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Chen J, Xiao H, Xue R, Kumar V, Aslam R, Mehdi SF, Luo H, Malhotra A, Lan X, Singhal P. Nicotine exacerbates diabetic nephropathy through upregulation of Grem1 expression. Mol Med 2023; 29:92. [PMID: 37415117 DOI: 10.1186/s10020-023-00692-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 06/27/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Diabetic nephropathy (DN) is a major complication of diabetes mellitus. Clinical reports indicate that smoking is a significant risk factor for chronic kidney disease, and the tobacco epidemic exacerbates kidney damage in patients with DN. However, the underlying molecular mechanisms remain unclear. METHOD In the present study, we used a diabetic mouse model to investigate the molecular mechanisms for nicotine-exacerbated DN. Twelve-week-old female mice were injected with streptozotocin (STZ) to establish a hyperglycemic diabetic model. After four months, the control and hyperglycemic diabetic mice were further divided into four groups (control, nicotine, diabetic mellitus, nicotine + diabetic mellitus) by intraperitoneal injection of nicotine or PBS. After two months, urine and blood were collected for kidney injury assay, and renal tissues were harvested for further molecular assays using RNA-seq analysis, real-time PCR, Western blot, and immunohistochemistry. In vitro studies, we used siRNA to suppress Grem1 expression in human podocytes. Then we treated them with nicotine and high glucose to compare podocyte injury. RESULT Nicotine administration alone did not cause apparent kidney injury, but it significantly increased hyperglycemia-induced albuminuria, BUN, plasma creatinine, and the kidney tissue mRNA expression of KIM-1 and NGAL. Results from RNA-seq analysis, real-time PCR, Western blot, and immunohistochemistry analysis revealed that, compared to hyperglycemia or nicotine alone, the combination of nicotine treatment and hyperglycemia significantly increased the expression of Grem1 and worsened DN. In vitro experiments, suppression of Grem1 expression attenuated nicotine-exacerbated podocyte injury. CONCLUSION Grem1 plays a vital role in nicotine-exacerbated DN. Grem1 may be a potential therapeutic target for chronic smokers with DN.
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Affiliation(s)
- Jianning Chen
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
| | - Haiting Xiao
- Key Laboratory of Luzhou City for Aging Medicine, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Rui Xue
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
| | - Vinod Kumar
- Feinstein Institute for Medical Research and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, 11030, USA
| | - Rukhsana Aslam
- Feinstein Institute for Medical Research and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, 11030, USA
| | - Syed Faizan Mehdi
- Feinstein Institute for Medical Research and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, 11030, USA
| | - Huairong Luo
- Key Laboratory of Luzhou City for Aging Medicine, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Ashwani Malhotra
- Feinstein Institute for Medical Research and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, 11030, USA
| | - Xiqian Lan
- Key Laboratory of Luzhou City for Aging Medicine, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, China.
| | - Pravin Singhal
- Feinstein Institute for Medical Research and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, 11030, USA.
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Yuan Y, Xiong X, Li L, Luo P. Novel targets in renal fibrosis based on bioinformatic analysis. Front Genet 2022; 13:1046854. [PMID: 36523757 PMCID: PMC9745177 DOI: 10.3389/fgene.2022.1046854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 10/25/2022] [Indexed: 08/14/2024] Open
Abstract
Background: Renal fibrosis is a widely used pathological indicator of progressive chronic kidney disease (CKD), and renal fibrosis mediates most progressive renal diseases as a final pathway. Nevertheless, the key genes related to the host response are still unclear. In this study, the potential gene network, signaling pathways, and key genes under unilateral ureteral obstruction (UUO) model in mouse kidneys were investigated by integrating two transcriptional data profiles. Methods: The mice were exposed to UUO surgery in two independent experiments. After 7 days, two datasets were sequenced from mice kidney tissues, respectively, and the transcriptome data were analyzed to identify the differentially expressed genes (DEGs). Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were executed. A Protein-Protein Interaction (PPI) network was constructed based on an online database STRING. Additionally, hub genes were identified and shown, and their expression levels were investigated in a public dataset and confirmed by quantitative real time-PCR (qRT-PCR) in vivo. Results: A total of 537 DEGs were shared by the two datasets. GO and the KEGG analysis showed that DEGs were typically enriched in seven pathways. Specifically, five hub genes (Bmp1, CD74, Fcer1g, Icam1, H2-Eb1) were identified by performing the 12 scoring methods in cytoHubba, and the receiver operating characteristic (ROC) curve indicated that the hub genes could be served as biomarkers. Conclusion: A gene network reflecting the transcriptome signature in CKD was established. The five hub genes identified in this study are potentially useful for the treatment and/or diagnosis CKD as biomarkers.
