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Background: Colorectal cancer (CRC) is a prevalent type of gastrointestinal cancer, and its poor prognosis is mainly attributed to the occurrence of invasion and metastasis. CYP1B1-AS1, as non-coding RNA, plays an important role in tumorigenesis and progression. However, the mechanism by which CYP1B1-AS1 acts in CRC is not yet understood. Aims: The objective of this study was to investigate how CYP1B1-AS1 contributes to the development of CRC, and provide a base for CRC diagnosis and treatment. Methods: RT-qPCR was used to detect the expression level of CYP1B1-AS1 in CRC and adjacent tissues. CCK-8, Edu, scratch healing, and transwell experiments were used to detect the changes of proliferation, migration, and invasion ability of CRC cells after overexpression or knockdown of CYP1B1-AS1 respectively. The RNA binding protein NOP58 combined with CYP1B1-AS1 was verified by RIP and RNA Pull-down experiments. Functional recovery experiments validated the interaction between CYP1B1-AS1 and NOP58 in CRC cells. The changes of EMT-related proteins were detected by Western blot, and the half-life of transcription factor SNAIL mRNA were detected by RT-qPCR after overexpression or knockdown of NOP58. Results: CYP1B1-AS1 was found to be significantly downregulated in CRC compared to adjacent noncancerous tissues. Experiments conducted in vitro and in vivo confirmed that upregulation of CYP1B1-AS1 significantly inhibited the proliferation, migration, and invasion of CRC cells. In addition, CYP1B1-AS1 can directly bind to NOP58 and negatively regulate NOP58. The effect of overexpression CYP1B1-AS1 was reversed by NOP58 overexpression. NOP58 regulates the EMT process of CRC cells by affecting the stability of EMT-related transcription factor SNAIL mRNA, and then affects the progress of CRC. Conclusion: This research proves that CYP1B1-AS1 can inhibit the occurrence of EMT in CRC by binding with NOP58, thus delaying the progress of CRC. This finding indicates that CYP1B1-AS1 may be a novel biomarker to improve the diagnosis and treatment of CRC. [ABSTRACT FROM AUTHOR]

Simple Summary: Since ferroptosis was proposed in 2012, it has been a popular field of study for researchers. So far, the regulatory mechanism of ferroptosis involves many aspects, such as DNA, RNA, and proteins. Accumulating studies have shown that ferroptosis and epigenetic modifications are crucial in multiple diseases. This review provides information on resistance systems to ferroptosis and advanced studies of epigenetic modifications in DNA methylation, RNA methylation, non-coding RNAs, and histone modifications through regulating ferroptosis in cancer and other diseases. A summary of the targets of epigenetic modifications regulating ferroptosis could help identify new prognostic indicators in human diseases and provide potential therapeutic strategies for these diseases. Ferroptosis is a non-apoptotic mode of cell death driven by membrane lipid peroxidation and is characterized by elevated intracellular levels of Fe2+, ROS, and lipid peroxidation. Studies have shown that ferroptosis is related to the development of multiple diseases, such as cancer, neurodegenerative diseases, and acute myeloid leukemia. Ferroptosis plays a dual role in the occurrence and development of these diseases. Ferroptosis mainly involves iron metabolism, ROS, and lipid metabolism. Various mechanisms, including epigenetic regulation, have been reported to be deeply involved in ferroptosis. Abnormal epigenetic modifications have been reported to promote tumor onset or other diseases and resistance to chemotherapy drugs. In recent years, diversified studies have shown that epigenetic modification is involved in ferroptosis. In this review, we reviewed the current resistance system of ferroptosis and the research progress of epigenetic modification, such as DNA methylation, RNA methylation, non-coding RNAs, and histone modification in cancer and other diseases by regulating ferroptosis. [ABSTRACT FROM AUTHOR]

