Long Non-Coding RNA HULC Promotes the Development of Breast Cancer Through Regulating LYPD1 Expression by Sponging miR-6754-5p

AMPFLDAP: Adaptive Message Passing and Feature Fusion on Heterogeneous Network for LncRNA-Disease Associations Prediction

Exploration of the intricate connections between long noncoding RNA (lncRNA) and diseases, referred to as lncRNA-disease associations (LDAs), plays a pivotal and indispensable role in unraveling the underlying molecular mechanisms of diseases and devising practical treatment approaches. It is imperative to employ computational methods for predicting lncRNA-disease associations to circumvent the need for superfluous experimental endeavors. Graph-based learning models have gained substantial popularity in predicting these associations, primarily because of their capacity to leverage node attributes and relationships within the network. Nevertheless, there remains much room for enhancing the performance of these techniques by incorporating and harmonizing the node attributes more effectively. In this context, we introduce a novel model, i.e., Adaptive Message Passing and Feature Fusion (AMPFLDAP), for forecasting lncRNA-disease associations within a heterogeneous network. Firstly, we constructed a heterogeneous network involving lncRNA, microRNA (miRNA), and diseases based on established associations and employing Gaussian interaction profile kernel similarity as a measure. Then, an adaptive topological message passing mechanism is suggested to address the information aggregation for heterogeneous networks. The topological features of nodes in the heterogeneous network were extracted based on the adaptive topological message passing mechanism. Moreover, an attention mechanism is applied to integrate both topological and semantic information to achieve the multimodal features of biomolecules, which are further used to predict potential LDAs. The experimental results demonstrated that the performance of the proposed AMPFLDAP is superior to seven state-of-the-art methods. Furthermore, to validate its efficacy in practical scenarios, we conducted detailed case studies involving three distinct diseases, which conclusively demonstrated AMPFLDAP's effectiveness in the prediction of LDAs.

Association of serum Ly6/PLAUR domain-containing protein 1 levels with skin sclerosis in systemic sclerosis

Systemic sclerosis (SSc) is a connective tissue disease characterized by aberrant immune activation, vascular injury, and fibrosis of the skin and internal organs. Ly6/PLAUR domain-containing protein 1 (LYPD1) was reported to be secreted and to have various physiological functions such as anti-angiogenic effects. Here we investigated serum LYPD1 levels in SSc patients and the association of serum LYPD1 levels with clinical features of SSc. Serum samples were obtained from 75 SSc patients and 22 healthy individuals as controls. We measured serum LYPD1 levels using enzyme-linked immunosorbent assay kits. Then, the relationship between serum LYPD1 levels and clinical features of SSc was analyzed. Serum LYPD1 levels in diffuse cutaneous SSc (dcSSc) patients were significantly higher than those in the limited cutaneous SSc (lcSSc) patients (median [25-75th percentiles], 1693.43 [1086.61-1917.57] vs. 904.55 [714.356-1285.56] pg/mL), while there were no significant differences in the serum LYPD1 levels between lcSSc and healthy controls (904.55 [714.356-1285.56] vs. 750.71 pg/mL [544.00-912.14]). Further analysis revealed that serum LYPD1 levels in patients correlated with skin thickness scores and serum interleukin (IL)-6 levels, which were known to reflect the extent of skin thickening in SSc. Moreover, serum LYPD1 levels showed a decrease with improvement in skin thickness after treatment, along with a decrease in serum IL-6 levels. These results indicate that LYPD1 might be a potential marker for monitoring skin sclerosis and evaluating the efficacy of skin fibrosis treatment in SSc patients.