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Affiliation(s)
- Yuan Yuan
- Department of Urology, Wuhan Third Hospital and Tongren Hospital of Wuhan University, Wuhan, China
| | - Xi Xiong
- Department of Urology, Wuhan Third Hospital School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Lili Li
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Pengcheng Luo
- Department of Urology, Wuhan Third Hospital and Tongren Hospital of Wuhan University, Wuhan, China
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Pu J, Yu H, Guo Y. A Novel Strategy to Identify Prognosis-Relevant Gene Sets in Cancers. Genes (Basel) 2022; 13:862. [PMID: 35627247 PMCID: PMC9141699 DOI: 10.3390/genes13050862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/16/2022] Open
Abstract
Molecular prognosis markers hold promise for improved prediction of patient survival, and a pathway or gene set may add mechanistic interpretation to their prognostic prediction power. In this study, we demonstrated a novel strategy to identify prognosis-relevant gene sets in cancers. Our study consists of a first round of gene-level analyses and a second round of gene-set-level analyses, in which the Composite Gene Expression Score critically summarizes a surrogate expression value at gene set level and a permutation procedure is exerted to assess prognostic significance of gene sets. An optional differential coexpression module is appended to the two phases of survival analyses to corroborate and refine prognostic gene sets. Our strategy was demonstrated in 33 cancer types across 32,234 gene sets. We found oncogenic gene sets accounted for an increased proportion among the final gene sets, and genes involved in DNA replication and DNA repair have ubiquitous prognositic value for multiple cancer types. In summary, we carried out the largest gene set based prognosis study to date. Compared to previous similar studies, our approach offered multiple improvements in design and methodology implementation. Functionally relevant gene sets of ubiquitous prognostic significance in multiple cancer types were identified.
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Affiliation(s)
- Junyi Pu
- School of Life Sciences, Northwest University, Xi’an 710069, China;
| | - Hui Yu
- Comprehensive Cancer Center, New Mexico University, Albuquerque, NM 87131, USA;
| | - Yan Guo
- Comprehensive Cancer Center, New Mexico University, Albuquerque, NM 87131, USA;
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Yu H, Wang L, Chen D, Li J, Guo Y. Conditional transcriptional relationships may serve as cancer prognostic markers. BMC Med Genomics 2021; 14:101. [PMID: 34856998 PMCID: PMC8638091 DOI: 10.1186/s12920-021-00958-3] [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: 03/28/2021] [Accepted: 04/08/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND While most differential coexpression (DC) methods are bound to quantify a single correlation value for a gene pair across multiple samples, a newly devised approach under the name Correlation by Individual Level Product (CILP) revolutionarily projects the summary correlation value to individual product correlation values for separate samples. CILP greatly widened DC analysis opportunities by allowing integration of non-compromised statistical methods. METHODS Here, we performed a study to verify our hypothesis that conditional relationships, i.e., gene pairs of remarkable differential coexpression, may be sought as quantitative prognostic markers for human cancers. Alongside the seeking of prognostic gene links in a pan-cancer setting, we also examined whether a trend of global expression correlation loss appeared in a wide panel of cancer types and revisited the controversial subject of mutual relationship between the DE approach and the DC approach. RESULTS By integrating CILP with classical univariate survival analysis, we identified up to 244 conditional gene links as potential prognostic markers in five cancer types. In particular, five prognostic gene links for kidney renal papillary cell carcinoma tended to condense around cancer gene ESPL1, and the transcriptional synchrony between ESPL1 and PTTG1 tended to be elevated in patients of adverse prognosis. In addition, we extended the observation of global trend of correlation loss in more than ten cancer types and empirically proved DC analysis results were independent of gene differential expression in five cancer types. CONCLUSIONS Combining the power of CILP and the classical survival analysis, we successfully fetched conditional transcriptional relationships that conferred prognosis power for five cancer types. Despite a general trend of global correlation loss in tumor transcriptomes, most of these prognosis conditional links demonstrated stronger expression correlation in tumors, and their stronger coexpression was associated with poor survival.
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Affiliation(s)
- Hui Yu
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM, 87131, USA.
| | - Limei Wang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Kaikou, Hainan, 571199, China.,College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, Heilongjiang, China
| | - Danqian Chen
- Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China
| | - Jin Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Kaikou, Hainan, 571199, China
| | - Yan Guo
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM, 87131, USA.
