Original Research | Open Access
Vol.9 (2023) | Issue-3 | Page No: 92-106
Yulou Luo1, Lan Chen2, Ximing Qu3, Na Yi3, Jihua Ran4, Yan Chen3,5*
Affiliations + Expand
1.Department of Breast Surgery, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
2.The Second Department of Gastroenterology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
3.Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, Xinjiang, China
4.Clinical Laboratory Diagnostic Center, General Hospital of Xinjiang Military Region, Urumqi, Xinjiang, China
5.Xinjiang Key Laboratory of Molecular Biology for Endemic Diseases, Xinjiang Medical University, Urumqi, Xinjiang, China
* Corresponding Author
Corresponding author:
Yan Chen, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Xinjiang Medical University, Shangde North Road, Shuimogou District, Urumqi 830017, Xinjiang, China;
Xinjiang Key Laboratory of Molecular Biology for Endemic Diseases, Xinjiang Medical University, Xinyi Road, Xinshi District, Urumqi 830017, Xinjiang, China. E-mail: yanchen@xjmu.edu.cn
Important Dates + Expand
Date of Submission: 16-Jun-2023
Date of Acceptance: 01-Sep-2023
Date of Publication: 09-Oct-2023
Objective: Esophageal squamous cell carcinoma (ESCC) is one of the most common cancer types worldwide. This present study aims to explore the prognostic roles of m6A methylation regulators in ESCC.
Methods: We acquired the RNA-sequencing data of ESCC from The Cancer Genome Atlas (TCGA) database. Nineteen m6A methylation-related genes were obtained from a previous study. Expression analysis, correlation analysis, consensus clustering, LASSO regression analysis, Cox regression analysis, immune-related analysis and stemness estimation were conducted.
Results: Fourteen m6A methylation-related genes were significantly upregulated in ESCC. Most m6A methylation regulators were positively correlated with each other. The ESCC cohort was divided into 6 subgroups, and we observed a significant difference in the ‘overall survival’ between these subgroups. A two-gene prognostic signature consisting of RBM15 and YTHDF3 was constructed. Cox regression analysis identified pN stage, pTNM stage, new tumor events and risk score as independent prognostic factors for ESCC. Furthermore, the risk score was associated with 4 immune cells. Additionally, the expression levels of 4 m6A methylation regulators were correlated with the stemness features of ESCC.
Conclusion: Our study established a new m6A methylation-related gene signature for prognostic prediction of patients with ESCC, which may better assist clinical work..
Keywords: N6-methyladenosine methylation; Esophageal squamous cell carcinoma; Prognostic signature; Immune infiltration; Stemness; Cancer
According to global cancer statistics in 2018, esophageal carcinoma has risen to the seventh most common carcinoma with the sixth highest mortality rate among all cancer types worldwide.[1] Although with the emergence of new therapeutic techniques like endoscopic esophageal mucosal dissection and radiofrequency ablation in recent years, the prognosis of esophageal carcinoma has remained unsatisfying; its 5-year survival rate is only 15% to 20%.[2] Generally, there are two histological subtypes of esophageal carcinoma, they are esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). The morbidity of ESCC is higher compared to the morbidity of EAC globally, and over 80% of ESCC cases occur in developing countries, while EAC continues to be the most common subtype of esophageal cancer in developed countries.[3] However, to the latest knowledge, the prognostic prediction of esophageal carcinoma has not yet been fully achieved.
There are many factors affecting gene expression, among which RNA modification, a sort of post-transcriptional modification method, is associated with RNA metabolic direction, to function normally or to be degraded. Over 100 internal modification methods of RNA have been observed in mammalian cells,[4] which are widely applied to almost all types of RNA, like mRNA, miRNA, lncRNA, circRNA, tRNA, etc. N6-methyladenosine (m6A), which means methylation modification at the sixth nitrogen atom of adenine accounts for more than 60% of RNA modifications,[5] exerting a great influence on RNA metabolic dynamics.
