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Lu Y, Berenson A, Lane R, Guelin I, Li Z, Chen Y, Shah S, Yin M, Soto-Ugaldi LF, Fiszbein A, Fuxman Bass JI. A large-scale cancer-specific protein-DNA interaction network. Life Sci Alliance 2024; 7:e202402641. [PMID: 39013578 PMCID: PMC11252446 DOI: 10.26508/lsa.202402641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/18/2024] Open
Abstract
Cancer development and progression are generally associated with gene dysregulation, often resulting from changes in the transcription factor (TF) sequence or expression. Identifying key TFs involved in cancer gene regulation provides a framework for potential new therapeutics. This study presents a large-scale cancer gene TF-DNA interaction network, as well as an extensive promoter clone resource for future studies. Highly connected TFs bind to promoters of genes associated with either good or poor cancer prognosis, suggesting that strategies aimed at shifting gene expression balance between these two prognostic groups may be inherently complex. However, we identified potential for oncogene-targeted therapeutics, with half of the tested oncogenes being potentially repressed by influencing specific activators or bifunctional TFs. Finally, we investigate the role of intrinsically disordered regions within the key cancer-related TF ESR1 in DNA binding and transcriptional activity, and found that these regions can have complex trade-offs in TF function. Altogether, our study broadens our knowledge of the TFs involved in cancer gene regulation and provides a valuable resource for future studies and therapeutics.
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Affiliation(s)
- Yunwei Lu
- https://ror.org/05qwgg493 Biology Department, Boston University, Boston, MA, USA
| | - Anna Berenson
- https://ror.org/05qwgg493 Biology Department, Boston University, Boston, MA, USA
- https://ror.org/05qwgg493 Molecular Biology, Cellular Biology and Biochemistry Program, Boston University, Boston, MA, USA
| | - Ryan Lane
- https://ror.org/05qwgg493 Biology Department, Boston University, Boston, MA, USA
| | - Isabelle Guelin
- https://ror.org/05qwgg493 Biology Department, Boston University, Boston, MA, USA
| | - Zhaorong Li
- https://ror.org/05qwgg493 Bioinformatics Program, Boston University, Boston, MA, USA
| | - Yilin Chen
- https://ror.org/05qwgg493 Biology Department, Boston University, Boston, MA, USA
| | - Sakshi Shah
- https://ror.org/05qwgg493 Biology Department, Boston University, Boston, MA, USA
| | - Meimei Yin
- https://ror.org/05qwgg493 Biology Department, Boston University, Boston, MA, USA
| | | | - Ana Fiszbein
- https://ror.org/05qwgg493 Biology Department, Boston University, Boston, MA, USA
- https://ror.org/05qwgg493 Molecular Biology, Cellular Biology and Biochemistry Program, Boston University, Boston, MA, USA
- https://ror.org/05qwgg493 Bioinformatics Program, Boston University, Boston, MA, USA
| | - Juan Ignacio Fuxman Bass
- https://ror.org/05qwgg493 Biology Department, Boston University, Boston, MA, USA
- https://ror.org/05qwgg493 Molecular Biology, Cellular Biology and Biochemistry Program, Boston University, Boston, MA, USA
- https://ror.org/05qwgg493 Bioinformatics Program, Boston University, Boston, MA, USA
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2
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Lu Y, Berenson A, Lane R, Guelin I, Li Z, Chen Y, Shah S, Yin M, Soto-Ugaldi LF, Fiszbein A, Fuxman Bass JI. A large-scale cancer-specific protein-DNA interaction network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.24.577099. [PMID: 38352498 PMCID: PMC10862707 DOI: 10.1101/2024.01.24.577099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Cancer development and progression are generally associated with dysregulation of gene expression, often resulting from changes in transcription factor (TF) sequence or expression. Identifying key TFs involved in cancer gene regulation provides a framework for potential new therapeutics. This study presents a large-scale cancer gene TF-DNA interaction network as well as an extensive promoter clone resource for future studies. Most highly connected TFs do not show a preference for binding to promoters of genes associated with either good or poor cancer prognosis, suggesting that emerging strategies aimed at shifting gene expression balance between these two prognostic groups may be inherently complex. However, we identified potential for oncogene targeted therapeutics, with half of the tested oncogenes being potentially repressed by influencing specific activator or bifunctional TFs. Finally, we investigate the role of intrinsically disordered regions within the key cancer-related TF estrogen receptor ɑ (ESR1) on DNA binding and transcriptional activity, and found that these regions can have complex trade-offs in TF function. Altogether, our study not only broadens our knowledge of TFs involved in the cancer gene regulatory network but also provides a valuable resource for future studies, laying a foundation for potential therapeutic strategies targeting TFs in cancer.
