TIMER (Li et al., 2017) is a comprehensive resource that can be used to systematically analyze the immune infiltration of different cancer types (https://cistrome.shinyapps.io/timer/). Its differential expression (DiffExp) module was used to study the differential expression of any gene of interest in the cancer genome atlas (TCGA) between the tumor and adjacent normal tissues. The distribution of gene expression levels was shown using box plots, and the statistical significance of differential expression was evaluated using Wilcoxon’s test. Its correlation module was used to draw a scatter plot of expression between a pair of user-defined genes in a given cancer type, as well as Spearman’s rho value and estimated statistical significance.

Forest plots and Kaplan-Meier curves were used to examine the relationship between YAP1 expression and the survival rates of 33 types of cancer. A univariate Cox regression survival analysis was used to calculate the hazard ratio (HR) and 95% confidence interval in a given cancer type. The prognostic significance of YAP1 expression was evaluated with overall survival (OS) and disease-free survival (DFS).

GEPIA (http://gepia.cancer-pku.cn) (Tang et al., 2017) is a website based on TCGA and Genotype-Tissue Expression (GTEx) projects cancer data mining. We analyzed the differential expression of YAP1 in PAAD and matched TCGA normal and GTEx data through GEPIA. The correlation between YAP1 expression and advanced pathological stage in PAAD was analyzed by GEPIA. Patient survival analysis based on the expression of YAP1 in PAAD (OS and DFS) and Brain Lower Grade Glioma (LGG) (OS) were conducted by GEPIA.

All GSE datasets used in this article were obtained from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) (Clough and Barrett, 2016). Limma package of R software was used to study the differential expression of mRNAs in GSE66949, GSE49406, and GSE163110. GSE32597, GSE35004, and GSE92335 were screened using the GEO2R tool to identify genes that were differentially expressed. A p value < 0.05 and |Fold change| > 1.5 were used as the threshold to identify differential expression genes (DEGs). FunRich software was used to find overlapping DEGs between GSE66949 and GSE32597 datasets. The overlapping DEGs were further analyzed to identify the most commonly regulated genes across datasets. The volcano plots and heat map for hierarchical clustering of DEGs were drawn by R language.

Metascape is a powerful and efficient tool designed to provide users with comprehensive gene list annotation and analysis resources (Zhou et al., 2019). Pathway and process enrichment analyses of the overlapping DEGs between GSE66949 and GSE32597 cohorts were performed with Metascape using default parameters. The Molecular Complex Detection tool (MCODE) could screen and identify the most significant module in the PPI network.

The ClusterProfiler package in R v4.0.3 was used to determine the functions of DEGs identified in GO (Gene Ontology Consortium, 2015) and KEGG (Kanehisa et al., 2016) pathway analysis, respectively.

The SKP2 expression matrix of 32 tumor cell lines was obtained from the CCLE dataset (https://portals.broadinstitute.org/ccle/about). The analysis was constructed by the R v4.0.3 software package ggplot2 (v3.3.3).

For the TCGA database, we downloaded tumor RNA-seq (FPKM) from the Genomic Data Commons (GDC). We converted PFKM data to TPM and normalized the data log2 (TPM+1), while keeping samples with clinical information recorded. We predicted the chemotherapeutic response of each sample based on the largest public pharmacogenomics database the Genomics of Drug Sensitivity in Cancer (GDSC), https://www.cancerrxgene.org. The prediction process was implemented by R package “pRRophetic,” where the half-maximum inhibitory concentration (IC50) of the sample was estimated through ridge regression and the prediction accuracy. All parameters were set by default values, eliminating the batch effect of combat and tissue types of all cancers, and repeated gene expression was summarized as the average value. All the above analysis methods and R package were implemented by R foundation for statistical computing (2020) version 4.0.3.

The YAP1 expression profile in pan-cancer was analyzed using TIMER database. The gene expression levels were presented as log2 TPM values.

By combining normal tissue data from the GTEx database with data from TCGA, the dysregulation of YAP1 and SKP2 expression between various cancers and normal tissues was studied. RNA sequencing data for patients with 33 types of cancers, including adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma (GBM), LGG, head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), PAAD, pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS), and uveal melanoma (UVM), were obtained from the TCGA database. All expression data were normalized via log2 conversion.

Paraffin-embedded pancreatic cancer tissue microarray containing 130 PAAD patient samples was approved by the Ethics Committee of Tianjin Medical University Cancer Institute and Hospital, China. All specimens were handled and made anonymous according to ethical and legal standards. All patients included in this study had no history of other malignancies and none of them had received chemotherapy, radiotherapy, and other treatment prior to surgery. Histopathology review and diagnosis confirmation were performed by three independent pathologists. Histological grade was classified into highly differentiated (G1), moderately differentiated (G2), and poorly differentiated (G3). Immunohistochemistry for YAP (#12395, 1:350, CST, United States) and Ki-67 (ab92742, 1:500, Abcam, United Kingdom) were performed using standard techniques. Two pathologists evaluated staining. Nuclear staining for YAP and Ki67 was considered positive. The immunoreactivity of YAP was scored based on the staining intensity and the percentage of cells stained positively. Sections for Ki67 were classified as follows: low (0%–25% of stained cells) and high (26%–100% of stained cells).

