Cyperi Rhizoma-Chuanxiong Rhizoma herb pair (CCHP) against depression using a network pharmacology approach.  We aimed to investigate the mechanisms underlying the effects of the  A network pharmacology approach, including screening of active compounds, target prediction, construction of a protein-protein interaction (PPI) network, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and molecular docking, molecular dynamics (MD) simulations, and molecular mechanics Poisson–Boltzmann surface area (MMPBSA), were used to explore the mechanisms of CCHP against depression.  Twenty-six active compounds and 315 and 207 targets of CCHP and depression, respectively, were identified. The PPI network suggested that AKT1, IL-6, TP53, DRD2, MAPK1, NR3C1, TNF, etc., were core targets. GO enrichment analyses showed that positive regulation of transcription from RNA polymerase II promoter, plasma membrane, and protein binding were of great significance. Neuroactive ligand-receptor interaction, PI3K-Akt signaling pathway, dopaminergic synapse, and mTOR signaling pathway were important pathways. Molecular docking results revealed good binding affinities for the core compounds and core targets. MD simulations and MMPBSA validated that quercetin can stably bind to 6hhi.  The effects of CCHP against depression involve multiple components, targets, and pathways, and these findings will promote further research on and clinical application of CCHP. Depression is a highly prevalent psychiatric illness with a global incidence of 258 million cases in 2017 and is predicted to be the leading contributor to worldwide disease burden by 2030, with a disability-adjusted life year (DALY) value of 84.32 million [Many studies have reported that traditional Chinese medicine (TCM) can treat depression effectively with fewer adverse events –8, sinceCyperi Rhizoma and Chuanxiong Rhizoma is deemed the Cyperi Rhizoma-Chuanxiong Rhizoma herb pair (CCHP) in TCM [Danxi Xinfa and Chaihu Shugan San from Jingyue Quanshu. Both Yueju pill and Chaihu Shugan San were found to exert significant antidepressant effects in clinical trials [Herb pairs used in TCM are composed of two herbs with synergistic effects; herb pairs are of particular clinical significance and provide an important perspective in studies of herb compatibility . The com) in TCM . It is al trials –15. Yuejl trials –18, whill trials , 20. HowNetwork pharmacology is a new approach derived from systems biology, polypharmacology, network theory, and so on. TCM is characterized by holism, and TCM formulas are known to treat diseases by employing multiple ingredients and targets from a systematic perspective. Since the properties of network pharmacology are in accordance with the holistic philosophy of TCM, network pharmacology offers a new approach to innovating drug discovery and an effective tool for exploring TCM from a systematic perspective , 22. TheThus, the purpose of this study was to investigate the multiple mechanisms of CCHP in treating depression using network pharmacology and molecular docking to provide insights into the research and therapy of depression. A detailed workflow is shown in https://tcmspw.com/tcmsp.php). The core compounds of CCHP that were recorded in the literature and not included in TCMSP were also obtained. TCMSP can provide information on the ingredients, corresponding targets, and pharmacokinetic properties of TCM [The active compounds of CCHP were predominantly retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  to acquire the targets of each compound [Homo sapiens” as the species and chose targets with a combined score of ≥0.7. The targets of the compounds obtained were standardized in the UniProt (https://www.uniprot.org) database, and “reviewed” and “human” UniProtKB was selected [We used TCMSP and the search tool for interacting chemicals  [To illustrate the relationships between herbs, compounds, and targets of CCHP, Cytoscape 3.2.1 Software (pe.org/)  was utilhttps://db.idrblab.org/ttd/) [https://www.drugbank.ca/) [https://www.genecards.org/) [Targets related to depression were retrieved from the therapeutic target database  , DrugBanank.ca/) , and Gends.org/)  databasehttp://bioinfogp.cnb.csic.es/tools/venny/index.html) mapping tool.To obtain the targets of CCHP in treating depression, the predicted targets of the compounds of CCHP were intersected with targets related to depression, and a Venn diagram was obtained using the Venny 2.1 (https://string-db.org/) for protein-protein interaction (PPI) analysis [Homo sapiens” was chosen as the species, and a combined score >0.9 was used as the threshold. The results for the PNG and TSV formats were exported. The PPI network was visualized by Cytoscape 3.2.1 and analyzed using the “Network analyzer” plug-in, which is a tool of Cytoscape. The screening thresholds were the median values of the degrees of all nodes.To illuminate the interactions among proteins, the targets of CCHP in treating depression were input into STRING 11.0  [p < 0.01 and false discovery rate (FDR) <0.05.The Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 (rf.gov/) , 33 was Based on KEGG analysis, Cytoscape was employed to construct a target-pathway network of the top 20 key signaling pathways and the enriched targets. The relationships between pathways and enriched targets are shown in the network. The network nodes are the pathways and enriched targets, and the size of the nodes represents the topological importance of the nodes.https://pubchem.ncbi.nlm.nih.gov/) [https://www.rcsb.org/) [The nodes with the top six degrees of the herb-compound-target network and PPI network were chosen as core compounds and targets for molecular docking. First, the 2D structures of the core compounds were acquired from the PubChem database (ih.gov/)  and inpusb.org/)  and delesb.org/) . DockingSince AKT1 (PDB ID: 6hhi) was the core target and quercetin was the core compound, the docking conformation of 6hhi and quercetin, which had low binding energy, was selected as the initial conformation for molecular dynamics (MD) simulations. G4N, the primitive ligand of 6hhi, was used as the positive control. MD simulations were performed using the GROMACS 2018.4 program  under coFirst, energy minimization was performed in the two systems using 5000 steps of steepest descent algorithm with the convergence of energy minimization of 100 kJ/mol/nm to eliminate excessive interatomic contact. Then, the systems were heated gradually from 0 to 300 K in the canonical ensemble (NVT) and equilibrated at 300 K for 1000 ps in the constant pressure-constant temperature ensemble (NPT). Finally, the systems were subjected to MD simulations for 50 ns and the conformation was preserved every 10 ps. The simulation results were visualized using the GROMACS embedding program and visual molecular dynamics (VMD).The molecular mechanics Poisson–Boltzmann surface area (MMPBSA) method  was usedA total of 26 compounds of CCHP were acquired from TCMSP and the literature.Cyperi Rhizoma and 9 were from Chuanxiong Rhizoma. The details of the compounds in each herb are shown in Cyperi Rhizoma and 73 targets of Chuanxiong Rhizoma. Cyperi Rhizoma and Chuanxiong Rhizoma shared 59 targets that may mediate their synergistic effects.Among the compounds, 18 were from The herb-compound-target network  built byWe retrieved 207 targets related to depression from the TTD, DrugBank, and GeneCards databases , 16 entries for cellular component (CC), and 16 entries for molecular function (MF). The top 16 entries in BP analysis included positive regulation of transcription from RNA polymerase II promoter, response to drug, positive regulation of transcription (DNA-templated), and signal transduction , PI3K-Akt signaling pathway (hsa04151), pathways in cancer (hsa05200), dopaminergic synapse (hsa04728), and mTOR signaling pathway (hsa04150). The top 20 enriched pathways are displayed in detail in KEGG pathway enrichment analyses were performed using the DAVID with the screening criterion of We input the top 20 key pathways and the enriched targets into Cytoscape to construct and analyze the target-pathway network . The degMolecular docking aims to predict the interactions between proteins and small molecules. The core compounds were quercetin, luteolin, kaempferol, beta-sitosterol, isorhamnetin, and stigmasterol. The core targets were AKT1 (PDB ID: 6hhi) , IL-6  indicates the sum of all atomic deviations between the conformation at a certain time and the target conformation, which is an important basis for measuring the stability of the system. The system of the binding complex of 6hhi and its primitive ligand G4N was named 6hhi_G4N, and the system of the binding complex of 6hhi and quercetin was named 6hhi_Quercetin. Root-mean-square fluctuations (RMSFs) can indicate the flexibility of amino acid residues in proteins. The amino acid flexibility distribution of 6hhi_G4N and 6hhi_Quercetin is shown in The results of MMPBSA show that the binding energy of the substrate and protein in 6hhi_G4N (binding energy = −125.522 ± 14.620 kJ/mol) is higher than that in 6hhi_Quercetin (binding energy = −103.144 ± 10.692 kJ/mol) . The resDepression, as a highly prevalent psychiatric illness, has serious effects on physical and mental health and can even lead to suicide . AlthougCyperi Rhizoma and Chuanxiong Rhizoma. Quercetin, a flavonoid, is present in numerous plants and exerts antidepressant effects by regulating the signaling related to BDNF [We obtained compounds and corresponding targets from the TCMSP and STITCH databases. Sitosterol was a common compound in  to BDNF , 52, all to BDNF , and inh to BDNF . Similar to BDNF . The mec to BDNF , 56 and  to BDNF .The herb-compound-target network  showed tTargets of depression were retrieved from the TTD, DrugBank, and GeneCards databases; the targets of depression and CCHP were