A novel anoikis-related gene signature identifies LYPD1 as a novel therapy target for bladder cancer

Bladder cancer (BLCA) is a malignant tumor associated with unfavorable outcomes. Studies suggest that anoikis plays a crucial role in tumor progression and cancer cell metastasis. However, its specific role in bladder cancer remains poorly understood. Our objective was to identify anoikis-related genes (ARGs) and subsequently construct a risk model to assess their potential for predicting the prognosis of bladder cancer.The transcriptome data and clinical data of BLCA patients were sourced from The Cancer Genome Atlas and GEO database. We then performed the differential expression analysis to screen differentially expressed ARGs. Subsequently, we conducted non-negative matrix factorization (NMF) clustering analysis to establish molecular subtypes based on the differentially expressed ARGs. The CIBERSORT algorithm was used to estimate the quantification of different cell infiltration in BLCA tumor microenviroment. A prognostic risk model containing 7 ARGs was established using Lasso-Cox regression analysis. The nomogram was built for predicting the survival probability of BLCA patients. To determine the drug sensitivity of each sample from the high- and low-risk groups, the R package "pRRophetic" was performed. Finally, the role of LYPD1 was explored in BLCA cell lines.We identified 90 differential expression ARGs and NMF clustering categorizated the BLCA patientss into two distinct groups (cluster A and B). Patients in cluster A had a better prognosis than those in cluster B. Then, we established a ARGs risk model including CALR, FASN, FOSL1, JUN, LYPD1, MST1R, and SATB1, which was validated in the train and test set. The results suggested overall survival rate was much higher in low risk group than high risk group. The cox regression analysis, ROC curve analysis, and nomogram collectively demonstrated that the risk model served as an independent prognostic factor. The high risk group had a higher level TME scores compared to the low risk group. Furthermore, LYPD1 was low expression in BLCA cells and overexpression of LYPD1 inhibits the prolifearation, migration and invasion.In the current study, we have identified differential expression ARGs and constructed a risk model with the promise for guiding prognostic predictions and provided a therapeutic target for patients with BLCA.

Breast cancer risk is associated with the HULC rs7763881, MTMR3 rs12537 polymorphisms, and serum levels of HULC and MTMR3 in Egyptian patients

BACKGROUND

Highly upregulated in liver cancer (HULC) is one of the LncRNAs that was documented to enhance cancer progression, and its downregulation is associated with cell cycle arrest and apoptosis. Myotubularin-related protein 3 (MTMR3) is required for autophagy, and many studies consider MTMR3 to be a negative regulator of autophagy processes. However, nothing is understood about how they regulate breast cancer.

MATERIAL AND METHODS

This case-control study included 245 patients (Group A: 85 early BC Group B: 40 metastatic BC cases, Group C: 40 fibroadenoma cases; and Group D: 80 age matched healthy control subjects. TaqMan Real-time PCR was used to analyse rs7158663 and rs12537. MTMR3 and HULC gene expression levels were measured using RT-PCR.

RESULT

Breast cancer patients exhibited elevated serum MTMR3 and HULC compared to fibroadenomas and control cases. The MTMR3 rs12537 "T/T" genotype was highly expressed in cases of breast cancer (early and metastatic) compared to controls (risk genotype). On the other hand, the HULC rs7158663 genotypes were not statistically associated with breast cancer. However, when compared to the control, the C/C genotype of the HULC gene is higher in the case.MTMR3 gene expression was higher in the T/T genotype compared to both the C/C and C/T genotypes, while HULC gene expression was lower in the A/C genotype compared to both the A/A and C/C genotypes. Positive correlation between MTMR3 and HULC. MTMR3 and ALT, as well as HULC and alkaline phosphatase, both showed a statistically significant positive correlation.

CONCLUSION

Our findings reveal that MTMR3 and HULC serum expression and their SNPs (HULC rs7763881, MTMR3 rs12537) are associated with a higher risk for the development of breast cancer in the Egyptian population.

Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization

Breast cancer and colorectal cancer are two of the most common malignant tumors worldwide. They cause the leading causes of cancer mortality. Many researches have demonstrated that long noncoding RNAs (lncRNAs) have close linkages with the occurrence and development of the two cancers. Therefore, it is essential to design an effective way to identify potential lncRNA biomarkers for them. In this study, we developed a computational method (LDA-RWLMF) by integrating random walk with restart and Logistic Matrix Factorization to investigate the roles of lncRNA biomarkers in the prognosis and diagnosis of the two cancers. We first fuse disease semantic and Gaussian association profile similarities and lncRNA functional and Gaussian association profile similarities. Second, we design a negative selection algorithm to extract negative LncRNA-Disease Associations (LDA) based on random walk. Third, we develop a logistic matrix factorization model to predict possible LDAs. We compare our proposed LDA-RWLMF method with four classical LDA prediction methods, that is, LNCSIM1, LNCSIM2, ILNCSIM, and IDSSIM. The results from 5-fold cross validation on the MNDR dataset show that LDA-RWLMF computes the best AUC value of 0.9312, outperforming the above four LDA prediction methods. Finally, we rank all lncRNA biomarkers for the two cancers after determining the performance of LDA-RWLMF, respectively. We find that 48 and 50 lncRNAs have the highest association scores with breast cancer and colorectal cancer among all lncRNAs known to associate with them on the MNDR dataset, respectively. We predict that lncRNAs HULC and HAR1A could be separately potential biomarkers for breast cancer and colorectal cancer and need to biomedical experimental validation.

Coding roles of long non-coding RNAs in breast cancer: Emerging molecular diagnostic biomarkers and potential therapeutic targets with special reference to chemotherapy resistance

Dysregulation of epigenetic mechanisms have been depicted in several pathological consequence such as cancer. Different modes of epigenetic regulation (DNA methylation (hypomethylation or hypermethylation of promotor), histone modifications, abnormal expression of microRNAs (miRNAs), long non-coding RNAs, and small nucleolar RNAs), are discovered. Particularly, lncRNAs are known to exert pivot roles in different types of cancer including breast cancer. LncRNAs with oncogenic and tumour suppressive potential are reported. Differentially expressed lncRNAs contribute a remarkable role in the development of primary and acquired resistance for radiotherapy, endocrine therapy, immunotherapy, and targeted therapy. A wide range of molecular subtype specific lncRNAs have been assessed in breast cancer research. A number of studies have also shown that lncRNAs may be clinically used as non-invasive diagnostic biomarkers for early detection of breast cancer. Such molecular biomarkers have also been found in cancer stem cells of breast tumours. The objectives of the present review are to summarize the important roles of oncogenic and tumour suppressive lncRNAs for the early diagnosis of breast cancer, metastatic potential, and chemotherapy resistance across the molecular subtypes.

Roles and Mechanisms of Long Non-Coding RNAs in Breast Cancer

Breast cancer is a major health threat and the second leading cause of cancer-related deaths in women worldwide. The detailed mechanisms involved in the initiation and progression of breast cancer remain unclear. In recent years, amounting evidence indicated that long non-coding RNAs (lncRNAs) played crucial roles in regulating various biological processes and malignancy tumors, including breast cancer. In this review, we briefly introduce the functions and underlying mechanisms by which lncRNAs are involved in breast cancer. We summarize the roles of the lncRNAs in regulating malignant behaviors of breast cancer, such as cell proliferation, migration and invasion, epithelial-mesenchymal transition (EMT), apoptosis, and drug resistance. Additionally, we also briefly summarize the roles of circular RNAs (circRNAs) in breast cancer carcinogenesis.

HULC targets the IGF1R-PI3K-AKT axis in trans to promote breast cancer metastasis and cisplatin resistance

