iProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis.

Large-scale omics studies have generated a wealth of mass spectrometry-based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature-related, and correlation analyses, including protein-protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein-protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.

Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives.

Molecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1,987 ovarian cancer patients spanning 26 independent GEO cohorts. A total of four consensus subtypes (C1-C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand-receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtype-specific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust 10-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.

Integrated analysis of Wnt signalling system component gene expression.

Wnt signalling controls patterning and differentiation across many tissues and organs of the developing embryo through temporally and spatially restricted expression of multi-gene families encoding ligands, receptors, pathway modulators and intracellular components. Here, we report an integrated analysis of key genes in the 3D space of the mouse embryo across multiple stages of development. We applied a method for 3D/3D image transformation to map all gene expression patterns to a single reference embryo for each stage, providing both visual analysis and volumetric mapping allowing computational methods to interrogate the combined expression patterns. We identify territories where multiple Wnt and Fzd genes are co-expressed and cross-compare all patterns, including all seven Wnt paralogous gene pairs. The comprehensive analysis revealed regions in the embryo where no Wnt or Fzd gene expression is detected, and where single Wnt genes are uniquely expressed. This work provides insight into a previously unappreciated level of organisation of expression patterns, as well as presenting a resource that can be utilised further by the research community for whole-system analysis.

PROSE: phenotype-specific network signatures from individual proteomic samples.

Proteomic studies characterize the protein composition of complex biological samples. Despite recent advancements in mass spectrometry instrumentation and computational tools, low proteome coverage and interpretability remains a challenge. To address this, we developed Proteome Support Vector Enrichment (PROSE), a fast, scalable and lightweight pipeline for scoring proteins based on orthogonal gene co-expression network matrices. PROSE utilizes simple protein lists as input, generating a standard enrichment score for all proteins, including undetected ones. In our benchmark with 7 other candidate prioritization techniques, PROSE shows high accuracy in missing protein prediction, with scores correlating strongly to corresponding gene expression data. As a further proof-of-concept, we applied PROSE to a reanalysis of the Cancer Cell Line Encyclopedia proteomics dataset, where it captures key phenotypic features, including gene dependency. We lastly demonstrated its applicability on a breast cancer clinical dataset, showing clustering by annotated molecular subtype and identification of putative drivers of triple-negative breast cancer. PROSE is available as a user-friendly Python module from https://github.com/bwbio/PROSE.

Integrated Transcriptomics-Proteomics Analysis Identifies Molecular Phenotypic Alterations Associated with Colorectal Cancer.

Understanding the pathogenesis and finding diagnostic markers for colorectal cancer (CRC) are the key to its diagnosis and treatment. Integrated transcriptomics and proteomics analysis can be used to characterize alterations of molecular phenotypes and reveal the hidden pathogenesis of CRC. This study employed a novel strategy integrating transcriptomics and proteomics to identify pathological molecular pathways and diagnostic biomarkers of CRC. First, differentially expressed proteins and coexpressed genes generated from weighted gene coexpression network analysis (WGCNA) were intersected to obtain key genes of the CRC phenotype. In total, 63 key genes were identified, and pathway enrichment analysis showed that the process of coagulation and peptidase regulator activity could both play important roles in the development of CRC. Second, protein-protein interaction analysis was then conducted on these key genes to find the central genes involved in the metabolic pathways underpinning CRC. Finally, Itih3 and Lrg1 were further screened out as diagnostic biomarkers of CRC by applying statistical analysis on central genes combining transcriptomics and proteomics data. The deep involvement of central genes in tumorigenesis demonstrates the accuracy and reliability of this novel transcriptomics-proteomics integration strategy in biomarker discovery. The identified candidate biomarkers and enriched metabolic pathways provide insights for CRC diagnosis and treatment.

Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations.

BACKGROUND: In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD. METHODS: We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases. RESULTS: We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations. CONCLUSIONS: Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.

Consensus gene modules strategy identifies candidate blood-based biomarkers for primary Sjögren's disease.

Primary Sjögren disease (pSD) is an autoimmune disease characterized by lymphoid infiltration of exocrine glands leading to dryness of the mucosal surfaces and by the production of autoantibodies. The pathophysiology of pSD remains elusive and no treatment with demonstrated efficacy is available yet. To better understand the biology underlying pSD heterogeneity, we aimed at identifying Consensus gene Modules (CMs) that summarize the high-dimensional transcriptomic data of whole blood samples in pSD patients. We performed unsupervised gene classification on four data sets and identified thirteen CMs. We annotated and interpreted each of these CMs as corresponding to cell type abundances or biological functions by using gene set enrichment analyses and transcriptomic profiles of sorted blood cell subsets. Correlation with independently measured cell type abundances by flow cytometry confirmed these annotations. We used these CMs to reconcile previously proposed patient stratifications of pSD. Importantly, we showed that the expression of modules representing lymphocytes and erythrocytes before treatment initiation is associated with response to hydroxychloroquine and leflunomide combination therapy in a clinical trial. These consensus modules will help the identification and translation of blood-based predictive biomarkers for the treatment of pSD.

