Construction and validation of a prognostic model of lncRNAs associated with RNA methylation in lung adenocarcinoma

Introduction

Lung cancer (LC) is currently the second leading cause of cancer worldwide and one of the main causes of high cancer mortality worldwide, lung adenocarcinoma (LUAD) is one of its major subtypes (1,2). In recent years, a large number of genomic studies have elucidated the complex molecular patterns of lung tumors, and targeted therapies for LUAD have been developed and applied. Study has shown that KLF5 can promote cell proliferation and migration of LUAD by upregulating STK24 (3). STK24 may also be involved in the immune regulation process of LUAD (3). A novel circular RNA (circRNA) (circ_0004140), which originates from the oncogene YAP1, is found to be elevated in LUAD. The high levels of circ_0004140 correlate with poor outcomes and dysfunction of cytotoxic T lymphocytes (CTLs) in LUAD patients. Silencing circ_0004140 can influence the proliferation, migration, and apoptosis of LUAD cells. Moreover, increased expression of circ_0004140 is associated with cytotoxic lymphocyte depletion, while C-021 (CCL22/CCR4 axis inhibitor) combined with anti-programmed cell death protein 1 (anti-PD-1) therapy can attenuate the development of LUAD and immune resistance (4). Currently, some of the latest treatment methods, including targeted drugs and immunotherapy drugs, are based on the results of LC gene research (5). With the widespread application of these treatments, patients’ disease-free survival has been improved (6). However, the 5-year survival rate of patients remains low, at about 16.6% (7). Constructing risk diagnostic models and developing new tumor markers can help optimize treatment strategies for patients and improve treatment prognosis.

RNA methylation is considered a crucial process in epigenetic regulation, and occurs in both messenger RNA (mRNA) and long non-coding RNA (lncRNA) (8). Different methylation sites exhibit various forms of RNA methylation, including N1-methyladenosine (m1A), 5-methylcytosine (m5C), N6-methyladenosine (m6A), 7-methylguanosine (m7G), and 2-O-dimethyladenosine (m6Am) (9). RNA methylation plays a role in numerous physiological and pathological processes, and its improper regulation is strongly linked to the occurrence and progression of human cancers. In eukaryotic organisms, m6A methylation is the most widely used mRNA modification. It involves a fundamental process with m6A addition by methyltransferases, removal by demethylases, and identification by specific recognition molecules. Through these interactions, m6A plays a crucial role in regulating RNA metabolism and can influence target gene expression patterns and modulate physiological functions like self-renewal, invasion, and proliferation (10). The mammalian target of rapamycin (mTOR) signaling pathway is a well-studied area in tumor investigation. Recent evidence has indicated a possible close link between mTOR signaling and m6A methylation in the regulation of tumor metabolism. By suppressing m6A methylation levels, the Wnt and phosphoinositide 3-kinase-protein kinase B (PI3K-Akt)-mTOR signaling pathways are potentially activated, accelerating cancer development. In contrast, upregulation of m6A methylation can counteract these molecular changes in gastric cancer, which suggests that the oncogenic phenotype and activation signals may be directly modulated by m6A-associated enzymes and not only by the target genes directly regulated by m6A methylation (11,12). Several studies have indicated that RNA methylation, m6A-related regulatory factor METTL3, exhibits elevated expression levels in various types of cancers such as gastric cancer (13), liver cancer (14), and colorectal cancer (15). The expression of most m5C and m1A regulators was significantly different between lung squamous cell carcinoma (LUSC) and normal samples, among which m5C regulators were associated with poor prognosis (16). The m5C-related factors regulate known tumor promoters such as heparin binding growth factor (HDGF), transforming growth factor beta (TGF-β), and Ras GTPase-activating protein-binding protein 1 (G3BP1), thereby shaping the tumor microenvironment suitable for cancer cell migration and metastasis (17). Additionally, RNA methylation also plays a role in tumor suppression processes; in hepatocellular carcinoma, decreased m6A modification levels of microRNA126 affect its competing endogenous RNA (ceRNA) function, thereby accelerating tumor progression (18). M7G regulators can affect the infiltration, maturation, and activation of T cells in the tumor immune microenvironment (TIME). Among these regulators, METTL1 and WDR4 are m7G methyltransferases that have been widely studied in the context of T cell biology (19,20). Research by Ma et al. demonstrates that METTL1/WDR4-mediated m7GtRNA modification promotes mRNA translation and LC progression (21).

