lncRNAs In Lung Adenocarcinoma Research Articles

A Novel Necroptosis-Related lncRNA Signature Predicts the Prognosis of Lung Adenocarcinoma.

Background: Necroptosis is closely related to the tumorigenesis and development of cancer. An increasing number of studies have demonstrated that targeting necroptosis could be a novel treatment strategy for cancer. However, the predictive potential of necroptosis-related long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) still needs to be clarified. This study aimed to construct a prognostic signature based on necroptosis-related lncRNAs to predict the prognosis of LUAD. Methods: We downloaded RNA sequencing data from The Cancer Genome Atlas database. Co-expression network analysis, univariate Cox regression, and least absolute shrinkage and selection operator were adopted to identify necroptosis-related prognostic lncRNAs. We constructed the predictive signature by multivariate Cox regression. Kaplan–Meier analysis, time-dependent receiver operating characteristics, nomogram, and calibration curves were used to validate and evaluate the signature. Subsequently, we used gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) to explore the relationship between the predictive signature and tumor immune microenvironment of risk groups. Finally, the correlation between the predictive signature and immune checkpoint expression of LUAD patients was also analyzed. Results: We constructed a signature composed of 7 necroptosis-related lncRNAs (AC026355.2, AC099850.3, AF131215.5, UST-AS2, ARHGAP26-AS1, FAM83A-AS1, and AC010999.2). The signature could serve as an independent predictor for LUAD patients. Compared with clinicopathological variables, the necroptosis-related lncRNA signature has a higher diagnostic efficiency, with the area under the receiver operating characteristic curve being 0.723. Meanwhile, when patients were stratified according to different clinicopathological variables, the overall survival of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the low-risk group. ssGSEA further confirmed that the predictive signature was significantly related to the immune status of LUAD patients. The immune checkpoint analysis displayed that low-risk patients had a higher immune checkpoint expression, such as CTLA-4, HAVCR2, PD-1, and TIGIT. This suggested that immunological function is more active in the low-risk group LUAD patients who might benefit from checkpoint blockade immunotherapies. Conclusion: The predictive signature can independently predict the prognosis of LUAD, helps elucidate the mechanism of necroptosis-related lncRNAs in LUAD, and provides immunotherapy guidance for patients with LUAD.

Prognostic Value of Genomic Instability of m6A-Related lncRNAs in Lung Adenocarcinoma

Background: Genomic instability of N6-methyladenosine (m6A)–related long noncoding RNAs (lncRNAs) plays a pivotal role in the tumorigenesis of lung adenocarcinoma (LUAD). Our study identified a signature of genomic instability of m6A-associated lncRNA signature and revealed its prognostic role in LUAD. Methods: We downloaded RNA-sequencing data and somatic mutation data for LUAD from The Cancer Genome Atlas (TCGA) and the GSE102287 dataset from the Gene Expression Omnibus (GEO) database. The “Limma” R package was used to identify a network of regulatory m6A-related lncRNAs. We used the Wilcoxon test method to identify genomic-instability–derived m6A-related lncRNAs. A competing endogenous RNA (ceRNA) network was constructed to identify the mechanism of the genomic instability of m6A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct a prognostic model for internal testing and validation of the prognostic m6A-related lncRNAs using the GEO dataset. Performance analysis was conducted to compare our prognostic model with the previously published lncRNA models. The CIBERSORT algorithm was used to explore the relationship of m6A-related lncRNAs and the immune microenvironment. Prognostic m6A-related lncRNAs in prognosis, the tumor microenvironment, stemness scores, and anticancer drug sensitivity were analyzed to explore the role of prognostic m6A-related lncRNAs in LUAD. Results: A total of 42 genomic instability–derived m6A-related lncRNAs were differentially expressed between the GS (genomic stable) and GU (genomic unstable) groups of LUAD patients. Four differentially expressed lncRNAs, 17 differentially expressed microRNAs, and 75 differentially expressed mRNAs were involved in the genomic-instability–derived m6A-related lncRNA-mediated ceRNA network. A prediction model based on 17 prognostic m6A-associated lncRNAs was constructed based on three TCGA datasets (all, training, and testing) and validated in the GSE102287 dataset. Performance comparison analysis showed that our prediction model (area under the curve [AUC] = 0.746) could better predict the survival of LUAD patients than the previously published lncRNA models (AUC = 0.577, AUC = 0.681). Prognostic m6A-related-lncRNAs have pivotal roles in the tumor microenvironment, stemness scores, and anticancer drug sensitivity of LUAD. Conclusion: A signature of genomic instability of m6A-associated lncRNAs to predict the survival of LUAD patients was validated. The prognostic, immune microenvironment and anticancer drug sensitivity analysis shed new light on the potential novel therapeutic targets in LUAD.

