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Yu Y, Li L, Luo B, Chen D, Yin C, Jian C, You Q, Wang J, Fang L, Cai D, Sun J. Predicting potential therapeutic targets and small molecule drugs for early-stage lung adenocarcinoma. Biomed Pharmacother 2024; 174:116528. [PMID: 38555814 DOI: 10.1016/j.biopha.2024.116528] [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: 01/16/2024] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 04/02/2024] Open
Abstract
Lung cancer is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) constituting the majority, and its main subtype being lung adenocarcinoma (LUAD). Despite substantial advances in LUAD diagnosis and treatment, early diagnostic biomarkers inadequately fulfill clinical requirements. Thus, we conducted bioinformatics analysis to identify potential biomarkers and corresponding therapeutic drugs for early-stage LUAD patients. Here we identified a total of 10 differentially expressed genes (DEGs) with survival significance through the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Subsequently, we identified a promising small molecule drug, Aminopurvalanol A, based on the 10 key genes using the L1000FWD application, which was validated by molecular docking followed by in vivo and in vitro experiments. The results highlighted TOP2A, CDH3, ASPM, CENPF, SLC2A1, and PRC1 as potential detection biomarkers for early LUAD. We confirmed the efficacy and safety of Aminopurvalanol A, providing valuable insights for the clinical management of LUAD.
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Affiliation(s)
- Yongxin Yu
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Lingchen Li
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Bangyu Luo
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Diangang Chen
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Chenrui Yin
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Chunli Jian
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Qiai You
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Jianmin Wang
- Department of Oncology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing 400021, China
| | - Ling Fang
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Dingqin Cai
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Jianguo Sun
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China.
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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.
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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
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Qin S, Li L, Liu D. Metastasis-related gene signature associates with immunity and predicts prognosis accurately in patients with osteosarcoma. Aging (Albany NY) 2023; 15:7219-7236. [PMID: 37494671 PMCID: PMC10415573 DOI: 10.18632/aging.204902] [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: 03/14/2023] [Accepted: 06/30/2023] [Indexed: 07/28/2023]
Abstract
Osteosarcoma is the most prevalent malignant bone tumor. In this study, we identified metastasis-related genes (MRG) that are differentially expressed between primary and metastatic osteosarcoma and employed them to create metastasis-related risk tags (MRSs) for the overall survival of osteosarcoma patients. Using consistent cluster analysis, patients with osteosarcoma in the TARGET database were divided into subgroups with different metastatic scoring patterns. The clinicopathological traits, survival rates, tumor microenvironment traits, immune-related scores, and therapeutic responses of these patients varied. Additionally, we constructed MRS-based risk characteristics and nomographs and developed an MRG Score to improve patient characteristics. Thorough evaluations demonstrated that prognostic models and metastasis scores can distinguish high-risk patients from low-risk individuals, offering excellent predictive value. Finally, western blotting was used to confirm the expression of five identified MRG markers, which are crucial for osteosarcoma invasion and migration in terms of mechanism. Our findings represent a novel and practical predictive biomarker for osteosarcoma.
