{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:09:31Z","timestamp":1787062171286,"version":"build-2736575974"},"reference-count":48,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Oxidative Medicine and Cellular Longevity"],"published-print":{"date-parts":[[2023,4,20]]},"abstract":"<jats:p>\n                    Background. MicroRNA-1246 (miR-1246), an oncomiR that regulates the expression of multiple cancer-related genes, has been attracted and studied as a promising indicator of various tumors. However, diverse conclusions on diagnostic accuracy have been shown due to the small sample size and limited studies included. This meta-analysis is aimed at systematically assessing the performance of extracellular circulating miR-1246 in screening common cancers. Methods. We searched the PubMed\/MEDLINE, Web of Science, Cochrane Library, and Google Scholar databases for relevant studies until November 28, 2022. Then, the summary receiver operating characteristic (SROC) curves were drawn and calculated area under the curve (AUC), diagnostic odds ratio (DOR), sensitivity, and specificity values of circulating miR-1246 in the cancer surveillance. Results. After selection and quality assessment, 29 eligible studies with 5914 samples (3232 cases and 2682 controls) enrolled in the final analysis. The pooled AUC, DOR, sensitivity, and specificity of circulating miR-1246 in screening cancers were 0.885 (95% confidence interval (CI): 0.827-0.892), 27.7 (95% CI: 17.1-45.0), 84.2% (95% CI: 79.4-88.1), and 85.3% (95% CI: 80.5-89.2), respectively. Among cancer types, superior performance was noted for breast cancer (\n                    <jats:inline-formula>\n                      <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mtext>AUC<\/a:mtext>\n                        <a:mo>=<\/a:mo>\n                        <a:mn>0.950<\/a:mn>\n                        <a:mo>,<\/a:mo>\n                        <a:mtext>DOR<\/a:mtext>\n                        <a:mo>=<\/a:mo>\n                        <a:mn>98.5<\/a:mn>\n                      <\/a:math>\n                    <\/jats:inline-formula>\n                    ) compared to colorectal cancer (\n                    <jats:inline-formula>\n                      <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mtext>AUC<\/c:mtext>\n                        <c:mo>=<\/c:mo>\n                        <c:mn>0.905<\/c:mn>\n                        <c:mo>,<\/c:mo>\n                        <c:mtext>DOR<\/c:mtext>\n                        <c:mo>=<\/c:mo>\n                        <c:mn>47.6<\/c:mn>\n                      <\/c:math>\n                    <\/jats:inline-formula>\n                    ), esophageal squamous cell carcinoma (\n                    <jats:inline-formula>\n                      <e:math xmlns:e=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\">\n                        <e:mtext>AUC<\/e:mtext>\n                        <e:mo>=<\/e:mo>\n                        <e:mn>0.757<\/e:mn>\n                        <e:mo>,<\/e:mo>\n                        <e:mtext>DOR<\/e:mtext>\n                        <e:mo>=<\/e:mo>\n                        <e:mn>8.0<\/e:mn>\n                      <\/e:math>\n                    <\/jats:inline-formula>\n                    ), hepatocellular carcinoma (\n                    <jats:inline-formula>\n                      <g:math xmlns:g=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\">\n                        <g:mtext>AUC<\/g:mtext>\n                        <g:mo>=<\/g:mo>\n                        <g:mn>0.872<\/g:mn>\n                        <g:mo>,<\/g:mo>\n                        <g:mtext>DOR<\/g:mtext>\n                        <g:mo>=<\/g:mo>\n                        <g:mn>18.6<\/g:mn>\n                      <\/g:math>\n                    <\/jats:inline-formula>\n                    ), pancreatic cancer (\n                    <jats:inline-formula>\n                      <i:math xmlns:i=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\">\n                        <i:mtext>AUC<\/i:mtext>\n                        <i:mo>=<\/i:mo>\n                        <i:mn>0.767<\/i:mn>\n                        <i:mo>,<\/i:mo>\n                        <i:mtext>DOR<\/i:mtext>\n                        <i:mo>=<\/i:mo>\n                        <i:mn>12.3<\/i:mn>\n                      <\/i:math>\n                    <\/jats:inline-formula>\n                    ), and others (\n                    <jats:inline-formula>\n                      <k:math xmlns:k=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M6\">\n                        <k:mtext>AUC<\/k:mtext>\n                        <k:mo>=<\/k:mo>\n                        <k:mn>0.887<\/k:mn>\n                        <k:mo>,<\/k:mo>\n                        <k:mtext>DOR<\/k:mtext>\n                        <k:mo>=<\/k:mo>\n                        <k:mn>27.5<\/k:mn>\n                        <k:mo>,<\/k:mo>\n                        <k:mi>P<\/k:mi>\n                        <k:mo>=<\/k:mo>\n                        <k:mn>0.007<\/k:mn>\n                      <\/k:math>\n                    <\/jats:inline-formula>\n                    ). No significant publication bias in DOR was observed in the meta-analysis (funnel plot asymmetry test with\n                    <jats:inline-formula>\n                      <m:math xmlns:m=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M7\">\n                        <m:mi>P<\/m:mi>\n                        <m:mo>=<\/m:mo>\n                        <m:mn>0.652<\/m:mn>\n                      <\/m:math>\n                    <\/jats:inline-formula>\n                    ;\n                    <jats:inline-formula>\n                      <o:math xmlns:o=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M8\">\n                        <o:mtext>skewness<\/o:mtext>\n                        <o:mtext>\u2009<\/o:mtext>\n                        <o:mtext>value<\/o:mtext>\n                        <o:mo>=<\/o:mo>\n                        <o:mn>0.672<\/o:mn>\n                      <\/o:math>\n                    <\/jats:inline-formula>\n                    ,\n                    <jats:inline-formula>\n                      <q:math xmlns:q=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M9\">\n                        <q:mi>P<\/q:mi>\n                        <q:mo>=<\/q:mo>\n                        <q:mn>0.071<\/q:mn>\n                      <\/q:math>\n                    <\/jats:inline-formula>\n                    ). Conclusion. Extracellular circulating miR-1246 may serve as a reliable biomarker with good sensitivity and specificity in screening cancers, especially breast cancer.\n                  <\/jats:p>","DOI":"10.1155\/2023\/8379231","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T14:05:56Z","timestamp":1681999556000},"page":"1-13","source":"Crossref","is-referenced-by-count":22,"title":["The Diagnostic Power of Circulating miR-1246 in Screening Cancer: An Updated Meta-analysis"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1403-0257","authenticated-orcid":true,"given":"Khanh Quang","family":"Huynh","sequence":"first","affiliation":[{"name":"The Breast Unit, Cancer Center, Cho Ray Hospital, HCMC 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anh Tuan","family":"Le","sequence":"additional","affiliation":[{"name":"Department of Chemo-Radiotherapy, Cancer Center, Cho Ray Hospital, HCMC 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3107-9906","authenticated-orcid":true,"given":"Thang Thanh","family":"Phan","sequence":"additional","affiliation":[{"name":"The Laboratory D Unit, Cancer Center, Cho Ray Hospital, HCMC 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toan Trong","family":"Ho","sequence":"additional","affiliation":[{"name":"The Laboratory D Unit, Cancer Center, Cho Ray Hospital, HCMC 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suong Phuoc","family":"Pho","sequence":"additional","affiliation":[{"name":"The Laboratory D Unit, Cancer Center, Cho Ray Hospital, HCMC 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang Thuy","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Clinical Pathology, Cho Ray Hospital, HCMC 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binh Thanh","family":"Le","sequence":"additional","affiliation":[{"name":"Department of General Director, Cho Ray Hospital, HCMC 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thuc Tri","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of General Director, Cho Ray Hospital, HCMC 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Son Truong","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of General Director, Cho Ray Hospital, HCMC 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1911793"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2019.12.010"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.7150\/jca.46661"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.3421"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2018.3712"},{"issue":"6","key":"8","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.3390\/cancers12061442","article-title":"Improving multi-tumor biomarker health check-up tests with machine learning algorithms","volume":"12","author":"H. 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