Most lncRNAs played an active role in epigenetic modifications, transcription, and multiple regulatory levels, and affect tumor initiation, necrosis, apoptosis, proliferation, cell cycle, and metastasis (22). Recently, the connection between RNA methylation and lncRNAs in tumors has emerged as a prominent area of research. Sun et al. found that the m5C methylation of lncRNA H19 mediated by NSUN2 could facilitate the progression and expansion of hepatocellular carcinoma by altering its interaction with the oncogenic protein G3BP1 (23). In gastric cancer cells, the demethylase ALKBH5 reduces the stability of lncRNA TP53TG1 and downregulates its expression, thereby promoting gastric cancer cell proliferation, migration, and cell cycle progression (24).

However, there is currently no research on the four types of RNA methylation (m6A, m1A, m5C, m7G)-related lncRNAs in LUAD. This study innovatively focused on lncRNA related to RNA methylation modification, screened and explored the expression of lncRNAs related to RNA methylation regulators (RMlncRNAs) in LUAD and its prognostic value, providing a new perspective for understanding the function of lncRNA in LUAD, especially its association with RNA methylation, an important epigenetic modification mechanism. Through bioinformatics analysis, a prognostic model of lncRNA was successfully constructed, providing a new tool for the prognosis assessment of LUAD patients. In addition, the study also deeply analyzed the correlation between the model and tumor mutational burden (TMB), microsatellite instability (MSI), immune infiltration analysis, immunotherapy response, and conducted drug sensitivity analysis, comparing the half-inhibitory concentration of multiple drugs between different risk groups, providing an important basis for the personalized treatment of LUAD. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1085/rc).

Methods

Data source

The RNA sequencing (RNA-seq) and clinical data were extracted from The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga), including 524 samples of LUAD and 59 normal controls (NCs). Subsequently, mRNA and lncRNA were annotated using Ensembl (https://asia.ensembl.org/index.html). The GSE31210 dataset was downloaded from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) and used as a validation cohort. Previous studies have reported a total of 76 genes related to methylation (9,12,25-29) (Table S1). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Differential expression and mutation analysis of RNA methylation regulators

RNA methylation regulators were obtained from recent literature. The LIMMA package (version 3.52.1) (|logFC| >1 and P<0.05) (30) was used to identify differentially expressed genes (DEGs) between LUAD and NC samples. The differentially expressed RNA methylation regulators were identified by overlapping DEGs and RNA methylation regulator screening. The MAFTOOLS package was used to create mutation profiles of methylation regulators that exhibit differential expression in LUAD patients (31). The RCircos package (32) was used to locate the copy number variations (CNVs) of methylation regulatory genes on chromosomes.

Differential expression analysis of lncRNA

Differential expression of lncRNA (DE-lncRNAs) was obtained using the LIMMA package (30). Pearson correlation analysis was performed to extract lncRNAs related to methylation regulatory factors that were differentially expressed [|Pearson correlation coefficient (R)| >0.5, P<0.001] and correlated with methylation regulatory factors.

