Circulating serum miRNAs predict response to platinum chemotherapy in high‐grade serous ovarian cancer
Article Swipe
Kazuhiro Suzuki
,
Akira Yokoi
,
Juntaro Matsuzaki
,
Kosuke Yoshida
,
Yusuke Yamamoto
,
Tomoyasu Kato
,
Mitsuya Ishikawa
,
Takahiro Ochiya
,
Hiroaki Kajiyama
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/cam4.70251
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/cam4.70251
Background Platinum chemotherapy is the cornerstone of treatment for high‐grade serous ovarian cancer (HGSOC); however, validated biomarkers that can accurately predict platinum response are lacking. Based on their roles in the underlying pathophysiology, circulating microRNAs are potential, noninvasive biomarkers in cancer. In the present study, we aimed to evaluate the circulating miRNA profiles of patients with HGSOC and to assess their potential utility as biomarkers to predict platinum response. Methods Pretreatment serum samples collected from patients who received platinum chemotherapy for Stage III–IV HGSOC between 2008 and 2016 were analyzed using miRNA microarray. LASSO logistic regression analysis was used to construct predictive models for treatment‐free interval of platinum (TFIp). Results The median follow‐up was 54.6 (range, 3.5–144.1) months. The comprehensive analysis of 2588 miRNAs was performed in serum samples of 153 eligible patients, and predictive models were constructed using a combination of circulating miRNAs with an area under the receiver operating characteristic curve of 0.944 for TFIp >1 month, 0.637 for TFIp ≥6 months, 0.705 for TFIp ≥12 months, and 0.938 for TFIp ≥36 months. Each predictive model provided a significant TFIp classification ( p = 0.001 in TFIp >1 month, p = 0.013 in TFIp ≥6 months, p < 0.001 in TFIp ≥12 months, and p < 0.001 in TFIp ≥36 months). Conclusion Circulating miRNA profiles has potential utility in predicting platinum response in patients with HGSOC and can aid clinicians in choosing appropriate treatment strategies.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/cam4.70251
- OA Status
- gold
- Cited By
- 1
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404728118
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4404728118Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/cam4.70251Digital Object Identifier
- Title
-
Circulating serum
miRNAs predict response to platinum chemotherapy in high‐grade serous ovarian cancerWork title - Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-01Full publication date if available
- Authors
-
Kazuhiro Suzuki, Akira Yokoi, Juntaro Matsuzaki, Kosuke Yoshida, Yusuke Yamamoto, Tomoyasu Kato, Mitsuya Ishikawa, Takahiro Ochiya, Hiroaki KajiyamaList of authors in order
- Landing page
-
https://doi.org/10.1002/cam4.70251Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1002/cam4.70251Direct OA link when available
- Concepts
-
Medicine, Internal medicine, Oncology, Ovarian cancer, Serous ovarian cancer, Serous fluid, Chemotherapy, Logistic regression, microRNA, Receiver operating characteristic, Microarray, Stage (stratigraphy), Area under the curve, Cancer, Biology, Gene expression, Biochemistry, Gene, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.>1 | 158, 190 |
| abstract_inverted_index.0.001 | 187, 201, 209 |
| abstract_inverted_index.0.013 | 194 |
| abstract_inverted_index.0.637 | 160 |
| abstract_inverted_index.0.705 | 165 |
| abstract_inverted_index.0.938 | 171 |
| abstract_inverted_index.0.944 | 155 |
| abstract_inverted_index.Based | 26 |
| abstract_inverted_index.HGSOC | 57, 84, 228 |
| abstract_inverted_index.LASSO | 94 |
| abstract_inverted_index.Stage | 82 |
| abstract_inverted_index.aimed | 47 |
| abstract_inverted_index.curve | 153 |
| abstract_inverted_index.miRNA | 52, 92, 216 |
| abstract_inverted_index.model | 178 |
| abstract_inverted_index.roles | 29 |
| abstract_inverted_index.serum | 72, 128 |
| abstract_inverted_index.their | 28, 61 |
