Long non-coding RNA expression profiles predict metastasis in lymph node-negative breast cancer independently of traditional prognostic markers

Ensemble-based classification using microRNA expression identifies a breast cancer patient subgroup with an ultralow long-term risk of metastases

BACKGROUND

Current clinical markers overestimate the recurrence risk in many lymph node negative (LNN) breast cancer (BC) patients such that a majority of these low-risk patients unnecessarily receive systemic treatments. We tested if differential microRNA expression in primary tumors allows reliable identification of indolent LNN BC patients to provide an improved classification tool for overtreatment reduction in this patient group.

METHODS

We collected freshly frozen primary tumors of 80 LNN BC patients with recurrence and 80 recurrence-free patients (mean follow-up: 20.9 years). The study comprises solely systemically untreated patients to exclude that administered treatments confound the metastasis status. Samples were pairwise matched for clinical-pathological characteristics to minimize dependence of current markers. Patients were classified into risk-subgroups according to the differential microRNA expression of their tumors via classification model building with cross-validation using seven classification methods and a voting scheme. The methodology was validated using available data of two independent cohorts (n = 123, n = 339).

RESULTS

Of the 80 indolent patients (who would all likely receive systemic treatments today) our ultralow-risk classifier correctly identified 37 while keeping a sensitivity of 100% in the recurrence group. Multivariable logistic regression analysis confirmed independence of voting results from current clinical markers. Application of the method in two validation cohorts confirmed successful classification of ultralow-risk BC patients with significantly prolonged recurrence-free survival.

CONCLUSION

Profiles of differential microRNAs expression can identify LNN BC patients who could spare systemic treatments demanded by currently applied classifications. However, further validation studies are required for clinical implementation of the applied methodology.

Long non-coding RNAs in bone metastasis: progresses and perspectives as potential diagnostic and prognostic biomarkers

In a precision medicine perspective, among the biomarkers potentially useful for early diagnosis of cancers, as well as to define their prognosis and eventually to identify novel and more effective therapeutic targets, there are the long non-coding RNAs (lncRNAs). The term lncRNA identifies a class of non-coding RNA molecules involved in the regulation of gene expression that intervene at the transcriptional, post-transcriptional, and epigenetic level. Metastasis is a natural evolution of some malignant tumours, frequently encountered in patients with advanced cancers. Onset and development of metastasis represents a detrimental event that worsen the patient's prognosis by profoundly influencing the quality of life and is responsible for the ominous progression of the disease. Due to the peculiar environment and the biomechanical properties, bone is a preferential site for the secondary growth of breast, prostate and lung cancers. Unfortunately, only palliative and pain therapies are currently available for patients with bone metastases, while no effective and definitive treatments are available. The understanding of pathophysiological basis of bone metastasis formation and progression, as well as the improvement in the clinical management of the patient, are central but challenging topics in basic research and clinical practice. The identification of new molecular species that may have a role as early hallmarks of the metastatic process could open the door to the definition of new, and more effective, therapeutic and diagnostic approaches. Non-coding RNAs species and, particularly, lncRNAs are promising compounds in this setting, and their study may bring to the identification of relevant processes. In this review, we highlight the role of lncRNAs as emerging molecules in mediating the formation and development of bone metastases, as possible biomarkers for cancer diagnosis and prognosis, and as therapeutic targets to counteract cancer spread.

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.

Predictive and Prognostic Value of Non-Coding RNA in Breast Cancer

For decades since the central dogma, cancer biology research has been focusing on the involvement of genes encoding proteins. It has been not until more recent times that a new molecular class has been discovered, named non-coding RNA (ncRNA), which has been shown to play crucial roles in shaping the activity of cells. An extraordinary number of studies has shown that ncRNAs represent an extensive and prevalent group of RNAs, including both oncogenic or tumor suppressive molecules. Henceforth, various clinical trials involving ncRNAs as extraordinary biomarkers or therapies have started to emerge. In this review, we will focus on the prognostic and diagnostic role of ncRNAs for breast cancer.

Vitamin D May Protect against Breast Cancer through the Regulation of Long Noncoding RNAs by VDR Signaling

Dietary vitamin D3 has attracted wide interest as a natural compound for breast cancer prevention and therapy, supported by in vitro and animal studies. The exact mechanism of such action of vitamin D3 is unknown and may include several independent or partly dependent pathways. The active metabolite of vitamin D3, 1α,25-dihydroxyvitamin D3 (1,25(OH)2D, calcitriol), binds to the vitamin D receptor (VDR) and induces its translocation to the nucleus, where it transactivates a myriad of genes. Vitamin D3 is involved in the maintenance of a normal epigenetic profile whose disturbance may contribute to breast cancer. In general, the protective effect of vitamin D3 against breast cancer is underlined by inhibition of proliferation and migration, stimulation of differentiation and apoptosis, and inhibition of epithelial/mesenchymal transition in breast cells. Vitamin D3 may also inhibit the transformation of normal mammary progenitors into breast cancer stem cells that initiate and sustain the growth of breast tumors. As long noncoding RNAs (lncRNAs) play an important role in breast cancer pathogenesis, and the specific mechanisms underlying this role are poorly understood, we provided several arguments that vitamin D3/VDR may induce protective effects in breast cancer through modulation of lncRNAs that are important for breast cancer pathogenesis. The main lncRNAs candidates to mediate the protective effect of vitamin D3 in breast cancer are lncBCAS1-4_1, AFAP1 antisense RNA 1 (AFAP1-AS1), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), long intergenic non-protein-coding RNA 511 (LINC00511), LINC00346, small nucleolar RNA host gene 6 (SNHG6), and SNHG16, but there is a rationale to explore several other lncRNAs.

A seven-lncRNA signature for predicting prognosis in breast carcinoma

Background

Long non-coding RNAs (lncRNAs) play an important part in tumorigenesis and cancer metastasis and can serve as a potential biosignature for cancer prognosis. However, the use of lncRNA signatures to predict survival in breast carcinoma is yet unreported.

Methods

The lncRNA expression profiles and homologous clinical data of 913 breast carcinoma samples from the Cancer Genome Atlas (TCGA), were analyzed to obtain 2,547 differentially expressed lncRNAs. Univariate Cox proportional risk regression was applied to both the training and testing datasets to screen the common prognostic lncRNAs. Potential prognostic LncRNAs were screened by multivariate Cox proportional risk regression in the training data set of the selected LncRNAs.

Results

Seven lncRNAs (LINC02037, MAPT-AS1, RP1-37C10.3, RP11-344E13.4, RP11-454P21.1, RP11-616M22.1, SPACA6P-AS) were prominently associated with overall survival. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves indicated that these indicators were sensitive and specific for survival prediction. The areas under the ROC curve of the seven-lncRNA signature in predicting 3- and 5-year survival rates were 0.771 and 0.780 respectively in the combined cohort. Furthermore, enrichment analysis revealed that these seven lncRNAs might participate multiple pathways related to tumorigenesis and prognosis.

