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An Y, Lu W, Li S, Lu X, Zhang Y, Han D, Su D, Jia J, Yuan J, Zhao B, Tu M, Li X, Wang X, Fang N, Ji S. Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients. Discov Oncol 2023; 14:234. [PMID: 38112859 PMCID: PMC10730790 DOI: 10.1007/s12672-023-00847-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
Prostate cancer (PC) is one of the most common cancers in men and becoming the second leading cause of cancer fatalities. At present, the lack of effective strategies for prognosis of PC patients is still a problem to be solved. Therefore, it is significant to identify potential gene signatures for PC patients' prognosis. Here, we summarized 71 different prognostic gene signatures for PC and concluded 3 strategies for signature construction after extensive investigation. In addition, 14 genes frequently appeared in 71 different gene signatures, which enriched in mitotic and cell cycle. This review provides extensive understanding and integrated analysis of current prognostic signatures of PC, which may help researchers to construct gene signatures of PC and guide future clinical treatment.
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Affiliation(s)
- Yang An
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China.
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China.
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China.
| | - Wenyuan Lu
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Shijia Li
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Xiaoyan Lu
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Yuanyuan Zhang
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Dongcheng Han
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Dingyuan Su
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Jiaxin Jia
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Jiaxin Yuan
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Binbin Zhao
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Mengjie Tu
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Xinyu Li
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Xiaoqing Wang
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Na Fang
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China.
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China.
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China.
| | - Shaoping Ji
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China.
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China.
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China.
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Tu S, Zuo J. Systematic single cell RNA sequencing analysis reveals unique transcriptional regulatory networks of Atoh1-mediated hair cell conversion in adult mouse cochleae. PLoS One 2023; 18:e0284685. [PMID: 38079436 PMCID: PMC10712870 DOI: 10.1371/journal.pone.0284685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 04/05/2023] [Indexed: 12/18/2023] Open
Abstract
Regeneration of mammalian cochlear hair cells (HCs) by modulating molecular pathways or transcription factors is a promising approach to hearing restoration; however, immaturity of the regenerated HCs in vivo remains a major challenge. Here, we analyzed a single cell RNA sequencing (scRNA-seq) dataset during Atoh1-induced supporting cell (SC) to hair cell (HC) conversion in adult mouse cochleae (Yamashita et al. (2018)) using multiple high-throughput sequencing analytical tools (WGCNA, SCENIC, ARACNE, and VIPER). Instead of focusing on differentially expressed genes, we established independent expression modules and confirmed the existence of multiple conversion stages. Gene regulatory network (GRN) analysis uncovered previously unidentified key regulators, including Nhlh1, Lhx3, Barhl1 and Nfia, that guide converted HC differentiation. Comparison of the late-stage converted HCs with the scRNA-seq data from neonatal mouse cochleae (Kolla et al. (2020)) revealed that they closely resemble postnatal day 1 wild-type OHCs, in contrast to other developmental stages. Using ARACNE and VIPER, we discovered multiple key regulators likely to promote conversion to a more mature OHC-like state, including Zbtb20, Nfia, Zmiz1, Gm14418, Bhlhe40, Six2, Fosb and Klf9. Our findings provide insights into the regulation of HC regeneration in adult mammalian cochleae in vivo and demonstrate an approach for analyzing GRNs in large scRNA-seq datasets.