intersected; 40 targets were identified as targets of CCHP in treating depression (Additional File 2: AKT1 gene polymorphisms may be a sign of depression severity and treatment response [DRD2 gene polymorphisms [MAPK1 polymorphisms may be related to the remission of antidepressant treatment [The targets of CCHP in the treatment of depression showed numerous interactions. The PPI network of CCHP in treating depression , which wresponse , 62, 63.response . IL-6 isresponse . An incrresponse . IL-6 leresponse , 68. TP5response , 70 thatresponse . The dopresponse . DRD2 isresponse . The vulorphisms –75. MAPKorphisms , 77. MAPThe results of GO analysis are shown in As shown in α/TNFR1/NF-κB signaling alleviated neuroinflammation and depression [The target-pathway network illustrated that AKT1, MAPK1, GSK3B, TNF, MTOR, and PTEN were core targets enriched in key signaling pathways that played crucial roles in the treatment of depression by CCHP. GSK3B may be involved in the development of depression by inhibiting Erk-CREB-BDNF signaling , and PI3pression .Molecular docking was employed to validate the interactions between the core compounds of CCHP and the core targets, and affinity analyses were used to estimate the binding energy of a ligand and the intensity of the interactions. The results indicated that multiple core compounds of CCHP could bind to multiple core targets, and this may be the basis of the mechanism underlying the therapeutic effects of CCHP.MD simulations are able to predict the motion of each atom over time and refine the conformation of the receptor-ligand complex –104. MD Nevertheless, this study had some limitations. The compound and target information used in the evaluations was mainly obtained from databases; however, some bioactive ingredients and targets may not be included in the databases. The inhibitory and activated effects of the targets are difficult to differentiate. The ingredients obtained from the databases may be distinct from those absorbed and utilized in the patient's body. Moreover, potential complex interactions between the ingredients were not taken into consideration. Accordingly, further experimental verification of the multiple mechanisms of CCHP in treating depression both in vivo and in vitro is required to validate the obtained results.In this study, quercetin, luteolin, kaempferol, beta-sitosterol, and isorhamnetin were the core compounds of CCHP in treating depression. CCHP treated depression by acting on multiple key targets, such as AKT1, IL-6, TP53, DRD2, MAPK1, NR3C1, TNF, ESR1, SST, OPRM1, DRD3, ADRA2A, and ADRA2C, and through important biological processes, such as positive regulation of transcription from RNA polymerase II promoter, plasma membrane, and protein binding. CCHP exerted antidepressant effects by regulating multiple key signaling pathways such as neuroactive ligand-receptor interaction, PI3K-Akt signaling pathway, dopaminergic synapse, and mTOR signaling pathway. AKT1, MAPK1, GSK3B, TNF, MTOR, and PTEN were the core targets enriched in key signaling pathways. CCHP treats depression through multiple components, targets, and pathways. However, the specific antidepressant effects of CCHP require experimental verification.


Deep learning has proven advantageous in solving cancer diagnostic or classification problems. However, it cannot explain the rationale behind human decisions. Biological pathway databases provide well-studied relationships between genes and their pathways. As pathways comprise knowledge frameworks widely used by human researchers, representing gene-to-pathway relationships in deep learning structures may aid in their comprehension. Here, we propose a deep neural network (PathDeep), which implements gene-to-pathway relationships in its structure. We also provide an application framework measuring the contribution of pathways and genes in deep neural networks in a classification problem. We applied PathDeep to classify cancer and normal tissues based on the publicly available, large gene expression dataset. PathDeep showed higher accuracy than fully connected neural networks in distinguishing cancer from normal tissues (accuracy = 0.994) in 32 tissue samples. We identified 42 pathways related to 32 cancer tissues and 57 associated genes contributing highly to the biological functions of cancer. The most significant pathway was G-protein-coupled receptor signaling, and the most enriched function was the G1/S transition of the mitotic cell cycle, suggesting that these biological functions were the most common cancer characteristics in the 32 tissues. Cancer is one of the most aggressive diseases worldwide, accounting for nearly nine million deaths universally. One of the defining features of cancer is the rapid duplication of abnormal cells beyond their usual boundaries, then invading adjoining parts of the body. Various efforts have been conducted in terms of deep learning for the classification and interpretation of cancer especially by using medical image as major data source ,2,3. As n = 626) RNA-seq data from The Cancer Genome Atlas (TCGA) were used as training datasets [n = 2952) were used for validation [Although deep neural network (DNN)-based research helps to resolve goal-oriented problems, its use in biological interpretation is limited . To provdatasets . Microarlidation . DeepCC lidation .Another study using deep learning and pathway information used the Kyoto Encyclopedia of Genes and Genomes (KEGG) BRITE hierarchy to image gene expression. These images were the input for a convolutional network . This apPathDNN is a DNN that comprises gene-to-pathway relationship data from 323 KEGG pathways in the first hidden and input layers . PathDNNThe Gene–pathway–disease (the GPD model) model connects input genes to the first layer, comprising of 1708 reactome pathways, to the output layer, representing the disease phenotype as a node; thus, the disease phenotype is predicted . Using 4PASNet connects genes to a node that represents a biological pathway, which is further connected to fully connected hidden layers . This mePrevious studies including those utilizing PathDNN, DeepCC, the GPD model, and PASNet and the study of López-García et al., made use of pathway knowledge in the DNN structure. Differences between these approaches and our proposed method are examined below. PathDNN uses the pathway structure for DNN, but it learns the relationship between the drug and drug target as well as the gene expression of the drug-administered cell. PathDNN applies only to drug efficacy prediction, which is different from the purpose of PathDeep. DeepCC and the method described by López-García et al. use genes-to-pathway information for deep learning training, but they do not provide both the pathway contribution index and a comprehensive explanation of their member genes. The GPD model provides the pathway index and performs perturbations to change the pathway node value when analyzing the association between the pathway and phenotype. The GPD model differs from our approach because PathDeep searches for statistically significant pathways using permutations of the phenotype label. PASNet provides a calculation method different from PathDeep for the association between the pathway and phenotype. PASNet summates all the weights from the pathway node to the output node as an index, but PathDeep uses the pathway node value itself as an index.https://github.com/sipark5340/PathDeep.git, accessed on 21 October 2021), which uses the pathway structure to learn the gene difference between cancer from normal tissues and provides a pathway based interpretation of it (Pathway index and pathway contribution gene index). We aimed to study the performance of PathDeep in classifying cancers from normal tissues based on gene expression data. PathDeep showed a favorable performance and provided a pathway knowledge-based interpretation.In this study, we propose PathDeep . Thus, we concluded that the c2 reactome provides more information when discriminating cancer from normal tissues than other pathway databases. We chose the c2 reactome database for further analysis , connecting the gene nodes to a pathway node at random but with the same number of edges that a pathway database provides. We repeatedly generated this random network and then trained each network to reveal that the biological relationship model demonstrates better performance than random networks. Among 15 pathway databases considered, the c2 reactome showed the highest test performance (accuracy = 0.994) and the lowest analysis .p < 2.22 × 10−16). It also showed higher accuracy than PathDeep random linked. The results show that the representation of PathDeep of the biological relationship between genes and pathways contributed to its training performance. PathDeep performed better than the MLNN (p < 2.22 × 10−16). The average test accuracy of other machine learning methods—SVM, Extreme Gradient Boosting Machine (XGboost), Gradient Boosting Machine (GBM), and RF—were 0.9925, 0.9921, 0.9905, and 0.9895, respectively [p < 2.22 × 10−16). This shows that the performance of a DNN with a biological relationship between the input gene and the pathway node is higher than that of a DNN with a fully connected layer. Furthermore, it is superior to other machine learning methods . The following four significant functions were related to DNA replication . The functions of the top 1% genes according to pathway contribution gene indexes are shown in We obtained pathway contribution gene indexes . Pathwayn = 5796) were used, the average test accuracy of PathDeep was 0.9931. When 50% of genes (n = 2898) with high pathway contribution gene indexes were used, the average test accuracy of PathDeep was 0.9925. When only 1% of genes with the high pathway contribution gene indexes were used, the average test accuracy of PathDeep