Cancer subtypes classification using long non-coding RNA

The pivotal role of long non-coding RNAs as potential biomarkers and modulators of chemoresistance in ovarian cancer (OC)

Ovarian cancer (OC) is a fatal gynecological disease that is often diagnosed at later stages due to its asymptomatic nature and the absence of efficient early-stage biomarkers. Previous studies have identified genes with abnormal expression in OC that couldn't be explained by methylation or mutation, indicating alternative mechanisms of gene regulation. Recent advances in human transcriptome studies have led to research on non-coding RNAs (ncRNAs) as regulators of cancer gene expression. Long non-coding RNAs (lncRNAs), a class of ncRNAs with a length greater than 200 nucleotides, have been identified as crucial regulators of physiological processes and human diseases, including cancer. Dysregulated lncRNA expression has also been found to play a crucial role in ovarian carcinogenesis, indicating their potential as novel and non-invasive biomarkers for improving OC management. However, despite the discovery of several thousand lncRNAs, only one has been approved for clinical use as a biomarker in cancer, highlighting the importance of further research in this field. In addition to their potential as biomarkers, lncRNAs have been implicated in modulating chemoresistance, a major problem in OC. Several studies have identified altered lncRNA expression upon drug treatment, further emphasizing their potential to modulate chemoresistance. In this review, we highlight the characteristics of lncRNAs, their function, and their potential to serve as tumor markers in OC. We also discuss a few databases providing detailed information on lncRNAs in various cancer types. Despite the promising potential of lncRNAs, further research is necessary to fully understand their role in cancer and develop effective strategies to combat this devastating disease.

Multiple roles of LncRNA-BMNCR on cell proliferation and apoptosis by targeting miR-145/CBFB axis in BMECs

Bovine mastitis is one of the most serious and costly disease affecting dairy cattle production. The present study explored the inflammatory response and autoprotective mechanism of a novel specific high expression BMNCR (bovine mastitis related long non-coding RNA) in S. aureus induced mastitis by miR-145/CBFB axis in dairy cows from the perspective of molecular genetics. In bovine mammary epithelial cells, we preformed loss of function experiments to detect changes in cytokine, proliferation and apoptosis by qRT-PCR, western blot, flow cytometry and EdU staining. The results demonstrated that BMNCR significantly increased cell apoptosis, and inhibited cell proliferation. However, the secretion of IL-1α, IL-2, IL-6, IL-8 and IL-12 were enhanced after knock-down BMNCR. Bioinformatics analysis demonstrated that BMNCR could target 8 miRNAs, in-depth analyses indicated that BMNCR acts as a molecular sponge for bta-miR-145 and CBFB was one of 23 target gene of bta-miR-145 . The results of the present study demonstrated that the role of BMNCR in S. aureus induced mastitis can be mediated by sponge bta-miR-145 activating CBFB expression. BMNCR could be a potential target for mastitis diagnosis and therapy, which may enrich the theoretical research of therapeutic intervention, and further increase milk yield and improve milk quality.

Exosomal lncRNAs in gastrointestinal cancer

Gastrointestinal cancer (GIC) remains a leading cause of morbidity and mortality worldwide. Unfortunately, these cancers are diagnosed in advanced metastatic stages due to lack of reliable biomarkers that are sufficiently specific and sensitive in early disease. There has been growing evidence that circulating exosomes can be used to diagnose cancer non-invasively with limited risks and side effects. Furthermore, exosomal long non-coding RNAs (lncRNAs) are emerging as a new class of promising biomarkers in cancer. This review provides an overview of the extraction and detection of exosomal lncRNAs with a focus on their potential role in GIC.

A promising new cancer marker: Long noncoding RNA EGFR-AS1

Cancer consists of a group of diseases with the salient properties of an uncontrolled cell cycle, metastasis, and evasion of the immune response, mainly driven by the genomic instability of somatic cells and the physicochemical environment. Long noncoding RNAs (lncRNAs) are defined as noncoding RNAs with a length of more than 200 nucleotides. LncRNA dysregulation participates in diverse disease types and is tightly associated with patient clinical features, such as age, disease stage, and prognosis. In addition, an increasing number of lncRNAs have been confirmed to regulate a series of biological and pathological processes through numerous mechanisms. The lncRNA epidermal growth factor receptor antisense RNA 1 (EGFR-AS1) was recently discovered to be aberrantly expressed in many types of diseases, particularly in cancers. A high level of EGFR-AS1 was demonstrated to correlate with multiple patient clinical characteristics. More importantly, EGFR-AS1 was found to be involved in the mediation of various cellular activities, including cell proliferation, invasion, migration, chemosensitivity, and stemness. Therefore, EGFR-AS1 is a promising marker for cancer management. In this review, we introduce the expression profile, molecular mechanisms, biological functions, and clinical value of EGFR-AS1 in cancers.

