SNRPG serves as a novel predictor of prognosis and tumor development for hepatocellular carcinoma by activating Wnt/β-catenin signaling

doi:10.21203/rs.3.rs-3294456/v1

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Research Article

SNRPG serves as a novel predictor of prognosis and tumor development for hepatocellular carcinoma by activating Wnt/β-catenin signaling

https://doi.org/10.21203/rs.3.rs-3294456/v1

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Background

Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies with poor prognosis and high mortality. The study aimed to identify crucial genes associated with HCC prognosis and treat responses, as well as the underlying mechanisms.

Methods

mRNA and protein levels in tumor cells and tissues were detected by immunohistochemistry (IHC), qRT-PCR, and western blot. CCK-8 assay was used to assess the proliferation of HCC cells and Cell cycle status was analyzed by flow cytometer. The protein interaction between SNRPB and SNRPG was verified using Co-immunoprecipitation (Co-IP). The effects of SNRPB and SNRPG expression on tumor response to XAV939 and immune checkpoint blocking (ICB) therapies were deduced through the analysis of TIDE and genomic of the GDSC dataset.

Results

SNRPB and SNRPG were significantly up-regulated in HCC tissues than that in adjacent non-tumor tissues. HCC patients with higher expressions of SNRPB and SNRPG had poor prognoses and lower survival rates. SNRPG knockdown resulted in an obvious decrease in proliferation and EMT of HCC cells and cell cycle arrest in the G2 phase, as well as reduced protein expressions of components and targets of the Wnt/β-catenin pathway. SNRPG overexpression simultaneously with shSNRPB recovered proliferation inhibition caused by SNRPB knockdown. Importantly, the double-genes prognostic signature formed by SNRPB and SNRPG had a good prognostic value for HCC patients and was negatively associated with the responses to chemo drug XAV939 and immune checkpoint blocking (ICB) therapies.

Conclusion

SNRPG was a potential interactive tumor-promoting factor and may guide clinical chemotherapy and immunotherapy for HCC.

Hepatocellular carcinoma

proliferation

metastasis

cell cycle

SNRPG

Wnt signaling pathway

Primary liver cancer, the majority of which are hepatocellular carcinomas (HCC), is one of the leading causes of cancer-related death worldwide [1]. Due to resistance to traditional treatments and tumor recurrence after therapy, therapeutic outcomes were frequently poor [2]. HCC displays high heterogeneity of molecular phenotypes [3]. Furthermore, HCC arises from hepatocytes through the sequential accumulation of multiple genomic and epigenetic alterations resulting from the Darwinian selection. Genes from various signaling pathways such as telomere maintenance, cycle regulation, oxidative stress, Wnt/β-catenin, AKT/mTOR, P53/cell epigenetic modifiers, and MAPK kinase are frequently mutated in HCC [4]. Despite the emergence of targeted therapy, advanced HCC remains largely untreatable owing to therapeutic resistance and low response rate [5]. Developing new intervention targets is needed for more effective therapies in HCC patients.

The SNRPG gene, encoding small nuclear ribonucleoprotein polypeptide G, is a core element of U1, U2, U4, and U5 small nuclear ribonucleoprotein (snRNP) complexes, which are the building blocks (or originators) of the spliceosome [6]. As another member of the snRNP family, SNRPB had been proved to function as an oncogene in HCC [7, 8]. Furthermore, down-regulation of SNRPB could inhibit proliferation, migration, and invasion of cervical cancer cells and improve G2/M cell cycle arrest, and all of these processes are primarily due to interactions with p53 [9]. Revelatory evidence of different expression levels of SNRPG has been reported in several human tumors, including lung cancer, breast cancer, colon cancer, and prostate cancer [10]. SNRPG has attracted significant attention due to its implicated roles in tumor incidence and development, but its exact function in HCC has not yet to be elucidated. SNRPG has a wide interaction network, and its functions are predominantly mediated by protein-protein interactions (PPIs), making it a promising anticancer therapeutic target in PPI-focused drug technology.

In our study, we found the abnormal expression of SNRPG in HCC and high SNRPG is associated with metastasis and poor prognosis in HCC patients. Furthermore, SNRPG can interact with SNRPB to regulate its roles in the progression of HCC. We characterized the functional role of SNRPG in HCC through a series of in vitro studies as well as bioinformatics analysis. Finally, a double gene prognostic signature was established based on SNRPG and SNRPB to explore its prognostic value and influence on clinical responses to chemotherapy and immunotherapy. Therefore, the present study can help to accelerate the identification of robust predictive biomarkers of response to clinical targeted biotherapy for HCC. Meanwhile, combinations of targeted therapies can be applied as a promising strategy in the future.