Insulin-like growth factor I receptor (IGF1R) is frequently upregulated in breast cancer. Due to its intrinsic tyrosine kinase activity, aberrant activation of the IGF1R signaling axis may enhance tumor cell proliferation and cancer stemness, causing tumor relapse, metastasis and resistance to chemotherapy. We utilized a chromatin RNA in situ reverse transcription (CRIST) approach to characterize molecular factors that regulate the IGF1R network. We identified lncRNA HULC (Highly Upregulated in Liver Cancer) as a key trans-regulator of IGF1R in breast cancer cells. Loss of HULC suppressed the expression of IGF1R and the activation of its downstream PI3K/AKT pathway, while HULC overexpression activated the axis in breast cancer cells. Using a transcription-associated trap (RAT) assay, we demonstrated that HULC functioned as a nuclear lncRNA and epigenetically activated IGF1R by directly binding to the intragenic regulatory elements of the gene, orchestrating intrachromosomal interactions, and promoting histone H3K9 acetylation. The activated HULC-IGF1R/PI3K/AKT pathway mediated tumor resistance to cisplatin through the increased expression of cancer stemness markers, including NANOG, SOX2, OCT4, CD44 and ALDH1A1. In immunodeficient mice, stimulation of the HULC-IGF1R pathway promoted tumor metastasis. These data suggest that HULC may be a new epigenetic target for IGF1R axis-targeted therapeutic intervention.

DeepMNE: Deep Multi-network Embedding for lncRNA-Disease Association prediction

Long non-coding RNA (lncRNA) participates in various biological processes, hence its mutations and disorders play an important role in the pathogenesis of multiple human diseases. Identifying disease-related lncRNAs is crucial for the diagnosis, prevention, and treatment of diseases. Although a large number of computational approaches have been developed, effectively integrating multi-omics data and accurately predicting potential lncRNA-disease associations remains a challenge, especially regarding new lncRNAs and new diseases. In this work, we propose a new method with deep multi-network embedding, called DeepMNE, to discover potential lncRNA disease associations, especially for novel diseases and lncRNAs. DeepMNE extracts multi-omics data to describe diseases and lncRNAs, and proposes a network fusion method based on deep learning to integrate multi-source information. Moreover, DeepMNE complements the sparse association network and uses kernel neighborhood similarity to construct disease similarity and lncRNA similarity networks. Furthermore, A graph embedding method is adopted to predict potential associations. Experimental results demonstrate that compared to other state-of-the-art methods, DeepMNE has a higher predictive performance on new associations, new lncRNAs and new diseases. Besides, DeepMNE also elicits a considerable predictive performance on perturbed datasets. Additionally, the results of two different types of case studies indicate that DeepMNE can be used as an effective tool for disease-related lncRNA prediction. The code of DeepMNE is freely available at https://github.com/Mayingjun20179/ DeepMNE.

Whole-genome-scale identification of novel non-protein-coding RNAs controlling cell proliferation and survival through a functional forward genetics strategy

Identification of cell fate-controlling lncRNAs is essential to our understanding of molecular cell biology. Here we present a human genome-scale forward-genetics approach for the identification of lncRNAs based on gene function. This approach can identify genes that play a causal role, and immediately distinguish them from those that are differentially expressed but do not affect cell function. Our genome-scale library plus next-generation-sequencing and bioinformatic approach, radically upscales the breadth and rate of functional ncRNA discovery. Human gDNA was digested to produce a lentiviral expression library containing inserts in both sense and anti-sense orientation. The library was used to transduce human Jurkat T-leukaemic cells. Cell populations were selected using continuous culture ± anti-FAS IgM, and sequencing used to identify sequences controlling cell proliferation. This strategy resulted in the identification of thousands of new sequences based solely on their function including many ncRNAs previously identified as being able to modulate cell survival or to act as key cancer regulators such as AC084816.1*, AC097103.2, AC087473.1, CASC15*, DLEU1*, ENTPD1-AS1*, HULC*, MIRLET7BHG*, PCAT-1, SChLAP1, and TP53TG1. Independent validation confirmed 4 out of 5 sequences that were identified by this strategy, conferred a striking resistance to anti-FAS IgM-induced apoptosis.

Association between the rs3802201 polymorphism of the lncRNA MIR2052HG gene and the risk of recurrent miscarriage in a Southern Chinese population

BACKGROUND

Plenty of studies have indicated that some genetic polymorphisms of the breast cancer which associated with its susceptibility may also be related to the susceptibility of abortion. MIR2052HG plays an important role in the onset and progression of breast cancer by maintaining the level of ERα, but to the best of our knowledge, the correlation between risk of recurrent abortion and MIR2052HG rs3802201 C>G polymorphism is still unclear. Therefore, we conducted this case-control study to investigate whether MIR2052HG rs3802201 C>G polymorphism is associated with susceptibility of recurrent miscarriage (RM).

