Supplementary webappendix This webappendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors. Supplement to: Ueda T, Volinia S, Okumura H, et al. Relation between microrna expression and progression and prognosis of gastric cancer: a microrna expression analysis. Lancet Oncol 2009; published online Dec 21. DOI:10.1016/S1470-2045(09)70343-2.
Ueda et al webappendix 1 Supplementary Methods Tissue samples Helicobacter pylori infection status was unavailable for this study. We used Lauren s classification for histological typing. 1 All samples were categorised into 2 histological types; for mixed types, we used the dominant type. To classify clinical factors, we used the UICC TNM classification, 5th edition 2 and the Japanese Classification of Gastric Cancer, 2nd English Edition. 3 RNA and protein extraction We extracted total RNA and protein with Dr. P kit (BioChain Institute, Hayward, CA, USA) according to manufacture s instructions. Protein concentration was determined with Bio-Rad protein assay (Hercules, CA, USA). Statistical analysis For cancer and non-tumour mucosa analyses, we performed class comparisons using the paired t-test. For other unpaired comparisons, we identified genes that were differentially expressed using a random-variance t-test, which is an improvement over the standard separate t-test because it permits sharing information about within-class variation without assuming that all genes have the same variance. 4 Genes were considered statistically significant if their p value was less than the chosen value of each analysis. A stringent significance threshold was used to limit the number of false positive findings. We also performed a global test of whether the expression profiles differed between the classes by permuting the labels of which arrays corresponded to which classes. For each permutation, the p values were re-computed and the number of genes significant at the chosen level was noted. The proportion of the permutations that gave at least as many significant micrornas as with the actual data was the significance level of the global test. Class prediction algorithms in BRB-ArrayTools were used to determine whether microrna expression patterns could accurately differentiate between cancer and paired non-tumour mucosa. We developed models based on the compound covariate predictor, diagonal linear discriminant analysis, nearest neighbour classification, nearest centroid classification, and support vector machine. 5 The models incorporated genes that were differentially expressed among genes at the significance level (0 01) as assessed by the random variance t-test. We estimated the prediction error of each model using leave-one-out cross-validation (LOOCV). For each LOOCV training set, the entire model building process was repeated, including the gene selection process. We also evaluated whether the cross-validated error rate estimate for a model was significantly less than one would expect from random prediction. The class labels were randomly permuted and the entire LOOCV process was repeated. The significance level is the proportion of the random permutations that gave a cross-validated error rate no greater than the cross-validated error rate obtained with the real data. One thousand random permutations were used. The percentage of tissues that were correctly differentiated is reported as the percentage accuracy. We identified micrornas that were correlated with T (depth of invasion) or disease stage using significance analysis of microarrays (SAM) version 3.0 application (Stanford University, Stanford, CA, USA) with rankregression option. 6 MicroRNAs were selected by q value, which is the lowest false-discovery rate (FDR) at which the gene is called significant and measures how significant the gene is: as SAM score (d>0) increases, the corresponding q value decreases. We also used SAM to find genes expressed differentially between diffuse-type and intestinal-type gastric cancer. Quantitative reverse transcription-pcr (qrt-pcr) of microrna TaqMan microrna assays (Applied Biosystems, Foster City, CA, USA) specific for mir-21 (hsa-mir-21, P/N: 4373090), mir-375 (hsa-mir-375, P/N: 4373027), let-7g (hsa-let-7g, P/N: 4373163), and mir-214 (hsa-mir-214, P/N: 4373085) were used to detect and quantify mature micrornas on Applied Biosystems real-time PCR instruments in accordance with manufacturer s instructions. Normalisation was performed with the small nuclear RNA, RNU49 (P/N: 4373376, Applied Biosystems). RT reactions, including no-template controls and RT minus controls, were run in a GeneAmp PCR 9700 thermocycler (Applied Biosystems), starting from 1 ng of total RNA and using the looped primers in the condition of 16 C for 30 minutes, 42 C for 30 minutes and 85 C for 5 minutes. MicroRNA expression levels were quantified using a sequence detection system (ABI Prism 7900HT; Applied Biosystems) in triplicate, including no-template controls in the condition of 95 C for 10 minutes, followed by 40 cycles of 95 C for 15 seconds and 60 C for 1 minute, and threshold cycle (Ct) for each sample was determined. Relative expression was calculated using the comparative Ct method. These validating procedures were performed by one of the investigators (HA) who was blinded to the results derived from microrna array.
