Identifying and Validating Potential Biomarkers of Early Stage Lung Adenocarcinoma Diagnosis and Prognosis

Immune Clustering Reveals Molecularly Distinct Subtypes of Lung Adenocarcinoma

Background/objectives: Lung adenocarcinoma, the most prevalent type of non-small cell lung cancer, consists of two driver mutations in KRAS or EGFR. These mutations are generally mutually exclusive and biologically and clinically different. In this study, we aimed to test if lung adenocarcinoma tumors could be separated by their immune profiles using an unsupervised machine learning method. The underlying assumption was that differences in the immune response to tumors are characteristic of tumor subtypes. Methods: RNA-seq data were projected into inferred immune profiles. Unsupervised learning was used to divide the lung adenocarcinoma population based on their projected immune profiles. Results: The patient population was divided into three subgroups, one of which appeared to contain mostly EGFR patients. The tumors in the different clusters significantly differed in their expression of some of their known immune checkpoints (TIGIT, PD-1/PD-L1, and CTLA4). Discussion: We argue that EGFR mutations in each subgroup are immunologically different, which implies a distinct tumor microenvironment and might relate to the relatively high resistance of EGFR-positive tumors to immune checkpoint inhibitors. However, we cannot make the same claim about KRAS mutations.

LINC00894, YEATS2-AS1, and SUGP2 genes as novel biomarkers for N0 status of lung adenocarcinoma

Research on genes affecting tumors without lymph node metastasis is limited, hence this study employed both bioinformatic and experimental approaches to identify specific genes associated with lung cancer adenocarcinoma (LUAD) before lymph node metastasis occurs. Expression profiles of mRNAs and lncRNAs and LUAD clinical data were downloaded from the Cancer Genome Atlas (TCGA) using R software to identify differentially expressed genes (DEGs) associated with N0 and N + status. TargetScan, miRTarBase, and miRDB databases were used to identify interactions between miRNAs and mRNAs. The DIANA database and lncBase tool were used to find the association between lncRNA and miRNA. After selecting some genes, the expression of candidate genes was confirmed by real-time RT-PCR technique. Following the knockdown LINC00894 gene using the shRNA technique, its effect on invasion, migration, and apoptosis in the calu-3 cell line was investigated. In total, we found 321 specific DEGs not only in N0 vs. normal but also in N0 Vs. N + in LUAD, most of which were lncRNA and we identified a ceRNA network containing nine lncRNAs with the highest degree of connectivity. Among them, in addition to bioinformatic analyses, LINC00894 and YEATS2-AS1 were significantly increased in tumor tissues compared to normal tissues (P = 0.0001). also, SUGP2 that was shared in both lncRNA-related ceRNA subnetworks was significantly upregulated (P = 0.0001). Additionally, following the knockdown of LINC00894 in Calu-3 cell line, a significant decrease in migration and invasion was observed, but early apoptosis was significantly increased in the shLINC00894(48 h) group (P = 0.007). The findings of the present study show that lncRNAs play an important role in the N0 status of LUAD. Moreover, LINC00894, YEATS2-AS1, and SUGP2 can act as biomarkers in patients with N0 LUAD.

Transcription Factor FOSL1 Promotes Cisplatin Resistance in Non-Small Cell Lung Cancer Cells by Modulating the Wnt3a/β-Catenin Signaling through Upregulation of PLIN3 Expression

BACKGROUND

Lung adenocarcinoma (LUAD) is the most prevalent histological subtype of lung cancer, accounting for 45.3% of all cases and serving as a major cause of cancer-related mortality. Although cisplatin (DDP) is a cornerstone in LUAD therapy, its efficacy is often compromised by resistance, leading to therapeutic failure and poor patient outcomes. Lipid metabolism and associated proteins, such as perilipin 3 (PLIN3), have been increasingly implicated in cancer progression and chemoresistance. However, the precise mechanisms through which PLIN3 contributes to cisplatin (DDP) resistance in LUAD remain poorly understood.

METHODS

To investigate the role of PLIN3 in DDP resistance, its expression in LUAD tissues and its correlation with patient prognosis were analyzed using bioinformatics databases and validated through clinical sample analysis. The effects of PLIN3 knockdown and overexpression on DDP resistance and Wnt3a/β-catenin signaling were assessed using quantitative real-time PCR (qPCR), western blotting, cytotoxicity assays, and colony formation assays. Bioinformatics screening identified FOS-like antigen 1 (FOSL1) as a transcription factor positively correlated with PLIN3, and its involvement in DDP resistance was further examined both in vitro and in vivo.

