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

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 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.


| INTRODUC TI ON
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 (LUAD) stands out as the most prevalent.The escalating number of LUAD cases underscores the critical need for ongoing research into the disease's underlying mechanisms and the formulation of effective strategies. 2 Currently, surgery, radiotherapy, chemotherapy, targeted therapy and immunotherapy are the predominant treatments in clinical practice and have shown progress.[5] Thus, there is a pressing need to develop more effective prognostic models to enhance prediction accuracy and improve clinical outcomes.
The regulation of eukaryotic messenger RNA (mRNA) involves modifications such as N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A).][8][9][10] m6A, the most prevalent modification in eukaryotic messenger RNA (mRNA), is catalysed by a writer complex, including methyltransferase proteins like METTL3, METTL14 and WTAP.This modification is dynamically regulated by erasers (FTO and ALKBH5) and recognized by readers, such as YTH domain-containing proteins, influencing mRNA splicing, stability, translation and decay. 11m5C, predominantly found in non-coding RNAs, is introduced by RNA methyltransferases (DNMT2 and NSUN family members). 12It contributes significantly to RNA structure, stability and RNA-protein interactions, playing essential roles in RNA metabolism and gene expression regulation. 12m1A, another prevalent modification in mRNA, is installed by RNA methyltransferases (METTL6 and TRMT6/61A). 13m1A modification influences mRNA stability, translation efficiency and splicing, impacting cellular processes and disease progression. 13Understanding these modifications' mechanisms, their writers, erasers and readers, is crucial for unravelling their roles in gene regulation, cellular processes and diseases, paving the way for potential therapeutic interventions.In the RNA methylation modification process, writers, erasers and readers play crucial roles in regulating RNA molecules, particularly messenger RNA (mRNA).
An instance is FEZF1-AS1, whose m6A modification influences the ITGA11/miR-516b-5p axis, leading to its upregulation in non-small cell lung cancer (NSCLC). 22lncRNA FEZF1-AS1 is linked to unfavourable outcomes in NSCLC patients.Recent findings 22 indicate that FEZF1-AS1 is an oncogenic regulator, boosting cell proliferation and invasion.It competes with miR-516b-5p for binding, leading to increased ITGA11 expression.Consequently, targeting the FEZF1-AS1/miR-516b-5p/ITGA11 axis holds promise as a valuable strategy for both predicting the prognosis and treating NSCLC. 22rthermore, in NSCLC patients, METTL3-induced ABHD11-AS1 lncRNA is upregulated, and its ectopic expression correlates with worse outcomes. 23The modification known as m6A plays a vital role in regulating immune suppression, anti-tumour immunity and tumour-immune evasion, thereby maintaining the proper functioning and homeostasis of immune cells. 24In contrast, m5C, which is a prevalent mRNA modification, was initially identified in the untranslated region in 1925. 25Studies have indicated that m5C plays a significant role in RNA export, ribosome assembly and translation. 25Specifically, m5C writers have been found to regulate oncogenes or suppressor genes, promoting metastasis in several cancer types. 26Some writers and readers have been linked to cancer metastasis through unclear mechanisms. 26Methylases and m5C-binding proteins work together to promote metastasis. 26In their study, Yin et al. 26 found that levels of m5C in immune cells present in peripheral blood can diagnose colorectal cancer more accurately and with better reclassification performance compared to commonly used blood tumour biomarkers.Earlier research has revealed that regulators of m1A are dysregulated in gastrointestinal 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.cancers and have a connection with ErbB and mTOR pathways. 18 addition, Shi et al.s' 27 investigation established that regulatory genes linked to m1A play vital roles in regulating the progression of hepatocellular carcinoma.In the study by Gao et al., 28 three different m1A modification patterns were crucial in identifying and characterizing TME-infiltrating immune cells.Much evidence has shown that m6A, m5C or m1A intersect with tumour prognosis and immune infiltration.Li et al. 29 conducted a study to assess the immune crosstalk ability and prognosis in liver cancer using a combination of m6A/m5C/m1A.However, it is currently unknown whether m6A/m5C/m1A have a significant impact on the prognosis of LUAD, or whether they can serve as a guide for immunotherapy and clinical medication.
To date, non-coding RNA (ncRNA) has been identified as associated with various complex diseases, [30][31][32][33] with a particular emphasis on its relevance to lung cancer.Numerous compelling findings indicate that dysregulated lncRNAs play a crucial role in the development and advancement of various cancers, notably lung cancer. 34These aberrantly expressed lncRNAs hold promise as potential biomarkers for cancer diagnosis, treatment and prognosis, offering avenues for personalized therapeutic interventions. 34awing on the work of our predecessors, we have been inspired to undertake a new study.The goal of this research is to develop a prognostic signature for m6A/m5C/m1A-related lncRNAs in LUAD.Furthermore, we aim to identify potential treatment targets and agents for patients with high signature scores.Using verified m6A/ m5C/m1A-regulated genes, we created a lncRNA signature capable of forecasting LUAD outcomes.In this study, we validated the prognostic potential of our signature in a large independent cohort.Using real-time PCR, we validated the differential expression of signature lncRNAs between normal and tumour lung tissues in real-world conditions.Additionally, we assessed the potential of immunotherapy and identified IL10, IL2, CD40LG, SELP, BTLA and CD28 as potential indicators for our signature.These findings suggest that these targets may hold promise for immunotherapy in patients with LUAD.
Our study identified gemcitabine as a potential treatment option for high-risk patients, and we also evaluated the prognostic value and differential expression of signature lncRNAs across different types of cancer.