SNRPG is required for the proliferation, migration, and EMT of HCC cells, which interacted with SNRPB

Recently, it has been reported that SNRPB could serve as a prognostic predictor for patients with HCC, and promote tumor progression [11]. To explore how SNRPB functions the tumorigenic potential in HCC, we selected several significantly dysregulated genes after SNRPB knockdown from RNA-sequencing data. Meanwhile, we also selected the top co-expressed genes with SNRPB ranked by the sum of their edges’LLS score through the online tool Coexpedia (https://www.coexpedia.org/hs_single.php?gene=SNRPB). As shown in Fig. 1A, the Venn diagram demonstrates the overlaps of the DEGs from RNA-sequence and the top 10 of co-expressed genes, and then six genes (PCNA, UBE2S, SNRPG, MCM2, PRMT1, and RANBP1) were presented in overlaps part. The table in Fig. 1B showed the expression value of these six genes through RNA sequencing, and a PPI network presented the interaction between SNRPB and six selected genes through the online STRING tool (Fig. 1C). Through RT-qPCR analysis, we observed that a most significant decrease in SNRPG expression after SNRPB expression was inhibited in SK-HEP-1 cells (Fig. 1D). Endogenous SNRPG was immunoprecipitated from 293T cell lysates by SNRPB antibody. The Co-IP assay demonstrated that SNRPB could interact with SNRPG (Fig. 1E). Thus, SNRPG was selected for further analysis.

To investigate whether SNRPG plays a key role in the malignant progression of HCC in vitro, we first evaluated SNRPG expression in two HCC cell lines after being transfected with shSNRPG or shCtrl vectors. The protein expression of SNRPG in Hep3B and SK-HEP-1 cells were significantly downregulated after shSNRPG transfection by western blot (Fig. 1F). The epithelial-mesenchymal transition (EMT) was associated with tumor initiation, invasion, metastasis, and resistance to therapy in several human cancers [12]. To explore whether SNRPG participated in HCC metastasis, we next examined the role of SNPRG in the expression of EMT markers in HCC cells. Western blotting indicated that protein expression levels of EMT markers, including epithelial marker (E-cadherin), were upregulated after knockdown of SNRPG, while mesenchymal markers (N-cadherin and Vimentin) were downregulated in two HCC cells (Fig. 1F). As shown in Fig. 1G, SNRPG silence significantly decreased cell growth of Hep3B and SK-HEP-1 cells. Additionally, the flow cytometer assay showed that the cell cycle was markedly interrupted in the G2 phase in the SNRPG knockdown cells (Fig. 1H), which may be the key event in which SNRPG affects the viability of tumor cells. These findings indicated that SNRPG was a tumor promoter, contributing to the malignant phenotypes of HCC cells.

Pathway’s enrichment analysis

Differential pathways between high- and low SNRPB or SNRPG expressions were enriched through GSVA and the enrichment heat map was drawn according to the enrichment fraction (Fig. 2A). GSVA analysis showed differences in KEGG functional enrichment between high- and low- SNRPG expression groups. The same results were found in the high and low SNRPB groups. Furthermore, a total of nine pathways regulated by SNRPG or SNRPB were significantly correlated with each other (Fig. 2B), including the spliceosome, RNA degradation, cell cycle, DNA replication, glycolysis gluconeogenesis, apoptosis, focal adhesion, the MAPK signaling pathway, and mTOR signaling pathway, which were closely related to Wnt signaling pathways in the development and/or progression of HCC [13–15]. Several Wnt/β-catenin targets, including Cyclin D1 and c-Myc, are vital for the growth and metastasis of HCC [16]. Therefore, we then examined the effect of SNRPG knockdown on related proteins of the Wnt/β-catenin signaling pathway and found that the expressions of β-catenin, Cyclin D1 and c-Myc were significantly reduced in Hep3B and SK-HEP-1 cells after the elimination of SNRPG (Fig. 2C). These results suggest that the roles of SNRPG in HCC proliferation and metastasis may be partly mediated by the activation of the Wnt/β-catenin signaling pathway.

