The microarray dataset of the anticancer efficiency of PD0332991 was obtained from the National Centre for Biotechnology Information Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo). Datasets were reanalyzed if they met the following conditions: i) The data was genome-wide; ii) the dataset contained human and mouse samples; iii) the number of cases and controls in each dataset was ≥3; and iv) complete raw microarray data were available. Based on these criteria, the GSE40514 dataset [contributed by Choi et al (13)] was selected for reanalysis. This superseries is composed of two subseries including GSE40512 and GSE40513. In the dataset of GSE40512, human T-ALL cell line KOPTK1 cells were cultured in the presence of the CDK4/6 inhibitor PD 0332991 with 1 mM or vehicle (VO) for 48 h and the experiment was performed in triplicate. A total of 6 RNA samples (3 vehicle treated and 3 PD0332991 treated samples) were tested by using the platform of Affymetrix Human Genome U133 Plus 2.0 Array (HG-U133_Plus_2, GPL570). In the dataset of GSE40513, mouse breast cancer V720 cells were cultured in the presence of the CDK4/6 inhibitor PD 0332991 with 1 mM or vehicle (VO) for 24 h and the experiment was performed in triplicate. A total of 6 RNA samples (3 vehicle treated and 3 PD 0332991 treated samples) were tested by using the platform of Affymetrix Mouse Genome 430 2.0 Array (Mouse430_2, GPL1261).

R version 3.2.0 (www.r-project.org) and Bioconductor version 3.5 (www.bioconductor.org) were used for data preprocessing (14). The Robust Multichip Average (RMA) algorithm was used in the oligo package to normalize the raw expression data and to generate normalized gene expression intensity in human and mouse cell lines (15). Gene annotation of the probes of human and mouse cell lines was performed using custom written Python code version 2.7 (www.python.org). Probes with no gene annotation or ones that matched multiple gene symbols were removed. Then, the average expression value of multiple probe identities that matched to an official gene symbol was calculated, and this value represented the expression intensity of the corresponding gene symbol. Finally, 20,307 human gene symbols and 20,968 mouse gene symbols were identified. There were 13,976 homologous genes in both human and mouse cell lines. These 13,976 genes were selected for further analysis.

Differential expression gene analysis was performed using R version 3.2.2 and the Bioconductor library. The empirical Bayes algorithm (eBayes) in the limma package was used to detect differentially expressed genes between PD0332991-treated and vehicle-treated samples in human and mouse cell lines (16). Fold-change (FC) was calculated as the mean gene expression value in PD0332991-treated samples divided by the mean gene expression value in the vehicle-treated samples. Upregulated genes were considered as a logarithmic transformed fold-changes (log₂(FC))≥log₂(1.5) and a false discovery rate (FDR) was the adjusted P-value of ≤0.05. Downregulated genes were considered as log₂(FC)≤-log₂(1.5) and a FDR P-value of ≤0.05.

Java Gene set Enrichment Analysis (GSEA) version 2.2.2 (http://software.broadinstitute.org/gsea/index.jsp) was used for human and mouse samples. The curated Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway software version 5.1 (http://www.genome.jp/kegg) was chosen as the gene sets database to perform the enrichment analysis. Gene sets with <15 or >500 genes were excluded. Phenotype label was set as breast cancer vs. control. The t-statistic mean of the genes was calculated for each KEGG pathway using a permutation test 1,000 times. The upregulated pathways were considered as a normalized enrichment score (NES) >0 and the downregulated pathways were considered as a NES <0. Pathways with a FDR P-value of ≤0.05 were considered to be significantly enriched. A Venn diagram was produced using InteractiVenn (www.interactivenn.net) to demonstrate the enriched KEGG pathways in the human and mouse groups (17).

In total, there were 13,976 homologous genes expressed in human and mouse gene expression profiles. Volcano plots are presented in Fig. 1. A greater number of dysregulated genes were identified in the human group compared with the mouse group. A total of 585 upregulated genes and 715 downregulated genes of PD0332991-treated samples were identified in human KOPTK1 cells. By comparison, 125 upregulated genes and 284 downregulated genes were identified in mouse V720 cells. There were 233 common downregulated genes in both human and mouse samples, which was presented in the Venn diagram in Fig. 2. However, there were only 18 upregulated genes that overlapped in these two groups (Fig. 2). The overlapping genes which were significantly up or downregulated across human and mouse cells are presented in Tables I and II.

