Identification of a four-long non-coding RNA signature in predicting breast cancer survival

Exosomal lncRNAs as regulators of breast cancer chemoresistance and metastasis and their potential use as biomarkers

Breast cancer is the most common cancer in women and the leading cause of female deaths by cancer in the world worldwide. Hence, understanding the molecular mechanisms associated with breast cancer development and progression, including drug resistance and breast cancer metastasis, is essential for achieving the best management of breast cancer patients. Cancer-related long noncoding RNAs have been shown to be involved in the regulation of each stage of breast cancer progression. Additionally, exosomes are extracellular microvesicles that are central to intercellular communication and play an important role in tumorigenesis. Exosomes can be released from primary tumor cells into the bloodstream and transmit cellular signals to distant body sites. In this work, we review the findings regarding the cellular mechanisms regulated by exosomal lncRNAs that are essentials to chemoresistance development and metastasis of breast cancer. Likewise, we evaluate the outcomes of the potential clinical use of exosomal lncRNAs as breast cancer biomarkers to achieve personalized management of the patients. This finding highlights the importance of transcriptomic analysis of exosomal lncRNAs to understand the breast cancer tumorigenesis as well as to improve the clinical tests available for this disease.

Prognostic and diagnostic values of non-coding RNAs as biomarkers for breast cancer: An umbrella review and pan-cancer analysis

Background: Breast cancer (BC) is the most common cancer in women. The incidence and morbidity of BC are expected to rise rapidly. The stage at which BC is diagnosed has a significant impact on clinical outcomes. When detected early, an overall 5-year survival rate of up to 90% is possible. Although numerous studies have been conducted to assess the prognostic and diagnostic values of non-coding RNAs (ncRNAs) in breast cancer, their overall potential remains unclear. In this field of study, there are various systematic reviews and meta-analysis studies that report volumes of data. In this study, we tried to collect all these systematic reviews and meta-analysis studies in order to re-analyze their data without any restriction to breast cancer or non-coding RNA type, to make it as comprehensive as possible. Methods: Three databases, namely, PubMed, Scopus, and Web of Science (WoS), were searched to find any relevant meta-analysis studies. After thoroughly searching, the screening of titles, abstracts, and full-text and the quality of all included studies were assessed using the AMSTAR tool. All the required data including hazard ratios (HRs), sensitivity (SENS), and specificity (SPEC) were extracted for further analysis, and all analyses were carried out using Stata. Results: In the prognostic part, our initial search of three databases produced 10,548 articles, of which 58 studies were included in the current study. We assessed the correlation of non-coding RNA (ncRNA) expression with different survival outcomes in breast cancer patients: overall survival (OS) (HR = 1.521), disease-free survival (DFS) (HR = 1.33), recurrence-free survival (RFS) (HR = 1.66), progression-free survival (PFS) (HR = 1.71), metastasis-free survival (MFS) (HR = 0.90), and disease-specific survival (DSS) (HR = 0.37). After eliminating low-quality studies, the results did not change significantly. In the diagnostic part, 22 articles and 30 datasets were retrieved from 8,453 articles. The quality of all studies was determined. The bivariate and random-effects models were used to assess the diagnostic value of ncRNAs. The overall area under the curve (AUC) of ncRNAs in differentiated patients is 0.88 (SENS: 80% and SPEC: 82%). There was no difference in the potential of single and combined ncRNAs in differentiated BC patients. However, the overall potential of microRNAs (miRNAs) is higher than that of long non-coding RNAs (lncRNAs). No evidence of publication bias was found in the current study. Nine miRNAs, four lncRNAs, and five gene targets showed significant OS and RFS between normal and cancer patients based on pan-cancer data analysis, demonstrating their potential prognostic value. Conclusion: The present umbrella review showed that ncRNAs, including lncRNAs and miRNAs, can be used as prognostic and diagnostic biomarkers for breast cancer patients, regardless of the sample sources, ethnicity of patients, and subtype of breast cancer.

SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA-disease associations

Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA-disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations. However, these methods do not consider the use of microbe-disease association information to enrich disease similarity, and also do not make more use of similarity information in the decomposition process. To address these issues, we here propose a correction-based similarity-constrained probability matrix decomposition method (SCCPMD) to predict lncRNA-disease associations. The microbe-disease associations are first used to enrich the disease semantic similarity matrix, and then the logistic function is used to correct the lncRNA and disease similarity matrix, and then these two corrected similarity matrices are added to the probability matrix decomposition as constraints to finally predict the potential lncRNA-disease associations. The experimental results show that SCCPMD outperforms the five advanced comparison algorithms. In addition, SCCPMD demonstrated excellent prediction performance in a case study for breast cancer, lung cancer, and renal cell carcinoma, with prediction accuracy reaching 80, 100, and 100%, respectively. Therefore, SCCPMD shows excellent predictive performance in identifying unknown lncRNA-disease associations.

Docetaxel-resistant triple-negative breast cancer cell-derived exosomal lncRNA LINC00667 reduces the chemosensitivity of breast cancer cells to docetaxel <em>via</em> targeting miR-200b-3p/Bcl-2 axis

Development of docetaxel (TXT) resistance is a major obstacle for triple-negative breast cancer (TNBC) treatment. Additionally, chemoresistant cell-derived exosomes were able to change the chemo-response of chemosensitive recipient cells via transportation of lncRNAs. It has been shown that lncRNA LINC00667 level was significantly elevated in breast cancer tissues. Therefore, we explored whether LINC00667 level is increased in TXT-resistant TNBC cell-derived exosomes. In addition, whether exosomal LINC00667 derived from TXT-resistant TNBC cell could affect TXT sensitivity in TXT-sensitive TNBC cells was investigated as well. In the present study, exosomes were isolated from the TXT-resistant TNBC cells and from TXT-sensitive TNBC cells. Next, the level of LINC00667 in the isolated exosomes was detected with RT-qPCR. We found that LINC00667 expression was obviously elevated in TXT-resistant TNBC cell-derived exosomes compared to that in TXT-sensitive TNBC cell-derived exosomes. In addition, LINC00667 could be transferred from TXT-resistant TNBC cells to TNBC cells via exosomes. Moreover, TXT-resistant TNBC cell secreted exosomal LINC00667 markedly reduced the sensitivity of TNBC cells to TXT via upregulation of Bcl-2. Meanwhile, downregulation of LINC00667 notably enhanced the sensitivity of TXT-resistant TNBC cells to TXT through downregulation of Bcl-2. Additionally, LINC00667 was considered to be a ceRNA to sponge miR-200b-3p, thereby elevating Bcl-2 expression. Collectively, TXT-resistant TNBC cell-derived exosomal LINC00667 could decrease the chemosensitivity of TNBC cells to TXT via regulating miR-200b-3p/Bcl-2 axis. These findings suggested that LINC00667 might serve as a promising target for enhancing sensitivity of TNBC cells to TXT therapy.

lncRNA PVT1: a novel oncogene in multiple cancers

Long noncoding RNAs are involved in epigenetic gene modification, including binding to the chromatin rearrangement complex in pre-transcriptional regulation and to gene promoters in gene expression regulation, as well as acting as microRNA sponges to control messenger RNA levels in post-transcriptional regulation. An increasing number of studies have found that long noncoding RNA plasmacytoma variant translocation 1 (PVT1) plays an important role in cancer development. In this review of a large number of studies on PVT1, we found that PVT1 is closely related to tumor onset, proliferation, invasion, epithelial–mesenchymal transformation, and apoptosis, as well as poor prognosis and radiotherapy and chemotherapy resistance in some cancers. This review comprehensively describes PVT1 expression in various cancers and presents novel approaches to the diagnosis and treatment of cancer.

