The expression of RPLP2 in HCC

The flowchart of this entire study was showed in Fig. 1. The pan-cancer analysis obtained from TCGA and GTEx showed that mRNA of RPLP2 was up-regulated in 22 types of cancer out of the 33 (Fig. 2A). The result from HCCDB database demonstrated the high expression of RPLP2 mRNA in HCC (Fig. 2B). In addition, the unpaired (Fig. 2C) and paired (Fig. 2D) sample analysis of the RPLP2 expression level from TCGA and GTEx both indicated the elevated mRNA level of RPLP2 in HCC. Moreover, compared with normal tissues, RPLP2 was also significantly higher in HCC from GSE84402 dataset (Additional file 1: Fig. S1A). To further examine the expression level of RPLP2 in different HCC cell lines, we first used the HPA dataset to find that the mRNA expression level of RPLP2 was up-regulated in HCC cell lines compared with normal liver tissue cells (Fig. 2E). Then, we detected the protein expression level of RPLP2 in normal liver cells WRL68 and several HCC cell lines, and the results showed that RPLP2 was elevated in liver cancer cells (Fig. 2F). The IHC results further proved the staining intensity of RPLP2 was greater in HCC (Fig. 2G, Additional file 1: Fig. S1B, Table S1). Additionally, in order to more intuitively and specifically detect the specific localization of RPLP2, we used the immunofluorescence results of RPLP2 from HPA to demonstrated that RPLP2 was mainly localized to the nuclear speckles and cytosol in HEK293 (Fig. 2H), PC3 and U2OS (Additional file 1: Fig. S1C, D).

Fig. 1

The flow chart of this study

Fig. 2

RPLP2 expression levels in HCC. A RPLP2 mRNA expression level in normal tissues and cancers from TCGA and GTEx databases. B The mRNA expression level of RPLP2 in adjacent tissues and HCC tissues from HCCDB database. C, D Unpaired (C) and paired (D) analysis of RPLP2 mRNA expression in paracancerous tissues (n = 50) and HCC tissues (n = 377) from TCGA database. E RPLP2 mRNA expression level in normal liver and HCC cell lines from HPA database. F Western blot for detecting the protein level of RPLP2 in normal live and HCC cell lines. G IHC test of RPLP2 protein expression in 12 pairs of clinical tissues (magnification, ×200, scale bar = 100 μm; magnification, ×400, scale bar = 50 μm). H The immunofluorescence staining of RPLP2 and microtubules in HEK293 cell line in HPA database. ns nonsignificant (P > 0.05), *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Association between RPLP2 expression and multiple clinicopathological characteristics in HCC

As shown in Table 1 and Fig. 3 based on the TCGA-LIHC dataset, the expression level of RPLP2 was significantly correlated with age, histological grade, histological type, race, alpha-fetoprotein (AFP) levels, overall survival (OS) and tumor status. In addition, the logistic regression analysis showed that RPLP2 expression levels significantly correlated with age [odds ratio [OR] = 0.602, 95% CI = (0.400–0.907, P = 0.015)], race [odds ratio [OR] = 0.358, 95% CI = (0.233–0.549, P < 0.001)], histological grade [odds ratio [OR] = 2.842, 95% CI = (1.829–4.417, P < 0.001)], tumor status [odds ratio [OR] = 1.651, 95% CI = (1.081–2.523, P = 0.020)], weight [odds ratio [OR] = 0.353, 95% CI = (0.228–0.546, P < 0.001)] and AFP [odds ratio [OR] = 4.094, 95% CI = (2.190–7.655, P < 0.001)] (Table 2).

