MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer.

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.

Loss of NDUFS1 promotes gastric cancer progression by activating the mitochondrial ROS-HIF1α-FBLN5 signaling pathway.

BACKGROUND: Recent studies suggested that NDUFS1 has an important role in human cancers; however, the effects of NDUFS1 on gastric cancer (GC) are still not fully understood. METHODS: We confirmed that NDUFS1 is downregulated in GC cells through western blot immunohistochemistry and bioinformation analysis. The effect of NDUFS1 on GC was studied by CCK-8, colony formation, transwell assay in vitro and Mouse xenograft assay in vivo. Expression and subcellular localization of NDUFS1 and the content of mitochondrial reactive oxygen species (mROS) was observed by confocal reflectance microscopy. RESULTS: Reduced expression of NDUFS1 was found in GC tissues and cell lines. Also, NDUFS1 overexpression inhibited GC cell proliferation, migration, and invasion in vitro as well as growth and metastasis in vivo. Mechanistically, NDUFS1 reduction led to the activation of the mROS-hypoxia-inducible factor 1α (HIF1α) signaling pathway. We further clarified that NDUFS1 reduction upregulated the expression of fibulin 5 (FBLN5), a transcriptional target of HIF1α, through activation of mROS-HIF1α signaling in GC cells. CONCLUSIONS: The results of this study indicate that NDUFS1 downregulation promotes GC progression by activating an mROS-HIF1α-FBLN5 signaling pathway.

Novel Approach to Enriching Glycosylated RNAs: Specific Capture of GlycoRNAs via Solid-Phase Chemistry.

Ribonuclease (RNA) modifications can alter cellular function and lead to differential immune responses by acting as discriminators between RNAs from different phyla. RNA glycosylation has recently been observed at the cell surface, and its dysregulation in disease may change RNA functions. However, determining which RNA substrates can be glycosylated remains to be explored. Here, we develop a solid-phase chemoenzymatic method (SPCgRNA) for targeting glycosylated RNAs, by which glycosylated RNA substrates can be specifically recognized. We found the differential N-glycosylation of small RNAs in hTERT-HPNE and MIA PaCa-2 cancer cells using SPCgRNA. RNA-Seq showed that the changes in glyco-miRNAs prepared from SPCgRNA were consistent with those of traditional methods. The KEGG signaling pathway analysis revealed that differential miRNA glycosylation can affect tumor cell proliferation and survival. Further studies found that NGI-1 significantly inhibited the proliferation, migration, and circulation of MIA PaCa-2 and promoted cell apoptosis. In addition, ß-1,4-galactosyltransferase 1 (B4GALT1) not only affected the expression level of glycosylated miRNAs hsa-miR-21-5p but also promoted cell apoptosis and inhibited the cell cycle possibly through the p53 signaling pathway, while B4GALT1 and p53 were also affected following the hsa-miR-21-5p increase. These results suggest that B4GALT1 may catalyze miRNAs glycosylation, which further promotes cancer cell progression.

Translational informatics for human microbiota: data resources, models and applications.

With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.

MiR-200/183 family-mediated module biomarker for gastric cancer progression: an AI-assisted bioinformatics method with experimental functional survey.

BACKGROUND: Gastric cancer (GC) is a major cancer burden throughout the world with a high mortality rate. The performance of current predictive and prognostic factors is still limited. Integrated analysis is required for accurate cancer progression predictive biomarker and prognostic biomarkers that help to guide therapy. METHODS: An AI-assisted bioinformatics method that combines transcriptomic data and microRNA regulations were used to identify a key miRNA-mediated network module in GC progression. To reveal the module's function, we performed the gene expression analysis in 20 clinical samples by qRT-PCR, prognosis analysis by multi-variable Cox regression model, progression prediction by support vector machine, and in vitro studies to elaborate the roles in GC cells migration and invasion. RESULTS: A robust microRNA regulated network module was identified to characterize GC progression, which consisted of seven miR-200/183 family members, five mRNAs and two long non-coding RNAs H19 and CLLU1. Their expression patterns and expression correlation patterns were consistent in public dataset and our cohort. Our findings suggest a two-fold biological potential of the module: GC patients with high-risk score exhibited a poor prognosis (p-value < 0.05) and the model achieved AUCs of 0.90 to predict GC progression in our cohort. In vitro cellular analyses shown that the module could influence the invasion and migration of GC cells. CONCLUSIONS: Our strategy which combines AI-assisted bioinformatics method with experimental and clinical validation suggested that the miR-200/183 family-mediated network module as a "pluripotent module", which could be potential marker for GC progression.

