A potential panel of six-long non-coding RNA signature to improve survival prediction of diffuse large-B-cell lymphoma. Sci Rep. 2016;6:27842. [PMID: 27292966 PMCID: PMC4904406 DOI: 10.1038/srep27842]

An integrative network-based approach to identify driving gene communities in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is an etiologically complex disease characterized by acute exacerbations and stable phases. We aimed to identify biological functions modulated in specific COPD conditions, using whole blood samples collected in the AERIS clinical study (NCT01360398). Considered conditions were exacerbation onset, severity of airway obstruction, and presence of respiratory pathogens in sputum samples. With an integrative multi-network gene community detection (MNGCD) approach, we analyzed expression profiles to identify communities of correlated genes. The approach combined different layers of gene interactions for each explored condition/subset of samples: gene expression similarity, protein-protein interactions, transcription factors, and microRNAs validated regulons. Heme metabolism, interferon-alpha, and interferon-gamma pathways were modulated in patients at both exacerbation and stable-state visits, but with the involvement of distinct sets of genes. An important gene community was enriched with G2M checkpoint, E2F targets, and mitotic spindle pathways during exacerbation. Targets of TAL1 regulator and hsa-let-7b - 5p microRNA were modulated with increasing severity of airway obstruction. Bacterial infections with Moraxella catarrhalis and, particularly, Haemophilus influenzae triggered a specific cellular and inflammatory response in acute exacerbations, indicating an active reaction of the host to infections. In conclusion, COPD is a complex multifactorial disease that requires in-depth investigations of its causes and features during its evolution and whole blood transcriptome profiling can contribute to capturing some relevant regulatory mechanisms associated with this disease. In this work, we explored multi-network modeling that integrated diverse layers of regulatory gene networks and enhanced our comprehension of the biological functions implicated in the COPD pathogenesis.

NEAT1 and CHROMR lncRNAs: a promising diagnostic tool for diffuse large B-cell lymphoma especially in elderly patients

Background: Long non-coding (lnc) RNAs have crucial regulatory roles in molecular pathways, and their dysregulation is associated with the pathogenesis of malignancies such as Diffuse large B-cell lymphoma (DLBCL). Therefore, we aimed to study the NEAT1 and CHROMR expression in DLBCL and explore their association with clinicopathological characteristics.Methods & materials: DLBCL and non-tumor lymph node specimens were obtained to assess the expression levels.Results: NEAT1 and CHROMR expressions were significantly increased in DLBCL, and were linked with the age of DLBCL patients (aged >60). NEAT1 and CHROMR overexpression may serve as moderate-to-good diagnostic biomarkers, with NEAT1 and CHROMR exhibiting area under the curve values of 0.781 and 0.831, respectively.

Noncoding RNAs in B cell non-Hodgkins lymphoma

B-cell non-Hodgkins lymphomas (BCNHLs) are a category of B-cell cancers that show heterogeneity. These blood disorders are derived from different levels of B-cell maturity. Among NHL cases, ∼80-90 % are derived from B-cells. Recent studies have demonstrated that noncoding RNAs (ncRNAs) contribute to almost all parts of mechanisms and are essential in tumorigenesis, including B-cell non-Hodgkins lymphomas. The study of ncRNA dysregulations in B-cell lymphoma unravels important mysteries in lymphoma's molecular etiology. It seems also necessary for discovering novel trials as well as investigating the potential of ncRNAs as markers for their diagnosis and prognosis. In the current study, we summarize the role of ncRNAs involving miRNAs, long noncoding RNAs, as well as circular RNAs in the development or progression of BCNHLs.

Predicting Cancer Lymph-Node Metastasis From LncRNA Expression Profiles Using Local Linear Reconstruction Guided Distance Metric Learning

Lymph-node metastasis is the most perilous cancer progressive state, where long non-coding RNA (lncRNA) has been confirmed to be an important genetic indicator in cancer prediction. However, lncRNA expression profile is often characterized of large features and small samples, it is urgent to establish an efficient judgment to deal with such high dimensional lncRNA data, which will aid in clinical targeted treatment. Thus, in this study, a local linear reconstruction guided distance metric learning is put forward to handle lncRNA data for determination of cancer lymph-node metastasis. In the original locally linear embedding (LLE) approach, any point can be approximately linearly reconstructed using its nearest neighborhood points, from which a novel distance metric can be learned by satisfying both nonnegative and sum-to-one constraints on the reconstruction weights. Taking the defined distance metric and lncRNA data supervised information into account, a local margin model will be deduced to find a low dimensional subspace for lncRNA signature extraction. At last, a classifier is constructed to predict cancer lymph-node metastasis, where the learned distance metric is also adopted. Several experiments on lncRNA data sets have been carried out, and experimental results show the performance of the proposed method by making comparisons with some other related dimensionality reduction methods and the classical classifier models.

LncRNA OR2A1-AS1 index predicts survival in germinal center-like diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive form of non-Hodgkin lymphoma. Long noncoding RNA (lncRNA) has been evaluated as prognostic markers in various carcinomas. However, the prognostic value of the lncRNA index in DLBCL has not been fully understood. Hence, this study aimed to identify the prognostic value of lncRNA olfactory receptor family 2 subfamily A member 1-antisense RNA 1 (OR2A1-AS1) in DLBCL.

METHODS

The Gene Expression Omnibus (GEO) database was used to obtain the GSE97336 dataset comprising lncRNA expression profiles. Quantitative reverse transcription polymerase chain reaction (QRT-PCR) was conducted to evaluate the expression of OR2A1-AS1 in 98 cases of DLBCL.

RESULTS

OR2A1-AS1 expression was considerably reduced in DLBCL patients, reduced OR2A1-AS1 expression was linked to a shorter overall survival (OS) and progression-free survival (PFS) in DLBCL patients, especially those with the germinal center B-cell-like subtype (GCB). Multivariate analysis (MVA) revealed that the OR2A1-AS1 index had prognostic significance. Patients with low OR2A1-AS1 expression have a poor prognosis.

CONCLUSIONS

OR2A1-AS may represent an effective predictor of patients' outcomes with DLBCL.

Identification of a Two-lncRNA Signature with Prognostic and Diagnostic Value for Hepatocellular Carcinoma

Background

Accumulating evidence has revealed the important role of long noncoding RNAs (lncRNA) in tumorigenesis and progression of hepatocellular carcinoma (HCC). This study aimed to identify potential lncRNAs that can serve as diagnostic and prognostic signatures for HCC.

Methods

Expression profiling analysis was performed to identify differentially expressed lncRNAs (DElncRNA) between HCC and matched normal samples by integrating two independent microarray datasets. Functional Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were explored by Gene Set Variation Analysis. The prognostic and diagnostic models were developed based on two DElncRNAs. Real-time PCR was used to quantify the relative expressions of candidate lncRNAs.

Results

Two robust DElncRNAs were identified and verified by quantitative PCR between HCC and matched normal samples. Function enrichment analysis revealed that they were associated with the wound healing process. The two lncRNAs were subsequently used to construct a prognostic risk model for HCC. Patients with high-risk scores estimated by the model showed a shorter survival time than low-risk patients (P < 0.001). Besides, the two lncRNA-based HCC diagnostic models exhibited good performance in discriminating HCC from normal samples on both training and test sets. The values of area under the curve (AUC) for early (I–II) and late (III–IV) HCC detection were 0.88 and 0.93, respectively.

Conclusions

The two wound healing-related DElncRNAs showed robust performance for HCC prognostic prediction and detection, implying their potential role as diagnostic and prognostic markers for HCC.

Long noncoding RNAs (lncRNAs) in human lymphomas

Lymphomas are a diverse group of haematologic malignancies, which occur in infection-fighting cells of the lymphatic system. Long non-coding RNAs (lncRNAs) are non-coding RNAs, which have recently received significant attention as the main mediators of gene expression. In this review, we summarize the current knowledge on lncRNAs involved in lymphomas, their molecular functions, as well as their potential clinical value. Relevant literature was identified by a PubMed search of English language papers using the following terms: Lymphoma, LncRNA, leukemia, proliferation, apoptosis, and prognosis. LncRNAs are imperative for lymphoma carcinogenesis through affecting apoptosis, cell proliferation, invasion, and response to chemotherapy. The expression level of lncRNAs can affect chemotherapy-induced apoptosis. Taken together, lncRNA dysregulation in lymphoma cells is not only an epiphenomenon but also lncRNA transcription is critically related to the initiation and progression of lymphomas. Aberrant expression of lncRNAs can lead to the transformation of normal lymphocytes into lymphoma cells.

