Screening of a Novel Upregulated lncRNA, A2M-AS1, That Promotes Invasion and Migration and Signifies Poor Prognosis in Breast Cancer

The role of long non-coding RNA A2M-AS1 in early diagnosis and prognosis evaluation of acute myocardial infarction

AIM

The objective was to assess the clinical efficacy of long non-coding RNA (lncRNA) alpha-2-macroglobulin-antisense 1 (A2M-AS1) in acute myocardial infarction (AMI).

METHODS

One hundred patients with AMI and eighty patients with chest pain were recruited in the case-control study. A2M-AS1 expression was examined by quantitative real-time polymerase chain reaction (qRT-PCR). Receiver operating characteristic (ROC) analysis was utilized for evaluating the diagnostic value. Pearson's correlation analysis was used to analyze the correlation between A2M-AS1 and conventional AMI biomarkers. AMI-associated risk indicators were identified using logistic regression analysis.

RESULTS

A significant reduction of serum A2M-AS1 was measured in AMI patients relative to chest pain patients. A2M-AS1 had an area under the curve (AUC) of 0.927 to distinguish AMI patients from those with chest pain. Pearson's correlation analysis showed that A2M-AS1 was adversely correlated with white blood cell (WBC) (r=-0.6682, P < 0.001), low density lipoprotein cholesterol (LDL-C) (r=-0.5795, P < 0.001), creatine kinase MB (CK-MB) (r=-0.6022, P < 0.001) and cTnl (r=-0.5473; P < 0.001), while positively correlated with high density lipoprotein cholesterol (HDL-C) (r = 0.6445, P < 0.001). Relative to non-Major Adverse Cardiovascular Events (non-MACE) group, serum A2M-AS1 was obviously declined in the MACE group of AMI patients with high capacity to distinguish the MACE group from the non-MACE patients (AUC = 0.802). Additionally, A2M-AS1 (P = 0.013; OR = 0.268; 95%CI = 0.095-0.760) was a risk indicator for predicting MACE with AMI patients, as well as age (P = 0.014; OR = 3.478; 95%CI = 1.285-9.414).

CONCLUSION

A reduction in A2M-AS1 expression was observed in AMI patients, suggesting its potential as an underlying indicator for AMI diagnosis.

MONet: cancer driver gene identification algorithm based on integrated analysis of multi-omics data and network models

Cancer progression is orchestrated by the accrual of mutations in driver genes, which endow malignant cells with a selective proliferative advantage. Identifying cancer driver genes is crucial for elucidating the molecular mechanisms of cancer, advancing targeted therapies, and uncovering novel biomarkers. Based on integrated analysis of Multi-Omics data and Network models, we present MONet, a novel cancer driver gene identification algorithm. Our method utilizes two graph neural network algorithms on protein-protein interaction (PPI) networks to extract feature vector representations for each gene. These feature vectors are subsequently concatenated and fed into a multi-layer perceptron model (MLP) to perform semi-supervised identification of cancer driver genes. For each mutated gene, MONet assigns the probability of being potential driver, with genes identified in at least two PPI networks selected as candidate driver genes. When applied to pan-cancer datasets, MONet demonstrated robustness across various PPI networks, outperforming baseline models in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve. Notably, MONet identified 37 novel driver genes that were missed by other methods, including 29 genes such as APOBEC2, GDNF, and PRELP, which are corroborated by existing literature, underscoring their critical roles in cancer development and progression. Through the MONet framework, we successfully identified known and novel candidate cancer driver genes, providing biologically meaningful insights into cancer mechanisms.

Integrated analysis of lncRNA and mRNA expression profiles in cutaneous leishmaniasis lesions caused by Leishmania tropica

Background

Cutaneous leishmaniasis (CL), caused by Leishmania (L.) species, remains a neglected tropical disease in many developing countries. We and others have shown that different Leishmania species can alter the gene expression profile of human host cells. Long non-coding RNAs (lncRNAs) have been found to play a role in the pathogenesis of leishmaniasis through dysregulation of transcriptome signatures. Understanding the regulatory roles of lncRNAs in the biological networks involved in leishmaniasis can improve our understanding of the disease.