Table 1 Clinicopathological features of high- and low-RPLP2 expression groups in HCC patientsFig. 3

The relationship between RPLP2 expression and clinicopathological features of HCC patients. A Age. B Histological grade. C Histological type. D Race. E AFP levels. F OS. G Tumor status. H Pathologic stage. I T stages. J N stages. K M stages. L DSS. ns nonsignificant (P > 0.05), *P < 0.05, **P < 0.01, ***P < 0.001. (The data was obtained from TCGA-LIHC)

Table 2 Logistic regression analysis of the relationship between RPLP2 expression levels and clinicopathological characteristics in HCC patientsCorrelation between RPLP2 expression with methylation level, molecular or immune subtypes in HCC

For elucidating the potential mechanism of RPLP2 overexpression in HCC tissues, we first used UALCAN database to explore the relationship between RPLP2 expression and DNA methylation levels of the promoter. The results demonstrated that HCC tissues showed an obviously lower level of promoter methylation than normal liver tissues (Fig. 4A), and the expression level of RPLP2 was negatively correlated to the tumor grades and individual cancer stages separately (Fig. 4B, C). Furthermore, we explored the specific methylation status of different methylation sites of RPLP2 and its correlation with the prognosis of HCC patients via using MethSurv tool. The results indicated that most of methylation sites in the DNA sequences of RPLP2 were hypomethylated in HCC (Fig. 4D), and methylation level of five CpG islands were associated with patient outcomes. Specifically, elevated methylation levels of RPLP2 in four islands including cg19520219, cg01813026, cg14016074 and cg05109266 were correlated with poor prognosis (Fig. 4E–I). In addition, we further analyzed the correlation between RPLP2 expression and molecular or immune subtypes in HCC from the TISIDB database. The analysis results indicated that there was no obvious difference shown in different molecular subtypes (Additional file 1: Fig. S2A), but for immune subtypes, RPLP2 expression was significantly different in HCC (Additional file 1: Fig. S2B).

Fig. 4

DNA methylation level of RPLP2 and its relationship with the prognosis of HCC patients. A The promoter methylation features of RPLP2 in HCC obtained from UALCAN database (n of normal = 165, n of tumor = 165). B, C The promoter methylation level of RPLP2 was analyzed by the tumor grade (B) and the main pathological stages (C) of HCC via UALCAN database. D Correlation between RPLP2 expression and methylation level in HCC obtained from MethSurv database. E–I Kaplan–Meier survival curves showing the effect of methylation levels in the CpG sites of RPLP2 on the prognosis of HCC patients obtained form MethSurv database. ns nonsignificant (P > 0.05), ***P < 0.001, ****P < 0.0001

Correlation between RPLP2 expression and the infiltration of multiple immune cell types in HCC

ssGSEA was used to evaluate the infiltration status of 24 kinds of immune cells, and the association between RPLP2 expression and immune cell infiltration was estimated by the Spearman’s correlation analysis (Fig. 5A). The analysis result showed that RPLP2 significantly positively correlated with NK CD56 bright and Th2, and negatively correlated with Tcm (Fig. 5B–D). In addition, the enrichment scores of NK CD56 bright, Th2 and Tcm were consistent with the Spearman’s analysis results (Fig. 5E–G). Moreover, the results of IHC further proved that high RPLP2 expression was associated with more NK CD56 bright and Th2 cell infiltration and less infiltration of Tcm cells in HCC (Fig. 5H).

Fig. 5

The correlation between RPLP2 expression and immune cell infiltration in HCC. A Spearman’s correlation analysis between RPLP2 expression and relative abundance of 24 types of immune infiltrating cells. B–D The immune infiltration levels of NK CD56 bright (B), Th2 (C) and Tcm (D). E–G The relationship between RPLP2 expression and NK CD56 bright (E), Th2 (F) and Tcm (G). (The data was obtained from TCGA-LIHC). H IHC test verified the infiltration level of immune cells in HCC with high or low RPLP2 expression (magnification, ×200, scale bar = 100 μm). ns nonsignificant (P > 0.05), *P < 0.05, **P < 0.01, ***P < 0.001