Competitive Endogenous RNA Network Involving miRNA and lncRNA in Non-Hodgkin Lymphoma: Current Advances and Clinical Perspectives

Non-Hodgkin lymphoma (NHL) is a heterogeneous malignancy with variable patient outcomes. There is still a lack of understanding about the different players involved in lymphomagenesis, and the identification of new diagnostic and prognostic biomarkers is urgent. MicroRNAs and long non-coding RNAs emerged as master regulators of B-cell development, and their deregulation has been associated with the initiation and progression of lymphomagenesis. They can function by acting alone or, as recently proposed, by creating competing endogenous RNA (ceRNA) networks. Most studies have focused on individual miRNAs/lncRNAs function in lymphoma, and there is still limited data regarding their interactions in lymphoma progression. The study of miRNAs' and lncRNAs' deregulation in NHL, either alone or as ceRNAs networks, offers new insights into the molecular mechanisms underlying lymphoma pathogenesis and opens a window of opportunity to identify potential diagnostic and prognostic biomarkers. In this review, we summarized the current knowledge regarding the role of miRNAs and lncRNAs in B-cell lymphoma, including their interactions and regulatory networks. Finally, we summarized the studies investigating the potential of miRNAs and lncRNAs as clinical biomarkers, with a special focus on the circulating profiles, to be applied as a non-invasive, easy-to-obtain, and reproducible liquid biopsy for dynamic management of NHL patients.

Construction and validation of a risk scoring model for diffuse large B-cell lymphoma based on ferroptosis-related genes and its association with immune infiltration

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Categorization of DLBCL into four clusters based on survival- and ferroptosis-related factors.

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Establishment of an efficient risk scoring model is established for patients with DLBCL.

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Ferroptosis-based risk scoring model reveals immune infiltration correlation in DLBCL.

Backgrounds

The prognostic significance of ferroptosis-related genes is well known. However, survival- and ferroptosis-related genes are not currently considered in risk scoring models for diffuse large B-cell lymphoma (DLBCL).

Materials and methods

Ferroptosis regulators and markers were downloaded from the FerrDb database. The transcriptome profiling data were collected from the cancer genome atlas (TCGA). Transcriptome data and corresponding clinical information of DLBCL were downloaded from the gene expression omnibus (GEO). The validation data were downloaded using the UCSC Xena browser. ConsensusClusterPlus was used to categorize DLBCL samples according to gene expression profiles. The survival function was plotted with the Kaplan-Meier plots. The nomogram was built using multivariate logistic regression analysis and the Cox proportional hazards regression model.

Results

Based on the GSE11318 dataset of 203 samples and 267 ferroptosis-related gene expression profiles, we identified four clusters. A total of 19 survival-related genes were found associated with ferroptosis. The prognostic risk scoring model was constructed based on the regression coefficients. The obtained area under the receiver operating characteristic curve (AUC) values were 0.769, 0.801, and 0.791 for 1-, 3-, and 5-year survival, respectively. DLBCL samples with cluster 2 or cancer stage IV have shorter survival. Correlations between the immune infiltration and risk scores of the 12 immune cells were demonstrated. The response of DLBCL to doxorubicin was effectively validated by the risk scoring model.

Conclusions

In this study, a ferroptosis-based risk scoring model for patients with DLBCL was constructed and validated in an independent dataset. This risk score model has a better efficacy in predicting survival compared to clinical characteristics.

Development of a novel five-lncRNA prognostic signature for predicting overall survival in elderly patients with breast cancer

Background

Breast cancer (BC) is an age‐related disease. Long noncoding RNAs (lncRNAs) have been proven to be crucial contributors in tumorigenesis. This study aims to develop a novel lncRNA‐based signature to predict elderly BC patients’ prognosis.

Methods

The RNA expression profiles and corresponding clinical information of 182 elderly BC patients were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) between BC and adjacent normal samples were used to construct the signature in the training set through univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. Kaplan–Meier analysis and time‐dependent receiver operating characteristic (ROC) analysis were used to evaluate the predictive performance. Besides, we developed the nomogram. Gene set enrichment analysis (GSEA) was performed to reveal the underlying molecular mechanisms.

Results

We constructed the five‐lncRNA signature (including LEF1‐AS1, MEF2C‐AS1, ST8SIA6‐AS1, LINC01224, and LINC02408) in the training set, which successfully divided the patients into low‐ and high‐risk groups with significantly different prognosis (p = 0.000049), and the AUC at 3 and 5 years of the signature was 0.779 and 0.788, respectively. The predictive performance of this signature was validated in the test and entire set. The 5‐lncRNA signature was an independent prognostic factor of OS (p = 0.007) and the nomogram constructed by independent prognostic factors was an accurate predictor of predicting overall survival probability. Besides, several pathways associated with tumorigenesis have been identified by GSEA.

Conclusions

The 5‐lncRNA signature and nomogram are reliable in predicting elderly BC patients’ prognosis and provide clues for clinical decision‐making.

MicroRNA and Other Non-Coding RNAs in Epstein-Barr Virus-Associated Cancers

EBV is a direct causative agent in around 1.5% of all cancers. The oncogenic properties of EBV are related to its ability to activate processes needed for cellular proliferation, survival, migration, and immune evasion. The EBV latency program is required for the immortalization of infected B cells and involves the expression of non-coding RNAs (ncRNAs), including viral microRNAs. These ncRNAs have different functions that contribute to virus persistence in the asymptomatic host and to the development of EBV-associated cancers. In this review, we discuss the function and potential clinical utility of EBV microRNAs and other ncRNAs in EBV-associated malignancies. This review is not intended to be comprehensive, but rather to provide examples of the importance of ncRNAs.

Comprehensive analyses of correlation and survival reveal informative lncRNA prognostic signatures in colon cancer

BACKGROUND

Colon cancer is a commonly worldwide cancer with high morbidity and mortality. Long non-coding RNAs (lncRNAs) are involved in many biological processes and are closely related to the occurrence of colon cancer. Identification of the prognostic signatures of lncRNAs in colon cancer has great significance for its treatment.

METHODS

We first identified the colon cancer-related mRNAs and lncRNAs according to the differential analysis methods using the expression data in TCGA. Then, we performed correlation analysis between the identified mRNAs and lncRNAs by integrating their expression values and secondary structure information to estimate the co-regulatory relationships between the cancer-related mRNAs and lncRNAs. Besides, the competing endogenous RNA regulation network based on co-regulatory relationships was constructed to reveal cancer-related regulatory patterns. Meanwhile, we used traditional regression analysis (univariate Cox analysis, random survival forest analysis, and lasso regression analysis) to screen the cancer-related lncRNAs. Finally, by combining the identified colon cancer-related lncRNAs according to the above analyses, we constructed a risk prognosis model for colon cancer through multivariate Cox analysis and also validated the model in the colon cancer dataset in TCGA cohorts.

RESULTS

Six lncRNAs were found highly correlated with the overall survival of colon cancer patients, and a risk prognosis model based on them was constructed to predict the overall survival of colon cancer patients. In particular, EVX1-AS, ZNF667-AS1, CTC-428G20.6, and CTC-297N7.9 were first reported to be related to colon cancer by using our model, among which EVX1-AS and ZNF667-AS1 have been predicted to be related to colon cancer in LncRNADisease database.

CONCLUSIONS

This study identified the potential regulatory relationships between lncRNAs and mRNAs by integrating their expression values and secondary structure information and presented a significant 6-lncRNA risk prognosis model to predict the overall survival of colon cancer patients.

A 6-lncRNA signature predicts prognosis of diffuse large B-cell lymphoma

Diffuse large B cell lymphoma (DLBCL) comprises distinct entities due to its heterogeneity. The currently used international prognostic index for DLBCL prognosis prediction is only based on clinical factors and cannot reflect the molecular mechanisms underlying its progression. Here, we aimed to establish a long noncoding RNAs (lncRNA)-based signature for DLBCL prognosis prediction. The data were retrieved from the Gene Expression Omnibus and The Cancer Genome Atlas database. After identifying the differentially expressed lncRNAs (DELs), univariate COX regression, LASSO regression, and stepwise regression analysis were performed to construct a 6-lncRNA risk score system. Kaplan-Meier survival presented that the high-risk group had a significantly poorer overall survival. Based on the risk score and clinical characters, a nomogram was established, which had better predictive accuracy than each factor alone. Finally, weighted gene co-expression network analysis showed that these lncRNAs might regulate immune response, metabolism process, and signal transduction to influence the outcome. Conclusively, our model and nomogram could be reliable prognostic tools for DLBCL patients.

