Exploring the Clinical Implications of RPL3 Presence in BRCA-Associated Cancers: Unraveling the Interplay With Cancer Immunity

Abstract

Background:

Few studies have explored the expression profile of RPL3 in breast cancer (BRCA). Our research provided an in-depth analysis of RPL3 expression patterns in BRCA, highlighting its significance for therapy prediction and prognosis.

Methods:

RPL3 was notably elevated in malignant cells, and its expression level was closely associated with tumor size and overall survival outcomes. Our study also identified RPL3-related terms and pathways and revealed a strong correlation between RPL3 expression and breast carcinoma immunity, demonstrating inconsistent expression levels in various immune cell lines. In addition, we examined the relationship between RPL3 expression and tumor mutational burden (TMB) in BRCA. To assess the clinical implications of BRCA chemotherapy, we investigated the correlation between RPL3 expression levels and drug sensitivity.

Results:

Our findings suggest that RPL3 plays a critical role in the BRCA process and is associated with immune infiltration, indicating its potential as a novel immunotherapy target in BRCA treatment.

Conclusions:

In summary, our research underscores the importance of RPL3 expression levels in tumorigenesis and its potential for guiding BRCA immunotherapy.

Keywords

RPL3 breast cancer immune therapy immune infiltration tumor

Introduction

The BRCA has surpassed lung carcinoma as the most common cause of cancer and the fifth cause of death globally.¹ There are various therapies used in BRCA management, including surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy.^2,3 Nevertheless, tumor management is not always adequate. Approximately 2.3 million patients were newly diagnosed, and nearly 68 000 died with these therapies in 2020. The 5-year survival rate in metastatic BRCA is less than 30%.¹

Among various treatments, immunotherapy—particularly immune checkpoint inhibitors—has garnered significant attention due to its high safety profile and favorable therapeutic effects. However, the effectiveness of current immunotherapies is limited to certain patient populations, primarily due to the scarcity of therapeutic targets. Consequently, there is an urgent need to identify novel immunotherapy targets to benefit a broader range of BRCA patients.

Ribosomal proteins (RPs), located in the cytoplasm, play a crucial role in the transfer of genetic information. As key components of protein synthesis, RPs not only regulate transcriptional and translational processes but also participate in various biological mechanisms.⁴ The RPL3, a subset of RPs, is confirmed to show a substantial part in cancer progress and chemotherapy sensitivity.^5,6 Earlier reports have discovered that both hematopoietic and non-hematopoietic carcinoma cell lines with increased RPL3 expression reveal greater sensitivity to drugs,⁵ whereas the loss of RPL3 creates chemotherapy medications unproductive.⁶ Associated with the adjacent normal tissues, the expression of RPL3 in colorectal carcinoma tissues is downregulated.⁷ In addition, it was discovered that RPL3 can regulate the capability of metastasis and invasion of colorectal cancer cells.⁸

Single-cell RNA sequencing (scRNA-seq) technologies prepare a chance to investigate cellular constituents in the tumor microenvironment (TME) and the functions they show in the tumor incidence and progress.⁹ Unlike population-level sequencing, scRNA-seq focuses on the TME and intratumor heterogeneity (ITH). This approach differentiates between the molecular profiles of various cell types within the TME, including tumor cells, stromal cells, and immune cells.¹⁰ The ITH of diverse cells and communications among these multicellular constituents can be additionally explored as well.¹¹

In this study, we examined the association between RPL3 expression levels and breast cancer (BRCA) development. We analyzed RPL3 expression concerning tumor infiltration, tumor immunity, and tumor mutational burden (TMB). Gene set enrichment analysis (GSEA) was conducted to identify the principal mechanisms involved. Our findings provided evidence of the role of RPL3 in BRCA, elucidating its impact on tumor immunity and clarifying potential underlying mechanisms. In addition, we assessed the sensitivity of chemotherapy drugs to RPL3 expression levels.

Materials and Methods

Patient data sets and processes

The original data were taken from GENE EXPRESSION OMNIBUS (GEO) data sets (https://www.ncbi.nlm.nih.gov/gds/), including initial breast tumor samples (GSE176078) and normal breast samples (GSE180878). We only used the open-access data in this study, so the condition of authorization from the Ethics Committee could be excluded.

