Functional characterization of disease/comorbidity-associated lncRNA

Jing Tang , Yongheng Wang , Jianbo Fu , Xianglu Wu , Zhijie Han , Chuan Wang , Maiyuan Guo , Yingxiong Wang , Yubin Ding , Bo Yang , Feng Zhu

Quant. Biol. ›› 2021, Vol. 9 ›› Issue (4) : 411 -425.

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Quant. Biol. ›› 2021, Vol. 9 ›› Issue (4) : 411 -425. DOI: 10.15302/J-QB-021-0247
RESEARCH ARTICLE
RESEARCH ARTICLE

Functional characterization of disease/comorbidity-associated lncRNA

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Abstract

Background: Functional characterization of the long noncoding RNAs (lncRNAs) in disease attracts great attention, which results in a limited number of experimentally characterized lncRNAs. The major problems underlying the lack of experimental verifications are considered to come from the significant false-positive assignments and extensive genetic-heterogeneity of disease. These problems are even worse when it comes to the functional characterization in comorbidity (simultaneous/sequential presence of multiple diseases in a patient, and showing much wider prevalence, poorer treatment-response and longer illness-course than a single disease).

Methods: Herein, FCCLnc was developed to characterize lncRNA function by (1) integrating diverse SNPs that were associated with 193 diseases standardized by International Classification of Diseases (ICD-11), (2) condition-specific expression of lncRNAs, (3) weighted correlation network of lncRNAs and protein-coding neighboring genes.

Results: FCCLnc can characterize lncRNA function in both disease and comorbidity by not only controlling false discovery but also tolerating their disease heterogeneity. Moreover, FCCLnc can provide interactive visualization and full download of lncRNA-centered co-expression network.

Conclusion: In summary, FCCLnc is unique in characterizing lncRNA function in diverse diseases and comorbidities and is highly expected to emerge to be an indispensable complement to other available tools. FCCLnc is accessible at https://idrblab.org/fcclnc/.

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Keywords

comorbidity / long noncoding RNA / functional characterization / disease-associated SNPs / guilt-by-association

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Jing Tang, Yongheng Wang, Jianbo Fu, Xianglu Wu, Zhijie Han, Chuan Wang, Maiyuan Guo, Yingxiong Wang, Yubin Ding, Bo Yang, Feng Zhu. Functional characterization of disease/comorbidity-associated lncRNA. Quant. Biol., 2021, 9(4): 411-425 DOI:10.15302/J-QB-021-0247

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References

[1]

Kopp, F. and Mendell, J. T. (2018) Functional classification and experimental dissection of long noncoding RNAs. Cell, 172, 393–407

[2]

Zhou, J., Zhang, S., Wang, H. and Sun, H. (2017) LncFunNet: an integrated computational framework for identification of functional long noncoding RNAs in mouse skeletal muscle cells. Nucleic Acids Res., 45, e108

[3]

Antonov, I. V., Mazurov, E., Borodovsky, M. and Medvedeva, Y. A. (2019) Prediction of lncRNAs and their interactions with nucleic acids: benchmarking bioinformatics tools. Brief. Bioinform., 20, 551–564

[4]

Yin, J., Sun, W., Li, F., Hong, J., Li, X., Zhou, Y., Lu, Y., Liu, M., Zhang, X., Chen, N., (2020) VARIDT 1.0: variability of drug transporter database. Nucleic Acids Res., 48, D1042–D1050

[5]

Jiang, S., Cheng, S. J., Ren, L. C., Wang, Q., Kang, Y. J., Ding, Y., Hou, M., Yang, X. X., Lin, Y., Liang, N., (2019) An expanded landscape of human long noncoding RNA. Nucleic Acids Res., 47, 7842–7856

[6]

Stojic, L., Niemczyk, M., Orjalo, A., Ito, Y., Ruijter, A. E., Uribe-Lewis, S., Joseph, N., Weston, S., Menon, S., Odom, D. T., (2016) Transcriptional silencing of long noncoding RNA GNG12-AS1 uncouples its transcriptional and product-related functions. Nat. Commun., 7, 10406

[7]

