Analysis
Pan-cancer analysis of m1A writer gene RRP8: implications for immune infiltration and prognosis in human cancers
Zhihui Huang 1,2,8 . Koo Han Yoo3 . Duohui Li4 . Qingxin Yu5,9 . Luxia Ye6 . Wuran Wei7
Received: 12 June 2024 / Accepted: 2 September 2024
Published online: 12 September 2024
@ The Author(s) 2024 OPEN
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.
Keywords Pan cancer . RNA 1-methylcytosine . Ribosomal RNA Processing 8 . Tumor-infiltrating cells
Abbreviations
| m6A | N6-methyladenosine |
| m5C | 5-Methylcytosine |
| m1A | N1-methyladenosine |
| RRP8 | Ribosomal RNA Processing 8 |
| TCGA | The Cancer Genome Atlas |
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s12672-024- 01299-0.
☒ Duohui Li, 2860263085@qq.com; ☒ Qingxin Yu, qingxinyu0220@163.com; ☒ Luxia Ye, ylx941016@163.com; ☒ Wuran Wei, weiwuranwch@126.com; Zhihui Huang, 624539501@qq.com; Koo Han Yoo, yookoohan@khu.ac.kr | 1Operating Room, West China Hospital, Sichuan University, Chengdu, China. 2West China School of Nursing, Sichuan University, Chengdu, China. 3Department of Urology, Kyung Hee University, Seoul, South Korea. 4Department of Pharmacy Management, Anqing Municipal Hospital, Anqing 246000, Anhui, China. 5Department of Pathology, Ningbo Clinical Pathology Diagnosis Center, Ningbo 315211, Zhejiang, China. 6Department of Public Research Platform, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China. 7Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China. 8West China Tianfu Hospital, Sichuan University, Chengdu, China. 9Department of pathology, Ningbo Medical Centre Lihuili Hospital, Ningbo, China.
Check for updates
Discover Oncology
(2024) 15:437
| https://doi.org/10.1007/s12672-024-01299-0
Discover
| 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 |
1 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 [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 Alt- man 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 epigenom- ics 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
Discover
splicing machinery, and the functional roles of non-coding genomic elements [6, 7]. Epigenetic mechanisms dynami- cally 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 modifica- tions 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-tran- scriptionally, 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 immuno- therapy 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 preci- sion oncology.
2 Materials and methods
2.1 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 tis- sue, 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.
Discover
2.2 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.
2.3 Analysis of tumor heterogeneity, stemness and mutation landscape
Tumor stemness indicators were calculated by analyzing tumor methylation and mRNA expression signatures. These indi- cators 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, Spear- man 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 heterozy- gosity (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 evalu- ated using the Chi-square test [33].
2.4 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 his- tocompatibility 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].
2.5 Gene enrichment analysis and nomogram
The LIHC cohort of RNAseq data type from the LinkedOmics database (http://www.linkedomics.org/login.php) was uti- lized 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.
2.6 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
Discover
deviation. All analyses were conducted using Sanger platform [33]. A p-value below 0.05 was considered statistical sig- nificance. ns, P≥0.05; * , P< 0.05; ** , P< 0.01; *** , P < 0.001.
3 Results
3.1 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 carci- noma (LUSC), Colon adenocarcinoma (COAD), and pheochromocytoma and paraganglioma (PCPG). Moreover, our find- ings 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).
3.2 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). Addi- tionally, 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).
Tumor gene mutations play a crucial role in determining their biological behavior. In this study, we focused on analyz- ing 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).
3.3 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 cor- related 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). Consist- ent 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.
