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Tang Q, Zhang H, Tang R. Identification of two immune subtypes and four hub immune-related genes in ovarian cancer through multiple analysis. Medicine (Baltimore) 2023; 102:e35246. [PMID: 37800814 PMCID: PMC10553066 DOI: 10.1097/md.0000000000035246] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/24/2023] [Indexed: 10/07/2023] Open
Abstract
Immune classification of ovarian cancer (OV) becomes more and more influential for its immunotherapy. However, current studies had few immune subtypes of OV. It is urgent to explore the immune subtypes and deeper hub immune-related genes (IRGs) of OV for follow-up treatment. A total number of 379 OV samples were obtained from UCSC online website. Single sample gene set enrichment analysis of 29 immune gene sets was used for identifying immune subtypes of OV and gene set variation analysis were used for exploring the hallmarks and Kyoto Encyclopedia of Genes and Genomes pathways of immune types. Two immunity subtypes (Immunity_H and Immunity_L) were identified by single sample gene set enrichment analysis. The OV patients in Immunity_H group had longer overall survival compared with those in Immunity_L group. The Immunity_H had higher stromal score, immune score and estimate score and the tumor purity had the adverse tendency. Besides, the gene set variation analysis enrichment results showed positive relationship between improved immunoreaction and pathways correlated to classical signaling pathway (PI3K/AKT/MTOR, P53, TNFA/NFkB signaling pathways) and immune responses (T/B cell receptor signaling pathways and primary immunodeficiency). Furthermore, 4 hub IRGs (CCR5, IL10RA, ITGAL and PTPRC) were jointly dug by weighted gene co-expression network construction and Cytoscape. Our team also explored the mutations of 4 hub IRGs and PTPRC showed nearly 7% amplification. Besides, 8 immune-checkpoint genes had higher expression in Immuity_H group compared with Immuity_L group, except CD276. The correlation between PD-1/PD-L1 and 4 hub IRGs were explored and gene set enrichment analysis were conducted to explore the underlying mechanisms of PTPRC in OV. Finally, western-blotting showed PTPRC could regulate immune checkpoint PD-L1 expression via JAK-STAT signaling pathway. In a word, 2 immune subtypes and 4 hub IRGs of OV were identified by multiple analysis.
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Affiliation(s)
- Qin Tang
- Department of Obstetrics and Gynecology, The Jingmen Center Hospital, Jingmen, PR China
| | - Haojie Zhang
- Department of Operating Room, The Jingmen Center Hospital, Jingmen, PR China
| | - Rong Tang
- Department of Pathology, The Jingmen Center Hospital, Jingmen, PR China
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Yang W, An L, Li Y, Qian S. A cellular senescence-related genes model allows for prognosis and treatment stratification of cervical cancer: a bioinformatics analysis and external verification. Aging (Albany NY) 2023; 15:9408-9425. [PMID: 37768206 PMCID: PMC10564413 DOI: 10.18632/aging.204981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/20/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Cervical cancer (CC) is highly lethal and aggressive with an increasing trend of mortality for females. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. METHODS The mRNAs expression data of CC patients and cellular senescence-related genes were obtained from the Cancer Genome Atlas (TCGA) and CellAge databases, respectively. Differentially expressed genes (DEGs) of senescence related genes between tumor and normal tissues were used for Least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model. Univariate and LASSO regression analyses were applied to establish a predictive nomogram. The performance of the nomogram were evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index), and calibration curve. GSE44001 and GSE52903 were used for external validation. RESULTS We established a cellular senescence-related genes-based stratified model, and a multivariable-based nomogram, which could accurately predict the prognosis of CC patients in the TCGA database. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better overall survival (OS, P =2.021e-05). The area under the ROC curve (AUC) of this model was 0.743 for OS. Multivariate analysis found that the 6-gene risk signature (HR=3.166, 95%CI: 1.660-6.041, P<0.001) was an independent risk factor for CC patients. We then designed an OS-associated nomogram that included the risk signature and clinicopathological factors. The AUC reached 0.860 for predicting 5-year OS. The nomogram showed excellent consistency between the predictions and actual survival observations. Two external GEO validations were corresponding to the gene expression pattern in TCGA. CONCLUSIONS Our results suggested a six-senescence related signature and established a prognostic nomogram that reliably predicted the overall survival for CC. These findings may be beneficial to personalized treatment and medical decision-making.