Univariate Cox regression analysis, multivariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis

Univariate Cox regression analysis was used to screen RMlncRNAs associated with the prognosis of LUAD patients [P<0.05, | hazard ratio (HR) −1| >0.1]. LUAD patients were divided into training and validation sets at a 1:1 ratio, and then LASSO and multivariate Cox regression analysis were used to further screen for feature genes. Additionally, the survival package (version 3.2-7) was utilized to calculate the risk scores for the training, testing, and validation sets. Risk scores were calculated using the following algorithm:

Riskscore=h0(t)∗exp⁡(β1X1+β2X2+…+βnXn)[1]

Then, using the median risk scores, patients in the training, validation, and testing sets were categorized into high- and low-risk groups. The Survminer package (version 0.4.8) was used to plot Kaplan-Meier curves, comparing the survival rates between the high- and low-risk groups. To further evaluate the effectiveness of the prognostic model, TimeROC software (version 0.4) was used to plot the receiver operating characteristic (ROC) curve with a 5-year survival time node. Survival status and RMlnc score curves were plotted to validate the prognostic model. Additionally, the predictive model was validated on the testing and validation sets.

Subgroup analysis and nomogram construction

In subgroup analysis, the 177 samples with complete data from the TCGA-LUAD dataset were divided into different groups based on age, gender (male and female), stage (I, II, III, IV), pathological M (M0 or M1), pathological T (T1, T2, T3, T4), and pathological N (N0 or N1/N2). The expression levels of feature genes in different groups were analyzed. Stratified analysis was used to explore the correlation between different pathological features and risk scores. Then, single-factor and multi-factor Cox regression analyses were conducted to investigate whether the risk score was an independent prognostic factor. A nomogram was constructed based on the risk score and multivariable analysis to predict the possible 1-, 3-, and 5-year survival rates of LUAD patients. Finally, the performance of the nomogram was evaluated using calibration curves.

Principal component analysis (PCA) and immunotherapy response analysis

To further validate the prognostic value of lncRNAs, PCA was performed on RMlncRNAs from high- and low-risk groups using the Scatterplot3d package. Then, TMB and MSI were compared between high- and low-risk populations to assess the immunotherapy response. The impact of the prognostic model on immune heterogeneity was studied using the Tumor Immune Dysfunction and Exclusion (TIDE) model. In addition, the Cibersort algorithm was employed to perform immune infiltration analysis on the lncRNA profiles from the TCGA database, comparing disease groups (524 samples) with control groups (59 samples) to identify differential immune cells. Perform correlation analysis between differentially expressed immune cells and RMlncRNAs. The marker genes for 22 types of immune cells were obtained from a designated reference (table available at https://cdn.amegroups.cn/static/public/tcr-24-1085-1.xls).

Drug sensitivity analysis

The half-maximal inhibitory concentration (IC₅₀) of common chemotherapy drugs and molecular targeted drugs was calculated using the pRophetic package (33). The Wilcoxon test was employed to compare the IC₅₀ of different drugs between high- and low-risk populations. The DrugBank database was utilized to obtain the target genes of drugs. Finally, the expression of target genes in the high- and low-risk populations of the training set was analyzed.

Statistical analysis

Sankey diagrams were generated using the giant package (34). Heatmaps were plotted using the pheatmap package (35). Kaplan-Meier analysis was performed to compare the survival differences between two groups of patients. A two-sided P<0.05 was considered statistically significant.

Results

A total of 10 differentially expressed RNA methylation regulators were identified in TCGA-LUAD

A total of 4,885 DEGs were detected between LUAD and NC samples, with 3,027 genes upregulated and 1,858 genes downregulated. Additionally, 10 differentially expressed RNA methylation regulators were identified, showing high expression levels in LUAD samples (Figure 1A). The mutation frequency of differentially expressed RNA methylation regulators is shown in Figure 1B. The location variation of RNA methylation regulators on chromosomes due to CNVs is depicted in Figure 1C.

Figure 1 Differential expression and mutation analysis of RNA methylation regulatory factors in LUAD group compared to normal group. (A) Heatmap of differentially expressed RNA methylation regulatory factors. (B) Waterfall plot showing the somatic mutation frequency of differentially expressed RNA methylation regulatory factors. (C) CNV of methylation regulatory genes at chromosomal locations. TMB, tumor mutation burden; NA, no significance; CNV, copy number variation; LUAD, lung adenocarcinoma; UTR, untranslated region; Cor, correlation.

