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Li Y, Meng L, Lou G. Revealing the inhibitory effect of VASH1 on ovarian cancer from multiple perspectives. Cancer Biol Ther 2023; 24:2285817. [PMID: 38010374 PMCID: PMC10783835 DOI: 10.1080/15384047.2023.2285817] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/16/2023] [Indexed: 11/29/2023] Open
Abstract
The function of Vasohibin-1 (VASH1) in human cancer has not been thoroughly or comprehensively examined. Here, we identified the tumor suppressor part of VASH1 across cancers, including epithelial ovarian tumors. Our study carefully contrasted the expression of VASH1 in pancancer and nontumorous tissues in a public database to explore its regulatory role in clinical prognosis, diagnosis, tumor purity, and immune cell infiltration. Next, we explored the antitumor mechanism of VASH1 through drug sensitivity, functional enrichment, and phenotypic experiments in ovarian cancer. Research suggests that the expression of VASH1 in neoplastic tissues is lower than that in normal tissues. VASH1 affects the OS and RFS of several tumor types. In addition, VASH1 expression resulted in a high OS and RFS in the diagnosis of tumor and nontumor tissues and negatively regulated tumor purity. Moreover, VASH1 controls the tumor microenvironment by regulating immunocyte infiltration. In ovarian cancer, VASH1 can serve as a biomarker to estimate the efficacy of chemotherapy. Functional enrichment analysis suggests that VASH1 plays a tumor suppressor role by regulating the extracellular matrix receptor pathway. VASH1 inhibition of the malignant phenotype of ovarian cancer cells was further confirmed by in vivo experiments. These results indicate that VASH1 acts as a cancer-inhibiting factor and potential therapeutic target in ovarian cancer.
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Affiliation(s)
- Yan Li
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
- Department of Obstetrics and Gynecology, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Liang Meng
- State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China
| | - Ge Lou
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
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Han S, Zhang Z, Ma W, Gao J, Li Y. Nucleotide-Binding Oligomerization Domain (NOD)-Like Receptor Subfamily C (NLRC) as a Prognostic Biomarker for Glioblastoma Multiforme Linked to Tumor Microenvironment: A Bioinformatics, Immunohistochemistry, and Machine Learning-Based Study. J Inflamm Res 2023; 16:523-537. [PMID: 36798872 PMCID: PMC9926983 DOI: 10.2147/jir.s397305] [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: 11/19/2022] [Accepted: 02/03/2023] [Indexed: 02/12/2023] Open
Abstract
Purpose Glioblastoma multiforme (GBM) remains the deadliest primary brain tumor. We aimed to illuminate the role of nucleotide-binding oligomerization domain (NOD)-like receptor subfamily C (NLRC) in GBM. Patients and Methods Based on public database data (mainly The Cancer Genome Atlas [TCGA]), we performed bioinformatics analysis to visually evaluate the role and mechanism of NLRCs in GBM. Then, we validated our findings in a glioma tissue microarray (TMA) by immunohistochemistry (IHC), and the prognostic value of NOD1 was assessed via random forest (RF) models. Results In GBM tissues, the expression of NLRC members was significantly increased, which was related to the low survival rate of GBM. Additionally, Cox regression analysis revealed that the expression of NOD1 (among NLRCs) served as an independent prognostic marker. A nomogram based on multivariate analysis proved the effective predictive performance of NOD1 in GBM. Enrichment analysis showed that high expression of NOD1 could regulate extracellular structure, cell adhesion, and immune response to promote tumor progression. Then, immune infiltration analysis showed that NOD1 overexpression correlated with an enhanced immune response. Then, in a glioma TMA, the results of IHC revealed that the increase in NOD1 expression indicated high recurrence and poor prognosis of human glioma. Furthermore, the expression level of NOD1 showed good prognostic value in the TMA cohort via RF. Conclusion The value of NOD1 as a biomarker for GBM was demonstrated. The possible mechanisms may lie in the regulatory role of NLRC-related pathways in the tumor microenvironment.