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Affiliation(s)
- Weiwei Yang
- Gynecology Department 2, Cangzhou Central Hospital, Yunhe District, Cangzhou 061000, Hebei Province, China
| | - Lijuan An
- Gynecology Department 2, Cangzhou Central Hospital, Yunhe District, Cangzhou 061000, Hebei Province, China
| | - Yanfei Li
- Gynecology Department 2, Cangzhou Central Hospital, Yunhe District, Cangzhou 061000, Hebei Province, China
| | - Sumin Qian
- Gynecology Department 2, Cangzhou Central Hospital, Yunhe District, Cangzhou 061000, Hebei Province, China
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Feng J, Yu Y, Yin W, Qian S. Development and verification of a 7-lncRNA prognostic model based on tumor immunity for patients with ovarian cancer. J Ovarian Res 2023; 16:31. [PMID: 36739404 PMCID: PMC9898952 DOI: 10.1186/s13048-023-01099-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/11/2023] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Both immune-reaction and lncRNAs play significant roles in the proliferation, invasion, and metastasis of ovarian cancer (OC). In this study, we aimed to construct an immune-related lncRNA risk model for patients with OC. METHOD Single sample GSEA (ssGSEA) algorithm was used to analyze the proportion of immune cells in The Cancer Genome Atlas (TCGA) and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells for OC patients. The stromal and immune scores were computed utilizing the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analyses were utilized to detect immune cluster-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression was conducted for lncRNA selection. The selected lncRNAs were used to construct a prognosis-related risk model, which was then validated in Gene Expression Omnibus (GEO) database and in vitro validation. RESULTS We identify two subtypes based on the ssGSEA analysis, high immunity cluster (immunity_H) and low immunity cluster (immunity_L). The proportion of patients in immunity_H cluster was significantly higher than that in immunity_L cluster. The ESTIMATE related scores are relative high in immunity_H group. Through WGCNA and LASSO analyses, we identified 141 immune cluster-related lncRNAs and found that these genes were mainly enriched in autophagy. A signature consisting of 7 lncRNAs, including AL391832.3, LINC00892, LINC02207, LINC02416, PSMB8.AS1, AC078788.1 and AC104971.3, were selected as the basis for classifying patients into high- and low-risk groups. Survival analysis and area under the ROC curve (AUC) of the signature pointed out that this risk model had high accuracy in predicting the prognosis of patients with OC. We also conducted the drug sensitive prediction and found that rapamycin outperformed in patient with high risk score. In vitro experiments also confirmed our prediction. CONCLUSIONS We identified 7 immune-related prognostic lncRNAs that effectively predicted survival in OC patients. These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for these patients.