RESULTS

PLIN3 expression is significantly elevated in LUAD tissues and correlates with poor overall survival. In LUAD cells, PLIN3 overexpression enhanced DDP resistance by upregulating Wnt3a expression and promoting β-catenin nuclear translocation. Bioinformatics analysis identified FOSL1 as a key transcription factor regulating PLIN3 expression. Experimental validation confirmed that FOSL1 directly binds to the PLIN3 promoter, activating the Wnt3a/β-catenin pathway and promoting DDP resistance. Knockdown of PLIN3 or inhibition of Wnt3a signaling reversed the effects of FOSL1 overexpression on DDP resistance.

CONCLUSION

This study demonstrates that PLIN3 contributes to DDP resistance in LUAD by activating the Wnt3a/β-catenin signaling pathway, with FOSL1 acting as a critical upstream regulator. Targeting the FOSL1/PLIN3/Wnt/β-catenin axis may provide a promising therapeutic strategy for overcoming chemoresistance in LUAD.

PRSS3 is a potential prognostic biomarker for lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a highly prevalent and deadly form of lung cancer and is a significant health concern worldwide. Although the expression of serine protease 3 (PRSS3) is elevated in certain cancers, its function in LUAD is yet unclear. The aim of this study was to investigate the mechanism of PRSS3 in lung adenocarcinoma, and validate PRSS3 as a reliable prognostic biomarker in lung adenocarcinoma.

Methods

The Cancer Genome Atlas (TCGA) provides RNA expression data and patient medical information for LUAD patients. To determine which genes are expressed differently in LUAD and normal lung tissues, we carefully examined these data. We then used Cox regression analysis to examine the expression and survival data to pinpoint the genes that are strongly associated with patient survival. The PRSS3 gene affects patient prognosis. Afterward, we divided LUAD patients into low- and high-expression groups on the basis of the median PRSS3 expression to examine the relationship between immune cells and PRSS3. The results of the CIBERSORT and CIBERSORTx studies revealed correlations between PRSS3 and the degree of infiltration of several immune cell types. After the groups with low and high PRSS3 expressions were compared, PRSS3-related genes were identified, and functional enrichment analysis was performed. Furthermore, a model was developed to predict patient prognosis according to clinical characteristics and PRSS3 expression. After the bioinformatics analyses were completed, we validated the differential expression of PRSS3 in samples obtained from our center via Western blotting and immunohistochemistry (IHC).

Results

We found that PRSS3 expression is highly upregulated in LUAD and that high PRSS3 expression is associated with a poorer prognosis in the TCGA database. Single-sample gene enrichment analysis revealed a strong correlation between PRSS3 and the immunological microenvironment. The clinical model developed on the basis of the PRSS3 showed great accuracy and can be used as a significant diagnostic indicator for LUAD. Western blotting and IHC confirmed a substantial increase in PRSS3 expression in LUAD. Herein, we analyzed an available dataset for a clinical cohort and revealed that elevated levels of PRSS3 are indicative of unfavorable outcomes in patients diagnosed with LUAD.

Conclusions

PRSS3 is significantly upregulated in LUAD and can be used as a marker for LUAD diagnosis and prognosis assessment. Further study of PRSS3 could provide valuable insight into the mechanisms underlying the occurrence and progression of LUAD.

N6-methyladenosine-mediated LINC01087 promotes lung adenocarcinoma progression by regulating miR-514a-3p to upregulate centrosome protein 55