ChIP-AP: an integrated analysis pipeline for unbiased ChIP-seq analysis.

Chromatin immunoprecipitation coupled with sequencing (ChIP-seq) is a technique used to identify protein-DNA interaction sites through antibody pull-down, sequencing and analysis; with enrichment 'peak' calling being the most critical analytical step. Benchmarking studies have consistently shown that peak callers have distinct selectivity and specificity characteristics that are not additive and seldom completely overlap in many scenarios, even after parameter optimization. We therefore developed ChIP-AP, an integrated ChIP-seq analysis pipeline utilizing four independent peak callers, which seamlessly processes raw sequencing files to final result. This approach enables (1) better gauging of peak confidence through detection by multiple algorithms, and (2) more thoroughly surveys the binding landscape by capturing peaks not detected by individual callers. Final analysis results are then integrated into a single output table, enabling users to explore their data by applying selectivity and sensitivity thresholds that best address their biological questions, without needing any additional reprocessing. ChIP-AP therefore presents investigators with a more comprehensive coverage of the binding landscape without requiring additional wet-lab observations.

Identification of potential therapeutic targets for plaque vulnerability based on an integrated analysis.

BACKGROUND AND AIMS: This study aimed to explore potential hub genes and pathways of plaque vulnerability and to investigate possible therapeutic targets for acute coronary syndrome (ACS). METHODS AND RESULTS: Four microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene coexpression networks (WGCNA) and immune cell infiltration analysis (IIA) were used to identify the genes for plaque vulnerability. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology, Gene Ontology annotation and protein-protein interaction (PPI) network analyses were performed to explore the hub genes. Random forest and artificial neural networks were constructed for validation. Furthermore, the CMap and Herb databases were employed to explore possible therapeutic targets. A total of 168 DEGs with an adjusted P < 0.05 and approximately 1974 IIA genes were identified in GSE62646. Three modules were detected and associated with CAD-Class, including 891 genes that can be found in GSE90074. After removing duplicates, 114 hub genes were used for functional analysis. GO functions identified 157 items, and 6 pathways were enriched for the KEGG pathway at adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). Random forest and artificial neural network models were built based on the GSE48060 and GSE34822 datasets, respectively, to validate the previous hub genes. Five genes (GZMA, GZMB, KLRB1, KLRD1 and TRPM6) were selected, and only two of them (GZMA and GZMB) were screened as therapeutic targets in the CMap and Herb databases. CONCLUSION: We performed a comprehensive analysis and validated GZMA and GZMB as a target for plaque vulnerability, which provides a therapeutic strategy for the prevention of ACS. However, whether it can be used as a predictor in blood samples requires further experimental verification.

Comparative proteomic and metabolomic analyses reveal stress responses of hemp to salinity.

KEY MESSAGE: Integrated omics analyses outline the cellular and metabolic events of hemp plants in response to salt stress and highlight several photosynthesis and energy metabolism related pathways as key regulatory points. Soil salinity affects many physiological processes of plants and leads to crop yield losses worldwide. For hemp, a crop that is valued for multiple aspects, such as its medical compounds, fibre, and seed, a comprehensive understanding of its salt stress responses is a prerequisite for resistance breeding and tailoring its agronomic performance to suit certain industrial applications. Here, we first observed the phenotype of salt-stressed hemp plants and found that under NaCl treatment, hemp plants displayed pronounced growth defects, as indicated by the significantly reduced average height, number of leaves, and chlorophyll content. Next, we conducted comparative proteomics and metabolomics to dissect the complex salt-stress response mechanisms. A total of 314 proteins and 649 metabolites were identified to be differentially behaving upon NaCl treatment. Functional classification and enrichment analysis unravelled that many differential proteins were proteases associated with photosynthesis. Through metabolic pathway enrichment, several energy-related pathways were found to be altered, such as the biosynthesis and degradation of branched-chain amino acids, and our network analysis showed that many ribosomal proteins were involved in these metabolic adaptations. Taken together, for hemp plants, influences on chloroplast function probably represent a major toxic effect of salinity, and modulating several energy-producing pathways possibly through translational regulation is presumably a key protective mechanism against the negative impacts. Our data and analyses provide insights into our understanding of hemp's stress biology and may lay a foundation for future functional genomics studies.