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Affiliation(s)
- Shu Tu
- Department of Biomedical Sciences, Creighton University School of Medicine, Omaha, NE, United States of America
| | - Jian Zuo
- Department of Biomedical Sciences, Creighton University School of Medicine, Omaha, NE, United States of America
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Zhao H, Wu T, Luo Z, Huang Q, Zhu S, Li C, Zhang Z, Zhang J, Zeng J, Zhang Y. Construction and validation of a fatty acid metabolism-related gene signature for predicting prognosis and therapeutic response in patients with prostate cancer. PeerJ 2023; 11:e14854. [PMID: 36778142 PMCID: PMC9910187 DOI: 10.7717/peerj.14854] [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: 11/17/2022] [Accepted: 01/13/2023] [Indexed: 02/09/2023] Open
Abstract
Background Reprogramming of fatty acid metabolism is a newly-identified hallmark of malignancy. However, no studies have systematically investigated the fatty acid metabolism related-gene set in prostate cancer (PCa). Methods A cohort of 381 patients with gene expression and clinical data from The Cancer Genome Atlas was used as the training set, while another cohort of 90 patients with PCa from GEO (GSE70769) was used as the validation set. Differentially expressed fatty acid metabolism-related genes were subjected to least absolute shrinkage and selection operator (LASSO)-Cox regression to establish a fatty acid metabolism-related risk score. Associations between the risk score and clinical characteristics, immune cell infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) score, and response to chemotherapy were analyzed. Finally, the expression level of genes included in the model was validated using real-time PCR. Results A prognostic risk model based on five fatty acid metabolism related genes (ALDH1A1, CPT1B, CA2, CROT, and NUDT19) were constructed. Tumors with higher risk score were associated with larger tumor size, lymph node involvement, higher Gleason score, and poorer biochemical recurrence (BCR)-free survival. Furthermore, the high- and low-risk tumors exhibited distinct immune cell infiltration features and immune-related pathway activation. High-risk tumors were associated with favorable response to immunotherapy as indicated by high TMB and low TIDE score, but poor response to bicalutamide and docetaxel chemotherapy. Conclusion This study established a fatty acid metabolism-related gene signature which was predictive of BCR and response to chemotherapy and immunotherapy, providing a novel therapeutic biomarker for PCa.
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Affiliation(s)
- Hongjun Zhao
- Department of Urology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Tong Wu
- Department of Urology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Zehao Luo
- Department of Urology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Qinyao Huang
- Department of Urology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Sihua Zhu
- Department of Urology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Chunling Li
- Department of Urology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Zubing Zhang
- Department of Urology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Jiahao Zhang
- Department of Urology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Jianwen Zeng
- Department of Urology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Yuying Zhang
- Shenzhen Longhua Maternity and Child Healthcare Hospital, Shenzhen, China
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Dovrolis N, Filidou E, Tarapatzi G, Kokkotis G, Spathakis M, Kandilogiannakis L, Drygiannakis I, Valatas V, Arvanitidis K, Karakasiliotis I, Vradelis S, Manolopoulos VG, Paspaliaris V, Bamias G, Kolios G. Co-expression of fibrotic genes in inflammatory bowel disease; A localized event? Front Immunol 2022; 13:1058237. [PMID: 36632136 PMCID: PMC9826764 DOI: 10.3389/fimmu.2022.1058237] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/08/2022] [Indexed: 12/27/2022] Open
Abstract
Introduction Extracellular matrix turnover, a ubiquitous dynamic biological process, can be diverted to fibrosis. The latter can affect the intestine as a serious complication of Inflammatory Bowel Diseases (IBD) and is resistant to current pharmacological interventions. It embosses the need for out-of-the-box approaches to identify and target molecular mechanisms of fibrosis. Methods and results In this study, a novel mRNA sequencing dataset of 22 pairs of intestinal biopsies from the terminal ileum (TI) and the sigmoid of 7 patients with Crohn's disease, 6 with ulcerative colitis and 9 control individuals (CI) served as a validation cohort of a core fibrotic transcriptomic signature (FIBSig), This signature, which was identified in publicly available data (839 samples from patients and healthy individuals) of 5 fibrotic disorders affecting different organs (GI tract, lung, skin, liver, kidney), encompasses 241 genes and the functional pathways which derive from their interactome. These genes were used in further bioinformatics co-expression analyses to elucidate the site-specific molecular background of intestinal fibrosis highlighting their involvement, particularly in the terminal ileum. We also confirmed different transcriptomic profiles of the sigmoid and terminal ileum in our validation cohort. Combining the results of these analyses we highlight 21 core hub genes within a larger single co-expression module, highly enriched in the terminal ileum of CD patients. Further pathway analysis revealed known and novel inflammation-regulated, fibrogenic pathways operating in the TI, such as IL-13 signaling and pyroptosis, respectively. Discussion These findings provide a rationale for the increased incidence of fibrosis at the terminal ileum of CD patients and highlight operating pathways in intestinal fibrosis for future evaluation with mechanistic and translational studies.