Conclusions

The proposed seven-lncRNA signature could serve as a latent prognostic biomarker for survival prediction in patients with breast carcinoma.

LncRNA SOCS2-AS1 promotes the progression of glioma via regulating ITGB1 expression

BACKGROUND

Accumulating evidence has underscored the important role of long non-coding RNAs (lncRNAs) in the development and progression of glioma. However, the role of lncRNA SOCS2-AS1 in glioma is largely unknown.

METHODS

lncRNA SOCS2-AS1 silencing was achieved by specific siRNAs. Proliferation of glioma cell line after lncRNA SOCS2-AS1 silencing was examined by MTT assay, Transwell assay was used to confirm changes of invasion and migration of glioma cells, and study the molecular mechanism of lncRNA SOCS2-AS1 by RT-qPCR and bioinformatics analysis.

RESULTS

We identified that lncRNA SOCS2-AS1 was significantly upregulated in glioma, and its overexpression was closely related with malignant clinical features and poor prognosis. To explore the cellular function of SOCS2-AS1, we performed loss-of function assays in two glioma cells. We demonstrated that SOCS2-AS1 knockdown repressed glioma cell proliferation, migration and invasion. Mechanistically, SOCS2-AS1 expression was positively correlated with the expression levels of core factors ITGB1 of ECM-receptor interaction signaling pathway in glioma. Moreover, SOCS2-AS1 knockdown suppressed ITGB1 expression in glioma cells. Finally, rescue assays were carried out to determine that ITGB1 involved in SOCS2-AS1-mediated glioma cell proliferation, migration and invasion.

CONCLUSION

Our findings provided the first evidence suggested that SOCS2-AS1 promoted the progression of glioma via upregulating ITGB1 expression.

Comparison of the Metastasis Predictive Potential of mRNA and Long Non-Coding RNA Profiling in Systemically Untreated Breast Cancer

Simple Summary

To support health care providers in clinical decision-making for breast cancer (BC) patients, profiles of gene activity patterns have previously been developed, where the majority have been based on messenger RNAs (mRNAs), molecules coding for proteins. However, we and others have recently developed profiles based on functional molecules that do not code for proteins—e.g., long non-coding RNAs (lncRNAs)—demonstrating great prognostic potential. Unfortunately, studies comparing such profiles for predicting relapse in BC patients are very scarce. Therefore, we aimed to compare these two types of molecules (mRNAs and lncRNAs) to forecast relapse in low-risk BC patients using advanced machine learning methods with two different approaches. Regardless of approach, our data suggested that profiles based on lncRNAs improved prediction of relapse and demonstrated potential advantages for future profile development.

Abstract

Several gene expression signatures based on mRNAs and a few based on long non-coding RNAs (lncRNAs) have been developed to provide prognostic information beyond clinical evaluation in breast cancer (BC). However, the comparison of such signatures for predicting recurrence is very scarce. Therefore, we compared the prognostic utility of mRNAs and lncRNAs in low-risk BC patients using two different classification strategies. Frozen primary tumor samples from 160 lymph node negative and systemically untreated BC patients were included; 80 developed recurrence—i.e., regional or distant metastasis while 80 remained recurrence-free (mean follow-up of 20.9 years). Patients were pairwise matched for clinicopathological characteristics. Classification based on differential mRNA or lncRNA expression using seven individual machine learning methods and a voting scheme classified patients into risk-subgroups. Classification by the seven methods with a fixed sensitivity of ≥90% resulted in specificities ranging from 16–40% for mRNA and 38–58% for lncRNA, and after voting, specificities of 38% and 60% respectively. Classifier performance based on an alternative classification approach of balanced accuracy optimization also provided higher specificities for lncRNA than mRNA at comparable sensitivities. Thus, our results suggested that classification followed by voting improved prognostic power using lncRNAs compared to mRNAs regardless of classification strategy.

Vitamin D receptor upregulates lncRNA TOPORS-AS1 which inhibits the Wnt/β-catenin pathway and associates with favorable prognosis of ovarian cancer

Long non-coding RNAs (lncRNAs) have important biological functions, but their involvement in ovarian cancer remains elusive. We analyzed high-throughput data to identify lncRNAs associated with ovarian cancer outcomes. Our search led to the discovery of lncRNA TOPORS Antisense RNA 1 (TOPORS-AS1). Patients with high TOPORS-AS1 expression had favorable overall survival compared to low expression. This association was replicated in our study and confirmed by meta-analysis. In vitro experiments demonstrated that overexpressing TOPORS-AS1 in ovarian cancer cells suppressed cell proliferation and inhibited aggressive cell behaviors, including migration, invasion, and colony formation. Analysis of tumor cell transcriptomes indicated TOPORS-AS1′s influence on the Wnt/β-catenin signaling. Additional experiments revealed that TOPORS-AS1 increased the phosphorylation of β-catenin and suppressed the expression of CTNNB1, disrupting the Wnt/β-catenin pathway. Our experiments further discovered that vitamin D receptor (VDR) upregulated TOPORS-AS1 expression and that inhibition of β-catenin by TOPORS-AS1 required a RNA binding protein, hnRNPA2B1 (heterogeneous nuclear ribonucleoprotein A2B1). Taken together, these findings suggest that TOPORS-AS1 may behave like a tumor suppressor in ovarian cancer through interrupting the Wnt/β-catenin signaling and that VDR upregulates the expression of TOPORS-AS1. Assessing TOPORS-AS1 expression in ovarian cancer may help predict disease prognosis and develop treatment strategy

Over-Expression of Centromere Protein U Participates in the Malignant Neoplastic Progression of Breast Cancer

The expression of Centromere Protein U (CENP-U) is closely related to tumor malignancy. Till now, the role of CENP-U in the malignant progression of breast cancer remains unclear. In this study, we found that CENP-U protein was highly expressed in the primary invasive breast cancer tissues compared to the paired adjacent histologically normal tissues and ductal carcinoma in situ (DCIS) tissues. After CENP-U was knocked down, the proliferation and colony-forming abilities of breast cancer cells were significantly suppressed, whereas the portion of apoptotic cells was increased. Meanwhile, the PI3K/AKT/NF-κB pathway was significantly inhibited. In vivo studies showed that, the inhibition of CENP-U repressed the tumor growth in orthotopic breast cancer models. Therefore, our study demonstrated that the CENP-U might act as an oncogene and promote breast cancer progression via activation of the PI3K/AKT/NF-κB pathway, which suggests a promising direction for targeting therapy in breast cancer.

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.