SNRPG is regulated by SNRPB through the Wnt/β-catenin signaling pathway in HCC

To validate the involvement of SNRPB in SNRPG-mediated HCC cell proliferation and metastasis, we stably introduced SNRPG or empty vector controls into SNRPB knockout HCC cells. As shown in Fig. 3A, SNRPG overexpression promoted cell proliferation of Hep3B and SK-HEP-1 cells, and forced expression of SNRPG in SNRPB knockdown cells remarkably reversed the proliferation inhibition. These data suggested that SNRPG could partly rescue the biological effects of SNRPB-knockdown on HCC cells.

Given that chemotherapy is currently commonly used to treat HCC, our study attempted to evaluate the response of SNRPB and SNRPG expressions to the chemo drug, XAV939, which was an inhibitor of the Wnt/β-catenin signaling pathway. Wnt signaling is well known to control many aspects of cell behavior in cancer [17]. The IC50 value for each sample was estimated in the TCGA-LIHC dataset based on the predictive model of XAV939. Notably, it was observed that HCC samples with low-SNRPB and SNRPG were more sensitive to XAV939 chemotherapy as shown in Fig. 3B (p < 0.001). Furthermore, SK-HEP-1 cells were treated with XAV939 (50 µmol/L) after different transfections. As shown in Fig. 3C, overexpression of SNRPG upregulated β-catenin, Cyclin D1, and c-Myc, while shSNRPB slightly inhibited the up-regulation of these proteins. Importantly, XAV939, similar to shSNRPB, could also significantly inhibit the Wnt/β-catenin signaling pathway in SK-HEP-1 cells (Fig. 3C). These data indicated that SNRPG might be regulated by SNRPB through the Wnt/β-catenin signaling pathway in HCC.

An aberrantly high expression of SNRPB and SNRPG correlates with worse survival in patients with HCC

Through the detection of SNRPB in HCC tumor tissues and adjacent non-tumor tissues, it could be observed that proportions of the high expression of SNRPB in HCC tumor tissues were higher than that in adjacent non-tumor tissues (Fig. 4A). The SNRPB protein was predominantly nuclear and significantly elevated in HCC tumor tissues compared to non-tumor tissues through IHC staining (Fig. 4B). Furthermore, we confirmed the higher expression of SNRPB in HCC primary tumor compared to para-cancer from the GSE45114 cohort, as well as SNRPB expression in the metastasis tumor was significantly higher than that in the primary tumor (Fig. 4C). These results confirmed that SNRPB participated in the process of HCC metastasis. Baseline clinical data were combined for 70 cases of patients with HCC, the univariate regression analysis showed that SNRPB and several other factors (tumor size, AJCC, and T stage) were independent risk prognostic factors for HCC with HR ≥ 1 (p < 0.05) (Fig. 4D). Kaplan-Meier analysis showed that higher expression of SNRPB indicated the poor OS of HCC patients (p = 0.00094) (Fig. 4E). Moreover, the results in Fig. 4F showed that HCC patients with higher levels of tumor size and AJCC were related to a lower probability of overall survival. The increased levels of the SNRPB protein in four pairs of HCC tumor and non-tumor tissues were also confirmed by western blot analysis (Fig. 4G). Therefore, SNRPB plays a pro-aggressive role during HCC malignant progression.

Similar to the SNRPB expression in HCC tissues and adjacent non-tumor tissues, we found that proportions of the high expression level of SNRPG in HCC tumor tissues were also higher than in adjacent non-tumor tissues (Fig. 5A). Through IHC staining, the SNRPG protein was also predominantly nuclear in HCC tumor tissues (Fig. 5B). Univariate regression analysis showed that SNRPG and several other factors (tumor size, AJCC, and T stage) also independent risk prognostic factors for HCC with HR ≥ 1 (p < 0.05) (Fig. 5C). The Kaplan-Meier analysis showed that higher SNRPG expression and higher tumor size indicated the poor OS for HCC patients (p = 0.029 and p = 0.02) (Fig. 5D). The increased levels of the SNRPG protein in four pairs of tumor and non-tumor tissues were also confirmed by Western blotting (Fig. 5E). Further Pearson correlation coefficient corroborated a positive correlation between SNRPB and SNRPG in HCC tissues (Fig. 5F). These data indicate that SNRPG, similar to SNRPB, was a potential prognostic marker in HCC.