Preprocessing and dimension reduction of single-cell transcriptome data

Full-length scRNA-seq was used to generate the transcriptome data in a single batch. We removed cells expressing either lower than 200 or higher than 2500 genes. Cells with mitochondria and hemoglobin content higher than 5% were also excluded.

To envisage the association between individual cells based on high-dimensional gene expression data, we ran Uniform Manifold Approximation and Projection (UMAP) for dimension reduction and performed clustering analysis. The major cell types were isolated and annotated centered on the marker gene expression by the singleR package.

RPL3 and tumor immunity

The tumor immunity estimation resource (TIMER, https://cistrome.shinyapps.io/timer/) is one of the common methods of the immune infiltration level of malignancy for the systematic study.¹² In TIMER, a statistical deconvolution method is employed to regulate the level of tumor-infiltrating immune cells based on gene expression data.¹³ Using the TIMER algorithm, we explored the involvement between RPL3 levels and the infiltration levels of 10 different immune cell types (T cells, B cells, epithelial cells, monocytes, fibroblasts, endothelial cells, tissue stem cells, chondrocytes, and mesenchymal stem cell [MSC]).

Tumor mutational burden is a method to evaluate the number of mutations in a specific cancer genome. Amounts of research indicated that TMB could be a potential biomarker for immune checkpoint inhibitor sensitivity.¹⁴ We obtained the somatic alteration data of GEO samples, analyzed the TMB scores, and regulated the involvement among TMB and RPL3 expression levels.

Differential expression of RPL3 and screening of cancer survival outcomes

We obtained RPL3 gene expression data from GEO data sets to compare expression levels between various cancer types and corresponding normal samples. To investigate the relationship between RPL3 expression levels and patient overall survival (OS) outcomes, we conducted a Kaplan-Meier (KM) analysis. The log-rank test was performed to assess the correlation between RPL3 expression levels and tumor size grade in BRCA. Statistical significance was determined at P-values less than .05 (*), .01 (**), .001(***), and .0001 (****).

Gene set enrichment analysis

We performed an additional analysis to refine the gene set using the GSEA software version 2.2.1, accessible at http://www.broadinstitute.org/gsea/index.jsp. By using 100 random sample permutations and setting the significance threshold at P < .05, we employed the R software (http://rproject.org/) and Bioconductor (http://bioconductor.org/) to visualize our findings.

Cell cultures and growth conditions

The MDA-MB-231, BT-549, and MCF-10A cell lines used in this study were obtained from the Shanghai Cell Biology Institute of the Chinese Academy of Sciences in Shanghai, China. The MDA-MB-231 and MCF-7 cells were cultured in Dulbecco’s Modified Eagle’s Medium (Gibco, Grand Island, NY, USA) with 10% fetal bovine serum (FBS) from Gibco. BT-549 cells were grown in Roswell Park Memorial Institute 1640 medium (Gibco) supplemented with 10% FBS. MCF-10A cells were maintained in Dulbecco’s Modified Eagle Medium-F12 (Gibco) with 100 U/mL penicillin, 100 μg/mL streptomycin, 2 mM l-glutamine, 20 ng/mL epidermal growth factor, and 10% FBS from Gibco.

Cell transfection, RNA extraction, and quantitative real-time polymerase chain reaction

MCF-7, MDA-MB-231, and BT-549 cells underwent transfection using the Lipofectamine RNAiMAX transfection reagent from Thermo Fisher Scientific. Approximately 100,000 breast cancer (BC) cells were plated 24 hours before transfection. The downregulation of SYNPO2 was achieved through siRNA, with the specific sequences being as follows: forward 5'-GCCUCCAGAGGAUUGGAAUTT-3' and reverse 5'-AUUCCAAUCCUCUGGAGGCTT-3'. Both siRNAs were provided by GenePharma in Shanghai, People’s Republic of China.