Background: Auxin is critical to plant growth and development, as well as stress responses. Small auxin-up RNA (SAUR) is the largest family of early auxin responsive genes in higher plants. However, the function of few SAUR genes is known owing to functional redundancy among the many family members. Results: In this study, we conducted a phylogenetic analysis using protein sequences of 795 SAURs from Anthoceros angustus, Marchantia polymorpha, Physcomitrella patens, Selaginella moellendorffii, Ginkgo biloba, Gnetum montanum, Amborella trichopoda, Arabidopsis thaliana, Oryza sativa, Zea mays, Glycine max, Medicago truncatula and Setaria italica. The phylogenetic trees showed that the SAUR proteins could be divided into 10 clades and three subfamilies, and that SAUR proteins of three bryophyte species were only located in subfamily III, which suggested that they may be ancestral. From bryophyta to anthophyta, SAUR family have appeared very large expansion. The number of SAUR gene in Fabaceae species was considerably higher than that in other plants, which may be associated with independent whole genome duplication event in the Fabaceae lineages. The phylogenetic trees also showed that SAUR genes had expanded independently monocotyledons and dicotyledons in angiosperms. Conserved motif and protein structure prediction revealed that SAUR proteins were highly conserved among higher plants, and two leucine residues in motif I were observed in almost all SAUR proteins, which suggests the residues plays a critical role in the stability and function of SAUR proteins. Expression analysis of SAUR genes using publicly available RNA-seq data from rice and soybean indicated functional similarity of members in the same clade, which was also further confirmed by qRT-PCR. Summarization of SAUR functions also showed that SAUR functions were usually consistent within a subclade. Conclusions: This study provides insights into the evolution and function of the SAUR gene family from bryophyta to anthophyta, particularly in Fabaceae plants. Future investigation to understand the functions of SAUR family members should employ a clade as the study unit. [ABSTRACT FROM AUTHOR]

Background: Pancreatic cancer is one of the most lethal malignancies and has an extremely poor diagnosis and prognosis. The development of resistance to gemcitabine is still a major challenge. The long noncoding RNA PVT1 was reported to be involved in carcinogenesis and chemoresistance; however, the mechanism by which PVT1 regulates the sensitivity of pancreatic cancer to gemcitabine remains poorly understood. Methods: The viability of pancreatic cancer cells was assessed by MTT assay in vitro and xenograft tumor formation assay in vivo. The expression levels of PVT1 and miR-619-5p were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Western blotting analysis and qRT-PCR were performed to assess the protein and mRNA levels of Pygo2 and ATG14, respectively. Autophagy was explored via autophagic flux detection under confocal microscopy and autophagic vacuole investigation under transmission electron microscopy (TEM). The functional role and mechanism of PVT1 were further investigated by gain- and loss-of-function assays in vitro. Results: In the present study, we demonstrated that PVT1 was up-regulated in gemcitabine-resistant pancreatic cancer cell lines. Gain- and loss-of-function assays revealed that PVT1 impaired sensitivity to gemcitabine in vitro and in vivo. We further found that PVT1 up-regulated the expression of both Pygo2 and ATG14 and thus regulated Wnt/β-catenin signaling and autophagic activity to overcome gemcitabine resistance through sponging miR-619-5p. Moreover, we discovered three TCF/LEF binding elements (TBEs) in the promoter region of PVT1, and activation of Wnt/β-catenin signaling mediated by the up-regulation of Pygo2 increased PVT1 expression by direct binding to the TBE region. Furthermore, PVT1 was discovered to interact with ATG14, thus promoting assembly of the autophagy specific complex I (PtdIns3K-C1) and ATG14-dependent class III PtdIns3K activity. Conclusions: These data indicate that PVT1 plays a critical role in the sensitivity of pancreatic cancer to gemcitabine and highlight its potential as a valuable target for pancreatic cancer therapy. [ABSTRACT FROM AUTHOR]