Methods

Herein, we used our previous RNA sequencing data (GSE216638) to investigate the profile of lncRNAs in the skin lesions of L. tropica-infected patients. We employed the weighted gene correlation network analysis (WGCNA) algorithm to establish co-expression networks of shared genes between CL patients and infer the potential role of lncRNAs in CL patients. We identified hub genes and trans- and cis-acting lncRNAs, and carried out functional enrichment analysis on a key co-expressed module related to L. tropica-infected patients.

Results

We found substantial involvement of lncRNAs in the CL patient dataset. Using the WGCNA method, we classified all included genes into seven modules, with a module (turquoise) being significantly correlated with the studied clinical traits and identified as the key module. This module was mainly involved in the "interferon gamma signaling" and "cytokine signaling" pathways. We highlighted several lncRNAs and their co-expressed mRNA pairs, like SIRPG-AS1, IL21R-AS1, IL24, and TLDC2, as hub genes of the key module. Quantitative RT-PCR validated the expression of several genes in the lesions of an independent cohort of L. tropica-infected patients.

Conclusions

These findings enhance our understanding of the human skin response to L. tropica infection. Furthermore, the hub genes identified in this study are worthy of further evaluation as potential targets in the development of more effective treatments and preventive measures for CL caused by L. tropica.

Molecular mechanism underlying the protective effects of ischemic preconditioning in total knee arthroplasty

PROPOSE

To investigate the molecular mechanisms underlying the protective effects of ischemic preconditioning (IPC) in patients undergoing total knee arthroplasty.

METHODS

GSE21164 was extracted from an online database, followed by an investigation of differentially expressed genes (DEGs) between IPC treatment samples at 2 time points (T0T and T1T). Function and pathway enrichment analyses were performed on the DEGs. A protein-protein interaction network was constructed to identify hub genes according to 5 different algorithms, followed by enrichment analysis. In addition, long noncoding RNAs (lncRNAs) were identified between the T0T and T1T samples. Furthermore, a competing endogenous RNA network was predicted based on the identified lncRNA-messenger RNA (mRNA), lncRNA-microRNA (miRNA), and mRNA-miRNA relationships revealed in this study. Finally, a drug-gene network was investigated. Statistical analyses were performed using GraphPad Prism 8.0. Differences between groups were determined using an unpaired t-test. p < 0.05 was considered significant.

RESULTS

A total of 343 DEGs at T0 and 10 DEGs at T1 were identified and compared with their respective control groups, followed by 100 DEGs between T0T and T1T. Based on these 100 DEGs, protein-protein interaction network analysis revealed 9 hub genes, mainly with mitochondria-related functions and the carbon metabolism pathway. Six differentially expressed lncRNAs were investigated between T0T and T1T. A competing endogenous RNA network was constructed using 259 lncRNA-miRNA-mRNA interactions, including alpha-2-macroglobulin antisense RNA 1-miR-7161-5p-iron-sulfur cluster scaffold. Finally, 13 chemical drugs associated with the hub genes were explored.

CONCLUSION

Iron-sulfur cluster scaffold may promote IPC-induced ischemic tolerance mediated by alpha-2-macroglobulin antisense RNA 1-miR-7161-5p axis. Moreover, IPC may induce a protective response after total knee arthroplasty via mitochondria-related functions and the carbon metabolism pathway, which should be further validated in the near future.

Initial Evaluation of lncRNA A2M-AS1 Gene Expression in Multiple Sclerosis Patients

Background

Multiple sclerosis (MS) is one of the three leading neurodegenerative diseases worldwide. Gene expression profile studies play an important role in recognizing and preventing disease. Considering the inherent ability of biomarkers to diagnose and prognose the occurrence of a disease, with the aim of gene therapy and changing gene expression, it can be helped to treat it. In this study, by examining the gene interaction and expression of non-coding genes in patients with MS, using bioinformatics analyses, laboratory research and potential non-coding diagnostic biomarkers of MS were selected for further investigations.

