Cloning of the mRNA of overexpression in colon carcinoma-1: a sequence overexpressed in a subset of colon carcinomas

Targeted splicing therapy: new strategies for colorectal cancer

RNA splicing is the process of forming mature mRNA, which is an essential phase necessary for gene expression and controls many aspects of cell proliferation, survival, and differentiation. Abnormal gene-splicing events are closely related to the development of tumors, and the generation of oncogenic isoform in splicing can promote tumor progression. As a main process of tumor-specific splicing variants, alternative splicing (AS) can promote tumor progression by increasing the production of oncogenic splicing isoforms and/or reducing the production of normal splicing isoforms. This is the focus of current research on the regulation of aberrant tumor splicing. So far, AS has been found to be associated with various aspects of tumor biology, including cell proliferation and invasion, resistance to apoptosis, and sensitivity to different chemotherapeutic drugs. This article will review the abnormal splicing events in colorectal cancer (CRC), especially the tumor-associated splicing variants arising from AS, aiming to offer an insight into CRC-targeted splicing therapy.

Predicting lncRNA-protein interactions with bipartite graph embedding and deep graph neural networks

Background: Long non-coding RNAs (lncRNAs) play crucial roles in numerous biological processes. Investigation of the lncRNA-protein interaction contributes to discovering the undetected molecular functions of lncRNAs. In recent years, increasingly computational approaches have substituted the traditional time-consuming experiments utilized to crack the possible unknown associations. However, significant explorations of the heterogeneity in association prediction between lncRNA and protein are inadequate. It remains challenging to integrate the heterogeneity of lncRNA-protein interactions with graph neural network algorithms. Methods: In this paper, we constructed a deep architecture based on GNN called BiHo-GNN, which is the first to integrate the properties of homogeneous with heterogeneous networks through bipartite graph embedding. Different from previous research, BiHo-GNN can capture the mechanism of molecular association by the data encoder of heterogeneous networks. Meanwhile, we design the process of mutual optimization between homogeneous and heterogeneous networks, which can promote the robustness of BiHo-GNN. Results: We collected four datasets for predicting lncRNA-protein interaction and compared the performance of current prediction models on benchmarking dataset. In comparison with the performance of other models, BiHo-GNN outperforms existing bipartite graph-based methods. Conclusion: Our BiHo-GNN integrates the bipartite graph with homogeneous graph networks. Based on this model structure, the lncRNA-protein interactions and potential associations can be predicted and discovered accurately.

Recent advances in predicting lncRNA-disease associations based on computational methods

Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA-disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effectively narrow down the screening scope of biological experiments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.

Cancer susceptibility 18 positively regulates NUAK Family Kinase 1 expression to promote migration and invasion via sponging of miR-5586-5p in cervical cancer cells

INTRODUCTION

Cervical squamous cell carcinoma (CESC) is the most common gynecological malignancy worldwide. Although the cancer susceptibility 18 (CASC18) gene was involved in the regulation of cancer biology, its specific role in CESC is not well characterized.

METHODS

CASC18-related axis was predicted by bioinformatic analyses, and the competing endogenous RNA (ceRNA) interaction was further validated using quantitative real-time PCR, western blotting, RNA pulldown, and luciferase reporter assays. Transwell and wound healing assays were performed to verify the effect of CASC18 on SiHa and HeLa cell motility.

RESULTS

We found that CASC18 was upregulated in CESC tissues. Moreover, interference with CASC18 attenuated NUAK1-mediated epithelial-mesenchymal transition (EMT) and thus suppressed cancer cell motility. Furthermore, the effects of CASC18 knockdown on CESC cells were partly rescued by transfection with the miR-5586-5p inhibitor. Additionally, our findings indicated that CASC18 acts as a ceRNA to enhance NUAK1 expression by sponging miR-5586-5p.

CONCLUSION

Our study showed a novel CASC18/miR-5586-5p/NUAK1 ceRNA axis that could regulate cell invasion and migration by modulating EMT in CESC. These findings suggest that CASC18 may potentially serve as a novel therapeutic target in CESC treatment.

Recent advances in machine learning methods for predicting LncRNA and disease associations

Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists' understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.

Long non-coding RNA and RNA-binding protein interactions in cancer: Experimental and machine learning approaches

Understanding the complex and specific roles played by non-coding RNAs (ncRNAs), which comprise the bulk of the genome, is important for understanding virtually every hallmark of cancer. This large group of molecules plays pivotal roles in key regulatory mechanisms in various cellular processes. Regulatory mechanisms, mediated by long non-coding RNA (lncRNA) and RNA-binding protein (RBP) interactions, are well documented in several types of cancer. Their effects are enabled through networks affecting lncRNA and RBP stability, RNA metabolism including N⁶-methyladenosine (m⁶A) and alternative splicing, subcellular localization, and numerous other mechanisms involved in cancer. In this review, we discuss the reciprocal interplay between lncRNAs and RBPs and their involvement in epigenetic regulation via histone modifications, as well as their key role in resistance to cancer therapy. Other aspects of RBPs including their structural domains, provide a deeper knowledge on how lncRNAs and RBPs interact and exert their biological functions. In addition, current state-of-the-art knowledge, facilitated by machine and deep learning approaches, unravels such interactions in better details to further enhance our understanding of the field, and the potential to harness RNA-based therapeutics as an alternative treatment modality for cancer are discussed.

LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA-protein interaction identification

Background

Long noncoding RNAs (lncRNAs) play important roles in various biological and pathological processes. Discovery of lncRNA–protein interactions (LPIs) contributes to understand the biological functions and mechanisms of lncRNAs. Although wet experiments find a few interactions between lncRNAs and proteins, experimental techniques are costly and time-consuming. Therefore, computational methods are increasingly exploited to uncover the possible associations. However, existing computational methods have several limitations. First, majority of them were measured based on one simple dataset, which may result in the prediction bias. Second, few of them are applied to identify relevant data for new lncRNAs (or proteins). Finally, they failed to utilize diverse biological information of lncRNAs and proteins.

Results

Under the feed-forward deep architecture based on gradient boosting decision trees (LPI-deepGBDT), this work focuses on classify unobserved LPIs. First, three human LPI datasets and two plant LPI datasets are arranged. Second, the biological features of lncRNAs and proteins are extracted by Pyfeat and BioProt, respectively. Thirdly, the features are dimensionally reduced and concatenated as a vector to represent an lncRNA–protein pair. Finally, a deep architecture composed of forward mappings and inverse mappings is developed to predict underlying linkages between lncRNAs and proteins. LPI-deepGBDT is compared with five classical LPI prediction models (LPI-BLS, LPI-CatBoost, PLIPCOM, LPI-SKF, and LPI-HNM) under three cross validations on lncRNAs, proteins, lncRNA–protein pairs, respectively. It obtains the best average AUC and AUPR values under the majority of situations, significantly outperforming other five LPI identification methods. That is, AUCs computed by LPI-deepGBDT are 0.8321, 0.6815, and 0.9073, respectively and AUPRs are 0.8095, 0.6771, and 0.8849, respectively. The results demonstrate the powerful classification ability of LPI-deepGBDT. Case study analyses show that there may be interactions between GAS5 and Q15717, RAB30-AS1 and O00425, and LINC-01572 and P35637.

Conclusions

Integrating ensemble learning and hierarchical distributed representations and building a multiple-layered deep architecture, this work improves LPI prediction performance as well as effectively probes interaction data for new lncRNAs/proteins.

