التفاصيل البيبلوغرافية
| العنوان: |
Splicing factor proline- and glutamine-rich is a prognostic biomarker and correlated with clinical pathologic features and immune infiltrates in hepatocellular carcinoma. |
| المؤلفون: |
Chao-Ran Zhu, Shi-Chen Zhu |
| المصدر: |
Medical Data Mining; 2024, Vol. 7 Issue 2, p1-15, 15p |
| مصطلحات موضوعية: |
GENE ontology, CANCER-related mortality, BIOMARKERS, GENE expression, HEPATOCELLULAR carcinoma, PSYCHONEUROIMMUNOLOGY, COMPETITIVE endogenous RNA, RNA sequencing |
| مستخلص: |
Background: Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related deaths globally. Splicing factor proline- and glutamine-rich (SFPQ) is a multifunctional protein that controls various biological functions. As a potential therapeutic target and a promising prognostic indicator, the potential effects and processes of SFPQ in HCC require further investigation. Methods: The RNA sequencing data were obtained from the Gene Expression Omnibus, International Cancer Genome Consortium, and The Cancer Genome Atlas databases to analyze SFPQ expression and differentially expressed genes (DEGs). We utilized the LinkedOmics database to identify co-expressed genes. A Venn diagram was constructed to determine the overlapping genes between the DEGs and the co-expressed genes. Functional enrichment analysis was performed on the overlapping genes and DEGs. Furthermore, our study involved functional enrichment analysis, a protein-protein interaction network analysis, and an analysis of immune cell infiltration. The cBioPortal and Tumor Immune Single-cell Hub were utilized to investigate the genetic alterations of SFPQ and the single-cell transcriptome visualization of the tumor microenvironment. A ceRNA network was established with the assistance of the ENCORI website. Finally, we elucidated the clinical significance of SFPQ in HCC by employing Kaplan-Meier survival analysis, univariate and multivariate Cox regression, and prognostic nomogram models. Results: The expression of SFPQ in HCC tissues was significantly elevated compared to normal tissues. GSEA results indicated that increased expression of SFPQ was associated with pathways related to HCC. The ceRNA network, including SFPQ, hsa-miR-101-3p, AC023043.4, AC124798.1, AC145207.5, and GSEC, was constructed with the assistance of ENCORI. High SFPQ expression was related to a poor prognosis in HCC and its subtypes. Univariate and multivariate Cox regression analysis showed that elevated SFPQ expression is an independent predictive factor. Conclusions: The overexpression of SFPQ may serve as a potential prognostic biomarker, indicating a poor prognosis in HCC. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
