Long non-coding RNA linc00152 acting as a promising oncogene in cancer progression

MALAT1-regulated gene expression profiling in lung cancer cell lines

BACKGROUND

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and has a poor prognosis. Identifying biomarkers based on molecular mechanisms is critical for early diagnosis, timely treatment, and improved prognosis of lung cancer. MALAT1 has been reported to have overexpressed and tumor-promoting functions in NSCLC. It has been proposed as a potential biomarker for the diagnosis and prognosis of cancer. Therefore, this study was conducted to profile the changes in gene expression according to the regulation of expression of MALAT1 in NSCLC cell lines and to investigate the correlation through bioinformatic analysis of differentially expressed genes (DEGs).

METHODS

MALAT1 expression levels were measured using RT-qPCR. The biological functions of MALAT1 in NSCLC were analyzed by cell counting, colony forming, wound-healing, and Transwell invasion assays. In addition, gene expression profiling in response to the knockdown of MALAT1 was analyzed by transcriptome sequencing, and differentially expressed genes regulated by MALAT1 were performed by GO and KEGG pathway enrichment analyses. Bioinformatic databases were used for gene expression analysis and overall survival analysis.

RESULTS

Comparative analysis versus MALAT1 expression in MRC5 cells (a normal lung cell line) and the three NSCLC cell lines showed that MALAT1 expression was significantly higher in the NSCLC cells. MALAT1 knockdown decreased cell survival, proliferation, migration, and invasion in all three NSCLC cell lines. RNA-seq analysis of DEGs in NSCLC cells showed 198 DEGs were upregulated and 266 DEGs downregulated by MALAT1 knockdown in all three NSCLC cell lines. Survival analysis on these common DEGs performed using the OncoLnc database resulted in the selection of five DEGs, phosphoglycerate mutase 1 (PGAM1), phosphoglycerate mutase 4 (PGAM4), nucleolar protein 6 (NOL6), nucleosome assembly protein 1 like 5 (NAP1L5), and sestrin1 (SESN1). The gene expression levels of these selected DEGs were proved to gene expression analysis using the TNMplot database.

CONCLUSION

MALAT1 might function as an oncogene that enhances NSCLC cell survival, proliferation, colony formation, and invasion. RNA-seq and bioinformatic analyses resulted in the selection of five DEGs, PGAM1, PGAM4, NOL6, NAP1L5, and SESN1, which were found to be closely related to patient survival and tumorigenesis. We believe that further investigation of these five DEGs will provide valuable information on the oncogenic role of MALAT1 in NSCLC.

Liquid biopsy: an examination of platelet RNA obtained from head and neck squamous cell carcinoma patients for predictive molecular tumor markers

Aim

Recently, a tumor cell-platelet interaction was identified in different tumor entities, resulting in a transfer of tumor-derived RNA into platelets, named further "tumor-educated platelets (TEP)". The present pilot study aims to investigate whether such a tumor-platelet transfer of RNA occurs also in patients suffering from head and neck squamous cell carcinoma (HNSCC).

Methods

Sequencing analysis of RNA derived from platelets of tumor patients (TPs) and healthy donors (HDs) were performed. Subsequently, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used for verification of differentially expressed genes in platelets from TPs and HDs in a second cohort of patients and HDs. Data were analyzed by applying bioinformatic tools.

Results

Sequencing of RNA derived from the tumor as well as from platelets of TPs and HDs revealed 426 significantly differentially existing RNA, at which 406 RNA were more and 20 RNA less abundant in platelets from TPs in comparison to that of HDs. In TPs' platelets, abundantly existing RNA coding for 49 genes were detected, characteristically expressed in epithelial cells and RNA, the products of which are involved in tumor progression. Applying bioinformatic tools and verification on a second TP/HD cohort, collagen type I alpha 1 chain (COL1A1) and zinc finger protein 750 (ZNF750) were identified as the strongest potentially platelet-RNA-sequencing (RNA-seq)-based biomarkers for HNSCC.

Conclusions

These results indicate a transfer of tumor-derived messenger RNA (mRNA) into platelets of HNSCC patients. Therefore, analyses of a patient's platelet RNA could be an efficient option for liquid biopsy in order to diagnose HNSCC or to monitor tumorigenesis as well as therapeutic responses at any time and in real time.

