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

Ribosomal biogenesis factor, a novel biomarker for predicting progression-free survival in prostate cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ribosomal biogenesis factor, a novel biomarker for predicting progression-free survival in prostate cancer Zexiao Chen, Yutong Fang, Jianhua Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4899995/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction 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. Methods 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. Result 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. 1 A, 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. 1 B) (p < 0.001) and in 52 matched pairs of PCa and normal tissues (Fig. 1 C) (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. 1 D and 1 E) (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. 1 F) (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 2 A 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. 2 B). 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. 2 C) (p < 0.001). The result was also validated in the GSE116918 dataset (Fig. 2 D) (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. 2 E). Moreover, the distribution of patient survival status in RBIS low- and high-expression groups is shown in Fig. 2 F. 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 (%) 60 126 (25.1%) 150 (29.9%) PSA(ng/ml), n (%) = 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. 3 A, 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. 3 B) (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. 3 C). The calibration plots demonstrate a strong concordance between the predicted and observed PFS values for the patients (Fig. 3 D). In addition, we evaluated the nomogram model's predictive ability for PFS using ROC curves (Fig. 3 E). 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. 4 A), 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. 4 B and 4 C, 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. 4 D and 4 E) 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. 4 F and 4 G) 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. 4 H). 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. 5 A). 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. 5 B). 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. 5 C). 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. 5 D). 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. 6 A), predominantly through amplification mechanisms. A schematic representation of these RBIS mutations is illustrated in Fig. 6 B. 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. 6 C). 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. 6 D. Conversely, no significant disparity in disease-free survival (DFS) was observed between the two cohorts (p = 0.249), as indicated in Fig. 6 E. Additionally, the presence of RBIS alterations was associated with a higher incidence and frequency of alterations in other genes (Fig. 6 F and 6 G). 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. 6 H). 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. 4. Discussion 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. 5. Conclusion 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. Abbreviations PCa prostate cancer RB 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 KM Kaplan–Meier PFS progression-free survival GO 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 BP biological process CC cellular component MF molecular function DFS disease-free survival mRNA messenger RNA ICIs immune checkpoint inhibitors Declarations 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. References Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin. 72 (1), 7–33 (2022). Basiri, A. et al. Incidence, Gleason Score and Ethnicity Pattern of Prostate Cancer in the Multi-ethnicity Country of Iran During 2008–2010. Urol. J. 17 (6), 602–606 (2020). Mottet, N. et al. EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer-2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur. Urol. 79 (2), 243–262 (2021). Miller, K. D. et al. Cancer treatment and survivorship statistics, 2022. CA Cancer J. Clin. 72 (5), 409–436 (2022). Rebello, R. J. et al. Prostate cancer. Nat. Rev. Dis. Primers . 7 (1), 9 (2021). Elhamamsy, A. R., Metge, B. J., Alsheikh, H. A., Shevde, L. A. & Samant, R. S. Ribosome Biogenesis: A Central Player in Cancer Metastasis and Therapeutic Resistance. Cancer Res. 82 (13), 2344–2353 (2022). Penzo, M., Montanaro, L., Treré, D. & Derenzini, M. The Ribosome Biogenesis-Cancer Connection. Cells . 8 (1), 55 (2019). Vasaikar, S. V., Straub, P., Wang, J. & Zhang, B. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res. 46 (D1), D956–D963 (2018). Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47 (D1), D607–D613 (2019). Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 14 , 7 (2013). Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov . 2 (5), 401–404 (2012). Geeleher, P., Cox, N. J. & Huang, R. S. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. 15 (3), R47 (2014). Baßler, J. & Hurt, E. Eukaryotic Ribosome Assembly. Annu. Rev. Biochem. 88 , 281–306 (2019). Sharifi, S. & Bierhoff, H. Regulation of RNA Polymerase I Transcription in Development, Disease, and Aging. Annu. Rev. Biochem. 87 , 51–73 (2018). Pecoraro, A., Pagano, M., Russo, G. & Russo, A. Ribosome Biogenesis and Cancer: Overview on Ribosomal Proteins. Int. J. Mol. Sci. 22 (11), 5496 (2021). Gaffney, C. et al. Tumor size and genomic risk in localized prostate cancer. Urol Oncol. ;39(7):434.e17-434.e22. (2021). Adams, J. & Cheng, L. Lymph node-positive prostate cancer: current issues, emerging technology and impact on clinical outcome. Expert Rev. Anticancer Ther. 11 (9), 1457–1469 (2011). Helpap, B. et al. The Significance of Accurate Determination of Gleason Score for Therapeutic Options and Prognosis of Prostate Cancer. Pathol. Oncol. Res. 22 (2), 349–356 (2016). Schwartz, L., Supuran, C. T. & Alfarouk, K. O. The Warburg Effect and the Hallmarks of Cancer. Anticancer Agents Med. Chem. 17 (2), 164–170 (2017). Xiao, Y. et al. Emerging therapies in cancer metabolism. Cell. Metab. 