Ueda et al webappendix 2 MicroRNA target prediction Targets for microrna were predicted by using TargetScanS (http://www.targetscan.org), Pic Tar (http://pictar.bio.nyu.edu), and miranda (http://cbio.mskcc.org/cgi-bin/mirnaviewer/mirnaviewer.pl). Western blot analysis and microrna-target correlation analysis Western blot analysis was performed using standard procedures. We investigated the microrna-target correlations in a set of 39 or 41 patients for whom both cancer and non-tumour mucosa were available from group 1. Thirty micrograms of each protein were separated on 4 to 20% gradient Criterion Precast Gel (Bio-Rad, Hercules, CA, USA) and transferred to Nitrocellulose membrane (Bio-Rad). The membrane was incubated in 5% nonfat dry milk in T-TBS (18mM Tris HCl, ph 7 6 122 mm NaCl, 0 1% Tween 20) at room temperature for 1 5 hours followed by incubation with a primary antibody against the target protein for microrna at room temperature for 1 5 hours and a secondary antibody of horseradish peroxidase-conjugated donkey anti-rabbit IgG (Amersham Biosciences, Piscataway, NJ, USA: NA934) or sheep anti-mouse IgG (Amersham Biosciences: NA9310), diluted at 1:5000, at room temperature for 1 5 hours. Primary antibodies were rabbit polyclonal anti-bim (Bcl-2-like 11 [BCL2L11]) antibody (catalog sc-11425; Santa Cruz Biotechnology Inc., Santa Cruz, CA, USA; diluted 1:150) and rabbit polyclonal anti-runx3 (AML2) antibody (catalog PC286L; Calbiochem, San Diego, CA, USA; diluted 1:100). The loading control was mouse monoclonal anti-β-actin antibody (catalog A1978; Sigma, St. Louis, MO, USA; diluted 1:4,200). Signals were detected with the enhanced chemiluminescense (ECL) system (Amersham). Signals of blotting were quantified using the Personal Densitometer SI (Molecular Dynamics, Sunnyvale, CA, USA) and ImageQuant analysis software version 5.2 (Molecular Dynamics). A ratio between target protein and β-actin corresponding bands was used to quantify target protein modulation by microrna. Expression correlations between micrornas and predicted targets were assessed with Spearman's rank correlation coefficient. Supplementary Results and Discussion In recent years, considerable information has accumulated concerning microrna function. To increase the understanding of the significance of the common microrna signature (the gastric cancer signature) found in this large set of gastric cancer samples, we performed Western blot analyses using 39 or 41 pairs of non-tumour mucosa and cancer samples (for description, see webfigure 5 legend) to evaluate correlation with 2 computationally predicted targets: Bim and RUNX3. We found that expression of Bim and mir-181a and of RUNX3 and mir-19a had significant negative Spearman correlations (both had correlation coefficient ρ= 0 31 and p<0 05). Representative results are shown in webfigure 5. By Western blot analysis, despite the existence of mechanisms other than microrna levels that regulate expression of targets, we found statistically significant inverse correlations between the expression of 2 targets and micrornas that are upregulated in gastric cancer using human solid tumours. The first of those 2 targets, Bim acts as a proapoptotic factor by thwarting antiapoptotic Bcl-2 proteins. We found an inverse correlation between mir- 181a and Bim expression in protein level; mir-181a is associated with progression of gastric cancer (T and stage). The other target, RUNX3, is one of the important tumor-suppressor genes of gastric cancer and is suppressed by hypermethylation. 7 RUNX3 is a transcription factor related to transforming growth factor beta (TGF-β)-induced apoptosis by upregulating Bim in gastric epithelial cells. 8 We found a negative correlation between mir-19a and RUNX3 expression; mir-19a is related to progression of gastric cancer (T) and is an independent prognostic factor for disease-free survival of gastric cancer. Possibly both Bim and RUNX3 are directly regulated by micrornas in gastric cancer. MicroRNAs regulate targets that are important for the biological behaviour of gastric cancer.
