
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39266915
1299
10.1007/s12672-024-01299-0
Analysis
Pan-cancer analysis of m1A writer gene RRP8: implications for immune infiltration and prognosis in human cancers
Huang Zhihui 128
Yoo Koo Han 3
Li Duohui 2860263085@qq.com

4
Yu Qingxin qingxinyu0220@163.com

59
Ye Luxia ylx941016@163.com

6
Wei Wuran weiwuranwch@126.com

7
1 grid.412901.f 0000 0004 1770 1022 Operating Room, West China Hospital, Sichuan University, Chengdu, China
2 https://ror.org/011ashp19 grid.13291.38 0000 0001 0807 1581 West China School of Nursing, Sichuan University, Chengdu, China
3 https://ror.org/01zqcg218 grid.289247.2 0000 0001 2171 7818 Department of Urology, Kyung Hee University, Seoul, South Korea
4 Department of Pharmacy Management, Anqing Municipal Hospital, Anqing, 246000 Anhui China
5 Department of Pathology, Ningbo Clinical Pathology Diagnosis Center, Ningbo, 315211 Zhejiang China
6 grid.469636.8 Department of Public Research Platform, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
7 grid.412901.f 0000 0004 1770 1022 Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041 China
8 https://ror.org/011ashp19 grid.13291.38 0000 0001 0807 1581 West China Tianfu Hospital, Sichuan University, Chengdu, China
9 https://ror.org/030zcqn97 grid.507012.1 Department of pathology, Ningbo Medical Centre Lihuili Hospital, Ningbo, China
12 9 2024
12 9 2024
12 2024
15 43712 6 2024
2 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Ribosomal RNA Processing 8 (RRP8) is a gene associated with RNA modification and has been implicated in the development of several types of tumors in recent research. Nevertheless, the biological importance of RRP8 in pan-cancer has not yet been thoroughly and comprehensively investigated.

Methods

In this study, we conducted an analysis of various public databases to investigate the biological functions of RRP8. Our analysis included examining its correlation with pan-cancer prognosis, heterogeneity, stemness, immune checkpoint genes, and immune cell infiltration. Furthermore, we utilized the GDSC and CTRP databases to assess the sensitivity of RRP8 to small molecule drugs.

Results

Our findings indicate that RRP8 exhibits differential expression between tumor and normal samples, particularly impacting the prognosis of various cancers such as Adrenocortical carcinoma (ACC) and Kidney Chromophobe (KICH). The expression of RRP8 is intricately linked to tumor heterogeneity and stemness markers. Additionally, RRP8 shows a positive correlation with the presence of tumor-infiltrating cells, with TP53 being the predominant mutated gene in these malignancies.

Conclusion

Our findings suggest that RRP8 may serve as a potential prognostic marker and therapeutic target in a variety of cancer types.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-024-01299-0.

Keywords

Pan cancer
RNA 1-methylcytosine
Ribosomal RNA Processing 8
Tumor-infiltrating cells
regional innovation cooperation project of Sichuan Province23QYCX0136 Wei Wuran issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

A central dogma of molecular biology is to elucidate the fundamental principles of the flow of genetic information within biological systems, contributing to our understanding of cellular processes [1, 2]. This principle assumes that genetic information passes from DNA to RNA to protein, involving transcription, translation, and replication processes. It has also been expanded to include RNA self-replication in certain viruses (e.g., borna disease virus), a process that challenges the conventional flow of information and offers insights into the origins of life and molecular evolution [3]. The historical development of RNA self-replication can be traced back to the discovery of ribozymes by Thomas Cech and Sidney Altman in the 1980s [4]. Their work led to the award of the Nobel Prize in Chemistry in 1989. Ribozymes are RNA molecules that can catalyze specific biochemical reactions, such as RNA splicing, showcasing the dual genetic and catalytic roles of RNA. The role of epigenetic modifications in cancer has been a major focus of research since the late twentieth century. Abnormal DNA methylation patterns are one of the earliest epigenetic changes associated with cancer, characterized by hypomethylation of oncogenes and hypermethylation of tumor suppressor genes [5]. Additionally, the field of epigenomics has revealed the intricate layers of regulation in eukaryotic cells. This encompasses various covalent modifications to histones and nucleic acids, changes in nucleosome arrangement, three-dimensional chromatin conformation, RNA splicing machinery, and the functional roles of non-coding genomic elements [6, 7]. Epigenetic mechanisms dynamically regulate chromatin structure and fine-tune gene expression, significantly influencing various biological properties. These mechanisms are integral to epigenetic-based transgenic technologies and play a crucial role in the pathogenesis and intervention of diseases, particularly cancer [8, 9].

