Introduction
Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide and poses a serious public health concern. It is estimated that 905,677 new cases of hepatocellular carcinoma were diagnosed in 2020, and 830,180 died from the disease [
1]. HCC has become the sixth most common malignancy and also the third leading cause of cancer-related deaths worldwide [
2]. There is no doubt that surgical treatment of HCC is crucial for long-term survival. However, because HCC develops insidiously, over 80% of patients with HCC are first diagnosed at the late stages and have missed the opportunity to receive radical surgery to cure the disease [
3]. In addition, the recurrence rate of HCC is high, with a 70% rate of recurrence within 5 years [
4]. There are still limited clinical options available for delaying or prolonging tumor progression, despite research into the biological and environmental mechanisms that underlie liver cancer occurrence and progression. Therefore, there is an urgent need to identify predictive biomarkers of diagnosis and treatment.
Several studies have demonstrated that mitochondria play a key role in apoptosis, metabolism and that their dysfunction may contribute to cancer development [
5,
6]. The mitochondrial DNA (mtDNA) of a human cell is 16,569 bp long and contains 37 genes and codes for 13 proteins involved in cell energy metabolism [
7,
8]. Due to the ability of mitochondria to adapt quickly to environmental cues, they are one of the main mediators of tumorigenesis. During the last two decades, numerous mtDNA mutations have been reported in cancers of various types, including renal adenocarcinomas, colon cancers, head and neck tumors, and ovarian cancers [
9,
10]. Throughout the mitochondrial matrix, mitochondrial ribosomes translate 13 mtDNA-encoded proteins, all of which are mitochondrial respiratory chain enzymes. The oxidative phosphorylation (OXPHOS) system is mediated by mitochondrial ribosomes, which synthesize mtDNA-encoded subunits. Research has shown that mitochondrial ribosomal proteins (MRPS), which are composed of small and large subunits, play a leading role in these processes [
11]. MRPS abnormal expression may be one of the factors involved in mitochondrial dysfunction, which then causes mitochondrial disease and even cancer. In ovarian cancer, a total of 10 MRPLs and 11 MRPSs showed significant prognostic significance, with MRPL49 proving to be the most predictive [
12,
13]. In breast cancer, downregulating MRPS23 expression can reduce cell proliferation and increase apoptosis [
14], while the overexpression of both MRPL13 and MRPS30 are associated with poorer survival [
15,
16]. The MRPS have also been reported to be active in HCC, and it has been shown that high levels of MRPL13 promote the invasion of liver cancer cells [
17]. In summary, the MRPS may play a crucial role in the progression of tumors.
MRPL48 belongs to the MRPS family, which is involved in the formation of ATP and fuels the growth of cells. Studies have shown that MRPL48 is of predictive value for the occurrence and prognosis of osteosarcoma, as well as promotes the growth of colorectal cancer cells [
18,
19]. However, MRPL48 is not yet well understood in terms of its prognosis and regulatory mechanisms in HCC. Furthermore, immunological analysis of the tumor microenvironment appears to indicate the prognosis of HCC patients, as well as evidence of the benefit of immunotherapy. In contrast, little research has been conducted to demonstrate a link between MRPS and immune infiltration patterns.
In this study, we first evaluated the prognostic significance of the expression of MRPL48 mRNA and methylation in patients with HCC based on data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Secondly, survival predictions based on the expression of MRPL48 mRNA in HCC were validated using the ICGC LIRI-JP datasets (HCCDB18). In addition, we performed GSEA analysis to further understand the biological involvement of MRPL48 in HCC pathogenesis. Finally, we conducted in vitro functional experiments using cells with MRPL48 knockdown and evaluated MRPL48 expression in HCC cell lines. In summary, the findings of this study provide insights into the clinical significance, potential functions, interactive network, and association of MRPL48 with immune infiltration in HCC, providing a novel prognostic biomarker for predicting the survival and targeting targeted treatment of HCC during its early stages.
Materials and methods
Repository of data
Liver hepatocellular carcinoma (LIHC) gene expression profiles were obtained from The Cancer Genome Atlas (TCGA), at level 3 of expression (level 3 data) (
https://cancergenome.nih.go). This study included 374 samples from the LIHC tissues and 50 samples from paracancerous tissue (Workflow Type: HTSeq-FPKM). Then, we converted the HTSeq-FPKM values into transcript per million (TPM) values to compare the differential expression among samples. The TCGA data portal was also used to obtain relevant clinical information of the HCC patients. For the pan-cancer analysis, normal RNASeq data were collected from 33 types of tumors derived from TCGA and Genotype-Tissue Expression (GTEx) samples using UCSC Xena (
https://xenabrowser.net/). Meanwhile, this study relied on R to obtain MRPL48 mRNA expression and clinical data from GSE121248, GSE164760, and GSE17967 data in the GEO databases.