Long Non-coding RNA NEAT1 as an Emerging Biomarker in Breast and Gynecologic Cancers: a Systematic Overview

Long non-coding RNAs (lncRNAs) are emerging regulators of cellular pathways, especially in cancer development. Among the lncRNAs, nuclear paraspeckle assembly transcript 1 (NEAT1) forms a scaffold for a nuclear body; the paraspeckle and aberrant expression of NEAT1 have been reported in breast and gynecologic cancers (ovarian, cervical, endometrial, and vulvar). Abundantly expressed NEAT1 in breast and gynecologic cancers generally contribute to tumor development by sponging its corresponding tumor-suppressive microRNAs or interacting with various regulatory proteins. The distinct expression of NEAT1 and its contribution to tumorigenic pathways make it a promising therapeutic target in breast and gynecologic cancers. Herein, we summarize the functions and molecular mechanisms of NEAT1 in human breast, ovarian, cervical, endometrial, and vulvar cancers. Furthermore, we emphasize its critical role in the formation of paraspeckle development and its functions. Conclusively, NEAT1 is a considerable biomarker with a bright prospect and can be therapeutically targeted to manage breast and gynecologic cancers.

Long non-coding RNAE330013P06 promotes progression of breast cancer with type 2 diabetes

Background

In previous research, we found diabetes rather than obesity was an independent risk factor of breast cancer. However, why diabetes could lead to increased risk of breast cancer patients remains elusive. Long non‐coding RNAE330013P06 has been shown to be upregulated in diabetes, and long non‐coding RNAs generally promote progression of cancer.

Methods

About 200 specimens of breast patients were obtained in previous clinical trial; 34 samples diagnosed as type 2 diabetes in breast cancer patient were enrolled in this research. Blood samples from 36 patients diagnosed as breast cancer without diabetes; 35 diabetic patients and 35 healthy peoples were obtained as control. All blood samples were measured by quantitative real‐time PCR (qRT‐PCR). Invasion and migration were tested by Transwell assay. Cell proliferation assay was tested by CCK‐8. Protein analysis was determined by Western blot.

Results

Compared with breast cancer patients without diabetes, diabetic patients without breast cancer and healthy peoples, LncRNAE330013P06 was upregulated in breast cancer patient with diabetes. Furthermore, of 34 breast patients, high LncRNAE330013P06 expression was significantly associated with family history, tumor‐node‐metastasis stage and lymph node metastasis. E33 promoted cancer cell growth in vitro via downregulation of P53.

Conclusion

Upregulation of LncRNAE330013P06 driven by type 2 diabetes is one of the factors which promoted progression of breast cancer.

A Comparative Study of HOTAIR Expression in Breast Cancer Patient Tissues and Cell Lines

Objective

Recent data suggest that increased levels of the HOTAIR long non-coding RNA (lncRNA) are involved in the development of various types of malignancy, including breast cancer. The aim of present study was to investigate HOTAIR lncRNA expression profile in breast cancer (BC) patients and cell lines.

Materials and Methods

In this experimental study, expression level of HOTAIR lncRNA was evaluated in BC and normal tissues of 15 patients as well as MDA-MB-231, MCF-7 and MCF-10A cell lines, using quantitative reverse- transcription polymerase chain reaction (qRT-PCR). HOTAIR lncRNA expression levels were estimated using 2^-ΔΔCt method. Further, receiver operating characteristic (ROC) curve analysis was done to evaluate the selected lncRNA diagnostic potential. The Cox’s proportional hazards regression model was performed to evaluate the predictive value of this lncRNA level in BC patients.

Results

The results of present study demonstrated no significant difference in the expression of HOTAIR lncRNA in MCF7 and MDA-MB-231 cancer cell lines compared to MCF-10A as normal cell line (P>0.05). However, we observed a significantly increase in the expression of HOTAIR in BC patients compared to normal tissues (P<0.001). Significant associations were found between gene expression and tumour size and margin. We found 91.1% sensitivity and 95.7% specificity of circulating HOTAIR with an area under the ROC curve of 0.969. The Kaplan-Meier analysis indicated significant correlation between HOTAIR expression and overall survival.

Conclusion

This study demonstrated that expression of HOTAIR is increased in BC and might be associated with its progression. According to these findings, HOTAIR expression could be proposed as biomarkers for BC early diagnosis and prognosis.

Long non-coding RNAs AC026904.1 and UCA1: a "one-two punch" for TGF-β-induced SNAI2 activation and epithelial-mesenchymal transition in breast cancer

Transforming growth factor-β (TGF-β) has received much attention as a major inducer of epithelial-mesenchymal transition (EMT) during cancer progression, mainly by activating a set of pleiotropic transcription factors including SNAI2/Slug. However, the involvement of long non-coding RNAs (lncRNAs) in TGF-β-induced Slug activation and EMT remains largely unknown.

Methods: In this study, we used microarray analysis to compare lncRNA expression profiles between TGF-β treated and untreated breast cancer cells. Then, the clinical significance of lncRNAs in breast cancer was investigated by qPCR and Kaplan-Meier survival analysis. The molecular mechanisms and EMT-promoting effects in vitro were analyzed by confocal laser microscopy, Western blotting, chromosome conformation capture (3C), chromatin isolation by RNA purification (ChIRP), ChIP, luciferase reporter assay and transwell migration assay. Lastly, the pro-metastatic effects in vivo were evaluated by bioluminescent imaging and hematoxylin and eosin (H&E) staining.

Results: We observed that TGF-β induced genome-wide changes in lncRNA levels in breast cancer cells, among which AC026904.1 and UCA1 were highly expressed in metastatic breast cancer and closely associated with poor prognosis. Mechanistic study revealed that AC026904.1 and UCA1 were upregulated by non-canonical and canonical TGF-β pathways, respectively. Further analysis showed that AC026904.1 functions as an enhancer RNA in the nucleus, whereas UCA1 exerts a competitive endogenous RNA (ceRNA) activity in the cytoplasm. In addition, the biological functions of these two lncRNAs converged on the activation and maintenance of Slug, constituting a one-two punch in promoting EMT and tumor metastasis.

Conclusion: These findings uncover for the first time that AC026904.1 and UCA1 could cooperatively upregulate Slug expression at both transcriptional and post-transcriptional levels, exerting critical roles in TGF-β-induced EMT. The present work provides new evidence that lncRNAs function as key regulators of EMT and hold great promise to be used as novel biomarkers and therapeutic targets for metastatic breast cancer.

BI-RADS 3-5 microcalcifications: prediction of lymph node metastasis of breast cancer

Purpose

To determine whether the clinicopathological parameters and Breast Imaging Reporting and Data System (BI-RADS) 3–5 microcalcifications differed between lymph node positive (LN (+)) and lymph node negative (LN (−)) invasive ductal carcinoma (IDC).

Results

For microcalcification-associated breast cancers, seven selected features (age, tumor size, Ki-67 status, lymphovascular invasion, calcification range, calcification diameter and calcification density) were significantly associated with LN status (all P < 0.05). Multivariate logistic regression analysis found that three risk factors (age: older vs. younger OR: 0.973 P = 0.006, tumor size: larger vs. smaller OR: 1.671, P < 0.001 and calcification density: calcifications > 20/cm² vs. calcifications ≤ 20/cm² OR: 1.698, P < 0.001) were significant independent predictors. This model had an area under the receiver operating characteristic curve (AUC) of 0.701. The nodal staging (N0 and N1 χ² = 5.701, P = 0.017; N0 and N2 χ² = 6.614, P = 0.013) was significantly positively associated with calcification density. The luminal B subtype had the highest risk of LN metastasis. Multivariate analysis demonstrated that calcification > 2 cm in range (OR: 2.209) and larger tumor size (OR: 1.882) were independently predictive of LN metastasis in the luminal B subtype (AUC = 0.667).

