Table 1.
Characteristics of the 88 patients with soft tissue sarcoma.
Figure 1.
A schematic of gene selection and the simulation based on the permutation test.
(A) The knowledge (OMIM)-based method. The list of OMIM numbers related to cancer (e.g., cancer, carcinoma, sarcoma, tumor, and neoplasm) was selected and converted into Affymetrix probe IDs in Ensembl. (B) Prefiltering of probe sets. This procedure was based on the number of absent calls and the range of signals. A signal range (95th percentile to 5th percentile) of >2000 was used as a percentile filter. Furthermore, we excluded probe sets for which the number of absent calls was >50% (44/88). Probe sets related to cancer were selected using the OMIM-based method. (C) Integration of survival analysis and discriminant analysis. (D) Clinical data from all patients were permutated. Permutated data for 72 STS patients (20 UPS, 15 MFS, 20 MLS, and 17 SS patients) were extracted from the permutated data of all patients. For these data, p values (p1) were calculated by applying ANOVA to the log-transformed gene expression data to discriminate among UPS, MFS, MLS, and SS. In addition, permutated data from 88 patients were used for survival analysis. For these data, p values (p2) were calculated by applying the logrank test to the binarized gene expression data to analyze the outcomes in the STS group. The integrated statistic p′ was defined as p1×p2. The lowest p′ value was selected for each repetition. This procedure was repeated 100,000 times, and an empirical null distribution was constructed. Using the distribution, the actual p′ value obtained from the real data was converted to the adjusted p value (based on the correction for multiple testing problems).
Figure 2.
Kaplan-Meier curves for 4 histological types of STS.
P value was calculated by logrank test. UPS: undifferentiated pleomorphic sarcoma, MLS: myxoid liposarcoma, SS: synovial sarcoma, MFS: myxofibrosarcoma.
Table 2.
Genes extracted using the simulation based on the permutation test.
Table 3.
Correlation analysis based on Spearman’s rank correlation coefficient between gene expression data and the histological grade (or metastasis status).
Figure 3.
Heatmap and hierarchical clustering analyses.
Twenty-nine probe sets were extracted using a simulation based on the permutation test (with adjusted p<0.05). The 29 probe sets were roughly divided into 4 clusters (clusters A–D). Columns represent probe sets, and rows represent samples. Red and green indicate high and low expression, respectively. UPS: undifferentiated pleomorphic sarcoma, MLS: myxoid liposarcoma, SS: synovial sarcoma, MFS: myxofibrosarcoma.
Figure 4.
Principal component analysis using 29 probe sets for 4 histological types.
The x-axis and y-axis represent the first and second principal components (PC1 and PC2), respectively. Each dot represents a sample colored according to its histological type. UPS: undifferentiated pleomorphic sarcoma, MLS: myxoid liposarcoma, SS: synovial sarcoma, MFS: myxofibrosarcoma.
Figure 5.
Principal component analysis using 9 probe sets for UPS and MFS.
The x-axis and y-axis represent the first and second principal components (PC1 and PC2), respectively. Each dot represents a sample colored according to its histological type. UPS: undifferentiated pleomorphic sarcoma, MLS: myxoid liposarcoma, SS: synovial sarcoma, MFS: myxofibrosarcoma.
Table 4.
Pairwise comparison between histological types using Welch’s t test for 29 probe sets.
Figure 6.
A Venn diagram of gene classification based on pairwise comparisons of histological types using Welch’s t test.
Genes inside the red circle were statistically significant (q <0.05 calculated using Welch’s t test and the BH method) in the comparison of UPS with SS. Genes inside the green oval were statistically significant (q <0.05) in the comparison of UPS with MLS. Genes inside the blue oval were statistically significant (q <0.05) in the comparison of UPS and MFS. Genes inside the pink oval are common to CINSARC and our 25-gene set. For PCA of the 9-probe set, MIF and CD34 highlighted in red were the first and third largest contributing coefficients to PC1, respectively. PTK7 and PRDX1 highlighted in blue were the first and second largest contributing coefficients to PC2, respectively. ENO1/MBP1 highlighted in purple was the second largest contributing coefficient to PC1 and the third largest contributing coefficient to PC2. SCD1 highlighted in green was the largest contributing coefficient to PC3.
Figure 7.
The Kaplan-Meier curve and the logrank test for STAT1 in UPS patients.
The STAT1-positive group (STAT1 expression level >4871.5) consisted of 14 patients (blue line), and the STAT1-negative group consisted of 6 patients (red line). A hazard ratio (exp(B) = 30.2) was calculated using the Cox proportional hazards model.
Figure 8.
A hypothetical regulation model of metabolic and signaling control in highly malignant STS.
(A) Signaling pathways, excluding cell cycle and DNA repair. (B) Cell cycle and DNA repair pathways. The pink oval indicates the genes selected in the present study. MUFA, monounsaturated fatty acid; SFA, saturated fatty acid; SCD1, stearoyl-CoA desaturase 1; MIF, macrophage migration inhibitory factor; CXCR, CXC chemokine receptor; PI3K, phosphoinositide 3-kinase; MAPK, extracellular signal-regulated kinase; ERK, mitogen-activated protein kinase; PTTG1, pituitary tumor-transforming 1; ASPM, abnormal spindle-like microcephaly-associated protein; CDC20, cell division cycle protein 20; KIF20A, kinesin family member 20A; ENO1, enolase 1; P4HA, prolyl 4-hydroxylase subunit α; PRDX1, peroxiredoxin 1; FAM162A, family with sequence similarity 162, member A; STAT1, signal transducer and activator of transcription 1; CDK1, cyclin-dependent kinase 1; TACC3, transforming, acidic coiled-coil containing protein 3; PRKDC, protein kinase, DNA-activated, catalytic polypeptide; H2AFY, H2A histone family, member Y; SLC16A1, solute carrier family 16, member 1; VEGF, vascular endothelial growth factor; HIF, hypoxia inducible factor; PLOD2, procollagen-lysine,2-oxoglutarate 5-dioxygenase 2; NF-κB, nuclear factor-kappa B.