Following the manufacturer’s guidelines (Thermo Fisher Scientific), total RNA extraction was performed using TRIzol reagent, and the quantification of isolated RNA was conducted at 260/280 nm using spectrophotometry from Thermo Fisher Scientific. The RNA samples were stored at −80°C. Real-time reactions were executed and analyzed using the ABI 7500 quantitative polymerase chain reaction (PCR) System (Applied Biosystems, Foster City, CA, USA). The relative mRNA expression was determined using the comparative cycle threshold (CT) (2^−ΔΔCT) method, with GAPDH serving as the endogenous control for data normalization. The primer sequences used were as follows:

RPL3 forward: 5'-ATGAAGCACCAACCGTATC-3' and reverse: 5'-CTGAATTGACCTTGACTGATG-3';

GAPDH forward: 5'-GTCTCCTCTGACTTCAACAGCG-3' and reverse: 5'-ACCACCCTGTTGCTGTAGCCAA-3'.

Chemicals and reagents

Sorafenib, alisertib, buparlisib, and IGF1R_3801 were acquired from Medchem Express in China. The AROS assay kit, Cell Counting Kit-8 (CCK-8), and N-acetyl-l-cysteine (NAC) were obtained from Solarbio Life Sciences in China. An Annexin VeFITC (fluorescein isothiocyanate) apoptosis detection kit was procured from BD Biosciences Pharmingen in San Diego, CA, USA. Mouse polyclonal antibodies targeting B-cell lymphoma 2 (BCL-2) and the BCL-2-associated X (BAX) protein were purchased from Santa Cruz Biotechnology in Dallas, TX, USA.

Cell viability assay

Cell viability was assessed through the use of CCK-8. In 96-well plates, each cell line was seeded at a density of 1×10⁴ cells per well, followed by a 24-hour incubation period. Subsequently, the cells were exposed to varying concentrations of sorafenib, alisertib, buparlisib, and IGF1R_3801 for specified durations, with control cells treated with dimethyl sulfoxide. At each designated time point, 10 μL of CCK-8 reagent was introduced to each well, and the incubation continued at 37℃ for 2 hours. The optical density (OD) was then measured at 450 nm using an ELISA plate reader. Cell viability (%) was computed as (OD experimental – OD blank)/(OD control – OD blank) ×100. The half-maximal inhibitory concentration (IC50) for each drug was determined using the weighted linear regression method.

Results

RPL3 expression level among different cells in BRCA

We applied scRNA-seq analyses to normal breast samples and BRCA samples from GEO data sets healthy and malignant cells were distinguished by characteristic canonical cell markers (Figure 1A). Similar to the observations in previous studies, malignant cells showed higher heterogeneity. Meanwhile, 10 major cell types were also detected and clustered together by different cell types, which were classified as immune cells (T cells, B cells, monocytes), epithelial cells, stromal cells (fibroblasts, chondrocytes, and endothelial cells), and stem cells (tissue stem cells and MSC) (Figure 1B).

Figure 1.

UMAP plot of all cells clustered and color-coded by prevalence situation and samples (A), tissue origin (B), and clusters were assigned to the indicated cell types by differentially expressed genes (DEGs).

Differences in single-cell profiles of normal and tumor cells show the expression level of RPL3 in the major cell types. Compared with normal samples, the expression of RPL3 was lower in immune cells and MSC of BRCA tissue. In contrast, other cell types did the opposite (Figure 2A). We further analyzed the expression of RPL3 in T cells and epithelial cells, which showed the most significant difference between normal and malignant samples (Figure 2B). However, There were statistically significant differences only in T cells, epithelial cells, endothelial cells, and monocytes (P < .05) (Figure 2C).

Figure 2.

RPL3 expression in BRCA. (A) UAMP plot for RPL3 expression levels in different cell lines. (B) RPL3 expression levels in T cells and epithelial cells. (C) Violin plot of RPL3 expression in different cell lines. The blue and red bar graphs indicate normal and tumor tissues, respectively.

RPL3 expression level in cancer

Our analysis showed that the expression levels of RPL3 were upregulated in malignant samples, which showed significant inconsistency from normal samples (Figure 3A). Furthermore, we analyzed the differences in RPL3 expression between normal samples and BRCA cells (Figure 3B).

Figure 3.

RPL3 expression and cancer. (A) RPL3 expression levels in pan-cancer. (B) RPL3 expression levels in BRCA. (C) Kaplan-Meier analyses show the association between RPL3 expression and OS. (D) RPL3 expression in different tumor size grades. The blue and red bar graphs indicate normal and tumor tissues, respectively.

In the OS outcome study, KM regression revealed that higher expression of RPL3 is a protection factor in BRCA (P < .005) (Figure 3C).

