{"id":24745799,"url":"https://github.com/bibymaths/bachelor_thesis","last_synced_at":"2025-03-23T00:22:02.590Z","repository":{"id":273566935,"uuid":"904239267","full_name":"bibymaths/bachelor_thesis","owner":"bibymaths","description":"Identification of Potent Biomarkers for Prostate Cancer Through AR, Mapk and M-TOR Signaling Pathways Mining","archived":false,"fork":false,"pushed_at":"2025-03-15T01:49:28.000Z","size":13810,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-15T02:35:31.395Z","etag":null,"topics":["bachelor","bioinformatics","cancer","pathway-analysis","prostate-cancer","thesis"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bibymaths.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"citation/thesis.bibtex","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-16T14:06:28.000Z","updated_at":"2025-03-15T01:49:31.000Z","dependencies_parsed_at":"2025-03-15T05:45:47.392Z","dependency_job_id":null,"html_url":"https://github.com/bibymaths/bachelor_thesis","commit_stats":null,"previous_names":["bibymaths/bachelor_thesis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bibymaths%2Fbachelor_thesis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bibymaths%2Fbachelor_thesis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bibymaths%2Fbachelor_thesis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bibymaths%2Fbachelor_thesis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bibymaths","download_url":"https://codeload.github.com/bibymaths/bachelor_thesis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245037438,"owners_count":20550871,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bachelor","bioinformatics","cancer","pathway-analysis","prostate-cancer","thesis"],"created_at":"2025-01-28T03:29:46.878Z","updated_at":"2025-03-23T00:22:02.570Z","avatar_url":"https://github.com/bibymaths.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Identification of Potent Biomarkers for Prostate Cancer Through AR, Mapk and M-TOR Signaling Pathways Mining \n\n## **Overview**\nThis repository contains all scripts, results, and documentation related to the **bachelor's thesis project** focused on identifying biomarkers for **prostate cancer** by analyzing the **AR (Androgen Receptor), MAPK, and m-TOR signaling pathways** using microarray datasets. The study integrates **bioinformatics, pathway analysis, and statistical approaches** to discover genes that may serve as potential therapeutic targets.\n\n## **Project Components**\nThe repository is structured as follows:\n\n### **1. Scripts**\n- **`view_dataset.R`** – Loads and explores datasets using the `GEOquery` package.\n- **`parse_CEL.R`** – Preprocesses `.CEL` files from GEO datasets and normalizes the data.\n- **`packages.R`** – Installs and loads necessary Bioconductor packages.\n- **`WBDEGS.R`** – Runs WB-DEGS (a Shiny app for differential gene expression analysis).\n- **`get_supplement.R`** – Downloads supplementary GEO datasets.\n\n### **2. Data and Results**\n- **`results_annotation.xlsx`** – Annotated results of significant genes identified.\n- **`results_STRING.xlsx`** – STRING network analysis results for gene interactions.\n- **`results.docx`** – Summary of STRING and GeneMANIA interactions, listing key biomarkers.\n- **`results.xlsx`** – Comprehensive results including differentially expressed genes (DEGs) across datasets.\n\n### **3. Documentation**\n- **`methods.pdf`** – Details the methodology, including preprocessing, statistical analysis, and pathway mapping.\n- **`datasets.pdf`** – Lists all GEO datasets used for analysis, including descriptions and accession numbers.\n- **`thesis.pdf`** – The full **bachelor's thesis** report.\n\n## **Methodology**\n1. **Data Collection**  \n   - Microarray gene expression datasets were retrieved from **GEO (NCBI Gene Expression Omnibus)**.\n   - Selected datasets targeted **AR, MAPK, and m-TOR pathways**.\n   \n2. **Preprocessing \u0026 Normalization**  \n   - **Background correction** and **quantile normalization** were performed using `affy` and `limma` packages.\n   - **RMA normalization** was applied to preprocess `.CEL` files.\n\n3. **Differential Expression Analysis**  \n   - Conducted using **GEO2R, MeV (MultiExperiment Viewer), and WB-DEGS**.\n   - Applied statistical tests: **t-test, linear models, twilight, and SAM (Significance Analysis of Microarrays)**.\n   - Identified **overexpressed and underexpressed genes** in prostate cancer samples.\n\n4. **Pathway \u0026 Network Analysis**  \n   - **STRING and GeneMANIA** were used to analyze gene interactions.\n   - Identified **intra-pathway** and **inter-pathway** interactions between AR, MAPK, and m-TOR genes.\n   - **Key biomarkers** were selected based on network connectivity and literature evidence.\n\n## **Key Findings**\n- **13 candidate genes** identified as potential biomarkers for prostate cancer.\n- **Hub genes** found with strong interactions across pathways:\n  - **AR Pathway:** _PRKACB, CDK1, EIF5B_\n  - **MAPK Pathway:** _EDN1, RPS6, SERBP1_\n  - **m-TOR Pathway:** _RPL23, RPS20, UCHL5_\n- **Inter-pathway connections** suggest interactions between AR, MAPK, and m-TOR genes in prostate cancer progression.\n\n## **How to Use**\n1. **Install Required Packages**  \n   ```r\n   source(\"http://bioconductor.org/biocLite.R\")\n   biocLite(c(\"GEOquery\", \"affy\", \"limma\", \"gcrma\", \"shiny\"))\n   ```\n2. **Run Data Preprocessing**  \n   ```r\n   source(\"parse_CEL.R\")\n   ```\n3. **Perform Differential Expression Analysis**  \n   ```r\n   source(\"WBDEGS.R\")\n   ```\n4. **Explore Pathway Interactions**  \n   - Open **results_STRING.xlsx** and **results_annotation.xlsx** to review gene interactions.\n\n## **Authors \u0026 Acknowledgments**\n- **Abhinav Mishra**, **Nimisha Asati**\n- **Supervisor:** Dr. Tiratha Raj Singh\n- **Institution:** Jaypee University of Information Technology, Waknaghat\n- **Year:** 2017\n\n## License\nThis project is open-source under the [MIT License](LICENSE).\n\n### Reference \nA. Mishra and N. Asati, Identification of Potent Biomarkers for Prostate Cancer Through AR, MAPK, and m-TOR Signaling Pathways Mining. Solan, HP: Jaypee University of Information Technology, 2017.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbibymaths%2Fbachelor_thesis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbibymaths%2Fbachelor_thesis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbibymaths%2Fbachelor_thesis/lists"}