- Li Yang
- Key Laboratory of Geriatric Diseases of Xinyang, Institute of Inspection Technology, Xinyang Vocational and Technical College, Xinyang, 464000, China
| | - Yi Long
- Department of Joint Surgery, The Affiliated Zhuzhou Hospital Xiangya Medical College CSU, Zhuzhou, 412000, China.
| | - Shengshi Xiao
- Department of Joint Surgery, The Affiliated Zhuzhou Hospital Xiangya Medical College CSU, Zhuzhou, 412000, China.
| |
Collapse
|
4
|
Liu B, Liu XY, Wang GP, Chen YX. The immune cell infiltration-associated molecular subtypes and gene signature predict prognosis for osteosarcoma patients. Sci Rep 2024; 14:5184. [PMID: 38431660 PMCID: PMC10908810 DOI: 10.1038/s41598-024-55890-0] [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: 05/26/2023] [Accepted: 02/28/2024] [Indexed: 03/05/2024] Open
Abstract
Host immune dysregulation involves in the initiation and development of osteosarcoma (OS). However, the exact role of immune cells in OS remains unknown. We aimed to distinguish the molecular subtypes and establish a prognostic model in OS patients based on immunocyte infiltration. The gene expression profile and corresponding clinical feature of OS patients were obtained from TARGET and GSE21257 datasets. MCP-counter and univariate Cox regression analyses were applied to identify immune cell infiltration-related molecular subgroups. Functional enrichment analysis and immunocyte infiltration analysis were performed between two subgroups. Furthermore, Cox regression and LASSO analyses were performed to establish the prognostic model for the prediction of prognosis and metastasis in OS patients. The subgroup with low infiltration of monocytic lineage (ML) was related to bad prognosis in OS patients. 435 DEGs were screened between the two subgroups. Functional enrichment analysis revealed these DEGs were involved in immune- and inflammation-related pathways. Three important genes (including TERT, CCDC26, and IL2RA) were identified to establish the prognostic model. The risk model had good prognostic performance for the prediction of metastasis and overall survival in OS patients. A novel stratification system was established based on ML-related signature. The risk model could predict the metastasis and prognosis in OS patients. Our findings offered a novel sight for the prognosis and development of OS.
Collapse
Affiliation(s)
- Bin Liu
- Department of Spine Surgery, Hunan Provincial People's Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, 410005, Hunan, China
| | - Xiang-Yang Liu
- Department of Spine Surgery, Hunan Provincial People's Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, 410005, Hunan, China
| | - Guo-Ping Wang
- Department of Spine Surgery, Hunan Provincial People's Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, 410005, Hunan, China
| | - Yi-Xin Chen
- Department of Rehabilitation Medicine, Xiangya Hospital of Central South University, No. 87, Xiangya Road, Changsha, 410008, Hunan, China.
| |
Collapse
|
5
|
Liu Y, Lin Y, Liao S, Feng W, Liu J, Luo X, Wei Q, Tang H. Single-cell RNA sequencing reveals the immune microenvironment landscape of osteosarcoma before and after chemotherapy. Heliyon 2024; 10:e23601. [PMID: 38332885 PMCID: PMC10851305 DOI: 10.1016/j.heliyon.2023.e23601] [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: 08/25/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 02/10/2024] Open
Abstract
Chemotherapy, a primary treatment for osteosarcoma (OS), has limited knowledge regarding its impact on tumor immune microenvironment (TIME). Here, tissues from 6 chemotherapy-naive OS patients underwent single-cell RNA sequencing (scRNA-seq) and were analyzed alongside public dataset (GSE152048) containing 7 post-chemotherapy OS tissues. CD45+ (PTPRC+) cells were used for cell clustering and annotation. Changes in immune cell composition pre- and post-chemotherapy were characterized. Totally, 28,636 high-quality CD45+ (PTPRC+) cells were extracted. Following chemotherapy, the proportions of regulatory T cells (Tregs) and activated CD8 T cells decreased, while CD8 effector T cells increased. GO analysis indicated that differentially expressed genes (DEGs) in T cells were associated with cell activation, adaptive immune response, and immune response to tumor cells. Furthermore, the proportions of plasma cells increased, while naive B cells decreased. B cell surface receptors expression was upregulated, and GO analysis revealed DEGs of B cells were mainly enriched in B cell-mediated immunity and B cell activation. Moreover, M2 polarization of macrophages was suppressed post-chemotherapy. Overall, this study elucidates chemotherapy remodels the OS TIME landscape, triggering immune heterogeneity and enhancing anti-tumor properties.