Differentially expressed genes (DEGs)
The data were extracted from TCGA-LIHC and divided into high and low MRPL48 expression groups, and the original counts matrix was differently analyzed using the DESeq2 [1.36.0] package [
20]. The threshold of DEGs were determined using a | log2-fold change|> 1.0 and adjusted
p < 0.05. The differential analysis results were visualized using the ggplot2 package in R (version 4.2.1).
Enrichment analysis of gene ontology (GO)
MRPL48 and its relatives DEGs (|log2-fold change|> 1.5 and adjusted
P < 0.01) were enriched to obtain GO annotations. GO enrichment analysis was conducted using the clusterProfiler (4.4.4) package in R (version 4.2.1) [
21].
Statistical analysis of gene set enrichment
For the Gene Set Enrichment Analysis (GSEA), an analytical method was used to determine whether the two phenotypes were statistically different in the presence of a previously defined set of genes. This study used the R package (version 4.2.1), clusterProfiler (4.4.4), for GSEA [
21]. The purpose of this study was to determine whether the high and low MRPL48 expression groups displayed significant differences in function and pathways. To perform each analysis, 500 gene set permutations were performed. A phenotyping label was established by measuring the MRPL48 mRNA expression levels. Reference genes were chosen from the MSigDB Collections c2.cp.all.v2022.1.Hs.symbols.gmt [All Canonical Pathways]. Significant enrichment was defined as an adjusted p value of 0.05, a false discovery rate (FDR) value of 0.25, and a normalized enrichment score (NES) of more than 1.
MRPL48 expression is correlated with immune infiltration
By integrating genes in the published signature gene lists [
22], we quantified the relative tumor infiltration levels of immune cell types using the ssGSEA (single-sample Gene Set Enrichment Analysis) method in the GSVA package (1.46.0) [
23]. We applied the Wilcoxon rank-sum test to examine the abundance of immune cells in different MRPL48 mRNA expression groups. A correlation between immune cell infiltration and MRPL48 mRNA expression levels was determined in TCGA HCC samples.
Methylation levels and prognoses based on MRPL48 expression
To obtain MRPL48 copy number variation (CNV) and methylation level data, we accessed the web platform, cBioPortal (
https://www.cbioportal.org/). We further examined MRPL48 gene expression variation between MRPL48 copy number variation groups (Kruskal–Wallis test) and the relationship between MRPL48 methylation level and MRPL48 gene expression (Person correlation). In addition, we analyzed and compared MRPL48 methylation levels in pan-cancer tissues and normal tissues using the SMART web platform (
http://www.bioinfo-zs.com/smartapp/). At the same time, we used the UALCAN online tool (
http://ualcan.path. uab.edu/) to analyze TCGA data on MRPL48 promoter methylation level differences between HCC and normal tissues. Finally, we analyzed HCC methylation status (TCGA data) using MethSurv online tool (
https://biit.cs.ut.ee/methsurv/) to determine its prognostic value.
Generating and predicting predictive models
Firstly, Akaike's information criterion (AIC) method and multivariate Cox regression analysis were used to determine the optimal prognostic model. Secondly, the R software package, rms, was used to construct a nomogram to predict prognosis. Meanwhile, based on median risk scores, patients were categorized into high- and low-risk groups. The Kaplan–Meier and a two-sided log-rank test were used to determine the OS differences between the two groups. Finally, receiver operating characteristic (ROC) curves were constructed to calculate the prediction accuracy of the prognostic model intensity.
Culturing and transfecting cells
Immortalized liver (MIHA) and human HCC (Hep-G2, SNU-387, and Huh-7) cell lines were obtained from the American Type Culture Collection (ATCC) and China Cell Bank. The cells were cultured in DMEM medium (Gibco, Grand Island, USA) containing 10% fetal bovine serum (Gibco, Grand Island, USA). The cells were cultured in an incubator with 5% carbon dioxide at 37 °C. MRPL48 siRNA sequences (5′-GCAACTCTCTCTCCATTAAAG-3′and 5′-ACTTCAAGGGACGATTCAAAG-3′). Cells with MRPL48 knockdown were collected 72 h after transfection.
A quantitative real-time PCR approach based on RNA extraction
Cells from human HCC cell lines were lysed in TRIzol reagent (Life Technologies, USA) to obtain total RNA. A RevertAid TM First Strand cDNA Synthesis Kit (Life Technologies, USA) was used to reverse transcribe the RNA. The following primers were used: MRPL48 forward 5′-TCGGTTTGCACAGCTAGAGG-3′ and reverse 5′-GGCACAGCACCTTTTCCAAG-3′. An internal control gene, GAPDH, was used to normalize transcriptional levels.
