Background
Methods
Ethics statement
Patients
Parameters | Discovery cohort (n = 12) | Validation cohort (n = 31) |
---|---|---|
Age (years, mean ± SD) | 55.9 ± 10.4 | 57.9 ± 12.0 |
Gender (male/female) | 2/10 | 8/23 |
Disease duration (months, mean ± SD) | 36.5 ± 30.6 | 41.5 ± 21.5 |
Erythrocyte sedimentation rate (ESR, mm/H, mean ± SD) | 58.4 ± 21.3 | 40.5 ± 27.3 |
C-reactive protein (CRP, mg/dL, mean ± SD) | 25.6 ± 22.9 | 21.5 ± 36.7 |
Positive rheumatoid factor (n, %) | 10, 83.3 | 24, 77.4 |
Positive anti-cyclic citrullinated peptide (CCP) antibodies (n, %) | 9, 75 | 24, 77.4 |
Gene expression profiling
Differentially expressed mRNAs screening
Gene signal transduction network analysis
Pathway enrichment analysis
Quantitative PCR analysis
Evaluation of model performance by independent dataset test
Statistical analyses
Results
Differentially expressed genes associated with response to TG tablets
Identification of candidate gene biomarkers that predict response to TG tablets based on the discovery cohort
Gene | Network topological features | Differential expression patterns | ||||
---|---|---|---|---|---|---|
Degree | Closeness | Betweenness | P_value | Fold_change | Style | |
ACTL6B | 2 | 2.69 | 1.37 | 0.04 | 0.82 | Down |
CRK
|
5
|
2.71
|
7.99
|
0.02
|
1.52
|
Up
|
GHR | 3 | 2.69 | 1.37 | 0.03 | 1.25 | Up |
GRAPL
|
3
|
2.73
|
12.48
|
0.02
|
1.59
|
Up
|
IGF1 | 4 | 2.69 | 1.43 | 0.02 | 0.81 | Down |
MX1
|
5
|
2.72
|
11.21
|
0.02
|
0.55
|
Down
|
OASL
|
5
|
2.70
|
5.02
|
0.01
|
0.62
|
Down
|
RAB28 | 2 | 2.69 | 2.64 | 0.02 | 1.22 | Up |
RAB33B | 2 | 2.66 | 1.37 | 0.02 | 1.24 | Up |
RNF2
|
3
|
2.68
|
2.64
|
0.02
|
1.65
|
Up
|
RNF8 | 2 | 2.66 | 1.37 | 0.04 | 0.80 | Down |
RPL23 | 3 | 2.67 | 1.37 | 0.04 | 0.79 | Down |
SPINK1
|
3
|
2.71
|
6.66
|
0.02
|
0.63
|
Down
|
UST | 3 | 2.68 | 2.64 | 0.04 | 1.23 | Up |
VAV2 | 2 | 2.68 | 1.37 | 0.03 | 1.21 | Up |
ZNF384 | 2 | 2.66 | 1.37 | 0.01 | 1.23 | Up |
Gene biomarkers | Pathways | Relevance to RA |
---|---|---|
MX1 | Cytokine signaling in immune system | Inflammatory cell infiltration Inflammation Synovial pannus formation |
Innate immune system | ||
Peginterferon alpha-2a/peginterferon alpha-2b pathway (Hepatocyte), pharmacodynamics | Drug metabolism | |
OASL | Cytokine signaling in Immune system | Inflammatory cell infiltration Inflammation Synovial pannus formation |
Innate immune system | ||
Immune response IFN alpha/beta signaling pathway | ||
Interferon signaling | ||
RNF2 | Cellular senescence | Drug metabolism |
SUMOylation of DNA damage response and repair proteins | ||
Metabolism of proteins | ||
Chromatin regulation/acetylation | ||
DNA damage | ||
SPINK1 | Regulation of peptidase activity | Inflammatory cell infiltration Inflammation Synovial pannus formation |
Nitric oxide mediated signal transduction | ||
Regulation of peptidyl-tyrosine phosphorylation | ||
CRK | RET signaling | Angiogenesis |
MET promotes cell motility | Joint destruction | |
Focal adhesion | Bone resorption | |
Integrin alphaIIb beta3 signaling | Angiogenesis | |
GRAPL | RET signaling | Angiogenesis |