Like any other biological activity, m6A methylation modification is regulated by multiple related proteins to maintain a functional balance dynamically. These regulators of m6A methylation modification are sorted as demethylases (eraser), methyltransferases (writer), and binding proteins (reader). ALKBH5 and FTO have been reported to be “erasers” of m6A methylation modification, which mainly catalyze the process of removing m6A from targeted RNAs.[6],[7] ZC3H13, METTL3, METTL14, WTAP, RBM15, and KIAA1429 are identified as “writers” of m6A methylation modification in terms of transferring methyl to targeted RNAs and catalyzing stable bonding.[8],[9],[10],[11],[12],[13] HNRNPC, HNRNPA2B1, together with YT521-B homology (YTH) domain family members (YTHDF1, YTHDF2, YTHDF3, YTHDC1, and YTHDC2) are known as “readers” of m6A methylation modification, for their capability to bind to the m6A site and affect subsequent RNA-protein interaction.[14],[15],[16]
As early as 1979, exploration of the association between malignant tumors and m6A methylation had begun.[17] Nowadays, m6A methylation is one of the research frontiers in molecular oncology. The present knowledge strongly informs us that dysregulation of m6A methylation may function as an important component of tumorigenesis and tumor progression,[18],[19],[20],[21],[22] but the detailed molecular mechanism of which has not been fully elucidated. Scientists and clinical doctors have always been dedicated to exploiting efficient molecular biomarkers to assist in the prognosis prediction of patients with cancer. With the construction of a new medical model, which refers to environment-biology-physiology-society-engineering, bioinformatics analysis has emerged to be an indispensable tool for oncology research, and a lot of achievements have been made possible. Thus, it is valuable to apply bioinformatic analysis for prognosis prediction of patients with cancer. There have been numerous studies to apply m6A methylation-related genes to predict the survival duration of patients with cancer via bioinformatic analysis,[23],[24],[25] but a similar study in ESCC is not fully completed yet.
In this study, based on TCGA, expression level assessment, PPI network analysis and expressive correlationship analysis were utilized to provide an overview of the 19 m6A methylation regulators in ESCC. Then, based on the expression of 19 m6A methylation regulators, a consensus clustering analysis was performed, and a prognostic signature that contained two genes, together with high/low-risk groups was constructed to verify the prognostic predictability for patients with ESCC of our signature. Subsequent uni/multivariate Cox regression analysis further identified two selected genes as independent prognostic predictors. Tumor immune microenvironment (TIME) and cancer cell stemness may be associated with tumor metastasis, immune check point response, and recurrence, thus to better understand m6A methylation-related genes’ role in ESCC, immune infiltration analysis and stemness estimation were performed.
Data acquisition
Clinical data of 82 samples with ESCC was obtained from TCGA, including status, age, gender, race, smoking, pTNM stage, and new tumor event information, together with corresponding RNA-sequencing data. 11 normal samples from TCGA were collected as normal samples in our study. A total of 19 m6A methylation-related genes (IGF2BP3, RBM15, YTHDF1, IGF2BP1, IGF2BP2, WTAP, YTHDC1, RBM15B, YTHDF2, HNRNPC, METTL3, YTHDC2, HNRNPA2B1, ALKBH5, RBMX, METTL14, YTHDF3, ZC3H13, FTO) were collected from a previous study and enrolled for further research.[LinkRef 26-188126
Analysis of the 19 m6A methylation-related genes in ESCC
The “ggplot2” package and “pheatmap” package in R software (version 4.1.0; http://www.bioconductor.org/packages/release/bioc/html) were utilized to assess the differential expression of the 19 m6A methylation-related genes based on RNA-sequencing data and plot the heatmap between ESCC group and normal group. The significance of the two groups passed the Wilcox test. Using the Search Tool for the Retrieval of Interacting Genes/proteins database (STRING, version 11.5; http://string-db.org/), we primarily explored the inner interactions among the 19 genes, and their functional distribution in gene ontology (GO) categories were also analyzed, such as biological process, molecular function, and cellular components. Spearman correlation analysis was performed to determine the expressive correlation among the 19 genes by the “ggstatsplot” package in R software based on RNA-sequencing data from TCGA.
Prognostic effects of m6A methylation-related genes in ESCC
We elucidated the effect of each m6A methylation-related gene on the 82 ESCC patients’ overall survival based on RNA-sequencing data and corresponding clinical information from TCGA. Kaplan-Meier survival analysis with the log-rank test was used to compare the survival differences between the two groups, which was supported by the “survival” package and the “survminer” package in R software.
Consensus clustering of patients with ESCC based on m6A methylation-related genes
With the assistance of the “ConsensusClusterPlus” package in R software, we divided the 82 patients into several subgroups, the results of which were verified by the principal component analysis (PCA). Differences among the overall survival curves of these subgroups were analyzed by Kaplan-Meier survival analysis using the “survival” package and “survminer” package in R software.
Construction of the prognostic signature based on m6A methylation-related genes
We used univariate Cox regression to analyze the 19 m6A methylation-related genes that significantly correlated with the overall survival of the 82 patients with ESCC and the “forestplot” package in R software was used to plot. Only genes with P < 0.05 were selected. Then the absolute shrinkage and selection operator (LASSO) regression algorithm was performed to construct a prognostic signature, in which two genes were algorithmically picked up as prognostic factors, and a risk score formula was refined. The 82 patients were further divided into low-risk and high-risk groups according to their calculated risk scores, the difference in overall survival was assessed between the two groups by Kaplan-Meier survival analysis and log-rank test. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) value later indicated the accuracy of our signature.