Pancreatic cancer, glioma, ovarian cancer, and colorectal cancer tissues and adjacent normal tissues were collected from Tianjin Medical University Cancer Institute and Hospital, China. They were stored immediately in liquid nitrogen and kept at −80°C. None of the patients had received neoadjuvant chemotherapy or preoperative radiation therapy. The study was approved by the Ethics Committee of Tianjin Medical University Cancer Institute and Hospital and conducted following the Declaration of Helsinki.

Total RNA was isolated from cells using Trizol reagent (Invitrogen, Carlsbad, CA, United States ) according to the manufacturer’s instructions. cDNA was generated by the RTase M-MLV (Takara, Shiga-ken, Japan) as described in the manufacturer’s protocol. Quantification of YAP1 mRNA level was done by RT-qPCR using SYBR Green PCR Master Mix (TaKaRa, Shiga-ken, Japan), and the expression of GAPDH was used as the internal control. The following primers were used to amplify target genes: GAPDH, 5′-CAG​GAG​GCA​TTG​CTG​ATG​AT-3′, 5′-GAA​GGC​TGG​GGC​TCA​TTT-3’; YAP1, 5′-AGT​GGA​CTA​AGC​ATG​AGC​AG-3′, 5′-TGT​TCA​TTC​CAT​CTC​CTT​CC-3'. Fold changes were calculated using the ΔΔCt method in Microsoft Excel.

Correlations between two variables were carried out by Spearman’s Rank-Correlation test or Pearson correlation analysis. The Kaplan-Meier method and log-rank test were applied to evaluate the relationship of different gene expression levels with OS, DFS, and RFS in certain tumor types. Univariate and multivariate Cox proportional hazards analyses were performed to assess the potential impact of YAP expression on OS and RFS in PAAD. Analyses were performed using SPSS 22.0 statistical analysis software or Graphpad 8.0 or R 4.0.3 software packages. Unpaired Student’s t test was used for two-group comparison. A two-sided p < 0.05 was considered statistically significant.

According to the results from the TIMER database, YAP1 exhibited inconsistent mRNA expression in 33 types of common cancers. Compared with adjacent normal tissues, YAP1 expression in cancer was significantly higher in CHOL, COAD, LIHC, STAD (Figure 1A). We also compared YAP1 expression using the data from TCGA and GTEx databases. Compared with normal tissues, the upregulated YAP1 mRNA expression was observed consistently in tumor tissues in CHOL, COAD, LIHC, STAD, and moreover in DLBC, ESCA, GBM, KICH, KIRC, KIRP, LGG, PAAD, READ, STAD, TGCT, and THCA (Figure 1B). Besides, the expressions of YAP1 in cancer and normal tissues of four cancer types, including pancreatic cancer, glioma, ovarian cancer, and colorectal cancer were verified by GSE16515, GSE4290, GSE14407, and GSE24514 (Figure 1C). We found that YAP1 was highly expressed in cancer tissues, as compared to adjacent normal tissues. The expression of YAP1 was further examined by qPCR in pancreatic cancer, glioma, ovarian cancer, and colorectal cancer tissue samples and the corresponding normal tissue samples. As shown in Figure 1D, these results consistently showed that the expression of YAP1 in pancreatic cancer, glioma, ovarian cancer, and colorectal cancer were significantly higher than the corresponding normal tissues. Data above indicated that YAP1 might play a role as an oncogene in the development of varieties of tumors.

Next, we studied the prognostic value of YAP1 by pan-cancer analysis in different databases. Firstly, we utilized univariate Cox proportional hazard regression models to analyze the association between YAP1 expression in pan-cancer and OS or DFS. Cox regression analysis results from 33 types of cancer suggested that YAP1 expression positively correlated with poor OS in patients with PAAD and LGG with significance (Figure 2A). Kaplan-Meier survival curves indeed indicated that the upregulated YAP1 expression was significantly associated with poor OS in patients with PAAD and LGG (Figures 2B,C). Then, we examined the relationship between YAP1 expression and the DFS of patients with cancer. The YAP1 expression affected DFS with statistical significance in patients with PAAD (Figure 2D). Kaplan-Meier analysis also showed that the increased YAP1 expression corresponded to the poor DFS in patients with PAAD (Figure 2E).

