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研究生:賴舒婷
研究生(外文):Su-Ting Lai
論文名稱:解密在肺腺癌中微小RNA異構物的調節以及其串擾網路
論文名稱(外文):Deciphering isomiR regulation and crosstalk network in lung adenocarcinoma
指導教授:林振慶
指導教授(外文):Chen-Ching Lin
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:生物醫學資訊研究所
學門:生命科學學門
學類:生物化學學類
論文種類:學術論文
論文出版年:2020
畢業學年度:109
語文別:英文
論文頁數:53
中文關鍵詞:異形小分子核醣核酸種子區域小分子核糖核酸肺腺癌蛋白質自磷酸化跨膜受體蛋白酪胺酸激酶訊號途徑調控細胞移動組織細胞骨架
外文關鍵詞:isomiRmiRNAseed regionlung adenocarcinomaprotein autophosphorylationtransmembrane receptor protein tyrosine kinase signaling pathwayregulation of cell migrationcytoskeleton organization
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MicroRNA (miRNA)是一段長度約22個核苷酸的短鍊非編碼核糖核酸(non-coding RNA),能透過抑制mRNA的轉譯或降解mRNA來調節其表現。成熟的5’ miRNA第二到第八個核苷酸,這段區域稱為seed region,主要帶領miRNA與其target gene透過序列互補而結合。MicroRNA isoform (isomiR)是由於Dicer及Drosha不精確地剪切所形成的產物。Seed region的序列會受5’核苷酸的增減影響形成不一樣的seed region,進而與不同的target gene互補最終導致調控不同的生物網路。
本研究使用TCGA-LUAD資料集解密isomiR的調控,並使用四個預測miRNA target的工具來預測isomiR的target以建構isomiR之間的串擾網路(crosstalk network)。藉由分析在LUAD的miRNome中isomiR的占比,我們發現isomiR在肺腺癌中展現了異質性(heterogeneous)並且為構成LUAD的miRNome的一員。我們緊接著發現了表現量中佔主導地位的isomiR以及miRNA archetype,並且發現佔主導地位的isomiR能夠影響其產生。根據我們研究的基本原理,如果從seed region的角度來分析isomiR的話能夠發現具有高度保留的seed region可能在miRNA archetype以及isomiR上都具有功能性,意味著isomiR可能經演化而參與了癌症的發展。最後我們建構了miRNA archetypes以及isomiRs的串擾網路,其中參與調控的功能主要涵蓋下調蛋白質的自我磷酸化、跨膜受體蛋白酪胺酸激酶訊號途徑、調控細胞移動以及組織細胞骨架。此結果描繪了isomiR成為癌症治療的標靶的可能性。
MicroRNAs (miRNA) are short non-coding RNAs (~22nt), which regulate mRNA expression by inhibiting the translation or degrading the mRNAs. The seed region—sequence from the second to eighth nucleotides of 5’ mature miRNA—mainly leads the miRNA to target genes through complementary pairing. MicroRNA isoforms (isomiRs) are the product of imprecise cleavage by Drosha and Dicer. By adding or deleting nucleotides at the 5’ end, the nucleotides pattern at the seed region can be totally different, thus targeting different target genes and regulating different networks.
Here we used the TCGA-LUAD dataset to decipher the regulation of isomiRs and use four miRNA prediction tools to predict miRNA-regulated targets for constructing the isomiRs crosstalk network. By analyzing the partition of isomiRs in the miRNome of LUAD, we found that isomiRs show heterogeneous in lung adenocarcinoma and comprise the miRNome of LUAD. We next focused on the dominant prevalence of isomiRs and archetypes and found that dominant isomiRs are able to affect the production of isomiRs. Based on our research rationale, we analyzed isomiR from the point of view of the seed region and found that highly conserved seed region which probably has functional both exist in miRNAs and isomiRs, implying isomiRs might evolve to participate the cancer development. At last, we constructed the crosstalk network of miRNA archetypes and isomiRs. Functions covered mainly in protein autophosphorylation, transmembrane receptor protein tyrosine kinase signaling pathway, regulation of cell migration, and cytoskeleton organization, and were downregulated. The results pictured the possibility of isomiRs as a therapeutic target in cancers.