Recent technological advances have significantly propelled the field of epigenomics, enabling more in-depth and thorough analysis of epigenetic modifications. High-throughput sequencing technology has facilitated detailed mapping of DNA methylation and chromatin immunoprecipitation sequencing, allowing for rapid and cost-effective sequencing of entire genomes and epigenomes [10, 11]. Furthermore, single-cell RNA sequencing at an unprecedented resolution has provided valuable insights into cellular heterogeneity and chromatin accessibility [12, 13]. In addition, CRISPR-based epigenome editing technologies have made targeted modification of epigenetic marks possible, enabling functional studies of epigenetic regulation [14, 15]. These advancements have played a crucial role in identifying numerous chemical modifications in DNA and RNA, thereby enhancing our understanding of epigenetic regulation. Epigenetic modifications are mediated by specialized enzymes known as 'writers', 'erasers', and 'readers', each playing a distinct role in the attachment, removal, and recognition of chemical groups [16]. Since the 1960s, over 100 RNA modifications have been discovered, which have diverse functions in determining cell fate [17]. As research progresses, it has become evident that RNA not only participates in protein synthesis but also has a direct impact on gene expression through microRNAs and long non-coding RNAs [18]. RNA modifications, which are chemical alterations made to RNA molecules post-transcriptionally, have significant impacts on RNA metabolism, including processes such as splicing, stability, translation, and decay [19, 20]. The most common internal modification in eukaryotic mRNA, N6-methyladenosine (m6A), plays a critical role in regulating embryonic development, stem cell differentiation, circadian rhythms, and stress responses [21]. Other important modifications include 5-methylcytosine (m5C), which stabilizes RNA and improves translation efficiency, and N1-methyladenosine (m1A), which promotes RNA stability and proper folding [22, 23]. In the context of tumors, aberrant RNA modifications are associated with tumorigenesis. For instance, inhibiting the expression of m6A demethylase ALKBH5 can suppress the proliferation and invasion of neuroblastoma, while alterations in m5C levels regulated by NSUN2 are linked to poor prognosis in colorectal cancer and bladder cancer [24]. Targeting these pathways holds therapeutic promise: Specific enzyme inhibitors have demonstrated efficacy in preclinical models, highlighting the importance of understanding RNA modifications in cancer biology and the potential for new therapeutic interventions. This study aimed to investigate the immuno-oncology role of ribosomal RNA processing 8 (RRP8) in human cancers through a pan-cancer analysis. Recent studies have indicated differential expression of RRP8 in tumors, suggesting its significance in tumorigenesis and its potential as a prognostic indicator for liver cancer [25]. RRP8, the yeast ortholog of mammalian nuclear methyl protein (NML), is linked to the 1m1A modification of 25S rRNA [26]. Additionally, it plays a role in energy-dependent silencing of ribosomal DNA, histone recruitment, and DNA repair processes [27, 28]. DNA damage triggers the generation of NML complexes, leading to the formation of rDNA isochromes and suppression of rRNA transcription. Immunohistochemistry findings indicate that breast tumors lacking detectable nucleosomal NML expression are associated with a lower survival rate [28]. Exploring the influence of RRP8 on immune regulatory genes and immune checkpoints can offer valuable insights into its potential as a therapeutic target and biomarker for immunotherapy response. Additionally, conducting pan-cancer analyses can provide a comprehensive understanding of RRP8's role across various cancer types, which is crucial for assessing its broader relevance and potential applications in precision oncology.

