Identification of lncRNA biomarkers in lung squamous cell carcinoma using comprehensive analysis of lncRNA mediated ceRNA network

Unfurling the functional association between long intergenic noncoding RNAs (lincRNAs) and HPV16-related cervical cancer pathogenesis through weighted gene co-expression network analysis of differentially expressed lincRNAs and coding genes

Long intergenic noncoding RNAs (lincRNAs) do not overlap annotated coding genes and are located in intergenic regions, as opposed to antisense and sense-intronic lncRNAs, located in genic regions. LincRNAs influence gene expression profiles and are thereby key to disease pathogenesis. In this study, we assessed the association between lincRNAs and HPV16-positive cervical cancer (CaCx) pathogenesis using weighted gene co-expression network analysis (WGCNA) with coding genes, comparing differentially expressed lincRNA and coding genes (DElincGs and DEcGs, respectively) in HPV16-positive patients with CaCx (n = 44) with those in HPV-negative healthy individuals (n = 34). Our analysis revealed five DElincG modules, co-expressing and correlating with DEcGs. We validated a substantial number of such module-specific correlations in the HPV16-positive cancer TCGA-CESC dataset. Four such modules, displayed significant correlations with patient traits, such as HPV16 physical status, lymph node involvement and overall survival (OS), highlighting a collaborative effect of all genes within specific modules on traits. Using the DAVID bioinformatics knowledgebase, we identified the underlying biological processes associated with these modules as cancer development and progression-associated pathways. Next, we identified the top 10 DElincGs with the highest connectivity within each functional module. Focusing on the prognostic module hub genes, downregulated CTD-2619J13.13 expression was associated with poor patient OS. This lincRNA gene interacted with 25 coding genes of its module and was associated with such biological processes as keratinization loss and keratinocyte differentiation, reflecting severe disease phenotypes. This study has translational relevance in fighting various cancers with high mortality rates in underdeveloped countries.

LINC01232 promotes lung squamous cell carcinoma progression through modulating miR-181a-5p/SMAD2 axis

BACKGROUND

LINC01232 has been implicated in the progression of multiple malignancies. Yet, the function of LINC01232 in the carcinogenesis of lung squamous cell carcinoma (LUSC) remains unclear. This study aims to examine the role LINC01232 plays in LUSC progression.

METHODS

mRNA and protein levels were assessed using qRT-PCR and western blot, respectively. Cell proliferation was assessed by CCK-8 and colony formation assays. Cell migration and invasion were evaluated by transwell assay. The interactions between LINC01232, miR-181a-5p, and SMAD2 were assessed using luciferase reporter, RNA pull-down, and RNA immunoprecipitation (RIP) assays. The subcellular distribution of LINC01232 was examined by cytosolic/nuclear fractionation assay RESULTS: LINC01232 was upregulated in both LUSC tissues and cell lines. Knockdown of LINC01232 impaired cell proliferation, migration and invasion capability in H1229 and A549 cells, a phenotype that could be reversed by miR-181a-5p silencing. In addition, LINC01232 silencing reduced levels of N-cadherin, Vimentin, and Snail in H1229 and A549 cells, but increased the level of E-cadherin, which can be abrogated by miR-181a-5p inhibitors.

CONCLUSIONS

In summary, our study demonstrates that LINC01232 expression increases in LUSC tissues and cell lines and promotes LUSC progression by modulating the miR-181a-5p/SMAD2 signaling, providing new potential drug targets for LUSC treatment.

LncRNA PITPNA-AS1/miR-223-3p/PTN axis regulates malignant progression and stemness in lung squamous cell carcinoma

Background

Long noncoding RNAs (lncRNAs) are a kind of molecule that cannot code proteins, and their expression is dysregulated in diversified cancers. LncRNA PITPNA‐AS1 has been shown to act as a tumor promoter in a variety of malignancies, but its function and regulatory mechanisms in lung squamous cell carcinoma (LUSC) are yet unknown.