METHODS

We recruited 392 healthy controls and 248 patients with RM to process this research, the participants were all from southern China, and genotyping was performed by TaqMan method.

RESULTS

Our results showed that there was no evidence indicates the MIR2052HG rs3802201 C>G is related to RM (CG and CC: adjusted OR = 0.970, 95% CI = 0.694-1.355, p = 0.8577; GG and CC: adjusted OR = 0.743, 95% CI = 0.416-1.330, p = 0.3174; dominant model: adjusted OR = 0.925, 95% CI = 0.672-1.272, p = 0.6298; recessive model: adjusted OR = 0.751, 95% CI = 0.430-1.321, p = 0.3233).

CONCLUSION

We verified that the MIR2052HG rs3802201 C>G allele might be uncorrelated to the RM risk, but these findings require further validation in multicenter studies with larger sample size and different ethnicities.

What is beyond LncRNAs in breast cancer: A special focus on colon cancer-associated Transcript-1 (CCAT-1)

Long non-coding RNAs (LncRNAs) play a vital role in the process of malignant transformation. In breast cancer (BC), lncRNAs field is currently under intensive investigations. Yet, the role of lncRNAs as promising diagnostic and/or prognostic biomarkers and as therapeutic target/tool among BC patients still needs a special focus from the biomedical scientists. In BC, triple negative breast cancer patients (TNBC) are the unlucky group as they are always represented with the worst prognosis and the highest mortality rates. For that reason, a special focus on TNBC and associated lncRNAs was addressed in this review. Colon cancer-associated transcript 1 (CCAT-1) is a newly discovered oncogenic lncRNA that has been emerged as a vital biomarker for diagnosis, prognosis and therapeutic interventions in multiple malignancies and showed differential expression among TNBC patients. In this review, the authors shed the light onto the general role of lncRNAs in BC and the specific functional activities, molecular mechanisms, competing endogenous ncRNA role of CCAT-1 in TNBC.

lncRNA and breast cancer: Progress from identifying mechanisms to challenges and opportunities of clinical treatment

Breast cancer is a malignant tumor that has a high mortality rate and mostly occurs in women. Although significant progress has been made in the implementation of personalized treatment strategies for molecular subtypes in breast cancer, the therapeutic response is often not satisfactory. Studies have reported that long non-coding RNAs (lncRNAs) are abnormally expressed in breast cancer and closely related to the occurrence and development of breast cancer. In addition, the high tissue and cell-type specificity makes lncRNAs particularly attractive as diagnostic biomarkers, prognostic factors, and specific therapeutic targets. Therefore, an in-depth understanding of the regulatory mechanisms of lncRNAs in breast cancer is essential for developing new treatment strategies. In this review, we systematically elucidate the general characteristics, potential mechanisms, and targeted therapy of lncRNAs and discuss the emerging functions of lncRNAs in breast cancer. Additionally, we also highlight the advantages and challenges of using lncRNAs as biomarkers for diagnosis or therapeutic targets for drug resistance in breast cancer and present future perspectives in clinical practice.

Multifaceted roles of long non-coding RNAs in triple-negative breast cancer: biology and clinical applications

Triple-negative breast cancer (TNBC) is a heterogeneous breast cancer subtype that lacks targeted therapy due to the absence of estrogen, progesterone, and HER2 receptors. Moreover, TNBC was shown to have a poor prognosis, since it involves aggressive phenotypes that confer significant hindrance to therapeutic treatments. Recent state-of-the-art sequencing technologies have shed light on several long non-coding RNAs (lncRNAs), previously thought to have no biological function and were considered as genomic junk. LncRNAs are involved in various physiological as well as pathological conditions, and play a key role in drug resistance, gene expression, and epigenetic regulation. This review mainly focuses on exploring the multifunctional roles of candidate lncRNAs, and their strong association with TNBC development. We also summarise various emerging research findings that establish novel paradigms of lncRNAs function as oncogenes and/or tumor suppressors in TNBC development, suggesting their role as prospective therapeutic targets.