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Affiliation(s)
- Shiyuan Han
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), Beijing, People’s Republic of China
| | - Zimu Zhang
- Department of General Surgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), Beijing, People’s Republic of China
| | - Wenbin Ma
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), Beijing, People’s Republic of China
| | - Jun Gao
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), Beijing, People’s Republic of China
| | - Yongning Li
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), Beijing, People’s Republic of China,Department of International Medical Service, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital (Dongdan campus), Beijing, People’s Republic of China,Correspondence: Yongning Li, Department of Neurosurgery and Department of International Medical Service, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital (Dongdan campus), No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, People’s Republic of China, Tel +86 13901074129, Fax +86 1069152530, Email
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Li H, He J, Li M, Li K, Pu X, Guo Y. Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma. Front Immunol 2022; 13:1027631. [PMID: 36532035 PMCID: PMC9751405 DOI: 10.3389/fimmu.2022.1027631] [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: 08/25/2022] [Accepted: 11/15/2022] [Indexed: 12/04/2022] Open
Abstract
Introduction As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM. Methods and results In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents. Conclusion Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.
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Zhou Q, Zhang P, Man J, Zhang B, Xue C, Ke X, Zhou J. Correlation analysis of tumor purity with clinicopathological, molecular, and imaging features in high-grade gliomas. Neurosurg Rev 2022; 45:3699-3708. [PMID: 36156749 DOI: 10.1007/s10143-022-01871-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/23/2022] [Accepted: 09/16/2022] [Indexed: 10/14/2022]
Abstract
High-grade gliomas (HGG) have high malignancy, high heterogeneity, and a poor prognosis. Tumor purity is an intrinsic feature of the HGG microenvironment and an independent prognostic factor. The purpose of this study was to analyze the correlation of tumor purity with clinicopathological, molecular, and imaging features. We performed a retrospective analysis of 112 patients diagnosed with HGG (grades III and IV) in our center. Eleven regions of interest (ROI) were randomly selected on whole-slide images (WSI, 40 × magnification) based on HGG tissue paraffin sections and hematoxylin-eosin (H&E) staining. Of these 11 ROIs, five ROIs were visually estimated by pathologists and six ROIs were automatically analyzed using ImageJ software. Last, the average tumor purity (%) of the 11 ROIs was calculated. Correlation analysis of tumor purity with clinicopathological, molecular, and imaging features was conducted. Of the 112 patients included in the study, the mean tumor purity of HGG was 70.96%. There were differences in tumor purity between WHO grades III and IV; the tumor purity of grade IV patients (67.59%) was lower than that of grade III patients (76.00%) (p < 0.001). There were also differences in tumor purity between IDH1 mutant and wild type, and the tumor purity of IDH1 mutant patients was higher than that of IDH1 wild-type patients (p = 0.006). The average range of peritumoral edema was about 19.18 mm, and the diameter of edema, ADCmean, and ADCmin were negatively correlated with tumor purity(r = - 0.236, r = - 0.306, and r = - 0.242; p < 0.05). The grade of HGG, IDH1 mutant/wild type, peritumoral edema, and ADC value were correlated with tumor purity. HGG grade, IDH1 mutant/wild type, peritumoral edema, and ADC value can predict tumor purity and indirectly reflect patient prognosis.
