Author	Date	Title
Shivani Malik	2025-06-20	SKCM README

R Markdown

🧬 Identification of Multiple Prognostic Biomarkers sets for Risk stratification in SKCM

This project identifies and validates prognostic biomarker sets in skin cutaneous melanoma (SKCM) using statistical and machine learning approaches.

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🔍 Overview

• Data Retrieval: Gene expression data (473 samples, 60,660 genes) was downloaded from TCGA using TCGAbiolinks in R.
• Normalization: Data was normalized to TPM.
• Statistical Analysis: Performed correlation analysis, survival analysis (Cox, Kaplan-Meier), and LASSO.
• Survival Class Creation: OS time was used to create 4 survival classes (0--1, 1--3, 3--5, >5 years → Class 0,1,2,3).
• Feature Selection: Applied SVC-L1, RFE, SelectKBest, and SFS to identify top genes.
• Class Balancing: Used SMOTE to balance the dataset.
• Biomarker Sets: Created 7 biomarker sets (each with 20 genes).
• Model Performance: AUC ranged from 0.84 to 0.91, and Kappa from 0.48 to 0.64 across sets.
• Model Training: Applied many models but best Trained classifiers (CatBoost) and evaluated using AUC-ROC.
• Validation: Validated on GSE65904 (214 samples) using overlapping genes.
  • First Set: 15 matching genes → AUC 0.85 (train), 0.83 (test)
  • Third Set: 12 matching genes → AUC 0.85 (train), 0.86 (test)

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This repository contains all relevant files and scripts used to identify and validate prognostic biomarkers in SKCM using statistical and machine learning approaches.

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📁 Repository Structure

1. Biomarkers/

Contains lists of genes identified through feature selection and validated on a new dataset.

• Primary_biomarker/

  • List_1st_set.txt – First set of 20 genes selected from TCGA using SVC-L1.
  • List_2nd_set.txt – Second set of 20 genes selected using alternative feature selection methods.
  • List_3rd_set.txt – Third set of 20 genes selected from TCGA.
  • List_4th_set.txt – Fourth set of 20 selected biomarker genes.
  • List_5th_set.txt – Fifth set of 20 selected biomarker genes.
  • List_6th_set.txt – Sixth set of 20 selected biomarker genes.
  • List_7th_set.txt – Seventh set of 20 selected biomarker genes.

• Validated_biomarker/

  • List_match_1st.txt – List of 15 matched genes from the first biomarker set validated on GEO dataset (GSE65904).
  • List_match_3rd.txt – List of 12 matched genes from the third biomarker set validated on GEO dataset (GSE65904).

2. Dataset/

Includes scripts and data access instructions for both primary (TCGA) and validation (GEO) datasets.

a. Primary_data_TCGA/

• Script/
  • TCGA_SKCM.R – R script for downloading SKCM expression data using TCGAbiolinks.
• Link/
  • Data_link.txt – Contains links to TCGA data portal and UCSC Xena browser for direct download.

b. Validation_data_GEO/

• Script/
  • GEO_SKCM.R – R script for downloading GSE65904 dataset.
• Link/
  • Data_link.txt – Contains GEO accession links.

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3. Scripts/

Includes preprocessing, feature selection, and machine learning implementation scripts.

• Preprocessing_TCGA.py – Preprocessing steps: Remove 50% zeroes and low variance gene.

• Feature_selection_ML_TCGA.py – Feature selection using SVC-L1 on the basis of ranking, ML training and evaluate performance

• 15_1stset_ML_GEO.py – ML analysis on 15 matched genes from the primary biomarker set.

• 12_3rdset_ML_GEO.py – ML analysis on 12 matched genes from the third biomarker set.

use the same code for other set of biomarkers##

All the GEO and TCGA relevant data files and models could be downloaded from the google drive link: https://drive.google.com/drive/folders/1HIVw_rfpHolB3tAvJTtOZVV0USOANil-.