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Development and validation of a N6 methylation regulator-related gene signature for prognostic and immune response prediction in non-small cell lung cancer.

ABSTRACT: N6 methylation (m6A) has been reported to play an important role in tumor progression. Non-small cell lung cancer (NSCLC) is the predominant pathological type of lung cancer with a high mortality rate. The purpose of this study was to develop and validate a N6 methylation regulator-related gene signature for assessing prognosis and response to immunotherapy in NSCLC. Data from The Cancer Genome Atlas was used as the training cohort. Data from Gene Expression Omnibus and Xena served as the two validation cohorts. We performed Cox regression, last absolute shrinkage and selection operator, receiver operating characteristic curves and Kaplan-Meier survival analysis to generate and validate a prognostic signature based on m6A regulator-related genes. We explored the association between the signature and tumor microenvironment including genomic mutation, immune cell infiltration and tumor mutation burden. We also analyzed the association between the signature and immunotherapy. Finally, among the genes that constituted the signature, GGA2 was the only favorable factor for NSCLC prognosis. Molecular experiments were used to explore GGA2 function in NSCLC. We generated a prognostic signature based on seven m6A regulator-related genes (GGA2, CD70, BMP2, GPX8, YWHAZ, NOG and TEAD4). And the data from three cohorts showed that the signature could effectively assess prognosis in NSCLC. Patients with high risk scores had the higher mutational load and lower immune infiltration levels and were more likely to not respond to immunotherapy. The experiments revealed overexpression of GGA2 inhibited proliferation and motility of NSCLC cells. Mechanically, GGA2 downregulated METTL3 expression and thus reduced m6A abundance in NSCLC. This study developed and validated a prognostic signature based on m6A regulator-related genes, providing useful insights for the management of NSCLC. And GGA2 may be a target of m6A regulation.

SUBMITTER: Li X

PROVIDER: S-EPMC10408494 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Development and validation of a N6 methylation regulator-related gene signature for prognostic and immune response prediction in non-small cell lung cancer.

Li Xiang X Ma Jinlong J Sun Zhenqian Z Li Na N Jiao Guangjun G Zhang Tianqi T Cao Hongxin H

American journal of cancer research 20230715 7

N6 methylation (m6A) has been reported to play an important role in tumor progression. Non-small cell lung cancer (NSCLC) is the predominant pathological type of lung cancer with a high mortality rate. The purpose of this study was to develop and validate a N6 methylation regulator-related gene signature for assessing prognosis and response to immunotherapy in NSCLC. Data from The Cancer Genome Atlas was used as the training cohort. Data from Gene Expression Omnibus and Xena served as the two va ...[more]

PMID: 37560004

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Project description:Background: N6-methyladenosine (m6A) RNA methylation is an important epigenetic modification affecting alternative splicing (AS) patterns of genes to regulate gene expression. AS drives protein diversity and its imbalance may be an important factor in tumorigenesis. However, the clinical significance of m6A RNA methylation regulator-related AS in the tumor microenvironment has not been investigated in low-grade glioma (LGG). Methods: We used 12 m6A methylation modulatory genes (WTAP, FTO, HNRNPC, YTHDF2, YTHDF1, YTHDC2, ALKBH5, YTHDC1, ZC3H13, RBM15, METTL14, and METTL3) from The Cancer Genome Atlas (TCGA) database as well as the TCGA-LGG (n = 502) dataset of AS events and transcriptome data. These data were downloaded and subjected to machine learning, bioinformatics, and statistical analyses, including gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Univariate Cox, the Least Absolute Shrinkage and Selection Operator (LASSO), and multivariable Cox regression were used to develop prognostic characteristics. Prognostic values were validated using Kaplan-Maier survival analysis, proportional risk models, ROC curves, and nomograms. The ESTIMATE package, TIMER database, CIBERSORT method, and ssGSEA algorithm in the R package were utilized to explore the role of the immune microenvironment in LGG. Lastly, an AS-splicing factor (SF) regulatory network was examined in the case of considering the role of SFs in regulating AS events. Results: An aggregate of 3,272 m6A regulator-related AS events in patients with LGG were screened using six machine learning algorithms. We developed eight AS prognostic characteristics based on splice subtypes, which showed an excellent prognostic prediction performance. Furthermore, quantitative prognostic nomograms were developed and showed strong validity in prognostic prediction. In addition, prognostic signatures were substantially associated with tumor immune microenvironment diversity, ICB-related genes, and infiltration status of immune cell subtypes. Specifically, UGP2 has better promise as a prognostic factor for LGG. Finally, splicing regulatory networks revealed the potential functions of SFs. Conclusion: The present research offers a novel perspective on the role of AS in m6A methylation. We reveal that m6A methylation regulator-related AS events can mediate tumor progression through the immune-microenvironment, which could serve as a viable biological marker for clinical stratification of patients with LGG so as to optimize treatment regimens.