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Affiliation(s)
- Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Peng Zhang
- Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.,Department of Pathology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
| | - Jiangwei Man
- Department of Surgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China. .,Second Clinical School, Lanzhou University, Lanzhou, Gansu, China. .,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China. .,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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5
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Reyes-González J, Barajas-Olmos F, García-Ortiz H, Magraner-Pardo L, Pons T, Moreno S, Aguirre-Cruz L, Reyes-Abrahantes A, Martínez-Hernández A, Contreras-Cubas C, Barrios-Payan J, Ruiz-Garcia H, Hernandez-Pando R, Quiñones-Hinojosa A, Orozco L, Abrahantes-Pérez MDC. Brain radiotoxicity-related 15CAcBRT gene expression signature predicts survival prognosis of glioblastoma patients. Neuro Oncol 2022; 25:303-314. [PMID: 35802478 PMCID: PMC9925695 DOI: 10.1093/neuonc/noac171] [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: 11/13/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Glioblastoma is the most common and devastating primary brain cancer. Radiotherapy is standard of care; however, it is associated with brain radiation toxicity (BRT). This study used a multi-omics approach to determine whether BRT-related genes (RGs) harbor survival prognostic value and whether their encoded proteins represent novel therapeutic targets for glioblastoma. METHODS RGs were identified through analysis of single-nucleotide variants associated with BRT (R-SNVs). Functional relationships between RGs were established using Protein-Protein Interaction networks. The influence of RGs and their functional groups on glioblastoma prognosis was evaluated using clinical samples from the Glioblastoma Bio-Discovery Portal database and validated using the Chinese Glioma Genome Atlas dataset. The identification of clusters of radiotoxic and putative pathogenic variants in proteins encoded by RGs was achieved by computational 3D structural analysis. RESULTS We identified the BRT-related 15CAcBRT molecular signature with prognostic value in glioblastoma, by analysis of the COMT and APOE protein functional groups. Its external validation confirmed clinical relevance independent of age, MGMT promoter methylation status, and IDH mutation status. Interestingly, the genes IL6, APOE, and MAOB documented significant gene expression levels alteration, useful for drug repositioning. Biological networks associated with 15CAcBRT signature involved pathways relevant to cancer and neurodegenerative diseases. Analysis of 3D clusters of radiotoxic and putative pathogenic variants in proteins coded by RGs unveiled potential novel therapeutic targets in neuro-oncology. CONCLUSIONS 15CAcBRT is a BRT-related molecular signature with prognostic significance for glioblastoma patients and represents a hub for drug repositioning and development of novel therapies.
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Affiliation(s)
| | | | - Humberto García-Ortiz
- Immunogenomics and Metabolic Diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | | | - Tirso Pons
- Department of Immunology and Oncology, National Center for Biotechnology, Spanish National Research Council (CNB-CSIC), Madrid, Spain
| | - Sergio Moreno
- Radioneurosurgery Unit, National Institute of Neurology and Neurosurgery;Mexico City, Mexico
| | - Lucinda Aguirre-Cruz
- Neuroendocrinology Laboratory, National Institute of Neurology and Neurosurgery; Mexico City, Mexico
| | - Andy Reyes-Abrahantes
- Precision Translational Oncology Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Angélica Martínez-Hernández
- Immunogenomics and Metabolic Diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Cecilia Contreras-Cubas
- Immunogenomics and Metabolic Diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Jorge Barrios-Payan
- Department of Pathology, National Institute of Medical Sciences and Nutrition Salvador Zubiran, Mexico City, Mexico
| | - Henry Ruiz-Garcia
- Department of Neurosurgery and Brain Tumor Stem Cell Research Laboratory, Mayo Clinic, Jacksonville, Florida,USA
| | - Rogelio Hernandez-Pando
- Department of Pathology, National Institute of Medical Sciences and Nutrition Salvador Zubiran, Mexico City, Mexico
| | - Alfredo Quiñones-Hinojosa
- Department of Neurosurgery and Brain Tumor Stem Cell Research Laboratory, Mayo Clinic, Jacksonville, Florida,USA
| | - Lorena Orozco
- Immunogenomics and Metabolic Diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - María del Carmen Abrahantes-Pérez
- Corresponding Author: María del Carmen Abrahantes-Pérez, PhD, Precision Translational Oncology Laboratory, National Institute of Genomic Medicine, Periférico Sur 4809, Tlalpan, Mexico City C.P. 14610, Mexico ()
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Tian Y, Liu H, Zhang C, Liu W, Wu T, Yang X, Zhao J, Sun Y. Comprehensive Analyses of Ferroptosis-Related Alterations and Their Prognostic Significance in Glioblastoma. Front Mol Biosci 2022; 9:904098. [PMID: 35720126 PMCID: PMC9204216 DOI: 10.3389/fmolb.2022.904098] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/27/2022] [Indexed: 12/23/2022] Open
Abstract
Background: This study was designed to explore the implications of ferroptosis-related alterations in glioblastoma patients.Method: After obtaining the data sets CGGA325, CGGA623, TCGA-GBM, and GSE83300 online, extensive analysis and mutual verification were performed using R language-based analytic technology, followed by further immunohistochemistry staining verification utilizing clinical pathological tissues.Results: The analysis revealed a substantial difference in the expression of ferroptosis-related genes between malignant and paracancerous samples, which was compatible with immunohistochemistry staining results from clinicopathological samples. Three distinct clustering studies were run sequentially on these data. All of the findings were consistent and had a high prediction value for glioblastoma. Then, the risk score predicting model containing 23 genes (CP, EMP1, AKR1C1, FMOD, MYBPH, IFI30, SRPX2, PDLIM1, MMP19, SPOCD1, FCGBP, NAMPT, SLC11A1, S100A10, TNC, CSMD3, ATP1A2, CUX2, GALNT9, TNFAIP6, C15orf48, WSCD2, and CBLN1) on the basis of “Ferroptosis.gene.cluster” was constructed. In the subsequent correlation analysis of clinical characteristics, tumor mutation burden, HRD, neoantigen burden and chromosomal instability, mRNAsi, TIDE, and GDSC, all the results indicated that the risk score model might have a better predictive efficiency.Conclusion: In glioblastoma, there were a large number of abnormal ferroptosis-related alterations, which were significant for the prognosis of patients. The risk score-predicting model integrating 23 genes would have a higher predictive value.
