Four m6A RNA Methylation Gene Signatures and Their Prognostic Values in Lung Adenocarcinoma

Association between methyltransferase-like 3 and non-small cell lung cancer: pathogenesis, therapeutic resistance, and clinical applications

Non-small cell lung cancer (NSCLC) is a malignant cancer that with high incidence, recurrence, and mortality rates in human beings, posing significant threats to human health. Moreover, effective early diagnosis of NSCLC remains limited primarily by the lack of accurate biomarkers. Therefore, there is an urgent need to understand the mechanisms underlying NSCLC pathogenesis and treatment failure. Methyltransferase-like 3 (METTL3) is a prototypical member of a family of which its members transfer methyl groups. It has been implicated in modulating the pathogenesis of NSCLC, as well as conferring resistance to NSCLC therapeutics. The targeting of METTL3 for NSCLC treatment has been reported. However, the relationship between METTL3 and NSCLC remains to be demonstrated. In this review, we discuss relevant interrelationships by summarising the studies on METTL3 in NSCLC pathogenesis, therapeutic resistance, and clinical applications. Current research suggests that the upregulation of METTL3 expression propels the tumorigenesis, progression, and treatment resistance of NSCLC. Therefore, we propose that METTL3 is an excellent candidate biomarker for NSCLC diagnosis and prognosis. Therapeutic targeting of METTL3 has significant potential for NSCLC treatment. This review provides a summary of the association between METTL3 and NSCLC, which would be a valuable reference for both basic and clinical research.

The critical roles of m6A RNA methylation in lung cancer: from mechanism to prognosis and therapy

Lung cancer, a highly malignant disease, greatly affects patients' quality of life. N6-methyladenosine (m6A) is one of the most common posttranscriptional modifications of various RNAs, including mRNAs and ncRNAs. Emerging studies have demonstrated that m6A participates in normal physiological processes and that its dysregulation is involved in many diseases, especially pulmonary tumorigenesis and progression. Among these, regulators including m6A writers, readers and erasers mediate m6A modification of lung cancer-related molecular RNAs to regulate their expression. Furthermore, the imbalance of this regulatory effect adversely affects signalling pathways related to lung cancer cell proliferation, invasion, metastasis and other biological behaviours. Based on the close association between m6A and lung cancer, various prognostic risk models have been established and novel drugs have been developed. Overall, this review comprehensively elaborates the mechanism of m6A regulation in the development of lung cancer, suggesting its potential for clinical application in the therapy and prognostic assessment of lung cancer.

A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes

Lung adenocarcinoma represents a significant global health challenge. Despite advances in diagnosis and treatment, the prognosis remains poor for many patients. In this study, we aimed to identify cuproptosis-related genes and to develop a deep neural network model to predict the prognosis of lung adenocarcinoma. We screened differentially expressed genes from The Cancer Genome Atlas data through differential analysis of cuproptosis-related genes. We then used this information to establish a prognostic model using a deep neural network, which we validated using data from the Gene Expression Omnibus. Our deep neural network model incorporated nine cuproptosis-related genes and achieved an area under the curve of 0.732 in the training set and 0.646 in the validation set. The model effectively distinguished between distinct risk groups, as evidenced by significant differences in survival curves (p < 0.001), and demonstrated significant independence as a standalone prognostic predictor (p < 0.001). Functional analysis revealed differences in cellular pathways, the immune microenvironment, and tumor mutation burden between the risk groups. Furthermore, our model provided personalized survival probability predictions with a concordance index of 0.795 and identified the drug candidate BMS-754807 as a potentially sensitive treatment option for lung adenocarcinoma. In summary, we presented a deep neural network prognostic model for lung adenocarcinoma, based on nine cuproptosis-related genes, which offers independent prognostic capabilities. This model can be used for personalized predictions of patient survival and the identification of potential therapeutic agents for lung adenocarcinoma, which may ultimately improve patient outcomes.

