Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data.

MOTIVATION: Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability. RESULTS: To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%-14.7% in F1 score for cancer survival prediction, 5.1%-12% for cancer stage prediction, and 8.1%-13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer's potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g. scavenger receptor pathway) and their crosstalk in cancer patients' blood, providing potential candidate targets for cancer microenvironment study. AVAILABILITY AND IMPLEMENTATION: Pathformer is implemented and freely available at https://github.com/lulab/Pathformer.

cfOmics: a cell-free multi-Omics database for diseases.

Liquid biopsy has emerged as a promising non-invasive approach for detecting, monitoring diseases, and predicting their recurrence. However, the effective utilization of liquid biopsy data to identify reliable biomarkers for various cancers and other diseases requires further exploration. Here, we present cfOmics, a web-accessible database (https://cfomics.ncRNAlab.org/) that integrates comprehensive multi-omics liquid biopsy data, including cfDNA, cfRNA based on next-generation sequencing, and proteome, metabolome based on mass-spectrometry data. As the first multi-omics database in the field, cfOmics encompasses a total of 17 distinct data types and 13 specimen variations across 69 disease conditions, with a collection of 11345 samples. Moreover, cfOmics includes reported potential biomarkers for reference. To facilitate effective analysis and visualization of multi-omics data, cfOmics offers powerful functionalities to its users. These functionalities include browsing, profile visualization, the Integrative Genomic Viewer, and correlation analysis, all centered around genes, microbes, or end-motifs. The primary objective of cfOmics is to assist researchers in the field of liquid biopsy by providing comprehensive multi-omics data. This enables them to explore cell-free data and extract profound insights that can significantly impact disease diagnosis, treatment monitoring, and management.

Dynamics of rhizosphere microbial structure and function associated with the biennial bearing of moso bamboo.

Moso bamboo (Phyllostachys edulis) is a valuable nontimber forestry product with a biennial cycle, producing abundant bamboo shoots within one year (on-year) and few shoots within the following year (off-year). Moso bamboo plants undergo clonal reproduction, resulting in similar genetic backgrounds. However, the number of moso bamboo shoots produced each year varies. Despite this variation, the impact of soil nutrients and the root microbiome on the biennial bearing of moso bamboo is poorly understood. We collected 139 soil samples and determined 14 major physicochemical properties of the rhizosphere, rhizoplane, and bulk soil in different seasons (i.e., the growing and deciduous seasons) and different years (i.e., on- and off-years). Based on 16S rRNA and metagenomic sequencing, major variations were found in the rhizospheric microbial composition during different seasons and years in the moso bamboo forest. Environmental driver analysis revealed that essential nutrients (i.e., SOC, TOC, TN, P, and NH4+) were the main drivers of the soil microbial community composition and were correlated with the on- and off-year cycles. Moreover, 19 MAGs were identified as important biomarkers that could distinguish on- and off-years. We found that both season and year influenced both the microbial community structure and functional pathways through the biosynthesis of nutrients that potentially interact with the moso bamboo growth rhythm, especially the on-year root-associated microbiome, which had a greater abundance of specific nutrients such as gibberellins and vitamin B6. This work provides a dynamic perspective of the differential responses of various on- and off-year microbial communities and enhances our understanding of bamboo soil microbiome biodiversity and stability.

Cell-free multi-omics analysis reveals potential biomarkers in gastrointestinal cancer patients' blood.

During cancer progression, tumorigenic and immune signals are spread through circulating molecules, such as cell-free DNA (cfDNA) and cell-free RNA (cfRNA) in the blood. So far, they have not been comprehensively investigated in gastrointestinal cancers. Here, we profile 4 categories of cell-free omics data from patients with colorectal cancer and patients with stomach adenocarcinoma and then assay 15 types of genomic, epigenomic, and transcriptomic variations. We find that multi-omics data are more appropriate for detection of cancer genes compared with single-omics data. In particular, cfRNAs are more sensitive and informative than cfDNAs in terms of detection rate, enriched functional pathways, etc. Moreover, we identify several peripheral immune signatures that are suppressed in patients with cancer. Specifically, we establish a γδ-T cell score and a cancer-associated-fibroblast (CAF) score, providing insights into clinical statuses like cancer stage and survival. Overall, we reveal a cell-free multi-molecular landscape that is useful for blood monitoring in personalized cancer treatment.

Identification of potential biomarkers and candidate therapeutic drugs for clear cell renal cell carcinoma by bioinformatic analysis and reverse network pharmacology.