Materials and methods

Data acquisition and processing

In accordance with our preceding research, we obtained the Cancer Genome Atlas (TCGA) pan-cancer dataset from the USCS database [29, 30]. We extracted the expression data of RRP8 in each sample by integrating the TCGA prognostic dataset from previous studies [31]. We screened samples with an expression level of 0, starting from normal solid tissue, primary tumor, and primary cancer-derived blood—peripheral blood. To enhance the robustness of our analysis, we subjected each expression value to a log2 (x + 0.001) transformation. Cancer types represented by a sample size of fewer than 3 were systematically excluded. For identifying significant variances, we employed the unpaired Wilcoxon rank-sum test in conjunction with the sign test.

Pan-cancer survival analysis and relationship with clinical features

Metastatic samples from Primary Blood Derived Cancer-Peripheral Blood, primary tumor, and TCGA-SKCM databases. Expression data for 39 cancer types were obtained by excluding samples with an expression level of 0 or a follow-up period of less than 30 days. We stratified patients into either high- or low-expression cohort, predicated on the median expression value corresponding to each gene. The prognostic value of RRP8 was analyzed using the Cox proportional hazards regression model, considering overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS) and progression-free interval (PFI) as prognostic analysis indicators [32, 33]. Furthermore, the correlation between gene expression and clinical stage, gender, and other clinical characteristics was evaluated using the unpaired Wilcoxon rank sum test, sign test, and Kruskal test. A dedicated exploration was also undertaken to discern the potential correlation between RRP8 expression and patient age.

Analysis of tumor heterogeneity, stemness and mutation landscape

Tumor stemness indicators were calculated by analyzing tumor methylation and mRNA expression signatures. These indicators include six categories: DNA methylation-based (DNAss), differentially methylated probe-based (DMPss), enhancer element/DNA methylation-based (ENHss), RNA expression-based (RNAss), appearance-based genetically regulated DNA methylation (EREG-METHss), and RNA methylation based on epigenetic regulation (EREG-METHss). Additionally, Spearman analysis was performed to determine the correlation between tumor stemness characteristics and RRP8 expression. Tumor mutation burden (TMB), mutant allelic tumor heterogeneity (MATH), tumor ploidy, tumor purity, loss of heterozygosity (LOH), neoantigens (NEO), Microsatellite instability (MSI) and homologous recombination deficiency (HRD) serve as reflective indicators of tumor heterogeneity, using Spearman's rank correlation coefficient [33, 34]. We analyzed gene expression and mutations in Adrenocortical carcinoma (ACC), Kidney Chromophobe (KICH), Brain Lower Grade Glioma (LGG), and Liver hepatocellular carcinoma (LIHC). The mutation frequency between samples in each group was evaluated using the Chi-square test [33].

Analysis of RNA modifications, checkpoints, tumor immune microenvironment (TME) and drug sensitivity

We undertook a comprehensive analysis to discern the potential correlations between the expression levels of RRP8 mRNA and an array of immune-related genes. These genes span several categories, including stimulatory checkpoints, heterogeneous checkpoints, and an extensive array of immunomodulatory genes (encompassing receptors, major histocompatibility complex molecules, chemokines, immunosuppressive, and immunostimulatory factors). Employing a structured data matrix, we investigated the relationship between RRP8 expression and 44 genes distributed across three RNA modification subcategories: m1A (containing 10 genes), m5C (containing 13 genes), and m6A (containing 21 genes). To gain insights into the tumor microenvironment, the Timer tool was utilized [35]. Concurrently, an exploration of drug sensitivities was conducted using datasets from the Genomics of Cancer Drug Sensitivity (GDSC) and the Pan-Cancer Cancer Therapy Response Portal (CTRP) via the GSCALite platform [36].

Gene enrichment analysis and nomogram

The LIHC cohort of RNAseq data type from the LinkedOmics database (http://www.linkedomics.org/login.php) was utilized as the research subject [37]. Gene Set Enrichment Analysis (GSEA) tool was employed to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on RRP8-related genes. Additionally, nomogram analysis and visualization were based on survival data of LIHC from TCGA.