| Selection of datasets and removal of batch effects
The study's prognostic model was created using the training cohort, and its effectiveness was assessed by validating it in the validation cohort.The TCGA-LUAD project, which offers comprehensive clinical and high-throughput data, was selected for the training cohort.The project's expression and other associated data were accessed via the Xena Hub online portal (https:// xenab rowser.net/ ).The validation cohort's data were sourced from the Gene Expression Omnibus (GEO) database, which was accessed through its official website https:// www.ncbi.nlm.nih.gov/ geo/ .Our search was tailored to identify a dataset related to 'lung adenocarcinoma', where we filtered out any results that did not contain expression and survival data to create our candidate dataset.We opted for GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081 datasets from GEO.It is essential to highlight that these datasets underwent preprocessing before being used.To carry out preprocessing, we utilized the R package 'inSilicoMerging' 35 to merge them, and we eliminated batch effects using the approach established by Johnson et al. 36 The preprocessed GEO data were utilized as the validation cohort.
We selected 21 m6A-regulated genes, which are writer: KIAA1429, RBM15B, METTL3, CBLL1, METTL14, ZC3H13, RBM15, WTAP; reader: IGF2BP1, LRPPRC, ELAVL1, HNRNPA2B1, HNRNPC, FMR1, YTHDC2, YTHDF2, YTHDF3, YTHDC1 and YTHDF1; eraser: ALKBH5 and FTO.We selected a total of 44 genes, and after removing duplicate ones, the remaining 40 genes were waiting to be dispatched.The R language package 'limma' 37 was employed to verify if the 40 distinct genes exhibited varied expression levels in normal tissues and LUAD tumours.The genes exhibiting differential expression were selected by applying a threshold of FDR < 0.05 for the differential expression analysis.Subsequently, the selected genes were fed into the 'ConsensusClusterPlus' 38 algorithm of the R language package for unsupervised clustering of the LUAD training samples.The optimal number of subtypes was decided by evaluating the value of k and examining whether there were any survival differences between the subtypes.To evaluate variations in survival among mRG clusters, we conducted Kaplan-Meier (KM) analysis, and to exhibit differences between clusters, we employed principal component analysis (PCA).The execution of both KM and PCA analyses was dependent on specific R language packages, namely 'survival 39 ', 'survminer 40 ' and 'scatterplot3d 41 '.Additionally, we employed several R packages, including 'GSEABase 42 ', 'reshape2 43 ', 'limma 37 ', 'ggpubr 44 ' and 'GSVA 45 ', to execute the single-sample gene set enrichment analysis (ssGSEA) and generate visualizations.7][48] We utilized the 'limma' R package with an FDR threshold of less than 0.05 to identify differentially expressed genes related to the mRG clusters (mRG-DEGs) between the clusters.

| Development of mRG-DEG cluster and signature of m6A/m5C/m1A-regulated lncRNAs (mRLncSig)
We categorized patients in the training cohort based on mRG-DEG and generated KM curves to evaluate survival disparities across the mRG-DEG clusters.To assess the level of differentiation among the different clusters, we utilized PCA.Then, we conducted the ssGSEA and generate visualizations.Next, we employed the 'limma', 'GSEABase', 'GSVA' and 'pheatmap' R packages to perform GSVA to identify the top significant KEGG pathways among the mRG-DEG clusters.We explored the lncRNA transcripts that were differentially expressed between the mRG-DEG clusters with an FDR threshold of less than 0.05.Subsequently, we conducted univariate Cox and KM analyses on these differentially expressed lncRNAs (DELs) to identify the ones that showed potential prognostic significance with a pvalue of less than 0.05.To further decrease the dimensionality of the prognostic DELs and avoid overfitting, we utilized the R language package 'glmnet' 49 to implement the LASSO algorithm.By subjecting the DELs to a 10-fold cross-validation test, we obtained a set of lncRNAs and their corresponding coefficients through the LASSO analysis.The risk score of each LUAD was calculated as the sum of the product of each lncRNA's expression level and its corresponding coefficient.The formula is as follows: Risk score = (lncRNA 1 expression*lncRNA 1 coefficient) + (lncRNA 2 expression*lncRNA 2 coefficient) + … + (lncRNA n expression*lncRNA n coefficient).

| Validation of mRLncSig in a large independent cohort
A risk score was assigned to each LUAD of the cohort and used to divide the cohort into high-risk and low-risk groups based on the median risk score.The predictive ability, accuracy and discrimination of mRLncSig were evaluated using various bioinformatics methods, including Cox analysis, 50 KM analysis, 51 ROC analysis, 50 tAUC analysis 52 and survival nomogram. 53The analysis was conducted in R software, utilizing packages such as 'timeROC 54 ', 'survival', 'survminer', 'rms 55 ' and 'regplot'.A gene set of immunotherapy-predicted pathways was collected from Hu et al.'s study. 56We also collected other gene set, oncogenic signature gene set, from Human MSigDB Collections (C6: oncogenic signature gene sets, v2022.1.Hs updated August 2022, https:// www.gsea-msigdb.org/ gsea/ msigdb/ human/ colle ctions.jsp# C6).The enrichment scores of these signatures were calculated using the GSVA R package. 56

| Identification of the role of mRLncSig in the immunological status of LUAD
The R package 'ESTIMATE' utilizes the gene expression levels of the training cohort to compute stromal, immune and ESTIMATE scores for individual patients. 57We evaluated the correlation between mRLncSig and the above category scores using statistical analysis methods like the Pearson coefficient and the Wilcoxon rank-sum test.With R package 'IOBR', immuno-oncology exploration can be facilitated, tumour-immune interactions can be explored, and precision immunotherapy can be expedited. 58The R package 'IOBR' or its algorithms included, namely CIBERSORT, 59 CIBERSORT-ABS, 59 quanTIseq, 60 TIMER, 61 MCPCounter, 62 xCell 63 and EPIC, 64 were applied to assess immune-infiltrating levels of every LUAD in the TCGA-LUAD.To assess the relationship between mRLncSig and immune-infiltrating levels, we employed the Pearson coefficient and the Wilcoxon rank-sum test, and the outcomes were presented as lollipop plots and heatmaps.We summarized the findings through Venn and cloud diagrams and assessed the immune function of mRL-ncSig utilizing the 'ssGSEA' function available in the 'gsva' R package.

| Identification of mRLncSig's role in immunotherapy and its potential checkpoint targets
Initially, we employed the 'maftools' R package to visualize the mutation landscape in LUAD.Our primary emphasis was on the top 20 genes with the most mutations, and we aimed to analyse and exhibit them.We utilized the chi-square test to compare the mutation frequencies of these 20 genes between the low-risk and highrisk groups.TMB is a gauge of the incidence of specific mutations in cancer genes and is increasingly being adopted as an indicator of immunotherapy responsiveness. 65To assess TMB rank scores for LUAD cases, we followed established protocols.To evaluate the correlation between the risk score and TMB, we utilized a combination of Pearson's coefficient and Wilcoxon rank sum.