Systematic Investigation of the Diagnostic and Prognostic Impact of LINC01087 in Human Cancers

(1) Background: Long non-coding RNAs may constitute epigenetic biomarkers for the diagnosis, prognosis, and therapeutic response of a variety of tumors. In this context, we aimed at assessing the diagnostic and prognostic value of the recently described long intergenic non-coding RNA 01087 (LINC01087) in human cancers. (2) Methods: We studied the expression of LINC01087 across 30 oncological indications by interrogating public resources. Data extracted from the TCGA and GTEx databases were exploited to plot receiver operating characteristic curves (ROC) and determine the diagnostic performance of LINC01087. Survival data from TCGA and KM-Plotter directories allowed us to graph Kaplan-Meier curves and evaluate the prognostic value of LINC01087. To investigate the function of LINC01087, gene ontology (GO) annotation and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed. Furthermore, interactions between LINC01087 and both miRNA and mRNA were studied by means of bioinformatics tools. (3) Results: LINC01087 was significantly deregulated in 7 out of 30 cancers, showing a predominant upregulation. Notably, it was overexpressed in breast (BC), esophageal (ESCA), and ovarian (OV) cancers, as well as lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD), and uterine carcinosarcoma (UCS). By contrast, LINC01087 displayed downregulation in testicular germ cell tumors (TGCT). ROC curve analyses identified LINC01087 as a potential diagnostic indicator in BC, ESCA, OV, STAD, and TGCT. Moreover, high and low expression of LINC01087 predicted a favorable prognosis in BC and papillary cell carcinoma, respectively. In silico analyses indicated that deregulation of LINC01087 in cancer was associated with a modulation of genes related to ion channel, transporter, and peptide receptor activity. (4) Conclusions: the quantification of an altered abundance of LINC01087 in tissue specimens might be clinically useful for the diagnosis and prognosis of some hormone-related tumors, including BC, OV, and TGCT, as well as other cancer types such as ESCA and STAD. Moreover, our study revealed the potential of LINC01087 (and perhaps other lncRNAs) to regulate neuroactive molecules in cancer.

Liquid biopsy: Exosomal microRNAs as novel diagnostic and prognostic biomarkers in cancer

BACKGROUND

Detecting cancer at an early stage before clinical manifestation could be an effective strategy to decrease cancer mortality. Thus, identifying liquid biopsy biomarkers with high efficacy could be a promising approach for non-invasive diagnosis of cancer.

MAIN TEXT

Liquid biopsies are increasingly used as a supplement to biopsy, as it enables disease progression to be detected months before clinical and radiographic confirmation. Many bodily fluids contain exosomal microRNAs (miRNAs) which could provide a new class of biomarkers for early and minimally invasive cancer diagnosis due to the stability of miRNAs in exosomes. In this review, we mainly focused on the exosomal miRNAs (liquid biopsy) as biomarkers in the diagnosis and prognosis of various cancers.

CONCLUSION

Exosomal miRNAs can be used as diagnostic and prognosis biomarkers that provide unique insights and a more dynamic perspective of the progression and therapeutic responses in various malignancies. Therefore, the development of novel and more sensitive technologies that exploit exosomal miRNAs should be a priority for cancer management.

Emerging Role of Exosomal-Derived Long Noncoding RNAs in Human PDAC

Background

The incidence and mortality of pancreatic ductal adenocarcinoma (PDAC) are increasing recently. Most patients with PDAC are diagnosed at advanced stage because of the high invasiveness of cancer cells and the lack of typical early symptoms. Therefore, early diagnosis of PDAC is very important to improve the prognosis. Exosomes play crucial role in intercellular communication and deliver the contents to recipient cells to regulate their biological behaviors. Recent evidence suggests emerging role of exosomes in the carcinogenesis of a variety of cancers including PDAC. Long noncoding RNAs (LncRNAs) have been reported to be involved in the development of PDAC. It has been proved that LncRNAs have the potential to be biomarkers and therapeutic targets for PDAC. Moreover, increasing number of studies focus on the role of exosomal LncRNAs in PDAC.

