A four-m6A methylation regulator risk score was an independent prognostic biomarker for adrenocortical carcinoma

doi:10.21203/rs.3.rs-4991875/v1

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A four-m6A methylation regulator risk score was an independent prognostic biomarker for adrenocortical carcinoma

https://doi.org/10.21203/rs.3.rs-4991875/v1

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N6-methyladenosine (m6A) is crucial in cancer prognosis, yet its role in adrenocortical carcinoma (ACC) is not well understood. This study investigates the prognostic value of m6A RNA methylation regulators in ACC using mRNA data and clinical information from the TCGA-ACC database. Patients were divided into two clusters based on the expression of 13 m6A genes. Univariate and LASSO Cox regression analyses identified four key prognostic m6A regulators (METTL14, RBM15, HNRNPC, WTAP), leading to the construction of a risk score (RS) model: RS = (-0.1828 × METTL14) + (0.3292 × RBM15) + (0.0219 × HNRNPC) + (0.0010 × WTAP). Kaplan-Meier survival analysis showed significant differences between high and low RS groups, with an AUC of 0.787, indicating good predictive accuracy. Univariate and multivariate Cox regression analyses revealed that T stage and RS were independent prognostic factors. RS was also closely correlated with stage and N status. High RS was associated with higher expression levels of RBM15, HNRNPC, and WTAP, and lower expression of METTL14. This study highlights the prognostic significance of m6A RNA methylation regulators in ACC, presenting a four-gene RS model as an independent prognostic biomarker. These results provide new insights into ACC prognosis, emphasizing the potential of targeting m6A RNA methylation in future therapeutic strategies and enhancing patient management through more accurate prognostic tools. Furthermore, this research underscores the importance of continued investigation into the molecular mechanisms of m6A RNA methylation and its broader implications in oncology, potentially guiding personalized treatment approaches for ACC patients.

Biological sciences/Cancer

Biological sciences/Cancer/Cancer models

Biological sciences/Cancer/Tumour biomarkers

Biological sciences/Cancer/Urological cancer

Biological sciences/Molecular biology/Translation

adrenocortical carcinoma

m6A RNA methylation regulators

risk score

prognostic biomarker

TCGA

Adrenocortical carcinoma (ACC) is a cancer with a dismal prognosis arising from the adrenal cortex and affecting 1–2 people per million each year^[1]. It is a rare and aggressive malignancy with a recurrence rate of 40–70% and a 5-year survival rate of 55% even after complete surgical resection which is the mainstay of treatment of localized ACC (Stage I–III)^[1–3]. Clinical factors such as patient age, initial stage, and hormonally functioning tumors, pathological factors such as Weiss score and Ki67 index, and molecular factors determine the prognosis of patients with ACC ^{[4, 5]}. Although lots of clinical and pathological factors were developed to evaluate the prognosis of ACC, few molecular factors were developed. The methylation profile is an important factor in molecular factors ^[5]. Therefore, methylation may serve as a new way to predict the prognosis of ACC.

N6-methyladenosine (m6A) methylation is the most common type in RNA methylation modification and plays an important role in regulating gene expression and phenotype in both health and disease^[6–8]. The regulation of m6A methylation is dynamically regulated by methyltransferases, demethylases and m6A binding proteins^[7]. The m6A methylation site appears in the nuclear RNA under the action of methyltransferases mainly including KIAA1429, METTL3, METTL14, RBM15, WTAP and ZC3H13, which are responsible for the addition of the methyl group to the nitrogen on the sixth carbon of the aromatic ring of an adenosine residue^{[6] [9, 10]}. The m6A methylation site in the nucleus can also be erased under the action of demethylases mainly including ALKBH5 and FTO, which can specifically target RNA m6A^{[6] [10–12]}. Subsequently, in the further processing of RNA, the m6A binding proteins mainly including HNRNPC, YTHDC1, YTHDC2, YTHDF1 and YTHDF2, will bind to the m6A methylated site in the nucleus or bind to the m6A site of the processed mature RNA outside the nucleus^{[6, 10, 13]}. The specific role of m6A methylation in regulating the stability, localization, transportation, translation, and degradation of target mRNAs largely depends on the recognition of different kinds of the m6A binding proteins^{[14, 15]}. Growing evidence has proved that m6A methylation plays a critical role in the prognosis of various human malignancies ^{[7, 14]}. However, little is known about the role of m6A methylation in the prognosis of ACC.

