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Zhao H, Dang R, Zhu Y, Qu B, Sayyed Y, Wen Y, Liu X, Lin J, Li L. Hub genes associated with immune cell infiltration in breast cancer, identified through bioinformatic analyses of multiple datasets. Cancer Biol Med 2022; 19:j.issn.2095-3941.2021.0586. [PMID: 35819135 PMCID: PMC9500228 DOI: 10.20892/j.issn.2095-3941.2021.0586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE The aim of this study was to identify hub genes associated with immune cell infiltration in breast cancer through bioinformatic analyses of multiple datasets. METHODS Nonparametric (NOISeq) and robust rank aggregation-ranked parametric (EdgeR) methods were used to assess robust differentially expressed genes across multiple datasets. Protein-protein interaction network, GO, KEGG enrichment, and sub-network analyses were performed to identify immune-associated hub genes in breast cancer. Immune cell infiltration was evaluated with the CIBERSORT, XCELL, and TIMER methods. The association between the hub gene-based risk signature and survival was determined through Kaplan-Meier survival analysis, multivariate Cox analysis, and a nomogram with external verification. RESULTS We identified 163 robust differentially expressed genes in breast cancer through applying both nonparametric and parametric methods to multiple GEO (n = 2,212) and TCGA (n = 1,045) datasets. Integrated bioinformatic analyses further identified 10 hub genes: CXCL10, CXCL9, CXCL11, SPP1, POSTN, MMP9, DPT, COL1A1, ADAMDEC1, and RGS1. The 10 hub-gene-based risk signature significantly correlated with the prognosis of patients with breast cancer. Moreover, these hub genes were strongly associated with the extent of infiltration of CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and myeloid dendritic cells into breast tumors. CONCLUSIONS Integrated analyses of multiple databases led to the discovery of 10 robust hub genes that together may serve as a risk factor characteristic of the immune microenvironment in breast cancer.
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Affiliation(s)
- Huanyu Zhao
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, China
| | - Ruoyu Dang
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, China
| | - Yipan Zhu
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, China
| | - Baijian Qu
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, China
| | - Yasra Sayyed
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, China
| | - Ying Wen
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, China
| | - Xicheng Liu
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China
| | - Jianping Lin
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, China
| | - Luyuan Li
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, China,Correspondence to: Luyuan Li E-mail:
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Alam MS, Sultana A, Reza MS, Amanullah M, Kabir SR, Mollah MNH. Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies. PLoS One 2022; 17:e0268967. [PMID: 35617355 PMCID: PMC9135200 DOI: 10.1371/journal.pone.0268967] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 05/11/2022] [Indexed: 02/06/2023] Open
Abstract
Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.
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Affiliation(s)
- Md. Shahin Alam
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- * E-mail: (MNHM); (MSA)
| | - Adiba Sultana
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Md. Selim Reza
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Md Amanullah
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Syed Rashel Kabir
- Department of Biochemistry and Molecular Biology, Rajshahi University, Rajshahi, Bangladesh
| | - Md. Nurul Haque Mollah
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- * E-mail: (MNHM); (MSA)
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Yu Y, Sung SK, Lee CH, Ha M, Kang J, Kwon EJ, Kang JW, Kim Y, Kim GH, Heo HJ, Lee H, Kim TW, Lee Y, Myung K, Oh CK, Kim YH. SOCS3 is Related to Cell Proliferation in Neuronal Tissue: An Integrated Analysis of Bioinformatics and Experiments. Front Genet 2021; 12:743786. [PMID: 34646310 PMCID: PMC8502821 DOI: 10.3389/fgene.2021.743786] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/02/2021] [Indexed: 12/13/2022] Open
Abstract
Glioma is the most common primary malignant tumor that occurs in the central nervous system. Gliomas are subdivided according to a combination of microscopic morphological, molecular, and genetic factors. Glioblastoma (GBM) is the most aggressive malignant tumor; however, efficient therapies or specific target molecules for GBM have not been developed. We accessed RNA-seq and clinical data from The Cancer Genome Atlas, the Chinese Glioma Genome Atlas, and the GSE16011 dataset, and identified differentially expressed genes (DEGs) that were common to both GBM and lower-grade glioma (LGG) in three independent cohorts. The biological functions of common DEGs were examined using NetworkAnalyst. To evaluate the prognostic performance of common DEGs, we performed Kaplan-Meier and Cox regression analyses. We investigated the function of SOCS3 in the central nervous system using three GBM cell lines as well as zebrafish embryos. There were 168 upregulated genes and 50 downregulated genes that were commom to both GBM and LGG. Through survival analyses, we found that SOCS3 was the only prognostic gene in all cohorts. Inhibition of SOCS3 using siRNA decreased the proliferation of GBM cell lines. We also found that the zebrafish ortholog, socs3b, was associated with brain development through the regulation of cell proliferation in neuronal tissue. While additional mechanistic studies are necessary, our results suggest that SOCS3 is an important biomarker for glioma and that SOCS3 is related to the proliferation of neuronal tissue.