Long noncoding RNAs are key players in the development of lung adenocarcinoma (LUAD). The present study elucidated the role of LINC01087 in LUAD development. Cell vitality and apoptosis were assessed by the CCK-8 assay and flow cytometry, respectively. The transwell assay was adopted to evaluate cell migration and invasion. Levels of m6A modification of LINC01087 were determined using the methylated RNA binding protein immunoprecipitation assay. The interactions among LINC01087, miR-514a-3p, and centrosome protein 55 (CEP55) were evaluated using dual-luciferase reporter, RNA immunoprecipitation, and RNA-RNA pull-down assays. LINC01087 was highly expressed in LUAD, and its downregulation restrained cancer cell proliferation, migration, invasion, and epithelial-mesenchymal transition in vitro as well as tumor growth in a xenograft tumor model. Overexpression of miR-514a-3p inhibited malignant phenotypes in LUAD cells by inactivating RhoA/ROCK1 signaling via the suppression of CEP55 expression. Mechanistically, RBM15 increased the expression and mRNA stability of LINC01087 by mediating its m6A modification and LINC01087 induced CEP55 expression by sponging miR-514a-3p. RBM15-induced LINC01087 upregulation accelerated LUAD progression by regulating the miR-514a-3p/CEP55/RhoA/ROCK1 axis, illustrating the potential of LINC01087 as a novel target for LUAD therapy.

Differential GPR56 Expression in T Cell Subpopulations for Early-Stage Lung Adenocarcinoma Patient Identification

OBJECTIVE

This study aimed to investigate the expression of GPR56 in the T cells of early-stage lung adenocarcinoma (LUAD) patients and clarify its diagnostic significance.

METHODS

Blood samples were collected from 32 patients with stage IA LUAD and 31 healthy controls. GPR56 and perforin were analysed in circulating T-cell subsets by flow cytometry. In addition, a correlation between perforin and GPR56 expression was detected. Changes in GPR56+ cells in early LUAD patients were analysed, and the diagnostic significance of GPR56+ T cells for early LUAD was studied by receiver operating characteristic (ROC) curve analysis.

RESULTS

The expression of GPR56 in CD8+ T cells from early-stage LUAD patients was significantly greater than that in CD4+ T cells. The percentage of perforin-positive GPR56+ cells in early-stage LUAD patients was high. GPR56 levels in the T cells of LUAD patients were significantly lower than those in healthy controls. ROC analysis revealed that the area under the curve for the percentage of GPR56-positive CD8+ TEMRA cells to distinguish early-stage LUAD patients from healthy individuals- reached 0.7978.

CONCLUSION

The decreased expression of GPR56 in the peripheral blood of early-stage LUAD patients correlated with perforin levels, reflecting compromised antitumor immunity and aiding early-stage LUAD screening.

Comprehensive analysis of prognostic value and immune infiltration of Regulator of Chromosome Condensation 2 in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) incidence and mortality take the leading place of most malignancies. Previous studies have revealed the regulator of chromosome condensation 1 (RCC1) family members played an essential role during tumorigenesis. However, its biological functions in LUAD still need further investigation. Methods: Several databases were applied to explore potential effects of RCC1 family members on LUAD, such as Oncomine, GEPIA, and cBioPortal. Real-time PCR and immunohistochemistry were used to verify the expression of RCC2 in stage I LUAD. H1975 and A549 were selected to explore the biological function of RCC2 in cellular malignant phenotype. Results: The expressions of RCC1 and RCC2 showed marked differences in malignant tissue compared to lung tissue. The higher the expression levels of RCC1 or RCC2 in LUAD patients, the shorter their overall survival (OS). In normal lung tissues, RCC1 expression was highly enriched in alveolar cells and endothelial cells. Compare with RCC1, RCC2 expression in normal lung tissue was significantly enriched in macrophages, B cells and granulocytes. Additionally, RCC2 expression level was correlated with multiple immune cell infiltration in LUAD. Moreover, the mutation or different sCNA status of RCC2 exerted influence on multiple immune cell infiltration distribution. We found that the upregulation of RCC1 and RCC2 were obviously related to TP53 mutation. GSEA analysis revealed that RCC2 was involved in the process of DNA replication, nucleotide excision repair and cell cycle, which might affect tumor progression through P53 signaling pathway. We further elucidated that downregulation of RCC2 could dramatically repress the migration and invasion of LUAD cells. Conclusions: The study demonstrated that RCC1 and RCC2 expression were markedly increased in early-stage of LUAD. Patients with high expression of RCC1 or RCC2 had a worse prognosis. Based on our analysis, RCC1 and RCC2 might exert influence on LUAD process through DNA replication, nucleotide excision repair and cell cycle, as well as cells migration and invasion. Different from RCC1, RCC2 also involved in immune infiltration. These analyses provided a novel insight into the identification of diagnostic biomarker.