Kondrashov, A. V., Kiefmann, M., Ebnet, K., Khanam, T., Muddashetty, R. S. and Brosius, J. (2005) Inhibitory effect of naked neural BC1 RNA or BC200 RNA on eukaryotic in vitro translation systems is reversed by poly(A)-binding protein (PABP). J. Mol. Biol., 353, 88–103

[8]

Signal, B., Gloss, B. S. and Dinger, M. E. (2016) Computational approaches for functional prediction and characterisation of long noncoding RNAs. Trends Genet., 32, 620–637

[9]

Fu, T. T., Tu, G., Ping, M., Zheng, G. X., Yang, F. Y., Yang, J. Y., Zhang, Y., Yao, X. J., Xue, W. W. and Zhu, F. (2020) Subtype-selective mechanisms of negative allosteric modulators binding to group I metabotropic glutamate receptors. Acta Pharmacol. Sin., doi: 10.1038/s41401-020-00541-z

[10]

Yang, Q., Li, B., Chen, S., Tang, J., Li, Y., Li, Y., Zhang, S., Shi, C., Zhang, Y., Mou, M., (2021) MMEASE: Online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis. J. Proteomics, 232, 104023

[11]

Qu, S., Shi, Q., Xu, J., Yi, W. and Fan, H. (2020) Weighted gene coexpression network analysis reveals the dynamic transcriptome regulation and prognostic biomarkers of hepatocellular carcinoma. Evol. Bioinform. Online, 16, 1176934320920562

[12]

Zhou, Y., Lutz, P. E., Wang, Y. C., Ragoussis, J. and Turecki, G. (2018) Global long non-coding RNA expression in the rostral anterior cingulate cortex of depressed suicides. Transl. Psychiatry, 8, 224

[13]

Li, Y. H., Li, X. X., Hong, J. J., Wang, Y. X., Fu, J. B., Yang, H., Yu, C. Y., Li, F. C., Hu, J., Xue, W. W., (2020) Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs. Brief. Bioinform., 21, 649–662

[14]

Tang, J., Mou, M., Wang, Y., Luo, Y. and Zhu, F. (2020) MetaFS: Performance assessment of biomarker discovery in metaproteomics. Brief. Bioinform., bbaa105

[15]

Chen, G., Wang, Z., Wang, D., Qiu, C., Liu, M., Chen, X., Zhang, Q., Yan, G. and Cui, Q. (2013) LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res., 41, D983–D986

[16]

Chen, Y. G., Satpathy, A. T. and Chang, H. Y. (2017) Gene regulation in the immune system by long noncoding RNAs. Nat. Immunol., 18, 962–972

[17]

Tang, J., Wang, Y., Fu, J., Zhou, Y., Luo, Y., Zhang, Y., Li, B., Yang, Q., Xue, W., Lou, Y., (2020) A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies. Brief. Bioinform., 21, 1378–1390

[18]

Hong, J., Luo, Y., Mou, M., Fu, J., Zhang, Y., Xue, W., Xie, T., Tao, L., Lou, Y. and Zhu, F. (2020) Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery. Brief. Bioinform., 21, 1825–1836

[19]

Lu, C., Wei, Y., Wang, X., Zhang, Z., Yin, J., Li, W., Chen, L., Lyu, X., Shi, Z., Yan, W., (2020) DNA-methylation-mediated activating of lncRNA SNHG12 promotes temozolomide resistance in glioblastoma. Mol. Cancer, 19, 28

[20]

Yin, J., Li, F., Zhou, Y., Mou, M., Lu, Y., Chen, K., Xue, J., Luo, Y., Fu, J., He, X., (2021) INTEDE: interactome of drug-metabolizing enzymes. Nucleic Acids Res., 49, D1233–D1243

[21]

Bao, Z., Yang, Z., Huang, Z., Zhou, Y., Cui, Q. and Dong, D. (2019) LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases. Nucleic Acids Res., 47, D1034–D1037

[22]

Parikshak, N. N., Swarup, V., Belgard, T. G., Irimia, M., Ramaswami, G., Gandal, M. J., Hartl, C., Leppa, V., Ubieta, L. T., Huang, J., (2016) Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature, 540, 423–427

[23]