Statistical analysis

Based on the normality and homogeneity of variance within the data, either a one-way ANOVA or the Mann‒Whitney U test was employed for the statistical analysis of continuous variables across three or more groups. For quantitative data comparisons between two groups, the Student’s t-test was utilized. All data presented are expressed as the standard deviation. All analyses were conducted using Sanger platform [33]. A p-value below 0.05 was considered statistical significance. ns, P ≥ 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Results

Differential expression and clinical value

Our study revealed notable variations in the expression levels of RRP8 in different types of human cancers as compared to normal samples. Specifically, we found high expression of RRP8 in 15 tumor tissues, while 2 tumor tissues showed low expression (Fig. 1A). Furthermore, our analysis demonstrated a strong correlation between this gene and OS (Fig. 1B), DFS (Fig. 1C), DSS (Fig. 1D), and PFI (Fig. 1E) in numerous cancer types. Notably, these included ACC, KICH, pan-kidney cohort carcinoma (KIPAN), renal papillary cell carcinoma (KIRP), LIHC, glioma (GBMLGG), LGG, lung squamous cell carcinoma (LUSC), Colon adenocarcinoma (COAD), and pheochromocytoma and paraganglioma (PCPG). Moreover, our findings revealed a significant association between RRP8 and ACC as well as LIHC across all the aforementioned prognostic indicators (Fig. 2B–E). Among the 9 types of cancer mentioned above, RRP8 mRNA expression exhibited a significant correlation with age. Specifically, there were 3 positive correlations and 6 negative correlations (Fig. 2B). Additionally, this gene displayed varying degrees of correlation with clinical characteristics (Figure S1).Fig. 1 Differential expression and prognosis analysis of RRP8. A Pan-cancer analysis of RRP8 for differential expression between tumor and normal tissues; B pan-cancer analysis of RRP8 for OS; C pan-cancer analysis of RRP8 for DFS; D pan-cancer analysis of RRP8 for DSS; E pan-cancer analysis of RRP8 for PFI; OS: overall survival; DFS: disease-free survival; DSS: disease-specific survival; PFI: progression-free interval

Fig. 2 The pan-cancer Spearman analysis of tumor heterogeneity and RRP8 expression. A The correlation between HRD and RRP8 level; B the correlation between LOH and RRP8 level; C the correlation between MATH and RRP8 level; D the correlation between MSI and RRP8 level; E the correlation between NEO and RRP8 level; F the correlation between TMB and RRP8 level. HRD: homologous recombination deficiency; LOH: loss of heterozygosity; MATH: mutant-allele tumor heterogeneity; MSI: microsatellite instability; NEO: neoantigen; TMB: tumor mutation burden

Relationship of RRP8 with tumor heterogeneity, stemness and gene mutation

The correlation between RRP8 expression levels and tumor heterogeneity and stemness was further investigated in our study. We discovered a significant correlation between RRP8 expression levels and HRD status in 15 tumors (Fig. 2A). Additionally, a positive correlation between RRP8 expression and LOH was observed in 6 tumors (Fig. 2B). Regarding MATH, we found a negative correlation between RRP8 mRNA expression and 10 tumors (Fig. 2C). Our results demonstrated that RRP8 expression was significantly correlated with MSI in 11 tumors, including GBMLGG, LUSC, and KIRC (Fig. 2D). However, NEO was only associated with RRP8 expression in 6 tumors (Fig. 2E). RRP8 expression was found to be correlated with TMB in 13 tumors (Fig. 2F). In our analysis of tumor stemness, we observed a correlation between the expression level of RRP8 in Thymoma (THYM) and all six tumor stemness (Fig. 3A–F).Fig. 3 The pan-cancer Spearman analysis of tumor stemness and RRP8 expression. A The correlation between tumor stemness and RRP8 level using DMPss; B the correlation between tumor stemness and RRP8 level using DNAss; C the correlation between tumor stemness and RRP8 level using ENHss; D the correlation between tumor stemness and RRP8 level using EREG.EXPss; E the correlation between tumor stemness and RRP8 level using EREG-METHss; F the correlation between tumor stemness and RRP8 level using RNAss. DNAss: DNA methylation based; DMPss: differentially methylated probes-based; EHNss: enhancer elements/DNA methylation-based; RNAss: RNA expression-based; EREG-METHss: epigenetically regulated DNA methylation-based; EREG-METHss: epigenetically regulated RNA methylation-based