Although cryopreservation of bull semen is widely used commercially in the livestock breeding industry, cryopreservation results in low fertility of bull sperm. As an important regulatory factor, the alteration of small non-coding RNA (sncRNA) profile during cryopreservation of bull sperm is not yet completely known. In the present study, we sequenced sncRNAs of frozen and fresh sperm to study the link of alteration of the sncRNA profiles (particularly in miRNAs and mRNA fragments) with low sperm fertility caused by cryopreservation. We identified 55 miRNAs and 526 mRNA fragments differentially expressed (DE) between frozen and fresh sperm. Subsequently, the functional analysis revealed that targeted genes of DE miRNAs in sperm had roles in the fertilization, ATP, and apoptosis. Instead, targeted genes of DE miRNAs in cow metaphase II oocyte were significantly enriched in the MAPK signaling pathway, autophagy-animal pathway, and mitophagy-animal pathway. Additionally, biological processes of DNA repair, spermatid development, response to temperature stimulus, and cellular response to DNA damage stimulus were enriched by mRNA fragments. In conclusion, we found that DE miRNAs or DE mRNA fragments in cryopreservation may influence the fertility of sperm, these findings will provide the reference to improve the cryopreservation technology of bull semen. [ABSTRACT FROM AUTHOR]

Aerobic glycolysis (the Warburg effect) is a robust metabolic hallmark of most tumors, including oral squamous cell carcinoma (OSCC). Glucose transporter 1 (GLUT1), a major glucose transporter regulating the glucose uptake, is upregulated in OSCC and participated in the cell glycolysis of OSCC. The deregulation and function of noncoding RNAs in cancers have been widely reported. Reportedly, hsa_circular RNA (circRNA)_100290 (circ_SLC30A7) is significantly upregulated (fold change = 6.91, p < 0.0000001) in OSCC. According to online tools prediction (miRWalk, miRanda, and Targetscan), miR‐378a could simultaneously target circRNA_100290 and GLUT1. Herein, the expression of circRNA_100290 and GLUT1 remarkably increased in oral tumor tissue specimens and cells. In OSCC cell lines, cell proliferation and glycolysis could be remarkably downregulated by circRNA_100290 silence, which could be rescued by GLUT1 overexpression. Conversely, miR‐378a expression could be remarkably inhibited in tumor tissue specimens and cells. The effect of miR‐378a overexpression on OSCC cells was similar to those of circRNA_100290 silence. miR‐378a directly bound to circRNA_100290 and GLUT1 3′‐untranslated region, circRNA_100290 could remarkably relieve miR‐378a‐induced inhibition on GLUT1 via acting as a competing endogenous RNA (ceRNA). miR‐378a inhibition remarkably attenuated the effect of circRNA_100290 silence on cell proliferation and glycolysis in OSCC cell lines. In summary, circRNA_100290 serves as a ceRNA to counteract miR‐378a‐mediated GLUT1 suppression, thus promoting glycolysis and cell proliferation in OSCC. We provide a reliable experimental basis for understanding the mechanism of cell growth and glycolysis deregulation in OSCC. [ABSTRACT FROM AUTHOR]

In recent years, increasing associations between microRNAs (miRNAs) and human diseases have been identified. Based on accumulating biological data, many computational models for potential miRNA-disease associations inference have been developed, which saves time and expenditure on experimental studies, making great contributions to researching molecular mechanism of human diseases and developing new drugs for disease treatment. In this paper, we proposed a novel computational method named Ensemble of Decision Tree based MiRNA-Disease Association prediction (EDTMDA), which innovatively built a computational framework integrating ensemble learning and dimensionality reduction. For each miRNA-disease pair, the feature vector was extracted by calculating the statistical measures, graph theoretical measures, and matrix factorization results for the miRNA and disease, respectively. Then multiple base learnings were built to yield many decision trees (DTs) based on random selection of negative samples and miRNA/disease features. Particularly, Principal Components Analysis was applied to each base learning to reduce feature dimensionality and hence remove the noise or redundancy. Average strategy was adopted for these DTs to get final association scores between miRNAs and diseases. In model performance evaluation, EDTMDA showed AUC of 0.9309 in global leave-one-out cross validation (LOOCV) and AUC of 0.8524 in local LOOCV. Additionally, AUC of 0.9192+/-0.0009 in 5-fold cross validation proved the model’s reliability and stability. Furthermore, three types of case studies for four human diseases were implemented. As a result, 94% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 96% (Breast Neoplasms) and 88% (Carcinoma Hepatocellular) of top 50 predicted miRNAs were confirmed by experimental evidences in literature. [ABSTRACT FROM AUTHOR]

