Ribosomal biogenesis factor, a novel biomarker for predicting progression-free survival in prostate cancer

doi:10.21203/rs.3.rs-4899995/v1

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Ribosomal biogenesis factor, a novel biomarker for predicting progression-free survival in prostate cancer

https://doi.org/10.21203/rs.3.rs-4899995/v1

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Background

Prostate cancer (PCa) is the second most common malignancy among men worldwide, with significant variability in incidence rates across different regions. Effective management of PCa is crucial, especially for advanced stages where the survival rates are notably low. Ribosome biogenesis (RB) plays a critical role in cancer cell proliferation, yet the specific function of the ribosomal biogenesis factor (RBIS) gene in PCa remains unexplored..

Methods

RNA sequencing data from the TCGA database and three GEO datasets were analyzed to assess RBIS expression in PCa. Clinicopathological features, survival rates, and drug sensitivity were evaluated in relation to RBIS expression. Gene co-expression and functional enrichment analyses were performed to investigate potential biological mechanisms. Additionally, immune cell infiltration and genetic alterations of RBIS were analyzed.

Results

RBIS expression was significantly elevated in PCa tissues compared to normal tissues. High RBIS expression correlated with adverse clinical outcomes, including advanced tumor stages and higher Gleason scores. Elevated RBIS levels were associated with poorer progression-free survival (PFS) and served as an independent prognostic marker. Co-expression analysis revealed that RBIS and its associated genes were involved in key cellular processes such as energy metabolism and protein synthesis. Furthermore, RBIS expression was linked to immune cell infiltration and drug sensitivity, indicating potential therapeutic implications.

Conclusion

RBIS emerges as a novel biomarker for the diagnosis and prognosis of PCa, with significant potential as a therapeutic target. Further research is needed to validate these findings and explore RBIS's role in clinical applications, aiming to improve PCa management and patient outcomes.

Biological sciences/Cancer

Biological sciences/Computational biology and bioinformatics

Biological sciences/Drug discovery

Biological sciences/Immunology

Prostate cancer

ribosomal biogenesis factor

biomarker

prognosis

drug sensitivity

Prostate cancer (PCa) ranks as the second most common malignant neoplasm in men globally, posing a significant threat to male health[1]. The global incidence of prostate cancer shows marked variation, with notably higher rates observed in North America, Australia, New Zealand, and several European nations. In contrast, the incidence rates are comparatively lower in Asian and African countries[2]. This discrepancy may be attributed to differing levels of genetic susceptibility, variations in lifestyle, and advancements in medical technology. Currently, radical prostatectomy, radiation therapy, and cryotherapy are the primary treatment modalities for localized PCa[3]. While early-stage PCa is generally manageable, the progression of tumors presents a considerable clinical challenge in the management of PCa. A significant number of patients fail to undergo curative surgery, leading to progression to advanced stages. Reports indicate that the 5-year survival rate for individuals with metastatic PCa is only 31%, making it a major contributor to cancer-related mortality among men[4, 5]. Therefore, the identification of biomarkers with high specificity and sensitivity is paramount for the accurate diagnosis and prognostic evaluation of PCa, thereby effectively guiding therapeutic strategies for patients afflicted with this condition.

Ribosome biogenesis (RB) is an essential process underpinning cellular growth and proliferation. Recent research underscores its pivotal role in the onset and progression of cancer. This intricate, multi-step process initiates in the nucleolus and culminates with the formation of functional ribosomes within the cytoplasm. It encompasses the transcription and meticulous processing of rRNA, coupled with the precise assembly of ribosomal proteins[6]. In cancer cells, RB increases significantly to support their rapid growth and division. Cancer cells enhance ribosomal biosynthesis through various mechanisms. For instance, RNA polymerase I (RNA Pol I) activity is markedly upregulated in cancer cells, leading to heightened ribosomal RNA (rRNA) synthesis. This abnormal ribosome biogenesis tends to translate oncogenes and anti-apoptotic genes, thereby facilitating cancer progression and metastasis[6, 7]. Due to the pivotal role of RB in cancer cells, inhibiting this process has been considered a potential anticancer strategy. Certain critical steps in RB, such as the activity of RNA polymerase I, can be effectively inhibited by specific drugs, thereby reducing the proliferative capacity of cancer cells[6, 7].