Ueda et al webappendix 3 Supplementary References 1 Lauren P. The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification. Acta Pathol Microbiol Scand 1965; 64: 31-49. 2 Sobin LH, Wittekind C. TNM Classification of Malignant Tumours. 5th edn. New York, NY: Wiley-Liss; 1997. 3 Association JGC. Japanese classification of gastric carcinoma - 2nd English edition. Gastric Cancer 1998; 1: 10-24. 4 Wright GW, Simon RM. A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics 2003; 19: 2448-55. 5 Raza M, Gondal I, Green D, Coppel RL. Comparative study of multivariate classification methods using microarray gene expression data for BRCA1/BRCA2 cancer tumors. Third International Conference on Information Technology and Applications (ICITA'05) 2005; 1: 475-80. 6 Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001; 98: 5116-21. 7 Li QL, Ito K, Sakakura C, et al. Causal relationship between the loss of RUNX3 expression and gastric cancer. Cell 2002; 109: 113-24. 8 Yano T, Ito K, Fukamachi H, et al. The RUNX3 tumor suppressor upregulates Bim in gastric epithelial cells undergoing transforming growth factor beta-induced apoptosis. Mol Cell Biol 2006; 26: 4474-88.
Ueda et al webappendix 4 Webfigure 1: Average linkage clustering with centered Pearson correlation using 169 non-tumour mucosae and 184 gastric cancers (353 samples in groups 1 and 2, including 9 unpaired non-tumour mucosae and 24 unpaired gastric cancers) MicroRNAs are in rows, samples in columns. The non-tumour mucosa branch is on the left, and the gastric cancer branch is on the right. These 35 micrornas were commonly up- or downregulated in both groups (table 2). Because some micrornas had 2 probes on the microrna array and some micrornas are located on different chromosomes but their mature form is the same, we selected each one of them. The sample number TG1N indicates non-tumour mucosa No.1 in group 1, whereas HG1T indicates cancer No.1 in group 2. Expression color bar is shown at upper right. Red means that a microrna expression value was higher than its average expression across all samples, and green means a lower expression value.
Ueda et al webappendix 5 Webfigure 2: Validation of the results of microrna array by quantitative reverse transcription-pcr (qrt- PCR) Twenty-four pairs of non-tumour mucosa and cancer samples initially investigated by microarray analysis (12 pairs in each group) were used. Graph shows the mean (±SD [error bars]) relative change in expression of micrornas in gastric cancer compared with non-tumour mucosa by qrt-pcr and microrna array. The degree of change in qrt- PCR was calculated using the comparative threshold cycle (Ct) method. qrt-pcr validated the results of microrna array: mir-21 was upregulated in cancer and mir-375 was downregulated in cancer in both groups 1 and 2.
Ueda et al webappendix 6 Webfigure 3: Average linkage clustering with centered Pearson correlation using 103 diffuse-type gastric cancers and 81 intestinal-type gastric cancers (184 gastric cancers) MicroRNAs are in rows, samples in columns. The intestinal-type branch is on the left, and the diffuse-type branch is on the right. These 19 micrornas were selected according to the p value of the class comparison (p 2 10-6 ) (webtable 1). Because some micrornas had 2 probes on the microrna array and some micrornas are located on different chromosomes but their mature form is the same, we selected each one of them. The sample number TG1T indicates cancer No.1 in group 1, whereas HG1T indicates cancer No.1 in group 2, shown with histological type (diffuse or intestinal). Expression color bar is shown at upper right. Red means that a microrna expression value was higher than its average expression across all samples, and green means a lower expression value. Although the center subbranch located in the diffuse-type branch includes some samples of the opposite histological type (because of the mixed histological types), other branches show good classification.