Discover
A
**
*
*
*
*
*
*
7
6
Expression
5
T
T
T
Group
4
1
I
T
T
T
·
1
I
O
.
1
I
T
Tumor Normal
T
P
L
E
3
I
O 1
I
2
1
0
GBM(T=153,N=5)
GBMLGG(T=662,N=5)
LGG(T=509,N=5)
CESC(T=304,N=3)
LUAD(T=513,N=109)
COAD(T=288,N=41)
COADREAD(T=380,N=51)
BRCA(T=1092,N=113)
ESCA(T=181,N=13)
STES(T=595,N=49)
STAD(T=414,N=36)
HNSC(T=518,N=44)
LIHC(T=369,N=50)
READ(T=92,N=10)
PCPG(T=177,N=3)
KICH(T=66,N=129)
CHOL(T=36,N=9)
B
C
CancerCode
pvalue
Hazard Ratio(95%C1)
TOGA-LIHC(N-294)
3.0e-3
1.87(1 24,2.83)
TOGA-ACC(N=44)
3.8c-3
6.16(1.76,21.61)
TOGA-COAD(N=103)
0.01
TOGA-KIRP(N-177)
9.92(2.02,48.78)
0,06
2.98(0.95,9.31)
TOGA-LGG(N=126)
0.10
2.60(0.84,8.06)
TOGA-COADREAD(N-132)
0.11
2.99(0.82,10.94)
TOGA-GBMLOG(N-127)
0.12
2.46(0.80,7.58)
TOGA-KICH(N-29)
0,14
98.60(0.16,59648.13)
TCGA-LUSC(N-292)
0.26
1.43(0.77,2.68)
TOGA-KIPAN(N-319)
0.30
1.49(0.71,3.11)
TCGA-HNSC(N-128)
0.31
1.57(0.66,3.71)
TCGA-PRAD(N-337)
0.37
1.74(0.52,5.84)
TCGA-SARC(N-149)
1.20(0.69,2.08)
TOGA-PCPG(N=152)
0.52 0.53
2.02(0.22,18.50)
TCGA-TGCT(N=101)
0.59
1.32(0.47,3.69)
TCGA-CESC(N=171)
0.67
1.25(0.44,3.54)
TCGA-DLBC(N=26)
0.91
1.16(0.10,13.81)
TOGA-LUAD(N=294)
0.93
TOGA-BLCA(N=184)
1.02(0.64,1.63)
0.97
1.02(0.45,2.27)
TOGA-UCEC(N-115)
0,97
1-0-1
1.01(0.52,1.98)
TOGA-ESCA(N-84)
5.2c-3
TCGA-STES(N-316)
0.18(0.06,0.60)
0.03
0.50(0.27,0.93)
TOGA-OV(N-203)
0.05
0.74(0.54,1.00)
TOGA-READ(N-29)
0,07
TOGA-THCA(N=352)
0.06(2.0e-3,1.56)
0.19
0.47(0.15,1.46)
TCGA-STAD(N-232)
0.42
1
TCGA-BRCA(N-905)
0.73(0.34,1.57)
0.44
0.84(0.54,1.30)
TOGA-MESO(N=14)
0.52
0.48(0.05,4.62)
TCGA-PAAD(N=68)
0.57
TCGA-UCS(N=26)
0.77(0.30,1.94)
0.64
TCGA-CHOL(N=23)
0.71(0.17,3.04)
0.78
0.81(0.18,3.59)
TCGA-KIRC(N-113)
0.92
0.94(0 27.3.32)
6 log2(Hazard Ratio(95%CI))
10 12 14
D
E
log2(Hazard Ratio(95%CI))
-4
4
log2(Hazard Ratio(95%CI))
log2(Hazard Ratio(95%CI))
| CancerCode | pvalue | Hazard Ratio(95%(1) | ||
|---|---|---|---|---|
| TOGA-GBMLGG(N=619) | 2.2e-16 | 3.49(2.59,4.71) | ||
| TOGA-LIHC(N=341) | 2.004 | 2.81(1.84,4.30) | ||
| TOGA-ACC(N=77) | 1.2e-3 | 3.45(1.63,7.28) | ||
| TOGA-KICH(N=64) | 0.01 | 7.24(1.59,32.91) | ||
| TOGA-PCPG(N=170) | 0.02 | 10.67(1.32,86.04) | ||
| TOGA-LGG(N-474) | 0,03 | 1/64(1.04,2 58) | ||
| TOGA-LUSC(N=468) | 0.16 | 1.28(0.91,1.80) | ||
| TOGA-HNSC(N=509) | 0.18 | 1.24(0,90,1.70) | ||
| TOGA-GBM(N=144) | 0.21 | 1.32(0.86,2.02) | ||
| TOGA-KIRC(N-515) | 0.21 | 1.31(0.86,1.99) | ||
| TOGA-MESO(N=84) | 0.23 | 1.58(0.75,3.31) | ||
| TOGA-COAD(N=278) | 0.27 | 1.43(0.76,2.70) | ||
| TOGA-SKCM-M(N=347) | 0.27 | 1.18(0.88,1.58) | ||
| TOGA-KIPAN(N=855) | 0.28 | 1.21(0.85,1.72) | ||
| TOGA-THCA(N=501) | 0.39 | 1.85(0.46,7.37) | ||
| TOGA-SKCM(N-444) | 0.40 | 1.12(0.85,1.48) | ||
| TOGA-LAML(N=144) | 0.43 | 1.24(0.73,2.13) | ||
| TOGA-BLCA(N-398) | 0.48 | 1.12(0.82,1.54) | ||
| TOGA-UVM(N=74) | 0.52 | 1.46(0.46,4.70) | ||
| TOGA-ESCA(N-175) | 0.55 | 1.17(0.70,1.95) | ||
| TOGA-STES(N=547) | 0.56 | 1.10(0.80,1.52) | ||
| TOGA-BRCA(N-1044) | 0.59 | 1.30(0.78,1.56) | ||
| TOGA-COADREAD(N=368) | 0.59 | 1.17(0.67,2.02) | ||
| TOGA-LUAD(N-490) | 0.65 | 1.08(0.77,1.53) | ||
| TOGA-PAAD(N=172) | 0.74 | 1.09(0.66,1.79) | ||
| TOGA-PRAD(N=492) | 1.2%(0.14,12.11) | |||
| TOGA-STAD(N=372) | 0.93 | 1.02(0.68,1.53) | ||
| TOGA-THYM(N=117) | 0.15 | 0.19(0.02,1.81) | ||
| TOGA-UCEC(N=166) | 0.23 | 0.74(0.45,1.21) | ||
| TOGA-READ(N-90) | 0.26 | 0.47(0.12,1.76) | ||
| TOGA-DLBC(N=44) | 0.26 | 0.40(0.08,1.99) | ||
| TOGA-CESC(N=273) | 0.42 | 0.78(0.43,1.43) | ||
| TOGA-CHOL(N=33) | 0.43 | 0.64(0.21,1.94) | ||
| TOGA-SARCIN-254) | 0.58 | 0.88(0.57,1.37) | ||
| TOGA-UCS(N=55) | 0.59 | -- 4 | 0.82(0.40,1.70) | |
| TOGA-TOCT(N=128) | 0.65 | 0.49(0.02,11.09) | ||
| TOGA-OV(N=406) | 0.71 | 0.96(0.77,1.20) | ||
| TOGA-KIRIN-276) | 0.84 | 0.91(0.37,2.24) | ||
| TOGA-SK.CM-P(N=97) | 1.00 | 1.00(0.45,2.20) | ||
| CancerCode | pvalue | Hazard Ratio(95%(T) | ||
|---|---|---|---|---|
| TOGA-GBMLGG(N-598) | 3.40-17 | 3.97(2.88,5.48) | ||
| TOGA-ACC(N=75) | 2.6c-3 | 3.27(1 51,7.07) | ||
| TOGA-LIHC(N-333) | 3.60-3 | 2.26(1 30,3,91) | ||
| TOGA-LGG(N=466) | 7.2e-3 | 1.95(1 20,3.17) | ||
| TOGA-LUSCIN-418) | 8.4c-3 | 2.11(1.22,3.64) | ||
| TOGA-KIPAN(N-840) | 0.02 | 1.66(1.09,2.52) | ||
| TOGA-KIRC(N=504) | 0.03 | 1.77(1.08,2.90) | ||
| TOGA-KICH(N=64) | 0.03 | 7.54(1.32,42.98) | ||
| TOGA-PCPG(N=170) | 0.06 | 8.55(0.76,95.95) | ||