Exploring the Mechanism of Zilongjin in Treating Lung Adenocarcinoma Based on Network Pharmacology Combined with Experimental Verification

Zilongjin (ZLJ) is a common traditional Chinese medicine for lung adenocarcinoma (LUAD) treatment. However, its mechanisms of action remain to be elucidated. Network pharmacology was used to explore the underlying mechanisms of ZLJ on LUAD treatment. The disease-related targets were determined from the Gene-Cards and DisGeNET databases. Active compounds and targets of ZLJ were obtained from the HIT, TCMSP, and TCMID databases. Then the protein-protein interaction (PPI) network was built by the STRING database to identify core-hub targets of ZLJ in LUAD. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were employed to analyze the enriched regulatory pathways of targets. Molecular docking analysis was used to evaluate interactions between potential targets and active compounds. Finally, qRT-PCR was used to further verify the results of network pharmacology. A total of 124 LUAD-related targets of ZLJ and 5 active compounds of ZLJ from the relevant databases were screened out. Among these target proteins, JUN, CDH1, PPARG, and FOS were core hub-genes in the PPI network. GO and KEGG pathway enrichment analysis indicated that these targets might regulate the PPAR signaling pathway in LUAD. JUN, PPARG, and FOS levels were upregulated, while CDH1 level was downregulated in LUAD cells. This study discerned that ZLJ may target genes such as JUN, FOS, PPARG, and CDH1 via the PPAR signaling pathway in LUAD, offering foundational insights for further exploration of ZLJ in clinical applications.

DSCC1 interacts with HSP90AB1 and promotes the progression of lung adenocarcinoma via regulating ER stress

Lung cancer is a leading cause of cancer-related deaths, and the most common type is lung adenocarcinoma (LUAD). LUAD is frequently diagnosed in people who never smoked, patients are always diagnosed at advanced inoperable stages, and the prognosis is ultimately poor. Thus, there is an urgent need for the development of novel targeted therapeutics to suppress LUAD progression. In this study, we demonstrated that the expression of DNA replication and sister chromatid cohesion 1 (DSCC1) was higher in LUAD samples than normal tissues, and the overexpression of DSCC1 or its coexpressed genes were highly correlated with poor outcomes of LUAD patients, highlighting DSCC1 might be involved in LUAD progression. Furthermore, the expression of DSCC1 was positively correlated with multiple genetic mutations which drive cancer development, including TP53, TTN, CSMD, and etc. More importantly, DSCC1 could promote the cell proliferation, stemness, EMT, and metastatic potential of LUAD cells. In addition, DSCC1 interacted with HSP90AB1 and promoted the progression of LUAD via regulating ER stress. Meanwhile, DSCC1 expression negatively correlated with immune cell infiltration in lung cancer, and DSCC1 positively regulated the expression of PD-L1 in LUAD cells. Collectively, this study revealed that DSCC1 is a novel therapeutic target to treat LUAD and a biomarker for predicting the efficiency of PD-1/PD-L1 blockade treatment.

[Expression of MEF2D in Lung Adenocarcinoma and Its Correlation with Prognosis]

BACKGROUND

Myocyte enhancer factor 2D (MEF2D) can participate in the process of tumor lesions by regulating the transcription of oncogenes. In a previous study, MEF2D was demonstrated to enhance the proliferation and metastasis of lung adenocarcinoma cells A549 and H1299 by promoting the transcription of NUSAP1. The research aimed to explore the expression level and clinical significance of MEF2D in lung adenocarcinoma.

METHODS

A total of 199 patients with lung adenocarcinoma were collected. Immunohistochemical staining was used to detect MEF2D expression levels in cancer and adjacent tissues. After the clinical and follow-up data were collated, the correlation between MEF2D expression level and clinical characteristics and prognosis of the patients was analyzed.

RESULTS

In the lung adenocarcinoma, the high expression rate of MEF2D in cancer tissues was significantly higher than that in adjacent tissues (P<0.05). According to immunohistochemical score, MEF2D expression level in lung adenocarcinoma tissues was correlated with tumor differentiation, N stage, M stage and intrapulmonary metastasis (P<0.05). Kaplan-Meier analysis showed that patients with low MEF2D expression had significantly better prognosis than patients with high MEF2D expression (P<0.05). Cox multivariate analysis showed that MEF2D expression level, M stage, N stage and bone metastasis of lung cancer were independent risk factors for prognosis of lung adenocarcinoma patients.