LDAH2V: Exploring Meta-Paths Across Multiple Networks for lncRNA-Disease Association Prediction

Accumulating evidence has demonstrated dysfunctions of long non-coding RNAs (lncRNAs) are involved in various complex human diseases. However, even today, the relationships between lncRNAs and diseases remain unknown in most cases. Developing effective computational approaches to identify potential lncRNA-disease associations has become a hot topic. Existing network-based approaches are usually focused on the intrinsic features of lncRNAs and diseases but ignore the heterogeneous information of biological networks. Considering the limitations in previous methods, we propose LDAH2V, an efficient computational framework for predicting potential lncRNA-disease associations. LDAH2V uses the HIN2Vec to calculate the meta-path and feature vector for each lncRNA-disease pair in the heterogeneous information network (HIN), which consists of lncRNA similarity network, disease similarity network, miRNA similarity network, and the associations between them. Then, a Gradient Boosting Tree (GBT) classifier to predict lncRNA-disease associations is built with the feature vectors. The results show that LDAH2V performs significantly better than the four existing state-of-the-art methods and gains an AUC of 0.97 in the 10-fold cross-validation test. Furthermore, case studies of colon cancer and ovarian cancer-related lncRNAs have been confirmed in related databases and medical literature.

Implications of protein ubiquitination modulated by lncRNAs in gastrointestinal cancers

Long non-coding RNAs (lncRNAs) are a class of RNA transcripts longer than 200 nucleotides and mostly cannot be translated into proteins. Next-generation transcriptome sequencing of various cell types has enabled the annotation of tens of thousands of lncRNAs in human genome. Varying levels of evidence supports the implications of lncRNAs in the onset and progression of cancers. Ubiquitin is an evolutionarily conserved protein and could post-translationally mark a number of proteins. The most important proteolytic role of ubiquitination is degradation of substrate proteins by the 26S proteasome. Compiling evidences demonstrated that lncRNAs are involved in the accurate execution of protein stability programs via the ubiquitin-proteasome system. In the current review, we systematically summarize the detailed mechanisms how lncRNAs modulate ubiquitination of target proteins, regulate cancerous signaling pathways and control tumorigenesis of gastrointestinal cancers. Although there are still considerable studies on unraveling the complicated interactions between lncRNAs and proteins, we believe that lncRNAs are promising but challenging molecules which may strongly facilitate precision cancer therapeutics in the future.

Long Noncoding RNA NEAT1 as a Potential Candidate Biomarker for Prostate Cancer

Background: Prostate cancer (PCa) remains one of the leading causes of cancer-related mortality in men worldwide, mainly due to unsatisfactory diagnostic methods used at present, which lead to overdiagnosis, unnecessary biopsies and treatment, or misdiagnosis in early asymptomatic stages. New diagnostic biomarkers are needed for a correct and early diagnosis. Long noncoding RNAs (lncRNAs) have been broadly studied for their involvement in PCa biology, as well as for their potential role as diagnostic biomarkers. Methods: We conducted lncRNA profiling in plasma and microdissected formalin-fixed paraffin-embedded (FFPE) tissues of PCa patients and attempted validation for commonly dysregulated individual lncRNAs. Results: Plasma profiling revealed eight dysregulated lncRNAs, while microarray analysis revealed 717 significantly dysregulated lncRNAs, out of which only nuclear-enriched abundant transcript 1 (NEAT1) was commonly upregulated in plasma samples and FFPE tissues. NEAT1’s individual validation revealed statistically significant upregulation (FC = 2.101, p = 0.009). Receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) value of 0.7298 for NEAT1 (95% CI = 0.5812–0.8785), suggesting a relatively high diagnostic value, thus having a potential biomarker role for this malignancy. Conclusions: We present herein data suggesting that NEAT1 could serve as a diagnostic biomarker for PCa. Additional studies of larger cohorts are needed to confirm our findings, as well as the oncogenic mechanism of NEAT1 in the development of PCa.

IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier

Background

Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential.

Results

Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611.

Conclusions

We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04104-9.

Knocking Down Long Noncoding RNAs Using Antisense Oligonucleotide Gapmers

Long noncoding RNAs (lncRNAs) are a class of RNA with 200 nucleotides or longer that are not translated into protein. lncRNAs are highly abundant; a study estimates that at least four times more lncRNAs are typically present than coding RNAs in humans. However, function of more than 95% of human lncRNAs are still unknown. Synthetic antisense oligonucleotides called gapmers are powerful tools for lncRNA loss-of-function studies. Gapmers contain a central DNA part, which activates RNase H-mediated RNA degradation, flanked by modified oligonucleotides, such as 2'-O-methyl RNA (2'OMe), 2'-O-methoxyethyl RNA (2'MOE), constrained ethyl nucleosides (cEt), and locked nucleic acids (LNAs). In contrast to siRNA or RNAi-based methods, antisense oligonucleotide gapmer-based knockdown is often more effective against nuclear-localized lncRNA targets, since RNase H is mainly localized in nuclei. As such, gapmers are also potentially a powerful tool for therapeutics targeting lncRNAs in various diseases, including cancer, cardiovascular diseases, lung fibrosis, and neurological/neuromuscular diseases. This chapter will discuss the development and applications of gapmers for lncRNA loss-of-function studies and tips to design effective antisense oligonucleotides.

Spatially and cell-type resolved quantitative proteomic atlas of healthy human skin

Human skin provides both physical integrity and immunological protection from the external environment using functionally distinct layers, cell types and extracellular matrix. Despite its central role in human health and disease, the constituent proteins of skin have not been systematically characterized. Here, we combine advanced tissue dissection methods, flow cytometry and state-of-the-art proteomics to describe a spatially-resolved quantitative proteomic atlas of human skin. We quantify 10,701 proteins as a function of their spatial location and cellular origin. The resulting protein atlas and our initial data analyses demonstrate the value of proteomics for understanding cell-type diversity within the skin. We describe the quantitative distribution of structural proteins, known and previously undescribed proteins specific to cellular subsets and those with specialized immunological functions such as cytokines and chemokines. We anticipate that this proteomic atlas of human skin will become an essential community resource for basic and translational research ( https://skin.science/ ).

SNP-SNP interactions of oncogenic long non-coding RNAs HOTAIR and HOTTIP on gastric cancer susceptibility

Genetic variants within oncogenic long non-coding RNAs HOTAIR and HOTTIP may affect their gene expression levels, thereby modifying genetic susceptibility to gastric cancer (GC). In a hospital-based study in Ardabil-a very high-risk area in North-West Iran, 600 blood samples from 300 GC patients and 300 healthy controls were recruited for genotyping. Seven HOTAIR (i.e., rs17720428, rs7958904, rs1899663, and rs4759314) and HOTTIP (i.e., rs3807598, rs17501292, and rs1859168) 'tag' single nucleotide polymorphisms (SNPs) were genotyped by the Infinium HTS platform. The rs17720428, rs7958904, and rs1899663 tagSNPs significantly increased GC risk under dominant models by 1.5-, 1.57-, and 1.5-fold, respectively. The G-C-T-A haplotype of HOTAIR tagSNPs increased the risk of GC by 1.31-fold. No significant association was found between HOTTIP SNPs and the risk of GC. HOTAIR and HOTTIP variants were also not associated with any clinicopathologic characteristics. The SNP-SNP interaction of HOTAIR rs17720428/rs7958904 with HOTTIP rs1859168 was associated with an increased risk of GC (rs17720428 TG-rs1859168 CC, OR = 1.76; rs7958904 GC-rs1859168 CC, OR = 1.85; rs7958904 CC-rs1859168 CC, OR = 1.86). Interestingly, the SNP-SNP interaction of HOTAIR rs1899663 with HOTTIP rs1859168 strongly increased the risk of GC (rs1899663 GT-rs1859168 CC, OR = 4.3; rs1899663 TT-rs1859168 CC, OR = 9.37; rs1899663 TT-rs1859168 CA, OR = 6.59). We showed that the HOTAIR rs17720428, rs7958904, and rs1899663 tagSNPs and their interactions with the HOTTIP rs1859168 polymorphism significantly increased the risk of GC. Specifically, novel SNP-SNP interactions between HOTAIR and HOTTIP tagSNPs have a larger impact than individual SNP effects on GC risk, thereby providing us with valuable information to reveal potential biological mechanisms for developing GC.