| abstract_inverted_index.under | 148 |
| abstract_inverted_index.using | 91, 139 |
| abstract_inverted_index.≥12 | 168, 204 |
| abstract_inverted_index.≥36 | 174, 212 |
| abstract_inverted_index.assess | 60 |
| abstract_inverted_index.cancer | 13 |
| abstract_inverted_index.median | 112 |
| abstract_inverted_index.miRNAs | 124, 144 |
| abstract_inverted_index.models | 103, 136 |
| abstract_inverted_index.month, | 159, 191 |
| abstract_inverted_index.serous | 11 |
| abstract_inverted_index.study, | 45 |
| abstract_inverted_index.(TFIp). | 109 |
| abstract_inverted_index.(range, | 116 |
| abstract_inverted_index.Methods | 70 |
| abstract_inverted_index.Results | 110 |
| abstract_inverted_index.between | 85 |
| abstract_inverted_index.cancer. | 41 |
| abstract_inverted_index.months, | 164, 169, 198, 205 |
| abstract_inverted_index.months. | 118, 175 |
| abstract_inverted_index.ovarian | 12 |
| abstract_inverted_index.predict | 21, 67 |
| abstract_inverted_index.present | 44 |
| abstract_inverted_index.samples | 73, 129 |
| abstract_inverted_index.utility | 63, 220 |
| abstract_inverted_index.(HGSOC); | 14 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.III–IV | 83 |
| abstract_inverted_index.Platinum | 2 |
| abstract_inverted_index.analysis | 97, 121 |
| abstract_inverted_index.analyzed | 90 |
| abstract_inverted_index.choosing | 234 |
| abstract_inverted_index.eligible | 132 |
| abstract_inverted_index.evaluate | 49 |
| abstract_inverted_index.however, | 15 |
| abstract_inverted_index.interval | 106 |
| abstract_inverted_index.lacking. | 25 |
| abstract_inverted_index.logistic | 95 |
| abstract_inverted_index.months). | 213 |
| abstract_inverted_index.patients | 55, 76, 226 |
| abstract_inverted_index.platinum | 22, 68, 79, 108, 223 |
| abstract_inverted_index.profiles | 53, 217 |
| abstract_inverted_index.provided | 179 |
| abstract_inverted_index.received | 78 |
| abstract_inverted_index.receiver | 150 |
| abstract_inverted_index.response | 23, 224 |
| abstract_inverted_index.collected | 74 |
| abstract_inverted_index.construct | 101 |
| abstract_inverted_index.microRNAs | 35 |
| abstract_inverted_index.operating | 151 |
| abstract_inverted_index.patients, | 133 |
| abstract_inverted_index.performed | 126 |
| abstract_inverted_index.potential | 62, 219 |
| abstract_inverted_index.response. | 69 |
| abstract_inverted_index.treatment | 8, 236 |
| abstract_inverted_index.validated | 16 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Conclusion | 214 |
| abstract_inverted_index.accurately | 20 |
| abstract_inverted_index.biomarkers | 17, 39, 65 |
| abstract_inverted_index.clinicians | 232 |
| abstract_inverted_index.potential, | 37 |
| abstract_inverted_index.predicting | 222 |
| abstract_inverted_index.predictive | 102, 135, 177 |
| abstract_inverted_index.regression | 96 |
| abstract_inverted_index.underlying | 32 |
| abstract_inverted_index.Circulating | 215 |
| abstract_inverted_index.appropriate | 235 |
| abstract_inverted_index.circulating | 34, 51, 143 |
| abstract_inverted_index.combination | 141 |
| abstract_inverted_index.constructed | 138 |
| abstract_inverted_index.cornerstone | 6 |
| abstract_inverted_index.follow‐up | 113 |
| abstract_inverted_index.microarray. | 93 |
| abstract_inverted_index.noninvasive | 38 |
| abstract_inverted_index.significant | 181 |
| abstract_inverted_index.strategies. | 237 |
| abstract_inverted_index.3.5–144.1) | 117 |
| abstract_inverted_index.Pretreatment | 71 |
| abstract_inverted_index.chemotherapy | 3, 80 |
| abstract_inverted_index.high‐grade | 10 |
| abstract_inverted_index.comprehensive | 120 |
| abstract_inverted_index.characteristic | 152 |
| abstract_inverted_index.classification | 183 |
| abstract_inverted_index.pathophysiology, | 33 |
| abstract_inverted_index.treatment‐free | 105 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.67349309 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |