Identification of differentially expressed miRNAs in individual breast cancer patient and application in personalized medicine

Personalized differential expression analysis in triple-negative breast cancer

Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.

Deregulation of miR-1245b-5p and miR-92a-3p and their potential target gene, GATA3, in epithelial-mesenchymal transition pathway in breast cancer

BACKGROUND

MicroRNAs (miRNAs) are small molecules that have prominent roles in tumor development and metastasis and can be used for diagnostic and therapeutic purposes. This study evaluated the expression of miR-92a-3p and miR-1245b-5p and their potential target gene, GATA3 in patients with breast cancer (BC).

MATERIALS AND METHODS

In the search for BC-related microRNAs, miR-124b-5p and miR-92a-3p were selected using Medline through PubMed, miR2disease, miRcancer and miRTarBase. Moreover, target gene GATA3 and their possible interaction in the regulating epithelial-mesenchymal transition (EMT) and invasion was evaluated using in silico tools including miRTarBase, TargetScan, STRING-db, and Cytoscape. The expression level of miR-92a-3p, miR1245b-5p, and GATA3 were assessed on extracted RNAs of tumor and nontumor tissues from 36 patients with BC using qPCR. Additionally, clinical-pathologic characteristics, such as tumor grade, tumor stage, lymph node were taken into consideration and the diagnostic power of these miRNAs and GATA3 was evaluated using the ROC curve analysis.

RESULTS

In silico evaluation of miR-92a-3p and miR-1245b-5p supports their potential association with EMT and invasion signaling pathways in BC pathogenesis. Comparing tumor tissues to nontumor tissues, we found a significant downregulation of miR-1245b-5p and miR-92a-3p and upregulation of GATA3. Patients with BC who had decreased miR-92a-3p expression also had higher rates of advanced stage/grade and ER expression, whereas decreased miR-1245b-5p expression was only linked to ER expression and was not associated with lymph node metastasis. The AUC of miR-1245b-5p, miR-92a-3p, and GATA3 using ROC curve was determined 0.6449 (p = .0239), 0.5980 (p = .1526), and 0.7415 (p < .0001), respectively, which showed a significant diagnostic accuracy of miR-1245b-5p and GATA3 between the BC patients and healthy individuals.

CONCLUSION

MiR-1245b-5p, miR-92a-3p, and GATA3 gene contribute to BC pathogenesis and they may be having potential regulatory roles in signaling pathways involved in invasion and EMT pathways in BC pathogenesis, as a result of these findings. More research is needed to determine the regulatory mechanisms that they control.

Exosomal microRNA-92b Is a Diagnostic Biomarker in Breast Cancer and Targets Survival-Related MTSS1L to Promote Tumorigenesis

Exosomal microRNAs (miRNAs) are novel, non-invasive biomarkers for facilitating communication and diagnosing cancer. However, only a few studies have investigated their function and role in the clinical diagnosis of breast cancer. To address this gap, we established a stable cell line, MDA-MB-231-CD63-RFP, and recruited 112 female participants for serum collection. We screened 88 exosomal miRNAs identified through microarray analysis of 231-CD63 and literature screening using real-time PCR; only exosomal miR-92b-5p was significantly increased in patients with breast cancer. It had a significant correlation with stage and discriminated patients from the control with an AUC of 0.787. Exosomal miR-92b-5p impacted the migration, adhesion, and spreading ability of normal human mammary epithelial recipient cells through the downregulation of the actin dynamics regulator MTSS1L. In clinical breast cancer tissue, the expression of MTSS1L was significantly inversely correlated with tissue miR-92b-5p, and high expression of MTSS1L was associated with better 10-year overall survival rates in patients undergoing hormone therapy. In summary, our studies demonstrated that exosomal miR-92b-5p might function as a non-invasive body fluid biomarker for breast cancer detection and provide a novel therapeutic strategy in the axis of miR-92b-5p to MTSS1L for controlling metastasis and improving patient survival.