Construction and evaluation of double-gene prognostic signature model

In light of the abnormal expression patterns of SNRPB and SNRPG in HCC, their expression levels (FPKM) were used to build the double gene prognostic signature model (Model). Based on the data of patients with HCC from the TCGA data set and 65 HCC patients collected at our hospital, we applied Kaplan-Meier survival and ROC curves to identify the prognostic functions of this model for HCC. As shown in Fig. 6A, the K-M survival curves confirmed that lower levels of the model were associated with better OS (p = 0.022), and the ROC curves showed that this model harbored a promising ability to predict OS for HCC patients in the TCGA data set (training set) (1-year AUC = 0.655, 2-year AUC = 0.637, 3-year AUC = 0.620, 5-year OS = 0.669). Similarly, in the validated set containing 65 HCC patients, a higher level of the model was associated with worse OS (p = 0.0049) and this model also had a promising ability to predict OS for HCC (20 months AUC = 0.662, 30 months AUC = 0.669, and 50 months AUC = 0.673; Fig. 6B). To create a clinically applicable quantitative tool to predict OS for HCC patients, we established a nomogram based on the model, age, tumor stage and gender (Fig. 6C). The OS of patients with HCC was calculated, and then the 1-year, 2-year, 3-year, and 5-year OS were predicted. Calibration plots of 1-, 2-, 3- and 5-year OS indicated that the nomogram was of excellent predictive effect (Figs. 6D), implying that it was incredibly valuable to integrate the double-gene prognostic signature model into the clinical prognostic factors for HCC.

Pan-cancer analysis of SNRPB and SNRPG and the sensitivity of their expression to immunotherapies in HCC

To further explore the possible roles of SNRPB and SNRPG in carcinogenesis, we first analyze the level of their double-gene prognostic signature model in 24 types of human cancer in the TCGA database. As shown in Fig. 7A, compared to normal samples, the model levels were significantly increased in most of the 24 cancer types, including UCEC, THCA, STAD, READ, PRAD, LUSC, LUAD, LIHC, KIRP, KIRC, HNSC, GBM, ESCA, COAD, CHOL, CESC, BRCA, and BLCA. These data indicated that SNRPB and SNRPG could function as crucial regulators in carcinogenesis, as candidate genes for developing independent prognostic factors for these cancer-type tumors.

In addition, tissue-/organ-specific SNRPB and SNRPG were identified by BioGPS. As shown in Fig. 7B, it was observed that SNRPB and SNRPG were both specifically enriched in the immune system, especially in the CD34 + cells, CD105 + Endothelial, and 721_B_lymphoblasts. Meanwhile, SNRPB was specifically enriched in leukemia lymphoblastic (MOLT-4), lymphoma_burkitts (Daudi) and lymphoma_burkitts (Raji), while SNRPG was specifically enriched in bronchial epithelial cells and chronic myelogenous. These data suggested that SNRPB and SNRPG could be involved in the progression of tumors or immune-related diseases by regulating the immune response. Subsequent correlation analysis also confirmed that their double-gene signature model was marked positively correlated with regulatory T cells (Tregs), follicular helper T cells, and memory B cells in the immune system (Fig. 7C), indicating that SNRPB and SNRPG were inextricably linked to the immune process. Additionally, the double-gene prognostic signature model was found to be associated with spliceosome, RNA degradation, cell cycle, DNA replication, glycolysis gluconeogenesis, apoptosis, focal adhesion, MAPK signaling pathway and mTOR signaling pathway in most cancers, including LIHC (Fig. 7D). These enriched signaling pathways are indeed closely related to SNRPB and SNRPG (Fig. 2B), as well as the Wnt signaling pathway, echoing the previously confirmed Wnt/β-Catenin signaling pathway downstream-regulated by the SNRPG in HCC.

To predict the response of SNRPB and SNRPG to ICB immune checkpoint blockade (ICB), we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. The findings showed that the TIDE scores were higher in the high SNRPB and high-SNRPG groups than that in the low SNRPB and low-SNRPG groups (P < 0.001) (Fig. 7E), indicating that HCC patients with high-SNRPB and SNRPG were more likely to respond worse to immunotherapy. Therefore, patients with low-SNRPB and SNRPG may benefit more from immunotherapy.

