CoChat
Sign Up
Cited by 54

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects

Grasso, Margherita, Piscopo, Paola, Talarico, Giuseppina, Ricci, Leonardo, Crestini, Alessio, Tosto, Giuseppe, Gasparini, Marina, Bruno, Giuseppe, Denti, Michela A., Confaloni, Annamaria·2019·Neurobiology of Aging
DOI 10.1016/j.neurobiolaging.2019.01.024
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.neurobiolaging.2019.01.024

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 10 references

Research works printed but not linked: 72

  1. Andersen, 2004, Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets, Cancer Res., 64, 5245, 10.1158/0008-5472.CAN-04-0496 · DOI · Paper page
  2. Anderson, 2006, MIR-206 regulates connexin43 expression during skeletal muscle development, Nucleic Acids Res., 34, 5863, 10.1093/nar/gkl743 · DOI
  3. Arai, 2006, TDP-43 is a component of ubiquitin-positive tau-negative inclusions in frontotemporal lobar degeneration and amyotrophic lateral sclerosis, Biochem. Biophys. Res. Commun., 351, 602, 10.1016/j.bbrc.2006.10.093 · DOI · Paper page
  4. Balakrishnan, 2014, Genome-wide analysis of miRNA-mRNA interactions in marrow stromal cells, Stem Cells, 32, 662, 10.1002/stem.1531 · DOI
  5. Bartel, 2004, MicroRNAs: genomics, biogenesis, mechanism, and function, Cell, 116, 281, 10.1016/S0092-8674(04)00045-5 · DOI
  6. Basak, 2016, microRNAs as neuroregulators, biomarkers and therapeutic agents in neurodegenerative diseases, Cell. Mol. Life Sci., 73, 811, 10.1007/s00018-015-2093-x · DOI · Paper page
  7. Blazer, 2013, Neurocognitive disorders in DSM-5, Am. J. Psychiatry, 170, 585, 10.1176/appi.ajp.2013.13020179 · DOI
  8. Blondal, 2013, Assessing sample and miRNA profile quality in serum and plasma or other biofluids, Methods, 59, S1, 10.1016/j.ymeth.2012.09.015 · DOI
  9. Buratti, 2010, Nuclear factor TDP-43 can affect selected microRNA levels, FEBS J., 277, 2268, 10.1111/j.1742-4658.2010.07643.x · DOI
  10. Cruts, 2008, Loss of progranulin function in frontotemporal lobar degeneration, Trends Genet., 24, 186, 10.1016/j.tig.2008.01.004 · DOI
  11. De Gasperi, 2017, Denervation-related alterations and biological activity of miRNAs contained in exosomes released by skeletal muscle fibers, Sci. Rep., 7, 12888, 10.1038/s41598-017-13105-9 · DOI · Paper page
  12. Denk, 2018, Specific serum and CSF microRNA profiles distinguish sporadic behavioural variant of frontotemporal dementia compared with Alzheimer patients and cognitively healthy controls, PLoS One, 13, e0197329, 10.1371/journal.pone.0197329 · DOI
  13. Di Leva, 2010, MicroRNA cluster 221-222 and estrogen receptor alpha interactions in breast cancer, J. Natl. Cancer Inst., 102, 706, 10.1093/jnci/djq102 · DOI
  14. Fagan, 2011, Comparison of analytical platforms for cerebrospinal fluid measures of β-amyloid 1-42, total tau, and p-tau181 for identifying Alzheimer disease amyloid plaque pathology, Arch. Neurol., 68, 1137, 10.1001/archneurol.2011.105 · DOI
  15. Feng, 2009, Evidence for X-chromosomal schizophrenia associated with microRNA alterations, PLoS One, 4, e6121, 10.1371/journal.pone.0006121 · DOI
  16. Fontana, 2015, A network of RNA and protein interactions in Fronto Temporal Dementia, Front. Mol. Neurosci., 8, 9, 10.3389/fnmol.2015.00009 · DOI