Pyroptosis-Related LncRNA Signatures Correlate With Lung Adenocarcinoma Prognosis.

BackgroundPyroptosis is a new type of programmed cell death, accompanied by an intense inflammatory response. Previous studies have shown that pyroptosis can modify long-chain non-coding RNA (lncRNA), thereby affecting the occurrence and progression of tumors. However, the underlying role of pyroptosis-related lncRNA in lung adenocarcinoma (LUAD) remains to be elucidated. Therefore, the purpose of our study was to evaluate the prognostic value of pyrolysis-related lncRNA in patients with LUAD.MethodsA total of 454 LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) database. Pearson’s correlation coefficient was used to identify the pyroptosis-related lncRNAs. Unsupervised consensus clustering was used to identify the various LUAD molecular subtypes. A least absolute shrinkage and selection operator (LASSO) analysis was conducted to construct a prognostic signature.ResultsAn 11-lncRNA prognostic signature out of 19 identified pyroptosis-related prognostic lncRNAs was constructed. The patients with LUAD were divided into low-risk and high-risk groups. Patients in the high-risk group had higher score values and mortality. The immune score, stromal score, and estimate score were lower in the high-risk group. The risk score was an independent predictor for OS in multivariate Cox regression analyses (HR > 1, p < 0.01). BTLA, PD-1, PD-L1, CTLA, and CD47 were lower expressed in the high-risk group.ConclusionsOur study identified an 11-pyroptosis-related lncRNA signature. These findings could further clarify the role of pyroptosis in LUAD and guide the prognosis and individualized treatment of patients.

Identification and Validation of 7-lncRNA Signature of Epigenetic Disorders by Comprehensive Epigenetic Analysis.

The survival rate of patients with lung adenocarcinoma (LUAD) is low. This study analyzed the correlation between the expression of long noncoding RNA (lncRNA) and epigenetic alterations along with the investigation of the prognostic value of these outcomes for LUAD. Differentially expressed lncRNAs were identified based on multiomic data and positively related genes using DESeq2 in R, differentially histone-modifying genes specific to LUAD based on histone modification data, gene enhancers from information collected from the FANTOM5 (Function Annotation Of The Mammalian Genome-5) (fantom.gsc.riken.jp/5) human enhancer database, gene promoters using the ChIPseeker and the human lincRNAs Transcripts database in R, and differentially methylated regions (DMRs) using Bumphunter in R. Overall survival was estimated by Kaplan-Meier, comparisons were performed among groups using log-rank tests to derive differences between sample subclasses, and epigenetic lncRNAs (epi-lncRNAs) potentially relevant to LUAD prognosis were identified. A total of seven dysregulated epi-lncRNAs in LUAD were identified by comparing histone modifications and alterations in histone methylation regions on lncRNA promoter and enhancer elements, including H3K4me2, H3K27me3, H3K4me1, H3K9me3, H4K20me1, H3K9ac, H3K79me2, H3K27ac, H3K4me3, and H3K36me3. Furthermore, 69 LUAD-specific dysregulated epi-lncRNAs were identified. Moreover, lncRNAs-based prognostic analysis of LUAD samples was performed and explored that seven of these lncRNAs, including A2M-AS1, AL161431.1, DDX11-AS1, FAM83A-AS1, MHENCR, MNX1-AS1, and NKILA (7-EpiLncRNA), showed the potential to serve as markers for LUAD prognosis. Additionally, patients having a high 7-EpiLncRNA score showed a generally more unfavorable prognosis compared with those which scored lower. Seven lncRNAs were identified as markers of prognosis in patients with LUAD. The outcomes of this research will help us understand epigenetically aberrant regulation of lncRNA expression in LUAD in a better way and have implications for research advances in the regulatory role of lncRNAs in LUAD.