Univariate Cox regression analysis and multivariate Cox regression analysis were performed to identify independent prognostic factors for patients with ESCC. P-value, hazard ratio, and 95% CI were exhibited vividly by using the “forestplot” package in R software. The “glmnet” package in R software was used to display the distribution of risk scores and status of the 82 patients with ESCC, as well as the heatmap of prognostic predictors’ expression in both the high-risk group and low-risk group.
Immune infiltration analysis
The “immunedeconv” package in R software included 6 algorithms (TIMER, XCELL, EPIC, CIBERSORT, QUANTISEQ, MCPCOUNTER) to calculate the immune score of the target sample. We chose the TIMER algorithm to calculate the immune scores of our present ESCC cohort and the normal cohort (11 adjacent samples from TCGA), and compared the difference of immune scores between the two cohorts in terms of 6 main immune cell types. The significance between the two cohorts passed the Wilcox test. The “ggplot2” package in R software was used to plot. Then we used Spearman correlation analysis to evaluate the correlation between the risk scores calculated by prognostic signature and the immune score, in other words, the infiltration level of the immune cells of each patient. The “ggstatsplot” package in R software was used to plot.
Cell stemness analysis
We downloaded ESCC RNA-sequencing data (FPKM) from the Genomic Data Commons (GDC), 19 m6A methylation-related genes’ data were picked up. FPKM data was converted to TPM data, which was subsequently normalized as log2 (TPM + 1). After that, mRNAsi, in other words, stemness index was calculated by OCLR algorithm constructed by Malta et al.,[27] which was based on the characteristics of mRNA expression and the gene expression profile contained 11774 genes. Linear transformation was utilized to map the stemness index to the range [0, 1]. Wilcox test was used to check the stemness difference between the present ESCC cohort and the normal cohort. Spearman correlation analysis was used to check the relation of m6A methylation-related genes’ expression with stemness index.
Statistical analysis
The comparison of K-M survival curve was achieved by Cox regression analysis. Difference of expression level between groups were compared by the Wilcoxon rank sum test. Spearman correlation was taken for correlation analysis. |r| > 0.1 was considered to be relevant and P < 0.05 was deemed as statistically significant.
Sample collection and clinical information
A total of 82 ESCC samples and 11 adjacent normal samples from TCGA were enrolled in this study. Clinical information of the 82 ESCC samples has been illustrated in Table 1.
Expression of m6A methylation-related genes in ESCC
To understand the expression levels of the 19 genes in the ESCC group and normal group, a heatmap was produced to vividly show the differential expression of genes between the ESCC group and normal group [Figure 1]. We can tell those 14 genes (WTAP, HNRNPC, RBMX, HNRNPA2B1, YTHDF2, YTHDC1, YTHDF1, ALKBH5, METTL3, FTO, IGF2BP3, IGF2BP1, IGF2BP2, VIRMA) show significant upregulation in ESCC group compared to normal group, and this kind of differential expression may act as a foundation for onco-functioning.
Interaction, correlation, and functional annotation of m6A methylation-related genes
Based on the STRING database, the inner interactions and potential functional associations among the 19 genes were explored, which was presented as a protein-protein interaction (PPI) network [Figure 2A]. The PPI network we acquired contained 19 nodes and 138 edges. HNRNPC, METTL14, YTHDF3, and HNRNPA2B1 seemed to be core genes.
Express correlations of the 19 genes were calculated and graphed in Figure 2B. The vast majority of the 19 genes were positively correlated with each other, while a few gene pairs correlated negatively. The correlation index of HNRNPC and RBMX came up to 0.78 positively, which was the highest.
Furthermore, a functional enrichment analysis was operated by STRING database later to understand the distribution of the 19 genes’ biological function in biological process, molecular function, and cellular components, the result of which was refined in Table 2. We can learn that regulation of mRNA metabolic process, mRNA binding, and RNA N6-methyladenosin methyltransferase complex were the most significantly enriched gene ontology items. There were no significant pathway enrichments observed in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Disease-gene associations (DISEASES), Tissue expression (TISSUES), and Protein Domains (SMART).
Effect of the 19 m6A methylation-related genes on survival of patients with ESCC
Survival analysis of each m6A methylation-related gene was performed by R software. The results revealed that RBM15 (P = 0.0343) and YTHDF3 (P = 0.00882), the two genes’ high expression levels, were significantly associated with ESCC patients’ better overall survival, while other genes had no statistical significance [Figure 3].
Consensus clustering identified six subgroups of patients with ESCC
Consensus clustering analysis was performed to divide the 82 patients with ESCC into several subgroups based on the expression levels of the 19 methylation-related genes. Trend of the cumulative distribution function (CDF) of the consensus cluster from k = 2 to 6 and the delta area under the CDF curve from k = 2 to 6 are displayed in Figures 4A, 4B. We found that the six subgroups were the most suitable classification, almost all patients can be categorized, and k = 6 achieved adequate selection [Figure 4C]. The tracking plot from k = 2 to 6 is depicted in Figure 4D. As for Figure 4E, our consensus clustering result was verified by principal component analysis (PCA), which revealed that the six subgroups can assemble respectively. It appears to be that subgroup 4 has the highest expression levels of the 19 methylation-related genes. Moreover, Kaplan-Meier survival analysis showed that there indeed exists a significant difference of overall survival probability between different subgroups (P = 7.30E-04) [Figure 4F].