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Affiliation(s)
- Yunwei Lu
- Biology Department, Boston University, Boston, MA, 02215, USA
| | - Anna Berenson
- Biology Department, Boston University, Boston, MA, 02215, USA
- Molecular Biology, Cellular Biology and Biochemistry Program, Boston University, Boston, MA, 02215, USA
| | - Ryan Lane
- Biology Department, Boston University, Boston, MA, 02215, USA
| | - Isabelle Guelin
- Biology Department, Boston University, Boston, MA, 02215, USA
| | - Zhaorong Li
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA
| | - Yilin Chen
- Biology Department, Boston University, Boston, MA, 02215, USA
| | - Sakshi Shah
- Biology Department, Boston University, Boston, MA, 02215, USA
| | - Meimei Yin
- Biology Department, Boston University, Boston, MA, 02215, USA
| | | | - Ana Fiszbein
- Biology Department, Boston University, Boston, MA, 02215, USA
- Molecular Biology, Cellular Biology and Biochemistry Program, Boston University, Boston, MA, 02215, USA
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA
| | - Juan Ignacio Fuxman Bass
- Biology Department, Boston University, Boston, MA, 02215, USA
- Molecular Biology, Cellular Biology and Biochemistry Program, Boston University, Boston, MA, 02215, USA
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA
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Duan M, Wang Y, Zhao D, Liu H, Zhang G, Li K, Zhang H, Huang L, Zhang R, Zhou F. Orchestrating information across tissues via a novel multitask GAT framework to improve quantitative gene regulation relation modeling for survival analysis. Brief Bioinform 2023; 24:bbad238. [PMID: 37427963 DOI: 10.1093/bib/bbad238] [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: 04/09/2023] [Revised: 05/29/2023] [Accepted: 06/08/2023] [Indexed: 07/11/2023] Open
Abstract
Survival analysis is critical to cancer prognosis estimation. High-throughput technologies facilitate the increase in the dimension of genic features, but the number of clinical samples in cohorts is relatively small due to various reasons, including difficulties in participant recruitment and high data-generation costs. Transcriptome is one of the most abundantly available OMIC (referring to the high-throughput data, including genomic, transcriptomic, proteomic and epigenomic) data types. This study introduced a multitask graph attention network (GAT) framework DQSurv for the survival analysis task. We first used a large dataset of healthy tissue samples to pretrain the GAT-based HealthModel for the quantitative measurement of the gene regulatory relations. The multitask survival analysis framework DQSurv used the idea of transfer learning to initiate the GAT model with the pretrained HealthModel and further fine-tuned this model using two tasks i.e. the main task of survival analysis and the auxiliary task of gene expression prediction. This refined GAT was denoted as DiseaseModel. We fused the original transcriptomic features with the difference vector between the latent features encoded by the HealthModel and DiseaseModel for the final task of survival analysis. The proposed DQSurv model stably outperformed the existing models for the survival analysis of 10 benchmark cancer types and an independent dataset. The ablation study also supported the necessity of the main modules. We released the codes and the pretrained HealthModel to facilitate the feature encodings and survival analysis of transcriptome-based future studies, especially on small datasets. The model and the code are available at http://www.healthinformaticslab.org/supp/.
