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Wang Y, Liu C, Qiao X, Han X, Liu ZP. PKI: A bioinformatics method of quantifying the importance of nodes in gene regulatory network via a pseudo knockout index. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2023; 1866:194911. [PMID: 36804477 DOI: 10.1016/j.bbagrm.2023.194911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 01/09/2023] [Accepted: 01/30/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND Gene regulatory network (GRN) is a model that characterizes the complex relationships between genes and thereby provides an informatics environment to measure the importance of nodes. The evaluation of important nodes in a GRN can effectively refer to their functional implications severing as key players in particular biological processes, such as master regulator and driver gene. Currently, it is mainly based on network topological parameters and focuses only on evaluating a single node individually. However, genes and products play their functions by interacting with each other. It is worth noting that the effects of gene combinations in GRN are not simply additive. Key combinations discovery is of significance in revealing gene sets with important functions. Recently, with the development of single-cell RNA-sequencing (scRNA-seq) technology, we can quantify gene expression profiles of individual cells that provide the potential to identify crucial nodes in gene regulations regarding specific condition, e.g., stem cell differentiation. RESULTS In this paper, we propose a bioinformatics method, called Pseudo Knockout Importance (PKI), to quantify the importance of node and node sets in a specific GRN structure using time-course scRNA-seq data. First, we construct ordinary differential equations to approach the gene regulations during cell differentiation. Then we design gene pseudo knockout experiments and define PKI score evaluation criteria based on the coefficient of determination. The importance of nodes can be described as the influence on the ODE system of removing variables. For key gene combinations, PKI is derived as a combinatorial optimization problem of quantifying the in silico gene knockout effects. CONCLUSIONS Here, we focus our analyses on the specific GRN of embryonic stem cells with time series gene expression profile. To verify the effectiveness and advantage of PKI method, we compare its node importance rankings with other twelve kinds of centrality-based methods, such as degree and Latora closeness. For key node combinations, we compare the results with the method based on minimum dominant set. Moreover, the famous combinations of transcription factors in induced pluripotent stem cell are also employed to verify the vital gene combinations identified by PKI. These results demonstrate the reliability and superiority of the proposed method.
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Affiliation(s)
- Yijuan Wang
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Chao Liu
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Xu Qiao
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Xianhua Han
- Faculty of Science, Yamaguchi University, Yamaguchi 753-8511, Japan
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.
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Li L, Liu ZP. Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models. J Transl Med 2021; 19:514. [PMID: 34930307 PMCID: PMC8686664 DOI: 10.1186/s12967-021-03180-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group. METHODS Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, [Formula: see text]-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts. RESULTS The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ([Formula: see text]). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ([Formula: see text]) in both internal and external validations. CONCLUSIONS The proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.
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Affiliation(s)
- Lingyu Li
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
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3
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Ta HDK, Minh Xuan DT, Tang WC, Anuraga G, Ni YC, Pan SR, Wu YF, Fitriani F, Putri Hermanto EM, Athoillah M, Andriani V, Ajiningrum PS, Wang CY, Lee KH. Novel Insights into the Prognosis and Immunological Value of the SLC35A (Solute Carrier 35A) Family Genes in Human Breast Cancer. Biomedicines 2021; 9:1804. [PMID: 34944621 PMCID: PMC8698499 DOI: 10.3390/biomedicines9121804] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/17/2021] [Accepted: 11/18/2021] [Indexed: 12/12/2022] Open
Abstract
According to statistics 2020, female breast cancer (BRCA) became the most commonly diagnosed malignancy worldwide. Prognosis of BRCA patients is still poor, especially in population with advanced or metastatic. Particular functions of each members of the solute carrier 35A (SLC35A) gene family in human BRCA are still unknown regardless of awareness that they play critical roles in tumorigenesis and progression. Using integrated bioinformatics analyses to identify therapeutic targets for specific cancers based on transcriptomics, proteomics, and high-throughput sequencing, we obtained new information and a better understanding of potential underlying molecular mechanisms. Leveraging BRCA dataset that belongs to The Cancer Genome Atlas (TCGA), which were employed to clarify SLC35A gene expression levels. Then we used a bioinformatics approach to investigate biological processes connected to SLC35A family genes in BRCA development. Beside that, the Kaplan-Meier estimator was leveraged to explore predictive values of SLC35A family genes in BCRA patients. Among individuals of this family gene, expression levels of SLC35A2 were substantially related to poor prognostic values, result from a hazard ratio of 1.3 (with 95 percent confidence interval (95% CI: 1.18-1.44), the p for trend (ptrend) is 3.1 × 10-7). Furthermore, a functional enrichment analysis showed that SLC35A2 was correlated with hypoxia-inducible factor 1A (HIF1A), heat shock protein (HSP), E2 transcription factor (E2F), DNA damage, and cell cycle-related signaling. Infiltration levels observed in specific types of immune cell, especially the cluster of differentiation found on macrophages and neutrophils, were positively linked with SLC35A2 expression in multiple BRCA subclasses (luminal A, luminal B, basal, and human epidermal growth factor receptor 2). Collectively, SLC35A2 expression was associated with a lower recurrence-free survival rate, suggesting that it could be used as a biomarker in treating BRCA.