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Affiliation(s)
- Nikolas Dovrolis
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Laboratory of Biology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece,*Correspondence: George Kolios, ; Nikolas Dovrolis,
| | - Eirini Filidou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Gesthimani Tarapatzi
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Georgios Kokkotis
- Gastrointestinal (GI) Unit, 3 Department of Internal Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Michail Spathakis
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Leonidas Kandilogiannakis
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Ioannis Drygiannakis
- Gastroenterology and Hepatology Research Laboratory, Medical School, University of Crete, Heraklion, Greece
| | - Vassilis Valatas
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Gastroenterology and Hepatology Research Laboratory, Medical School, University of Crete, Heraklion, Greece
| | - Konstantinos Arvanitidis
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Ioannis Karakasiliotis
- Laboratory of Biology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - Stergios Vradelis
- Second Department of Internal Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, Alexandroupolis, Greece
| | - Vangelis G. Manolopoulos
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | | | - Giorgos Bamias
- Gastrointestinal (GI) Unit, 3 Department of Internal Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - George Kolios
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece,*Correspondence: George Kolios, ; Nikolas Dovrolis,
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Algabri YA, Li L, Liu ZP. scGENA: A Single-Cell Gene Coexpression Network Analysis Framework for Clustering Cell Types and Revealing Biological Mechanisms. Bioengineering (Basel) 2022; 9:bioengineering9080353. [PMID: 36004879 PMCID: PMC9405199 DOI: 10.3390/bioengineering9080353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/27/2022] [Accepted: 07/27/2022] [Indexed: 11/16/2022] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput technique that can measure gene expression, reveal cell heterogeneity, rare and complex cell populations, and discover cell types and their relationships. The analysis of scRNA-seq data is challenging because of transcripts sparsity, replication noise, and outlier cell populations. A gene coexpression network (GCN) analysis effectively deciphers phenotypic differences in specific states by describing gene–gene pairwise relationships. The underlying gene modules with different coexpression patterns partially bridge the gap between genotype and phenotype. This study presents a new framework called scGENA (single-cell gene coexpression network analysis) for GCN analysis based on scRNA-seq data. Although there are several methods for scRNA-seq data analysis, we aim to build an integrative pipeline for several purposes that cover primary data preprocessing, including data exploration, quality control, normalization, imputation, and dimensionality reduction of clustering as downstream of GCN analysis. To demonstrate this integrated workflow, an scRNA-seq dataset of the human diabetic pancreas with 1600 cells and 39,851 genes was implemented to perform all these processes in practice. As a result, scGENA is demonstrated to uncover interesting gene modules behind complex diseases, which reveal biological mechanisms. scGENA provides a state-of-the-art method for gene coexpression analysis for scRNA-seq data.
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Su Q, Liu Z, Zhu Y, Tian J. Metabolic-related gene signature model forecasts biochemical relapse in primary prostate cancer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:65-68. [PMID: 36083923 DOI: 10.1109/embc48229.2022.9871189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Metabolism plays an important role in the pathogenesis of prostate cancer (PCa). Hence, we explored candidate metabolic-related genes attributed to biochemical relapse (BCR) of PCa. Gene expression profile and clinical parameters were downloaded from GSE70769 as a "training set". Using univariate Cox and LASSO-COX regression models, risk scores (RSs) were constructed. Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (t-ROC) curves were employed. Univariate and multivariate Cox models were utilized to validate prognostic factors for biochemical relapse-free survival (BCRFS). Nomogram was plotted to facilitate clinical application. The dataset obtained from GSE70768 served as "validation set". RSs were constructed by using 7 metabolic-related genes. RSs could significantly predict 1, 3, 5-year BCRFS (AUCs for training set: 0.810-0.836; AUC for validation set: 0.673-0.827). Nomograms could effectively predicted BCRFS (training set: C-index=0.831; validation set: C-index=0.737). RSs model is an independent prognostic factor for BCR, holding greater predictive value than traditional clinicopathological parameters. Clinical Relevance- We built the prognostic nomogram based on metabolic-related gene signatures and clinicopathological features. The nomogram might further optimize biochemical relapse risk stratification for prostate cancer patients with crucial accuracy.