HCC is a global health problem that kills a large number of patients every year [18]. Therefore, in-depth exploration of new prognostic markers and new drug targets is essential to improve the survival and prognosis of HCC patients. In our study, we found that the upregulation of SNRPB and SNRPG were both significantly associated with the poor prognosis of HCC patients, and they were all able to be regarded as independent risk prognostic factors for HCC. We also identify SNRPG as a vital target involved in SNRPB-mediated HCC cell growth and metastasis. SNRPB-SNRPG regulates the cell cycle and EMT process of HCC through the Wnt pathway. Importantly, the double-gene model formed by SNRPB and SNRPG has a high prognostic value and is related to immune response, which can significantly affect the sensitivity of HCC patients to immune/chemotherapies.

Previous studies showed that SNRPG knockdown played a functional role in the development of cancer cells by inhibiting cell growth and inducing cell cycle arrest by targeting the prevention of Myc and p53 [6]. Similar to previous studies, the present study showed that SNRPG silencing decreased proliferative activities of HCC cells and induce cell cycle arrest in the G2 phase, as well as EMT progress. These results confirm that SNRPG is a potential tumor promoter for HCC. Subsequently, we explored the molecular mechanism by which SNRPG promotes the malignant progression process of HCC. Through the online tool-Coexpedia and the RNA-sequencing data, the overlapping hub genes related to SNRPB were analyzed. We then found that SNRPB interacted with SNRPG from the PPI network and there is a positive correlation between them. Further function assays showed that SNRPG overexpression partly restored the capacity of cell proliferation capacity in SNRPB-knockdown HCC cells. Hence, SNRPG may be involved in SNRPB-induced hepatocarcinogenesis. To our knowledge, this is the first study to indicate that SNRPG played a role in weakening the tumor promoter SNRPB and reveal the potential clinical value of SNRPG in HCC.

To further search the prognostic significance of SNRPB and SNRPG for HCC, a double-genes model was established based on their expression for predicting the OS of HCC patients. Based on the median level of the model, all HCC patients in TCGA (training set) and all HCC patients (test set) were divided into high-model and low-model subgroups, and HCC patients with high-model had shorter OS time. By drawing the ROC curves, the conclusion is that the model had a good predictive effect for the 1/2 /3/5-year survival time. Furthermore, by combining the model with age, tumor stage, and gender, we built a nomogram that had a good predictive effect being verified by drawing the calibration curves. Amazingly, the double-genes model showed high levels not only in liver cancer but similar patterns in many other cancers, suggesting that this model may also have high clinical prognostic value for other tumors.

The Wnt signaling cascade has developed together with multicellularity to orchestrate the development and homeostasis of complex structures, and a direct link with the G2/M cell cycle control has recently been established [19]. In this study, through KEGG enrichment analysis, we found that the nine different signaling pathways, including spliceosome, RNA degradation, cell cycle, DNA replication, glycolysis gluconeogenesis, apoptosis, focal adhesion, MAPK signaling pathway, and mTOR signaling pathway, were the potential pathways both related to SNRPB and SNRPG. Unexpectedly, the double-gene model was proved to be closely related to these nine signaling pathways, which could link with the Wnt signaling pathways in the development and/or progression of HCC [13–15]. A previous study reported that lanatoside C exerted its anti-cancer activity by arresting the G2/M phase of the cell cycle by blocking Wnt signaling pathways in the liver cancer cell line [20]. The Wnt signaling pathway induces EMT conversion by inhibiting its targeted genes and β-catenin degradation in the cytoplasm. Our experiments show that the expression levels of β-catenin, Cyclin D1, and c-Myc are downregulated by SNRPG knockdown, while were upregulated by the addition of SNRPG. Myc can mediate a transcriptional program that encompasses cell growth, cell cycle, and survival in cancer cells [21]. These experimental results indicate for the first time the potential mechanism of SNRPB/SNRPG in modulating the cell cycle and the EMT process by regulating the Wnt pathway in HCC.

