Identification of a novel m5C/m6A-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma

Abstract

Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer (NSCLC) and is associated with high mortality rates. However, effective methods to guide clinical therapeutic strategies for LUAD are still lacking. The goals of this study were to analyze the relationship between an m5C/m6A-related signature and LUAD and construct a novel model for evaluating prognosis and predicting drug resistance and immunotherapy efficacy. We obtained data from LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Based on the differentially expressed m5C/m6A-related genes, we identified distinct m5C/m6A-related modification subtypes in LUAD by unsupervised clustering and compared the differences in functions and pathways between different clusters. In addition, a risk model was constructed using multivariate Cox regression analysis based on prognostic m5C/m6A-related genes to predict prognosis and immunotherapy response. We showed the landscape of 36 m5C/m6A regulators in TCGA-LUAD samples and identified 29 differentially expressed m5C/m6A regulators between the normal and LUAD groups. Two m5C/m6A-related subtypes were identified in 29 genes. Compared to cluster 2, cluster 1 had lower m5C/m6A regulator expression, higher OS (overall survival), higher immune activity, and an abundance of infiltrating immune cells. Four m5C/m6A-related gene signatures consisting of HNRNPA2B1, IGF2BP2, NSUN4, and ALYREF were used to construct a prognostic risk model, and the high-risk group had a worse prognosis, higher immune checkpoint expression, and tumor mutational burden (TMB). In patients treated with immunotherapy, samples with high-risk scores had higher expression of immune checkpoint genes and better immunotherapeutic efficacy than those with low-risk scores. We concluded that the m5C/m6A regulator-related risk model could serve as an effective prognostic biomarker and predict the therapeutic sensitivity of chemotherapy and immunotherapy.

Characterization of the m6A/m1A/m5C/m7G‐related regulators on the prognosis and immune microenvironment of glioma by integrated analysis of scRNA‐seq and bulk RNA‐seq data

“…43 A growing number of studies are finding possible links between different RNA modification regulators. 17,18,[44][45][46][47] NSUN2 catalyzes m5C modification and METTL3/METTL14 m6A modification in the three "non-coding" regions of p21 mRNA, in addition, methylation of METTL3/METTL14 promotes methylation of NSUN2 and vice versa. 18 EIF3A may play the role of a chaperone protein as a reader of NOP2 (m5C binding protein), thus regulating NOP2-mediated m5C modification.…”

“…A growing number of studies are finding possible links between different RNA modification regulators 17,18,44–47 . NSUN2 catalyzes m5C modification and METTL3/METTL14 m6A modification in the three “non‐coding” regions of p21 mRNA, in addition, methylation of METTL3/METTL14 promotes methylation of NSUN2 and vice versa 18 .…”

LINC02159 promotes non-small cell lung cancer progression via ALYREF/YAP1 signaling

“…Liu et al have constructed a risk model by using 5 m 5 C RNA regulators, including ALYREF, to classify patients with lung cancer into high-risk (poorer OS) and low-risk (better response to immune checkpoint blockade therapy) groups [26]. Similarly, Ma et al have constructed a prognostic risk model by using m 5 C/m 6 A-related gene signatures including ALYREF to evaluate prognosis and predict drug resistance and immunotherapy efficacy [17]. The functional roles of ALYREF in NSCLC have not been well studied thus far.…”

“…Recent studies have shown that ALYREF is involved in tumor cell proliferation, metabolism, and metastasis [14][15][16]. The roles of ALYREF in NSCLC remain unclear although a recent study suggests that it, together with other m 5 C/m 6 A-related genes, could predict prognosis and immunotherapy efficacy in lung cancer [17]. Therefore, we first investigated the expression pattern and biological function of ALYREF in NSCLC.…”

Development and Validation of a Machine Learning Prognostic Model of m5C Related immune Genes in Lung Adenocarcinoma

“… 8 Further, researches reported that the novel m5C-related molecular subtypes of LUAD cohorts were constructed by unsupervised clustering analysis to assess the similarity and difference information in m5C methylation as well as m5C gene expression patterns of LUAD samples, providing a preliminary rationale for further screening of key genes in LUAD progression. 9 , 10 On the other hand, the immune related prognostic signature for predicting survival of LUAD cohorts has been explored, 11 Inspired by these studies, the relationship of m5C methylation and immune microenvironments alterations was revealed gradually. 12 Therefore, it is of great significance to search for novel therapeutic directions such as immune-related genes significantly associated with m5C methylation (m5C-IRGs) in LUAD for early diagnosis and prognosis assessment of LUAD patients.…”