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Affiliation(s)
- Yuan Tian
- Somatic Radiotherapy Department, Shandong Second Provincial General Hospital, Jinan, China
- *Correspondence: Yuan Tian, ; Yuping Sun,
| | - Hongtao Liu
- Department of Pathology, Shandong Medicine and Health Key Laboratory of Clinical Pathology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Institute of Nephrology, Jinan, China
| | - Caiqing Zhang
- Department of Respiratory and Critical Care Medicine, Shandong Second Provincial General Hospital, Shandong University, Jinan, China
| | - Wei Liu
- Somatic Radiotherapy Department, Shandong Second Provincial General Hospital, Jinan, China
| | - Tong Wu
- Somatic Radiotherapy Department, Shandong Second Provincial General Hospital, Jinan, China
| | - Xiaowei Yang
- Department of Hepatobiliary Intervention, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Junyan Zhao
- Nursing Department, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yuping Sun
- Phase I Clinical Trial Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- *Correspondence: Yuan Tian, ; Yuping Sun,
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Wang J, Li Y, Zhang C, Chen X, Zhu L, Luo T. Characterization of diagnostic and prognostic significance of cell cycle-linked genes in hepatocellular carcinoma. Transl Cancer Res 2022; 10:4636-4651. [PMID: 35116320 PMCID: PMC8799204 DOI: 10.21037/tcr-21-1145] [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: 07/01/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
Background The high degree of heterogeneity of hepatocellular carcinoma (HCC) imposes a significant challenge to predict the prognosis. Currently, increasing evidence has indicated that cell cycle-linked genes are strongly linked to occurrence and progress of HCC. Herein, we purposed to create a prediction model on the basis of cell cycle-linked genes. Methods The transcriptome along with clinicopathological data abstracted from The Cancer Genome Atlas (TCGA) were used as a training cohort. Lasso regression analysis was employed to create a prediction model in TCGA cohort. The data of samples obtained from the International Cancer Genome Consortium (ICGC) data resource were applied in the verification of the model. A series of bioinformatics analyzed the relationship of the risk signature with overall survival (OS), biological function, and clinicopathological features. Results Six cell cycle-linked genes (PLK1, CDC20, HSP90AA1, CHEK1, HDAC1, and NDC80) were chosen to create the prognostic model, demonstrating a good prognostic capacity. Further analyses indicated that the model could independently assess the OS of HCC patients. A single-sample gene set enrichment analysis (ssGSEA) indicated that the risk signature was remarkably linked to immune status. Additionally, there was a remarkable association of the risk signature with TP53 mutation frequency, as well as immune checkpoint molecule expression levels. Conclusions We created a prediction model using six cell cycle-linked genes to predict HCC prognosis. The six genes are expected to be novel markers for HCC diagnosis, as well as treatment.
