The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.

BACKGROUND: Type 2B von Willebrand disease (VWD) is a less common subtype and is difficult to diagnose. This case report and literature review highlights a rare neonatal onset of type 2B VWD initially misdiagnosed as neonatal alloimmune thrombocytopenia (NAIT).
METHODS: The neonate presented with severe thrombocytopenia and was unresponsive to NAIT treatments. Genetic testing was conducted because of the unclear family history of thrombocytopenia.
RESULTS: Next-generation sequencing revealed a p.Arg1306Trp von Willebrand factor variant, confirming type 2B VWD.
CONCLUSIONS: This study underscores the critical role of genetic testing in diagnosing challenging cases of neonatal thrombocytopenia, irrespective of family history, and aims to elucidate the clinical manifestations and course of neonatal onset type 2B VWD.

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

BACKGROUND: Angiostrongyliasis cantonensis is a severe yet rare parasitic infection caused by the larvae of Angiostrongylus cantonensis. The primary characteristic feature of this foodborne illness in humans is eosinophilic meningitis. Recently, there has been a gradual increase in reported cases globally. Due to the lack of typical clinical symptoms, signs, and specific laboratory tests, early diagnosis of this disease poses significant challenges. Failure to diagnose and treat this condition promptly can result in fatalities.
METHODS: We present the case of a 13-year-old male patient who initially presented with fever and headache. The patient was preliminarily diagnosed with bacterial meningitis and received treatment with antibacterial drugs. However, the patient\'s condition worsened, and he developed progressive consciousness disturbances. Eventually, metagenomic next-generation sequencing (mNGS) testing of cerebrospinal fluid samples indicated Angiostrongylus cantonensis infection. Following treatment with albendazole and prednisone, the patient made a full recovery. We include this case report as part of a literature review to emphasize the potential applications of mNGS in the early diagnosis of Angiostrongyliasis cantonensis.
CONCLUSIONS: mNGS technology plays a crucial role in the diagnosis of angiostrongyliasis cantonensis. As this technology continues to evolve and be applied, we believe it will play an increasingly important role in diagnosing, treating, and monitoring angiostrongyliasis cantonensis.

Studying individual ecological niches within the oral cavity is a logical first step to understanding the distribution of antimicrobial resistance genes (ARGs); however, it is not representative of the whole oral resistome. The aim of our systematic review was to provide a map of the oral resistome by reviewing the composition of individual niches. A total of 580 papers were retrieved from a search of all English language publications investigating the presence of oral ARGs in five electronic databases between January 2015 and August 2023. Fifteen studies [10 PCR and 5 next-generation sequencing (NGS)] were included in this review. The heterogeneity of methods precluded meta-analysis. ARGs are present throughout the oral cavity with 158 unique ARGs identified across 6 locations - supra and sub-gingival biofilm, mucosa, oropharynx, root canal system (RCS) and saliva. The supragingival biofilm had the highest resistome richness, while the RCS had the least. Tetracycline was the dominant antimicrobial resistance (AMR) class found. Three core genes were identified - tet(M), tet(O) and ermB.This review highlights the necessity of NGS studies to comprehensively characterize the oral resistome in its entirety. This is the logical foundation for future \'omics studies to truly understand the scope of the resistome and its contribution to AMR.

Zoonotic viruses are widely seen as the primary threat for future pandemics. Bats are the most diverse group of mammals, with more than 1400 species distributed across most habitats on Earth. So far, 31 known virus families were associated with bats, although the understanding of most viruses were insufficient. Continuous efforts to discover, understand and monitor these bats viruses, is thereby an area of public health interest. This systematic review was designed to catalogue publications reporting novel bat virus discoveries within PubMed, SCOPUS, and Web of Science databases, within a 5-year period from 2018 to 2022. Various experimental parameters, including sampling locations, methodology, bat species diversity, similarity to known viruses, species demarcation of new viruses, and genomic sequencing strategies, were extracted from 41 publications and analyzed. In total, 72 novel viruses from 19 virus families were identified between 2018 and 2022, particularly from Genomoviridae (DNA viruses) and Coronaviridae (RNA viruses). That said, only a limited number of bat families featured extensively despite noticeable shift towards next generation sequencing methods and metagenomics pipeline for virus identification across different sampling methods. This review aims to provide a comprehensive analysis of the global efforts made over the past five years to identify and characterize emerging viruses in bat species, and to provide a detailed overview of the current technologies and methodologies used in these studies.