The expression of RPL3 was measured to examine whether has a difference in the RPL3 expression across different tumor size grades (Figure 3D). Statistical differences were observed between all pairs of tumor grades except between T1 and T2. The expression level of the RPL3 gene was proportional to the size of the tumor before it invaded the chest wall or skin.

Correlation with the level of biomarkers

To investigate the function of RPL3 in BRCA, we analyzed the relationship between the expression of RPL3 and other genes involved in protein transport and synthesis. The RPL3 expression level was negatively associated with MAP3K2, ANKIB1, XPO1, SEC24A, and SCYL2. In contrast, the expression of RPL3 was positive correlated with MAP2K2 RPS9, RPL13A, RPL10A, RPL7A, RPL11, and RPS8 (Figure 4).

Figure 4.

Correlation with the level of biomarkers. (A) The correlation between RPL3 expression level and SCYL2, SEC24A, RPL11, and MAP2K2. (B) Chord diagram is correlated with further gene expression.

Functional analysis

The impact of RPL3 expression on biological processes was evaluated using GSEA. Increased RPL3 expression levels were associated with the enrichment of various Gene Ontology (GO) terms, including nucleosome assembly, nucleosome organization, protein–DNA complex assembly, chromatin assembly, protein–DNA complex subunit organization, chromatin remodeling, DNA replication-dependent chromatin assembly, DNA replication-dependent chromatin organization, detection of chemical stimuli involved in the sensory perception of bitter taste, sensory perception of bitter taste, DNA packaging complex, nucleosome, protein–DNA complex, nuclear chromosome, endocytic vesicle lumen, male germ cell nucleus, lamellar body, CENP-A containing nucleosome, CENP-A containing chromatin, chromosome, centromeric core domain, receptor ligand activity, signaling receptor activator activity, protein heterodimerization activity, anion transmembrane transporter activity, cytokine activity, nucleosomal DNA binding, structural constituent of muscle, bitter taste receptor activity, immunoglobulin binding, and taste receptor activity (Figure 5A).

Figure 5.

The enrichment results of GO and KEGG pathways. (A and B) The enrichment results of GO and KEGG pathways.

Furthermore, RPL3 expression levels were associated with the enrichment of specific KEGG (Kyoto Encyclopedia of Genes and Genomes) terms, encompassing alcoholism, systemic lupus erythematosus, neutrophil extracellular trap formation, neuroactive ligand-receptor interaction, taste transduction, cardiac muscle contraction, dilated cardiomyopathy, leukocyte transendothelial migration, and renin secretion (Figure 5B).

Correlation with the level of immune infiltration

Through TIMER analysis, we observed a correlation between the expression levels of RPL3 and immune infiltration in breast invasive carcinoma (BRCA). The results indicated a significant positive correlation between RPL3 expression and the infiltration of CD8 + T cells, activated NK cells, T regulatory cells, resting dendritic cells, plasma cells, and monocytes. Conversely, there was a negative correlation with activated dendritic cells, M0 macrophages, M1 macrophages, and M2 macrophages, as well as resting and activated CD4 + T memory cells, neutrophils, and resting NK cells (Figure 6A to C). Subsequently, an analysis of the association between RPL3 expression and immune-related markers in various immunocytes revealed several immune markers that exhibited a correlation with RPL3 expression (Figure 6D and E).

Figure 6.

Correlation with the level of immune infiltration. (A) TIMER predicts that the RPL3 level is related to the degree of immune infiltration within BRCA. (B) The box plot represents RPL3 expression in different immune cell types. (C) RPL3 expression in different immune cell types. (D) Relationship between 17 immune checkpoint genes and the gene expression of RPL3. (E) The heat map represents the relationship between 17 immune checkpoint genes and the gene expression of RPL3. For each pair, the right triangle is colored to represent the P-value; the bottom left o is colored to indicate the Spearman correlation coefficient.

The potential of RPL3 in BRCA treatment

We discovered that the RPL3 expression level was negatively correlated with TMB in BRCA (Figure 7A). Compared with CTLA4 positive cells, the number of CTLA4 negative cells was more affected by RPL3 increased expression level (Figure 7B). The RPL3 expression was higher in tumor cells by immunohistochemistry, but its mRNA was expressed at a lower level in tumor cells (Figure 7C and E). The association between RPL3 expression and several drug sensitivities was analyzed, which indicated that the sensitivity got lower in the RPL3 low expression group (Figure 7D).

