1
Abstract
2
Methods
3
Results
3.1
Dataset properties
3.1.1
All selected projects
3.1.1.1
Project id subtypes
3.1.1.2
Primary diagnosis subtypes
3.1.2
TCGA-SARC
3.1.2.1
Primary diagnosis subtypes
3.1.2.2
Paper histology subtypes
3.1.3
LM22 signature matrix
3.2
Co-expression network with WGCNA
3.2.1
Projects
3.2.1.1
TCGA-SARC dataset
3.2.1.2
TCGA-BLCA dataset
3.2.1.3
TCGA-BRCA dataset
3.2.1.4
TCGA-OV dataset
3.2.1.5
TCGA-LIHC dataset
3.2.1.6
TCGA-LUAD dataset
3.2.1.7
TCGA-LUSC dataset
3.2.1.8
TCGA-MESO dataset
3.2.1.9
TCGA-OV dataset
3.2.1.10
TCGA-PAAD dataset
3.2.1.11
TCGA-STAD dataset
3.2.2
Primary diagnosis subsets
3.2.2.1
Leiomyosarcoma
3.2.2.2
Dedifferentiated liposarcoma
3.2.2.3
Undifferentiated sarcoma
3.2.2.4
Fibromyxosarcoma
3.2.2.5
Malignant fibrous histiocytoma
3.2.2.6
Malignant peripheral nerve sheath tumor
3.2.3
Paper histology subsets
3.2.3.1
STLMS
3.2.3.2
ULMS
3.2.3.3
DDLPS
3.2.3.4
UPS
3.2.3.5
MFS
3.2.4
Synthesis
3.2.4.1
Hallmarks
3.2.4.1.1
Alphabetically-ordered hallmarks and all datasets
3.2.4.1.2
Occurency-ordered hallmarks and all datasets
3.2.4.1.3
Log10 transformed color scale
3.2.4.2
Kegg
3.2.4.2.1
Manually-selected and ordered pathways and datasets
3.2.4.2.2
Alphabetically-ordered Kegg and all datasets
3.2.4.2.3
Occurency-ordered Kegg and all datasets
3.2.4.2.4
Log10 transformed color scale
3.2.4.3
Pathway
3.2.4.3.1
Alphabetically-ordered Pathway and all datasets
3.2.4.3.2
Occurency-ordered Pathway and all datasets
3.2.4.3.3
Log10 transformed color scale
3.3
Differential expression analyses with DESeq2
3.3.1
VNN1 High VS Low
3.3.1.1
Projects subsets
3.3.1.1.1
TCGA-SARC
3.3.1.1.1.1
Gender covariate
3.3.1.1.1.2
Gender and primary diagnosis as covariates
3.3.1.1.2
TCGA-BLCA
3.3.1.1.2.1
Gender covariate
3.3.1.1.2.2
Gender and primary diagnosis as covariates
3.3.1.1.3
TCGA-BRCA
3.3.1.1.3.1
Gender covariate
3.3.1.1.3.2
Gender and primary diagnosis as covariates
3.3.1.1.4
TCGA-ESCA
3.3.1.1.4.1
Gender covariate
3.3.1.1.4.2
Gender and primary diagnosis as covariates
3.3.1.1.5
TCGA-LIHC
3.3.1.1.5.1
Gender covariate
3.3.1.1.5.2
Gender and primary diagnosis as covariates
3.3.1.1.6
TCGA-LUAD
3.3.1.1.6.1
Gender covariate
3.3.1.1.6.2
Gender and primary diagnosis as covariates
3.3.1.1.7
TCGA-LUSC
3.3.1.1.7.1
Gender covariate
3.3.1.1.7.2
Gender and primary diagnosis as covariates
3.3.1.1.8
TCGA-MESO
3.3.1.1.8.1
Gender covariate
3.3.1.1.8.2
Gender and primary diagnosis as covariates
3.3.1.1.9
TCGA-OV
3.3.1.1.9.1
No covariate
3.3.1.1.9.2
Primary diagnosis as covariate
3.3.1.1.10
TCGA-PAAD
3.3.1.1.10.1
Gender covariate
3.3.1.1.10.2
Gender and primary diagnosis as covariates
3.3.1.1.11
TCGA-STAD
3.3.1.1.11.1
Gender covariate
3.3.1.1.11.2
Gender and primary diagnosis as covariates
3.3.1.2
Primary diagnosis subsets
3.3.1.2.1
Leiomyosarcoma
3.3.1.2.2
Dedifferentiated liposarcoma
3.3.1.2.3
Undifferentiated sarcoma
3.3.1.2.4
Fibromyxosarcoma
3.3.1.2.5
Malignant fibrous histiocytoma
3.3.1.2.6
Malignant peripheral nerve sheath tumor
3.3.1.3
Paper histology subtypes
3.3.1.3.1
STLMS
3.3.1.3.2
ULMS
3.3.1.3.3
DDLPS
3.3.1.3.4
UPS
3.3.1.3.5
MFS
3.4
Cell deconvolution with CIBSERSORTx
3.4.1
Highlight Neutrophils
3.5
Correlation analyses
3.5.1
All selected projects
3.5.2
TCGA-SARC only with cell deconvolution
3.6
PCA
3.6.1
All selected projects
3.6.1.1
On most genes
3.6.1.2
Top 2000 variance genes
3.6.2
Well- or de-differentiated liposarcoma
3.6.2.1
On most genes
3.6.2.2
Top 2000 variance genes
3.7
UMAP
3.7.1
All selected projects
3.7.1.1
Most genes
3.7.1.2
Top 2000 variance genes
3.7.2
Well or dedifferentiated liposarcomas
3.7.2.1
Most genes
3.7.2.2
Top 2000 variance genes
3.8
Survival analyses
3.8.1
VNN1 high VS low
3.8.1.1
By project id
3.8.1.2
By primary diagnosis
3.8.2
VNN1 tertiles
3.8.2.1
By project id
3.8.2.2
By primary diagnosis
3.8.3
Neutrophil infiltration Yes Vs No
3.8.3.1
By project id
3.8.3.2
By paper short histo
3.8.3.2.1
STLMS
3.8.3.2.2
DDLPS
3.8.3.2.3
UPS
3.8.3.3
VNN1 survival for non-infiltrated-by-neutrophil sarcoma
3.8.3.4
VNN1 survival for infiltrated-by-neutrophil sarcoma
4
Timetracking
VNN1 role in sarcoma outcome
4
Timetracking
Summary 2021
Detailed 2021
18 hours in 2020
140h already paid