Circulating RNA biomarkers in diffuse large B-cell lymphoma: a systematic review

Diffuse large B-cell lymphoma (DLBCL) is the most common histological subtype of non-Hodgkin's lymphomas (NHL). DLBCL is an aggressive malignancy that displays a great heterogeneity in terms of morphology, genetics and biological behavior. While a sustained complete remission is obtained in the majority of patients with standard immunochemotherapy, patients with refractory of relapsed disease after first-line treatment have a poor prognosis. This patient group represents an important unmet need in lymphoma treatment. In recent years, improved understanding of the underlying molecular pathogenesis had led to new classification and prognostication tools, including the development of cell-free biomarkers in liquid biopsies. Although the majority of studies have focused on the use of cell-free fragments of DNA (cfDNA), there has been an increased interest in circulating-free coding and non-coding RNA, including messenger RNA (mRNA), microRNA (miRNA), long non-coding RNA (lncRNA) and circular RNA (circRNA), as well as RNA encapsulated in extracellular vesicles or tumor-educated platelets (TEPs). We performed a systematic search in PubMed to identify articles that evaluated circulating RNA as diagnostic, subtype, treatment response or prognostic biomarkers in a human DLBCL population. A total of 35 articles met the inclusion criteria. The aim of this systematic review is to present the current understanding of circulating RNA molecules as biomarker in DLBCL and to discuss their future potential.

Circulating long non-coding RNAs HOTAIR, Linc-p21, GAS5 and XIST expression profiles in diffuse large B-cell lymphoma: association with R-CHOP responsiveness

The reliable identification of diffuse large B-cell lymphoma (DLBCL)-specific targets owns huge implications for its diagnosis and treatment. Long non-coding RNAs (lncRNAs) are implicated in DLBCL pathogenesis; however, circulating DLBCL-related lncRNAs are barely investigated. We investigated plasma lncRNAs; HOTAIR, Linc-p21, GAS5 and XIST as biomarkers for DLBCL diagnosis and responsiveness to R-CHOP therapy. Eighty-four DLBCL patients and thirty-three healthy controls were included. Only plasma HOTAIR, XIST and GAS5 were differentially expressed in DLBCL patients compared to controls. Pretreatment plasma HOTAIR was higher, whereas GAS5 was lower in non-responders than responders to R-CHOP. Plasma GAS5 demonstrated superior diagnostic accuracy (AUC = 0.97) whereas a panel of HOTAIR + GAS5 superiorly discriminated responders from non-responders by ROC analysis. In multivariate analysis, HOTAIR was an independent predictor of non-response. Among patients, plasma HOTAIR, Linc-p21 and XIST were correlated. Plasma GAS5 negatively correlated with International Prognostic Index, whereas HOTAIR positively correlated with performance status, denoting their prognostic potential. We constructed the lncRNAs-related protein-protein interaction networks linked to drug response via bioinformatics analysis. In conclusion, we introduce plasma HOTAIR, GAS5 and XIST as potential non-invasive diagnostic tools for DLBCL, and pretreatment HOTAIR and GAS5 as candidates for evaluating therapy response, with HOTAIR as a predictor of R-CHOP failure. We provide novel surrogates for future predictive studies in personalized medicine.

Long Non-Coding RNAs in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid malignancy in adults. Although significant progress has been made in recent years to treat DLBCL patients, 30%-40% of the patients eventually relapse or are refractory to first line treatment, calling for better therapeutic strategies for DLBCL. Long non-coding RNAs (lncRNAs) have emerged as a highly diverse group of non-protein coding transcripts with intriguing molecular functions in human disease, including cancer. Here, we review the current understanding of lncRNAs in the pathogenesis and progression of DLBCL to provide an overview of the field. As the current knowledge of lncRNAs in DLBCL is still in its infancy, we provide molecular signatures of lncRNAs in DLBCL cell lines to assist further lncRNA research in DLBCL.

Non-Coding RNAs in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma worldwide. The molecular mechanisms underlying DLBCL have not been fully elucidated, and approximately 40% of patients who undergo standard chemoimmunotherapy still present with primary refractory disease or relapse. Non-coding RNAs (ncRNAs), a group of biomolecules functioning at the RNA level, are increasingly recognized as vital components of molecular biology. With the development of RNA-sequencing (RNA-Seq) technology, accumulating evidence shows that ncRNAs are important mediators of diverse biological processes such as cell proliferation, differentiation, and apoptosis. They are also considered promising biomarkers and better candidates than proteins and genes for the early recognition of disease onset, as they are associated with relative stability, specificity, and reproducibility. In this review, we provide the first comprehensive description of the current knowledge regarding three groups of ncRNAs-microRNAs (miRNAs), circular RNAs (circRNAs), and long non-coding RNAs (lncRNAs)-focusing on their characteristics, molecular functions, as well as diagnostic and therapeutic potential in DLBCL. This review provides an exhaustive account for researchers to explore novel biomarkers for the diagnosis and prognosis of DLBCL and therapeutic targets.

Transcriptome profiling reveals an integrated mRNA-lncRNA signature with predictive value for long-term survival in diffuse large B-cell lymphoma

For patients with diffuse large B-cell lymphoma (DLBCL), survival at 24 months is a milestone for long-term survival. The purpose of this study was to develop a multigene risk score (MGRS) to refine the International Prognostic Index (IPI) model to identify patients with DLBCL at high risk of death within 24 months. Using a robust statistical strategy, we built a MGRS incorporating nine mRNAs and two lncRNAs. Stratification and multivariable Cox regression analysis confirmed the MGRS as an independent risk factor. A nomogram based on IPI+MGRS model was constructed and its calibration plot showed close agreement between predicted 2-year survival rate and observed rate. The 2-year AUC was bigger with the IPI+MGRS model (ΔAUC=0.162; 95%CI 0.1295–0.1903) than with the IPI model, and the IPI+MGRS model more accurately predicted the prognostic risk of DLBCL. The 2-year survival decision curve revealed the IPI+MGRS model was more useful clinically than the IPI model. Functional enrichment analysis showed that the MGRS correlated with cell cycle, DNA replication and repair. The results were validated using an independent external dataset. In conclusion, we successfully developed an integrated mRNA–lncRNA signature to refine the IPI model for predicting long-term survival of patients with DLBCL.

Biomarkers and novel therapeutic approaches for diffuse large B-cell lymphoma in the era of precision medicine

Despite the great efforts for better treatment options for diffuse large B-cell lymphoma (DLBCL) (most common form of non-Hodgkin lymphoma, NHL) to treat and prevent relapse, it continues to be a challenge. Here, we present an overview of DLBCL and address the diagnostic assays and molecular techniques used in its diagnosis, role of biomarkers in detection, treatment of early and advanced stage DLBCL, and novel drug regimens. We discuss the significant biomarkers that have emerged as essential tools for stratifying patients according to risk factors and for providing insights into the use of more targeted and individualized therapeutics. We discuss techniques such as gene expression studies, including next-generation sequencing, which have enabled a more understanding of the complex pathogenesis of DLBCL and have helped determine molecular targets for novel therapeutic agents. We examine current treatment approaches, outline the findings of completed clinical trials, and provide updates for ongoing clinical trials. We highlight clinical trials relevant to the significant fraction of DLBCL patients who present with complex cases marked by high relapse rates. Supported by an increased understanding of targetable pathways in DLBCL, clinical trials involving specialized combination therapies are bringing us within reach the promise of an effective cure to DLBCL using precision medicine. Optimization of therapy remains a crucial objective, with the end goal being a balance between high survival rates through targeted and personalized treatment while reducing adverse effects in DLBCL patients of all subsets.