The Role of MicroRNAs in Breast Cancer and the Challenges of Their Clinical Application

MicroRNAs (miRNAs) constitute a subclass of non-coding RNAs that exert substantial influence on gene-expression regulation. Their tightly controlled expression plays a pivotal role in various cellular processes, while their dysregulation has been implicated in numerous pathological conditions, including cancer. Among cancers affecting women, breast cancer (BC) is the most prevalent malignant tumor. Extensive investigations have demonstrated distinct expression patterns of miRNAs in normal and malignant breast cells. Consequently, these findings have prompted research efforts towards leveraging miRNAs as diagnostic tools and the development of therapeutic strategies. The aim of this review is to describe the role of miRNAs in BC. We discuss the identification of oncogenic, tumor suppressor and metastatic miRNAs among BC cells, and their impact on tumor progression. We describe the potential of miRNAs as diagnostic and prognostic biomarkers for BC, as well as their role as promising therapeutic targets. Finally, we evaluate the current use of artificial intelligence tools for miRNA analysis and the challenges faced by these new biomedical approaches in its clinical application. The insights presented in this review underscore the promising prospects of utilizing miRNAs as innovative diagnostic, prognostic, and therapeutic tools for the management of BC.

Generative pretraining from large-scale transcriptomes for single-cell deciphering

Exponential accumulation of single-cell transcriptomes poses great challenge for efficient assimilation. Here, we present an approach entitled generative pretraining from transcriptomes (tGPT) for learning feature representation of transcriptomes. tGPT is conceptually simple in that it autoregressive models the ranking of a gene in the context of its preceding neighbors. We developed tGPT with 22.3 million single-cell transcriptomes and used four single-cell datasets to evalutate its performance on single-cell analysis tasks. In addition, we examine its applications on bulk tissues. The single-cell clusters and cell lineage trajectories derived from tGPT are highly aligned with known cell labels and states. The feature patterns of tumor bulk tissues learned by tGPT are associated with a wide range of genomic alteration events, prognosis, and treatment outcome of immunotherapy. tGPT represents a new analytical paradigm for integrating and deciphering massive amounts of transcriptome data and it will facilitate the interpretation and clinical translation of single-cell transcriptomes.

RNA interference: Promising approach for breast cancer diagnosis and treatment

Today, cancer is one of the main health-related challenges, and in the meantime, breast cancer (BC) is one of the most common cancers among women, with an alarming number of incidences and deaths every year. For this reason, the discovery of novel and more effective approaches for the diagnosis, treatment, and monitoring of the disease are very important. In this regard, scientists are looking for diagnostic molecules to achieve the above-mentioned goals with higher accuracy and specificity. RNA interference (RNAi) is a posttranslational regulatory process mediated by microRNA intervention and small interfering RNAs. After transcription and edition, these two noncoding RNAs are integrated and activated with the RNA-induced silencing complex (RISC) and AGO2 to connect the target mRNA by their complementary sequence and suppress their translation, thus reducing the expression of their target genes. These two RNAi categories show different patterns in different BC types and stages compared to healthy cells, and hence, these molecules have high diagnostic, monitoring, and therapeutic potentials. This article aims to review the RNAi pathway and diagnostic and therapeutic potentials with a special focus on BC.

Application of individualized differential expression analysis in human cancer proteome

Liquid chromatography–mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https://github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.

Cure lies in nature: medicinal plants and endophytic fungi in curbing cancer

Success of targeted cancer treatment modalities has generated an ambience of plausible cure for cancer. However, cancer remains to be the major cause of mortality across the globe. The emergence of chemoresistance, relapse after treatment and associated adverse effects has posed challenges to the present therapeutic regimes. Thus, investigating new therapeutic agents of natural origin and delineating the underlying mechanism of action is necessary. Since ages and still in continuum, the phytochemicals have been the prime source of identifying bioactive agents against cancer. They have been exploited for isolating targeted specific compounds to modulate the key regulating signaling pathways of cancer pathogenesis and progression. Capsaicin (alkaloid compound in chilli), catechin, epicatechin, epigallocatechin and epigallocatechin-3-gallate (phytochemicals in green tea), lutein (carotenoid found in yellow fruits), Garcinol (phenolic compound present in kokum tree) and many other naturally available compounds are also very valuable to develop the drugs to treat the cancer. An alternate repository of similar chemical diversity exists in the form of endophytic fungi inhabiting the medicinal plants. There is a high diversity of plant associated endophytic fungi in nature which are potent producers of anti-cancer compounds and offers even stronger hope for the discovery of an efficient anti-cancer drug. These fungi provide various bioactive molecules, such as terpenoids, flavonoids, alkaloids, phenolic compounds, quinines, steroids etc. exhibiting anti-cancerous property. The review discusses the relevance of phytochemicals in chemoprevention and as modulators of miRNA. The perspective advocates the imperative role of anti-cancerous secondary metabolites containing repository of endophytic fungi, as an alternative route of drug discovery.

