PiRNA Biogenesis and Their Role in Human Cancers and Other Diseases: A Narrative Review

PIWI-interacting RNAs (piRNAs) with the length of approximately 26-30 nucleotides are a distinct class of small non-coding RNAs that mainly expressed in the animal gonads. Other small RNAs originate from double stranded precursors but piRNAs derive from long single-stranded primary transcripts, which expressed from distinct genomic regions. piRNAs are involved in silencing of mobile elements named transposons and their main role is germline maintenance. Recent studies have opened new insights on biological and clinical significance of piRNAs in various diseases. Abnormal expression of piRNAs is a remarkable feature in many diseases especially human cancers, which emphasize on their important biological role in disease progression. Furthermore, they can be served as biomarkers and therapeutic targets for tumor diagnostics and treatment. In this review, we explained piRNAs characteristics, biogenesis process and functions, discuss new findings about involvement of these elements in various disease and their potential to be used as diagnostic biomarkers.

Accurate detection of circulating tumor DNA using nanopore consensus sequencing

Levels of circulating tumor DNA (ctDNA) in liquid biopsies may serve as a sensitive biomarker for real-time, minimally-invasive tumor diagnostics and monitoring. However, detecting ctDNA is challenging, as much fewer than 5% of the cell-free DNA in the blood typically originates from the tumor. To detect lowly abundant ctDNA molecules based on somatic variants, extremely sensitive sequencing methods are required. Here, we describe a new technique, CyclomicsSeq, which is based on Oxford Nanopore sequencing of concatenated copies of a single DNA molecule. Consensus calling of the DNA copies increased the base-calling accuracy ~60×, enabling accurate detection of TP53 mutations at frequencies down to 0.02%. We demonstrate that a TP53-specific CyclomicsSeq assay can be successfully used to monitor tumor burden during treatment for head-and-neck cancer patients. CyclomicsSeq can be applied to any genomic locus and offers an accurate diagnostic liquid biopsy approach that can be implemented in clinical workflows.

PATH-44. RAMAN SPECTROSCOPY AS A DIAGNOSTIC TOOL IN NEUROPATHOLOGY

BACKGROUND Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" which could be used to differentiate tissue heterogeneity or diagnostic entities. RS has so far been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS. METHODS To address this issue, we examined FFPE samples of a broad range of intracranial tumors (e.g. glioblastoma and primary CNS lymphoma) and also different areas of morphologically highly heterogeneous glioblastoma tumor tissue. The latter in order to classify not only the tumor entity but also histologically defined GBM areas according to their spectral properties. We applied linear and nonlinear machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine) on our spectroscopic data and compared statistical performance of resulting classifiers. RESULTS We found that Random Forest classification distinguished between glioblastoma and primary CNS lymphoma with a balanced accuracy of 94%, only using Raman measurements on FFPE tissue. Furthermore, our established support vector machine-based classifier identified distinct histological areas in glioblastoma such as tumor core and necroses with an overall accuracy of 70.5% and showed a clear separation between the areas of necrosis and peritumoral zone. CONCLUSIONS This relatively cheap and easy-to-apply tool may serve useful to complement histopathological and molecular diagnostics. It provides an unbiased approach to tumor diagnostics with very little requirements (e.g. histopathological feature completeness of the tumor entity) to the sample. As a conclusion, we propose RS as a potential future additional method in the (neuro)-pathological toolbox for tumor diagnostics.

MRI Image Classification of Brain Tumor Using Deep Neural Network and Deployment using Web Framework

A brain tumor is a mass or growth of abnormal cells in our brain. Many different types of brain tumors exist. Some brain tumors are noncancerous (benign), and some brain tumors are cancerous (malignant). Brain tumors can begin in your brain (primary brain tumors), or cancer can begin in other parts of your body and spread to your brain (secondary, or metastatic, brain tumors). Brain tumor treatment options depend on the type of brain tumor you have, as well as its size and location. The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image classification is the convolution neural network (CNN). It is predicted that the success of the obtained results will increase if the CNN method is supported by adding extra feature extraction methods and classify successfully brain tumor normal and abnormal image.