Ribosomal biogenesis factor (RBIS), also known as chromosome 8 open reading frame 59 (C8orf59), is a protein-coding gene that plays a significant role in the progression of RB. Despite the established importance of RB in cancer cell proliferation, there is a paucity of research concerning the specific role of the RBIS gene in cancer. The functions of RBIS and its potential oncogenic mechanisms remain largely unexplored. In this study, we presented the inaugural analysis of the clinical significance of RBIS in patients with PCa. Leveraging bioinformatics, we have delved into the potential biological mechanisms through which RBIS may facilitate the progression of PCa. Our findings suggested that RBIS emerges as a novel diagnostic and prognostic biomarker for PCa, with promising potential as a therapeutic target.

2.1. Data collection

For a comprehensive differential expression analyses analysis in pan-cancer, RNA sequencing data for 33 cancer types, along with their respective normal counterparts, were sourced from the TCGA database (https://www.cancer.gov/tcga). This dataset comprised 501 PCa samples, each annotated with prognostic information, and 52 normal samples for comparison. The expression data were standardized as log2-transformed TPM (Transcripts Per Million) values. In addition, clinicopathological information pertaining to the PCa patients were retrieved from the TCGA database. To further validate our findings, three datasets from the GEO database (https://www.ncbi.nlm.nih.gov/gds), specifically GSE70768, GSE71016, and GSE116918, were meticulously selected as external validation cohorts.

2.2. Clinicopathological features analysis

PCa specimens were stratified into groups with high and low RBIS expression, using the median value of RBIS expression as the threshold. We then explored the disparities in clinical characteristics across these groups. To assess the diagnostic utility of RBIS in distinguishing between normal and cancerous tissues, we generated receiver operating characteristic (ROC) curves. Furthermore, we employed Kaplan–Meier (KM) survival analysis coupled with log-rank tests to investigate variations in progression-free survival (PFS) rates between the two cohorts. Utilizing the "survival" and "timeROC" packages in R, we conducted temporal ROC analysis to ascertain the prognostic significance of RBIS expression levels. In addition, we conducted both univariate and multivariate Cox regression analyses to ascertain the independent prognostic value of RBIS. Following this, we constructed a nomogram with the aid of the "rms" R package. This model integrates RBIS expreaaion along with clinical features to accurately forecast the probabilities of 1-year, 3-year, and 5-year PFS in patients with PCa.

2.3. RBIS co-expression identification and functional enrichment analysis

In our study, we employed the LinkedOmics database (http://www.linkedomics.org/login.php)[8] to identify genes co-expressed with RBIS within the TCGA-PRAD cohort. Subsequently, we focused on the top 200 co-expressed genes for an in-depth analysis. To elucidate the potential roles of RBIS in PCa, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the "clusterProfiler" and "org.Hs.eg.db" packages in R. Additionally, we uploaded the top 200 co-expressed genes to the STRING database (https://cn.string-db.org/)[9] for constructing a protein-protein interaction (PPI) network, requiring a minimum interaction score of 0.9 for inclusion. The resulting PPI network was then visualized using the network visualization tool Cytoscape (version 3.10.1), providing a comprehensive view of the molecular interactions associated with RBIS in the context of PCa.

2.4. Immunity analysis

We employed the single sample gene set enrichment analysis (ssGSEA) algorithm[10] to quantify the presence of 24 well-known immune cell types in each sample. Additionally, we used the ESTIMATE algorithm to calculate the immune, stromal, and ESTIMATE scores for each PCa sample. To assess the potential response of PCa patients from the TCGA database to immunotherapy, we applied the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Furthermore, we explored the relationships between RBIS expression and 8 common immune checkpoint genes (ICGs) to gain insights into the immunological implications of RBIS in the context of cancer.