A systematic study of critical miRNAs on cells proliferation and apoptosis by the shortest path

BACKGROUND

MicroRNAs are a class of important small noncoding RNAs, which have been reported to be involved in the processes of tumorigenesis and development by targeting a few genes. Existing studies show that the imbalance between cell proliferation and apoptosis is closely related to the initiation and development of cancers. However, the impact of miRNAs on this imbalance has not been studied systematically.

RESULTS

In this study, we first construct a cell fate miRNA-gene regulatory network. Then, we propose a systematical method for calculating the global impact of miRNAs on cell fate genes based on the shortest path. Results on breast cancer and liver cancer datasets show that most of the cell fate genes are perturbed by the differentially expressed miRNAs. Most of the top-identified miRNAs are verified in the Human MicroRNA Disease Database (HMDD) and are related to breast and liver cancers. Function analysis shows that the top 20 miRNAs regulate multiple cell fate related function modules and interact tightly based on their functional similarity. Furthermore, more than half of them can promote sensitivity or induce resistance to some anti-cancer drugs. Besides, survival analysis demonstrates that the top-ranked miRNAs are significantly related to the overall survival time in the breast and liver cancers group.

CONCLUSION

In sum, this study can help to systematically study the important role of miRNAs on proliferation and apoptosis and thereby uncover the key miRNAs during the process of tumorigenesis. Furthermore, the results of this study will contribute to the development of clinical therapy based miRNAs for cancers.

Model-Based Integration Analysis Revealed Presence of Novel Prognostic miRNA Targets and Important Cancer Driver Genes in Triple-Negative Breast Cancers

Background: miRNAs (microRNAs) play a key role in triple-negative breast cancer (TNBC) progression, and its heterogeneity at the expression, pathological and clinical levels. Stratification of breast cancer subtypes on the basis of genomics and transcriptomics profiling, along with the known biomarkers’ receptor status, has revealed the existence of subgroups known to have diverse clinical outcomes. Recently, several studies have analysed expression profiles of matched mRNA and miRNA to investigate the underlying heterogeneity of TNBC and the potential role of miRNA as a biomarker within cancers. However, the miRNA-mRNA regulatory network within TNBC has yet to be understood. Results and Findings: We performed model-based integrated analysis of miRNA and mRNA expression profiles on breast cancer, primarily focusing on triple-negative, to identify subtype-specific signatures involved in oncogenic pathways and their potential role in patient survival outcome. Using univariate and multivariate Cox analysis, we identified 25 unique miRNAs associated with the prognosis of overall survival (OS) and distant metastases-free survival (DMFS) with “risky” and “protective” outcomes. The association of these prognostic miRNAs with subtype-specific mRNA genes was established to investigate their potential regulatory role in the canonical pathways using anti-correlation analysis. The analysis showed that miRNAs contribute to the positive regulation of known breast cancer driver genes as well as the activation of respective oncogenic pathway during disease formation. Further analysis on the “risk associated” miRNAs group revealed significant regulation of critical pathways such as cell growth, voltage-gated ion channel function, ion transport and cell-to-cell signalling. Conclusion: The study findings provide new insights into the potential role of miRNAs in TNBC disease progression through the activation of key oncogenic pathways. The results showed previously unreported subtype-specific prognostic miRNAs associated with clinical outcome that may be used for further clinical evaluation.

MicroRNA-4316 inhibits gastric cancer proliferation and migration via directly targeting VEGF-A

Background and aims

microRNAs (miRNAs) have been reported to regulate proliferation and migration by down-regulating the expression of target genes. The aims of this study were to investigate whether miR-4316 inhibited proliferation and migration by downregulating vascular endothelial growth factor A (VEGF-A) and its clinical significance in gastric cancer (GC).