The ability of Tumour Immune Dysfunction and Exclusion (TIDE)
7][68] Our primary objective was to determine the correlation between our signature and the TIDE.In our study, we chose a set of 60 immune checkpoints that had been previously investigated, which included 24 inhibitory and 36 stimulatory checkpoints 69 (Table S1).To evaluate the relationships between our mRLncSig and the 60 selected immune checkpoints, we conducted integration analysis including Pearson coefficient and Wilcoxon rank-sum analyses.We sought to determine if our mRLncSig could serve as a guide for immunotherapy.To this end, we utilized the KM and Cox analysis to assess the outcome predictive value of 60 immune checkpoints.Using a Venn diagram, we summarized the results to identify potential checkpoints with targeting ability relate to that of the mRLncSig.Furthermore, we conducted a search of public databases to locate datasets that include information on immunotherapy to evaluate the influence of the checkpoints highlighted earlier on immunotherapy.The 'Regulatory Prioritization' function in the TIDE online tool facilitated our visualization of the results of immunotherapy. 67

| Drug selection for patients with high mRLncSig score LUAD
A comprehensive examination of numerous human cancer models was conducted through the initiation of the Cancer Cell Line Encyclopedia (CCLE) project in 2008.The drug sensitivity data utilized in this investigation were sourced from the Cancer Therapeutics Response Portal (CTRP, https:// porta ls.broad insti tute.org/ ctrp) and PRISM (https:// depmap.org/ portal/ prism ) databases, with the former providing information on 481 compounds from 835 cancer cell lines (CCLs) and the latter assessing 1448 compounds from 482 CCLs.In both datasets, the drug sensitivity was determined by the area under the dose-response curve (AUC), with lower values indicating greater sensitivity.Our study involves the analysis of drug response data from CTRP and PRISM to identify feasible drug candidates from the high-scoring group. 70To do this, we compared drug responses between patients with the highest and lowest decile risk scores and used a threshold of log2FC > 0.1 to screen for drugs with lower AUC in high-scoring patients. 71To choose the target compounds, 71 we performed Spearman correlation analysis with a threshold of r < −0.18 to determine the correlation between drug AUC values and risk scores.

| Connectivity Map (CMAP) to validate drug candidates
Afterward, additional validation analyses were conducted on the results of the drug candidate, which involved reviewing the data of clinical trial and published experimental evidence, and the use of CMap to further confirm its potential in LUAD. 71The Connectivity Map, or CMap, facilitates drug discovery by creating and analysing massive datasets of altered biological conditions, providing insights into human diseases and accelerating the search for novel therapies. 71In this study, we employed CMap analysis as a supplementary approach to explore the potential efficacy of the identified drug candidates in LUAD.There were 2429 compounds accessible for analysis on CMap.The top 150 upregulated and top 150 downregulated genes in the differential ranking were chosen after conducting differential analysis on LUAD tumour and normal tissue samples.These selected genes were then taken to the CMap online analysis portal for drug validation.Each compound's CMap result is represented as a value between −100 and 100, with a result closer to −100 indicating a greater potential for therapeutic power.

| Comparing mRLncSig with previous studies
To conclude whether our study is more robust than previous, we searched PubMed using the keywords 'm1a lncRNA signature lung adenocarcinoma prognosis', 'm5c lncRNA signature lung adenocarcinoma prognosis' and 'm6a lncRNA signature lung adenocarcinoma prognosis' to find candidate studies.We included the research that contains a lncRNA signature and the related coefficient.Because most of the candidate studies did not upload raw data or used different or unmentioned data preprocessing methods, therefore, to ensure the standard consistency of the comparison, we use the official TCGA data for analysis here, which are TCGA-LUAD_PanCanAtlas from Genomic Data Commons, Pan-Cancer Atlas (https:// gdc.cancer.gov/ about -data/ publi catio ns/ panca natlas), and TCGA-LUAD_Count and TCGA-LUAD_ FPKM_UQ from Genomic Data Commons Data Portal (https:// portal.gdc.cancer.gov/ ).For specific comparative analysis, we used Cox regression analysis.

| Using real-time PCR to measure the expression levels of lncRNAs and analyse data from multiple databases to determine if mRLncSig has the potential to impact various cancers
The situation of the target gene in the real world can be described by laboratory data obtained from human samples.The expression level of each mRLncSig lncRNA was investigated by collecting nine pairs of LUAD and adjacent tissues from the clinic. 70All patients included in this study did not receive any relevant treatment before collecting samples.The Ethical Review Committee of the First Affiliated Hospital of Zhengzhou University approved our approaches, and informed consent was obtained from all patients before the operation.Tissue samples were immediately frozen and stored in liquid nitrogen after extraction during the surgery. 72TRIzol reagent (Invitrogen, Thermo Fisher Scientific Corporation, MA, USA) was utilized to extract total RNA from tissues.Reverse transcription was performed using a PrimeScript™ RT reagent Kit with gDNA Eraser (TAKARA BIO INC., Kusatsu, Shiga, Japan).Real-time PCR was conducted using a SYBR Premix Ex Taq™ II kit (TAKARA BIO INC.) on a CFX Opus 96 Real-Time PCR System (Bio-Rad Laboratories, Hercules, CA, USA).Relative gene expression was calculated automatically using 2 −ΔΔC t . 72Detection of genes between normal and tumour samples was conducted utilizing Student's t-test, with statistical significance established for adjusted p-values below 0.05. 72r our pan-cancer analysis, 70 we opted for the TCGA pancancer data that we obtained from the UCSC database (https:// xenab rowser.net/ ).After downloading the data, we filtered out the haematological tumour data and retained only cancer types that featured both normal and tumour tissues.R packages 'ggplot2', 'clus-terProfiler', 'ComplexHeatmap' and 'limma' were adopted for the calculation and visualization.Then, we conducted the prognostic ability determination and only cancer types that contain expression and survival data were selected.R packages 'survival' and 'pheatmap' were used for this approach.