2.5. Genetic alteration analysis

In our research, we investigated the genetic alteration of RBIS in PCa patients, as well as the correlation between RBIS alteration and prognosis, by utilizing three datasets (MCTP, Nature 2012; SU2C/PCF Dream Team, PNAS 2019; TCGA, PanCancer Atlas) available in the cBioPortal database (www.cbioportal.org)[11].

2.6. Drug sensitivity analysis

We leveraged the R package "pRRophetic" to estimate the 50% inhibitory concentration (IC50) values of various drugs, utilizing data from the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/)[12]. Subsequently, we conducted a comparative analysis of the IC50 values for drugs between the RBIS high-expression and low-expression groups to elucidate potential differences in drug sensitivity associated with RBIS expression levels.

2.7. Cell culture and quantitative real-time PCR (qRT-PCR)

The human prostate cancer cell lines C4-2, DU145, PC-3, and normal prostate epithelial cells RWPE-1 were procured from the Cell Bank of the Chinese Academy of Sciences. All cell cultures were maintained at 37°C in a humidified atmosphere containing 5% CO2. RPMI-1640 medium (Gibco, Waltham, MA, USA) supplemented with 10% fetal bovine serum (FBS, Gibco, Australia) and 1% penicillin/streptomycin (Gibco) was used for cell culture. Total cellular RNA was extracted using TRIzol reagent (Invitrogen, USA) following the manufacturer's protocol. Subsequently, the total RNA was reverse transcribed into cDNA using HiScript RT Mix (Vazyme, Nanjing, China). QRT-PCR was conducted using SYBR Green Master Mix (Vazyme, Nanjing, China), and relative expression levels were analyzed using the 2^−∆∆CT method with GAPDH serving as the internal reference gene. The primer sequences were as follows: GAPDH: forward, 5′- GTCAAGGCTGAGAACGGGAA′, and reverse, 5′-TGGACTCCACGACGTACTCA-3′; RBIS: forward, 5′- AAAGCAAAACCAGTTACCACTAATC-3′and reverse, 5′- GGTTCAAGTGAAATGCTTTTTGCG-3′.

2.8. Statistical analysis

Statistical evaluations were conducted utilizing R software, version 4.0.5. To assess differences between two groups, the Wilcoxon signed-rank test was employed, while the Kruskal-Wallis test was applied for comparisons involving more than two groups. For paired samples within the two groups, analysis was performed via the paired t-test. Spearman’s correlation coefficient was used for correlation analyses. A P-value below 0.05 was deemed to indicate statistical significance.

3.1. The expression of RBIS in PCa was higher than normal tissues

In this study, we investigated the expression patterns of RBIS across a diverse array of 33 cancer types and their respective normal tissues, utilizing RNA-seq data from TCGA. As depicted in Fig. 1A, RBIS expression levels were notably elevated in a majority of cancer tissues, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), and cholangiocarcinoma (CHOL), compared to their corresponding normal tissues. This trend was further substantiated in our focused analysis on PCa within the TCGA dataset, where RBIS expression was significantly higher in PCa tissues compared to normal tissues, both in non-matched samples (Fig. 1B) (p < 0.001) and in 52 matched pairs of PCa and normal tissues (Fig. 1C) (p = 0.005). To corroborate these findings, we utilized data from GSE70768 and GSE71016 within the GEO database, which comprised 126 PCa samples and 73 normal samples for GSE70768, and 48 PCa and 47 normal samples for GSE71016. Consistent with our TCGA analysis, RBIS expression was markedly higher in PCa tissues across both GEO datasets (Fig. 1D and 1E) (p < 0.05). In addition, we validated the differential expression of RBIS at the cellular level through qRT-PCR. In the PCa cell lines C4-2, DU145, and PC-3, the expression of RBIS was significantly higher compared to normal prostate epithelial cells RWPE-1 (Fig. 1F) (p < 0.005).