Methods

The clinical tissues of the GC patients for miR-4316 and VEGF-A were detected by qRT-PCR. The protein levels of VEGF-A and c-Met were determined by western blotting. Cell Proliferation, migration, and colony forming assays were conducted to show whether miR-4316 affects proliferation by CCK-8, migration by transwell, wound healing and colony formation assays. The bioinformatic methods and luciferase reporter assay were applied to detect the relationship between miRNA and VEGF-A on its targeting 3-untranslated regions (3-UTRs). CCK-8, colony formation, wound healing, and transwell assay were performed to explore the function of miR-4316.

Results

The results of qRT-PCR indicated that miR-4316 expression level was significantly downregulated in human GC tissues and GC cell lines compared with their control. miR-4316 inhibited proliferation, migration and colony formation in GC cell lines by reducing VEGF-A. And western blot results indicated that miR-4316 significantly inhibited GC through repressing VEGF-A and c-Met. The investigation of Luciferase assay indicated that VEGF-A is a direct target gene of miR-4316.

Conclusions

miR-4316 suppressed proliferation and migration of GC through the VEGF-A gene. MiR-4316 acts as a tumor suppressor by targeting VEGF-A and this indicated that MiR-4316 might be a potential therapeutic target for GC.

Identification of differentially expressed microRNAs in outgrowth embryos compared with blastocysts and non-outgrowth embryos in mice

Recurrent implantation failure (RIF) is one of the main causes for the repeated failure of IVF, and the major reason for RIF is thought to be a miscommunication between the embryo and uterus. However, the exact mechanism underlying embryo-uterus cross-talk is not fully understood. The aim of the present study was to identify differentially expressed microRNAs (miRNAs) among blastocysts, non-outgrowth and outgrowth embryos in mice using microarray analysis. A bioinformatics analysis was performed to predict the potential mechanisms of implantation. The miRNA expression profiles differed significantly between non-outgrowth and outgrowth embryos. In all, 3163 miRNAs were detected in blastocysts and outgrowth embryos. Of these, 10 miRNA candidates (let-7b, miR-23a, miR-27a, miR-92a, miR-183, miR-200c, miR-291a, miR-425, miR-429 and miR-652) were identified as significant differentially expressed miRNAs of outgrowth embryos by in silico analysis. The expression of the miRNA candidates was markedly changed during preimplantation embryo development. In particular, let-7b-5p, miR-200c-3p and miR-23a-3p were significantly upregulated in outgrowth embryos compared with non-outgrowth blastocysts. Overall, differentially expressed miRNAs in outgrowth embryos compared with blastocysts and non-outgrowth embryos could be involved in embryo attachment, and interaction between the embryo proper and maternal endometrium during the implantation process.

Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes

The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.

Validation of methylation-based forensic age estimation in time-series bloodstains on FTA cards and gauze at room temperature conditions

We previously proposed a prediction model consisting of 9 CpG sites for forensic age estimation with high practical potentials in Chinese males. Here, we further evaluated the performance of this prediction model in two independent batches of time-series bloodstain samples naturally exposed to room temperature conditions. The first batch consists of 30 Han Chinese males (18-59 years of age) whose peripheral blood was converted into bloodstains on Flinders Technology Association (FTA) cards and naturally exposed to room temperature conditions for different time points up to 3 months. The second batch consists of 99 Han Chinese males (21-66 years of age) whose peripheral blood was divided into 3 replicates, converted into bloodstains on gauze, and naturally exposed to room temperature conditions for 3 months. For each time point and each replicate, the methylation levels at the 9 CpG sites were detected using the EpiTYPER system. Applying the 9-CpG age prediction model to these bloodstain samples resulted in highly accurate age predictions for all time points and replicates (0.81 <R² < 0.91, 2.94 < MAD < 3.55 years). The updated model combining our previous and current data achieved similarly high prediction results. Therefore, our 9-CpG age prediction model was successfully validated in time-series bloodstain samples converted on both FTA card and gauze under natural room temperature conditions, demonstrating high potentials in future forensic applications to Han Chinese males.