Western blotting analysis
The protein concentrations of the lysed cells were measured using bicinchoninic acid assay. The cells were washed with an ice-cold PBS solution and lysed in a 100 mL RIPA buffer containing 100 mM PMSF on ice. The protein lysates were further separated on 10% polyacrylamide gels (Invitrogen), and then transferred onto PVDF (polyvinylidene fluoride) membranes. A 5% skim milk solution in PBS containing 0.1% Tween 20 was used to block the membranes for 2.5 h. Then, the membranes were incubated overnight with antibodies targeting anti-b-Tubulin (1:1000, Proteintech, Chicago, IL, USA) and anti-MRPL48 (1:1000, Proteintech). Protein–antibody complexes were detected using chemiluminescence (Life Technologies, USA) and recorded on Hyperfine-ECI detection films after conjugating with the corresponding HRP-coupled secondary antibodies.
Cell proliferation, invasion, and migration assays
According to the manufacturer's instructions, a CCK-8 kit (Cell Signal Technology, New York, USA) was used for cell proliferation testing. And 5 × 103 cells were inoculated into each well of a 96-well plate, and 200 μL of fresh culture medium containing 10 μL of CCK-8 reagent was added into them at 24, 48, 36, and 72 h after transfection, and incubated at 37 °C for 4 h. OD was measured on a 450 nm tablet reader (Bio Rad, Harkles, CA, USA). In the colony formation assay, 3000 cancer cells were inoculated into a 6-well plate and the culture medium was changed every other day. The colonies were immobilized with formaldehyde and stained with crystal violet. The cell migration ability was investigated using wound healing assays. The wounds were made using a 200 mL pipette tip at 90 percent confluency of the transfected cells. After incubation in serum-free medium for 72 h, the migration distance of the cells was calculated.
Statistical analysis
To analyze statistical differences, t-tests or one-way ANOVA were performed. Kaplan–Meier analysis was used to assess the survival rate of the patients. Moreover, the log-rank test was used to evaluate survival differences. MRPL48 expression level and other clinical parameters associated with OS and DSS in patients with HCC were evaluated via univariate and multivariate Cox analyses to evaluate their independent prognostic significance. ROC curves (AUC) were established to evaluate the diagnostic significance of MRPL48 expression using the pROC package in R software. Generally, AUCs > 0.7 indicate good accuracy, while AUCs 0.5–0.7 indicate weak accuracy. We set up a nomogram to predict the OS of HCC patients based on MRPL48 expression and other clinical parameters. A p value of 0.05 was considered statistically significant. Means ± standard deviations were presented for continuous data.
Discussion
During recent years, methods of surveillance and diagnosis of HCC have evolved. Novel biomarkers have been identified and existing surveillance tools have been repurposed [
26]. However, the prevention and treatment of HCC remains a challenge in China. According to the latest estimates, the death rate due to HCC reached 4.12 million, accounting for 49.6% of the global total, respectively, in China in 2022 [
27]. China has become the country with the highest incidence of HCC and the largest number of related deaths [
27]. As far as HCC is concerned, it is crucial that it is detected and diagnosed as early as possible. Relevant studies have shown that the 5-year survival rate after radical surgery for small HCC (D≤5 cm) can reach 60–70% [
28], while the 5-year survival rate for overall HCC is 18.4% [
29]. Therefore, identifying markers that can predict and diagnose HCC occurrence and progression as early as possible has been a main focus.
The genetic and metabolic basis of most cancers are being unraveled by advances in tumor and cancer cell genomics and proteomics. Several studies indicated that cancer cells rely heavily on mitochondrial OXPHOS to provide energy, and mitochondrial ribosomes are crucial regulators of the OXPHOS system [
6]. Therefore, mitochondria ribosomal biogenesis has been a critical cellular process involved during the progression of neoplastic transformation. Some progress has been achieved in determining the role of MRPS in HCC occurrence and development. It has been shown that the high levels of MRPL13 promote invasion of liver cancer cells [
17]. A study by Tang found that MRPL9 is of a positive prognostic value for HCC and that the knockdown of MRPL9 and SMG5 significantly inhibits cell proliferation and migration in HCC [
30]. Some researchers have found that high MRPS23 levels can predict a poor outcome in HCC, and this protein plays an important role in tumor progression [
31]. Others have demonstrated that MRPS31 loss in conjunction with the upregulation of COL1A1/DDR can be used to develop a diagnostic marker for HCC [
32]. In contrast, MRPL48 does not appear to have been previously reported on in HCC.