3.2. High RBIS expression was associated with adverse clinical outcomes in PCa

The clinical details of PCa patients from the TCGA database are delineated in Table 1. By segregating patients according to clinical attributes, we scrutinized the association of RBIS expression with variables such as age, T stage, N stage, M stage, levels of prostate-specific antigen (PSA), Gleason score, and outcomes from primary treatments. Figure 2A reveals that elevated RBIS expression correlates with increased age (p = 0.007), higher T (p < 0.001) and N stages (p < 0.001), elevated Gleason scores (p < 0.001), and less favorable treatment outcomes (p < 0.001). Intriguingly, RBIS expression demonstrated no significant association with PSA levels (p = 0.869) and M stage (p = 0.091). Nevertheless, it's noteworthy that patients at the M1 stage presented with higher RBIS expression levels, albeit without statistical significance. These insights underscore the potential linkage of RBIS upregulation with detrimental clinical characteristics in PCa patients, suggesting its close association with the disease's progression. We further assessed the diagnostic potential of RBIS by employing ROC curve analysis. Within the TCGA-PRAD cohort, the area under the curve (AUC) for RBIS in distinguishing between PCa tissues and normal samples was 0.847, with a sensitivity of 0.747 and specificity of 0.808 (Fig. 2B). To delve deeper into the relationship between RBIS expression and the prognosis or advancement of PCa, we classified patients from the TCGA dataset into groups of low (n = 250) and high (n = 251) RBIS expression, based on the median expression level. KM survival analysis showed that the PFS time of the high-expression group was shorter than that of the low-expression group (Fig. 2C) (p < 0.001). The result was also validated in the GSE116918 dataset (Fig. 2D) (p = 0.047). Furthermore, we conducted time-related ROC analysis to ascertain the prognostic significance of RBIS expression levels. In the TCGA dataset, the AUC of RBIS at 1, 3, and 5 years were 0.691, 0.732, and 0.704 respectively (Fig. 2E). Moreover, the distribution of patient survival status in RBIS low- and high-expression groups is shown in Fig. 2F.

Table 1

The clinical details of PCa patients from the TCGA database
Characteristics	Low expression of RBIS	High expression of RBIS
n	250	251
Pathologic T stage, n (%)
T2	119 (24.1%)	70 (14.2%)
T3&T4	126 (25.5%)	179 (36.2%)
Pathologic N stage, n (%)
N0	183 (42.8%)	165 (38.6%)
N1	20 (4.7%)	60 (14%)
Clinical M stage, n (%)
M0	222 (48.3%)	235 (51.1%)
M1	0 (0%)	3 (0.7%)
Age, n (%)
<= 60	124 (24.8%)	101 (20.2%)
> 60	126 (25.1%)	150 (29.9%)
PSA(ng/ml), n (%)
< 4	212 (47.7%)	205 (46.2%)
>= 4	13 (2.9%)	14 (3.2%)
Primary therapy outcome, n (%)
CR	195 (44.3%)	146 (33.2%)
PD&SD&PR	28 (6.4%)	71 (16.1%)
Gleason score, n (%)
6&7	184 (36.7%)	110 (22%)
8&9&10	66 (13.2%)	141 (28.1%)
Race, n (%)
Asian	5 (1%)	7 (1.4%)
Black or African American	32 (6.6%)	26 (5.3%)
White	209 (43%)	207 (42.6%)
Residual tumor, n (%)
R0	176 (37.4%)	140 (29.8%)
R1	55 (11.7%)	94 (20%)
R2	3 (0.6%)	2 (0.4%)
Zone of origin, n (%)
Central	2 (0.7%)	2 (0.7%)
Multiple	43 (15.5%)	84 (30.3%)
Peripheral	40 (14.4%)	98 (35.4%)
Transition	4 (1.4%)	4 (1.4%)

3.3. RBIS was an independent prognostic marker for PCa patients

To ascertain if RBIS serves as an independent prognostic marker for PCa, we investigated the association among clinical characteristics, RBIS expression, and PFS in patients with PCa from the TCGA database, employing both univariate and multivariate Cox regression analyses. As depicted in Fig. 3A, the univariate analysis demonstrated significant correlations between RBIS expression levels, T stage, N stage, PSA levels, Gleason scores, treatment outcomes, and PFS (p < 0.05). Subsequent multivariate Cox regression analysis incorporating these variables showed that elevated RBIS expression correlated with an increased PFS rate (Fig. 3B) (p < 0.05), underscoring RBIS as an independent prognostic indicator for PCa patients. Building on this, we merged clinical data from the TCGA dataset to construct a nomogram that includes RBIS, aiming to forecast the PFS of PCa patients at 1, 3, and 5 years (Fig. 3C). The calibration plots demonstrate a strong concordance between the predicted and observed PFS values for the patients (Fig. 3D). In addition, we evaluated the nomogram model's predictive ability for PFS using ROC curves (Fig. 3E). The AUCs of the ROC curves for predicting 1-year, 3-year, and 5-year PFS were 0.806, 0.817, and 0.839, respectively, highlighting the nomogram model’s capability to effectively predict PFS in PCa patients.