Materials and Methods

Mammographic images of 419 female breast cancer patients were included. Associations between the risk factors and LN status were evaluated using a Chi-square test, ANOVA and binary logistic regression analysis.

Conclusions

This study found that age, tumor size and calcifications density can be conveniently used to facilitate the preoperative prediction of LN metastasis. The luminal B subtype has the highest risk of LN metastasis among the microcalcification-associated breast cancers.

Characterization of dysregulated lncRNA-mRNA network based on ceRNA hypothesis to reveal the occurrence and recurrence of myocardial infarction

Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play important roles in initiation and development of human diseases. However, the mechanism of ceRNA regulated by lncRNA in myocardial infarction (MI) remained unclear. In this study, we performed a multi-step computational method to construct dysregulated lncRNA-mRNA networks for MI occurrence (DLMN_MI_OC) and recurrence (DLMN_MI_Re) based on “ceRNA hypothesis”. We systematically integrated lncRNA and mRNA expression profiles and miRNA-target regulatory interactions. The constructed DLMN_MI_OC and DLMN_MI_Re both exhibited biological network characteristics, and functional analysis demonstrated that the networks were specific for MI. Additionally, we identified some lncRNA-mRNA ceRNA modules involved in MI occurrence and recurrence. Finally, two new panel biomarkers defined by four lncRNAs (RP1-239B22.5, AC135048.13, RP11-4O1.2, RP11-285F7.2) from DLMN_MI_OC and three lncRNAs (RP11-363E7.4, CTA-29F11.1, RP5-894A10.6) from DLMN_MI_Re with high classification performance were, respectively, identified in distinguishing controls from patients, and patients with recurrent events from those without recurrent events. This study will provide us new insight into ceRNA-mediated regulatory mechanisms involved in MI occurrence and recurrence, and facilitate the discovery of candidate diagnostic and prognosis biomarkers for MI.

Functional and prognostic significance of long non-coding RNA MALAT1 as a metastasis driver in ER negative lymph node negative breast cancer. Oncotarget. 2016;7:40418-40436. [PMID: 27250026 PMCID: PMC5130017 DOI: 10.18632/oncotarget.9622]

MALAT1 (metastasis associated lung adenocarcinoma transcript1) is a conserved long non-coding RNA, known to regulate gene expression by modulating transcription and post-transcriptional pre-mRNA processing of a large number of genes. MALAT1 expression is deregulated in various tumors, including breast cancer. However, the significance of such abnormal expression is yet to be fully understood. In this study, we demonstrate that regulation of aggressive breast cancer cell traits by MALAT1 is not predicted solely based on an elevated expression level but is context specific. By performing loss- and gain-of-function studies, both under in vitro and in vivo conditions, we demonstrate that MALAT1 facilitates cell proliferation, tumor progression and metastasis of triple-negative breast cancer (TNBC) cells despite having a comparatively lower expression level than ER or HER2-positive breast cancer cells. Furthermore, MALAT1 regulates the expression of several cancer metastasis-related genes, but displays molecular subtype specific correlations with such genes. Assessment of the prognostic significance of MALAT1 in human breast cancer (n=1992) revealed elevated MALAT1 expression was associated with decreased disease-specific survival in ER negative, lymph node negative patients of the HER2 and TNBC molecular subtypes. Multivariable analysis confirmed MALAT1 to have independent prognostic significance in the TNBC lymph node negative patient subset (HR=2.64, 95%CI 1.35 − 5.16, p=0.005). We propose that the functional significance of MALAT1 as a metastasis driver and its potential use as a prognostic marker is most promising for those patients diagnosed with ER negative, lymph node negative breast cancer who might otherwise mistakenly be stratified to have low recurrence risk.

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.

Association of miR-548c-5p, miR-7-5p, miR-210-3p, miR-128-3p with recurrence in systemically untreated breast cancer

Current prognostic markers allocate the majority of lymph node (LN) negative and estrogen receptor (ER) positive breast cancer patients into the high-risk group. Accordingly, most patients receive systemic treatments although approximately 40% of these patients may have been cured by surgery and radiotherapy alone. Two studies identified seven prognostic microRNAs in systemically untreated, LN negative and ER positive breast cancer patients which may allow more precise patient classification. However, six of the seven microRNAs were analyzed in both studies but only found to be prognostic in one study. To validate their prognostic potential, we analyzed microRNA expression in an independent cohort (n = 110) using a pair-matched study design minimizing dependence of classical markers. The expression of hsa-miR-548c-5p was significantly associated with abridged disease-free survival (hazard ratio [HR]:1.96, p = 0.027). Contradicting published results, high hsa-miR-516-3p expression was associated with favorable outcome (HR:0.29, p = 0.0068). The association is probably time-dependent indicating later relapse. Additionally, re-analysis of previously published expression data of two matching cohorts (n = 100, n = 255) supports an association of hsa-miR-128-3p with shortened disease-free survival (HR:2.48, p = 0.0033) and an upregulation of miR-7-5p (p = 0.0038; p = 0.039) and miR-210-3p (p = 0.031) in primary tumors of patients who experienced metastases. Further analysis may verify the prognostic potential of these microRNAs.

Increased expression of long-noncoding RNA ZFAS1 is associated with epithelial-mesenchymal transition of gastric cancer

LncRNAs play critical roles in gastric cancer (GC). In this study, the expression of fourteen cancer related lncRNAs were investigated in paired tissues of 66 patients with GC, Realtime RT-PCR revealed that ZFAS1 was significantly upregulated. We then examined the expression of ZFAS1 in plasmas derived from 77 GC patients before- and post-operations and 60 healthy individuals, and found that circulating ZFAS1 was also upregulated in GC patients and operation can reduce its presence in plasma. To investigate the potential mechanisms, we compared the expression of ZFAS1 in multiple gastric cell lines and one normal cell line and found that ZFAS1 was up-regulated in GC cell lines. Furthermore, circulating tumor cells (CTC) were simulated by mixing GC cells with peripheral blood. After EpCAM antibody-based cell sorting, we found that the expression of ZFAS1 was positively correlated with EMT property of CTCs. In GC patient tissue samples, we found that Twist was positively correlated with ZFAS1 by immunohistochemical staining. Taken together, our results suggested that ZFAS1 was up-regulated in both tissues and plasmas of GC patients, and may be involved in regulation of EMT in GC progression. Thus, ZFAS1 might serve as a potential diagnostic marker and/or therapeutic target for GC.

The Long Noncoding RNA HOTAIR in Breast Cancer: Does Autophagy Play a Role?