Identification of LHFPL3-AS2 as a prognostic biomarker in lung adenocarcinoma.

Deregulation of long noncoding RNAs (lncRNAs) was considered one of the main characteristics of several human cancers. However, detailed genome-wide expression and functional significance studies of lnc RNAs in lung adenocarcinoma are still limited. This study aims to discover a new lncRNA that may play an important role in regulating the pathogenesis of lung adenocarcinoma (ADC). We conducted a comprehensive analysis of three Gene Expression Omnibus (GEO) microarray datasets and TCGA datasets. Differentially expressed lncRNAs between ADC and normal tissues were screened and verified using Gene Expression Profiling Interactive Analysis (GEPIA). Moreover, Kaplan-Meier plotter was used to construct the gene prognosis profile. The downstream targets of miRNA and related functional pathways were predicted and validated. Results: With microarray gene expression analysis, we found that only lncRNAs-PCAT6 was commonly upregulated among four datasets, and four lncRNAs (LINC00968, PGM5-AS1, LHFPL3-AS2 and SFTA1P) were significantly downregulated in the ADC samples as compared to the normal tissues. Meanwhile, for LHFPL3-AS2, high-risk patients showed better overall survival (HR=0.6 or 0.62; P < .0001 or P = 0.0014), overall survival from TCGA datasets (HR=0.72; P = 0.015) and recurrence-free survival (HR=0.72; P = 0.015). Then, LHFPL3-AS2 was predicted to bind to two miRNAs, miR-127-5p and miR-424-5p. Finally, validation and functional enrichment analysis of the downstream key mRNAs showed significant enrichment in some cancer-related pathways, such as cell adhesion in cancer and small cell lung cancer. Taken together, our study indicated that LHFPL3-AS2 was associated with tumorigenesis, and it could be used as a useful biomarker in the diagnosis, prognosis and treatment of ADC.

Understanding the Critical Role of Glycolysis-Related lncRNAs in Lung Adenocarcinoma Based on Three Molecular Subtypes.

Background Glycolysis is closely associated with tumor progression, but the roles of lncRNAs in glycolysis have not been comprehensively investigated in lung adenocarcinoma (LUAD). This study is aimed at studying the possible mechanisms of glycolysis-related lncRNAs in tumor development and providing a guidance for targeted therapy. Methods Unsupervised consensus clustering was used to identify molecular subtypes. Gene enrichment analysis was applied to screen important pathways involved in tumor progression. A series of immune analysis was performed to assess immune infiltration. Critical transcription factors (TFs) interacting with lncRNAs were selected by Pearson correlation analysis. A first-order partial correlation analysis was implemented to identify critical lncRNAs with prognostic significance. Results Three molecular subtypes (C1, C2, and C3) were identified with distinct overall survival. Three subtypes showed differential immune infiltration, and C3 subtype was the optimal for immunotherapy treatment. Ten lncRNA-TF pairs among four glycolysis-related lncRNAs (FTX, LINC00472, PSMA3-AS1, and SNHG14) and six TFs (FOXP1, SP1, MYC, FOXM1, HIF1A, and FOS) were involved in tumor progression. We identified four critical glycolysis-related lncRNAs significantly associated with prognosis. Conclusions This study identified three molecular subtypes that could guide personalized therapy. The four-lncRNA prognostic model can serve as an indicator for predicting prognosis or early screening of lung adenocarcinoma patients. The current results improve the understanding of the relation between lncRNAs and glycolysis.

Construction of a Ferroptosis-Related Long Non-coding RNA Prognostic Signature and Competing Endogenous RNA Network in Lung Adenocarcinoma.