Key Enabling Technologies for Point-of-Care Diagnostics

A major trend in biomedical engineering is the development of reliable, self-contained point-of-care (POC) devices for diagnostics and in-field assays. The new generation of such platforms increasingly addresses the clinical and environmental needs. Moreover, they are becoming more and more integrated with everyday objects, such as smartphones, and their spread among unskilled common people, has the power to improve the quality of life, both in the developed world and in low-resource settings. The future success of these tools will depend on the integration of the relevant key enabling technologies on an industrial scale (microfluidics with microelectronics, highly sensitive detection methods and low-cost materials for easy-to-use tools). Here, recent advances and perspectives will be reviewed across the large spectrum of their applications.

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common abnormal miRNA expression patterns and their potential roles in cancer have not yet been evaluated. To account for individual differences between patients, we retrieved miRNA sequencing data for 575 patients with both tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA). We then performed differential expression analysis using DESeq2 and edgeR. Results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns between tumor and non-tumor samples. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) in a single cancer. We also found 21 key SDEmiRNAs (nine over-expressed and 12 under-expressed) associated with at least eight cancers each and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effects of these 21 SDEmiRNAs on cellular function were evaluated through enrichment and pathway analysis of 7186 protein-coding gene targets mined from literature reports of differential expression of miRNAs in cancer. This analysis enables identification of SDEmiRNA functional similarity in cell proliferation control across a wide range of cancers, and assembly of common regulatory networks over cancer-related pathways. These findings were validated by construction of a regulatory network in the PI3K pathway. This study provides evidence for the value of further analysis of SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

Novel MicroRNA-Based Risk Score Identified by Integrated Analyses to Predict Metastasis and Poor Prognosis in Breast Cancer

BACKGROUND

The use of biomarkers that allow early therapeutic intervention or intensive follow-up evaluation is expected to be a powerful means for reducing breast cancer mortality. MicroRNAs (miRNAs) are known to play major roles in cancer biology including metastasis. This study aimed to develop a novel miRNA risk score to predict patient survival and metastasis in breast cancer.

METHODS

An integrated unbiased approach was applied to derive a composite risk score for prognosis based on miRNA expression in primary breast tumors in 1051 breast cancer patients from The Cancer Genome Atlas (TCGA). Further analysis of the risk score with metastasis/recurrence was performed using the TCGA data set and validated in a separate patient population using small RNA sequencing.

RESULTS

The three-miRNAs risk score (miR-19a, miR-93, and miR-106a) was developed using the TCGA cohort, which predicted poor prognosis (p = 0.0005) independently of known clinical risk factors. The prognostic value was validated in another three following independent cohorts: GSE19536 (p = 0.0009), GSE22220 (p = 0.0003), and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (p = 0.0023). The three-miRNAs risk score predicted bone recurrence in TCGA (p = 0.0052), and the findings were validated in another independent population of patients who experienced bone recurrence and age/stage-matched patients without any recurrence. The three-miRNAs risk score enriched multiple metastasis-related gene sets such as angiogenesis and epithelial mesenchymal transition in a gene-set-enrichment analysis.

CONCLUSIONS

The authors developed the novel miRNA-based risk score, which is a promising biomarker for prediction of worse survival and bone recurrence potential in breast cancer.

Differential expression of hsa-miR-221, hsa-miR-21, hsa-miR-135b, and hsa-miR-29c suggests a field effect in oral cancer

BACKGROUND

The theory of field effect suggests that the tumor-adjacent area, besides histopathologically normal, undergoes genetic and epigenetic changes that can eventually affect epithelial homeostasis, predisposing the patient to cancer development. One of the many molecular changes described in cancer are microRNAs (miRNAs), which regulates the expression of important genes during carcinogenesis. Thus, the aim of this study was to investigate the field effect in oral cancer.

METHODS

We investigated the differential expression profile of four miRNAs (hsa-miR-221, hsa-miR-21, hsa-miR-135b, and hsa-miR-29c) in cancerous oral tissue, in tumor-adjacent tissue and and in non-cancerous tissue samples from healthy volunteers.

RESULTS

Our results showed significant overexpression profiles of all four studied miRNAs in cancerous oral tissue compared to non-cancerous samples, as well as in tumor-adjacent tissue compared to cancer-free tissue. No significant difference was found when comparing the expression profile of cancerous and tissue-adjacent tissue groups. We found a negative correlation between the expression of hsa-miR-21 expression and STAT3 in oral squamous cell carcinoma.