This study aims to analyze the potential biomarkers using bioinformatics technology, explore the pathogenesis, and investigate potential Chinese herbal ingredients for the Clear cell renal cell carcinoma (ccRCC), which could provide theoretical basis for early diagnosis and effective treatment of ccRCC. The gene expression datasets GSE6344 and GSE53757 were obtained from the Gene Expression Omnibus database to screen differentially expressed genes (DEGs) involved in ccRCC carcinogenesis and disease progression. Enrichment analyses, protein-protein interaction networks construction, survival analysis and herbal medicines screening were performed with related software and online analysis platforms. Moreover, network pharmacology analysis has also been performed to screen potential target drugs of ccRCC and molecular docking analysis has been used to validate their effects. Total 274 common DEGs were extracted through above process, including 194 up-regulated genes and 80 down-regulated genes. The enrichment analysis revealed that DEGs were significantly focused on multiple amino acid metabolism and HIF signaling pathway. Ten hub genes, including FLT1, BDNF, LCP2, AGXT2, PLG, SLC13A3, SLC47A2, SLC22A8, SLC22A7, and SLC13A3, were screened. Survival analysis showed that FLT1, BDNF, AGXT2, PLG, SLC47A2, SLC22A8, and SLC12A3 were closely correlated with the overall survival of ccRCC, and AGXT2, SLC47A2, SLC22A8, and SLC22A7 were closely associated with DFS. The potential therapeutic herbs that have been screened were Danshen, Baiguo, Yinxing, Huangqin and Chuanshanlong. The active compounds which may be effective in ccRCC treatment were kaempferol, Scillaren A and (-)-epigallocatechin-3-gallate.

An Efficient Prephenate Dehydrogenase Gene for the Biosynthesis of L-tyrosine: Gene Mining, Sequence Analysis, and Expression Optimization.

L-tyrosine is a key precursor for synthesis of various functional substances, but the microbial production of L-tyrosine faces huge challenges. The development of new microbial chassis cell and gene resource is especially important for the biosynthesis of L-tyrosine. In this study, the optimal host strain Bacillus amyloliquefaciens HZ-12 was firstly selected by detecting the production capacity of L-tyrosine. Subsequently, the recombinant expression of 15 prephenate dehydrogenase genes led to the discovery of the best gene, Bao-tyrA from B. amyloliquefaciens HZ-12. After the overexpression of Bao-tyrA, the L-tyrosine yield of the recombinant strain HZ/P43-Bao-tyrA reach 411 mg/L, increased by 42% compared with the control strain (HZ/pHY300PLK). Moreover, the nucleic acid sequence and deduced amino acid sequence of the gene Bao-tyrA were analyzed, and their conservative sites and catalytic mechanisms were proposed. Finally, the expression of Bao-tyrA was regulated through a promoter and 5'-UTR sequence to obtain the optimal expression elements. Thereby, the maximum L-tyrosine yield of 475 mg/L was obtained from HZ/P43-UTR3-Bao-tyrA. B. amyloliquefaciens was applied for the first time to produce L-tyrosine, and the optimal prephenate dehydrogenase gene Bao-tyrA and corresponding expression elements were obtained. This study provides new microbial host and gene resource for the construction of efficient L-tyrosine chassis cells, and also lays a solid foundation for the production of various functional tyrosine derivatives.

A comprehensive evaluation of full-spectrum cell-free RNAs highlights cell-free RNA fragments for early-stage hepatocellular carcinoma detection.

BACKGROUND: Various studies have reported cell-free RNAs (cfRNAs) as noninvasive biomarkers for detecting hepatocellular carcinoma (HCC). However, they have not been independently validated, and some results are contradictory. We provided a comprehensive evaluation of various types of cfRNA biomarkers and a full mining of the biomarker potential of new features of cfRNA. METHODS: We first systematically reviewed reported cfRNA biomarkers and calculated dysregulated post-transcriptional events and cfRNA fragments. In 3 independent multicentre cohorts, we further selected 6 cfRNAs using RT-qPCR, built a panel called HCCMDP with AFP using machine learning, and internally and externally validated HCCMDP's performance. FINDINGS: We identified 23 cfRNA biomarker candidates from a systematic review and analysis of 5 cfRNA-seq datasets. Notably, we defined the cfRNA domain to describe cfRNA fragments systematically. In the verification cohort (n = 183), cfRNA fragments were more likely to be verified, while circRNA and chimeric RNA candidates were neither abundant nor stable as qPCR-based biomarkers. In the algorithm development cohort (n = 287), we build and test the panel HCCMDP with 6 cfRNA markers and AFP. In the independent validation cohort (n = 171), HCCMDP can distinguish HCC patients from control groups (all: AUC = 0.925; CHB: AUC = 0.909; LC: AUC = 0.916), and performs well in distinguishing early-stage HCC patients (all: AUC = 0.936; CHB: AUC = 0.917; LC: AUC = 0.928). INTERPRETATION: This study comprehensively evaluated full-spectrum cfRNA biomarker types for HCC detection, highlighted the cfRNA fragment as a promising biomarker type in HCC detection, and provided a panel HCCMDP. FUNDING: National Natural Science Foundation of China, and The National Key Basic Research Program (973 program).