MiR-378a-3p as a putative biomarker for hepatocellular carcinoma diagnosis and prognosis: Computational screening with experimental validation.

BACKGROUND: Hepatocellular carcinoma (HCC) is a malignant disease with high morbidity and mortality, and the molecular mechanism for the genesis and progression is complex and heterogeneous. Biomarker discovery is crucial for the personalized and precision treatment of HCC. The accumulation of reported microRNA biomarkers makes it possible to combine computational identification with experimental validation to accelerate the discovery of novel biomarker. RESULTS: In the present work, we applied a rational computer-aided biomarker discovery model to screen for the HCC diagnosis biomarker. Two HCC-associated networks were constructed based on the microRNA and mRNA expression profiles, and the potential microRNA biomarkers were identified based on their unique regulatory and influential power in the network. These putative biomarkers were then experimentally validated. One prominent example among these identified biomarkers is MiR-378a-3p: It was shown to independently regulate several important transcription factors such as PLAGL2 and ß-catenin, affecting the ß-catenin signaling. Such mechanism may indicate a potential tumor suppressor role of MiR-378a-3p and the impact of its abnormal expression on the cell growth and invasion of HCC. CONCLUSIONS: A bioinformatics model with network topological and functional characterization was successfully applied to the identification of HCC biomarkers. The predicted microRNA biomarkers were than validated with experiments using human HCC cell lines, model animal, and clinical specimens. The results confirmed the prediction by our proposed model that miR-378a-3p was a putative biomarker for diagnosis and prognosis of HCC.

ING4 suppresses hepatocellular carcinoma via a NF-κB/miR-155/FOXO3a signaling axis.

The tumor suppressor ING4 has been shown to be reduced in human HCC. The alteration of ING4 contributes to HCC progression. However, its effect in HCC and the potential mechanism is largely unclear. Herein, we found that downregulation of ING4 in HCC tumor tissues was closely associated with cancer staging, tumor size and vascular invasion. Lentivirus-mediated ING4 overexpression significantly inhibited proliferation, migration and invasion, and induced cell cycle G1 phase arrest and apoptosis in MHCC97H human HCC cells. Moreover, overexpression of ING4 dramatically suppressed MHCC97H tumor cell growth and metastasis to lung in vivo in athymic BALB/c nude mice. Mechanistic studies revealed that overexpression of ING4 markedly increased expression of FOXO3a both at the mRNA and protein level as well as enhanced nuclear level and transcriptional activity of FOXO3a in MHCC97H tumor cells. In addition, ING4 repressed transcriptional activity of NF-κB and expression of miR-155 targeting FOXO3a. Knockdown of ING4 exhibited opposing effects in MHCC97L human HCC cells. Interestingly, knockdown of FOXO3a attenuated not only ING4-elicited tumor suppression but also ING4-mediated regulatory effect on FOXO3a downstream targets, confirming that FOXO3a is involved in ING4-directed tumor-inhibitory effect in HCC. Overexpression of miR-155 attenuated ING4-induced upregulation of FOXO3a, whereas inhibition of miR-155 blunted ING4 knockdown-induced reduction of FOXO3a. Furthermore, inhibition of NF-κB markedly impaired ING4 knockdown-induced upregulation of miR-155 and downregulation of FOXO3a. Taken together, our study provided the first compelling evidence that ING4 can suppress human HCC growth and metastasis to a great extent via a NF-κB/miR-155/FOXO3a pathway.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications.

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

Translational Bioinformatics for Cholangiocarcinoma: Opportunities and Challenges.

Translational bioinformatics is becoming a driven force and a new scientific paradigm for cancer research in the era of big data. To promote the cross-disciplinary communication and research, we take cholangiocarcinoma as an example to review the present status and the future perspectives of the bioinformatics models applied in cancer study. We first summarize the present application of computational methods to the study of cholangiocarcinoma ranged from pattern recognition of biological data, knowledge based data annotation to systems biological level modeling and clinical translation. Then the future opportunities and challenges about database or knowledge base building, novel model developing and molecular mechanism exploring as well as the intelligent decision supporting system construction for the precision diagnosis, prognosis and treatment of cholangiocarcinoma are discussed.

Translational biomedical informatics in the cloud: present and future.

Next generation sequencing and other high-throughput experimental techniques of recent decades have driven the exponential growth in publicly available molecular and clinical data. This information explosion has prepared the ground for the development of translational bioinformatics. The scale and dimensionality of data, however, pose obvious challenges in data mining, storage, and integration. In this paper we demonstrated the utility and promise of cloud computing for tackling the big data problems. We also outline our vision that cloud computing could be an enabling tool to facilitate translational bioinformatics research.