Figure 7.

RPL3 potential in BRCA therapy. (A) Correction with RPL3 expression level and TMB. (B) The number of cells in different CTLA4 conditions among low expression group and high expression group. (C) RPL3 expression by immunohistochemistry. (D) Drug sensitivity in low expression group and high expression group. (E) RPL3 mRNA expression in different cells. and (F) RPL3 expression in BC tissue.

The expression of RPL3 in BC tissue is lower than that in adjacent tissue (Figure 7F).

Discussion

With the growth of knowledge and medical capability, therapies of BRCA have been more comprehensive and diversified, including surgery, chemotherapy, radiotherapy, endocrine therapy, immune therapy, and so on.^15,16

The discovery of immunotherapy, especially immune check inhibitors, is a milestone in the treatment of BRCA patients.^17-19 However, the application of immune check inhibitor therapy is restricted with strict terms that cannot improve survival. It is crucial to distinguish tumor-specific targets or features for individualized management to increase the chance of curing cancer patients.²⁰ Therefore, we discussed what role RPL3 plays in BRCA.

We analyzed data sets from GEO, a comprehensive and authoritative repository of cancer sample data. Our findings revealed significant differences in RPL3 expression levels between BRCA and normal breast tissues and various other cancer types. We observed that larger tumor sizes were associated with higher RPL3 expression levels. In addition, high RPL3 expression was correlated with longer OS and was identified as a protective prognostic factor in BRCA through KM survival analyses.

Previous research proved that RPL11 took part in cancer suppression by promoting apoptosis and inhibiting cancer cell proliferation.^21,22 Alternatively, SCYL2 and SEC24A were established to play a significant part in tumor progress and gene mutation.^23-25 In our study, several gene expression levels were associated with the expression levels of RPL3. Among them, RPL3 showed a great positive relevance to RPL11 and strongly negative relevance to SCYL2 and SEC24A, which suggests that RPL3 may make a difference in cancer suppression by protein transfer and translation.

Tumor-infiltrating lymphocytes (TILs) independently predict sensitive lymph node involvement and prognosis.^26,27 By using TIMER analysis, we revealed the association between RPL3 expression levels and the number of infiltrations of immune cells, such as CD8 + T cells, monocytes, and M2 macrophages.

Cancer’s adaptive immune responses are triggered by self-heterologous antigens on tumor cell surfaces, which may originate from specific mutated genes. TMB, representing genetic alterations, has been identified as a contributing factor in cancer development. In addition, TMB levels correlate with the quantity of immunogenic peptides, indicating the strength of immune responses. To some extent, TMB can also predict therapeutic outcomes, especially in immune checkpoint inhibitor therapy.^28-30 Our analysis revealed a negative correlation between RPL3 expression levels and TMB in BRCA. In addition, RPL3 expression was significantly higher in CTLA-4 negative samples. The inverse relationship between RPL3 expression and RPL3 mRNA levels in tumor cells confirmed that RPL3 expression was upregulated in these cells. Furthermore, susceptibility tests suggested that RPL3 might reduce drug sensitivity, corroborating findings from a previous study.⁶ Nevertheless, the molecular mechanism underlying this phenomenon is still under investigation.

Conclusions

To conclude, the expression level of PRL3 is crucial in BRCA. However, further research is necessary to determine if RPL3 can be a predictive biomarker for BRCA immunotherapy. Consequently, the results of this study provide a basis for future exploration of the relationship between RPL3 expression levels and the potential application of immunotherapy in BRCA.

Footnotes

Acknowledgements

The authors thank Dr Adheesh Bhandari and all the doctors in the Department of Breast Surgery at The First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China) for providing all the necessary information required for this study.

Author Contributions

LW wrote the article. ZM and MC collected and analyzed the raw data. HZ helped to revise the article. SX and BX designed the whole work.

Funding:

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests:

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Availability of the Data and Materials

The data sets generated during the current study are available from the corresponding author upon reasonable request.

Consent for Publication

Not applicable.

Ethical Approval

Not applicable. Since this study only used open-access data, the requirement for approval from the Ethics Committee could be waived.

ORCID iDs

Bojian Xie

Shiwen Xu

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