摘要...............................................................................I
Abstract...........................................................................II
Contents...........................................................................III
List of Figures....................................................................IV
List of Tables.....................................................................V
Chapter 1. Introduction............................................................1
1. MicroRNA.......................................................................1
2. MicroRNA isoform: isomiR.......................................................3
3. The roles of miRNAs/isomiRs in lung cancer.....................................4
Chapter 2. Materials and Methods...................................................5
1. MicroRNA and mRNA transcriptomics profiles and preprocessing...................5
2. IsomiR nomenclature............................................................6
3. Statistical methods and packages...............................................7
4. Target prediction..............................................................8
5. MicroRNA Crosstalk network.....................................................10
5.1. Predicting the regulatory function of the isomiR crosstalk modules.........11
Chapter 3. Results.................................................................14
1. MicroRNAs (archetype and isomiRs) production are heterogeneous in lung cancer..14
2. IsomiRs are constituent in the miRNome of lung cancer..........................16
3. The dominant non-archetype miRNAs shape the production of isomiRs..............21
4. The conserved seed regions select the functional isomiRs in lung cancer........26
5. The functional modules regulated by crosstalk conserved isomiRs................32
Chapter 4. Discussion and Conclusion...............................................39
References.........................................................................42

List of Figures
Figure 1 The miRNA biogenesis..............................................2
Figure 2 The isomiR nomenclature...........................................6
Figure 3 Crosstalk candidate selection.....................................10
Figure 4 Framework of our research.........................................13
Figure 5 Average type of isomiRs in the tumor and normal...................15
Figure 6 Distribution of expressed isomiRs in LUAD samples.................16
Figure 7 Distribution of numbers of isomiRs................................18
Figure 8 Expression of isomiR subsets......................................19
Figure 9 Distribution of dominant isomiRs..................................20
Figure 10 Effect size of isomiRs expression................................20
Figure 11 Dominant isomiRs percentage in tumor and normal..................22
Figure 12 The most popular dominant isomiRs in tumor and normal............24
Figure 13 Siblings of the most popular dominant isomiRs in tumor...........25
Figure 14 Occurrence of isomiRs grouped by seed............................27
Figure 15 Average expression of isomiRs grouped by seeds...................28
Figure 16 Siblings from the seed covered the most isomiRs..................29
Figure 17 Consensus logo of isomiRs........................................30
Figure 18 Results of functional analysis...................................37
Figure 19 Expression of the first target genes from selected modules.......38

List of Tables
Table 1 Profile of miRNA prediction tools...............................................9
Table 2 Description of isomiR numbers in TCGA-LUAD......................................15
Table 3 Table of the expression of isomiR subsets examined by Wilcoxon rank sum test....19
Table 4 Detail information in seeds: tumor type.........................................31
Table 5 Detail information in seeds: normal type........................................31
Table 6 Spearman’s correlation coefficient of isomiRs...................................32
Table 7 Numbers of isomiR-target gene find in each prediction tools.....................34
Table 8 Statistics of the correlation between candidate pairs...........................34
Table 9 Statistics of the crosstalk selection and the correlation between miRNAs........35
Table 10 Statistics of crosstalk networks...............................................35
1 Lee, Y. et al. The nuclear RNase III Drosha initiates microRNA processing. Nature 425, 415-419, doi:10.1038/nature01957 (2003).
2 Winter, J., Jung, S., Keller, S., Gregory, R. I. & Diederichs, S. Many roads to maturity: microRNA biogenesis pathways and their regulation. Nature Cell Biology 11, 228-234, doi:10.1038/ncb0309-228 (2009).
3 Bartel, D. P. Metazoan MicroRNAs. Cell 173, 20-51, doi:10.1016/j.cell.2018.03.006 (2018).
4 Mulrane, L., McGee, S. F., Gallagher, W. M. & O'Connor, D. P. miRNA Dysregulation in Breast Cancer. Cancer Research 73, 6554-6562, doi:10.1158/0008-5472.can-13-1841 (2013).