CONCLUSIONS

MEF2D expression level is closely related to the metastasis of lung adenocarcinoma and other clinical characteristics, and can be used as an independent risk factor for the prognosis of patients with lung adenocarcinoma, which has the potential to be developed as a clinical diagnosis and treatment target of lung adenocarcinoma.

Diverse roles of UBE2T in cancer (Review)

As a leading cause of mortalities worldwide, cancer results from accumulation of both genetic and epigenetic alterations. Disruption of epigenetic regulation in cancer, particularly aberrant ubiquitination, has drawn increasing interest in recent years. The present study aimed to review the roles of ubiquitin‑conjugating enzyme E2 T (UBE2T) and its associated pathways in the pathogenesis of pan‑cancer, and the development of small‑molecule modulators to regulate ubiquitination for treatment strategies. The current study comprehensively investigated the expression landscape and functional significance of UBE2T, as well as its correlation with cancer cell sensitivity to chemotherapy/radiotherapy. Multiple levels of evidence suggested that aberrant UBE2T played important roles in pan‑cancer. Information was collected from 16 clinical trials on ubiquitin enzymes, and it was found that these molecules had an important role in the ubiquitin‑proteasome system. Further studies are necessary to explore their feasibility and effectiveness as diagnostic and prognostic biomarkers, or as up/down‑stream and therapeutic targets for cancer treatment.

NCAPD2 is a novel marker for the poor prognosis of lung adenocarcinoma and is associated with immune infiltration and tumor mutational burden

Lung adenocarcinoma (LUAD) is at present the most prevalent subtype of lung cancer worldwide. Non-SMC condensin I complex subunit D2 (NCAPD2) is one of the 3 non-SMC subunits in condensin I. Previous studies have confirmed that NCAPD2 plays a critical role in chromosome cohesion and segregation. NCAPD2 may be involved in tumorigenesis and progression by participating in abnormal cell cycle division, but the prognostic value of NCAPD2 in LUAD remains unclear. We investigated differences in the expression levels of NCAPD2 and determined their association with clinical features, as well as their diagnostic and prognostic value using the cancer genome atlas database. The function of NCAPD2 was analyzed using gene ontology, Kyoto encyclopedia of genes and genomes, and gene set enrichment analysis. CIBERSORT, single-sample gene set enrichment analysis, and ESTIMATE were used to analyze the immune microenvironment of tumor patients. Tumor mutational burden (TMB) and immune checkpoints were analyzed, while hub genes were identified using weighted gene coexpression network analysis and were used to construct prognostic models. Subsequently, the competing endogenous RNAs network of NCAPD2 in LUAD was explored. Finally, we performed qPCR to verify differences in NCAPD2 expression between the tumor and normal tissues. The expression of NCAPD2 in LUAD was significantly upregulated compared with normal lung tissues. NCAPD2 has been linked to the T stage, N stage, and tumor stage. The elevated expression of NCAPD2 in LUAD can predict a poor prognosis. Functional enrichment analysis indicated that the main function of NCAPD2 was in cell cycle regulation. Moreover, NCAPD2 was also associated with immune cell infiltration and TMB. NCAPD2 is a novel prognostic marker in LUAD and is associated with immune infiltration and TMB.

A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection

Great advances in deep learning have provided effective solutions for prediction tasks in the biomedical field. However, accurate prognosis prediction using cancer genomics data remains challenging due to the severe overfitting problem caused by curse of dimensionality inherent to high-throughput sequencing data. Moreover, there are unique challenges to perform survival analysis, arising from the difficulty in utilizing censored samples whose events of interest are not observed. Convolutional neural network (CNN) models provide us the opportunity to extract meaningful hierarchical features to characterize cancer subtype and prognosis outcomes. On the other hand, feature selection can mitigate overfitting and reduce subsequent model training computation burden by screening out significant genes from redundant genes. To accomplish model simplification, we developed a concise and efficient survival analysis model, named CNN-Cox model, which combines a special CNN framework with prognosis-related feature selection cascaded Wx, with the advantage of less computation demand utilizing light training parameters. Experiment results show that CNN-Cox model achieved consistent higher C-index values and better survival prediction performance across seven cancer type datasets in The Cancer Genome Atlas cohort, including bladder carcinoma, head and neck squamous cell carcinoma, kidney renal cell carcinoma, brain low-grade glioma, lung adenocarcinoma (LUAD), lung squamous cell carcinoma, and skin cutaneous melanoma, compared with the existing state-of-the-art survival analysis methods. As an illustration of model interpretation, we examined potential prognostic gene signatures of LUAD dataset using the proposed CNN-Cox model. We conducted protein-protein interaction network analysis to identify potential prognostic genes and further analyzed the biological function of 13 hub genes, including ANLN, RACGAP1, KIF4A, KIF20A, KIF14, ASPM, CDK1, SPC25, NCAPG, MKI67, HJURP, EXO1, HMMR, whose high expression is significantly associated with poor survival of LUAD patients. These findings confirmed that CNN-Cox model is effective in extracting not only prognosis factors but also biologically meaningful gene features. The codes are available at the GitHub website: https://github.com/wangwangCCChen/CNN-Cox .