Ueda et al webappendix 7 Webfigure 4: Validation of the results of microrna array for the prognostic factor micrornas by quantitative reverse transcription-pcr (qrt-pcr) The expression levels of let-7g and mir-214 by qrt-pcr were compared for samples selected from high- and lowexpression groups of each microrna determined by microrna array. Twelve samples selected from each group were analyzed by qrt-pcr, and the graph shows the mean (±SD [error bars]) relative expression levels calculated using the comparative threshold cycle (Ct) method. p values by t-test are shown.
Ueda et al webappendix 8 Webfigure 5: Inverse correlation between expression of mirornas and their target proteins in gastric cancers Representative results of Western blot analysis for the targets Bim and RUNX3 are shown with β-actin as the loading control. Bim, 25 kda; RUNX3, 44 kda; β-actin, 42 kda. Sample numbers appear at the top of each blot: TG42N indicates non-tumour mucosa No.42 in group 1, whereas TG42T indicates cancer No.42 in group 1. The numbers beneath the blots indicate the expression ratios of cancer to non-tumour mucosa by Western blot analysis for the targets (normalised by β-actin) and by microrna array for the micrornas. Upregulation in cancer is shown in red and downregulation in cancer is shown in green. Forty-eight pairs of samples from group 1 were used, but 9 pairs for Bim and 7 pairs for RUNX3 were removed because of the inadequate quality of the protein, according to the β-actin signals. Expression of Bim and mir-181a and of RUNX3 and mir-19a showed significant negative Spearman correlation (correlation coefficient r= 0 31 and p<0 05 for both).
Ueda et al 9 Webtable 1: List of the most significant micrornas in histotype signature between diffuse- and intestinal-type gastric cancers MicroRNA p value FDR (%) Fold change (diffuse/intestinal) Chromosomal location mir-100 <1 10-7 <0 01 2 6 11q24 1 mir-125b-1 <1 10-7 <0 01 2 7 11q24 1 mir-100 <1 10-7 <0 01 2 0 11q24 1 mir-373* <1 10-7 <0 01 0 6 19q13 42 mir-105-2 <1 10-7 <0 01 2 4 Xq28 mir-143 <1 10-7 <0 01 2 3 5q32 mir-125b-2 <1 10-7 <0 01 2 2 21q21 1 mir-373* <1 10-7 <0 01 0 6 19q13 42 mir-199a-1 <1 10-7 <0 01 2 4 19p13 2 mir-126 <1 10-7 <0 01 2 0 9q34 3 mir-24-2 <1 10-7 <0 01 1 4 19p13 12 mir-199a-2 <1 10-7 <0 01 2 3 1q24 3 mir-99a <1 10-7 <0 01 1 7 21q21 1 mir-24-1 <1 10-7 <0 01 1 4 9q22 32 mir-10a 1 10-7 <0 01 1 6 17q21 32 mir-27b 1 10-7 <0 01 1 6 9q22 32 mir-202* 2 10-7 <0 01 0 6 10q26 3 mir-101-2 3 10-7 <0 01 2 0 9p24 1 mir-145 9 10-7 <0 01 1 8 5q32 mir-10b 1 10-6 <0 01 1 5 2q31 1 mir-498 1 10-6 <0 01 0 6 19q13 42 mir-133a-1 1 10-6 <0 01 2 6 18q11 2 mir-195 1 10-6 <0 01 1 4 17p13 1 mir-494 2 10-6 <0 01 0 6 14q32 31 FDR=false discovery rate. Because some micrornas had 2 probes on the microrna array, the same microrna names appear twice, corresponding to the each used probe. This redundancy represents additional confirmation of the results. These micrornas were used in the clustering of webfigure 3. p values are the result of unpaired class comparison analysis of microrna expression patterns using BRB-ArrayTools 3.5.0. FDR was calculated with BRB-ArrayTools. A 1% FDR predicts that this list is 99% accurate.