Tumor gene mutations play a crucial role in determining their biological behavior. In this study, we focused on analyzing the mutation patterns of RRP8, a gene known for its prominent role in tumors. We compared the mutation profiles of the RRP8 high-expression group with the low-expression group and identified the significantly mutated genes. Our findings revealed that TP53 was the most mutated gene. Additionally, we observed mutations in CTNBI, MUC, TTN and HMCN in ACC, TP53, PTEN, ZAN, TTN and CFAP47 in KICH, IDH1, TP53, ATRX.CIC and EGFR in LGG, TP53, ARID1A, JMUC17 and PCDH7 in LIHC, were significant mutated between the two groups (Fig. 4A–E).Fig. 4 Mutation landscape of RRP8. A Mutation landscapes of RRP8 for pan-cancer; B the top 5 mutation genes between high and low-expression of RRP8 in ACC patients; C the top 5 mutation genes between high and low-expression of RRP8 in KICH patients; D the top 5 mutation genes between high and low-expression of RRP8 in LGG patients; E the top 5 mutation genes between high and low-expression of RRP8 in LIHC patients. ACC: Adrenocortical carcinoma; KICH: Kidney Chromophobe; LGG: Brain Lower Grade Glioma; LIHC: Liver hepatocellular carcinoma

Relationship between RRP8 expression with immune regulation, checkpoints, RNA modification and drug sensitivity

Our findings indicate that RRP8 exhibits a positive correlation with immune regulatory genes in most urinary system tumors, including ACC, KIRP, KICH, KIPAN, and BLCA (Fig. 5A). Notably, RRP8 expression levels are largely negatively correlated with immune regulatory genes (Fig. 5A). Similarly, we observed a positive correlation between RRP8 and several pre-immune checkpoints in various urological tumors (BLCA, KICH, KIRP) as well as UVM, OV, and LGG (Fig. 5B). Consistent with the aforementioned results, RRP8 displays a negative correlation with the majority of tumor infiltrating cells in THYM and THCA, while OV, KIRC, and KIRP exhibit a positive correlation with most tumor infiltrating cells. Of particular interest, BLCA demonstrates a strong correlation with CD4 T+ cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells (Fig. 6A, B). Furthermore, we identified a relationship between RRP8 expression and drug sensitivity, as depicted in Fig. 6C and D. Notably, lapatinib, erlotinib, Saracatinib and gefitinib exhibited relatively promising efficacy among the experimental drugs.Fig. 5 The Spearman analysis of RRP8 expression and regulatory genes and immune checkpoints. A The correlation of RRP8 expression with immune regulatory genes; B the correlation of RRP8 expression with immune checkpoint genes

Fig. 6 The Spearman analysis of RRP8 expression and RNA modification; Tumor immune environment and its correlation with RRP8 expression and drug sensitivity analysis. A The correlation of RRP8 expression with genes of RNA modification; B the correlation of RRP8 expression with immune infiltrating cells using TIMER; C the correlation between gene expression and the sensitivity of GDSC drugs (top 10) in pan-cancer; D The correlation between gene expression and the sensitivity of CTRP drugs (top 3) in pan-cancer

Gene enrichment analysis and nomogram

GSEA analysis showed that enrichment of mitogen-activated protein kinase and extracellular matrix-receptor pathways in LIHC in relation to RRP8 (Fig. 7A, C, D). Additionally, a nomogram for LIHC was constructed incorporating survival data and clinical characteristics (Fig. 7B).Fig. 7 GSEA of RRP8 in the TCGA LIHC cohort and nomogram. A KEGG enrichment analysis of RRP8 in LIHC; B the nomogram of TCGA LIHC cohort; C enrichment plot of RRP8 in MAPK signaling pathway; D enrichment plot of RRP8 in ECM-receptor interaction. GSEA: Gene set enrichment analysis; LIHC: Liver hepatocellular carcinoma; KEGG: Kyoto encyclopedia of genes and genomes. MAPK: Mitogen-activated protein kinase: ECM-receptor: Extracellular Matrix-receptor