Collapse
Affiliation(s)
- Yun Liu
- Department of Spine and Osteopathic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yunhua Lin
- Department of Trauma Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Shijie Liao
- Department of Trauma Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Wenyu Feng
- Department of Orthopedics, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jianhong Liu
- Department of Spine and Osteopathic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xiaoting Luo
- Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Qingjun Wei
- Department of Trauma Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Haijun Tang
- Department of Spine and Osteopathic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| |
Collapse
|
6
|
Urbiola-Salvador V, Jabłońska A, Miroszewska D, Huang Q, Duzowska K, Drężek-Chyła K, Zdrenka M, Śrutek E, Szylberg Ł, Jankowski M, Bała D, Zegarski W, Nowikiewicz T, Makarewicz W, Adamczyk A, Ambicka A, Przewoźnik M, Harazin-Lechowicz A, Ryś J, Filipowicz N, Piotrowski A, Dumanski JP, Li B, Chen Z. Plasma protein changes reflect colorectal cancer development and associated inflammation. Front Oncol 2023; 13:1158261. [PMID: 37228491 PMCID: PMC10203952 DOI: 10.3389/fonc.2023.1158261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 04/05/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of death worldwide. Efficient non-invasive blood-based biomarkers for CRC early detection and prognosis are urgently needed. Methods To identify novel potential plasma biomarkers, we applied a proximity extension assay (PEA), an antibody-based proteomics strategy to quantify the abundance of plasma proteins in CRC development and cancer-associated inflammation from few μL of plasma sample. Results Among the 690 quantified proteins, levels of 202 plasma proteins were significantly changed in CRC patients compared to age-and-sex-matched healthy subjects. We identified novel protein changes involved in Th17 activity, oncogenic pathways, and cancer-related inflammation with potential implications in the CRC diagnosis. Moreover, the interferon γ (IFNG), interleukin (IL) 32, and IL17C were identified as associated with the early stages of CRC, whereas lysophosphatidic acid phosphatase type 6 (ACP6), Fms-related tyrosine kinase 4 (FLT4), and MANSC domain-containing protein 1 (MANSC1) were correlated with the late-stages of CRC. Discussion Further study to characterize the newly identified plasma protein changes from larger cohorts will facilitate the identification of potential novel diagnostic, prognostic biomarkers for CRC.
Collapse
Affiliation(s)
- Víctor Urbiola-Salvador
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Gdańsk, Poland
| | - Agnieszka Jabłońska
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Gdańsk, Poland
| | - Dominika Miroszewska
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Gdańsk, Poland
| | - Qianru Huang
- Center for Immune-Related Diseases at Shanghai Institute of Immunology, Department of Respiratory and Critical Care Medicine of Ruijin Hospital, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | | | - Marek Zdrenka
- Department of Tumor Pathology and Pathomorphology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Ewa Śrutek
- Department of Tumor Pathology and Pathomorphology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Łukasz Szylberg
- Department of Tumor Pathology and Pathomorphology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
- Department of Obstetrics, Gynaecology and Oncology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Michał Jankowski
- Surgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in ToruńSurgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Toruń, Poland
- Department of Surgical Oncology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Dariusz Bała
- Surgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in ToruńSurgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Toruń, Poland
- Department of Surgical Oncology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Wojciech Zegarski
- Surgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in ToruńSurgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Toruń, Poland
- Department of Surgical Oncology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Tomasz Nowikiewicz
- Surgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in ToruńSurgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Toruń, Poland
- Department of Breast Cancer and Reconstructive Surgery, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Wojciech Makarewicz
- Clinic of General and Oncological Surgery, Specialist Hospital of Kościerzyna, Kościerzyna, Poland
| | - Agnieszka Adamczyk
- Department of Tumor Pathology, Maria Skłodowska-Curie National Research Institute of Oncology, Kraków, Poland
| | - Aleksandra Ambicka
- Department of Tumor Pathology, Maria Skłodowska-Curie National Research Institute of Oncology, Kraków, Poland
| | - Marcin Przewoźnik
- Department of Tumor Pathology, Maria Skłodowska-Curie National Research Institute of Oncology, Kraków, Poland
| | - Agnieszka Harazin-Lechowicz
- Department of Tumor Pathology, Maria Skłodowska-Curie National Research Institute of Oncology, Kraków, Poland
| | - Janusz Ryś