3.4. Co-expressed genes identification and enrichment analyses

To deepen our understanding of the carcinogenic mechanisms of RBIS in PCa, we explored RBIS co-expressed genes within PCa utilizing the LinkedOmics online database. Our comprehensive analysis yielded a dataset of 20,050 genes co-expressed with RBIS (Supplementary Table S1). A detailed examination, illustrated in the volcano plot (Fig. 4A), revealed that 10,702 of these genes exhibited a negative correlation with RBIS, whereas 9,348 showed a positive correlation. To provide a focused insight, the top 50 positively correlated genes and the top 50 negatively correlated genes with RBIS were visualized in two distinct heatmaps (Fig. 4B and 4C, respectively). Further analytical efforts involved conducting GO and KEGG pathway enrichment analyses on the top 200 co-expressed genes (Supplementary Tables S2 and S3). The outcomes of the GO analyses (Fig. 4D and 4E) indicated a significant enrichment of the co-expressed genes in processes crucial for cellular metabolism, including cytoplasmic translation, aerobic electron transport chain, and mitochondrial ATP synthesis coupled electron transport, across the biological process (BP) ontology. In terms of cellular component (CC) ontology, notable enrichment was observed in the ribosomal subunit, ribosome, cytosolic ribosome, and mitochondrial protein-containing complex. Molecular function (MF) ontology highlighted enrichment in key functions such as structural constituent of ribosome, oxidoreduction-driven active transmembrane transporter activity, electron transfer activity, and NADH dehydrogenase (ubiquinone) activity. Similarly, the KEGG pathway enrichment analysis (Fig. 4F and 4G) underscored the co-expressed genes' significant involvement in pathways associated with ribosome, coronavirus disease (COVID-19), oxidative phosphorylation, Parkinson's disease, and Huntington's disease. Collectively, these analyses suggest that RBIS and its co-expressed genes may facilitate the progression of prostate cancer by modulating essential biological processes and pathways, including cellular metabolism, energy production, and protein synthesis. This insight provides valuable directions for further investigation into the role of RBIS in PCa and its viability as a therapeutic target. Additionally, we uploaded 200 co-expressed genes to the STRING database to construct a PPI network, subsequently visualized using the gene network tool Cytoscape (Fig. 4H). The PPI network comprises 165 nodes and 1894 edges, offering a graphical representation of the intricate interactions among these co-expressed genes, further elucidating their potential collective role in PCa carcinogenesis.

3.5. Immunity analysis

In our study, we delved into the relationship between RBIS gene expression and the levels of infiltration by various immune cells, uncovering that RBIS expression is inversely related to the infiltration levels of a broad spectrum of immune cells, including neutrophils, THF, Th17 cells, mast cells, eosinophils, Th1 cells, NK cells, among others (p < 0.05). Conversely, a positive correlation was observed with CD8 T cells and pDCs. (p < 0.05) (Fig. 5A). Leveraging the ESTIMATE algorithm, we observed that the group with high RBIS expression exhibited lower stromal scores (p < 0.05). However, no significant variations were noted in the immune and ESTIMATE scores between the groups with high and low RBIS expression (Fig. 5B). We further explored the correlation between RBIS expression and the predictive efficacy of immune therapy, and unfortunately, we found no significant differences in TIDE scores, Dysfunction scores, and Exclusion scores between the high and low expression groups (Fig. 5C). Moreover, our correlation analysis concerning the expression of ICGs demonstrated a positive association of RBIS expression with CTLA4 (r = 0.121, p = 0.006) and LAG3 (r = 0.090, p = 0.044), while showing an inverse correlation with CD274 (r=-0.091, p = 0.041), SIGLEC15 (r=-0.188, p < 0.001), and PDCD1LG2 (r=-0.131, p = 0.003) (Fig. 5D). This data provides a nuanced insight into the complex interplay between RBIS expression and immune cell infiltration, hinting at its potential implications for the efficacy of immune therapies.