was 0.9741. Thus, even if only 1% of the genes (n = 57) with a high pathway contribution gene index were used, PathDeep could discriminate between cancer and normal phenotypes. The result shows only a few genes that PathDeep counts important is sufficient to discriminate cancer from normal.We evaluated the performance of PathDeep by reducing the number of gene expression values used for training by limiting the number of genes according to the pathway contribution gene index value . When alp = 2.22 × 10−16), indicating that the higher the pathway contribution gene index, the more information is available for the discrimination between cancer and normal phenotypes.As shown in −100). The average test accuracy from building 100 models with different hyperparameters of PathDeep with 42 pathways—which are related to the distinction of cancer and normal phenotypes—for the training and test of five sets was 0.9914. The average test accuracy from 100 trials of PathDeep with all 674 pathways was 0.9935; this indicates that only 42 pathways related to cancer prediction were found to be suitable for discriminating between cancer and normal tissues.We identified pathways essential for cancer discrimination in a pan-cancer manner through label permutation experiments . Of the −100), indicating that GPCRs and their downstream signaling affect cancer growth and development [Uncovering cancer-related biological functions could offer a multifaceted solution to researchers for treating this disease. In this study, the PathDeep model was developed for pan-cancer analysis of biological functions that have statistically significant effects on the prediction of cancer. PathDeep demonstrates why the biological knowledge of the c2 reactome is the most appropriate biological knowledge for the discrimination between cancer and normal tissues among the 15 pathway databases considered. PathDeep has a superior performance in predicting cancer and normal tissues compared to other machine learning techniques, and it also shows a higher performance of DNNs in discriminating cancer and normal tissues when a biological knowledge structure is used than a conventional machine learning approach. PathDeep identified 42 pathways related to discriminating cancer and normal tissues among 674 pathways in the c2 reactome database using representative values representing pathways. The first and second most significant pathways were REACTOME GPCR downstream signaling  and REACTOME GPCR ligand binding . The G1/S transition occurs late in G1, and the absence or improper application of this highly regulated checkpoint can lead to cellular transformation and disease states such as cancer [PathDeep can indicate the contribution of a gene in the pathway layer, which is an indicator of the relation of genes to a biological function. The gene with the highest pathway contribution gene index was PLA2G10 (pathway contribution gene index = 1.149), a member of the A2 phospholipases that regulate fatty acid cleavage . PLA2G10s cancer . Other rs cancer .In conclusion, PathDeep, constructed by utilizing biological function structure-based DNNs, showed cancer and normal tissue discrimination performance and demonstrated the possibility of biological functional analysis and explanation. PathDeep implemented the diagnosis of cancer and normal tissues for four data platforms: TCGA, TARGET, GTEx, and K-562, and performed cancer-related biological function analysis. PathDeep shows better diagnosis performance in discriminating cancer and normal tissues than other methods. Applying PathDeep with the capabilities of biological functional analysis to other data platforms and problems may represent a possibility for offering biological functional advice to various studies  and clinical practice when biological interpretation is required.For this study, we used TCGA TARGET GTEx data with the Toil RNA-seq recompute fragments per kilobase of exon per million (FPKM) version provided by the UCSC Xena database . The totFor information on pathways and member genes of pathways, a collection of 15 molecular signatures provided by MSigDB v6.0 was used. The number of pathways per molecular signature collection, the number of symbols for member genes in the pathways, and the number of member genes in pathways with common gene symbols of TCGA, TARGET, and GTEx are shown in The cancer and normal samples in the training and test datasets were stratified. We randomly selected half of the cancer and normal samples for each of the following four projects: TCGA, TARGET, GTEx, and K-562. Half of the extracted samples were used in the training sample set, and the other half were used as the test sample set. The training sample set had 9565 samples, and the test sample set was comprised of 9566 samples. The same process as above was repeated 20 times to create 20 training and test sets . We first performed sample-wise standardize of data using the mean and standard deviation of 60,498 genes from each sample to process the input data for PathDeep to learn. In addition, we selected gene symbols for pathway members and listed 15 molecular signature collections in common with the gene symbols lists of TCGA, TARGET, and GTEx, using MSigDB. The genes selected for each molecular signature collection were the input for the PathDeep model.We compared the test accuracy of PathDeep with that of RF, SVM, GBM, and XGboost. Furthermore, we compared the test accuracy of PathDeep with the test accuracy of the multi-layer neural network and PathDeep random linked network to prove the superior performance of PathDeep. Then, we analyzed the pathway index, pathway contribution gene index, and label permutation using PathDeep. The data processing and analysis workflow is shown in We created a DNN that includes the relationship between pathways and their member genes. To create a DNN that shows the relationship between pathways and input genes, the first layer of the DNN was defined as the pathway layer. Each node of the pathway layer represented one pathway, and only the pathway member genes among the input genes were connected to each pathway layer node. For example, among the input genes, only the member genes of the epidermal growth factor receptor (EGFR) signaling pathway were connected to the pathway layer node of the EGFR signaling pathway. After defining the first layer of the DNN as a pathway layer, fully connected layers were connected to the pathway layer to complete the PathDeep model , which lWe created a PathDeep model with random links between gene nodes to pathway nodes. This was to investigate how the randomly generated gene-to-pathway relationship affects the discrimination between cancerous and normal tissues when compared to the linked gene nodes and pathway nodes, which are linked according to their known biological relationships in the pathway database. The total number of links between the gene nodes and pathway nodes were determined by the pathway database and was the same when the random link was generated. Therefore, in random link generation, the total number of links between gene nodes and pathway nodes are conserved, but participating gene and pathway pairs are random.p value using one sample z-test of PathDeep test accuracy using the mean and standard deviation of the 100 test accuracy of PathDeep random linked.We conducted the following experiment to identify suitable biological functional relationships for PathDeep among the 15 molecular signature collections of MSigDB. In each molecular signature collection, we implemented discriminating cancer and normal tissues using PathDeep once and using PathDeep random linked 100 times. Next, we calculated the z-score and We performed R programming to analyze the library of t-SNE values and to visualize pathway layer values . As a hyP is the pathway, i is the pathway number and s represents the sample. j, which is a member gene of pathway i, and j of sample s. j of the pathway i. j expression of sample s and the equivalent weight in all samples and is a summary value of the pathway layer node of sample of PathDeep. The equation for the pathway index is shown in Equation (1):The pathway index of the sample is defined as G is the gene symbol and j is the gene number. j, which is a member gene of pathway i, and j of sample s. j of pathway i, and N is the number of samples. j expression of sample s and the equivalent weight in all samples and represents the contribution that each gene has in the pathway layer of PathDeep. The equation for the pathway contribution gene index is shown in Equation (2):The pathway contribution gene index is represented as We performed GeneMANIA for biological functional analysis of the top 1% of pathway-contributing genes .We obtained the pathway indexes using PathDeep, with the highest test accuracy in the Train & Test set 5, and calculated the average difference between the pathway indexes of cancer and normal tissues. Then, permutation of the original labels of the cancer and normal tissues in the Train & Test 5 set was performed 1000 times to obtain the permutation labels. For each permutation label, the pathway index was obtained by discriminating between cancer and normal tissues using PathDeep. A total of 1000 PathDeep learning permutation labels were conducted and the average and standard deviation of the average difference of the pathway index between cancer and normal samples were calculated. Then, we performed z-tests for each pathway index of the average difference of the original label using the average and standard deviation obtained from the permutation label .Taejin Ahn, Sangick Park, and Taesung Park. Methods for diagnosis and therapeutic decision using artificial neural network trained with functional gene module and apparatus. KR Patent 10-2020-0120165 filled 18 September 2020, was based on the contents of this paper.


Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient’s “bed pathway” - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020.In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: “Ward, CC, Ward”, “Ward, CC”, “CC” and “CC, Ward”. Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days.For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities.We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19.ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.The ISARIC WHO CCP-UK study The online version contains supplementary material available at 10.1186/s12913-021-06509-x. Patients with Coronavirus disease 2019 (COVID-19) can display a range of symptoms which vary in severity . HoweverTo prioritise resource allocation, models that can project bed occupancy are used at the hospital, regional and national level –12. For Here, we use both hospital and national level COVID-19 inpatient data to identify the range of “bed pathways”, the sequence of transfers of individual patients between bed types, for a single hospital stay. We then estimate the length of stay (LoS) by bed type for each stage in these pathways. Finally, we develop and apply a model to examine the importance of using these bed pathways for bed occupancy predictions, notably for key indicators such as peak bed and capacity requirements over time.https://github.com/qleclerc/COVID_bed_occupancy).All of the analyses below were conducted in R , using tA bed pathway is the sequence of transfers of individual patients between bed types during a hospital stay. We considered two bed types used in acute care hospitals in England: general ward and critical care (CC) beds. Note that in England, CC beds include both intensive care unit (ICU) beds and high dependency unit (HDU) beds , but in We derived patient bed pathways from two datasets. Our first dataset was provided by University College Hospital (UCH), a teaching hospital that is part of the University College London Hospitals National Health Service (NHS) Foundation Trust, and one of the designated hospitals in London for admission of patients with COVID-19. This first dataset contains date of admission, type of bed used and date of discharge information on 168 inpatients with COVID-19 admitted to UCH between 6th March 2020 and 17th April 2020. All of these patients are now discharged or dead, therefore the LoS from this dataset were uncensored. The patients are not identifiable in this dataset, and are only associated with a number between 1 and 168. If a patient stayed in more than one bed type during their hospitalisation, the dataset includes an admission date, bed type, and discharge date for each stage.To identify all possible bed pathways, we gathered all recorded stages by patient, and ordered them in chronological order. For each patient, the LoS for each stage was then estimated by taking the difference between the admission date and discharge date, and adding 1 day (such that a patient admitted and discharged on the same day would have an LoS of 1). The data was regrouped by bed pathway, and for each pathway we calculated the number of stages and the proportion of total patients following the pathway. Finally, for each stage in each bed pathway we calculated the mean LoS and standard deviation (s.d.) To compare these estimates with those typically used in bed occupancy models –12, we aThe second dataset was from the ISARIC WHO CCP-UK study  an ongoing prospective cohort study in England, Scotland, Wales and Northern Ireland . The datFor the CO-CIN dataset, multi-state modelling analyses were used to estimate the time patients spent in each bed type, before transitioning to a different type, as well as time to discharge or in-hospital death , 27. UsiThis resulted in a set of empiric LoS distributions, one for each stage in each bed pathway. We drew 100,000 samples from each empiric distribution to estimate mean LoS and s.d. for each stage. As with the dataset from UCH, we again calculated an “average LoS by bed type”, which is the mean LoS of all stages of that bed type recorded across all pathways. For sensitivity, we also estimated median LoS and interquartile range for each stage and pathway.To obtain an international comparison of the possible types of bed pathways and the proportions of patients going through each, we used studies collated in a systematic literature review of LoS values for patients with COVID-19 . From thi, the following characteristics:pi, where ∑ipi = 1)the probability that any patient will go down this pathway (i (Ni)the number of stages for each pathway j for each pathway i an average LoS in each stage si,j)a standard deviation for each LoS  daily hospital admissions over any given period of time, and, for each bed pathway W,CC = 2) with a first stage where the patient stays in a ward bed for an average of d,1{W,CC} days and s.d. of s,1{W,CC} days, followed by a second stage in a CC bed for an average of d{W,CC},2 days, s.d. s{W,CC},2.For example, the pathway “Ward, CC” contains two stages (Nt0) of the admission data time series, we assign a pathway, k, to each admitted patient by sampling from all of the possible pathways, weighted by the probabilities, p. Then, for each patient, we randomly assign a LoS for each of the Nk stages by sampling from a Weibull distribution  and standard deviation sk,j, and record that a bed of the corresponding type will be needed for that duration and for the specific time window determined by the order of the stages. Note that if a bed is needed for a fraction of a day, we conservatively assume that it is needed for that entire day (effectively rounding up the LoS to the next day). For sensitivity, we explored the impact of using a Lognormal instead of a Weibull distribution, and of rounding the LoS to the nearest day instead of rounding up.The model then proceeds as follows: on the first day (ibutions ) with mei and stage j, the model takes as input a vector of values for the distribution  and a vector of the corresponding probabilities for each value in the distribution . For a patient assigned to a pathway, k, the LoS at each stage, j, is then sampled from the values provided in akj, weighted by the probabilities provided in rkj.Alternatively, instead of the mean and s.d., we can specify an empirical distribution for the LoS in each stage of the bed pathway (which we use with CO-CIN bed pathways data - see below). In that case, for each pathway For all results, 100 simulations of the model are performed, for which we report the daily mean overall bed requirements and 95% confidence intervals (mean +/− 1.96 * standard error). For each bed type, we also calculate across these 100 simulations the median time and mean size of peak bed occupancy during the period over which admissions are generated, alongside the interquartile range (IQR) and standard deviation for peak time and peak size respectively.To obtain a time-series of daily hospital admissions and bed occupancy for UCH between 6th March 2020 to 17th April 2020, we re-used the same UCH dataset on COVID-19 inpatients described above. To generate the admissions time-series, we counted the earliest recorded admission date for each patient as one hospital admission on that date, regardless of bed type and bed pathway. Our time-series of admissions therefore represents admissions to hospital, rather than admissions to a specific bed type. To generate a time-series of general ward and CC bed occupancy, we looked at each bed pathway recorded for each patient. For each stage in each pathway, we noted the hospital bed type that was occupied by the patient during that stage , and recorded that one hospital bed of that type was occupied between the beginning and end dates for that stage.We then compared the time-series of UCH bed occupancy to model-predicted occupancy for the same period. This serves both as a model validation step, and an opportunity to explore the impact of patient bed pathways as a service evaluation for which model to use for UCH. Model-predicted occupancy for UCH was generated using the UCH hospital admissions time-series, and either the UCH bed pathways characteristics, or the UCH average LoS by bed type values. The UCH bed pathways characteristics used to parameterise the model  were derived from our analysis above (see “Methods - Patient bed pathways”). This also applies to the average LoS by bed type estimates.As a sensitivity analysis, we repeated this, keeping the UCH hospital admissions time-series, but using CO-CIN bed pathways characteristics and average LoS by bed type, instead of the UCH equivalents.As CO-CIN covers patients across the UK, we use the LoS estimates to predict bed occupancy at a national level. To support this analysis, we used publicly available data from the UK Government COVID-19 dashboard on daily hospital admissions in England  between 19th March 2020 and 19th August 2020 . This daWe then compared the time-series of England bed occupancy to model-predicted occupancy for the same period. Model-predicted occupancy for England was generated using the England hospital admissions