Crosstalk Between MYC and lncRNAs in Hematological Malignancies

The human genome project revealed the existence of many thousands of long non-coding RNAs (lncRNAs). These transcripts that are over 200 nucleotides long were soon recognized for their importance in regulating gene expression. However, their poor conservation among species and their still controversial annotation has limited their study to some extent. Moreover, a generally lower expression of lncRNAs as compared to protein coding genes and their enigmatic biochemical mechanisms have impeded progress in the understanding of their biological roles. It is, however, known that lncRNAs engage in various kinds of interactions and can form complexes with other RNAs, with genomic DNA or proteins rendering their functional regulatory network quite complex. It has emerged from recent studies that lncRNAs exert important roles in gene expression that affect many cellular processes underlying development, cellular differentiation, but also the pathogenesis of blood cancers like leukemia and lymphoma. A number of lncRNAs have been found to be regulated by several well-known transcription factors including Myelocytomatosis viral oncogene homolog (MYC). The c-MYC gene is known to be one of the most frequently deregulated oncogenes and a driver for many human cancers. The c-MYC gene is very frequently activated by chromosomal translocations in hematopoietic cancers most prominently in B- or T-cell lymphoma or leukemia and much is already known about its role as a DNA binding transcriptional regulator. Although the understanding of MYC's regulatory role controlling lncRNA expression and how MYC itself is controlled by lncRNA in blood cancers is still at the beginning, an intriguing picture emerges indicating that c-MYC may execute part of its oncogenic function through lncRNAs. Several studies have identified lncRNAs regulating c-MYC expression and c-MYC regulated lncRNAs in different blood cancers and have unveiled new mechanisms how these RNA molecules act. In this review, we give an overview of lncRNAs that have been recognized as critical in the context of activated c-MYC in leukemia and lymphoma, describe their mechanism of action and their effect on transcriptional reprogramming in cancer cells. Finally, we discuss possible ways how an interference with their molecular function could be exploited for new cancer therapies.

lncRNAs-mRNAs Co-Expression Network Underlying Childhood B-Cell Acute Lymphoblastic Leukaemia: A Pilot Study

Simple Summary

Acute lymphoblastic leukemia (ALL) is one of the most common childhood cancers. The ALL onset involves abnormal proliferation and arrest of differentiation of B or T cell progenitors. Recently, long non–coding RNAs (lncRNAs) gained great interest in the B–ALL leukemogenesis, however, so far few “omic” studies investigate lncRNAs and protein–coding gene networks. In our retrospective study, we conceived an integrated bioinformatic approach, by using NGS platform, to discover lncRNAs strongly correlated with aberrantly expressed protein–coding genes. We provided dysregulated lncRNA–mRNA pairs potentially underlying B–ALL pathogenesis. Diagnosis incidence peak of ALL appears approximatively between 1 and 19 years old. lncRNAs may be of clinical utility as non–invasive biomarker for B–ALL onset or therapy response in support of precision medicine. The identification of lncRNA as key regulators in B–ALL could lead to the identification of the altered pathways able to sustain the leukemic growth.

Abstract

Long non–coding RNAs (lncRNAs) are emerging as key gene regulators in the pathogenesis and development of various cancers including B lymphoblastic leukaemia (B–ALL). In this pilot study, we used RNA–Seq transcriptomic data for identifying novel lncRNA–mRNA cooperative pairs involved in childhood B–ALL pathogenesis. We conceived a bioinformatic pipeline based on unsupervised PCA feature extraction approach and stringent statistical criteria to extract potential childhood B–ALL lncRNA signatures. We then constructed a co–expression network of the aberrantly expressed lncRNAs (30) and protein–coding genes (754). We cross–validated our in–silico findings on an independent dataset and assessed the expression levels of the most differentially expressed lncRNAs and their co–expressed mRNAs through ex vivo experiments. Using the guilt–by–association approach, we predicted lncRNA functions based on their perfectly co–expressed mRNAs (Spearman’s correlation) that resulted closely disease–associated. We shed light on 24 key lncRNAs and their co–expressed mRNAs which may play an important role in B–ALL pathogenesis. Our results may be of clinical utility for diagnostic and/or prognostic purposes in paediatric B–ALL management.

Long Noncoding RNAs in Diffuse Large B-Cell Lymphoma: Current Advances and Perspectives

Diffuse large B-cell lymphoma (DLBCL) is a complex and aggressive malignancy originating from B lymphocytes and characterized by extensive clinical, phenotypic and molecular heterogeneity. Although research conducted over the past decades has substantially improved our understanding of DLBCL, its pathogenesis has not yet been fully elucidated. The development of RNA sequencing technology has allowed the identification of numerous long noncoding RNAs (lncRNAs) that exhibit aberrant expression in DLBCL. These lncRNAs play crucial roles in DLBCL development and pathogenesis and are thus good candidates for use as diagnostic biomarkers or therapeutic targets. In this review, we describe the lncRNAs associated with DLBCL, summarize their characteristics and molecular functions, and discuss their relationships with clinical practice.

Development of a Reproducible Prognostic Gene Signature to Predict the Clinical Outcome in Patients with Diffuse Large B-Cell Lymphoma

Alongside various clinical prognostic factors for diffuse large B-cell lymphoma (DLBCL) such as the international prognostic index (IPI) components (ie, age, tumor stage, performance status, serum lactate dehydrogenase concentration, and number of extranodal sites), prognostic gene signatures have recently shown promising efficacy. However, previously developed signatures for DLBCL suffer from many major inadequacies such as lack of reproducibility in external datasets, high number of members (genes) in a signature, and inconsistent association with the survival time in various datasets. Accordingly, we sought to find a reproducible prognostic gene signature with a minimal number of genes. Seven datasets—namely GSE10856 (420 samples), GSE31312 (470 samples), GSE69051 (157 samples), GSE32918 (172 samples), GSE4475 (123 samples), GSE11318 (203 samples), and GSE34171 (91 samples)—were employed. The datasets were randomly categorized into training (1219 samples comprising GSE10856, GSE31312, GSE69051, and GSE32918) and validation (417 samples consisting of GSE4475, GSE11318, and GSE34171) groups. Through the univariate Cox proportional hazards analysis, common genes associated with the overall survival time with a P value less than 0.001 and a false discovery rate less than 5% were identified in 1219 patients included in the 4 training datasets. Thereafter, the common genes were entered into a multivariate Cox proportional hazards analysis encompassing the common genes and the international prognostic index (IPI) factors as covariates, and then only common genes with a significant level of difference (P < 0.01 and z-score >2 or <−2) were selected to reconstruct the prognostic signature. After the analyses, a 7-gene prognostic signature was developed, which efficiently predicted the survival time in the training dataset (Ps < 0.0001). Subsequently, this signature was tested in 3 validation datasets. Our signature was able to strongly predict clinical outcomes in the validation datasets (Ps < 0.0001). In the multivariate Cox analysis, our outcome predictor was independent of the routine IPI components in both training datasets (Ps < 0.0001). Furthermore, our outcome predictor was the most powerful independent prognostic variable (Ps < 0.0001). We developed a potential reproducible prognostic gene signature which was able to robustly discriminate low-risk patients with DLBCL from high-risk ones.

Integrative analysis of miRNA and mRNA expression profiles reveals a novel mRNA/miRNA signature to improve risk classification for patients with gastric cancer

Gastric cancer (GC) is one of the most common types of malignant cancer and is associated with poor prognosis. Although the prognosis of patients with GC is associated with grade, stage and lymph node metastases, these traditional clinical features are inadequate to predict the outcome of GC. Therefore, there has been an increased focus on identifying novel molecular biomarkers for early diagnosis and prognosis, in order to improve outcomes in GC. In the present study, an integrative analysis of microRNA (miRNA) expression profiles, mRNA expression profiles and clinical characteristics was performed in a large cohort of patients with GC in order to identify an integrative prognostic model for improving postoperative risk classification. An integrative mRNA/miRNA signature (IMMIS), comprised of three miRNAs and one mRNA, was identified from a large number of differentially expressed miRNAs and mRNAs using univariate and multivariate Cox regression analysis. The prognostic value of the IMMIS was validated in the discovery cohort, testing cohort and The Cancer Genome Atlas (TCGA) cohort. The present results suggested that the identified signature had a reliable predictive performance and could classify the patients into high- and low-risk groups with significantly different overall survival times. In the discovery cohort, the hazard ratio (HR) was 2.805 with a 95% CI=1.722–4.567 (P<0.001). The median overall survival time as 1.49 vs. 3.85 years. In the testing cohort, the HR was 1.625 with a 95% CI=1.004–2.638 (P=0.039) and the median overall survival time was 2.17 vs. 4.62 years. In the TCGA cohort, the HR was 2.139 with a 95% CI=1.519–3.012 (P<0.001) and the median overall survival time was 1.53 vs. 4.62 years. The IMMIS constituted a reliable independent prognostic factor compared with clinical covariates, including age, sex, grade and stage, as indicated by multivariate and stratified analyses. Furthermore, comparative analysis revealed that the predictive value of the IMMIS was superior to the mRNA-based signature alone. The present results suggested the potential value of the IMMIS as a promising novel biomarker for improving the clinical management of patients with GC.