| Patient characteristics
The critical steps of this study are illustrated in Figure 1.To build our validation cohort, we selected 500 LUADs from TCGA-LUAD.
Additionally, we gathered 554 LUAD patients from five datasets in the GEO database (GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081) to augment our validation cohort.The elimination of batch effects associated with the data merging was carried out following the approach described by Johnson et al., 36 The UMAP diagram (Figure 2A) revealed that prior to the elimination of batch effects, the merged data set was segregated, whereas after removing the batch effect, the data sets became intertwined, indicating a successful elimination of the batch effect.The cohorts' status and the clinical baseline information of the patients included in our study are presented in Table 1.

| Constriction of mRG clusters in LUADs using consensus clustering
We selected a total of 44 mRGs as mentioned in the method section, and after removing duplicate ones, there were 40 genes remaining.
Table 2 demonstrates that out of the 40 candidate genes, 33 fulfilled our criteria based on the differentially expressed FDR values.
These genes were subjected to the consensus clustering algorithm to classify LUAD patients, resulting in two mRG clusters.Figure 2B depicts the KM survival curves of the two clusters, demonstrating significant differences in terms of prognosis, with cluster B having a better outcome than cluster A. Additionally, a distinct separation between clusters A and B is noticeable from the PCA analysis shown in Figure 2C.Based on the ssGSEA analysis depicted in Figure 2D, 16 types of immune cells, activated B cell, activated CD8 T cell, activated dendritic cell, CD56dim natural killer cell, eosinophil, immature B cell, immature dendritic cell, MDSC, macrophage, mast cell, monocyte, natural killer cell, regulatory T cell, T follicular helper cell, Type 1 T helper cell and Type 17 T helper cell, were statistically distributed in two mRG clusters.Moreover, there was a pronounced difference between the two LUAD patient clusters in the aspect of the expression of 33 m6A/m5C/m1A-regulated genes (Figure 2E).
To identify the most important KEGG pathways, we compared the two mRG clusters and conducted GSVA analysis (Figure 2F, Table S2).Interestingly, the top 10 ranked pathways were related to spliceosome, base excision repair, RNA degradation, basal transcription factors, lysine degradation, mismatch repair, nucleotide excision repair, aminoacyl-tRNA biosynthesis, homologous recombination and one carbon pool by folate.The dissimilarities observed between two populations can frequently be accounted for by genes that are expressed differently between them.To gain insights into F I G U R E 1 Research design and analysis process. 70 the underlying mechanisms responsible for the divergence between the two mRG clusters, we delved deeper into the genes that were expressed differentially, ultimately identifying 256 mRG clusterrelated differentially expressed genes (mRG-DEGs) (Table S3).

| Two mRG-DEG clusters constructed and a mRLncSig generated
After adopting mRG-DEG, a consensus clustering approach was employed to partition the training cohort's LUADs into two distinct mRG-DEG clusters.The prognostic ability of the mRG-DEG clusters was assessed using their KM survival curves, revealing that cluster A had a more favourable prognosis compared to cluster B (Figure 3A).Furthermore, a clear separation between clusters A and B was observed through PCA analysis (Figure 3B).The distribution of 14 immune cells, activated B cell, activated CD4 T cell, CD56bright natural killer cell, eosinophil, gamma delta T cell, immature dendritic cell, mast cell, monocyte, natural killer T cell, neutrophil, plasmacytoid dendritic cell, regulatory T cell, Type 17 T helper cell and Type 2 T helper cell, across different mRG-DEG clusters was differentially visualized through ssGSEA (Figure 3C).Additionally, TA B L E 1 Baseline clinical status of cohorts and patients included in this study.

Characteristics
Training cohort (TCGA-LUAD, n = 500)   S5).To further refine our findings, we carried out LASSO analysis using these 18 DELs for selection and shrinkage.This analysis enabled the identification of 10 lncRNAs (Figure 4A,B and Figure S1), and we obtained the coefficient for each gene (Table 3).To better comprehend the procedures, we examined and the relationships among them, we depicted them as Sankey diagrams (Figure 4C), with the aid of the mRG clusters, mRG-DEG clusters, risk levels and vital status.These variables may provide greater insight into the analyses we conducted and the associations between them.We also used box plots to show that the risk score distribution within mRG-DEG clusters varied significantly (Figure 4D).After examining the expression pattern of the 33 mRGs in both high-and low-risk groups, we identified 25 genes (ALKBH1, DNMT1, DNMT3A, ELAVL1, FMR1, FTO, HNRNPA2B1, HNRNPC, IGF2BP1, METTL3, NSUN3, NSUN4, NSUN5, NSUN6, NSUN7, RBM15, RBM15B, TRMT61A, TRMT61B, VIRMA, YTHDC1, YTHDC2, YTHDF1, YTHDF2 and YTHDF3) that showed significant differences in expression (Figure 4E).Out of the 25 identified genes, only IGF2BP1 was upregulated, while the remaining genes were downregulated in the high-risk group.Additionally, we have included a display of the correlations between each of the 10 lncRNAs and the 33 mRGs in Figure S2A.

| Correlations between mRLncSig and the enrichment scores of immunotherapy-predicted pathways and oncogenic signature gene sets
We analysed the correlations between mRLncSig and the immunotherapy-predicted pathways.The top 10 pathways that mRLncSig correlated with were progesterone mediated oocyte  5J).

