Objective: To analyze the molecular markers associated with occurrence, development and poor prognosis of acute myeloid leukemia (AML) by using the data of GEO and TCGA database, as well as multiomics analysis.
Methods: The transcriptome data meeting requirements were down-loaded from GEO database, the differentially expressed genes were screened by using the R language limma package, and the GO function enrichment analysis and KEGG pathway analysis were performed for differentially expressed genes, at the same time, the protein interaction network was contracted by using STRING database and cytoscape software to screen out the hub gene, then the prognosis analysis was carried out for hub gene by combination with the clinical information affected in TCGA database.
Results: 620 differentially expressed genes were screened out, among which 162 differentially expressed genes were up-regulated, and 458 differentially expressed genes were down-regulated. Based on the results of GO functional enrichment, the KEGG pathway enrichment and protein interaction network, CXCL4, CXCR4, CXCR1, CXCR2, CCL5 and JUN were selected as hub genes. The survival analysis showed that the high expression of CXCL4, CXCR1, and CCL5 was a risk factor for poor prognosis of patiants.
Conclusion: CXCL4, CXCR1 and CCL5 can be used as biomarkers for the occurrence and development of AML, which relateds with the unfavorable prognosis and can provide a basis for further study.
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http://dx.doi.org/10.19746/j.cnki.issn.1009-2137.2019.02.004 | DOI Listing |
Curr Protein Pept Sci
January 2025
Dr. Zafar H. Zaidi Center for Proteomics, University of Karachi, Karachi-75270, Pakistan.
Background: Triple-negative breast cancer (TNBC) is an aggressive type of breast cancer with a high recurrence rate. A new therapeutic intervention is urgently needed to combat this lethal subtype. The identification of biomarkers is also crucial for improving outcomes in TNBC.
View Article and Find Full Text PDFCurr Med Chem
January 2025
Department of Cardiology, Taizhou Hospital of Zhejiang Province, affiliated to Wenzhou Medical University, Linhai, Zhejiang Province, China.
Aims: This study was to explore the relationship between plasma exosomes and Acute myocardial infarction (AMI).
Background: Acute myocardial infarction (AMI) is one of the most common cardiovascular complications. Recent studies have shown that exosomes play a crucial role in the development and progression of cardiovascular diseases.
Metab Brain Dis
January 2025
Department of Pharmacy, the Second Affiliated Hospital of Shaoyang University, Shaoyang, Hunan, China.
Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β (Aβ) plaques and the aggregation of tau protein, resulting in intense memory loss and dementia. Diabetes-associated cognitive dysfunction (DACD) is a complication of diabetes mellitus, which is associated with decreased cognitive function and impaired memory. A growing body of literature emphasize the involvement of microglia in AD and DACD.
View Article and Find Full Text PDFAlzheimers Res Ther
January 2025
Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou, Taipei, 112304, Taiwan.
Background: Effective treatment for Alzheimer's disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention.
Methods: Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n = 28), stable MCI (S-MCI, n = 39), and AD patients (n = 49).
J Ovarian Res
January 2025
Reproductive Medicine Center, Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. It is characterized by symptoms such as hyperandrogenemia, oligo or anovulation and polycystic ovarian, significantly impacting quality of life. However, the practical implementation of machine learning (ML) in PCOS diagnosis is hindered by the limitations related to data size and algorithmic models.
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