35 (8), 1283–1303 (2023). Ghasemishahrestani, Z., Melo Mattos, L. M., Tilli, T. M., Santos, A. L. S. D. & Pereira, M. D. Pieces of the Complex Puzzle of Cancer Cell Energy Metabolism: An Overview of Energy Metabolism and Alternatives for Targeted Cancer Therapy. Curr. Med. Chem. 28 (18), 3514–3534 (2021). Kryczek, I. et al. Phenotype, distribution, generation, and functional and clinical relevance of Th17 cells in the human tumor environments. Blood . 114 (6), 1141–1149 (2009). Raskov, H., Orhan, A., Christensen, J. P. & Gögenur, I. Cytotoxic CD8 + T cells in cancer and cancer immunotherapy. Br. J. Cancer . 124 (2), 359–367 (2021). Fu, C., Zhou, L., Mi, Q. S. & Jiang, A. Plasmacytoid Dendritic Cells and Cancer Immunotherapy. Cells . 11 (2), 222 (2022). Vafaei, S. et al. Combination therapy with immune checkpoint inhibitors (ICIs); a new frontier. Cancer Cell. Int. 22 (1), 2 (2022). Bagchi, S., Yuan, R. & Engleman, E. G. Immune Checkpoint Inhibitors for the Treatment of Cancer: Clinical Impact and Mechanisms of Response and Resistance. Annu. Rev. Pathol. 16 , 223–249 (2021). Shen, D. et al. Emerging roles of bexarotene in the prevention, treatment and anti-drug resistance of cancers. Expert Rev. Anticancer Ther. 18 (5), 487–499 (2018). Sun, G., Sun, K. & Sun, J. Combination prostate cancer therapy: Prostate-specific membranes antigen targeted, pH-sensitive nanoparticles loaded with doxorubicin and tanshinone. Drug Deliv . 28 (1), 1132–1140 (2021). Chen, Y. et al. FH535 Inhibits Proliferation and Motility of Colon Cancer Cells by Targeting Wnt/β-catenin Signaling Pathway. J. Cancer . 8 (16), 3142–3153 (2017). Wu, M. Y. et al. FH535 inhibited metastasis and growth of pancreatic cancer cells. Onco Targets Ther. 8 , 1651–1670 (2015). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.xlsx SupplementaryTableS2.xlsx SupplementaryTableS3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4899995","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":354942346,"identity":"a416e854-443c-4e50-ba8d-c8d1a631589d","order_by":0,"name":"Zexiao Chen","email":"","orcid":"","institution":"Cancer Hospital of Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zexiao","middleName":"","lastName":"Chen","suffix":""},{"id":354942347,"identity":"a4edb176-4d29-44f7-b28d-1d1cc78029a9","order_by":1,"name":"Yutong Fang","email":"","orcid":"","institution":"Cancer Hospital of Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Fang","suffix":""},{"id":354942348,"identity":"de211c21-f4da-4221-bcee-5112781478d7","order_by":2,"name":"Jianhua Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3RPQrCMBTA8RcKcUn92FIK9QRCJKBL0as0CHUTwaWbLcLrFTyJc6WgS/QMFi/QI9iOTskomP/8foT3AuBy/WDrXJNXIuIjHRRF09oQkmtPtFmaDFl9ktyOMDo56zqJ+BbHzIZ45HHzfEx3NGgQOKyiWW4gFJ7p28f4QEOFrz1s5KIyEAZ6KbtXCIaqFBwqdTERDnoR+lgTDK7ImQ0RHQm69RVyYk9kf2RJmeqOLCx2WZ/1vP/KaFrem6bNVpGRAP+aEKbxvlFuM+VyuVx/3Qfpl0RFpm+DYgAAAABJRU5ErkJggg==","orcid":"","institution":"Cancer Hospital of Shantou University Medical College","correspondingAuthor":true,"prefix":"","firstName":"Jianhua","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-08-12 11:10:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4899995/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4899995/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66672208,"identity":"2b3f7005-b235-4b37-92fd-7e18dab61490","added_by":"auto","created_at":"2024-10-15 10:40:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151191,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of RBIS in PCa and pan-carcinoma. \u003cstrong\u003eA\u003c/strong\u003e Expression of RBIS in different types of cancers and their respective normal tissues. \u003cstrong\u003eB-C\u003c/strong\u003e RBIS expression levels in unpaired (B) and paired (C) PCa tissues and normal prostate of TCGA. \u003cstrong\u003eD-E\u003c/strong\u003e RBIS expression levels in PCa tissues and normal prostate of GSE70768 (D) and GSE71016 datasets (E). \u003cstrong\u003eF\u003c/strong\u003e RBIS expression levels in PCa cell lines and normal prostate epithelial cell line. NS indicates no statistical difference, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/f177b3a28ad054400d2e4163.png"},{"id":66672513,"identity":"c536315e-68ca-49d1-a5f5-609157bb0675","added_by":"auto","created_at":"2024-10-15 10:48:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171947,"visible":true,"origin":"","legend":"\u003cp\u003eClinical significance of RBIS expression in PCa. \u003cstrong\u003eA\u003c/strong\u003e Relationship between RBIS expression and clinical characteristics of PCa patients in TCGA. \u003cstrong\u003eB\u003c/strong\u003e ROC curve assessing the diagnostic value of RBIS for PCa. \u003cstrong\u003eC-D\u003c/strong\u003e Kaplan-Meier analysis of PFS between RBIS low- and high-expression groups (C), and validated in the GSE116918 dataset (D). \u003cstrong\u003eE\u003c/strong\u003eTime-dependent ROC curves evaluating the predictive ability of RBIS for 1-year, 3-year, and 5-year PFS. \u003cstrong\u003eF\u003c/strong\u003e distribution of patient survival status in RBIS low- and high-expression groups. NS indicates no statistical difference, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/4d35f28dec3ec183fc8d2295.png"},{"id":66671377,"identity":"905be149-e0b0-4bef-a9a0-4699e99f6640","added_by":"auto","created_at":"2024-10-15 10:32:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":181812,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of nomogram. \u003cstrong\u003eA-B \u003c/strong\u003eUnivariate (A) and multivariate (B) Cox regression analysis of the RBIS expression and clinical characteristics in TCGA. \u003cstrong\u003eC\u003c/strong\u003e Nomogram for predicting the 1-, 3- and 5-year PFS probabilities. \u003cstrong\u003eD\u003c/strong\u003e Calibration curves of the nomogram for predicting 1-, 3- and 5-year PFS probabilities. \u003cstrong\u003eE\u003c/strong\u003e Time-dependent ROC curves of the nomogram model for predicting the 1-, 3- and 5-year PFS probabilities.