Identification of 233 DE-lncRNAs

A total of 986 lncRNAs were extracted from the TCGA-LUAD dataset, with 712 upregulated and 274 downregulated (Figure 2A,2B). A total of 233 DE-lncRNAs were identified. A Sankey diagram was generated based on the co-expression patterns of mRNA-lncRNA to illustrate the relationship between DE-lncRNAs and differentially expressed RNA methylation regulators (Figure 2C).

Figure 2 Identification of DE-lncRNAs in LUAD patients. (A,B) The heatmap and volcano plot illustrate the DE-lncRNAs between the lung adenocarcinoma group and the normal group. (C) Sankey diagram to illustrate the correlation between DE-lncRNAs and differentially expressed RNA methylation regulatory factors. DE-lncRNAs, differentially expressed long non-coding RNAs; LUAD, lung adenocarcinoma; FC, fold change; Cor, correlation.

Construction and validation of prognostic model

A total of 18 RMlncRNAs associated with the prognosis of LUAD patients were selected (Figure 3A), including nine risk factors and nine protective factors (Figure 3B). Then, through LASSO and multivariate Cox regression analysis, six feature genes (NFYC-AS1, OGFRP1, MIR4435-2HG, TDRKH-AS1, DANCR, and TMPO-AS1) were identified (Figure 3C,3D). The overall survival (OS) curve showed statistically significant differences in survival rates between the high- and low-risk groups (P<0.001), with lower survival rates observed in the high-risk group (Figure 3E). The area under the curve (AUC) for predicting the 5-year survival rate of LUAD patients was 0.661, indicating good predictive performance of the model (Figure 3F). Additionally, patients with shorter survival times and censoring events were more abundant in the high-risk group (Figure 3G,3H). The heatmap showed high expression of OGFRP1, MIR4435_2HG, and TMPO_AS1 in the high-risk group (HR >1) (Figure 3I). The results of the test and validation sets were consistent with the training set (Figure 4A-4E, Figure S1).

Figure 3 Construction and validation of the prognostic model. (A) Heatmap of the expression differences of 18 prognostic-related RMlncRNAs in the LUAD group and the normal group (blue represents low expression, red represents high expression). (B) Forest plot for univariate Cox regression analysis of RMlncRNAs. (C) LASSO regression analysis for feature gene selection. (D) Forest plot for multivariate Cox regression analysis of the six feature genes. (E) The Kaplan-Meier curve of the prognostic risk model shows that the overall survival rate in the high-risk group is lower than that in the low-risk group. (F) Determine the AUC of the prognostic risk model based on the ROC curve. (G-I) The distribution of risk scores in LUAD patients based on prognostic features, survival status, and the expression of RMlncRNAs. RMlncRNAs, lncRNAs related to RNA methylation regulators; LUAD, lung adenocarcinoma; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic; lncRNA, long non-coding RNA; HR, hazard ratio; CI, confidence interval; TPR, true positive rate; FPR, false positive rate; AUC, area under the curve.

Figure 4 Prognostic value evaluation of the risk model in the test set. (A) The Kaplan-Meier curve shows that the overall survival rate is lower in the high-risk group than in the low-risk group. (B) Determine the AUC of the prognostic risk model based on the ROC curve. (C-E) Distribution of patient risk scores (based on prognostic features, survival status, and RMlncRNAs expression). RMlncRNAs, lncRNAs related to RNA methylation regulators; ROC, receiver operating characteristic; TPR, true positive rate; FPR, false positive rate; AUC, area under the curve.