In the present study, utilizing the data of ACC cohort from The Cancer Genome Atlas (TCGA) database and bioinformatic analysis, we investigate the role of the m6A methylation regulators in the prognosis of ACC. We construct a four-m6A methylation regulator risk score (RS) as an independent prognostic biomarker in ACC, which was calculated as follows based on the expression levels of genes: -0.182757311315352 × METTL14 + 0.329207248192332 × RBM15 + 0.0218703531461402 × HNRNPC + 0.00097106415792933 × WTAP.

Collection of primary data

Data Release 24.0 - May 07, 2020 of the TCGA-ACC mRNA FPKM transcriptome profiling data (with 0 normal samples and 79 tumor samples) and corresponding clinical information (N = 92) was downloaded from TCGA Research Network (https://cancergenome.nih.gov/) ^[16]. All mRNA expression matrix and associated clinical information were extracted via Perl (version: strawberry-perl-5.30.2.1-64bit for Windows). The extracted matrix is used for subsequent bioinformatics analysis with Perl (version: strawberry-perl-5.30.2.1-64bit for Windows), R (version: 4.0.1 for Windows), RStudio (version: 1.1.456 for Windows), and corresponding R packages.

Consensus clustering of m6A RNA methylation regulators

As previously described, to identify whether the cancer tissues can be grouped into distinct clusters according to the expression levels of the 13 m6A genes, we divide ACC patients into k (k = 2–9) clusters using consensus clustering by “ConsensusClusterPlus” package (http://www.bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus.html) ^[7]. Moreover, to verify whether the clustering is reasonable, we performed PCA and survival analyses using “ggplot2” package (cran.r‑project.org/web/packages/ggplot2/index.html) and “survival” package (cran.r‑project.org/web/packages/survival/index.html) respectively.

Selection of potential prognostic m6A RNA methylation regulators

To better understand the prognostic value of m6A RNA methylation regulators in ACC, we did a univariate cox regression analysis of 13 m6A RNA methylation regulators by “survival” package (cran.r‑project.org/web/packages/survival/index.html) in R software as previously described ^[7]. The m6A RNA methylation regulators with a p-value < 0.05 were considered as prognostic genes and selected as candidate genes for further procedure.

Construction of risk score (RS)

In order to construct a risk score as a potential prognostic biomarker in ACC, we implemented Least Absolute Shrinkage and Selection Operator (LASSO) cox regression analysis to the candidate genes. LASSO algorithm ^{[17, 18]} was performed utilizing “glmnet” package (https://cran.r‑project.org/web/packages/glmnet/index.html) and “survival” package in R software. As a result, genes and their coefficients were determined. The RS was calculated by the formula: \(\:RS=\:\sum\:_{i=1}^{n}coefi\) \(\:\times\:\) x_i, in which the coefi was the coefficient, and x_i was the expression value of each selected gene. Based on the median cutoff point of RS, a RS model was constructed, in which all the patients were divided into the high risk group and low risk group.

Overall survival (OS) analysis for RS

To identify whether the RS model was statistically significant, we performed Kaplan–Meier (K–M) survival analysis for the high risk and low risk groups using “survival” package in R software to identify whether the RS model was statistically significant. A p < 0.05 was considered to indicate a statistically significant difference. In addition, to predict the accuracy of the RS model, we performed a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) using “timeROC” (cran.r‑project.org/web/packages/timeROC/index.html) package in R software.

Cox regression analysis of RS

To identify the independency of RS as a prognostic factor in ACC patients, we performed univariate and multivariate cox regression analyses using “survival” and “forestplot” (https://cran.r-project.org/web/packages/forestplot/index.html) packages ^[7]. p < 0.05 was statistically significant. Except for RS, we analyzed the impact of gender, T status, N status, and stage status. All the ambiguous value was excluded, including Tx, Nx, N/A and unknown. Each factor with a p value < 0.05 in univariate cox regression analysis was considered to be associated with the prognosis of ACC. Each factor with a p value < 0.05 and HR > 1 in multivariate cox regression analysis was considered as an independent prognostic factor in ACC.

