Data from miRNA Landscape in Stage I Epithelial Ovarian Cancer Defines the Histotype Specificities Article Swipe
Enrica Calura
,
Robert Fruscio
,
Lara Paracchini
,
Eliana Bignotti
,
Antonella Ravaggi
,
Paolo Martini
,
Gabriele Sales
,
Luca Beltrame
,
Luca Clivio
,
Lorenzo Ceppi
,
Mariacristina Di Marino
,
Ilaria Fuso Nerini
,
Laura Zanotti
,
Duccio Cavalieri
,
Giorgio Cattoretti
,
Patrizia Perego
,
Rodolfo Milani
,
Dionyssios Katsaros
,
Germana Tognon
,
Enrico Sartori
,
Sërgio Pecorelli
,
Costantino Mangioni
,
Maurizio D’Incalci
,
Chiara Romualdi
,
Sergio Marchini
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1158/1078-0432.c.6520955
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1158/1078-0432.c.6520955
Purpose: Epithelial ovarian cancer (EOC) is one of the most lethal gynecologic diseases, with survival rate virtually unchanged for the past 30 years. EOC comprises different histotypes with molecular and clinical heterogeneity, but up till now the present gold standard platinum-based treatment has been conducted without any patient stratification. The aim of the present study is to generate microRNA (miRNA) profiles characteristic of each stage I EOC histotype, to identify subtype-specific biomarkers to improve our understanding underlying the tumor mechanisms.Experimental Design: A collection of 257 snap-frozen stage I EOC tumor biopsies was gathered together from three tumor tissue collections and stratified into independent training (n = 183) and validation sets (n = 74). Microarray and quantitative real-time PCR (qRT-PCR) were used to generate and validate the histotype-specific markers. A novel dedicated resampling inferential strategy was developed and applied to identify the highest reproducible results. mRNA and miRNA profiles were integrated to identify novel regulatory circuits.Results: Robust miRNA markers for clear cell and mucinous histotypes were found. Specifically, the clear cell histotype is characterized by a five-fold (log scale) higher expression of miR-30a and miR-30a*, whereas mucinous histotype has five-fold (log scale) higher levels of miR-192/194. Furthermore, a mucinous-specific regulatory loop involving miR-192/194 cluster and a differential regulation of E2F3 in clear cell histotype were identified.Conclusions: Our findings showed that stage I EOC histotypes have their own characteristic miRNA expression and specific regulatory circuits. Clin Cancer Res; 19(15); 4114–23. ©2013 AACR.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1158/1078-0432.c.6520955
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361944721
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4361944721Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1158/1078-0432.c.6520955Digital Object Identifier
- Title
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Data from miRNA Landscape in Stage I Epithelial Ovarian Cancer Defines the Histotype SpecificitiesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-31Full publication date if available
- Authors
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Enrica Calura, Robert Fruscio, Lara Paracchini, Eliana Bignotti, Antonella Ravaggi, Paolo Martini, Gabriele Sales, Luca Beltrame, Luca Clivio, Lorenzo Ceppi, Mariacristina Di Marino, Ilaria Fuso Nerini, Laura Zanotti, Duccio Cavalieri, Giorgio Cattoretti, Patrizia Perego, Rodolfo Milani, Dionyssios Katsaros, Germana Tognon, Enrico Sartori, Sërgio Pecorelli, Costantino Mangioni, Maurizio D’Incalci, Chiara Romualdi, Sergio MarchiniList of authors in order
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https://doi.org/10.1158/1078-0432.c.6520955Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1158/1078-0432.c.6520955Direct OA link when available
- Concepts
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microRNA, Cancer research, Biology, Ovarian cancer, Cancer, Oncology, Medicine, Internal medicine, Gene, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.The | 49 |
| abstract_inverted_index.aim | 50 |
| abstract_inverted_index.and | 29, 99, 107, 114, 123, 136, 145, 161, 182, 203, 229 |
| abstract_inverted_index.any | 46 |
| abstract_inverted_index.but | 32 |
| abstract_inverted_index.for | 18, 158 |
| abstract_inverted_index.has | 42, 187 |
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| abstract_inverted_index.one | 6 |
| abstract_inverted_index.our | 74 |
| abstract_inverted_index.own | 225 |
| abstract_inverted_index.the | 8, 19, 36, 52, 77, 125, 140, 167 |
| abstract_inverted_index.was | 91, 134 |
| abstract_inverted_index.(log | 176, 189 |
| abstract_inverted_index.183) | 106 |
| abstract_inverted_index.74). | 112 |
| abstract_inverted_index.Res; | 235 |
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| abstract_inverted_index.cell | 160, 169, 211 |
| abstract_inverted_index.each | 63 |
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| abstract_inverted_index.gold | 38 |
| abstract_inverted_index.have | 223 |
| abstract_inverted_index.into | 101 |
| abstract_inverted_index.loop | 199 |
| abstract_inverted_index.mRNA | 144 |
| abstract_inverted_index.most | 9 |
| abstract_inverted_index.past | 20 |
| abstract_inverted_index.rate | 15 |
| abstract_inverted_index.sets | 109 |
| abstract_inverted_index.that | 218 |
| abstract_inverted_index.till | 34 |
| abstract_inverted_index.used | 120 |
| abstract_inverted_index.were | 119, 148, 164, 213 |