Association of EGLN2 rs10680577 Polymorphism with the Risk and Clinicopathological Features of Patients with Prostate Cancer

Several studies have evaluated the association between EGLN2 4-bp insertion/deletion (ins/del) polymorphism (rs10680577) and many cancers. However, up to date, no study has inspected the impact of rs10680577 polymorphism on prostate cancer (PCa) risk. This case-control study was achieved on 170 pathologically confirmed PCa patients and 196 cancer free men to inspect whether rs10680577 variant is related to the risk and clinicopathological features of patients with PCa. Genotyping was performed by mismatched polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The findings did not support an association between the variant with the risk and clinicopathological characteristics of PCa patients. When we pooled our results with six preceding studies, the findings suggested that rs10680577 variant significantly augmented the risk of overall cancer in heterozygous (OR=1.38, 95 % CI=1.26-1.52, p<0.00001, ins/del vs ins/ins), homozygous (OR=1.66, 95 % CI=1.05-2.61, p=0.029, del/del vs ins/ins), codominant (OR=1.44, 95%CI=1.32-1.58, p<0.00001, ins/del+del/del vs ins/ins), and allele (OR=1.32, 95%CI=1.18-1.49, p<0.00001, del vs ins) genetic models. Additional well designed studies with larger sample sizes are necessary to confirm our findings.

LNC00673 suppresses proliferation and metastasis of pancreatic cancer via target miR-504/ HNF1A

Pancreatic cancer is a highly invasive malignant tumor of the digestive system. To explore the mechanism of pancreatic cancer development, development, invasion and metastasis, in this study we focused on long non-coding RNA (LncRNA), which has been reported to be involved in tumorigenesis. We identified a LINC00673, which is highly correlated with the pancreatic cancer risk. LINC00673 Overexpression is associated with good survival in pancreatic cancer patients, Effects of LINC00673 on pancreatic cancer cell apoptosis, viability, migration. LINC00673 negatively correlated with miR-504 and MiR-504 overexpression promoted cancer progression in Pancreatic cancer. MiR-504 negatively correlated with HNF1A, which was highly expressed Pancreatic cancer. HNF1A inhibited cell progression in pancreatic cancer cells. LINC00673 overexpression inhibited caner progression in nude mice. Taken together, LINC00673 can through suppress miR-504/ HNF1A regulating invasion and migration in pancreatic cancer. Also, we identified miR-504 as a target of LINC00673 in pancreatic cancer and LINC00673 can be used as a novel therapeutic target for the pancreatic cancer.

IDLDA: An Improved Diffusion Model for Predicting LncRNA-Disease Associations

It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA–disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA–disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and vice versa, we developed an improved diffusion model to predict potential lncRNA–disease associations (IDLDA). As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research.

A novel target convergence set based random walk with restart for prediction of potential LncRNA-disease associations

BACKGROUND

In recent years, lncRNAs (long-non-coding RNAs) have been proved to be closely related to the occurrence and development of many serious diseases that are seriously harmful to human health. However, most of the lncRNA-disease associations have not been found yet due to high costs and time complexity of traditional bio-experiments. Hence, it is quite urgent and necessary to establish efficient and reasonable computational models to predict potential associations between lncRNAs and diseases.

RESULTS

In this manuscript, a novel prediction model called TCSRWRLD is proposed to predict potential lncRNA-disease associations based on improved random walk with restart. In TCSRWRLD, a heterogeneous lncRNA-disease network is constructed first by combining the integrated similarity of lncRNAs and the integrated similarity of diseases. And then, for each lncRNA/disease node in the newly constructed heterogeneous lncRNA-disease network, it will establish a node set called TCS (Target Convergence Set) consisting of top 100 disease/lncRNA nodes with minimum average network distances to these disease/lncRNA nodes having known associations with itself. Finally, an improved random walk with restart is implemented on the heterogeneous lncRNA-disease network to infer potential lncRNA-disease associations. The major contribution of this manuscript lies in the introduction of the concept of TCS, based on which, the velocity of convergence of TCSRWRLD can be quicken effectively, since the walker can stop its random walk while the walking probability vectors obtained by it at the nodes in TCS instead of all nodes in the whole network have reached stable state. And Simulation results show that TCSRWRLD can achieve a reliable AUC of 0.8712 in the Leave-One-Out Cross Validation (LOOCV), which outperforms previous state-of-the-art results apparently. Moreover, case studies of lung cancer and leukemia demonstrate the satisfactory prediction performance of TCSRWRLD as well.

CONCLUSIONS

Both comparative results and case studies have demonstrated that TCSRWRLD can achieve excellent performances in prediction of potential lncRNA-disease associations, which imply as well that TCSRWRLD may be a good addition to the research of bioinformatics in the future.

Expression and function of long non-coding RNA LINC01420 in thyroid cancer

The Human Genome Project revealed that >90% of the human genome was found to transcribe non-coding RNAs, including micro RNAs and long non-coding RNAs (lncRNAs). lncRNAs have been identified to play a crucial role in cancer progression. Thyroid cancer (TC) is a common type of endocrine cancer; however, the functional roles of lncRNAs in TC have yet to be fully elucidated. The present study investigated whether LINC01420 was upregulated in TC tissues, compared with normal thyroid tissues, and the results suggested that LINC01420 may play a regulatory role in TC. Bioinformatics analysis demonstrated that LINC01420 was associated with translation, rRNA processing, mRNA splicing, regulation of transcription, DNA repair and double-strand break repair. Furthermore, the exact role of LINC01420 in TC was explored by performing a loss-of-function assay, which revealed that the knockdown of LINC01420 inhibited TC cell proliferation and cell cycle progression. The findings of the present study provide a novel insight into the molecular mechanisms underlying TC development. Moreover, they suggest that LINC01420 may serve as a potential therapeutic target for the treatment of TC, and that increased LINC01420 expression levels show potential as a prognostic marker for the disease.

Long non-coding RNA LINC00152 promotes tumorigenesis via sponging miR-193b-3p in osteosarcoma

The majority of the human genome has been revealed to be non-protein-coding, which are transcribed into noncoding RNAs (ncRNA), RNAs which are not translated into protein. Long non-coding RNAs (lncRNAs), including LINC00152, may be associated with the pathogenesis of different types of cancer. LINC00152 serves as an endogenous sponge by binding to micro-RNAs (miRNAs) and inhibiting their activity. The current study revealed that LINC00152 is overexpressed in osteosarcoma cells, leading to increased cell proliferation, and decreased G0/G1 cell cycle arrest and apoptosis. The binding of miR-193b-3p to LINC00152 was demonstrated by dual-luciferase assay, and led to miR-193b-3p downregulation in osteosarcoma cells. Knockdown of LINC00152 revealed an antitumorigenic effect by reducing cell proliferation and increasing G0/G1 arrest and apoptosis. Inhibiting miR-193b-3p reversed the effects of LINC00152 knockdown. These results suggested that LINC00152 binds to miR-193b-3p and reduces its expression level, leading to increased cell proliferation and decreased G0/G1 cell cycle arrest and apoptosis in osteosarcoma cells.

