Identification of a novel m6A-related lncRNA pair signature for predicting the prognosis of gastric cancer patients

Gastric cancer (GC) is a major global disease, and it is the fifth most common cancer and the fourth most lethal malignancy. There were more than one million new cases and an estimated 769,000 deaths in 2020 [1], and more than 40% of the new cases and deaths occurred in China [2, 3]. In addition, 80% of patients with GC are diagnosed at an advanced stage [4]. Notably, the 5-year mortality rate for advanced GC is between 30 and 50% [5]. Overall, the prognosis of GC is not very optimistic, and it is necessary to identify novel biomarkers to reliably predict the survival outcomes of GC patients.

Of the over 160 RNA post-transcriptional regulatory marks in multiple RNA species, N6-methyladenosine (m6A) is the most common form modification on mRNA in higher eukaryotes, and it plays a vital role in RNA splicing, export, stability and translation [6]. Recently, accumulating studies have revealed that m6A modification is involved in multiple processes of tumorigenesis [7‐11], and m6A modification, which is a reversible and dynamic process, is regulated by m6A regulators, including “writers” (methyltransferases), “readers” (signal transducers) and “erasers” (demethylases) [12]. Writers, including METTL3, METTL16, KIAA1429, WTAP, RBM15, RBM15B, and ZC3H13, mediate the RNA methylation modification process. Erasers include FTO and ALKBH5, and mediate the RNA demethylation process. In addition, signal transducers, including YT521-B homology (YTH) domain family members (YTHDF1, YTHDF2, and YTHDF3), YTH domain-containing proteins (YTHDC1 and YTHDC2), heterogeneous nuclear ribonucleoproteins family members (HNRNP and HNRNPA2B1), and insulin-like growth factor 2 mRNA-binding proteins (IGF2BPs; including IGF2BP1, IGF2BP2, and IGF2BP3), affect the reading of RNA methylation information, translation, stability and degradation of downstream RNAs [4, 13, 14]. In summary, m6A RNA methylation has a significant impact on RNA production and metabolism and is involved in the pathogenesis of multiple diseases, including GC [15].

Long non-coding RNAs (lncRNAs) represent the largest group of non-coding RNAs produced from the genome [16], and they are more than 200 nucleotides in length. Accumulating evidence has revealed that various lncRNAs contribute to gene expression at both the post-transcriptional and transcriptional levels. Additionally, aberrant lncRNA expression is strongly related to multiple cancers [12, 17] and serves as a diagnostic and prognostic marker for tumours [18]. Furthermore, lncRNAs can direct the expression of genes related to the activation of immune cells, thus altering the immune microenvironment and further contributing to the malignant phenotypes of some cancers [17, 19]. m6A-related lncRNAs are potential biomarkers for predicting the overall survival (OS) of lower-grade glioma patients and might be novel therapeutic targets [12]. However, m6A-related lncRNA signatures in GC patients need further exploration.

Epithelial–mesenchymal transition (EMT) is a process that enables polarized epithelial cells to transition towards a mesenchymal phenotype with increased cellular motility, and EMT occurs in many types of cancers [20]. In GC, the loss of E-cadherin expression stimulates cell transformation into a more invasive and less differentiated state through the EMT process [21]. However, the association between m6A-related lncRNAs and EMT factors in GC is not entirely clear.

In the present study, we analysed the value of a m6A-related lncRNA pair signature (m6A-LPS) in predicting the OS of GC patients and further validated the m6A-LPS in the testing dataset and the whole dataset. Notably, m6A-LPS served as an independent prognostic marker for GC independent of other clinical variables. Additionally, we identified differences in the expression of EMT biomarkers and immune cell infiltration between the high-risk and low-risk groups.

With the rapid development of high-throughput sequencing and bioinformatics analyses, we are entering a new era of biological big data. A tremendous amount of genomic information, including potential biomarkers, can be detected in clinical samples, promoting the diagnosis, prognostication and prediction of diseases [26]. Genomic signatures are novel biomarkers in which genomic data are combined in a defined manner and have been proven to be able to predict the prognosis of patients with diseases, especially those with malignant tumours [27]. GC remains one of the most prevalent and deadly cancers worldwide, especially in China. Due to the lack of diagnostic biomarkers, most patients are diagnosed at an advanced stage, and not all patients benefit equally from surgical resection, chemotherapy or chemoradiotherapy because of disease heterogeneity [28]. In recent years, an increasing number of studies have focused on establishing signatures with both coding genes and noncoding RNAs to evaluate the prognosis of patients with cancer [29]. Several studies have revealed that m6A-related lncRNAs participate in the development of various cancers, including GC. Thus, exploring the role of lncRNAs in the prognosis and diagnosis of GC will contribute to a better understanding of the molecular mechanism of GC [13]. However, most prognostic signatures published to date [13, 23‐25, 30, 31] require proper standardization of gene expression profile data for further analysis, which is a major limitation in clinical application. In the current study, we employed a strategy considering immune-related gene pairs [28] and attempted to construct an efficient model with two-lncRNA combinations regardless of exact expression levels [32]; this strategy not only eliminates batch effects among different platforms but also lacks the need for the normalization and scaling of data, thus successfully solving the problems surrounding the use of different data platforms to determine expression [33, 34].