Potential prognostic and diagnostic value of RPLP2 in HCC

Kaplan–Meier method was carried out to analyse the relationship between RPLP2 expression and the prognosis of HCC patients. The survival curve showed that compared with low RPLP2 expression group, HCC patients with high RPLP2 level exhibited worse prognosis of OS, DSS and PFI (Fig. 6A–C). Then we further explored the effect of RPLP2 on prognosis in different subgroups of HCC patients. And the results indicated that the high level of RPLP2 predicted unfavorable OS in various subgroups including T1 and T2, T3 and T4, N0, M0, Stage III and IV, tumor free, age ≤ 60, hepatocellular carcinoma, R0, G3 and G4 and Child–Pugh grade A (Fig. 6D–N). For DSS, RPLP2 played a risk role in several subgroups, such as Stage III and IV, age ≤ 60, hepatocellular carcinoma and R0 (Additional file 1: Fig. S3A–D). And the prognosis of PFI was notably poor in many subgroups including N0, MO, with tumor, age ≤ 60, hepatocellular carcinoma, R0 and G1 and G2 (Additional file 1: Fig. S3E–K). In addition, we used univariate and multivariate cox regression analyses to figure out potential prognostic indicators (Additional file 1: Tables S2–S4). The multivariate cox regression analysis demonstrated that the pathological T stage was an independent risk factor of OS and DSS, the tumor status and the expression level of RPLP2 were valuable prognostic predictors of OS and PFI, and the vascular invasion exhibited a great value in clinical predicting PFI (Fig. 6O, Additional file 1: Fig. S4A, B).

Fig. 6

Survival analysis of RPLP2 in HCC. A–C Kaplan–Meier curves for patient’s OS (A), DSS (B) or PFI (C) classified by different expression level of RPLP2 in HCC (n of low = 187, n of high = 186). D–N Kaplan–Meier curves indicating the OS prognostic value of RPLP2 expression in different HCC subgroups including, T1 and T2 (D) (n of low = 139, n of high = 138), T3 and T4 (E) (n of low = 46, n of high = 47), N0 (F) (n of low = 127, n of high = 127), M0 (G) (n of low = 134, n of high = 134), Stage III and IV (H) (n of low = 45, n of high = 45), tumor free (I) (n of low = 101, n of high = 101), age ≤ 60 (J) (n of low = 88, n of high = 89), hepatocellular carcinoma (K) (n of low = 182, n of high = 181), R0 (L) (n of low = 163, n of high = 163), G3 and G4 (M) (n of low = 68, n of high = 67) and Child–Pugh grade A (N) (n of low = 109, n of high = 109). O Forest plots showing the potential prognostic indicators for OS. (The data was obtained from TCGA-LIHC)

We plotted the receiver operating curve (ROC) to investigate the diagnostic value of RPLP2 in HCC. And the ROC curve analysis demonstrated that RPLP2 had great performance (AUC = 0.906) in distinguishing HCC tumor from normal control (Fig. 7A). Then, we evaluated the diagnostic value of RPLP2 expression in different subgroup of HCC patients. Specifically, RPLP2 exhibited excellent diagnostic value (AUC > 0.90) in many subgroups including.

Fig. 7

The nomogram with RPLP2 shows excellent performance in the diagnosis and prognosis of HCC. A ROC analysis of RPLP2 in HCC. B–H Diagnostic value of RPLP2 mRNA level in different HCC subgroups including, G3 and G4 (B), T1 and T2 (C), T3 and T4 (D), N0 (E), M0 (F), Stage I and II (G) and Stage III and IV (H). I Time-dependent survival ROC analysis for predicting the probability of HCC patients with 1-, 3- and 5-year survival. J Nomogram for predicting the 1-, 3- and 5-year overall survival rates of HCC patients. (The data was obtained from TCGA-LIHC)

G3 and G4, T1 and T2, T3 and T4, N0, M0, Stage I and II and Stage III and IV (Fig. 7B–H). In addition, the result of time-dependent ROC curve showed that RPLP2 had certain prediction accuracy (AUC = 0.589, 0.584 and 0.591) for 1-, 3-, and 5-year survival rates of HCC patients (Fig. 7I). At last, we established a nomogram combining RPLP2 expression and some critical clinical features which exhibited significantly high value in predicting the 1-, 3-, and 5-year survival probability of the HCC patients (Fig. 7J, Additional file 1: Fig. S5A–C).