Summary

In this review, we summarize the current status on our understanding of the role of exosomal-derived LncRNAs in the progression and metastasis of PDAC.

Key Messages

We focus on challenges in the potential of exosomal-derived LncRNAs as novel diagnostic and prognostic markers and therapeutic targets of PDAC. In addition, we provide an overview about the demonstrated important role of exosomal LncRNAs in the progression of PDAC.

Diagnostic, prognostic and therapeutic potential of long noncoding RNAs in cancer

Long noncoding RNAs (lncRNAs) are longer than 200 nucleotides in length and undergo splicing, capping, polyadenylation, and editing just like mRNA. Evidence is growing that they regulate transcription, splicing, RNA degradation, and translation of genes and that their expression has been linked to a variety of illnesses, including cancer. The advancement of next-generation and high-throughput sequencing has changed the way lncRNAs are identified and characterized, revealing a relationship between lncRNAs and several tumor types. Since then, they have gained a significant attraction as a promising candidate in cancer diagnosis, prognosis, and therapy. Furthermore, they are a good candidate for consideration as tumor biomarkers due to their high stability, better tissue/cell selectivity, aberrant expression in certain malignancies, and easy and noninvasive detection. In addition, lncRNAs are being examined as therapeutic targets in clinical trials for a variety of malignancies. This review highlights the potential of lncRNAs as biomarkers or therapeutic targets in light of the current progress, clinical investigations, and patents filed so far.

LncRNA MATN1-AS1 for Prediction of Prognosis in Osteosarcoma Patients and Its Cellular Function

Long non-coding RNAs show essential roles in various cancer processes. This study aimed at the expression features, prognosis significance, and biological effect of lnc MATN1-AS1 in osteosarcoma (OS). Five kinds of cell lines and 117 pairs of tissues were analyzed by qRT-PCR for quantification of lnc MATN1-AS1 and miR-1299 level. Clinical data were analyzed using Chi-Square Tests to show the association with lnc MATN1-AS1 level. Kaplan-Meier analysis and Cox regression were used to judge the prognostic value. Cell counting kit-8 and Transwell assay were conducted, respectively, to analyze the effect of lnc MATN1-AS1 on cell proliferation and metastasis. The target miRNA was predicted. lnc MATN1-AS1 level was significantly elevated in OS cells and tissues and related to Enneking staging, lung metastasis, and histologic type. Patients with high lnc MATN1-AS1 level showed a shorter overall survival and recurrence-free survival. Lnc MATN1-AS1 knockdown inhibited OS cell proliferation, migration, and invasion by sponging miR-1299. Lnc MATN1-AS1 has oncogenic features and prognostic significance in OS and is a novel therapeutic strategy for OS.

Non-coding RNAs in pancreatic ductal adenocarcinoma: New approaches for better diagnosis and therapy

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignancies with a 5-year survival rate less than 8%, which has remained unchanged over the last 50 years. Early detection is particularly difficult due to the lack of disease-specific symptoms and a reliable biomarker. Multimodality treatment including chemotherapy, radiotherapy (used sparingly) and surgery has become the standard of care for patients with PDAC. Carbohydrate antigen 19-9 (CA 19-9) is the most common diagnostic biomarker; however, it is not specific enough especially for asymptomatic patients. Non-coding RNAs are often deregulated in human malignancies and shown to be involved in cancer-related mechanisms such as cell growth, differentiation, and cell death. Several micro, long non-coding and circular RNAs have been reported to date which are involved in PDAC. Aim of this review is to discuss the roles and functions of non-coding RNAs in diagnosis and treatments of PDAC.