As shown in Figure 3A, YAP1 expression was significantly higher in PAAD tissues compared to normal tissues. In addition, the increased expression of YAP1 was notably associated with the advanced pathological stage (Figure 3B). To learn more about the role of YAP in PAAD, we examined the expression of YAP in a tissue microarray composed of 130 human PAAD samples through immunohistochemistry assays. YAP staining was detected in all PAAD tissues and the expression of nuclear YAP was positively correlated with the histological grade of PAAD (Supplementary Table S1; Figure 3C). Importantly, Kaplan-Meier analysis showed that PAAD patients with high YAP protein expression had significantly worse OS and RFS than patients with low YAP expression (Figures 3D,E). Univariate and multivariate cox proportional hazards analysis also indicated that upregulation of YAP expression is an independent risk factor for PAAD patients (Supplementary Table S2). In addition, to assess the role YAP played in PAAD proliferation, we used Ki67 as a proliferation indicator and found a positive correlation between YAP and Ki67 expression in PAAD samples (Figures 3F,G). In the GSE57495 cohort, the YAP1 and MKI67 mRNA levels were also positively correlated in PAAD (Figure 3H). Taken together, these results indicated that YAP played an extremely important role in PAAD cell proliferation.

TIMER results noted that YAP1 expression was positively correlated with MKI67 in 24 types of tumors, including ACC, BLCA, BRCA, CESC, COAD, DLBC, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUSC, OV, PAAD, PRAD, READ, SKCM, STAD, TGCT, THCA, UCEC, UCS, and UVM (Figure 4). Collectively, these findings indicated that YAP1 expression positively correlated with the proliferation of multiple tumors.

Although the Hippo-YAP signaling pathway has been extensively studied over the past years and it has been reported that cyclins like cyclin D1 and E2F1 are involved in YAP function (Nicolay et al., 2011; Mizuno et al., 2012), the full range of YAP target genes that regulate cell cycle processes has not yet been identified. Therefore, we performed the analysis of differential expression of mRNAs between the control and siYAP1 groups. Limma package of R software was used to study the differential expression of mRNAs in GSE66949. GSE32597 was screened using the GEO2R tool to identify genes that were differentially expressed. A total of 2,151 and 572 genes were identified as the DEGs from their respective datasets (Figure 5A). Next, FunRich software was used to identify overlapping DEGs between GSE66949 and GSE32597. We found 145 overlapping genes (Figure 5A), which were used for functional and pathway enrichment analysis using the online gene annotation tool Metascape (Zhou et al., 2019). As demonstrated in Figure 5B, the enrichment results mainly presented its involvement of cellular response to interleukin-1, retinoblastoma gene in cancer, cell cycle, etc. The protein-protein interaction (PPI) topology analysis of the overlapping DEGs was performed in order to systemically learn the functions of YAP target genes. The PPI network and Molecular Complex Detection (MCODE) components identified in 145 aforementioned DEGs were shown in Figures 5C–E. The three most significant MCODE components were extracted from the PPI network. After pathway and process enrichment analysis was independently applied to each MCODE component, the results showed that its biological function was mainly related to the cell cycle, DNA repair, and interferon-gamma signaling. On the basis of the enrichment analysis, YAP was determined to play an important role in the cell cycle of multiple tumors.

To more clearly figure out the target genes that were regulated by YAP with its participation in the cell cycle process of multiple tumors, we analyzed GSE49406 using the Limma package of R software in YAP1-knockdown and control HEK293 cells. The expression changes of overlapping DEGs that participated in the cell cycle process in GSE66949 and GSE32597 were assessed. As shown in Figure 5F, the expressions of CDC20, SKP2, and CDT1 were significantly changed. The changes in expression patterns of SKP2, CDC20, and CDT1 were further assessed based on the inhibition of YAP1 in GSE66949, GSE92335, GSE32597, GSE35004, and GSE49406 (Figures 5G–J). Details of the YAP1 inhibition datasets from the GEO database were shown in Table 1. As the bar charts showed, SKP2 and CDC20, especially SKP2, exhibited the same expression trends with the inhibition of YAP1 in all 5 datasets (Figures 5H–I). But CDT1 exhibited an inconsistent trend with the expression pattern of YAP1 in the GSE datasets above (Figure 5J). It is worth noting that only SKP2 exhibited the same expression pattern with statistical significance based on the inhibition of YAP1 in all 5 datasets (Table 2). Moreover, stable cell lines with downregulation of YAP in four cancer types, including pancreatic cancer, glioma, ovarian cancer, and colorectal cancer, were established. Knockdown of YAP expression correlated well with decreased SKP2 protein and mRNA levels (Supplementary Figure S1). To sum up, SKP2, as the target gene regulated by YAP, participates in the cell cycle process of multiple tumors.

