MKI67 protein, human

We performed a multivariate analysis using Cox proportional hazards regression including the gene expression markers and clinical variables including stage, age, Lauren classification, differentiation, and gender. In addition to the clinical data, we also determined the HER2 and MKI67 expression using data provided on the gene chips. We computed HER2 status by using the probe set 216836_s_at and setting the cutoff for positivity at 4800 [42 (link)]. To assess correlation to proliferation, Spearman correlation to MKI67 expression (probes set 212021_s_at) was computed for each of the genes separately [43 (link)]. In addition, Spearman correlation was also run for HER2 without using the dichotomization.

The raw read counts of all samples were merged in a single read count matrix. This matrix was used as input for each of the different normalization methods. The most commonly used RNA-seq normalization methods are TMM, implemented in edgeR [2 (link)] and RLE, in DESeq2 [3 (link), 4 (link)]. Both these methods do not employ any gene length normalization since their aim is to identify DE genes between samples and thus assume that the gene length is constant across samples. The TPM method adds to the previously used RPKM - for single-end sequencing protocols - or its paired-end counterpart FPKM. TPM uses a simple normalization scheme, where the raw read counts of each gene are divided by its length in kb (Reads per Kilobase, RPK), and the total sum of RPK is considered the library size of that sample. Next, the library size is divided by a million, and that is used as scaling factor to scale each genes’ RPK value. Thus, TPM does correct for gene length, but is lacking a sophisticated between-sample correction; it does not account for a possible small number of highly expressed genes, thus comprising a large portion of the total library size of that sample. DESeq2 and edgeR address this problem by estimating correction factors that are used to rescale the counts (see [2 (link), 3 (link)] for more details). In short, edgeR employs the Trimmed Means of M values (TMM) [2 (link)] in which highly expressed genes and those that have a large variation of expression are excluded, whereupon a weighted average of the subset of genes is used to calculate a normalization factor. DESeq2 uses RLE that also assumes most genes are not DE; here, for each gene the ratio of its read count in a sample over the geometric mean of that gene in all samples is calculated. The median of the ratios of all genes in a sample is used as correction factor. Where TMM (edgeR) estimates a correction factor that is applied to the library size, the correction factor of RLE (DESeq2) is applied to the read counts of the individual genes.
Such normalized data are better comparable between samples, but still suffer from the inability to compare gene expression levels within a sample. To obtain a normalized data set that is equally suitable for between-samples and within-sample analyses, the following GeTMM method is proposed: first, the RPK is calculated for each gene in a sample: raw read counts/length gene (kb). In edgeR, which uses TMM-normalization, normally the library size (total read count; RC) is corrected by the estimated normalization factor and scaled to per million reads, but in GeTMM the total RC is substituted with the total RPK (Fig. 1).Fig. 1

normalization using GeTMM method with n = number of genes and i = given gene i

In practice, to obtain GeTMM normalized data, pre-calculate the RPK values from the raw read counts and gene length (in kb), and use these values as input for the edgeR package. See Additional file 4 for a step by step procedure in R. The gene length is calculated using the annotation by gencode: the length of all exons with a unique exon_id annotated to the same gene_id is summed. DESeq2 only allows integers as input, thus the fractions generated by the gene length correction are rejected for input by DESeq2.
edgeR and DESeq2 are available as R-packages (https://bioconductor.org/), and subsequent analyses were performed using R (v3.2.2). To obtain normalized data, the raw read count matrix (tab-delimited text file) was used as input. R commands to obtain normalized data are listed in Additional file 4. Each method outputs normalized read counts, that were log2-transformed (setting genes to NA when having 0 read counts).
The CMS classification was performed using the “CMSclassifier” package (https://github.com/Sage-Bionetworks/CMSclassifier), using the single-sample prediction parameter. The Oncotype DX® [14 (link)] recurrence score was performed as described for the RT-qPCR data, and using the RNA-seq normalized values as input for the algorithm. In short, expression data of 7 genes are used; BGN, FAP, INHBA (stromal panel), MKI67, MYC, MYBL2 (cell cycle panel) and GADD45B. An unscaled recurrence score (Rsu) is calculated as (0.1263 x average stromal panel) – (0.3158 x average cell cycle panel) + (0.3406 x GADD45B). The Recurrence Score (RS) is calculated as 44.16 x (Rsu + 0.30). The signal-to-noise ratio (SNR) was calculated as the (mean1 – mean2)/Sp, where Sp is the square root of the pooled variance Vp. This is calculated as Vp = [(n1–1) V1 + (n2–1)V2]/(n1 + n2–2), where V1 and V2 are the variance for each of the groups, and n1 and n2 the sample group sizes.