Project description:ImportanceThe prevalence of early-stage non-small cell lung cancer (NSCLC) is expected to increase with recent implementation of annual screening programs. Reliable prognostic biomarkers are needed to identify patients at a high risk for recurrence to guide adjuvant therapy.ObjectiveTo develop a robust, individualized immune signature that can estimate prognosis in patients with early-stage nonsquamous NSCLC.Design, setting, and participantsThis retrospective study analyzed the gene expression profiles of frozen tumor tissue samples from 19 public NSCLC cohorts, including 18 microarray data sets and 1 RNA-Seq data set for The Cancer Genome Atlas (TCGA) lung adenocarcinoma cohort. Only patients with nonsquamous NSCLC with clinical annotation were included. Samples were from 2414 patients with nonsquamous NSCLC, divided into a meta-training cohort (729 patients), meta-testing cohort (716 patients), and 3 independent validation cohorts (439, 323, and 207 patients). All patients underwent surgery with a negative surgical margin, received no adjuvant or neoadjuvant therapy, and had publicly available gene expression data and survival information. Data were collected from July 22 through September 8, 2016.Main outcomes and measuresOverall survival.ResultsOf 2414 patients (1205 men [50%], 1111 women [46%], and 98 of unknown sex [4%]; median age [range], 64 [15-90] years), a prognostic immune signature of 25 gene pairs consisting of 40 unique genes was constructed using the meta-training data set. In the meta-testing and validation cohorts, the immune signature significantly stratified patients into high- vs low-risk groups in terms of overall survival across and within subpopulations with stage I, IA, IB, or II disease and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.72 [95% CI, 1.26-2.33; P < .001] to 2.36 [95% CI, 1.47-3.79; P < .001]) after adjusting for clinical and pathologic factors. Several biological processes, including chemotaxis, were enriched among genes in the immune signature. The percentage of neutrophil infiltration (5.6% vs 1.8%) and necrosis (4.6% vs 1.5%) was significantly higher in the high-risk immune group compared with the low-risk groups in TCGA data set (P < .003). The immune signature achieved a higher accuracy (mean concordance index [C-index], 0.64) than 2 commercialized multigene signatures (mean C-index, 0.53 and 0.61) for estimation of survival in comparable validation cohorts. When integrated with clinical characteristics such as age and stage, the composite clinical and immune signature showed improved prognostic accuracy in all validation data sets relative to molecular signatures alone (mean C-index, 0.70 vs 0.63) and another commercialized clinical-molecular signature (mean C-index, 0.68 vs 0.65).Conclusions and relevanceThe proposed clinical-immune signature is a promising biomarker for estimating overall survival in nonsquamous NSCLC, including early-stage disease. Prospective studies are needed to test the clinical utility of the biomarker in individualized management of nonsquamous NSCLC.

Dataset Information

Development and validation of a N6 methylation regulator-related gene signature for prognostic and immune response prediction in non-small cell lung cancer.

Publications

Development and validation of a N6 methylation regulator-related gene signature for prognostic and immune response prediction in non-small cell lung cancer.

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