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Identification of co-expression hub genes for ferroptosis in kidney renal clear cell carcinoma based on weighted gene co-expression network analysis and The Cancer Genome Atlas clinical data. Sci Rep 2022; 12:4821. [PMID: 35314744 PMCID: PMC8938444 DOI: 10.1038/s41598-022-08950-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/15/2022] [Indexed: 12/14/2022] Open
Abstract
Renal clear cell carcinoma (KIRC) is one of the most common tumors worldwide and has a high mortality rate. Ferroptosis is a major mechanism of tumor occurrence and development, as well as important for prognosis and treatment of KIRC. Here, we conducted bioinformatics analysis to identify KIRC hub genes that target ferroptosis. By Weighted gene co-expression network analysis (WGCNA), 11 co-expression-related genes were screened out. According to Kaplan Meier's survival analysis of the data from the gene expression profile interactive analysis database, it was identified that the expression levels of two genes, PROM2 and PLIN2, are respectively related to prognosis. In conclusion, our findings indicate that PROM2 and PLIN2 may be effective new targets for the treatment and prognosis of KIRC.
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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Li X, Gao X, Yuan J, Wang F, Xu X, Wang C, Liu H, Guan W, Zhang J, Xu G. The miR-33a-5p/CROT axis mediates ovarian cancer cell behaviors and chemoresistance via the regulation of the TGF-β signal pathway. Front Endocrinol (Lausanne) 2022; 13:950345. [PMID: 36120434 PMCID: PMC9478117 DOI: 10.3389/fendo.2022.950345] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 08/15/2022] [Indexed: 11/25/2022] Open
Abstract
Due to the lack of symptoms and detection biomarkers at the early stage, most patients with ovarian cancer (OC) are diagnosed at an advanced stage and often face chemoresistance and relapse. Hence, defining detection biomarkers and mechanisms of chemoresistance is imperative. A previous report of a cDNA microarray analysis shows a potential association of carnitine O-octanoyltransferase (CROT) with taxane resistance but the biological function of CROT in OC remains unknown. The current study explored the function and regulatory mechanism of CROT on cellular behavior and paclitaxel (PTX)-resistance in OC. We found that CROT was downregulated in OC tissues and PTX-resistant cells. Furthermore, CROT expression was negatively correlated with the prognosis of OC patients. Overexpression of CROT inhibited the OC cell proliferation, migration, invasion, and colony formation, arrested the cell cycle at the G2/M phase, and promoted cell apoptosis. In addition, miR-33a-5p bound directly to the 3'UTR of CROT to negatively regulate the expression of CROT and promoted OC cell growth. Finally, overexpression of CROT decreased the phosphorylation of Smad2, whereas knockdown of CROT increased the nuclear translocation of Smad2 and Smad4, two transducer proteins of TGF-β signaling, indicating that CROT is a tumor suppressor which mediates OC cell behaviors through the TGF-β signaling pathway. Thus, targeting the miR-33a-5p/CROT axis may have clinical potential for the treatment of patients with OC.
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Affiliation(s)
- Xin Li
- Research Center for Clinical Medicine, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xuzhu Gao
- Research Center for Clinical Medicine, Jinshan Hospital, Fudan University, Shanghai, China
| | - Jia Yuan
- Research Center for Clinical Medicine, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Fancheng Wang
- Research Center for Clinical Medicine, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaolin Xu
- Research Center for Clinical Medicine, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chenglong Wang
- Research Center for Clinical Medicine, Jinshan Hospital, Fudan University, Shanghai, China
| | - Huiqiang Liu
- Research Center for Clinical Medicine, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wencai Guan
- Research Center for Clinical Medicine, Jinshan Hospital, Fudan University, Shanghai, China
| | - Jihong Zhang
- Research Center for Clinical Medicine, Jinshan Hospital, Fudan University, Shanghai, China
| | - Guoxiong Xu
- Research Center for Clinical Medicine, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- *Correspondence: Guoxiong Xu,
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10
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Yang X, Luo S, Zhang Z, Wang Z, Zhou T, Zhang J. Silent transcription intervals and translational bursting lead to diverse phenotypic switching. Phys Chem Chem Phys 2022; 24:26600-26608. [DOI: 10.1039/d2cp03703c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
For complex process of gene expression, we use theoretical analysis and stochastic simulations to study the phenotypic diversity induced by silent transcription intervals and translational bursting.