Materials and Methods

First, by using micro-array data analysis of the GEO database, the expression status of the long non-coding ribonucleic acid (RNA) (lncRNA) A2M-AS1 gene was investigated in patients with MS. lncRNA-mRNA interaction analysis was performed in the lncRRisearch database. After sample collection, the total RNA extracted using the RNA extraction kit from 20 patient samples and 20 healthy samples was synthesized into cDNA with the synthesis kit. The quantitative reverse transcriptase polymerase chain reaction experiment was performed for the final validation of expression change.

Results

Based on bioinformatic and laboratory analysis, the expression of the A2M-AS1 gene in MS samples showed a significant decrease in expression compared to healthy samples. Also, based on the receiver operating characteristic analysis, lncRNA A2M-AS1 can be introduced as an acceptable diagnostic biomarker to distinguish MS samples from healthy samples.

Conclusion

lncRNA A2M-AS1, by reducing its expression as an acceptable diagnostic biomarker, can increase the risk of developing MS.

The engineered exosomes targeting ferroptosis: A novel approach to reverse immune checkpoint inhibitors resistance

Immune checkpoint inhibitors (ICIs) have been extensively used in immunological therapy primarily due to their ability to prolong patient survival. Although ICIs have achieved success in cancer treatment, the resistance of ICIs should not be overlooked. Ferroptosis is a newly found cell death mode characterized by the accumulation of reactive oxygen species (ROS), glutathione (GSH) depletion, and glutathione peroxidase 4 (GPX4) inactivation, which has been demonstrated to be beneficial to immunotherapy and combining ferroptosis and ICIs to exploit new immunotherapies may reverse ICIs resistance. Exosomes act as mediators in cell-to-cell communication that may regulate ferroptosis to influence immunotherapy through the secretion of biological molecules. Thus, utilizing exosomes to target ferroptosis has opened up exciting possibilities for reversing ICIs resistance. In this review, we summarize the mechanisms of ferroptosis improving ICIs therapy and how exosomes regulate ferroptosis through adjusting iron metabolism, blocking the ROS accumulation, controlling ferroptosis defense systems, and influencing classic signaling pathways and how engineered exosomes target ferroptosis and improve ICIs efficiency.

Prognostic significance of alpha-2-macrglobulin and low-density lipoprotein receptor-related protein-1 in various cancers

Cancer is the leading cause of death worldwide. The World Health Organization (WHO) estimates that 10 million fatalities occurred in 2023. Breast cancer (BC) ranked first among malignancies with 2.26 million cases, lung cancer (LC) second with 2.21 million cases, and colon and rectum cancers (CC, CRC) third with 1.93 million cases. These results highlight the importance of investigating novel cancer prognoses and anti-cancer markers. In this study, we investigated the potential effects of alpha-2 macroglobulin and its receptor, LRP1, on the outcomes of breast, lung, and colorectal malignancies. Immunohistochemical staining was used to analyze the expression patterns of A2M and LRP1 in 545 cases of invasive ductal breast carcinoma (IDC) and 51 cases of mastopathies/fibrocystic breast disease (FBD); 256 cases of non-small cell lung carcinomas (NSCLCs) and 45 cases of non-malignant lung tissue (NMLT); and 108 cases of CRC and 25 cases of non-malignant colorectal tissue (NMCT). A2M and LRP1 expression levels were also investigated in breast (MCF-7, BT-474, SK-BR-3, T47D, MDA-MB-231, and MDA-MB-231/BO2), lung (NCI-H1703, NCI-H522, and A549), and colon (LS 180, Caco-2, HT-29, and LoVo) cancer cell lines. Based on our findings, A2M and LRP1 exhibited various expression patterns in the examined malignancies, which were related to one another. Additionally, the stroma of lung and colorectal cancer has increased levels of A2M/LRP1 areas, which explains the significance of the stroma in the development and maintenance of tumor homeostasis. A2M expression was shown to be downregulated in all types of malignancies under study and was positively linked with an increase in cell line aggressiveness. Although more invasive cells had higher levels of A2M expression, an IHC analysis showed the opposite results. This might be because exogenous alpha-2-macroglobulin is present, which has an inhibitory effect on several cancerous enzymes and receptor-dependent signaling pathways. Additionally, siRNA-induced suppression of the transcripts for A2M and LRPP1 revealed their connection, which provides fresh information on the function of the LRP1 receptor in A2M recurrence in cancer. Further studies on different forms of cancer may corroborate the fact that both A2M and LRP1 have high potential as innovative therapeutic agents.