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/d390f965fed8e2c740569c66.png"},{"id":66670905,"identity":"bc5f988d-8a10-4c1c-8464-b1fbee33be1e","added_by":"auto","created_at":"2024-10-15 10:24:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":688328,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expressed genes identification and enrichment analyses. \u003cstrong\u003eA\u003c/strong\u003e Volcano plot shows RBIS and its co-expressed genes. \u003cstrong\u003eB-C\u003c/strong\u003e Heatmaps respectively show the top 50 co-expressed genes positively (B) and negatively (C) correlated with RBIS. \u003cstrong\u003eD-E \u003c/strong\u003eBar chart (D) and network diagram (E) show the results of the GO analysis. \u003cstrong\u003eF-G\u003c/strong\u003eBar chart (F) and network diagram (G) show the results of the KEGG pathway enrichment analysis. \u003cstrong\u003eH\u003c/strong\u003e PPI network of co-expressed genes.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/a4442f15be52311c3988a6ef.png"},{"id":66670911,"identity":"bd4d0ebd-941e-4e6f-b583-6f51aa1b06ab","added_by":"auto","created_at":"2024-10-15 10:24:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":246027,"visible":true,"origin":"","legend":"\u003cp\u003eImmunity analysis of RBIS expression in PCa. \u003cstrong\u003eA\u003c/strong\u003eRelationships between RBIS expression and 24 common immune cells infiltration. \u003cstrong\u003eB\u003c/strong\u003e Box diagrams show the stromal scores, immune scores, and ESTIMATE score in RBIS low- and high-expression groups. \u003cstrong\u003eC\u003c/strong\u003e Box diagrams show the TIDE scores, Dysfunction scores, and Exclusion score in RBIS low- and high-expression groups. \u003cstrong\u003eD\u003c/strong\u003e Scatter plots depict the correlation between RBIS expression and eight ICGs.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/1c01ee1799e87de7b381a88e.png"},{"id":66670913,"identity":"f6ad017a-2d75-40f0-bf7c-0f0efca7b155","added_by":"auto","created_at":"2024-10-15 10:24:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":163941,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic alteration of RBIS in PCa. \u003cstrong\u003eA\u003c/strong\u003e OncoPrint visual summary of RBIS alteration. \u003cstrong\u003eB \u003c/strong\u003eSchematic representation of RBIS mutations. \u003cstrong\u003eC \u003c/strong\u003eMutation frequencies of RBIS across the MCTP, SU2C/PCF Dream Team, and TCGA datasets. \u003cstrong\u003eD-E\u003c/strong\u003e Kaplan-Meier analysis of OS (D) and DFS (E) between RBIS altered and unaltered groups. \u003cstrong\u003eF-G\u003c/strong\u003e Scatter plot and volcano plot illustrate the mutation frequencies of the altered and unaltered groups. \u003cstrong\u003eH\u003c/strong\u003e Top ten genes with the highest alteration frequencies in RBIS altered and unaltered groups.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/16a7ec116137a0081feac7a5.png"},{"id":66671381,"identity":"16a73919-c145-414b-b852-e48588c7b906","added_by":"auto","created_at":"2024-10-15 10:32:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":126064,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/4cf0ce0bc2edf3bd33669a66.png"},{"id":66674071,"identity":"7bf55d78-79eb-43b9-9b2b-22ca0869368e","added_by":"auto","created_at":"2024-10-15 10:56:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2313606,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/baa760d9-149c-4d36-b5dc-65ba8a807931.pdf"},{"id":66672210,"identity":"ff61ef00-3ca8-4106-9132-9022dc979b57","added_by":"auto","created_at":"2024-10-15 10:40:09","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1464536,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/ddb6ccf7aadeea70909c931a.xlsx"},{"id":66670901,"identity":"c3248b27-02e5-4612-a25e-18ef637b8186","added_by":"auto","created_at":"2024-10-15 10:24:09","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":33024,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/c7de73e3888249cd16a8dff4.xlsx"},{"id":66670906,"identity":"1a2aa8af-e302-4e10-ba65-eb61aad2c888","added_by":"auto","created_at":"2024-10-15 10:24:09","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12174,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4899995/v1/36dbf55dafe9ed1a41b6dfa5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ribosomal biogenesis factor, a novel biomarker for predicting progression-free survival in prostate cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eProstate cancer (PCa) ranks as the second most common malignant neoplasm in men globally, posing a significant threat to male health[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. 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[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. 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[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eRibosome 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[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. 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[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. 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[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRibosomal 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.