Yang, Y., Chen, L., Gu, J., Zhang, H., Yuan, J., Lian, Q., Lv, G., Wang, S., Wu, Y., Yang, Y. T., (2017) Recurrently deregulated lncRNAs in hepatocellular carcinoma. Nat. Commun., 8, 14421

[24]

Volders, P. J., Anckaert, J., Verheggen, K., Nuytens, J., Martens, L., Mestdagh, P. and Vandesompele, J. (2019) LNCipedia 5: towards a reference set of human long non-coding RNAs. Nucleic Acids Res., 47, D135–D139

[25]

Alam, T., Uludag, M., Essack, M., Salhi, A., Ashoor, H., Hanks, J. B., Kapfer, C., Mineta, K., Gojobori, T. and Bajic, V. B. (2017) FARNA: knowledgebase of inferred functions of non-coding RNA transcripts. Nucleic Acids Res, 45, 2838–2848

[26]

Liley, J., Todd, J. A. and Wallace, C. (2017) A method for identifying genetic heterogeneity within phenotypically defined disease subgroups. Nat. Genet., 49, 310–316

[27]

Yan, X., Liang, A., Gomez, J., Cohn, L., Zhao, H. and Chupp, G. L. (2017) A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression. BMC Bioinformatics, 18, 309

[28]

Kornienko, A. E., Dotter, C. P., Guenzl, P. M., Gisslinger, H., Gisslinger, B., Cleary, C., Kralovics, R., Pauler, F. M. and Barlow, D. P. (2016) Long non-coding RNAs display higher natural expression variation than protein-coding genes in healthy humans. Genome Biol., 17, 14

[29]

Peng, F., Wang, R., Zhang, Y., Zhao, Z., Zhou, W., Chang, Z., Liang, H., Zhao, W., Qi, L., Guo, Z., (2017) Differential expression analysis at the individual level reveals a lncRNA prognostic signature for lung adenocarcinoma. Mol. Cancer, 16, 98

[30]

Castellanos-Rubio, A. and Ghosh, S. (2019) Disease-associated SNPs in inflammation-related lncRNAs. Front. Immunol., 10, 420

[31]

Han, Z., Xue, W., Tao, L., Lou, Y., Qiu, Y. and Zhu, F. (2020) Genome-wide identification and analysis of the eQTL lncRNAs in multiple sclerosis based on RNA-seq data. Brief. Bioinform., 21, 1023–1037

[32]

Li, P., Guo, M., Wang, C., Liu, X. and Zou, Q. (2015) An overview of SNP interactions in genome-wide association studies. Brief. Funct. Genomics, 14, 143–155

[33]

Ecker, S., Pancaldi, V., Rico, D. and Valencia, A. (2015) Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia. Genome Med., 7, 8

[34]

Li, L., Cheng, W. Y., Glicksberg, B. S., Gottesman, O., Tamler, R., Chen, R., Bottinger, E. P. and Dudley, J. T. (2015) Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci. Transl. Med., 7, 311ra174

[35]

Nguyen, Q. and Carninci, P. (2016) Expression specificity of disease-associated lncRNAs: toward personalized medicine. Curr. Top. Microbiol. Immunol., 394, 237–258

[36]

Shah, M. Y., Ferracin, M., Pileczki, V., Chen, B., Redis, R., Fabris, L., Zhang, X., Ivan, C., Shimizu, M., Rodriguez-Aguayo, C., (2018) Cancer-associated rs6983267 SNP and its accompanying long noncoding RNA CCAT2 induce myeloid malignancies via unique SNP-specific RNA mutations. Genome Res., 28, 432–447

[37]

Liu, S. J. and Lim, D. A. (2018) Modulating the expression of long non-coding RNAs for functional studies. EMBO Rep., 19, e46955

[38]

Wang, Y., Li, F., Zhang, Y., Zhou, Y., Tan, Y., Chen, Y. and Zhu, F. (2020) Databases for the targeted COVID-19 therapeutics. Br. J. Pharmacol., 177, 4999–5001

[39]

Tang, J., Wang, Y., Luo, Y., Fu, J., Zhang, Y., Li, Y., Xiao, Z., Lou, Y., Qiu, Y. and Zhu, F. (2020) Computational advances of tumor marker selection and sample classification in cancer proteomics. Comput. Struct. Biotechnol. J., 18, 2012–2025

