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Yu L, Liu J, Jia J, Yang J, Tong R, Zhang X, Zhang Y, Yin S, Li J, Sun D. Fusion Genes Landscape of Lung Cancer Patients From Inner Mongolia, China. Genes Chromosomes Cancer 2024; 63:e23258. [PMID: 39011998 DOI: 10.1002/gcc.23258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/04/2024] [Accepted: 06/19/2024] [Indexed: 07/17/2024] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths globally. Gene fusion, a key driver of tumorigenesis, has led to the identification of numerous driver gene fusions for lung cancer diagnosis and treatment. However, previous studies focused on Western populations, leaving the possibility of unrecognized lung cancer-associated gene fusions specific to Inner Mongolia due to its unique genetic background and dietary habits. To address this, we conducted DNA sequencing analysis on tumor and adjacent nontumor tissues from 1200 individuals with lung cancer in Inner Mongolia. Our analysis established a comprehensive fusion gene landscape specific to lung cancer in Inner Mongolia, shedding light on potential region-specific molecular mechanisms underlying the disease. Compared to Western cohorts, we observed a higher occurrence of ALK and RET fusions in Inner Mongolian patients. Additionally, we discovered eight novel fusion genes in three patients: SLC34A2-EPHB1, CCT6P3-GSTP1, BARHL2-APC, HRAS-MELK, FAM134B-ERBB2, ABCB1-GIPC1, GPR98-ALK, and FAM134B-SALL1. These previously unreported fusion genes suggest potential regional specificity. Furthermore, we characterized the fusion genes' structures based on breakpoints and described their impact on major functional gene domains. Importantly, the identified novel fusion genes exhibited significant clinical and pathological relevance. Notably, patients with SLC34A2-EPHB1, CCT6P3-GSTP1, and BARHL2-APC fusions showed sensitivity to the combination of chemotherapy and immunotherapy. Patients with HRAS-MELK, FAM134B-ERBB2, and ABCB1-GIPC1 fusions showed sensitivity to chemotherapy. In summary, our study provides novel insights into the frequency, distribution, and characteristics of specific fusion genes, offering valuable guidance for the development of effective clinical treatments, particularly in Inner Mongolia.
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Affiliation(s)
- Lan Yu
- Clinical Medical Research Center, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Jinyang Liu
- Department of Sciences, Geneis Beijing Co. Ltd., Beijing, China
- Department of Data Mining, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jianchao Jia
- Clinical Medical Research Center, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Jie Yang
- Clinical Medical Research Center, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Ruiying Tong
- Clinical Medical Research Center, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Xiao Zhang
- Clinical Medical Research Center, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Yun Zhang
- Department of Sciences, Geneis Beijing Co. Ltd., Beijing, China
- Department of Data Mining, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Songtao Yin
- Department of Medical Imaging, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Junlin Li
- Department of Medical Imaging, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Dejun Sun
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Pulmonary and Critical Care Medicine, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
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Pan P, Li J, Wang B, Tan X, Yin H, Han Y, Wang H, Shi X, Li X, Xie C, Chen L, Chen L, Bai Y, Li Z, Tian G. Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis. Front Oncol 2023; 13:1067849. [PMID: 37546388 PMCID: PMC10401844 DOI: 10.3389/fonc.2023.1067849] [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: 10/15/2022] [Accepted: 06/21/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Colorectal adenoma can develop into colorectal cancer. Determining the risk of tumorigenesis in colorectal adenoma would be critical for avoiding the development of colorectal cancer; however, genomic features that could help predict the risk of tumorigenesis remain uncertain. Methods In this work, DNA and RNA parallel capture sequencing data covering 519 genes from colorectal adenoma and colorectal cancer samples were collected. The somatic mutation profiles were obtained from DNA sequencing data, and the expression profiles were obtained from RNA sequencing data. Results Despite some similarities between the adenoma samples and the cancer samples, different mutation frequencies, co-occurrences, and mutually exclusive patterns were detected in the mutation profiles of patients with colorectal adenoma and colorectal cancer. Differentially expressed genes were also detected between the two patient groups using RNA sequencing. Finally, two random forest classification models were built, one based on mutation profiles and one based on expression profiles. The models distinguished adenoma and cancer samples with accuracy levels of 81.48% and 100.00%, respectively, showing the potential of the 519-gene panel for monitoring adenoma patients in clinical practice. Conclusion This study revealed molecular characteristics and correlations between colorectal adenoma and colorectal cancer, and it demonstrated that the 519-gene panel may be used for early monitoring of the progression of colorectal adenoma to cancer.