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Affiliation(s)
- Jing Feng
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
| | - Yiping Yu
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
| | - Wen Yin
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
| | - Sumin Qian
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
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Ma R, Tang Z, Wang J. PTTG1IP (PBF) is a prognostic marker and correlates with immune infiltrate in ovarian cancer. Am J Transl Res 2023; 15:27-46. [PMID: 36777854 PMCID: PMC9908464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 11/08/2022] [Indexed: 02/14/2023]
Abstract
OBJECTIVE An oncogenic protein, pituitary tumor transforming gene 1 binding factor (PTTG1IP, also called PBF), has been found to be expressed in various cancers. However, few studies have explored its prognostic significance and biologic function in epithelial ovarian cancer (EOC). METHODS Based on the Cancer Genome Atlas (TCGA) database, this study determined the differential expression of PBF at the mRNA level in EOC and normal tissues, which was then verified using real-time PCR and western blotting. Moreover, the Kaplan-Meier method and the Cox regression method were adopted to assess the clinical value of PBF in EOC. A nomogram model was constructed to evaluate the prognostic performance of PBF in EOC. Gene set enrichment analysis (GSEA) was employed to evaluate the signaling and pathway enrichment of PBF in EOC. The association between PBF expression and tumor-infiltrating immune cells (TIICs) in EOC was examined by single-sample GSEA and TIMER. RESULTS PBF was significantly higher in EOC than normal tissues as shown through TCGA database, and this result was verified by qRT-PCR and western blotting of EOC tissues and different cell lines. High PBF was associated with tumor size and lymphatic metastasis status. Kaplan-Meier (KM) analysis indicated that high PBF expression correlated with poor prognosis in patients with EOC (P < 0.0001). Moreover, multivariate Cox regression analysis was used to verify that PBF is an independent prognostic factor for EOC. The nomogram model exhibited moderate predictive accuracy and clinical utility in predicting EOC prognosis. The GSEA revealed that the expression of signaling pathways, such DNA damage replication, p53 pathway, Akt phosphorylation pathway, and estrogen-dependent nuclear pathway, were increased in the phenotype with high PBF expression. PBF expression was associated with neutrophil cells, iDC cells, NK cells, and Tem cells. CONCLUSION As a prognostic biomarker for EOC, PBF was found to be correlated with immune infiltration, and may therefore be a promising target for immunotherapy for EOC.
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Liu Z, Liu H, Wang Y, Li Z. A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma. BMC Gastroenterol 2022; 22:435. [PMID: 36241983 PMCID: PMC9564244 DOI: 10.1186/s12876-022-02510-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/31/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Stomach adenocarcinoma (STAD) is a highly heterogeneous disease and is among the leading causes of cancer-related death worldwide. At present, TNM stage remains the most effective prognostic factor for STAD. Exploring the changes in gene expression levels associated with TNM stage development may help oncologists to better understand the commonalities in the progression of STAD and may provide a new way of identifying early-stage STAD so that optimal treatment approaches can be provided. METHODS The RNA profile retrieving strategy was utilized and RNA expression profiling was performed using two large STAD microarray databases (GSE62254, n = 300; GSE15459, n = 192) from the Gene Expression Omnibus (GEO) and the RNA-seq database within the Cancer Genome Atlas (TCGA, n = 375). All sample expression information was obtained from STAD tissues after radical resection. After excluding data with insufficient staging information and lymph node number, samples were grouped into earlier-stage and later-stage. Samples in GSE62254 were randomly divided into a training group (n = 172) and a validation group (n = 86). Differentially expressed genes (DEGs) were selected based on the expression of mRNAs in the training group and the TCGA group (n = 156), and hub genes were further screened by least absolute shrinkage and selection operator (LASSO) logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the hub genes in distinguishing STAD stage in the validation group and the GSE15459 dataset. Univariate and multivariate Cox regressions were performed sequentially. RESULTS 22 DEGs were commonly upregulated (n = 19) or downregulated (n = 3) in the training and TCGA datasets. Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression. The model achieved discrimination in the validation group (AUC = 0.704), training-validation group (AUC = 0.743), and GSE15459 dataset (AUC = 0.658), respectively. Gene Set Enrichment Analysis (GSEA) was used to identify the potential stage-development pathways, including the PI3K-Akt and Calcium signaling pathways. Univariate Cox regression indicated that the nine-gene score was a significant risk factor for overall survival (HR = 1.28, 95% CI 1.08-1.50, P = 0.003). In the multivariate Cox regression, only SCRG1 was an independent prognostic predictor of overall survival after backward stepwise elimination (HR = 1.21, 95% CI 1.11-1.32, P < 0.001). CONCLUSION Through a series of bioinformatics and validation processes, a nine-gene signature that can distinguish STAD stage was identified. This gene signature has potential clinical application and may provide a novel approach to understanding the progression of STAD.
