Novel miRNA markers for the diagnosis and prognosis of endometrial cancer

As endometrial cancer (EC) is a major threat to female health worldwide, the ability to provide an accurate diagnosis and prognosis of EC is promising to improve its treatment guidance. Since the discovery of miRNAs, it has been realized that miRNAs are associated with every cell function, including malignant transformation and metastasis. This study aimed to explore diagnostic and prognostic miRNA markers of EC. In this study, differential analysis and machine learning were performed, followed by correlation analysis of miRNA‐mRNA based on the miRNA and mRNA expression data. Nine miRNAs were identified as diagnostic markers, and a diagnostic classifier was established to distinguish between EC and normal endometrium tissue with overall correct rates >95%. Five specific prognostic miRNA markers were selected to construct a prognostic model, which was confirmed more effective in identifying EC patients at high risk of mortality compared with the FIGO staging system. This study demonstrates that the expression patterns of miRNAs may hold promise for becoming diagnostic and prognostic biomarkers and novel therapeutic targets for EC.     

Circulating miRNA and cancer diagnosis

miRNAs are a class of small RNA molecules with regulatory function, and play an important role in tumor development and progression. It has been demonstrated that tumor-derived miRNAs exist in the circulating nucleic acids of cancer patients. This phenomenon implies that detection of the circulating miRNA may be an effective method for non-invasive diagnosis of cancer. In this review, we summarize the applications of the circulating miRNA as biomarkers in cancer diagnosis, as well as the latest research progress in this area.     

miRNA expression profiling for identification of potential breast cancer biomarkers

To identify micro RNA (miRNA) biomarker candidates for early detection of breast cancer and detection of minimal residual breast cancer, we performed miRNA expression profiling in pooled RNA samples from breast tumors, and from bone marrow mononuclear cells, peripheral blood mononuclear cells and plasma from healthy controls. We found substantially higher levels of five miRNAs in the breast tumors compared to the normal samples. However, validation of these miRNA levels, and seven other candidates selected from the literature, in individual samples from healthy controls and patients with non-metastatic breast cancer did not suggest further examination of their biomarker potential.     

miRNA expression profiling for identification of potential breast cancer biomarkers

To identify micro RNA (miRNA) biomarker candidates for early detection of breast cancer and detection of minimal residual breast cancer, we performed miRNA expression profiling in pooled RNA samples from breast tumors, and from bone marrow mononuclear cells, peripheral blood mononuclear cells and plasma from healthy controls. We found substantially higher levels of five miRNAs in the breast tumors compared to the normal samples. However, validation of these miRNA levels, and seven other candidates selected from the literature, in individual samples from healthy controls and patients with non-metastatic breast cancer did not suggest further examination of their biomarker potential.     

Legumain: a biomarker for diagnosis and prognosis of human ovarian cancer

Legumain is a member of the asparaginyl endopeptidase family that is over‐expressed in response to hypoxic stress on mammary adenocarcinoma, colorectal cancer, proliferating endothelial cells, and tumor‐associated macrophages (TAMs). Here, we demonstrate that elevated expression of legumain in ovarian cancer by a proteomic approach using isobaric tags for relative and absolute quantification (iTRAQ) followed by liquid chromatography–mass spectrometry (LC–MS/MS). To investigate the relationship between legumain expression and ovarian cancer development, we tested legumain expression in malignant human ovarian tumors (n = 60), borderline ovarian tumors (n = 20), benign ovarian tumors (n = 20), and normal ovary samples (n = 20) using immunohistochemical assay (IHC). A correlation between legumain expression, and clinocopathologic and biological variables was also established. Importantly, increased legumain expression was validated by real‐time PCR and Western blots, correlated positively with an increased malignancy of ovarian tumors (P < 0.01). In fact, patients with strong legumain expression had a worse prognosis (P = 0.03). In addition, results of in vitro experiments revealed that over‐expression of legumain correlates with increased cell migration and invasion of ovarian cancer cells. Although legumain's functional role and clinical utility remain to be established, our results indicated that a sensitive assay for early expression of legumain may serve as both a potential biomarker and a molecular target for treatment of ovarian cancer. J. Cell. Biochem. 113: 2679–2686, 2012. © 2012 Wiley Periodicals, Inc.     