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Affiliation(s)
- Peng Pan
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Jingnan Li
- Department of Gastroenterology, Peking Union Medical College Hospital, Beijing, China
| | - Bo Wang
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaoyan Tan
- Department of Gastroenterology, Maoming People's Hospital, Maoming, China
| | - Hekun Yin
- Department of Gastroenterology, Jiangmen Central Hospital, Jiangmen, China
| | - Yingmin Han
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
| | - Haobin Wang
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
| | - Xiaoli Shi
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaoshuang Li
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Cuinan Xie
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Longfei Chen
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Lanyou Chen
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Yu Bai
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Zhaoshen Li
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Geng Tian
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
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Li C, Tang H, Yang Z, Tang Z, Cheng N, Huang J, Zhou X. Mechanism of CAV and CAVIN Family Genes in Acute Lung Injury based on DeepGENE. Curr Gene Ther 2023; 23:72-80. [PMID: 36043785 DOI: 10.2174/1566523222666220829140649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/01/2022] [Accepted: 07/06/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND The fatality rate of acute lung injury (ALI) is as high as 40% to 60%. Although various factors, such as sepsis, trauma, pneumonia, burns, blood transfusion, cardiopulmonary bypass, and pancreatitis, can induce ALI, patients with these risk factors will eventually develop ALI. The rate of developing ALI is not high, and the outcomes of ALI patients vary, indicating that it is related to genetic differences between individuals. In a previous study, we found multiple functions of cavin-2 in lung function. In addition, many other studies have revealed that CAV1 is a critical regulator of lung injury. Due to the strong relationship between cavin-2 and CAV1, we suspect that cavin-2 is also associated with ALI. Furthermore, we are curious about the role of the CAV family and cavin family genes in ALI. METHODS To reveal the mechanism of CAV and CAVIN family genes in ALI, we propose DeepGENE to predict whether CAV and CAVIN family genes are associated with ALI. This method constructs a gene interaction network and extracts gene expression in 84 tissues. We divided these features into two groups and used two network encoders to encode and learn the features. RESULTS Compared with DNN, GBDT, RF and KNN, the AUC of DeepGENE increased by 7.89%, 16.84%, 20.19% and 32.01%, respectively. The AUPR scores increased by 8.05%, 15.58%, 22.56% and 23.34%. DeepGENE shows that CAVIN-1, CAVIN-2, CAVIN-3 and CAV2 are related to ALI. CONCLUSION DeepGENE is a reliable method for identifying acute lung injury-related genes. Multiple CAV and CAVIN family genes are associated with acute lung injury-related genes through multiple pathways and gene functions.
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Affiliation(s)
- Changsheng Li
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hexiao Tang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zetian Yang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zheng Tang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Nitao Cheng
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jingyu Huang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xuefeng Zhou
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
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Xu Y, Zhao J, Ma Y, Liu J, Cui Y, Yuan Y, Xiang C, Ma D, Liu H. The microbiome types of colorectal tissue are potentially associated with the prognosis of patients with colorectal cancer. Front Microbiol 2023; 14:1100873. [PMID: 37025624 PMCID: PMC10072283 DOI: 10.3389/fmicb.2023.1100873] [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: 02/28/2023] [Indexed: 04/08/2023] Open
Abstract
As the second leading cause of cancer worldwide, colorectal cancer (CRC) is associated with a poor prognosis. Although recent studies have explored prognostic markers in patients with CRC, whether tissue microbes carry prognostic information remains unknown. Here, by assessing the colorectal tissue microbes of 533 CRC patients, we found that Proteobacteria (43.5%), Firmicutes (25.3%), and Actinobacteria (23.0%) dominated the colorectal tissue microbiota, which was different from the gut microbiota. Moreover, two clear clusters were obtained by clustering based on the tissue microbes across all samples. By comparison, the relative abundances of Proteobacteria and Bacteroidetes in cluster 1 were significantly higher than those in cluster 2; while compared with cluster 1, Firmicutes and Actinobacteria were more abundant in cluster 2. In addition, the Firmicutes/Bacteroidetes ratios in cluster 1 were significantly lower than those in cluster 2. Further, compared with cluster 2, patients in cluster 1 had relatively poor survival (Log-rank test, p = 0.0067). By correlating tissue microbes with patient survival, we found that the relative abundance of dominant phyla, including Proteobacteria, Firmicutes, and Bacteroidetes, was significantly associated with survival in CRC patients. Besides, the co-occurrence network of tissue microbes at the phylum level of cluster 2 was more complicated than that of cluster 1. Lastly, we detected some pathogenic bacteria enriched in cluster 1 that promote the development of CRC, thus leading to poor survival. In contrast, cluster 2 showed significant increases in the abundance of some probiotics and genera that resist cancer development. Altogether, this study provides the first evidence that the tissue microbiome of CRC patients carries prognostic information and can help design approaches for clinically evaluating the survival of CRC patients.
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Affiliation(s)
- Yixin Xu
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jing Zhao
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yu Ma
- Department of Pathology, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jia Liu
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yingying Cui
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yuqing Yuan
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Chenxi Xiang
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Dongshen Ma
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- *Correspondence: Hui Liu, ; Dongshen Ma,
| | - Hui Liu
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Department of Pathology, Xuzhou Medical University, Xuzhou, Jiangsu, China
- *Correspondence: Hui Liu, ; Dongshen Ma,
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Leng X, Yang J, Liu T, Zhao C, Cao Z, Li C, Sun J, Zheng S. A bioinformatics framework to identify the biomarkers and potential drugs for the treatment of colorectal cancer. Front Genet 2022; 13:1017539. [PMID: 36238159 PMCID: PMC9551025 DOI: 10.3389/fgene.2022.1017539] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.