| Identification of mRLncSig's potential in immunological status of LUAD
The progression of cancer is driven by the collaboration between subclonal populations, which comprise cancerous and noncancerous cells in the tumour microenvironment.This intricate system forms a dynamic ecosystem.Therefore, it is crucial to conduct a comprehensive examination of the tumour microenvironment.
In this study, we utilized data from the TCGA cohort and utilized the R package 'ESTIMATE' to measure various scores such as immune score, stromal score and ESTIMATE score.S6. Figure 6F presents a comprehensive analysis that employs a combined Venn diagram and word cloud to visualize the results of the heatmap and lollipop analysis.The analysis identifies cells that are most closely related to our signature, such as CD4 T cells, memory B cells, resting T cells, myeloid dendritic cells and CD8 T cells.Furthermore, Figure 6G illustrates the immune function analysis that reveals the differential distribution of immune function scores between high-and low-risk groups.Notably, chemokine receptors, checkpoint, human leukocyte antigen, T cell co-inhibition, T cell co-stimulation and type 2 interferon response exhibit the most pronounced differences.Taken together, these findings suggest that our signature may be linked to the immune status in LUAD.

| mRLncSig participates in immunotherapy and targets immune checkpoints
According to our extensive analysis of mutational characteristics (Figure 7A), TP53 emerged as the most mutated gene, with a frequency of approximately 53.8% within the cohort.Following closely were TTN and MUC16, accounting for 51.0% and 44.2% of the mutations, respectively.Missense mutation was the most frequently observed type of mutation.The Wilcoxon test verified that the LUAD with a higher risk score demonstrated an elevated level of TMB, while Pearson's analysis revealed a positive correlation between TMB and risk score (Figure 7B).
Clinical studies have shown that patients with higher TMB tend to respond better to immune checkpoint blockade therapy, resulting in more long-lasting clinical benefits, including treatment responses and improved survival. 73,74Our findings indicated that patients with high-risk LUADs might respond better to immunotherapy to some extent.The TIDE score is a surrogate biomarker that can be utilized to forecast the likelihood of NSCLC patients responding to immune checkpoint blockade therapies, such as anti-PD1 and anti-CTLA4.7][68] Our study evaluated genes warrant further investigation (Figure 7H). Figure 7I highlights six checkpoint genes that could have an impact on the immune system and immunotherapy.The immunotherapy cohort represented by the black module ranked these genes based on their abilities, with IL10 having the highest rank, followed by IL2, CD40LG, SELP, BTLA and CD28.These results suggest the possibility of in-depth crosstalk studies beneath our mRLncSig and immunotherapy.

| Discovering potential therapeutic agents for LUAD with high mRLncSig score
The The distribution of the relative expression of immune checkpoint genes among high-and low-risk patients were displayed using violin plots.The significance of the distribution was determined using the Wilcoxon rank-sum test, and only immune checkpoints with significant distributions were included in the plot.(F) KM curves were generated to assess the prognostic significance of the immune checkpoint genes.Eight genes were found to have significant prognostic value.(G) The Cox proportional hazards model was employed to scrutinize all the checkpoint genes and single out those that possess prognostic significance.Our results exhibit only the indicators that were deemed statistically significant, and a total of 15 checkpoints were identified with prognostic power.(H) A Venn diagram was constructed to display the intersection of the outcomes obtained from correlation analysis, difference analysis, KM analysis and Cox analysis.(I) To visualize the impact of six checkpoint genes in immunotherapy, a heatmap was generated utilizing data from various published online datasets.mRLncSig, m6A/m5C/m1A-regulated lncRNA signature; TIDE, Tumour Immune Dysfunction and Exclusion; TMB, Tumour mutational burden; a p-value less than 0.05 was deemed to be statistically significant; results with a p-value greater than or equal to 0.05 were considered non-significant and denoted by 'ns'; asterisks were used to indicate the level of significance: one asterisk (*) for p-values < 0.05, two asterisks (**) for p-values < 0.01, three asterisks (***) for p-values < 0.001 and four asterisks (****) for p-values < 0.0001.
these datasets encompass a total of 1770 compounds (Figure 8A and Table S7), with 160 compounds common to both.The roadmap for identifying sensitive drugs for patients with high-risk scores is detailed in Figure 8B.These analyses led to the identification of six CTRP-derived compounds, including paclitaxel, methotrexate, selumetinib, leptomycin B, SB-743921 and PD318088 (Figure 8C), as well as six PRISM-derived compounds, including echinomycin, cabazitaxel, vincristine, gemcitabine, NVP-AUY922 and Ro-4987655 (Figure 8D).Our results demonstrate that the discovered compounds had lower AUC values in the high-risk score group, and there was a negative correlation between their AUCs and the risk score.
Although the 12 candidate compounds demonstrated heightened drug sensitivity in high mRLncSig risk patients, the aforementioned analyses solo are not able to substantiate their efficacy.
Therefore, additional multi-dimensional analyses were conducted to evaluate their therapeutic capacity in patients with LUAD.CMap analysis indicated that among these compounds, selumetinib and gemcitabine stood out with CMap scores of <−95, suggesting potential therapeutic benefits for LUADs (Figure 8E and Table S7).
Furthermore, we performed an analysis to calculate the fold-change values of drug candidates in tumour and normal tissues.The increased values observed in Figure 8E and Table S8 8E and Table S7.Based on the comprehensive analysis and presentation outlined above, as well as its performance in both in silico and in vitro studies, gemcitabine emerged as the most promising drug candidate with the highest potential for treating LUAD.
Based on the screening criteria we set, we found eight studies, [75][76][77][78][79][80][81][82] but one of them was removed because it did not contain coefficient information.Finally, seven studies came into our view 75,[77][78][79][80][81][82] (Table 4).To compare previous signatures with ours, we performed Cox regression analysis for OS, DSS and PFS using three formats of official TCGA data (Figure 9), respectively.The analysis confirmed that mRLncSig has solid predictive ability in overall, disease-specific and progression-free survival in three testing cohorts (p < 1.21e-04).In particular, our signature occupies the first place in terms of p-value in the OS and DSS prediction.mRLncSig ranked second in terms of p-value in PFS prediction in TCGA-LUAD_PanCanAtlas and TCGA-LUAD_FPKM_UQ.mRLncSig ranked third in in terms of pvalue in PFS prediction in TCGA-LUAD_Count.

| Identification of expression patterns of mRLncSig and its ability in pan-cancer
To assess the real-world effectiveness of 10 signature lncRNAs, we utilized real-time PCR to compare their expression levels in human LUAD tissue (n = 9) and adjacent normal lung tissue (n = 9).