First, raw lncRNA data were downloaded from the GC project of TCGA. By performing Pearson correlation coefficient analysis, iteration loop, 0-or-1 matrix, univariate Cox regression and LASSO-penalized regression analyses, we constructed an m6A-LPS (containing 14 m6A-related lncRNA pairs consisting of 25 unique lncRNAs). Based on the median risk score, patients were divided into high-risk and low-risk groups, and Kaplan–Meier curve analysis revealed that the high-risk group had shorter OS. Further ROC analysis revealed that the m6A-LPS had a higher accuracy in predicting the 5-year OS of GC than other traditional clinicopathological features. Moreover, multivariate Cox regression analysis revealed that m6A-LPS was an independent risk factor for GC. Notably, we also compared the accuracy of our model with that of other reported models. The AUC values of the Lv et al. seven-mRNA signature in predicting the 1-, 3-, and 5-year OS were 0.682, 0.603, and 0.630, respectively, and the AUC values of the Liu et al. four-gene signature in predicting the 1-, 3-, and 5-year OS were 0.535, 0.617, and 0.675, respectively. The AUC values of the Mao et al. six-gene signature in predicting the 1-, 3-, and 5-year OS were 0.557, 0.615, and 0.577, respectively, while the AUCs for our m6A-LPS model in predicting the OS at 1, 3, and 5 years were 0.795, 0.818, and 0.882, respectively. All of the above results demonstrate that our m6A-LPS provides efficient and robust prognostic prediction and might serve as an efficient biomarker for the prognosis of GC. In addition, a nomogram based on the m6A-LPS and clinicopathological factors may be applied in the clinical management of GC patients.

Furthermore, the GSEA results showed that patients in the high-risk group mainly showed enrichment of ECM receptor interactions and focal adhesion. Notably, previous studies have demonstrated that the ECM plays a vital role in cancer progression, and focal adhesion kinase (FAK) is often associated with poor clinical outcome, highlighting FAK as a potential determinant of tumour progression and metastasis [35]. The above results provide new directions for exploring the potential molecular mechanisms of GC.

Moreover, previous studies revealed that tumour-infiltrating immune cells can be used as independent prognostic markers in GC [36]. Therefore, we used CIBERSORT to explore the relationship between the risk score and tumour-infiltrating immune cells. The results showed that resting memory CD4 T cells, resting dendritic cells, monocytes, and M2 macrophages were positively related to the risk score, while activated memory CD4 T cells were inversely correlated with the risk score. Published studies have shown that increased monocytes and activated memory CD4 T cells are related to the poor prognosis of GC [37, 38], which is consistent with our research.

Finally, we also analysed the differential expression of EMT biomarkers between the high-risk and low-risk groups because the EMT process is a key molecular step in distant metastasis and is associated with poor prognosis [39]. The results showed that N-cadherin and vimentin, biomarkers of mesenchymal cells, were abundantly expressed in the high-risk group of patients. These results may provide new ideas for individualized treatment of GC patients.

Overall, we developed a prognostic model based on 14 m6A-related lncRNA pairs, and only the relative expression of the pairs had to be detected instead of examining specific expression values of every lncRNA, significantly lowering the cost of sequencing and carrying high clinical practicability. Furthermore, the prognostic model showed a robust, high value for predicting the survival of GC. However, this study has several limitations that need to be addressed. First, our prognostic model was constructed based only on TCGA data, and we failed to use other public databases or patient cohorts for further validation. Second, the relationship between m6A regulators and lncRNAs should be further explored in experiments in vitro and in vivo.

In the current study, we constructed an m6A-LPS prognostic model with high predictive value that can serve as an independent prognostic factor for GC. To the best of our knowledge, this is the first study to construct a prognostic model based on m6A-related lncRNA pairs that does not require assessment of the exact expression levels of each lncRNA. Obviously, it has substantial value in clinical applications. Additionally, our results provide a new direction for individualized therapy.