Roles of miRNA and lncRNA in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is currently the most malignant subtype of breast cancer without effective targeted therapies, which makes its pathogenesis an important target for research. A growing number of studies have shown that non-coding RNA (ncRNA), including microRNA (miRNA) and long non-coding RNA (lncRNA), plays a significant role in tumorigenesis. This review summarizes the roles of miRNA and lncRNA in the progression, diagnosis, and neoadjuvant chemotherapy of TNBC. Aberrantly expressed miRNA and lncRNA are listed according to their roles. Further, it describes the multiple mechanisms that lncRNA shows for regulating gene expression in the nucleus and cytoplasm, and more importantly, describes lncRNA-regulated TNBC progression through complete combining with miRNA at the post-transcriptional level. Focusing on miRNA and lncRNA associated with TNBC can provide new insights for early diagnosis and treatment-they can be targeted in the future as a novel anticancer target of TNBC.

Dynamics of m6A RNA Methylome on the Hallmarks of Hepatocellular Carcinoma

Epidemiological data consistently rank hepatocellular carcinoma (HCC) as one of the leading causes of cancer-related deaths worldwide, often posing severe economic burden on health care. While the molecular etiopathogenesis associated with genetic and epigenetic modifications has been extensively explored, the biological influence of the emerging field of epitranscriptomics and its associated aberrant RNA modifications on tumorigenesis is a largely unexplored territory with immense potential for discovering new therapeutic approaches. In particular, the underlying cellular mechanisms of different hallmarks of hepatocarcinogenesis that are governed by the complex dynamics of m6A RNA methylation demand further investigation. In this review, we reveal the up-to-date knowledge on the mechanistic and functional link between m6A RNA methylation and pathogenesis of HCC.

Dual relationship between long non-coding RNAs and STAT3 signaling in different cancers: New insight to proliferation and metastasis

Uncontrolled growth and metastasis of cancer cells is an increasing challenge for overcoming cancer, and improving survival of patients. Complicated signaling networks account for proliferation and invasion of cancer cells that need to be elucidated for providing effective cancer therapy, and minimizing their malignancy. Long non-coding RNAs (lncRNAs) are RNA molecules with a length of more than 200 nucleotides. They participate in cellular events, and their dysregulation in a common phenomenon in different cancers. Noteworthy, lncRNAs can regulate different molecular pathways, and signal transducer and activator of transcription 3 (STAT3) is one of them. STAT3 is a tumor-promoting factors in cancers due to its role in cancer proliferation (cell cycle progression and apoptosis inhibition) and metastasis (EMT induction). LncRNAs can function as upstream mediators of STAT3 pathway, reducing/enhancing its expression. This dual relationship is of importance in affecting proliferation and metastasis of cancer cells. The response of cancer cells to therapy such as chemotherapy and radiotherapy is regulated by lncRNA/STAT3 axis. Tumor-promoting lncRNAs including NEAT1, SNHG3 and H19 induces STAT3 expression, while tumor-suppressing lncRNAs such as MEG3, PTCSC3 and NKILA down-regulate STAT3 expression. Noteworthy, upstream mediators of STAT3 such as microRNAs can be regulated by lncRNAs. These complicated signaling networks are mechanistically described in the current review.

RETRACTED: LncRNA GAS6-AS1 facilitates the progression of breast cancer by targeting the miR-324-3p/SETD1A axis to activate the PI3K/AKT pathway

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the current Editor in Chief, with the agreement of all authors. The article is being retracted due to inappropriate manipulation of the original data for presentation in Figures 2C, 5C and 6C. Upon further investigation and discussion with the authors, insufficient evidence was provided to support a reasonable explanation for these mistakes. In addition, the authors could not provide convincing original data supporting other figures of the manuscript, e.g. Figures 2G, 4 and 6A. Based on these findings, the Editor in Chief and the authors have decided to retract the study. The authors apologize for any inconvenience caused.