To investigate the anticancer efficiency of PD0332991, 9 genes with diverse regulatory patterns between mouse and human were identified (Fig. 3). In the PD0332991 group, LIM homeobox 2 (LHX2) was overexpressed in humans compared with the vehicle group, whereas in mice, LHX2 expression was reduced (Fig. 3). Dysregulated expression of LHX2 in human cancers has been previously reported (18). In addition, cancer-associated epigenetic alterations were significantly associated with LHX2 regulation, including DNA methylation and microRNA regulation (19–22). In the present study, adrenomedullin (ADM), bone marrow stromal cell antigen 1 (BST1), caveolin 1 (CAV1), histone cluster 1 H2B family member C (HIST1H2BC), histone cluster 1 H3 family member F (HIST1H3F), low density lipoprotein-receptor related protein 11 (LRP11), prolyl 4-hydroxylase subunit α1 (P4HA1) and torsin family 3 member A (TOR3A) were downregulated in human KOPTK1 cells, and were upregulated in mouse V720 cells. HIST1H3F demonstrated the largest difference between PD0332991- and vehicle-treated samples in human and mouse cell lines (log₂(FC)=−1.77, FDR=5.99E-6 in human; log₂(FC)=1.21, FDR=8.20E-4 in mice) from the 8 genes investigated. The histone HIST1H3F has been previously identified as a biomarker for predicting cancer risk (23). In addition, the mean log₂ gene expression value of HIST1H2BC in humans was ~4. However, the mean value in mice was ~12. This suggested that there was a large difference between human and mouse cell lines. HIST1H2BC is one of the most significant susceptible locus in the transcriptome study of schizophrenia, which has been also reported to be a suitable reference gene in non-small cell lung cancer (24,25). The association between ADM and epithelial-mesenchymal transition, and the underlying signaling pathways in intrahepatic cholangiocellular carcinoma has been previously elucidated (26). The overexpression of ADM in tumor cells may accelerate breast cancer bone metastasis, which suggests that it may be a potential target for therapeutic intervention against bone metastases (27). The association between movement disorders and genetic polymorphisms of BST1 has been confirmed in different populations, including Parkinson's and Alzheimer's disease (28–30). BST1 has been also investigated in a previous study on the drug resistance of tumors, including anticancer drugs for artesunate resistance (31). CAV1 may have a tumor suppression role in breast cancer and its expression is regulated by CpG island shore methylation (32). LRP11 is a member of the low-density lipoprotein receptor family, and has a potential role in mediating cellular drug uptake (33). One of the isoforms of P4HA, P4HA1, has an important role in prostate cancer progression and is associated with microRNA-124 (34). TOR3A functions with the gene encoding the Ras-related protein RAB1B, which regulates molecular transport, protein trafficking and developmental disorders (35).

GSEA analysis was performed to identify the signaling pathways that were significantly associated with anticancer effect of PD0332991. The findings of the present study revealed that there were 15 upregulated pathways and 11 downregulated pathways in PD0332991-treated human KOPTK1 cells. In addition, there were 14 upregulated pathways and 13 downregulated pathways in PD0332991-treated mouse V720 cells (Fig. 4A and B). There were 7 common upregulated pathways and 9 common downregulated pathways in the human and mouse samples. However, oppositely dysregulated pathways in the two groups were not identified. The details of these pathways are presented in Tables I and II. The downregulated pathways in human and mouse cell lines were primarily associated with cell cycle, DNA replication and nucleotide metabolism. The upregulated pathways in the two groups treated with PD0332991 were involved in various intracellular signaling pathways, including the JAK-STAT, Toll-like receptor, T cell receptor and nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathways.

It has been previously reported that the JAK/STAT pathway may be aberrantly activated in solid tumors including breast cancer, whose activation in malignant and nonmalignant cells contributes to the cancer pathogenesis and therapeutic response (36,37). Toll-like receptor (TLR)3 signaling is an integral component of solid tumors, and inhibition of this pathway may increase the effectiveness of current treatments of breast cancer in combination with anticancer drugs (38). The TLR8 signaling pathway has been associated with the functional regulation of tumor-specific ∆γ T cells, which are important contributors to innate immunity against cancer (39). Gene polymorphisms of NOD1/caspase recruitment domain (CARD)4 and NOD2/CARD15 may be associated with altered risk of diverse malignancies, including breast cancer (40).

In conclusion, the present study identified 9 overlapping genes with opposite expression patterns across PD0332991-treated human T-ALL cells and mouse breast cancer cells. In addition, various cellular signaling pathways were identified to be closely associated with the efficacy of anticancer drugs. Therefore, this data may aid future breast cancer drug screening and design.