5 O'Carroll, D. & Schaefer, A. General Principals of miRNA Biogenesis and Regulation in the Brain. Neuropsychopharmacology 38, 39-54, doi:10.1038/npp.2012.87 (2013).
6 Chendrimada, T. P. et al. TRBP recruits the Dicer complex to Ago2 for microRNA processing and gene silencing. Nature 436, 740-744, doi:10.1038/nature03868 (2005).
7 Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215-233, doi:10.1016/j.cell.2009.01.002 (2009).
8 Grimson, A. et al. MicroRNA Targeting Specificity in Mammals: Determinants beyond Seed Pairing. Molecular Cell 27, 91-105, doi:10.1016/j.molcel.2007.06.017 (2007).
9 Liang, C, William & Phillip. Argonaute Divides Its RNA Guide into Domains with Distinct Functions and RNA-Binding Properties. Cell 151, 1055-1067, doi:10.1016/j.cell.2012.10.036 (2012).
10 William, Samson, Melissa, Phillip & Serebrov, V. Single-Molecule Imaging Reveals that Argonaute Reshapes the Binding Properties of Its Nucleic Acid Guides. Cell 162, 84-95, doi:10.1016/j.cell.2015.06.029 (2015).
11 Agarwal, V., Bell, G. W., Nam, J.-W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4, doi:10.7554/elife.05005 (2015).
12 Morin, R. D. et al. Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Research 18, 610-621, doi:10.1101/gr.7179508 (2008).
13 Glazov, E. A. et al. A microRNA catalog of the developing chicken embryo identified by a deep sequencing approach. Genome Research 18, 957-964, doi:10.1101/gr.074740.107 (2008).
14 Li, S.-C. et al. Discovery and characterization of medaka miRNA genes by next generation sequencing platform. BMC Genomics 11, S8, doi:10.1186/1471-2164-11-s4-s8 (2010).
15 Zhou, H. et al. Deep annotation of mouse iso-miR and iso-moR variation. Nucleic Acids Research 40, 5864-5875, doi:10.1093/nar/gks247 (2012).
16 Landgraf, P. et al. A Mammalian microRNA Expression Atlas Based on Small RNA Library Sequencing. Cell 129, 1401-1414, doi:10.1016/j.cell.2007.04.040 (2007).
17 Lee, L. W. et al. Complexity of the microRNA repertoire revealed by next-generation sequencing. RNA 16, 2170-2180, doi:10.1261/rna.2225110 (2010).
18 Neilsen, C. T., Goodall, G. J. & Bracken, C. P. IsomiRs – the overlooked repertoire in the dynamic microRNAome. Trends in Genetics 28, 544-549, doi:10.1016/j.tig.2012.07.005 (2012).
19 Burroughs, A. M. et al. A comprehensive survey of 3' animal miRNA modification events and a possible role for 3' adenylation in modulating miRNA targeting effectiveness. Genome Research 20, 1398-1410, doi:10.1101/gr.106054.110 (2010).
20 Wyman, S. K. et al. Post-transcriptional generation of miRNA variants by multiple nucleotidyl transferases contributes to miRNA transcriptome complexity. Genome Research 21, 1450-1461, doi:10.1101/gr.118059.110 (2011).
21 Newman, M. A., Mani, V. & Hammond, S. M. Deep sequencing of microRNA precursors reveals extensive 3' end modification. RNA 17, 1795-1803, doi:10.1261/rna.2713611 (2011).
22 Cloonan, N. et al. MicroRNAs and their isomiRs function cooperatively to target common biological pathways. Genome Biology 12, R126, doi:10.1186/gb-2011-12-12-r126 (2011).
23 Mercey, O. et al. Characterizing isomiR variants within the microRNA-34/449 family. doi:10.1002/1873-3468.12595 (2017).