Ferroptosis-related genes play an important role in the progression of lung adenocarcinoma (LUAD). However, the potential function of ferroptosis-related lncRNAs in LUAD has not been fully elucidated. Thus, to explore the potential role of ferroptosis-related lncRNAs in LUAD, the transcriptome RNA-seq data and corresponding clinical data of LUAD were downloaded from the TCGA dataset. Pearson correlation was used to mine ferroptosis-related lncRNAs. Differential expression and univariate Cox analysis were performed to screen prognosis related lncRNAs. A ferroptosis-related lncRNA prognostic signature (FLPS), which included six ferroptosis-related lncRNAs, was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression. Patients were divided into a high risk-score group and low risk-score group by the median risk score. Receiver operating characteristic (ROC) curves, principal component analysis (PCA), and univariate and multivariate Cox regression were performed to confirm the validity of FLPS. Enrichment analysis showed that the biological processes, pathways and markers associated with malignant tumors were more common in high-risk subgroups. There were significant differences in immune microenvironment and immune cells between high- and low-risk groups. Then, a nomogram was constructed. We further investigated the relationship between six ferroptosis-related lncRNAs and tumor microenvironment and tumor stemness. A competing endogenous RNA (ceRNA) network was established based on the six ferroptosis-related lncRNAs. Finally, we detected the expression levels of ferroptosis-related lncRNAs in clinical samples through quantitative real-time polymerase chain reaction assay (qRT-PCR). In conclusion, we identified the prognostic ferroptosis-related lncRNAs in LUAD and constructed a prognostic signature which provided a new strategy for the evaluation and prediction of prognosis in LUAD.

Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer

BackgroundIncreasing amount of long non-coding RNAs (lncRNAs) have been found involving in many biological processes and played salient roles in cancers. However, up until recently, functions of most lncRNAs in lung cancer have not been fully discovered, particularly in the co-regulated lncRNAs. Thus, this study aims to investigate roles of lncRNA modules and uncover a module-based biomarker in lung adenocarcinoma (LUAD).ResultsWe used gene expression profiles from The Cancer Genome Atlas (TCGA) to construct the lncRNA association networks, from which the highly-associated lncRNAs are connected as modules. It was found that the expression of some modules is significantly associated with patient’s survival, including module N1 (HR = 0.62, 95% CI = 0.46–0.84, p = 0.00189); N2 (HR = 0.68, CI = 0.50–0.93, p = 0.00159); N4 (HR = 0.70, CI = 0.52–0.95, p = 0.0205) and P3 (HR = 0.68, CI = 0.50–0.92, p = 0.0123). The lncRNA signature consisting of these four prognosis-related modules, a 4-modular lncRNA signature, is associated with favourable prognosis in TCGA-LUAD (HR = 0.51, CI = 0.37–0.69, p value = 2.00e−05). Afterwards, to assess the performance of the generic modular signature as a prognostic biomarker, we computed the time-dependent area under the receiver operating characteristics (AUC) of this 4-modular lncRNA signature, which showed AUC equals 68.44% on 336th day. In terms of biological functions, these modules are correlated with several cancer hallmarks and pathways, including Myc targets, E2F targets, cell cycle, inflammation/immunity-related pathways, androgen/oestrogen response, KRAS signalling, DNA repair and epithelial-mesenchymal transition (EMT).ConclusionTaken together, we identified four novel LUAD prognosis-related lncRNA modules, and assessed the performance of the 4-modular lncRNA signature being a prognostic biomarker. Functionally speaking, these modules involve in oncogenic hallmarks as well as pathways. The results unveiled the co-regulated lncRNAs in LUAD and may provide a framework for further lncRNA studies in lung cancer.

A Prognostic Signature of Glycolysis-Related Long Noncoding RNAs for Molecular Subtypes in the Tumor Immune Microenvironment of Lung Adenocarcinoma.