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Affiliation(s)
- Hoang Dang Khoa Ta
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Academia Sinica, Taipei 11031, Taiwan; (H.D.K.T.); (G.A.)
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (D.T.M.X.); (Y.-C.N.); (S.-R.P.)
| | - Do Thi Minh Xuan
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (D.T.M.X.); (Y.-C.N.); (S.-R.P.)
| | - Wan-Chun Tang
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan;
| | - Gangga Anuraga
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Academia Sinica, Taipei 11031, Taiwan; (H.D.K.T.); (G.A.)
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (D.T.M.X.); (Y.-C.N.); (S.-R.P.)
- Department of Statistics, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya 60234, Indonesia; (F.F.); (E.M.P.H.); (M.A.)
| | - Yi-Chun Ni
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (D.T.M.X.); (Y.-C.N.); (S.-R.P.)
| | - Syu-Ruei Pan
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (D.T.M.X.); (Y.-C.N.); (S.-R.P.)
| | - Yung-Fu Wu
- National Defense Medical Center, School of Medicine, Department of Medical Research, Tri-Service General Hospital, Taipei 11490, Taiwan;
| | - Fenny Fitriani
- Department of Statistics, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya 60234, Indonesia; (F.F.); (E.M.P.H.); (M.A.)
| | - Elvira Mustikawati Putri Hermanto
- Department of Statistics, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya 60234, Indonesia; (F.F.); (E.M.P.H.); (M.A.)
| | - Muhammad Athoillah
- Department of Statistics, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya 60234, Indonesia; (F.F.); (E.M.P.H.); (M.A.)
| | - Vivin Andriani
- Department of Biological Science, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya 60234, Indonesia; (V.A.); (P.S.A.)
| | - Purity Sabila Ajiningrum
- Department of Biological Science, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya 60234, Indonesia; (V.A.); (P.S.A.)
| | - Chih-Yang Wang
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Academia Sinica, Taipei 11031, Taiwan; (H.D.K.T.); (G.A.)
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (D.T.M.X.); (Y.-C.N.); (S.-R.P.)
| | - Kuen-Haur Lee
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Academia Sinica, Taipei 11031, Taiwan; (H.D.K.T.); (G.A.)
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (D.T.M.X.); (Y.-C.N.); (S.-R.P.)
- Cancer Center, Wan Fang Hospital, Taipei Medical University, Taipei 11031, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei 11031, Taiwan
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Identification and Complete Validation of Prognostic Gene Signatures for Human Papillomavirus-Associated Cancers: Integrated Approach Covering Different Anatomical Locations. J Virol 2021; 95:JVI.02354-20. [PMID: 33361419 DOI: 10.1128/jvi.02354-20] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 12/10/2020] [Indexed: 12/13/2022] Open
Abstract
Human papillomavirus (HPV) infects squamous epithelium and is a major cause of cervical cancer (CC) and a subset of head and neck cancers (HNC). Virus-induced tumorigenesis, molecular alterations, and related prognostic markers are expected to be similar between the two cancers, but they remain poorly understood. We present integrated molecular analysis of HPV-associated tumors from TCGA and GEO databases and identify prognostic biomarkers. Analysis of gene expression profiles identified common upregulated genes and pathways of DNA replication and repair in the HPV-associated tumors. We established 34 prognostic gene signatures with a universal cutoff value in TCGA-CC using Elastic Net Cox regression analysis. We were able to externally validate our results in the TCGA-HNC and several GEO data sets, and demonstrated prognostic power in HPV-associated HNC, but not in HPV-negative cancers. The HPV-related prognostic and predictive indicator did not discriminate other cancers, except bladder urothelial carcinoma. These results identify and completely validate a highly selective prognostic system and its cross-usefulness in HPV-associated cancers, regardless of the tumor's anatomical subsite.IMPORTANCE Persistent infection with high-risk HPV interferes with cell function regulation and causes cell mutations, which accumulate over the long term and eventually develop into cancer. Results of pathway enrichment analysis presumably showed this accumulation of intracellular damage during the chronic HPV-infected state. We used highly advanced statistical methods to identify the most appropriate genes and coefficients and developed the HPV-related prognostic and predictive indicator (HPPI) risk scoring system. We applied the same cutoff value to training and validation sets and demonstrated good prognostic performance in both data sets, and confirmed a consistent trend in external validation. Moreover, HPPI presented significant validation results for bladder cancer suspected to be related to HPV. This suggested that our risk scoring system based on the prognostic gene signature could play an important role in the development of treatment strategies for patients with HPV-related cancer.
