Selection of reference genes for quantitative analysis of microRNA expression in three different types of cancer

Raman Spectroscopy in the Diagnosis of Brain Gliomas: A Literature Review

Raman spectroscopy (RS) is increasingly applied in medical fields to distinguish neoplastic from normal tissues, with recent advancements enabling its use in neurosurgery. This review explores RS as a diagnostic and surgical aid for brain gliomas, detailing its various modalities and applications. Through a comprehensive search in databases including PubMed, Google Scholar, and eLibrary, over 300 references were screened, resulting in 74 articles that met inclusion criteria. Key findings reveal RS's potential in neuro-oncology for examining native biopsy specimens, frozen and paraffin-embedded tissues, and body fluids, as well as performing intraoperative assessments. RS offers promise for identifying gliomas, differentiating them from healthy brain tissue, and establishing precise tumor boundaries during resection.

Normalization and selecting non-differentially expressed genes improve machine learning modelling of cross-platform transcriptomic data

Normalization is a critical step in quantitative analyses of biological processes. Recent works show that cross-platform integration and normalization enable machine learning (ML) training on RNA microarray and RNA-seq data, but no independent datasets were used in their studies. Therefore, it is unclear how to improve ML modelling performance on independent RNA array and RNA-seq based datasets. Inspired by the house-keeping genes that are commonly used in experimental biology, this study tests the hypothesis that non-differentially expressed genes (NDEG) may improve normalization of transcriptomic data and subsequently cross-platform modelling performance of ML models. Microarray and RNA-seq datasets of the TCGA breast cancer were used as independent training and test datasets, respectively, to classify the molecular subtypes of breast cancer. NDEG (p>0.85) and differentially expressed genes (DEG, p<0.05) were selected based on the p values of ANOVA analysis and used for subsequent data normalization and classification, respectively. Models trained based on data from one platform were used for testing on the other platform. Our data show that NDEG and DEG gene selection could effectively improve the model classification performance. Normalization methods based on parametric statistical analysis were inferior to those based on nonparametric statistics. In this study, the LOG_QN and LOG_QNZ normalization methods combined with the neural network classification model seem to achieve better performance. Therefore, NDEG-based normalization appears useful for cross-platform testing on completely independent datasets. However, more studies are required to examine whether NDEG-based normalization can improve ML classification performance in other datasets and other omic data types.

Multiple Myeloma: Genetic and Epigenetic Biomarkers with Clinical Potential

Multiple myeloma (MM) is characterized by the uncontrolled proliferation of monoclonal plasma cells and accounts for approximately 10% of all hematologic malignancies. The clinical outcomes of MM can exhibit considerable variability. Variability in both the genetic and epigenetic characteristics of MM undeniably contributes to tumor dynamics. The aim of the present study was to identify biomarkers with the potential to improve the accuracy of prognosis assessment in MM. Initially, miRNA sequencing was conducted on bone marrow (BM) samples from patients with MM. Subsequently, the expression levels of 27 microRNAs (miRNA) and the gene expression levels of ASF1B, CD82B, CRISP3, FN1, MEF2B, PD-L1, PPARγ, TERT, TIMP1, TOP2A, and TP53 were evaluated via real-time reverse transcription polymerase chain reaction in BM samples from patients with MM exhibiting favorable and unfavorable prognoses. Additionally, the analysis involved the bone marrow samples from patients undergoing examinations for non-cancerous blood diseases (NCBD). The findings indicate a statistically significant increase in the expression levels of miRNA-124, -138, -10a, -126, -143, -146b, -20a, -21, -29b, and let-7a and a decrease in the expression level of miRNA-96 in the MM group compared with NCBD (p < 0.05). No statistically significant differences were detected in the expression levels of the selected miRNAs between the unfavorable and favorable prognoses in MM groups. The expression levels of ASF1B, CD82B, and CRISP3 were significantly decreased, while those of FN1, MEF2B, PDL1, PPARγ, and TERT were significantly increased in the MM group compared to the NCBD group (p < 0.05). The MM group with a favorable prognosis demonstrated a statistically significant decline in TIMP1 expression and a significant increase in CD82B and CRISP3 expression compared to the MM group with an unfavorable prognosis (p < 0.05). From an empirical point of view, we have established that the complex biomarker encompassing the CRISP3/TIMP1 expression ratio holds promise as a prognostic marker in MM. From a fundamental point of view, we have demonstrated that the development of MM is rooted in a cascade of complex molecular pathways, demonstrating the interplay of genetic and epigenetic factors.