SMAR1 inhibits proliferation, EMT and Warburg effect of bladder cancer cells by suppressing the activity of the Wnt/ß-catenin signaling pathway.

This study aimed to investigate the effects of scaffold matrix attachment region binding protein 1 (SMAR1) on the development of bladder cancer (BCa). SMAR1 expression in paired tumor and corresponding adjacent normal tissues from 55 BCa patients was detected by quantitative reverse transcription-polymerase chain reaction. BCa cells were transfected to regulate SMAR1 expression. BCa cells were treated with XAV-939, LiCl and 2-deoxyglucose. The effect of SMAR1 on the viability, proliferation, migration, invasion and Warburg effect of BCa cells was researched by counting kit-8, colony formation assay, Transwell and aerobic glycolysis assays. Western blot was performed to detect protein expression. BCa cell growth in vivo was recorded in nude mice. Immunohistochemical staining was performed for clinical and xenografted tumor tissue specimens. SMAR1 expression was down-regulated in BCa patients, associating with worse prognoses. SMAR1 knockdown enhanced the viability, proliferation, migration, invasion, EMT and Warburg effect of BCa cells. The opposite effect was found in the SMAR1 overexpression BCa cells. XAV-939 treatment reversed the elevation of ß-catenin, c-Myc and Cyclin D1 proteins expression and Warburg effect in Bca cells post-SMAR1 knockdown. LiCl treatment abrogated the inhibition of ß-catenin, c-Myc and Cyclin D1 proteins expression and Warburg effect proteins due to SMAR1 overexpression in BCa cells. SMAR1 overexpression inhibited the growth of BCa cells in vivo. SMAR1 might suppress the Wnt/ß-catenin signaling pathway activity to inhibit the progression of BCa. It might be an effective treatment target for BCa.

Global research trends on precision oncology: A systematic review, bibliometrics, and visualized study.

BACKGROUND: Advances in next-generation sequencing technologies are changing the ways cancer diagnosis and treatment, which leads to a new branch of precision medicine: "Precision Oncology". This study aims to deliver a structured overview to carry out a bibliometric analysis of precision oncology research over the past 10 years retrospectively. METHODS: Bibliometric methods including clustering analysis and co-occurrence visualized study were conducted based on publications of academic databases Web of Science Main Collection from 1st January 2012, to 31st December 2021. This study analyzed the information about related research outputs, countries, institutions, authors, cited papers, and hot topics. RESULTS: 7163 papers related to precision oncology were identified. Since 2014, the number of articles has proliferated, and oncology precision has attracted significant attention from scholars worldwide in recent years. The USA leads the research in this field, and the League of European Research Universities is the primary research institution. Research institutions from Asia paid more attention to this field through high-level international cooperation. Besides, there are still many issues expected to be explored and evaluated correctly. Such as the considerable uncertainty that pharmacogenomic methods have no significant influence on patient outcomes. CONCLUSIONS: Precision oncology serves as an essential method in clinical treatment, and is closely related to biological study, including biochemistry, molecular and genetics, advanced technology, and pharmacology discovery. The future research prospect would be the broad involvement of social participation and global cooperation in oncology precision research to acquire better results via the balance of technology and public health policy.

POSTAR3: an updated platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins.