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Hernández-Lemus E, Martínez-García M. Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics. Front Oncol 2021; 10:605680. [PMID: 33520715 PMCID: PMC7841291 DOI: 10.3389/fonc.2020.605680] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 11/24/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors-let us call that the silver bullet approach to cancer therapeutics-to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns-we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases "shrapnel gunshots" may become more effective than "silver bullets". Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.
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Affiliation(s)
- Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Mireya Martínez-García
- Sociomedical Research Unit, National Institute of Cardiology “Ignacio Chávez”, Mexico City, Mexico
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Pak K, Oh SO, Goh TS, Heo HJ, Han ME, Jeong DC, Lee CS, Sun H, Kang J, Choi S, Lee S, Kwon EJ, Kang JW, Kim YH. A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study. J Med Internet Res 2020; 22:e16084. [PMID: 32369034 PMCID: PMC7238095 DOI: 10.2196/16084] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 11/13/2019] [Accepted: 03/25/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. OBJECTIVE Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. METHODS We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). RESULTS Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. CONCLUSIONS Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.
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Affiliation(s)
- Kyoungjune Pak
- Department of Nuclear Medicine, Pusan National University Hospital, Busan, Republic of Korea
| | - Sae-Ock Oh
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Tae Sik Goh
- Department of Orthopaedic Surgery, Pusan National University Hospital, Busan, Republic of Korea
| | - Hye Jin Heo
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Myoung-Eun Han
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Dae Cheon Jeong
- Deloitte Analytics Group, Deloitte Consulting LLC, Seoul, Republic of Korea
| | - Chi-Seung Lee
- Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.,Department of Biomedical Engineering, School of Medicine, Pusan National University, Busan, Republic of Korea
| | - Hokeun Sun
- Department of Statistics, Pusan National University, Busan, Republic of Korea
| | - Junho Kang
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Suji Choi
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Soohwan Lee
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Eun Jung Kwon
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Ji Wan Kang
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Yun Hak Kim
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea.,Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea
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Oh CK, Ha M, Han ME, Heo HJ, Myung K, Lee Y, Oh SO, Kim YH. FAM213A is linked to prognostic significance in acute myeloid leukemia through regulation of oxidative stress and myelopoiesis. Hematol Oncol 2020; 38:381-389. [PMID: 32124993 DOI: 10.1002/hon.2728] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 01/27/2020] [Accepted: 02/08/2020] [Indexed: 12/11/2022]
Abstract
Accurate prediction of malignancies is important in choosing therapeutic strategies. Although there are many genetic and cytogenetic prognostic factors for acute myeloid leukemia (AML), prognosis is difficult to predict because of the heterogeneity of AML. Prognostic factors, including messenger RNA (mRNA) expression, have been determined for other malignancies, but not for AML. A total of 402 patients from The Cancer Genome Atlas, GSE12417 (GPL96, 97), and GSE12417 (GPL570) were included in this study. In Kaplan-Meier curve analyses, high expression of family with sequence similarity 213 member A (FAM213A), which activates antioxidant proteins, was associated with worse prognosis of AML. Similar to the results of the survival curve, C-indices and area under the curve values were high. Current prognostic factors of AML, unlike those of other cancers, do not take mRNA expression into consideration. Thus, the development of mRNA-based prognostic factors would be beneficial for accurate prediction of the survival of AML patients. Additionally, in vivo validation using zebrafish revealed that fam213a is important for myelopoiesis at the developmental stage and is a negative regulator of the p53 tumor suppressor gene. The findings implicate fam213a as a novel prognostic factor for AML patients.