[40]

Barnett, K., Mercer, S. W., Norbury, M., Watt, G., Wyke, S. and Guthrie, B. (2012) Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet, 380, 37–43

[41]

Zhang, Y., Ying, J. B., Hong, J. J., Li, F. C., Fu, T. T., Yang, F. Y., Zheng, G. X., Yao, X. J., Lou, Y., Qiu, Y., (2019) How does chirality determine the selective inhibition of histone deacetylase 6? A lesson from Trichostatin A enantiomers based on molecular dynamics. ACS Chem. Neurosci., 10, 2467–2480

[42]

Cui, X., Yang, Q., Li, B., Tang, J., Zhang, X., Li, S., Li, F., Hu, J., Lou, Y., Qiu, Y., (2019) Assessing the effectiveness of direct data merging strategy in long-term and large-scale pharmacometabonomics. Front. Pharmacol., 10, 127

[43]

Xue, W., Yang, F., Wang, P., Zheng, G., Chen, Y., Yao, X. and Zhu, F. (2018) What contributes to serotonin-norepinephrine reuptake inhibitors’ dual-targeting mechanism? The key role of transmembrane domain 6 in human serotonin and norepinephrine transporters revealed by molecular dynamics simulation. ACS Chem. Neurosci., 9, 1128–1140

[44]

McIntyre, R. S., Rosenbluth, M., Ramasubbu, R., Bond, D. J., Taylor, V. H., Beaulieu, S. and Schaffer, A., and the Canadian Network for Mood and Anxiety Treatments (CANMAT) Task Force. (2012) Managing medical and psychiatric comorbidity in individuals with major depressive disorder and bipolar disorder. Ann Clin Psychiatry, 24, 163–169

[45]

Kato, M. and Natarajan, R. (2014) Diabetic nephropathy‒emerging epigenetic mechanisms. Nat. Rev. Nephrol., 10, 517–530

[46]

Reddy, M. A., Zhang, E. and Natarajan, R. (2015) Epigenetic mechanisms in diabetic complications and metabolic memory. Diabetologia, 58, 443–455

[47]

Geronazzo-Alman, L., Guffanti, G., Eisenberg, R., Fan, B., Musa, G. J., Wicks, J., Bresnahan, M., Duarte, C. S. and Hoven, C. (2018) Comorbidity classes and associated impairment, demographics and 9/11-exposures in 8,236 children and adolescents. J. Psychiatr. Res., 96, 171–177

[48]

Schuckit, M. A. (2006) Comorbidity between substance use disorders and psychiatric conditions. Addiction, 101, 76–88

[49]

Gao, Y., Wang, P., Wang, Y., Ma, X., Zhi, H., Zhou, D., Li, X., Fang, Y., Shen, W., Xu, Y., (2019) Lnc2Cancer v2.0: updated database of experimentally supported long non-coding RNAs in human cancers. Nucleic Acids Res., 47, D1028–D1033

[50]

Cheng, L., Wang, P., Tian, R., Wang, S., Guo, Q., Luo, M., Zhou, W., Liu, G., Jiang, H. and Jiang, Q. (2019) LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res., 47, D140–D144

[51]

Frankish, A., Diekhans, M., Ferreira, A. M., Johnson, R., Jungreis, I., Loveland, J., Mudge, J. M., Sisu, C., Wright, J., Armstrong, J., (2019) GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res., 47, D766–D773

[52]

Ma, L., Cao, J., Liu, L., Du, Q., Li, Z., Zou, D., Bajic, V. B. and Zhang, Z. (2019) LncBook: a curated knowledgebase of human long non-coding RNAs. Nucleic Acids Res., 47, D128–D134

[53]

Yang, Q. X., Wang, Y. X., Li, F. C., Zhang, S., Luo, Y. C., Li, Y., Tang, J., Li, B., Chen, Y. Z., Xue, W. W., (2019) Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence-based method of enhanced reproducibility. CNS Neurosci. Ther., 25, 1054–1063

[54]

