The expression profiles and potential signaling pathways of ARLs in GC
As shown in Figure 1a, we firstly investigate the expression differences of ARLs between GC tissues and adjacent normal tissues using TCGA, GTEx and Oncomine databases (Figure 1b). We find that six ARLs (i.e., ARL4C, ARL5A, ARL5B, ARL8A, ARL8B, ARL13B) are obviously up-regulated in GC tissues compared to adjacent normal tissues in TCGA and GTEx datasets (P<0.01). Meanwhile, 6 ARLs (i.e., ARL2, ARL4C, ARL10, ARL11, ARL13B and ARL14) are aberrantly regulated in GC patients based on Oncomine (Figure S1a). Both in TCGA and Oncomine databases, ARL4C and ARL13B are significantly upregulated (Figure S1b).
Since small GTPase proteins commonly work synergistically as function hubs to regulate cell biological functions [21, 22], we conduct network analysis of ARLs at the gene level using the GeneMANIA tool, and find a large number of shared protein domains among ARLs (Figure 1c).
we further perform the correlation analysis of ARLs in GC using TCGA database. As shown in Figure 1d and Table S1: significant correlations are discovered among these genes. To explore the potential oncogenic pathways which ARLs are involved in, we analyze the correlation between the expression levels of ARLs and the activity of hallmark-related pathways using GSVA. As shown in Figure 1e and 1f, various hallmark pathways of cancer are significantly associated with the expression of ARLs, including CHOLESTEROL HOMEOSTASIS (7/22), MYOGENESIS (5/22), UV RESPONSE UP (5/22), P53_PATHWAY (5/22). Meanwhile, the expression levels of ARL10 (n = 22), ARL13A (n = 12), ARL5B (n = 10), ARL15 (n = 8), and ARL4C (n = 8) are correlated with a higher number of pathways (Table S2).
Genetic alteration analysis of ARLs in GC
To comprehensively understand the expression profiles of ARLs in GC, we analyze the genetic alteration of ARLs in GC. The chromosome status (GRCh38/hg38) showed in Figure 2a and Table S3 clearly displays the genomic locations of 22 ARLs, and we find ARLs are unevenly distributed on different chromosomes. Furthermore, we conduct the exact genetic analysis using cBioPortal for Cancer Genomic. From the changes in protein structure of ARLs (mutation sites≥3), we find that ARL13B has more mutation sites than others (Figure 2b). Moreover, we discover varying degrees of genetic variation among the 22 ARLs (1.7% to 10.0%), and the mutation ratios of ARL4A, ARL13B and ARL16 are relatively higher, up to 10.0% (Figure S2a). We further check the alteration frequency of ARLs (mutation ratio≥8%) in various GC types. As shown in Figure 2c, copy number amplification obviously contributes to the mRNA expression alteration of ARLs in different GC types. More interestingly, DNA methylation analysis demonstrates that there is a negative correlation between mRNA expression and DNA methylation for most ARLs (R≥0.3, P<0.05) (Figure 2d). A recent study showed that deregulation of ARL4C is due to hypomethylation in its 3’-UTR in lung squamous cell carcinoma (22). Therefore, we further investigate the specific methylation sites of ARL4C using MEXPRESS tool and find that DNA methylation status of cg24441922 and cg11509907 sites of the 3’-UTR is significantly negatively related to ARL4C mRNA expression (Figure S2b and Table S4). Taken together, these results suggest that DNA methylation is also involved in the epigenetic regulation of ARLs.
The diagnostic and prognostic values of ARLs for GC
We further assess the diagnostic and prognostic values of ARLs in GC patients based on the TCGA, GTEx and GEO datasets. Firstly, we construct the Logistic Regression model to test the usefulness of ARLs in GC diagnosis. All samples are randomly separated into training (75%) and validation (25%) cohorts. All ARLs in the training cohort are identified and featured with nonzero coefficients by Logistic regression model. Then, diagnostic markers with high significance are selected using stepwise method (“both” method). As shown in Figure 3a, we identify nine ARLs as the potential diagnostic markers for GC. Moreover, we evaluate the ability of predicted diagnostic markers in differentiating the GC patients from the normal in validation cohort. The result suggest that our selected diagnostic markers have a high accuracy of prediction (Area Under Curve (AUC) = 0.929) (Figure S3a).