The Role of lncRNAs in the Stem Phenotype of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma is one of the deadliest tumors. This neoplasia is characterized by an important cellular and phenotypic heterogeneity. In particular, it has been shown that at least two subtypes can be found: basal-like, which presents stem-like properties, and classical. Cancer stem cells have been isolated and characterized from these tumors, showing their dependance on general and tissue-specific stem transcription factors and signaling pathways. Nevertheless, little is known about their tissue microenvironment and cell non-autonomous regulators, such as long-non-coding RNAs. (lncRNAs). In this review, we summarize the current knowledge about the positive and negative effects of lncRNAs in the stemness phenotype of pancreatic ductal adenocarcinoma cancer (PDAC).

Extracellular Vesicles and Pancreatic Cancer: Insights on the Roles of miRNA, lncRNA, and Protein Cargos in Cancer Progression

Pancreatic cancer (PC) is among the most devastating digestive tract cancers worldwide. This cancer is characterized by poor diagnostic detection, lack of therapy, and difficulty in predicting tumorigenesis progression. Although mutations of key oncogenes and oncosuppressor involved in tumor growth and in immunosurveillance escape are known, the underlying mechanisms that orchestrate PC initiation and progression are poorly understood or still under debate. In recent years, the attention of many researchers has been concentrated on the role of extracellular vesicles and of a particular subset of extracellular vesicles, known as exosomes. Literature data report that these nanovesicles are able to deliver their cargos to recipient cells playing key roles in the pathogenesis and progression of many pancreatic precancerous conditions. In this review, we have summarized and discussed principal cargos of extracellular vesicles characterized in PC, such as miRNAs, lncRNAs, and several proteins, to offer a systematic overview of their function in PC progression. The study of extracellular vesicles is allowing to understand that investigation of their secretion and analysis of their content might represent a new and potential diagnostic and prognostic tools for PC.

Prediction of Lymph-Node Metastasis in Cancers Using Differentially Expressed mRNA and Non-coding RNA Signatures

Accurate prediction of lymph-node metastasis in cancers is pivotal for the next targeted clinical interventions that allow favorable prognosis for patients. Different molecular profiles (mRNA and non-coding RNAs) have been widely used to establish classifiers for cancer prediction (e.g., tumor origin, cancerous or non-cancerous state, cancer subtype). However, few studies focus on lymphatic metastasis evaluation using these profiles, and the performance of classifiers based on different profiles has also not been compared. Here, differentially expressed mRNAs, miRNAs, and lncRNAs between lymph-node metastatic and non-metastatic groups were identified as molecular signatures to construct classifiers for lymphatic metastasis prediction in different cancers. With this similar feature selection strategy, support vector machine (SVM) classifiers based on different profiles were systematically compared in their prediction performance. For representative cancers (a total of nine types), these classifiers achieved comparative overall accuracies of 81.00% (67.96-92.19%), 81.97% (70.83-95.24%), and 80.78% (69.61-90.00%) on independent mRNA, miRNA, and lncRNA datasets, with a small set of biomarkers (6, 12, and 4 on average). Therefore, our proposed feature selection strategies are economical and efficient to identify biomarkers that aid in developing competitive classifiers for predicting lymph-node metastasis in cancers. A user-friendly webserver was also deployed to help researchers in metastasis risk determination by submitting their expression profiles of different origins.

Exosomal long non-coding RNAs in the diagnosis and oncogenesis of pancreatic cancer

Extracellular vesicles, specifically exosomes, play a significant role as an extracellular messenger through their transporting cargo. Of particular interest are the potential roles they play in pancreatic cancer, one of the leading causes of cancer-related mortality worldwide. Pancreatic Ductal Adenocarcinoma displays high chemo-resistance and metastatic ability, which may be influenced by cancer-derived exosomes carrying proteins, lipids and RNA. To date, among the most extensively examined exosomal molecular cargo there are long non-coding RNAs (lncRNAs) that, despite the increasing interest in their role and functions, are relatively poorly understood compared to other RNA transcripts. Nevertheless, we have witnessed an increasing interest for lncRNAs roles and functions in the past decade. For example, lncRNAs have been investigated as potential biomarkers for diagnosing pancreatic cancer and may have a role as therapeutics targets for precision medicine, but may also directly intervene in tumour progression features such as metastasis, epithelial to mesenchymal transition and resistance of cancer cells towards chemotherapy agents. The function of lncRNAs within various cancer exosomes is still undefined. In this review, we summarize the current knowledge on pancreatic cancer-derived exosome specific lncRNAs having prominent roles in genome integrity, pancreatic cancer progression and in other oncogenic hallmarks.