Discussion

RNA modifications play a crucial role in the epigenomic machinery, offering a unique perspective to comprehend cancer biology. Unlike static DNA changes, RNA modifications provide a dynamic and reversible mechanism for regulating gene expression [38, 39]. This adaptability is particularly important in cancer, where rapid and adaptive changes in gene expression are vital for survival and proliferation in diverse microenvironments [40]. One of the primary functions of RNA modifications in cancer is their influence on the destiny of mRNA molecules. Modifications like m6A methylation have been observed to affect mRNA stability, decay, and translation efficiency [41–43]. These post-transcriptional modifications can result in altered expression of key oncogenes and tumor suppressors, thereby driving the oncogenic process. For instance, modified mRNAs may evade standard degradation pathways, leading to sustained expression of growth-promoting genes. The overexpression of the methyltransferase METTL3 in BCLA results in the downregulation of PTEN in an m6A-dependent manner, leading to a poor response to treatment in patients [44]. Moreover, elevated METTL3 is found to be an independent factor for poor prognosis in patients with LIHC and gastric cancer [45, 46]. The impact of RNA modification extends beyond mRNA to include various types of non-coding RNA, such as microRNA and long non-coding RNA. These molecules play crucial roles in regulating gene expression and cell signaling pathways associated with cancer. RNA modifications could influence the production, stability, and function of these noncoding RNAs, thereby affecting important cellular processes like apoptosis, angiogenesis, and metastasis [47, 48]. RNA methylation has been shown to play a role in promoting tumorigenesis through the regulation of metabolic pathways. Wang et al. discovered that TRMT8 and TRMT61A can combine to form an m1A methyltransferase complex, leading to an increase in m1A methylation. This increase in methylation further enhances the expression of PPARδ, which in turn triggers cholesterol synthesis and ultimately activates Hedgehog signaling, thereby driving tumorigenesis [49]. Cancer cells exploit the flexibility provided by RNA modifications to adapt to environmental stresses like hypoxia or nutrient deprivation, and to develop resistance to therapeutic interventions. Changes in RNA modification patterns can confer drug resistance through metabolic enzymes. Overexpression of METTL3 in BRCA cell lines has been observed to result in an increased rate of fatty acid beta oxidation, which is a key enzyme leading to chemotherapy resistance [50, 51]. This resistance mechanism enables tumor cells to become resistant to multiple drugs.

The discovery of RNA m1A modifications dates to the second half of the twentieth century [51]. This is a reversible methylation process that involves adding a methyl group to the N1 position of adenosine in cellular transcripts [52]. The modification of m1A in RNA can also alter the secondary structure of the RNA and its interactions with proteins, consequently impacting RNA metabolism, structure, stability, and ultimately regulating gene expression and various cellular processes. Specific methyltransferases primarily mediate this process, and it has been observed in various RNA types, including coding and non-coding RNAs [53, 54]. The significance of m1A modifications is particularly notable in cancer research because it can regulate the expression of tumor suppressor genes in response to changes in cellular conditions, making it a key factor in cancer cell adaptability and resilience. In hepatocellular carcinoma, elevated m1A scores are associated with poorer prognosis and increased immune cell infiltration in tumor tissues, underscoring their significance within the tumor immune microenvironment [55]. The m1A demethylase ALKBH3 regulates glycolysis in cancer cells in a manner dependent on its demethylation activity, highlighting its role in the metabolic reprogramming of cancer cells [56]. In colorectal cancer, m1A modification patterns markedly influence tumor progression, invasion, and metastasis, with high m1A levels correlating with worse prognosis and greater tumor burdens [57]. Furthermore, TRMT6-mediated m1A modification in colorectal cancer enhances cancer stem cell self-renewal and activates the EGFR/ERK signaling pathway, contributing to tumorigenesis [58]. In gynecological cancers, the m1A regulator TRMT10C is a predictor of poorer survival and promotes malignant behaviors, while its silencing results in reduced cancer cell proliferation and migration [59]. Studies have found that m1A regulatory factors can promote the proliferation of cancer cells in gastric tumor [60] and LIHC [61] by regulating the PI3K/AKT pathway [62]. Additionally, ALKBH3, which acts as an eraser for m1A, can also contribute to cancer cell invasion by destabilizing tRNA [63]. Detecting and analyzing m1A modifications primarily rely on advanced sequencing technology and immunoprecipitation methods [64, 65]. However, techniques for accurately identifying and mapping m1A modifications still require further refinement and development. The emergence of new computational methods and improved sequencing technologies holds promise for deepening our understanding of m1A modifications and their role in various biological contexts, particularly in tumorigenesis.