ALS is imprinted in the chromatin accessibility of blood cells.

Amyotrophic Lateral Sclerosis (ALS) is a complex and incurable neurodegenerative disorder in which genetic and epigenetic factors contribute to the pathogenesis of all forms of ALS. The interplay of genetic predisposition and environmental footprints generates epigenetic signatures in the cells of affected tissues, which then alter transcriptional programs. Epigenetic modifications that arise from genetic predisposition and systemic environmental footprints should in theory be detectable not only in affected CNS tissue but also in the periphery. Here, we identify an ALS-associated epigenetic signature ('epiChromALS') by chromatin accessibility analysis of blood cells of ALS patients. In contrast to the blood transcriptome signature, epiChromALS includes also genes that are not expressed in blood cells; it is enriched in CNS neuronal pathways and it is present in the ALS motor cortex. By combining simultaneous ATAC-seq and RNA-seq with single-cell sequencing in PBMCs and motor cortex from ALS patients, we demonstrate that epigenetic changes associated with the neurodegenerative disease can be found in the periphery, thus strongly suggesting a mechanistic link between the epigenetic regulation and disease pathogenesis.

Bioinformatics analysis of a disease-specific lncRNA-miRNA-mRNA regulatory network in recurrent spontaneous abortion (RSA).

BACKGROUND: This study investigated the molecular mechanisms of long non-coding RNAs (lncRNAs) in RSA using the lncRNA-miRNA-mRNA regulatory network. METHODS: The present study obtained expression datasets of long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), and microRNAs (miRNAs) from blood samples of individuals with unexplained recurrent spontaneous abortion (RSA) and healthy controls. Differentially expressed lncRNAs (DELs), mRNAs (DEMs), and miRNAs (DEmiRs) were identified. A regulatory network comprising lncRNA, miRNA, and mRNA was constructed, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEM. Also, a protein-protein interaction (PPI) network was made and key genes were identified. RESULTS: A total of 57 DELs, 212 DEmiRs, and 301 DEMs regarding RSA were identified. Later analysis revealed a lncRNA-miRNA-mRNA network comprising nine lncRNAs, 14 miRNAs, and 65 mRNAs. Then, the ceRNA network genes were subjected to functional enrichment and pathway analysis, which showed their association with various processes, such as cortisol and thyroid hormone synthesis and secretion, human cytomegalovirus infection, and parathyroid hormone synthesis. In addition, ten hub genes (ITGB3, GNAI2, GNAS, SRC, PLEC, CDC42, RHOA, RAC1, CTNND1, and FN1) were identified based on the PPI network results. CONCLUSION: In summary, the outcomes of our study provided some data regarding the alteration genes involved in RSA pathogenic mechanism via the lncRNA-miRNA-mRNA network and reveal the possibility of identifying new lncRNAs and miRNAs as promising molecular biomarkers.

NRP1 knockdown inhibits the invasion and migration of rhabdoid tumor of the kidney cells.

PURPOSE: The aim of this study was to detect candidate oncogenes of rhabdoid tumor of the kidney (RTK) and evaluate their roles in RTK in vitro. METHODS: An integrated analysis of messenger RNA (mRNA) and microRNA (miRNA) sequencing was performed to determine the expression profile of exosome-derived miRNAs and mRNAs in human RTK-derived cell lines and a human embryonic renal cell line. A Gene Ontology enrichment analysis was performed to analyze the functional characteristics of differentially expressed mRNAs in RTK cells. Matrigel invasion and wound-healing assays were performed to evaluate the cell invasion and migration abilities. RESULTS: Forty mRNAs were highly expressed in RTK cells targeted by exosomal miRNAs, the expression of which was lower in RTK cells than in the controls. These mRNAs were primarily related to cell adhesion. Of these mRNAs, we selected neuropilin 1 (NRP1) as a candidate oncogene because its upregulated expression is associated with a poor prognosis of several types of tumors. RTK cells in which NRP1 had been knocked down exhibited decreased invasive and migratory abilities. CONCLUSION: Our study indicates that NRP1 acts as an oncogene by promoting the invasion and migration of RTK cells and that it could serve as a therapeutic target.

Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective.

