Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD

Sep 22, 2026

Links between m6A, m5C and m1A RNA modifications and tumor development are established, yet their role in LUAD prognosis remains unclear. Using TCGA-LUAD for signature training and merged GEO cohorts for validation (GSE29013, GSE30219, GSE31210, GSE37745, GSE50081), the study builds an m6A/m5C/m1A-regulated lncRNA signature (mRLncSig) from 10 lncRNAs via LASSO and mRG-DEG clustering, then validates performance with Kaplan–Meier, Cox regression and ROC/tAUC/PCA plus a nomogram. Multiple immunoinformatics analyses assess immunotherapy relevance with TMB, TIDE and checkpoints; real-time PCR confirms LUAD tissue expression.

LanguageEnglish

CategoryResearch & Report

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Abstract

Introduction

Background: lung cancer burden and need for prognostic models

RNA modifications: m6A, m5C and m1A and their regulatory machinery

DOI: 10. 1111/jcmm.18282 OR I G I NAL ART I C L E Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD Chao Ma | Zhuoyu Gu | Yang Yang Department of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Correspondence Yang Yang, Department of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. Email: [fccyangy1@zzu.edu.cn](fccyangy1@zzu.edu.cn) Abstract Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081 . The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters) . Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan–Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd. in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively. K E Y WO R D S drug prediction, immunotherapy, lncRNA signature, lung adenocarcinoma, m6A/m5C/m1A, prognosis 1 | INTRODUCTION In spite of remarkable strides made in comprehending its intricacies, diagnosis and therapy, lung cancer continues to be the leading cancer in both occurrence and fatality rates, and the numbers are on the rise.1 Among the various types of lung cancer, lung adenocarcinoma (LU

FAQs

How was the mRLncSig signature constructed for LUAD?

TCGA-LUAD was used for training. The study focused on m6A/m5C/m1A-regulated genes, formed mRG clusters and mRG-DEG clusters, then used LASSO regression to select prognostic lncRNAs to build the mRLncSig model.

How was the prognostic value of mRLncSig evaluated?

The signature’s accuracy was validated using Kaplan–Meier analysis and Cox regression, supported by ROC analysis and tAUC evaluation, along with PCA examination and nomogram predictor validation in the validation cohort.

What evidence connects mRLncSig with immunotherapy and candidate drugs?

Immunoinformatics analyses evaluated immunotherapeutic potential using algorithms plus TMB, TIDE and immune checkpoint assessments. The study identifies checkpoint links (e.g., IL10, IL2, CD40LG, SELP, BTLA, CD28) and proposes 12 candidate drugs, with gemcitabine highlighted as the most significant for targeting the high-risk signature.

Sep 22, 2026

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