Identifying circulating miRNA biomarkers for early diagnosis and monitoring of lung cancer

Liquid biopsy refers to the sampling, screening, and detecting potential biomarkers in unique liquid samples for clinical use. Lung cancer is one of the most highly frequent cancer subtypes, which is hard to be early diagnosed and monitored by radiological and histopathological evaluation that are the most general and accurate methods. Circulating miRNA is a potential clinical examination index for tumor detection and monitoring tumorigenesis progression using liquid biopsy. However, recognizing and validating the unique clinical values of each candidate circulating miRNA is expensive and time consuming. In this study, we presented a novel computational approach for identifying significant circulating miRNAs that may be applied to early screening, diagnosis, and constant monitoring of lung cancer progression. This approach incorporated several machine learning algorithms and was applied on the expression profiles of circulating miRNAs on lung cancer patients and control samples. In brief, a powerful feature selection method, minimum redundancy maximum relevance, was adopted to evaluate the importance of all features, resulting in a feature list. Then, incremental feature selection incorporating random forest followed to extract key circulating miRNAs. At the same time, an efficient classifier with MCC 0.740 was built. Top five circulating miRNAs, including miR-92a, miR-140-5p, miR-331-3p, miR-223, miR-374a, were analyzed and confirmed that they participated in the pathogenesis of lung cancer, indicating their significant prognosis power in lung cancer.     

Molecular profiling of human cancer

Traditionally, tumours have been categorized on the basis of histology. However, the staining pattern of cancer cells viewed under the microscope is insufficient to reflect the complicated underlying molecular events that drive the neoplastic process. By surveying thousands of genes at once, using DNA arrays, it is now possible to read the molecular signature of an individual patient's tumour. When the signature is analysed with clustering algorithms, new classes of cancer emerge that transcend distinctions based on histological appearance alone. Using DNA arrays, protein arrays and appropriate experimental models, the ultimate goal is to move beyond correlation and classification to achieve new insights into disease mechanisms and treatment targets.     

miRNA profiling in colorectal cancer highlights miR-1 involvement in MET-dependent proliferation

Altered expression of miRNAs is associated with development and progression of various human cancers by regulating the translation of oncogenes and tumor suppressor genes. In colorectal cancer, these regulators complement the Vogelstein multistep model of pathogenesis and have the potential of becoming a novel class of tumor biomarkers and therapeutic targets. Using quantitative real-time PCR, we measured the expression of 621 mature miRNAs in 40 colorectal cancers and their paired normal tissues and identified 23 significantly deregulated miRNAs. We subsequently evaluated their association with clinical characteristics of the samples and presence of alterations in the molecular markers of colorectal cancer progression. Expression levels of miR-31 were correlated with CA19-9 and miR-18a, miR-21, and miR-31 were associated with mutations in APC gene. To investigate the downstream regulation of the differentially expressed miRNAs identified, we integrated putative mRNA target predictions with the results of a meta-analysis of seven public gene expression datasets of normal and tumor samples of colorectal cancer patients. Many of the colorectal cancer deregulated miRNAs computationally mapped to targets involved in pathways related to progression. Here one promising candidate pair (miR-1 and MET) was studied and functionally validated. We show that miR-1 can have a tumor suppressor function in colorectal cancer by directly downregulating MET oncogene both at RNA and protein level and that reexpression of miR-1 leads to MET-driven reduction of cell proliferation and motility, identifying the miR-1 downmodulation as one of the events that could enhance colorectal cancer progression.     

Serum proteomic profiling for the early diagnosis of colorectal cancer

No ideal serum biomarker currently exists for the early diagnosis of colorectal cancer (CRC). Magnetic bead‐based fractionation coupled with MALDI‐TOF MS was used to screen serum samples from CRC patients, healthy controls, and other cancer patients. A diagnostic model with five proteomic features (m/z 1778.97, 1866.16, 1934.65, 2022.46, and 4588.53) was generated using Fisher algorithm with best performance. The Fisher‐based model could discriminate CRC patients from the controls with 100% (46/46) sensitivity and 100% (35/35) specificity in the training set, 95.6% (43/45) sensitivity and 83.3% (35/42) specificity in the test set. We further validated the model with 94.4% (254/269) sensitivity and 75.5% (83/110) specificity in the external independent group. In other cancers group, the Fisher‐based model classified 25 of 46 samples (54.3%) as positive and the other 21 as negative. With FT‐ICR‐MS, the proteomic features of m/z 1778.97, 1866.16, 1934.65, and 2022.46, of which intensities decreased significantly in CRC, were identified as fragments of complement C3f. Therefore, the Fisher‐based model containing five proteomic features was able to effectively differentiate CRC patients from healthy controls and other cancers with a high sensitivity and specificity, and may be CRC‐specific. Serum complement C3f, which was significantly decreased in CRC group, may be relevant to the incidence of CRC. J. Cell. Biochem. 114: 448–455, 2013. © 2012 Wiley Periodicals, Inc.     