3.6. Genetic alteration analysis

Analyzed across three pivotal datasets (MCTP (Nature 2012), SU2C/PCF Dream Team (PNAS 2019), and TCGA PanCancer Atlas) within the cBioPortal database, our study encompassed a collective total of 1,066 samples. We determined that RBIS gene alterations occur in 10.0% of PCa cases (Fig. 6A), predominantly through amplification mechanisms. A schematic representation of these RBIS mutations is illustrated in Fig. 6B. Specifically, the mutation frequencies of RBIS across the MCTP, SU2C/PCF Dream Team, and TCGA datasets were found to be 26.23%, 21.85%, and 6.61%, respectively (Fig. 6C). KM survival analysis revealed that patients with RBIS gene alterations exhibited significantly reduced overall survival (OS) in comparison to those without such alterations (p < 0.001), as shown in Fig. 6D. Conversely, no significant disparity in disease-free survival (DFS) was observed between the two cohorts (p = 0.249), as indicated in Fig. 6E. Additionally, the presence of RBIS alterations was associated with a higher incidence and frequency of alterations in other genes (Fig. 6F and 6G). Among both groups, the top ten genes exhibiting the highest alteration frequencies were identified as E2F5, LRRCC1, CA13, CA1, CA3, RALYL, FABP4, CA2, FABP12, and SNX16 (Fig. 6H).

3.7. Drug sensitivity analysis

Through drug sensitivity analysis, we discovered that the IC50 values for A-770041, AKT inhibitor VIII, Embelin, Erlotinib, Foretinib, Lapatinib, MG-132, Nilotinib, Paclitaxel, Ruxolitinib, Sunitinib, and Z-LLNle-CHO were lower in the low-expression group (all p < 0.05) (Fig. 7), indicating that patients with lower RBIS expression might have higher sensitivity to these drugs. Moreover, in the high-expression group, the IC50 values for Bexarotene, Doxorubicin, and FH535 were lower than in the low-expression group.

The ribosome, an evolutionarily conserved supramolecular ribonucleoprotein complex, translates the genetic information embedded in messenger RNA (mRNA) into functional proteins, representing a pivotal and final step in the process of gene expression[13]. In eukaryotic cells, the assembly of ribosomal subunits is a complex process that involves not only rRNA and ribosomal proteins but also more than 200 assembly factors[13]. Over the past few decades, research has uncovered a significant link between the dysregulation of RB and tumorigenesis. Tumor cells achieve rapid growth by upregulating RB, thereby maintaining high efficiency in protein synthesis[6, 7]. Previous studies have shown that the hyperactivation of oncogenes or the inactivation of tumor suppressor genes can stimulate RNA Pol I transcription, leading to increased rRNA synthesis and consequently promoting cell growth and proliferation[14]. Additionally, ribosomal proteins have the capacity to directly or indirectly influence the cell cycle, contribute to DNA damage repair, initiate apoptosis, regulate cell migration and invasion, respond to endoplasmic reticulum stress, and impact various other cellular activities, thereby exerting significant regulatory effects on tumor cell growth[15]. Therefore, we believed that BRIS may play a significant role in the progression of cancer.