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Affiliation(s)
- Sen Qin
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China
| | - Lei Li
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China
| | - Da Liu
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China
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Wang F, Yang K, Pan R, Xiang Y, Xiong Z, Li P, Li K, Sun H. A glycometabolic gene signature associating with immune infiltration and chemosensitivity and predicting the prognosis of patients with osteosarcoma. Front Med (Lausanne) 2023; 10:1115759. [PMID: 37293295 PMCID: PMC10244582 DOI: 10.3389/fmed.2023.1115759] [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: 12/09/2022] [Accepted: 05/05/2023] [Indexed: 06/10/2023] Open
Abstract
Background Accumulating evidence has suggested that glycometabolism plays an important role in the pathogenesis of tumorigenesis. However, few studies have investigated the prognostic values of glycometabolic genes in patients with osteosarcoma (OS). This study aimed to recognize and establish a glycometabolic gene signature to forecast the prognosis, and provide therapeutic options for patients with OS. Methods Univariate and multivariate Cox regression, LASSO Cox regression, overall survival analysis, receiver operating characteristic curve, and nomogram were adopted to develop the glycometabolic gene signature, and further evaluate the prognostic values of this signature. Functional analyses including Gene Ontology (GO), kyoto encyclopedia of genes and genomes analyses (KEGG), gene set enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), and competing endogenous RNA (ceRNA) network, were used to explore the molecular mechanisms of OS and the correlation between immune infiltration and gene signature. Moreover, these prognostic genes were further validated by immunohistochemical staining. Results A total of four genes including PRKACB, SEPHS2, GPX7, and PFKFB3 were identified for constructing a glycometabolic gene signature which had a favorable performance in predicting the prognosis of patients with OS. Univariate and multivariate Cox regression analyses revealed that the risk score was an independent prognostic factor. Functional analyses indicated that multiple immune associated biological processes and pathways were enriched in the low-risk group, while 26 immunocytes were down-regulated in the high-risk group. The patients in high-risk group showed elevated sensitivity to doxorubicin. Furthermore, these prognostic genes could directly or indirectly interact with other 50 genes. A ceRNA regulatory network based on these prognostic genes was also constructed. The results of immunohistochemical staining showed that SEPHS2, GPX7, and PFKFB3 were differentially expressed between OS tissues and adjacent normal tissues. Conclusion The preset study constructed and validated a novel glycometabolic gene signature which could predict the prognosis of patients with OS, identify the degree of immune infiltration in tumor microenvironment, and provide guidance for the selection of chemotherapeutic drugs. These findings may shed new light on the investigation of molecular mechanisms and comprehensive treatments for OS.
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Affiliation(s)
- Fengyan Wang
- Department of Orthopaedics, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Kun Yang
- Department of Orthopaedics, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Runsang Pan
- School of Basic Medicine, Guizhou Medical University, Guiyang, China
| | - Yang Xiang
- Department of Orthopaedics, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Zhilin Xiong
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
| | - Pinhao Li
- Department of Pathology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Ke Li
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Hong Sun
- Department of Orthopaedics, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
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Liang XZ, Liu XC, Li S, Wen MT, Chen YR, Luo D, Xu B, Li NH, Li G. IRF8 and its related molecules as potential diagnostic biomarkers or therapeutic candidates and immune cell infiltration characteristics in steroid-induced osteonecrosis of the femoral head. J Orthop Surg Res 2023; 18:27. [PMID: 36627660 PMCID: PMC9832881 DOI: 10.1186/s13018-022-03381-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 11/02/2022] [Indexed: 01/12/2023] Open
Abstract