LncRNA HULC shRNA disinhibits miR-377-5p to suppress the growth and invasion of hepatocellular carcinoma in vitro and hepatocarcinogenesis in vivo

Background

Aberrant expression of up-regulated long non-coding RNA [LncRNA highly upregulated in liver cancer (HULC)] has been observed to play an important regulatory role in the development of multiple human diseases. However, the molecular mechanism underlying the role of HULC and miR-377-5p in HCC needs to be urgently explored.

Methods

The mRNA and protein expression levels of HULC were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and western blot in hepatocellular carcinoma (HCC) cell line HB611, HepG2 and H22, respectively. HULC-shRNA was transfected into HepG-2 cells, which were randomly divided into the control, shRNA-NC, and sh-HULC groups. The correlation between HULC and miR-377-5p was analyzed by performing a luciferase reporter assay. The targeting relationship between miR-377-5p and hypoxia-inhibitory factor-1α (HIF-1α) was also investigated using a luciferase reporter assay. Sh-HULC and miR-377-5p inhibitors were transfected either alone or together into HepG2 cells, and which were divided into the control group, the sh-HULC group, the miR-377-5p inhibitor, and the sh-HULC + inhibitor group for subsequent experiments. HepG2 cell proliferation and invasion were measured by 5-Ethynyl-2-Deoxyuridine (EdU) staining and Transwell invasion assay, respectively. Western plot was carried out to detect the protein expression levels of Ki67, PCNA, E-cadherin, and N-cadherin. Tumor xenograft mouse models were established to confirm the effect of HULC down-regulation on the development of HCC in vivo.

Results

The mRNA and protein expression levels of HULC were markedly increased, whereas the mRNA expression levels of miR-377-5p were decreased in HCC cell lines. HepG2 cell proliferation and invasion were suppressed in the Sh-HULC group, while miR-377-5p showed the opposite. Further experiments exhibited that miR-377-5p was targeted by HULC, and an negative correlation between HULC and miR-377-5p was observed. Importantly, the in vivo experiments indicated that down-regulation of HULC could inhibit tumor growth. Taken together, our research demonstrated that down-regulation of HULC plays an anti-cancer role through restrainingHepG2 cell proliferation and invasion.

Conclusions

In summary, our in vitro and in vivo findings confirmed HULC to play a role in the progression of HCC, with the underlying mechanism possibly involving the miR-377-5p/HIF-1α pathway.

RNA-Binding Proteins as Important Regulators of Long Non-Coding RNAs in Cancer

The majority of the genome is transcribed into pieces of non-(protein) coding RNA, among which long non-coding RNAs (lncRNAs) constitute a large group of particularly versatile molecules that govern basic cellular processes including transcription, splicing, RNA stability, and translation. The frequent deregulation of numerous lncRNAs in cancer is known to contribute to virtually all hallmarks of cancer. An important regulatory mechanism of lncRNAs is the post-transcriptional regulation mediated by RNA-binding proteins (RBPs). So far, however, only a small number of known cancer-associated lncRNAs have been found to be regulated by the interaction with RBPs like human antigen R (HuR), ARE/poly(U)-binding/degradation factor 1 (AUF1), insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1), and tristetraprolin (TTP). These RBPs regulate, by various means, two aspects in particular, namely the stability and the localization of lncRNAs. Importantly, these RBPs themselves are commonly deregulated in cancer and might thus play a major role in the deregulation of cancer-related lncRNAs. There are, however, still many open questions, for example regarding the context specificity of these regulatory mechanisms that, in part, is based on the synergistic or competitive interaction between different RBPs. There is also a lack of knowledge on how RBPs facilitate the transport of lncRNAs between different cellular compartments.