| DISCUSS ION
There is a substantial body of research demonstrating that m6A modification plays a crucial role in multiple types of cancer.This modification frequently occurs via writers, which catalyse m6A modification in the mRNA of oncogenes or tumour suppressor genes.On the other hand, erasers can also modify m6A by removing it from the mRNA of these genes, leading to an upregulation of oncogene expression or a downregulation of tumour suppressor gene expression. 835][86] Furthermore, research suggests that globally modified m5C and its regulators, including writers, erasers and readers, are expressed abnormally in different cancer types.Methylation status appears to be closely associated with cancer pathogenesis, including initiation, metastasis, progression, drug resistance and tumour recurrence. 12reover, elevated levels of RNA m5C can be identified in the circulating tumour cells of individuals with lung cancer, as per Comparison of previous signatures 75, [77][78][79][80][81][82] with mRLncSig by performing Cox regression analysis for overall, disease-specific and progression-free survival using three formats of official TCGA data.mRLncSig, m6A/m5C/m1A-regulated lncRNA signature. | recent findings. 87As an emerging hotspot of discussion, the research on the correlation between m1A modification and various cancers has gradually become the basis of widespread attention. 88cording to previous research, m1A methylation plays a significant role in tumour development and occurrence. 18,89The study by Bao et al. 90  We utilized a pioneering research approach by incorporating the underutilized concepts of m6A, m5C and m1A.To increase the reliability of our conclusions, we employed an array of sophisticated bioinformatics statistical methods alongside real-time PCR validation of clinical samples.It is worth mentioning that we trained and validated our results through multiple drug databases to identify suitable drugs for high-risk populations and supported our findings using diverse evidence sources.
Immunotherapy for cancer has significantly improved the survival rates of patients with life-threatening cancer.This groundbreaking approach is transforming the field of oncology as more patients are deemed eligible for immune-based treatments. 95,96e introduction of new drug targets and therapeutic combinations is expanding the scope of immunotherapy in cancer treatment.
Targeted techniques can impede tumour progression by disrupting key molecular pathways, while immunotherapy leverages the host's own response for long-lasting and effective tumour eradication. 95,96wever, identifying the appropriate biomarker for each host and optimizing the application strategy remains a crucial challenge in the field of immunotherapy. 97The study provides insights into the optimal use of immunotherapy targets and their application in different circumstances.The findings indicate that the risk score is linked to TMB and TIDE, implying that the signature can be used to guide immunotherapy.Additionally, the research identified six checkpoints-IL10, IL2, CD40LG, SELP, BTLA and CD28-that are associated with our mRLncSig score.In the immunotherapy cohorts analysed, IL10, IL2 and CD40LG were the top three ranked checkpoints, in descending order of importance.IL-10 is a cytokine known for its potent anti-inflammatory properties and plays a critical role in preserving a balanced tissue environment to protect the host. 98IL-10 is capable of restraining the growth of tumours by suppressing Th17 T cells and macrophages. 98Vahl et al.s' 99 research revealed that the competition between IL-10 and IFNγ might be a contributing factor to the resistance of lung cancer patients to PD1/PDL1 immunotherapy.IL-2 plays a crucial role in stimulating the immune system, which has the potential to eliminate cancer. 100In the treatment of metastatic renal cell carcinoma and metastatic melanoma, IL-2 has been approved by the FDA as a monotherapy. 100Conversely, decreased levels of IL-2 and elevated concentrations of soluble IL-2 receptors have been detected in end-stage NSCLC, and this has been linked to unfavourable outcomes. 100Additionally, research has shown that activating IL-2 can help restore lymphocyte immunocompetence F I G U R E 1 0 Real-time PCR identifying the mRLncSig lncRNAs expression patterns and multi analyses assessing their potential in pancancer.(A) The expression levels of mRLncSig lncRNAs in the normal lung (n = 9) and LUAD (n = 9) tissues were visualized using box plots.Real-time PCR was employed for detecting the expression levels.The statistical analysis was conducted using Student's t-test.(B) A heatmap was constructed to depict the differential expression ability of mRLncSig lncRNAs in tumour and normal tissues.The 'pan-cancer TCGA TARGET GTEx' database was used for the heatmap, where each column represented a cancer type, and each row represented a lncRNA.The 'limma' R language package was used for detecting the differences.(C) The prognostic ability of mRLncSig lncRNAs in tumours was evaluated by constructing a heatmap.The data were obtained from the 'pan-cancer TCGA TARGET GTEx' database, and the Cox regression model was used for testing the prognostic ability.mRLncSig, m6A/m5C/m1A-regulated lncRNA signature.
against lung cancer. 100CD40LG, also known as CD154, is a protein that is mainly found on activated T cells and belongs to the TNF superfamily of molecules.Acting as a co-stimulatory molecule, CD154 facilitates the maturation and function of B cells by binding to CD40 located on the surface of B cells, thus encouraging intercellular communication.Initially, CD154 was known to play a crucial part in T cell-dependent humoral responses by binding to its classical receptor CD40. 101However, further investigations revealed that CD154 also participates in inflammation and cell-mediated immunity through its interactions with CD40 alone or with newly identified integrin family members, which can result in the onset of various diseases. 101Furthermore, CD154 is recognized as a molecule with significant potential for cancer treatment, in addition to its role in disease progression. 101e to the high levels of heterogeneity exhibited by individuals with LUAD, it is challenging to find an effective treatment that works for everyone. 102The mRLncSig risk score not only provides information on prognosis but also offers potential benefits in precision oncology by guiding targeted therapy.We identified a range of potential drug candidates for high-risk LUAD.Among these, gemcitabine emerged as the most promising one.Gemcitabine, a synthetic antimetabolite tumour drug, is a common treatment non-small cell lung cancer. 103During the 1980s, Larry Hertel discovered the efficacy of gemcitabine against leukaemia cells. 104 1998, the FDA approved gemcitabine for treating NSCLC.
Clinical trials, which enrolled over 500 patients, demonstrated that gemcitabine monotherapy led to remarkable response rates with fewer side effects. 103Despite its extensive study and effectiveness against most lung cancers, the heterogeneity of lung cancer means that gemcitabine may not be effective for certain patients. 102Drug resistance, low response rates and tumour recurrence have been widely reported. 102,105,106The mRLncSig score we have developed can be a valuable tool in addressing this pain point, serving as a potential promising indicator to guide the clinical application of gemcitabine.
Our study was having some limitations.Despite the validation of mRLncSig's stable prognostic power in another large independent cohort and the confirmation of its stronger predictive ability through comparison with similar published studies, the data source in this study were solely obtained from open-access databases.While real-time PCR confirmed some of our findings, additional laboratory experiments are required to establish the underlying mechanisms.
Therefore, more experiments are crucial to gather further evidence and confirm the potential of mRLncSig as a future therapeutic target.