PURPOSE Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people, but the pathogenesis of SONFH remained unclear. We aimed to identify the potential genes and screen potential therapeutic compounds for SONFH. METHODS The microarray was obtained for blood tissue from the GEO database, and then it identifies differentially expressed genes (DEGs). The DEGs were analyzed to obtain the differences in immune cell infiltration. The gene functional enrichment analysis of SONFH was analyzed. The PPI of DEGs was identified through the STRING database, and the cluster modules and hub genes were ascertained using MCODE and CytoHubba, and the ROC curve of hub genes was analyzed, and the tissues distribution of hub genes was understood by the HPA, Bgee and BioGPS databases. The hub genes and target miRNAs and corresponding upstream lncRNAs were predicted by TargetScan, miRDB and ENCORI database. Subsequently, we used CMap, DGIdb and L1000FWD databases to identify several potential therapeutic molecular compounds for SONFH. Finally, the AutoDockTools Vina, PyMOL and Discovery Studio were employed for molecular docking analyses between compounds and hub genes. RESULTS The microarray dataset GSE123568 was obtained related to SONFH. There were 372 DEGs including 197 upregulated genes and 175 downregulated genes by adjusted P value < 0.01 and |log2FC|> 1. Several significant GSEA enrichment analysis and biological processes and KEGG pathway associated with SONFH were identified, which were significantly related to cytoskeleton organization, nucleobase-containing compound catabolic process, NOD-like receptor signaling pathway, MAPK signaling pathway, FoxO signaling pathway, neutrophil-mediated immunity, neutrophil degranulation and neutrophil activation involved in immune response. Activated T cells CD4 memory, B cells naïve, B cells memory, T cells CD8 and T cells gamma delta might be involved in the occurrence and development of SONFH. Three cluster modules were identified in the PPI network, and eleven hub genes including FPR2, LILRB2, MNDA, CCR1, IRF8, TYROBP, TLR1, HCK, TLR8, TLR2 and CCR2 were identified by Cytohubba, which were differed in bone marrow, adipose tissue and blood, and which had good diagnostic performance in SONFH. We identified IRF8 and 10 target miRNAs that was utilized including Targetsan, miRDB and ENCORI databases and 8 corresponding upstream lncRNAs that was revealed by ENCORI database. IRF8 was detected with consistent expression by qRT-PCR. Based on the CMap, DGIdb and L1000FWD databases, the 11 small molecular compounds that were most strongly therapeutic correlated with SONFH were estradiol, genistein, domperidone, lovastatin, myricetin, fenbufen, rosiglitazone, sirolimus, phenformin, vorinostat and vinblastine. All of 11 small molecules had good binding affinity with the IRF8 in molecular docking. CONCLUSION The occurrence of SONFH was associated with a "multi-target" and "multi-pathway" pattern, especially related to immunity, and IRF8 and its noncoding RNA were closely related to the development of SONFH. The CMap, DGIdb and L1000FWD databases could be effectively used in a systematic manner to predict potential drugs for the prevention and treatment of SONFH. However, additional clinical and experimental research is warranted.
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Affiliation(s)
- Xue-Zhen Liang
- grid.479672.9Orthopaedic Microsurgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, 16369 Jingshi Road, Jinan City, 250014 Shandong Province China ,grid.464402.00000 0000 9459 9325The First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, 250355 Shandong China
| | - Xiao-Chen Liu
- grid.479672.9Orthopaedic Microsurgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, 16369 Jingshi Road, Jinan City, 250014 Shandong Province China
| | - Song Li
- grid.464402.00000 0000 9459 9325Library, Shandong University of Traditional Chinese Medicine, Jinan, 250355 Shandong China
| | - Ming-Tao Wen
- grid.464402.00000 0000 9459 9325The First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, 250355 Shandong China
| | - Yan-Rong Chen
- grid.479672.9Orthopaedic Microsurgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, 16369 Jingshi Road, Jinan City, 250014 Shandong Province China
| | - Di Luo
- grid.479672.9Orthopaedic Microsurgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, 16369 Jingshi Road, Jinan City, 250014 Shandong Province China
| | - Bo Xu
- grid.464402.00000 0000 9459 9325The First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, 250355 Shandong China
| | - Nian-Hu Li
- grid.464402.00000 0000 9459 9325The First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, 250355 Shandong China ,grid.479672.9Spinal Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, 16369 Jingshi Road, Jinan City, 250014 Shandong Province China
| | - Gang Li
- grid.479672.9Orthopaedic Microsurgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, 16369 Jingshi Road, Jinan City, 250014 Shandong Province China ,grid.464402.00000 0000 9459 9325The First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, 250355 Shandong China