time-series, and either the CO-CIN bed pathways characteristics, or the CO-CIN average LoS by bed type values. The CO-CIN bed pathways characteristics were derived from our analysis above (see “Methods - Patient bed pathways”), and provide empirical distributions from which to sample LoS at each stage in each pathway. On the other hand, the CO-CIN average LoS by bed type values only rely on mean LoS and standard deviation.As a sensitivity analysis, we repeated this, keeping the England hospital admissions time-series, but using UCH bed pathways characteristics and average LoS by bed type, instead of the CO-CIN equivalents.The UK Government COVID-19 dashboard also provides daily hospital admissions and bed occupancy for each of the seven NHS Regions in England, and for 208 NHS Trusts , 30. We As a sensitivity analysis, we also fit the model-predicted bed occupancy at the NHS Regions level by adjusting the proportion of patients going to a CC bed in each Region, while maintaining the original CO-CIN average LoS by bed type values.n = 137, 81.5%), only staying in a ward bed during their hospitalisation at UCH. The other patient pathways, ranked from most to least common, were: “Ward, CC, Ward” (6.0%), “Ward, CC” (5.3%), “CC” (4.8%) and “CC, Ward” (2.4%).We identified five possible bed pathways for the 168 patients with COVID-19 admitted to UCH between 6th March 2020 and 11th April 2020  3.60 days), while the mean LoS in a CC bed was 4.67 days s.d. 3.24). However, LoS in each bed type varied between different pathways Table . The sho4. Howeven = 29,975, 82.1%); however, the proportion of patients going through the other bed pathways differ slightly compared to UCH: “Ward, CC, Ward” (9.9%), “CC, Ward” (6.9%), “CC” (0.6%) and “Ward, CC” (0.5%).We found the same five bed pathways using the CO-CIN dataset Table . Most paLoS estimates and standard deviations are higher for all stages and all bed pathways in CO-CIN compared to UCH Table . Across In our analysis of studies reporting length of stay, none gave bed pathway information but 21 did give information on the “proportion of patients ever requiring an ICU bed” see . The weiModel-predicted bed occupancy for UCH is similar to, but underestimates, true occupancy Fig. a-b. UsinFor ward beds, 95% of data points lie within the 95% confidence interval of the model-predicted bed occupancy using bed pathways, while for CC beds this value is 89%. The median peak time in ward bed occupancy estimated by the model is 3rd April 2020 (IQR 31st March - 3rd April) and 31st March 2020 (IQR 30th March - 3rd April), when using bed pathways or average LoS respectively, while the true peak occurred on 31st March 2020  naturally led to reduced bed occupancy predictions. In the case of UCH, ceiling instead of rounding improved the match to data  required only a ward bed, for an average of 9.08 days. The remaining patients followed one of four other bed pathways, with lengths of stay (LoS) that varied by pathway and bed type. Incorporating bed pathways improved model-predicted bed occupancy at a local hospital level, compared to only using average LoS values by bed type. However, analyses at the England, NHS Regions and Trust level indicate that simply using national LoS values, even taking into account the bed pathways, did not generate reported regional or national bed occupancy.Our results are subject to limitations, notably linked to our model assumptions. Firstly, we chose to round-up sampled LoS estimates to the next day instead of rounding to the closest. In the case of UCH, this improved the fit of the model-predicted bed occupancy to the data . As for the second model, it focused on machine-learning methods to estimate transition probabilities between the clinical states moderate/severe and critical, and is hence not comparable [In the early months of the pandemic, the simplifying assumptions made by many capacity planning tools were necessary, as detailed data were not available on bed pathways. The next generation of bed capacity predictions tools should use patient bed pathways with local information on length of stay. To the best of our knowledge, there are only three other published models available which used patient bed pathways –35. The mparable . The thiWe found that our initial hypothesis, that the LoS distribution for patients with COVID-19 should be the same everywhere in England, was unlikely to hold true. Instead, we observed regional variation, of up to 2 days differences for ward LoS and 4 days for CC LoS in the best fitting model estimates, as well as variation by NHS Trusts, and therefore within-NHS Regions. This could be explained in several ways. LoS for other diseases has been shown to vary between countries , and betHowever, the regional differences could also be due to true differences between NHS Regions in the COVID-19 care pathway and clinical practice. They could also reflect variation in risk factors for COVID-19 hospitalisation and disease severity, which would affect the proportion of patients in CC . While wAt the NHS Trust level, estimated differences in LoS are harder to interpret. In a single region, Trusts may form a network where each Trust fulfills a specific role, such as specialising in the care of critically ill patients, leading to patient flows between hospitals . This coA greater issue is likely to be inconsistency between data definitions  and sources of data (e.g. which Trusts are included) . In the Our findings indicate that the majority (82%) of hospitalised patients with COVID-19 in England only stayed in a general ward bed. Hospitals should plan infection control measures accordingly to prevent nosocomial transmission of SARS-CoV-2.In addition, the regional heterogeneities in length of stay we have identified imply that, whenever possible, hospitals should monitor local patient bed pathways and adapt their organisation based on these, instead of relying on data at the national level. In the work presented in this manuscript, the identification of patient pathways and their use to improve bed occupancy predictions for audit were greatly facilitated thanks to interdisciplinary collaboration. We therefore recommend that other hospitals should consider such collaborations to address organisational questions, especially in the context of public health crises such as COVID-19.Moreover, in predicting bed occupancy it is not just the physical number of beds that needs to be measured but also the additional resources in terms of equipment and personnel. This organisation may vary between hospitals. For example, different hospitals may have different numbers of staff per bed , 44. ThiBed occupancy predictions crucially rely on predictions of hospital admissions, which are difficult to produce, especially at lower levels, such as the hospital. Using local past estimates of LoS can help narrow down uncertainty, though a key driver will be admission prediction variability. For example, the timing of the UCH & England peak bed occupancy were quite different. Our model would rely on good bed admission predictions to forecast bed occupancy, alongside accurate LoS values. Without these, errors in timing of peak can result in long tails of errors in levels of bed occupancy.Understanding the reasons for the regional and sub-regional heterogeneity is a key next step. Matching this to known risk factor variation (e.g. in socio-economic status and comorbidities) could improve our understanding of clinical care impact. Future work clarifying the structure of patient bed pathways could be used to better understand the factors that contribute to patient outcomes, such as whether a patient is likely to die or be discharged at the end of their hospitalisation .Our model could be expanded to consider additional resource complexity (such as staffing needs) and spare capacity requirements, alongside exploring trigger times for the need of future beds. This could be explored with simple adaptations to this model. Similarly, an exploration of time varying elements, such as admission & discharge procedures, alongside COVID-19 prevalence would be important.This model structure is not linked specifically to the English situation and hence could be adapted to any setting. We found that patient bed pathways are likely to be similar globally but further data is required on length of stay to parameterise the bed pathways.Our results emphasise the importance of local knowledge in predicting bed occupancy and hence capacity planning. We found that using only average LoS by bed types could underestimate bed occupancy at the hospital level. There may be important underlying heterogeneities in LoS, which should be further investigated as they may provide insight into the prevalence of risk factors for COVID-19 or clinical care disparities. As cases of COVID-19 are currently increasing again in England, and for future epidemic preparedness, it is essential to develop the best possible understanding of the bed pathways of patients with COVID-19 now, to avoid local under- or overestimates of forecasted bed occupancy.Additional file 1.