DEGs between high- and low-RPLP2 expressing HCC patients and PPI network analysis

Using absolute log-fold change > 1.5 and P < 0.01 as the threshold parameters, we identified 1141 differentially expressed genes (777 up-regulated and 364 down-regulated) between high- and low-RPLP2 expressing HCC groups (Additional file 1: Fig. S6A). The top ten significant DEGs (including LGALS14, DLK1, AC093894.2, CYP11B2, CEACAM7, SST, AL109838.1, AFP, HOXC10 and ARHGAP36) were shown the single gene co-expression heat map (Additional file 1: Fig. S6B). In order to explore the interactions between all RPLP2-related DEGs, we carried out the online STRING tool to construct a PPI network (Additional file 1: Fig. S6C), and further used cytoscope to figure out the top 10 hub genes which were CHGA, ISL1, AFP, ASCL1, CALB2, KRT19, NKX2-2, SST, EPCAM and ESR1 (Additional file 1: Fig. S6D).

Knockdown of RPLP2 promotes ferroptosis

Considering the accumulation of ROS caused by the inhibition of RPLP2 in gynecologic tumors [20], and the significant effect of RPLP2 on ferroptosis-related pathway showed by GSEA in AML [21], we further investigated whether RPLP2 had an effect on ferroptosis of HCC cells. First, we used Gene set enrichment analysis (GSEA) to reveal that several critical ferroptosis-related pathways including “Oxidative Phosphorylation”, “Regulation of Lipid Catabolic Process”, “Iron Ion Homeostasis” and “WP Ferroptosis” exhibited significant enrichment differences between high and low RPLP2 expression groups (Fig. 8A). In addition, the correlation analysis between RPLP2 and key ferroptosis gene regulators in TCGA HCC samples from FerrDb showed that RPLP2 positively correlated with ferroptosis suppressor gene GPX4, and negatively associated with ferroptosis driver genes IREB2, BECN1 and NCOA4 (Fig. 8B). Furthermore, the result of western blot demonstrated that GPX4 was decreased by the knockdown of RPLP2 (Fig. 8C), and IHC assay also proved that the staining intensity of RPLP2 was positively correlated with GPX4 in HCC (Fig. 8D, E). Next, we treated Hep3B and HepG2 cells with ferroptosis inducer RSL-3 which could activate oxidative stress pathways, and we found that compared with the DMSO treated group, Hep3B and HepG2 cells treated with RSL-3 had a decreased expression level of RPLP2 (Fig. 8F). Then, we treated Hep3B cells with RSL-3, and the CCK8 analysis indicated that RPLP2 knockdown promoted the ferroptosis of Hep3B cells (Fig. 8G). Moreover, the detection of GSH and lipid ROS (surrogate markers for ferroptosis) in Hep3B cells treated with RSL-3 showed that RPLP2 knockdown could lead to the decrease of GSH (Fig. 8H) and increase of lipid ROS (Fig. 8I). Notably, we further explored whether RPLP2 upregulation was linked to ferroptosis in seven other cancers where RPLP2 was most significantly upregulated, and the results indicated that RPLP2 also showed significant correlation with GPX4 in five cancer types including GBM, LGG, PAAD, TGCT and THYM (Additional file 1: Fig. S7A). And GSEA results indicated that RPLP2 expression significantly alter the enrichment of several ferroptosis-related pathways in these five cancer types, especially in THYM (Additional file 1: Fig. S7B–D).