For the clinical treatments of HCC, it is increasingly difficult to achieve an effective treatment with single-target drugs. ICB can bring long-lasting clinical benefits, but only a small percentage of cancer patients respond to treatment. Despite this, immunotherapy is considered a promising alternative to cancer treatment because it may help patients overcome the challenge of drug resistance. The prognostic importance of immune cell infiltration has been verified in various types of solid tumors [22, 23]. Here, the results revealed the specific enrichment of SNRPB and SNRPG in immune cells. This research also revealed the positive relationship between the double gene model and immune cell subsets, including Tregs, helper follicular T cells, and memory B cells. Furthermore, TIDE analyzes demonstrated that CTLA4-immunotherapy was more promising in patients with HCC with low-SNRPB and SNRPG. The GDSC database also implied that the low-SNRPB and SNRPG group could be more sensitive to chemotherapies of an inhibitor of the Wnt/β-catenin compared to the high-SNRPB and SNRPG group. Therefore, patients with HCC with high expressions of SNRPB or SNRPG may benefit more from the combination of chemotherapy and immunotherapy.

There are some limitations to this study. The specific mechanism by which the SNRPB/SNRPG axis regulates the Wnt pathway has not been fully elucidated, which requires further experiments in vitro and in vivo. The clinical application of SNRPB and SNRPG as biomarkers of HCC needs to be studied comprehensively and deeply; further clinical studies are needed.

In summary, based on current research results, we demonstrated that SNRPG plays a crucial role in HCC progression by the Wnt/β-catenin signaling pathway through its interaction with SNRPB. Also, SNRPG recovered cell growth deficiencies caused by SNRPB knocking down in proliferation. The proposed double-genes model represents promising novel signatures for the diagnosis and prognosis prediction of HCC. The findings also revealed an important role of this model in predicting clinical outcomes. This comprehensive assessment of the double-gene model in HCC provides insights into patients with different expressions of SNRPG respond to chemotherapy/immunotherapy and may help clinicians explore the development of new drugs. Our study highlights the potential of SNRPG as a promising therapeutic target for the treatment of HCC.

Patient samples

All experiments in human subjects were conducted in accordance with the Helsinki Declaration and approved by the Ethics Committee of the First Medical Center, Chinese PLA General Hospital. 70 cases of fresh tissue samples, HCC tissues, and adjacent nontumor tissues were obtained from HCC patients who underwent surgical excision at the First Medical Centre, Chinese PLA General Hospital and written informed consents were acquired from each participating patient. The clinicopathological features of all HCC patients were collected for Kaplan-Meier (K-M) survival analyses.

Immunohistochemistry (IHC)

Paraffin-embedded, formalin-fixed HCC and adjacent non-tumor tissue sections were deparaffinized and rehydrated. The sections were pre-treated using pressure cooker heat mediated antigen retrieval with sodium citrate buffer (pH = 6) for 30 mins, and incubated overnight at 4°C with the rabbit polyclonal anti-SNRPG or SNRPB antibody. IHC Select® HRP/DAB kits (Cat. DAB50, Millipore) were used to detect indicated antigens according to the manufacturers’ instructions. The section was counterstained with hematoxylin and observed under a microscope. Nucleus immunoreactivity for SNRPB and SNRPG proteins was scored by evaluating the sum of percentage and staining intensity scores, ranging from 0 to 8. Sections were scored by pathologists with no knowledge of the ligand-binding results or patient outcomes.

Cell culture

Human HCC cell lines (Hep3B and SK-HEP-1) and the human embryonic kidney cell lines 293T (293T) were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Cells were cultured in Dulbecco’s modified Eagle medium (DMEM), supplemented with 10% fetal bovine serum, 100U/ml penicillin, and 100 µg/ml streptomycin, at 37 ° C in a 5% CO₂ atmosphere. Cells were tested regularly for mycoplasma.

Cell transfections

Hep3B and SK-HEP-1 cells were transduced with short hairpin RNA (shRNA) against SNRPG (shSNRPG) or SNRPB (shSNRPB) and the corresponding negative control NC (shCtrl), pcDNA3.1-SNRPG and empty pcDNA3.1 vector. Cell transfections after 48 h were routinely performed using Lipofectamine 2000 (Invitrogen).

Cell proliferation

The cell proliferation assay was performed by counting the number of cells. Briefly, transfected HCC cells were seeded in 96-well plates (1000 cells/well) at day 0. After cell adherence, the cell number change was then counted at indicated time points using the cell counting kit-8 (CCK-8) (Dojindo Corp. Japan) every day for 5 days according to the manufacturer’s protocol. The number of cells per well was measured by absorbance (450 nm).