| CON CLUS ION
A novel and effective m6A/m5C/m1A-related lncRNA signature, called mRLncSig, was developed for LUAD in this study.Validation of our developed mRLncSig in an independent large cohort confirmed its validity and stability, and its potential for targeted therapy and immunotherapy in treating LUAD was demonstrated by its ability in the immune state.The mRLncSig score can guide clinicians in selecting drugs for specific populations, leading to maximum benefits.In addition, mRLncSig not only predicts the survival of LUAD but also holds potential for personalized and precise tumour therapy.Nonetheless, further exploration of its mechanisms is necessary.
AUC, area under the ROC curve; FP, false positive rate; HR, hazard ratio; lncRNA, long non-coding RNA; LUAD, lung adenocarcinoma; mRLncSig, m6A/m5C/m1A-regulated lncRNA signature; PC, principal component; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; TP, true positive rate.F I G U R E 2 Batch effect removal of the validation cohort and mRG cluster establishment.(A) Comparison of UMAP plots before and after batch effect removal for the validation cohort.(B) Survival differences among different mRG clusters were assessed using KM curves.(C) The scatterplot generated from the principal component analysis indicates the degree of heterogeneity between the clusters, revealing a distinct segregation of the two clusters.(D) Bioinformatics algorithms were utilized to visualize the distribution of immune cells within two mRG clusters.(E) Displayed in the heatmap are the distribution patterns of 33 mRGs across mRG clusters, along with clinical parameter distribution within the clusters.The clinical parameters are represented in the upper portion while the lower part shows each gene represented as a row and each sample as a column.(F) KEGG analysis performed using GSVA to visualize pathways dominating in different clusters.Only the most significant pathways are plotted.DEGs, differentially expressed genes; GSVA, Gene set variation analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; KM, Kaplan-Meier estimator; mRG, m6A/m5C/m1A-regulated gene; UMAP, uniform manifold approximation and projection; we considered a p-value of less than 0.05 as statistically significant.The notation *, ** and *** indicate p-values of <0.05, <0.01 and <0.001, respectively.

Figure S2B ,
Figure S2B,C display the risk plots we created for the general situation of mRLncSig in the two cohorts.The graphs are partitioned into three sections.The top section presents patients sorted in ascending order of risk score from left to right.The middle scatterplot illustrates the vital status of LUADs using blue for alive and red for dead.Finally, the heatmap at the bottom shows the relative expression levels of the 10 lncRNAs in the mRLnc-Sig signature.The visualization in Figure 5A upper depicts the KM analysis of the training cohort, revealing that LUAD in the

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Figure 6A-C depicts our visualizations of boxplots and correlation analyses.These visualizations reveal that the 'ESTIMATE' algorithm scores were lower in the high-risk population, and the risk score demonstrated F I G U R E 4 LASSO regression model built the signature mRLncSig.(A) This figure shows how the LASSO algorithm simplifies the data by reducing the number of important features (prognostic lncRNAs) needed to analyse cancer risk.It displays the strength of association (LASSO coefficient) between each lncRNA and the risk score.(B) The plot illustrates the LASSO regression process employing 10-fold cross-validation and minimal Lambda to identify 10 prognostic lncRNAs.(C) The relationship between mRG clusters, mRG-DEG clusters, risks and vital status in general is illustrated by the Sankey diagram.The diagram reveals that a notable portion of the A cluster in mRG-DEG display low-risk scores, while most of its B cluster exhibit high-risk scores.(D) The box plots demonstrate distinct statistical variations in the distributions of risk scores across the two mRG-DEG cluster.(E) Box plots display expression pattern of the 33 mRGs in the high-and low-risk groups.DEGs, differentially expressed genes; FDR, false discovery rate; KM, Kaplan-Meier estimator; LASSO, least absolute shrinkage and selection operator; mRG, m6A/m5C/m1A-regulated gene; mRLncSig, m6A/m5C/m1A-regulated lncRNA signature; statistical significance was determined if the p-value was less than 0.05.Symbols were used to indicate varying levels of significance: * for p-values < 0.05, ** for p-values < 0.01 and *** for p-values < 0.001.et al. a negative correlation with the 'ESTIMATE' algorithm scores.Using the seven primary immune algorithms, we assigned immune scores to individuals within the training cohort.Subsequently, we utilized statistical methods such as the Wilcoxon rank-sum test and Pearson correlation coefficient to compare differences and correlations between high and low risks.The results were presented as heatmaps and lollipop plots in Figure 6D,E, respectively.Only significant factors were highlighted in the plots, while detailed information was provided in Table

5 F I G U R E 6
Figure 7G.To ensure a well-rounded outcome for each analysis, we utilized a Venn diagram to overlap the results from the plots.Upon examination, we observed that CD40LG, BTLA, SELP, IL2, CD28 and IL10 not only displayed a significant association with our mRLncSig, but also had an impact on LUAD prognosis.Consequently, these