PurposeLong noncoding RNAs (lncRNAs) and glycolysis regulate multiple types of cancer. However, the prognostic roles and biological functions of glycolysis-related lncRNAs in lung adenocarcinoma (LUAD) remain unclear. In this study, we investigated the role of glycolysis-related lncRNAs in LUAD.Patients and MethodsWe retrieved glycolysis-related genes from the Molecular Signatures Database and screened for prognostic glycolysis-related lncRNAs from The Cancer Genome Atlas.ResultsWe identified three LUAD subtypes (clusters 1–3) by univariate Cox regression analysis and consensus clustering. Patients in cluster 1 had the best overall survival rates. Immune, stromal, and cytolytic-activity scores were the highest in cluster 1. The expression of immune checkpoint molecules (programmed cell death protein 1 and cytotoxic T-lymphocyte-associated protein 4) and other immune-related indicators was the highest in cluster 1, whereas that of epithelial cell biomarkers (Cadherin 1, Cadherin 2, and MET) was the lowest. Therefore, patients in cluster 1 may benefit from immunotherapy. Lasso–Cox regression and multivariate Cox regression analyses were used to select nine lncRNAs to build a robust prognostic model of LUAD. The area under the curve classifier values and a nomogram performed well in predicting survival times for patients with LUAD. The expression levels of nine lncRNAs were validated by quantitative reverse transcriptase-polymerase chain reaction analysis, and most of these lncRNAs were significantly related to immune-related mRNAs. Gene set enrichment analysis revealed that the high-risk group was enriched for cell cycle-related pathways and the low-risk group was enriched for pathways associated with immunity or immune-related diseases.ConclusionThe LUAD subtypes and prognostic model developed here may help in clinical risk stratification, prognosis management, and treatment decisions for patients with LUAD.

Identification of Tumor Microenvironment-Related Prognostic lncRNAs in Lung Adenocarcinoma.

BackgroundLung adenocarcinoma (LUAD) is the most common type of lung cancer and is a severe threat to human health. Although many therapies have been applied to LUAD, the long-term survival rate of patients remains unsatisfactory. We aim to find reliable immune microenvironment-related lncRNA biomarkers to improve LUAD prognosis.MethodsESTIMATE analysis was performed to evaluate the degree of immune infiltration of each patient in TAGA LUAD cohort. Correlation analysis was used to identify the immune microenvironment-related lncRNAs. Univariate cox regression analysis, LASSO analysis, and Kaplan Meier analysis were used to construct and validate the prognostic model based on microenvironment-related lncRNAs.ResultsWe obtained 1,178 immune microenvironment-related lncRNAs after correlation analysis. One hundred and eighty of them are independent prognostic lncRNAs. Sixteen key lncRNAs were selected by LASSO method. This lncRNA-based model successfully predicted patients’ prognosis in validation cohort, and the risk score was related to pathological stage. Besides, we also found that TP53 had the highest frequency mutation in LUAD, and the mutation of TP53 in the high-risk group, which was identified by our survival model, has a poor prognosis. lncRNA-mRNA co-expression network further suggested that these lncRNAs play a vital role in the prognosis of LUAD.ConclusionHere, we filtered 16 key lncRNAs, which could predict the survival of LUAD and may be potential biomarkers and therapeutic targets.

Multi-Omics Integrative Bioinformatics Analyses Reveal Long Non-coding RNA Modulates Genomic Integrity via Competing Endogenous RNA Mechanism and Serves as Novel Biomarkers for Overall Survival in Lung Adenocarcinoma.

