https://github.com/bibymaths/bachelor_thesis
Identification of Potent Biomarkers for Prostate Cancer Through AR, Mapk and M-TOR Signaling Pathways Mining
https://github.com/bibymaths/bachelor_thesis
bachelor bioinformatics cancer pathway-analysis prostate-cancer thesis
Last synced: 2 months ago
JSON representation
Identification of Potent Biomarkers for Prostate Cancer Through AR, Mapk and M-TOR Signaling Pathways Mining
- Host: GitHub
- URL: https://github.com/bibymaths/bachelor_thesis
- Owner: bibymaths
- License: mit
- Created: 2024-12-16T14:06:28.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-03-15T01:49:28.000Z (2 months ago)
- Last Synced: 2025-03-15T02:35:31.395Z (2 months ago)
- Topics: bachelor, bioinformatics, cancer, pathway-analysis, prostate-cancer, thesis
- Language: R
- Homepage:
- Size: 13.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: citation/thesis.bibtex
Awesome Lists containing this project
README
# Identification of Potent Biomarkers for Prostate Cancer Through AR, Mapk and M-TOR Signaling Pathways Mining
## **Overview**
This 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.
## **Project Components**
The repository is structured as follows:
### **1. Scripts**
- **`view_dataset.R`** – Loads and explores datasets using the `GEOquery` package.
- **`parse_CEL.R`** – Preprocesses `.CEL` files from GEO datasets and normalizes the data.
- **`packages.R`** – Installs and loads necessary Bioconductor packages.
- **`WBDEGS.R`** – Runs WB-DEGS (a Shiny app for differential gene expression analysis).
- **`get_supplement.R`** – Downloads supplementary GEO datasets.
### **2. Data and Results**
- **`results_annotation.xlsx`** – Annotated results of significant genes identified.
- **`results_STRING.xlsx`** – STRING network analysis results for gene interactions.
- **`results.docx`** – Summary of STRING and GeneMANIA interactions, listing key biomarkers.
- **`results.xlsx`** – Comprehensive results including differentially expressed genes (DEGs) across datasets.
### **3. Documentation**
- **`methods.pdf`** – Details the methodology, including preprocessing, statistical analysis, and pathway mapping.
- **`datasets.pdf`** – Lists all GEO datasets used for analysis, including descriptions and accession numbers.
- **`thesis.pdf`** – The full **bachelor's thesis** report.
## **Methodology**
1. **Data Collection**
- Microarray gene expression datasets were retrieved from **GEO (NCBI Gene Expression Omnibus)**.
- Selected datasets targeted **AR, MAPK, and m-TOR pathways**.
2. **Preprocessing & Normalization**
- **Background correction** and **quantile normalization** were performed using `affy` and `limma` packages.
- **RMA normalization** was applied to preprocess `.CEL` files.
3. **Differential Expression Analysis**
- Conducted using **GEO2R, MeV (MultiExperiment Viewer), and WB-DEGS**.
- Applied statistical tests: **t-test, linear models, twilight, and SAM (Significance Analysis of Microarrays)**.
- Identified **overexpressed and underexpressed genes** in prostate cancer samples.
4. **Pathway & Network Analysis**
- **STRING and GeneMANIA** were used to analyze gene interactions.
- Identified **intra-pathway** and **inter-pathway** interactions between AR, MAPK, and m-TOR genes.
- **Key biomarkers** were selected based on network connectivity and literature evidence.
## **Key Findings**
- **13 candidate genes** identified as potential biomarkers for prostate cancer.
- **Hub genes** found with strong interactions across pathways:
- **AR Pathway:** _PRKACB, CDK1, EIF5B_
- **MAPK Pathway:** _EDN1, RPS6, SERBP1_
- **m-TOR Pathway:** _RPL23, RPS20, UCHL5_
- **Inter-pathway connections** suggest interactions between AR, MAPK, and m-TOR genes in prostate cancer progression.
## **How to Use**
1. **Install Required Packages**
```r
source("http://bioconductor.org/biocLite.R")
biocLite(c("GEOquery", "affy", "limma", "gcrma", "shiny"))
```
2. **Run Data Preprocessing**
```r
source("parse_CEL.R")
```
3. **Perform Differential Expression Analysis**
```r
source("WBDEGS.R")
```
4. **Explore Pathway Interactions**
- Open **results_STRING.xlsx** and **results_annotation.xlsx** to review gene interactions.
## **Authors & Acknowledgments**
- **Abhinav Mishra**, **Nimisha Asati**
- **Supervisor:** Dr. Tiratha Raj Singh
- **Institution:** Jaypee University of Information Technology, Waknaghat
- **Year:** 2017
## License
This project is open-source under the [MIT License](LICENSE).
### Reference
A. 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.