Breast cancer is well-known to be a highly heterogenous disease. This facet of cancer makes finding a research model that mirrors the disparate intrinsic features challenging. With advances in multi-omics technologies, establishing parallels between the various models and human tumors is increasingly intricate. Here we review the various model systems and their relation to primary breast tumors using available omics data platforms. Among the research models reviewed here, breast cancer cell lines have the least resemblance to human tumors since they have accumulated many mutations and copy number alterations during their long use. Moreover, individual proteomic and metabolomic profiles do not overlap with the molecular landscape of breast cancer. Interestingly, omics analysis revealed that the initial subtype classification of some breast cancer cell lines was inappropriate. In cell lines the major subtypes are all well represented and share some features with primary tumors. In contrast, patient-derived xenografts (PDX) and patient-derived organoids (PDO) are superior in mirroring human breast cancers at many levels, making them suitable models for drug screening and molecular analysis. While patient derived organoids are spread across luminal, basal- and normal-like subtypes, the PDX samples were initially largely basal but other subtypes have been increasingly described. Murine models offer heterogenous tumor landscapes, inter and intra-model heterogeneity, and give rise to tumors of different phenotypes and histology. Murine models have a reduced mutational burden compared to human breast cancer but share some transcriptomic resemblance, and representation of many breast cancer subtypes can be found among the variety subtypes. To date, while mammospheres and three- dimensional cultures lack comprehensive omics data, these are excellent models for the study of stem cells, cell fate decision and differentiation, and have also been used for drug screening. Therefore, this review explores the molecular landscapes and characterization of breast cancer research models by comparing recent published multi-omics data and analysis.

The chromosome-level genome of Akebia trifoliata as an important resource to study plant evolution and environmental adaptation in the Cretaceous.

The environmental adaptation of eudicots is the most reasonable explanation for why they compose the largest clade of modern plants (>70% of angiosperms), which indicates that the basal eudicots would be valuable and helpful to study their survival and ability to thrive throughout evolutionary processes. Here, we detected two whole-genome duplication (WGD) events in the high-quality assembled Akebia trifoliata genome (652.73 Mb) with 24 138 protein-coding genes based on the evidence of intragenomic and intergenomic collinearity, synonymous substitution rate (KS ) values and polyploidization and diploidization traces; these events putatively occurred at 85.15 and 146.43 million years ago (Mya). The integrated analysis of 16 species consisting of eight basal and eight core eudicots further revealed that there was a putative ancient WGD at the early stage of eudicots (temporarily designated θ) at 142.72 Mya, similar to the older WGD of Akebia trifoliata, and a putative core eudicot-specific WGD (temporarily designated ω). Functional enrichment analysis of retained duplicate genes following the θ event is suggestive of adaptation to the extreme environment change in both the carbon dioxide concentration and desiccation around the Jurassic-Cretaceous boundary, while the retained duplicate genes following the ω event is suggestive of adaptation to the extreme droughts, possibly leading to the rapid spread of eudicots in the mid-Cretaceous. Collectively, the A. trifoliata genome experienced two WGD events, and the older event may have occurred at the early stage of eudicots, which likely increased plant environmental adaptability and helped them survive in ancient extreme environments.

Structure-preserving integrated analysis for risk stratification with application to cancer staging.

To provide appropriate and practical level of health care, it is critical to group patients into relatively few strata that have distinct prognosis. Such grouping or stratification is typically based on well-established risk factors and clinical outcomes. A well-known example is the American Joint Committee on Cancer staging for cancer that uses tumor size, node involvement, and metastasis status. We consider a statistical method for such grouping based on individual patient data from multiple studies. The method encourages a common grouping structure as a basis for borrowing information, but acknowledges data heterogeneity including unbalanced data structures across multiple studies. We build on the "lasso-tree" method that is more versatile than the well-known classification and regression tree method in generating possible grouping patterns. In addition, the parametrization of the lasso-tree method makes it very natural to incorporate the underlying order information in the risk factors. In this article, we also strengthen the lasso-tree method by establishing its theoretical properties for which Lin and others (2013. Lasso tree for cancer staging with survival data. Biostatistics 14, 327-339) did not pursue. We evaluate our method in extensive simulation studies and an analysis of multiple breast cancer data sets.

Network analyses in microbiome based on high-throughput multi-omics data.

Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.

Constructing a seventeen-gene signature model for non-obstructive azoospermia based on integrated transcriptome analyses and WGCNA.