Long non-coding RNA (lncRNA) plays a crucial role in modulating genome instability, immune characteristics, and cancer progression, within which genome instability was identified as a critical regulator in tumorigenesis and tumor progression. However, the existing accounts fail to detail the regulatory role of genome instability in lung adenocarcinoma (LUAD). We explored the clinical value of genome instability-related lncRNA in LUAD with multi-omics bioinformatics analysis. We extracted the key genome instability-related and LUAD-related gene modules using weighted gene co-expression network analysis (WGCNA) and established a competing endogenous RNA (ceRNA) network using four lncRNAs (LINC01224, LINC00346, TRPM2-AS, and CASC9) and seven target mRNAs (CCNF, PKMYT1, GCH1, TK1, PSAT1, ADAM33, and DDX11). We found that LINC01224 is primarily located in the cytoplasm and that LINC00346 and TRPM2-AS are primarily located in the nucleus (CASC9 unknown). We found that all 11 genes were positively related to tumor mutational burden and involve drug resistance, cancer stemness, and tumor microenvironment infiltration. Additionally, an eight-lncRNA genome instability-related lncRNA signature was established and validated, predicting the overall survival and immunotherapy outcomes in LUAD. To conclude, we discovered that sponging microRNA, genome instability-related lncRNA functions as ceRNA, modulating genomic integrity. This research provides clinical references for LUAD immunotherapy and prognosis and interprets a potential genome instability-related ceRNA regulatory network in which LINC01224-miR-485-5p/miR-29c-3p-CCNF-RRM2 and LINC01224-miR485-5p-PKMYT1-CDK1 axes were the most promising pathways. However, the potential mechanisms underlying our findings still need biological validation through in vitro and in vivo experiments.

Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma.

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

Identification of a Ferroptosis-Related LncRNA Signature as a Novel Prognosis Model for Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic.

LncRNA LINC00680 promotes lung adenocarcinoma growth via binding to GATA6 and canceling GATA6-mediated suppression of SOX12 expression

Lung adenocarcinoma (LUAD) is a major subtype of non-small-cell lung cancers (NSCLC). LINC00680 has been characterized as a novel oncogenic lncRNA in LUAD, but its regulatory mechanisms remain largely unclear. This study aimed to explore the subcellular localization of LINC00680 in LUAD and its regulation on the transcriptional process. LUAD cell lines (A549, H1650, and H1299) were used for in vitro and in vivo studies. Results showed LINC00680 depletion resulted in G0/G1 phase arrest of LUAD cells and reduced CDK4 and cyclin D1 expression in H1650 and H1299 cells. LINC00680 overexpression promoted A549 cell proliferation and increased CDK4 and cyclin D1 expression. RNA-fluorescence in situ hybridization (FISH) assay showed that LINC00680 has both cytoplasmic and nuclear distribution in LUAD cells. RNA pulldown and western blotting assays confirmed a physical interaction between LINC00680 and GATA6. In LUAD cells, GATA6 overexpression only slightly suppressed SOX12 transcription. ChIP-qPCR and dual-luciferase assay showed that GATA6 only weakly bound to the SOX12 promoter and decreased its activity. However, when LINC00680 was depleted, these transcriptional suppressive effects were significantly enhanced. These findings suggested that LINC00680 forms a complex with GATA6 and weakens its transcriptional suppressive effect on SOX12 expression. In the nude mice model, LINC00680 overexpression partly abrogated the growth-suppressive effects of GATA6 on A549 derived tumors. In summary, this study revealed a novel LINC00680-GATA6-SOX12 axis in promoting LUAD cell cycle progression and proliferation. Future studies should be conducted for a better understanding of the complex networking of LINC00680 in LUAD.

A novel autophagy-related lncRNA survival model for lung adenocarcinoma.

Long non‐coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy‐related lncRNA in lung adenocarcinoma. In this study, autophagy‐related mRNAs‐lncRNAs were screened from lung adenocarcinoma and a co‐expression network of autophagy‐related mRNAs‐lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy‐related lncRNAs and finally obtained a survival model composed of 11 autophagy‐related lncRNAs. Through Kaplan‐Meier analysis, univariate and multivariate Cox regression analysis and time‐dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low‐risk and high‐risk groups. These 11 lncRNAs were GAS6‐AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA‐DQB1‐AS1, LINC01116, LINC01806, FAM83A‐AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196‐1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149‐1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy‐related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy‐related therapeutic targets.

Long non-coding RNA LEISA promotes progression of lung adenocarcinoma via enhancing interaction between STAT3 and IL-6 promoter.