TFPI inhibits breast cancer progression by suppressing ERK/p38 MAPK signaling pathway

BACKGROUND

Tissue factor pathway inhibitor-1 (TFPI) is a serine protease inhibitor, which is responsible for inactivating TF-induced coagulation. Recently, increasing studies revealed that TFPI was lowly expressed in tumor cells and exhibited the antitumor activity.

OBJECTIVE

The aim of this study was to explore the role and underlying molecular mechanisms of TFPI in breast cancer.

METHODS

The expression and prognostic value of TFPI were analyzed using UALCAN and Kaplan-Meier plotter website. The expression level of TFPI in breast cancer tissues and cells was examined by immunohistochemistry (IHC) and western blot analysis, respectively. Cellular proliferation was evaluated by CCK-8 and colony formation assays. Cell migration and invasion were determined by transwell assay. The methylation level of TFPI promoter was determined by methylation-specific PCR.

RESULTS

TFPI expression was significantly lower in breast cancer tissues and cells compared to normal breast tissues and normal breast cells. Patients with low TFPI levels showed worse overall survival (OS). Furthermore, overexpression of TFPI significantly inhibited the proliferation, migration and invasion of breast cancer cells. Conversely, knockdown of TFPI promoted the proliferation, migration and invasion of breast cancer cells. Mechanistically, TFPI inhibited the ERK/p38 MAPK signaling pathway in breast cancer. Moreover, DNA hypermethylation of TFPI promoter was responsible for the downregulation of TFPI in breast cancer cells.

CONCLUSION

TFPI inhibited breast cancer cell proliferation, migration and invasion through inhibition of the ERK/p38 MAPK signaling pathway, suggesting that TFPI may serve as a novel prognostic biomarker and therapeutic target for breast cancer.

Identification of Epigenetic-Dysregulated lncRNAs Signature in Osteosarcoma by Multi-Omics Data Analysis

Background

Alterations of epigenetic modification patterns are potential markers of cancer. The current study characterized six histone modifications in osteosarcoma and identified epigenetically dysregulated long non-coding RNAs (epi-lncRNAs).

Methods

Multi-omics data were obtained from osteosarcoma cell line SJSA1 and a normal cell line. Differentially expressed lncRNAs (DElncRNAs) between osteosarcoma and normal skeletal muscle were analyzed using Limma. MACS2 was applied to identify the “peaks” modified by each histone in the cell. Promoters or enhancers of DElncRNA were overlapped with differential histone-modified regions (DHMR) to screen epi-lncRNAs. Univariate and multivariate Cox regression analysis were performed to detect the genes closely related to the prognosis of osteosarcoma and to construct risk models.

Results

A total of 17 symbolic epi-lncRNA in osteosarcoma were screened, and 13 of them were differentially expressed between osteosarcoma and normal samples. Eight epi-lncRNAs were retained by Univariate Cox regression analysis. Four of these epi-lncRNAs were used to construct an epi-lncRNA signature. The risk score of each osteosarcoma sample in the high- or low-risk group was estimated according to the epi-lncRNA signature. The overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The area under the receiver operating characteristic (ROC) curve of the model was 0.79 and 0.82 for 1-, 3-, and 5-year OS, respectively.

Conclusion

Our results revealed the histone modification pattern in osteosarcoma and developed 4-epi-lncRNA signature to predict the prognosis of osteosarcoma, laying a foundation for the identification of highly specific epigenetic biomarkers for osteosarcoma.

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 hub genes for early detection of bone metastasis in breast cancer

BACKGROUND

Globally, among all women, the most frequently detected and diagnosed and the most lethal type of cancer is breast cancer (BC). In particular, bone is one of the most frequent distant metastases 24in breast cancer patients and bone metastasis arises in approximately 80% of advanced patients. Thus, we need to identify and validate early detection markers that can differentiate metastasis from non-metastasis breast cancers.