Objectives: SMAD3 is pivotal in the biology functions of various tumors. This study is aiming to study the relationship among SMAD3, long noncoding RNAs (lncRNAs) OPA‐interacting protein 5 antisense transcript 1 (OIP5‐AS1), and miR‐143‐3p, and their effects on cervical cancer. Methods: In our research, real‐time polymerase chain reaction and western blot assay were conducted to detect the expression level of messenger RNA and protein in tumor tissues and cells. Transfection of lncRNA OIP5‐AS1, miR‐143‐3p, or SMAD3 was performed to investigate their potential effects on the function of cell as well as the relationship among them in cervical cell lines via 3‐(4,5‐dimethylthiazolyl‐2)‐2,5‐diphenyltetrazolium bromide) together with transwell assays or dual‐luciferase reporter assay respectively. Results: SMAD3, lncRNA OIP5‐AS1 expression is significantly enhanced in cervical cancer tissues and cell lines, but miR‐143‐3p was inhibited. LncRNA OIP5‐AS1 is demonstrated to mediate the physiological process of cervical cancer cells. Moreover, silencing SMAD3 via siRNA suppressed cell number, viability, migration and invasion, whereas overexpression of OIP5‐AS1 promoted these abilities. Furthermore, lncRNA OIP5‐AS1 exert its function via sponging miR‐143‐3p to regulate SMAD3 expression. Conclusions: LncRNA OIP5‐AS1 promoted SMAD3 expression via mediating miR‐143‐3p to promote migration and invasion of cervical cancer cells. 1.Our results were the first to establish a functional relationship between lncRNA OIP5‐AS1, miR‐143‐3p, and SMAD3 and their roles in the migration and invasion of cervical cancer cells. 2. SMAD3 and lncRNA OIP5‐AS1 were overexpressed, and miR‐143‐3p was low‐expressed in cervical cancer cells. 3. lncRNA OIP5‐AS1 played its tumorigenic activity through its promotion of SMAD3 expression through miR‐143‐3p. [ABSTRACT FROM AUTHOR]

Emerging evidence has shown microRNAs (miRNAs) play an important role in human disease research. Identifying potential association among them is significant for the development of pathology, diagnose and therapy. However, only a tiny portion of all miRNA-disease pairs in the current datasets are experimentally validated. This prompts the development of high-precision computational methods to predict real interaction pairs. In this paper, we propose a new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In particular, we introduce miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA-disease prediction model. In the cross-validation experiment, LMTRDA obtained 90.51% prediction accuracy with 92.55% sensitivity at the AUC of 90.54% on the HMDD V3.0 dataset. To further evaluate the performance of LMTRDA, we compared it with different classifier and feature descriptor models. In addition, we also validate the predictive ability of LMTRDA in human diseases including Breast Neoplasms, Breast Neoplasms and Lymphoma. As a result, 28, 27 and 26 out of the top 30 miRNAs associated with these diseases were verified by experiments in different kinds of case studies. These experimental results demonstrate that LMTRDA is a reliable model for predicting the association among miRNAs and diseases. [ABSTRACT FROM AUTHOR]

Background: In the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly. Results: In this work, we proposed a computational model of Random Walk and Binary Regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease associations. RWBRMDA obtained AUC of 0.8076 in the leave-one-out cross validation. Additionally, we carried out three different patterns of case studies on four human complex diseases. Specifically, Esophageal cancer and Prostate cancer were conducted as one kind of case study based on known miRNA-disease associations in HMDD v2.0 database. Out of the top 50 predicted miRNAs, 94 and 90% were respectively confirmed by recent experimental reports. To simulate new disease without known related miRNAs, the information of known Breast cancer related miRNAs was removed. As a result, 98% of the top 50 predicted miRNAs for Breast cancer were confirmed. Lymphoma, the verified ratio of which was 88%, was used to assess the prediction robustness of RWBRMDA based on the association records in HMDD v1.0 database. Conclusions: We anticipated that RWBRMDA could benefit the future experimental investigations about the relation between human disease and miRNAs by generating promising and testable top-ranked miRNAs, and significantly reducing the effort and cost of identification works. [ABSTRACT FROM AUTHOR]