Construction of prognostic signature
Univariate Cox regression analysis was used to check the prognostic value of the 19 methylation-related genes in ESCC [Figure 5A]. The results showed that RBM15 (P = 0.0343) and YTHDF3 (P = 0.0088) were significantly associated with the increased overall survival of the 82 patients with ESCC, and the two genes’ hazard ratio was less than 1, indicating their potential role as prognostic factors. Then the LASSO regression algorithm was performed to construct a two variates prognostic signature and calculate the coefficients of RBM15 and YTHDF3 [Figure 5B]. λ was selected when the median of the sum of squared residuals was the smallest [Figure 5C]. The prognostic signature we got was as follows: Risk score = (-0.1436) * RBM15 expression + (-0.2309) * YTHDF3 expression, λ = 0.0557. This formula indicated that both RBM15 and YTHDF3 were protective genes for patients with ESCC.
The Risk scores of all 82 patients with ESCC were calculated, which were then devided into high-risk and low-risk groups with 41 patients in each group. Kaplan-Meier survival analysis with log-rank test was utilized again to judge the overall survival probability between the high-risk group and low-risk group [Figure 5D]. Clearly, the low-risk group (3.7 years) showed a significantly longer survival duration compared to the high-risk group (1.7 years) (P = 0.0136). Furthermore, time-dependent ROC curves were depicted to check the predictive efficiency of our prognostic signature [Figure 5E]. As a result, the AUCs at 1, 3, and 4 years were 0.612, 0.744, and 0.769, respectively, suggesting a favorable differentiating performance of RBM15 and YTHDF3 for patients with ESCC.
Prognostic value of prognostic signature
Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors, especially, the risk score calculated by the prognostic signature was observed [Figures 6A, 6B]. Cox regression analysis showed that pN stage (P = 0.02555), pTNM stage (P < 0.0001), and new tumor event (P < 0.0001) were indicated as independent prognostic factors. What’s more important, the risk score based on prognostic signature was also determined as an independent prognostic factor (P < 0.001). The distribution of risk scores of the 82 patients with ESCC is displayed in Figure 6C ⅰ. Patients’ status in both high-risk and low-risk groups is depicted in Figure 6C ⅱ; we can tell that there are more alive patients in the low-risk group than in the high-risk group. The relative expressions of RBM15 and YTHDF3 in the high-risk and low-risk groups are displayed in Figure 6C ⅲ. It showed that the low-risk group had higher expression of RBM15 and YTHDF3, while the high-risk group had lower expression of RBM15 and YTHDF3.
Immune-related analysis
The TIMER algorithm score of the present ESCC cohort and normal cohort is displayed in Figure 7A. It showed that the present ESCC cohort had significantly higher infiltration levels of CD4+ T cells (P < 0.01), neutrophil cells (P < 0.001), and myeloid dendritic cells (P < 0.001) compared with the normal cohort. To further assess the effect of RBM15’s and YTHDF3’s expressive dynamics on immune cell infiltration, we examined the correlation between the risk scores based on prognostic signature and infiltration levels of 6 main immune cells. The results of Spearman correlation analysis showed that the risk score significantly correlates with the infiltration level of 4 immune cells: B cells (P = 0.022), neutrophil cells (P = 0.002), CD4+ T cells (P = 0.005), and macrophage cells (P = 0.001), while there was no significant correlation between risk score and infiltration level of CD8+ T cell and myeloid dendritic cell [Figure 7B]. It indicated that the two emerging prognostic predictors for patients with ESCC, also as important regulators of m6A methylation, may exert an influence on tumor cells via manipulating the tumor immune microenvironment (TIME).
Stemness estimation of the present ESCC cohort
Stemness feature identification is a rising realm in oncology research, thus in this study, we evaluated the stemness feature of the ESCC cohort involved in our study based on the OCLR algorithm. At first, we found that the ESCC cohort involved in our study exhibits significant stemness features compared to the normal cohort, which includes 11 adjacent samples from TCGA and 1,445 normal samples from GTEx (P < 0.0001) [Figure 8A]. Furthermore, the correlation between stemness index and expression of 19 m6A methylation-related genes was explored. There were merely 4 m6A methylation-related genes showing significant correlation with the stemness index: IGF2BP1 (P = 0.013); IGF2BP2 (P = 0.016); IGF2BP3 (P = 8.82E-07) and HNRNPC (P = 0.042), while the two selected prognostic predictors, RBM15 and YTHDF3 did not [Figure 8B]. We can infer from the results that RBM15 and YTHDF3 may hardly engage in the manipulation of stemness features, meanwhile, IGF2BP1, IGF2BP2, IGF2BP3, and HNRNPC, the 4 m6A methylation-related genes may take part in stemness feature regulation in ESCC.