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Affiliation(s)
- Chang-Kyu Oh
- Center for Genomic Integrity, Institute for Basic Science (IBS), Ulsan, Republic of Korea
| | - Mihyang Ha
- Interdisplinary Program of Genomic Science, Pusan National University, Yangsan, Republic of Korea
| | - Myoung-Eun Han
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Hye J Heo
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Kyungjae Myung
- Center for Genomic Integrity, Institute for Basic Science (IBS), Ulsan, Republic of Korea
| | - Yoonsung Lee
- Center for Genomic Integrity, Institute for Basic Science (IBS), Ulsan, Republic of Korea
| | - Sae-Ock Oh
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Yun Hak Kim
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea.,Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.,Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea
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Ha M, Kim J, Park SM, Hong CM, Han ME, Song P, Kang CD, Lee D, Kim YH, Hur J, Oh SO. Prognostic Role of Zinc Finger Homeobox 4 in Ovarian Serous Cystadenocarcinoma. Genet Test Mol Biomarkers 2020; 24:145-149. [PMID: 32105524 DOI: 10.1089/gtmb.2019.0185] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Introduction: The zinc finger homeobox 4 (ZFHX4) protein is a crucial molecular regulator of tumor-initiating stem cell-like functions. Objective: This study aimed to determine the role of ZFHX4 in the progression of ovarian serous cystadenocarcinoma (OSC). Methods: Differential gene expression ZFHX4 among low-stage (stages I and II), high-stage (stages III and IV), low-grade (grades I and II), and high-grade (grades III and IV) OSC patients was identified using four independent cohorts from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We compared ZFHX4 expression as a prognostic factor using Kaplan-Meier survival curves, multivariate analysis, the time-dependent area under the curve (AUC) of Uno's C-index, and the AUC of the receiver operating characteristics at 4 years post diagnosis. Results: ZFHX4 gene expression in high-stage tumors is significantly higher than in low-stage tumors (TCGA, p = 0.007; GSE9891, p = 0.001). A Kaplan-Meier analysis revealed that elevated expression of ZFHX4 was associated with a poor prognosis in OSC patients for all cohorts, regardless of stage and grade (TCGA, p = 1e-04; GSE9891, p = 0.0044; GSE13876, p = 0.00078; GSE26712, p = 0.039). Analysis of C-indices and the area under the receiver operating characteristic curve further supported this result (C-index: TCGA, 0.599; GSE9891, 0.642; GSE13876, 0.585; GSE26712, 0.597). Moreover, univariate and multivariate Cox hazards analyses confirmed the prognostic significance of ZFHX4 levels. Conclusion: Collectively, these findings suggest that ZFHX4 is a prognostic factor for OSC.
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Affiliation(s)
- Mihyang Ha
- Department of Anatomy, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Jayoung Kim
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Su Min Park
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Chae Mi Hong
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Myoung-Eun Han
- Department of Anatomy, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Parkyong Song
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Chi-Dug Kang
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea.,Department of Biochemistry, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Dongjun Lee
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Yun Hak Kim
- Department of Anatomy, Biomedical Research Institute, Pusan National University School of Medicine, Yangsan, Republic of Korea.,Department of Biomedical Informatics, Biomedical Research Institute, Pusan National University School of Medicine, Yangsan, Republic of Korea.,Biomedical Research Institute, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Jin Hur
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Sae-Ock Oh
- Department of Anatomy, Pusan National University School of Medicine, Yangsan, Republic of Korea
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Kim K, Sun H. Incorporating genetic networks into case-control association studies with high-dimensional DNA methylation data. BMC Bioinformatics 2019; 20:510. [PMID: 31640538 PMCID: PMC6805595 DOI: 10.1186/s12859-019-3040-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 08/21/2019] [Indexed: 12/23/2022] Open
Abstract
Background In human genetic association studies with high-dimensional gene expression data, it has been well known that statistical selection methods utilizing prior biological network knowledge such as genetic pathways and signaling pathways can outperform other methods that ignore genetic network structures in terms of true positive selection. In recent epigenetic research on case-control association studies, relatively many statistical methods have been proposed to identify cancer-related CpG sites and their corresponding genes from high-dimensional DNA methylation array data. However, most of existing methods are not designed to utilize genetic network information although methylation levels between linked genes in the genetic networks tend to be highly correlated with each other. Results We propose new approach that combines data dimension reduction techniques with network-based regularization to identify outcome-related genes for analysis of high-dimensional DNA methylation data. In simulation studies, we demonstrated that the proposed approach overwhelms other statistical methods that do not utilize genetic network information in terms of true positive selection. We also applied it to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. Conclusions The proposed variable selection approach can utilize prior biological network information for analysis of high-dimensional DNA methylation array data. It first captures gene level signals from multiple CpG sites using data a dimension reduction technique and then performs network-based regularization based on biological network graph information. It can select potentially cancer-related genes and genetic pathways that were missed by the existing methods. Electronic supplementary material The online version of this article (10.1186/s12859-019-3040-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kipoong Kim
- Department of Statistic, Pusan National University, Busan, 46241, Korea
| | - Hokeun Sun
- Department of Statistic, Pusan National University, Busan, 46241, Korea.