Identification of expression signatures for non-small-cell lung carcinoma subtype classification

MOTIVATION

Non-small-cell lung carcinoma (NSCLC) mainly consists of two subtypes: lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). It has been reported that the genetic and epigenetic profiles vary strikingly between LUAD and LUSC in the process of tumorigenesis and development. Efficient and precise treatment can be made if subtypes can be identified correctly. Identification of discriminative expression signatures has been explored recently to aid the classification of NSCLC subtypes.

RESULTS

In this study, we designed a classification model integrating both mRNA and long non-coding RNA (lncRNA) expression data to effectively classify the subtypes of NSCLC. A gene selection algorithm, named WGRFE, was proposed to identify the most discriminative gene signatures within the recursive feature elimination (RFE) framework. GeneRank scores considering both expression level and correlation, together with the importance generated by classifiers were all taken into account to improve the selection performance. Moreover, a module-based initial filtering of the genes was performed to reduce the computation cost of RFE. We validated the proposed algorithm on The Cancer Genome Atlas (TCGA) dataset. The results demonstrate that the developed approach identified a small number of expression signatures for accurate subtype classification and particularly, we here for the first time show the potential role of LncRNA in building computational NSCLC subtype classification models.

AVAILABILITY AND IMPLEMENTATION

The R implementation for the proposed approach is available at https://github.com/RanSuLab/NSCLC-subtype-classification.

Research Progress on Long Non-coding RNAs and Drug Resistance of Breast Cancer

Breast cancer, as the foremost cause of women's death in the world, is highly metastatic and mutable. Resistance to drugs for chemotherapies, endocrine therapies, and targeted therapies is an important factor that impacts the prognosis of breast cancer. Long non-coding ribonucleic acids (LncRNAs) are crucial regulators of intracellular gene expressions. Some researchers have suggested that expression level of several types of LncRNAs were closely related to the prognosis of patients with breast cancer. LncRNAs significantly impact biological processes such as drug transport, detoxication, apoptosis, epithelial to mesenchymal transition (EMT), and autophagy by regulating intracellular signaling pathways such as multi-drug resistance gene 1 (MDR1), nuclear factor erythroid 2-related factor 2 (NRF2), phosphoinositide 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR), transforming growth factor-β (TGF-β), BRCA1/2, and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB). This paper will summarize research progress on correlations between LncRNA and drug resistance of breast cancer. It will particularly expound molecular mechanisms through which LncRNAs regulate drug resistance of breast cancer. It will further discuss the feasibility as molecular markers for forecasting drug resistance of breast cancer and may be becoming new targets for treating breast cancer in the future.

Long non-coding RNAs in genitourinary malignancies: a whole new world

Long non-coding RNAs (lncRNAs) are regulators of cellular machinery that are commonly dysregulated in genitourinary malignancies. Accordingly, the investigation of lncRNAs is improving our understanding of genitourinary cancers, from development to progression and dissemination. lncRNAs are involved in major oncogenic events in genitourinary malignancies, including androgen receptor (AR) signalling in prostate cancer, hypoxia-inducible factor (HIF) pathway activation in renal cell carcinoma and invasiveness in bladder cancer, as well as multiple other proliferation and survival mechanisms. In line with their putative oncogenic roles, new lncRNA-based classifications are emerging as potent predictors of prognosis. In clinical practice, detection of oncogenic lncRNAs in serum or urine might enable early cancer detection, and lncRNAs might also be promising therapeutic targets for patients with genitourinary cancer. Furthermore, as predictors of sensitivity to anticancer treatments, lncRNAs could be integrated into future precision medicine strategies. Overall, lncRNAs are promising new candidates for molecular studies and for discovery of innovative biomarkers and are putative therapeutic targets in genitourinary oncology.

Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients

BACKGROUND

Deciphering the meaning of the human DNA is an outstanding goal which would revolutionize medicine and our way for treating diseases. In recent years, non-coding RNAs have attracted much attention and shown to be functional in part. Yet the importance of these RNAs especially for higher biological functions remains under investigation.