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Affiliation(s)
- Jukun Wang
- Department of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yu Li
- Department of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Chao Zhang
- Department of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xin Chen
- Department of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Linzhong Zhu
- Department of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Tao Luo
- Department of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China
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Zhang ZC, Guo JN, Zhang N, Wang ZQ, Lou G, Cui BB, Yang C. Identification and Validation of Immune-Related Gene for Predicting Prognosis and Therapeutic Response in Ovarian Cancer. Front Immunol 2021; 12:763791. [PMID: 34880862 PMCID: PMC8645858 DOI: 10.3389/fimmu.2021.763791] [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: 08/24/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Ovarian cancer (OC) is a devastating malignancy with a poor prognosis. The complex tumor immune microenvironment results in only a small number of patients benefiting from immunotherapy. To explore the different factors that lead to immune invasion and determine prognosis and response to immune checkpoint inhibitors (ICIs), we established a prognostic risk scoring model (PRSM) with differential expression of immune-related genes (IRGs) to identify key prognostic IRGs. Patients were divided into high-risk and low-risk groups according to their immune and stromal scores. We used a bioinformatics method to identify four key IRGs that had differences in expression between the two groups and affected prognosis. We evaluated the sensitivity of treatment from three aspects, namely chemotherapy, targeted inhibitors (TIs), and immunotherapy, to evaluate the value of prediction models and key prognostic IRGs in the clinical treatment of OC. Univariate and multivariate Cox regression analyses revealed that these four key IRGs were independent prognostic factors of overall survival in OC patients. In the high-risk group comprising four genes, macrophage M0 cells, macrophage M2 cells, and regulatory T cells, observed to be associated with poor overall survival in our study, were higher. The high-risk group had a high immunophenoscore, indicating a better response to ICIs. Taken together, we constructed a PRSM and identified four key prognostic IRGs for predicting survival and response to ICIs. Finally, the expression of these key genes in OC was evaluated using RT-qPCR. Thus, these genes provide a novel predictive biomarker for immunotherapy and immunomodulation.
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Affiliation(s)
- Zhao-Cong Zhang
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Jun-Nan Guo
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ning Zhang
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Zhi-Qiang Wang
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ge Lou
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Bin-Bin Cui
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Chang Yang
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
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METTL7B is a novel prognostic biomarker of lower-grade glioma based on pan-cancer analysis. Cancer Cell Int 2021; 21:383. [PMID: 34281539 PMCID: PMC8287669 DOI: 10.1186/s12935-021-02087-4] [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: 03/22/2021] [Accepted: 07/10/2021] [Indexed: 12/14/2022] Open
Abstract
Methyltransferase-like 7B (METTL7B) is a member of the methyltransferase-like protein family that plays an important role in the development and progression of tumors. However, its prognostic value and the correlation of METTL7B expression and tumor immunity in some cancers remain unclear. By analyzing online data, we found that METTL7B is abnormally overexpressed in multiple human tumors and plays an important role in the overall survival (OS) of patients with 8 cancer types and disease-free survival (DFS) of patients with 5 cancer types. Remarkably, METTL7B expression was positively correlated with the OS and DFS of patients with lower-grade glioma (LGG). In addition, a positive correlation between METTL7B expression and immune cell infiltration in LGG was observed. Moreover, we identified a strong correlation between METTL7B expression and immune checkpoint gene expression in kidney chromophobe (KICH), LGG and pheochromocytoma and paraganglioma (PCPG). Furthermore, METTL7B was involved in the extracellular matrix (ECM) and immune-related pathways in LGGs. Finally, in vitro experiments showed that knockdown of METTL7B inhibited the growth, migration, invasion and the epithelial–mesenchymal transition (EMT) of LGG cells. METTL7B expression potentially represents a novel prognostic biomarker due to its significant association with immune cell infiltration in LGG.
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A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning. Biomolecules 2021; 11:biom11040565. [PMID: 33921457 PMCID: PMC8070530 DOI: 10.3390/biom11040565] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 02/06/2023] Open
Abstract
Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.