| TOGA-COAD(N=263) | 0.11 | 2.15(0.85,5.46) | ||
| TOGA-COADREAD(N-347) | 0.14 | 1.92(0.82,4.50) | ||
| TOGA-HNSC(N-485) | 0.20 | 1.31(0.87,1.98) | ||
| TOGA-THCA(N-495) | 0.21 | 3.85(0.48,30.71) | ||
| TOGA-MESO(N-64) | 0.28 | 1.70(0.65,4.43) | ||
| TOGA-GBM(1-131) | 0.30 | 1.29(0.80,2.08) | ||
| TOGA-UVM(N-74) | 0.36 | 1.83(0.51,641) | ||
| TOGA-SKCM-M(N=341) | 0.44 | 1.13(0.83,1.54) | ||
| TOGA.BRCA(N=1025) | 0.47 | 1.19(0.74,1.89) | ||
| TOGA-SKCM(N-438) | 0.47 | L.11(0.83,1.49) | ||
| TOGA-PAAD(N=166) | 0.58 | 1.18(0.66,2.10) | ||
| TOGA-BLCA(N=385) | 0.63 | 1-0-1 | 1.10(0.75,1.61) | |
| TOGA-KIRP(N=272) | 0.65 | 1.28(0.44,3.75) | ||
| TOGA-SKCM-P(N=97) | 0.65 | 1.25(0.47,3.36) | ||
| TOGA-THYM(N=117) | 0.73 | 1.68(0.09,29.76) | ||
| TOGA-STES(N-524) | 0.83 | 1.01 1-9-1 | 1.05(0.70,1.57) | |
| TOGA-ESCAIN-173) | 1.06(0.56,2.00) | |||
| TOGA-DLBC(N=44) | 0.38 | 0.37(0.04,3.48) | ||
| TOGA-CESC(N-269) | 0.45 | 0.77(0.39,1.52) | ||
| TOGA-UCEC(N=164) | 0.45 | 0.79(0.44,1.44) | ||
| TOGA-CHOL(N-12) | 0.46 | 0.65(0.21,2.04) | ||
| TOGA-TGCT(N=128) | 0.52 | 0.29(6.8e-3,12.43) | ||
| TOGA-SARC(N=248) | 0.58 | 0.87(0.54,1.41) | ||
| TOGA-PRAD(N=490) | 0.62 | 0.45(0.02.10.63) | ||
| TOGA-OV(N=377) | 0.68 | 0.95(0.75,1.21) | ||
| TOGA-LUAD(N=457) | 0.79 | 0.94(0.61,1.45) | ||
| TOGA-READ(N-84) | 0.93 | 0.89(0.08,9.91) | ||
| TOGA-UCS(N=53) | 0.95 | 0.98(0.45,2.10) | ||
| TOGA-STAD(N-151) | 0.99[0 59.1.67) |
| CancerCode | pvalue | Hazard Ratio(95%CT) | ||
|---|---|---|---|---|
| TOGA-GBMLGG(N-616) | 4.4c-14 | 2.75(2.12,3.58) | ||
| TOGA-ACC(N=76) | 1.le-4 | 3.50(1.86,6.60) | ||
| TOGA-LIHC(N-340) | 2.10-4 | 2.00(1.39,2.89) | ||
| TOGA-PCPG(N=168) | 2.46-3 | 5.04(1.73,14.65) | ||
| TOGA-KIPAN(N-845) | 5.3e-3 | 1.62(1.16,2.27) | ||
| TOGA-LUSC(N=467) | 0.02 | 1.66(1.11,2 50) | ||
| TOGA-KICH(N-64) | 0.02 | 4.86(1.33,17.74) | ||
| TOGA-LGG(N=472) | 0.03 | 1.48(1.04,2.12) | ||
| TOGA-GBM(N=143) | 0.03 | 1.63(1.06,2.50) | ||
| TOGA-KIRC(N=508) | 0.04 | 1.57(1.03,2.39) | ||
| TOGA-KIRP(N=273) | 0.05 | 2.19(1.01,4.74) | ||
| TOGA-COAD(N=275) | 0.08 | 1.71(0.95,3.08) | ||
| TOGA-HNSC(N=508) | 0.12 | 1.30(0.93,1.80) | ||
| TOGA-UVM(N=73) | 0.29 | 1.73(0.62,4.85) | ||
| TOGA-PRAD(N-492) | 0.42 | 1.33(0.66,2.69) | ||
| TOGA-COADREAD(N=363) | 0.43 | 1.23(0.74,2.03) | ||
| TOGA-MESO(N=82) | 0.51 | 1.31(0.59,2.90) | ||
| TOGA-THYM(N-117) | 0.53 | 1.53(0.41,5.75) | ||
| TOGA-TGCT(N-126) | 0.60 | 1.29(0.50,3.32) | ||
| TOGA-PAAD(N=171) | 0.77 | 1.07(0.67,1.71) | ||
| TOGA-THCA(N-499) | 0.83 | 1.09(0.50,2.39) | ||
| TOGA-BLCA(N-397) | 0.90 | 1.02(0.74,1.41) | ||
| TOGA-READ(N-88) | 0.01 | 0.19(0.05,0.71) | ||
| TOGA-STES(N=548) | 0.06 | 0.72(0.52,1.01) | ||
| TOGA-STAD(N-375) | 0.11 | 0.70(0.45,1.08) | ||
| TOGA-CHOL(N-33) | 0.21 | 0.48(0.15,1.50) | ||
| TOGA-ESCA(N-173) | 0.25 | 0.74(0.44,1.24) | ||
| TOGA-OV(N-406) | 0.32 | 0.90(0.73,1.11) | ||
| TOGA-UCEC(N-166) | 0.36 | 0.82(0.54,1.25) | ||
| TOGA-LUAD(N-486) | 0.40 | 0.87(0.63,1.20) | ||
| TOGA-CESC(N-273) | 0.51 | 0.82(0.46,1.48) | ||
| TOGA-SARC(N=250) | 0.57 | 0.90(0.61.1.31) | ||
| TOGA-SKCM(N-434) | 0.58 | 0.94(0.74,1.18) | ||
| TOGA-SKCM-M(N-338) | 0.64 | 1-0-1 | 0.94(0.74,1.21) | |
| TOGA-DLBC(N-43) | 0.73 | 0.78(0.19,3.23) | ||
| TOGA-UCS(N=55) | 0.75 | 0.89(0.42,1.87) | ||
| TOGA-SKCM-P(N-96) | 0.79 | 0.91(0.47,1.78) | ||
| TOGA-BRCA(N-1043) | 0.98 | 0.99(0.71,1.40) |
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
3.4 Gene enrichment analysis and nomogram
GSEA analysis showed that enrichment of mitogen-activated protein kinase and extracellular matrix-receptor path- ways 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).
Discover
A
B
UCEC(N=178)
pValue
pValue
BRCA(N=1044)
0.0
ACC(N=74)
READ(N=87)
0.0
ESCA(N=158)
UCEC(N=175)
LUAD(N=500)
-0.2
CHOL(N=36)
-0.2
CHOL(N=36)
-0.4
CESC(N=291
CESC(N=291)
BRCA(N=1035
-0.4
OV(N=406)
UVM(N=79)
-0.6
STAD(N=400)
LUAD(N=495
-0.6
STES(N=563)
STAD(N=405)
-0.8
STES(N=558)
ESCA(N=158)
-0.8
READ(N-90)
-1.0
THYM(N=102)
KICH(N-66)
LUSC(N=476)
1.0
SKCM(N=102)
TGCT(N=147)
LGG(N=503)
DLBC(N=46)
OV(N=400)
UCS(N=56)
HNSC(N=505)
HNSC(N=500)
THYM(N-103)
PAAD(N=158)
BLCA(N-397
PRAD(N=469)
SARC(N=241)
GBMLGG(N=646)
MESO(N=81)
COADREAD(N=362)
PAAD(N=158)
BLCA(N=397
PRAD(N=470)
LAML(N=111)
MESO(N=81)
PCPG(N-160)
SARC(N=241)
SKCM(N=102)
LUSC(N=490)
KIRC(N=478)
LAML(N=111)
KIRC(N=495)
GBM(N=143)
COAD(N=275
COADREAD(N=373)