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10
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Lee D, Ha M, Hong CM, Kim J, Park SM, Park D, Sohn DH, Shin HJ, Yu HS, Kim CD, Kang CD, Han ME, Oh SO, Kim YH. GABRQ expression is a potential prognostic marker for patients with clear cell renal cell carcinoma. Oncol Lett 2019; 18:5731-5738. [PMID: 31788046 PMCID: PMC6865077 DOI: 10.3892/ol.2019.10960] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 06/27/2019] [Indexed: 01/08/2023] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. Novel biomarkers of ccRCC may provide crucial information on tumor features and prognosis. The present study aimed to determine whether the expression of γ-aminobutyric acid (GABA) A receptor subunit θ (GABRQ) could serve as a novel prognostic marker of ccRCC. GABA is the main inhibitory neurotransmitter in the brain that activates the receptor GABAA, which is comprised of three subunit isoforms: GABRA3, GABRB3 and GABRQ. A recent study reported that GABRQ is involved in the initiation and progression of hepatocellular carcinoma; however, the role of GABRQ in ccRCC remains unknown. In the present study, clinical and transcriptomic data were obtained from cohorts of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Differential GABRQ expression levels among early (TI and II), late (TIII and IV), nonmetastatic (M0) and metastatic (M1, primary tumor) stages of ccRCC samples were then identified. Furthermore, the use of GABRQ as a prognostic gene was analyzed using Uno's C-index based on the time-dependent area under the curve (AUC), the AUC of the receiver operating characteristic curve at 5 years, the Kaplan-Meier survival curve and multivariate analysis. The survival curve analysis revealed that low GABRQ mRNA expression was significantly associated with a poor prognosis of ccRCC (P<0.001 and P=0.0012 for TCGA and ICGC data, respectively). In addition, analyses of the C-index and AUC values further supported this discriminatory power. Furthermore, the prognostic value of GABRQ mRNA expression was confirmed by multivariate Cox regression analysis. Taken together, these results suggested that GABRQ mRNA expression may be considered as a novel prognostic biomarker of ccRCC.
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Affiliation(s)
- Dongjun Lee
- Department of Convergence Medical Science, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Mihyang Ha
- Department of Anatomy, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Chae Mi Hong
- Department of Convergence Medical Science, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Jayoung Kim
- Department of Convergence Medical Science, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Su Min Park
- Department of Convergence Medical Science, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Dongsu Park
- Department of Molecular Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.,Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.,Center for Skeletal Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Dong Hyun Sohn
- Department of Microbiology and Immunology, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Ho Jin Shin
- Department of Hematology-Oncology, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Hak-Sun Yu
- Department of Parasitology, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Chi Dae Kim
- Department of Pharmacology, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Chi-Dug Kang
- Department of Convergence Medical Science, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea.,Department of Biochemistry, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Myoung-Eun Han
- Department of Anatomy, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Sae-Ock Oh
- Department of Anatomy, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea
| | - Yun Hak Kim
- Department of Anatomy, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea.,Department of Biomedical Informatics, Pusan National University School of Medicine, Yangsan, Gyeongsangnam-do 50612, Republic of Korea.,Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Republic of Korea
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11
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Ha M, Kim DW, Kim J, Hong CM, Park SM, Woo IA, Kim MY, Koo H, Namkoong J, Kim J, Han ME, Song P, Hur J, Kang CD, Kim YH, Lee D, Oh SO. Prognostic role of the beta-2 adrenergic receptor in clear cell renal cell carcinoma. Anim Cells Syst (Seoul) 2019; 23:365-369. [PMID: 31700702 PMCID: PMC6830282 DOI: 10.1080/19768354.2019.1658638] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/31/2019] [Accepted: 08/14/2019] [Indexed: 12/18/2022] Open
Abstract
The beta-2 adrenergic receptor (ADRB2) regulates the proliferation, apoptosis, angiogenesis, migration, and metastasis of cancer cells. However, its function in the progression of clear cell renal cell carcinoma (ccRCC) is unknown. Here, we report that ADRB2 can be a novel prognostic factor for patients with ccRCC. The differential expression of ADRB2 in low-stage (stages I and II), high-stage (stages III and IV), low-grade (grades I and II), and high-grade (grades III and IV) ccRCC was identified in cohorts of patients from The Cancer Genome Atlas and the International Cancer Genome Consortium. We evaluated ADRB2 expression as a prognostic factor using the Kaplan-Meier survival curve, multivariate analysis, time-dependent area under the curve (AUC) of Uno’s C-index, and AUC of the receiver operating characteristics (ROC) at five years. Kaplan-Meier analysis revealed that reduced ADRB2 expression is associated with poor prognosis in ccRCC patients. Analysis of C-indices and AUC-ROC further confirmed this result. Moreover, multivariate analysis confirmed the prognostic significance of ADRB2 expression. Collectively, these findings suggest that ADRB2 is a potential prognostic factor for ccRCC.