Relationship between RS and clinicopathological characteristics

The clinicopathological characteristics usually play an important role in the clinical outcome of ACC patients. Therefore, we investigated the relationship between RS and the clinicopathological characteristics of ACC by “pheatmap” package in R software. The clinicopathological characteristics included gender, stage status, T status, and N status. All the ambiguous value was excluded, including Tx, Nx, N/A and unknown. Besides, we also investigated the expression levels of the related m6A genes in RS high risk and low risk groups using “pheatmap” package in R software. A p < 0.05 was statistically significant.

Correlation Between m6A RNA Methylation Regulators and Immune Cells

The CIBERSORT tool (https://cibersort.stanford.edu/) was utilized to analyze gene expression data from the GEO dataset GSE10927. This analysis aimed to infer the proportions of various immune cell types within each sample. Data visualization was performed using R and Python, which included the creation of stacked bar charts to display the proportions of different immune cells across samples and a heatmap to illustrate the correlation between immune cell proportions and gene expression levels.

Clustering of m6A RNA methylation regulators

The 79 ACC tumor samples were divided into cluster 1 and cluster 2 according to the expression levels of the 13 m6A genes (Fig. 1A-C). The results of PCA and survival analysis showed that cluster 1 and cluster 2 were two distinct clusters with a significant difference (p = 0.027) (Fig. 1D, E).

Prognostic m6A RNA methylation regulators

Univariate cox regression analysis based on the 13 m6A RNA methylation regulators revealed five genes (RBM15, HNRNPC, YTHDF2, WTAP, and METTL14) were considered as prognostic genes. The result was shown in Fig. 2.

Formula and OS analysis of the RS

Four m6A RNA methylation regulators (RBM15, HNRNPC, WTAP, and METTL14) were filtered by the LASSO cox regression analysis of the five genes, which were selected to construct the RS model (Fig. 3A, B). The total RS was calculated as follows: (-0.182757311315352 × METTL14) + (0.329207248192332 × RBM15) + (0.0218703531461402 × HNRNPC) + (0.00097106415792933 × WTAP). The 5-year OS and ROC curve were presented in Fig. 3C, D. It revealed the significant difference between the high and low risk groups (p = 3.29E-5). And the time-dependent AUC further proved the accuracy of the result (AUC = 0.787).

RS and other clinicopathological characteristics

The result of univariate analysis showed statistical significance in stage status, T status and RS (all p value < 0.001) (Fig. 4A). And the result of multivariate analysis showed statistical significance in T status (p = 0.006) and RS (p = 0.027) (Fig. 4B). In addition, as shown in Fig. 4C, it revealed that RS was closely correlated to the clinicopathological characteristics including stage status, T status, and N status. Besides, it seemed that the expression levels of RBM15, HNRNPC and WTAP were higher in the high risk group than the low risk group, while the expression level of METTL14 was higher in the low risk group than the high risk group (Fig. 4C).

Relationship Between Four m6A RNA Methylation Regulators and Immune Cells

A stacked bar chart was created to display the relative proportions of various immune cell types (e.g., neutrophils, monocytes, T cells, etc.) across different samples (Fig. 5A). To further analyze the relationship between immune cell proportions and gene expression levels, we calculated the correlations between the proportions of different immune cell types and the expression levels of genes such as WTAP, RBM15, HNRNPC, and METTL14 (Fig. 5B). In the correlation heatmap, the colors represent the strength and direction of the correlation coefficients, with red indicating positive correlations and blue indicating negative correlations. The intensity of the colors indicates the significance of the correlations (-log10(p-value))

ACC is a rare but aggressive endocrine disease with a poor prognosis, whose estimated five-year overall survival rate is < 50% ^[19]. The recurrence rate of ACC patients with radical resection is also up to 85%, which is usually with concurrent metastases ^[19]. Therefore, prognostic stratification is important for individualized therapies for ACC patients. At present, the tumor extension that is best reflected by the ENSAT stage, and the tumor proliferation which is estimated either by mitotic count or Ki67 proliferation index were the main prognostic factors used in clinical practice^[20]. However, the prognosis still varies widely among tumors with the same tumor stage and proliferation index^[21]. Currently, pan-genomic studies have shown that molecular classification is closely associated with prognosis in ACC, and targeted molecular markers have been proposed to complete the routine prognostic assessment^{[20, 22, 23]}. Recently, m6A RNA methylation regulators play an important role in the prognostic assessment and add to a better understanding of the pathogenesis of cancer ^[24]. Therefore, it is necessary to explore the prognostic significance of m6A RNA methylation regulators in ACC.