PCPG(N=159)
TGCT(N=147)-
KIPAN(N=844)
UVM(N=79)
KIPAN(N=821)
COAD(N=283)
KIRP(N=278)
LIHC(N=356)
LGG(N=501)
UCS(N=56)
LIHC(N=353
GBM(N=143)
KICH(N=65
KIRP(N=283)
THCA(N=462
THCA(N=461
DLBC(N=46
ACC(N=75)
GBMLGG(N=644)
-0.4
-0.2
0.0
0.2
0,4
-0.2
0.0
0.2
Correlation coefficient(spearman)
Correlation coefficient(spearman)
C
D
GBM(N=149)
pValue
UCEC(N=175)
0.0
GBMLGG(N=657)
pValue
GBMLGG(N=649)
ACC(N=77
0.0
ACC(N=77)
-0.2
CESC(N=302)
SKCM(N=102)
PCPG(N=177
-0.2
SARC(N=234)
-0.4
THYM(N=118)
THYM(N=118)
READ(N=89)
-0.4
BLCA(N=407)
-0.6
GBM(N=151
LGG(N=506)
-0.6
LGG(N=500)
LAML(N=123)
-0.8
PRAD(N=495)
MESO(N=83
-0.8
BRCA(N=980)
HNSC(N=498)
-1.0
SARC(N=252)
1.0
DLBC(N-37
COADREAD(N=374)
PAAD(N=168)
OV(N=303
COAD(N=285
PRAD(N=492)
THCA(N=487
BRCA(N=1039)
STAD(N=409)
BLCA(N=407
PAAD(N=176)
PCPG(N=176)
KIRC(N=334)
LAML(N=129)
CESC(N=286
KIRP(N=285)
UCS(N-57
READ(N-90)
OV(N=303)
THCA(N=493)
CHOL(N-35
LUAD(N=511)
LIHC(N=367
STES(N=589)
LUSC(N=485)
CHOL(N=36)
LUAD(N=508)
ESCA(N=180)
COADREAD(N=372)
TGCT(N=148)
LIHC(N=356)
STES(N=592)
COAD(N=282)
STAD(N=412)
LUSC(N=490
KIRP(N=279)
KIPAN(N=679)
HNSC(N=500)
MESO(N=81
KIPAN(N=688)
KICH(N=66
UCS(N=57
ESCA(N=180)
UCEC(N=180)
UVM(N=79
KIRC(N=337
UVM(N=79
TGCT(N=143)
SKCM(N=102)
KICH(N=66
DLBC(N=47
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Correlation coefficient(spearman)
Correlation coefficient(spearman)
E
F
DLBC(N=33)
pValue
0.0
THYM(N=118)
pValue
THCA(N=175)
BRCA(N=981)
0.0
CHOL(N=30)
-0.2
LAML(N=126)
KICH(N=39)
UVM(N=79)
-0.2
LUAD(N=462)
-0.4
CHOL(N=36)
LUAD(N=509)
-0.4
MESO(N=65)
LGG(N=403)
-0.6
PRAD(N=492)
KIRP(N=279)
-0.6
SKCM(N=83)
-0.8
KIPAN(N=679)
PRAD(N=341)
DLBC(N=37
-0.8
BRCA(N=857)
1.0
CESC(N=286)
GBMLGG(N=520)
BLCA(N=407)
-1.0
THCA(N=489
SARC(N=177)
LUSC(N=486)
LUSC(N=447)
COAD(N=282)
ACC(N=57)
OV(N=303
READ(N=81)
COADREAD(N-372)
CESC(N-244)
SARC(N=234)
HNSC(N=498)
HNSC(N=446)
READ(N=90
TGCT(N=94)
UCS(N=57)
UVM(N=38)
TGCT(N=143)
GBM(N=117
KIRC(N=334)
KIPAN(N=636)
KICH(N-66)
THYM(N=64)
PCPG(N=177)
SKCM(N=102)
KIRP(N=260)
MESO(N=82)
UCS(N=50)
LIHC(N=357)
COADREAD(N=336)
ESCA(N=180)
KIRC(N=337)
GBM(N=149)
LIHC(N=337)
STES(N=589)
STAD(N=409)
BLCA(N=375)
PAAD(N=171)
COAD(N=255)
LGG(N-501)
PAAD(N=113)
UCEC(N=175)
PCPG(N=60)
ACC(N=77)
UCEC(N=166)
GBMLGG(N=650)
-0.2
-0.2
0.0
0.2
0.0
0.2
0.4
Correlation coefficient(spearman)
Correlation coefficient(spearman)
Discover
A
B
THYM(N=119)
pValue
THCA(N=499)
O.C
THYM(N=119)
pValue
BLCA(N=403)
THCA(N=499
0.0
LUAD(N=451)
-0.2
BLCA(N=403)
OV(N-9
-0.2
CESC(N=301
OV(N=9)
-0.4
CESC(N=301)
MESO(N=87)
LUAD(N=451)
-0.4
LAML(N=170)
- 0.€
BRCA(N=774)
LAML(N=170)
-0.6
BRCA(N=774)
SARC(N-253)
-0.8
CHOL(N=36)
DLBC(N=47)
SARC(N=253)
-0.8
TGCT(N=147)
- 1.0
KICH(N-65
LIHC(N-366)
1.0
LIHC(N=366)
KICH(N=65)
LUSC(N=361
MESO(N=87
CHOL(N=36)
DLBC(N=47
LUSC(N=361)
PRAD(N=491
TGCT(N=147)
COAD(N=271
READ(N=87)
COAD(N=271)
COADREAD(N=358)
COADREAD(N=358)
UCEC(N=173)
PCPG(N=176
UCEC(N=173)
STAD(N=369)
KIRP(N=268)
PCPG(N=176)
GBM(N=51)
KIRP(N=268)
GBM(N=51)
PRAD(N=491
STAD(N=369)
STES(N=548)
STES(N=548)
HNSC(N=512
READ(N=87
PAAD(N=156)
PAAD(N=156)
ESCA(N=179)
HNSC(N=512)
KIRC(N-309
KIRC(N=309)
LGG(N=507
KIPAN(N=642)
UCS(N=57
KIPAN(N=642)
LGG(N=507
ESCA(N=179
ACC(N=76
SKCM(N=102)
ACC(N=76
SKCM(N=102)
GBMLGG(N=558)
UCS(N=57
UVM(N=79)
GBMLGG(N=558)
UVM(N=79)
-0.4
-0.2
0.0
0.2
0.4
-0.4
-0.2
0.0
0.2
0.4
Correlation coefficient(spearman)
Correlation coefficient(spearman)
C
D
THYM(N=119)
pValue
KICH(N=65
0
TGCT(N=147)
pValue
CHOL(N-36)
0.0
BLCA(N=403)
OV(N-9
-0
BRCA(N=1080)
SARC(N=253)
-0.2
SARC(N=253)
LUAD(N=451)
-0
LGG(N=507
CESC(N=301
OV(N=297)
-0.4
CHOL(N-36
-0
GBMLGG(N=659)
PAAD(N=156)
-0.6
BRCA(N=774)
LAML(N=170)
-0
BLCA(N=403)
LIHC(N-366
UCEC(N=177)
0.8
THCA(N=499
-1
KIRP(N=283)
-1.0
LUSC(N=361
HNSC(N=512)
MESO(N=87
UCS(N=57)
DLBC(N=47
THCA(N=499)
STAD(N-369
KIRC(N=512)
PAAD(N=156
ESCA(N=179)
COAD(N-271
LUSC(N=483)
KIRP(N=268
KIPAN(N=860)
KIRC(N=309
LUAD(N=507)
PCPG(N=176
CESC(N=301
STES(N=548
PRAD(N-491)
COADREAD(N=358)
UVM(N=79)
KIPAN(N=642)
MESO(N-87)
TGCT(N=147
STES(N=578)
PRAD(N=491
LAML(N=167)
HNSC(N=512)
GBM(N=152)
READ(N=87)
KICH(N=65)
UCEC(N=173
STAD(N=399)
ESCA(N=179)
LIHC(N-366)
ACC(N=76
SKCM(N=102)
LGG(N=507
COAD(N-281)
GBM(N=51)
PCPG(N=176)
UCS(N=57
THYM(N-119)
SKCM(N=102
COADREAD(N=369)
GBMLGG(N=558)
ACC(N-76)
UVM(N=79
DLBC(N=47)
READ(N=88)
-0.4
-0.2
0.0
0.2
0.4
-0.4
-0.2
0.0
0.2
0.4
Correlation coefficient(spearman)