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Affiliation(s)
- Zining Liu
- Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Hua Liu
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China
| | - Yinkui Wang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Ziyu Li
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
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Li Z, Yu Q, Zhu Q, Yang X, Li Z, Fu J. Applications of machine learning in tumor-associated macrophages. Front Immunol 2022; 13:985863. [PMID: 36211379 PMCID: PMC9538115 DOI: 10.3389/fimmu.2022.985863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 09/07/2022] [Indexed: 11/29/2022] Open
Abstract
Evaluation of tumor-host interaction and intratumoral heterogeneity in the tumor microenvironment (TME) is gaining increasing attention in modern cancer therapies because it can reveal unique information about the tumor status. As tumor-associated macrophages (TAMs) are the major immune cells infiltrating in TME, a better understanding of TAMs could help us further elucidate the cellular and molecular mechanisms responsible for cancer development. However, the high-dimensional and heterogeneous data in biology limit the extensive integrative analysis of cancer research. Machine learning algorithms are particularly suitable for oncology data analysis due to their flexibility and scalability to analyze diverse data types and strong computation power to learn underlying patterns from massive data sets. With the application of machine learning in analyzing TME, especially TAM’s traceable status, we could better understand the role of TAMs in tumor biology. Furthermore, we envision that the promotion of machine learning in this field could revolutionize tumor diagnosis, treatment stratification, and survival predictions in cancer research. In this article, we described key terms and concepts of machine learning, reviewed the applications of common methods in TAMs, and highlighted the challenges and future direction for TAMs in machine learning.
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Affiliation(s)
- Zhen Li
- Radiation Oncology Department, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Qijun Yu
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China
- Institute of Respiratory Diseases, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qingyuan Zhu
- Radiation Oncology Department, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Xiaojing Yang
- Radiation Oncology Department, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Zhaobin Li
- Radiation Oncology Department, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Jie Fu
- Radiation Oncology Department, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
- *Correspondence: Jie Fu,
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Liu J, Liu L, Antwi PA, Luo Y, Liang F. Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning. Front Genet 2022; 13:858466. [PMID: 35719392 PMCID: PMC9198487 DOI: 10.3389/fgene.2022.858466] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning. Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level. Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis. Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.
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Affiliation(s)
- Jinya Liu
- Department of Plastic Surgery, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Leping Liu
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Paul Akwasi Antwi
- Department of Plastic Surgery, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Yanwei Luo
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Fang Liang
- Department of Hematology and Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China
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Si M, Cao X. Considering Computational Mathematics IGHG3 as Malignant Melanoma Is Associated with Immune Infiltration of Malignant Melanoma. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4168937. [PMID: 35480143 PMCID: PMC9038404 DOI: 10.1155/2022/4168937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/19/2022] [Indexed: 11/17/2022]
Abstract
Malignant melanoma is one of the most threatening cancers to human health. Only 14% of patients with malignant melanoma have a remaining life span of 5 years. At present, there have been some studies looking for potential prognostic indicators of esophageal cancer from the level of genes and infiltrating immune cells, but there are still some problems that need to be resolved urgently. This paper proposes IGHG3 as the immune infiltration of malignant melanoma, which takes into account the computational mathematics. It aims to deduce the characteristics of immune cell infiltration in malignant melanoma and study the relationship between different immune cell infiltration characteristics and prognosis. The method in this article is to establish a computational mathematical model for the immunotherapy of melanoma, then study the method of identification of the affinity of the IGHG3 reagent, and finally obtain the gene expression of immune infiltration. The functions of these methods are, respectively, to predict the dynamic behavior of T cells with two different specificities through mathematical models and to test the matching degree of different concentrations of IGHG3 reagent with the human body. Then use the ssGSEA algorithm to obtain immune infiltration related data and calculate the difference between the weighted empirical cumulative distribution function of all genes in the effect of IGHG3 on melanoma that was carried out. The experimental results showed the computational mathematical method genome and all the remaining genes. In this study, a computational mathematical method to detect the IGHG3 gene expression had a significant inhibitory effect on A375 cells in the experimental group, and the knockdown efficiency reached 85.6%.