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Affiliation(s)
- Mihyang Ha
- Department of Anatomy, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Dong Woo Kim
- Department of Premedicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Jayoung Kim
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Chae Mi Hong
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Su Min Park
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - In Ae Woo
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Min Yong Kim
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Hyunjun Koo
- Department of Premedicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Jin Namkoong
- Department of Premedicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Jaehyun Kim
- Department of Premedicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Myoung-Eun Han
- Department of Anatomy, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Parkyong Song
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Jin Hur
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Chi-Dug Kang
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Yun Hak Kim
- Department of Anatomy, Department of Biomedical Informatics, and Biomedical Research Institute, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Dongjun Lee
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Sae-Ock Oh
- Department of Anatomy, Pusan National University School of Medicine, Yangsan, Republic of Korea
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12
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Goh TS, Lee JS, Il Kim J, Park YG, Pak K, Jeong DC, Oh SO, Kim YH. Prognostic scoring system for osteosarcoma using network-regularized high-dimensional Cox-regression analysis and potential therapeutic targets. J Cell Physiol 2019; 234:13851-13857. [PMID: 30604867 DOI: 10.1002/jcp.28065] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 12/18/2018] [Indexed: 07/20/2023]
Abstract
With the recent emphasis on the importance of personalized genomic medicine, studies have performed prognostic stratification using gene signatures in cancers. However, these studies have not considered gene networks with clinical data. Therefore, this study aimed to develop a novel prognostic score using grouped variable selection for patients with osteosarcoma. We assessed messenger RNA (mRNA) expression and clinical data from Gene Expression Omnibus to develop a novel prognostic scoring system for patients with osteosarcoma. Variable selection using Network-Regularized high-dimensional Cox-regression analysis with information regarding gene networks obtained from six large pathway databases was performed. We determined the risk score on the linear combination of regression coefficients and mRNA expression values. Log-rank test, UNO's c-index, and area under the curve (AUC) values were determined to evaluate the discriminatory power between the low- and high-risk groups. A recently reported next-generation Connectivity Map was used to identify future therapeutic targets for osteosarcoma. Our novel model had significantly high discriminatory power in predicting overall survival. An optimal c-index of 0.967 was obtained and time-dependent receiver operating characteristic analysis revealed an acceptable predictive value of AUC between 0.953 and 1.000. Knockdown of BACE2 or ING2 and linifanib treatment may improve the prognosis of patients with osteosarcoma. Herein, this novel prognostic scoring system would not only facilitate a more accurate prediction of patient prognosis, but also contribute to the selection of suitable therapeutic alternatives for osteosarcoma patients.
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Affiliation(s)
- Tae Sik Goh
- Department of Orthopaedic Surgery and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Jung Sub Lee
- Department of Orthopaedic Surgery and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Jeung Il Kim
- Department of Orthopaedic Surgery and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Yong Geon Park
- Department of Orthopaedic Surgery and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Kyoungjune Pak
- Department of Nuclear Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Dae Cheon Jeong
- Deloitte Analytics Group, Deloitte Consulting LLC, Seoul, Republic of Korea
| | - Sae-Ock Oh
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Gyeongnam, Republic of Korea
| | - Yun Hak Kim
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Gyeongnam, Republic of Korea
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea
- Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
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13
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Ha M, Moon H, Choi D, Kang W, Kim JH, Lee KJ, Park D, Kang CD, Oh SO, Han ME, Kim YH, Lee D. Prognostic Role of TMED3 in Clear Cell Renal Cell Carcinoma: A Retrospective Multi-Cohort Analysis. Front Genet 2019; 10:355. [PMID: 31057605 PMCID: PMC6478656 DOI: 10.3389/fgene.2019.00355] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 04/02/2019] [Indexed: 12/02/2022] Open
Abstract
Transmembrane p24 trafficking protein 3 (TMED3) is a metastatic suppressor in colon cancer and hepatocellular carcinoma. However, its function in the progression of clear cell renal cell carcinoma (ccRCC) is unknown. Here, we report that TMED3 could be a new prognostic marker for ccRCC. Patient data were extracted from cohorts in the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Differential expression of TMED3 was observed between the low stage (Stage I and II) and high stage (Stage III and IV) patients in the TCGA and ICGC cohorts and between the low grade (Grade I and II) and high grade (Grade III and IV) patients in the TCGA cohort. Further, we evaluated TMED3 expression as a prognostic gene using Kaplan-Meier survival analysis, multivariate analysis, the time-dependent area under the curve (AUC) of Uno’s C-index, and the AUC of the receiver operating characteristics at 5 years. The Kaplan-Meier analysis revealed that TMED3 overexpression was associated with poor prognosis for ccRCC patients. Analysis of the C-indices and area under the receiver operating characteristic curve further supported this. Multivariate analysis confirmed the prognostic significance of TMED3 expression levels (P = 0.005 and 0.006 for TCGA and ICGC, respectively). Taken together, these findings demonstrate that TMED3 is a potential prognostic factor for ccRCC.