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Chen Y, Sun X, Yang J. Prediction of Gastric Cancer-Related Genes Based on the Graph Transformer Network. Front Oncol 2022; 12:902616. [PMID: 35847949 PMCID: PMC9281472 DOI: 10.3389/fonc.2022.902616] [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: 03/23/2022] [Accepted: 04/26/2022] [Indexed: 02/01/2023] Open
Abstract
Gastric cancer is a complex multifactorial and multistage process that involves a large number of tumor-related gene structural changes and abnormal expression. Therefore, knowing the related genes of gastric cancer can further understand the pathogenesis of gastric cancer and provide guidance for the development of targeted drugs. Traditional methods to discover gastric cancer-related genes based on biological experiments are time-consuming and expensive. In recent years, a large number of computational methods have been developed to identify gastric cancer-related genes. In addition, a large number of experiments show that establishing a biological network to identify disease-related genes has higher accuracy than ordinary methods. However, most of the current computing methods focus on the processing of homogeneous networks, and do not have the ability to encode heterogeneous networks. In this paper, we built a heterogeneous network using a disease similarity network and a gene interaction network. We implemented the graph transformer network (GTN) to encode this heterogeneous network. Meanwhile, the deep belief network (DBN) was applied to reduce the dimension of features. We call this method “DBN-GTN”, and it performed best among four traditional methods and five similar methods.
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Liu J, Lan Y, Tian G, Yang J. A Systematic Framework for Identifying Prognostic Genes in the Tumor Microenvironment of Colon Cancer. Front Oncol 2022; 12:899156. [PMID: 35664768 PMCID: PMC9161737 DOI: 10.3389/fonc.2022.899156] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/19/2022] [Indexed: 12/23/2022] Open
Abstract
As one of the most common cancers of the digestive system, colon cancer is a predominant cause of cancer-related deaths worldwide. To investigate prognostic genes in the tumor microenvironment of colon cancer, we collected 461 colon adenocarcinoma (COAD) and 172 rectal adenocarcinoma (READ) samples from The Cancer Genome Atlas (TCGA) database, and calculated the stromal and immune scores of each sample. We demonstrated that stromal and immune scores were significantly associated with colon cancer stages. By analyzing differentially expressed genes (DEGs) between two stromal and immune score groups, we identified 952 common DEGs. The significantly enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms for these DEGs were associated with T-cell activation, immune receptor activity, and cytokine–cytokine receptor interaction. Through univariate Cox regression analysis, we identified 22 prognostic genes. Furthermore, nine key prognostic genes, namely, HOXC8, SRPX, CCL22, CD72, IGLON5, SERPING1, PCOLCE2, FABP4, and ARL4C, were identified using the LASSO Cox regression analysis. The risk score of each sample was calculated using the gene expression of the nine genes. Patients with high-risk scores had a poorer prognosis than those with low-risk scores. The prognostic model established with the nine-gene signature was able to effectively predict the outcome of colon cancer patients. Our findings may help in the clinical decisions and improve the prognosis for colon cancer.
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Affiliation(s)
- Jinyang Liu
- Department of Sciences, Geneis Beijing Co., Ltd., Beijing, China
- Department of Data Mining,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Yu Lan
- Department of Sciences, Geneis Beijing Co., Ltd., Beijing, China
- Department of Data Mining,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Geng Tian
- Department of Sciences, Geneis Beijing Co., Ltd., Beijing, China
- Department of Data Mining,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jialiang Yang
- Department of Sciences, Geneis Beijing Co., Ltd., Beijing, China
- Department of Data Mining,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
- PhD Workstation, Chifeng Municipal Hospital, Chifeng, China
- *Correspondence: Jialiang Yang,
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Sun Y, Li T, Qian X. Biological Role of Nodal Modulator: A Comprehensive Review of the Last Two Decades. DNA Cell Biol 2022; 41:336-341. [PMID: 35133875 DOI: 10.1089/dna.2021.0944] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Nodal modulator (NOMO) is a type I transmembrane protein that is conserved in various human tissues. Humans have three highly similar NOMO proteins, namely NOMO1, NOMO2, and NOMO3. These three proteins are closely related and may have similar functions. NOMO has been identified as a part of a protein complex that mediates a wide range of biological processes such as tumor formation, bone and cartilage formation, embryo formation, facial asymmetry, and development of congenital heart disease. To date, a few studies have focused on the role of NOMO; however, the mechanism underlying its effects remains unknown. To improve our understanding regarding NOMO, we reviewed the role of NOMO in different diseases and investigated the mechanism underlying its effects.