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Affiliation(s)
- Mengqing Si
- Queen Mary College, Medical Department, Nanchang University, Nanchang, China
- The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xianwei Cao
- Department of Dermatology, The First Affiliated Hospital of Nanchang University, Nanchang, China
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Ren H, Bazhin AV, Pretzsch E, Jacob S, Yu H, Zhu J, Albertsmeier M, Lindner LH, Knösel T, Werner J, Angele MK, Bösch F. A novel immune-related gene signature predicting survival in sarcoma patients. Mol Ther Oncolytics 2022; 24:114-126. [PMID: 35024438 PMCID: PMC8718575 DOI: 10.1016/j.omto.2021.12.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/07/2021] [Indexed: 02/08/2023] Open
Abstract
Sarcomas are a heterogeneous group of rare mesenchymal tumors. The migration of immune cells into these tumors and the prognostic impact of tumor-specific factors determining their interaction with these tumors remain poorly understood. The current risk stratification system is insufficient to provide a precise survival prediction and treatment response. Thus, valid prognostic models are needed to guide treatment. This study analyzed the gene expression and outcome of 980 sarcoma patients from seven public datasets. The abundance of immune cells and the response to immunotherapy was calculated. Immune-related genes (IRGs) were screened through a weighted gene co-expression network analysis (WGCNA). A least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a powerful IRG signature predicting prognosis. The identified IRG signature incorporated 14 genes and identified high-risk patients in sarcoma cohorts. The 14-IRG signature was identified as an independent risk factor for overall and disease-free survival. Moreover, the IRG signature acted as a potential indicator for immunotherapy. The nomogram based on the risk score was built to provide a more accurate survival prediction. The decision tree with IRG risk score discriminated risk subgroups powerfully. This proposed IRG signature is a robust biomarker to predict outcomes and treatment responses in sarcoma patients.
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Affiliation(s)
- Haoyu Ren
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Alexandr V Bazhin
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Elise Pretzsch
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Sven Jacob
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Haochen Yu
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Jiang Zhu
- Department of Liver Surgery and Liver Transplantation Centre, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Markus Albertsmeier
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Lars H Lindner
- Department of Medicine III, SarKUM, University Hospital, LMU Munich, Munich, Germany
| | - Thomas Knösel
- Institute of Pathology, University Hospital, LMU Munich, Munich, Germany
| | - Jens Werner
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Martin K Angele
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Florian Bösch
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
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Zou J, Li Y, Liao N, Liu J, Zhang Q, Luo M, Xiao J, Chen Y, Wang M, Chen K, Zeng J, Mo Z. Identification of key genes associated with polycystic ovary syndrome (PCOS) and ovarian cancer using an integrated bioinformatics analysis. J Ovarian Res 2022; 15:30. [PMID: 35227296 PMCID: PMC8886837 DOI: 10.1186/s13048-022-00962-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 02/20/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Accumulating evidence suggests a strong association between polycystic ovary syndrome (PCOS) and ovarian cancer (OC), but the potential molecular mechanism remains unclear. In this study, we identified previously unrecognized genes that are significantly correlated with PCOS and OC via bioinformatics. MATERIALS AND METHODS Multiple bioinformatic analyses, such as differential expression analysis, univariate Cox analysis, functional and pathway enrichment analysis, protein-protein interaction (PPI) network construction, survival analysis, and immune infiltration analysis, were utilized. We further evaluated the effect of OGN on FSHR expression via immunofluorescence. RESULTS TCGA-OC, GSE140082 (for OC) and GSE34526 (for PCOS) datasets were downloaded. Twelve genes, including RNF144B, LPAR3, CRISPLD2, JCHAIN, OR7E14P, IL27RA, PTPRD, STAT1, NR4A1, OGN, GALNT6 and CXCL11, were identified as signature genes. Drug sensitivity analysis showed that OGN might represent a hub gene in the progression of PCOS and OC. Experimental analysis found that OGN could increase FSHR expression, indicating that OGN could regulate the hormonal response in PCOS and OC. Furthermore, correlation analysis indicated that OGN function might be closely related to m6A and ferroptosis. CONCLUSIONS Our study identified a 12-gene signature that might be involved in the prognostic significance of OC. Furthermore, the hub gene OGN represent a significant gene involved in OC and PCOS progression by regulating the hormonal response.