24 Karali, M. et al. High-resolution analysis of the human retina miRNome reveals isomiR variations and novel microRNAs. Nucleic Acids Research 44, 1525-1540, doi:10.1093/nar/gkw039 (2016).
25 Manzano, M., Forte, E., Raja, A. N., Schipma, M. J. & Gottwein, E. Divergent target recognition by coexpressed 5'-isomiRs of miR-142-3p and selective viral mimicry. RNA 21, 1606-1620, doi:10.1261/rna.048876.114 (2015).
26 Marzi, M. J. et al. Degradation dynamics of microRNAs revealed by a novel pulse-chase approach. Genome Research 26, 554-565, doi:10.1101/gr.198788.115 (2016).
27 Guo, Y. et al. Characterization of the mammalian miRNA turnover landscape. Nucleic Acids Research 43, 2326-2341, doi:10.1093/nar/gkv057 (2015).
28 Gutiérrez-Vázquez, C. et al. 3′ Uridylation controls mature microRNA turnover during CD4 T-cell activation. Rna 23, 882-891, doi:10.1261/rna.060095.116 (2017).
29 Katoh, T., Hojo, H. & Suzuki, T. Destabilization of microRNAs in human cells by 3′ deadenylation mediated by PARN and CUGBP1. Nucleic Acids Research 43, 7521-7534, doi:10.1093/nar/gkv669 (2015).
30 Katoh, T. et al. Selective stabilization of mammalian microRNAs by 3' adenylation mediated by the cytoplasmic poly(A) polymerase GLD-2. 23, 433-438, doi:10.1101/gad.1761509 (2009).
31 Yamane, D. et al. Differential hepatitis C virus RNA target site selection and host factor activities of naturally occurring miR-122 3′ variants. Nucleic Acids Research, gkw1332, doi:10.1093/nar/gkw1332 (2017).
32 Yu, F. et al. Naturally existing isoforms of miR-222 have distinct functions. Nucleic Acids Research 45, 11371-11385, doi:10.1093/nar/gkx788 (2017).
33 Guo, L., Liang, T., Yu, J. & Zou, Q. A Comprehensive Analysis of miRNA/isomiR Expression with Gender Difference. PLOS ONE 11, e0154955, doi:10.1371/journal.pone.0154955 (2016).
34 Loher, P., Londin, E. R. & Rigoutsos, I. IsomiR expression profiles in human lymphoblastoid cell lines exhibit population and gender dependencies. Oncotarget 5, 8790-8802, doi:10.18632/oncotarget.2405 (2014).
35 Telonis, A. G., Loher, P., Jing, Y., Londin, E. & Rigoutsos, I. Beyond the one-locus-one-miRNA paradigm: microRNA isoforms enable deeper insights into breast cancer heterogeneity. Nucleic Acids Research 43, 9158-9175, doi:10.1093/nar/gkv922 (2015).
36 Telonis, A. G. et al. Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types. Nucleic Acids Research 45, 2973-2985, doi:10.1093/nar/gkx082 (2017).
37 Salem, O. et al. The highly expressed 5’isomiR of hsa-miR-140-3p contributes to the tumor-suppressive effects of miR-140 by reducing breast cancer proliferation and migration. BMC Genomics 17, doi:10.1186/s12864-016-2869-x (2016).
38 Calin, G. A. et al. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A 99, 15524-15529, doi:10.1073/pnas.242606799 (2002).
39 Peng, Y. & Croce, C. M. The role of MicroRNAs in human cancer. Signal Transduct Target Ther 1, 15004, doi:10.1038/sigtrans.2015.4 (2016).
40 Lu, J. et al. MicroRNA expression profiles classify human cancers. Nature 435, 834-838, doi:10.1038/nature03702 (2005).
41 Volinia, S. et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A 103, 2257-2261, doi:10.1073/pnas.0510565103 (2006).
42 Croce, C. M. Causes and consequences of microRNA dysregulation in cancer. Nat Rev Genet 10, 704-714, doi:10.1038/nrg2634 (2009).