In the present study, we grouped all patients with ACC into 2 distinctly different clusters with different clinical outcome based on the expression levels of the 13 m6A RNA methylation regulators. It implied a potential prognostic value of m6A RNA methylation regulators in ACC. Therefore, we finally selected four genes (RBM15, HNRNPC, WTAP, and METTL14) associated with prognosis by univariate cox regression and LASSO regression constructing a RS model for prognostic assessment of ACC. It revealed the evident difference in 5-year OS between the high and low risk groups. And the time-dependent AUC was 0.787. More importantly, it suggested that RS, stage status and T status were all associated with prognosis, but RS and T status were independent prognostic biomarkers. It also suggested RS was closely correlated to the stage status, T status, and N status. A previous study reported that patient age and initial stage at diagnosis were considered two of the most important predictors of survival, with a poorer prognosis in patients with advanced age or metastatic disease at the time of diagnosis ^[4]. Unfortunately, the age and M status were not included in our study because all these data were unknown. Accurate staging was also a crucial step in treatment planning and in determining the prognosis of ACC ^[21]. Survival dropped precipitously with advanced stages. The 1-year survival for Stage I was 90%, while it dropped to 29% for Stage IV disease^[25]. Our study also demonstrated that stage status was associated with prognosis of ACC, although it seemed not an independent prognostic biomarker. More importantly, our study indicated T status was an independent prognostic biomarker and RS was closely associated with the stage status, T status, and N status in ACC. Thus, combination of RS, along with clinical features, might provide a better way for the prognostic assessment of ACC. It also offered a new understanding of the tumorigenesis and the correlation between the molecular, clinical, and pathological profile of ACC.

Emerging evidence has demonstrated that m6A RNA methylation regulators are involved in the occurrence and development of many cancers, such as hepatocellular carcinoma and colorectal cancer ^{[26, 27]}. However, the role of m6A RNA methylation regulators in ACC remains unknown. At the present study, we found the expression levels of RBM15, HNRNPC and WTAP were higher in the high risk group than the low risk group, while that of METTL14 was higher in the low risk group than the high risk group. It seemed that RBM15, HNRNPC and WTAP were poor prognostic factors, while METTL14 was a protective prognostic factor in ACC. Though there were few studies about these m6A methylation regulators in ACC, the role of them was investigated in other cancers. A recent study reported that HNRNPC was highly expressed in bladder cancer, and HNRNPC knockdown reduced the proliferation and tumor growth of breast cancer cells ^{[6, 28]}. HNRNPC was also proved to be an independent prognostic biomarker in oral squamous cell carcinoma, and HNRNPC overexpression promoted its carcinogenesis via epithelial- mesenchymal transition ^[29]. WTAP was evaluated in osteosarcoma with a poor prognosis, which served as an independent prognostic factor for osteosarcoma patients ^[30]. WTAP/HMBOX1 dramatically promoted osteosarcoma proliferation and metastasis in a PI3K/AKT-dependent pattern^[30]. High expression of WTAP also resulted in a poor prognosis by influencing tumor-associated T lymphocyte infiltration in gastric cancer ^[31]. METTL14 was lowly expressed in bladder cancer and colorectal cancer, and METTL14 might inhibit the malignant process via regulating RNA stability of Notch1 mRNA in bladder cancer and SOX4-mediated EMT process and PI3K/Akt signals in colorectal cancer ^{[32, 33]}. knockdown of METTL14 drastically enhanced proliferative and invasive ability of colorectal cancer cells in vitro and promoted tumorigenicity and metastasis in vivo by substantially abolishing m6A level of lncRNA XIST and augmenting the expression of lncRNA XIST, which was a downstream target of METTL14 ^[34]. METTL14 was also an independent prognostic factor in colorectal cancer, and decreased METTL14 was associated with poor OS ^[32]. These findings not only presented a similar prognostic value of HNRNPC, WTAP and METTL14 in other cancers, but also implied their functions and mechanisms in ACC. However, more further investigations of m6A RNA methylation regulators were necessary in ACC.