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Affiliation(s)
- Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, P. R. China
| | - Songhao Luo
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Zhenquan Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Zihao Wang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Tianshou Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Jiajun Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
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11
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Eksi SE, Chitsazan A, Sayar Z, Thomas GV, Fields AJ, Kopp RP, Spellman PT, Adey AC. Epigenetic loss of heterogeneity from low to high grade localized prostate tumours. Nat Commun 2021; 12:7292. [PMID: 34911933 PMCID: PMC8674326 DOI: 10.1038/s41467-021-27615-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 11/30/2021] [Indexed: 12/13/2022] Open
Abstract
Identifying precise molecular subtypes attributable to specific stages of localized prostate cancer has proven difficult due to high levels of heterogeneity. Bulk assays represent a population-average, which mask the heterogeneity that exists at the single-cell level. In this work, we sequence the accessible chromatin regions of 14,424 single-cells from 18 flash-frozen prostate tumours. We observe shared chromatin features among low-grade prostate cancer cells are lost in high-grade tumours. Despite this loss, high-grade tumours exhibit an enrichment for FOXA1, HOXB13 and CDX2 transcription factor binding sites, indicating a shared trans-regulatory programme. We identify two unique genes encoding neuronal adhesion molecules that are highly accessible in high-grade prostate tumours. We show NRXN1 and NLGN1 expression in epithelial, endothelial, immune and neuronal cells in prostate cancer using cyclic immunofluorescence. Our results provide a deeper understanding of the active gene regulatory networks in primary prostate tumours, critical for molecular stratification of the disease.
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Affiliation(s)
- Sebnem Ece Eksi
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR, 97239, USA.
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR, 97209, USA.
| | - Alex Chitsazan
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR, 97239, USA
| | - Zeynep Sayar
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR, 97239, USA
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR, 97209, USA
| | - George V Thomas
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR, 97239, USA
- Department of Pathology & Laboratory Medicine, School of Medicine, OHSU, Portland, OR, 97239, USA
| | - Andrew J Fields
- Department of Molecular and Medical Genetics, School of Medicine, OHSU, Portland, OR, 97239, USA
| | - Ryan P Kopp
- Department of Urology, School of Medicine, OHSU, Portland, OR, 97239, USA
| | - Paul T Spellman
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR, 97239, USA
- Department of Molecular and Medical Genetics, School of Medicine, OHSU, Portland, OR, 97239, USA
| | - Andrew C Adey
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR, 97239, USA.
- Department of Molecular and Medical Genetics, School of Medicine, OHSU, Portland, OR, 97239, USA.
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12
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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: 20] [Impact Index Per Article: 6.7] [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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13
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Pan Y, Meng Y, Zhai Z, Xiong S. Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis. PeerJ 2021; 9:e11320. [PMID: 34249481 PMCID: PMC8247704 DOI: 10.7717/peerj.11320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/31/2021] [Indexed: 12/05/2022] Open
Abstract
Background Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles. Methods Gene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters. Results GSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients. Conclusion In summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients.
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Affiliation(s)
- Ying Pan
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ye Meng
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhimin Zhai
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shudao Xiong
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
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14
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Chen X, Xu J, Zeng F, Yang C, Sun W, Yu T, Zhang H, Li Y. Inferring Cell Subtypes and LncRNA Function by a Cell-Specific CeRNA Network in Breast Cancer. Front Oncol 2021; 11:656675. [PMID: 33987091 PMCID: PMC8111082 DOI: 10.3389/fonc.2021.656675] [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: 01/21/2021] [Accepted: 03/30/2021] [Indexed: 12/11/2022] Open
Abstract
Single-cell RNA sequencing is a powerful tool to explore the heterogeneity of breast cancer. The identification of the cell subtype that responds to estrogen has profound significance in breast cancer research and treatment. The transcriptional regulation of estrogen is an intricate network involving crosstalk between protein-coding and non-coding RNAs, which is still largely unknown, particularly at the single cell level. Therefore, we proposed a novel strategy to specify cell subtypes based on a cell-specific ceRNA network (CCN). The CCN was constructed by integrating a cell-specific RNA-RNA co-expression network (RCN) with an existing ceRNA network. The cell-specific RCN was built based on single cell expression profiles with predefined reference cells. Heterogeneous cell subtypes were inferred by enriching RNAs in CCN to the estrogen response hallmark. Edge biomarkers were identified in the early estrogen response subtype. Topological analysis revealed that NEAT1 was a hub lncRNA for the early response subtype, and its ceRNAs could predict patient survival. Another hub lncRNA, DLEU2, could potentially be involved in GPCR signaling, based on CCN. The CCN method that we proposed here facilitates the inference of cell subtypes from a network perspective and explores the function of hub lncRNAs, which are promising targets for RNA-based therapeutics.
