0% Complete
the 4th international edition and 13th Iranian Conference on Bioinformatics
international edition and Iranian Conference on Bioinformatics
صفحه اصلی
/
4th international edition and 13th Iranian Conference on Bioinformatics
Upregulation of IL6 as a Hub Gene in Metastatic Breast Cancer: Insights from Gene Expression and Network Analysis
نویسندگان :
Roxana Tajdini
1
Farinaz Behfarjam
2
Maryam Shahhoseini
3
Mostafa Rafiepour
4
1- دانشگاه علم و فرهنگ تهران
2- دانشگاه البرز آبیک
3- پژوهشگاه رویان
4- دانشگاه البرز آبیک
کلمات کلیدی :
Breast cancer،IL6،BC
چکیده :
Abstract: Introduction: Breast cancer is the most common cancer affecting women worldwide each year. It is a multifactorial disease caused by various factors such as gender, age, genetic mutations, and being overweight. Early detection of breast cancer can save lives. One method used for diagnosis is next-generation sequencing (NGS). In this research project, data from RNA sequencing of metastatic-stage breast cancer tissue, obtained using the NGS method, is analyzed to identify dysregulated genes involved in the development of breast cancer. These genes can serve as biomarkers for the prognosis and diagnosis of the disease. Methods: Three RNA-seq data sets from metastatic-stage breast cancer tissue (sample group) and three RNA-seq data sets from healthy breast tissue (control group) were extracted from the NCBI Sequence Read Archive (SRA) database. First, the data quality was assessed using FastQC software, ensuring its quality standards. Next, STAR software was used to compare and align the RNA-seq sequences from healthy and cancerous tissue to the human reference genome. For data normalization and statistical analysis, the DESeq2 package in the R software environment was used, identifying genes with differential expression. Results: potential genes involved in breast cancer progression were identified. By applying thresholds of P-value < 0.01 and Log2 Fold Change > 2 on differentially expressed genes, 2717 genes with increased expression were found. One of the most important is IL6. Differential expression analysis identified IL6 as one of the most significantly upregulated genes, with a log2 fold change of 3.68 (adjusted p-value: 0.006). In STRING analysis, IL6 ranked as the top hub gene in the upregulated network with a confidence score of 123, highlighting its central role in the metastatic breast cancer gene interaction network. These findings underscore the potential importance of IL6 in metastatic progression and its utility as a prognostic biomarker. Discussion and Conclusion: IL6, identified as the top hub gene in the STRING network and significantly upregulated in metastatic breast cancer, underscores its critical role in cancer progression. IL6 is known to promote tumor growth and metastasis by modulating inflammatory pathways and enhancing the tumor microenvironment's immunosuppressive characteristics .Previous studies have demonstrated its involvement in activating the STAT3 signaling cascade, which is crucial for cancer cell proliferation, survival, and invasion .The high STRING score of 123 indicates IL6's central position in the protein-protein interaction network, further supporting its role as a key regulator in metastatic processes. These findings align with earlier reports highlighting IL6 as a prognostic biomarker and a therapeutic target in metastatic cancers. However, the unique expression and interaction profile of IL6 in this study suggests additional unexplored mechanisms that warrant further investigation. Future studies should focus on functional validation of IL6's role in metastasis and its potential as a target for immunotherapy or other precision treatments. In conclusion, IL6 emerges as a pivotal gene in metastatic breast cancer, offering valuable insights into the disease's molecular underpinnings. Its integration into clinical workflows may enhance patient stratification and guide targeted therapeutic strategies, ultimately improving outcomes for breast cancer patients.
لیست مقالات
لیست مقالات بایگانی شده
Upregulation of IL6 as a Hub Gene in Metastatic Breast Cancer: Insights from Gene Expression and Network Analysis
Roxana Tajdini - Farinaz Behfarjam - Maryam Shahhoseini - Mostafa Rafiepour
Bioinformatics and computational Studies on Highly Conserved Neurocalcin Protein
Ali Eyvazi - Khosrow Khalifeh - Emran Heshmati
Element-Specific Estimation of Background Mutation Rates in Whole Cancer Genomes Through Transfer Learning
ّFarideh Bahari - Reza Ahangari Cohan - Hesam Montazeri
Multi-Target Drug Discovery for Rheumatoid Arthritis: A Comprehensive Computational Approach Using Bioactive Compounds
Pegah Mansouri - Pardis Mansouri - Sohrab Najafipour - Seyed Amin Kouhpayeh - Akbar Farjadfar - Esmaeil Behmard
BiogenExplorer: A Robust Solution for Rapid Gene Presence Detection and Allelic Diversity Analysis in large Genomic Datasets
Mohammadreza Najafi Disfani - Danial Ghofrani - Mahdiar Mansouri - Parastoo Saniee
Elucidating Inflammatory Pathways in Polycystic Ovary Syndrome: A Transcriptomic Investigation of Granulosa Cell Gene Expression
Mobina Afshari Kave - Farinaz Behfarjam - Maryam Shahhosseini - Mostafa Rafiepour - Zahra Safaei Nezhad
Improved COVID‐19 Diagnosis Using a Hybrid Transfer Learning Model with Fuzzy Edge Detection on CT Scan Images
Hassan Salarabadi - Mohammad Saber Iraji - Mehdi Salimi - Mehdi Zoberi
Comprehensive Computational Strategies for Multi-Target Drug Discovery in Inflammatory Bowel Disease Utilizing Bioactive Compounds
Pardis Mansouri - Pegah Mansouri - Sohrab Najafipour - Seyed Amin Kouhpayeh - Akbar Farjadfar - Esmaeil Behmard
Curcumin’s Journey Through Cellulose: Binding Dynamics Across Cellulose-derived Bio-Nanofibers
Ayla Esmaeilzadeh - Maryam Azimzadeh Irani - Mehdi Jahanfar - Naser Farrokhi
Investigation of the interaction between 2-aminothiazole and bovine serum albumin (BSA), using the methods of molecular docking calculations and density functional theory (DFT)
Forough Pakzadi - Yaghub Pazhang - Ebrahim Nemati-Kande
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.5.0