Finding biomarkers for non-small cell lung cancer diagnosis and prognosis

Despite of several decades of efforts,lung cancer remains one of most deadly diseases,with a 5-year survival rate approximately 15％ worldwide.In China,the situation is even worse.Although there is no o...     

Direct and sensitive miRNA profiling from low-input total RNA

We have developed a sensitive, accurate, and multiplexed microRNA (miRNA) profiling assay that is based on a highly efficient labeling method and novel microarray probe design. The probes provide both sequence and size discrimination, yielding in most cases highly specific detection of closely related mature miRNAs. Using a simple, single-vial experimental protocol, 120 ng of total RNA is directly labeled using Cy3 or Cy5, without fractionation or amplification, to produce precise and accurate measurements that span a linear dynamic range from 0.2 amol to 2 fmol of input miRNA. The results can provide quantitative estimates of the miRNA content for the tissues studied. The assay is also suitable for use with formalin-fixed paraffin-embedded clinical samples. Our method allows rapid design and validation of probes for simultaneous quantitative measurements of all human miRNA sequences in the public databases and to new miRNA sequences as they are reported.     

miRAS: a data processing system for miRNA expression profiling study

Background  

The study of microRNAs (miRNAs) is attracting great considerations. Recent studies revealed that miRNAs play as important regulators of gene expression and some even as cancer players or inhibitors. Many studies try to discover new miRNAs and reveal the miRNA expression profile in cancer using a SAGE-based total RNA clone method. However, the data processing of this method is labor-intensive with several different biological databases and more than ten data processing steps involved.     

Global profiling of miRNAs and the hairpin precursors: insights into miRNA processing and novel miRNA discovery

MicroRNAs (miRNAs) constitute an important class of small regulatory RNAs that are derived from distinct hairpin precursors (pre-miRNAs). In contrast to mature miRNAs, which have been characterized in numerous genome-wide studies of different organisms, research on global profiling of pre-miRNAs is limited. Here, using massive parallel sequencing, we have performed global characterization of both mouse mature and precursor miRNAs. In total, 87 369 704 and 252 003 sequencing reads derived from 887 mature and 281 precursor miRNAs were obtained, respectively. Our analysis revealed new aspects of miRNA/pre-miRNA processing and modification, including eight Ago2-cleaved pre-miRNAs, eight new instances of miRNA editing and exclusively 5′ tailed mirtrons. Furthermore, based on the sequences of both mature and precursor miRNAs, we developed a miRNA discovery pipeline, miRGrep, which does not rely on the availability of genome reference sequences. In addition to 239 known mouse pre-miRNAs, miRGrep predicted 41 novel ones with high confidence. Similar as known ones, the mature miRNAs derived from most of these novel loci showed both reduced abundance following Dicer knockdown and the binding with Argonaute2. Evaluation on data sets obtained from Caenorhabditis elegans and Caenorhabditis sp.11 demonstrated that miRGrep could be widely used for miRNA discovery in metazoans, especially in those without genome reference sequences.     

MicroRNAs: Potential biomarkers for cancer diagnosis,prognosis and targets for therapy

MicroRNAs have a revolutionary impact on cancer research over recent years. They emerge as important players in tumorigenesis, leading to a paradigm shift in oncology. The widespread and comprehensive use of microRNA microarrays has enabled the identification of a number of microRNAs as potential biomarkers for cancer. It is encouraging to report that microRNAs have remarkable stability in both formalin-fixed tissue and blood. Many microRNAs have been identified to act as oncogenes, tumor suppressors, or even modulators of cancer stem cells and metastasis. Some studies not only reported the identified microRNA biomarkers, but also deciphered their target genes and the underlying mechanisms. The rapid discovery of many microRNA targets and their relevant pathways has contributed to the development of microRNA-based therapeutics, but the developing progress of antisense or siRNA drugs has been hampered by stability, specificity and delivery problems. This review summarizes the most significant and latest findings of original researches on microRNAs involvement in cancer, focusing on the potential of cancer-related microRNAs as biomarkers for diagnosis, prognosis and targets for therapy.