Fig. 8

RPLP2 silencing promotes ferroptosis of HCC cells. A Gene set enrichment plots of “Oxidative Phosphorylation”, “Regulation of Lipid Catabolic Process”, “Iron Ion Homeostasis” and “WP Ferroptosis” from GSEA of RPLP2-related DEGs. (The data was obtained from TCGA-LIHC). B The correlation between RPLP2 expression and key ferroptosis gene regulators GPX4, IREB2, BECN1 and NCOA4 via FerrDb V2. C The expression levels of RPLP2 and GPX4 in Hep3B cells with RPLP2 knockdown were detected using western blot. D The correlation between RPLP2 and GPX4 protein level was analyzed in 12 clinical HCC tissues. E IHC analysis of the relationship between RPLP2 and GPX4 expression in 12 clinical HCC tissues. F The expression level of RPLP2 in Hep3B cells and HepG2 cells treated with ferroptosis inducer RSL-3 were detected via western blot. G CCK8 assays detected the responses of Hep3B cells knockdown of RPLP2 to Fer-1 and RSL-3. (n = 3 independent experiments). H, I The levels of total GSH (H) and lipid ROS (I) in Hep3B cells knockdown of RPLP2 were detected. (n = 3 independent experiments). ns nonsignificant (P > 0.05), *P < 0.05, **P < 0.01, ***P < 0.001

RPLP2 knockdown inhibits tumor growth

To explore the role of RPLP2 in HCC, we first selected Hep3B cell line which had a high RPLP2 expression level for functional analysis in vitro. The results of CCK8, transwell and colony formation assays indicated that RPLP2 silencing significantly reduced the cell proliferation, cell migration and colony formation ability of Hep3B cells (Fig. 9A–F). Then we further confirm the tumorigenic function of RPLP2 on HCC in vivo. After transplanting RPLP2 knockdown Hep3B cells subcutaneously into nude mice, we found that tumor size and weight were obviously decreased upon the silencing of RPLP2 (Fig. 9G, H). Taken together, these findings suggested that RPLP2 promoted HCC growth and migration.

Fig. 9

Knockdown of RPLP2 inhibits tumor growth of HCC. A Western blot detecting the transfection efficiency of siRPLP2#1 and siRPLP2#2 in the Hep3B cell. B CCK8 assays was applied to detect the effect of RPLP2 knockdown on the cell proliferation of Hep3B cells. (n = 3 independent experiments). C, D The transwell assay of Hep3B cells with RPLP2 knockdown. (n = 3 independent experiments). E, F The effect of RPLP2 knockdown on the colony formation of Hep3B cells was evaluated by colony formation assay. (n = 3 independent experiments). G, H The nude mice xenograft model was established to explore the effect of RPLP2 knockdown on tumor volume (G) and weight (H). (n = 3 mice per group). ns nonsignificant (P > 0.05), *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Potential therapeutic drugs targeting RPLP2 for treatment

Considering the critical role of RPLP2 in promoting liver cancer, it’s necessary to search for drugs specifically targeting RPLP2 with high sensitivity. We used the RNAactDrug database to analyse the correlation between the drug sensitivity and mRNA expression or methylation level of RPLP2. The results showed that the drug sensitivity of methylundecylpiperidine, trans, iyomycin b1, destruxin b, artelasticin, gw406731x and prodiginine hcl, butylcycloheptyl-increased with elevated mRNA expression of RPLP2 (Fig. 10A–F). In addition, we found that patients with high RPLP2 methylation level had great sensitivity for tp4ek-k6, Ibrutinib, indole-2,3-dione, 3-[(o-chlorophenyl)hydrazone], indole-2,3-dione, 3-[(o-nitrophenyl)hydrazone], 5-(5,6-dichloro-1H-benzo[d]imidazol-2-yl)-6-(4-fluoroph… and cyanoaminopyranopyridine derivatives (Fig. 10G–L).

Fig. 10

Predicting potential drugs targeting RPLP2 for treatment. A–F Correlation analysis between drug sensitivity and mRNA expression of RPLP2 based on RNAactDrug database. G–L Correlation analysis between drug sensitivity and methylation level of RPLP2 based on RNAactDrug database