ESCC is the most common histological subtype of esophageal carcinoma with extremely poor survival duration, thus the disease burden of ESCC still remains heavy globally. Exploitation of a dependable prognostic predictor has always been one of the goals for ESCC prognosis improvement. It has been observed in tumor cells that differential expression of m6A methylation regulators, which represents a dysregulation of m6A methylation modulation, is involved in tumorigenesis.[21],[28],[29],[30],[31] Thus, a new way of directing prognosis prediction via m6A methylation regulators is becoming clear.[23],[24],[25] In this bioinformatic study, we primarily explored the expression pattern, inner interaction, prognostic effects and values, immune infiltration association, and stemness correlation of the 19 m6A methylation-related genes. The expression levels of 14 m6A methylation-related genes were significantly higher in the ESCC group compared to the normal group, among which HNRNPC and IGF2BP3,[32] ALKBH5[33] and METTL3[34] have been reported to be independent prognostic factors for patients with ESCC. The PPI network has strongly shown that there are complicated inner interactions among the 19 m6A methylation regulators and their functions are collaborated in both physiology and cancer. What’s more, functional enrichment analysis based on the STRING database has been performed to understand the gene ontology distributions of the 19 genes. According to Spearman analysis, the correlation index between HNRNPC and RBMX was identified to be the highest, which was 0.78. Next, we determined that the expression levels of RBM15 and YTHDF3 are significantly correlated with the overall survival of patients with ESCC. These results indicate that m6A methylation-related genes may be potential tumorigenesis regulators and they deserve further exploration.
Consensus clustering of multiple patients is advantageous to find the collective characteristics of a subgroup, which makes it easier to understand a single individual and helps to guide treatment. The whole ESCC cohort was divided into 6 subgroups by consensus clustering based on the 19 m6A methylation-related genes, and PCA has verified the accuracy of our clustering. What’s more important, the overall survival possibility among the 6 subgroups has revealed a significant difference and subgroup 3 has the longest overall survival duration. Additionally, we established a two-gene prognostic signature to calculate the risk score of each patient based on the LASSO regression algorithm. Two genes (RBM15 and YTHDF3) were identified as potential prognostic predictors for ESCC. The high-risk group and low-risk group were divided according to the risk scores calculated by our prognostic signature, and the overall survival difference between the high-risk group and low-risk group was significant. Time-dependent ROC curves were depicted to judge our signature’s prognostic ability, it turned out that AUCs at 1, 3, and 4 years were 0.612, 0.744, and 0.769, suggesting a good performance of our prognostic signature to predict the survival duration of patients with ESCC. Results of univariate Cox regression and multivariate Cox regression identified pN stage, pTNM stage, new tumor events, and risk scores calculated by our signature as independent prognostic factors for patients with ESCC. Higher expression levels of RBM15 and YTHDF3 were observed in the low-risk group, indicating the two genes’ protective effect for patients with ESCC. Ni et al.[35] reported that overexpressed lncRNA GAS5 is able to significantly suppress the proliferation and invasive capacity of colorectal cancer cells in vitro, while YTHDF3, one of the “readers” in m6A methylation, is observed to propel the degradation of m6A-modified lncRNA GAS5. Chang et al.[36] observed a positive correlation between upregulated YTHDF3 expression and brain metastases in patients with breast cancer, mechanistically, YTHDF3 enhances the translation of m6A-enriched transcripts for several brain metastasis-related genes. Anita et al.[37] pointed out that overexpression of YTHDF3 together with YTHDF1 is significantly correlated with intrinsic subclasses and lymph node metastasis in patients with breast cancer, and overexpression of YTHDF1 and YTHDF3 is associated with poor prognosis. Hu et al.[38] reported a negative feedback loop in pancreatic cancer in response to glucose deletion, the enhanced interaction between YTHDF3 and lncRNA DICER1-AS1 induces the degradation of lncRNA DICER1-AS1, then subsequent downregulation of DICER1 reduces the maturation of miR-5586-5p, which promotes the transcription of several glycolysis-related genes to enhance glycolysis. Correlation between high expression levels of YTHDF3 and poor overall survival of patients has also been reported in soft tissue sarcoma,[39] ocular melanoma,[40] osteosarcoma,[41] and lung adenocarcinoma.[42] Another predictor, RBM15, has been reported to facilitate the progression of colorectal cancer,[43] laryngeal squamous cell carcinoma,[44] and pancreatic adenocarcinoma.[45] Furthermore, Zhao et al.[45]45 pointed out the potential prognostic value of RBM15 in pancreatic adenocarcinoma by a comprehensive pancancer analysis. However, the role of RBM15 and YTHDF3 in ESCC has not been well demonstrated because of limited literature. Our study has laid a good foundation for subsequent research.
