Integrative Pan -Cancer Analysis of RNMT a Potential Prognostic and Immunological Biomarker Shuqiang Huang1 Cuiyu Tan1 Jinzhen Zheng1 Zhugu Huang2 Zhihong Li1 Ziyin Lv3

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Integrative Pan-Cancer Analysis of RNMT: a Potential

Prognostic and Immunological Biomarker

Shuqiang Huang1, Cuiyu Tan1, Jinzhen Zheng1, Zhugu Huang2, Zhihong Li1, Ziyin Lv3,

Wanru Chen4

1The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, Guangdong

511500, China.

2School of Pediatrics, Guangzhou Medical University, Guangzhou, Guangdong 510000, China

3The First Clinical College of Guangzhou Medical University, Guangzhou, Guangdong 511436,

China.

4The Third Clinical College of Guangzhou Medical University, Guangzhou, Guangdong

511436, China.

ABSTRACT

Background: RNA guanine-7 methyltransferase (RNMT) is one of the main regulators

of N7-methylguanosine (m7G), and the deregulation of RNMT correlated with tumor

development and immune metabolism. However, the specific function of RNMT in

pan-cancer remains unclear.

Methods: RNMT expression in different cancers was analyzed using multiple

databases, including Cancer Cell Line Encyclopedia (CCLE), Genotype-Tissue

Expression Project (GTEx), and The Cancer Genome Atlas (TCGA). Cox regression

analysis and Kaplan-Meier analysis were used to estimate the correlation of RNMT

expression to prognosis. The data was also used to research the relationship between

RNMT expression and common immunoregulators, tumor mutation burden (TMB),

microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase

(DNMT). Additionally, the cBioPortal website was used to evaluate the characteristics

of RNMT alteration. The TISDB database was used to obtain the expression of different

subtypes. The Tumor Immune Estimation Resource (TIMER) database was used to

analyze the association between RNMT and tumor immune infiltration. Gene set

enrichment analysis (GSEA) was used to identify the relevant pathways.

Results: RNMT was ubiquitously highly expressed across cancers and survival

analysis revealed that its expression was highly associated with the clinical prognosis

of various cancer types. Remarkably, RNMT participates in immune regulation and

plays a crucial part in the tumor microenvironment (TME). A positive association was

found between RNMT expression and six immune cell types expression (B cell, CD4

+ T cell, CD8 + T cell, dendritic, macrophage, and neutrophil) in colon adenocarcinoma

(COAD), kidney renal clear cell carcinoma (KIRC), and liver hepatocellular carcinoma

(LIHC). Moreover, RNMT expression was highly associated with immunoregulators in

most cancer types, and correlated to TMB, MSI, MMR, and DNMT. Finally, GSEA

indicated that RNMT may correlate with tumor immunity.

Conclusion: RNMT was ubiquitously related to tumor prognosis and tumor immune

infiltrates, and played an essential role in the occurrence and progression of various

tumors. RNMT may be a promising prognostic biomarker in various tumors and

provide new ideas for future tumor immune studies and treatment strategies.

Keywords: RNMT, tumor immune, prognostic biomarker, immune infiltration,

pan-cancer

INTRODUCTION

It is an intimidating and everlasting struggle for the human race to fight cancer. As the

framework of cancer diversity has been constantly polished (1), searching for powerful

tumor markers offers a promising avenue for individual-based tumor diagnosis and

treatment. Over the last few decades, modern immunotherapy, as a novel and promising

direction for cancer treatment, has made considerable progress and revolutionized the

therapy of tumors in many ways. At present, there are a variety of immunotherapy

strategies including the use of monoclonal antibodies, adjuvants, checkpoint inhibitors

and so on (2). In the case of immune inhibitors, Immune checkpoints refer to numerous

inhibitory pathways hardwired into the immune system that are essential for the

maintenance of self-tolerance and the prevention of autoimmunity (3). In practical

clinical applications, the blockade of immune checkpoints is one of the most promising

approaches to activating therapeutic antitumor immunity. For example, combined PD-

1 and CTLA-4 pathway inhibition approaches demonstrated increased survival

markedly in multiple malignancies, including melanoma, renal cell carcinoma,

esophageal squamous-cell carcinoma, and non–small cell lung cancer (4-7). Driven by

such promising results, pre-clinical research on checkpoint inhibitors has become a

rapidly evolving field. Nonetheless, there are still many challenges in cancer

immunotherapy, which range from a lack of confidence in translating pre-clinical

findings to confirming the best combination for any given patient (8). Therefore, more

clinical and translational research is undoubtedly needed to explore potential and

optimal targets for cancer immunotherapy.