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Affiliation(s)
- Yuhui Sun
- Department of Pediatrics, Taihe Hospital, Hubei University of Medicine, Shiyan, P.R. China
| | - Tao Li
- Department of Pediatrics, Taihe Hospital, Hubei University of Medicine, Shiyan, P.R. China
| | - Xin Qian
- Department of Pulmonary and Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, P.R. China
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Li C, Huang J, Tang H, Liu B, Zhou X. Revealing Cavin-2 Gene Function in Lung Based on Multi-Omics Data Analysis Method. Front Cell Dev Biol 2022; 9:827108. [PMID: 35174175 PMCID: PMC8841408 DOI: 10.3389/fcell.2021.827108] [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: 12/01/2021] [Accepted: 12/15/2021] [Indexed: 11/23/2022] Open
Abstract
Research points out that it is particularly important to comprehensively evaluate immune microenvironmental indicators and gene mutation characteristics to select the best treatment plan. Therefore, exploring the relevant genes of pulmonary injury is an important basis for the improvement of survival. In recent years, with the massive production of omics data, a large number of computational methods have been applied in the field of biomedicine. Most of these computational methods are devel-oped for a certain type of diseases or whole diseases. Algorithms that specifically identify genes associated with pulmonary injury have not yet been developed. To fill this gap, we developed a novel method, named AdaRVM, to identify pulmonary injury-related genes in large scale. AdaRVM is the fusion of Adaboost and Relevance Vector Machine (RVM) to achieve fast and high-precision pattern recognition of pulmonary injury genetic mechanism. AdaRVM found that Cavin-2 gene has strong potential to be related to pulmonary injury. As we known, the formation and function of Caveolae are mediated by two family proteins: Caveolin and Cavin. Many studies have explored the role of Caveolin proteins, but people still knew little about Cavin family members. To verify our method and reveal the functions of cavin-2, we integrated six genome-wide association studies (GWAS) data related to lung function traits, four expression Quantitative Trait Loci (eQTL) data, and one methylation Quantitative Trait Loci (mQTL) data by Summary data level Mendelian Randomization (SMR). We found strong relationship between cavin-2 and canonical signaling pathways ERK1/2, AKT, and STAT3 which are all known to be related to lung injury.
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Affiliation(s)
- Changsheng Li
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jingyu Huang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hexiao Tang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Bing Liu
- Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
- Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, China
| | - Xuefeng Zhou
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
- *Correspondence: Xuefeng Zhou,
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Zhao Z, Shi J, Zhao G, Gao Y, Jiang Z, Yuan F. Large Scale Identification of Osteosarcoma Pathogenic Genes by Multiple Extreme Learning Machine. Front Cell Dev Biol 2021; 9:755511. [PMID: 34646831 PMCID: PMC8502917 DOI: 10.3389/fcell.2021.755511] [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: 08/09/2021] [Accepted: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
At present, the main treatment methods of osteosarcoma are chemotherapy and surgery. Its 5-year survival rate has not been significantly improved in the past decades. Osteosarcoma has extremely complex multigenomic heterogeneity and lacks universally applicable signal blocking targets. Osteosarcoma is often found in adolescents or children under the age of 20, so it is very important to explore its genetic pathogenic factors. We used known osteosarcoma-related genes and computer algorithms to find more osteosarcoma pathogenic genes, laying the foundation for the treatment of osteosarcoma immune microenvironment-related treatments, so as to carry out further explorations on these genes. It is a traditional method to identify osteosarcoma related genes by collecting clinical samples, measuring gene expressions by RNA-seq technology and comparing differentially expressed gene. The high cost and time consumption make it difficult to carry out research on a large scale. In this paper, we developed a novel method “RELM” which fuses multiple extreme learning machines (ELM) to identify osteosarcoma pathogenic genes. The AUC and AUPR of RELM are 0.91 and 0.88, respectively, in 10-cross validation, which illustrates the reliability of RELM.
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Affiliation(s)
- Zhipeng Zhao
- Department of Basic Medical Sciences, Taizhou University, Taizhou, China
| | - Jijun Shi
- Department of Orthopedics, Songyuan Central Hospital, Songyuan, China
| | - Guang Zhao
- Department of Orthopedics, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Yanjun Gao
- Department of Orthopedics, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Zhigang Jiang
- Department of Hand Surgery, Changchun Central Hospital, Changchun, China
| | - Fusheng Yuan
- Department of Orthopedics, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
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Zhong LK, Xie CL, Jiang S, Deng XY, Gan XX, Feng JH, Cai WS, Liu CZ, Shen F, Miao JH, Xu B. Prioritizing Susceptible Genes for Thyroid Cancer Based on Gene Interaction Network. Front Cell Dev Biol 2021; 9:740267. [PMID: 34497810 PMCID: PMC8421023 DOI: 10.3389/fcell.2021.740267] [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: 07/12/2021] [Accepted: 08/02/2021] [Indexed: 12/05/2022] Open
Abstract
Thyroid cancer ranks second in the incidence rate of endocrine malignant cancer. Thyroid cancer is usually asymptomatic at the initial stage, which makes patients easily miss the early treatment time. Combining genetic testing with imaging can greatly improve the diagnostic efficiency of thyroid cancer. Researchers have discovered many genes related to thyroid cancer. However, the effects of these genes on thyroid cancer are different. We hypothesize that there is a stronger interaction between the core genes that cause thyroid cancer. Based on this hypothesis, we constructed an interaction network of thyroid cancer-related genes. We traversed the network through random walks, and sorted thyroid cancer-related genes through ADNN which is fusion of Adaboost and deep neural network (DNN). In addition, we discovered more thyroid cancer-related genes by ADNN. In order to verify the accuracy of ADNN, we conducted a fivefold cross-validation. ADNN achieved AUC of 0.85 and AUPR of 0.81, which are more accurate than other methods.