Brain Tumor Detection System

Brain tumor is an abnormal growth of brain cells within the brain. Detection of brain tumor is a challenging problem, due to complex structure of the brain. The automatic segmentation has great potential in clinical medicine by freeing physicians from the burden of manual labeling; whereas only a quantitative measurement allows to track and modeling precisely the disease. Magnetic resonance (MR) images are an awfully valuable tool to determine the tumor growth in brain. But, accurate brain image segmentation is a complicated and time consuming process. MR is generally more sensitive in detecting brain abnormalities during the early stages of disease, and is excellent in early detection of cases of cerebral infarction, brain tumors, or infections. So, in this project we put forward a method for automatic brain tumor diagnostics using MR images. The proposed system identifies and segments the tumor portions of the images successfully. Keywords: MR, 2D Image, BrainTumor

The role of artificial intelligence in cancer diagnostics - a review

Introduction and purposeArtificial intelligence (AI) is more advanced than ever and finds more and more new applications. Attempts are being made to use computer data analysis in medicine. The aim of this study is to summarize the knowledge on the use of AI in the diagnosis of breast, prostate, skin and colorectal cancer with particular emphasis on the applications and effectiveness of AI in making diagnoses. A brief description of the state of knowledgeThe most frequently used form of artificial intelligence in diagnostics are algorithms that analyze databases and recognize patterns. They can capture the features of samples characteristic of tumors, such as abnormal cells in the biopsy material or the alarming size and color of the skin lesion. Additionally, AI is capable of analyzing magnetic resonance images, radiographs, and other standardized test results. In most cases, AI is more effective than clinicians, sometimes as effective as they are, and almost never less effective. As a rule, the most accurate and adequate diagnosis can be obtained by joining the forces of AI and medical specialists. Working with learning algorithms requires the use of very extensive data sets. Every effort should be made to protect sensitive information from patients' medical history. ConclusionsThe results of research on the effectiveness of AI in cancer diagnostics are very promising. Further research and development of information technology systems may positively affect the quality and effectiveness of tumor diagnostics.

Variations in the Management of Diffuse Low-Grade Gliomas – a Scandinavian Multicenter Study

Background Early extensive surgery is a cornerstone in treatment of diffuse low-grade gliomas (DLGGs), and an additional survival benefit has been demonstrated from early radiochemotherapy in selected “high-risk” patients. Still, there are a number of controversies related to DLGG management. The objective of this multicenter population-based cohort study was to explore potential variations in diagnostic work-up and treatment between treating centers in two Scandinavian countries with similar public healthcare systems. Methods Patients screened for inclusion underwent primary surgery of a histopathologically verified diffuse WHO grade II glioma in the time period 2012 through 2017. Clinical and radiological data were collected from medical records and locally conducted research projects, whereupon differences between countries and inter-hospital variations were explored. Results A total of 642 patients were included (male:female ratio 1.4), and annual age-standardized incidence rates were 0.9 and 0.8 per 100 000 in Norway and Sweden, respectively. Considerable inter-hospital variations were observed in preoperative work-up, tumor diagnostics, surgical strategies, techniques for intraoperative guidance, as well as choice and timing of adjuvant therapy. Conclusions Despite geographical population-based case selection, similar healthcare organization and existing guidelines, there were considerable variations in DLGG management. While some can be attributed to differences in clinical implementation of current scientific knowledge, some of the observed inter-hospital variations reflect controversies related to diagnostics and treatment. Quantification of these disparities renders possible identification of treatment patterns associated with better or worse outcomes and may thus represent a step toward more uniform evidence-based care.

Intracardiac EUS-B-Guided FNA for Diagnosing Cardiac Tumors

Primary cardiac tumors are extremely rare. Obtaining a tissue diagnosis is difficult and commonly requires open-heart surgery with associated morbidity. Esophageal endoscopic ultrasound (EUS) and EUS with the EBUS scope (EUS-B) provide real-time sampling of centrally located lung tumors and mediastinal lymph nodes. They also provide an excellent view of the left atrium, since it is located adjacent to the esophagus. To date, left atrium tumor diagnostics by endosonography is poorly explored. We describe 2 exceptional diagnostic cases of left atrium tumors in which cardiac surgery was hazardous due to the clinical condition or previous surgical interventions. During EUS-B-guided fine-needle aspiration (FNA), the left atrial masses were successfully and safely sampled, revealing a Burkitt lymphoma and a synovial sarcoma. FNA including cell block analysis enabled specific tumor diagnosis and molecular subtyping. Our findings suggest that in selected cases, linear endosonography qualifies as a minimally invasive technique for intracardiac tumor diagnostics.

Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine

Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.