Construction and verification of a novel circadian clock related long non-coding RNA model and prediction of treatment for survival prognosis in patients with hepatocellular carcinoma

Circadian clock genes are significant in the occurrence and development of HCC and long-non coding RNAs (lncRNAs) are closely related to HCC progression. In this study, we aimed to establish a prognostic risk model for HCC. Circadian clock-related lncRNAs expressed in HCC were extracted from The Cancer Genome Atlas. A nomogram was established to predict individual survival rate. Biological processes enriched for risk model transcripts were investigated by Gene Set Enrichment Analysis. Further, we evaluated the relationship between risk score and immune-checkpoint inhibitor-related gene expression level. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to assess the sensitivity of tumors in high- and low-risk score groups to different drugs. A total of 11 circadian clock-related lncRNAs were included in multi-Cox proportional hazards model analysis to establish a risk model. Univariate and multivariate Cox regression analysis showed that the risk model was an independent risk factor in HCC. The risk model was a significantly associated with the immune signature. Further GDSC analysis indicated that patients in each risk score group may be sensitive to different anti-cancer drugs. QRT-PCR analysis results showed that C012073.1, PRRT3-AS1, TMCC1-AS1, LINC01138, MKLN1-AS, KDM4A-AS1, AL031985.3, POLH-AS1, LINC01224, and AC099850.3 were more highly expressed in Huh-7 and HepG2, compared to LO2, while AC008549.1 were lower expressed. Our work established a prognostic model for HCC. Risk score analysis indicated that the model is significantly associated with modulation tumor immunity and could be used to guide more effective therapeutic strategies in the future.

Function and prognostic value of N6-methyladenosine-modified RNAs in lung adenocarcinoma

BACKGROUND

Aberrant regulation of N6-methyladenosine (m6A) modification is reportedly vital for cancer progression, including lung adenocarcinoma (LUAD). However, current studies mainly focus on the function and mechanism of m6A-modified regulators, such as m6A writers (METTL3 and METTL14), erasers (ALKBH5 and FTO), and readers (YTHDF1 and YTHDF2). The landscape, function, and prognostic value of RNAs by m6A-modified have not been fully clarified until now.

METHODS

The present study identified 57 RNAs with significantly different m6A-methylation levels in LUAD tissues using epitranscriptomic microarray analysis.

RESULTS

Among the 57 RNAs, 28 and 29 were hypermethylated and hypomethylated, respectively. The m6A-methylation level increased in mRNA and long non-coding RNA (lncRNA) but decreased in small non-coding RNA. After pathway enrichment analyses, RNA metabolism-associated pathways such as nucleotide metabolism were enriched in total and m6A-hypermethylated mRNAs. Furthermore, lncRNA networks were built using miRNet tools, revealing that the immune system was closed to m6A-modified lncRNAs. To evaluate the prognostic value of mRNAs with hypermethylated or hypomethylated, we calculated the risk scores, and constructed signatures to predict the survival time of patients with LUAD using multicox regression analysis. In addition, hypermethylated-mRNA and hypomethylated-mRNA signatures were established. The survival plotter showed that these two signatures effectively predicted the survival time of patients with LUAD.

CONCLUSIONS

The results of the present study support the evidence for understanding the expression, function, and potential prognostic values of m6A-modified RNAs, possibly promoting effective therapies for patients with LUAD.

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Background: An imbalance in the redox homeostasis has been reported in multiple cancers and is associated with a poor prognosis of disease. However, the prognostic value of redox-related genes in non-small-cell lung cancer (NSCLC) remains unclear.

Methods: RNA sequencing data, DNA methylation data, mutation, and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Redox-related differentially expressed genes (DEGs) were used to construct the prognostic signature using least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan–Meier survival curve and receiver operator characteristic (ROC) curve analyses were applied to validate the accuracy of the gene signature. Nomogram and calibration plots of the nomogram were constructed to predict prognosis. Pathway analysis was performed using gene set enrichment analysis. The correlations of risk score with tumor stage, immune infiltration, DNA methylation, tumor mutation burden (TMB), and chemotherapy sensitivity were evaluated. The prognostic signature was validated using GSE31210, GSE26939, and GSE68465 datasets. Real-time polymerase chain reaction (PCR) was used to validate dysregulated genes in NSCLC.

Results: A prognostic signature was constructed using the LASSO regression analysis and was represented as a risk score. The high-risk group was significantly correlated with worse overall survival (OS) (p < 0.001). The area under the ROC curve (AUC) at the 5-year stage was 0.657. The risk score was precisely correlated with the tumor stage and was an independent prognostic factor for NSCLC. The constructed nomogram accurately predicted the OS of patients after 1-, 3-, and 5-year periods. DNA replication, cell cycle, and ECM receptor interaction were the main pathways enriched in the high-risk group. In addition, the high-risk score was correlated with higher TMB, lower methylation levels, increased infiltrating macrophages, activated memory CD4⁺ T cells, and a higher sensitivity to chemotherapy. The signature was validated in GSE31210, GSE26939, and GSE68465 datasets. Real-time PCR validated dysregulated mRNA expression levels in NSCLC.

Conclusions: A prognostic redox-related gene signature was successfully established in NSCLC, with potential applications in the clinical setting.