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation. Accurate identification of RBP binding sites in multiple cell lines and tissue types from diverse species is a fundamental endeavor towards understanding the regulatory mechanisms of RBPs under both physiological and pathological conditions. Our POSTAR annotation processes make use of publicly available large-scale CLIP-seq datasets and external functional genomic annotations to generate a comprehensive map of RBP binding sites and their association with other regulatory events as well as functional variants. Here, we present POSTAR3, an updated database with improvements in data collection, annotation infrastructure, and analysis that support the annotation of post-transcriptional regulation in multiple species including: we made a comprehensive update on the CLIP-seq and Ribo-seq datasets which cover more biological conditions, technologies, and species; we added RNA secondary structure profiling for RBP binding sites; we provided miRNA-mediated degradation events validated by degradome-seq; we included RBP binding sites at circRNA junction regions; we expanded the annotation of RBP binding sites, particularly using updated genomic variants and mutations associated with diseases. POSTAR3 is freely available at http://postar.ncrnalab.org.

Integrative analysis of long extracellular RNAs reveals a detection panel of noncoding RNAs for liver cancer.

Rationale: Long extracellular RNAs (exRNAs) in plasma can be profiled by new sequencing technologies, even with low abundance. However, cancer-related exRNAs and their variations remain understudied. Methods: We investigated different variations (i.e. differential expression, alternative splicing, alternative polyadenylation, and differential editing) in diverse long exRNA species (e.g. long noncoding RNAs and circular RNAs) using 79 plasma exosomal RNA-seq (exoRNA-seq) datasets of multiple cancer types. We then integrated 53 exoRNA-seq datasets and 65 self-profiled cell-free RNA-seq (cfRNA-seq) datasets to identify recurrent variations in liver cancer patients. We further combined TCGA tissue RNA-seq datasets and validated biomarker candidates by RT-qPCR in an individual cohort of more than 100 plasma samples. Finally, we used machine learning models to identify a signature of 3 noncoding RNAs for the detection of liver cancer. Results: We found that different types of RNA variations identified from exoRNA-seq data were enriched in pathways related to tumorigenesis and metastasis, immune, and metabolism, suggesting that cancer signals can be detected from long exRNAs. Subsequently, we identified more than 100 recurrent variations in plasma from liver cancer patients by integrating exoRNA-seq and cfRNA-seq datasets. From these datasets, 5 significantly up-regulated long exRNAs were confirmed by TCGA data and validated by RT-qPCR in an independent cohort. When using machine learning models to combine two of these validated circular and structured RNAs (SNORD3B-1, circ-0080695) with a miRNA (miR-122) as a panel to classify liver cancer patients from healthy donors, the average AUROC of the cross-validation was 89.4%. The selected 3-RNA panel successfully detected 79.2% AFP-negative samples and 77.1% early-stage liver cancer samples in the testing and validation sets. Conclusions: Our study revealed that different types of RNA variations related to cancer can be detected in plasma and identified a 3-RNA detection panel for liver cancer, especially for AFP-negative and early-stage patients.

Long non-coding RNA SNHG3 promotes the progression of clear cell renal cell carcinoma via regulating BIRC5 expression.

BACKGROUND: Research has shown that the progression of clear cell renal cell carcinoma (ccRCC) is modulated by long non-coding RNAs (lncRNAs). However, the roles of specific lncRNAs in the malignancy of ccRCC are still unknown. METHODS: TCGA and GSE66272 datasets were used to predict differentially expressed genes (DEGs) in ccRCC. ENCORI database was employed to display BIRC5 miRNA network and potential lncRNA interactions for miRNAs. KM plotter and correlation analyses were performed to identify the overall survival (OS)- and BIRC5-related miRNAs. Quantitative real-time PCR (qRT-PCR) was used to verify the BIRC5 mRNA in the seventy paired clinical samples of ccRCC tissues. The ccRCC A498 and 786-O were individually transfected with lncRNA SNHG3 and LINC00997 and then western blotting was used to detect the BIRC5 protein expression. The Dual-luciferase reporter assay was used to examine the regulatory interaction between lncRNA SNHG3 and microRNA (miRNA/miR)-10b-5p. RESULTS: BICR5 is associated with the progression of ccRCC. The two novel lncRNAs (LINC00997, SNHG3) were up-regulated in ccRCC tissues and positively with the BICR5 protein expression. However, Suppressing SNHG3 expression reduced BIRC5 protein expression compared with the LINC00997, most importantly, Suppressing SNHG3 expression suppressed tumor progression in vitro. In addition, SNHG3 promotes the expression of BIRC5 protein by sponging microRNA-10b-5p. CONCLUSIONS: Our findings suggest that SNHG3 plays a vital role in promoting ccRCC via the microRNA-10b-5p/BIRC5 axis and may serve as a novel therapeutic target for the treatment of patients with ccRCC.