  17. Hébert, 2008, Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer’s disease correlates with increased BACE1/beta-secretase expression, Proc. Natl. Acad. Sci. U. S. A., 105, 6415, 10.1073/pnas.0710263105 · DOI · Paper page
  18. Holm, 2007, A reassessment of the neuropathology of frontotemporal dementia linked to chromosome 3, J. Neuropathol. Exp. Neurol., 66, 884, 10.1097/nen.0b013e3181567f02 · DOI
  19. Holm, 2009, Absence of FUS-immunoreactive pathology in frontotemporal dementia linked to chromosome 3 (FTD-3) caused by mutation in the CHMP2B gene, Acta Neuropathol., 118, 719, 10.1007/s00401-009-0593-1 · DOI
  20. Hudson, 2012, MicroRNA-1 is a candidate tumor suppressor and prognostic marker in human prostate cancer, Nucleic Acids Res., 40, 3689, 10.1093/nar/gkr1222 · DOI
  21. Jian, 2011, Retinoic acid induces HL-60 cell differentiation via the upregulation of miR-663, J. Hematol. Oncol., 4, 20, 10.1186/1756-8722-4-20 · DOI
  22. Jun, 2015, TMEM106B, a frontotemporal lobar dementia (FTLD) modifier, associates with FTD-3-linked CHMP2B, a complex of ESCRT-III, Mol. Brain, 8, 85, 10.1186/s13041-015-0177-z · DOI
  23. Kedde, 2007, RNA-binding protein Dnd1 inhibits microRNA access to target mRNA, Cell, 131, 1273, 10.1016/j.cell.2007.11.034 · DOI
  24. Keller, 2011, Toward the blood-borne miRNome of human diseases, Nat. Methods, 8, 841, 10.1038/nmeth.1682 · DOI · Paper page
  25. King, 2014, The RNA-binding Protein TDP-43 selectively disrupts MicroRNA-1/206 incorporation into the RNA-induced silencing complex, J. Biol. Chem., 289, 14263, 10.1074/jbc.M114.561902 · DOI
  26. Koutsoulidou, 2017, Identification of exosomal muscle-specific miRNAs in serum of myotonic dystrophy patients relating to muscle disease progress, Hum. Mol. Genet., 26, 3285, 10.1093/hmg/ddx212 · DOI
  27. Latruffe, 2015, Exploring new ways of regulation by resveratrol involving miRNAs, with emphasis on inflammation, Ann. N. Y. Acad. Sci., 1348, 97, 10.1111/nyas.12819 · DOI
  28. Lee, 2012, Tau and tauopathies, Prog. Mol. Biol. Transl Sci., 107, 263, 10.1016/B978-0-12-385883-2.00004-7 · DOI
  29. Lee, 2012, miR-206 regulates brain-derived neurotrophic factor in Alzheimer disease model, Ann. Neurol., 72, 269, 10.1002/ana.23588 · DOI · Paper page
  30. Lewis, 2003, Prediction of mammalian microRNA targets, Cell, 115, 787, 10.1016/S0092-8674(03)01018-3 · DOI · Paper page
  31. Li, 2014, microRNA-206 overexpression inhibits cellular proliferation and invasion of estrogen receptor α-positive ovarian cancer cells, Mol. Med. Rep., 9, 1703, 10.3892/mmr.2014.2021 · DOI
  32. Liu, 2014, Identification of suitable plasma-based reference genes for miRNAome analysis of major depressive disorder, J. Affect. Disord., 163, 133, 10.1016/j.jad.2013.12.035 · DOI
  33. Ma, 2015, MiR-206, a key modulator of skeletal muscle development and disease, Int. J. Biol. Sci., 11, 345, 10.7150/ijbs.10921 · DOI
  34. Maciotta, 2013, The involvement of microRNAs in neurodegenerative diseases, Front. Cell. Neurosci., 7, 265, 10.3389/fncel.2013.00265 · DOI
  35. Masè, 2017, Selection of reference genes is critical for miRNA expression analysis in human cardiac tissue. A focus on atrial fibrillation, Sci. Rep., 7, 41127, 10.1038/srep41127 · DOI
  36. Munoz, 2009, FUS pathology in basophilic inclusion body disease, Acta Neuropathol., 118, 617, 10.1007/s00401-009-0598-9 · DOI
  37. Muroya, 2015, Grazing affects exosomal circulating microRNAs in cattle, PLoS One, 10, e0136475, 10.1371/journal.pone.0136475 · DOI