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data collection\u003c/h2\u003e \u003cp\u003eFor 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 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). 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 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), specifically GSE70768, GSE71016, and GSE116918, were meticulously selected as external validation cohorts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Clinicopathological features analysis\u003c/h2\u003e \u003cp\u003ePCa 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\u0026ndash;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. RBIS co-expression identification and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eIn our study, we employed the LinkedOmics database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.linkedomics.org/login.php\u003c/span\u003e\u003cspan address=\"http://www.linkedomics.org/login.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] 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 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Immunity analysis\u003c/h2\u003e \u003cp\u003eWe employed the single sample gene set enrichment analysis (ssGSEA) algorithm[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Genetic alteration analysis\u003c/h2\u003e \u003cp\u003eIn 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 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.cancer.gov/tcga\" target=\"_blank\"\u003ewww.cbioportal.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eWe 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 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Cell culture and quantitative real-time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eThe 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\u0026deg;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\u003csup\u003e\u0026minus;∆∆\u003c/sup\u003eCT method with GAPDH serving as the internal reference gene. The primer sequences were as follows: GAPDH: forward, 5\u0026prime;- GTCAAGGCTGAGAACGGGAA\u0026prime;, and reverse, 5\u0026prime;-TGGACTCCACGACGTACTCA-3\u0026prime;; RBIS: forward, 5\u0026prime;- AAAGCAAAACCAGTTACCACTAATC-3\u0026prime;and reverse, 5\u0026prime;- GGTTCAAGTGAAATGCTTTTTGCG-3\u0026prime;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical 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\u0026rsquo;s correlation coefficient was used for correlation analyses. A P-value below 0.05 was deemed to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. The expression of RBIS in PCa was higher than normal tissues\u003c/h2\u003e \u003cp\u003eIn 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in 52 matched pairs of PCa and normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) (p\u0026thinsp;=\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) (p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. High RBIS expression was associated with adverse clinical outcomes in PCa\u003c/h2\u003e \u003cp\u003eThe clinical details of PCa patients from the TCGA database are delineated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. 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\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA reveals that elevated RBIS expression correlates with increased age (p\u0026thinsp;=\u0026thinsp;0.007), higher T (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and N stages (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), elevated Gleason scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and less favorable treatment outcomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Intriguingly, RBIS expression demonstrated no significant association with PSA levels (p\u0026thinsp;=\u0026thinsp;0.869) and M stage (p\u0026thinsp;=\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). 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\u0026thinsp;=\u0026thinsp;250) and high (n\u0026thinsp;=\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The result was also validated in the GSE116918 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) (p\u0026thinsp;=\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Moreover, the distribution of patient survival status in RBIS low- and high-expression groups is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical details of PCa patients from the TCGA database\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow expression of RBIS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh expression of RBIS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u0026amp;T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (42.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical M stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (51.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSA(ng/ml), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary therapy outcome, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195 (44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (33.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u0026amp;SD\u0026amp;PR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGleason score, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026amp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026amp;9\u0026amp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual tumor, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZone of origin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (35.