Overexpression of ZEB2-AS1 lncRNA is associated with poor clinical outcomes in acute myeloid leukemia

Acute myeloid leukemia (AML) is a fatal hematopoietic malignancy with poor clinical outcomes. To determine whether the expression of the long non-coding (lnc)RNA zinc finger E-box binding homeobox 2 (ZEB2) antisense RNA 1 (ZEB2-AS1) is associated with clinical outcomes, its expression was analyzed in a retrospective cohort of 62 AML and 10 non-malignant cases. The results revealed that the expression of ZEB2-AS1 lncRNA was notably high and closely associated with adverse clinical outcomes in AML cases compared with the non-malignant cases, based on either modified Medical Research Council or European Leukemia Net risk stratification systems. Univariate analyses indicated that patients with a higher expression of ZEB2-AS1 lncRNA had significantly shorter overall survival (OS) (P=0.036) and disease-free survival (DFS) rates (P=0.039) compared with patients with a lower expression of ZEB2-AS1 lncRNA. In addition, patients with a higher expression of ZEB2-AS1 lncRNA had a significant lower complete remission rate in response to induction by chemotherapy compared with patients with a lower expression of ZEB2-AS1 lncRNA (P=0.031). In cases with low levels of ZEB2-AS1 lncRNA, patients treated with allogenic hematopoietic stem cell transplantation had significantly longer OS and DFS rates compared with that of chemotherapy-treated patients (P=0.037 and P=0.049 respectively). Furthermore, the knockdown of ZEB2-AS1 lncRNA effectively inhibited AML cell invasion and migration, which was closely associated with the downregulation of ZEB2 and upregulation of E-cadherin expression. Collectively, although its independent prognostic value for survival was not rigorously determined, ZEB2-AS1 lncRNA may function as a candidate marker to improve conventional risk stratification systems and the evaluation of therapeutic responses for AML.

Role of long non-coding RNAs in lymphoma: A systematic review and clinical perspectives

Long non-coding RNAs (lncRNAs), are over 200 nucleotides in length, and they rarely act as templates for protein synthesis. Mounting studies have shown that lncRNAs play a crucial regulatory role in various processes that sustain life, such as epigenetic regulation, cell cycle control, splicing, and post-transcriptional regulation. LncRNAs were aberrantly expressed in most hematological malignancies including lymphoma, participating in tumor suppression or promoting oncogenesis and modulating key genes in different pathways. The specific expression patterns of lncRNAs in lymphoma make them good candidates to be used as diagnostic biomarkers or as therapeutic targets. LncRNAs can be targeted by multiple approaches including nucleic acid therapeutics, CRISPR/Cas genome editing techniques, small molecule inhibitors, and gene therapy. Efforts are made to develop therapeutic strategies aimed at targeting lncRNAs, but there are still some avenues to be covered before they can be applied to the clinical treatment of lymphoma.

A prognostic signature based on three non-coding RNAs for prediction of the overall survival of glioma patients

Recent studies have identified certain non‐coding RNAs (ncRNAs) as biomarkers of disease progression. Glioma is the most common primary intracranial cancer, with high mortality. Here, we developed a prognostic signature for prediction of overall survival (OS) of glioma patients by analyzing ncRNA expression profiles. We downloaded gene expression profiles of glioma patients along with their clinical information from the Gene Expression Omnibus and extracted ncRNA expression profiles via a microarray annotation file. Correlations between ncRNAs and glioma patients’ OS were first evaluated through univariate Cox regression analysis and a permutation test, followed by random survival forest analysis for further screening of valuable ncRNA signatures. Prognostic signatures could be established as a risk score formula by including ncRNA signature expression values weighted by their estimated regression coefficients. Patients could be divided into high risk and low risk subgroups by using the median risk score as cutoff. As a result, glioma patients with a high risk score tended to have shorter OS than those with low risk scores, which was confirmed by analyzing another set of glioma patients in an independent dataset. Additionally, gene set enrichment analysis showed significant enrichment of cancer development‐related biological processes and pathways. Our study may provide further insights into the evaluation of glioma patients’ prognosis.

A three-lncRNA expression signature predicts survival in head and neck squamous cell carcinoma (HNSCC)

Increasing evidence has shown that long non-coding RNAs (lncRNAs) have important biological functions and can be used as a prognostic biomarker in human cancers. However, investigation of the prognostic value of lncRNAs in head and neck squamous cell carcinoma (HNSCC) is in infancy. In the present study, we analyzed the lncRNA expression data in a large number of HNSCC patients (n=425) derived from The Cancer Genome Atlas (TCGA) to identify an lncRNA expression signature for improving the prognosis of HNSCC. Three lncRNAs are identified to be significantly associated with survival in the training dataset using Cox regression analysis. Three lncRNAs were integrated to construct an lncRNA expression signature that could stratify patients of training dataset into the high-risk group and low-risk group with significantly different survival time (median survival 1.85 years vs. 5.48 years; P=0.0018, log-rank test). The prognostic value of this three-lncRNA signature was confirmed in the testing and entire datasets, respectively. Further analysis revealed that the prognostic power of three-lncRNA signature was independent of clinical features by multivariate Cox regression and stratified analysis. These three lncRNAs were significantly associated with known genetic and epigenetic events by means of functional enrichment analysis. Therefore, our results indicated that the three-lncRNA expression signature can predict HNSCC patients’ survival.

Novel three‑lncRNA signature predicts survival in patients with pancreatic cancer

A growing body of evidence confirms that long non-coding RNAs (lncRNAs) have an important role in biological processes by regulating gene expression at multiple levels. Dysregulated lncRNAs may be potential prognostic biomarkers or targets for the development of cancer treatments. However, the prognostic role of an lncRNA signature in pancreatic cancer has not been investigated. Pancreatic cancer lncRNA expression profiles from The Cancer Genome Atlas (TCGA) were analyzed in the current study. The prognostic value of differentially expressed lncRNAs (DElncRNAs) was evaluated via the Kaplan-Meier method. A risk score model was established based on the potential prognostic lncRNAs. The biological functions of lncRNAs were predicted by functional enrichment analysis. Then, an lncRNA-mRNA co-expression network was established and predicted the function of the lncRNAs. Seven DElncRNAs that were significantly associated with the prognosis of pancreatic cancer were identified. Patients were classified into high-risk and low-risk groups using a risk score based on a three-lncRNA signature. There was a significant difference in overall survival (OS) between the groups (median OS 1.33 vs. 3.65 years; log-rank test, P=0.0000). Cox regression analysis and ROC curves demonstrated that the three-lncRNA signature may be an effective independent prognostic biomarker in patients with pancreatic. The functional enrichment analysis showed that lncRNA AL137789.1, one component of the three-lncRNA signature, may be associated with tumor immune responses. In the present study, a novel three-lncRNA signature that was established that may be useful in predicting survival among patients with pancreatic cancer. These lncRNAs may be involved in tumor immunity and thus affect the prognosis of patients.

Long Non-Coding RNAs Guide the Fine-Tuning of Gene Regulation in B-Cell Development and Malignancy

With the introduction of next generation sequencing methods, such as RNA sequencing, it has become apparent that alterations in the non-coding regions of our genome are important in the development of cancer. Particularly interesting is the class of long non-coding RNAs (lncRNAs), including the recently described subclass of circular RNAs (circRNAs), which display tissue- and cell-type specific expression patterns and exert diverse regulatory functions in the cells. B-cells undergo complex and tightly regulated processes in order to develop from antigen naïve cells residing in the bone marrow to the highly diverse and competent effector cells circulating in peripheral blood. These processes include V(D)J recombination, rapid proliferation, somatic hypermutation and clonal selection, posing a risk of malignant transformation at each step. The aim of this review is to provide insight into how lncRNAs including circRNAs, participate in normal B-cell differentiation, and how deregulation of these molecules is involved in the development of B-cell malignancies. We describe the prognostic value and functional significance of specific deregulated lncRNAs in diseases such as acute lymphoblastic leukemia, chronic lymphocytic leukemia, mantle cell lymphoma, diffuse large B-cell lymphoma, follicular lymphoma, Burkitt lymphoma and multiple myeloma, and we provide an overview of the current knowledge on the role of circRNAs in these diseases.

Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma

AIM

To construct a long non-coding RNA (lncRNA) signature for predicting hepatocellular carcinoma (HCC) prognosis with high efficiency.

METHODS

Differentially expressed lncRNAs (DELs) between HCC specimens and peritumor liver specimens were identified using the edgeR package to analyze The Cancer Genome Atlas (TCGA) LIHC dataset. Univariate Cox proportional hazards regression was performed to obtain the DELs significantly associated with overall survival (OS) in a training set. These OS-related DELs were further analyzed using a stepwise multivariate Cox regression model. Those lncRNAs fitted in the multivariate Cox regression model and independently associated with overall survival were chosen to build a prognostic risk formula. The prognostic value of this formula was then validated in the test group and the entire cohort and further compared with two previously identified prognostic signatures for HCC. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to explore the potential biological functions of the lncRNAs in the signature.

RESULTS

Based on lncRNA expression profiling of 370 HCC patients from the TCGA database, we constructed a 5-lncRNA signature (AC015908.3, AC091057.3, TMCC1-AS1, DCST1-AS1 and FOXD2-AS1) that was significantly associated with prognosis. HCC patients with high-risk scores based on the expression of the 5 lncRNAs had significantly shorter survival times compared to patients with low-risk scores in both the training and test groups. Multivariate Cox regression analysis demonstrated that the prognostic value of the 5 lncRNAs was independent of clinicopathological parameters. A comparison study involving two previously identified prognostic signatures for HCC demonstrated that this 5-lncRNA signature showed improved prognostic power compared with the other two signatures. Functional enrichment analysis indicated that the 5 lncRNAs were potentially involved in metabolic processes, fibrinolysis and complement activation.

CONCLUSION

Our present study constructed a 5-lncRNA signature that improves survival prediction and can be used as a prognostic biomarker for HCC patients.

LncRNA SNHG16 drives proliferation, migration, and invasion of hemangioma endothelial cell through modulation of miR-520d-3p/STAT3 axis

It has been verified that long noncoding RNAs (lncRNAs) have great effects on various biological behaviors of human diseases. Although more and more lncRNAs have been studied in human cancers, countless lncRNAs still need to be excavated. This study aims to investigate the impacts of lncRNA SNHG16 on proliferation and metastasis of human hemangioma endothelial cell (HemECs). qRT-PCR analysis was carried out to explore the expression pattern of SNHG16, miR-520d-3p, and STAT3. The effect of SNHG16 on cell proliferation was detected by MTT and colony formation assay. Flow cytometry analysis was performed to test the apoptosis of HemECs cells. Migration and invasion of HemECs cells were determined and examined by transwell assays. Tube formation assay helped to observe the influence of SNHG16 expression on the vasoformation of HemECs cells. The correlations among SNHG16, miR-520d-3p, and STAT3 were certified by bioinformatics analysis, pull-down assay, and dual-luciferase reporter assay. Finally, rescue assays were conducted to demonstrate the effects of SNHG16-miR-520d-3p-STAT3 axis on biological behaviors of HemECs cell. SNHG16 was strongly expressed in proliferating phase hemangioma tissues and HemECs cells. Silenced SNHG16 negatively affected proliferation, migration, and invasion of HemECs cell. LncRNA SNHG16 acted as a ceRNA to upregulate STAT3 through binding with miR-520d-3p in HemECs cell. LncRNA SNHG16 acted as a ceRNA to drive proliferation, vasoformation, migration, and invasion of HemECs cells through modulating miR-520d-3p/STAT3 axis.

Long non-coding RNA H19 promotes the migration and invasion of colon cancer cells via MAPK signaling pathway

Long non-coding RNA H19 has been identified to be dysregulated in a number of tumor types, and is closely associated with cancer progression. RAS/mitogen-activated protein kinase (MAPK) is an important intracellular signaling transduction pathway. Activation of the RAS-MAPK signaling pathway is one of the most frequent carcinogenic events in human cancer. However, the mechanism of H19 in promoting the migration and invasion of colorectal cancer (CRC) cells, and the association between H19 and RAS-MAPK signaling pathway is not well understood. The aim of the present study was to investigate the function of H19 on CRC metastasis and invasion, and assess the association between H19 and the RAS-MAPK signaling pathway. The migration and invasion of CRC cells were analyzed using Transwell migration and invasion assays. To elucidate the association between H19 and the RAS-MAPK signaling pathway and determine the expression level of active RAS in CRC cells, Ras activity assay and Western blotting were performed. It was indicated that the overexpression of H19 was able to increase the migration and invasion of CRC cells and this may be mediated by the regulation of RAS activation. Therefore, H19 may promote metastasis and invasion in colorectal cancer by activating the RAS-MAPK signaling pathway.

Identifying autophagy gene-associated module biomarkers through construction and analysis of an autophagy-mediated ceRNA‑ceRNA interaction network in colorectal cancer

Autophagy is crucial in cellular homeostasis and has been implicated in the development of malignant tumors. However, the regulatory function of autophagy in cancer remains to be fully elucidated. In the present study, the autophagy-mediated competing endogenous RNA (ceRNA)-ceRNA interaction networks in colorectal cancer (CRC) were constructed by integrating systematically expression profiles of long non-coding RNAs and mRNAs. It was found that a large proportion of autophagy genes were inclined to target hub nodes, including a fraction of autophagy genes, by comparing with other genes within ceRNA networks, and showed preferential interaction with themselves. The present study also revealed that autophagy genes may be used as prognostic markers for cancer therapy. A risk score model based on multivariable Cox regression analysis was then used to capture novel biomarkers in connection with lncRNA for the prognosis of CRC. These biomarkers were confirmed in the test dataset and an additional independent dataset. Furthermore, the prognostic value of biomarkers is independent of conventional clinical factors. These results provide improved understanding of autophagy-mediated ceRNA regulatory mechanisms in CRC and provide novel potential molecular therapeutic targets for the diagnosis and treatment of CRC.

Comprehensive analysis of differentially expressed profiles of lncRNAs and mRNAs reveals ceRNA networks in the transformation of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is one of the malignancies with a high mortality rate. The molecular mechanisms involved in transformation of DLBCL remain unclear. Therefore, it is critically important to investigate the biological mechanisms of DLBCL. Accumulating evidence indicates that long non-coding RNAs (lncRNAs) serve key functions in tumorigenesis, cancer progression and metastasis. Compared with follicular lymphoma (FL), a total of 123 upregulated lncRNAs and 192 downregulated lncRNAs in DLBCL were identified. Subsequently, a specific DLBCL-associated competing endogenous RNA (ceRNA) network and a specific FL-associated ceRNA network was constructed. Gene Oncology and Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that differentially expressed lncRNAs served key functions in regulating signal transduction, transcription, cell adhesion, development and protein amino acid phosphorylation. Furthermore, the molecular functions of PRKCQ antisense RNA 1, HLA complex P5, OIP5 antisense RNA 1, growth arrest specific 5 and taurine upregulated 1 were investigated, and it was revealed that these lncRNAs served important functions in regulating a series of biological processes, including anti-apoptosis, cell cycle, DNA repair, response to oxidative stress and transcription. The present study may provide a potential novel therapeutic and prognostic target for the treatment of DLBCL.

Identification of novel long non-coding RNA biomarkers for prognosis prediction of papillary thyroid cancer

Papillary thyroid carcinoma (PTC) is the most frequent type of malignant thyroid tumor. Several lncRNA signatures have been established for prognosis prediction in some cancers. However, the prognostic value of lncRNAs has not been investigated in PTC yet. In this study, we performed genome-wide analysis of lncRNA expression profiles in a large cohort of PTC patients from The Cancer Genome Atlas and identified 111 differentially expressed lncRNAs between tumor and non-tumor samples and between recurrent and recurrence-free samples. From the 111 differentially expressed lncRNAs, four independent lncRNA biomarkers associated with prognosis were identified and were integrated into a four-lncRNA signature which classified the patients of training dataset into the high-risk group and low-risk group with significantly different overall survival (p=0.016, log-rank test). The prognostic value of the four-lncRNA signature was validated in the independent testing dataset. Multivariate analysis indicated that the four-lncRNA signature was an independent prognostic predictor. Moreover, identified four lncRNA biomarkers demonstrates good performance in predicting disease recurrence with AUC of 0.833 using leave one out cross-validation. Our study not only highlighted the potential role for lncRNAs to improve clinical prognosis prediction in patients with PTC and but also provided alternative biomarkers and therapeutic targets for PTC patients.