Fu, J., Tang, J., Wang, Y., Cui, X., Yang, Q., Hong, J., Li, X., Li, S., Chen, Y., Xue, W., (2018) Discovery of the consistently well-performed analysis chain for SWATH-MS based pharmacoproteomic quantification. Front. Pharmacol., 9, 681

[55]

Miao, Y. R., Liu, W., Zhang, Q. and Guo, A. Y. (2018) lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs. Nucleic Acids Res., 46, D276–D280

[56]

Paraskevopoulou, M. D., Vlachos, I. S., Karagkouni, D., Georgakilas, G., Kanellos, I., Vergoulis, T., Zagganas, K., Tsanakas, P., Floros, E., Dalamagas, T., (2016) DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts. Nucleic Acids Res., 44, D231–D238

[57]

Zheng, L. L., Li, J. H., Wu, J., Sun, W. J., Liu, S., Wang, Z. L., Zhou, H., Yang, J. H. and Qu, L. H. (2016) deepBase v2.0: identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data. Nucleic Acids Res., 44, D196–D202

[58]

Li, X. X., Yin, J., Tang, J., Li, Y., Yang, Q., Xiao, Z., Zhang, R., Wang, Y., Hong, J., Tao, L., (2018) Determining the balance between drug efficacy and safety by the network and biological system profile of its therapeutic target. Front. Pharmacol., 9, 1245

[59]

Quek, X. C., Thomson, D. W., Maag, J. L., Bartonicek, N., Signal, B., Clark, M. B., Gloss, B. S. and Dinger, M. E. (2015) lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs. Nucleic Acids Res., 43, D168–D173

[60]

Fu, T., Zheng, G., Tu, G., Yang, F., Chen, Y., Yao, X., Li, X., Xue, W. and Zhu, F. (2018) Exploring the binding mechanism of metabotropic glutamate receptor 5 negative allosteric modulators in clinical trials by molecular dynamics simulations. ACS Chem. Neurosci., 9, 1492–1502

[61]

Liu, K., Yan, Z., Li, Y. and Sun, Z. (2013) Linc2GO: a human LincRNA function annotation resource based on ceRNA hypothesis. Bioinformatics, 29, 2221–2222

[62]

Xue, W., Wang, P., Tu, G., Yang, F., Zheng, G., Li, X., Li, X., Chen, Y., Yao, X. and Zhu, F. (2018) Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder. Phys. Chem. Chem. Phys., 20, 6606–6616

[63]

Liao, Z. J., Li, D. P., Wang, X. R., Li, L. S. and Zou, Q. (2018) Cancer diagnosis through isomiR expression with machine learning method. Curr. Bioinform., 13, 57–63

[64]

Ke, L., Yang, D. C., Wang, Y., Ding, Y. and Gao, G. (2020) AnnoLnc2: the one-stop portal to systematically annotate novel lncRNAs for human and mouse. Nucleic Acids Res., 48, W230–W238

[65]

Zhao, Z., Bai, J., Wu, A., Wang, Y., Zhang, J., Wang, Z., Li, Y., Xu, J. and Li, X. (2015) Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data. Database (Oxford), 2015, bav082

[66]

Guo, X., Gao, L., Liao, Q., Xiao, H., Ma, X., Yang, X., Luo, H., Zhao, G., Bu, D., Jiao, F., (2013) Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks. Nucleic Acids Res., 41, e35

[67]

Wang, P., Zhang, X., Fu, T., Li, S., Li, B., Xue, W., Yao, X., Chen, Y. and Zhu, F. (2017) Differentiating physicochemical properties between addictive and nonaddictive ADHD drugs revealed by molecular dynamics simulation studies. ACS Chem. Neurosci., 8, 1416–1428

[68]

Li, Y. H., Xu, J. Y., Tao, L., Li, X. F., Li, S., Zeng, X., Chen, S. Y., Zhang, P., Qin, C., Zhang, C., (2016) SVM-Prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity. PLoS One, 11, e0155290

[69]

Li, B., Tang, J., Yang, Q., Cui, X., Li, S., Chen, S., Cao, Q., Xue, W., Chen, N. and Zhu, F. (2016) Performance evaluation and online realization of data-driven normalization methods used in LC/MS based untargeted metabolomics analysis. Sci. Rep., 6, 38881

[70]