Impact of Stilbenes as Epigenetic Modulators of Breast Cancer Risk and Associated Biomarkers

With the recent advancement of genetic screening for testing susceptibility to mammary oncogenesis in women, the relevance of the gene−environment interaction has become progressively apparent in the context of aberrant gene expressions. Fetal exposure to external stressors, hormones, and nutrients, along with the inherited genome, impact its traits, including cancer susceptibility. Currently, there is increasing interest in the role of epigenetic biomarkers such as genomic methylation signatures, plasma microRNAs, and alterations in cell-signaling pathways in the diagnosis and primary prevention of breast cancer, as well as its prognosis. Polyphenols like natural stilbenes have been shown to be effective in chemoprevention by exerting cytotoxic effects that can stall cell proliferation. Besides possessing antioxidant properties against the DNA-damaging effects of reactive oxygen species, stilbenes have also been observed to modulate cell-signaling pathways. With the increasing trend of early-life screening for hereditary breast cancer risks, the potency of different phytochemicals in harnessing the epigenetic biomarkers of breast cancer risk demand more investigation. This review will explore means of exploiting the abilities of stilbenes in altering the underlying factors that influence breast cancer risk, as well as the appearance of associated biomarkers.

Comprehensive Analysis of lncRNA and miRNA Regulatory Network Reveals Potential Prognostic Non-coding RNA Involved in Breast Cancer Progression

Breast cancer is one of the most common malignant tumors in women and is the second leading cause of cancer deaths among women. The tumorigenesis and progression of breast cancer are not well understood. The existing researches have indicated that non-coding RNAs, which mainly include long non-coding RNA (lncRNA) and microRNA (miRNA), have gradually become important regulators of breast cancer. We aimed to screen the differential expression of miRNA and lncRNA in the different breast cancer stages and identify the key non-coding RNA using TCGA data. Based on series test of cluster (STC) analysis, bioinformatics analysis, and negatively correlated relationships, 122 lncRNAs, 67 miRNAs, and 119 mRNAs were selected to construct the regulatory network of lncRNA and miRNA. It was shown that the miR-93/20b/106a/106b family was at the center of the regulatory network. Furthermore, 6 miRNAs, 10 lncRNAs, and 15 mRNAs were significantly associated with the overall survival (OS, log-rank P < 0.05) of patients with breast cancer. Overexpressed miR-93 in MCF-7 breast cancer cells was associated with suppressed expression of multiple lncRNAs, and these downregulated lncRNAs (MESTIT1, LOC100128164, and DNMBP-AS1) were significantly associated with poor overall survival in breast cancer patients. Therefore, the miR-93/20b/106a/106b family at the core of the regulatory network discovered by our analysis above may be extremely important for the regulation of lncRNA expression and the progression of breast cancer. The identified key miRNA and lncRNA will enhance the understanding of molecular mechanisms of breast cancer progression. Targeting these key non-coding RNA may provide new therapeutic strategies for breast cancer treatment and may prevent the progression of breast cancer from an early stage to an advanced stage.

Expression Analysis of Long Non-Coding RNAs Related With FOXM1, GATA3, FOXA1 and ESR1 in Breast Tissues

Breast cancer is the most common neoplasm among females. Estrogen receptor (ESR) signaling has a prominent impact in the pathogenesis of breast cancer. Among the transcription factors associated with ESR signaling, FOXM1, GATA3, FOXA1 and ESR1 have been suggested as a candidate in the pathogenesis of this neoplasm. In the current project, we have designed an in silico approach to find long non-coding RNAs (lncRNAs) that regulate these transcription factors. Then, we used clinical samples to carry out validation of our in silico findings. Our systems biology method led to the identification of APTR, AC144450.1, linc00663, ZNF337.AS1, and RAMP2.AS1 lncRNAs. Subsequently, we assessed the expression of these genes in breast cancer tissues compared with the adjacent non-cancerous tissues (ANCTs). Expression of GATA3 was significantly higher in breast cancer tissues compared with ANCTs (Ratio of mean expressions (RME) = 4.99, P value = 3.12E−04). Moreover, expression levels of APTR, AC144450.1, and ZNF337.AS1 were elevated in breast cancer tissues compared with control tissues (RME = 2.27, P value = 5.40E−03; Ratio of mean expressions = 615.95, P value = 7.39E−19 and RME = 1.78, P value = 3.40E−02, respectively). On the other hand, the expression of RAMP2.AS1 was lower in breast cancer tissues than controls (RME = 0.31, P value = 1.87E−03). Expression levels of FOXA1, ESR1, and FOXM1 and linc00663 were not significantly different between the two sets of samples. Expression of GATA3 was significantly associated with stage (P value = 4.77E−02). Moreover, expressions of FOXA1 and RAMP2.AS1 were associated with the mitotic rate (P values = 2.18E−02 and 1.77E−02, respectively). Finally, expressions of FOXM1 and ZNF337.AS1 were associated with breastfeeding duration (P values = 3.88E−02 and 4.33E−02, respectively). Based on the area under receiver operating characteristics curves, AC144450.1 had the optimal diagnostic power in differentiating between cancerous and non-cancerous tissues (AUC = 0.95, Sensitivity = 0.90, Specificity = 0.96). The combination of expression levels of all genes slightly increased the diagnostic power (AUC = 0.96). While there were several significant pairwise correlations between expression levels of genes in non-tumoral tissues, the most robust correlation was identified between linc00663 and RAMP2.AS1 (r = 0.61, P value = 3.08E−8). In the breast cancer tissues, the strongest correlations were reported between FOXM1/ZNF337.AS1 and FOXM1/RAMP2.AS1 pairs (r = 0.51, P value = 4.79E−5 and r = 0.51, P value = 6.39E−5, respectively). The current investigation suggests future assessment of the functional role of APTR, AC144450.1 and ZNF337.AS1 in the development of breast neoplasms.