HOTAIR (HOX transcript antisense RNA) plays a critical role in chromatin dynamics through the interaction with histone modifiers resulting in transcriptional gene silencing. The promoter of the HOTAIR gene contains multiple estrogen response elements (EREs) and is transcriptionally activated by estradiol in estrogen receptor-positive breast cancer cells. HOTAIR competes with BRCA1, a critical protein in breast cancer and is a critical regulator of genes involved in epithelial-to-mesenchymal transition. It mediates an oncogenic action of c-Myc, essential for breast carcinogenesis. The carcinogenic action of HOTAIR was confirmed in breast cancer stem-like cells, in which it was essential for self-renewal and proliferation. Several miRNAs regulate the expression of HOTAIR and HOTAIR interacts with many miRNAs to support cancer transformation. Many studies point at miR-34a as a major component of HOTAIR–miRNAs–cancer cross-talk. The most important role of HOTAIR can be attributed to cancer progression as its overexpression stimulates invasion and metastasis. HOTAIR can regulate autophagy, important for breast cancer cells survival, through the interaction with miRNAs specific for autophagy genes and directly with these genes. The role of HOTAIR-mediated autophagy in breast cancer progression can be underlined by its interaction with matrix metalloproteinases, essential for cancer invasion, and β-catenin can be important for this interaction. Therefore, there are several mechanisms of the interplay between HOTAIR and autophagy important for breast cancer, but further studies are needed to determine more details of this interplay.

A six-long non-coding RNAs signature as a potential prognostic marker for survival prediction of ER-positive breast cancer patients

Dysregulated expression of lncRNAs has been observed in various human complex diseases (including cancers) by recent transcriptional profiling studies, highlighting potentials of lncRNAs as biomarkers for cancer diagnosis and prognosis. Despite some efforts have been made to search for novel lncRNA signature in breast cancer, the prognostic value of lncRNAs for ER-positive breast cancer patients still needs to be systematically investigated. In this study, we analyzed lncRNA expression profiles in a large of more than 600 breast cancer patients with ER-positive status from The Cancer Genome Atlas (TCGA) and identified six lncRNAs that are significantly associated with survival. Then a linear risk score model comprising six prognostic lncRNAs, termed six-lncRNA signature, was developed to identify high-risk patients from low-risk cases. The results of Kaplan-Meier analysis and ROC curves demonstrated the good sensitivity and specificity in survival prediction both in the training and testing datasets. Multivariate Cox regression analysis and stratified analysis showed that the six-lncRNA signature is an independent prognostic marker in survival prediction for ER-positive breast cancer patients. The GO enrichment analysis suggested that the six-lncRNA might involve with known breast cancer-related biological processes. With further experimental validation, these identified prognostic lncRNAs might have clinical implications for more personalized risk assessment for ER-positive breast cancer patients.

Selective expression of long non-coding RNAs in a breast cancer cell progression model

Long non-coding RNAs (lncRNAs) are acknowledged as regulators of cancer biology and pathology. Our goal was to perform a stringent profiling of breast cancer cell lines that represent disease progression. We used the MCF-10 series, which includes the normal-like MCF-10A, HRAS-transformed MCF-10AT1 (pre-malignant), and MCF-10CA1a (malignant) cells, to perform transcriptome wide sequencing. From these data, we have identified 346 lncRNAs with dysregulated expression across the progression series. By comparing lncRNAs from these datasets to those from an additional set of cell lines that represent different disease stages and subtypes, MCF-7 (early stage, luminal), and MDA-MB-231 (late stage, basal), 61 lncRNAs that are associated with breast cancer progression were identified. Querying breast cancer patient data from The Cancer Genome Atlas, we selected a lncRNA, IGF-like family member 2 antisense RNA 1 (IGFL2-AS1), of potential clinical relevance for functional characterization. Among the 61 lncRNAs, IGFL2-AS1 was the most significantly decreased. Our results indicate that this lncRNA plays a role in downregulating its nearest neighbor, IGFL1, and affects migration of breast cancer cells. Furthermore, the lncRNAs we identified provide a valuable resource to mechanistically and clinically understand the contribution of lncRNAs in breast cancer progression.

Identification of potential long non-coding RNA biomarkers associated with the progression of colon cancer

Increasing evidence has suggested that dysregulated lncRNA expression played important roles in the development and progression of human cancers. Although prognostic roles of lncRNAs have been recognized for colon cancer (CC) patients, the search for novel lncRNA biomarkers potentially involved in CC progression is an urgent and still largely unmet medical need. In this study, we evaluated the lncRNA expression changes during the progression of CC by analyzing two cohorts of previously published expression profiles of CC patients and identified hundreds of differentially expressed lncRNAs. Then we identified eight lncRNAs that closely associated with the progression of CC patients from a large number of significantly altered lncRNAs using random forest supervised classification algorithm. Finally, an SVM-based lncRNA risk classifier was developed to discriminate high-risk CC patients from persons with early-stage and validated in both the training dataset and testing dataset by survival analysis and five-fold cross-validation strategy. Our pathway enrichment analysis based on protein-coding genes that are co-expressed with lncRNAs, suggested that variation in expression of eight lncRNAs biomarkers might affect critical pathways involved in CC progression. With further validation, these eight lncRNAs might have significant implications for the clinical management of CC patients with early stage and improve our understanding of cancer progression.

Identification of long non-coding RNAs biomarkers associated with progression of endometrial carcinoma and patient outcomes

Endometrial carcinoma is a complex disease characterized by both genetic, epigenetic and environmental factors. Increasing evidence has suggested that long non-coding RNAs (lncRNAs) play important roles in the development and progression of cancers. In this study, we performed a comparison analysis for lncRNA expression between patients with early-stage (stage I/II) and those with advanced-stage (stage III/IV) derived from The Cancer Genome Atlas (TCGA) project and identified 17 differentially expressed lncRNAs using student t-test. Five of the 17 differentially expressed lncRNAs were selected as optimal biomarkers that are significantly associated with progression of UCEC using random forest feature selection procedure. A risk classifier of five lncRNAs was developed to as a molecular signature that identifies patients at high risk for progression using support vector machine. Results of five-lncRNA risk classifier achieved high discriminatory performance in distinguishing advanced stage from early stage with 78% prediction accuracy, 96.6% sensitivity and 76.6% specificity. Functional analysis suggested that these five lncRNA biomarkers may play critical roles in the progression of UCEC by participating in important cancer-related biological processes. Our study will help to improve our understanding of underlying mechanisms in the progression of UCEC and provide novel lncRNAs as candidate predictive biomarkers for the identification of patients with high risk for progression.