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Affiliation(s)
- Lin-Kun Zhong
- Department of General Surgery, Zhongshan City People's Hospital, Zhongshan, China
| | - Chang-Lian Xie
- Intensive Care Unit, Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Chinese Medicine, Zhongshan, China
| | - Shan Jiang
- Reproductive Medicine Center, Boai Hospital of Zhongshan, Zhongshan, China
| | - Xing-Yan Deng
- Department of Thyrovascular Surgery, Maoming People's Hospital, Maoming, China
| | - Xiao-Xiong Gan
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Jian-Hua Feng
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wen-Song Cai
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Chi-Zhuai Liu
- Department of General Surgery, Zhongshan City People's Hospital, Zhongshan, China
| | - Fei Shen
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Jian-Hang Miao
- Department of General Surgery, Zhongshan City People's Hospital, Zhongshan, China
| | - Bo Xu
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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12
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Liu Y, Jin G, Wang X, Dong Y, Ding F. Identification of New Genes and Loci Associated With Bone Mineral Density Based on Mendelian Randomization. Front Genet 2021; 12:728563. [PMID: 34567079 PMCID: PMC8456003 DOI: 10.3389/fgene.2021.728563] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 08/02/2021] [Indexed: 02/05/2023] Open
Abstract
Bone mineral density (BMD) is a complex and highly hereditary trait that can lead to osteoporotic fractures. It is estimated that BMD is mainly affected by genetic factors (about 85%). BMD has been reported to be associated with both common and rare variants, and numerous loci related to BMD have been identified by genome-wide association studies (GWAS). We systematically integrated expression quantitative trait loci (eQTL) data with GWAS summary statistical data. We mainly focused on the loci, which can affect gene expression, so Summary data-based Mendelian randomization (SMR) analysis was implemented to investigate new genes and loci associated with BMD. We identified 12,477 single-nucleotide polymorphisms (SNPs) regulating 564 genes, which are associated with BMD. The genetic mechanism we detected could make a contribution in the density of BMD in individuals and play an important role in understanding the pathophysiology of cataclasis.
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Affiliation(s)
- Yijun Liu
- Department of Orthopedics, The First Hospital of Jilin University, Changchun, China
| | - Guang Jin
- Department of Orthopedics, The First Hospital of Jilin University, Changchun, China
| | - Xue Wang
- Department of Anesthesiology, The First Hospital of Jilin University, Changchun, China
| | - Ying Dong
- The Third Department of Radiotherapy, Jilin Provincial Tumor Hospital, Changchun, China
| | - Fupeng Ding
- Department of Orthopedics, The First Hospital of Jilin University, Changchun, China
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13
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Du Y, Kong N, Zhang J. Genetic Mechanism Revealed of Age-Related Macular Degeneration Based on Fusion of Statistics and Machine Learning Method. Front Genet 2021; 12:726599. [PMID: 34422023 PMCID: PMC8375266 DOI: 10.3389/fgene.2021.726599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 07/13/2021] [Indexed: 11/13/2022] Open
Abstract
Age-related macular degeneration (AMD) is the most common cause of irreversible vision loss in the developed world which affects the quality of life for millions of elderly individuals worldwide. Genome-wide association studies (GWAS) have identified genetic variants at 34 loci contributing to AMD. To better understand the disease pathogenesis and identify causal genes for AMD, we applied random walk (RW) and support vector machine (SVM) to identify AMD-related genes based on gene interaction relationship and significance of genes. Our model achieved 0.927 of area under the curve (AUC), and 65 novel genes have been identified as AMD-related genes. To verify our results, a statistics method called summary data-based Mendelian randomization (SMR) has been implemented to integrate GWAS data and transcriptome data to verify AMD susceptibility-related genes. We found 45 genes are related to AMD by SMR. Among these genes, 37 genes overlap with those found by SVM-RW. Finally, we revealed the biological process of genetic mutations leading to changes in gene expression leading to AMD. Our results reveal the genetic pathogenic factors and related mechanisms of AMD.
