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ITLNI identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial
JinHui Liu1, SiYue Li1, JunYa Liang2
1Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China, wenjunchengdoc@163.com.
Background:
Epithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable.
Materials And Methods:
Gene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the "limma" package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein-protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples.
Results:
We analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA.
Conclusion:
In this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.
Insights
This study identifies ITLN1 as a potential tumor suppressor for epithelial ovarian cancer (EOC). Lower ITLN1 levels correlate with poorer EOC prognosis, suggesting its use as a diagnostic biomarker.
Area of Science:
- Oncology
- Bioinformatics
- Molecular Biology
Background:
- Epithelial ovarian cancer (EOC) is a significant cause of cancer-related mortality in women.
- Bioinformatics approaches are crucial for identifying genes involved in cancer progression.
- Targeted therapies based on specific molecular factors offer promising treatment avenues for EOC.
Purpose of the Study:
- To identify key genes and pathways implicated in epithelial ovarian cancer (EOC) progression.
- To explore the potential of identified genes as biomarkers for EOC diagnosis and prognosis.
- To investigate the role of ITLN1 as a potential tumor suppressor in EOC.
Main Methods:
- Differential gene expression analysis of EOC versus normal ovarian tissues using Gene Expression Omnibus (GEO) datasets (GSE18520, GSE27651).
- Functional and pathway enrichment analyses of differentially expressed genes (DEGs).
- Protein-protein interaction (PPI) network construction and hub gene identification using Cytoscape.
- Weighted gene co-expression network analysis (WGCNA) to identify EOC-related modules.
- Validation of key genes using public databases, Gene Expression Profiling Interactive Analysis (GEPIA), and quantitative real-time PCR (qRT-PCR) on clinical samples.
Main Results:
- Upregulated DEGs were enriched in phosphate ion binding, while downregulated DEGs were enriched in glycosaminoglycan binding.
- CDK1 was identified as a key hub gene in the PPI network.
- The gene ITLN1, identified within an EOC-related module, was found to be downregulated in EOC tissues and positively correlated with improved patient prognosis, suggesting a tumor-suppressive role.
Conclusions:
- ITLN1 functions as a potential tumor suppressor in epithelial ovarian cancer.
- ITLN1 may serve as a valuable prognostic biomarker for EOC.
- The study highlights key genes and pathways involved in EOC, providing insights for targeted therapeutic strategies.
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