Signalling pathway analysis is a popular approach that is used to identify significant cancer‐related pathways based on differentially expressed genes (DEGs) from biological experiments. The main advantage of signalling pathway analysis lies in the fact that it assesses both the number of DEGs and the propagation of signal perturbation in signalling pathways. However, this method simplifies the interactions between genes by categorising them only as activation (+1) and suppression (−1), which does not encompass the range of interactions in real pathways, where interaction strength between genes may vary. In this study, the authors used newly developed signalling pathway impact analysis (SPIA) methods, SPIA based on Pearson correlation coefficient (PSPIA), and mutual information (MSPIA), to measure the interaction strength between pairs of genes. In analyses of a colorectal cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset, PSPIA and MSPIA identified more candidate cancer‐related pathways than were identified by SPIA. Generally, MSPIA performed better than PSPIA. Korucuoglu et al. [et al. [et al. integrated information related to protein–protein interactions and microRNAs to identify disease‐associated pathways [With the establishment of pathway databases, including the KEGG, BioCata, and Reactome databases, analysis of differentially expressed genes (DEGs) has become a dominant analytical method in systemic biological research. In such analysis, the first step is to list DEGs according to gene expression profiles. Next, pathways with significant number of DEGs are identified, after which gene ontology functional enrichment analysis is performed on sets of DEGs in affected pathways, allowing researchers and clinicians to better understand interactions between diseases and genes. Early methods of pathway analysis mainly included techniques based on overexpression analysis (ORA) and functional class scoring (FCS) , 2, 3. Oet al.  introduc [et al. , 10 prop [et al.  improvedu et al.  depicted [et al.  integratpathways , 15, 16.In a signalling pathway, the effect of upstream gene perturbation on downstream genes is closely related to the intensity of the interaction between such pairs of genes. In order to simplify model parameters, SPIA only considers the relationships of activation and inhibition; it does not consider the intensity of interactions between genes. However, interaction intensity can be measured by Pearson correlation analysis or mutual information computation for gene expression profiles. Therefore, we developed modified SPIA methods based on Pearson's correlation coefficient (PSPIA) and mutual information (MSPIA). Through the application of PSPIA and MSPIA to colon cancer, lung cancer, and pancreatic cancer datasets, we found that, in comparison with SPIA, GSEA, and BPA, proposed PSPIA and MSPIA recognise more pathways related to diseases and have more stable performance.et al. (ID = GSE27262) [et al. [In this paper, we use the following three cancer data sets: a colon cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset. The colon cancer dataset included 12 colon cancer samples and 10 normal samples (the identification of the datasets in Gene Expression Omnibus dataset (ID) = GSE4107) . The lunSE27262) , 19 incl [et al.  (ID = GShttp://www.kegg.jp/kegg/xml/), every pathway corresponds to an XML document stored in KGML format. There are two main types of nodes in the KGML format: gene product and compound. To compare with SPIA method, we analyse the 137 signalling pathways listed in the SPIA R package. First, we downloaded the signalling pathways from the KEGG database one by one. Next, the graphite package [In the KEGG database  captures the significance of a given subpathway Pi by an over‐representation analysis of the NDE contained in the pathway. H0 represents the null hypothesis, in which random DEGs appear in a given subpathway. The probability PNDE is obtained by assuming that the NDE follows a hypergeometric distribution. If the whole genome has a total of m genes, of which t are involved in the pathway under investigation, while the set of genes submitted for analysis has a total of n genes, of which r are involved in the same pathway, then the p ‐value can be calculated to evaluate the significance of the enrichment of a group of DEGs for that pathway as followsPPERT is calculated based on the amount of perturbation measured in each pathway. The gene perturbation factor is defined asE (gi) represents the signed, normalised, measured expression change of gene gi (log fold‐change if two conditions are compared). The second term in . βij quantifies the interaction between genes gi and gj (activation or inhibition). SPIA uses all |β | = 1 in order to minimise the number of model parameters. Detailed information can be found in [The first probability ined asPF(gi)=Δfound in .wij represents the intensity of the interaction between two genes. For PSPIA, the interaction intensity is represented by Pearson's correlation coefficient and wij is calculated as followsn represents the sample size of the expression profiles, Eik represents the expression value of gene gi in the k th sample; Ei¯ represents the average expression value of gene gi; and sEi represents the standard deviation of the expression values of gene gi.To consider the influence of the interaction strength between two adjacent genes with different intensities on perturbation, formula  was modiwij is calculated as follows:X and Y represent the binarised expression value space of genes gi and gj, respectively, such as X = {1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0}. Then, the probability space of X is as followsY is as followsX and Y is as followsgi and gj can be caculated as formula ) <0.01 were considered as significant pathways. In the GSEA results, signalling pathways with q ‐values <0.01 were considered significant.Fair comparisons of methods are difficult because of the unavailability of gold standard cancer‐pathway association data. In addition to the differences in the techniques used in the compared methods, the calculations of the significance of the identified pathways are also performed differently. Therefore, one common method of comparison is to evaluate them using a specific PG (FDR) of each pathway. In addition, pathways identified via GSEA and BPA are also indicated. Table Tables First, the number of significant pathways identified via PSPIA and MSPIA was much greater than that identified via SPIA. This result suggests that consideration of the interaction intensity of genes improves the sensitivity of SPIA, i.e. the proposed methods can identify more potential disease‐related pathways.colorectal cancer pathway [, calcium signalling pathway [transcriptional misregulation in cancer, and salmonella infection [colorectal cancer pathway is directly related to colon cancer [calcium signalling pathway plays an important role in proliferation and migration of colon cancer cells [Second, some pathways identified via PSPIA and MSPIA, but not via SPIA, were related to particular type of cancer . When the sample size is sufficiently large, MSPIA can capture more non‐linear interactions between genes than can PSPIA. Conversely, in the case of a small sample size, the linear correlation intensity of genes reflected by PSPIA is more reliable than that provided by MSPIA.ECM‐receptor interaction pathwa y [PPAR signalling pathway [calcium signalling pathway and salivary secretion were not identified via PSPIA. The calcium signalling pathway has been reported to be related to lung cancer. In the pancreatic cancer dataset, PSPIA identified all pathways identified via SPIA. The pancreatic cancer pathway is obviously a significant pathway associated with pancreatic cancer, but it could not be identified by PSPIA, MSPIA, and SPIA with p ‐value <0.01. The p ‐values of this pathway were 0.058, 0.059, and 0.13 by PSPIA, MSPIA, and SPIA respectively. Therefore, the PSPIA and MSPIA methods identified this pathway more significantly than the SPIA method. This indicates that the two proposed methods are more efficient than the SPIA methods.Third, all pathways identified in the three cancer data sets via SPIA were also identified by MSPIA. In the colon cancer dataset, only the pathwa y  and PPAR pathway , which hParkinson's disease pathway, Alzheimer's disease pathway, and Huntington's disease pathway were identified in the colon cancer dataset by PSPIA, MSPIA, and SPIA. These anomalous results might have been caused by the quality of the microarray data set or by the inherent limitations of SPIA‐based methods. The anomalous pathways were easily eliminated by the subpathway method [Finally, some pathways were identified by PSPIA, MSPIA, and SPIA that might have no association with the corresponding cancer. For example, the y method .focal adhesion pathway [regulation of the actin cytoskeleton pathway [, MAPK signalling pathway [ECM‐receptor interaction pathway, Wnt signalling pathway [p53 signalling pathway [p ‐values smaller than 0.01, providing indirect evidence for the validity of the SPIA and SPIA‐based methods.Several pathways have been reliably associated with cancer, including the  pathway , 42, 43, pathway , MAPK si pathway , 46, 47, pathway , 48, 49, pathway , 50. Tabp ‐values below 0.1, with the exception of the p53 signalling pathway, which had relatively larger p ‐values in the significance analysis by each of the three methods for the colon cancer and lung cancer datasets. In the PSPIA results, the p ‐values for regulation of the actin cytoskeleton (colon cancer dataset), ECM‐receptor interaction (colon cancer dataset), regulation of the actin cytoskeleton (lung cancer dataset), MAPK signalling pathway (pancreatic cancer dataset), and p53 signalling pathway (pancreatic cancer dataset) were 0.068, 0.042, 0.078, 0.1 and 0.042, respectively. In the MSPIA results, the p ‐values for regulation of actin cytoskeleton (colon cancer dataset), regulation of actin cytoskeleton (lung cancer dataset), and MAPK signalling pathway (pancreatic cancer dataset) were 0.063, 0.06, and 0.05, respectively. In the SPIA results, the p ‐values for regulation of actin cytoskeleton (colon cancer dataset), regulation of actin cytoskeleton (lung cancer dataset), MAPK signalling pathway (pancreatic cancer dataset), and p53 signalling pathway (pancreatic cancer dataset), were 0.034, 0.062, 0.11, and 0.056, respectively. The number of pathways identified and corresponding p ‐values show that MSPIA had the best performance among the three methods, whereas SPIA performed slightly better than PSPIA.In the three data sets, all pathways had p ‐value or q ‐value threshold was 0.01, the performance of the two methods was not stable. In the colon cancer and pancreatic cancer datasets, the two methods failed to identify any relevant pathway. In the lung cancer dataset, GSEA identified only two significant pathways, while BPA identified 29 significant pathways. In general, PSPIA, MSPIA, and SPIA were all better than GSEA and BPA. The main disadvantage of GSEA is that it only considers the total NDE in a pathway, but neglects the perturbation induced by differential signals. The major disadvantage of BPA is the loss of some pathway information caused by deleting some relationships according to the correlation of genes during the transformation of the signal pathway into a directed acyclic graph. In addition, in the case of a small sample size, the correlation may be distorted.Classical GSEA and BPA methods were also selected for comparison. As shown in Table Recognition of pathways related to cancer or other diseases is of great importance for understanding their pathogenetic mechanisms and developing effective therapies. In recent years, researchers have proposed many pathway analysis methods. The advantage of SPIA and major reason for its popularity is the combination of information regarding DEGs and their perturbation within pathways. In real biological pathways, the transmission of a perturbation signal is closely related to the intensity of the interaction between genes. For this reason, in this article based on the SPIA, we adopted PSPIA and MSPIA to obtain the interaction intensity, which was integrated into the transmission process of the perturbation signal. Thus, two signalling pathway analysis methods based on Pearson's correlation coefficient and mutual information were proposed: PSPIA and MSPIA.With the comparison of results from the colon cancer, lung cancer, and pancreatic cancer datasets, we found that the modified PSPIA and MSPIA methods recognised more signalling pathways possibly related to cancer than did the original SPIA method. In addition, among the significant pathways identified through PSPIA and MSPIA, some pathways were not identified through SPIA, but have been reported as related to some cancers in previous articles. Among the three data sets, MSPIA identified more significant pathways in lung cancer and pancreatic cancer datasets than did PSPIA, but identified fewer significant pathways in the colon cancer dataset. We believe that this difference may be related to the sample size. In the case of a large sample size, mutual information can better interpret the interaction between genes than can Pearson's correlation coefficient. In addition, through comparison of the capacity of the three methods to identify seven common cancer pathways, we found that MSPIA had the best performance, whereas SPIA was slightly better than PSPIA. Therefore, it can be concluded that consideration of the intensity of the interaction between genes can further improve the capacity of SPIA to recognise cancer‐related pathways. In general, the recognition capacity of MSPIA was better than that of PSPIA.We also investigated the pathway recognition capacities of the GSEA and BPA methods in the three cancer datasets. The results of the analyses indicate that the recognition capacity and stability of GSEA and BPA are inferior to those of SPIA and SPIA‐based methods.Supplementary DataClick here for additional data file.Supplementary DataClick here for additional data file.Supplementary DataClick here for additional data file.