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Affiliation(s)
- Yeuni Yu
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Busan, South Korea
| | - Soon Ki Sung
- Department of Neurosurgery, Pusan National University Yangsan Hospital, Yangsan, South Korea
| | - Chi Hyung Lee
- Department of Neurosurgery, Pusan National University Yangsan Hospital, Yangsan, South Korea
| | - Mihyang Ha
- Interdisciplinary Program of Genomic Science, Pusan National University, Yangsan, South Korea
| | - Junho Kang
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Busan, South Korea
| | - Eun Jung Kwon
- Interdisciplinary Program of Genomic Science, Pusan National University, Yangsan, South Korea
| | - Ji Wan Kang
- Interdisciplinary Program of Genomic Science, Pusan National University, Yangsan, South Korea
| | - Youngjoo Kim
- Interdisciplinary Program of Genomic Science, Pusan National University, Yangsan, South Korea
| | - Ga Hyun Kim
- Interdisciplinary Program of Genomic Science, Pusan National University, Yangsan, South Korea
| | - Hye Jin Heo
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, South Korea
| | - Hansong Lee
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Busan, South Korea
| | - Tae Woo Kim
- Department of Orthopaedic Surgery, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, South Korea
| | - Yoonsung Lee
- Center for Genomic Integrity, Institute for Basic Science (IBS), Ulsan, South Korea.,Department of Core Research Laboratory, Clinical Research Institute, Kyung Hee University Hospital at Gangdong, Seoul, South Korea
| | - Kyungjae Myung
- Center for Genomic Integrity, Institute for Basic Science (IBS), Ulsan, South Korea
| | - Chang-Kyu Oh
- Center for Genomic Integrity, Institute for Basic Science (IBS), Ulsan, South Korea.,Department of Anatomy, College of Medicine, Inje University, Busan, South Korea
| | - Yun Hak Kim
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Busan, South Korea.,Department of Anatomy, School of Medicine, Pusan National University, Yangsan, South Korea
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Guo W, Liang W, Deng Q, Zou X. A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients. Front Genet 2021; 12:709027. [PMID: 34490038 PMCID: PMC8417828 DOI: 10.3389/fgene.2021.709027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/29/2021] [Indexed: 01/27/2023] Open
Abstract
Accurate survival prediction of breast cancer holds significant meaning for improving patient care. Approaches using multiple heterogeneous modalities such as gene expression, copy number alteration, and clinical data have showed significant advantages over those with only one modality for patient survival prediction. However, existing survival prediction methods tend to ignore the structured information between patients and multimodal data. We propose a multimodal data fusion model based on a novel multimodal affinity fusion network (MAFN) for survival prediction of breast cancer by integrating gene expression, copy number alteration, and clinical data. First, a stack-based shallow self-attention network is utilized to guide the amplification of tiny lesion regions on the original data, which locates and enhances the survival-related features. Then, an affinity fusion module is proposed to map the structured information between patients and multimodal data. The module endows the network with a stronger fusion feature representation and discrimination capability. Finally, the fusion feature embedding and a specific feature embedding from a triple modal network are fused to make the classification of long-term survival or short-term survival for each patient. As expected, the evaluation results on comprehensive performance indicate that MAFN achieves better predictive performance than existing methods. Additionally, our method can be extended to the survival prediction of other cancer diseases, providing a new strategy for other diseases prognosis.
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Affiliation(s)
- Weizhou Guo
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Wenbin Liang
- Key Laboratory of Luminescence Analysis and Molecular Sensing, Ministry of Education, College of Chemistry and Chemical Engineering, Southwest University, Chongqing, China
| | - Qingchun Deng
- Department of Gynecology, The Second Affiliated Hospital of Hainan Medical University, Hainan, China
| | - Xianchun Zou
- College of Computer and Information Science, Southwest University, Chongqing, China
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5
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Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas. Biosci Rep 2021; 41:229248. [PMID: 34308980 PMCID: PMC8314434 DOI: 10.1042/bsr20211280] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 06/28/2021] [Accepted: 07/13/2021] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.