Furthermore, we analyze the effects of ARLs on the overall survival (OS) of GC patients using the Kaplan-Meier (K-M) plotter. We observe that 9 ARLs (i.e., ARL1, ARL4C, ARL5A, ARL5B, ARL9, ARL13B, ARL15, ARL17A and ARL17B) are significantly related to patient prognosis (Figure S3b). Thus, ARLs are of great significance for assessing prognosis for GC patients. Then, we further identify the key prognostic markers for the purpose of avoiding overfitting of the predictive model with the minimum criteria s via conducting LASSO Cox regression model univariate and multivariate Cox regression models (Figure 3b and Figure S3c),where eight ARLs (ARL1, ARL4C, ARL5C, ARL6, ARL13B, ARL14, ARL15 and ARL16) are selected that are reliably associated with OS. The univariate and multivariate Cox regression models are also undertaken to study the prognostic values of all ARLs for GC (Figure 3c and 3d). As the Venn diagram integrated diagnostic analysis model and prognostic analysis models shown in Figure 3e, we acknowledge that ARL4C and ARL13B are the most important markers for diagnosis and prognosis for GC patients among all ARLs.
ARL13B has been previously reported to play a critical role in promoting proliferation, migration and invasion of GC cells and is associated with poor prognosis of GC patients (23). However, the biological functions of ARL4C in gastric tumorigenesis remain unclear. Therefore, we evaluate the protein expression level of ARL4C by IHC in a cohort of 142 GC patients (Cohort Ⅰ). Higher ARL4C expression is found in primary GC samples compared with normal gastric mucosa tissues (Figure 3f). Furthermore, we identify the ARL4C expression in frozen tumor and adjacent mucosa tissues of 12 GC patients at the Xijing Hospital of Digestive Diseases (Cohort Ⅱ) by Western blot analysis. The results indicate that the protein expression of ARL4C in the tumor tissues is significantly higher than that in the adjacent mucosa tissues (Figure 3g). Meanwhile, ARL4C overexpression could remarkably dampen the prognosis of GC patients after adjusting for several confounding factors, including subtype, Lauren classification, stage, age at surgery and gender (Figure S3d).
ARL4C knockdown decreases the proliferation and metastasis of GC cells in vitro and in vivo as well as reverses EMT
Given that ARL4C is involved in regulating the biological behaviors of various tumors, we examine whether ARL4C acts as an oncogene in GC cells in vitro and in vivo. In contrast to other small G proteins, ARL4C activity is regulated by its expression level rather than the switch between GDP- and GTP-bound status induced by regulators. Thus, we explore the role of ARL4C in the tumorigenesis of GC by constructing ARL4C knockdown GC cells. AGS and MKN45 cells are transfected with shRNA and siRNA against ARL4C. Multiple clones stably transfected with lentivirus are selected and confirmed by PCR and western blot analyses (Figure S4a and S4b). CCK-8 assays reveal that ARL4C downregulation significantly reduces cell growth compared with the control (Figure 4a), which is further confirmed by colony forming assays (Figure 4b). Furthermore, the in vivo analysis shows that silencing ARL4C in MKN45 cells causes obvious reductions in tumor weight and volume in nude mice (Figure 4c). The 3D invasion experiment, as shown in Figure 4d, indicates that ARL4C knockdown can decrease the invasion ability of GC cells in 3D culture. The in vivo metastatic assay also indicates that the downregulation of ARL4C decreases the incidence of lung metastasis and the number of metastatic lung nodules (Figure 4e). Overall, these results suggest that ARL4C may play a critical role in GC growth and metastasis both in vitro and in vivo.
Epithelial-mesenchymal transition (EMT) is involved in tumor aggressive progression. To confirm the role of ARL4C in regulating EMT of GC cells, we evaluate the expression changes of EMT markers after ARL4C silencing. Western-blot and RT-PCR analyses shos that downregulation of ARL4C leads to the increased expression of E-cadherin and decreased expression of N-cadherin and Vimentin compared with the control group (Figure 5a and 5b). Furthermore, the IF assays show similar results (Figure 5c). Additionally, we investigate the correlation coefficients between ARL4C and EMT markers based on the TCGA data and find that ARL4C is positively related to Vimentin (Figure S5c).