When other confounding factors were adjusted, serum APOA1 level was shown as an independent risk factor for the prevalence of NAFLD . Our results suggested that the three PPARs  might promote the expression and molecular transportation of APOA1 to form a PPAR-APOA1 signaling pathway that demonstrated a beneficial role in the pathogenesis of NAFLD.Peroxisome proliferator-activated receptors (PPARs) have been suggested to play crucial roles in the pathology of NAFLD with a vague understanding of the underlying mechanism. Here, we integrated large-scale literature data and clinical data to explore the potential role of the PPAR-APOA1 signaling pathway in the pathology of NAFLD. First, the signaling pathway connecting PPARs, APOA1, and NAFLD was constructed. Then, we employed clinical data to explore the association between APOA1 levels and NAFLD. In addition, we built the APOA1-driven pathway analysis to explore the potential mechanism of the APOA1-NAFLD association. Pathway analysis showed that APOA1 serves as a hubprotein connecting PPARs and NAFLD through a beneficial modulation of 16 out of 21 NAFLD upstream regulators. Each relationship within the composed pathway was supported by results from multiple previous studies. Clinical data analysis showed that an increase of APOA1 level was associated with a significantly decreased NAFLD prevalence ( Nonalcoholic fatty liver disease (NAFLD) is characterized by an excessive fat build-up in the liver without a clear cause, such as alcohol use , 2. NAFLδ (PPARD) and peroxisome proliferator-activated receptor α (PPARA) have been suggested as therapeutic targets to alleviate NAFLD [γ (PPARG) has also been shown essential to protect against nonalcoholic steatohepatitis [Peroxisome proliferator-activated receptors have been suggested to play crucial roles in the pathology of NAFLD , 6. Specte NAFLD , 7. Peroepatitis , 9.As the major component of high-density lipoprotein (HDL) particles, apolipoprotein A1 (APOA1) is a protein encoded by the APOA1 gene to have a specific role in lipid metabolism , 11. TheAll three PPARs  were implicated as promoters to increase APOA1 secretion and expression from the liver –18, whicThe rest of this study was organized as follows. First, we conducted a literature-based pathway analysis to study the PPAR-APOA1 signaling pathway and its role in the pathology of NAFLD. Second, we used large-scale clinical data to study the association between APOA1 levels and NAFLD. In addition, we constructed the genetic and small molecule pathways to explore the potential mechanism of APOA1-NAFLD association.http://www.pathwaystudio.com) knowledge database, we constructed the PPAR-APOA1 signaling pathway to explore the connection between PPARs, APOA1, and NAFLD. The entities within the network included the three PPARs , the protein APOA1, the diseases , and the two potential drugs for the treatment of NAFLD (fenofibrate and gemfibrozil). The relationships between the entities were identified by using the network building module (https://supportcontent.elsevier.com/Support%20Hub/Pathway%20Studio/Guide%20to%20Building%20Pathways%20in%20ChemEffect%20and%20DiseaseFX%20with%20Pathway%20Studio%20Web.pdf). The relationship between the entities within the signaling pathway was supported by at least three references. Then, a manual quality control process was conducted to ensure the reliability of the relationships. A reference list was provided in Supplementary Material Assisted by Elsevier Pathway Studio . The average age was (47.2 ± 15.1) years. Of these 12,823 subjects, 3147 (24.5%) fulfilled the diagnostic criteria of NAFLD, and the prevalence in men and women was 28.8% and 16.3%, respectively. More information regarding the clinical data and related data process is available in Supplementary Material Fasting venous blood from the cubital vein and centrifuged was used to prepare the serum for biochemical analysis. The APOA1 levels were studied using the AU640 fully automatic biochemical analyzer . To explore the relationship between APOA1 levels and the prevalence of NAFLD, the 12,823 subjects were separated into four different groups according to their serum APOA1 levels, which were described as follows. (1) Q1 group: APOA1 level ≤ 1.18 g/L; (2) Q2 group: APOA1 level was between 1.19 and 1.32 g/L; (2) Q3 group: APOA1 level was between 1.33 and 1.55 g/L; and (2) Q4 group: APOA1 level ≥ 1.56 g/L in the Q4 group. A chi-square test was used to test the prevalence of NAFLD under different APOA1 levels. Multivariate logistic regression was used to analyze corisk factors of NAFLD besides APOA1 levels.http://www.pathwaystudio.com) knowledge database, we identified common genes and small molecules that were downstream targets of APOA1 and upstream regulators of NAFLD. These relation data were then used to compose the APOA1 seeded pathways, which was tentative to explore the possible mechanisms underlying the role of APOA1 in the pathology of NAFLD. A manual quality control process was conducted to ensure the reliability of the relationships and their polarities. A reference list was provided in Supplementary Material Assisted by the literature-based Pathway Studio  employed in NAFLD treatment were identified as promoters for all three PPARs, which also increase APOA1 mRNA in human liver biopsies as well as APOA1 plasma concentration. These studies extended the PPAR-APOA1 signaling pathway and provided further support for its role in NAFLD pathology. In total, over 1,200 references support the relationships presented in Due to the limited support from previous studies regarding the role of serum APOA1 in NAFLD, in this study, we employed large-scale clinical data to test the association between the variation of APOA1 levels and the prevalence of NAFLD. Our results indicated that the prevalence of NAFLD could decrease with elevated APOA1 levels, and that low serum APOA1 levels might be an independent risk factor for the prevalence of NAFLD.χ2 = 292.109, P < 0.001), as shown in NAFLD prevalence in Q1, Q2, Q3, and Q4 groups was 32.58%, 28.21%, 21.55%, and 15.58%, respectively. With the increase of APOA1 concentration, the prevalence of NAFLD was significantly decreased (We explored the possibility of the decreased APOA1 levels as a risk for NAFLD incidence with/without adjusting other confounding factors and presented the results in When other risk factors were adjusted (model 2 and model 3), the ORs were increased, suggesting APOA1 as an independent risk factor for NAFLD. Especially in the group where APOA1 levels were high (group 3 and group 4), the influence of APOA1 levels was statistically significant in all models with/without adjusting other confounding factors. These results indicated that the increased serum APOA1 levels could be more critical than the deficiency of serum APOA1 in their influence on NAFLD development. Our results supported the APOA1–NAFLD relationship within the PPAR-APOA1 signaling pathway constructed in Both literature-based pathway analysis and our clinical data suggested that increased APOA1 levels might play a beneficial role in NAFLD. To understand the underlying mechanisms, we conducted a literature-based molecule pathway analysis to identify downstream targets of APOA1 that were also upstream regulators of NAFLD, based on which APOA1-driven signaling pathways influencing NAFLD development were constructed and presented in Overall, APOA1 beneficially regulated 16 out of 21 NAFLD regulators, highlighted in green-yellow or green-blue in Figures All three peroxisome proliferator-activated receptors  attenuate NAFLD and thus were suggested as therapeutic targets for the treatment of NAFLD –7. ResulMultiple previous studies supported the positive regulation of PPARs on APOA1. For instance, Gervois et al. showed that the PPARA activation could induce hepatic APOA1 and APOA2 expression in humans and lead to increased plasma HDL cholesterol . Singh eMoreover, multiple studies supported the association between decreased levels of APOA1 and the prevalence of NAFLD , 20, andInterestingly, literature-based pathway analysis also showed that PPARs , as well as APOA1, were downstream targets of two drugs (fenofibrate and gemfibrozil) that have the potential to be used in the treatment of NAFLD , 22, as As the mechanism of the APOA1-NAFLD relationship was largely unknown, we conducted another literature-based molecule pathway analysis. Our results showed that APOA1 could modulate 16 out of 21 NAFLD regulators in favor of a protective role against NAFLD development . For exaThis study has one limitation as follows. The PPAR-APOA1 pathway was built mainly based on previous studies and partially on clinical data. Due to the complexity of the relationship between PPARs and NAFLD, the composed pathway in this study should be tested in a biology experiment.This study integrated literature-based pathway analysis and clinical data analysis to study the PPAR-APOA1 signaling pathway and their role in the pathologic development of NAFLD. Our results showed that the three PPARs  might promote the expression and molecular transportation of APOA1, which mainly plays a beneficial role in the development of NAFLD. Our results may add new insights into the understanding of the role PPARs play in NAFLD.