Risk score, pathological N, and pathological M were independent prognostic factors for LUAD

Comparison of the number of patients with different clinical features between the high- and low-risk groups showed no statistically significant differences (Table S2). Analysis of risk scores for different clinical features showed differences in stage and pathological N stage (Figure 5A). The expression analysis of feature genes in different clinical feature groups is shown in Figure 5B. Furthermore, significant differences were observed in the correlation between risk scores and different pathological features between the high- and low-risk groups (Figure 5C). Results of univariate and multivariate COX regression analysis showed that risk score, pathological N, and pathological M could serve as independent prognostic factors (Figure 6A,6B). The nomogram is presented in Figure 6C. The calibration curve of the nomogram indicated the effectiveness of the predictive model (Figure 6D).

Figure 5 Comprehensive analysis of clinicopathologic features in relation to risk scores and gene expression. (A) Differences in risk scores among clinical and pathological features in different groups. (B) Expression of genes in the model across different clinical feature groups. (C) The Kaplan-Meier curve of stratified analysis exploring the correlation between risk score and different pathological features. ns, no significance; *, P<0.05; ***, P<0.001.

Figure 6 Correlation analysis of risk scores with clinical features and construction of prognostic model. (A) Forest plot for univariate Cox analysis of risk score and clinical features. (B) Forest plot for multivariate Cox analysis of risk score and clinical features. (C) Nomogram constructed based on the risk score. (D) Calibration curves for the risk score model at 1, 3, and 5 years. HR, hazard ratio; CI, confidence interval; OS, overall survival.

Differences in immunotherapy response between high- and low-risk groups

The results of principal component analysis indicated that samples could be distinctly classified into high- and low-risk groups based on characteristic lncRNAs (Figure 7A). Visualization results of somatic mutations in the high- and low-risk groups are shown in Figure 7B. Figure 7C,7D demonstrate significant differences in TMB and TIDE scores between the high- and low-risk groups. The high-risk group exhibited higher TMB compared to the low-risk group, suggesting a higher correlation between the subindex based on the prognostic model and TMB. Moreover, higher TIDE scores in the high-risk population suggest potentially greater immune resistance, indicating a higher potential for immune evasion and possibly poorer efficacy of immune checkpoint inhibitor (ICI) therapy. In addition, the expression results of the 22 types of immune cells in both the tumor and control groups revealed that B cells memory and Plasma cells were highly expressed in the tumor group (Figure S2A,S2B). Furthermore, with the exception of T cells CD4 naive, the expressions of the other 21 types of immune cells exhibited significant differences (P<0.001) between the tumor and control groups (Figure S2C). The correlation results between six RMlncRNAs (NFYC-AS1, OGFRP1, MIR4435-2HG, TDRKH-AS1, DANCR, TMPO-AS1) and the 21 types of immune cells are presented in Figure S2D.

Figure 7 Study on the main component analysis of high and low risk groups and the difference of immune-related indexes based on multi-group characteristics. (A) Principal component analysis between the high- and low-risk groups based on entire gene expression profiles, RNA methylation related genes, RMlncRNAs, and risk model based on six RMlncRNAs. (B) Waterfall map of somatic mutation frequency between high and low risk groups. (C) Differences of TMB and MSI in high and low risk groups. (D) Differences of dysfunction and TIDE scores in high and low risk groups. ns, no significance; *, P<0.05; **, P<0.01. TMB, tumor mutation burden; NA, no significance; MSI, microsatellite instability; TIDE, Tumor Immune Dysfunction and Exclusion; PC, principal component; RMlncRNAs, long non-coding RNAs related to RNA methylation regulators.

The IC₅₀ of 11 drugs was significant differences between high- and low-risk groups

Wilcoxon test revealed significant differences in IC₅₀ values for 11 drugs (AICAR, BIBW2992, Bryostatin.1, EHT.1864, GDC0941, Gefitinib, JNK.Inhibitor.VIII, KU.55933, MS.275, NU.7441, and Nutlin.3a) between the high- and low-risk groups (Figure 8A). Subsequently, 12 target genes corresponding to the drugs were obtained, among which only three (ERBB4, CASP8, and CD86) showed differences between the high- and low-risk groups (Figure 8B).