N7-methylguanosine (m7G), occurring in the N7-position of guanosine, is one of the

electropositive modifications of messenger RNA (mRNA) 5′ cap structure, internal

mRNA, ribosomal RNA (rRNA), transfer RNA (tRNA), microRNA (miRNA), and long

non-coding RNAs (lncRNA) (9-11). The m7G modification plays a significant role in

mRNA export, metabolism, stability, splicing, and translation (12). Recent evidence

suggests that the change of m7G has a great effect on the development of cancer, which

means that m7G molecules may be potential therapeutic targets and prognostic

biomarkers (13). RNA guanine-7 methyltransferase (RNMT), localized on

chromosome 18p11.21, contains an N-terminal regulatory domain that regulates RNMT

activity and facilitates recruitment to transcription initiation sites (14, 15).

RNMT/RNMT-activating miniprotein (RAM)-mediated m7G mRNA methylome

modification at the 5’ caps is important for ordinary RNA translation, the transformation

of mammary epithelial and fibroblast cell, and T cell activation (16, 17). Previous

research has established that aberrant expression of RNMT is also closely associated

with a variety of cancers. For example, the promoters of RNMT are hypomethylated in

hepatocellular carcinoma tumorigenesis, which promotes the development of HCC (18).

Inhibition of RNMT induces the apoptosis of HeLa cells, while decreasing the growth

of breast cancer cells in a PIK3CA mutant background (19, 20). Moreover, the

expression of RNMT is decreased in glioma stem-like cells (GSLCs) and inhibits tumor

growth via the B7-H6/c-Myc axis (21). Although the function of RNMT in several

tumors has been explored, its key functions in tumor immunity are still obscure.

With the rise of bioinformatics, multiple datasets in existence can be used to forecast

the effect of numerous genes. In this research, we completed Pan-cancer data collection

and processing by looking for the databases such as The Cancer Genome Atlas (TCGA)

database, the Genotype-Tissue Expression (GTEx) database, and The Tumor Immune

Estimation Resource（TIMER） database. Afterwards, we make analyses of survival

and immune prognosis, TMB, MSI, DNA MMR Genes, DNMT and Gene Set

Enrichment Analysis using a variety of statistical methods, including Cox regression

and Kaplan–Meier analysis, log-rank statistic, spearman correlation analysis and so on

to validate the value of RNMT in pan-cancer indicative function.

MATERIALS AND METHODS

Sample Source Obtained and the Expression of RNMT in Pan-Cancer

In this study, a series of standardized pan-cancer data were downloaded from The

Cancer Genome Atlas(TCGA) database （https://portal.gdc.cancer.gov）, the Genotype-

Tissue Expression (GTEx) database(https://commonfund.nih.gov/GTEx/)and Cancer

Cell Line Encyclopedia (CCLE) database(https://portals.broadinstitute.org/ccle).

Furthermore, we extract the expression information of RNMT in different samples so

as to identify the role of RNMT in Pan-cancer. And the abbreviation and the full name

of all the cancers are shown in Table 1.

TABLE 1 Abbreviations of the tumors from TCGA database

Abbreviations

Tumor name

ACC

Adrenocortical carcinoma

BLCA

Bladder Urothelial Carcinoma

BRCA

Breast invasive carcinoma

CESC

Cervical squamous cell carcinoma and endocervical adenocar

cinoma

CHOL

Cholangiocarcinoma

COAD

Colon adenocarcinoma

COADREAD

Colon and rectal cancer

DLBC

Lymphoid Neoplasm Diffuse Large B-cell Lymphoma

ESCA

Esophageal carcinoma

FPPP

FFPE Pilot Phase II FFPE

GBM

Glioblastoma multiforme

GBMLGG

Glioma

HNSC

Head and Neck squamous cell carcinoma

KICH

Kidney Chromophobe

KIPAN

Pan-kidney cohort (KICH+KIRC+KIRP)

KIRC

Kidney renal clear cell carcinoma

KIRP

Kidney renal papillary cell carcinoma

LAML

Acute Myeloid Leukemia

LGG

Brain Lower Grade Glioma

LIHC

Liver hepatocellular carcinoma

LUAD

Lung adenocarcinoma

LUSC

Lung squamous cell carcinoma

MESO

Mesothelioma

Ovarian serous cystadenocarcinoma

PAAD

Pancreatic adenocarcinoma

PCPG

Pheochromocytoma and Paraganglioma

PRAD

Prostate adenocarcinoma

READ

Rectum adenocarcinoma

SARC

Sarcoma

SKCM

Skin Cutaneous Melanoma

STAD

Stomach adenocarcinoma

STES

Stomach and Esophageal carcinoma

TGCT

Testicular Germ Cell Tumors

THCA

Thyroid carcinoma

THYM

Thymoma

UCEC

Uterine Corpus Endometrial Carcinoma

UCS

Uterine Carcinosarcoma

UVM

Uveal Melanoma

Cox Regression and Kaplan–Meier Prognosis Analysis

The RNMT expression data of normal samples from GTEx and cancer samples from

TCGA were extracted and formed into an expression matrix. Univariate cox model was

used to reveal the relationship between RNMT expression and patient survival. On the

basis of the best separation of expression of RNMT, the Kaplan-Meier (K-M) method

by log-rank test was used to compare the prognosis of patients (overall survival: OS;

disease-specific survival: DSS; disease-free interval: DFI; and progression-free interval:

PFI) from the high and low group. The images were plotted as forest plots (Cox

regression analysis) and K-M curves (K-M prognosis analysis).

Genetic Alteration Analysis of RNMT in Human Pan-Cancer

The cBioPortal website (http://www.cbioportal.org) provides a platform for analyzing,

visualizing, and exploring multidimensional cancer genomic data (22). The database

was used to analyze the characteristics of genetic alteration, including missense

mutation, deep deletion, copy number amplification, and RNMT mRNA upregulation.

Immune and Molecular Subtype Analysis

In order to have a better understanding of the relationship between immune and

molecular subtypes and RNMT expression among human cancers, the TISDB database

(http://cis.hku.hk/TISIDB/index.php) was used to obtain the analysis result and

determine the expression of different subtypes. It was considered statistically

significant when the p < 0.05.

Immune Infiltration and Tumor Microenvironment Assess

The Tumor Immune Estimation Resource (TIMER) database

(http://cistrome.dfci.harvard.edu/TIMER/) is an influential website, which can evaluate

the condition of immune infiltration and tumor microenvironment, so we used it to

score the RNMT expression in diverse immune cell infiltrations and tumor

microenvironment types by the means of the standardized expression matrix.

Correlation Analysis of RNMT Expression Level and Immunoregulator and

Immune Checkpoints

The TCGA dataset was downloaded to extract the expression data of RNMT gene and

immunoregulator (chemokines, receptors, TILs, immunostimulators, and

immunoinhibitors), immune checkpoints (24 inhibitors and 36 stimulators). Spearman's

correlation test was used to analyze the association between RNMT expression and

immunoregulator, immune checkpoints.

Analysis of Tumor Mutation Burden(TMB), Microsatellite Instability(MSI),

Mismatch Repair(MMR) and DNA Methyltransferase(DNMT)

TMB, MSI, DNA MMR and DNA methylation were considered prognosis-related and

immune-related factors in cancer immunotherapy (23). The correlations of RNMT

expression with TMB and MSI in human pan-cancer were based on the TCGA pan-

cancer atlas. We also analyzed five MMR genes (MLH1, MSH2, MSH6, EPCAM, and

PMS2) and four methyltransferases (DNMT1, DNMT2, DNMT3A, and DNMT3B) in

human pan-cancer from the TCGA database. In this study, spearman correlation

analysis was applied to evaluate the role of RNMT expression in TMB, MSI, MMR,

and DNMT.

Gene Set Enrichment Analysis (GSEA) of RNMT in Human Pan-Cancer

GSEA is a common method used to interpret gene expression data and compare

different groups with differing biological states to determine statistically significant

differences (24). The signaling pathway of RNMT was analyzed with the R package

clusterProfiler. The Kyoto Encyclopedia of Genes and Genomes (KEGG;

https://www.kegg.jp.) was applied. Implementation criteria were normalized

enrichment score | NES | > 1, p < 0.05, false discovery rate (FDR) ≤ 0.25.

Statistical Analysis

Statistical analysis methods were described in the above parts. p < 0.05 was recorded

as statistically significant for all analyses unless otherwise specified.

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IntegrativePan-CancerAnalysisofRNMT:aPotentialPrognosticandImmunologicalBiomarkerShuqiangHuang1,CuiyuTan1,JinzhenZheng1,ZhuguHuang2,ZhihongLi1,ZiyinLv3,WanruChen41TheSixthAffiliatedHospitalofGuangzhouMedicalUniversity,Qingyuan,Guangdong511500,China.2SchoolofPediatrics,GuangzhouMedicalUniversity,Guan...

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Integrative Pan -Cancer Analysis of RNMT a Potential Prognostic and Immunological Biomarker Shuqiang Huang1 Cuiyu Tan1 Jinzhen Zheng1 Zhugu Huang2 Zhihong Li1 Ziyin Lv3.pdf

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Integrative Pan -Cancer Analysis of RNMT a Potential Prognostic and Immunological Biomarker Shuqiang Huang1 Cuiyu Tan1 Jinzhen Zheng1 Zhugu Huang2 Zhihong Li1 Ziyin Lv3

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