Identification of long non-coding RNAs biomarkers for early diagnosis of myocardial infarction from the dysregulated coding-non-coding co-expression network

Long non-coding RNAs (lncRNAs) have recently been shown as novel promising diagnostic or prognostic biomarkers for various cancers. However, lncRNA expression patterns and their predictive value in early diagnosis of myocardial infarction (MI) have not been systematically investigated. In our study, we performed a comprehensive analysis of lncRNA expression profiles in MI and found altered lncRNA expression pattern in MI compared to healthy samples. We then constructed a lncRNA-mRNA dysregulation network (DLMCEN) by integrating aberrant lncRNAs, mRNAs and their co-dysregulation relationships, and found that some of mRNAs were previously reported to be involved in cardiovascular disease, suggesting the functional roles of dysregulated lncRNAs in the pathogenesis of MI. Therefore, using support vector machine (SVM) and leave one out cross-validation (LOOCV), we developed a 9-lncRNA signature (termed 9LncSigAMI) from the discovery cohort which could distinguish MI patients from healthy samples with accuracy of 95.96%, sensitivity of 93.88% and specificity of 98%, and validated its predictive power in early diagnosis of MI in another completely independent cohort. Functional analysis demonstrated that these nine lncRNA biomarkers in the 9LncSigAMI may be involved in myocardial innate immune and inflammatory response, and their deregulation may lead to the dysfunction of the inflammatory and immune system contributing to MI recurrence. With prospective validation, the 9LncSigAMI identified by our work will provide additional diagnostic information beyond other known clinical parameters, and increase the understanding of the molecular mechanism underlying the pathogenesis of MI.

Long non-coding RNA AK058003, as a precursor of miR-15a, interacts with HuR to inhibit the expression of γ-synuclein in hepatocellular carcinoma cells

Long non-coding RNAs (lncRNAs) have been identified as critical players in multiple cancers and lncRNAs are tightly linked to cancer progression. However, only little amount of lncRNAs have been identified to participate in the molecular mechanisms of the progression of hepatocellular carcinoma. In this study, we found that lncRNA-AK058003 is down-regulated in hepatocellular carcinoma tissues and it is associated with the relapse and metastasis of the cancer. Furthermore, lncRNA-AK058003 acts as a tumor suppressor, suppressing hepatocellular carcinoma cell proliferation and metastasis in vitro and in vivo. lncRNA-AK058003 can reduce mRNA stabilizing protein HuR, which results in the inhibition of the expression of γ-synuclein. In addition, a bioinformatics study indicated that γ-synuclein is a target of miR-15a. To verify whether lncRNA-AK058003 plays a role in miR-15a-mediated inhibition of γ-synuclein, we demonstrated that lncRNA-AK058003 is very likely to be a precursor of miR-15a. Collectively, lncRNA-AK058003 can reduce the expression of mRNA stabilizing protein HuR and act as a precursor of miR-15a to suppress γ-synuclein-mediated cell proliferation and the metastasis of hepatocellular carcinoma.

Identification of novel prognostic indicators for triple-negative breast cancer patients through integrative analysis of cancer genomics data and protein interactome data

Triple negative breast cancers (TNBCs) are highly heterogeneous and aggressive without targeted treatment. Here, we aim to systematically dissect TNBCs from a prognosis point of view by building a subnetwork atlas for TNBC prognosis through integrating multi-dimensional cancer genomics data from The Cancer Genome Atlas (TCGA) project and the interactome data from three different interaction networks. The subnetworks are represented as the protein-protein interaction modules perturbed by multiple genetic and epigenetic interacting mechanisms contributing to patient survival. Predictive power of these subnetwork-derived prognostic models is evaluated using Monte Carlo cross-validation and the concordance index (C-index). We uncover subnetwork biomarkers of low oncogenic GTPase activity, low ubiquitin/proteasome degradation, effective protection from oxidative damage, and tightly immune response are linked to better prognosis. Such a systematic approach to integrate massive amount of cancer genomics data into clinical practice for TNBC prognosis can effectively dissect the molecular mechanisms underlying TNBC patient outcomes and provide potential opportunities to optimize treatment and develop therapeutics.

Let-7c Inhibits the Proliferation, Invasion, and Migration of Glioma Cells via Targeting E2F5

As a member of the miRNA family, let-7c has been identified as a tumor suppressor in many cancers. However, the molecular biological function of let-7c in glioma has not been elucidated. The aim of this study was to explore let-7c expression levels and evaluate its function in glioma cells. We first measured the expression of let-7c in four glioma cell lines and a normal cell line by quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR), and the results showed that let-7c was downregulated in glioma cells. By applying gain-of-function and loss-of-function assays, the experiments suggested that dysregulation of let-7c could obviously affect cell proliferation, metastasis, and invasion. Based on online bioinformatics analysis and Dual-Luciferase Reporter assays, we found that E2F5 was a target gene of let-7c and contributed to the function of let-7c in glioma cells. Our investigations indicated that loss of let-7c contributed to the progression of glioma cells.

Construction and analysis of dysregulated lncRNA-associated ceRNA network identified novel lncRNA biomarkers for early diagnosis of human pancreatic cancer

It is increasing evidence that ceRNA activity of long non-coding RNAs (lncRNAs) played critical roles in both normal physiology and tumorigenesis. However, functional roles and regulatory mechanisms of lncRNAs as ceRNAs in pancreatic ductal adenocarcinoma (PDAC), and their potential implications for early diagnosis remain unclear. In this study, we performed a genome-wide analysis to investigate potential lncRNA-mediated ceRNA interplay based on "ceRNA hypothesis". A dysregulated lncRNA-associated ceRNA network (DLCN) was constructed by utilizing sample-matched miRNA, lncRNA and mRNA expression profiles in PDAC and normal samples in combination with miRNA regulatory network. The results of network analysis uncovered seven novel lncRNAs as functional ceRNAs whose aberrant expression will result in the extensive variation in tumorigenic or tumor-suppressive gene expression through DLCN at the post-transcriptional level contributing to PDAC. Therefore, we developed a 7-lncRNA signature (termed LncRisk-7) based on the expression data of seven lncRNAs and SVM algorithm as a novel diagnostic tool to improve early diagnosis of PDAC. The LncRisk-7 achieved high performance in distinguishing PDAC patients from nonmalignant pancreas samples in the discovery cohort and was further confirmed in another two independent validation cohorts. Functional analysis demonstrated that seven lncRNA biomarkers act as ceRNAs involving the regulation of cell death, cell adhesion and cell cycle. This study will help to improve our understanding of the lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of PDAC and provide novel lncRNAs as candidate diagnostic biomarkers or potential therapeutic targets.

Identification of a progression-associated long non-coding RNA signature for predicting the prognosis of lung squamous cell carcinoma

Long non-coding RNAs (lncRNAs) have been indicated to have prognostic roles in various cancer types. However, the association between lncRNAs and lung squamous cell carcinoma (LSCC) progression, and the prognostic value of lncRNAs as a marker for early detection of LSCC have not been systematically investigated. The present study performed a genome-wide comparative analysis in order to determine the expression profiles of 10,207 lncRNAs to investigate the expression patterns between patients with early stages of LSCC (stage I–II) and those with late-stage disease (stage III–IV). It was found that 114 lncRNAs were significantly differentially expressed between late- and early-stage LSCC and therefore associated with the progression of the malignancy. By focusing on progression-associated lncRNAs, eight lncRNAs were found to be significantly associated with overall survival of LSCC patients according to univariate Cox proportional hazards regression analysis. These eight prognostic lncRNAs were integrated into a progression-associated eight-lncRNA signature, which stratified patients into two groups with significantly different overall survival (median survival, 2.25 vs. 7.68 years; P=2.06×10⁻⁵). In addition, the prognostic value of the progression-associated eight-lncRNA signature was independent of known clinical factors. Functional analysis suggested that eight prognostic lncRNAs may be involved in adipocytokine signaling pathway and glycerophospholipid metabolism. Taken together, the progression-associated eight-lncRNA signature identified by our study not only represents a candidate prognostic biomarker for LSCC patients but also provides insight into the molecular mechanisms in the progression of LSCC.