Long noncoding RNA OCC-1 suppresses cell growth through destabilizing HuR protein in colorectal cancer

Overexpressed in colon carcinoma-1 (OCC-1) is one of the earliest annotated long noncoding RNAs (lncRNAs) in colorectal cancer (CRC); however, its function remains largely unknown. Here, we revealed that OCC-1 plays a tumor suppressive role in CRC. OCC-1 knockdown by RNA interference promotes cell growth both in vitro and in vivo, which is largely due to its ability to inhibit G0 to G1 and G1 to S phase cell cycle transitions. In addition, overexpression of OCC-1 can suppress cell growth in OCC-1 knockdown cells. OCC-1 exerts its function by binding to and destabilizing HuR (ELAVL1), a cancer-associated RNA binding protein (RBP) which can bind to and stabilize thousands of mRNAs. OCC-1 enhances the binding of ubiquitin E3 ligase β-TrCP1 to HuR and renders HuR susceptible to ubiquitination and degradation, thereby reducing the levels of HuR and its target mRNAs, including the mRNAs directly associated with cancer cell growth. These findings reveal that lncRNA OCC-1 can regulate the levels of a large number of mRNAs at post-transcriptional level through modulating RBP HuR stability.

Expression analysis of circulating plasma long noncoding RNAs in colorectal cancer: The relevance of lncRNAs ATB and CCAT1 as potential clinical hallmarks

Long noncoding RNAs (lncRNAs) have been demonstrated to regulate a variety of cell processes and involve in the development and progression of colorectal cancer (CRC). Recently, the circulating lncRNAs have emerged as minimally invasive biomarkers for cancer diagnosis and prognosis. We aimed to examine the plasma expression level of long noncoding RNAs lnc-ATB, lnc-CCAT1, and lnc-OCC-1 in CRC patients and evaluate the clinical values. A total of 74 pretreatment CRC and 74 healthy blood biopsies were subjected to differentially evaluate the expression levels of three lncRNAs (OCC-1, CCAT1, and ATB). Briefly, after plasma separation and total RNA extraction, RNAs were reversely transcribed to complementary DNA followed by amplification using a quantitative real-time polymerase chain reaction technique for lncRNA expression analysis. The results showed that the expression levels of lnc-ATB (p < 0.001) and CCAT1 (p = 0.024), but not OCC-1 (p = 0.24), were significantly upregulated in the CRC compared with the healthy group. The calculated AUC of ROC was 0.78 (95% confidence interval [CI]: 0.811-0.94) for lnc-ATB and 0.64 (95% CI: 0.811-0.94) for CCAT1, which were indicative of a high discriminatory power (p < 0.001). The highest accuracy for lncRNA-ATB was obtained at a cutoff point of 2.5, which corresponded to sensitivity and specificity of 82% and 75%, respectively. Our results suggested a significant accuracy of lncRNA-ATB and lncRNA-CCAT1 in distinguishing CRC patients from healthy individuals.

Emerging Evidence of Translational Control by AU-Rich Element-Binding Proteins

RNA-binding proteins (RBPs) are key regulators of posttranscriptional gene expression and control many important biological processes including cell proliferation, development, and differentiation. RBPs bind specific motifs in their target mRNAs and regulate mRNA fate at many steps. The AU-rich element (ARE) is one of the major cis-regulatory elements in the 3′ untranslated region (UTR) of labile mRNAs. Many of these encode factors requiring very tight regulation, such as inflammatory cytokines and growth factors. Disruption in the control of these factors’ expression can cause autoimmune diseases, developmental disorders, or cancers. Therefore, these mRNAs are strictly regulated by various RBPs, particularly ARE-binding proteins (ARE-BPs). To regulate mRNA metabolism, ARE-BPs bind target mRNAs and affect some factors on mRNAs directly, or recruit effectors, such as mRNA decay machinery and protein kinases to target mRNAs. Importantly, some ARE-BPs have stabilizing roles, whereas others are destabilizing, and ARE-BPs appear to compete with each other when binding to target mRNAs. The function of specific ARE-BPs is modulated by posttranslational modifications (PTMs) including methylation and phosphorylation, thereby providing a means for cellular signaling pathways to regulate stability of specific target mRNAs. In this review, we summarize recent studies which have revealed detailed molecular mechanisms of ARE-BP-mediated regulation of gene expression and also report on the importance of ARE-BP function in specific physiological contexts and how this relates to disease. We also propose an mRNP regulatory network based on competition between stabilizing ARE-BPs and destabilizing ARE-BPs.

Downregulation of lncRNA CCAT1 enhances 5-fluorouracil sensitivity in human colon cancer cells

BACKGROUND

The purpose of this study was to determine the aberrant expression of the long noncoding RNA (lncRNA) colon cancer-associated transcript 1 (CCAT1) in 5-fluorouracil-resistant colonic neoplasm cells and to elucidate its effects on the 5-fluorouracil sensitivity of human colonic neoplasm cells. The aberrant expression of lncRNAs in normal tissues and colonic neoplasm tissues was detected by microarray assay. qRT-PCR analysis was performed to assess CCAT1 expression levels in colonic neoplasm cell lines and corresponding normal tissues. After constructing the 5-FU-resistant cell lines and validating the resistance by measuring the IC₅₀ value, the CCAT1 expression levels in parental and artificially resistant cell lines were determined by qRT-PCR. Transfection was used to modulate the expression of CCAT1. Cell proliferation and apoptosis were then detected by CCK-8 and flow cytometry, respectively.

RESULTS

CCAT1 in colon cancer tissues was higher than that in noncancer tissues, and the levels of CCAT1 in HCT 116, SW1417, HT-29, and KM12 cell lines were higher than those in the human normal colon epithelial NCM460 cell line. Moreover, the expression levels of CCAT1 were high in HCT 116/5-FU and HT-29/5-FU cell lines, whose apoptosis rates induced by 5-FU were lower than those in corresponding parental cells. The results of qRT-PCR and CCK-8 assay showed that enhancement of lncRNA CCAT1 expression levels in HCT 116 and HT-29 cell lines increased their IC₅₀ of 5-FU and decreased their apoptosis rates. Meanwhile, siRNA-CCAT1 effectively inhibited the expression of CCAT1 and enhanced the 5-FU-sensitivity of HCT 116/5-FU and HT-29/5-FU, in which apoptosis rates were increased at the same time.

CONCLUSIONS

Downregulation of CCAT1 effectively reversed the resistance of HCT 116/5-FU and HT-29/5-FU cells to 5-FU chemotherapeutic, opening a new avenue in colon cancer therapy.

Assessment of ApoC1, LuzP6, C12orf75 and OCC-1 in cystic glioblastoma using MALDI-TOF mass spectrometry, immunohistochemistry and qRT-PCR

Mass spectrometric analysis of glioblastoma cyst fluids has disclosed a protein peak with m/z 6424–6433. Among the proteins, potentially generating this peak are ApoC1 and LuzP6. To further elucidate protein expression of glioblastoma cells, we analyzed MALDI–TOF results of cyst fluid, performed immunohistochemistry and mRNA analysis. MALDI–TOF protein extraction from 24 glioblastoma cyst fluids was performed with a weak cation exchange. 50 glioblastoma samples were stained with two custom-made antibodies against LuzP6 and commercial antibodies against ApoC1, C12orf75 and OCC-1 and analyzed. For mRNA detection, 16 tissue samples were stored in RNAlater, extracted using the miRNeasy kit and reversely transcribed. For 12 patients, synopsis of results from all three examinations was possible. MALDI–TOF confirmed the peak at 6433 Da in 75% of samples. Immunohistochemically, LuzP6 was detected in 92% (LuzP6_1–29) and 96% (LuzP6_30–58) of samples and ApoC1 in 66%. Mean mRNA levels were highest for ApoC1, followed by LuzP6. No correlation between mRNA expression, immunohistochemical staining and intensity of the MALDI–TOF peaks was found. An unequivocal identification of one protein as the source for the 6433 peak is not possible, but our results point to ApoC1 and LuzP6 as the underlying proteins.