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Affiliation(s)
- Yongyi Du
- Department of Ophthalmology, Panyu Central Hospital, Guangzhou, China
| | - Ning Kong
- Department of Ophthalmology, Panyu Central Hospital, Guangzhou, China
| | - Jibin Zhang
- Department of Stomatology, Panyu Central Hospital, Guangzhou, China
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14
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Ye L, Lin Y, Fan XD, Chen Y, Deng Z, Yang Q, Lei X, Mao J, Cui C. Identify Inflammatory Bowel Disease-Related Genes Based on Machine Learning. Front Cell Dev Biol 2021; 9:722410. [PMID: 34381790 PMCID: PMC8352440 DOI: 10.3389/fcell.2021.722410] [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: 06/08/2021] [Accepted: 07/06/2021] [Indexed: 11/29/2022] Open
Abstract
The patients of Inflammatory bowel disease (IBD) are increasing worldwide. IBD has the characteristics of recurring and difficult to cure, and it is also one of the high-risk factors for colorectal cancer (CRC). The occurrence of IBD is closely related to genetic factors, which prompted us to identify IBD-related genes. Based on the hypothesis that similar diseases are related to similar genes, we purposed a SVM-based method to identify IBD-related genes by disease similarities and gene interactions. One hundred thirty-five diseases which have similarities with IBD and their related genes were obtained. These genes are considered as the candidates of IBD-related genes. We extracted features of each gene and implemented SVM to identify the probability that it is related to IBD. Ten-cross validation was applied to verify the effectiveness of our method. The AUC is 0.93 and AUPR is 0.97, which are the best among four methods. We prioritized the candidate genes and did case studies on top five genes.
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Affiliation(s)
- Lili Ye
- Daycare Chemotherapy Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yongwei Lin
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xing-di Fan
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yaoming Chen
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zengli Deng
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Qian Yang
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaotian Lei
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jizong Mao
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Chunhui Cui
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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15
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Zong Y, Li X. Identification of Causal Genes of COVID-19 Using the SMR Method. Front Genet 2021; 12:690349. [PMID: 34290742 PMCID: PMC8287881 DOI: 10.3389/fgene.2021.690349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 05/07/2021] [Indexed: 01/03/2023] Open
Abstract
Since the first report of COVID-19 in December 2019, more than 100 million people have been infected with SARS-CoV-2. Despite ongoing research, there is still limited knowledge about the genetic causes of COVID-19. To resolve this problem, we applied the SMR method to analyze the genes involved in COVID-19 pathogenesis by the integration of multiple omics data. Here, we assessed the SNPs associated with COVID-19 risk from the GWAS data of Spanish and Italian patients and lung eQTL data from the GTEx project. Then, GWAS and eQTL data were integrated by summary-data-based (SMR) methods using SNPs as instrumental variables (IVs). As a result, six protein-coding and five non-protein-coding genes regulated by nine SNPs were identified as significant risk factors for COVID-19. Functional analysis of these genes showed that UQCRH participates in cardiac muscle contraction, PPA2 is closely related to sudden cardiac failure (SCD), and OGT, as the interacting gene partner of PANO1, is associated with neurological disease. Observational studies show that myocardial damage, SCD, and neurological disease often occur in COVID-19 patients. Thus, our findings provide a potential molecular mechanism for understanding the complications of COVID-19.
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Affiliation(s)
- Yan Zong
- Department of Infectious Diseases, Yiwu Central Hospital, Jinhua, China
| | - Xiaofei Li
- Department of Infectious Diseases, Yiwu Central Hospital, Jinhua, China
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16
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Li C, Wang Y, Liu H, Zhang X, Baolige D, Zhao S, Hu W, Yang Y. Change in the Single Amino Acid Site 83 in Rabies Virus Glycoprotein Enhances the BBB Permeability and Reduces Viral Pathogenicity. Front Cell Dev Biol 2021; 8:632957. [PMID: 33634109 PMCID: PMC7900495 DOI: 10.3389/fcell.2020.632957] [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: 11/24/2020] [Accepted: 12/22/2020] [Indexed: 12/24/2022] Open
Abstract
Lab-attenuated rabies virus (RABV) is a highly cellular adaptation and less pathogenic than wild-type RABV. However, the molecular mechanisms that regulate the cellular adaptation and pathogenicity remain obscure. In this work, we isolated a wild-type RABV (CNIM1701) from a rabid bovine in northern China. The original CNIM1701 was lethal in adult mice and restricted replication in cell cultures. After 20 serial passages in the brains of suckling mice, the virus was renamed CNIM1701-P20, which was safe in adult mice and replicated well in cell cultures. In addition, sequence comparison analysis of the original CNIM1701 and CNIM1701-P20 identified 2 amino acid substitutions on G protein (Lys83 → Arg83 and Pro367 → Ser 367) related to pathogenesis and cellular adaptation. Using site-directed mutagenesis to exchange Lys83 with Arg83 and Pro367 with Ser 367 in the G protein of the RABV SAD strain, the pathogenicity of rSAD-K83R was significantly decreased. Our data indicate that the decreased pathogenicity of rSAD-K83R is due to increasing the expression of RABV-G, which also induced a higher level of apoptosis in infected cells. Furthermore, the K83 mutation induced high expression of MMP-2 and MMP-9 on DCs and promoted blood-brain barrier (BBB) permeability. These results demonstrate that the pathogenesis of RABV is partially dependent on G expression and BBB permeability, which may help in the design and development of highly safe, live-RABV vaccines.