METHODS

GSE55715, GSE103357, and GSE146661 gene expression profiling data were downloaded from the GEO database. There was 14 breast cancer with bone metastasis samples and 8 breast cancer tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). The volcano plots, Venn diagrams, and annular heatmap were generated by using the ggplot2 package. By using the cluster Profiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, K-M survival and ROC curves were generated to validate hub gene expression.

RESULTS

By GO enrichment analysis, 143 DEGs were enriched in the following GO terms: extracellular structure organization, extracellular matrix organization, leukocyte migration class II protein complex, collagen tridermic protein complex, extracellular matrix structural constituent, growth factor binding, and platelet-derived growth factor binding. In the KEGG pathway enrichment analysis, DEGs were enriched in Staphylococcus aureus infection, Complement and coagulation cascades, and Asthma. By PPI network analysis, we selected the top 10 genes, including SLCO2B1, STAB1, SERPING1, HLA-DOA, AIF1, GIMAP4, C1orf162, HLA-DMB, ADAP2, and HAVCR2. By using TCGA and THPA databases, we validated 2 genes, SERPING1 and GIMAP4, that were related to the early detection of bone metastasis in BC.

CONCLUSIONS

2 abnormally expressed hub genes could play a pivotal role in the breast cancer with bone metastasis by affecting bone homeostasis imbalance in the bone microenvironment.

Overexpression of lncRNA A2M-AS1 inhibits cell growth and aggressiveness via regulating the miR-587/bone morphogenetic protein 3 axis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a malignant tumor that occurs in the lungs. Numerous reports have substantiated the participation of long non-coding RNAs (lncRNAs) in the tumorigenesis of LUAD. Previously, lncRNA alpha-2-macroglobulin antisense RNA 1 (A2M-AS1) was confirmed to be an important regulator in the biological processes of LUAD and dysregulation of A2M-AS1 was associated with non-small cell lung cancer (NSCLC) progression. However, the precise mechanism of A2M-AS1 in LUAD has not been elucidated. Therefore, our study was designed to investigate the detailed molecular mechanism of A2M-AS1 in LUAD. Herein, the expression of lncRNA A2M-AS1, microRNA (miRNA) miR-587, and bone morphogenetic protein 3 (BMP3) in LUAD cell lines and tissues were detected by real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting. The viability, proliferation, migration and invasion of LUAD cells were tested by cell counting kit-8 (CCK-8), colony formation and Transwell assays. In vivo tumor growth was investigated by xenograft animal experiment. Interactions among A2M-AS1, miR-587 and BMP3 were measured by RNA pulldown and luciferase reporter assays. In this study, A2M-AS1 was downregulated in LUAD tissues and cells and related to poor prognosis in LUAD patients. A2M-AS1 overexpression suppressed LUAD cell proliferation, migration and invasion in vitro and inhibited tumor growth in vivo. Mechanistically, A2M-AS1 directly bound with miR-587 to promote BMP3 expression in LUAD cells. Low expression of BMP3 was found in LUAD tissues and cells and was closely correlated with poor prognosis in LUAD patients. BMP3 deficiency reserved the inhibitory influence of A2M-AS1 overexpression on LUAD cell behaviors. Overall, A2M-AS1 inhibits cell growth and aggressiveness via regulating the miR-587/BMP3 axis in LUAD.

LncRNAs Target Ferroptosis-Related Genes and Impair Activation of CD4+ T Cell in Gastric Cancer

