Background
Methods
Gene expression data
TCGA data
GEO data
Study_Code | Data source | Disease | Tissue | Number of samples | Paired control | Technology |
---|---|---|---|---|---|---|
BLCA | TCGA | Cancer | Bladder | 38 | Yes | RNA-seq (Illumina) |
BRCA | TCGA | Cancer | Breast | 220 | Yes | RNA-seq (Illumina) |
CHOL | TCGA | Cancer | Gallbladder, liver and parts of biliary tract | 18 | Yes | RNA-seq (Illumina) |
COAD | TCGA | Cancer | Colon and rectosigmoid junction | 82 | Yes | RNA-seq (Illumina) |
ESCA | TCGA | Cancer | Esophagus | 16 | Yes | RNA-seq (Illumina) |
HNSC | TCGA | Cancer | Base of tongue, floor of mouth, gum, hypo- and oro-pharynx, larynx, etc. | 86 | Yes | RNA-seq (Illumina) |
KICH | TCGA | Cancer | Kidney | 48 | Yes | RNA-seq (Illumina) |
KIRC | TCGA | Cancer | Kidney | 144 | Yes | RNA-seq (Illumina) |
KIRP | TCGA | Cancer | Kidney | 64 | Yes | RNA-seq (Illumina) |
LIHC | TCGA | Cancer | Liver and intrahepatic bile ducts | 100 | Yes | RNA-seq (Illumina) |
LUAD | TCGA | Cancer | Bronchus and lung | 114 | Yes | RNA-seq (Illumina) |
LUSC | TCGA | Cancer | Bronchus and lung | 98 | Yes | RNA-seq (Illumina) |
PRAD | TCGA | Cancer | Prostate gland | 104 | Yes | RNA-seq (Illumina) |
READ | TCGA | Cancer | Rectum rectosigmoid junction | 20 | Yes | RNA-seq (Illumina) |
STAD | TCGA | Cancer | Stomach | 62 | Yes | RNA-seq (Illumina) |
THCA | TCGA | Cancer | Thyroid gland | 116 | Yes | RNA-seq (Illumina) |
UCEC | TCGA | Cancer | Corpus uteri | 46 | Yes | RNA-seq (Illumina) |
GSE4607 | GEO | Septic Shock | Whole Blood | 84 | No | Microarray (Affymetrix HGU 133 Plus 2.0) |
GSE8121 | GEO | Septic Shock | Whole Blood | 75 | No | Microarray (Affymetrix HGU 133 Plus 2.0) |
GSE9692 | GEO | Septic Shock | Whole Blood | 45 | No | Microarray (Affymetrix HGU 133 Plus 2.0) |
GSE13904 | GEO | Septic Shock | Whole Blood | 124 | No | Microarray (Affymetrix HGU 133 Plus 2.0) |
GSE26378 | GEO | Septic Shock | Whole Blood | 103 | No | Microarray (Affymetrix HGU 133 Plus 2.0) |
GSE26440 | GEO | Septic Shock | Whole Blood | 130 | No | Microarray (Affymetrix HGU 133 Plus 2.0) |
Pathway enrichment analysis
Cluster analysis
Visualization of pathway-level and gene-level expression scores
Network analysis
Sample-level pathway score
Machine learning
Survival analysis
Code and data availability
Results
Hierarchical clustering revealed two groups of cancers
Network analysis
Machine learning-based prediction of cancer group (SLC/CA) from 66 pathway scores
Support Vector Machine | Predicted | ||
CA | SLC | ||
Given | CA | 349 | 1 |
SLC | 4 | 188 | |
Misclassification rate | 0.9 | ||
Neural Network | Predicted | ||
CA | SLC | ||
Given | CA | 347 | 3 |
SLC | 3 | 189 | |
Misclassification rate | 1.1 |