Screening and Validation of Significant Genes with Poor Prognosis in Pathologic Stage-I Lung Adenocarcinoma

Background

Although more pathologic stage-I lung adenocarcinoma (LUAD) was diagnosed recently, some relapsed or distantly metastasized shortly after radical resection. The study aimed to identify biomarkers predicting prognosis in the pathologic stage-I LUAD and improve the understanding of the mechanisms involved in tumorigenesis.

Methods

We obtained the expression profiling data for non-small cell lung cancer (NSCLC) patients from the NCBI-GEO database. Differentially expressed genes (DEGs) between early-stage NSCLC and normal lung tissue were determined. After function enrichment analyses on DEGs, the protein-protein interaction (PPI) network was built and analyzed with the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Overall survival (OS) and mRNA levels of genes were performed with Kaplan–Meier analysis and Gene Expression Profiling Interactive Analysis (GEPIA). qPCR and western blot analysis of hub genes in stage-I LUAD patients validated the significant genes with poor prognosis.

Results

A total of 172 DEGs were identified, which were mainly enriched in terms related to management of extracellular matrix (ECM), receptor signaling pathway, cell adhesion, activity of endopeptidase, and receptor. The PPI network identified 11 upregulated hub genes that were significantly associated with OS in NSCLC and highly expressed in NSCLC tissues compared with normal tissues by GEPIA. Elevated expression of ANLN, EXO1, KIAA0101, RRM2, TOP2A, and UBE2T were identified as potential risk factors in pathologic stage-I LUAD. Except for ANLN and KIAA0101, the hub genes mRNA levels were higher in tumors compared with adjacent non-cancerous samples in the qPCR analysis. The hub genes protein levels were also overexpressed in tumors. In vitro experiments showed that knockdown of UBE2T in LUAD cell lines could inhibit cell proliferation and cycle progression.

Conclusions

The DEGs can probably be used as potential predictors for stage-I LUAD worse prognosis and UBE2T may be a potential tumor promoter and target for treatment.

Construction and Characterization of Long Non-Coding RNA-Associated Networks to Reveal Potential Prognostic Biomarkers in Human Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one type of the malignant tumors with high morbidity and mortality. The molecular mechanism of LUAD is still unclear. Studies demonstrate that lncRNAs play crucial roles in LUAD tumorigenesis and can be used as prognosis biomarkers. Thus, in this study, to identify more robust biomarkers of LUAD, we firstly constructed LUAD-related lncRNA-TF network and performed topological analyses for the network. Results showed that the network was a scale-free network, and some hub genes with high clinical values were identified, such as lncRNA RP11-173A16 and TF ZBTB37. Module analysis on the network revealed one close lncRNA module, which had good prognosis performance in LUAD. Furthermore, through integrating ceRNAs strategy and TF regulatory information, we identified some lncRNA-TF positive feedback loops. Prognostic analysis revealed that ELK4- and BDP1-related feedback loops were significant. Secondly, we constructed the lncRNA-m6A regulator network by merging all the high correlated lncRNA-m6A regulator pairs. Based on the network analysis results, some key m6A-related lncRNAs were identified, such as MIR497HG, FENDRR, and RP1-199J3. We also investigated the relationships between these lncRNAs and immune cell infiltration. Results showed that these m6A-related lncRNAs were high correlated with tumor immunity. All these results provide a new perspective for the diagnostic biomarker and therapeutic target identification of LUAD.