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Affiliation(s)
- Juan Zou
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of University of South China, University of South China, Hengyang, Hunan, China
- Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology, Cancer Research Institute, University of South China, Hengyang, Hunan, China
| | - Yukun Li
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of University of South China, University of South China, Hengyang, Hunan, China
- Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology, Cancer Research Institute, University of South China, Hengyang, Hunan, China
| | - Nianchun Liao
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of University of South China, University of South China, Hengyang, Hunan, China
| | - Jue Liu
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of University of South China, University of South China, Hengyang, Hunan, China
| | - Qunfeng Zhang
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of University of South China, University of South China, Hengyang, Hunan, China
| | - Min Luo
- Clinical Anatomy & Reproductive Medicine Application Institute, Department of Histology and Embryology, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Jiao Xiao
- Department of Endocrinology, The Affiliated Nanhua Hospital, University of South China, Hengyang, Hunan, China
| | - Yanhua Chen
- Institute of Basic Medical Sciences, College of Basic Medicine, Guilin Medical University, Guilin, Guangxi, China
- Department of Laboratory Medicine, The Affiliated Nanhua Hospital, University of South China, Hengyang, Hunan, China
| | - Mengjie Wang
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of University of South China, University of South China, Hengyang, Hunan, China
| | - Kexin Chen
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of University of South China, University of South China, Hengyang, Hunan, China
| | - Juan Zeng
- Department of Anesthesiology, The Second Affiliated Hospital of University of South China, University of South China, Hengyang, Hunan, China.
| | - Zhongcheng Mo
- Institute of Basic Medical Sciences, College of Basic Medicine, Guilin Medical University, Guilin, Guangxi, China.
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11
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Prognostic immunologic signatures in epithelial ovarian cancer. Oncogene 2022; 41:1389-1396. [PMID: 35031772 DOI: 10.1038/s41388-022-02181-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 12/21/2021] [Accepted: 01/07/2022] [Indexed: 02/07/2023]
Abstract
Epithelial Ovarian Cancer (EOC) is a deadly gynecologic malignancy in which patients frequently develop recurrent disease following initial platinum-taxane chemotherapy. Analogous to many other cancer subtypes, EOC clinical trials have centered upon immunotherapeutic approaches, most notably programmed cell death 1 (PD-1) inhibitors. While response rates to these immunotherapies in EOC patients have been low, evidence suggests that ovarian tumors are immunogenic and that immune-related genomic profiles can serve as prognostic markers. This review will discuss recent advances in the development of immune-based prognostic signatures in EOC that predict patient clinical outcomes, as well as emphasize specific research areas that need to be addressed to drive this field forward.