Potential prognostic long non-coding RNA identification and their validation in predicting survival of patients with multiple myeloma

Multiple myeloma, a typical hematological malignancy, is characterized by malignant proliferation of plasma cells. This study was to identify differently expressed long non-coding RNAs to predict the survival of patients with multiple myeloma efficiently. Gene expressing profiles of diagnosed patients with multiple myeloma, GSE24080 (559 samples) and GSE57317 (55 samples), were downloaded from Gene Expression Omnibus database. After processing, survival-related long non-coding RNAs were identified by Cox regression analysis. The prognosis of multiple myeloma patients with differently expressed long non-coding RNAs was predicted by Kaplan–Meier analysis. Meanwhile, stratified analysis was performed based on the concentrations of serum beta 2-microglobulin (S-beta 2m), albumin, and lactate dehydrogenase of multiple myeloma patients. Gene set enrichment analysis was performed to further explore the functions of identified long non-coding RNAs. A total of 176 long non-coding RNAs significantly related to the survival of multiple myeloma patients (p < 0.05) were identified. In dataset GSE24080 and GSE57317, there were 558 and 55 patients being clustered into two groups with significant differences, respectively. Stratified analysis indicated that prediction of the prognoses with these long non-coding RNAs was independent from other clinical phenotype of multiple myeloma. Gene set enrichment analysis–identified pathways of cell cycle, focal adhesion, and G2-M checkpoint were associated with these long non-coding RNAs. A total of 176 long non-coding RNAs, especially RP1-286D6.1, AC008875.2, MTMR9L, AC069360.2, and AL512791.1, were potential biomarkers to evaluate the prognosis of multiple myeloma patients. These long non-coding RNAs participated indispensably in many pathways associated to the development of multiple myeloma; however, the molecular mechanisms need to be further studied.

Prognostic significance of long noncoding RNA Z38 as a candidate biomarker in breast cancer

BACKGROUND

Long noncoding RNA (lncRNA) Z38 has been shown to promote cell proliferation and tumorigenesis in breast cancer. However, expression pattern and prognostic value of lncRNA Z38 in breast cancer patients remain elusive.

METHODS

The expression levels of SPRY4-IT1 in 110 self-paired specimens of breast cancer and adjacent normal breast tissues were measured by quantitative real-time PCR (qRT-PCR), and its correlation with overall survival of patients with breast cancer was further statistically analyzed.

RESULTS

Compared with normal breast tissues, Z38 was upregulated in breast cancer tissues. Furthermore, of 110 breast cancer patients, high Z38 expression was significantly associated with tumor-node-metastasis stage and lymph node metastasis. Further analysis using the Cox regression model revealed that Z38 expression was an independent prognostic factor of overall survival in patients with breast cancer (hazard ratio=4.74, 95% confidence interval 2.41-9.32). The nomogram presents a good prediction of the probability of overall survival of breast cancer patients (c-index: 0.792), and its predictive efficiency was further confirmed by the calibration curve.

CONCLUSION

Our data highlighted the potential of lncRNA Z38 as novel candidate biomarker to identify patients with breast cancer at high risk of tumor death.

LncRNAs: From Basic Research to Medical Application

This review aimed to summarize the current research contents about long noncoding RNAs (lncRNAs) and some related lncRNAs as molecular biomarkers or therapy strategies in human cancer and cardiovascular diseases. Following the development of various kinds of sequencing technologies, lncRNAs have become one of the most unknown areas that need to be explored. First, the definition and classification of lncRNAs were constantly amended and supplemented because of their complexity and diversity. Second, several methods and strategies have been developed to study the characteristic of lncRNAs, including new species identifications, subcellular localization, gain or loss of function, molecular interaction, and bioinformatics analysis. Third, based on the present results from basic researches, the working mechanisms of lncRNAs were proved to be different forms of interactions involving DNAs, RNAs, and proteins. Fourth, lncRNA can play different important roles during the embryogenesis and organ differentiations. Finally, because of the tissue-specific expression of lncRNAs, they could be used as biomarkers or therapy targets and effectively applied in different kinds of diseases, such as human cancer and cardiovascular diseases.

Insights from Global Analyses of Long Noncoding RNAs in Breast Cancer

PURPOSE OF REVIEW

The goal of this review was to compare and contrast the results and implications from several recent transcriptomic studies that analyzed the expression of lncRNAs in breast cancer. How many lncRNAs are dysregulated in breast cancer? Do dysregulated lncRNAs contribute to breast cancer etiology? Are lncRNAs viable biomarkers in breast cancer?

RECENT FINDINGS

Transcriptomic profiling of breast cancer tissues, mostly from The Cancer Genome Atlas, identified thousands of long noncoding RNAs that are expressed and dysregulated in breast cancer. The expression of lncRNAs alone can divide patients into molecular subtypes. Subsequent functional studies demonstrated that several of these lncRNAs have important roles in breast cancer cell biology.

SUMMARY

Thousands of lncRNAs are dysregulated in breast cancer that can be developed as biomarkers for prognostic or therapeutic purposes. The reviewed reports provide a roadmap to guide functional studies to discover lncRNAs with critical biological functions relating to breast cancer development and progression.

Identification of a RNA-Seq Based 8-Long Non-Coding RNA Signature Predicting Survival in Esophageal Cancer

Background

Accumulating evidence suggests the involvement of long non-coding RNAs (lncRNAs) as oncogenic or tumor suppressive regulators in the development of various cancers. In the present study, we aimed to identify a lncRNA signature based on RNA sequencing (RNA-seq) data to predict survival in esophageal cancer.

Material/Methods

The RNA-seq lncRNA expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs were screened out between esophageal cancer and normal tissues. Univariate and multivariate Cox regression analysis were performed to establish a lncRNA-related prognostic model. Receiver operating characteristic (ROC) analysis was conducted to test the sensitivity and specificity of the model. GO (gene ontology) functional and KEGG pathway enrichment analyses were performed for mRNAs co-expressed with the lncRNAs to explore the potential functions of the prognostic lncRNAs.

Results

A total of 265 differentially expressed lncRNAs were identified between esophageal cancer and normal tissues. After univariate and multivariate Cox regression analysis, eight lncRNAs (GS1-600G8.5, LINC00365, CTD-2357A8.3, RP11-705O24.1, LINC01554, RP1-90J4.1, RP11-327J17.1, and LINC00176) were finally screened out to establish a predictive model by which patients could be classified into high-risk and low-risk groups with significantly different overall survival. Further analysis indicated independent prognostic capability of the 8-lncRNA signature from other clinicopathological factors. ROC curve analysis demonstrated good performance of the 8-lncRNA signature. Functional enrichment analysis showed that the prognostic lncRNAs were mainly associated with esophageal cancer related biological processes such as regulation of glucose metabolic process and amino acid and lipids metabolism.

Conclusions

Our study developed a novel candidate model providing additional and more powerful prognostic information beyond conventional clinicopathological factors for survival prediction of esophageal cancer patients. Moreover, it also brings us new insights into the molecular mechanisms underlying esophageal cancer.

