miTS_Pathway

A step to step guide on mining cancer biomarker based on microRNA data and Tissue Specificity of disease (miTS) + Molecular Pathway Investigation.

The miTS Pathway method has been discussed previously on IOP Journal of Physics: Conference Series (Indexes by SCOPUS) in article Mining Potential MicroRNA Biomarkers related to IQGAPs of Tyroid Carcinoma through in silico process. In this repository, the demonstration of the method was provided.

Goal of the demonstrated experiment:

• Find thyroid carcinoma's biomarker related to IQGAP1 gene based on the miRNA presence in patient thyroid tissue.
• Find the molecular interaction that promote the cancer.

Pipeline:

Index

• About
• Pipeline
• Developer

About

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Tools & Databases used in this experiment demonstration:

• National Cancer Institute GDC Data Portal Database
• Data Conversion from .JSON to .CSV Tools
• Phyton 3.6 Tools
• TCGA Assembler v.1 (Require R Studio Installation) Tools
• R Studio Tools
• Microsoft Excel 2016 Tools
• MATLAB R2018a Tools
• PubMed Database
• miRTarBase Database
• STRING Database
• KEGG Database

Compatibility

The method is compatible for any OS as long the tools and database mentioned above can be accessed. However, the tutorial here was shown based on Windows 10 OS

Pipeline

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The complete video tutorial can be accesed on:

📹 [A step to step guideline in Mining Cancer Biomarker and its pathway interaction based on miTS method]

Gene-miRNA correlation

This part was destined to find the exact microRNA that responsible for IQGAP1 gene regulation. Here, the author use statistical correlation analysis to find the miRNA.

1. Data Pre-Processing/Data Mining

A. Download the metadata file of cancer repository

• Download the metadata from National Cancer Institute GDC Data Portal database
• Using the parameter listed on "Data pre-processing" section of [Mining Potential MicroRNA Biomarkers related to IQGAPs of Tyroid Carcinoma through in silico process] paper.
• 📹 Video tutorial [Data Pre-Processing/Data Mining (1)]

B. Conver the metadata file from .json to .csv

• using tools such as: konklone, convert the data
• 📹 Video tutorial [Data Conversion (2)]

C. Find the patient ID that contain both gene and miRNA and download it (data filtration)

• using python, extract the patient id. Download find_id.py on link below
• ⬇️ Download the prebuilt zip file and unzip it.
• Install TCGA Assembler in R Studio
• Import TCGA Assembler to R
• Set up the R code based on TCGA Assembler guidelines
• Copy paste the result of extraction to R studio
• Download the data
• Import the data to Excel for further analysis
• 📹 Video tutorial [Data Filtration (3)]

2. Statistical/Correlation Analysis (Find the Spearman Rho and Significance Value)

• Using matlab, perform the spearman rho and significance value test. Download Spearman_Rho_&_Significance_Value.mat
• ⬇️ Download the prebuilt zip file and unzip it.
• :video camera: Video tutorial [Statistical Analysis (4)]

3. Validation

• Using excel, perform the spearman rho and significance value test manually.
• 📹 Video tutorial [Correlation Validation (5)]

Gene-miRNA pathway

This part was destined to find the miRNA interaction and IQGAP1 in molecular pathway.

1.Find Protein Target

• Using miRTarBase find the miRNA target
• 📹 Video tutorial [miRNA target on miRTarBase (6)]

2. Find the relation of protein target

• Using STRING find the interaction between protein
• 📹 Video tutorial [MiRNA protein interaction on STRING database (7)]

3. Validation of the protein target

• Using KEGG find the pathway of protein and miRNA interaction
• 📹 Video tutorial [Protein interaction pathway on KEGG (8)]

Developer Contribution & Contacts

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• Stefanus Satrio Hadi Wibowo ([email protected])

  Create the Gene-miRna pathway analysis process and execute it

• David Agustriawan ([email protected])

  Create the Gene-miRNA correlation process Matlab code and supervised the research

• Jeremias Ivan ([email protected])

  Create the Python coding and verificaiton process