Formalin-fixed paraffin-embedded (FFPE) tissue represents a valuable source for translational cancer research. However, the widespread application of various downstream methods remains challenging. Here, we aimed to assess the feasibility of a genomic and gene expression analysis workflow using FFPE breast cancer (BC) tissue. We conducted a systematic literature review for the assessment of concordance between FFPE and fresh-frozen matched tissue samples derived from patients with BC for DNA and RNA downstream applications. The analytical performance of three different nucleic acid extraction kits on FFPE BC clinical samples was compared. We also applied a newly developed targeted DNA Next-Generation Sequencing (NGS) 370-gene panel and the nCounter BC360® platform on simultaneously extracted DNA and RNA, respectively, using FFPE tissue from a phase II clinical trial. Of the 3701 initial search results, 40 articles were included in the systematic review. High degree of concordance was observed in various downstream application platforms. Moreover, the performance of simultaneous DNA/RNA extraction kit was demonstrated with targeted DNA NGS and gene expression profiling. Exclusion of variants below 5% variant allele frequency was essential to overcome FFPE-induced artefacts. Targeted genomic analyses were feasible in simultaneously extracted DNA/RNA from FFPE material, providing insights for their implementation in clinical trials/cohorts.

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), and its pathogenesis has not been clarified. Current research suggests that DKD involves multiple cell types and extra-renal factors, and it is particularly important to clarify the pathogenesis and identify new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) technology is high-throughput sequencing of the transcriptomes of individual cells at the single-cell level, which is an effective technology for exploring the development of diseases by comparing genetic information, reflecting the differences in genetic information between cells, and identifying different cell subpopulations. Accumulating evidence supports the role of scRNA-seq in revealing the pathogenesis of diabetes and strengthening our understanding of the molecular mechanisms of DKD. We reviewed the scRNA-seq data this time. Then, we analyzed and discussed the applications of scRNA-seq technology in DKD research, including annotation of cell types, identification of novel cell types (or subtypes), identification of intercellular communication, analysis of cell differentiation trajectories, gene expression detection, and analysis of gene regulatory networks, and lastly, we explored the future perspectives of scRNA-seq technology in DKD research.

OBJECTIVE: Circulating tumor DNA (ctDNA) testing has become a promising tool to guide first-line (1L) targeted treatment for advanced non-small cell lung cancer (aNSCLC). This study aims to estimate the clinical validity (CV) and clinical utility (CU) of ctDNA-based next-generation sequencing (NGS) for oncogenic driver mutations to inform 1L treatment decisions in aNSCLC through a systematic literature review and meta-analysis.
METHODS: A systematic literature search was conducted in PubMed/MEDLINE and Embase to identify randomized control trials or observational studies reporting CV/CU on ctDNA testing in patients with aNSCLC. Meta-analyses were performed using bivariate random-effects models to estimate pooled sensitivity and specificity. Progression-free/overall survival (PFS/OS) was summarized for CU studies.
RESULTS: A total of 20 studies were identified: 17 CV only, 2 CU only, and 1 both, and 13 studies were included for the meta-analysis on multi-gene detection. The overall sensitivity and specificity for ctDNA detection of any mutation were 0.69 (95% CI 0.63-0.74) and 0.99 (95% CI 0.97-1.00), respectively. However, sensitivity varied greatly by driver gene, ranging from 0.29 (95% CI 0.13-0.53) for ROS1 to 0.77 (95% CI 0.63-0.86) for KRAS. Two studies that compared PFS with ctDNA versus tissue-based testing followed by 1L targeted therapy found no significant differences. One study reported OS curves on ctDNA-matched and tissue-matched therapies but no hazard ratios were provided.
CONCLUSIONS: ctDNA testing demonstrated an overall acceptable diagnostic accuracy in patients with aNSCLC, however, sensitivity varied greatly by driver mutation. Further research is needed, especially for uncommon driver mutations, to better understand the CU of ctDNA testing in guiding targeted treatments for aNSCLC.

Gastric cancer (GC) is a complex and heterogeneous disease with significant phenotypic and genetic variation. Traditional classification systems rely mainly on the evaluation of clinical pathological features and conventional biomarkers and might not capture the diverse clinical processes of individual GCs. The latest discoveries in omics technologies such as next‑generation sequencing, proteomics and metabolomics have provided crucial insights into potential genetic alterations and biological events in GC. Clustering strategies for identifying subtypes of GC might offer new tools for improving GC treatment and clinical trial outcomes by enabling the development of therapies tailored to specific subtypes. However, the feasibility and therapeutic significance of implementing molecular classifications of GC in clinical practice need to addressed. The present review examines the current molecular classifications, delineates the prevailing landscape of clinically relevant molecular features, analyzes their correlations with traditional GC classifications, and discusses potential clinical applications.