Identification of prognostic and diagnostic signatures for cancer and acute myocardial infarction: multi-omics approaches for deciphering heterogeneity to enhance patient management
Patients diagnosed with cancer face an increased risk of cardiovascular events in the short term, while those experiencing acute myocardial infarction (AMI) have a higher incidence of cancer. Given limitations in clinical resources, identifying shared biomarkers offers a cost-effective approach to r...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2023.1249145/full |
| _version_ | 1797685285240176640 |
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| author | Na Yuan Hai-Hua Pan Yan-Shan Liang Hui-Lin Hu Chang-Lin Zhai Bo Wang |
| author_facet | Na Yuan Hai-Hua Pan Yan-Shan Liang Hui-Lin Hu Chang-Lin Zhai Bo Wang |
| author_sort | Na Yuan |
| collection | DOAJ |
| description | Patients diagnosed with cancer face an increased risk of cardiovascular events in the short term, while those experiencing acute myocardial infarction (AMI) have a higher incidence of cancer. Given limitations in clinical resources, identifying shared biomarkers offers a cost-effective approach to risk assessment by minimizing the need for multiple tests and screenings. Hence, it is crucial to identify common biomarkers for both cancer survival and AMI prediction. Our study suggests that monocyte-derived biomarkers, specifically WEE1, PYHIN1, SEC61A2, and HAL, hold potential as predictors for cancer prognosis and AMI. We employed a novel formula to analyze mRNA levels in clinical samples from patients with AMI and cancer, resulting in the development of a new risk score based on expression profiles. By categorizing patients into high-risk and low-risk groups based on the median risk score, we observed significantly poorer overall survival among high-risk patients in cancer cohorts using Kaplan-Meier analysis. Furthermore, calibration curves, decision curve analysis (DCA), and clinical impact curve analyses provided additional evidence supporting the robust diagnostic capacity of the risk score for AMI. Noteworthy is the shared activation of the Notch Signaling pathway, which may shed light on common high-risk factors underlying both AMI and cancer. Additionally, we validated the differential expression of these genes in cell lines and clinical samples, respectively, reinforcing their potential as meaningful biomarkers. In conclusion, our study demonstrates the promise of mRNA levels as biomarkers and emphasizes the significance of further research for validation and refinement. |
| first_indexed | 2024-03-12T00:42:56Z |
| format | Article |
| id | doaj.art-2dc330b78a5f4cef9d1b2c3d98ca2ce2 |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-03-12T00:42:56Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-2dc330b78a5f4cef9d1b2c3d98ca2ce22023-09-15T01:27:53ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122023-09-011410.3389/fphar.2023.12491451249145Identification of prognostic and diagnostic signatures for cancer and acute myocardial infarction: multi-omics approaches for deciphering heterogeneity to enhance patient managementNa Yuan0Hai-Hua Pan1Yan-Shan Liang2Hui-Lin Hu3Chang-Lin Zhai4Bo Wang5The First Hospital of Jiaxing Affiliated Hospitial of Jiaxing University, Jiaxing, Zhejiang, ChinaThe First Hospital of Jiaxing Affiliated Hospitial of Jiaxing University, Jiaxing, Zhejiang, ChinaAffiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, ChinaThe First Hospital of Jiaxing Affiliated Hospitial of Jiaxing University, Jiaxing, Zhejiang, ChinaThe First Hospital of Jiaxing Affiliated Hospitial of Jiaxing University, Jiaxing, Zhejiang, ChinaThe Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, ChinaPatients diagnosed with cancer face an increased risk of cardiovascular events in the short term, while those experiencing acute myocardial infarction (AMI) have a higher incidence of cancer. Given limitations in clinical resources, identifying shared biomarkers offers a cost-effective approach to risk assessment by minimizing the need for multiple tests and screenings. Hence, it is crucial to identify common biomarkers for both cancer survival and AMI prediction. Our study suggests that monocyte-derived biomarkers, specifically WEE1, PYHIN1, SEC61A2, and HAL, hold potential as predictors for cancer prognosis and AMI. We employed a novel formula to analyze mRNA levels in clinical samples from patients with AMI and cancer, resulting in the development of a new risk score based on expression profiles. By categorizing patients into high-risk and low-risk groups based on the median risk score, we observed significantly poorer overall survival among high-risk patients in cancer cohorts using Kaplan-Meier analysis. Furthermore, calibration curves, decision curve analysis (DCA), and clinical impact curve analyses provided additional evidence supporting the robust diagnostic capacity of the risk score for AMI. Noteworthy is the shared activation of the Notch Signaling pathway, which may shed light on common high-risk factors underlying both AMI and cancer. Additionally, we validated the differential expression of these genes in cell lines and clinical samples, respectively, reinforcing their potential as meaningful biomarkers. In conclusion, our study demonstrates the promise of mRNA levels as biomarkers and emphasizes the significance of further research for validation and refinement.https://www.frontiersin.org/articles/10.3389/fphar.2023.1249145/fullsignatureAMIcancermachine learningprognosis |
| spellingShingle | Na Yuan Hai-Hua Pan Yan-Shan Liang Hui-Lin Hu Chang-Lin Zhai Bo Wang Identification of prognostic and diagnostic signatures for cancer and acute myocardial infarction: multi-omics approaches for deciphering heterogeneity to enhance patient management Frontiers in Pharmacology signature AMI cancer machine learning prognosis |
| title | Identification of prognostic and diagnostic signatures for cancer and acute myocardial infarction: multi-omics approaches for deciphering heterogeneity to enhance patient management |
| title_full | Identification of prognostic and diagnostic signatures for cancer and acute myocardial infarction: multi-omics approaches for deciphering heterogeneity to enhance patient management |
| title_fullStr | Identification of prognostic and diagnostic signatures for cancer and acute myocardial infarction: multi-omics approaches for deciphering heterogeneity to enhance patient management |
| title_full_unstemmed | Identification of prognostic and diagnostic signatures for cancer and acute myocardial infarction: multi-omics approaches for deciphering heterogeneity to enhance patient management |
| title_short | Identification of prognostic and diagnostic signatures for cancer and acute myocardial infarction: multi-omics approaches for deciphering heterogeneity to enhance patient management |
| title_sort | identification of prognostic and diagnostic signatures for cancer and acute myocardial infarction multi omics approaches for deciphering heterogeneity to enhance patient management |
| topic | signature AMI cancer machine learning prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2023.1249145/full |
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