  38. Nagaraj, 2017, Profile of 6 microRNA in blood plasma distinguish early stage Alzheimer’s disease patients from non-demented subjects, Oncotarget, 8, 16122, 10.18632/oncotarget.15109 · DOI
  39. Neary, 1998, Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria, Neurology, 51, 1546, 10.1212/WNL.51.6.1546 · DOI · Paper page
  40. Neumann, 2009, A new subtype of frontotemporal lobar degeneration with FUS pathology, Brain, 132, 2922, 10.1093/brain/awp214 · DOI · Paper page
  41. Neumann, 2009, Abundant FUS-immunoreactive pathology in neuronal intermediate filament inclusion disease, Acta Neuropathol., 118, 605, 10.1007/s00401-009-0581-5 · DOI
  42. Neumann, 2006, Ubiquitinated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis, Science, 314, 130, 10.1126/science.1134108 · DOI · Paper page
  43. Ni, 2011, MicroRNA-663 upregulated by oscillatory shear stress plays a role in inflammatory response of endothelial cells, Am. J. Physiol. Heart Circ. Physiol., 300, H1762, 10.1152/ajpheart.00829.2010 · DOI
  44. Peltier, 2008, Normalization of microRNA expression levels in quantitative RT-PCR assays: Identification of suitable reference RNA targets in normal and cancerous human solid tissues, RNA, 14, 844, 10.1261/rna.939908 · DOI · Paper page
  45. Piscopo, 2016, Frontotemporal lobar degeneration and microRNAs, Front. Aging Neurosci., 8, 17, 10.3389/fnagi.2016.00017 · DOI
  46. Piscopo, 2016, Reduced miR-659-3p levels correlate with progranulin increase in hypoxic conditions: implications for frontotemporal dementia, Front. Mol. Neurosci., 9, 31, 10.3389/fnmol.2016.00031 · DOI
  47. Piscopo, 2010, Hypoxia induces up-regulation of progranulin in neuroblastoma cell lines, Neurochem. Int., 57, 893, 10.1016/j.neuint.2010.09.008 · DOI
  48. Ramezani, 2015, Circulating and urinary microRNA profile in focal segmental glomerulosclerosis: a pilot study, Eur. J. Clin. Invest., 45, 394, 10.1111/eci.12420 · DOI
  49. Rascovsky, 2011, Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia, Brain, 134, 2456, 10.1093/brain/awr179 · DOI · Paper page
  50. Ricci, 2015, Statistical analysis of a Bayesian classifier based on the expression of miRNAs, BMC Bioinformatics, 16, 287, 10.1186/s12859-015-0715-9 · DOI
  51. Satoh, 2015, MicroRNA-Seq data analysis pipeline to identify blood biomarkers for Alzheimer’s disease from public data, Biomark. Insights, 10, 21, 10.4137/BMI.S25132 · DOI
  52. Sharma, 2014, Influence of sex differences on microRNA gene regulation in disease, Biol. Sex Differ., 5, 3, 10.1186/2042-6410-5-3 · DOI
  53. Sheinerman, 2017, Circulating brain-enriched microRNAs as novel biomarkers for detection and differentiation of neurodegenerative diseases, Alzheimers. Res. Ther., 9, 89, 10.1186/s13195-017-0316-0 · DOI · Paper page
  54. Shu, 2015, APP intracellular domain acts as a transcriptional regulator of miR-663 suppressing neuronal differentiation, Cell Death Dis., 6, e1651, 10.1038/cddis.2015.10 · DOI
  55. Skalsky, 2012, The viral and cellular microRNA targetome in lymphoblastoid cell lines, Plos Pathog., 8, e1002484, 10.1371/journal.ppat.1002484 · DOI
  56. Sun, 2016, MiR-502-3P suppresses cell proliferation, migration, and invasion in hepatocellular carcinoma by targeting SET, Onco. Targets Ther., 9, 3281, 10.2147/OTT.S87183 · DOI