Long noncoding RNA identification in lymphoma

It has been found that long noncoding RNA (lncRNA) are abnormally expressed in lymphoma and play an important role in its pathogenesis. Through the detection and analysis of lncRNA, it was found that lncRNA could be used as an excellent biomarker for the diagnosis and prognosis of cancer. In lymphoma, abnormal lncRNA expression is associated with the patient's clinical characteristics, it can be used to determine the prognosis of the patient and serve as a therapeutic target in the disease.

Emerging roles for long noncoding RNAs in B-cell development and malignancy

Long noncoding (lnc)RNAs have emerged as essential mediators of cellular biology, differentiation and malignant transformation. LncRNAs have a broad range of possible functions at the transcriptional, posttranscriptional and protein level and their aberrant expression significantly contributes to the hallmarks of cancer cell biology. In addition, their high tissue- and cell-type specificity makes lncRNAs especially interesting as biomarkers, prognostic factors or specific therapeutic targets. Here, we review current knowledge on lncRNA expression changes during normal B-cell development, indicating essential functions in the differentiation process. In addition we address lncRNA deregulation in B-cell malignancies, the putative prognostic value of this as well as the molecular functions of multiple deregulated lncRNAs. Altogether, the discussed work indicates major roles for lncRNAs in normal and malignant B cells affecting oncogenic pathways as well as the response to common therapeutics.

Identification of potential prognostic ceRNA module biomarkers in patients with pancreatic adenocarcinoma

Accumulating evidence suggested that long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) to interact with other RNA transcripts and ceRNAs perturbation play important roles in cancer initiation and progression including pancreatic adenocarcinoma (PAAD). In this study, we constructed a PAAD-specific hallmark gene-related ceRNA network (HceNet) using paired genome-wide expression profiles of mRNA, lncRNA and miRNA and regulatory relationships between them. Based on “ceRNA hypothesis”, we analyzed the characteristics of HceNet and identified a ceRNA module comprising of 29 genes (12 lncRNAs, two miRNAs and 15 mRNAs) as potential prognostic biomarkers related to overall survival of patients with PAAD. The prognostic value of ceRNA module biomarkers was further validated in the train (Hazard Ratio (HR) =1.661, 95% CI: 1.275–2.165, p<1.00e-4), test (HR=1.546, 95% CI: 1.238-1.930, p<1.00e-4), and entire (HR=1.559, 95% CI: 1.321-1.839, p<1.00e-4) datasets. Our study provides candidate prognostic biomarkers for PAAD and increases our understanding of ceRNA-related regulatory mechanism in PAAD pathogenesis.

A novel seven-long non-coding RNA signature predicts survival in early stage lung adenocarcinoma

Increasing evidence has revealed the significant association between dysregulated lncRNA expression and cancers. The prognostic value of lncRNAs in predicting the risk of disease recurrence and identifying high-risk subgroup of early stage lung adenocarcinoma (LUAD) is still unclear. In this study, we analyzed lncRNA expression profiles of 415 early-stage LUAD patients from Gene Expression Omnibus and identified a novel seven-lncRNA signature that was significantly associated with survival in patients with early-stage LUAD (HR = 2.718, CI = 2.054–3.597, p < 0.001). Based on the seven-lncRNA signature, we constructed a risk score model which is able to classify patients of training dataset into the high-risk group and the low-risk group with significantly different clinical outcome (p < 0.001). The robustness of the seven-lncRNA signature was successfully validated through application in other two independent patient datasets. Furthermore, the prognostic value of seven-lncRNA signature was independent of other clinicopathological factors including age, gender, stage and smoking status. Functional analysis suggested that the seven-lncRNA signature may be involved in a variety of biological pathways including cell cycle, ECM-receptor interaction, Focal adhesion and p53 signaling pathway. Taken together, our study not only provides insights into the lncRNA association with LUAD, but also provide alternative molecular markers in prognosis prediction for early-stage LUAD patients.

Expression profiles analysis of long non-coding RNAs identified novel lncRNA biomarkers with predictive value in outcome of cutaneous melanoma

Recent advancements in cancer biology have identified a large number of lncRNAs that are dysregulated expression in the development and tumorigenesis of cancers, highlighting the importance of lncRNAs as a key player for human cancers. However, the prognostic value of lncRNAs still remains unclear and needs to be further investigated. In the present study, we aim to assess the prognostic value of lncRNAs in cutaneous melanoma by integrated lncRNA expression profiles from TCGA database and matched clinical information from a large cohort of patients with cutaneous melanoma. We finally identified a set of six lncRNAs that are significantly associated with survival of patients with cutaneous melanoma. A linear combination of six lncRNAs (LINC01260, HCP5, PIGBOS1, RP11-247L20.4, CTA-292E10.6 and CTB-113P19.5) was constructed as a six-lncRNA signature which classified patients of training cohort into the high-risk group and low-risk group with significantly different survival time. The prognostic value of the six-lncRNA signature was validated in both the validation cohort and entire TCGA cohort. Moreover, the six-lncRNA signature is independent of known clinic-pathological factors by multivariate Cox regression analysis and demonstrated good performance for predicting three- and five-year overall survival by time-dependent receiver operating characteristic (ROC) analysis. Our study provides novel insights into the molecular heterogeneity of cutaneous melanoma and also shows potentially important implications of lncRNAs for prognosis and therapy for cutaneous melanoma.

Genome-scale analysis identifies NEK2, DLGAP5 and ECT2 as promising diagnostic and prognostic biomarkers in human lung cancer

This study aims to identify promising biomarkers for the early detection of lung cancer and evaluate the prognosis of lung cancer patients. Genome-wide mRNA expression data obtained from the Gene Expression Omnibus (GSE19188, GSE18842 and GSE40791), including 231 primary tumor samples and 210 normal samples, were used to discover differentially expressed genes (DEGs). NEK2, DLGAP5 and ECT2 were found to be highly expressed in tumor samples. These results were experimentally confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The elevated expression of the three candidate genes was also validated using the Cancer Genome Atlas (TCGA) datasets, which consist of 349 tumor and 58 normal tissues. Furthermore, we performed receiver operating characteristics (ROC) analysis to assess the diagnostic value of these lung cancer biomarkers, and the results suggested that NEK2, DLGAP5 and ECT2 expression levels could robustly distinguish lung cancer patients from normal subjects. Finally, Kaplan-Meier analysis revealed that elevated NEK2, DLGAP5 and ECT2 expression was negatively correlated with both overall survival (OS) and relapse-free survival (RFS). Taken together, these findings indicate that these three genes might be used as promising biomarkers for the early detection of lung cancer, as well as predicting the prognosis of lung cancer patients.

Expression profiles analysis reveals an integrated miRNA-lncRNA signature to predict survival in ovarian cancer patients with wild-type BRCA1/2

Emerging evidence shows that dysregulated expression of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) were closely linked with disease progression, including cancers. However, the joint predictive power of miRNAs and lncRNAs in prognosis for ovarian cancer (OV) patients with wild-type BRCA1/2 is as yet unknown. In this study, we sought to assess the joint predictive power of miRNAs and lncRNAs by integrating miRNA and lncRNA expression profiles and clinical data of 281 OV patients with wild-type BRCA1/2 from The Cancer Genome Atlas (TCGA) project. Finally, we identified an integrated miRNA-lncRNA signature composing of two lncRNAs (LINC01234 and CCDC144NL-AS1) and two miRNAs (miR-637 and miR-129-5p) which can effectively classify OV patients with wild-type BRCA1/2 into groups with the good and poor outcome. The prognostic value of the integrated miRNA-lncRNA signature was validated in the testing cohort and entire TCGA cohort. Multivariate analysis demonstrated the independence of the integrated miRNA-lncRNA signature of known other clinical factors. Further analysis suggested that patients who were in the low-risk group based on the signature achieved a better CR from platinum-based chemotherapy compared with patients in the high-risk group. Our results indicated that this integrated miRNA-lncRNA signature may have important clinical implications for risk stratification of ovarian cancer patients with wild-type BRCA1/2.