From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function–related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future. [ABSTRACT FROM AUTHOR]

Recently, a growing number of biological research and scientific experiments have demonstrated that microRNA (miRNA) affects the development of human complex diseases. Discovering miRNA-disease associations plays an increasingly vital role in devising diagnostic and therapeutic tools for diseases. However, since uncovering associations via experimental methods is expensive and time-consuming, novel and effective computational methods for association prediction are in demand. In this study, we developed a computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction (MDHGI) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method, the miRNA functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and miRNAs into a heterogeneous network. Compared with previous computational models based on heterogeneous networks, our model took full advantage of matrix decomposition before the construction of heterogeneous network, thereby improving the prediction accuracy. MDHGI obtained AUCs of 0.8945 and 0.8240 in the global and the local leave-one-out cross validation, respectively. Moreover, the AUC of 0.8794+/-0.0021 in 5-fold cross validation confirmed its stability of predictive performance. In addition, to further evaluate the model's accuracy, we applied MDHGI to four important human cancers in three different kinds of case studies. In the first type, 98% (Esophageal Neoplasms) and 98% (Lymphoma) of top 50 predicted miRNAs have been confirmed by at least one of the two databases (dbDEMC and miR2Disease) or at least one experimental literature in PubMed. In the second type of case study, what made a difference was that we removed all known associations between the miRNAs and Lung Neoplasms before implementing MDHGI on Lung Neoplasms. As a result, 100% (Lung Neoplasms) of top 50 related miRNAs have been indexed by at least one of the three databases (dbDEMC, miR2Disease and HMDD V2.0) or at least one experimental literature in PubMed. Furthermore, we also tested our prediction method on the HMDD V1.0 database to prove the applicability of MDHGI to different datasets. The results showed that 50 out of top 50 miRNAs related with the breast neoplasms were validated by at least one of the three databases (HMDD V2.0, dbDEMC, and miR2Disease) or at least one experimental literature. [ABSTRACT FROM AUTHOR]

Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a L1-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction. [ABSTRACT FROM AUTHOR]

Background Because long non-coding RNA ATB (activated by TGF-β) is dysregulated in many cancers, we performed a meta-analysis to determine its prognostic potential in malignant tumors. Methods We searched electronic databases, including PubMed, Medline, OVID, Cochrane Library and Web of Science from inception until November 15, 2016 and identified eight studies with 818 cancer patients for the meta-analysis. We analyzed the hazard ratios (HRs) and 95% confidence intervals (CIs) to determine the relationship between lncRNA-ATB expression and overall survival (OS), recurrence -free survival (RFS), disease-free survival (DFS). We also use RevMan5.3 software to calculate odds ratio (ORs) to assess the association between lncRNA-ATB expression and pathological parameters including lymph node metastasis (LNM), distant metastasis (DM) and tumor stage. Results Our analysis showed that increased lncRNA-ATB expression was associated with OS (HR = 2.82, 95% CI:1.98–4.00, P < 0.00001), DFS (HR = 2.75, 95% CI:1.73–4.38, P < 0.0001), RFS(HR = 3.96, 95% CI:2.30–6.81, P < 0.00001), LNM (OR = 4.07, 95% CI 1.74–9.53, P = 0.001), DM (OR = 3.21, 95% CI 1.06–9.72, P = 0.04) and high tumor stage (OR = 2.81, 95% 1.78–4.43, P < 0.0001) in patients with other types of cancers that excluded pancreatic cancer. Conclusions Meta-analysis demonstrated that increased lncRNA-ATB expression can be a useful prognostic biomarker in human cancer. [ABSTRACT FROM AUTHOR]