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. RBIS was an independent prognostic marker for PCa patients\u003c/h2\u003e \u003cp\u003eTo 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, the univariate analysis demonstrated significant correlations between RBIS expression levels, T stage, N stage, PSA levels, Gleason scores, treatment outcomes, and PFS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequent multivariate Cox regression analysis incorporating these variables showed that elevated RBIS expression correlated with an increased PFS rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) (p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The calibration plots demonstrate a strong concordance between the predicted and observed PFS values for the patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In addition, we evaluated the nomogram model's predictive ability for PFS using ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). 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\u0026rsquo;s capability to effectively predict PFS in PCa patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Co-expressed genes identification and enrichment analyses\u003c/h2\u003e \u003cp\u003eTo 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 (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). A detailed examination, illustrated in the volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cb\u003erespectively\u003c/b\u003e). Further analytical efforts involved conducting GO and KEGG pathway enrichment analyses on the top 200 co-expressed genes (\u003cb\u003eSupplementary Tables S2 and S3\u003c/b\u003e). The outcomes of the GO analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG) 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Immunity analysis\u003c/h2\u003e \u003cp\u003eIn 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\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, a positive correlation was observed with CD8 T cells and pDCs. (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Leveraging the ESTIMATE algorithm, we observed that the group with high RBIS expression exhibited lower stromal scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant variations were noted in the immune and ESTIMATE scores between the groups with high and low RBIS expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). 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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Moreover, our correlation analysis concerning the expression of ICGs demonstrated a positive association of RBIS expression with CTLA4 (r\u0026thinsp;=\u0026thinsp;0.121, p\u0026thinsp;=\u0026thinsp;0.006) and LAG3 (r\u0026thinsp;=\u0026thinsp;0.090, p\u0026thinsp;=\u0026thinsp;0.044), while showing an inverse correlation with CD274 (r=-0.091, p\u0026thinsp;=\u0026thinsp;0.041), SIGLEC15 (r=-0.188, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and PDCD1LG2 (r=-0.131, p\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Genetic alteration analysis\u003c/h2\u003e \u003cp\u003eAnalyzed 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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), predominantly through amplification mechanisms. A schematic representation of these RBIS mutations is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB. 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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). KM survival analysis revealed that patients with RBIS gene alterations exhibited significantly reduced overall survival (OS) in comparison to those without such alterations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD. Conversely, no significant disparity in disease-free survival (DFS) was observed between the two cohorts (p\u0026thinsp;=\u0026thinsp;0.249), as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE. Additionally, the presence of RBIS alterations was associated with a higher incidence and frequency of alterations in other genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). 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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eThrough 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\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe 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[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. 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[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. 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[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. 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[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. 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[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, we believed that BRIS may play a significant role in the progression of cancer.\u003c/p\u003e \u003cp\u003eIn 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[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. 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[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eTo 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[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. 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[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, studies have shown that oncogenes and tumor suppressor proteins can impact cancer progression by regulating energy metabolism, presenting novel therapeutic targets[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 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[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. 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[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. 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[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. 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[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eThrough 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[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. 