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Affiliation(s)
- Chunfu Li
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yongzhi Wang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Huiting Liu
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Xinghua Zhang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Dalai Baolige
- Veterinary Research Institution, Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot, China
| | - Shihua Zhao
- Veterinary Research Institution, Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot, China
| | - Wei Hu
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yang Yang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
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17
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Shen F, Gan X, Zhong R, Feng J, Chen Z, Guo M, Li Y, Wu Z, Cai W, Xu B. Identifying Thyroid Carcinoma-Related Genes by Integrating GWAS and eQTL Data. Front Cell Dev Biol 2021; 9:645275. [PMID: 33614667 PMCID: PMC7889963 DOI: 10.3389/fcell.2021.645275] [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: 12/22/2020] [Accepted: 01/15/2021] [Indexed: 01/21/2023] Open
Abstract
Thyroid carcinoma (TC) is the most common endocrine malignancy. The incidence rate of thyroid cancer has increased rapidly in recent years. The occurrence and development of thyroid cancers are highly related to the massive genetic and epigenetic changes. Therefore, it is essential to explore the mechanism of thyroid cancer pathogenesis. Genome-Wide Association Studies (GWAS) have been widely used in various diseases. Researchers have found multiple single nucleotide polymorphisms (SNPs) are significantly related to TC. However, the biological mechanism of these SNPs is still unknown. In this paper, we used one GWAS dataset and two eQTL datasets, and integrated GWAS with expression quantitative trait loci (eQTL) in both thyroid and blood to explore the mechanism of mutations and causal genes of thyroid cancer. Finally, we found rs1912998 regulates the expression of IGFALS (P = 1.70E-06) and HAGH (P = 5.08E-07) in thyroid, which is significantly related to thyroid cancer. In addition, KEGG shows that these genes participate in multiple thyroid cancer-related pathways.
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Affiliation(s)
- Fei Shen
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Thyroid Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Xiaoxiong Gan
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Thyroid Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Ruiying Zhong
- Department of Hepatobiliary, Pancreatic and Splenic Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Jianhua Feng
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Thyroid Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Zhen Chen
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Thyroid Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Mengli Guo
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Thyroid Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Yayi Li
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Thyroid Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Zhaofeng Wu
- Department of Hepatobiliary, Pancreatic and Splenic Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wensong Cai
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Thyroid Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Bo Xu
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Thyroid Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
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18
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Tang J, Wu X, Mou M, Wang C, Wang L, Li F, Guo M, Yin J, Xie W, Wang X, Wang Y, Ding Y, Xue W, Zhu F. GIMICA: host genetic and immune factors shaping human microbiota. Nucleic Acids Res 2021; 49:D715-D722. [PMID: 33045729 PMCID: PMC7779047 DOI: 10.1093/nar/gkaa851] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/09/2020] [Accepted: 10/08/2020] [Indexed: 01/09/2023] Open
Abstract
Besides the environmental factors having tremendous impacts on the composition of microbial community, the host factors have recently gained extensive attentions on their roles in shaping human microbiota. There are two major types of host factors: host genetic factors (HGFs) and host immune factors (HIFs). These factors of each type are essential for defining the chemical and physical landscapes inhabited by microbiota, and the collective consideration of both types have great implication to serve comprehensive health management. However, no database was available to provide the comprehensive factors of both types. Herein, a database entitled 'Host Genetic and Immune Factors Shaping Human Microbiota (GIMICA)' was constructed. Based on the 4257 microbes confirmed to inhabit nine sites of human body, 2851 HGFs (1368 single nucleotide polymorphisms (SNPs), 186 copy number variations (CNVs), and 1297 non-coding ribonucleic acids (RNAs)) modulating the expression of 370 microbes were collected, and 549 HIFs (126 lymphocytes and phagocytes, 387 immune proteins, and 36 immune pathways) regulating the abundance of 455 microbes were also provided. All in all, GIMICA enabled the collective consideration not only between different types of host factor but also between the host and environmental ones, which is freely accessible without login requirement at: https://idrblab.org/gimica/.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Xianglu Wu
- Joint International Research Lab of Reproductive and Development, Department of Reproductive Biology, School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chuan Wang
- College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Lidan Wang
- College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Maiyuan Guo
- College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Wenqin Xie
- College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Xiaona Wang
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yingxiong Wang
- College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China.,Joint International Research Lab of Reproductive and Development, Department of Reproductive Biology, School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Yubin Ding
- Joint International Research Lab of Reproductive and Development, Department of Reproductive Biology, School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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19
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Liu B, Nan J, Zu X, Zhang X, Xiao Q. Identification of Genome Sequences of Polyphosphate-Accumulating Organisms by Machine Learning. Front Cell Dev Biol 2021; 8:626221. [PMID: 33537313 PMCID: PMC7848102 DOI: 10.3389/fcell.2020.626221] [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: 11/05/2020] [Accepted: 12/15/2020] [Indexed: 11/13/2022] Open
Abstract
In the field of sewage treatment, the identification of polyphosphate-accumulating organisms (PAOs) usually relies on biological experiments. However, biological experiments are not only complicated and time-consuming, but also costly. In recent years, machine learning has been widely used in many fields, but it is seldom used in the water treatment. The present work presented a high accuracy support vector machine (SVM) algorithm to realize the rapid identification and prediction of PAOs. We obtained 6,318 genome sequences of microorganisms from the publicly available microbial genome database for comparative analysis (MBGD). Minimap2 was used to compare the genomes of the obtained microorganisms in pairs, and read the overlap. The SVM model was established using the similarity of the genome sequences. In this SVM model, the average accuracy is 0.9628 ± 0.019 with 10-fold cross-validation. By predicting 2,652 microorganisms, 22 potential PAOs were obtained. Through the analysis of the predicted potential PAOs, most of them could be indirectly verified their phosphorus removal characteristics from previous reports. The SVM model we built shows high prediction accuracy and good stability.