CONCLUSION

These results suggest that the tissue adjacent to cancer cannot be considered a normal tissue because its molecular aspects are significantly altered. Our data corroborates the hypothesis of field cancerization.

Target-enrichment sequencing for detailed characterization of small RNAs

Identification of important, functional small RNA (sRNA) species is currently hampered by the lack of reliable and sensitive methods to isolate and characterize them. We have developed a method, termed target-enrichment of sRNAs (TEsR), that enables targeted sequencing of rare sRNAs and diverse precursor and mature forms of sRNAs not detectable by current standard sRNA sequencing methods. It is based on the amplification of full-length sRNA molecules, production of biotinylated RNA probes, hybridization to one or multiple targeted RNAs, removal of nontargeted sRNAs and sequencing. By this approach, target sRNAs can be enriched by a factor of 500-30,000 while maintaining strand specificity. TEsR enriches for sRNAs irrespective of length or different molecular features, such as the presence or absence of a 5' cap or of secondary structures or abundance levels. Moreover, TEsR allows the detection of the complete sequence (including sequence variants, and 5' and 3' ends) of precursors, as well as intermediate and mature forms, in a quantitative manner. A well-trained molecular biologist can complete the TEsR procedure, from RNA extraction to sequencing library preparation, within 4-6 d.

Mechanisms of human telomerase reverse transcriptase (hTERT) regulation: clinical impacts in cancer

Background

Limitless self-renewal is one of the hallmarks of cancer and is attained by telomere maintenance, essentially through telomerase (hTERT) activation. Transcriptional regulation of hTERT is believed to play a major role in telomerase activation in human cancers.

Main body

The dominant interest in telomerase results from its role in cancer. The role of telomeres and telomere maintenance mechanisms is well established as a major driving force in generating chromosomal and genomic instability. Cancer cells have acquired the ability to overcome their fate of senescence via telomere length maintenance mechanisms, mainly by telomerase activation.

hTERT expression is up-regulated in tumors via multiple genetic and epigenetic mechanisms including hTERT amplifications, hTERT structural variants, hTERT promoter mutations and epigenetic modifications through hTERT promoter methylation. Genetic (hTERT promoter mutations) and epigenetic (hTERT promoter methylation and miRNAs) events were shown to have clinical implications in cancers that depend on hTERT activation. Knowing that telomeres are crucial for cellular self-renewal, the mechanisms responsible for telomere maintenance have a crucial role in cancer diseases and might be important oncological biomarkers. Thus, rather than quantifying TERT expression and its correlation with telomerase activation, the discovery and the assessment of the mechanisms responsible for TERT upregulation offers important information that may be used for diagnosis, prognosis, and treatment monitoring in oncology. Furthermore, a better understanding of these mechanisms may promote their translation into effective targeted cancer therapies.

Conclusion

Herein, we reviewed the underlying mechanisms of hTERT regulation, their role in oncogenesis, and the potential clinical applications in telomerase-dependent cancers.

Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer

Background

Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors.

Results

Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization.

Conclusions

Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4446-y) contains supplementary material, which is available to authorized users.

Sulforaphane-Induced Cell Cycle Arrest and Senescence are accompanied by DNA Hypomethylation and Changes in microRNA Profile in Breast Cancer Cells

Cancer cells are characterized by genetic and epigenetic alterations and phytochemicals, epigenetic modulators, are considered as promising candidates for epigenetic therapy of cancer. In the present study, we have investigated cancer cell fates upon stimulation of breast cancer cells (MCF-7, MDA-MB-231, SK-BR-3) with low doses of sulforaphane (SFN), an isothiocyanate. SFN (5-10 µM) promoted cell cycle arrest, elevation in the levels of p21 and p27 and cellular senescence, whereas at the concentration of 20 µM, apoptosis was induced. The effects were accompanied by nitro-oxidative stress, genotoxicity and diminished AKT signaling. Moreover, SFN stimulated energy stress as judged by decreased pools of ATP and AMPK activation, and autophagy induction. Anticancer effects of SFN were mediated by global DNA hypomethylation, decreased levels of DNA methyltransferases (DNMT1, DNMT3B) and diminished pools of N6-methyladenosine (m6A) RNA methylation. SFN (10 µM) also affected microRNA profiles, namely SFN caused upregulation of sixty microRNAs and downregulation of thirty two microRNAs, and SFN promoted statistically significant decrease in the levels of miR-23b, miR-92b, miR-381 and miR-382 in three breast cancer cells. Taken together, we show for the first time that SFN is an epigenetic modulator in breast cancer cells that results in cell cycle arrest and senescence, and SFN may be considered to be used in epigenome-focused anticancer therapy.