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Jiang Y, Xi Y, Li Y, Zuo Z, Zeng C, Fan J, Zhang D, Tao H, Guo Y. Ethanol promoting the upregulation of C-X-C Motif Chemokine Ligand 1(CXCL1) and C-X-C Motif Chemokine Ligand 6(CXCL6) in models of early alcoholic liver disease. Bioengineered 2022; 13:4688-4701. [PMID: 35156518 PMCID: PMC8973977 DOI: 10.1080/21655979.2022.2030557] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Alcoholic liver disease (ALD) denotes a series of liver diseases caused by ethanol. Recently, immune-related genes (IRGs) play increasingly crucial role in diseases. However, it’s unclear the role of IRGs in ALD. Bioinformatic analysis was used to discern the core immune-related differential genes (IRDGs) in the present study. Subsequently, Cell Counting Kit-8 say, oil red O staining, and triglyceride detection were employed to explore optimal experimental conditions of establishing hepatocellular models of early ALD. Ultimately, real-time reverse transcription-PCR and immunohistochemistry/immunocytochemistry methods were adopted to verify the expressions of mRNA and proteins of core IRDGs, respectively. C-X-C Motif Chemokine Ligand 1 (Cxcl1) and Cxcl6 were regarded as core IRDGs via integrated bioinformatics analysis. Besides, Lieber Decarli Ethanol feeding and 200 mM and 300 mM ethanol stimulating L02 cells for 36 h can both successfully hepatocellular model. In ethanol groups, the levels of CXCL1 and CXCL6 mRNA were significantly upregulated than pair-fed groups (P < 0.0001). Also, immunohistochemistry revealed that positive particles of CXCL1 and CXCL6 in mice model of early ALD were obviously more than control groups (P < 0.0001). Besides, in L02 hepatocytes stimulated by ethanol, CXCL1 and CXCL6 mRNA were over-expressed, compared with normal L02 cells (P < 0.0001). Meanwhile, immunocytochemistry indicated that CXCL1 and CXCL6 proteins in hepatocellular model of early ALD were higher than normal L02 hepatocytes stimulus (P < 0.0001). Ethanol promoted the upregulation of Cxcl1 and Cxcl6 mRNA and proteins in models of early ALD, denoting their potentiality of acting as biomarkers of ALD.
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Affiliation(s)
- Yao Jiang
- Clinical Laboratory, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Yuge Xi
- Clinical Laboratory, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Yiqin Li
- Clinical Laboratory, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Zhihua Zuo
- Department of Clinical Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Chuyi Zeng
- Department of Clinical Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jia Fan
- Clinical Laboratory, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Dan Zhang
- Clinical Laboratory, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Hualin Tao
- Department of Clinical Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yongcan Guo
- Clinical Laboratory, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
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Lu S, Wang Y, Liu G, Wang L, Wu P, Li Y, Cheng C. Construction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study. J Orthop Surg Res 2021; 16:231. [PMID: 33785046 PMCID: PMC8008682 DOI: 10.1186/s13018-021-02376-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/21/2021] [Indexed: 02/07/2023] Open
Abstract
Background Osteosarcoma is most common malignant bone tumors. OS patients with metastasis have a poor prognosis. There are few tools to assess metastasis; we want to establish a nomogram to evaluate metastasis of osteosarcoma. Methods Data from the Surveillance, Epidemiology, and End Results (SEER) database of patients with osteosarcoma were retrieved for retrospective analysis. We identify risk factors through univariate logistic regression and multivariate logistic regression analysis. Based on the results of multivariate analysis, we established a nomogram to predict metastasis of patients with osteosarcoma and used the concordance index (C-index) and calibration curves to test models. Results One thousand fifteen cases were obtained from the SEER database. In the univariate and multivariate logistic regression analysis, age, primary site, grade, T stage, and surgery are risk factors. The nomogram for metastasis was constructed based on these factors. The C-index of the training and validation cohort was 0.754 and 0.716. This means that the nomogram predictions of patients with metastasis are correct, and the calibration plots also show the good prediction performance of the nomogram. Conclusion We successfully develop the nomogram which can reliably predict metastasis in different patients with osteosarcoma and it only required basic information of patients. The nomogram that we developed can help clinicians better predict the metastasis with OS and determine postoperative treatment strategies.
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Affiliation(s)
- Shouliang Lu
- NO.1 Orthopedics Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, China.
| | - Yanhua Wang
- ECG Examination Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, China
| | - Guangfei Liu
- NO.1 Orthopedics Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, China
| | - Lu Wang
- NO.1 Orthopedics Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, China
| | - Pengfei Wu
- NO.1 Orthopedics Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, China
| | - Yong Li
- NO.1 Orthopedics Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, China
| | - Cai Cheng
- NO.1 Orthopedics Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, China
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