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Affiliation(s)
- Meiyu Duan
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China, 130012
| | - Yueying Wang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China, 130012
| | - Dong Zhao
- School of Biology and Engineering, and Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou 550025, China
| | - Hongmei Liu
- School of Biology and Engineering, and Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou 550025, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
| | - Gongyou Zhang
- School of Biology and Engineering, and Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou 550025, China
| | - Kewei Li
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China, 130012
| | - Haotian Zhang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China, 130012
| | - Lan Huang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China, 130012
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
| | - Ruochi Zhang
- School of Artificial Intelligence, Jilin University, Changchun, China, 130012
| | - Fengfeng Zhou
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China, 130012
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
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Li H, Lin D, Yu Z, Li H, Zhao S, Hainisayimu T, Liu L, Wang K. A nomogram model based on the number of examined lymph nodes-related signature to predict prognosis and guide clinical therapy in gastric cancer. Front Immunol 2022; 13:947802. [PMID: 36405735 PMCID: PMC9667298 DOI: 10.3389/fimmu.2022.947802] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 09/30/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Increasing evidence suggests that the number of examined lymph nodes (ELNs) is strongly linked to the survivorship of gastric cancer (GC). The goal of this study was to assess the prognostic implications of the ELNs number and to construct an ELNs-based risk signature and nomogram model to predict overall survival (OS) characteristics in GC patients. METHODS This inception cohort study included 19,317 GC patients from the U.S. Surveillance, Epidemiology, and End Results (SEER) database, who were separated into a training group and an internal validation group. The nomogram was built with the training set, then internally verified with SEER data, and externally validated with two different data sets. Based on the RNA-seq data, ELNs-related DERNAs (DElncRNAs, DEmiRNAs, andDEmRNAs) and immune cells were identified. The LASSO-Cox regression analysis was utilized to construct ELNs-related DERNAs and immune cell prognostic signature in The Cancer Genome Atlas (TCGA) cohort. The OS of subgroups with high- and low-ELN signature was compared using the Kaplan-Meier (K-M) analysis. A nomogram was successfully constructed based on the ELNs signature and other clinical characteristics. The concordance index (C-index), calibration plot, receiver operating characteristic curve, and decision curve analysis (DCA) were all used to evaluate the nomogram model. The meta-analysis, the Gene Expression Profiling Interactive Analysis database, and reverse transcription-quantitative PCR (RT-qPCR) were utilized to validate the RNA expression or abundance of prognostic genes and immune cells between GC tissues and normal gastric tissues, respectively. Finally, we analyzed the correlations between immune checkpoints, chemotherapy drug sensitivity, and risk score. RESULTS The multivariate analysis revealed that the high ELNs improved OS compared with low ELNs (hazard ratio [HR] = 0.659, 95% confidence interval [CI]: 0.626-0.694, p < 0.0001). Using the training set, a nomogram incorporating ELNs was built and proven to have good calibration and discrimination (C-index [95% CI], 0.714 [0.710-0.718]), which was validated in the internal validation set (C-index [95% CI], 0.720 [0.714-0.726]), the TCGA set (C-index [95% CI], 0.693 [0.662-0.724]), and the Chinese set (C-index [95% CI], 0.750 [0.720-0.782]). An ELNs-related signature model based on ELNs group, regulatory T cells (Tregs), neutrophils, CDKN2B-AS1, H19, HOTTIP, LINC00643, MIR663AHG, TMEM236, ZNF705A, and hsa-miR-135a-5p was constructed by the LASSO-Cox regression analysis. The result showed that OS was remarkably lower in patients with high-ELNs signature compared with those with low-ELN signature (HR = 2.418, 95% CI: 1.804-3.241, p < 0.001). This signature performed well in predicting 1-, 3-, and 5-year survival (AUC [95% CI] = 0.688 [0.612-0.763], 0.744 [0.659-0.830], and 0.778 [0.647-0.909], respectively). The multivariate Cox analysis illustrated that the risk score was an independent predictor of survival for patients with GC. Moreover, the expression of prognostic genes (LINC00643, TMEM236, and hsa-miR-135a-5p) displayed differences between GC tissues and adjacent non-tumor tissues. The C-index of the nomogram that can be used to predict the OS of GC patients was 0.710 (95% CI: 0.663-0.753). Both the calibration plots and DCA showed that the nomogram has good predictive performance. Moreover, the signature was significantly correlated with the N stage and T stage. According to our analysis, GC patients in the low-ELN signature group may have a better immunotherapy response and OS outcome. CONCLUSIONS We explored the prognostic role of ELNs in GC and successfully constructed an ELNs signature linked to the GC prognosis in TCGA. The findings manifested that the signature is a powerful predictive indicator for patients with GC. The signature might contain potential biomarkers for treatment response prediction for GC patients. Additionally, we identified a novel and robust nomogram combining the characteristics of ELNs and clinical factors for predicting 1-, 3-, and 5-year OS in GC patients, which will facilitate personalized survival prediction and aid clinical decision-making in GC patients.