Long non-coding RNA POLR2E gene polymorphisms increased the risk of prostate cancer in a sample of the Iranian population

The current study aimed to examine the impact of POLR2E rs1046040 and rs3787016 polymorphisms on prostate cancer (PCa) risk in a sample of southeast Iranian population. The present case-control study was performed on 178 patients with PCa and 180 benign prostatic hyperplasia (BPH). Genotyping of the variants was done by mismatch PCR-RFLP. The findings showed that the rs3787016 C > T variant significantly increased the risk of PCa in codominant (OR = 1.84, 95% CI = 1.12-3.03, P = 0.018, CT vs CC), dominant (OR = 1.88, 95% CI = 1.63-3.05, P = 0.011, CT + TT vas CC) and allele (OR = 1.77, 95% CI = 1.52-2.72, P = 0.010, T vs C) inheritance model. Regarding rs1046040 C > T polymorphism, the findings revealed that the CT genotype significantly increased the risk of PCa compared to the CC genotype (OR = 1.60, 95% CI = 1.03-2.49, P = 0.043). Furthermore, rs3787016 CT/rs1046040 CC as well as rs3787016 CT/rs1046040 CT increased the risk of PCa compared to the CC/CC genotype (p = 0.029 and p = 0.014, respectively). Haplotype analysis proposed that rs3787016 T/rs1046040 C significantly increased the risk of PCa compared to C/C (p = 0.037). No significant association was observed between POLR2E variants and clinicopathological characteristics of PCa patients. In conclusion, the findings propose that POLR2E variants may be a risk factor for susceptibility to PCa in a sample of Iranian population.

Long noncoding RNAs: A new player in the prevention and treatment of diabetic cardiomyopathy?

Diabetic cardiomyopathy (DCM) can cause extensive necrosis of the heart muscle by metabolic disorders and microangiopathy, with subclinical cardiac dysfunction, and eventually progress to heart failure, arrhythmia, and cardiogenic shock; severe patients may even die suddenly. Long noncoding RNAs (lncRNAs) are a class of nonprotein-coding RNAs longer than 200 nucleotides. They have critical roles in various biological processes, including gene expression regulation, genomic imprinting, nuclear-cytoplasmic trafficking, RNA splicing, and translational control. Recent studies indicated that lncRNAs extensively participate in the development of diverse cardiac diseases, such as cardiac ischaemia, hypertrophy, and heart failure. Little is known about lncRNA in DCM. In this review, we summarize the current literature on lncRNAs in DCM studies, aiming to provide new methods for DCM's future prevention and treatment strategies.

Overexpressed in colorectal carcinoma gene (OCC-1) upregulation and APPL2 gene downregulation in breast cancer specimens

Breast cancer is the most common cancer type and the second cause of cancer death in women. Different mechanisms are contributed to the initiation and progression of the breast cancer. OCC-1 and APPL2 neighboring genes located in 12q.23.3 human chromosome region are related to colorectal cancer. Here, we intended to investigate OCC-1 newly reported transcript variants and APPL2 gene expression alteration in breast cancer specimens and investigate OCC-1 variants overexpression effect on APPL2 and on cell cycle status. Rt-qPCR analysis indicated that the expression level of OCC-1A/B and OCC-1D (not OCC-1C) transcript variants has been increased while, APPL2 gene expression level has been decreased in breast cancer specimen, compared to their normal pairs. Therefore, a negative correlation of expression is evident between APPL2 and OCC-1 genes in breast cancer specimen. Unlike OCC-1A/B which encodes a small protein, OCC-1D noncoding RNA overexpression lead to APPL2 downregulation in MCF7 cells. Consistently, OCC-1D overexpression resulted in increased sub-G1 cell population in MCF7 cells, detected by flow cytometry. Altogether, these results suggest that OCC1-D variant have an inhibitory effect on APPL2 expression and may regulate the cell cycle status.

Long non-coding RNA FENDRR inhibits cell proliferation and is associated with good prognosis in breast cancer

Introduction

FENDRR is a long non-coding RNA (lncRNA) that mediates the modification of the epigenetic landscape of target promoters by binding to polycomb repressive complex 2. However, the role of FENDRR in breast cancer remains unknown.

Materials and methods

We detected the expression of FENDRR in 52 breast cancer patients' tissues and five breast cancer cell lines. The association between FENDRR expression and clinicopathological features and prognosis of breast cancer patients was also analyzed. Moreover, cell proliferation assays, flow cytometry analysis, wound-healing assays, and transwell migration assays were performed to detect the biological effects of FENDRR in the breast cancer cells. A xenograft model was used to explore the role of FENDRR expression on tumor growth.

Results

We found that FENDRR expression was lower in breast cancer cell lines and cancerous tissues than in the adjacent normal tissues. Low expression of FENDRR was associated with a shorter overall survival and a shorter progression-free survival in breast cancer patients (p<0.001, p<0.001, respectively). We found that FENDRR inhibits breast cancer cell proliferation and migration and promotes cell apoptosis, while FENDRR knockdown promotes breast cancer cell proliferation and migration and suppresses cell apoptosis. Finally, we also detected that FENDRR overexpression could inhibit tumor growth in a xenograft model.

Conclusion

Our data suggested that FENDRR inhibits breast cancer cell proliferation, promotes cell apoptosis, and is associated with good prognosis in breast cancer. Thus, FENDRR plays an important role in the growth and progression of breast cancer.

FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model

Accumulating experimental studies have indicated the influence of lncRNAs on various critical biological processes as well as disease development and progression. Calculating lncRNA functional similarity is of high value in inferring lncRNA functions and identifying potential lncRNA-disease associations. However, little effort has been attempt to measure the functional similarity among lncRNAs on a large scale. In this study, we developed a Fuzzy Measure-based LNCRNA functional SIMilarity calculation model (FMLNCSIM) based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases. The performance improvement of FMLNCSIM mainly comes from the combination of information content and the concept of fuzzy measure, which was applied to the directed acyclic graphs of disease MeSH descriptors. To evaluate the effectiveness of FMLNCSIM, we further combined it with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association (LRLSLDA). As a result, the integrated model, LRLSLDA-FMLNCSIM, achieve good performance in the frameworks of global LOOCV (AUCs of 0.8266 and 0.9338 based on LncRNADisease and MNDR database) and 5-fold cross validation (average AUCs of 0.7979 and 0.9237 based on LncRNADisease and MNDR database), which significantly improve the performance of previous classical models. It is anticipated that FMLNCSIM could be used for searching functionally similar lncRNAs and inferring lncRNA functions in the future researches.

Long Non-coding RNAs in Prostate Cancer with Emphasis on Second Chromosome Locus Associated with Prostate-1 Expression

Long non-coding RNAs (lncRNAs) are a class of RNA with transcripts longer than 200 nucleotides that lack functional open reading frames. They play various roles in human carcinoma, such as dysregulating gene expression in prostate cancer (PCa), which results in cancer initiation, development, and progression. The non-coding RNA SChLAP1 (second chromosome locus associated with prostate-1) is highly expressed in approximately 25% of PCas with higher prevalence in metastatic compared to localized PCa. Its expression is detectable non-invasively in PCa patient urine samples. Experimental data suggest that targeting SChLAP1 may represent a novel therapeutic application in PCa. This contribution focuses on the role of lncRNAs SChLAP1 expression in PCa diagnosis and prognosis.