Next, we found out that the infiltration levels of CD4+ T cells, neutrophil cells, and myeloid dendritic cells were significantly higher in the ESCC cohort than in the normal cohort. A previous study has reported that a high infiltration level of CD4+ T cells predicts a favorable overall survival possibility for patients with ESCC,[46] while a higher infiltration level of neutrophil cells predicted a poor prognosis.[47] We also explored the correlation between the infiltration levels of immune cells and the risk scores based on prognostic signatures, in an attempt to further understand the role of m6A methylation-related genes in ESCC progression via manipulating TIME. The risk score based on prognostic signature was significantly negatively correlated with the infiltration level of B cells, neutrophil cells, CD4+ T cells, and macrophage cells. In other words, patients with higher immune scores revealed lower risk scores and longer survival duration. The ESCC cohort exhibited greatly significant stemness features compared to the normal cohort according to the mRNAsi score we calculated. Interestingly, it was the expression levels of IGF2BP1, IGF2BP2, IGF2BP3, and HNRNPC that significantly correlated with stemness degree, rather than the expression levels of RBM15 and YTHDF3. YTHDF2 has been identified to promote liver cancer stem cell phenotype via impairing OCT4 expression in vitro.[48] YTHDF1 was also determined to be indispensable for intestinal cancer cells’ stemness via blocking the Wnt signaling pathway.[49] FTO was reported to inhibit the self-renewal ability of ovarian cancer stem cells and suppress tumorigenesis in vivo,[50] and increased FTO expression mediated by berberine was involved in stemness regulation in colorectal cancer.[51] Knockdown of IGF2BP2 inhibited cholangiocarcinoma cell stemness induced by IL-6 treatment.[52] IGF2BP1 was able to promote liver cancer stem cell phenotype via regulating MGTA5 mRNA stability.[53] Our stemness estimation results provide new insight into the association between m6A methylation-related genes and ESCC stemness features, but the inner mechanisms require further exploration.
It was worth mentioning that Pu et al.[54] also reported a m6A methylation-related genes-based prognostic signature, which contained four genes (YTHDF3, RBM15, KIAA1429, and ALKBH5). Two major reasons were considered to distinguish our study from such similar studies. Firstly, the AUCs at 1-, 3-, and 4- years of our prognostic signature were 0.612, 0.744, and 0.769, which were higher than the result (AUC = 0.663) by Pu et al. Secondly, the correlation between cell stemness and m6A methylation-related genes was analyzed in our study. The Stemness index of the ESCC cohort was significantly correlated with the expression level of IGF2BP1, IGF2BP2, IGF2BP3, and HNRNPC. However, there are inherent limitations to our present study indeed. Firstly, the ESCC cohort involved simply contains 82 samples, the limitation of sample size may influence the accuracy of results. Thus, the sample size requires to be expanded. Besides, the ultimate purpose of this sort of study is to assist clinical work, verification with clinical data from actual patients is able to put the results into practice.
Our study explored the expression level of 19 m6A methylation-related genes in the ESCC cohort and normal cohort, visualized these m6A methylation regulators’ inner interactions, and summarized the gene ontology annotation in biological process, molecular function, and cellular component. A majority of m6A methylation regulators were positively correlated with each other. Consensus clustering divided the ESCC cohort into 6 subgroups and compared the survival differences between them. The most important thing is that a two-gene prognostic signature consisting of RBM15 and YTHDF3 was constructed, according to which each patient’s risk score was calculated. Risk scores together with other variables were determined to be independent prognostic factors for patients with ESCC. Immune-related analysis and stemness estimation revealed that the risk score based on prognostic signature was associated with the infiltration level of 4 immune cells and 4 m6A methylation regulators were correlated with the stemness feature of ESCC, indicating m6A methylation regulators may affect tumorigenesis by participating in TIME and stemness regulation.
ABBREVIATIONS
IGF2BP1, insulin-like frowth factor 2 mRNA binding protein 1; IGF2BP2, insulin-like frowth factor 2 mRNA binding protein 2; IGF2BP3, insulin-like frowth factor 2 mRNA binding protein 3; RBM15, RNA binding motif protein 15; YTHDF1, YTH N6-methyladenosine RNA binding protein 1; YTHDF2, YTH N6-methyladenosine RNA binding protein 2; YTHDF3, YTH N6-methyladenosine RNA binding protein 3; YTHDC1, YTH domain containing 1; YTHDC2, YTH domain containing 2; WTAP, WT1 associated protein; RBM15B, RNA binding motif protein 15B; HNRNPC, heterogeneous nuclear ribonucleoprotein C; METTL3, methytransferase 3; HNRNPA2B1, heterogeneous nuclear ribonucleoprotein A2/B1; ALKBH5, alkB homolog 5; RBMX, RNA binding motif protein X-linked; METTL14, methytransferase 14; ZC3H13, zinc finger CCCH-type containing 13; FTO, FTO alpha-ketoglutarate dependent dioxygenase; VIRMA, vir like m6A methyltransferase associated; OCT4, organic cation transporter 4; Wnt, Wnt oncogene; DICER1, ribonuclease Ⅲ.