To evaluate the influence of CB on the cell cycle progression, flow cytometry and gene expression analyses were performed. For flow cytometry analysis, hWJ-MSCs were seeded in 75 cm² flasks (Corning Incorporated, Corning, NY, USA). After 24 h in standard conditions, cells were exposed to CB 1 μM or DMSO for 24 h. Untreated cells were used as CTR. At the end of treatment, hWJ-MSCs were detached from the plastic support and counted. Cells were washed once in 1X PBS and then, for each condition, 500,000 cells were fixed with 70% ice-cold ethanol overnight at −20 °C. Then, cells were washed with 1X PBS and incubated with a solution containing 50 µg/mL RNase for 30 min at RT. hWJ-MSCs were washed to remove RNase and then were stained with 50 µg/mL propidium iodine (PI). DNA content was analyzed using the CytoFLEX S Flow cytometer (Beckman-Coulter Inc., Brea, CA, USA); data were analyzed by using the FlowJo v10.8 software (Tree Star, Ashland, OR, USA).
To evaluate the expression of genes involved in proliferation (MKI67), cell cycle control (CCND1, CDKN1A and CDKN2A), and stemness (OCT-4 or POUF51A), hWJ-MSCs seeded in 25 cm² flasks (Corning Incorporated, Corning, NY, USA) and treated as above reported, were harvested for RNA isolation as described in Section 4.7.

For each experimental condition, 25 ng of cDNA was amplified using the SsoAdvanced Universal SYBR Green Supermix (Bio-Rad Laboratories, Hercules, CA, USA) in technical triplicates using the Bio-Rad CFX96 real-time thermal cycler (Bio-Rad Laboratories, Hercules, CA, USA), as previously described [22 (link),68 (link)]. The gene expression was determined by CFX Manager Software version 3.1 (Bio-Rad Laboratories, Hercules, CA, USA) using the “delta-delta CT method” [70 (link)]. To normalize the expression of gene involvement in cell cycle progression, proliferation and stemness (MKI67, CCND1, CDKN1A, CDKN2A, and OCT-4), three reference genes GAPDH, TATA box binding protein (TBP), tyrosine 3 monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ) were used.
For osteogenesis experiments, the expression of the specific osteogenic markers RUNX2 and BGLAP, and of the autophagy related genes, ATG7, LC3A, and BECN1, was evaluated; YWHAZ, TBP and hypoxanthine phosphoribosyl transferase 1 (HPRT1) were used as reference genes.
GAPDH, TBP, HPRT1, CCND1, CDKN1A, CDKN2A, and OCT-4 primers were purchased from Bio-Rad (20X, Bio-Rad Laboratories, Hercules, CA, USA); all the other sequences were provided from Sigma-Aldrich (Sigma-Aldrich Co., St. Louis, MO, USA). The list of primer sequences was reported in Table 1. For each gene, the normalized expression value of untreated cells (CTR) or DMSO treated cells was set to 1, and all other gene expression values are reported to that value. Data are expressed as fold change ± SD.

The minigene containing exons 6–8 and introns 6–7 of the human MKI67 gene was amplified from HEK 293 cell genome DNA and cloned into pEGFP-N1 (Clontech) vector at EcoRI and BamHI sites. To map the essential motifs for exon 7 splicing, further deletions or mutations were introduced into minigene by overlapping PCR (Figure 6A,C,E). Primers used to construct the minigene plasmids are listed in Table S3.