Benchmarking miRNA reference genes in B-cell precursor acute lymphoblastic leukemia

MicroRNAs (miRNAs) play dual roles in acute lymphoblastic leukemia (ALL) as both tumor suppressors and oncogenes, and miRNA expression profiles can be used for patient risk stratification. Precise assessment of miRNA levels is crucial for understanding their role and function in gene regulation. Quantitative real-time polymerase chain reaction (qPCR) is a reliable, rapid, and cost-effective method for analyzing miRNA expression, assuming that appropriate normalization to stable references is performed to ensure valid data. In this study, we evaluated the stability of six commonly used miRNA references (5sRNA, SNORD44, RNU6, RNU1A1, miR-103a-3p, and miR-532-5p) across nine B-cell precursor (BCP) ALL cell lines, 22 patient-derived xenograft (PDX) BCP ALL samples from different organ compartments of leukemia bearing mice, and peripheral blood mononuclear cells (PBMCs) from six healthy donors. We used four different algorithms (Normfinder, ∆CT, geNorm, and BestKeeper) to assess the most stably expressed reference across all samples. Moreover, we validated our data in an additional set of 13 PDX ALL samples and six healthy controls, identifying miR-103a-3p and miR-532-5p as the most stable references for miRNA normalization in BCP ALL studies. Additionally, we demonstrated the critical importance of using a stable reference to accurately interpret miRNA data.

miRNA Expression Profiles in Ovarian Endometriosis and Two Types of Ovarian Cancer-Endometriosis-Associated Ovarian Cancer and High-Grade Ovarian Cancer

Endometriosis-associated ovarian cancer (EOC) consisting of endometrioid cancer and clear-cell ovarian cancer could be promoted by many factors. miRNAs, which are small, non-coding molecules of RNA, are among them. The aim of this study was to detect miRNAs connected with the malignant transformation of endometriosis. FFPE (formalin-fixed, paraffin-embedded) samples of 135 patients operated on for endometriosis and different types of ovarian cancer (EOC and HGSOC-high-grade serous ovarian cancer) were studied. Healthy ovarian tissue was used as a control group. From the expression panel of 754 miRNAs, 7 were chosen for further tests according to their ROC (receiver operating characteristic) curves: miR-1-3p, miR-125b-1-3p, miR-31-3p, miR-200b-3p, miR-502-5p, miR-503-5p and miR-548d-5p. Furthermore, other potentially important clinical data were analysed, which included age, BMI, Ca-125 concentration, miscarriages and deliveries and concomitant diseases such as hypertension, type 2 diabetes and smoking. Among the miRNAs, miR200b-3p had the lowest expression in neoplastic tissues. miR31-3p had the highest expression in women without any lesions in the ovaries. miR-502-5p and miR-548-5p did not differ between the studied groups. The examined miRNA panel generally distinguished significantly normal ovarian tissue and endometriosis, normal ovarian tissue and cancer, and endometriosis and cancer. The malignant transformation of endometriosis is dependent on different factors. miRNA changes are among them. The studied miRNA panel described well the differences between endometriosis and EOC but had no potential to differentiate types of ovarian cancer according to their origin. Therefore, examination of a broader miRNA panel is needed and might prove itself advantageous in clinical practice.

A one-pot isothermal Cas12-based assay for the sensitive detection of microRNAs

The use of microRNAs as clinical cancer biomarkers is hindered by the absence of accurate, fast and inexpensive assays for their detection in biofluids. Here we report a one-step and one-pot isothermal assay that leverages rolling-circle amplification and the endonuclease Cas12a for the accurate detection of specific miRNAs. The assay exploits the cis-cleavage activity of Cas12a to enable exponential rolling-circle amplification of target sequences and its trans-cleavage activity for their detection and for signal amplification. In plasma from patients with pancreatic ductal adenocarcinoma, the assay detected the miRNAs miR-21, miR-196a, miR-451a and miR-1246 in extracellular vesicles at single-digit femtomolar concentrations with single-nucleotide specificity. The assay is rapid (sample-to-answer times ranged from 20 min to 3 h), does not require specialized instrumentation and is compatible with a smartphone-based fluorescence detection and with the lateral-flow format for visual readouts. Simple assays for the detection of miRNAs in blood may aid the development of miRNAs as biomarkers for the diagnosis and prognosis of cancers.