The Lancet. (2019) ICD-11. Lancet, 393, 2275

[71]

Wang, Y., Zhang, S., Li, F., Zhou, Y., Zhang, Y., Wang, Z., Zhang, R., Zhu, J., Ren, Y., Tan, Y., (2020) Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res, 48, D1031–D1041

[72]

Li, Y. H., Yu, C. Y., Li, X. X., Zhang, P., Tang, J., Yang, Q., Fu, T., Zhang, X., Cui, X., Tu, G., (2018) Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res., 46, D1121–D1127

[73]

Otasek, D., Morris, J. H., Bouças, J., Pico, A. R. and Demchak, B. (2019) Cytoscape Automation: empowering workflow-based network analysis. Genome Biol., 20, 185

[74]

Ravi, A., Koster, J., Dijkhuis, A., Bal, S. M., Sabogal Piñeros, Y. S., Bonta, P. I., Majoor, C. J., Sterk, P. J. and Lutter, R. (2019) Interferon-induced epithelial response to rhinovirus 16 in asthma relates to inflammation and FEV1. J. Allergy Clin. Immunol., 143, 442–447.e10

[75]

Pai, S., Li, P., Killinger, B., Marshall, L., Jia, P., Liao, J., Petronis, A., Szabó P. E. and Labrie, V. (2019) Differential methylation of enhancer at IGF2 is associated with abnormal dopamine synthesis in major psychosis. Nat. Commun., 10, 2046

[76]

Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., Marshall, K. A., Phillippy, K. H., Sherman, P. M., Holko, M., (2013) NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res., 41, D991–D995

[77]

Herring, B. P., Chen, M., Mihaylov, P., Hoggatt, A. M., Gupta, A., Nakeeb, A., Choi, J. N. and Wo, J. M. (2019) Transcriptome profiling reveals significant changes in the gastric muscularis externa with obesity that partially overlap those that occur with idiopathic gastroparesis. BMC Med. Genomics, 12, 89

[78]

Speake, C., Skinner, S. O., Berel, D., Whalen, E., Dufort, M. J., Young, W. C., Odegard, J. M., Pesenacker, A. M., Gorus, F. K., James, E. A., (2019) A composite immune signature parallels disease progression across T1D subjects. JCI Insight, 4, e126917

[79]

Hutter, C. and Zenklusen, J. C. (2018) The Cancer Genome Atlas: creating lasting value beyond its data. Cell, 173, 283–285

[80]

Hu, J., Xu, J., Pang, L., Zhao, H., Li, F., Deng, Y., Liu, L., Lan, Y., Zhang, X., Zhao, T., (2016) Systematically characterizing dysfunctional long intergenic non-coding RNAs in multiple brain regions of major psychosis. Oncotarget, 7, 71087–71098

[81]

Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M. and Barabási, A. L. (2007) The human disease network. Proc. Natl. Acad. Sci. USA, 104, 8685–8690

[82]

Ko, Y., Cho, M., Lee, J. S. and Kim, J. (2016) Identification of disease comorbidity through hidden molecular mechanisms. Sci. Rep., 6, 39433

[83]

Leng, L., Zhang, C., Ren, L. and Li, Q. (2019) Construction of a long noncoding RNA-mediated competitive endogenous RNA network reveals global patterns and regulatory markers in gestational diabetes. Int J Mol Med, 43, 927–935

[84]

Brent, G. A. (2012) Mechanisms of thyroid hormone action. J. Clin. Invest., 122, 3035–3043

[85]

Pearce, E. N. (2012) Thyroid hormone and obesity. Curr. Opin. Endocrinol. Diabetes Obes., 19, 408–413

[86]

Biondi, B. (2010) Thyroid and obesity: an intriguing relationship. J. Clin. Endocrinol. Metab., 95, 3614–3617

[87]

Sinha, R. A., Singh, B. K. and Yen, P. M. (2014) Thyroid hormone regulation of hepatic lipid and carbohydrate metabolism. Trends Endocrinol. Metab., 25, 538–545

[88]

Biondi, B., Kahaly, G. J. and Robertson, R. P. (2019) Thyroid dysfunction and diabetes mellitus: two closely associated disorders. Endocr. Rev., 40, 789–824