43 Munker, R. & Calin, G. A. MicroRNA profiling in cancer. Clin Sci (Lond) 121, 141-158, doi:10.1042/CS20110005 (2011).
44 Jansson, M. D. & Lund, A. H. MicroRNA and cancer. Mol Oncol 6, 590-610, doi:10.1016/j.molonc.2012.09.006 (2012).
45 Kasinski, A. L. & Slack, F. J. Epigenetics and genetics. MicroRNAs en route to the clinic: progress in validating and targeting microRNAs for cancer therapy. Nat Rev Cancer 11, 849-864, doi:10.1038/nrc3166 (2011).
46 Iorio, M. V. & Croce, C. M. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol Med 4, 143-159, doi:10.1002/emmm.201100209 (2012).
47 Takamizawa, J. et al. Reduced expression of the let-7 microRNAs in human lung cancers in association with shortened postoperative survival. Cancer Res 64, 3753-3756, doi:10.1158/0008-5472.CAN-04-0637 (2004).
48 Johnson, C. D. et al. The let-7 microRNA represses cell proliferation pathways in human cells. Cancer Res 67, 7713-7722, doi:10.1158/0008-5472.CAN-07-1083 (2007).
49 Hayashita, Y. et al. A polycistronic microRNA cluster, miR-17-92, is overexpressed in human lung cancers and enhances cell proliferation. Cancer Res 65, 9628-9632, doi:10.1158/0008-5472.CAN-05-2352 (2005).
50 Abbas-Aghababazadeh, F., Li, Q. & Fridley, B. L. Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing. PLoS One 13, e0206312, doi:10.1371/journal.pone.0206312 (2018).
51 Kozomara, A. & Griffiths-Jones, S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Research 42, D68-D73, doi:10.1093/nar/gkt1181 (2014).
52 Kozomara, A. & Griffiths-Jones, S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Research 39, D152-D157, doi:10.1093/nar/gkq1027 (2011).
53 Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422-1423, doi:10.1093/bioinformatics/btp163 (2009).
54 Enright, A. J. et al. MicroRNA targets in Drosophila. Genome Biology 5, R1, doi:10.1186/gb-2003-5-1-r1 (2003).
55 John, B. et al. Human MicroRNA Targets. PLoS Biology 2, e363, doi:10.1371/journal.pbio.0020363 (2004).
56 Kertesz, M., Iovino, N., Unnerstall, U., Gaul, U. & Segal, E. The role of site accessibility in microRNA target recognition. Nature Genetics 39, 1278-1284, doi:10.1038/ng2135 (2007).
57 Kruger, J. & Rehmsmeier, M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Research 34, W451-W454, doi:10.1093/nar/gkl243 (2006).
58 Rehmsmeier, M. Fast and effective prediction of microRNA/target duplexes. RNA 10, 1507-1517, doi:10.1261/rna.5248604 (2004).
59 Zuker, M. & Stiegler, P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Research 9, 133-148, doi:10.1093/nar/9.1.133 (1981).
60 Lewis, B. P., Burge, C. B. & Bartel, D. P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15-20, doi:10.1016/j.cell.2004.12.035 (2005).
61 Peterson, S. M. et al. Common features of microRNA target prediction tools. Frontiers in Genetics 5, doi:10.3389/fgene.2014.00023 (2014).
62 Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25, 25-29, doi:10.1038/75556 (2000).
63 Li, T. et al. A scored human protein-protein interaction network to catalyze genomic interpretation. Nat Methods 14, 61-64, doi:10.1038/nmeth.4083 (2017).
64 Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289-300 (1995).
65 Ameres, S. L. & Zamore, P. D. Diversifying microRNA sequence and function. Nat Rev Mol Cell Biol 14, 475-488, doi:10.1038/nrm3611 (2013).
66 Neilsen, C. T., Goodall, G. J. & Bracken, C. P. IsomiRs--the overlooked repertoire in the dynamic microRNAome. Trends Genet 28, 544-549, doi:10.1016/j.tig.2012.07.005 (2012).