Long non-coding RNAs (lncRNAs) are emerging as a new class of regulators for a variety of biological processes and have been suggested to play pivotal roles in cancer development and progression. Our current study found that a lncRNA, designated enhancing IL-6/STAT3 signaling activation (LEISA, ENST00000603468), functioned as an oncogenic lncRNA in lung adenocarcinoma (LAD), a major form of non-small cell lung carcinoma, which is one of the most frequently diagnosed malignancies with high morbidity and mortality worldwide, and was involved in the regulation of STAT3 induced IL-6 transcription. Our data showed that LEISA was highly expressed in, and correlated with the clinical progression and prognosis of LAD. Ectopic expression of LEISA promoted the proliferation and suppressed apoptosis of LAD cells in vitro and in vivo. Mechanistically, we demonstrated that LEISA recruited STAT3 to bind the promoter of IL-6 and upregulated IL-6 expression. Taken together, our work identifies LEISA as a potential diagnostic biomarker and therapeutic target for LAD.

ITGB1-DT Facilitates Lung Adenocarcinoma Progression via Forming a Positive Feedback Loop With ITGB1/Wnt/β-Catenin/MYC.

Lung adenocarcinoma (LUAD) is the main histological type of lung cancer, which is the leading cause of cancer-related deaths. Long non-coding RNAs (lncRNAs) were recently revealed to be involved in various cancers. However, the clinical relevance and potential biological roles of most lncRNAs in LUAD remain unclear. Here, we identified a prognosis-related lncRNA ITGB1-DT in LUAD. ITGB1-DT was upregulated in LUAD and high expression of ITGB1-DT was correlated with advanced clinical stages and poor overall survival and disease-free survival. Enhanced expression of ITGB1-DT facilitated LUAD cellular proliferation, migration, and invasion, and also lung metastasis in vivo. Knockdown of ITGB1-DT repressed LUAD cellular proliferation, migration, and invasion. ITGB1-DT interacted with EZH2, repressed the binding of EZH2 to ITGB1 promoter, reduced H3K27me3 levels at ITGB1 promoter region, and therefore activated ITGB1 expression. Through upregulating ITGB1, ITGB1-DT activated Wnt/β-catenin pathway and its downstream target MYC in LUAD. The expressions of ITGB1-DT, ITGB1, and MYC were positively correlated with each other in LUAD tissues. Intriguingly, ITGB1-DT was found as a transcriptional target of MYC. MYC directly transcriptionally activated ITGB1-DT expression. Thus, ITGB1-DT formed a positive feedback loop with ITGB1/Wnt/β-catenin/MYC. The oncogenic roles of ITGB1-DT were reversed by depletion of ITGB1 or inhibition of Wnt/β-catenin pathway. In summary, these findings revealed ITGB1-DT as a prognosis-related and oncogenic lncRNA in LUAD via activating the ITGB1-DT/ITGB1/Wnt/β-catenin/MYC positive feedback loop. These results implicated ITGB1-DT as a potential prognostic biomarker and therapeutic target for LUAD.

Deregulation of a Cis-Acting lncRNA in Non-small Cell Lung Cancer May Control HMGA1 Expression.

BackgroundLong non-coding RNAs (lncRNAs) have long been implicated in cancer-associated phenotypes. Recently, a class of lncRNAs, known as cis-acting, have been shown to regulate the expression of neighboring protein-coding genes and may represent undiscovered therapeutic action points. The chromatin architecture modification gene HMGA1 has recently been described to be aberrantly expressed in lung adenocarcinoma (LUAD). However, the mechanisms mediating the expression of HMGA1 in LUAD remain unknown. Here we investigate the deregulation of a putative cis-acting lncRNA in LUAD, and its effect on the oncogene HMGA1.MethodsLncRNA expression was determined from RNA-sequencing data of tumor and matched non-malignant tissues from 36 LUAD patients. Transcripts with significantly deregulated expression were identified and validated in a secondary LUAD RNA-seq dataset (TCGA). SiRNA-mediated knockdown of a candidate cis-acting lncRNA was performed in BEAS-2B cells. Quantitative real-time PCR was used to observe the effects of lncRNA knockdown on the expression of HMGA1.ResultsWe identified the lncRNA RP11.513I15.6, which we refer to as HMGA1-lnc, neighboring HMGA1 to be significantly downregulated in both LUAD cohorts. Conversely, we found HMGA1 significantly overexpressed in LUAD and anticorrelated with HMGA1-lnc. In vitro experiments demonstrated siRNA-mediated inhibition of HMGA1-lnc in immortalized non-malignant lung epithelial cells resulted in a significant increase in HMGA1 gene expression.ConclusionOur results suggest that HMGA1-lnc is a novel cis-acting lncRNA that negatively regulates HMGA1 gene expression in lung cells. Further characterization of this regulatory mechanism may advance our understanding of the maintenance of lung cancer phenotypes and uncover a novel therapeutic intervention point for tumors driven by HMGA1.