Long non-coding RNA in glioma: novel genetic players in temozolomide resistance

Glioma is the most common primary malignant brain tumor in adults and accounts for approximately 80% of brain and central nervous system tumors. In 2021, the World Health Organization (WHO) published a new taxonomy for glioma based on its histological features and molecular alterations. Isocitrate dehydrogenase (IDH) catalyzes the decarboxylation of isocitrate, a critical metabolic reaction in energy generation in cells. Mutations in the IDH genes interrupt cell differentiation and serve as molecular biomarkers that can be used to classify gliomas. For example, the mutant IDH is widely detected in low-grade gliomas, whereas the wild type is in high-grade ones, including glioblastomas. Long non-coding RNAs (lncRNAs) are epigenetically involved in gene expression and contribute to glioma development. To investigate the potential use of lncRNAs as biomarkers, we examined lncRNA dysregulation dependent on the IDH mutation status. We found that several lncRNAs, namely, AL606760.2, H19, MALAT1, PVT1 and SBF2-AS1 may function as glioma risk factors, whereas AC068643.1, AC079228.1, DGCR5, FAM13A-AS1, HAR1A and WDFY3-AS2 may have protective effects. Notably, H19, MALAT1, PVT1, and SBF2-AS1 have been associated with temozolomide resistance in glioma patients. This review study suggests that targeting glioma-associated lncRNAs might aid the treatment of glioma.

Comprehensive analysis of N6 -methyladenosine-related long non-coding RNAs for prognosis prediction in liver hepatocellular carcinoma

Background

Liver hepatocellular carcinoma (LIHC) is a lethal cancer. This study aimed to identify the N⁶‐methyladenosine (m⁶A)‐targeted long non‐coding RNA (lncRNA) related to LIHC prognosis and to develop an m⁶A‐targeted lncRNA model for prognosis prediction in LIHC.

Methods

The expression matrix of mRNA and lncRNA was obtained, and differentially expressed (DE) mRNAs and lncRNAs between tumor and normal samples were identified. Univariate Cox and pathway enrichment analyses were performed on the m⁶A‐targeted lncRNAs and the LIHC prognosis‐related m⁶A‐targeted lncRNAs. Prognostic analysis, immune infiltration, and gene DE analyses were performed on LIHC subgroups, which were obtained from unsupervised clustering analysis. Additionally, a multi‐factor Cox analysis was used to construct a prognostic risk model based on the lncRNAs from the LASSO Cox model. Univariate and multivariate Cox analyses were used to assess prognostic independence.

Results

A total of 5031 significant DEmRNAs and 292 significant DElncRNAs were screened, and 72 LIHC‐specific m⁶A‐targeted binding lncRNAs were screened. Moreover, a total of 29 LIHC prognosis‐related m⁶A‐targeted lncRNAs were obtained and enriched in cytoskeletal, spliceosome, and cell cycle pathways. An 11‐m⁶A‐lncRNA prognostic model was constructed and verified; the top 10 lncRNAs included LINC00152, RP6‐65G23.3, RP11‐620J15.3, RP11‐290F5.1, RP11‐147L13.13, RP11‐923I11.6, AC092171.4, KB‐1460A1.5, LINC00339, and RP11‐119D9.1. Additionally, the two LIHC subgroups, Cluster 1 and Cluster 2, showed significant differences in the immune microenvironment, m⁶A enzyme genes, and prognosis of LIHC.

Conclusion

The m⁶A‐lncRNA prognostic model accurately and effectively predicted the prognostic survival of LIHC. Immune cells, immune checkpoints (ICs), and m⁶A enzyme genes could act as novel therapeutic targets for LIHC.

LncRNA LINC00152 promotes oral squamous cell carcinoma growth via enhancing Upstream Transcription Factor 1 mediated Mitochondrial Ribosomal Protein L52 transcription

BACKGROUND

LINC00152 (long intergenic non-protein coding RNA 152) was identified as an oncogenic lncRNA in multiple cancers. In the current study, we aimed to explore the transcriptional profile of LINC00152 in oral squamous cell carcinoma (OSCC) and its regulations at the transcriptional level.

METHODS

Bioinformatic analysis was performed by extracting the OSCC subset from The Cancer Genome Atlas (TCGA)-Head and Neck Squamous Cell Carcinoma (HNSC). LINC00152 subcellular localization and its interacting transcriptional factors (TFs) were explored. Dual-luciferase assay and ChIP-qPCR were applied to study transcriptional regulation. In vitro and in-vivo tumor cell growth models were used for functional assays.