- Department of Tumor Pathology, Maria Skłodowska-Curie National Research Institute of Oncology, Kraków, Poland
| | | | | | - Jan P. Dumanski
- 3P-Medicine Laboratory, Medical University of Gdańsk, Gdańsk, Poland
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Biology and Pharmaceutical Botany, Medical University of Gdańsk, Gdańsk, Poland
| | - Bin Li
- Center for Immune-Related Diseases at Shanghai Institute of Immunology, Department of Respiratory and Critical Care Medicine of Ruijin Hospital, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi Chen
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Gdańsk, Poland
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| |
Collapse
|
7
|
Li S, Que Y, Yang R, He P, Xu S, Hu Y. Construction of Osteosarcoma Diagnosis Model by Random Forest and Artificial Neural Network. J Pers Med 2023; 13:jpm13030447. [PMID: 36983630 PMCID: PMC10056981 DOI: 10.3390/jpm13030447] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/19/2023] [Accepted: 02/27/2023] [Indexed: 03/05/2023] Open
Abstract
Osteosarcoma accounts for 28% of primary bone malignancies in adults and up to 56% in children and adolescents (<20 years). However, early diagnosis and treatment are still inadequate, and new improvements are still needed. Missed diagnoses exist due to fewer traditional diagnostic methods, and clinical symptoms are often already present before diagnosis. This study aimed to develop novel and efficient predictive models for the diagnosis of osteosarcoma and to identify potential targets for exploring osteosarcoma markers. First, osteosarcoma and normal tissue expression microarray datasets were downloaded from the Gene Expression Omnibus (GEO). Then we screened the differentially expressed genes (DEGs) in the osteosarcoma and normal groups in the training group. Next, in order to explore the biologically relevant role of DEGs, Metascape and enrichment analyses were also performed on DEGs. The “randomForest” and “neuralnet” packages in R software were used to select representative genes and construct diagnostic models for osteosarcoma. The next step is to validate the model of the artificial neural network. Then, we performed an immune infiltration analysis by using the training set data. Finally, we constructed a prognostic model using representative genes for prognostic analysis. The copy number of osteosarcoma was also analyzed. A random forest classifier identified nine representative genes (ANK1, TGFBR3, RSF21, HSPB8, ITGA7, RHD, AASS, GREM2, NFASC). HSPB8, RHD, AASS, and NFASC were genes we identified that have not been previously reported to be associated with osteosarcoma. The osteosarcoma diagnostic model we constructed has good performance with areas under the curves (AUCs) of 1 and 0.987 in the training and validation groups, respectively. This study opens new horizons for the early diagnosis of osteosarcoma and provides representative markers for the future treatment of osteosarcoma. This is the first study to pioneer the establishment of a genetic diagnosis model for osteosarcoma and advance the development of osteosarcoma diagnosis and treatment.
Collapse
|
8
|
Lu Z, Xu J, Cao B, Jin C. Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:87. [PMID: 36819543 PMCID: PMC9929789 DOI: 10.21037/atm-22-6369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/10/2023] [Indexed: 01/30/2023]
Abstract
Background Gene play an important role in malignant tumors. However, there is still insufficient research on genetic variations in osteosarcoma (OS) patients. Therefore, we aimed to analyze the gene expression profile of OS using bioinformatics and to explore the pathogenesis of OS at the molecular level. Methods The gene chip dataset of OS samples was downloaded from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The R language clusterProfiler software package was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs. The central node proteins of the protein interaction network were analyzed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, Cytoscape, and its plug-ins cytoHubba and NetworkAnalyzer to find the key genes. Results A total of 631 DEGs were obtained, including 362 upregulated genes and 269 downregulated genes. DEGs were mainly involved in the regulation of leukocyte chemotaxis and migration, vascular development, and other biological processes (BPs); mediation of receptor ligand activity, growth factor binding, growth factor activity, integrin binding, and other molecular functions (MFs); and were enriched in the extracellular matrix (ECM). Conclusions DEGs in the ECM and growth factors play a key role in the development of OS. The leukocyte transendothelial migration pathway and the PI3K-AKT pathway are closely related to OS, and the related molecular mechanism is worthy of further study.
Collapse
Affiliation(s)
- Zhengyu Lu
- Department of Orthopedics, Taizhou First People’s Hospital, Huangyan Hospital of Wenzhou Medical University, Taizhou, China
| | - Jin Xu
- Department of Orthopedics, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Binhao Cao
- Department of Orthopedics, Taizhou First People’s Hospital, Huangyan Hospital of Wenzhou Medical University, Taizhou, China
| | - Chongqiang Jin
- Department of Orthopedics, Taizhou First People’s Hospital, Huangyan Hospital of Wenzhou Medical University, Taizhou, China
| |
Collapse
|