Methods

The mRNA and protein expression of genes were examined by RT‐qPCR, western blot, and IHC assay. The cell proliferation, migration, invasion, and stemness were detected through CCK‐8, colony formation, Transwell and spheroid formation assays. The CD44⁺ and CD166⁺‐positive cells were detected through flow cytometry. The binding ability among genes through luciferase reporter and RNA pull‐down assays. The tumor growth was detected through in vivo nude mice assay.

Results

The lncRNA PITPNA‐AS1 had increased expression in LUSC and was linked to a poor prognosis. In LUSC, PITPNA‐AS1 also enhanced cell proliferation, migration, invasion, and stemness. This mechanistic investigation showed that PITPNA‐AS1 absorbed miR‐223‐3p and that miR‐223‐3p targeted PTN. MiR‐223‐3p inhibition or PTN overexpression might reverse the inhibitory effects of PITPNA‐AS1 suppression on LUSC progression, as demonstrated by rescue experiments. In addition, the PITPNA‐AS1/miR‐223‐3p/PTN axis accelerated tumor development in vivo.

Conclusions

It is the first time we investigated the potential role and ceRNA regulatory mechanism of PITPNA‐AS1 in LUSC. The data disclosed that PITPNA‐AS1 upregulated PTN through sponging miR‐223‐3p to enhance the onset and progression of LUSC. These findings suggested the ceRNA axis may serve as a promising therapeutic biomarker for LUSC patients.

SPINNAKER: an R-based tool to highlight key RNA interactions in complex biological networks

Background

Recently, we developed a mathematical model for identifying putative competing endogenous RNA (ceRNA) interactions. This methodology has aroused a broad acknowledgment within the scientific community thanks to the encouraging results achieved when applied to breast invasive carcinoma, leading to the identification of PVT1, a long non-coding RNA functioning as ceRNA for the miR-200 family. The main shortcoming of the model is that it is no freely available and implemented in MATLAB®, a proprietary programming platform requiring a paid license for installing, operating, manipulating, and running the software.

Results

Breaking through these model limitations demands to distribute it in an open-source, freely accessible environment, such as R, designed for an ordinary audience of users that are not able to afford a proprietary solution. Here, we present SPINNAKER (SPongeINteractionNetworkmAKER), the open-source version of our widely established mathematical model for predicting ceRNAs crosstalk, that is released as an exhaustive collection of R functions. SPINNAKER has been even designed for providing many additional features that facilitate its usability, make it more efficient in terms of further implementation and extension, and less intense in terms of computational execution time.

Conclusions

SPINNAKER source code is freely available at https://github.com/sportingCode/SPINNAKER.git together with a thoroughgoing PPT-based guideline. In order to help users get the key points more conveniently, also a practical R-styled plain-text guideline is provided. Finally, a short movie is available to help the user to set the own directory, properly.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04695-x.

Autophagy Induced by BCL2-Related ceRNA Network Participates in the Occurrence of COPD

Purpose

Chronic obstructive pulmonary disease (COPD) is a predominant cause of mortality worldwide. Autophagy, which depends on a lysosomal degradation pathway, plays an essential role in the occurrence of COPD. The aim of our study was to identify the potential function of autophagy and construct a BCL2-related competing endogenous RNA (ceRNA) network that induces autophagy in COPD.

Methods

Blood sample data from GSE31568, GSE24709, and GSE61741 were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs in COPD and controls were identified via GEO2R. Transcription factors were obtained from FunRich. DIANA, miRDB, miRTarBase, and TargetScan were used to predict target genes of miRNAs. Autophagy genes were collected from the Human Autophagy Database (HADb). The GSE151052 dataset was used to identify autophagy-related differentially expressed genes in tissues. Functional enrichment and protein–protein interaction (PPI) network analyses were conducted via Metascape and the STRING network. Spearman correlation analysis was used to analyze the relationship between autophagy-related differentially expressed genes and lung function. The BCL2-related ceRNA network was modeled by Cytoscape.