67 Wheeler, B. M. et al. The deep evolution of metazoan microRNAs. Evolution & Development 11, 50-68, doi:10.1111/j.1525-142x.2008.00302.x (2009).
68 Li, Q., Yang, Z., Chen, M. & Liu, Y. Downregulation of microRNA-196a enhances the sensitivity of non-small cell lung cancer cells to cisplatin treatment. Int J Mol Med 37, 1067-1074, doi:10.3892/ijmm.2016.2513 (2016).
69 Liu, Q., Bai, W., Huang, F., Tang, J. & Lin, X. Downregulation of microRNA-196a inhibits stem cell self-renewal ability and stemness in non-small-cell lung cancer through upregulating GPX3 expression. Int J Biochem Cell Biol 115, 105571, doi:10.1016/j.biocel.2019.105571 (2019).
70 Xu, Q. & Xu, Z. miR-196b-5p Promotes Proliferation, Migration and Invasion of Lung Adenocarcinoma Cells via Targeting RSPO2. Cancer Manag Res 12, 13393-13402, doi:10.2147/CMAR.S274171 (2020).
71 Huang, X. et al. miR-196b-5p-mediated downregulation of FAS promotes NSCLC progression by activating IL6-STAT3 signaling. Cell Death Dis 11, 785, doi:10.1038/s41419-020-02997-7 (2020).
72 Liang, G. et al. miR-196b-5p-mediated downregulation of TSPAN12 and GATA6 promotes tumor progression in non-small cell lung cancer. Proc Natl Acad Sci U S A 117, 4347-4357, doi:10.1073/pnas.1917531117 (2020).
73 Wang, Q. et al. MiR-372-3p promotes cell growth and metastasis by targeting FGF9 in lung squamous cell carcinoma. Cancer Med 6, 1323-1330, doi:10.1002/cam4.1026 (2017).
74 Wu, A. et al. [MiR-373-3p Promotes Invasion and Metastasis of Lung Adenocarcinoma Cells]. Zhongguo Fei Ai Za Zhi 18, 427-435, doi:10.3779/j.issn.1009-3419.2015.07.07 (2015).
75 Fan, X., Xu, S. & Yang, C. miR-373-3p promotes lung adenocarcinoma cell proliferation via APP. Oncol Lett 15, 1046-1050, doi:10.3892/ol.2017.7372 (2018).
76 Tu, K., Liu, Z., Yao, B., Han, S. & Yang, W. MicroRNA-519a promotes tumor growth by targeting PTEN/PI3K/AKT signaling in hepatocellular carcinoma. Int J Oncol 48, 965-974, doi:10.3892/ijo.2015.3309 (2016).
77 Bai, Y. et al. Overexpression of miR-519d in lung adenocarcinoma inhibits cell proliferation and invasion via the association of eIF4H. Tumour Biol 39, 1010428317694566, doi:10.1177/1010428317694566 (2017).
78 Lv, X., Li, C. Y., Han, P. & Xu, X. Y. MicroRNA-520a-3p inhibits cell growth and metastasis of non-small cell lung cancer through PI3K/AKT/mTOR signaling pathway. Eur Rev Med Pharmacol Sci 22, 2321-2327, doi:10.26355/eurrev_201804_14822 (2018).
79 Liu, Y. et al. microRNA-520a-3p inhibits proliferation and cancer stem cell phenotype by targeting HOXD8 in non-small cell lung cancer. Oncol Rep 36, 3529-3535, doi:10.3892/or.2016.5149 (2016).
80 Liu, X., Liu, J., Zhang, X., Tong, Y. & Gan, X. MiR-520b promotes the progression of non-small cell lung cancer through activating Hedgehog pathway. J Cell Mol Med 23, 205-215, doi:10.1111/jcmm.13909 (2019).
81 Li, X. et al. MicroRNA-520c-3p functions as a novel tumor suppressor in lung adenocarcinoma. FEBS J 286, 2737-2752, doi:10.1111/febs.14835 (2019).