Correlation coefficient(spearman)
E
F
THYM(N=119)
pValue
O.C
GBMLGG(N=659)
pValue
THCA(N=499)
0.0
BLCA(N=403)
BRCA(N=1080)
-0.2
KIRP(N=283
LAML(N=170)
PRADIN-491
-0.2
CHOL(N=36)
BRCA(N=774)
-0.4
LGG(N-507
TGCT(N=147
-0.4
CESC(N=301
OV(N=9
- 0.€
UVM(N=79
SKCM(N=102
-0.6
LUAD(N=451
SARCIN-253
-0.8
BLCA(N=403
UCS(N=57
-0.8
LUSC(N=361
KICH(N=65
- 1.0
KICH(N-65)
KIRC(N=512)
-1.0
LIHC(N=366
CESC(N=301
DLBC(N=47
LAML(N=167
COAD(N=271)
GBM(N-152)
MESO(N=87)
PCPG(N=176
GBM(N=51)
LUAD(N=507
TGCT(N=147
KIPAN(N=860)
UCEC(N=173)
THCA(N=499
COADREAD(N=358)
ACC(N=76
PCPG(N=176
OV(N-297
STAD(N=369
CHOL(N=36
KIRP(N-268
LUSC(N=483
READ(N=87
PAAD(N=156)
STES(N=548)
STADIN-399
PRAD(N=491
STES(N=578
PAAD(N=156)
HNSC(N=512
LGG(N=507
DLBC(N=47
HNSC(N=512)
SARC(N=253
KIRC(N=309
COAD(N=281
KIPAN(N=642 UCS(N=57)
ESCA(N=179)
LIHC(N-366
SKCM(N=102
COADREAD(N=369)
ESCA(N=179
MESO(N=87
ACC(N=76)
UCEC(N=177
GBMLGG(N=558)
READ(N=88
UVM(N=79)
THYM(N=119)
-0.4
-0.2
0.0
0.2
0.4
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
Correlation coefficient(spearman)
Correlation coefficient(spearman)
Discover
A
GBM(N=149,0.7%)-
Missense_Mutation
GBMLGG(N=649,0.2%)
Splice_Site
CESC(N-286,0.3%)
Frame_Shift_Ins
LUAD(N-508,1.8%)
Frame Shift_Del
Nonsense_Mutation
COAD(N=282,0.7%)
Nonstop_Mutation
COADREAD(N-372,1.3%)
-2.0
LAML(N-123,0.8%)
STES(N=589,0.7%)
SARC(N-234,0.4%)
KIPAN(N-679,0.1%)
STAD(N-409,1.0%)
UCEC(N=175,5.7%)
1.5
HNSC(N-498,0.4%)-
KIRC(N-334,0.3%)
LUSC(N-485,1.4%)
LIHC(N-356,0.3%)
THCA(N-487,0.2%)
READ(N-90,3.3%)
-1.0
PAAD(N=168,1.2%)
OV(N-303,1.3%)
SKCM(N-102,2.0%)
BLCA(N=407,0.5%)-
B
457aa
AdoMet MTases
47
MutCount
Missense_Mutation Frame Shift Del Nonsense_Mutation
MutCount2.
0
SampleGroup
0
3
10
Splice_Site
In_Frame_Del Frame_Shift_Ins
TP53(0.20)
35.1%
SampleGroup: HighExp LowExp
CTNNB1(0.32)
32.4%
MUC16(0.21)
29.7%
TTN(0.14)
24.3%
HMCNI(0.97)
18.9%
C
MutCount
Missense_Mutation Nonsense_Mutation Splice_Site
MutCount2
-
J
SampleGroup
0
5
15 20
Frame_Shift_Del
SampleGroup: HighExp LowExp
TP53(0.21)
67.9%
PTEN(1.00)
17.9%
ZAN(0.94)
17.9%
TTΝ(0.65)
14,3%
CFAP47(1.00)
10.7%
D
Mut Count
Missense Mutation Framo_Shill_Del
MulCount
-
A
Nonsense_Mutatice
SampleGroup
0
100 200 300
Frame Shift_Irs
In_Frame_Del Splice_Site
IDH 1(5.93-4)
86.8%
SampleGroup:
Low Exp HighExp
TPS3(1.40-3)
51.5%
ATRX(3.2e-5)
37.2%
CIC(4.55-3)
22.7%
EGFR(0.01)
7.0%
E
-& MutCount
Frame_Shift_Del Missense_Mutation Nonsense Mutation
MutCount
/
SampleGroup
0
100
In_Frame_Del Frame_Shift_Ins Splice_Site In_Frame_Ins
TP53(2.4c-4)
71.8%
SampleGroup: HighExp LowExp
ARIDIA(9.2e-3)
17.6%
MUCI7(0.03)
10.6%
PCDH7(0.05)
9.9%
ANKRDI2(0.03)
8.5%
Discover
A
3
B
Type
correlation coefficient
IL13
correlation coefficient
-1.0-0.5 0.0 0.5 1
1.0
VTCNI
pValue
C10orf54
-1.0-0.5 0.0 0.5 1.0
05
LAG3
pValue
0.0
1.0
Type: chemokine
PDCD1
SLAMF7
0,0
0.5
1.0
receptor
MHC
CTLA4
Type:
Immunoinhibitor
TIGIT
Inhibitory Stimulaotry
Immunostimulator
CD274
HAVCR2
IL10
16
KIR2DL1
[13
KIR2DL3
BTLA
IDO1
ARGI
EDNRB
ADORA2A
IL4
VEGFB
VEGFA
IL12A
CD276
TGFB1
HMGBI
CD70
TNFRSF18
ICOSLG
TNFSF9
TNFRSF14
TNFRSF4
CD27
A2A
CD28
LG2
CD40LG
ICOS
ITGB2
PRFI
AB
GZMA
CCL5
BTN3A1
BTN3A2
CD80
ICAMI
13C
TNFSF4
CXCL10
$138
CXCL9
IFNG
E14
IL2RA
TNFRSF9
RSF18
ENTPDI
CD40
ILIA
CX3CLI
TLR4
ILIB
TNF
SF18
IFNAI
IL2
IFNA2
SELP
UVM(N=19) THYMIN HY)
DLBC(N=41) THCA(N-504)
CHOLIN-36
LAMLON-173
PAAD(N=178) PRADIN 495)
KIPAN(N-884)
KIRCIN- 530 GBMLGG(N=662)
BLCA(N=407
KICHEN
PCPO(N=197
KIRPIN ORS
ACC(N=77)
SKCM(N-102)
READ(N=92)
COAD(N=)28
COADREAL(N”380)
ESCAIN-181)
BRCA(N=1092)
LUADIN=513
LUSCO
STAD(NASA)
STESIN-SOS
SARCIN-258)
MESOINS
UCECIN=180)
CESC(N=304)
INFRSP25
HNSC(N=518)
OADRI
GBM
4 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,
Discover
A
Modification
Type
B
TRMTGIA
TRMT6IB
correlation coefficient
TRMTIOC
TRMT6
-1.0-0.5 0.0 0.5 1.0
0.38
0.27
0.21
0.27
YTHDF3
pValue
…
0.35
…
…
…
TCGA-PCPG(N=177)
correlation coefficient
YTHDCI
-0.16
0.21
0.15
0.08
YTHDF1
·
TCGA-GBMLGG(N=656)
YTHDF2
0,0
0.5
M
1.0
-0.16
-0.24
0.14
-0.31
0.11
Modification: mlA
·
-0.33
TCGA-THCA(N-503)
0.2 0.0 0.2
ALKBHI
ALKBH3
0.15
0.18
0.14
0.22
0.15
0.22
pValue
TCGA-KIRC(N=528)
NSUNS
msc
.**
0.20
0.24
NOP2
mbA
0.20
0.17
0.30 TCGA-KIRP(N=285)
DNMTI
Type:
**
0.0
-0.20
0.5
1.5
2.0
NSUN2
writer
*
TCGA-GBM(N=152)
NSUN4
reader
DNMT3A
eraser
0.17
·
).17
TCGA-PAAD(N=177)
DNMT3B
NSUN7
-0.10
0.12
TCGA-LGG(N=504)
NSUN6
NSUN3
0.11
e
0.13
0.14
0.16
TCGA-LIHC(N=363)
