Volume 1, Issue 2 - Special issue on tumor and bioinformatics

Objectives Genomic signatures like k -mers have become one of the most prominent approaches to describe genomic data. As a result, myriad real-world applications, such as the construction of de Bruijn graphs in genome assembly, have been benefited by recognizing genomic signatures. In other words, an efficient approach of genomic signature profiling is an essential need for tackling high-throughput sequencing reads. However, most of the existing approaches only recognize fixed-size k -mers while many research studies have shown the importance of considering variable-length k -mers. Methods In this paper, we present a novel genomic signature profiling approach, TahcoRoll, by extending the Aho–Corasick algorithm (AC) for the task of profiling variable-length k -mers. We first group nucleotides into two clusters and represent each cluster with a bit. The rolling hash technique is further utilized to encode signatures and read patterns for efficient matching. Results In extensive experiments, TahcoRoll significantly outperforms the most state-of-the-art k -mer counters and has the capability of processing reads across different sequencing platforms on a budget desktop computer. Conclusions The single-thread version of TahcoRoll is as efficient as the eight-thread version of the state-of-the-art, JellyFish, while the eight-thread TahcoRoll outperforms the eight-thread JellyFish by at least four times.

Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual’s genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.

In the past several years, nanopore sequencing technology from Oxford Nanopore Technologies (ONT) and single-molecule real-time (SMRT) sequencing technology from Pacific BioSciences (PacBio) have become available to researchers and are currently being tested for cancer research. These methods offer many advantages over most widely used high-throughput short-read sequencing approaches and allow the comprehensive analysis of transcriptomes by identifying full-length splice isoforms and several other posttranscriptional events. In addition, these platforms enable structural variation characterization at a previously unparalleled resolution and direct detection of epigenetic marks in native DNA and RNA. Here, we present a comprehensive summary of important applications of these technologies in cancer research, including the identification of complex structure variants, alternatively spliced isoforms, fusion transcript events, and exogenous RNA. Furthermore, we discuss the impact of the newly developed nanopore direct RNA sequencing (RNA-Seq) approach in advancing epitranscriptome research in cancer. Although the unique challenges still present for these new single-molecule long-read methods, they will unravel many aspects of cancer genome complexity in unprecedented ways and present an encouraging outlook for continued application in an increasing number of different cancer research settings.

Traditional Chinese Medicine (TCM), as an effective alternative medicine, utilizes tongue diagnosis as a major method to assess the patient’s health status by examining the tongue’s color, shape, and texture. Tongue images can also give the pre-disease indications without any significant disease symptoms, which provides a basis for preventive medicine and lifestyle adjustment. However, traditional tongue diagnosis has limitations, as the process may be subjective and inconsistent. Hence, computer-aided tongue diagnoses have a great potential to provide more consistent and objective health assessments. This paper reviewed the current trends in TCM tongue diagnosis, including tongue image acquisition hardware, tongue segmentation, feature extraction, color correction, tongue classification, and tongue diagnosis system. We also present a case of TCM constitution classification based on tongue images.

How cells sense and respond to environmental changes is still a key question. It has been identified that cellular metabolism is an important modifier of various epigenetic modifications, such as DNA methylation, histone methylation and acetylation and RNA N 6 -methyladenosine (m6A) methylation. This closely links the environmental nutrient availability to the maintenance of chromatin structure and gene expression, and is crucial to regulate cellular homeostasis, cell growth and differentiation. Cancer metabolic reprogramming and epigenetic alterations are widely observed, and facilitate cancer development and progression. In cancer cells, oncogenic signaling-driven metabolic reprogramming modifies the epigenetic landscape via changes in the key metabolite levels. In this review, we briefly summarized the current evidence that the abundance of key metabolites, such as S -adenosyl methionine (SAM), acetyl-CoA, α -ketoglutarate ( α -KG), 2-hydroxyglutarate (2-HG), uridine diphospho- N -acetylglucosamine (UDP-GlcNAc) and lactate, affected by metabolic reprogramming plays an important role in dynamically regulating epigenetic modifications in cancer. An improved understanding of the roles of metabolic reprogramming in epigenetic regulation can contribute to uncover the underlying mechanisms of metabolic reprogramming in cancer development and identify the potential targets for cancer therapies.

Tumor-associated myeloid cells constitute a series of plastic and heterogeneous cell populations within the tumor microenvironment (TME), and exhibit different phenotypes and functions in response to various microenvironmental signals. In light of promising preclinical data indicating that myeloid-based therapy can effectively suppress tumor growth, a series of novel immune-based therapies and approaches are currently undergoing clinical evaluation. A better understanding of the diversity and functional roles of different myeloid cell subtypes and of how they are associated with TME remodeling may help to improve cancer therapy. Herein, we focus on myeloid cells and discuss how tumor cells can simultaneously reprogram these cells through tumor-derived factors and metabolites. In addition, we discuss the interactions between myeloid cells and other cells in the TME that have the potential to directly or indirectly regulate tumor initiation, invasion, or angiogenesis. We further discuss the current and future potential applications of myeloid cells in the development of focused therapeutic strategies in cancer treatment.

Despite continual progress in the technologies and regimens for cancer therapy, the treatment outcome of fatal metastatic breast cancer is far from satisfactory. Encouragingly, nanotechnology has emerged as a valuable tool to optimize drug delivery process in cancer therapy via preventing the cargos from degradation, improving the tumor-targeting efficiency, enhancing therapeutic agents’ retention in specific sites, and controlling drug release. In the last decade, several mechanisms of suppressing tumor metastasis by functional nano drug delivery systems (NDDSs) have been revealed and a guidance for the rational design of anti-metastasis NDDSs is summarized, which consist of three aspects: optimization of physiochemical properties, tumor microenvironment remodeling, and biomimetic strategies. A series of medicinal functional biomaterials and anti-metastatic breast cancer NDDSs constructed by our team are introduced in this review. It is hoped that better anti-metastasis strategies can be inspired and applied in clinic.