BACKGROUND: Non-obstructive azoospermia (NOA) affects approximately 1% of the male population worldwide. The underlying mechanism and gene transcription remain unclear. This study aims to explore the potential pathogenesis for the detection and management of NOA. METHODS: Based on four microarray datasets from the Gene Expression Omnibus database, integrated analysis and weighted correlation network analysis (WGCNA) were used to obtain the intersected common differentially expressed genes (DESs). Differential signaling pathways were identified via GO and GSVA-KEGG analyses. We constructed a seventeen-gene signature model using least absolute shrinkage and selection operation (LASSO) regression, and validated its efficacy in another two GEO datasets. Three patients with NOA and three patients with obstructive azoospermia were recruited. The mRNA levels of seven key genes were measured in testicular samples, and the gene expression profile was evaluated in the Human Protein Atlas (HPA) database. RESULTS: In total, 388 upregulated and 795 downregulated common DEGs were identified between the NOA and control groups. ATPase activity, tubulin binding, microtubule binding, and metabolism- and immune-associated signaling pathways were significantly enriched. A seventeen-gene signature predictive model was constructed, and receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) values were 1.000 (training group), 0.901 (testing group), and 0.940 (validation set). The AUCs of seven key genes (REC8, CPS1, DHX57, RRS1, GSTA4, SI, and COX7B) were all > 0.8 in both the testing group and the validation set. The qRT-PCR results showed that consistent with the sequencing data, the mRNA levels of RRS1, GSTA4, and COX7B were upregulated, while CPS1, DHX57, and SI were downregulated in NOA. Four genes (CPS1, DHX57, RRS1, and SI) showed significant differences. Expression data from the HPA database showed the localization characteristics and trajectories of seven key genes in spermatogenic cells, Sertoli cells, and Leydig cells. CONCLUSIONS: Our findings suggest a novel seventeen-gene signature model with a favorable predictive power, and identify seven key genes with potential as NOA-associated marker genes. Our study provides a new perspective for exploring the underlying pathological mechanism in male infertility.

Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsia.

PURPOSE: In this study, we explored the relationship of genes in HIF-1 signaling pathway with preeclampsia and establish a logistic regression model for diagnose preeclampsia using bioinformatics analysis. METHOD: Two microarray datasets GSE75010 and GSE35574 were downloaded from the Gene Expression Omnibus database, which was using for differential expression analysis. DEGs were performed the Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene set enrichment analysis (GSEA). Then we performed unsupervised consensus clustering analysis using genes in HIF-1 signaling pathway, and clinical features and immune cell infiltration were compared between these clusters, as well as the least absolute shrinkage and selection operator (LASSO) method to screened out key genes to constructed logistic regression model, and receiver operating characteristic (ROC) curve was plotted to evaluate the accuracy of the model. RESULTS: 57 DEGs were identified, of which GO, KEGG and analysis GSEA showed DEGs were mostly involved in HIF-1 signaling pathway. Two subtypes were identified of preeclampsia and 7 genes in HIF1-signaling pathway were screened out to establish the logistic regression model for discrimination preeclampsia from controls, of which the AUC are 0.923 and 0.845 in training and validation datasets respectively. CONCLUSION: Seven genes (including MKNK1, ARNT, FLT1, SERPINE1, ENO3, LDHA, BCL2) were screen out to build potential diagnostic model of preeclampsia.

An integrated analytical strategy to decipher the metabolic profile of alkaloids in Compound Kushen injection based on UHPLC-ESI-QTOF/MSE.

Compound Kushen injection (CKI) is a kind of sterilised water-soluble traditional Chinese medicine preparation that has been used for the clinical treatment of a variety of cancers (hepatocellular carcinoma, lung cancer, etc.) for 19 years. However, to date, the metabolism-related study on CKI in vivo has not been conducted.An integrated analytical strategy was established to investigate the metabolic profile of alkaloids of CKI in rat plasma, urine, and faeces based on ultra-high performance liquid chromatography-electrospray quadrupole time-of-flight mass spectrometry in MSE mode (UHPLC-ESI-QTOF/MSE).Nineteen prototype alkaloids (including 12 matrine-type alkaloids, 2 cytisine-type alkaloids, 3 lupinine-type alkaloids, and 2 aloperine-type alkaloids) of CKI were identified in vivo. Furthermore, 71 metabolites of alkaloids (including 11 of lupanine-related metabolites, 14 of sophoridine-related metabolites, 14 of lamprolobine-related metabolites, and 32 of baptifoline-related metabolites) were tentatively characterised. Metabolic pathways involved in the metabolism of phase I (include oxidation, reduction, hydrolysis, and desaturation), phase II (mainly include glucuronidation, acetylcysteine or cysteine conjugation, methylation, acetylation, and sulphation), and associated combination reactions.The integrated analytical strategy was successfully used to characterise the prototype alkaloids and their metabolites of CKI in vivo, and the results laying a foundation for further study its pharmacodynamic substances.