Abstract

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

Introduction

   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
   [[34]1, [35]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 [[36]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 [[37]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 [[38]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 [[39]6, [40]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 [[41]8, [42]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 [[43]10, [44]11]. Furthermore, single-cell RNA
   sequencing at an unprecedented resolution has provided valuable
   insights into cellular heterogeneity and chromatin accessibility
   [[45]12, [46]13]. In addition, CRISPR-based epigenome editing
   technologies have made targeted modification of epigenetic marks
   possible, enabling functional studies of epigenetic regulation [[47]14,
   [48]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 [[49]16]. Since the 1960s, over 100 RNA
   modifications have been discovered, which have diverse functions in
   determining cell fate [[50]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 [[51]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 [[52]19, [53]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 [[54]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 [[55]22, [56]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 [[57]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 [[58]25]. RRP8, the yeast ortholog of mammalian nuclear methyl
   protein (NML), is linked to the 1m1A modification of 25S rRNA [[59]26].
   Additionally, it plays a role in energy-dependent silencing of
   ribosomal DNA, histone recruitment, and DNA repair processes [[60]27,
   [61]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 [[62]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 [[63]29,
   [64]30]. We extracted the expression data of RRP8 in each sample by
   integrating the TCGA prognostic dataset from previous studies [[65]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 [[66]32, [67]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 [[68]33, [69]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 [[70]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 [[71]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 [[72]36].

Gene enrichment analysis and nomogram

   The LIHC cohort of RNAseq data type from the LinkedOmics database
   ([73]http://www.linkedomics.org/login.php) was utilized as the research
   subject [[74]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
   [[75]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. [76]1A). Furthermore,
   our analysis demonstrated a strong correlation between this gene and OS
   (Fig. [77]1B), DFS (Fig. [78]1C), DSS (Fig. [79]1D), and PFI
   (Fig. [80]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. [81]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. [82]2B). Additionally, this gene
   displayed varying degrees of correlation with clinical characteristics
   (Figure S1).

Fig. 1.

   [83]Fig. 1
   [84]Open in a new tab

   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.

   [85]Fig. 2
   [86]Open in a new tab

   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. [87]2A). Additionally, a positive correlation
   between RRP8 expression and LOH was observed in 6 tumors (Fig. [88]2B).
   Regarding MATH, we found a negative correlation between RRP8 mRNA
   expression and 10 tumors (Fig. [89]2C). Our results demonstrated that
   RRP8 expression was significantly correlated with MSI in 11 tumors,
   including GBMLGG, LUSC, and KIRC (Fig. [90]2D). However, NEO was only
   associated with RRP8 expression in 6 tumors (Fig. [91]2E). RRP8
   expression was found to be correlated with TMB in 13 tumors
   (Fig. [92]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. [93]3A–F).

Fig. 3.

   [94]Fig. 3
   [95]Open in a new tab

   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. [96]4A–E).

Fig. 4.

   [97]Fig. 4
   [98]Open in a new tab

   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. [99]5A). Notably, RRP8 expression
   levels are largely negatively correlated with immune regulatory genes
   (Fig. [100]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. [101]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. [102]6A, [103]B). Furthermore,
   we identified a relationship between RRP8 expression and drug
   sensitivity, as depicted in Fig. [104]6C and D. Notably, lapatinib,
   erlotinib, Saracatinib and gefitinib exhibited relatively promising
   efficacy among the experimental drugs.

Fig. 5.

   [105]Fig. 5
   [106]Open in a new tab

   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.

   [107]Fig. 6
   [108]Open in a new tab

   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. [109]7A, [110]C, [111]D). Additionally, a nomogram for
   LIHC was constructed incorporating survival data and clinical
   characteristics (Fig. [112]7B).

Fig. 7.

   [113]Fig. 7
   [114]Open in a new tab

   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 [[115]38, [116]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 [[117]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 [[118]41–[119]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 [[120]44]. Moreover, elevated METTL3 is found to
   be an independent factor for poor prognosis in patients with LIHC and
   gastric cancer [[121]45, [122]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
   [[123]47, [124]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
   [[125]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 [[126]50, [127]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 [[128]51]. This is a reversible methylation process
   that involves adding a methyl group to the N1 position of adenosine in
   cellular transcripts [[129]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 [[130]53, [131]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 [[132]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 [[133]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 [[134]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 [[135]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 [[136]59]. Studies have found that m1A
   regulatory factors can promote the proliferation of cancer cells in
   gastric tumor [[137]60] and LIHC [[138]61] by regulating the PI3K/AKT
   pathway [[139]62]. Additionally, ALKBH3, which acts as an eraser for
   m1A, can also contribute to cancer cell invasion by destabilizing tRNA
   [[140]63]. Detecting and analyzing m1A modifications primarily rely on
   advanced sequencing technology and immunoprecipitation methods
   [[141]64, [142]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 [[143]54]. It is a protein-coding gene found in the cytoplasm
   and nucleus. Its functions include RNA polymerase 1 promoter opening
   and gene expression [[144]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 [[145]66]. While research in the field
   of cancer is limited, some studies suggest that overexpression of RRP8
   is a poor prognostic marker in LIHC [[146]67]. Furthermore, research by
   Han et al. demonstrated the correlation between RRP8 expression and the
   effectiveness of neoadjuvant chemotherapy in triple-negative breast
   cancer [[147]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 [[148]69, [149]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
   [[150]71, [151]72]. It plays a crucial role in mediating natural killer
   cell adhesion and triggering natural killer (NK) cell effector
   functions [[152]73]. PVR forms an immune synapse between NK cells and
   target cells by binding to CD96 and CD226, activating NK cell
   cytotoxicity [[153]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
   [[154]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 [[155]76, [156]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 [[157]78, [158]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
   [[159]80]. Stomach adenocarcinoma (STAD) accounts for over 1 million
   new cases annually and ranks as the fifth most prevalent malignant
   tumor worldwide [[160]81, [161]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 [[162]83, [163]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 [[164]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 [[165]86, [166]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 [[167]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 [[168]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

   [169]12672_2024_1299_MOESM1_ESM.tiff^ (1.6MB, tiff)

   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.

Acknowledgements