The RRP8 gene is 8.5 base pairs long and is located on chromosome 11p15.4 [54]. It is a protein-coding gene found in the cytoplasm and nucleus. Its functions include RNA polymerase 1 promoter opening and gene expression [26]. Additionally, it can bind to methylated histones and act as a methyltransferase. In vivo experiments have revealed that deficiency of RRP8 affects the translation of proteins involved in carbohydrate metabolism, making it a gene associated with metabolic diseases and obesity [66]. While research in the field of cancer is limited, some studies suggest that overexpression of RRP8 is a poor prognostic marker in LIHC [67]. Furthermore, research by Han et al. demonstrated the correlation between RRP8 expression and the effectiveness of neoadjuvant chemotherapy in triple-negative breast cancer [68]. Our study discovered that RRP8 exhibits differential expression in many tumors, including GMBLGG, LIHC, ACC, KICH, LGG, and LUSC, suggesting a correlation with solid tumors. Additionally, we observed a significant correlation between the expression of RRP8 and advanced age in multiple tumor types. Growing evidence supports the notion that alterations in the epigenetic landscape during aging contribute to tumorigenesis [69, 70]. The substantial association between RRP8 expression and advanced age in tumors underscores the importance of investigating the genetic overlap between aging and tumorigenesis, providing insights into the genomic mechanisms underlying tumor initiation and progression.

This study examined the correlation between RRP8 expression level and immune regulatory genes, immune checkpoints, and tumor infiltrating cells. The results consistently showed a strong correlation between RRP8 and tumor infiltrating cells in urological tumors (KIRC, KIRP, BLCA), as well as a positive correlation with poliovirus receptor (PVR). PVR, also known as CD155, is a transmembrane glycoprotein involved in cell adhesion, contact inhibition, and proliferation [71, 72]. It plays a crucial role in mediating natural killer cell adhesion and triggering natural killer (NK) cell effector functions [73]. PVR forms an immune synapse between NK cells and target cells by binding to CD96 and CD226, activating NK cell cytotoxicity [74]. However, when its expression increases, its isomers compete with membrane-bound PVR for the binding of DNAM-1, allowing tumors to evade detection and elimination by NK cells [75]. While PVR is constitutively expressed at low levels in various tissues, studies have shown that its overexpression is associated with poor prognosis in different malignant tumors, promoting tumor progression and metastasis [76, 77]. Based on these findings, we hypothesize that RRP8 may induce tumor cells to express PVR, thereby inhibiting the function of NK cells.

Tumor heterogeneity arises from variations in epigenetics and the tumor microenvironment, which play crucial roles in tumor growth, metastasis, and response to treatment [78, 79]. In our study, we investigated the association between RRP8 expression and tumor heterogeneity, specifically focusing on TMB (total number of mutations in the coding region of an exon) and MSI. As a marker for predicting immunotherapy response, TMB has shown a good correlation in melanoma [80]. Stomach adenocarcinoma (STAD) accounts for over 1 million new cases annually and ranks as the fifth most prevalent malignant tumor worldwide [81, 82]. Unfortunately, it is often diagnosed at an advanced stage, limiting the efficacy of combination chemotherapy in improving patient outcomes. As a result, immunotherapy is emerging as a promising first-line treatment for advanced gastric cancer patients [83, 84]. Our findings indicate a positive correlation between RRP8 expression and TMB as well as MSI in STAD, suggesting that patients with high RRP8 expression may benefit more from immunotherapy. Cellular stemness refers to the capacity of primitive cells to undergo self-renewal and differentiation. During tumor progression, epigenetic dysregulation of tumor cells can cause cancerous dedifferentiation and acquisition of stemness traits. Undifferentiated tumors are more prone to metastasis, resulting in disease progression and a poor prognosis. KIPAN cancer is a prevalent form of malignant tumors [85]. The survival rates for kidney cancer are notably high (90%) when the tumor remains localized in the kidney. However, these rates drastically decrease to 12% when metastasis occurs. The primary organs affected by metastasis are the lungs, bones, liver, and brain, all of which exhibit limited response to treatment [86, 87]. In this study, we observed a positive correlation between the expression of RRP8 in KIPAN and DMPss and EREG. These findings suggest that patients with higher RRP8 expression may have an increased susceptibility to tumor progression and metastasis.