Results

We obtained 41 differentially expressed miRNAs and 10 significantly different transcription factors. We identified 19 autophagy-related differentially expressed genes that were significantly different (P<0.05) in tissue samples. The most significant enrichment in Metascape was an autophagy item, which further confirmed autophagy participation in the occurrence of COPD. PPI network analysis found four genes (BCL2, BECN1, MAPK8, and ITPR1), among which BCL2 was correlated with both FEV1/FVC and FEV1 prediction. Finally, the BCL2-related ceRNA network was constructed to clarify the interaction of RNAs and occurrence of autophagy, including 18 miRNAs and 65 lncRNAs.

Conclusion

We identified 19 autophagy-related differentially expressed genes that participated in COPD; among them, BCL2 was correlated with lung function, and a BCL2-related ceRNA network was constructed, which further revealed the potential mechanism of autophagy involvement in COPD.

Epigenetic Regulation in Exposome-Induced Tumorigenesis: Emerging Roles of ncRNAs

Environmental factors, including pollutants and lifestyle, constitute a significant role in severe, chronic pathologies with an essential societal, economic burden. The measurement of all environmental exposures and assessing their correlation with effects on individual health is defined as the exposome, which interacts with our unique characteristics such as genetics, physiology, and epigenetics. Epigenetics investigates modifications in the expression of genes that do not depend on the underlying DNA sequence. Some studies have confirmed that environmental factors may promote disease in individuals or subsequent progeny through epigenetic alterations. Variations in the epigenetic machinery cause a spectrum of different disorders since these mechanisms are more sensitive to the environment than the genome, due to the inherent reversible nature of the epigenetic landscape. Several epigenetic mechanisms, including modifications in DNA (e.g., methylation), histones, and noncoding RNAs can change genome expression under the exogenous influence. Notably, the role of long noncoding RNAs in epigenetic processes has not been well explored in the context of exposome-induced tumorigenesis. In the present review, our scope is to provide relevant evidence indicating that epigenetic alterations mediate those detrimental effects caused by exposure to environmental toxicants, focusing mainly on a multi-step regulation by diverse noncoding RNAs subtypes.

Comprehensive analysis of lncRNA biomarkers in kidney renal clear cell carcinoma by lncRNA-mediated ceRNA network

Introduction

Kidney renal clear cell carcinoma (KIRC) has a high incidence globally, and its pathogenesis remains unclear. Long non-coding RNA (lncRNA), as a molecular sponge, participates in the regulation of competitive endogenous RNA (ceRNA). We aimed to construct a ceRNA network and screened out possible lncRNAs to predict KIRC prognosis.

Material and methods

All KIRC data were downloaded from the TCGA database and screened to find the possible target lncRNA; a ceRNA network was designed. Next, GO functional enrichment and KEGG pathway of differentially expressed mRNA related to lncRNA were performed. We used Kaplan-Meier curve analysis to predict the survival of these RNAs. We used Cox regression analysis to construct a model to predict KIRC prognosis.

Results

In the KIRC datasets, 1457 lncRNA, 54 miRNA and 2307 mRNA were screened out. The constructed ceRNA network contained 81 lncRNAs, nine miRNAs, and 17 mRNAs differentially expressed in KIRC. Survival analysis of all differentially expressed RNAs showed that 21 lncRNAs, four miRNAs, and two mRNAs were related to the overall survival rate. Cox regression analysis was performed again, and we found that eight lncRNAs were related to prognosis and used to construct predictive models. Three lnRNAs from independent samples were meaningful.

Conclusion

The construction of ceRNA network was involved in the process and transfer of KIRC, and three lncRNAs may be potential targets for predicting KIRC prognosis.