  57. Sun, 2017, Overexpression of miR-206 ameliorates chronic constriction injury-induced neuropathic pain in rats via the MEK/ERK pathway by targeting brain-derived neurotrophic factor, Neurosci. Lett., 646, 68, 10.1016/j.neulet.2016.12.047 · DOI
  58. Tang, 2015, Different normalization strategies might cause inconsistent variation in circulating microRNAs in patients with hepatocellular carcinoma, Med. Sci. Monit., 21, 617, 10.12659/MSM.891028 · DOI
  59. Taulli, 2009, The muscle-specific microRNA miR-206 blocks human rhabdomyosarcoma growth in xenotransplanted mice by promoting myogenic differentiation, J. Clin. Invest., 119, 2366
  60. Tian, 2014, MiR-206 decreases brain-derived neurotrophic factor levels in a transgenic mouse model of Alzheimer’s disease, Neurosci. Bull., 30, 191, 10.1007/s12264-013-1419-7 · DOI · Paper page
  61. Toivonen, 2014, MicroRNA-206: a potential circulating biomarker candidate for amyotrophic lateral sclerosis, PLoS One, 9, e89065, 10.1371/journal.pone.0089065 · DOI
  62. van Dijk, 2010, Diagnostic cerebrospinal fluid biomarkers for Parkinson’s disease: a pathogenetically based approach, Neurobiol. Dis., 39, 229, 10.1016/j.nbd.2010.04.020 · DOI
  63. Vandesompele, 2002, Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes, Genome Biol., 3, 10.1186/gb-2002-3-7-research0034 · DOI · Paper page
  64. Vlachos, 2015, DIANA-miRPath v3.0: deciphering microRNA function with experimental support, Nucleic Acids Res., 43, W460, 10.1093/nar/gkv403 · DOI
  65. Waller, 2017, Serum miRNAs miR-206, 143-3p and 374b-5p as potential biomarkers for amyotrophic lateral sclerosis (ALS), Neurobiol. Aging, 55, 123, 10.1016/j.neurobiolaging.2017.03.027 · DOI
  66. Xie, 2017, Increased serum miR-206 level predicts conversion from amnestic mild cognitive impairment to Alzheimer’s disease: A 5-year follow-up study, J. Alzheimers. Dis., 55, 509, 10.3233/JAD-160468 · DOI
  67. Xie, 2015, Serum miR-206 and miR-132 as potential circulating biomarkers for mild cognitive impairment, J. Alzheimers. Dis., 45, 721, 10.3233/JAD-142847 · DOI · Paper page
  68. Yan, 2009, MicroRNA-1/206 targets c-Met and inhibits rhabdomyosarcoma development, J. Biol. Chem., 284, 29596, 10.1074/jbc.M109.020511 · DOI
  69. Yi, 2012, MiR-663, a microRNA targeting p21WAF1/CIP1, promotes the proliferation and tumorigenesis of nasopharyngeal carcinoma, Oncogene, 31, 4421, 10.1038/onc.2011.629 · DOI
  70. Zanardini, 2016, Molecular pathways bridging frontotemporal lobar degeneration and psychiatric disorders, Front. Aging Neurosci., 8, 10, 10.3389/fnagi.2016.00010 · DOI
  71. Zhai, 2013, Inhibition of autophagy and tumor growth in colon cancer by miR-502, Oncogene, 32, 1570, 10.1038/onc.2012.167 · DOI
  72. Zhang, 2014, MicroRNA directly enhances mitochondrial translation during muscle differentiation, Cell, 158, 607, 10.1016/j.cell.2014.05.047 · DOI

Cited by

Sorted by citations

See all 54

Metadata

Authors
Grasso, M., Piscopo, P., Talarico, G., Ricci, L., Crestini, A., Tosto, G., Gasparini, M., Bruno, G., Denti, M. A., & Confaloni, A.
ORCID
Venue
Neurobiology of Aging
Publisher
Elsevier BV
Published
2019-12
Volume
84
Pages
240.e1-240.e12
ISSN
0197-4580
DOI
10.1016/j.neurobiolaging.2019.01.024
Cited by
54
References
10
Language
en
Licence
Funders
  • Futuro in Ricerca–Italian Ministry of Education
  • University and Research (RBFR-0895DC)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review