As the first step, we determined MRPL48 transcription levels in different types of cancer based on independent datasets of different sources (TCGA and GTEx, respectively). These research findings show that MRPL48 is highly expressed in a wide range of tumors. The expression of MRPL48 has been reported to be elevated in several types of cancer, including osteosarcomas and colorectal cancers. Zhang [
18] used the WGC-NA algorithm and LASSO, PPI, and MOCDE methods to verify that high MRPL48 expression is associated with the poor prognosis of osteosarcoma patients. HuTT [
19] reported that knockdown of MRPL48 expression using the CRISPR-Cas 9 technique could significantly increase the sensitivity of colorectal cancer cells to cetuximab. According to these findings, MRPL48 may be a factor involved in tumorigenesis. Secondly, we found that the HCC specimens had significantly high levels of MRPL48 transcription and protein, compared with the normal tissue samples. According to the current study, HCC samples from a variety of databases including HBV and NASH-related HCC samples showed higher transcriptional levels of MRPL48 than non-cancerous specimens. As was known all, hepatitis B, non-alcoholic fatty liver disease, and cirrhosis were considered as an important risk factor for the development of HCC. These findings favor our follow-up with more targeted studies. Meanwhile, a significant difference was also found between the expression of MRPL48 protein in the HCC tissues and that of normal liver tissues, as analyzed using the CPTAC database. As a follow-up to our bioinformatics analysis, we performed qRT-PCR to quantify the relative MRPL48 expression levels and our results are consistent with our bioinformatics analysis.
Furthermore, we examined the association between MRPL48 expression and clinical characteristics of patients with HCC and determined that MRPL48 expression was correlated with T classification, pathological tumor grade, AFP level, and vascular invasion. As determined by the Kaplan–Meier test, patients with HCC with high MRPL48 expression displayed unfavorable OS, DSS, and PFI prognosis. A multivariate and univariate regression analysis showed that the elevated expression of MRPL48 is an independent adverse prognostic factor in HCC. Currently, MRPL48 expression has not been reported in a prediction profile for HCC. However, our ROC curves indicate that MRPL48 expression is significant in predicting HCC. In addition, we integrated various clinical parameters into the TCGA dataset to construct a nomogram, which could be used to predict the mortality risk of individual patients and optimize their treatment. In addition, we studied the mechanisms of MRPL48 mRNA overexpression in HCC, and the results of our study suggest that MRPL48 mRNA overexpression may be associated with MRPL48 hypomethylation. Interestingly, the methylation of MRPL48 negatively affects the prognosis of HCC. HCC patients with hypomethylation have a shorter OS, which is consistent with the mRNA expression of this gene being associated with poorer survival. In spite of the fact that a wide variety of mechanisms can cause elevated gene expression, hypomethylation is one of the most important factors that contribute to it.
For a deeper understanding of how MRPL48 expression leads to abnormal changes in the downstream pathways in HCC, we analyzed DEGs in the high- and low-expressing MRPL48 in HCC patients. In the GO and KEGG enrichment analyses, the above DEGs were mostly involved in receptor ligand activity, signaling receptor activator activity, and copper metabolism. The results of the GSEA analysis indicated that the DEGs exhibited significant enrichment in mitosis, Cell Cycle Checkpoints, and cancer pathways. It has been found that all the pathways enriched above are significantly associated with the occurrence and progression of malignant tumors. A growing body of research has suggested that immune cell infiltration influences cancer development and progression [
33]. In turn, this adversely affects the effectiveness of immunotherapy and the prognosis of patients. Moreover, another important finding of this study is that MRPL48 mRNA levels are associated with immune cell infiltration in HCC. MRPL48 expression is significantly correlated with the number of NK CD56 bright cells, and especially Th2 cells. Normally, the Th1/Th2 ratio of the body maintains a dynamic balance between Th1 cells and Th2 cells [
34]. The release of Th2 cytokines in malignant tumors results in a Th1/Th2 imbalance caused by the Th1/Th2 drift [
35]. The Th1/Th2 balance is tipped in many tumors, such as lung cancer, liver cancer, and gastric cancer, and is frequently led by Th2 dominance in the body. As a result, this may be an indication that the tumor has escaped the immune system [
36]. In keeping with the above mentioned information, we observed that transcript levels of MRPL48 showed a significant correlation with Th2 cell infiltration in HCC.
Additionally, we assessed the effects of MRPL48 expression on the malignant phenotype of HCC cells in vitro. The knockdown of MRPL48 dramatically impaired Hep-G2 and SNU-387 cell proliferation, migration, and invasion. Therefore, these findings demonstrate that MRPL48 may play a crucial role in facilitating HCC cell development.
This study concluded that MRPL48 plays a significant role in HCC occurrence and development, but with some limitations. First, MRPL48 functional assessments were conducted based on an in vitro model and were not confirmed in vivo, which needs to be further explored in future studies. Second, MRPL48 expression and its prognostic value ought to be evaluated in clinical samples, since the use of publicly available datasets may lead to some inaccuracies. Finally, despite showing that MRPL48 regulates the cell cycle and influences immune infiltration, the exact biological mechanisms and signaling networks involved have not been investigated. We will perform future research to uncover the mechanism of action of MRPL48 expression in HCC.
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