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Affiliation(s)
- Huling Li
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Dandan Lin
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Zhen Yu
- Department of Gastrointestinal Surgery, The Third Affiliated Hospital, Xinjiang Medical University, Urumqi, China
| | - Hui Li
- Central Laboratory of Xinjiang Medical University, Urumqi, China
| | - Shi Zhao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Tuersun Hainisayimu
- Department of Biochemistry and Molecular Biology, Basic Medicine School, Xinjiang Medical University, Urumqi, China
| | - Lin Liu
- Department of Gastrointestinal Surgery, The Third Affiliated Hospital, Xinjiang Medical University, Urumqi, China,*Correspondence: Kai Wang, ; Lin Liu,
| | - Kai Wang
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China,*Correspondence: Kai Wang, ; Lin Liu,
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Zhang Y, Liu Y, Wang Z, Wang M, Xiong S, Huang G, Gong M. Uncovering the Relationship between Tissue-Specific TF-DNA Binding and Chromatin Features through a Transformer-Based Model. Genes (Basel) 2022; 13:1952. [PMID: 36360189 PMCID: PMC9690320 DOI: 10.3390/genes13111952] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/19/2022] [Accepted: 10/23/2022] [Indexed: 09/08/2024] Open
Abstract
Chromatin features can reveal tissue-specific TF-DNA binding, which leads to a better understanding of many critical physiological processes. Accurately identifying TF-DNA bindings and constructing their relationships with chromatin features is a long-standing goal in the bioinformatic field. However, this has remained elusive due to the complex binding mechanisms and heterogeneity among inputs. Here, we have developed the GHTNet (General Hybrid Transformer Network), a transformer-based model to predict TF-DNA binding specificity. The GHTNet decodes the relationship between tissue-specific TF-DNA binding and chromatin features via a specific input scheme of alternative inputs and reveals important gene regions and tissue-specific motifs. Our experiments show that the GHTNet has excellent performance, achieving about a 5% absolute improvement over existing methods. The TF-DNA binding mechanism analysis shows that the importance of TF-DNA binding features varies across tissues. The best predictor is based on the DNA sequence, followed by epigenomics and shape. In addition, cross-species studies address the limited data, thus providing new ideas in this case. Moreover, the GHTNet is applied to interpret the relationship among TFs, chromatin features, and diseases associated with AD46 tissue. This paper demonstrates that the GHTNet is an accurate and robust framework for deciphering tissue-specific TF-DNA binding and interpreting non-coding regions.
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Affiliation(s)
- Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yuhang Liu
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Zixuan Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Maocheng Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Shuwen Xiong
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Guo Huang
- School of Electronic Information and Artificial Intelligence, Leshan Normal University, Leshan 614000, China
| | - Meiqin Gong
- West China Second University Hospital, Sichuan University, Chengdu 610041, China
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Shen WK, Chen SY, Gan ZQ, Zhang YZ, Yue T, Chen MM, Xue Y, Hu H, Guo AY. AnimalTFDB 4.0: a comprehensive animal transcription factor database updated with variation and expression annotations. Nucleic Acids Res 2022; 51:D39-D45. [PMID: 36268869 PMCID: PMC9825474 DOI: 10.1093/nar/gkac907] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/30/2022] [Accepted: 10/05/2022] [Indexed: 01/29/2023] Open
Abstract
Transcription factors (TFs) are proteins that interact with specific DNA sequences to regulate gene expression and play crucial roles in all kinds of biological processes. To keep up with new data and provide a more comprehensive resource for TF research, we updated the Animal Transcription Factor Database (AnimalTFDB) to version 4.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB4/) with up-to-date data and functions. We refined the TF family rules and prediction pipeline to predict TFs in genome-wide protein sequences from Ensembl. As a result, we predicted 274 633 TF genes and 150 726 transcription cofactor genes in AnimalTFDB 4.0 in 183 animal genomes, which are 86 more species than AnimalTFDB 3.0. Besides double data volume, we also added the following new annotations and functions to the database: (i) variations (including mutations) on TF genes in various human cancers and other diseases; (ii) predicted post-translational modification sites (including phosphorylation, acetylation, methylation and ubiquitination sites) on TFs in 8 species; (iii) TF regulation in autophagy; (iv) comprehensive TF expression annotation for 38 species; (v) exact and batch search functions allow users to search AnimalTFDB flexibly. AnimalTFDB 4.0 is a useful resource for studying TF and transcription regulation, which contains comprehensive annotation and classification of TFs and transcription cofactors.
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Affiliation(s)
| | | | - Zi-Quan Gan
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yu-Zhu Zhang
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Tao Yue
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Miao-Miao Chen
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yu Xue
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Hui Hu
- Correspondence may also be addressed to Hui Hu.