Luminal long non-coding RNAs regulated by estrogen receptor alpha in a ligand-independent manner show functional roles in breast cancer

Estrogen Receptor alpha (ERα) activation by estrogenic hormones induces luminal breast cancer cell proliferation. However, ERα plays also important hormone-independent functions to maintain breast tumor cells epithelial phenotype. We reported previously by RNA-Seq that in MCF-7 cells in absence of hormones ERα down-regulation changes the expression of several genes linked to cellular development, representing a specific subset of estrogen-induced genes. Here, we report regulation of long non-coding RNAs from the same experimental settings. A list of 133 Apo-ERα-Regulated lncRNAs (AER-lncRNAs) was identified and extensively characterized using published data from cancer cell lines and tumor tissues, or experiments on MCF-7 cells. For several features, we ran validation using cell cultures or fresh tumor biopsies. AER-lncRNAs represent a specific subset, only marginally overlapping estrogen-induced transcripts, whose expression is largely restricted to luminal cells and which is able to perfectly classify breast tumor subtypes. The most abundant AER-lncRNA, DSCAM-AS1, is expressed in ERα+ breast carcinoma, but not in pre-neoplastic lesions, and correlates inversely with EMT markers. Down-regulation of DSCAM-AS1 recapitulated, in part, the effect of silencing ERα, i.e. growth arrest and induction of EMT markers. In conclusion, we report an ERα-dependent lncRNA set representing a novel luminal signature in breast cancer cells.

'Omics Approaches in Breast Cancer Research and Clinical Practice

Our understanding of the natural history of breast cancer has evolved alongside technologies to study its genomic, transcriptomic, proteomic, and metabolomics landscapes. These technologies have helped decipher multiple molecular pathways dysregulated in breast cancer. First-generation 'omics analyses considered each of these dimensions individually, but it is becoming increasingly clear that more holistic, integrative approaches are required to fully understand complex biological systems. The 'omics represent an exciting era of discovery in breast cancer research, although important issues need to be addressed to realize the clinical utility of these data through precision cancer care. How can the data be applied to predict response to molecular-targeted therapies? When should treatment decisions be based on tumor genetics rather than histology? And with the sudden explosion of "big data" from large 'omics consortia and new precision clinical trials, how do we now negotiate evidence-based pathways to clinical translation through this apparent sea of opportunity? The aim of this review is to provide a broad overview of 'omics technologies used in breast cancer research today, the current state-of-play in terms of applying this new knowledge in the clinic, and the practical and ethical issues that will be central to the public discussion on the future of precision cancer care.

A Genome-Wide Association Study to Identify Potential Germline Copy Number Variants for Sporadic Breast Cancer Susceptibility

Breast cancer (BC) predisposition in populations arises from both genetic and nongenetic risk factors. Structural variations such as copy number variations (CNVs) are heritable determinants for disease susceptibility. The primary objectives of this study are (1) to identify CNVs associated with sporadic BC using a genome-wide association study (GWAS) design; (2) to utilize 2 distinct CNV calling algorithms to identify concordant CNVs as a strategy to reduce false positive associations in the hypothesis-generating GWAS discovery phase, and (3) to identify potential candidate CNVs for follow-up replication studies. We used Affymetrix SNP Array 6.0 data profiled on Caucasian subjects (422 cases/348 controls) to call CNVs using algorithms implemented in Nexus Copy Number and Partek Genomics Suite software. Nexus algorithm identified CNVs associated with BC (731 autosomal CNVs with >5% frequency in the total sample and Q < 0.05). Thirteen CNVs were identified when Partek algorithm-called CNVs were overlapped with Nexus-identified CNVs; these CNVs showed concordances for frequency, effect size, and direction. Coding genes present within BC-associated CNVs were known to play a role in disease etiology and prognosis. Long noncoding RNAs identified within CNVs showed tissue-specific expression, indicating potential functional relevance of the findings. The identified candidate CNVs warrant independent replication.

Portraying breast cancers with long noncoding RNAs

Evidence is emerging that long noncoding RNAs (lncRNAs) may play a role in cancer development, but this role is not yet clear. We performed a genome-wide transcriptional survey to explore the lncRNA landscape across 995 breast tissue samples. We identified 215 lncRNAs whose genes are aberrantly expressed in breast tumors, as compared to normal samples. Unsupervised hierarchical clustering of breast tumors on the basis of their lncRNAs revealed four breast cancer subgroups that correlate tightly with PAM50-defined mRNA-based subtypes. Using multivariate analysis, we identified no less than 210 lncRNAs prognostic of clinical outcome. By analyzing the coexpression of lncRNA genes and protein-coding genes, we inferred potential functions of the 215 dysregulated lncRNAs. We then associated subtype-specific lncRNAs with key molecular processes involved in cancer. A correlation was observed, on the one hand, between luminal A-specific lncRNAs and the activation of phosphatidylinositol 3-kinase, fibroblast growth factor, and transforming growth factor-β pathways and, on the other hand, between basal-like-specific lncRNAs and the activation of epidermal growth factor receptor (EGFR)-dependent pathways and of the epithelial-to-mesenchymal transition. Finally, we showed that a specific lncRNA, which we called CYTOR, plays a role in breast cancer. We confirmed its predicted functions, showing that it regulates genes involved in the EGFR/mammalian target of rapamycin pathway and is required for cell proliferation, cell migration, and cytoskeleton organization. Overall, our work provides the most comprehensive analyses for lncRNA in breast cancers. Our findings suggest a wide range of biological functions associated with lncRNAs in breast cancer and provide a foundation for functional investigations that could lead to new therapeutic approaches.

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]

Long non-coding RNAs (lncRNAs) represent an emerging layer of cancer biology and have been implicated in the development and progression of cancers. However, the prognostic significance of lncRNAs in diffuse large-B-cell lymphoma (DLBCL) remains unclear and needs to be systematically investigated. In this study, we obtained and analyzed lncRNA expression profiles in three cohorts of 1043 DLBCL patients by repurposing the publicly available microarray datasets from the Gene Expression Omnibus (GEO) database. In the discovery series of 207 patients, we identified a set of six lncRNAs that was significantly associated with patients’ overall survival (OS) using univariate Cox regression analysis. The six prognostic lncRNAs were combined to form an expression-based six-lncRNA signature which classified patients of the discovery series into the high-risk group and low-risk group with significantly different survival outcome (HR = 2.31, 95% CI = 1.8 to 2.965, p < 0.001). The six-lncRNA signature was further confirmed in the internal testing series and two additional independent datasets with different array platform. Moreover, the prognostic value of the six-lncRNA signature is independent of conventional clinical factors. Functional analysis suggested that six-lncRNA signature may be involved with DLBCL through exerting their regulatory roles in known cancer-related pathways, immune system and signaling molecules interaction.

Prediction of Lymph Node Metastasis in Patients with Bladder Cancer Using Whole Transcriptome Gene Expression Signatures

PURPOSE

Clinical staging in patients with muscle invasive bladder cancer misses up to 25% of lymph node metastasis. These patients are at high risk for disease recurrence and improved clinical staging is critical to guide management.

MATERIALS AND METHODS

Whole transcriptome expression profiles were generated in 199 patients who underwent radical cystectomy and extended pelvic lymph node dissection. The cohort was divided randomly into a discovery set of 133 patients and a validation set of 66. In the discovery set features were identified and modeled in a KNN51 (K-nearest neighbor classifier 51) to predict pathological lymph node metastases. Two previously described bladder cancer gene signatures, including RF15 (15-gene cancer recurrence signature) and LN20 (20-gene lymph node signature), were also modeled in the discovery set for comparison. The AUC and the OR were used to compare the performance of these signatures.