| abstract_inverted_index.with | 13, 27 |
| abstract_inverted_index.(EOC) | 4 |
| abstract_inverted_index.clear | 159, 168, 210 |
| abstract_inverted_index.miRNA | 146, 156, 227 |
| abstract_inverted_index.novel | 129, 152 |
| abstract_inverted_index.stage | 64, 86, 219 |
| abstract_inverted_index.study | 54 |
| abstract_inverted_index.their | 224 |
| abstract_inverted_index.three | 95 |
| abstract_inverted_index.tumor | 78, 89, 96 |
| abstract_inverted_index.Cancer | 234 |
| abstract_inverted_index.Robust | 155 |
| abstract_inverted_index.cancer | 3 |
| abstract_inverted_index.found. | 165 |
| abstract_inverted_index.higher | 178, 191 |
| abstract_inverted_index.lethal | 10 |
| abstract_inverted_index.levels | 192 |
| abstract_inverted_index.scale) | 177, 190 |
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| abstract_inverted_index.tissue | 97 |
| abstract_inverted_index.years. | 22 |
| abstract_inverted_index.©2013 | 238 |
| abstract_inverted_index.(miRNA) | 59 |
| abstract_inverted_index.19(15); | 236 |
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| abstract_inverted_index.cluster | 202 |
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| abstract_inverted_index.improve | 73 |
| abstract_inverted_index.markers | 157 |
| abstract_inverted_index.ovarian | 2 |
| abstract_inverted_index.patient | 47 |
| abstract_inverted_index.present | 37, 53 |
| abstract_inverted_index.whereas | 184 |
| abstract_inverted_index.without | 45 |
| abstract_inverted_index.biopsies | 90 |
| abstract_inverted_index.clinical | 30 |
| abstract_inverted_index.findings | 216 |
| abstract_inverted_index.gathered | 92 |
| abstract_inverted_index.generate | 57, 122 |
| abstract_inverted_index.identify | 69, 139, 151 |
| abstract_inverted_index.markers. | 127 |
| abstract_inverted_index.microRNA | 58 |
| abstract_inverted_index.mucinous | 162, 185 |
| abstract_inverted_index.profiles | 60, 147 |
| abstract_inverted_index.results. | 143 |
| abstract_inverted_index.specific | 230 |
| abstract_inverted_index.standard | 39 |
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| abstract_inverted_index.training | 103 |
| abstract_inverted_index.validate | 124 |
| abstract_inverted_index.(qRT-PCR) | 118 |
| abstract_inverted_index.circuits. | 232 |
| abstract_inverted_index.comprises | 24 |
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| abstract_inverted_index.dedicated | 130 |
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| abstract_inverted_index.different | 25 |
| abstract_inverted_index.diseases, | 12 |
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| abstract_inverted_index.histotype | 170, 186, 212 |
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| abstract_inverted_index.molecular | 28 |
| abstract_inverted_index.real-time | 116 |
| abstract_inverted_index.treatment | 41 |
| abstract_inverted_index.unchanged | 17 |
| abstract_inverted_index.virtually | 16 |
| abstract_inverted_index.4114–23. | 237 |
| abstract_inverted_index.Epithelial | 1 |
| abstract_inverted_index.Microarray | 113 |
| abstract_inverted_index.biomarkers | 71 |
| abstract_inverted_index.collection | 82 |
| abstract_inverted_index.expression | 179, 228 |
| abstract_inverted_index.histotype, | 67 |
| abstract_inverted_index.histotypes | 26, 163, 222 |
| abstract_inverted_index.integrated | 149 |
| abstract_inverted_index.regulation | 206 |
| abstract_inverted_index.regulatory | 153, 198, 231 |
| abstract_inverted_index.resampling | 131 |
| abstract_inverted_index.stratified | 100 |
| abstract_inverted_index.underlying | 76 |
| abstract_inverted_index.validation | 108 |
| abstract_inverted_index.collections | 98 |
| abstract_inverted_index.gynecologic | 11 |
| abstract_inverted_index.independent | 102 |
| abstract_inverted_index.inferential | 132 |
| abstract_inverted_index.snap-frozen | 85 |
| abstract_inverted_index.Furthermore, | 195 |
| abstract_inverted_index.differential | 205 |
| abstract_inverted_index.quantitative | 115 |
| abstract_inverted_index.reproducible | 142 |
| abstract_inverted_index.<i>Clin | 233 |
| abstract_inverted_index.Specifically, | 166 |
| abstract_inverted_index.characterized | 172 |
| abstract_inverted_index.understanding | 75 |
| abstract_inverted_index.characteristic | 61, 226 |
| abstract_inverted_index.heterogeneity, | 31 |
| abstract_inverted_index.platinum-based | 40 |
| abstract_inverted_index.stratification. | 48 |
| abstract_inverted_index.subtype-specific | 70 |
| abstract_inverted_index.Design:</b> | 80 |
| abstract_inverted_index.mucinous-specific | 197 |
| abstract_inverted_index.histotype-specific | 126 |
| abstract_inverted_index.(<i>n</i> | 104, 110 |
| abstract_inverted_index.<i>E2F3</i> | 208 |
| abstract_inverted_index.<i>miR-30a</i> | 181 |
| abstract_inverted_index.<i>miR-30a</i>*, | 183 |
| abstract_inverted_index.<i>miR-192/194</i> | 201 |
| abstract_inverted_index.<i>miR-192/194</i>. | 194 |
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| abstract_inverted_index.<div>Abstract<p><b>Purpose:</b> | 0 |
| abstract_inverted_index.circuits.</p><p><b>Results:</b> | 154 |
| abstract_inverted_index.identified.</p><p><b>Conclusions:</b> | 214 |
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| institutions_distinct_count | 25 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
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