A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.

Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.

Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.

Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.

Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

LINC00667 Sponges miR-4319 to Promote the Development of Nasopharyngeal Carcinoma by Increasing FOXQ1 Expression

Accumulating evidence has indicated that lncRNAs regulate various biological and pathological processes in diverse malignant tumors. The roles of LINC00667 in cancer development have been explored in glioma, hepatocellular carcinoma and non-small cell lung cancer, but not in nasopharyngeal carcinoma (NPC). In the present study, we characterize the role and molecular mechanism of LINC00667 in NPC progression. It was found that LINC00667 was overexpressed in NPC cells compared to normal cells. Silencing LINC00667 suppressed the proliferation, migration, invasion and epithelial mesenchymal transition (EMT) in NPC cells. In addition, bioinformatics analysis revealed that LINC00667 acted as a ceRNA to absorb miR-4319. Further investigations illustrated that miR-4319 had low expression in NPC cells and functioned as a tumor suppressor in the progression of NPC. Mechanistic study identified forkhead box Q1 (FOXQ1) as a functional target of miR-4319. The effect of LINC00667 in NPC development was mediated by the miR-4319/FOXQ1 axis. Analysis on tumorxenograft mouse model demonstrated that knockdown of LINC00667 repressed NPC tumor growth in vivo and confirmed the in vitro results. Our present study suggested that LINC00667 promoted the malignant phenotypes of NPC cells by competitively binding to miR-4319 to up-regulate FOXQ1 expression. Our results reveled that LINC00667 could be a diagnostic and therapeutic target for NPC patients.

Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

Long noncoding RNA (lncRNA) plays a critical role in the development of tumors. The aim of our study was construction of a lncRNA signature model to predict breast cancer (BRCA) patient survival. We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. Corresponding protein-coding genes were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. Finally, 521 differentially expression lncRNA were obtained. We constructed a ten-lncRNA signature model (LINC01208, RP5-1011O1.3, LINC01234, LINC00989, RP11-696F12.1, RP11-909N17.2, CTC-297N7.9, CTA-384D8.34, CTC-276P9.4, and MAPT-IT1) to predict BRCA patient survival using the multivariate Cox proportional hazard regression model. The C-index was 0.712, and AUC scores of training, test, and entire sets were 0.746, 0.717, and 0.732, respectively. Univariate Cox regression analysis indicated that age, tumor status, N status, M status, and risk score were significantly related to overall survival in patients with BRCA. Further, the multivariate analysis showed that risk score and M status had outstanding independent prognostic values, both with p < 0.001. The Gene Ontology (GO) function and KEEG pathway analysis was primarily enriched in immune response, receptor binding, external surface of plasma membrane, signal transduction, cytokine-cytokine receptor interaction, and cell adhesion molecules (CAMs). Finally, we constructed a ten-lncRNA signature model that can serve as an independent prognostic model to predict BRCA patient survival.

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.