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Wu Y, Guo Y, Yu H, Guo T. RNA editing affects cis-regulatory elements and predicts adverse cancer survival. Cancer Med 2021; 10:6114-6127. [PMID: 34319007 PMCID: PMC8419749 DOI: 10.1002/cam4.4146] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND RNA editing exerts critical impacts on numerous biological processes and thus are implicated in crucial human phenotypes, including tumorigenesis and prognosis. While previous studies have analyzed aggregate RNA editing activity at the sample level and associated it with overall cancer survival, there is not yet a large-scale disease-specific survival study to examine genome-wide RNA editing sites' prognostic value taking into account the host gene expression and clinical variables. METHODS In this study, we solved comprehensive Cox proportional models of disease-specific survival on individual RNA-editing sites plus host gene expression and critical demographic covariates. This allowed us to interrogate the prognostic value of a large number of RNA-editing sites at single-nucleotide resolution. RESULTS As a result, we identified 402 gene-proximal RNA-editing sites that generally predict adverse cancer survival. For example, an RNA-editing site residing in ZNF264 indicates poor survival of uterine corpus endometrial carcinoma, with a hazard ratio of 2.13 and an adjusted p-value of 4.07 × 10-7 . Some of these prognostic RNA-editing sites mediate the binding of RNA binding proteins and microRNAs, thus propagating their impacts to extensive regulatory targets. CONCLUSIONS In conclusion, RNA editing affects cis-regulatory elements and predicts adverse cancer survival.
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Affiliation(s)
- Yuan‐Ming Wu
- School of Basic Medical SciencesGuizhou Medical UniversityGuiyangChina
- Stem Cell and Tissue Engineering Research CenterGuizhou Medical UniversityGuizhouChina
| | - Yan Guo
- Comprehensive Cancer CenterUniversity of New MexicoAlbuquerqueNMUSA
| | - Hui Yu
- Comprehensive Cancer CenterUniversity of New MexicoAlbuquerqueNMUSA
| | - Tao Guo
- Guizhou Provincial People’s HospitalGuiyangChina
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Guo Y, Yu H, Song H, He J, Oyebamiji O, Kang H, Ping J, Ness S, Shyr Y, Ye F. MetaGSCA: A tool for meta-analysis of gene set differential coexpression. PLoS Comput Biol 2021; 17:e1008976. [PMID: 33945541 PMCID: PMC8121311 DOI: 10.1371/journal.pcbi.1008976] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 05/14/2021] [Accepted: 04/18/2021] [Indexed: 01/24/2023] Open
Abstract
Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.
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Affiliation(s)
- Yan Guo
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Hui Yu
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Haocan Song
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jiapeng He
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Olufunmilola Oyebamiji
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Huining Kang
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Scott Ness
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Yu Shyr
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Vanderbilt Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Fei Ye
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Vanderbilt Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
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7
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Yu H, Guo Y, Chen J, Chen X, Jia P, Zhao Z. Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods. Genes (Basel) 2021; 12:665. [PMID: 33946654 PMCID: PMC8146818 DOI: 10.3390/genes12050665] [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: 03/13/2021] [Revised: 04/16/2021] [Accepted: 04/27/2021] [Indexed: 02/03/2023] Open
Abstract
Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found "Clostridium neurotoxicity" and "signaling events mediated by focal adhesion kinase" had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.
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Affiliation(s)
- Hui Yu
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA; (H.Y.); (Y.G.)
| | - Yan Guo
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA; (H.Y.); (Y.G.)
| | - Jingchun Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA; (J.C.); (X.C.)
| | - Xiangning Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA; (J.C.); (X.C.)
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA;
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA;
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
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Zhang C, Mathé E, Ning X, Zhao Z, Wang K, Li L, Guo Y. The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): computational methods and applications in medical genomics. BMC Med Genomics 2020; 13:47. [PMID: 32241271 PMCID: PMC7119270 DOI: 10.1186/s12920-020-0678-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
In this editorial, we briefly summarized the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9-11, 2019 at Columbus, Ohio, USA. We further introduced the 19 research articles included in this supplement issue, covering four major areas, namely computational method development, genomics analysis, network-based analysis and biomarker prediction. The selected papers perform cutting edge computational research applied to a broad range of human diseases such as cancer, neural degenerative and chronic inflammatory disease. They also proposed solutions for fundamental medical genomics problems range from basic data processing and quality control to functional interpretation, biomarker and drug prediction, and database releasing.
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Affiliation(s)
- Chi Zhang
- Department of Medical & Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN 46202 USA
| | - Ewy Mathé
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210 USA
| | - Xia Ning
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210 USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210 USA
| | - Yan Guo
- Department of internal medicine, comprehensive cancer center, University of New Mexico, Albuquerque, NM 87131 USA
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