Combined Analysis of RNA Sequence and Microarray Data Reveals a Competing Endogenous RNA Network as Novel Prognostic Markers in Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a highly aggressive cancer with short survival time. Unbalanced competing endogenous RNAs (ceRNAs) have been shown to participate in the tumor pathogenesis and served as biomarkers for the clinical prognosis. However, the comprehensive analyses of the ceRNA network in the prognosis of MPM are still rarely reported. In this study, we obtained the transcriptome data of the MPM and the normal samples from TCGA, EGA, and GEO databases and identified the differentially expressed (DE) mRNAs, lncRNAs, and miRNAs. The functions of the prognostic genes and the overlapped DEmRNAs were further annotated by the multiple enrichment analyses. Then, the targeting relationships among lncRNA–miRNA and miRNA–mRNA were predicted and calculated, and a prognostic ceRNA regulatory network was established. We included the prognostic 73 mRNAs and 13 miRNAs and 26 lncRNAs into the ceRNA network. Moreover, 33 mRNAs, three miRNAs, and seven lncRNAs were finally associated with prognosis, and a model including seven mRNAs, two lincRNAs, and some clinical factors was finally established and validated by two independent cohorts, where CDK6 and SGMS1-AS1 were significant to be independent prognostic factors. In addition, the identified co-expressed modules associated with the prognosis were overrepresented in the ceRNA network. Multiple enrichment analyses showed the important roles of the extracellular matrix components and cell division dysfunction in the invasion of MPM potentially. In summary, the prognostic ceRNA network of MPM was established and analyzed for the first time and these findings shed light on the function of ceRNAs and revealed the potential prognostic and therapeutic biomarkers of MPM.

The role of M6A modification in the regulation of tumor-related lncRNAs

N⁶-methyladenosine (m⁶A) is the most abundant modification in eukaryotic cells, and it regulates RNA transcription, processing, splicing, degradation, and translation. Long non-coding RNAs (lncRNAs), as transcriptional products with no or limited protein coding ability more than 200 nt in length, play an important role in epigenetic modification, mRNA transcription, splicing, stability, translation, and other biological functions. Extensive studies have shown that both m⁶A modification and lncRNAs are involved in the pathogenesis of various diseases, such as kinds of cancers, heart failure, Alzheimer’s disease, periodontitis, human abdominal aortic aneurysm, and obesity. To date, m⁶A modification has been identified as an important biological function in enrichment and regulation of lncRNAs. In this review, we summarize the role of m⁶A modification in the regulation and function of tumor-related lncRNAs. Moreover, we discuss the potential applications and possible future directions in the field.

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.

MicroRNAs and Long Non-Coding RNAs as Potential Candidates to Target Specific Motifs of SARS-CoV-2

The respiratory system is one of the most affected targets of SARS-CoV-2. Various therapies have been utilized to counter viral-induced inflammatory complications, with diverse success rates. Pending the distribution of an effective vaccine to the whole population and the achievement of "herd immunity", the discovery of novel specific therapies is to be considered a very important objective. Here, we report a computational study demonstrating the existence of target motifs in the SARS-CoV-2 genome suitable for specific binding with endogenous human micro and long non-coding RNAs (miRNAs and lncRNAs, respectively), which can, therefore, be considered a conceptual background for the development of miRNA-based drugs against COVID-19. The SARS-CoV-2 genome contains three motifs in the 5'UTR leader sequence recognized by selective nucleotides within the seed sequence of specific human miRNAs. The seed of 57 microRNAs contained a "GGG" motif that promoted leader sequence-recognition, primarily through offset-6mer sites able to promote microRNAs noncanonical binding to viral RNA. Similarly, lncRNA H19 binds to the 5'UTR of the viral genome and, more specifically, to the transcript of the viral gene Spike, which has a pivotal role in viral infection. Notably, some of the non-coding RNAs identified in our study as candidates for inhibiting SARS-CoV-2 gene expression have already been proposed against diverse viral infections, pulmonary arterial hypertension, and related diseases.

Immune cell landscape analysis reveals prognostic immune cells and its potential mechanism in squamous cell lung carcinoma

Background

Squamous cell lung carcinoma (LUSC) was closely associated with smoking which was known to have a distant immunosuppression effect. In this study, we aimed to explore the relationship between immune cells and clinical outcomes of LUSC patients with smoking history.