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Affiliation(s)
- Xin Chen
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Jing Xu
- Department of Oncology, Changhai Hospital, The Naval Military Medical University, Shanghai, China
| | - Feng Zeng
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Chao Yang
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Weijun Sun
- School of Automation, Guangdong University of Technology, Guangzhou, China.,Guangdong Key Laboratory of IoT Information Technology, School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Tao Yu
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Haokun Zhang
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Yan Li
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
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15
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Single-cell network biology for resolving cellular heterogeneity in human diseases. Exp Mol Med 2020; 52:1798-1808. [PMID: 33244151 PMCID: PMC8080824 DOI: 10.1038/s12276-020-00528-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 08/26/2020] [Accepted: 08/31/2020] [Indexed: 01/10/2023] Open
Abstract
Understanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profiling using tissue samples provide only average signals of diverse cell types. Therefore, reconstructing gene regulatory networks for a particular cell type is not feasible with tissue-based transcriptome data. Recently, single-cell omics technology has emerged and enabled the capture of the transcriptomic landscape of every individual cell. Although single-cell gene expression studies have already opened up new avenues, network biology using single-cell transcriptome data will further accelerate our understanding of cellular heterogeneity. In this review, we provide an overview of single-cell network biology and summarize recent progress in method development for network inference from single-cell RNA sequencing (scRNA-seq) data. Then, we describe how cell-type-specific gene networks can be utilized to study regulatory programs specific to disease-associated cell types and cellular states. Moreover, with scRNA data, modeling personal or patient-specific gene networks is feasible. Therefore, we also introduce potential applications of single-cell network biology for precision medicine. We envision a rapid paradigm shift toward single-cell network analysis for systems biology in the near future. Gene regulatory networks reconstructed from single-cell RNA sequencing datasets are allowing researchers to better understand the molecular circuits and cell states that contribute to complex human disease. Junha Cha and Insuk Lee from Yonsei University in Seoul, South Korea, review the concept of ‘single-cell network biology’, which involves using computational algorithms on genetic expression data from thousands of cells to infer functional interactions in various biological contexts. This systems biology approach to analyzing the profiles of messenger RNA in single cells is helping researchers discover new signaling pathways that could serve as disease biomarkers or therapeutic targets. In the future, patient-specific models of personal gene networks could explain why certain genetic variants affect disease risk. This research could also eventually lead to new types of individualized medical treatments.
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16
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Yang J, Shi W, Zhu S, Yang C. Construction of a 6-gene prognostic signature to assess prognosis of patients with pancreatic cancer. Medicine (Baltimore) 2020; 99:e22092. [PMID: 32925750 PMCID: PMC7489722 DOI: 10.1097/md.0000000000022092] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Pancreatic cancer (PaCa) is one of the most fatal cancers in the world. Although great efforts have made to explore the mechanisms of PaCa oncogenesis, the prognosis of PaCa patients is still unsatisfactory. Thus, it is imperative to further understand the potential carcinogenesis of PaCa and reliable prognostic models.The gene expression profile and clinical information of GSE21501 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to explore the potent genes associated with the overall survival (OS) events of PaCa patients. Cox regression model was applied to selecting prognostic genes and establish prognostic model. The prognostic values of six-gene signature were validated in TCGA-PAAD cohort.According to the WGCNA analysis, a total of 19 modules were identified and 115 hub genes in the mostly associated module were reserved for next analysis. According to the univariate and multivariate Cox regression analysis, we established a six-gene signature (FTSJ3, STAT1, STX2, CDX2, RASSF4, MACF1) which could effectively evaluate the overall survival (OS) of PaCa patients. In validated patients' cohorts, the six-gene signature exhibited excellent prognostic value in TCGA-PAAD cohort as well.We developed a six-gene signature to exactly predict OS of PaCa patients and provide a novel personalized strategy for evaluating prognosis. The findings may be contributed to medical customization and therapeutic decision in clinical practice.