Gastric cancer (GC) is a malignant disease of the digestive tract and a life-threatening disease worldwide. Ferroptosis, an iron-dependent cell death caused by lipid peroxidation, is reported to be highly correlated with gastric tumorigenesis and immune cell activity. However, the underlying relationship between ferroptosis and the tumor microenvironment in GC and potential intervention strategies have not been unveiled. In this study, we profiled the transcriptome and prognosis data of ferroptosis-related genes (FRGs) in GC samples of the TCGA-STAD dataset. The infiltrating immune cells in GC were estimated using the CIBERSORT and XCELL algorithms. We found that the high expression of the hub FRGs (MYB, PSAT1, TP53, and LONP1) was positively correlated with poor overall survival in GC patients. The results were validated in an external GC cohort (GSE62254). Further immune cell infiltration analysis revealed that CD4⁺ T cells were the major infiltrated cells in the tumor microenvironment of GC. Moreover, the hub FRGs were significantly positively correlated with activated CD4⁺ T cell infiltration, especially Th cells. The gene features in the high-FRG score group were enriched in cell division, DNA repair, protein folding, T cell receptor, Wnt and NIK/NF-kappaB signaling pathways, indicating that the hub FRGs may mediate CD4⁺ T cell activation by these pathways. In addition, an upstream transcriptional regulation network of the hub FRGs by lncRNAs was also developed. Three lncRNAs (A2M-AS1, C2orf27A, and ZNF667-AS1) were identified to be related to the expression of the hub FRGs. Collectively, these results showed that lncRNA A2M-AS1, C2orf27A, and ZNF667-AS1 may target the hub FRGs and impair CD4⁺ T cell activation, which finally leads to poor prognosis of GC. Effective interventions for the above lncRNAs and the hub FRGs can help promote CD4⁺ T cell activation in GC patients and improve the efficacy of immunotherapy. These findings provide a novel idea of GC immunotherapy and hold promise for future clinical application.

Identification of lncRNA-associated competing endogenous RNA networks for occurrence and prognosis of gastric carcinoma

Background

Gastric cancer (GC) is one of the common digestive malignancies worldwide and causes a severe public health issue. So far, the underlying mechanisms of GC are largely unclear. Thus, we aim to identify the long non‐coding RNA (lncRNA)‐associated competing endogenous RNA (ceRNA) for GC.

Methods

TCGA database was downloaded and used for the identification of differentially expressed (DE) lncRNAs, miRNAs, and mRNAs, respectively. Then, the ceRNA network was constructed via multiple online datasets and approaches. In addition, various in vitro assays were carried out to validate the effect of certain hub lncRNAs.

Results

We constructed a ceRNA network, including 76 lncRNAs, 18 miRNAs, and 159 mRNAs, which involved multiple critical pathways. Next, univariate and multivariate analysis demonstrated 11 lncRNAs, including LINC02731, MIR99AHG, INHBA‐AS1, CCDC144NL‐AS1, VLDLR‐AS1, LIFR‐AS1, A2M‐AS1, LINC01537, and LINC00702, and were associated with OS, and nine of those lncRNAs were considered as hub lncRNAs involved in the sub‐ceRNA network. The in vitro assay indicated two lncRNAs, INHBA‐AS1 and CCDC144NL‐AS1, which were positively related to the GC aggressive features, including proliferation, invasion, and migration.

Conclusions

We identified nine hub lncRNAs and the associated ceRNA network related to the prognosis of GC, and then validated two out of them as promising oncogenes in GC.

Long Non-Coding RNA A2M-AS1 Promotes Breast Cancer Progression by Sponging microRNA-146b to Upregulate MUC19

Background

Long non-coding RNA (lncRNA) A2M-AS1 has been indicated to be augmented in breast cancer (BC), with its specific function undetermined. Therefore, this study is designed to investigate the mechanism of lncRNA A2M-AS1 in BC.

Methods

The expression of A2M-AS1, microRNA (miR)-146b, and MUC19 in BC tissues and cells was measured. Then, the interaction among A2M-AS1, miR-146b, and MUC19 was detected. After A2M-AS1, miR-146b, and MUC19 expression were altered in BC cells, cell proliferation, invasion, and apoptosis were detected, and the protein levels of Hippo-related proteins (YAP and p-YAP) were evaluated. Tumor growth assay was also performed to validate the effects of A2M-AS1 and miR-146b in vivo.

Results

A2M-AS1 and MUC19 were highly expressed in BC, while miR-146b was poorly expressed. A2M-AS1 acts as a molecular sponge for miR-146b, which targeted and negatively modulated MUC19. A2M-AS1 accelerated BC cell proliferation, invasion, and colony formation and suppressed apoptosis via the miR-146b/MUC19/Hippo axis, which was confirmed in vivo.

Conclusion

Taken above together, an oncogenic role for A2M-AS1 in BC was elicited by acting as a miR-146b sponge to promote MUC19 expression. The findings will present some cues for a further approach to BC.