Methods

The immune cell infiltration and RNA expression profiles of LUSC patients were collected from The Cancer Genome Atlas (TCGA). Then, the correlation between immune cell infiltration and clinical characteristics was explored. According to the level of immune cell infiltration, LUSC patients with smoking history were divided into high or low group to screen the differentially expressed lncRNAs and mRNAs. The prediction of target genes was performed by miRanda. Finally, the prognostic value of a certain signature was confirmed in an independent dataset.

Results

Higher abundance of tumor-infiltrating T follicular helper (Tfh) cells together with a lower abundance of resting memory CD4 T cells had been found in LUSC current reformed smokers for ≤15 years and current smoking patients. Moreover, Tfh cell infiltration was not only associated with better overall survival (OS) but also varied from different degrees of TNM stage. Low expression of lncRNA PWRN1 and its potential regulating genes DMRTB1, PIRT, APOBEC1, and ZPBP2 were associated with better OS. Combining PWRN1 and four regulating genes as a signature, patients with higher-level expression of the signature had shorter survival time in not only the TCGA but also in the GEO dataset.

Conclusions

It was found that Tfh cells presented higher infiltration in LUSC current reformed smokers for ≤15 years and current smokers, while resting memory CD4 T cells had lower infiltration. The signature consisting of PWRN1 as well as its predicted targeted mRNAs was dysregulated in different levels of Tfh cell infiltration and might indicate patients' OS.

Comprehensive Analysis of a ceRNA Network Identifies lncR-C3orf35 Associated with Poor Prognosis in Osteosarcoma

Osteosarcoma is a highly malignant bone cancer which primarily occurs in children and young adults. Increasing evidence indicates that long noncoding RNAs (lncRNAs), which function as competing endogenous RNAs (ceRNAs) that sponge microRNAs (miRNAs) and messenger RNAs (mRNAs), play a pivotal role in the pathogenesis and progression of cancers. The regulatory mechanisms of lncRNA-mediated ceRNAs in osteosarcoma have not been fully elucidated. In this study, we identified differentially expressed lncRNAs, miRNAs, and mRNAs in osteosarcoma based on RNA microarray profiles in the Gene Expression Omnibus (GEO) database. A ceRNA network was constructed utilizing bioinformatic tools. Kaplan-Meier survival analysis showed that lncR-C3orf35 and HMGB1 were associated with poor prognosis of osteosarcoma patients. Furthermore, results of Gene Set Enrichment Analysis (GSEA) suggested that lncR-C3orf35 may be involved in cellular invasion, the Toll-like receptor signaling pathway, and immune cell infiltration in the tumor microenvironment. Further analysis showed that patients with osteosarcoma metastasis expressed higher levels of lncR-C3orf35 and HMGB1 compared to metastasis-free patients. Moreover, the metastasis-free survival rate of the high lncR-C3orf35/HMGB1 expression group was significantly lower than that of the low expression group. The ESTIMATE algorithm was used to calculate the immune score and stromal scores for each sample. High lncR-C3orf35 and HMGB1 levels were correlated with low immune scores. ImmuCellAI analysis revealed that a low proportion of macrophage infiltration was associated with high lncR-C3orf35 and HMGB1 expression. The differential expression of lncR-C3orf35, miR-142-3p, and HMGB1 was further verified by quantitative real-time PCR. This study indicates that lncR-C3orf35 could be considered as a novel potential biomarker and therapeutic target of osteosarcoma, which may contribute to a better understanding of ceRNA regulatory mechanisms.

LncRNA NNT-AS1 promotes lung squamous cell carcinoma progression by regulating the miR-22/FOXM1 axis

Background

Recent studies have revealed that dysregulated expression of long non-coding RNA nicotinamide nucleotide transhydrogenase antisense RNA 1 (lncRNA NNT-AS1) is associated with cell tumorigenicity in non-small cell lung cancer. However, the exact molecular mechanisms of NNT-AS1 in lung squamous cell carcinoma (LUSC) remain largely unknown.