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Affiliation(s)
- Bohan Liu
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, China
| | - Jun Nan
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, China
| | - Xuehui Zu
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, China
| | - Xinhui Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, China
| | - Qiliang Xiao
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, China
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20
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Liu Z, Zhang Y, Han X, Li C, Yang X, Gao J, Xie G, Du N. Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network. Front Cell Dev Biol 2020; 8:637. [PMID: 32850792 PMCID: PMC7432192 DOI: 10.3389/fcell.2020.00637] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 06/24/2020] [Indexed: 12/15/2022] Open
Abstract
Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. In recent years, long non-coding RNAs (lncRNAs) have been proven to play an important role in diseases, especially cancers. These lncRNAs execute their functions by regulating gene expression. Therefore, identifying lncRNAs which are related to cancers could help researchers gain a deeper understanding of cancer mechanisms and help them find treatment options. A large number of relationships between lncRNAs and cancers have been verified by biological experiments, which give us a chance to use computational methods to identify cancer-related lncRNAs. In this paper, we applied the convolutional neural network (CNN) to identify cancer-related lncRNAs by lncRNA's target genes and their tissue expression specificity. Since lncRNA regulates target gene expression and it has been reported to have tissue expression specificity, their target genes and expression in different tissues were used as features of lncRNAs. Then, the deep belief network (DBN) was used to unsupervised encode features of lncRNAs. Finally, CNN was used to predict cancer-related lncRNAs based on known relationships between lncRNAs and cancers. For each type of cancer, we built a CNN model to predict its related lncRNAs. We identified more related lncRNAs for 41 kinds of cancers. Ten-cross validation has been used to prove the performance of our method. The results showed that our method is better than several previous methods with area under the curve (AUC) 0.81 and area under the precision–recall curve (AUPR) 0.79. To verify the accuracy of our results, case studies have been done.
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Affiliation(s)
- Zihao Liu
- Department of Oncology, Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China.,Department of Oncology, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Xudong Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chenxi Li
- Department of Oncology, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xuhui Yang
- Department of Oncology, Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China
| | - Jie Gao
- Department of Oncology, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ganfeng Xie
- Department of Oncology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Nan Du
- Department of Oncology, Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China.,Department of Oncology, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, China
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21
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Zhao T, Hu Y, Zang T, Wang Y. Identifying Protein Biomarkers in Blood for Alzheimer's Disease. Front Cell Dev Biol 2020; 8:472. [PMID: 32626709 PMCID: PMC7314983 DOI: 10.3389/fcell.2020.00472] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 05/20/2020] [Indexed: 12/26/2022] Open
Abstract
Background: At present, the main diagnostic methods for Alzheimer's disease (AD) are positron emission tomography (PET) scanning of the brain and analysis of cerebrospinal fluid (CSF) sample, but these methods are expensive and harmful to patients. Recently, more researchers focus on diagnosing AD by detecting biomarkers in blood, which is a cheaper and harmless way. Therefore, identifying AD-related proteins in blood can help treatment and diagnosis. Methods: We proposed a hypothesis that similar diseases share similar proteins. Diseases with similar symptoms are caused by abnormalities of similar proteins. Assuming that the similarities between AD and other diseases obey the normal distribution, we developed an iterative method based on disease similarity (IBDS). We combined Elastic Network (EN) with Minimum angle regression (MAR) to find the optimal solution. Finally, we used case studies and Summary data Mendelian Random (SMR) to verify our method. Results: We selected 39 diseases which are highly related to AD. They correspond 1,481 kinds of proteins. One hundred and eighty-four proteins are reported to be related to AD in Uniprot and the number would be 284 with our method. The AUC of our method by cross-validation is 0.9251 which is much higher than previous methods. Conclusion: In this paper, we presented a novel method for prioritizing AD-related proteins. Seven proteins have tissue specificity in blood among these 284 proteins, which could be used to diagnose AD in future. Case studies and SMR have been used to prove the relationship between these 7 proteins and AD. Availability and Implementation: https://github.com/zty2009/Identifying-Protein-Biomarkers-in-Blood-for-Alzheimer-s-Disease.
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Affiliation(s)
- Tianyi Zhao
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yang Hu
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tianyi Zang
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yadong Wang
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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22
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Reply to Zhao et al.: NOMO1 is a potential target gene of MRTFB. Proc Natl Acad Sci U S A 2020; 117:7570-7571. [PMID: 32184332 DOI: 10.1073/pnas.2000772117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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