82 Matsubara, H. et al. Apoptosis induction by antisense oligonucleotides against miR-17-5p and miR-20a in lung cancers overexpressing miR-17-92. Oncogene 26, 6099-6105, doi:10.1038/sj.onc.1210425 (2007).
83 Han, J. et al. MicroRNA-20a-5p suppresses tumor angiogenesis of non-small cell lung cancer through RRM2-mediated PI3K/Akt signaling pathway. Mol Cell Biochem, doi:10.1007/s11010-020-03936-y (2020).
84 Peng, L. et al. Regulation of BTG3 by microRNA-20b-5p in non-small cell lung cancer. Oncol Lett 18, 137-144, doi:10.3892/ol.2019.10333 (2019).
85 Du, L. et al. miR-93, miR-98, and miR-197 regulate expression of tumor suppressor gene FUS1. Mol Cancer Res 7, 1234-1243, doi:10.1158/1541-7786.MCR-08-0507 (2009).
86 He, Q. Y. et al. miR-106a-5p Suppresses the Proliferation, Migration, and Invasion of Osteosarcoma Cells by Targeting HMGA2. DNA Cell Biol 35, 506-520, doi:10.1089/dna.2015.3121 (2016).
87 Yanaihara, N. et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 9, 189-198, doi:10.1016/j.ccr.2006.01.025 (2006).
88 Wei, K. et al. MiR-106b-5p Promotes Proliferation and Inhibits Apoptosis by Regulating BTG3 in Non-Small Cell Lung Cancer. Cell Physiol Biochem 44, 1545-1558, doi:10.1159/000485650 (2017).
89 Zhang, G. et al. miR-519d-3p Overexpression Inhibits P38 and PI3K/AKT Pathway via Targeting VEGFA to Attenuate the Malignant Biological Behavior of Non-Small Cell Lung Cancer. Onco Targets Ther 13, 10257-10266, doi:10.2147/OTT.S252795 (2020).
90 Zhang, Z. Y. et al. By downregulating Ku80, hsa-miR-526b suppresses non-small cell lung cancer. Oncotarget 6, 1462-1477, doi:10.18632/oncotarget.2808 (2015).
91 Mo, F. et al. Curcumin-Induced Global Profiling of Transcriptomes in Small Cell Lung Cancer Cells. Frontiers in Cell and Developmental Biology 8, doi:10.3389/fcell.2020.588299 (2021).
92 Bai, X. et al. MicroRNA-196b Inhibits Cell Growth and Metastasis of Lung Cancer Cells by Targeting Runx2. Cell Physiol Biochem 43, 757-767, doi:10.1159/000481559 (2017).
93 Xu, H. & Wen, Q. miR31205p acts as a diagnostic biomarker in nonsmall cell lung cancer and promotes cancer cell proliferation and invasion by targeting KLF4. Mol Med Rep 18, 4621-4628, doi:10.3892/mmr.2018.9454 (2018).
94 Zhao, W. et al. Induction of microRNAlet7a inhibits lung adenocarcinoma cell growth by regulating cyclin D1. Oncol Rep 40, 1843-1854, doi:10.3892/or.2018.6593 (2018).
95 Wang, Y. Y., Ren, T., Cai, Y. Y. & He, X. Y. MicroRNA let-7a inhibits the proliferation and invasion of nonsmall cell lung cancer cell line 95D by regulating K-Ras and HMGA2 gene expression. Cancer Biother Radiopharm 28, 131-137, doi:10.1089/cbr.2012.1307 (2013).
96 Qi, L. et al. lncRNA NEAT1 competes against let-7a to contribute to non-small cell lung cancer proliferation and metastasis. Biomed Pharmacother 103, 1507-1515, doi:10.1016/j.biopha.2018.04.053 (2018).
97 Jusufovic, E. et al. let-7b and miR-126 are down-regulated in tumor tissue and correlate with microvessel density and survival outcomes in non--small--cell lung cancer. PLoS One 7, e45577, doi:10.1371/journal.pone.0045577 (2012).