METHODS

In this paper, we analyze RNA-seq data, including non-coding and protein coding RNAs, from lung adenocarcinoma patients, a histologic subtype of non-small-cell lung cancer, with deep learning neural networks and other state-of-the-art classification methods. The purpose of our paper is three-fold. First, we compare the classification performance of different versions of deep belief networks with SVMs, decision trees and random forests. Second, we compare the classification capabilities of protein coding and non-coding RNAs. Third, we study the influence of feature selection on the classification performance.

RESULTS

As a result, we find that deep belief networks perform at least competitively to other state-of-the-art classifiers. Second, data from non-coding RNAs perform better than coding RNAs across a number of different classification methods. This demonstrates the equivalence of predictive information as captured by non-coding RNAs compared to protein coding RNAs, conventionally used in computational diagnostics tasks. Third, we find that feature selection has in general a negative effect on the classification performance which means that unfiltered data with all features give the best classification results.

CONCLUSIONS

Our study is the first to use ncRNAs beyond miRNAs for the computational classification of cancer and for performing a direct comparison of the classification capabilities of protein coding RNAs and non-coding RNAs.

Noncoding RNAs as circulating biomarkers in osteosarcoma patients

Noncoding RNAs (ncRNAs) identify a large family of RNAs that do not encode proteins and represent an important group of tumor biomarkers, directly involved in the process of tumor pathogenesis and progression. Many of them have also been identified in biological fluids of patients with cancer, especially blood, suggesting their role as an emerging class of circulating biomarkers. Many ncRNAs, both miRNAs and lncRNAs, are deregulated in sarcoma tissues, with the most consistent data in osteosarcomas. In patients with osteosarcoma, the role of ncRNAs as circulating biomarkers is taking enormous value, above all for their ability to vary expression levels during disease progression and in response to therapy. In this mini-review, we summarize the main studies supporting the role of circulating ncRNAs in monitoring disease status in patients with osteosarcoma.

Long Non-Coding RNAs as New Master Regulators of Resistance to Systemic Treatments in Breast Cancer

Predicting response to systemic treatments in breast cancer (BC) patients is an urgent, yet still unattained health aim. Easily detectable molecules such as long non-coding RNAs (lncRNAs) are the ideal biomarkers when they act as master regulators of many resistance mechanisms, or of mechanisms that are common to more than one treatment. These kinds of markers are pivotal in quasi-personalized treatment selection, and consequently, in improvement of outcome prediction. In order to provide a better approach to understanding development of disease and resistance to treatments, we reviewed current literature searching for lncRNA-associated systemic BC treatments including endocrine therapies, aromatase inhibitors, selective estrogen receptor modulators (SERMs), trastuzumab, paclitaxel, docetaxel, 5-fluorouracil (5-FU), anthracyclines, and cisplatin. We found that the engagement of lncRNAs in resistance is well described, and that lncRNAs such as urotelial carcinoma-associated 1 (UCA1) and regulator of reprogramming (ROR) are indeed involved in multiple resistance mechanisms, which offers tantalizing perspectives for wide usage of lncRNAs as treatment resistance biomarkers. Thus, we propose this work as the foundation for a wide landscape of functions and mechanisms that link more lncRNAs to resistance to current and new treatments in years of research to come.

Circulating biomarkers for early diagnosis of pancreatic cancer: facts and hopes

Pancreatic cancer (PC) is characterized by extremely high mortality and poor prognosis, which are largely ascribed to difficulties in early diagnosis and limited therapeutics. Although there is a sufficient window for intervention before preneoplastic lesions progress to invasive disease, effective early detection of PC remains difficult using current biomarkers and imaging techniques. Biomarkers with satisfactory diagnostic efficacy and convenient analysis methods are urgently required. In this review, we summarized recent advances in the identification of biomarkers in circulation for early detection of PC. A number of novel circulating biomarkers, such as metabolites, cell-free DNA (cfDNA), noncoding RNA, and exosomes, that show promising diagnostic value have been discovered using advances in sequencing techniques and "omics" analyses. Panels comprising several biomarkers may also exhibit better diagnostic performance. In the future, we need more efficient circulating biomarkers for the identification of noninvasive precursor lesions and early disease. Collaborative large-scale studies are also required to show the clinical validity and applicability of potential biomarkers.