Differential expression analysis at the individual level reveals a lncRNA prognostic signature for lung adenocarcinoma

BACKGROUND

Deregulations of long non-coding RNAs (lncRNAs) have been implicated in cancer initiation and progression. Current methods can only capture differential expression of lncRNAs at the population level and ignore the heterogeneous expression of lncRNAs in individual patients.

METHODS

We propose a method (LncRIndiv) to identify differentially expressed (DE) lncRNAs in individual cancer patients by exploiting the disrupted ordering of expression levels of lncRNAs in each disease sample in comparison with stable normal ordering. LncRIndiv was applied to lncRNA expression profiles of lung adenocarcinoma (LUAD). Based on the expression profile of LUAD individual-level DE lncRNAs, we used a forward selection procedure to identify prognostic signature for stage I-II LUAD patients without adjuvant therapy.

RESULTS

In both simulated data and real pair-wise cancer and normal sample data, LncRIndiv method showed good performance. Based on the individual-level DE lncRNAs, we developed a robust prognostic signature consisting of two lncRNA (C1orf132 and TMPO-AS1) for stage I-II LUAD patients without adjuvant therapy (P = 3.06 × 10^-6, log-rank test), which was confirmed in two independent datasets of GSE50081 (P = 1.82 × 10^-2, log-rank test) and GSE31210 (P = 7.43 × 10^-4, log-rank test) after adjusting other clinical factors such as smoking status and stages. Pathway analysis showed that TMPO-AS1 and C1orf132 could affect the prognosis of LUAD patients through regulating cell cycle and cell adhesion.

CONCLUSIONS

LncRIndiv can successfully detect DE lncRNAs in individuals and be applied to identify prognostic signature for LUAD patients.

Computational Analysis of Specific MicroRNA Biomarkers for Noninvasive Early Cancer Detection

Cancer is a complex disease residing in various tissues of human body, accompanied with many abnormalities and mutations in genomes, transcriptome, and epigenome. Early detection plays a crucial role in extending survival time of all major cancer types. Recent advances in microarray and sequencing techniques have given more support to identifying effective biomarkers for early detection of cancer. MicroRNAs (miRNAs) are more and more frequently used as candidates for biomarkers in cancer related studies due to their regulation of target gene expression. In this paper, the comparative analysis is used to discover miRNA expression patterns in cancer versus normal samples on early stage of eight prevalent cancer types. Our work focuses on the specific miRNAs biomarkers identification and function analysis. Several identified miRNA biomarkers in this paper are matched well with those reported in existing researches, and most of them could serve as potential candidate indicators for clinical early diagnosis applications.

Decoding the usefulness of non-coding RNAs as breast cancer markers

Although important advances in the management of breast cancer (BC) have been recently accomplished, it still constitutes the leading cause of cancer death in women worldwide. BC is a heterogeneous and complex disease, making clinical prediction of outcome a very challenging task. In recent years, gene expression profiling emerged as a tool to assist in clinical decision, enabling the identification of genetic signatures that better predict prognosis and response to therapy. Nevertheless, translation to routine practice has been limited by economical and technical reasons and, thus, novel biomarkers, especially those requiring non-invasive or minimally invasive collection procedures, while retaining high sensitivity and specificity might represent a significant development in this field. An increasing amount of evidence demonstrates that non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), are aberrantly expressed in several cancers, including BC. miRNAs are of particular interest as new, easily accessible, cost-effective and non-invasive tools for precise management of BC patients because they circulate in bodily fluids (e.g., serum and plasma) in a very stable manner, enabling BC assessment and monitoring through liquid biopsies. This review focus on how ncRNAs have the potential to answer present clinical needs in the personalized management of patients with BC and comprehensively describes the state of the art on the role of ncRNAs in the diagnosis, prognosis and prediction of response to therapy in BC.