RESULTS

NR_024206.2 was the dominant isoform that accounts for 80% of all transcripts of LINC00152. LINC00152 upregulation was associated with unfavorable survival of patients with OSCC. LINC00152 knockdown significantly impaired OSCC cell growth in vitro and in vivo. RNA FISH assay confirmed nuclear and cytoplasmic distribution of LINC00152. It physically interacted with Upstream Transcription Factor 1 (USF1), a common transcription factor in mammalian cells. USF1 could bind to the promoter region of MRPL52 (Mitochondrial Ribosomal Protein L52) and activate its transcription. LINC00152 could enhance the binding, thereby indirectly elevating MRPL52 expression. USF1 or MRPL52 knockdown slowed the proliferation of OSCC cells and partly canceled LINC00152 mediated growth-promoting effects.

CONCLUSION

This study revealed a novel LINC00152-USF1/MRPL52 axis promoting OSCC tumor growth.

Role of lncRNAs in the Development of an Aggressive Phenotype in Gallbladder Cancer

Long non-coding RNAs are sequences longer than 200 nucleotides that are involved in different normal and abnormal biological processes exerting their effect on proliferation and differentiation, among other cell features. Functionally, lncRNAs can regulate gene expression within the cells by acting at transcriptional, post-transcriptional, translational, or post-translational levels. However, in pathological conditions such as cancer, the expression of these molecules is deregulated, becoming elements that can help in the acquisition of tumoral characteristics in the cells that trigger carcinogenesis and cancer progression. Specifically, in gallbladder cancer (GBC), recent publications have shown that lncRNAs participate in the acquisition of an aggressive phenotype in cancer cells, allowing them to acquire increased malignant capacities such as chemotherapy resistance or metastasis, inducing a worse survival in these patients. Furthermore, lncRNAs are useful as prognostic and diagnostic biomarkers since they have been shown to be differentially expressed in tumor tissues and serum of individuals with GBC. Therefore, this review will address different lncRNAs that could be promoting malignant phenotypic characteristics in GBC cells and lncRNAs that may be useful as markers due to their capability to predict a poor prognosis in GBC patients.

Gene expression profiling after LINC00472 overexpression in an NSCLC cell line

Lung cancer accounts for a large proportion of cancer-related deaths worldwide. Personalized therapeutic medicine based on the genetic characteristics of non-small cell lung cancer (NSCLC) is a promising field, and discovering clinically applicable biomarkers of NSCLC is required. LINC00472 is a long non-coding RNA and has been recently suggested to be a biomarker of NSCLC, but little is known of its mechanism in NSCLC. Thus, the current study was performed to document changes in gene expression after LINC00472 overexpression in NSCLC cells. As a result of cell viability and migration assay, LINC00472 downregulated cell survival, proliferation, and motility. Transcriptome sequencing analysis showed 3,782 genes expression were changed in LINC00472 overexpressing cells. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed most genes were associated with intracellular metabolism. The PPP1R12B, RGS5, RBM5, RBL2, LDLR and PTPRM genes were upregulated by LINC00472 overexpression and these genes functioned as tumor suppressors in several cancers. In contrast, SPSB1, PCNA, CD24, CDK5, CDC25A, and EIF4EBP1 were downregulated by LINC00472, and they functioned as oncogenes in various cancers. Consequently, the function of LINC00472 in tumorigenesis might be related to changes in the expressions of other oncogenes and tumor suppressors.

Long Non-Coding RNA LINC00152 Regulates Self-Renewal of Leukemia Stem Cells and Induces Chemo-Resistance in Acute Myeloid Leukemia

Relapse of acute myeloid leukemia (AML) has a very poor prognosis and remains a common cause of treatment failure in patients with this disease. AML relapse is partially driven by the chemoresistant nature of leukemia stem cells (LSCs), which remains poorly understood, and our study aimed at elucidating the underlying mechanism. Accumulating evidences show that long noncoding RNAs (lncRNAs) play a crucial role in AML development. Herein, the lncRNA, LINC00152, was identified to be highly expressed in CD34⁺ LSCs and found to regulate the self-renewal of LSCs derived from AML patients. Importantly, LINC00152 upregulation was correlated with the expression of 16 genes within a 17-gene LSC biomarker panel, which contributed to the accurate prediction of initial therapy resistance in AML. Knockdown of LINC00152 markedly increased the drug sensitivity of leukemia cells. Furthermore, LINC00152 expression was found to be correlated with poly (ADP-ribose) polymerase 1 (PARP1) expression in AML, whereas LINC00152 knockdown significantly decreased the expression of PARP1. Upregulation of LINC00152 or PARP1 was associated with poor prognosis in AML patients. Collectively, these data highlight the importance and contribution of LINC00152 in the regulation of self-renewal and chemoresistance of LSCs in AML.