[89]

Broholm, C., Olsson, A. H., Perfilyev, A., Hansen, N. S., Schrölkamp, M., Strasko, K. S., Scheele, C., Ribel-Madsen, R., Mortensen, B., Jørgensen, S. W., (2016) Epigenetic programming of adipose-derived stem cells in low birthweight individuals. Diabetologia, 59, 2664–2673

[90]

Forstner, A. J., Basmanav, F. B., Mattheisen, M., Böhmer, A. C., Hollegaard, M. V., Janson, E., Strengman, E., Priebe, L., Degenhardt, F., Hoffmann, P., (2014) Investigation of the involvement of MIR185 and its target genes in the development of schizophrenia. J. Psychiatry Neurosci., 39, 386–396

[91]

Venkatasubramanian, G. (2015) Understanding schizophrenia as a disorder of consciousness: biological correlates and translational implications from quantum theory perspectives. Clin. Psychopharmacol. Neurosci., 13, 36–47

[92]

Swanger, S. A., Mattheyses, A. L., Gentry, E. G. and Herskowitz, J. H. (2016) ROCK1 and ROCK2 inhibition alters dendritic spine morphology in hippocampal neurons. Cell. Logist., 5, e1133266

[93]

Ross, K. A. (2011) Evidence for somatic gene conversion and deletion in bipolar disorder, Crohn’s disease, coronary artery disease, hypertension, rheumatoid arthritis, type-1 diabetes, and type-2 diabetes. BMC Med., 9, 12

[94]

Li, M. J., Liu, Z., Wang, P., Wong, M. P., Nelson, M. R., Kocher, J. P., Yeager, M., Sham, P. C., Chanock, S. J., Xia, Z., (2016) GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res., 44, D869–D876

[95]

Buniello, A., MacArthur, J. A. L., Cerezo, M., Harris, L. W., Hayhurst, J., Malangone, C., McMahon, A., Morales, J., Mountjoy, E., Sollis, E., (2019) The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res., 47, D1005–D1012

[96]

Zhong, C., Yang, Y. and Yooseph, S. (2019) GRASP2: fast and memory-efficient gene-centric assembly and homolog search for metagenomic sequencing data. BMC Bioinformatics, 20, 276

[97]

Tang, J., Fu, J., Wang, Y., Luo, Y., Yang, Q., Li, B., Tu, G., Hong, J., Cui, X., Chen, Y., (2019) Simultaneous improvement in the precision, accuracy, and pobustness of label-free proteome quantification by optimizing data manipulation chains. Mol. Cell. Proteomics, 18, 1683–1699

[98]

Yang, Q., Hong, J., Li, Y., Xue, W., Li, S., Yang, H. and Zhu, F. (2020) A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies. Brief. Bioinform., 21, 2142–2152

[99]

Ros, G., Pegoraro, S., De Angelis, P., Sgarra, R., Zucchelli, S., Gustincich, S. and Manfioletti, G. (2020) HMGA2 antisense long non-coding RNAs as new players in the regulation of HMGA2 expression and pancreatic cancer promotion. Front. Oncol., 9, 1526

[100]

Han, Z. J., Xue, W. W., Tao, L. and Zhu, F. (2018) Identification of novel immune-relevant drug target genes for Alzheimer’s disease by combining ontology inference with network analysis. CNS Neurosci. Ther., 24, 1253–1263

[101]

Wang, M., Yuan, D., Tu, L., Gao, W., He, Y., Hu, H., Wang, P., Liu, N., Lindsey, K. and Zhang, X. (2015) Long noncoding RNAs and their proposed functions in fibre development of cotton (Gossypium spp.). New Phytol., 207, 1181–1197

[102]

Cabili, M. N., Dunagin, M. C., McClanahan, P. D., Biaesch, A., Padovan-Merhar, O., Regev, A., Rinn, J. L. and Raj, A. (2015) Localization and abundance analysis of human lncRNAs at single-cell and single-molecule resolution. Genome Biol., 16, 20

[103]

Werner, M. S., Sullivan, M. A., Shah, R. N., Nadadur, R. D., Grzybowski, A. T., Galat, V., Moskowitz, I. P. and Ruthenburg, A. J. (2017) Chromatin-enriched lncRNAs can act as cell-type specific activators of proximal gene transcription. Nat. Struct. Mol. Biol., 24, 596–603