Long non-coding RNAs and complex diseases: from experimental results to computational models

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.

ILNCSIM: improved lncRNA functional similarity calculation model

Increasing observations have indicated that lncRNAs play a significant role in various critical biological processes and the development and progression of various human diseases. Constructing lncRNA functional similarity networks could benefit the development of computational models for inferring lncRNA functions and identifying lncRNA-disease associations. However, little effort has been devoted to quantifying lncRNA functional similarity. In this study, we developed an Improved LNCRNA functional SIMilarity calculation model (ILNCSIM) based on the assumption that lncRNAs with similar biological functions tend to be involved in similar diseases. The main improvement comes from the combination of the concept of information content and the hierarchical structure of disease directed acyclic graphs for disease similarity calculation. ILNCSIM was combined with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association to further evaluate its performance. As a result, new model obtained reliable performance in the leave-one-out cross validation (AUCs of 0.9316 and 0.9074 based on MNDR and Lnc2cancer databases, respectively), and 5-fold cross validation (AUCs of 0.9221 and 0.9033 for MNDR and Lnc2cancer databases), which significantly improved the prediction performance of previous models. It is anticipated that ILNCSIM could serve as an effective lncRNA function prediction model for future biomedical researches.

Association between genetic polymorphisms of long non-coding RNA PRNCR1 and prostate cancer risk in a sample of the Iranian population

The aim of the present study was to determine whether there is an association between the long non-coding RNA (lncRNA) prostate cancer-associated non-coding RNA 1 (PRNCR1) polymorphisms and prostate cancer (PCa) risk in a sample of the Iranian population. This case-control study was performed on 178 patients with PCa and 180 subjects with benign prostatic hyperplasia (BPH). Genotyping assay was performed by polymerase chain reaction-restriction fragment length polymorphism. The findings indicated that the GG genotype of the rs13252298 A>G variant significantly increased the risk of PCa (odds ratio=3.49, 95% confidence interval: 1.79-6.81, P=0.0001) compared with AA+AG. As regards the rs1456315 G>A polymorphism, the AG genotype and G allele significantly increased the risk of PCa. As regards the rs7841060 T>G variant, the findings demonstrated that this TG genotype and the G allele significantly increased the risk of PCa. The rs7007694 T>C variant was not found to be associated with the risk of PCa. Haplotype analysis indicated that GTGA and GTGG significantly increased the risk of PCa compared with rs1456315A/rs7007694T/rs7841060T/rs13252298G (ATTG). The PRNCR1 variants were not found to be significantly associated with the clinicopathological characteristics of PCa patients. In conclusion, our findings support an association between PRNCR1 variants and the risk of PCa in a sample of the Iranian population.

Long Noncoding RNAs as Diagnostic and Therapeutic Targets in Type 2 Diabetes and Related Complications

Protein-coding genes represent only a small fraction of the human genome. In the past, the majority of the genomic sequence has been considered transcriptionally silent, but recent large-scale studies have uncovered an array of functionally significant elements, including non-protein-coding transcripts, within these noncoding regions of the human genome. Long noncoding RNAs (lncRNAs), a class of noncoding transcripts with lengths >200 nucleotides, are pervasively transcribed in the genome and function as signals, decoys, guides, or scaffolds to regulate gene expression. More than 200 diseases have been associated with dysregulated or dysfunctional lncRNAs, and new associations continue to accumulate in the literature. The role of lncRNAs in the pathogenesis of type 2 diabetes mellitus and related complications has only recently been recognized, but there is already evidence for their involvement in many of the pathophysiological mechanisms underlying the disease. In this review, we summarize the current knowledge of the functions and underlying mechanisms of lncRNA activity with a focus on type 2 diabetes mellitus and related renal and retinal complications of the disease. We also discuss the potential of lncRNAs to serve as therapeutic targets for drug development and diagnostic markers for clinical applications in the management of diabetes.

Involvement of Non-coding RNAs in the Signaling Pathways of Colorectal Cancer

Colorectal cancer (CRC) is one of the most common diagnosed cancers worldwide. The metastasis and development of resistance to anti-cancer treatment are major challenges in the treatment of CRC. Understanding mechanisms underpinning the pathogenesis is therefore critical in developing novel agents for CRC treatments. A large number of evidence has demonstrated that non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs have functional roles in both the physiological and pathological processes by regulating the expression of their target genes. These molecules are engaged in the pathobiology of neoplastic diseases and are targets for the diagnosis, prognosis and therapy of a variety of cancers, including CRC. In this regard, ncRNAs have emerged as one of the hallmarks of CRC pathogenesis and they also play key roles in metastasis, drug resistance and the stemness of CRC stem cell by regulating various signaling networks. Therefore, a better understanding the ncRNAs involved in the signaling pathways of CRC may lead to the development of novel strategy for diagnosis, prognosis and treatment of CRC. In this chapter, we summarize the latest findings on ncRNAs, with a focus on miRNAs and lncRNAs involving in signaling networks and in the regulation of pathogenic signaling pathways in CRC.

The long noncoding RNA, TINCR, functions as a competing endogenous RNA to regulate PDK1 expression by sponging miR-375 in gastric cancer

BACKGROUND

Accumulating evidence indicates that the long noncoding RNA, TINCR, plays a critical role in cancer progression and metastasis. However, the overall biological role and mechanisms of TINCR that were involved in human gastric cancer (GC) progression remain largely unknown.

METHODS

TINCR expression was measured in 56 paired tumor and adjacent nontumor tissue samples by real-time polymerase chain reaction (PCR). Insights of the mechanism of competitive endogenous RNAs (ceRNAs) were gained from bioinformatic analysis, luciferase assays. The effects of TINCR and miR-375 on GC cell apoptosis and proliferation were studied by RNA interference approaches in vitro and in vivo. The correlation of TINCR and PDK1 was identified by real-time PCR and Western blot analysis.

RESULTS

Our results showed that miR-375 level decreased and TINCR level increased in tumor tissues. In addition, TINCR was a target of miR-375 and inhibited its expression in GC cells. Furthermore, the low expression of TINCR increased cell apoptosis and inhibited the proliferation of GC cells, while the downregulation of miR-375 reversed the function. In particular, TINCR could negatively regulate the miR-375 expression and increased the PDK1 expression in GC cells. Finally, tumor growth suppression was retarded with miR-375 downregulated in TINCR knockdown of GC cell xenografts.

CONCLUSION

The long noncoding RNA TINCR functions as a competing endogenous RNA to regulate PDK1 expression by sponging miR-375 in GC. The ceRNA regulatory network of TINCR/miR-375/PDK1 allows us to better understand the pathogenesis of GC and facilitate the development of long noncoding RNA (lncRNA)-directed diagnostics in GC.

Long non-coding RNAs in colorectal cancer

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Despite substantial progress in understanding the molecular mechanisms and treatment of CRC in recent years, the overall survival rate of CRC patients has not improved dramatically. The development of CRC is multifactor and multistep processes, in which abnormal gene expression may play an important role. With the advance of human tumor molecular biology, a series of studies have highlighted the role of long non-coding RNAs (lncRNAs) in the development of CRC. CRC-related lncRNAs have been demonstrated to regulate the genes by various mechanisms, including epigenetic modifications, lncRNA-miRNA and lncRNA-protein interactions, and by their actions as miRNA precursors or pseudogenes. Since some lncRNAs can be detected in human body fluid and have good specificity and accessibility, they have been suggested to be used as novel potential biomarkers for CRC diagnosis and prognosis as well as in the prediction of the response to therapy. Therefore, in this review, we will focus on lncRNAs in CRC development, the mechanisms and biomarkers of lncRNAs in CRC.