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12
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Sato M, Sato S, Shintani D, Hanaoka M, Ogasawara A, Miwa M, Yabuno A, Kurosaki A, Yoshida H, Fujiwara K, Hasegawa K. Clinical significance of metabolism-related genes and FAK activity in ovarian high-grade serous carcinoma. BMC Cancer 2022; 22:59. [PMID: 35027024 PMCID: PMC8756654 DOI: 10.1186/s12885-021-09148-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 12/22/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Administration of poly (ADP-ribose) polymerase (PARP) inhibitors after achieving a response to platinum-containing drugs significantly prolonged relapse-free survival compared to placebo administration. PARP inhibitors have been used in clinical practice. However, patients with platinum-resistant relapsed ovarian cancer still have a poor prognosis and there is an unmet need. The purpose of this study was to examine the clinical significance of metabolic genes and focal adhesion kinase (FAK) activity in advanced ovarian high-grade serous carcinoma (HGSC). METHODS The RNA sequencing (RNA-seq) data and clinical data of HGSC patients were obtained from the Genomic Data Commons (GDC) Data Portal and analysed ( https://portal.gdc.cancer.gov/ ). In addition, tumour tissue was sampled by laparotomy or screening laparoscopy prior to treatment initiation from patients diagnosed with stage IIIC ovarian cancer (International Federation of Gynecology and Obstetrics (FIGO) classification, 2014) at the Saitama Medical University International Medical Center, and among the patients diagnosed with HGSC, 16 cases of available cryopreserved specimens were included in this study. The present study was reviewed and approved by the Institutional Review Board of Saitama Medical University International Medical Center (Saitama, Japan). Among the 6307 variable genes detected in both The Cancer Genome Atlas-Ovarian (TCGA-OV) data and clinical specimen data, 35 genes related to metabolism and FAK activity were applied. RNA-seq data were analysed using the Subio Platform (Subio Inc, Japan). JMP 15 (SAS, USA) was used for statistical analysis and various types of machine learning. The Kaplan-Meier method was used for survival analysis, and the Wilcoxon test was used to analyse significant differences. P < 0.05 was considered significant. RESULTS In the TCGA-OV data, patients with stage IIIC with a residual tumour diameter of 1-10 mm were selected for K means clustering and classified into groups with significant prognostic correlations (p = 0.0444). These groups were significantly associated with platinum sensitivity/resistance in clinical cases (χ2 test, p = 0.0408) and showed significant relationships with progression-free survival (p = 0.0307). CONCLUSION In the TCGA-OV data, 2 groups classified by clustering focusing on metabolism-related genes and FAK activity were shown to be associated with platinum resistance and a poor prognosis.
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Affiliation(s)
- Masakazu Sato
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan.
| | - Sho Sato
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Daisuke Shintani
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Mieko Hanaoka
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Aiko Ogasawara
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Maiko Miwa
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Akira Yabuno
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Akira Kurosaki
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Hiroyuki Yoshida
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | | | - Kosei Hasegawa
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
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13
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Zhang D, Li Y, Yang S, Wang M, Yao J, Zheng Y, Deng Y, Li N, Wei B, Wu Y, Zhai Z, Dai Z, Kang H. Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients. Cancer Med 2021; 10:8222-8237. [PMID: 34609082 PMCID: PMC8607265 DOI: 10.1002/cam4.4317] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 08/22/2021] [Accepted: 08/31/2021] [Indexed: 12/17/2022] Open
Abstract
Background Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV. Methods The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets. Results A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed. Conclusion Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.