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11
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Zhao B, Wang Y, Wang Y, Chen W, Liu PH, Kong Z, Dai C, Wang Y, Ma W. Systematic identification, development, and validation of prognostic biomarkers involving the tumor-immune microenvironment for glioblastoma. J Cell Physiol 2020; 236:507-522. [PMID: 32572951 DOI: 10.1002/jcp.29878] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/17/2020] [Accepted: 05/31/2020] [Indexed: 01/31/2023]
Abstract
Gliomas are infiltrative neoplasms with a highly invasive nature. Due to its distinct genomic, genetic and epigenetic features, the immune prognostic signature (IPS) and immune microenvironment of glioblastoma (GBM) merit further research. We aimed to explore prognosis-related immune genes and develop an IPS model for predicting prognosis in GBM. RNA-sequencing data, as well as clinical information, from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) public cohorts were analyzed. To develop the IPS, least absolute shrinkage and selection operator (LASSO) Cox analysis was performed for immune-related genes that were differentially expressed between GBM and normal tissues. Then, interaction effects of the IPS on the immune microenvironment were systematically analyzed; the precise prognostic model was developed based on the IPS and clinical data and was then further validated. A total of 21 immune prognostic genes were identified based on GBM microenvironment status. An 8-gene IPS was established, and the GBM patients were effectively stratified into low- and high-risk groups in the TCGA cohort as a training set. Univariate and multivariate Cox analyses revealed that IPS was an independent prognostic factor, and the prognostic performance of individual IPS genes was systematically illustrated. In addition, a comprehensive and novel nomogram model was initially established to estimate overall survival in TCGA-GBM patients, and high-risk patients had higher levels of dendritic cell and neutrophil infiltration. Furthermore, the nomogram model was developed and validated in the CGGA validation set. The low-risk IPS was linked to a stronger response to anti-PD-L1 immunotherapy and clinical advantages in the IMvigor210 cohort. This novel IPS with promising biomarkers classifies GBM patients into subgroups with distinct clinical outcomes and immunophenotypes. Our findings and this resource may help to characterize the immune microenvironment, inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
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Affiliation(s)
- Binghao Zhao
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuekun Wang
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yaning Wang
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Wenlin Chen
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Peng Hao Liu
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Ziren Kong
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Congxin Dai
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yu Wang
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Wenbin Ma
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
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12
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Yang Q, Xiong Y, Jiang N, Zeng F, Huang C, Li X. Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma. J Cancer 2020; 11:3794-3802. [PMID: 32328184 PMCID: PMC7171505 DOI: 10.7150/jca.44032] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 03/29/2020] [Indexed: 01/19/2023] Open
Abstract
Background: Glioma is the most common type of primary central nervous system tumors. However, the relationship between gene mutations and transcriptome is unclear in diffuse glioma, and there are no systemic analyses with regard to the genotype-phenotype association currently. Methods: We performed the multi-omics analysis in large glioblastoma multiforme (GBM, n=126) and low-grade glioma (LGG, n=481) cohorts obtained from The Cancer Genome Atlas (TCGA) database. We used multivariate linear models to evaluate associations between driver gene mutations and global gene expression. We developed generalized linear models to evaluate associations between genetic/expression factors with clinicopathologic features. Multivariate Cox proportional hazards models were used to predict the overall survival. Results: The potential relationship between genotype and genetics, clinical as well as pathologic features, on diffused glioma was observed. At least one driver mutation correlated with expression changes of about 10% of genes in GBMs while about 80% of genes in LGGs. The strongest association between mutations and expression changes was observed for DRG2 and LRCC41 gene in GBMs and LGGs, respectively. Additionally, the association between genomics features and clinicopathologic features suggested the different underlying molecular mechanisms in molecular subtypes or histology subtypes. For predicting survival, among genetics, transcriptome and clinical variables, transcriptome features made the largest contribution. By combining all the available data, the accuracy in predicting the prognosis of diffuse glioma in patients was also improved. Conclusion: Our study results revealed the influences of driver gene mutations on global gene expression in diffuse glioma patients. A more accurate model in predicting the prognosis of patients was achieved when combining with all the available data than just transcriptomic data.
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Affiliation(s)
- Qi Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China
| | - Yi Xiong
- Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China
| | - Nian Jiang
- Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China
| | - Fanyuan Zeng
- Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China
| | - Chunhai Huang
- Department of Neurosurgery, First Affiliated Hospital of Jishou University, Jishou, Hunan, 416000 P. R. China.,Centre for Clinical and Translational Medicine Research, Jishou University, Jishou, Hunan, 416000 P. R. China
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China
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