The ceRNA network of lncRNA and miRNA in lung cancer

Since lung cancer is a major causative for cancer-related deaths, the investigations for discovering biomarkers to diagnose at an early stage and to apply therapeutic strategies have been continuously conducted. Recently, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are being exponentially studied as promising biomarkers of lung cancer. Moreover, supportive evidence provides the competing endogenous RNA (ceRNA) network between lncRNAs and miRNAs participating in lung tumorigenesis. This review introduced the oncogenic or tumor-suppressive roles of lncRNAs and miRNAs in lung cancer cells and summarized the involvement of the lncRNA/miRNA ceRNA networks in carcinogenesis and therapeutic resistance of lung cancer.

Zebrafish xenograft model of human lung cancer for studying the function of LINC00152 in cell proliferation and invasion

Background

Numerous studies have shown that long noncoding RNAs play important roles in human cancer progression. Although zebrafish xenografts have recently become a novel in vivo model for human cancer research, whether such models can be used to study the function of long noncoding RNAs remains unknown.

Methods

In vitro studies validated the roles of LINC00152 in the proliferation and invasion of lung cancer cells. In vivo studies of zebrafish xenografts also confirmed these roles of LINC00152. In vivo confocal imaging was used to more accurately evaluate the function of LINC00152 in cell proliferation and migration. Pharmacological experiments were further performed to study the potential ability of LINC00152 downregulation combined with an EGFR inhibitor to treat tumors in cultured cells and the zebrafish xenograft model.

Results

Silencing of LINC00152 suppressed cell proliferation and invasion in SPCA1 and A549 lung cancer cell lines in vitro. In the zebrafish xenograft model, knockdown of LINC00152 reduced the proliferation and migration of lung cancer cells, as indicated by the two imaging methods at different magnifications. Moreover, the knockdown of LINC00152 enhanced the inhibition effect of afatinib for lung cancer progression in cultured cells and the zebrafish xenograft model.

Conclusion

Our study reveals the oncogenic roles and potential for LINC00152 to be a target for tumor treatment in lung cancer using zebrafish xenograft models, and the findings suggest that this model could be used for functional and application studies of human long noncoding RNAs in tumor biology.

N6-Methylandenosine-Related lncRNAs Are Potential Biomarkers for Predicting the Overall Survival of Lower-Grade Glioma Patients

The prognostic value of N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) was investigated in 646 lower-grade glioma (LGG) samples from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) datasets. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, and then univariate Cox regression analysis was performed to screen their prognostic roles in LGG patients. Twenty-four prognostic m6A-related lncRNAs were identified as prognostic lncRNAs and they were inputted in a least absolute shrinkage and selection operator (LASSO) Cox regression to establish a m6A-related lncRNA prognostic signature (m6A-LPS, including 9 m6A-related prognostic lncRNAs) in the TCGA dataset. Corresponding risk scores of patients were calculated and divided LGG patients into low- and high-risk subgroups by the median value of risk scores in each dataset. The m6A-LPS was validated in the CGGA dataset and it showed a robust prognostic ability in the stratification analysis. Principal component analysis showed that the low- and high-risk subgroups had distinct m6A status. Enrichment analysis indicated that malignancy-associated biological processes, pathways and hallmarks were more common in the high-risk subgroup. Moreover, we constructed a nomogram (based on m6A-LPS, age and World Health Organization grade) that had a strong ability to forecast the overall survival (OS) of the LGG patients in both datasets. We also establish a competing endogenous RNA (ceRNA) network based on seven of the twenty-four m6A-related lncRNAs. Besides, we also detected five m6A-related lncRNA expression levels in 22 clinical samples using quantitative real-time polymerase chain reaction assay.