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Affiliation(s)
- Mihyang Ha
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, South Korea
| | - Hwan Moon
- Department of Premedicine, School of Medicine, Pusan National University, Yangsan, South Korea
| | - Dongwook Choi
- Division of Drug Process Development, New Drug Development Center, Osong Medical Innovation Foundation, Cheongju, South Korea
| | - Wonmo Kang
- Division of Drug Process Development, New Drug Development Center, Osong Medical Innovation Foundation, Cheongju, South Korea
| | - Ji-Hong Kim
- Division of Drug Process Development, New Drug Development Center, Osong Medical Innovation Foundation, Cheongju, South Korea
| | - Keon Jin Lee
- Division of Drug Process Development, New Drug Development Center, Osong Medical Innovation Foundation, Cheongju, South Korea
| | - Dongsu Park
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States.,Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, United States.,Center for Skeletal Medicine and Biology, Baylor College of Medicine, Houston, TX, United States
| | - Chi-Dug Kang
- Department of Biochemistry, Pusan National University School of Medicine, Yangsan, South Korea.,Department of Convergence Medical Science, Pusan National University School of Medicine, Yangsan, South Korea
| | - Sae-Ock Oh
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, South Korea
| | - Myoung-Eun Han
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, South Korea
| | - Yun Hak Kim
- Department of Anatomy, Biomedical Research Institute, School of Medicine, Pusan National University, Yangsan, South Korea.,Department of Biomedical Informatics, Biomedical Research Institute, School of Medicine, Pusan National University, Yangsan, South Korea
| | - Dongjun Lee
- Department of Convergence Medical Science, Pusan National University School of Medicine, Yangsan, South Korea
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14
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Goh TS, Ha M, Lee JS, Jeong DC, Jung ES, Han ME, Kim YH, Oh SO. Prognostic significance of EIF4G1 in patients with pancreatic ductal adenocarcinoma. Onco Targets Ther 2019; 12:2853-2859. [PMID: 31043796 PMCID: PMC6472433 DOI: 10.2147/ott.s202101] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Background Advances in genomics have greatly improved the survival rate in cancer patients. However, due to genetic heterogeneity, pancreatic ductal adenocarcinoma (PDAC) is still difficult to diagnose early, and its survival rate is extremely low. Therefore, we identified biomarkers that predict the prognosis of PDAC patients using independent cohort data. Materials and methods To develop a novel prognostic biomarker, we used the gene expression and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Kaplan-Meier survival curve using median values of genes as cutoff showed that EIF4G1 was the only statistically significant gene in the 3 cohorts. We analyzed the prognostic significance of EIF4G1 using the time-dependent area under the curve (AUC) of Uno's C-index, the AUC value of the receiver operating characteristics (ROC) at 3 years, and multivariate Cox analysis. We also compared EIF4G1 levels between tumors and matched non-tumor tissues. Results EIF4G1 is the only prognostic gene in patients with PDAC, which was selected by Kaplan-Meier survival analysis. The survival curve showed that high expression of EIF4G1 was associated with poor prognosis of PDAC with a good discriminative ability in 3 independent cohorts. The risk stratifying ability of EIF4G1 was demonstrated by analyzing C-indices and AUC values. Multivariate Cox regression confirmed its prognostic significance. EIF4G1 expression was significantly higher in PDAC tissues than in the matched normal tissues. Conclusion EIF4G1 could be used as a novel prognostic marker for PDAC and to determine suitable treatment options.
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Affiliation(s)
- Tae Sik Goh
- Department of Orthopaedic Surgery and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Mihyang Ha
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea,
| | - Jung Sub Lee
- Department of Orthopaedic Surgery and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Dae Cheon Jeong
- Deloitte Analytics Group, Deloitte Consulting LLC, Seoul, Republic of Korea
| | - Eun Sang Jung
- Department of Bioenvironmental Energy, College of Life & Resources Science, Pusan National University, Miryang, Republic of Korea
| | - Myoung-Eun Han
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea,
| | - Yun Hak Kim
- Department of Anatomy and Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea, .,Biomedical Research Institute, Pusan National University, Busan, Republic of Korea,
| | - Sae-Ock Oh
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea,
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15
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Pak K, Kim YH, Suh S, Goh TS, Jeong DC, Kim SJ, Kim IJ, Han ME, Oh SO. Development of a risk scoring system for patients with papillary thyroid cancer. J Cell Mol Med 2019; 23:3010-3015. [PMID: 30729678 PMCID: PMC6433682 DOI: 10.1111/jcmm.14208] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 01/02/2019] [Accepted: 01/16/2019] [Indexed: 12/30/2022] Open
Abstract
As the importance of personalized therapeutics in aggressive papillary thyroid cancer (PTC) increases, accurate risk stratification is required. To develop a novel prognostic scoring system for patients with PTC (n = 455), we used mRNA expression and clinical data from The Cancer Genome Atlas. We performed variable selection using Network-Regularized high-dimensional Cox-regression with gene network from pathway databases. The risk score was calculated using a linear combination of regression coefficients and mRNA expressions. The risk score and clinical variables were assessed by several survival analyses. The risk score showed high discriminatory power for the prediction of event-free survival as well as the presence of metastasis. In multivariate analysis, the risk score and presence of metastasis were significant risk factors among the clinical variables that were examined together. In the current study, we developed a risk scoring system that will help to identify suitable therapeutic options for PTC.