TIMER results noted that SKP2 expression was positively correlated with YAP1 expression in 31 types of tumors, including ACC, BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, MESO, OV, PAAD, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, THYM, UCEC, UCS, UVM (Figure 6). Consistent with almost all results analyzed through TIMER database, there were strong positive correlations between the expression of YAP1 and SKP2 in ACC, BLCA, BRCA, CESC, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD, SARC, SKCM, STAD, TGCT, THCA, UCEC, UCS, UVM through GEPIA database (Supplementary Figure S2). But CDC20 expression exhibited an inconsistent trend with YAP1 expression. CDC20 expression was positively correlated with YAP1 expression in ACC, PAAD, PCPG, TGCT, and UCEC, whereas negatively correlated with YAP1 expression in BRCA, COAD, LUAD, READ, and THYM (Supplementary Figure S3). And there was no significant relationship between CDC20 and YAP1 expression in other tumors. Hence, SKP2, instead of CDC20, positively correlates with YAP1 expression in pan-cancer.

To better understand the role of Skp2 played in pan-cancer, we firstly compared the SKP2 expression using the data from TCGA and GTEx databases. Interestingly, the upregulated SKP2 mRNA expression was observed consistently in tumor tissues versus normal tissues in 29 types of human common cancer, including ACC, BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, UCEC, and UCS (Figure 7A). The remaining four types of cancer, including LAML, MESO, THYM, and UVM, lack the expression data of SKP2 in normal tissue (Figure 7A). These findings revealed that Skp2 acted as an oncogenic protein in multiple cancers.

To further investigate the molecular function of Skp2 in tumors, the analysis of differential expression of mRNAs was performed between the control and shSKP2 group using the Limma package of R software in GSE163110. A total of 1,520 DEGs were observed in the control group compared to the shSKP2 group with |Fold Change| >1.5 and p < 0.05 as the cut-off criteria. Volcano and heat maps of these genes were drawn as shown in Figures 7B,C.

In order to determine the functions and pathways of DEGs related to the inhibition of SKP2, we performed the KEGG pathway and GO analysis. The top 10 KEGG pathways of the DEGs indicated that most DEGs were significantly enriched in the cell cycle signaling pathway (Figure 7D). And the 10 most significant GO functional annotations were enriched in the nuclear division, DNA replication, and so on (Figure 7E). These analysis results above indicated that Skp2 played an extremely important role in nuclear division and DNA replication in the cell cycle.

As Ki67 was a vital important proliferation indicator in the cell cycle, we assessed the relationship between SKP2 and MKI67 mRNA in pan-cancer using TIMER database. TIMER results noted that SKP2 expression was positively correlated with MKI67 expression in all types of tumors included in TIMER database (Figure 8). Taken together, SKP2, as the target of YAP, promotes cell proliferation in the cell cycle process of pan-cancer.

MLN4924 is an agent that targets the NEDD8-activating enzyme, thus affecting CUL1 neddylation and impairing Skp2-SCF complex formation and ubiquitin ligase activity. It was previously evaluated by the Pediatric Preclinical Testing Program, and it exhibited effective activity in vitro and could inhibit tumor growth (Chen and Tweddle, 2012). Another previous study reported that the activity of Skp2 could be inhibited by MLN4924 and subsequently triggered an apparent stabilization of p27, which in turn induced cell cycle arrest in PC3 cells (Mickova et al., 2021). To demonstrate the functional relevance of Skp2 in modulating MLN4924 sensitivity, we firstly assessed the expression of SKP2 in pan-cancer cell lines from the CCLE dataset (Figure 9A). Further, we explored the association between SKP2 expression and MLN4924 IC50 values in human cancer cell lines and pan-cancer. As shown in Figures 9B,C, the expression of SKP2 was negatively correlated with MLN4924 IC50 values in 454 human cancer cell lines (Figure 9B) and in almost all cancer types, including ACC, BLCA, BRCA, CESC, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, MESO, OV, PAAD, PRAD, READ, SARC, SKCM, STAD, THCA, UCEC, and UVM (Figure 9C). Besides, we detected the mRNA level and protein expression of SKP2 in various cancer cell lines in several cancer types. As shown in Supplementary Figures S4A,B, the expression of SKP2 in MiaPaCa-2 was higher than that of BxPC-3, and the expression of SKP2 in LN229 was higher than that of U87. Then we performed CCK8 assay on these cells, which were treated with different concentrations of MLN4924. Similar to Skp2 knockdown, BxPC-3 was less sensitive to MLN4924 than MiaPaCa-2, with an IC50 of 213 nM (Supplementary Figure S4C). Similarly, U87 was less sensitive to MLN4924 than LN229, with an IC50 of 4.28 μM (Supplementary Figure S4D). All data above suggested that the elevation of SKP2 expression could increase cancer cell sensitivity towards MLN4924 in pan-cancer.