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Affiliation(s)
| | | | | | - Cheng Yang
- Department of Gastroenterology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi 214023, China
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17
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Li Y, Ma A, Mathé EA, Li L, Liu B, Ma Q. Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics. Trends Genet 2020; 36:951-966. [PMID: 32868128 DOI: 10.1016/j.tig.2020.08.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 07/29/2020] [Accepted: 08/04/2020] [Indexed: 12/14/2022]
Abstract
Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in heterogeneous cell populations. The derived information is especially promising for computing cell-type-specific biological networks encoded in complex diseases and improving our understanding of the underlying gene regulatory mechanisms. The integration of these networks could, therefore, give rise to a heterogeneous regulatory landscape (HRL) in support of disease diagnosis and drug therapeutics. In this review, we provide an overview of this field and pay particular attention to how diverse biological networks can be inferred in a specific cell type based on integrative methods. Then, we discuss how HRL can advance our understanding of regulatory mechanisms underlying complex diseases and aid in the prediction of prognosis and therapeutic responses. Finally, we outline challenges and future trends that will be central to bringing the field of HRL in complex diseases forward.
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Affiliation(s)
- Yang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Ewy A Mathé
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health (NIH), Rockville, MD, 20892, USA
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China.
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
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18
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Chen J, Cai Y, Xu R, Pan J, Zhou J, Mei J. Identification of four hub genes as promising biomarkers to evaluate the prognosis of ovarian cancer in silico. Cancer Cell Int 2020; 20:270. [PMID: 32595417 PMCID: PMC7315561 DOI: 10.1186/s12935-020-01361-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 06/17/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Ovarian cancer (OvCa) is one of the most fatal cancers among females in the world. With growing numbers of individuals diagnosed with OvCa ending in deaths, it is urgent to further explore the potential mechanisms of OvCa oncogenesis and development and related biomarkers. METHODS The gene expression profiles of GSE49997 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to explore the most potent gene modules associated with the overall survival (OS) and progression-free survival (PFS) events of OvCa patients, and the prognostic values of these genes were exhibited and validated based on data from training and validation sets. Next, protein-protein interaction (PPI) networks were built by GeneMANIA. Besides, enrichment analysis was conducted using DAVID website. RESULTS According to the WGCNA analysis, a total of eight modules were identified and four hub genes (MM > 0.90) in the blue module were reserved for next analysis. Kaplan-Meier analysis exhibited that these four hub genes were significantly associated with worse OS and PFS in the patient cohort from GSE49997. Moreover, we validated the short-term (4-years) and long-term prognostic values based on the GSE9891 data, respectively. Last, PPI networks analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed several potential mechanisms of four hub genes and their co-operators participating in OvCa progression. CONCLUSION Four hub genes (COL6A3, CRISPLD2, FBN1 and SERPINF1) were identified to be associated with the prognosis in OvCa, which might be used as monitoring biomarkers to evaluate survival time of OvCa patients.
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Affiliation(s)
- Jingxuan Chen
- School of Basic Medical Sciences, Nanjing Medical University, Nanjing, 211166 China
- Cytoskeleton Research Group & First Clinical Medicine College, Nanjing Medical University, No. 101 Longmian Road, Nanjing, 211166 China
| | - Yun Cai
- Department of Bioinformatics, Nanjing Medical University, Nanjing, 211166 China
| | - Rui Xu
- School of Basic Medical Sciences, Nanjing Medical University, Nanjing, 211166 China
- Cytoskeleton Research Group & First Clinical Medicine College, Nanjing Medical University, No. 101 Longmian Road, Nanjing, 211166 China
| | - Jiadong Pan
- First Clinical Medicine College, Nanjing Medical University, Nanjing, 211166 China
| | - Jie Zhou
- Department of Gynecology and Obstetrics, Affiliated Wuxi Maternal and Child Health Hospital of Nanjing Medical University, No.48, Huaishu Road, Wuxi, 214023 China
| | - Jie Mei
- Cytoskeleton Research Group & First Clinical Medicine College, Nanjing Medical University, No. 101 Longmian Road, Nanjing, 211166 China
- First Clinical Medicine College, Nanjing Medical University, Nanjing, 211166 China
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