Colorectal Carcinoma: A General Overview and Future Perspectives in Colorectal Cancer

Colorectal cancer (CRC) is the third most common cancer and the fourth most common cause of cancer-related death. Most cases of CRC are detected in Western countries, with its incidence increasing year by year. The probability of suffering from colorectal cancer is about 4%–5% and the risk for developing CRC is associated with personal features or habits such as age, chronic disease history and lifestyle. In this context, the gut microbiota has a relevant role, and dysbiosis situations can induce colonic carcinogenesis through a chronic inflammation mechanism. Some of the bacteria responsible for this multiphase process include Fusobacterium spp, Bacteroides fragilis and enteropathogenic Escherichia coli. CRC is caused by mutations that target oncogenes, tumour suppressor genes and genes related to DNA repair mechanisms. Depending on the origin of the mutation, colorectal carcinomas can be classified as sporadic (70%); inherited (5%) and familial (25%). The pathogenic mechanisms leading to this situation can be included in three types, namely chromosomal instability (CIN), microsatellite instability (MSI) and CpG island methylator phenotype (CIMP). Within these types of CRC, common mutations, chromosomal changes and translocations have been reported to affect important pathways (WNT, MAPK/PI3K, TGF-β, TP53), and mutations; in particular, genes such as c-MYC, KRAS, BRAF, PIK3CA, PTEN, SMAD2 and SMAD4 can be used as predictive markers for patient outcome. In addition to gene mutations, alterations in ncRNAs, such as lncRNA or miRNA, can also contribute to different steps of the carcinogenesis process and have a predictive value when used as biomarkers. In consequence, different panels of genes and mRNA are being developed to improve prognosis and treatment selection. The choice of first-line treatment in CRC follows a multimodal approach based on tumour-related characteristics and usually comprises surgical resection followed by chemotherapy combined with monoclonal antibodies or proteins against vascular endothelial growth factor (VEGF) and epidermal growth receptor (EGFR). Besides traditional chemotherapy, alternative therapies (such as agarose tumour macrobeads, anti-inflammatory drugs, probiotics, and gold-based drugs) are currently being studied to increase treatment effectiveness and reduce side effects.

Alternative splicing of the OCC-1 gene generates three splice variants and a novel exonic microRNA, which regulate the Wnt signaling pathway

The Wnt signaling pathway is hyperactivated in most colorectal cancers (CRC). Finding new regulators of this pathway represents the potential for cancer diagnosis or treatment. OCC-1 was initially reported as an up-regulated gene in colon carcinoma, without knowing its mechanism of action. Here, two novel transcript variants and an exonic microRNA that originated from the OCC-1 gene are reported, showing positive effects on Wnt activity. Up-regulation of the known OCC-1 variant (assigned as OCC-1A/B) was limited to CRC, and its overexpression increased survival of CRC-originated SW480 cells (Wnt+), while resulting in apoptosis of Wnt-suppressed SW480 cells or HeLa cells (Wnt-) detected by PI staining. Immunocytochemistry showed that the OCC-1A/B-encoded peptide was localized to the nucleus, where its overexpression resulted in Wnt signaling up-regulation, detected by TOP/FOPflash assay. The noncoding portion of the OCC-1A/B transcript had a suppressive effect on Wnt activity and had a negative correlation with APPL2 neighboring gene expression. Unlike OCC-1A/B, the novel OCC-1C splice variant had no expression alteration in CRC, and it seemed to encode a smaller peptide with cytoplasmic localization. A 60-nucleotide (nt) fragment containing an AUG start codon is spliced out to produce an OCC-1D noncoding RNA variant. The 60-nt RNA was validated as the precursor of a novel microRNA, which we named miR-ex1 Both OCC-1D and miR-ex1 were coordinately up-regulated in CRC. MiR-ex1 functional analysis revealed that it is targeting the APC2 tumor suppressor gene and is an activator of the Wnt signaling pathway. Overall, the OCC-1 gene is now introduced as a novel Wnt signaling regulator and as a potential therapeutic target.

Introduction of novel splice variants for CASC18 gene and its relation to the neural differentiation

BACKGROUND

CASC18 along with APPL2, OCC-1 and NUAK1 flanking genes are located in 12q23.3 locus which is known as a potential cancer predisposition locus. Only an uncharacterized EST was initially reported for CASC18 and it was crucial to find its full length sequence and function.

METHODS AND RESULTS

In an attempt to search for the CASC18's full-length gene sequence, other related ESTs were bioinformatically collected and four novel splice variants (designated as; CASC18-A, -B, -C and -D) were deduced and some were experimentally validated. Two transcription start sites and two alternative polyadenylation sites were deduced for CASC18 gene, using EST data mining and RACE method. CASC18-A and CASC18-D were exclusively expressed in neural cell lines and CASC18-D expression level was gradually increased during the NT2 differentiation to the neuron-like cells. Consistently, overexpression of CASC18-D variant in NT2 cells resulted in remarkable up-regulation of PAX6 neural differentiation marker, suggesting a crucial role of this variant in neural differentiation.

CONCLUSION

Here, we introduced seven novel transcription variants for human CASC18 gene in which CASC18-D has the potential of being used as a neural cell differentiation marker.

Molecular mechanisms underlying fibrosis and elastin destruction in childhood interstitial lung diseases

OBJECTIVE

This study aimed to evaluate fibrosis and elastin destruction in childhood interstitial lung disease (chILD) patients.

METHODS

Sixty patients and twenty healthy children were recruited. On admission, evaluation of chILD severity was made using Fan chILD score. Participants provided urine and blood samples. Plasma levels of transforming growth factor (TGF)-β₁, connective tissue growth factor (CCN2), soluble factor related apoptosis (sFas) and long non-coding RNAs and urinary levels of desmosine/urinary creatinine (UDes/UCr) were measured.

RESULTS

In patients, clinical findings were crackles (100.00%), tachypnea (65.00%), cardiomegaly (45.00%), digital clubbing (43.30%), cough (33.00%), cyanosis (26.70%), hepatomegaly (28.30%) and wheezes (23.30%). Categorizing of the patients with Fan chILD clinical score revealed that most patients 33.30% scored (3, symptomatic with abnormal saturation/cyanosis during exercise) then 28.30% scored (5, symptomatic with clinical and echocardiographic features of pulmonary hypertension), 18.30% scored (2, symptomatic with normal room air saturations), 15.00% scored (1, asymptomatic) and 5.00% scored (4, symptomatic with abnormal room air saturation/cyanosis at rest). TGF-β₁, CCN2, sFas, lncrRNA-2700086A05Rik relative gene expression and UDes/UCr levels were higher in patients than controls (P=0.002, P=0.001, P=0.001, P=0.001, P=0.001, respectively). In patients, significant positive correlations were found between TGF-β₁ and CCN2, sFas, UDes/UCr; between CCN2 and both sFas and UDes/UCr; between UDes/UCr and sFas. Morbidity and mortality rates were 46.70% and 10.00%, respectively.

CONCLUSION

Markers of fibrosis (TGF-β₁, sFas, CCN2) and elastin destruction (UDes/UCr) were increased in chILD especially in patients with long disease duration. So blockage of their pathways signals may offer novel therapeutic targets.