Figure 8 Analysis of differences in drug sensitivity and target gene expression between high and low risk groups. (A) Differences of drug sensitivity in high and low risk groups. (B) Differences of target genes in high and low risk groups. ns, no significance; *, P<0.05; ***, P<0.001. IC₅₀, the half-maximal inhibitory concentration.

Discussion

As LC treatment methods continue to evolve, the application of predictive biomarkers has made significant progress (36). Due to the high heterogeneity of LUAD cells, there are still differences in treatment outcomes among patients with similar clinical characteristics and pathological presentations (37). In epigenetic modification, RNA methylation plays a crucial role in regulating gene expression and maintaining stability. For example, m6A methylation acts as a versatile checkpoint, that can couple different layers of gene regulation with one another (38). As research progresses, the close relationship between lncRNA and RNA methylation in tumor occurrence and development has been discovered. For instance, Chen et al. found that lncRNA lnc-H2AFV-1 alters m6A modification by modulating m6A methyltransferases METTL3/14 and FTO, mediating downstream target IFT80 to promote the progression of head and neck squamous cell carcinoma (39). Wanna-Udom et al. discovered that METTL3 is crucial in TGF-β-induced epithelial-mesenchymal transition (EMT) in LC. It primarily functions by promoting m6A modification, elevating total mRNA levels, and enhancing the stability of JUNB mRNA, which is a key transcriptional regulator of EMT (40). The study also revealed that YTHDF2 is overexpressed in LC tissues, facilitating the growth of LC cells. It directly interacts with the m6A modification site in the 3’ untranslated region (UTR) of 6-phosphogluconate dehydrogenase (6PGD), thereby promoting the translation of 6PGD mRNA in LC cells and enhancing the pentose phosphate pathway (PPP), which is essential for tumor growth (41). Moreover, research has indicated that m6A-modified lncRNAs can influence various biological processes. The impact of m6A modification on lncRNAs may involve several regulatory mechanisms: on one hand, it can modulate lncRNA function by creating binding sites for m6A reader proteins or altering local RNA structures to facilitate the binding of RNA-binding proteins (RBPs). On the other hand, m6A modification may also affect the interaction between RNA and DNA, as well as specific DNA sites, by influencing the triple helix structure of lncRNA (12). Therefore, RMlncRNAs have the potential to serve as innovative molecular indicators and possible therapeutic targets in cancer treatment. Currently, there is no research published on the characteristics of RMlncRNAs as tumor prognostic indicators. This study constructed an LUAD prognostic risk model based on RNA methylation-related lncRNAs, providing new clues for subsequent mechanistic research.

We identified 4,885 RMlncRNAs from the TCGA dataset. Through LASSO and multiple-factor analysis, we finally determined six RMlncRNAs to construct a model for predicting the survival rate of LUAD patients. The results indicated that patients in the high-risk group had shorter OS times. The AUC of the 5-year ROC curves for the training, testing, and validation sets were all greater than 0.6, demonstrating the effectiveness of these RMlncRNAs features in predicting the prognosis of LUAD patients. The calculated risk score, lymph node metastasis, and distant metastasis based on these RMlncRNAs were independent prognostic factors for LUAD patients, playing a predictive role in the prognosis of LUAD.