In this study, leveraging the TCGA database, we have identified for the first time that RBIS is highly expressed in various cancer tissues, including PCa. We further substantiated the differential expression of RBIS at the cellular level using data from the GEO database, specifically GSE70768 and GSE71016, as well as through qRT-PCR analysis. Our analysis of the clinical significance of RBIS expression in PCa patients revealed that elevated RBIS expression correlates with adverse clinical outcomes, including advanced age, higher T and N stages, elevated Gleason scores, and unfavorable treatment responses. In terms of tumor staging, increased T and N stages suggest greater local invasiveness of the tumor and a higher degree of lymph node involvement, both of which typically predict a poorer prognosis[16, 17]. The Gleason scoring system remains one of the principal methods for evaluating the malignancy of PCa, with higher Gleason scores generally associated with more aggressive cancer phenotypes and a worse prognosis[18]. Furthermore, we have identified RBIS as a potential diagnostic biomarker for prostate cancer, demonstrating high sensitivity and specificity. The elevated expression of RBIS is correlated with poorer PFS and may serve as an independent prognostic marker for PCa patients. Based on these findings, we integrated various clinical characteristics with RBIS expression to develop a nomogram model with enhanced predictive accuracy for PFS, thereby increasing its clinical applicability.

To gain deeper insights into the oncogenic mechanisms of RBIS in PCa, we identified genes co-expressed with RBIS and performed GO and KEGG pathway enrichment analyses. Our findings suggest that RBIS and its co-expressed genes may facilitate the progression of PCa by regulating processes such as energy metabolism and protein synthesis. The significance of energy metabolism in cancer research has garnered widespread attention in recent years. Tumor cells often exhibit distinct metabolic characteristics, including high glucose uptake, aerobic glycolysis, and increased lactate production, collectively known as the Warburg effect[19]. Targeting the energy metabolism of tumor cells has emerged as a pivotal strategy in contemporary cancer therapy. Research has demonstrated that inhibitors of key metabolic enzymes, such as mutant IDH, GPX4, and NAMPT, exhibit significant anti-tumor activity. Modulating the activity of these enzymes can effectively curb tumor cell proliferation and metastasis while enhancing anti-tumor immunity[20]. Additionally, studies have shown that oncogenes and tumor suppressor proteins can impact cancer progression by regulating energy metabolism, presenting novel therapeutic targets[21].

In our study, we analyzed the potential relationship between RBIS expression and immune cell infiltration. We have identified a negative correlation between MAPK8IP2 expression and the infiltration of specific immune cells, notably Th17 cells, Th1 cells, and NK cells. Prior research indicates that Th17 cells can induce the production of CXCL9 and CXCL10 through IL-17 and IFN-γ, thereby recruiting Th1 cells and NK cells into the tumor microenvironment and enhancing anti-tumor immunity[22]. This observation suggests that PCa cells may suppress anti-tumor immunity by upregulating RBIS expression. Intriguingly, our findings show that high RBIS expression is positively associated with the infiltration of anti-tumor immune cells, such as CD8 T cells and pDCs. CD8 T cells, recognized as the most potent effector cells in anti-cancer immune responses, can directly eliminate infected and cancerous cells, thereby playing a pivotal role in the adaptive immune system[23]. Additionally, studies have shown that pDCs are critical in cross-presentation, activating CD8 T cells by presenting exogenous antigens on MHC I, a process vital for anti-tumor immunity while also mediating immune tolerance[24]. In summary, RBIS gene expression appears to have dual roles in promoting and inhibiting tumor functions, suggesting a regulatory role in the immune microenvironment of prostate cancer. We also investigated the differences in ICGs expression between the high and low RBIS expression groups. Immune checkpoint inhibitors (ICIs) therapy, which enhances anti-tumor immune responses by modulating T cell activity, has shown significant potential in cancer treatment in recent years[25, 26]. We found that RBIS expression is positively correlated with CTLA4 and LAG3, while negatively correlated with CD274, SIGLEC15, and PDCD1LG2. These novel immune checkpoints may serve as potential immunotherapeutic targets for PCa.

Through drug sensitivity analysis, we discovered that PCa patients with high RBIS expression are more sensitive to Bexarotene, Doxorubicin, and FH535. Currently, research and clinical trials on the application of Bexarotene in PCa treatment are limited, primarily focusing on other types of cancer. For instance, Bexarotene has demonstrated significant efficacy in the treatment of cutaneous T-cell lymphoma and has been approved by the FDA[27]. In the treatment of PCa, Doxorubicin has also shown considerable potential. Studies indicate that Doxorubicin, when used in combination with other therapies, can produce synergistic anti-cancer effects. For example, research has explored a nanoparticle drug delivery system combining Doxorubicin with traditional Chinese medicine extracts to achieve better therapeutic outcomes in PCa[28]. FH535, a small molecule compound that inhibits the Wnt/β-catenin signaling pathway, holds promise as a novel therapeutic option for PCa patients[29, 30].