A tree shrew model for steroid-associated osteonecrosis.

Osteonecrosis is a common human disease in orthopedics. It is difficult to treat, and half of patients may need artificial joint replacement, resulting in a considerable economic burden and a reduction in quality of life. Hormones are one of the major causes of osteonecrosis and high doses of corticosteroids are considered the most dangerous factor. Because of the complexity of treatment, we still need a better animal model that can be widely used in drug development and testing. Tree shrews are more closely related to primates than rodents. As such, we constructed a successful tree shrew model to establish and evaluate steroid-associated osteonecrosis (SAON). We found that low-dose lipopolysaccharide (LPS) combined with high-dose methylprednisolone (MPS) over 12 weeks could be used to establish a tree shrew model with femoral head necrosis. Serum biochemical and histological analyses showed that an ideal model was obtained. Thus, this work provides a useful animal model for the study of SAON and for the optimization of treatment methods.

Establishment of an osteoporosis model in tree shrews by bilateral ovariectomy and comprehensive evaluation.

Osteoporosis (OP) treatment has always been challenging for elderly menopausal females. An animal model with a closer genetic association to human OP is essential for treatment research. Given its close genetic association to primates, the tree shrew is a suitable candidate for meeting the requirements for such an animal model. In the present study, a tree shrew OP model induced by ovariectomy (OVX), was established. Evaluation by multiple analysis methods, including blood biochemical indicators, uterus coefficients, micro-computed tomography analysis, histochemical analysis and scanning electron microscopic observation indicated that OVX was an appropriate method to establish the OP model in tree shrews. In addition, the biomolecular characteristics of OVX-induced osteoporosis were also assessed by transcriptome sequencing and bioinformatics analysis. The present study provides the methods used to confirm the successful establishment of the OP model in tree shrew, and suggests that the OP model is appropriate for human OP research.

Effect of Osteoking on the osteogenic and adipogenic differentiation potential of rat bone marrow mesenchymal stem cells in vitro.

BACKGROUND: Bone damage is a condition that affects the quality of life of patients. Mesenchymal stem cells (MSCs) are important for bone repair. Osteoking is a natural compound in traditional Chinese Medicine used to treat bone diseases; however, the effect of Osteoking on the differentiation of MSCs has not been reported. In this study, we aimed to investigate the effect of Osteoking on the osteogenic and adipogenic differentiation potential of rat bone marrow mesenchymal stem cells (rbMSCs). METHODS: The effects of Osteoking on the proliferation and differentiation of rbMSCs were investigated. Different concentrations of Osteoking were prepared, and its cytotoxicity was evaluated by CCK-8 assay. The expression of osteogenic and adipogenic genes were determined, and several staining methods were used to reveal the osteogenic and adipogenic differentiation potential of rbMSCs. RESULTS: Our results show that appropriate concentrations of Osteoking can enhance osteogenic differentiation of rbMSCs and reduce adipogenic differentiation without any effect on proliferation. This may be related to the changes in related gene expression. CONCLUSION: Osteoking enhances osteogenic differentiation and inhibits adipogenic differentiation of rbMSCs. Therefore, Osteoking may have a therapeutic potential for treating bone disease caused by changes in differentiation function of MSCs.

Controllable synthesis of submillimeter single-crystal monolayer graphene domains on copper foils by suppressing nucleation.

Submillimeter single-crystal monolayer and multilayer graphene domains were prepared by an atmospheric pressure chemical vapor deposition method with suppressing nucleation on copper foils through an annealing procedure. A facile oxidation visualization method was applied to study the nucleation density and morphology of graphene domains on copper foils. Scanning electron microscopy, transmission electron microscopy, atomic force microscopy, polarized optical microscopy, and Raman spectra showed that the submillimeter graphene domains were monolayer single crystals.

[Research on information extraction of electronic medical records in Chinese].

This is a research to enhance the application of natural language understanding and ontology in the Chinese medical text semantic annotation and content analysis, and so to provide technology support for the computer-readable electronic medical records (EMR). The Chinese EMR information extraction and statistical analysis of related subjects in accordance to the user's demands were performed through building the named entity rules, the classified word list and field ontology by using GATE platform on the basis of EMR text set's construction and pre-processing. The automatic and artificial semantic annotation of EMR text set was implemented. The situation of drugs used in medicinal treatment and the distribution of patients' age and sex were obtained. The ontology-based semantic information extraction can improve the function of computer for text understanding, and the discovery of knowledge in EMR through field ontology is feasible.