In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, ath-ased iRNA-isease ssociation (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background It has been reported that Colon cancer-associated transcript 2 (CCAT2) is dysregulated in various cancers. We performed this meta-analysis to clarify its promising functions as a prognosis marker in malignant tumors. Methods Electronic databases, including PubMed, Medline, OVID, Cochrane Library, and Web of Science, were searched from inception to October 20, 2016. The hazard ratio (HR) and 95% confidence interval (CI) were calculated to explore the relationship between CCAT2 expression and survival, which were extracted from the eligible studies. The odds ratio (OR) was calculated to assess the association between CCAT2 expression and pathological parameters using RevMan5.3 software. Results Six original studies were included in this meta-analysis including 725 cancer patients. The pooled HR suggested that high CCAT2 expression was significantly correlated with overall survival (OS) (HR = 2.30, 95% CI: 1.62–3.25, p < 0.00001) in cancer patients. Subgroup analysis revealed a significant association between CCAT2 and OS in urogenital system (HR = 1.70, 95% CI: 1.27–2.26, p < 0.003) and non-urogenital system cancer patients (HR = 3.18, 95% CI: 2.09–4.83, p < 0.0001). A significant association was observed between high CCAT2 expression and poor progression-free survival (PFS) in cancer patients (pooled HR = 2.76, 95% CI: 1.74–4.37). CCAT2 expression was significantly related to lymph node metastasis (LNM) (OR = 4.33, 95% CI 2.03–9.22), distant metastasis (DM) (OR = 11.66, 95% CI: 5.36–25.37) and tumor stage (OR = 2.58, 95% CI 1.86–3.57). Conclusions This meta-analysis demonstrated that high CCAT2 expression significantly predicts poor OS, poor PFS, LNM, DM and tumor stage, suggesting that high CCAT2 expression may serve as a novel biomarker for poor prognosis and metastasis in cancers. [ABSTRACT FROM AUTHOR]

Papillary thyroid carcinoma (PTC) is generally recognized as a slow-growing tumor. However, a small subset of patients may still experience relapse or metastasis shortly after therapy, leading to a poor prognosis and raising concerns about excessive medical treatment. One major challenge lies in the inadequacy of effective biomarkers for accurate risk stratification. Long non-coding RNAs (lncRNAs), which are closely related to malignant characteristics and poor prognosis, play a significant role in the genesis and development of PTC through various pathways. The objective of this review is to provide a comprehensive summary of the biological functions of lncRNAs in PTC, identify prognosis-relevant lncRNAs, and explore their potential mechanisms in drug resistance to BRAF kinase inhibitors, tumor dedifferentiation, and lymph node metastasis. By doing so, this review aims to offer valuable references for both basic research and the prediction of PTC prognosis. [ABSTRACT FROM AUTHOR]

An increasing number of studies have shown that FAM83A, a member of the family with sequence similarity 83 (FAM83), which consists of eight members, is a key tumor therapeutic target involved in multiple signaling pathways. It has been reported that FAM83A plays essential roles in the regulation of Wnt/β-catenin, EGFR, MAPK, EMT, and other signaling pathways and physiological processes in models of pancreatic cancer, lung cancer, breast cancer, and other malignant tumors. Moreover, the expression of FAM83A could be significantly affected by multiple noncoding RNAs that are dysregulated in malignant tumors, the dysregulation of which is essential for the malignant process. Among these noncoding RNAs, the most noteworthy is the antisense long noncoding (Lnc) RNA of FAM83A itself (FAM83A-AS1), indicating an outstanding synergistic carcinogenic effect between FAM83A and FAM83A-AS1. In the present study, the specific mechanisms by which FAM83A and FAM83A-AS1 cofunction in the Wnt/β-catenin and EGFR signaling pathways were reviewed in detail, which will guide subsequent research. We also described the applications of FAM83A and FAM83A-AS1 in tumor therapy and provided a certain theoretical basis for subsequent drug target development and combination therapy strategies. [Display omitted] • FAM83A and FAM83A-AS1 regulates Wnt, EGFR, PI3K, and EMT signaling pathways. • FAM83A-AS1 enhance the mRNA stability of FAM83A directly. • FAM83A and FAM83A-AS1 effectively enhanced multiple types of cancers development. • Both FAM83A and FAM83A-AS1 represent promising therapeutic targets for cancers. [ABSTRACT FROM AUTHOR]