Long non-coding RNA cartilage injury-related promotes malignancy in bladder cancer

Recent advances have highlighted the important roles of long non-coding RNAs (lncRNAs) in a number of biological processes, including oncogenesis. However, the function of lncRNA cartilage injury-related (lncRNA-CIR) in bladder cancer progression remains elusive. A novel function for lncRNA-CIR in bladder cancer was identified in the present study. Reverse transcription quantitative polymerase chain reaction, viability, invasion assay and in vivo implantation were used to evaluate the role of lncRNA-CIR. It was identified that the expression of lncRNA-CIR was frequently upregulated in 52 cancerous tissues and selected bladder cancer cell lines. Additionally, upregulating lncRNA-CIR was demonstrated to promote viability and invasion in T24 and SW780 cells, whereas siRNA-mediated lncRNA-CIR-knockdown consistently exhibited the opposite effects. High lncRNA-CIR levels also dictated poor overall survival among patients with bladder cancer. Furthermore, in vivo implantation experiments also supported a tumorigenic function for lncRNA-CIR, as decreasing lncRNA-CIR levels markedly attenuated Ki-67 staining and xenograft tumor growth. Overall, the present study identified a novel function of lncRNA-CIR and indicates that lncRNA-CIR may serve as a potential biomarker for bladder cancer treatment.

Long non-coding RNA LINC00968 acts as oncogene in NSCLC by activating the Wnt signaling pathway

Long non-coding RNAs (lncRNAs) have played critical roles in a variety of cancers, including non-small cell lung cancer (N SCLC). In our study, we focused on the biological function and clinical significance of lncRNA LINC00968 in NSCLC. It was indicated that LINC00968 was significantly increased in LUAD tissues, LUSC tissues and NSCLC cells compared to their corresponding controls. Inhibition of LINC00968 was able to repress NSCLC growth, migration, and invasion in vitro while upregulation of LINC00968 reversed this process. Additionally, downregulation of LINC00968 induced apoptosis capacity of A549 cell. Apoptosis-related proteins BCL-2 were decreased and BAX was increased by knockdown of LINC00968, respectively. Meanwhile we observed that Wnt signaling pathway was involved in the LINC00968-induced NSCLC progression. Finally, in vivo tumor xenografts were established using A549 cells to detect the function of LINC00968 in NSCLC tumorigenesis. Silencing LINC00968 greatly inhibited NSCLC tumor progression, which was consistent with the in vitro tests. In conclusion, we have uncovered that LINC00968 could be regarded as a novel prognostic biomarker and therapeutic target in NSCLC diagnosis and treatment.

Molecular Signatures for Tumor Classification: An Analysis of The Cancer Genome Atlas Data

Cancer classification in the clinic is primarily based on histological analysis in the proper clinical context, often supplemented by immunohistochemical and molecular studies. Recent genomic studies have shown the potential of integrated multiomics platforms for molecular classification. We performed unsupervised analyses of molecular platforms in The Cancer Genome Atlas data (n = 6,216 samples) in comparison with tumor type. Our data showed that mRNA signatures and DNA methylation signatures mapped to histological diagnosis with high accuracy (95% and 88%, respectively) as individual platforms. The accuracy of mRNA signatures alone for classification and subtype identification was comparable to accuracies reported in the previously published Pan-Cancer 12 analysis. When combined, mRNA and methylation revealed a set of outliers for which the mRNA- and methylation-based molecular signatures concordantly differed from the original histological diagnosis. A subset remained consistent as outliers after using alternative classification and clustering algorithms and analysis of an independent molecular platform (miRNA). Overall, our results indicate that unsupervised approaches with individual genomic platforms, especially gene expression and DNA methylation, provide substantial classification information and identify occasional outlier cases in which the molecular signature is distinct from signatures expected for a given histological diagnosis. Identification of cases in which the molecular signature correlates with a specific histology that differs from initial impressions may prompt reconsideration of tumor classification in specific cases.