ARL4C acts a mediator of TGF-β1/Smad signaling in GC
To uncover the underlying mechanisms of ARL4C in GC, we explore the TCGA database to identify the genes related to ARL4C. As shown in Figure S5a and S5b, we identify that numbers of GC-related genes are highly correlated with ARL4C, among which TGF-β1 is the most significant gene in GC (R=0.851, P<0.01). GO and KEGG enrichment analyses indicate that the ARL4C-associated genes (R≥0.5, P<0.05) are significantly involved in the cellular response to TGF-β stimulus and TGF-β signaling pathway (Figure 6a and 6b).
As TGF-β1 is identified as an important inducer of the malignant progression of cancer, we investigate whether ARL4C might participate in TGF-β1-induced progression of GC. We treat AGS and MKN45 cells with 10 ng/ml TGF-β1 for 24 and 48 h. Following TGF-β1 stimulation, compared with the control, ARL4C is significantly upregulated in AGS and MKN45 cells. In particular, TGF-β1-induced ARL4C expression is in a time-dependent manner in AGS cells (Figure 6c). In addition, Western blot analysis and immunofluorescence analysis show the downregulation of ARL4C decreases the expression levels of Smad3, phosphorylated-Smad2 (p-Smad2) and phosphorylated-Smad3 (p-Smad3) in the AGS and MKN45 cells (Figure 6d-6e and Figure S4c). Meanwhile, TCGA correlation analysis shows that ARL4C is positively related to Smad2 and Smad3 with high correlation coefficients (Figure S5c). These data suggest that ARL4C may mediate the TGF-β1/Smad signaling pathway. Besides, TGFβ-1-induced EMT is reversed when ARL4C is silenced in MKN45 cells (Figure 6f and 6g).
ARL4C enhances the TGF-β1-mediated poor prognosis of GC patients
To translate the above findings into clinical significance, we analyze clinical data of ARL4C and TGF-β1 expression in GC patients from GSE15459 cohort. We divide the samples into 4 groups according to the expression status of ARL4C and TGF-β1: group 1 (ARL4Clow/ TGF-β1low), group 2 (ARL4Chigh/ TGF-β1low), group 3 (ARL4Clow/ TGF-β1high) and group 4 (ARL4Chigh/ TGF-β1high). Kaplan-Meier analysis shows that elevated expression of TGF-β1 or ARL4C is associated with shorter OS of GC patients. Furthermore, patients with coexpression of TGF-β1 and ARL4C have the lowest OS (Figure 7a).
Next, we construct a predictive nomogram based on overall mortality (OM) via multivariate Cox regression model. The nomogram incorporates six variables: age, the expression status of TGF-β1 and ARL4C, gender, stage, molecular subtype and Lauren subtype. As shown in Figure 7b, to evaluate the individual’s probability of overall mortality, values for the prognostic factors must be determined. Each independent prognostic factor is assigned an exact score scale, the points must be added up to obtain the total risk score at 3, 5, and 8 years. The OM probability can be read from the X-axis (total risk score) to the predicted the corresponding probabilities of independent prognostic factors on the left Y-axis.
The nomogram demonstrates that stage of GC patients contributes significantly to the individual’s probability of overall mortality and patients in stage Ⅳ have the highest mortality. Secondly, the expression status of TGF-β1 and ARL4C is a critical prognostic factor for GC patients. Group 4 (ARL4Chigh/ TGF-β1high) has the higher probability of OM than group 1 (ARL4Clow/ TGF-β1low), group 2 (ARL4Chigh/ TGF-β1low) and group 3 (ARL4Clow/ TGF-β1high) at 3, 5 and 8 years. We then adopt DCA to verify the prognostic accuracy of the nomogram in OS prediction. The results show that the best net benefit is similar with the prediction of the nomogram at 3, 5 and 8 years (Figure 7c and 7d).
The calibration curves of the nomogram at 3, 5 and 8 years are very close to the best prediction curve, showing a great consistency between the predicted OS rates and the actual observations (Figure 7e). Taken together, these results suggest that ARL4C is critical for TGF-β1-mediated poor clinical outcomes for GC patients.