TRDMTI
0.10
0.21
0.26
0.19
0.26
…
…
…
TCGA-OV(N=416)
ALYREF
0.30
0.31
ZC3H13
**
-0.35
0.32
TCGA-THYM(N=118)
METTL14
CBLLI
TCGA-STAD(N=388)
KIAA1429
METTL3
TCGA-SKCM-M(N=351)
RBMISB
RBM15
TCGA-UVM(N=79)
WTAP
0.30
0.23
ALKBHS
**
TCGA-SKCM-P(N=101)
FTO
0.29
IGF2BP1
TCGA-KICH(N=65)
YTHDF3
YTHDC2
TCGA-UCEC(N=178)
YTHDCI
FMRI
TCGA-HNSC(N=517)
LRPPRC
0.25
0.30
HNRNPA2BI
*
**
TCGA-ACC(N=77)
HNRNPC
YTHDF2
TCGA-READ(N=91)
YTHDF1
ELAVLI
TCGA-MESO(N=85)
THYM(N-119)
0.20
*
TCGA-TGCT(N=132)
0.13
0.13
..
0.10
+
TCGA-PRAD(N=495)
0.27
0.13
0.30
C
**
0.17
0.38
D
TCGA-BLCA(N=405)
”
-0.17
-0.22
**
TCGA-SARC(N=258)
Lapatinib
TCGA-LUSC(N=491)
Erlotinib
TCGA-CHOL(N-36)
austocystin D
0.22
-0.18
Saracatinib
TCGA-ESCA(N=181)
TCGA-SKCM(N=452)
17-AAG
P
TCGA-CESC(N=291)
Docetaxel
0
0.0025
0.0050
TCGA-UCS(N=56)
0.0075
erlotinib
Gefitinib
0.06
0.0100
TCGA-BRCA(N=1077)
GSK1904529A
TCGA-COADREAD(N=373)
AKT inhibitor Vill
-0.14
TCGA-LUAD(N=500)
Cetuximab
vandetanib
TCGA-COAD(N=282)
-0.35
JNK Inhibitor VIII
.
TCGA-DLBC(N=46)
B cell
T cell CD4
T cell CD8
Neutrophil
Macrophage
DC
0.00
0.05
0.10
15
0.20
0.00
0.05
0.10
0.15
Correlation
Correlation
stability, and function of these noncoding RNAs, thereby affecting important cellular processes like apoptosis, angio- genesis, 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 PPAR8, 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, conse- quently 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,
Discover
A
B
FOR $ 0.05
FOR > 0.05
20
1.5
1.0
0.5
0.0
0.5
1.0
1,5
20
2,5
3.0
Ribosome
Oxidative phosphorylation
Points
0
20
40
60
80
100
Parkinson disease
Proteasome
Huntington disease
Non-alcoholic fatty liver disease (NAFLD)
Pathologic T stage
T2
Thermogenesis
Alzheimer disease
T1
T3&T4
Spliceosome
Retrograde endocannabinoid signaling
Female
Pyrimidine metabolism
Gender
Cardiac muscle contraction
Drug metabolism
Male
RNA polymerase
Ribosome biogenesis in eukaryotes
Age
> 60
Purine metabolism
Glutathione metabolism
⇐ 60
Cytosolic DNA-sensing pathway
⇐ 400
Base excision repair
AFP(ng/ml)
Protein export
Glyoxylate and dicarboxylate metabolism
Sulfur relay system Metabolic pathways
> 400
DNA replication
RRP8
High
Aminoacyl-tRNA biosynthesis
Low
Renin secretion
Wnt signaling pathway
Ras signaling pathway
Total Points
MAPK signaling pathway
Prolactin signaling pathway
0
100
200
300
Vascular smooth muscle contraction
Hippo signaling pathway
Linear Predictor
Rap1 signaling pathway
Hedgehog signaling pathway Neurotrophin signaling pathway
-1
-0.6
-0.2
0.2
0.6
1
1.4
TNF signaling pathway
1-year Survival Probability
Osteoclast differentiation
Focal adhesion
0.95
0.9
0.85
0.8 0.75 0.
7
CGMP-PKG signaling pathway
Inositol phosphate metabolism
3-year Survival Probability
Platelet activation
JAK-STAT signaling pathway
0.8
0.7
0.6
0.5
0.4
0.3
ECM-receptor interaction
TGF-beta signaling pathway
5-year Survival Probability
Signaling pathways regulating pluripotency of stem cells
MicroRNAs in cancer
0.7
0.6
0.5
0.4
0.3
0.2
2.0
-15
-1.0
0.5
Normalized Enrichment Score
0.0
0.5
1.0
1.5
20
2.5
3.0
C
D
Enrichment plot: MAPK signaling pathway
Enrichment plot: ECM-receptor interaction
0
0
a
à
Enrichment Score
Enrichment Score
0
O
10
8
2
:
0
¥
Ranked list metric
20
Ranked list metric
R
8
9
0
5000
10000
15000
0
5000
Rank in Ordered Dataset
10000
Rank in Ordered Dataset
15000
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 path- way, 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
Discover
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 check- points, 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 local- ized 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.
Discover
5 Conclusions
Our findings suggested that RRP8 could serves a biomarker in many cancers and should deserve more attention of researchers.
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.
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://creativeco mmons.org/licenses/by-nc-nd/4.0/.
References
1. Crick F. Central dogma of molecular biology. Nature. 1970;227(5258):561-3.
2. Li GW, Xie XS. Central dogma at the single-molecule level in living cells. Nature. 2011;475(7356):308-15.
3. Jordan I, Lipkin WI. Borna disease virus. Rev Med Virol. 2001;11(1):37-57.
4. Kruger K, et al. Self-splicing RNA: autoexcision and autocyclization of the ribosomal RNA intervening sequence of tetrahymena. Cell. 1982;31(1):147-57.