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[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. FH535, a small molecule compound that inhibits the Wnt/β-catenin signaling pathway, holds promise as a novel therapeutic option for PCa patients[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough 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.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOverall, 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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprostate cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eribosome biogenesis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA Pol I\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRNA polymerase I\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003erRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eribosomal RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRBIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eribosomal biogenesis factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC8orf59\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echromosome 8 open reading frame 59\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranscripts Per Million\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaplan\u0026ndash;Meier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein-protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003essGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle sample gene set enrichment analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIDE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Immune Dysfunction and Exclusion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmune checkpoint genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIC50\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e50% inhibitory concentration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenomics of Drug Sensitivity in Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBLCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebladder urothelial carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBRCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebreast invasive carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHOL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echolangiocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprostate-specific antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebiological process\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecellular component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emolecular function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edisease-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emessenger RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmune checkpoint inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Youth Science Foundation of the Cancer Hospital of Shantou University Medical College (Grant No. 2023A002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets extracted and/or analysed during the current study are available in the following repositories:\u003c/p\u003e\n\u003cp\u003eThe 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).\u003c/p\u003e\n\u003cp\u003eGene 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 \u0026nbsp;(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).\u003c/p\u003e\n\u003cp\u003eThese datasets are publicly available, and all data used in the study can be accessed through the provided links and accession numbers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest related to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R. L., Miller, K. D., Fuchs, H. E. \u0026amp; Jemal, A. Cancer statistics, 2022. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e (1), 7\u0026ndash;33 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasiri, A. et al. Incidence, Gleason Score and Ethnicity Pattern of Prostate Cancer in the Multi-ethnicity Country of Iran During 2008\u0026ndash;2010. \u003cem\u003eUrol. J.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (6), 602\u0026ndash;606 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMottet, N. et al. 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FH535 inhibited metastasis and growth of pancreatic cancer cells. \u003cem\u003eOnco Targets Ther.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 1651\u0026ndash;1670 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prostate cancer, ribosomal biogenesis factor, biomarker, prognosis, drug sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-4899995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4899995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProstate 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..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRBIS 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRBIS 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.\u003c/p\u003e","manuscriptTitle":"Ribosomal biogenesis factor, a novel biomarker for predicting progression-free survival in prostate cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 10:24:04","doi":"10.21203/rs.3.rs-4899995/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"169b7094-4a07-47e1-becd-ec1ac1358101","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37728326,"name":"Biological sciences/Cancer"},{"id":37728327,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":37728328,"name":"Biological sciences/Drug discovery"},{"id":37728329,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2024-10-15T10:24:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 10:24:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4899995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4899995","identity":"rs-4899995","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}