[104]

Teimuri, S., Hosseini, A., Rezaenasab, A., Ghaedi, K., Ghoveud, E., Etemadifar, M., Nasr-Esfahani, M. H. and Megraw, T. L. (2018) Integrative analysis of lncRNAs in Th17 cell lineage to discover new potential biomarkers and therapeutic targets in autoimmune diseases. Mol. Ther. Nucleic Acids, 12, 393–404

[105]

Li, S., Yu, X., Lei, N., Cheng, Z., Zhao, P., He, Y., Wang, W. and Peng, M. (2017) Genome-wide identification and functional prediction of cold and/or drought-responsive lncRNAs in cassava. Sci. Rep., 7, 45981

[106]

Wang, X., Yang, C., Guo, F., Zhang, Y., Ju, Z., Jiang, Q., Zhao, X., Liu, Y., Zhao, H., Wang, J., (2019) Integrated analysis of mRNAs and long noncoding RNAs in the semen from Holstein bulls with high and low sperm motility. Sci. Rep., 9, 2092

[107]

Schultz, B. M., Gallicio, G. A., Cesaroni, M., Lupey, L. N. and Engel, N. (2015) Enhancers compete with a long non-coding RNA for regulation of the Kcnq1 domain. Nucleic Acids Res., 43, 745–759

[108]

Ørom, U. A., Derrien, T., Beringer, M., Gumireddy, K., Gardini, A., Bussotti, G., Lai, F., Zytnicki, M., Notredame, C., Huang, Q., (2010) Long noncoding RNAs with enhancer-like function in human cells. Cell, 143, 46–58

[109]

Pyfrom, S. C., Luo, H. and Payton, J. E. (2019) PLAIDOH: a novel method for functional prediction of long non-coding RNAs identifies cancer-specific LncRNA activities. BMC Genomics, 20, 137

[110]

Khyzha, N., Khor, M., DiStefano, P. V., Wang, L., Matic, L., Hedin, U., Wilson, M. D., Maegdefessel, L. and Fish, J. E. (2019) Regulation of CCL2 expression in human vascular endothelial cells by a neighboring divergently transcribed long noncoding RNA. Proc. Natl. Acad. Sci. USA, 116, 16410–16419

[111]

The Gene Ontology Consortium. (2019) The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res., 47, D330–D338

[112]

Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. and Morishima, K. (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res., 45, D353–D361

[113]

Yang, Q., Li, B., Tang, J., Cui, X., Wang, Y., Li, X., Hu, J., Chen, Y., Xue, W., Lou, Y., (2020) Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data. Brief. Bioinform., 21, 1058–1068

[114]

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 102, 15545–15550

[115]

Hong, J., Luo, Y., Zhang, Y., Ying, J., Xue, W., Xie, T., Tao, L. and Zhu, F. (2020) Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning. Brief. Bioinform., 21, 1437–1447

[116]

Love, M. I., Huber, W. and Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol., 15, 550

[117]

Yang, Q., Wang, Y., Zhang, Y., Li, F., Xia, W., Zhou, Y., Qiu, Y., Li, H. and Zhu, F. (2020) NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res., 48, W436–W448

[118]

Li, B., Tang, J., Yang, Q., Li, S., Cui, X., Li, Y., Chen, Y., Xue, W., Li, X. and Zhu, F. (2017) NOREVA: normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res., 45, W162–W170

[119]

Tang, J., Fu, J., Wang, Y., Li, B., Li, Y., Yang, Q., Cui, X., Hong, J., Li, X., Chen, Y., (2020) ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief. Bioinform., 21, 621–636

[120]

Clough, E. and Barrett, T. (2016) The gene expression omnibus database. Methods Mol. Biol., 1418, 93–110

[121]

Li, F., Zhou, Y., Zhang, X., Tang, J., Yang, Q., Zhang, Y., Luo, Y., Hu, J., Xue, W., Qiu, Y., (2020) SSizer: determining the sample sufficiency for comparative biological study. J. Mol. Biol., 432, 3411–3421

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