Cell cycle assay

Transfected cells were collected after trypsinization into single cells and washed with ice-cold PBS. Then, cells were fixed with 75% cold ethanol overnight at 4°C. Cells were resuspended in a staining buffer [10 µg/mL propidium iodide (PI) and 10 µL RNase in 500 µL PBS] and incubated in the dark for 30 mins at 37°C. Cells stained were examined using a fluorescence-activated cell sorter (FACS), and cell cycle status was subjected to flow cytometer analyzes.

Co-immunoprecipitation Assay (co-IP)

Cells were harvested and washed with cold PBS, followed by lysing with Triton-lysis buffer. Whole cell lysates were centrifugated at 12,000×g for 10 min at 4 ° C, then supernatant added with protein A / G sepharose beads (Santa Cruz) and incubated with the indicated antibody overnight at 4 ° C. After washing with lysis buffer three times, the immunoprecipitants were separated by SDS-PAGE. To detect the interaction of endogenous proteins in 293T cells, the cell lysate was incubated with anti-SNRPB, anti-SNRPG, or negative control IgG along with the G-sepharose protein (Santa Cruz Biotechnology). Western blot was performed following standard procedures.

RT-PCR analysis

Total RNA from cells was extracted with TRIZOL reagent (Invitrogen) according to the manufacturer's instructions. Complementary DNA (cDNA) was synthesized using the PrimeScript™ reverse transcription Master Mix Reagent (Takara) following the manufacturer’s protocol. Real-time PCR (qRT-PCR) was performed for every cDNA with SYBR Green mix (Takara) using the Step-One-Plus Real-Time PCR system. Quantification was normalized using GAPDH as the internal control. Primers used for gene expression analysis were from PrimerBank. Primer sequences are listed as follows: SNRPB

F: 5’-CCGGATCTTCATTGGCACCT-3’, R: 5’-AGGACTCGCTTCTCTTCCCT-3’. Data were evaluated by the 2^−ΔΔCT method.

Western blot analysis

HCC tissue homogenates or cell lysates were lysed in ice cold RIPA lysis buffer (Cell Signaling Technology) with a 1% protease inhibitor cocktail (Roche) and 1 mM phenylmethylsulfonyl fluoride (Roche). The protein concentration was detected using the BSA kit. An equal amount of proteins was subjected to 1 × sodium dodecyl sulfate buffer, separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and then transferred to polyvinylidene difluoride membranes. The membranes were blocked with 5% non-fat milk for 2 h at room temperature and then incubated with the corresponding primary antibodies overnight at 4° C. After washing five times with TBST for 8 min each time, the membranes were incubated for 2 hours at room temperature with horseradish peroxidase (HRP)-conjugated secondary antibodies followed by exposure to enhanced chemiluminescence (ECL) reagents (Pierce, Rockford, IL).

Bioinformatics analyses

In the present study, the edgeR package was used to analyze the raw gene expression data from the TCGA database (http://cancergenome.nih.gov/). The gene expression levels and clinical data of HCC patients in the TCGA-LIHC database were obtained from the GEPIA2 website (http://gepia.cancer-pku.cn/). GEPIA2 was used to determine the level of the model in various types of human cancer based on TCGA and Genotype-Tissue Expression (GTEx) data, and a p value < 0.05 was considered statistically significant. The tissue / organ specificity of SNRPB and SNRPG was recognized by the online tool BioGPS (http://biogps.org/) to analyze their tissue distributions [24]. The screening criteria were as follows: 1) transcripts mapped to a single organ system with an expression value of > 10 multiples of the median, and 2) second-most abundant tissue expression was no more than a third as high [25]. To understand the cell functions and pathways affected by SNRPB and SNRPG expressions in HCC, we performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment through gene set variation analysis (GSVA). The largest publicly available pharmacogenomics database [Genomics of Drug Sensitivity in Cancer (GDSC) (https://www.cancerrxgene.org/)][26] was used to analyze the correlations between SNRPB/SNRPG expressions and the chemotherapeutic response of XAV939 (a Tankyrase inhibitor that targets the Wnt/β-catenin pathway) for HCC. Tumor Immune Dysfunction and Exclusion (TIDE) algorithms and subclass mapping were also used to predict the clinical response to immune checkpoint blockade (ICB) [27]. High TIDE score, poor efficacy of ICB therapy, short survival after ICB treatment.