| | - An-Yuan Guo
- To whom correspondence should be addressed. Tel: +86 27 8779 3177; Fax: +86 27 8779 3177;
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Zhou K, Chen Z, Du X, Huang Y, Qin J, Wen L, Pan X, Lin Y. SMRT Sequencing Reveals Candidate Genes and Pathways With Medicinal Value in Cipangopaludina chinensis. Front Genet 2022; 13:881952. [PMID: 35783279 PMCID: PMC9243326 DOI: 10.3389/fgene.2022.881952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/26/2022] [Indexed: 12/03/2022] Open
Abstract
Cipangopaludina chinensis is an economically important aquatic snail with high medicinal value. However, molecular biology research on C. chinensis is limited by the lack of a reference genome, so the analysis of its transcripts is an important step to study the regulatory genes of various substances in C. chinensis. Herein, we conducted the first full-length transcriptome analysis of C. chinensis using PacBio single-molecule real-time (SMRT) sequencing technology. We identified a total of 26,312 unigenes with an average length of 2,572 bp, of which the largest number of zf-c2h2 transcription factor families (120,18.24%) were found, and also observed that the majority of the 8,058 SSRs contained 4-7 repeat units, which provided data for subsequent work on snail genetics Subsequently, 91.86% (24,169) of the genes were successfully annotated to the four major databases, while the highest homology was observed with Pomacea canaliculata. Functional annotation revealed that the majority of transcripts were enriched in metabolism, signal transduction and Immune-related pathways, and several candidate genes involved in drug metabolism and immune response were identified (e.g., CYP1A1, CYP2J, CYP2U1, GST, ,PIK3, PDE3A, PRKAG). This study lays a foundation for future molecular biology research and provides a reference for studying genes associated with the medicinal value of C. chinensis.
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Affiliation(s)
| | | | | | | | | | | | | | - Yong Lin
- *Correspondence: Xianhui Pan, ; Yong Lin,
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Liang X, Chen Y, Fan Y. Bioinformatics approach to identify common gene signatures of patients with coronavirus 2019 and lung adenocarcinoma. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:22012-22030. [PMID: 34775559 PMCID: PMC8590527 DOI: 10.1007/s11356-021-17321-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/28/2021] [Indexed: 02/05/2023]
Abstract
Coronavirus disease 2019 (COVID-19) continues as a global pandemic. Patients with lung cancer infected with COVID-19 may develop severe disease or die. Treating such patients severely burdens overwhelmed healthcare systems. Here, we identified potential pathological mechanisms shared between patients with COVID-19 and lung adenocarcinoma (LUAD). Co-expressed, differentially expressed genes (DEGs) in patients with COVID-19 and LUAD were identified and used to construct a protein-protein interaction (PPI) network and to perform enrichment analysis. We used the NetworkAnalyst platform to establish a co-regulatory of the co-expressed DEGs, and we used Spearman's correlation to evaluate the significance of associations of hub genes with immune infiltration and immune checkpoints. Analysis of three datasets identified 112 shared DEGs, which were used to construct a protein-PPI network. Subsequent enrichment analysis revealed co-expressed genes related to biological process (BP), molecular function (MF), and cellular component (CC) as well as to pathways, specific organs, cells, and diseases. Ten co-expressed hub genes were employed to construct a gene-miRNA, transcription factor (TF)-gene, and TF-miRNA network. Hub genes were significantly associated with immune infiltration and immune checkpoints. Finally, methylation level of hub genes in LUAD was obtained via UALCAN database. The present multi-dimensional study reveals commonality in specific gene expression by patients with COVID-19 and LUAD. These findings provide insights into developing strategies for optimising the management and treatment of patients with LUAD with COVID-19.
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Affiliation(s)
- Xiao Liang
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Yali Chen
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Yuchao Fan
- Department of Anesthesiology, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, Renmin South Road, Chengdu, 610041, Sichuan Province, China.
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9
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Zhang L, Yang Y, Chai L, Li Q, Liu J, Lin H, Liu L. A deep learning model to identify gene expression level using cobinding transcription factor signals. Brief Bioinform 2021; 23:6447678. [PMID: 34864886 DOI: 10.1093/bib/bbab501] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/13/2021] [Accepted: 11/01/2021] [Indexed: 01/02/2023] Open
Abstract
Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the correlation between TFs and surveyed the TF interaction networks associated with gene expression in GM12878 and K562 cell lines. We identified six TF modules associated with gene expression in each cell line. Furthermore, according to the enrichment characteristics of TFs in these TF modules around a target gene, a convolutional neural network model, called TFCNN, was constructed to identify gene expression level. Results showed that the TFCNN model achieved a good prediction performance for gene expression. The average of the area under receiver operating characteristics curve (AUC) can reach up to 0.975 and 0.976, respectively in GM12878 and K562 cell lines. By comparison, we found that the TFCNN model outperformed the prediction models based on SVM and LDA. This is due to the TFCNN model could better extract the combinatorial interaction among TFs. Further analysis indicated that the abundant binding of regulatory TFs dominates expression of target genes, while the cooperative interaction between TFs has a subtle regulatory effects. And gene expression could be regulated by different TF combinations in a nonlinear way. These results are helpful for deciphering the mechanism of TF combination regulating gene expression.
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Affiliation(s)
- Lirong Zhang
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Yanchao Yang
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Lu Chai
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Qianzhong Li
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Junjie Liu
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Li Liu
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
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