5. Esteller M. Epigenetics in cancer. N Engl J Med. 2008;358(11):1148-59.
6. Parmar JJ, Padinhateeri R. Nucleosome positioning and chromatin organization. Curr Opin Struct Biol. 2020;64:111-8.
7. Casado-Pelaez M, Bueno-Costa A, Esteller M. Single cell cancer epigenetics. Trends Cancer. 2022;8(10):820-38.
8. Dawson MA, Kouzarides T. Cancer epigenetics: from mechanism to therapy. Cell. 2012;150(1):12-27.
9. Xiao K, et al. mRNA-based chimeric antigen receptor T cell therapy: basic principles, recent advances and future directions. Interdiscipl Med. 2024;2(1): e20230036.
10. Reuter JA, Spacek DV, Snyder MP. High-throughput sequencing technologies. Mol Cell. 2015;58(4):586-97.
11. Wu Z, et al. Genomic characterization of peritoneal lavage cytology-positive gastric cancer. Chin J Cancer Res. 2024;36(1):66-77.
12. Feng DC, Zhu WZ, Wang J, Li DX, Shi X, Xiong Q, You J, Han P, Qiu S, Wei Q, Yang L. The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy. Mil Med Res. 2024;11(1):21. https://doi.org/10.1186/s40779-024-00526-7.
13. Du H, et al. Single-cell RNA-seq and bulk-seq identify RAB17 as a potential regulator of angiogenesis by human dermal microvascular endothelial cells in diabetic foot ulcers. Burns & Trauma. 2023;11: tkad020.
14. Wang S-W, et al. Current applications and future perspective of CRISPR/Cas9 gene editing in cancer. Mol Cancer. 2022;21(1):57.
15. Dong M, et al. CRISPR/CAS9: a promising approach for the research and treatment of cardiovascular diseases. Pharmacol Res. 2022;185: 106480.
16. Zhao LY, et al. Mapping the epigenetic modifications of DNA and RNA. Protein Cell. 2020;11(11):792-808.
17. Roundtree IA, et al. Dynamic RNA modifications in gene expression regulation. Cell. 2017;169(7):1187-200.
18. Ghidotti P, Petraroia I, Fortunato O, Pontis F. Immunomodulatory role of EV-derived non-coding RNA in lung cancer. Extracell Vesicles Circ Nucleic Acids. 2023;4(1):59-71. https://doi.org/10.20517/evcna.2022.42.
Discover
| https://doi.org/10.1007/s12672-024-01299-0
(2024) 15:437
Discover Oncology
19. Li C, et al. N6-Methyladenosine in vascular aging and related diseases: clinical perspectives. Aging Dis. 2023. https://doi.org/10.14336/ AD.2023.0924-1.
20. Thompson MG, Sacco MT, Horner SM. How RNA modifications regulate the antiviral response. Immunol Rev. 2021;304(1):169-80.
21. Zou D, et al. Single-cell and spatial transcriptomics reveals that PTPRG activates the m6A methyltransferase VIRMA to block mitophagy- mediated neuronal death in Alzheimer’s disease. Pharmacol Res. 2024;201: 107098.
22. Jin H, et al. m(1)A RNA modification in gene expression regulation. Genes. 2022;13(5):910.
23. Yuan L, Mao L-H, Li J-Y. CAG repeat expansions increase N1-methyladenine to Alter TDP-43 phase separation: lights up therapeutic inter- vention for neurodegeneration. Aging Dis. 2024. https://doi.org/10.14336/AD.2024.0110.
24. Guan Q, et al. Variant rs8400 enhances ALKBH5 expression through disrupting miR-186 binding and promotes neuroblastoma progres- sion. Chin J Cancer Res. 2023;35(2):140-62.
25. You K, et al. RRP8, associated with immune infiltration, is a prospective therapeutic target in hepatocellular carcinoma. J Cancer Res Clin Oncol. 2024;150(5):245.
26. Peifer C, et al. Yeast Rrp8p, a novel methyltransferase responsible for m1A 645 base modification of 25S rRNA. Nucleic Acids Res. 2013;41(2):1151-63.
27. Zhu C, et al. Erroneous ribosomal RNAs promote the generation of antisense ribosomal siRNA. Proc Natl Acad Sci USA. 2018;115(40):10082-7.
28. Yang L, et al. Nucleolar repression facilitates initiation and maintenance of senescence. Cell Cycle. 2015;14(22):3613-23.
29. Feng D, et al. A pan-cancer analysis of the oncogenic role of leucine zipper protein 2 in human cancer. Exp Hematol Oncol. 2022;11(1):55.
30. Goldman MJ, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38(6):675-8.
31. Liu, J., et al., An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, 2018. 173(2): p. 400-416 e11.
32. Cortese G, Scheike TH, Martinussen T. Flexible survival regression modelling. Stat Methods Med Res. 2010;19(1):5-28.
33. Shen W, et al. Sangerbox: a comprehensive, interaction-friendly clinical bioinformatics analysis platform. iMeta. 2022. https://doi.org/10. 1002/imt2.36.
34. Ozga AJ, Chow MT, Luster AD. Chemokines and the immune response to cancer. Immunity. 2021;54(5):859-74.
35. Li T, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108-10.
36. Liu CJ, et al. GSCALite: a web server for gene set cancer analysis. Bioinformatics. 2018;34(21):3771-2.
37. Vasaikar SV, et al. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res. 2018;46(D1):D956-63.
38. Bi Z, et al. A dynamic reversible RNA N(6)-methyladenosine modification: current status and perspectives. J Cell Physiol. 2019;234(6):7948-56.
39. Zhao BS, Roundtree IA, He C. Post-transcriptional gene regulation by mRNA modifications. Nat Rev Mol Cell Biol. 2017;18(1):31-42.
40. Hanahan D, Robert A. Weinberg, Hallmarks of cancer: the next generation. Cell. 2011;144(5):646-74.
41. Zeng Z, et al. The m6A reader YTHDF2 alleviates the inflammatory response by inhibiting IL-6R/JAK2/STAT1 pathway-mediated high- mobility group box-1 release. Burns & Trauma. 2023;11: tkad023.
42. Chen L, et al. m6A methylation-induced NR1D1 ablation disrupts the HSC circadian clock and promotes hepatic fibrosis. Pharmacol Res. 2023;189: 106704.
43. Zhao J, et al. Emerging regulatory mechanisms of N6-methyladenosine modification in cancer metastasis. Phenomics. 2023;3(1):83-100.
44. Han J, et al. METTL3 promote tumor proliferation of bladder cancer by accelerating pri-miR221/222 maturation in m6A-dependent man- ner. Mol Cancer. 2019;18(1):110.
45. Zhou Y, et al. Expression profiles and prognostic significance of RNA N6-methyladenosine-related genes in patients with hepatocellular carcinoma: evidence from independent datasets. Cancer Manag Res. 2019;11:3921-31.
46. Yue B, et al. METTL3-mediated N6-methyladenosine modification is critical for epithelial-mesenchymal transition and metastasis of gastric cancer. Mol Cancer. 2019;18(1):142.
47. Ma S, et al. The interplay between m6A RNA methylation and noncoding RNA in cancer. J Hematol Oncol. 2019;12(1):121.
48. Tu B, et al. METTL3 boosts mitochondrial fission and induces cardiac fibrosis by enhancing LncRNA GAS5 methylation. Pharmacol Res. 2023;194: 106840.
