Bioinformatics Analysis of Circulating Tumor Cells (CTCs) in Lung Cancer: Implications for Diagnosis, Prognosis and Treatment
Bioinformatics Analysis of Circulating Tumor Cells (CTCs) in Lung Cancer: Implications for Diagnosis, Prognosis and Treatment
Masoumeh Nomani,1,*Adnan Khosravi,2Sharareh Seifi,3Babak Salimi,4Maryam Mabani,5Parsa Rostami,6
1. Research Center of Thoracic Oncology (RCTO), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Science, Tehran, Iran. 2. Research Center of Thoracic Oncology (RCTO), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Science, Tehran, Iran. 3. Research Center of Thoracic Oncology (RCTO), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Science, Tehran, Iran. 4. Research Center of Thoracic Oncology (RCTO), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Science, Tehran, Iran. 5. Research Center of Thoracic Oncology (RCTO), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Science, Tehran, Iran. 6. Research Center of Thoracic Oncology (RCTO), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Science, Tehran, Iran.
Introduction: The spread of cancer to other organs (metastasis) is the primary cause of cancer-related deaths globally. Lung cancer, known for its highly metastatic progression, remains among the most lethal of malignancies. Despite its high metastatic potential, the genomic profile of lung cancer metastases is often poorly understood, making it challenging to develop effective treatments. Cancer cells that detached from the original tumor or its spread (metastases) and travel through the blood are called circulating tumor cells (CTCs). These traveling cancer cells, were recognized as potential founders of metastatic lesions more than 100 years ago. Studies show that CTCs hold valuable information for predicting a patient's progression-free survival, overall survival, and treatment response. Furthermore, CTC analysis can aid in predicting and staging tumor recurrence and metastasis, guiding drug development, and personalizing treatment strategies. This study utilized a comprehensive analysis of gene expression data to gain a deeper understanding of the biological mechanisms of CTCs . The ultimate goal is to pinpoint reliable biomarkers for diagnosis, prognosis, and potential therapeutic targets for more effective treatment strategies
Methods: This study aimed to identify novel biomarkers for lung cancer by analyzing the gene expression profile of Circulating Tumor Cells (CTCs). The GSE249262 microarray dataset was retrieved from the Gene Expression Omnibus (GEO) database. The selected microarray data was analyzed using Transcriptome Analysis Console software, a powerful tool for gene expression analysis. Significance analysis of the expression of genes was implemented by fold change (FC) calculation. Analysis Enrichment of genes was done using Enrich r and KEGG pathway. In the present study, the STRING database was used to construct the network of hub genes with a minimum required interaction score of 0.4. The protein-protein interaction (PPI) network was then visually represented and further analyzed using Cytoscape software (version 3.6.1). This study aimed to predict the most influential genes within the network using four distinct centrality measures: Eigenvector centrality, degree centrality, betweenness centrality, and closeness centrality. By these analysis, the study identified key genes that play a crucial role in the development of CTCs in lung cancer. To further confirm the significance of the identified key genes, this study utilized the Gepia database. Kaplan-Meier curves was used to analyze the relationship between gene expression levels and survival rates in lung cancer patients and log-rank tests were performed to determine the statistical significance of any observed differences in survival
Results: This study identified 3840 genes with altered expression in circulating tumor cells (CTCs) of lung cancer patients, with 1045 showing over expression. By analyzing the centrality values, which reflect a gene's influence within the protein interaction network, were pinpointed the top 140 genes. Using Cytoscape and Gephi softwares, 15 of these genes were identified as hub genes due to their central role in the network. To understand the impact of these key genes on patient prognosis, we analyzed their relationship with survival rates.The hazard ratio (HR) with 95% confidence intervals and log-rank having p <0.05 values are considered as the cutoff value. Seven genes (ESR1, FGF2, CAV1, CDH17, GAD1, NCAM1, COL1A1) satisfied the said cutoff criteria and have been found to be associated with worse Overall Survival (OS) for lung cancer patient. This suggests these genes could serve as potential prognostic biomarkers for lung cancer. Analysis of the biological pathways enriched in the upregulated genes revealed their involvement in a variety of processes. These include pathways related to Chemical carcinogenesis, GnRH secretion, Arginine biosynthesis, Vitamin digestion and absorption, Proteoglycans in cancer, Protein digestion and absorption, and Estrogen signaling. This suggests that these upregulated genes might contribute to tumor development and progression through their roles in these pathways
Conclusion: Analyzing the genes present in circulating tumor cells (CTCs) offers crucial insights CTCs serve as valuable biological markers, providing important clues about a patient's prognosis. In this study, we have identified 15 genes as key genes. However, survival analysis based on the expression of these genes indicated that only seven genes are associated with the poor overall survival of CTCs of lung cancer patients. These key genes may help to future research of CTCs’s molecular mechanisms and biomarkers analysis. By using bioinformatics and data mining techniques to pinpoint key genes and pathways involved in CTC activity, can gain valuable insights into the complex biological processes driving metastasis