Up to now, the role and specific mechanism of m6A RNA methylation regulators are not fully understood and lack specificity. Though we have demonstrated the prognostic values of m6A RNA methylation regulators in patients with ACC, the accurate mechanism of them in the tumorigenesis, progression and prognosis still needs further studies. Additionally, there were several limitations existing in our study. Firstly, this ACC cohort lacked the samples of normal adrenal tissues. As a result, the expression landscape of m6A RNA methylation regulators in ACC was unclear. Secondly, the expression levels and prognostic value of m6A RNA methylation regulators were not verified in our own ACC samples, which needed a future verification. Moreover, though we investigated the role of the m6A RNA methylation regulators in the prognosis of ACC, we did not further explore the accurate mechanism of them. Finally, some important clinical and pathological information in this ACC cohort was missing, mainly including age and M status. Therefore, m6A RNA methylation regulators play a crucial role in the prognostic assessment, and modulating the m6A RNA methylation modification might be a novel strategy to guide the therapy of patients with ACC, but more further investigations are still needed.

In summary, using RBM15, HNRNPC, WTAP, and METTL14, we constructed a four-m6A methylation regulator RS signature, which was calculated as follows: (-0.182757311315352 × METTL14) + (0.329207248192332 × RBM15) + (0.0218703531461402 × HNRNPC) + (0.00097106415792933 × WTAP). This RS signature could serve as an independent prognostic biomarker to predict the prognosis of patients with ACC.

ACC

Adrenocortical carcinoma

m6A

N6-methyladenosine

K-M

Kaplan-Meier

risk score

LASSO

Least Absolute Shrinkage and Selection Operator

AUC

area under the curve

ROC

receiver operating characteristic

TCGA

The Cancer Genome Atlas

Overall survival

Acknowledgments:

We especially gratitude to The Cancer Genome Atlas (TCGA) project (https://portal.gdc.cancer.gov/) for its valuable public dataset.

Funding Information

The study was funded by Natural Science Foundation of Ningxia Hui Autonomous Region (2022AAC05054),

Conflict of Interest

The authors declare that they have no conflicts of interest.

Ethics Statement

- Approval of the research protocol by an Institutional Reviewer Board: N/A
- Informed Consent: N/A
- Registry and the Registration No. of the study/trial: N/A

- Animal Studies: N/A

Author Contributions

Ziyang Liu: Data curation, Formal analysis, Writing – original draft; Jiahao Shan: Data curation, Formal analysis; Jia Tian: Data curation, Formal analysis; Qiang Zhang: Data curation, Formal analysis; Feilong Yang: Conceptualization, Methodology, Supervision, Writing – review & editing; Lianghong Ma: Conceptualization, Methodology, Supervision, Writing – review & editing.

Data availability statement

Data related to our research work are available upon request from the corresponding authors of this study.

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No competing interests reported.

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You are reading this latest preprint version

A four-m6A methylation regulator risk score was an independent prognostic biomarker for adrenocortical carcinoma

Status:

Version 1

Abstract

Figures

Introduction

Material and Methods

Collection of primary data

Consensus clustering of m6A RNA methylation regulators

Selection of potential prognostic m6A RNA methylation regulators

Construction of risk score (RS)

Overall survival (OS) analysis for RS

Cox regression analysis of RS

Relationship between RS and clinicopathological characteristics

Correlation Between m6A RNA Methylation Regulators and Immune Cells

Results

Clustering of m6A RNA methylation regulators

Prognostic m6A RNA methylation regulators

Formula and OS analysis of the RS

RS and other clinicopathological characteristics

Relationship Between Four m6A RNA Methylation Regulators and Immune Cells

Discussion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1