49. Wang Y, et al. N(1)-methyladenosine methylation in tRNA drives liver tumourigenesis by regulating cholesterol metabolism. Nat Commun. 2021;12(1):6314.
50. Singh B, et al. Important role of FTO in the survival of rare panresistant triple-negative inflammatory breast cancer cells facing a severe metabolic challenge. PLoS ONE. 2016;11(7): e0159072.
51. Chen Z, et al. N6-methyladenosine-induced ERRgamma triggers chemoresistance of cancer cells through upregulation of ABCB1 and metabolic reprogramming. Theranostics. 2020;10(8):3382-96.
52. Dominissini D, et al. The dynamic N1-methyladenosine methylome in eukaryotic messenger RNA. Nature. 2016;530(7591):441-6.
53. El Yacoubi B, Bailly M, de Crécy-Lagard V. Biosynthesis and function of posttranscriptional modifications of transfer RNAs. Annu Rev Genet. 2012;46(1):69-95.
54. Sharma S, et al. Identification of a novel methyltransferase, Bmt2, responsible for the N-1-methyl-adenosine base modification of 25S rRNA in Saccharomyces cerevisiae. Nucleic Acids Res. 2013;41(10):5428-43.
55. Zhao M, Shen S, Xue C. A novel m1A-score model correlated with the immune microenvironment predicts prognosis in hepatocellular carcinoma. Front Immunol. 2022;13: 805967.
56. Wu Y, et al. RNA m1A methylation regulates glycolysis of cancer cells through modulating ATP5D. Proc Natl Acad Sci USA. 2022;119(28): e2119038119.
57. Jiang C, et al. Landscape of N1-methyladenosin (m1A) modification pattern in colorectal cancer. Cancer Rep. 2024;7(2): e1965.
58. Sui S, et al. Abstract 1713: TRMT6-mediated N1-methyladenosine methylation promotes tumorigenesis in colorectal cancer. Cancer Res. 2023;83(7_Supplement):1713-1713.
59. Wang Q, et al. m1A regulator TRMT10C predicts poorer survival and contributes to malignant behavior in gynecological cancers. DNA Cell Biol. 2020;39(10):1767-78.
Discover
60. Li J, et al. Differential analysis of RNA methylation regulators in gastric cancer based on TCGA data set and construction of a prognostic model. J Gastrointest Oncol. 2021;12(4):1384-97.
61. Shi Q, et al. Gene signatures and prognostic values of m1A-related regulatory genes in hepatocellular carcinoma. Sci Rep. 2020;10(1):15083.
62. Macari F, et al. TRM6/61 connects PKCalpha with translational control through tRNAi(Met) stabilization: impact on tumorigenesis. Onco- gene. 2016;35(14):1785-96.
63. Woo HH, Chambers SK. Human ALKBH3-induced m(1)A demethylation increases the CSF-1 mRNA stability in breast and ovarian cancer cells. Biochim Biophys Acta Gene Regul Mech. 2019;1862(1):35-46.
64. Thuring K, et al. Analysis of RNA modifications by liquid chromatography-tandem mass spectrometry. Methods. 2016;107:48-56.
65. Araujo Tavares RC, et al. MRT-ModSeq - rapid detection of RNA modifications with MarathonRT. J Mol Biol. 2023;435(22): 168299.
66. Sharma S, et al. A single N(1)-methyladenosine on the large ribosomal subunit rRNA impacts locally its structure and the translation of key metabolic enzymes. Sci Rep. 2018;8(1):11904.
67. Li D, et al. The m6A/m5C/m1A regulated gene signature predicts the prognosis and correlates with the immune status of hepatocellular carcinoma. Front Immunol. 2022;13: 918140.
68. Han Y, Wang J, Xu B. Novel biomarkers and prediction model for the pathological complete response to neoadjuvant treatment of triple- negative breast cancer. J Cancer. 2021;12(3):936-45.
69. Martin-Herranz DE, et al. Screening for genes that accelerate the epigenetic aging clock in humans reveals a role for the H3K36 methyl- transferase NSD1. Genome Biol. 2019;20(1):146.
70. Feng D, et al. Unraveling links between aging, circadian rhythm and cancer: Insights from evidence-based analysis. Chin J Cancer Res. 2024;36(3):341-50.
71. Mendelsohn CL, Wimmer E, Racaniello VR. Cellular receptor for poliovirus: molecular cloning, nucleotide sequence, and expression of a new member of the immunoglobulin superfamily. Cell. 1989;56(5):855-65.
72. O’Donnell JS, et al. Tumor intrinsic and extrinsic immune functions of CD155. Semin Cancer Biol. 2020;65:189-96.
73. de Andrade LF, Smyth MJ, Martinet L. DNAM-1 control of natural killer cells functions through nectin and nectin-like proteins. Immunol Cell Biol. 2014;92(3):237-44.
74. Martinet L, Smyth MJ. Balancing natural killer cell activation through paired receptors. Nat Rev Immunol. 2015;15(4):243-54.
75. Briukhovetska D, et al. T cell-derived interleukin-22 drives the expression of CD155 by cancer cells to suppress NK cell function and pro- mote metastasis. Immunity. 2023;56(1):143-61.
76. Chen J. Expression of CD155 protein in pancreatic cancer and its clinical significance. J Am Coll Surg. 2020;231(4):S158-9.
77. Li YC, et al. Overexpression of an immune checkpoint (CD155) in breast cancer associated with prognostic significance and exhausted tumor-infiltrating lymphocytes: a cohort study. J Immunol Res. 2020;2020:3948928.
78. Jardim DL, et al. The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell. 2021;39(2):154-73.
79. Zhang X, et al. Radiomics under 2D regions, 3D regions, and peritumoral regions reveal tumor heterogeneity in non-small cell lung cancer: a multicenter study. Radiol Med. 2023;128(9):1079-92.
80. Chan TA, Wolchok JD, Snyder A. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2015;373(20):1984.
81. Smyth EC, et al. Gastric cancer. Lancet. 2020;396(10251):635-48.
82. Yan X, et al. Stomach cancer burden in China: epidemiology and prevention. Chin J Cancer Res. 2023;35(2):81-91.
83. Fuchs CS, et al. Ramucirumab with cisplatin and fluoropyrimidine as first-line therapy in patients with metastatic gastric or junctional adenocarcinoma (RAINFALL): a double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Oncol. 2019;20(3):420-35.
84. Ohtsu A, et al. Bevacizumab in combination with chemotherapy as first-line therapy in advanced gastric cancer: a randomized, double- blind, placebo-controlled phase III study. J Clin Oncol. 2011;29(30):3968-76.
85. Qi J, et al. National and subnational trends in cancer burden in China, 2005-20: an analysis of national mortality surveillance data. Lancet Public Health. 2023;8(12):e943-55.
86. Ge W, et al. Review and prospect of immune checkpoint blockade therapy represented by PD-1/PD-L1 in the treatment of clear cell renal cell carcinoma. Oncol Res. 2023;31(3):255-70.
87. Capitanio U, Montorsi F. Renal cancer. Lancet. 2016;387(10021):894-906.
88. Kandoth C, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502(7471):333-9.
89. Kastenhuber ER, Lowe SW. Putting p53 in context. Cell. 2017;170(6):1062-78.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Discover