Although this study reveals the significance of RBIS in PCa through bioinformatics analysis, several limitations remain. The analysis primarily relies on data from the TCGA and GEO databases, which may present selection bias and lack sufficient sample representativeness. Additionally, further fundamental experiments are necessary to investigate the functional mechanisms by which RBIS promotes the progression of PCa.

Overall, our findings establish RBIS as a promising biomarker for the diagnosis, prognosis, and potential therapeutic targeting of PCa. Further research is warranted to elucidate the detailed mechanisms of RBIS function and to explore its utility in clinical trials, ultimately aiming to enhance the management and outcomes of PCa patients.

PCa

prostate cancer

ribosome biogenesis

RNA Pol I

RNA polymerase I

rRNA

ribosomal RNA

RBIS

ribosomal biogenesis factor

C8orf59

chromosome 8 open reading frame 59

TPM

Transcripts Per Million

ROC

receiver operating characteristic

Kaplan–Meier

PFS

progression-free survival

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

PPI

protein-protein interaction

ssGSEA

single sample gene set enrichment analysis

TIDE

Tumor Immune Dysfunction and Exclusion

ICG

immune checkpoint genes

IC50

50% inhibitory concentration

GDSC

Genomics of Drug Sensitivity in Cancer

BLCA

bladder urothelial carcinoma

BRCA

breast invasive carcinoma

CHOL

cholangiocarcinoma

PSA

prostate-specific antigen

AUC

area under the curve

biological process

cellular component

molecular function

DFS

disease-free survival

mRNA

messenger RNA

ICIs

immune checkpoint inhibitors

Acknowledgements

Not applicable.

Authors’ contributions

C.Z.X participated in the data analysis, organized the article writing, and critically modified the manuscript. Y.T.F modified the manuscript, drafted the manuscript and were responsiblefor the acquisition of data; J.H.Z contributed to the literature search, and correct language expression. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding

This work was supported by the Youth Science Foundation of the Cancer Hospital of Shantou University Medical College (Grant No. 2023A002).

Availability of data and materials

The datasets extracted and/or analysed during the current study are available in the following repositories:

The Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma (PRAD) dataset is available in the TCGA repository. The relevant dataset can be accessed through the following accession number: TCGA-PRAD (https://portal.gdc.cancer.gov/projects/TCGA-PRAD).

Gene Expression Omnibus (GEO) datasets used in this study include GSE70768, GSE71016, and GSE116918. These datasets can be accessed through the following links and accession numbers: GSE70768 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70768), GSE71016 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71016), GSE116918 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116918).

These datasets are publicly available, and all data used in the study can be accessed through the provided links and accession numbers.

Ethics approval

This study does not involve animal or clinical experiments. All data were obtained from public databases, and therefore, it does not require submission for ethical review.

Conflicts of interest

The authors declare no conflicts of interest related to this study.

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No competing interests reported.

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Ribosomal biogenesis factor, a novel biomarker for predicting progression-free survival in prostate cancer

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Abstract

Figures

1. Introduction

2. Methods

2.1. Data collection

2.2. Clinicopathological features analysis

2.3. RBIS co-expression identification and functional enrichment analysis

2.4. Immunity analysis

2.5. Genetic alteration analysis

2.6. Drug sensitivity analysis

2.7. Cell culture and quantitative real-time PCR (qRT-PCR)

2.8. Statistical analysis

3. Result

3.1. The expression of RBIS in PCa was higher than normal tissues

3.2. High RBIS expression was associated with adverse clinical outcomes in PCa

3.3. RBIS was an independent prognostic marker for PCa patients

3.4. Co-expressed genes identification and enrichment analyses

3.5. Immunity analysis

3.6. Genetic alteration analysis

3.7. Drug sensitivity analysis

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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