Th the median of the risk score as the threshold value. A high score indicated a poor 15900046 outcome. For each univariate Cox analysis, clinical and pathological factors associated with poor overall survival were identified among the available factors provided in the dataset. Factors significant on univariate analysis were entered into multivariate Cox analysis. Overall survival was analyzed and compared by the Kaplan-Meier method. Differences in survival were tested for MedChemExpress 223488-57-1 statistical significance by the log-rank test. P values of less thanPatient Sample DataPatient information, including both clinical data and gene expression data, was obtained from multiple independent sources. Gene expression datasets representing inflammatory bowel disease (GSE13367), rheumatoid arthritis (GSE12021), and cirrhosis (GSE14323) were downloaded from GEO. These datasets were chosen based on the availability of both diseased and normal tissue specimens, as well as having a large number of samples available at the time of analysis. Gene expression datasets representing human breast cancer, colon cancer, lung cancer, and glioma were downloaded from publicly available repositories. These datasets were chosen based on the large number of samples, the availability of clinical outcome data, and the diversity of tumor types. For each tumor type, training and validation cohorts were constructed. In breast cancer [10] (n = 295; Netherlands Cancer Institute; http:// bioinformatics.nki.nl/data.php) and colon cancer [11] (n = 232;Tumor Endothelial Inflammation in Cancer Prognosis0.05 were considered to indicate statistical significance. Two-tailed P values were calculated for training datasets, while one-tailed P values were calculated for testing datasets. All statistical analyses were performed using JMP 7.1.Results Characterization of inflammatory gene expression in tumor-associated endotheliumTo examine the role of TNF-a-mediated stromal inflammation in tumor growth, we used a syngeneic tumor model of B16-F1 murine melanoma Finafloxacin manufacturer established in wild-type (WT) mice and mice with immune dysfunction as a result of germline deletions of both TNF-a receptors (TNFR 1, 22/2, herein referred to as knockout [KO]). Disruption of stromal TNF-a signaling significantly impaired the growth of tumors in KO mice as compared to that in WT mice (relative volume (V/V0) [mean 6 SEM]; KO: 9.060.9; WT: 2162.5; p = 0.0033; Fig. 1A), thus supporting a role for stromal TNF-a signaling in tumor growth. In association with these data, we found an increased expression of the proinflammatory enzyme, COX2, in tumor-infiltrating vessels of WT mice compared to KO mice, as measured by the 
percentage of COX2-positive vascular endothelial cells (WT: 6564.9 ; KO: 9.365.1 ; p = 0.0014; Fig. 1B and C). In a replicate experiment, we purified endothelial cells from excised WT and KO tumors of equal volume and performed gene expression profiling of isolated tumor-associated endothelial cells (TAECs). We identified differential expression of 993 probe sets corresponding to 808 genes. WT TAECs exhibited increased expression of 686 probe sets and decreased expression 
of 307 probe sets relative to KO TAECs (Table S3). These genes encode enzymes (104), transcriptional regulators (66), kinases (35), transporters (20), and chemokines/ cytokines (7). We utilized Ingenuity Pathway Analysis (IPA) to classify the differentially expressed genes and identified “Inflammatory Response” as the most significantly enriched set of fun.Th the median of the risk score as the threshold value. A high score indicated a poor 15900046 outcome. For each univariate Cox analysis, clinical and pathological factors associated with poor overall survival were identified among the available factors provided in the dataset. Factors significant on univariate analysis were entered into multivariate Cox analysis. Overall survival was analyzed and compared by the Kaplan-Meier method. Differences in survival were tested for statistical significance by the log-rank test. P values of less thanPatient Sample DataPatient information, including both clinical data and gene expression data, was obtained from multiple independent sources. Gene expression datasets representing inflammatory bowel disease (GSE13367), rheumatoid arthritis (GSE12021), and cirrhosis (GSE14323) were downloaded from GEO. These datasets were chosen based on the availability of both diseased and normal tissue specimens, as well as having a large number of samples available at the time of analysis. Gene expression datasets representing human breast cancer, colon cancer, lung cancer, and glioma were downloaded from publicly available repositories. These datasets were chosen based on the large number of samples, the availability of clinical outcome data, and the diversity of tumor types. For each tumor type, training and validation cohorts were constructed. In breast cancer [10] (n = 295; Netherlands Cancer Institute; http:// bioinformatics.nki.nl/data.php) and colon cancer [11] (n = 232;Tumor Endothelial Inflammation in Cancer Prognosis0.05 were considered to indicate statistical significance. Two-tailed P values were calculated for training datasets, while one-tailed P values were calculated for testing datasets. All statistical analyses were performed using JMP 7.1.Results Characterization of inflammatory gene expression in tumor-associated endotheliumTo examine the role of TNF-a-mediated stromal inflammation in tumor growth, we used a syngeneic tumor model of B16-F1 murine melanoma established in wild-type (WT) mice and mice with immune dysfunction as a result of germline deletions of both TNF-a receptors (TNFR 1, 22/2, herein referred to as knockout [KO]). Disruption of stromal TNF-a signaling significantly impaired the growth of tumors in KO mice as compared to that in WT mice (relative volume (V/V0) [mean 6 SEM]; KO: 9.060.9; WT: 2162.5; p = 0.0033; Fig. 1A), thus supporting a role for stromal TNF-a signaling in tumor growth. In association with these data, we found an increased expression of the proinflammatory enzyme, COX2, in tumor-infiltrating vessels of WT mice compared to KO mice, as measured by the percentage of COX2-positive vascular endothelial cells (WT: 6564.9 ; KO: 9.365.1 ; p = 0.0014; Fig. 1B and C). In a replicate experiment, we purified endothelial cells from excised WT and KO tumors of equal volume and performed gene expression profiling of isolated tumor-associated endothelial cells (TAECs). We identified differential expression of 993 probe sets corresponding to 808 genes. WT TAECs exhibited increased expression of 686 probe sets and decreased expression of 307 probe sets relative to KO TAECs (Table S3). These genes encode enzymes (104), transcriptional regulators (66), kinases (35), transporters (20), and chemokines/ cytokines (7). We utilized Ingenuity Pathway Analysis (IPA) to classify the differentially expressed genes and identified “Inflammatory Response” as the most significantly enriched set of fun.