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Mo L, Liu J, Yang Z, Gong X, Meng F, Zou R, Hou L, Fang F. DNAJB4 identified as a potential breast cancer marker: evidence from bioinformatics analysis and basic experiments. Gland Surg 2020; 9:1955-1972. [PMID: 33447546 DOI: 10.21037/gs-20-431] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background Breast cancer (BC) is the leading cause of tumor-related death in women worldwide, but its pathogenesis is not clear. The efficient screening of new therapeutic targets for BC through bioinformatics and biological experimental techniques has become a hot topic in BC research. Methods The bioinformatics method was used to analyze the gene chips and obtain the hub genes, playing an important role in the development of BC. The biological processes (BP) involved in the hub genes were analyzed by Bingo, and the impact of each hub gene on disease-free survival (DFS) and overall survival (OS) in BC patients was evaluated in the Kaplan-Meier Plotter database. The expression of DNAJB4, the hub gene with the greatest degree and having an effect on the prognosis of BC patients, was detected in BC cell lines and clinicopathological specimens. And DNAJB4 was selected for further biological experiments and clinical prognosis verification. Results Ten hub genes including DNAJB4, the greatest degree genes, were found by bioinformatics analysis of BC gene chips. DNAJB4 expressions in both BC cell lines and clinicopathological specimens were detected and the results showed that DNAJB4 was significantly down-regulated in BC cell lines and tissues. After interfering with the expression of DNAJB4, it was found that the invasion and migration ability of MDA-MB-231 cell line was significantly enhanced in vitro. The clinical survival data of BC patients showed that patients with high DNAJB4 expression had longer DFS. Conclusions DNAJB4 may be a tumor suppressor gene in BC as it could regulate invasion and migration of BC cells and its expression level is related to the prognosis of BC patients. Nevertheless, further researches are still necessary to verify its role in BC so as to provide evidences for clinical guidance regarding diagnosis and treatment.
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Affiliation(s)
- Linlong Mo
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Jiayou Liu
- Human Anatomy Department of School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, China
| | - Ziquan Yang
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Xun Gong
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Fanlun Meng
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Rongyang Zou
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Lingmi Hou
- Department of Breast and Thyroid Surgery, Hepatobiliary and pancreatic institution, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Fang Fang
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
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7
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Nguyen QH, Le DH. Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data. Sci Rep 2020; 10:20521. [PMID: 33239644 PMCID: PMC7688645 DOI: 10.1038/s41598-020-77318-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 10/26/2020] [Indexed: 12/18/2022] Open
Abstract
The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene.
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Affiliation(s)
- Quang-Huy Nguyen
- Department of Computational Biomedicine, Vingroup Big Data Institute, Hanoi, Vietnam.,Faculty of Pharmacy, Dainam University, Hanoi, Vietnam
| | - Duc-Hau Le
- Department of Computational Biomedicine, Vingroup Big Data Institute, Hanoi, Vietnam. .,College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam.
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Nguyen QH, Nguyen H, Nguyen T, Le DH. Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer. Front Genet 2020; 11:574661. [PMID: 33193681 PMCID: PMC7594512 DOI: 10.3389/fgene.2020.574661] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 09/23/2020] [Indexed: 12/02/2022] Open
Abstract
The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed. In this study, we performed an integrative analysis of three-omics profiles, CNA, MET, and mRNA expression, in two BRCA patient cohorts (one for discovery and another for validation) – to elucidate those complicated relationships. To this purpose, we first established a set of CNAcor and METcor genes, which had CNA and MET levels significantly correlated (and anti-correlated) with their corresponding expression levels, respectively. Next, to revisit the current classification of BRCA, we performed single and integrated clustering analyses using our clustering method PINSPlus. We then discovered two biologically distinct subgroups that could be an improved and refined classification system for breast cancer patients, which can be validated by a third-party data. Further studies were then performed and realized each-subgroup-specific genes and different interactions between each of the two identified subgroups with the age factor. These findings can show promise as diagnostic and prognostic values in BRCA, and a potential alternative to the PAM50 intrinsic subtypes in the future.
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Affiliation(s)
- Quang-Huy Nguyen
- Department of Computational Biomedicine, Vingroup Big Data Institute, Hanoi, Vietnam.,Faculty of Pharmacy, Dainam University, Hanoi, Vietnam
| | - Hung Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, United States
| | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, United States
| | - Duc-Hau Le
- Department of Computational Biomedicine, Vingroup Big Data Institute, Hanoi, Vietnam.,School of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam
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