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Affiliation(s)
- Dai Zhang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, The Air Force Medical University, Xi'an, China
| | - Yiche Li
- Department of Tumor Surgery, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Si Yang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Meng Wang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jia Yao
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yi Zheng
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yujiao Deng
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Na Li
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Bajin Wei
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Ying Wu
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Zhen Zhai
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zhijun Dai
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Huafeng Kang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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14
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Yang Y, Wu G, Li Q, Zheng Y, Liu M, Zhou L, Chen Z, Wang Y, Guo Q, Ji R, Zhou Y. Angiogenesis-Related Immune Signatures Correlate With Prognosis, Tumor Microenvironment, and Therapeutic Sensitivity in Hepatocellular Carcinoma. Front Mol Biosci 2021; 8:690206. [PMID: 34262941 PMCID: PMC8273615 DOI: 10.3389/fmolb.2021.690206] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/01/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Hepatocellular carcinoma (HCC) is one of the highly heterogeneous cancers that lacks an effective risk model for prognosis prediction. Therefore, we searched for angiogenesis-related immune genes that affected the prognosis of HCC to construct a risk model and studied the role of this model in HCC. Methods: In this study, we collected the transcriptome data of HCC from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. Pearson correlation analysis was performed to identify the association between immune genes and angiogenesis-related genes. Consensus clustering was applied to divide patients into clusters A and B. Subsequently, we studied the differentially expressed angiogenesis-related immune genes (DEari-genes) that affected the prognosis of HCC. The most significant features were identified by least absolute shrinkage and selection operator (LASSO) regression, and a risk model was constructed. The reliability of the risk model was evaluated in the TCGA discovery cohort and the ICGC validation cohort. In addition, we compared the novel risk model to the previous models based on ROC analysis. ssGSEA analysis was used for function evaluation, and pRRophetic was utilized to predict the sensitivity of administering chemotherapeutic agents. Results: Cluster A patients had favorable survival rates. A total of 23 DEari-genes were correlated with the prognosis of HCC. A five-gene (including BIRC5, KITLG, PGF, SPP1, and SHC1) signature-based risk model was constructed. After regrouping the HCC patients by the median score, we could effectively discriminate between them based on the adverse survival outcome, the unique tumor immune microenvironment, and low chemosensitivity. Conclusion: The five-gene signature-based risk score established by ari-genes showed a promising clinical prediction value.
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Affiliation(s)
- Yuan Yang
- The First Clinical Medical College, Lanzhou University, Lanzhou, China.,Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
| | - Guozhi Wu
- The First Clinical Medical College, Lanzhou University, Lanzhou, China.,Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
| | - Qiang Li
- Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
| | - Ya Zheng
- Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
| | - Min Liu
- Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
| | - Lingshan Zhou
- The First Clinical Medical College, Lanzhou University, Lanzhou, China.,Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
| | - Zhaofeng Chen
- Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
| | - Yuping Wang
- Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
| | - Qinghong Guo
- Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
| | - Rui Ji
- Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
| | - Yongning Zhou
- Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.,Key Laboratory for Gastrointestinal Diseases of Gansu Province, Lanzhou University, Lanzhou, China
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15
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Yan S, Fang J, Chen Y, Xie Y, Zhang S, Zhu X, Fang F. Correction to: Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer. BMC Cancer 2021; 21:55. [PMID: 33435868 PMCID: PMC7802239 DOI: 10.1186/s12885-020-07724-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
An amendment to this paper has been published and can be accessed via the original article.
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Affiliation(s)
- Shibai Yan
- Department of Medical Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Zhuang Autonomous Region, China
| | - Juntao Fang
- Laboratory of Experimental Cardiology, Department of Cardiology, University Medical Center Utrecht, Utrecht, 3584, CX, The Netherlands
| | - Yongcai Chen
- Department of Obstetrics and Gynecology, The First People's Hospital of Foshan, 81 Lingnan North Avenue, Foshan, 528000, Guangdong, China
| | - Yong Xie
- Department of Obstetrics and Gynecology, The First People's Hospital of Foshan, 81 Lingnan North Avenue, Foshan, 528000, Guangdong, China
| | - Siyou Zhang
- Department of Obstetrics and Gynecology, The First People's Hospital of Foshan, 81 Lingnan North Avenue, Foshan, 528000, Guangdong, China
| | - Xiaohui Zhu
- Department of Pharmacology, College of Pharmacy, Shenzhen Technology University, Shenzhen, 518118, Guangdong, China.
| | - Feng Fang
- Department of Obstetrics and Gynecology, The First People's Hospital of Foshan, 81 Lingnan North Avenue, Foshan, 528000, Guangdong, China.
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