This study screened lncRNAs related to RMlncRNAs by correlating DE-lncRNAs with RNA methylation regulatory factors. A prognostic risk model based on six RMlncRNAs (including OGFRP1, MIR4435-2HG, TMPO-AS1, NFYC-AS1, TDRKH-AS1, DANCR) was constructed. Some studies have already shown that these lncRNAs play roles in cancer. Opioid growth factor receptor pseudogene 1 (OGFRP1) has carcinogenic effects on non-small cell lung cancer (NSCLC), colorectal cancer (CRC), and gastric cancer (GC). OGFRP1 regulates LYPD3 expression through sponge miR-124-3p, promoting the progression of NSCLC (42). Dong et al.’s study found that OGFRP1 promotes angiogenesis and EMT of CRC cells through the miR-423-5p/CTCF axis (43). Zhang et al. found that OGFRP1 promotes GC progression by activating the AKT/mTOR pathway (44). MicroRNA4435-2 Host Gene (MIR4435-2HG) activates TGF-β signaling by sponge TGF-β1, promoting migration and proliferation of NSCLC (45). MIR4435-2HG promotes tumorigenesis by participating in six signaling pathways, including TGF-β, Wnt/β-catenin, MDM2/p53, PI3K/AKT, Hippo, and MAPK/ERK signaling pathways. It can competitively bind to multiple miRNAs to create a complex ceRNA network, thereby regulating the expression of downstream target genes. Elevated levels of MIR4435-2HG are strongly associated with clinical pathological features and poor prognosis in various tumors, making its high expression in peripheral blood or serum a valuable predictor of cancer risk. Additionally, MIR4435-2HG plays a role in the mechanisms of action for three anticancer drugs: resveratrol in LC, cisplatin in NSCLC and colon cancer, and carboplatin in triple-negative breast cancer (46).

As a lncRNA, MIR4435-2HG is also involved in the molecular cascade of other diseases such as coronary artery disease, osteonecrosis, osteoarthritis, osteoporosis and periodontitis. MIR4435-2HG exerts its functions through multiple mechanisms, including inhibiting cell apoptosis, adsorbing microRNAs (miRNAs), promoting cell proliferation, increasing cell invasion and migration ability, and enhancing EMT (47).

It shows diagnostic and prognostic value in various tumors and is considered a pan-cancer biomarker (48). Thymopoietin-antisense RNA 1 (TMPO-AS1) regulates the proliferation of LUAD cells by upregulating SOX12 expression levels through sponge miR-326 (49). Qiu et al.’s study found that downregulation of TMPO-AS1 can induce apoptosis of LC cells by regulating the miR-143-3p/CDK1 axis (50). Nuclear transcription factor Y subunit gamma-antisense RNA 1 (NFYC-AS1) is a commonly expressed nuclear antisense RNA, highly expressed in pan-cancer, controlling the cell cycle mitosis process (51). In LUAD, NFYC-AS1 promotes the tumor cell proliferation, invasion, and apoptosis through autophagy, apoptosis, and MET/c-Myc oncogenic proteins (52). Furthermore, Tong et al.’s study found that the abnormal elevation of NFYC-AS1 may promote the progression of LUAD through the autophagy-related gene BIRC6 (53). In Ding et al.’s study, tudor and KH domain containing-antisense RNA 1 (TDRKH-AS1) acts as a sponge for miR-134-5p in breast cancer, thereby reducing the inhibitory effect of miR-134-5p on CREB1 expression and exerting a tumor-promoting effect (54). In CRC, TDRKH-AS1 promotes cancer cell proliferation and invasion through the β-catenin-activated Wnt signaling pathway (55). Its mechanism of action in LUAD remains to be further investigated. Differentiation antagonizing non-protein coding RNA (DANCR) was first discovered to be closely associated with the EMT process (56). DANCR is aberrantly expressed in various cancers and plays a key role in cancer cell proliferation, apoptosis, invasion, and drug resistance (57). LncRNA-DANCR promotes the progression of LUAD by regulating mTOR expression through sponge miR-496 (58). Besides miR-496, many other miRNAs have been verified as sponge targets for DANCR (59). The results of this study indicate that these six RMlncRNAs play important roles in the occurrence and prognosis of LUAD. However, there is currently no research on the potential significance of their association with RNA methylation in studying its relevance to the prognosis of LUAD patients.