LINC00942 Promotes Tumor Proliferation and Metastasis in Lung Adenocarcinoma via FZD1 Upregulation.

Background:Long non-coding RNAs (lncRNAs) have been reported to play important roles in the progression of human cancers. Herein, bioinformatic analysis identified that LINC00942 was a highly overexpressed lncRNA in lung adenocarcinoma (LUAD). The present study aimed to explore the roles and possible molecular mechanisms of LINC00942 in LUAD.Methods:First, on the basis of TCGA database, the expression and prognosis of LINC00942 were analyzed in LUAD tissues. Then, si-LINC00942 was transfected into A549 and H1299 cells to knockdown the expression of LINC00942. Cell viability was detected by MTT assay. Flow cytometry was used to analyze cell apoptosis. The expressions of PCNA, Bax, Bcl-2, and wnt/β-catenin pathway proteins were detected by western blotting. Dual-luciferase reporter assay was used to evaluate the regulatory relationship between LINC00942 and miR-5006-5p, or miR-5006-5p and FZD1.Results:We discovered that LINC00942 was up-regulated in LUAD tissues compared with adjacent tissues. Besides, we found the increased LINC00942 expression was associated with poor survival. In addition, silencing of LINC00942 suppressed the proliferation, migration, invasion and facilitated the apoptosis of A549 and H1299 cells. Moreover, silencing of LINC00942 repressed the expression of PCNA, Bcl-2, and enhanced Bax expression in A549 and H1299 cells. Mechanically, LINC00942 exerted its effects via enhancing Wnt signaling. LINC00942 functioned as competing endogenous RNA (ceRNA) by binding to miR-5006-5p, upregulating the expression of FZD1, which was a direct target of miR-5006-5p.Conclusion:Our findings indicated that LINC00942/miR-5006-5p/FZD1 axis played important roles in LUAD growth through enhancing Wnt signaling. LINC00942/miR-5006-5p/FZD1 axis might serve as a potential biomarker and therapeutic target for LUAD treatment.

Identification of Potential Long Non-coding RNA Expression Quantitative Trait Methylations in Lung Adenocarcinoma and Lung Squamous Carcinoma.

There are associations between DNA methylation and the expression of long non-coding RNA (lncRNA), also known as lncRNA expression quantitative trait methylations (lnc-eQTMs). Lnc-eQTMs may induce a wide range of carcinogenesis pathways. However, lnc-eQTMs have not been globally identified and studied, and their roles in lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) are largely unknown. In the present study, we identified some differential methylation sites located in genes of long intergenic non-coding RNAs (lincRNAs) and other types of lncRNAs in LUAD and LUSC. An integrated pipeline was established to construct two global cancer-specific regulatory networks of lnc-eQTMs in LUAD and LUSC. The associations between eQTMs showed common and specific features between LUAD and LUSC. Some lnc-eQTMs were also related with survival in LUAD- and LUSC-specific regulatory networks. Lnc-eQTMs were associated with cancer-related functions, such as lung epithelium development and vasculogenesis by functional analysis. Drug repurposing analysis revealed that these lnc-eQTMs may mediate the effects of some anesthesia-related drugs in LUAD and LUSC. In summary, the present study elucidates the roles of lnc-eQTMs in LUAD and LUSC, which could improve our understanding of lung cancer pathogenesis and facilitate treatment.