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Affiliation(s)
- Kyoungjune Pak
- Department of Nuclear Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Yun Hak Kim
- Department of Anatomy and Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea.,Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Sunghwan Suh
- Department of Internal Medicine, Dong-A University College of Medicine, Busan, Republic of Korea
| | - Tae Sik Goh
- Department of Orthopaedic Surgery and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Dae Cheon Jeong
- Deloitte Analytics Group, Deloitte Consulting LLC, Republic of Korea
| | - Seong Jang Kim
- Department of Nuclear Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - In Joo Kim
- Department of Nuclear Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Myoung-Eun Han
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Sae-Ock Oh
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea
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16
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SAC3D1: a novel prognostic marker in hepatocellular carcinoma. Sci Rep 2018; 8:15608. [PMID: 30353105 PMCID: PMC6199250 DOI: 10.1038/s41598-018-34129-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 10/07/2018] [Indexed: 02/06/2023] Open
Abstract
Centrosome-associated proteins are recognized as prognostic factors in many cancers because centrosomes are critical structures for the cell cycle progression and genomic stability. SAC3D1, however, is associated with centrosome abnormality, although its prognostic potential has not been evaluated in hepatocellular carcinoma (HCC). In this study, 3 independent cohorts (GSE10186, n = 80; TCGA, n = 330 and ICGC, n = 237) were used to assess SAC3D1 as a biomarker, which demonstrated SAC3D1 overexpression in HCC tissues when compared to the matched normal tissues. Kaplan-Meier survival analysis also showed that its overexpression was associated with poor prognosis of HCC with good discriminative ability in 3 independent cohorts (GSE10186, P = 0.00469; TCGA, P = 0.0000413 and ICGC, P = 0.0000114). Analysis of the C-indices and AUC values further supported its discriminative ability. Finally, multivariate analysis confirmed its prognostic significance (GSE10186, P = 0.00695; TCGA, P = 0.0000289 and ICGC, P = 0.0000651). These results suggest a potential of SAC3D1 as a biomarker for HCC.
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17
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Cho SH, Pak K, Jeong DC, Han M, Oh S, Kim YH. The AP2M1 gene expression is a promising biomarker for predicting survival of patients with hepatocellular carcinoma. J Cell Biochem 2018; 120:4140-4146. [DOI: 10.1002/jcb.27699] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 08/27/2018] [Indexed: 12/31/2022]
Affiliation(s)
- Sung Hwan Cho
- Department of Surgery Pusan National University Yangsan Hospital Yangsan Korea
| | - Kyoungjune Pak
- Department of Nuclear Medicine and Biomedical Research Institute Pusan National University Hospital Busan Korea
| | | | - Myoung‐Eun Han
- Department of Anatomy School of medicine, Pusan National University Yangsan Korea
| | - Sae‐Ock Oh
- Department of Anatomy School of medicine, Pusan National University Yangsan Korea
| | - Yun Hak Kim
- Department of Anatomy School of medicine, Pusan National University Yangsan Korea
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18
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Ha M, Han M, Kim J, Jeong DC, Oh S, Kim YH. Prognostic role of
TPD52
in acute myeloid leukemia: A retrospective multicohort analysis. J Cell Biochem 2018; 120:3672-3678. [DOI: 10.1002/jcb.27645] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2018] [Accepted: 08/14/2018] [Indexed: 01/08/2023]
Affiliation(s)
- Mihyang Ha
- Department of Anatomy School of Medicine, Pusan National University Yangsan Republic of Korea
| | - Myoung‐Eun Han
- Department of Anatomy School of Medicine, Pusan National University Yangsan Republic of Korea
| | - Ji‐Young Kim
- Department of Anatomy School of Medicine, Pusan National University Yangsan Republic of Korea
| | - Dae Cheon Jeong
- Deloitte Analytics Group, Deloitte Consulting LLC Seoul Republic of Korea
| | - Sae‐Ock Oh
- Department of Anatomy School of Medicine, Pusan National University Yangsan Republic of Korea
| | - Yun Hak Kim
- Department of Anatomy School of Medicine, Pusan National University Yangsan Republic of Korea
- BEER, Busan Society of Evidence‐Based Medicine and Research Busan Republic of Korea
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