An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods
Abstract Acute myocardial infarction (AMI) is a major contributor to cardiovascular-related mortality, and early diagnosis is crucial for effective treatment and better outcomes. While several biomarkers have been explored for AMI, there remains a need for reliable, non-invasive biomarkers that can...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92757-4 |
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| author | Fei Huang Zongning Chen Binjie Tan Rong He Xiaoyu Zhang Yali Chen Jinsong Gao Bo Sun |
| author_facet | Fei Huang Zongning Chen Binjie Tan Rong He Xiaoyu Zhang Yali Chen Jinsong Gao Bo Sun |
| author_sort | Fei Huang |
| collection | DOAJ |
| description | Abstract Acute myocardial infarction (AMI) is a major contributor to cardiovascular-related mortality, and early diagnosis is crucial for effective treatment and better outcomes. While several biomarkers have been explored for AMI, there remains a need for reliable, non-invasive biomarkers that can accurately differentiate AMI patients from healthy individuals. This study aims to identify potential mRNA biomarkers in peripheral blood that could aid in the diagnosis and monitoring of AMI. We performed transcriptomic analysis of blood samples from 81 individuals, including 16 healthy controls, 58 AMI patients, and 7 post-treated AMI individuals. Through a combination of Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA), random forest (RF), Weighted Gene Co-expression Network Analysis (WGCNA), and LASSO regression, we identified mRNA markers that are significantly correlated with AMI. Specifically, the mRNA expressions of ANKRD52, ART1, NRP2, and PPP1R15A were elevated in AMI patients, whereas BAIAP2L1 and CCNE1 were downregulated. However, while these mRNA biomarkers show potential for distinguishing AMI patients from healthy individuals, further studies are needed to confirm their clinical applicability. |
| format | Article |
| id | doaj-art-8ffa615276384f87ae064bd92519740f |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8ffa615276384f87ae064bd92519740f2025-08-20T02:55:29ZengNature PortfolioScientific Reports2045-23222025-03-0115111610.1038/s41598-025-92757-4An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methodsFei Huang0Zongning Chen1Binjie Tan2Rong He3Xiaoyu Zhang4Yali Chen5Jinsong Gao6Bo Sun7Medical School, People’s Hospital of Lijiang, Kunming University of Science and TechnologyMedical School, People’s Hospital of Lijiang, Kunming University of Science and TechnologyMedical School, Kunming University of Science and TechnologyMedical School, Kunming University of Science and TechnologyMedical School, Kunming University of Science and TechnologyMedical School, Kunming University of Science and TechnologyMedical School, Kunming University of Science and TechnologyMedical School, Kunming University of Science and TechnologyAbstract Acute myocardial infarction (AMI) is a major contributor to cardiovascular-related mortality, and early diagnosis is crucial for effective treatment and better outcomes. While several biomarkers have been explored for AMI, there remains a need for reliable, non-invasive biomarkers that can accurately differentiate AMI patients from healthy individuals. This study aims to identify potential mRNA biomarkers in peripheral blood that could aid in the diagnosis and monitoring of AMI. We performed transcriptomic analysis of blood samples from 81 individuals, including 16 healthy controls, 58 AMI patients, and 7 post-treated AMI individuals. Through a combination of Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA), random forest (RF), Weighted Gene Co-expression Network Analysis (WGCNA), and LASSO regression, we identified mRNA markers that are significantly correlated with AMI. Specifically, the mRNA expressions of ANKRD52, ART1, NRP2, and PPP1R15A were elevated in AMI patients, whereas BAIAP2L1 and CCNE1 were downregulated. However, while these mRNA biomarkers show potential for distinguishing AMI patients from healthy individuals, further studies are needed to confirm their clinical applicability.https://doi.org/10.1038/s41598-025-92757-4Acute myocardial infarction (AMI)mRNA biomarkersMachine learningDiagnostic screening |
| spellingShingle | Fei Huang Zongning Chen Binjie Tan Rong He Xiaoyu Zhang Yali Chen Jinsong Gao Bo Sun An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods Scientific Reports Acute myocardial infarction (AMI) mRNA biomarkers Machine learning Diagnostic screening |
| title | An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods |
| title_full | An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods |
| title_fullStr | An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods |
| title_full_unstemmed | An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods |
| title_short | An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods |
| title_sort | exploratory study of high throughput transcriptomic analysis reveals novel mrna biomarkers for acute myocardial infarction using integrated methods |
| topic | Acute myocardial infarction (AMI) mRNA biomarkers Machine learning Diagnostic screening |
| url | https://doi.org/10.1038/s41598-025-92757-4 |
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