FINANCIAL SUPPORT AND SPONSORSHIP
This study was supported by the grant from Natural Science Foundation of Xinjiang Uyghur Autonomous Region (No. 2020D01C171, to Yan Chen).
CONFLICTS OF INTEREST
The authors declare that they have no competing interests.
ETHNICS APPROVAL AND CONSENT TO PARTICIPATE
Not applicable.
AUTHOR CONTRIBUTIONS
Yulou Luo, Lan Chen and Yan Chen proposed the concept and designed the study. Yulou Luo and Lan Chen collected the data and conducted analyses. Yulou Luo, Ximing Qu, Na Yi and Jihua Ran drafted the manuscript and revised it critically for important intellectual content. The authors read and approved the final manuscript.
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Javier de la Rosa*, Alejandro Urdiciain*, Juan Jesús Aznar‑Morales, Bárbara Meléndez1,
Juan A. Rey2, Miguel A. Idoate3, Javier S. Castresana
Akiko Sasaki1, Yuko Tsunoda2, Kanji Furuya3, Hideto Oyamada1, Mayumi Tsuji1, Yuko Udaka1, Masahiro Hosonuma1, Haruna Shirako1, Nana Ichimura1, Yuji Kiuchi1
Hui Liu1, Hongwei Yang2, Xin Wang3, Yanyang Tu1
Xiaoshan Xu, Zhen Wang, Nan Liu, Pengxing Zhang, Hui Liu, Jing Qi, Yanyang Tu
Lei Zhang1,2, Fang‑Fang Yin1,2,3, Brittany Moore1,2, Silu Han1,2, Jing Cai1,2,4
Sulin Zeng1,2, Wen H. Shen2, Li Liu1
Yanhua Mou1, Quan Wang1, Bin Li1,2
Raquel Luque Caro, Carmen Sánchez Toro, Lucia Ochoa Vallejo
Shazima Sheereen1, Flora D. Lobo1, Waseemoddin Patel2, Shamama Sheereen3,
Abhishek Singh Nayyar4, Mubeen Khan5
Feiyifan Wang1, Christopher J. Pirozzi2, Xuejun Li1
Jian‑Hong Zhong1,2, Kang Chen1, Bhavesh K. Ahir3, Qi Huang4, Ye Wu4, Cheng‑Cheng Liao1, Rong‑Rong Jia1, Bang‑De Xiang1,2, Le‑Qun Li1,2
Antonio Lucena‑Cacace1,2,3, Amancio Carnero1,2
Michael Zhang, Kelvin Zheng, Muhammad Choudhury, John Phillips, Sensuke Konno
Ajay Sasidharan, Rahul Krishnatry
Leping Liu1, Xuejun Li1,2
Gerard Cathal Millen1, Karen A. Manias1,2, Andrew C. Peet1,2, Jenny K. Adamski1
Ge Ren1,2,3, Yawei Zhang1,2, Lei Ren1,2
Qing Du1, Xiaoying Ji2, Guangjing Yin3, Dengxian Wei3, Pengcheng Lin1, Yongchang Lu1,
Yugui Li3, Qiaohong Yang4, Shizhu Liu5, Jinliang Ku5, Wenbin Guan6, Yuanzhi Lu7
Lei Zhang1, Guoyu Qiu2, Xiaohui Xu2, Yufeng Zhou3, Ruiming Chang4
Aanchal Tandon, Bharadwaj Bordoloi, Safia Siddiqui, Rohit Jaiswal
Dongni Ren1, Xin Wang2, Yanyang Tu1,2
Xipeng Wang1,2, Mitsuteru Yokoyama2, Ping Liu3
Xiaohui Xu1, Guoyu Qiu1, Lupeng Ji2, Ruiping Ma3, Zilong Dang4, Ruling Jia1, Bo Zhao1
Mansoor C. Abdulla
Guru Prasad Sharma1, Anjali Geethadevi2, Jyotsna Mishra3, G. Anupa4, Kapilesh Jadhav5,
K. S. Vikramdeo6, Deepak Parashar2
Ge Zengzheng1, Huang-Sheng Ling2, Ming-Feng Li2, Xu Xiaoyan1, Yao Kai1, Xu Tongzhen3,
Ge Zengyu4, Li Zhou5
Guoyu Qiu1, Xiaohui Xu1, Lupeng Ji2, Ruiping Ma3, Zilong Dang4, Huan Yang5
