Detecting the Tumor Prognostic Factors From the YTH Domain Family Through Integrative Pan-Cancer Analysis.

Objectives: Emerging evidence suggests that N6-methyladenosine (mA) methylation plays a critical role in cancers through various mechanisms. This work aims to reveal the essential role of mA methylation "readers" in regulation of cancer prognosis at the pan-cancer level.

Methods: Herein, we focused on one special protein family of the "readers" of mA methylation, YT521-B homology (YTH) domain family genes, which were observed to be frequently dysregulated in tumor tissues and closely associated with cancer prognosis.

"Nutrient-fungi-host" tripartite interaction in cancer progression.

The human microbiome exhibits a profound connection with the cancer development, progression, and therapeutic response, with particular emphasis on its components of the mycobiome, which are still in the early stages of research. In this review, we comprehensively summarize cancer-related symbiotic and pathogenic fungal genera. The intricate mechanisms through which fungi impact cancer as an integral member of both gut and tissue-resident microbiomes are further discussed.

Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID-19.

The COVID-19 pandemic was characterized by multiple subsequent, overlapping outbreaks, as well as extremely rapid changes in viral genomes. The information about local epidemics spread and the epidemic control measures was shared on a daily basis (number of cases and deaths) via centralized repositories. The vaccines were developed within the first year of the pandemic.

Neo-intline: integrated pipeline enables neoantigen design through the in-silico presentation of T-cell epitope.

Neoantigen vaccines are one of the most effective immunotherapies for personalized tumour treatment. The current immunogen design of neoantigen vaccines is usually based on whole-genome sequencing (WGS) and bioinformatics prediction that focuses on the prediction of binding affinity between peptide and MHC molecules, ignoring other peptide-presenting related steps. This may result in a gap between high prediction accuracy and relatively low clinical effectiveness.

Revealing the characteristics of ZIKV infection through tissue-specific transcriptome sequencing analysis.

Background: Recently, Zika virus (ZIKV) re-emerged in India and was potentially associated with microcephaly. However, the molecular mechanisms underlying ZIKV pathogenesis remain to be explored.

Results: Herein, we performed a comprehensive RNA-sequencing analysis on ZIKV-infected JEG-3, U-251 MG, and HK-2 cells versus corresponding uninfected controls.

Probing Synergistic Targets by Natural Compounds for Hepatocellular Carcinoma.

Background: Designing combination drugs for malignant cancers has been restricted due to the scarcity of synergy-medicated targets, while some natural compounds have demonstrated potential to enhance anticancer effects.

Methods: We here explored the feasibility of probing synergy-mediated targets by Berberine (BER) and Evodiamine (EVO) in hepatocellular carcinoma (HCC). Using the genomics-derived HCC signaling networks of compound treatment, NF-κB and c-JUN were inferred as key responding elements with transcriptional activity coinhibited during the synergistic cytotoxicity induction in BEL-7402 cells.

H-RACS: a handy tool to rank anti-cancer synergistic drugs.

Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug synergy. Yet they normally require the drug-cell treatment results as an essential input, thus exclude the possibility to pre-screen those unexplored drugs without cell treatment profiling.

Detecting Prognosis Risk Biomarkers for Colon Cancer Through Multi-Omics-Based Prognostic Analysis and Target Regulation Simulation Modeling.

Background: Colon cancer is one of the most common health threats for humans since its high morbidity and mortality. Detecting potential prognosis risk biomarkers (PRBs) is essential for the improvement of therapeutic strategies and drug development. Currently, although an integrated prognostic analysis of multi-omics for colon cancer is insufficient, it has been reported to be valuable for improving PRBs' detection in other cancer types.

Reveal the Regulation Patterns of Prognosis-Related miRNAs and lncRNAs Across Solid Tumors in the Cancer Genome Atlas.

Background: The dysregulation of non-coding RNAs (ncRNAs) such as miRNAs and lncRNAs are associated with the pathogenesis and progression in multiple cancers including solid tumors. Comprehensive investigations of prognosis-related ncRNA markers could promote the development of therapeutic strategies for solid tumors, but rarely reported.

Methods: By taking advantage of The Cancer Genome Atlas (TCGA), pan-cancer prognosis analysis (PCPA) models were firstly constructed based on miRNA and lncRNA expression profiles of 8,450 samples in 19 solid tumors.

DSab-origin: a novel IGHD sensitive VDJ mapping method and its application on antibody response after influenza vaccination.

Background: Functional antibody genes are often assembled by VDJ recombination and then diversified by somatic hypermutation. Identifying the combination of sourcing germline genes is critical to understand the process of antibody maturation, which may facilitate the diagnostics and rapid generation of human monoclonal antibodies in therapeutics. Despite of successful efforts in V and J fragment assignment, method in D segment tracing remains weak for immunoglobulin heavy diversity (IGHD).

Searching Synergistic Dose Combinations for Anticancer Drugs.

Recent development has enabled synergistic drugs in treating a wide range of cancers. Being highly context-dependent, however, identification of successful ones often requires screening of combinational dose on different testing platforms in order to gain the best anticancer effects. To facilitate the development of effective computational models, we reviewed the latest strategy in searching optimal dose combination from three perspectives: (1) mainly experimental-based approach; (2) Computational-guided experimental approach; and (3) mainly computational-based approach.

Developing an Agent-Based Drug Model to Investigate the Synergistic Effects of Drug Combinations.

The growth and survival of cancer cells are greatly related to their surrounding microenvironment. To understand the regulation under the impact of anti-cancer drugs and their synergistic effects, we have developed a multiscale agent-based model that can investigate the synergistic effects of drug combinations with three innovations. First, it explores the synergistic effects of drug combinations in a huge dose combinational space at the cell line level.

Advances in computational approaches in identifying synergistic drug combinations.

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model.

Systematic large-scale study of the inheritance mode of Mendelian disorders provides new insight into human diseasome.

One important piece of information about the human Mendelian disorders is the mode of inheritance. Recent studies of human genetic diseases on a large scale have provided many novel insights into the underlying molecular mechanisms. However, most successful analyses ignored the mode of inheritance of diseases, which severely limits our understanding of human disease mechanisms relating to the mode of inheritance at the large scale.