Expression of long non-coding RNA MFI2-AS1 is a strong predictor of recurrence in sporadic localized clear-cell renal cell carcinoma

Prediction of recurrence is a challenge for the development of adjuvant treatments in clear-cell renal cell carcinoma (ccRCC). In these tumors, expression of long non-coding RNAs (lncRNAs) are deregulated and closely associated with prognosis. Thus, we aimed to predict ccRCC recurrence risk using lncRNA expression. We identified prognostic lncRNAs in a training set of 351 localized ccRCCs from The Cancer Genome Atlas and validated lncRNA-based recurrence classification in an independent cohort of 167 localized ccRCCs. We identified lncRNA MFI2-AS1 as best candidate in the training set. In the validation cohort, MFI2-AS1 expression was independently associated with shorter disease-free survival (Hazard Ratio (HR) for relapse 3.5, p = 0.0001). Combined with Leibovich classification, MFI2-AS1 status improved prediction of recurrence (C-index 0.70) compared to MFI2-AS1 alone (0.67) and Leibovich classification alone (0.66). In patients with aggressive tumors (Leibovich ≥5), MFI2-AS1 expression was associated with dramatically increased risk of relapse (HR 12.16, p < 0.0001) compared to patients with undetectable MFI2-AS1 who had favorable outcomes. Compared to normal samples, MFI2-AS1 was upregulated in tumor tissue, and higher expression was associated with metastatic dissemination. Overall, MFI2-AS1 status improves patient stratification in localized ccRCC, which supports further integration of lncRNAs in molecular cancer classifications.

Deregulated expression of long non-coding RNA UCA1 in multiple myeloma

OBJECTIVES

Long non-coding RNAs (lncRNAs) are RNA transcripts longer than 200 nucleotides that are not translated into proteins. They are involved in pathogenesis of many diseases including cancer and have a potential to serve as diagnostic and prognostic markers. We aimed to investigate lncRNA expression profiles in bone marrow plasma cells (BMPCs) of newly diagnosed multiple myeloma (MM) patients in comparison to normal BMPCs of healthy donors (HD) in a three-phase biomarker study.

METHODS

Expression profile of 83 lncRNA was performed by RT2 lncRNA PCR Array (Qiagen), followed by quantitative real-time PCR using specific TaqMan non-coding RNA assays analyzing 84 newly diagnosed MM patients and 25 HD.

RESULTS

Our analysis revealed dysregulation of two lncRNAs; NEAT1 (sensitivity of 55.0% and specificity of 79.0%) and UCA1 (sensitivity of 85.0% and specificity of 94.7%). UCA1 levels correlated with albumin and monoclonal immunoglobulin serum levels, cytogenetic aberrations, and survival of MM patients.

CONCLUSION

Our study suggests a possible prognostic impact of UCA1 expression levels on MM patients.

Long Noncoding RNAs as Biomarkers in Cancer

Long noncoding RNAs (lncRNAs) are a relatively well-characterized class of noncoding RNA (ncRNA) molecules, involved in the regulation of various cell processes, including transcription, intracellular trafficking, and chromosome remodeling. Their deregulation has been associated with the development and progression of various cancer types, the fact which makes them suitable as biomarkers for cancer diagnosis and prognosis. In recent years, detection of cancer-associated lncRNAs in body fluids of cancer patients has proven itself as an especially valuable method to effectively diagnose cancer. Cancer diagnosis and prognosis employing circulating lncRNAs are preferential when compared to classical biopsies of tumor tissues, especially due to their noninvasiveness, and have great potential for routine usage in clinical practice. Thus, this review focuses on summarizing the perspectives of lncRNAs as biomarkers in cancer, based on evaluating their expression profiles determined in body fluids of cancer patients.

The underlying mechanisms of non-coding RNAs in the chemoresistance of pancreatic cancer

Pancreatic cancer, which is often asymptomatic, is currently one of the most common causes of cancer-related death. This phenomenon is most likely due to a lack of early diagnosis, a high metastasis rate and a disappointing chemotherapy outcome. Thus, improving treatment outcomes by overcoming chemotherapy resistance may be a useful strategy in pancreatic cancer. Various underlying mechanisms involved in the chemoresistance of pancreatic cancer have been investigated. Notably, non-coding RNAs (ncRNAs), especially microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), play a pivotal role in regulating sensitivity to chemotherapy in pancreatic cancer. In this review, we highlight recent evidence regarding the role of miRNAs and lncRNAs in the chemoresistance of pancreatic cancer, including their expression levels, targets, biological functions and the regulation of chemoresistance, and discuss the potential clinical application of miRNAs and lncRNAs in the treatment of pancreatic cancer.