Steven Lehrer1, Peter H. Rheinstein2
Umair Ali Khan Saddozai1, Qiang Wang1, Xiaoxiao Sun1, Yifang Dang1, JiaJia Lv1,2, Junfang Xin1, Wan Zhu3, Yongqiang Li1, Xinying Ji1, Xiangqian Guo1
Elias Adikwu, Nelson Clemente Ebinyo, Beauty Tokoni Amgbare
Zengzheng Ge1, Xiaoyan Xu1, Zengyu Ge2, Shaopeng Zhou3, Xiulin Li1, Kai Yao1, Lan Deng4
Crystal R. Montgomery‑Goecker1, Andrew A. Martin2, Charles F. Timmons3, Dinesh Rakheja3, Veena Rajaram3, Hung S. Luu3
Elias Adikwu, Nelson Clemente Ebinyo, Loritta Wasini Harris
Ling Wang1,2, Run Wan1,2, Cong Chen1,2, Ruiliang Su1,2, Yumin Li1,2
Priyanka Priyaarshini1, Tapan Kumar Sahoo2
Debasish Mishra1, Gopal Krushna Ray1, Smita Mahapatra2, Pankaj Parida2
Yang Li1, Zhenfan Huang2, Haiping Jiang3
Srigopal Mohanty1, Yumkhaibam Sobita Devi2, Nithin Raj Daniel3, Dulasi Raman Ponna4,
Ph. Madhubala Devi5, Laishram Jaichand Singh2
Xiaohui Xu1, Zilong Dang2, Lei Zhang3, Lingxue Zhuang4, Wutang Jing5, Lupeng Ji6, Guoyu Qiu1
Debasish Mishra1, Dibyajyoti Sahoo1, Smita Mahapatra2, Ashutosh Panigrahi3
Nadeema Rafiq1, Tauseef Nabi2, Sajad Ahmad Dar3, Shahnawaz Rasool4
Palash Kumar Mandal1, Anindya Adhikari2, Subir Biswas3, Amita Giri4, Arnab Gupta5,
Arindam Bhattacharya6
Seyyed Majid Bagheri1,2, Davood Javidmehr3, Mohammad Ghaffari1, Ehsan Ghoderti‑Shatori4
Mun Kyoung Kim1, Aidin Iravani2, Matthew K. Topham2,3
Yan Liu1,2,3,4#, Chen Cui1,2,3,4#, Jidong Liu1,2,3,4, Peng Lin1,2,3,4,Kai Liang1,2,3,4, Peng Su5, Xinguo Hou1,2,3,4, Chuan Wang1,2,3,4, Jinbo Liu1,2,3,4, Bo Chen6, Hong Lai1,2,3,4, Yujing Sun1,2,3,4* and Li Chen 1,2,3,4*
Zhiyu Xia1,2, Haotian Tian1, Lei Shu1,2, Guozhang Tang3, Zhenyu Han4, Yangchun Hu1*, Xingliang Dai1*
Jianfeng Xu1,2, Hanwen Zhang1,2, Xiaohui Song1,2, Yangong Zheng3, Qingning Li1,2,4*
Bowen Hu1#, Lingyu Du2#, Hongya Xie1, Jun Ma1, Yong Yang1*, Jie Tan2*
Umair Ali Khan Saddozai, Zhendong Lu, Fengling Wang, Muhammad Usman Akbar, Saadullah Khattak, Muhammad Badar, Nazeer Hussain Khan, Longxiang Xie, Yongqiang Li, Xinying Ji, Xiangqian Guo
Suxia Hu, Abdusemer Reyimu, Wubi Zhou, Xiang Wang, Ying Zheng, Xia Chen, Weiqiang Li, Jingjing Dai
Yuting Chen, Yuzhen Rao, Zhiyu Zeng, Jiajie Luo, Chengkuan Zhao, Shuyao Zhang
Jun Li, Ziyong Wang, Qilin Wang
Jinghua Qi1,2, Hangping Chen3，Huaqing Lin2,4，Hongyuan Chen1,2,5* and Wen Rui2,3,5,6*
Xingli Qi1,2, Huaqing Lin2,3, Wen Rui2,3,4,5 and Hongyuan Chen1,2,3
Xiumin Zhang1,2#, Xinyue Lin1,3#, Siman Su1,3#, Wei He3, Yuying Huang4, Chengkuan Zhao3, Xiaoshan Chen3, Jialin Zhong3, Chong Liu3, Wang Chen3, Chengcheng Xu3, Ping Yang5, Man Zhang5, Yanli Lei5*, Shuyao Zhang1,3*
Min Jiang1#, Rui Zheng1#, Ling Shao1, Ning Yao2, Zhengmao Lu1*
Qiaoxin Lin1, Bin Liang1, Yangyang Li2, Ling Tian3*, Dianna Gu1*
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