In order to assess the practical clinical impact of the risk model, we also conducted an analysis of immune therapy response to evaluate the model’s predictive ability for ICIs efficacy. TMB can reflect the ability and extent of tumor to generate new antibodies, and it has been shown to predict the efficacy of ICIs in multiple cancers (60). For NSCLC patients receiving ICIs treatment, TMB alone or in combination testing has good predictive ability (61,62). In the results of this study, the TMB of the high-risk group exceeded that of the low-risk group, indicating a high correlation between the risk model’s classification index and TMB. The high-risk group is more likely to respond to immune therapy compared to the low-risk group. The TIDE scores predict tumor immune escape capability by comprehensively assessing the activity of two immune evasion mechanisms. In this study, the predictive results of the TIDE scores indicated that patients in the high-risk group had better responses to immune therapy. Based on these results, we believe that this predictive model may provide a reference for the immune therapy efficacy in LUAD patients, and these RMlncRNAs may serve as new immune biomarkers in LUAD treatment.

Finally, we conducted drug sensitivity analysis. By analyzing 138 commonly used drugs, we identified 11 drugs with significant differences in sensitivity. Subsequently, we predicted their target genes and identified three target genes (ERBB4, CASP8, and CD86) that showed significant differences between the high- and low-risk groups. The corresponding drugs for these genes were afatinib (BIBW2992) and bryostatin-1. Afatinib is a kinase inhibitor approved for first-line treatment of metastatic NSCLC harboring non-resistant epidermal growth factor receptor (EGFR) mutations. It demonstrates greater activity and better tolerability than chemotherapy in patients with EGFR mutations (63). Bryostatin.1 is currently in the research stage and has potential value in the treatment of LUAD. Phase II clinical trials have been completed in combination with paclitaxel for stage IIIB, IV, or recurrent NSCLC patients (64). The model reflects the drug sensitivity of LUAD patients to these two drugs, providing a reference for drug selection in LUAD patients.

The model constructed in this study included the following lncRNAs: NFYC-AS1, OGFRP1, MIR4435-2HG, TDRKH-AS1, DANCR, TMPO-AS1, which may represent relevant biomarkers with potentially good predictive capabilities, holding significant implications for improving treatment outcomes and prognosis in patients with LUAD. This study still has some limitations. Firstly, it is a retrospective study based on the TCGA and GEO databases, requiring multicenter, large-sample clinical trials to validate the clinical value of the model. Additionally, our research is currently mainly based on bioinformatics analysis. In the future, we can conduct in vivo and in vitro experiments to further verify the research results. For example, culturing LUAD cell lines and normal lung epithelial cells for subsequent in vitro experiments will enable us to more directly observe the functional role of RMlncRNAs in LC cells; using small interfering RNA (siRNA) or plasmid technology to conduct gene knockdown or overexpression studies on identified RMlncRNAs; using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot analysis techniques to verify the expression level of RMlncRNAs and their interaction with target proteins, which will provide us with important clues to reveal the molecular mechanism of RMlncRNAs in the occurrence and development of LC; constructing mouse models (such as xenograft models) injected with LUAD cell lines manipulated by RMlncRNAs for in vivo experimental studies. This will enable us to observe the functional role of RMlncRNAs in an environment closer to the human body and provide strong support for its development as a potential therapeutic target.

Conclusions

In this study, 18 RMlncRNAs associated with prognosis in LUAD patients were analyzed to establish a risk model with good reliability in predicting patient survival, and to reveal the correlation between immune characteristics and chemotherapy drug sensitivity in LUAD patients. This can provide important guidance for personalized treatment of clinical LUAD patients, and promote the development of tumor therapy.

Acknowledgments

The authors thank the TCGA dataset and GEO dataset for providing high quality data.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1085/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1085/prf

Funding: This work was supported by the Yunnan Fundamental Research Projects (Nos. 202301AT070094, 202201AT070380); and the Joint Special Funds for the Department of Science and Technology of Yunnan Province-Kunming Medical University (No. 202201AY070001-156).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1085/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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