CUDR promotes liver cancer stem cell growth through upregulating TERT and C-Myc

Cancer up-regulated drug resistant (CUDR) is a novel non-coding RNA gene. Herein, we demonstrate excessive CUDR cooperates with excessive CyclinD1 or PTEN depletion to accelerate liver cancer stem cells growth and liver stem cell malignant transformation in vitro and in vivo. Mechanistically, we reveal the decrease of PTEN in cells may lead to increase binding capacity of CUDR to CyclinD1. Therefore, CUDR-CyclinD1 complex loads onto the long noncoding RNA H19 promoter region that may lead to reduce the DNA methylation on H19 promoter region and then to enhance the H19 expression. Strikingly, the overexpression of H19 increases the binding of TERT to TERC and reduces the interplay between TERT with TERRA, thus enhancing the cell telomerase activity and extending the telomere length. On the other hand, insulator CTCF recruits the CUDR-CyclinD1 complx to form the composite CUDR-CyclinD1-insulator CTCF complex which occupancied on the C-myc gene promoter region, increasing the outcome of oncogene C-myc. Ultimately, excessive TERT and C-myc lead to liver cancer stem cell and hepatocyte-like stem cell malignant proliferation. To understand the novel functions of long noncoding RNA CUDR will help in the development of new liver cancer therapeutic and diagnostic approaches.

Reciprocal regulation of long noncoding RNAs THBS4‑003 and THBS4 control migration and invasion in prostate cancer cell lines

Increasing evidence implicates long noncoding RNAs (lncRNAs), a class of noncoding RNAs >200 nucleotides in length, in the development of cancer. However, the mechanism underlying the effects of lncRNAs in prostate cancer (PCa) remains to be elucidated. The present study aimed to investigate the role of lncRNA-THBS4-003 in the pathogensis of PCa. In the present study, a microarray containing 8,277 lncRNA probes and 32,207 mRNA probes were used to identify dysregulated mRNAs in three patients with PCa, and reverse transcription-quantitative polymerase chain reaction was used to determine the expression levels of thrombospondin 4 (THBS4) and lncRNA-THBS4-003 in 46 primary PCa and adjacent non-tumor tissue samples. The expression levels of THBS4 were determined in six samples of PCa and adjacent non-tumor tissues using Western blot analysis. The effects of forced THBS4 knockdown and lncRNA-THBS4-003 knockdown in the two PCa cell lines, DU145 and PC-3, were evaluated using cell migration and invasion assays, as well as using Western blot analysis. Of the 40,484 probes in the microarray, 354 were significantly upregulated (P<0.05; fold-change >2). The most significantly upregulated mRNA was THBS4. The expression levels of THBS4 and lncRNA-THBS4-003 in the 46 primary PCa samples was significantly higher, compared with that in the adjacent non-tumor tissue samples. Patients with Gleason scores >7 exhibited higher expression levels of lncRNA-THBS4-003, compared with patients with lower scores. Knockdown of THBS4 or lncRNA-THBS4-003 significantly reduced the migratory and invasive abilities of the PCa cells in vitro, and decreased the expression levels of p38 and matrix metal-loproteinase (MMP)-9. These findings suggested that the reciprocal regulation of lncRNA-THBS4-003 and THBS4 contributed to the pathogenesis of PCa. Therefore silencing lncRNA-THBS4-003 or THBS4 may inhibit PCa cell migration and invasion, and regulate the levels of MMP-9 through the mitogen-activated protein kinase signaling pathway.

KATZLDA: KATZ measure for the lncRNA-disease association prediction

Accumulating experimental studies have demonstrated important associations between alterations and dysregulations of lncRNAs and the development and progression of various complex human diseases. Developing effective computational models to integrate vast amount of heterogeneous biological data for the identification of potential disease-lncRNA associations has become a hot topic in the fields of human complex diseases and lncRNAs, which could benefit lncRNA biomarker detection for disease diagnosis, treatment, and prevention. Considering the limitations in previous computational methods, the model of KATZ measure for LncRNA-Disease Association prediction (KATZLDA) was developed to uncover potential lncRNA-disease associations by integrating known lncRNA-disease associations, lncRNA expression profiles, lncRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. KATZLDA could work for diseases without known related lncRNAs and lncRNAs without known associated diseases. KATZLDA obtained reliable AUCs of 7175, 0.7886, 0.7719 in the local and global leave-one-out cross validation and 5-fold cross validation, respectively, significantly improving previous classical methods. Furthermore, case studies of colon, gastric, and renal cancer were implemented and 60% of top 10 predictions have been confirmed by recent biological experiments. It is anticipated that KATZLDA could be an important resource with potential values for biomedical researches.

SP1-induced upregulation of the long noncoding RNA TINCR regulates cell proliferation and apoptosis by affecting KLF2 mRNA stability in gastric cancer

The long noncoding RNA TINCR shows aberrant expression in human squamous carcinomas. However, its expression and function in gastric cancer remain unclear. We report that TINCR is strongly upregulated in human gastric carcinoma (GC), where it was found to contribute to oncogenesis and cancer progression. We also revealed that TINCR overexpression is induced by nuclear transcription factor SP1. Silencing TINCR expression inhibited cell proliferation, colony formation, tumorigenicity and apoptosis promotion, whereas TINCR overexpression promoted cell growth, as documented in the SGC7901 and BGC823 cell lines. Mechanistic analyses indicated that TINCR could bind to STAU1 (staufen1) protein, and influence KLF2 mRNA stability and expression, then KLF2 regulated cyclin-dependent kinase genes CDKN1A/P21 and CDKN2B/P15 transcription and expression, thereby affecting the proliferation and apoptosis of GC cells. Together, our findings suggest that TINCR contributes to the oncogenic potential of GC and may constitute a potential therapeutic target in this disease.

Long Noncoding RNA CUDR Regulates HULC and β-Catenin to Govern Human Liver Stem Cell Malignant Differentiation

Long noncoding RNA cancer upregulated drug resistant (CUDR) is overexpressed in many tumors and promotes tumorigenesis. Herein, we demonstrate CUDR could enhance the human embryonic stem cells (ESC) differentiation into hepatocyte-like cells by reducing trimethylation on histone H3 twenty-seventh lysine (H3K27me3). On the other hand, excessive CUDR triggers hepatocyte-like cells malignant transformation. Mechanistically, we identify CUDR causes highly upregulated in liver cancer (HULC) and β-catenin abnormal expression by inhibiting HULC promoter methylation and promoting β-catenin promoter-enhancer chromatin looping formation mediated by CUDR-ccctc-binding factor (CTCF) complex, which recruits more RNA polII and P300. Strikingly, HULC and β-catenin activity are crucial for CUDR oncogenic function. These findings provide the first demonstration that CUDR plays a positive potential role in liver cancer stem cell through the cascade of CUDR-HULC/CUDR-β-catenin signaling, and offer insights into a novel link between long noncoding RNA (lncRNA) and the epigenetic modification in cancer stem cells.

Multidrug-Resistance Related Long Non-Coding RNA Expression Profile Analysis of Gastric Cancer

The effect of chemotherapy of gastric cancer (GC) remains very poor because of multidrug resistance (MDR). However, the mechanisms underlying MDR of GC remains far from fully understood. The aim of this study is to illustrate the potential mechanisms of the MDR of GC at mainly the long non-coding RNA (lncRNA) level. In this study, GC cell line, SGC7901, and two MDR sublines, SGC7901/VCR and SGC7901/ADR were subjected to an lncRNA microarray analysis. Bioinformatics and verification experiments were performed to investigate the potential lncRNAs involved in the development of MDR. Pathway analysis indicated that 15 pathways corresponded to down-regulated transcripts and that 20 pathways corresponded to up-regulated transcripts (p-value cut-off is 0.05). GO analysis showed that the highest enriched GOs targeted by up-regulated transcripts were “system development” and the highest esenriched GOs targeted by the down-regulated transcripts were “sterol biosynthetic process”. Our study is the first to interrogate differentially expressed lncRNAs in human GC cell line and MDR sublines and indicates that lncRNAs are worthwhile for further study to be the novel candidate biomarkers for the clinical diagnosis of MDR and potential targets for further therapy.