TP53 is the most frequently altered tumor suppressor gene in solid tumors [88]. As a transcription gene, the P53 protein participates in specific physiological activities depending on the type of cellular stress signal received. These signals can include oncogene activation, DNA damage, and repair [89]. Consistent with this, our findings indicate that the TP53 gene is the most mutated gene in ACC, KICH, and LIHC. Furthermore, we observed that patients with higher RRP8 expression also had a higher frequency of TP53 mutations, highlighting the importance of this gene in tumor development. Our study reveals the potential functions and clinical significance of RRP8 in various solid tumors.

This study has several limitations. The data utilized are exclusively sourced from TCGA and various external databases, which may introduce selection bias. The samples in these databases are often collected under specific conditions and may not comprehensively represent the general oncology patient population. Furthermore, the quality and completeness of the data in public databases may impact the results. Additionally, the role of RRP8 in tumors requires further verification through in vivo and in vitro experiments. Nevertheless, our pan-cancer analysis of RRP8 establishes a solid foundation and offers novel insights for future research.

Conclusions

Our findings suggested that RRP8 could serves a biomarker in many cancers and should deserve more attention of researchers.

Supplementary Information

Supplementary file1Correlation between RRP8 expression and clinicopathological features. the correlation of RRP8 expression with T stages; the correlation of RRP8; expression with N stage; the correlation of RRP8 expression with M stages; the correlation of RRP8 expression with clinical stages;The correlation of RRP8 expression with gender; the correlation of RRP8 expression with age.

Abbreviations

m6A N6-methyladenosine

m5C 5-Methylcytosine

m1A N1-methyladenosine

RRP8 Ribosomal RNA Processing 8

TCGA The Cancer Genome Atlas

OS Overall survival

PFI Progression-free interval

DFS Disease-free survival

DSS Disease-specific survival

DNAss DNA methylation based

DMPss Differentially methylated probes-based

EHNss Enhancer elements/DNA methylation-based

RNAss RNA expression-based

EREG-METHss Epigenetically regulated DNA methylation-based

EREG-METHss Epigenetically regulated RNA methylation-based

TMB Tumor mutation burden

MATH Mutant-allele tumor heterogeneity

LOH Loss of heterozygosity

NEO Neoantigen

HRD Homologous recombination deficiency

MSI Microsatellite instability

ACC Adrenocortical carcinoma

KICH Kidney Chromophobe

LGG Brain Lower Grade Glioma

LIHC Liver hepatocellular carcinoma

KIPAN Pan-kidney cohort carcinoma

KIRP Renal papillary cell carcinoma

GBMLGG Glioma

LUSC Lung squamous cell carcinoma

COAD Colon adenocarcinoma

PCPG Pheochromocytoma and paraganglioma

THYM Thymoma

NK Natural killer

STAD Stomach adenocarcinoma

GDSC Genomics of Cancer Drug Sensitivity

CTRP Pan-Cancer Cancer Therapy Response Portal

PVR Poliovirus receptor

GSEA Gene set enrichment analysis

KEGG Kyoto encyclopedia of genes and genomes

MAPK Mitogen-activated protein kinase

ECM-receptor Extracellular Matrix-receptor

Acknowledgements

We appreciated the Figdraw (www.figdraw.com) and Chengdu Basebiotech Co, Ltd for their assistance in drawing and data process.

Author contributions

ZHH proposed the project, conducted data analysis, interpreted the data, and wrote the manuscript; KHY and QXY ducted data analysis, interpreted the data; DHL, LXY and WRW supervised the project, and interpreted the data. All authors reviewed and edited the manuscript.

Funding

This research was funded by a regional innovation cooperation project of Sichuan Province (Grant No. 23QYCX0136).

Data availability

The data sets presented in this study are available in online repositories. The name and join number of the repository can be found in the article/supplement.

Declarations

Ethics approval and consent to participate

Not available.

Consent for publication

Not available.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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