RESULTS

In the validation set KNN51 achieved an AUC of 0.82 (range 0.71-0.93) to predict lymph node positive cases. It significantly outperformed RF15 and LN20, which had an AUC of 0.62 (range 0.47-0.76) and 0.46 (range 0.32-0.60), respectively. Only KNN51 showed significant odds of predicting LN metastasis with an OR of 2.65 (range 1.68-4.67) for every 10% increase in score (p <0.001). RF15 and LN20 had a nonsignificant OR of 1.21 (range 0.97-1.54) and 1.39 (range 0.52-3.77), respectively.

CONCLUSIONS

The new KNN51 signature was superior to previously described gene signatures for predicting lymph node metastasis. If validated prospectively in transurethral resection of bladder tumor samples, KNN51 could be used to guide patients at high risk to early multimodal therapy.

LncRNA PANDAR regulates the G1/S transition of breast cancer cells by suppressing p16(INK4A) expression

It has been reported that lncRNA PANDAR (promoter of CDKN1A antisense DNA damage-activated RNA) is induced as a result of DNA damage, and it regulates the reparation of DNA damage. In this study, we investigated the role of lncRNA PANDAR in the progression of breast cancer and found that PANDAR was up-regulated in breast cancer tissues and cell lines. The knockdown of PANDAR suppresses G1/S transition of breast cancer cells. We demonstrated mechanistically that the regulation of G1/S transition by PANDAR was partly due to the transcriptional modulation of p16^INK4A. Moreover, we showed that PANDAR impacted p16^INK4A expression by regulating the recruitment Bmi1 to p16^INK4A promoter. To our knowledge, this is the first study which showed the functional roles and mechanisms of PANDAR in regulating the progression of breast cancer. The PANDAR/Bmi1/p16^INK4A axis could serve as novel targets for breast cancer therapy.

Non-coding RNAs in Mammary Gland Development and Disease

Non-coding RNAs (ncRNAs) are untranslated RNA molecules that function to regulate the expression of numerous genes and associated biochemical pathways and cellular functions. NcRNAs include small interfering RNAs (siRNAs), microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs) and long non-coding RNAs (lncRNAs). They participate in the regulation of all developmental processes and are frequently aberrantly expressed or functionally defective in disease. This Chapter will focus on the role of ncRNAs, in particular miRNAs and lncRNAs, in mammary gland development and disease.

IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework

Accuracy plays a vital role in the medical field as it concerns with the life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement on which classifier produces the best results. A specific classifier may be better than others for a specific dataset, but another classifier could perform better for some other dataset. Ensemble of classifiers has been proved to be an effective way to improve classification accuracy. In this research we present an ensemble framework with multi-layer classification using enhanced bagging and optimized weighting. The proposed model called "HM-BagMoov" overcomes the limitations of conventional performance bottlenecks by utilizing an ensemble of seven heterogeneous classifiers. The framework is evaluated on five different heart disease datasets, four breast cancer datasets, two diabetes datasets, two liver disease datasets and one hepatitis dataset obtained from public repositories. The analysis of the results show that ensemble framework achieved the highest accuracy, sensitivity and F-Measure when compared with individual classifiers for all the diseases. In addition to this, the ensemble framework also achieved the highest accuracy when compared with the state of the art techniques. An application named "IntelliHealth" is also developed based on proposed model that may be used by hospitals/doctors for diagnostic advice.

A potential prognostic long non-coding RNA signature to predict metastasis-free survival of breast cancer patients

Long non-coding RNAs (lncRNAs) have been implicated in a variety of biological processes, and dysregulated lncRNAs have demonstrated potential roles as biomarkers and therapeutic targets for cancer prognosis and treatment. In this study, by repurposing microarray probes, we analyzed lncRNA expression profiles of 916 breast cancer patients from the Gene Expression Omnibus (GEO). Nine lncRNAs were identified to be significantly associated with metastasis-free survival (MFS) in the training dataset of 254 patients using the Cox proportional hazards regression model. These nine lncRNAs were then combined to form a single prognostic signature for predicting metastatic risk in breast cancer patients that was able to classify patients in the training dataset into high- and low-risk subgroups with significantly different MFSs (median 2.4 years versus 3.0 years, log-rank test p < 0.001). This nine-lncRNA signature was similarly effective for prognosis in a testing dataset and two independent datasets. Further analysis showed that the predictive ability of the signature was independent of clinical variables, including age, ER status, ESR1 status and ERBB2 status. Our results indicated that lncRNA signature could be a useful prognostic marker to predict metastatic risk in breast cancer patients and may improve upon our understanding of the molecular mechanisms underlying breast cancer metastasis.

Genomic-Wide Analysis with Microarrays in Human Oncology

DNA microarray technologies have advanced rapidly and had a profound impact on examining gene expression on a genomic scale in research. This review discusses the history and development of microarray and DNA chip devices, and specific microarrays are described along with their methods and applications. In particular, microarrays have detected many novel cancer-related genes by comparing cancer tissues and non-cancerous tissues in oncological research. Recently, new methods have been in development, such as the double-combination array and triple-combination array, which allow more effective analysis of gene expression and epigenetic changes. Analysis of gene expression alterations in precancerous regions compared with normal regions and array analysis in drug-resistance cancer tissues are also successfully performed. Compared with next-generation sequencing, a similar method of genome analysis, several important differences distinguish these techniques and their applications. Development of novel microarray technologies is expected to contribute to further cancer research.

ADAM12 expression predicts clinical outcome in estrogen receptor-positive breast cancer

OBJECTIVES

Our study was aimed to make sure whether ADAM12 could serve as a prognostic biomarker of estrogen receptor (ER) -positive breast cancer.

METHODS

127 patients with ER-positive breast cancer were included in the present study. The level of ADAM12 was assayed through real-time quantitative PCR (RT-qPCR). Levels of ADAM12 in tumor tissues and adjacent normal tissues were compared with paired t-test. The association of ADAM12 expression with clinical characteristics was analyzed via χ(2) test. Kaplan-Meier survival curve was used to evaluate the role of ADAM12 expression in overall survival (OS) of patients. Cox-regression analysis was performed to judge if ADAM12 could serve as a prognostic marker in breast cancer.

RESULTS

The level of ADAM12 was upregulated in tumor tissues of breast cancer compared to that of adjacent normal tissues (P < 0.05). The expression of ADAM12 was closely related to the Ki-67 and HER2 status (P < 0.05 for both). The results of Kaplan-Meier survival curve showed that patients with higher level of ADAM12 exhibited shorter survival time compared to that of low level of ADAM12 (P < 0.001). Cox regression analysis showed that ADAM12 might be a biomarker in predicting prognosis of patients with ER-positive breast cancer (HR = 7.116, 95% CI = 3.329-15.212).

CONCLUSION

ADAM12 appears to be a prognostic marker in ER-positive breast cancer.