Methods

The expression of NNT-AS1, microRNA (miR)-22 and Forkhead box protein M1 (FOXM1) was measured using quantitative real-time polymerase chain reaction or western blot, respectively. The interaction between miR-22 and NNT-AS1 or FOXM1 was confirmed using a dual-luciferase reporter assay and RNA immunoprecipitation assay. Cell migration and invasion abilities were measured by Transwell assay. Flow cytometry was used to detect apoptotic cells.

Results

NNT-AS1 and FOXM1 were up-regulated but miR-22 was down-regulated in LUSC tissues and cell lines. NNT-AS1 was a sponge of miR-22, and NNT-AS1 deletion suppressed the migration and invasion but induced apoptosis in LUSC cells. FOXM1 was a target of miR-22, and overexpression of miR-22 inhibited cell carcinogenesis in LUSC by targeting FOXM1. Additionally, NNT-AS1 could directly regulate FOXM1 expression by binding to miR-22 in LUSC cells.

Conclusion

LncRNA NNT-AS1 contributes to cell carcinogenesis in LUSC by regulating the miR-22/FOXM1 axis, providing a novel insight into the pathogenesis of LUSC and a new potential therapeutic target for LUSC treatment.

A nine-long non-coding RNA signature for prognosis prediction of patients with lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is malignant disease with poor therapeutic response and unfavourable prognosis.

OBJECTIVE

This study aims to develop a long non-coding RNA (lncRNA) signature for survival prediction in patients with LUSC.

METHODS

We obtained lncRNA expression profiles of 493 LUSC cases from The Cancer Genome Atlas, and randomly divided the samples into a training set (n= 296) and a testing set (n= 197). Univariate Cox regression and random survival forest algorithm were performed to select optimum survival-related lncRNAs.

RESULTS

A lncRNA-focused risk score model was then constructed for prognosis prediction in the training set and further validated in the testing set and the entire set. Finally, bioinformatics analysis was carried out to explore the potential signaling pathways associated with the prognostic lncRNAs. A set of 9 lncRNAs were found to be strongly correlated with overall survival of LUSC patients. These 9 lncRNAs were integrated into a prognostic signature, which could separate patients into high- and low-risk groups with significantly different survival times in the training set (median: 30.5 vs. 80.5 months, log-rank P< 0.001). This signature was also confirmed in the testing set and the entire set. Besides, the prognostic value of the 9-lncRNA signature was independent of clinical features and maintained stable in stratified analyses. Functional enrichment study suggested that the 9 lncRNAs may be mainly involved in metabolism-related pathways, phosphatidylinositol signaling system, p53 signaling pathway, and notch signaling pathway.

CONCLUSIONS

Our study demonstrated the potential clinical implication of the 9-lncRNA signature for survival prediction of LUSC patients.

Non-coding RNA profile in lung cancer

Lung cancer is the most frequently diagnosed malignancy and the leading source of cancer-associated mortality. This kind of cancer has heterogeneous nature and is divided into two broad classes of small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). In addition to aberrant expression of several signaling pathways and oncogenes, lung cancer is associated with dysregulation of expression of non-coding RNAs including both long non-coding RNAs (lncRNAs) and miRNAs. These aberrantly expressed transcripts are putative therapeutic targets and diagnostic/ prognostic markers. Integrative assessment of expression of lncRNAs, miRNAs and mRNAs has led to construction of competing endogenous RNA networks in which several lncRNAs act as molecular sponges to inhibit regulatory function of miRNAs on mRNAs. Notably, some of these networks seem to have subtype-specific functions in lung cancer. In this review, we summarize recent findings about the importance of these networks in the pathogenesis of lung cancer and provide a list of onco-miRNAs, tumor suppressor miRNAs, oncogenic lncRNAs and tumor suppressor lncRNAs based on their roles in the carcinogenic process in lung cancer.