98 Gong, J. et al. The relationship between miR-17-5p, miR-92a, and let-7b expression with non-small cell lung cancer targeted drug resistance. J BUON 22, 454-461 (2017).
99 Zhao, B. et al. MicroRNA let-7c inhibits migration and invasion of human non-small cell lung cancer by targeting ITGB3 and MAP4K3. Cancer Lett 342, 43-51, doi:10.1016/j.canlet.2013.08.030 (2014).
100 Zhu, W. Y. et al. Differential expression of miR-125a-5p and let-7e predicts the progression and prognosis of non-small cell lung cancer. Cancer Invest 32, 394-401, doi:10.3109/07357907.2014.922569 (2014).
101 Capodanno, A. et al. Let-7g and miR-21 expression in non-small cell lung cancer: correlation with clinicopathological and molecular features. Int J Oncol 43, 765-774, doi:10.3892/ijo.2013.2003 (2013).
102 Jiang, F. et al. MicroRNA-98-5p inhibits proliferation and metastasis in non-small cell lung cancer by targeting TGFBR1. Int J Oncol 54, 128-138, doi:10.3892/ijo.2018.4610 (2019).
103 Wang, K., Dong, L., Fang, Q., Xia, H. & Hou, X. Low serum miR-98 as an unfavorable prognostic biomarker in patients with non-small cell lung cancer. Cancer Biomark 20, 283-288, doi:10.3233/CBM-170124 (2017).
104 Hutvagner, G. et al. A cellular function for the RNA-interference enzyme Dicer in the maturation of the let-7 small temporal RNA. Science 293, 834-838, doi:10.1126/science.1062961 (2001).
105 Han, J. et al. Molecular basis for the recognition of primary microRNAs by the Drosha-DGCR8 complex. Cell 125, 887-901, doi:10.1016/j.cell.2006.03.043 (2006).
106 Wilson, R. C. et al. Dicer-TRBP complex formation ensures accurate mammalian microRNA biogenesis. Mol Cell 57, 397-407, doi:10.1016/j.molcel.2014.11.030 (2015).
107 Fukunaga, R. et al. Dicer Partner Proteins Tune the Length of Mature miRNAs in Flies and Mammals. Cell 151, 912, doi:10.1016/j.cell.2012.10.029 (2012).
108 Lee, H. Y., Zhou, K., Smith, A. M., Noland, C. L. & Doudna, J. A. Differential roles of human Dicer-binding proteins TRBP and PACT in small RNA processing. Nucleic Acids Res 41, 6568-6576, doi:10.1093/nar/gkt361 (2013).
109 Ruegger, S. & Grosshans, H. MicroRNA turnover: when, how, and why. Trends Biochem Sci 37, 436-446, doi:10.1016/j.tibs.2012.07.002 (2012).
110 van Rooij, E. et al. Control of stress-dependent cardiac growth and gene expression by a microRNA. Science 316, 575-579, doi:10.1126/science.1139089 (2007).
111 Baccarini, A. et al. Kinetic analysis reveals the fate of a microRNA following target regulation in mammalian cells. Curr Biol 21, 369-376, doi:10.1016/j.cub.2011.01.067 (2011).
112 Gantier, M. P. et al. Analysis of microRNA turnover in mammalian cells following Dicer1 ablation. Nucleic Acids Res 39, 5692-5703, doi:10.1093/nar/gkr148 (2011).
113 Bindi M. Doshi, P. Why protein phosphorylation is crucial in disease research, <http://blog.mblintl.com/protein_phosphorylation> (2019).
114 Cohen, P. The role of protein phosphorylation in human health and disease. The Sir Hans Krebs Medal Lecture. Eur J Biochem 268, 5001-5010, doi:10.1046/j.0014-2956.2001.02473.x (2001).
115 Paul, M. K. & Mukhopadhyay, A. K. Tyrosine kinase - Role and significance in Cancer. Int J Med Sci 1, 101-115, doi:10.7150/ijms.1.101 (2004).
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