Methods in molecular biology
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MicroRNAs (miRNAs) are 20-22 nucleotides long single-stranded noncoding RNAs. They regulate gene expression posttranscriptionally by base pairing with the complementary sequences in the 3'-untranslated region of their targeted mRNA. Aberrant expression of miRNAs leads to alterations in the expression of oncogenes and tumor suppressors, thereby affecting cellular growth, proliferation, apoptosis, motility, and invasion capacity of gastrointestinal cells, including cells of esophageal squamous cell carcinoma (ESCC). ⋯ Consequently, expression profiles of miRNAs could be useful as diagnostic, prognostic, and prediction biomarkers in ESCC. Herein, we describe the quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) and microarray methods for detection and quantitate miRNAs in ESCC. In addition, we summarize the roles of miRNAs in ESCC pathogenesis, progression, and prognosis.
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Pathological staging is the most important factor that determines the prognosis and management of patients with esophageal squamous cell carcinoma. The method for the pathological staging in esophageal squamous cell carcinoma involves assessment of standard parameters-extent of tumor (T), lymph node status (N), presence of distant metastasis (M), as well as grade (G) and anatomical location of the carcinoma. In addition, other relevant factors, such as use of neoadjuvant therapy, could affect the pathological staging of esophageal squamous cell carcinoma.
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Mouse models are important in the study of pathogenesis, testing new treatment, and monitoring the progress of treatment in patients with esophageal squamous cell carcinoma (ESCC). The mice commonly used are immunosuppressed. The first category of models is for basic research and includes genetically engineered mouse models and carcinogen- or diet-induced mouse models. ⋯ This model appears promising for personalized medicine and of high resemblance to the nature of human ESCC, but there are many limitations for the use. It is likely to be used more in research in ESCC in the future. In this chapter, we detailed the preparation and experiments of PDX model from a patient with ESCC.
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Cancer stem cells (CSCs) are a small subpopulation of cells associated with cancer initiation, progression, metastasis, therapy resistant, and recurrence. In esophageal squamous cell carcinoma (ESCC), several cell surface and intracellular markers, for example, CD44, ALDH, Pygo2, MAML1, Twist1, Musashi1, side population (SP), CD271, and CD90, have been proposed to identify CSCs. In addition, stem cell markers such as ALDH1, HIWI, Oct3/4, ABCG2, SOX2, SALL4, BMI-1, NANOG, CD133, and podoplanin were associated with pathological stages of cancer, cancer recurrence, prognosis, and therapy resistance of patients with ESCC. ⋯ However, none of these methods solely can guarantee complete isolation of CSC population. Therefore, a combination of methods is used for reliable detection and isolation of CSCs. Herein, we describe the identification and isolation of CSCs from ESCC cells by cell sorting after Hoechst 33342 staining followed by in vitro functional assays and in vivo mouse xenotransplantation techniques.
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ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, by means of precursor ion chromatogram integration. ⋯ To achieve this statistical step, it is possible to rely on ProStaR, which allows the user to (1) load correctly formatted data, (2) clean them by means of various filters, (3) normalize the sample batches, (4) impute the missing values, (5) perform null hypothesis significance testing, (6) check the well-calibration of the resulting p-values, (7) select a subset of differentially abundant proteins according to some false discovery rate, and (8) contextualize these selected proteins into the Gene Ontology. This chapter provides a detailed protocol on how to perform these eight processing steps with ProStaR.