F I G U R E 7
CTRP and PRISM datasets comprise gene expression profiles and drug sensitivity profiles of numerous CCLs, enabling the creation of a drug response prediction model.After eliminating duplicates, Identifying the relationship between the mRLncSig and immunotherapy.(A) A plot depicting the mutational landscape of the 20 most frequently mutated genes in LUAD is displayed in a waterfall format.The chart also illustrates the distribution of mutational disparities between high-and low-risk groups.(B, C) The left-side box plots in the panel indicate the variation in the distribution of TMB and TIDE between high-risk and low-risk patients.The Wilcoxon test was used to verify the difference.The right side of the figure represents the correlation of TMB and TIDE with the mRLncSig model, and the detection of correlation was based on the Pearson coefficient.(D) Lollipop plots were utilized to depict the correlations between the scores of the mRLncSig model and immune checkpoints.The Pearson coefficient was used to detect correlations, and only immune checkpoints that showed significant associations were included in the plot.(E)

F I G U R E 8
Identification of drugs with therapeutic potential for patients with high-risk scores based on multiple datasets.(A) The plot illustrates the databases utilized in our drug prediction study, namely CTRP and PRISM, through a Venn diagram.It depicts the number of compounds present in each database.(B) The general concept of our drug prediction study is demonstrated through a flowchart.We employed the Wilcoxon rank-sum test and Spearman rank correlation based on the CTRP and PRISM databases, respectively, to identify potential drugs for treating high-risk score populations.(C) The CTRP database yielded six compounds, with their respective Spearman correlation analysis results on the left and drug response AUC difference analysis results on the right.(D) The PRISM database identified six compounds, with the corresponding Spearman correlation analysis results displayed on the left and drug response AUC difference analysis results on the right.(E) The therapeutic potential of candidate drugs from CTRP and PRISM databases was assessed through CMap score, literature review and clinical trial evidence.The drugs obtained from the CTRP database are shown on the left, while those obtained from PRISM are on the right; a p-value < 0.05 was deemed to be statistically significant; a p-value < 0.001 was denoted by '***'.and normal lung tissues.Specifically, AC010327.4and ITGB1-DT lncRNAs were upregulated in LUAD tumour tissues, while the other lncRNAs were downregulated.Table 3 contains the primer sequences for the signature lncRNAs, which include AC010327.4,AC093010.2,AC107464.3,AL353622.1,COLCA1, ITGB1-DT, LIFR-AS1, LINC00324, LINC00639 and LINC00892.We conducted real-time PCR on nine pairs of LUAD and adjacent tissues to assess the expression levels of these lncRNAs.The comparison results, as shown in Figure 10A, revealed differential expression of the 10 lncRNAs in tumour and normal tissues.Notably, only AC010327.4and ITGB1-DT were upregulated, while the remaining eight lncRNAs exhibited decreased expression levels in tumour tissues.It is worth noting that the upregulation of AC010327.4and ITGB1-DT genes in LUAD tissues is consistent with the findings in Figure S2, which indicated their association with an unfavourable prognosis.On the other hand, the downregulated genes demonstrated protective effects on LUAD prognosis, which further supports the credibility of the gene signatures we discovered and provides guidance for future in-depth investigations.Starting with pan-cancer expression patterns, we investigated the potential of 10 lncRNAs.To explore the expression variance of the signature lncRNAs, we obtained their expression across 24 cancer types, as depicted in Figure 10B.The plots hinted that the ln-cRNAs, ITGB1-DT, AC010327.4,COLCA1, LIFR-AS1 and LINCO0892 ranked the different expression ability.The cancer types of KICH, KIPAN, NCSLC and THCA may strongly be impacted by the 10 ln-cRNAs.To delved deeper into the outcome predictive capabilities of 10 lncRNAs in pan-cancer, we meticulously used data from 33 types of cancers and constructed Cox models.The survival heatmap displayed in Figure 10C showed that the ITGB1-DT and AC010327.4might have an unfavourable impact on most part of the pan-cancer population.In contrast, the remaining lncRNAs mostly protected the pan-outcomes.Our concise examination of the 10 lncRNAs and their association with pan-cancer reinforces the significance of our mRL-ncSig.This could potentially guide further investigations in other types of cancers.

Furthermore, the validation
of real-time PCR in Figure10Arevealed differential expression of signature 10 lncRNAs between normal and tumour samples, reflecting real-world situation.The impact of these lncRNAs on LUAD prognosis, notably AC010327.4and ITGB1-DT exhibiting adverse effects, while other lncRNAs showed positive effects, is depicted in FigureS2.In the pan-cancer analysis performed (Figure10B), we detected the effects of all 10 lncRNAs on various cancers.The lncRNA ITGB1-DT stands out among the gene signatures due to its elevated expression levels in tumour tissue and its association with poor tumour prognosis, making it a promising marker for LUAD.Several studies have explored the impact of ITGB1-DT on cancer, and significant findings have been reported.[91][92][93]Jiang et al.92 utilized both basic research and bioinformatics techniques to reveal that ITGB1-DT is upregulated in stomach adenocarcinoma.Advanced T stage, treatment response, overall survival and progression-free survival are all correlated with high expression of ITGB1-DT in patients with gastric adenocarcinoma, indicating poor prognosis.Moreover, blocking the expression of ITGB1-DT can restrain the proliferation, invasion and migration of gastric adenocarcinoma cells.Research has shown that eliminating ITGB1-DT can cause a delay in the growth, movement and invasion The expression difference of m6A/m5C/m1A-regulated genes in LUAD and normal tissues.
significant differences in gender and tumour stage distribution were noted among the mRG-DEG clusters, as shown in Figure3D.We performed GSVA to determine the top significant KEGG pathways of the mRG-DEG clusters (Figure3E, TableS4) showing that KEGG_CELL_CYCLE, KEGG_DNA_REPLICATION, KEGG_ HOMOLOGOUS_RECOMBINATION, KEGG_MISMATCH_REPAIR, the most important 10 pathways.We examined the distribution of 33 mRGs, including NSUN2, DNMT3B, NOP2, NSUN5, DNMT3A, TA B L E 2 The characteristics of the similar categories of studies from predecessors.
suggests that modulators of m1A can aid in the outcome prediction and treatment of LUAD, which provides some preliminary data for further studies on m1A modulators in LUAD.
outcomes by utilizing data from publicly available databases, encompassing human tissue sample size of over 1000 cases in total.