Prognosis nomograms for the double-genes signature model in HCC

After the double gene prognostic signature model was established using SNRPB and SNRPG, the Receiver Operating Characteristic (ROC) curve was drawn to evaluate its predictive effectiveness. The prognostic ability of the model for overall survival (OS) in TCGA or OS in a validated set was evaluated using ROC curves and the area under the curve (AUC) values. We incorporated this model and clinical indicator to optimize its predictive power. A nomogram was established to assess the relationship between model, age, sex, tumor stage, and the prognosis of HCC patients. The performance of the nomogram was assessed using the calibration curve plots, which showed the predictive prognostic accuracy of the nomograms.

Statistical analysis

All data were analyzed with GraphPad Prism 8 or R version 4.0.3 and presented as the mean ± S.D. The correlation between SNRPB levels and SNRPG was assessed by the Pearson correlation test. A Cox partial proportional hazard regression model was performed for the univariate analyzes. The survival rate was plotted using the Kaplan-Meier method and analyzed using the log-rank test. Student’s t test or one-way ANOVA followed by Tukey’s multiple comparison test was applied to compare statistical analyzes between the data pairs where appropriate. p values of < 0.05 were considered statistically significant.

HCC: Hepatocellular carcinoma

IHC: Immunohistochemistry

CCK-8: Cell Counting Kit-8

Co-IP: Co-immunoprecipitation

ICB: Immune checkpoint blocking

SNRNP: Small nuclear ribonucleoprotein

SNRPG: Small nuclear ribonucleoprotein polypeptide G

PPI: Protein-protein interactions

K-M: Kaplan-Meier

shRNA: short hairpin RNA

cDNA: Complementary DNA

SDS-PAGE: Sodium dodecyl sulfate-polyacrylamide gel electrophoresis

KEGG: Kyoto Encyclopaedia of Genes and Genomes

GSVA: Gene Set Variation Analysis

GDSC: Genomics of Drug Sensitivity in Cancer

TIDE: Tumor Immune Dysfunction and Exclusion

ROC: Receiver Operating Characteristic

OS: Overall survival

AUC: Area under curve

EMT: Epithelial-mesenchymal transition

CTL: Cytotoxic T lymphocytes

Availability of data and materials

The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Funding

Not applicable.

Competing Interests

The authors declare that they have no competing interests.

Ethical approval and consent to participate

Study protocols were approved by the First Medical Center Hospital Ethics Committee, Chinese PLA General Hospital, and written informed consent was obtained from patients based on the Declaration of Helsinki.
Consent for publication

Not applicable.

Acknowledgements

Not applicable.

Authors' information

Authors and Affiliations

Faculty of Hepato-Pancreato-Biliary Surgery, First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, China

Zhipeng Zhou & Yang-Liu

Zhejiang University School of Medicine First Affiliated Hospital Beilun Branch, Ningbo, Zhejiang, 315800, China

Yunsheng Qin

Department of Hepatobiliary and Pancreatic Surgery, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, No.600 Yishan Road, Shanghai, 200233, China

Xiongzhi WangPu,

Department of Infectious Disease & Liver Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China

Tongdong Shi

Department of Liver Surgery and Transplantation, Fudan University Laboratory of Liver Cancer Institute, Zhongshan Hospital, Shanghai, 200032, China

Jian Sun

Authors' Contributions

Yang-Liu, Jian Sun, and Tongdong Shi conceived and designed the study. Yunsheng Qin and Zhipeng-Zhou performed the experiments. Yunsheng Qin and Xiongzhi WangPu analyzed and interpreted the data. Zhipeng-Zhou contributed the materials. Yunsheng Qin wrote the manuscript. Yang-Liu reviewed and edited the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Tongdong Shi, Jian Sun &Yang-Liu

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You are reading this latest preprint version

SNRPG serves as a novel predictor of prognosis and tumor development for hepatocellular carcinoma by activating Wnt/β-catenin signaling

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Background

Results

Pathway’s enrichment analysis

SNRPG is regulated by SNRPB through the Wnt/β-catenin signaling pathway in HCC

Construction and evaluation of double-gene prognostic signature model

Discussion

Conclusions

Materials and methods

Patient samples

Immunohistochemistry (IHC)

Cell culture

Cell transfections

Cell proliferation

Cell cycle assay

Co-immunoprecipitation Assay (co-IP)

RT-PCR analysis

Western blot analysis

Bioinformatics analyses

Prognosis nomograms for the double-genes signature model in HCC

Statistical analysis

Abbreviations

Declarations

References

Status:

Version 1