To investigate the molecular function of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta (PIK3CB) underlying Alzheimer's disease (AD).  RNA sequencing data were used to filtrate differentially expressed genes (DEGs) in AD/nondementia control and PIK3CB-low/high groups. An unbiased coexpression network was established to evaluate module-trait relationships by using weight gene correlation network analysis (WGCNA). Global regulatory network was constructed to predict the protein-protein interaction. Further cross-talking pathways of PIK3CB were identified by functional enrichment analysis.  The mean expression of PIK3CB in AD patients was significantly lower than those in nondementia controls. We identified 2,385 DEGs from 16,790 background genes in AD/control and PIK3CB-low/high groups. Five coexpression modules were established using WGCNA, which participated in apoptosis, axon guidance, long-term potentiation (LTP), regulation of actin cytoskeleton, synaptic vesicle cycle, FoxO, mitogen-activated protein kinase (MAPK), and vascular endothelial growth factor (VEGF) signaling pathways. DEGs with strong relation to AD and low PIK3CB expression were extracted to construct a global regulatory network, in which cross-talking pathways of PIK3CB were identified, such as apoptosis, axon guidance, and FoxO signaling pathway. The occurrence of AD could be accurately predicted by low PIK3CB based on the area under the curve of 71.7%.  These findings highlight downregulated PIK3CB as a potential causative factor of AD, possibly mediated via apoptosis, axon guidance, and FoxO signaling pathway. The elements –7. Pathoneration , 9. This (sAPPα) . Imbalane (PI3K) , 12. Mule (PI3K) . The maie (PI3K) , 15. Suce (PI3K) . As an ie (PI3K) . Despiteβ pathway [Traditionally, class I PI3K is the most concerned isoform of PI3K family, which consists of a regulatory subunit and a catalytic subunit that play pivotal roles in cell proliferation, differentiation, and survival (or apoptosis) –19. In t pathway . To the  pathway , which mnormalizeBetweenArrays function in R package [Microarray RNA sequencing (RNA-seq) data from middle temporal gyrus of 78 human samples (46 AD patients and 32 age-matched nondementia controls) were accessible through Gene Expression Omnibus (GEO) Series accession number GSE109887 , 26. Ill package .ClusterProfler, enrichplot and GSEABase packages [ggplot2 package. A pvaluecutoff of 0.05 was determined as the screening threshold for significant enrichment.GSEA is a widely used computational method assessing whether enrichment of a predefined gene set is statistically significant between two biological states . During packages . The vislmFit and eBayes functions, we identified the differences of gene expression in AD/control and PIK3CB-low/high groups, respectively [p cutoff of < 0.05 was adopted to define differentially expressed genes (DEGs) [limma package [Taken the average expression value of PIK3CB to be the boundary, enrolled samples were dichotomized into PIK3CB-low and PIK3CB-high cohort. Using ectively . A fold s (DEGs) –33. Anal package . Volcanohclust function. An appropriate soft-thresholding power of 12 was selected with pickSoftThreshold function to achieve the integral connectivity of gene coexpression modules, so that the network utmost approaches the authentic biological state [clusterProfiler package.Following the default parameters of weight gene correlation network analysis (WGCNA), overlapping DEGs between AD/control and PIK3CB-low/high cohorts were disposed to create an unbiased coexpression network. The preponderance of WGCNA is that it converts intricate microarray data into gene coexpressed modules, providing insight into a signal network that may be associated with phenotypic traits of interest . To ensual state . Module al state . Correlaal state , 38. FunverboseScatterplot function [http://www.stringdb.org/), the interaction between DEGs was explored by establishment of protein-protein interaction (PPI) network [cytoscape software was used to visualize the global regulatory network, as well as the cross-talking pathways of PIK3CB that were enriched by pathway enrichment analyses [Genetic phenotype and intramodular connectivity were measured by gene significance (GS) and module membership (MM), respectively, the relationships of which were plotted in a scatter diagram adopting function . Based o network . The cytanalyses , 42.pROC package in R. An AUC value of 100% indicated complete prediction, while 50% represented random selection.ROC analysis was conducted to estimate the classifier performance of sequential output, including sensitivity and specificity parameters, as measured by the area under the curve (AUC) , 44. Diat test or the nonparametric Mann–Whitney U test. During the process of WGCNA analysis, the statistical relationship between gene coexpressed modules and phenotypic traits of interest was estimated by Pearson correlation coefficient (PCC) analysis. Using the approach of Hanley-McNeil test, predictive accuracy of PIK3CB was verified by ROC analysis, the result of which was quantified by AUC value. Two-tailed p values of < 0.05 were defined as the standard of statistical significance. All statistical analyses were conducted by employing GraphPad Prism software (version 8.3.1) and R package (version 3.6.2).Continuous variables between AD patients and nondementia controls were compared by applying The workflow diagram of this research was detailed in p < 0.01)  . Through < 0.01) , while 4 < 0.01) . Subsequ < 0.01) , which ep = 4e − 06; turquoise: correlation coefficient = 0.55, p = 2e − 07) and negative correlation with PIK3CB expression ; while brown, blue, and yellow modules were negatively correlated with AD  and positively relevant to PIK3CB expression . Functional enrichment analysis of KEGG pathways  and could be enrolled in subsequent gene coexpression network analysis . As showpathways  showed tp < 1e − 200; brown: correlation coefficient = 0.68, p = 6.2e − 65; green: correlation coefficient = 0.46, p = 4.8e − 07; turquoise: correlation coefficient = 0.81, p < 1e − 200; yellow: correlation coefficient = 0.25, p = 0.0079), suggesting that the five coexpression modules were prominently associated with low expression of PIK3CB in AD patients. To accurately identify the mechanistic pathways of PIK3CB in AD onset, DEGs strongly interacting with PIK3CB (meeting the inclusion criteria of MM > 0.7 and GS > 0.5) were extracted to construct a global regulation network , AD may be at higher risk due to mitochondrial oxidative stress through FoxO signaling pathway [in vitro experiment from animal AD models, Aβ-induced Ros activates p66Shc, an adaptor protein that triggers phosphorylation  of FoxO, which aggravates the accumulation of oxidative stress and thus to neuronal and synaptic loss [Taking into consideration that A pathway , 78. Upr pathway . This ac pathway –82. Duri pathway , 84. As tic loss . Relatedtic loss , 87. Froβ peptide is a candidate site for PI3K phosphorylation, the toxicity of which can be suppressed by co-expression of PI3K [in vivo or in vitro experiments are needed to verify the mechanistic pathways of low PIK3CB-mediated AD neurodegeneration that are proposed in this in silico research.Based on the scatter diagram of the relationship between MM and GS, DEGs strongly interacting with PIK3CB were extracted to construct the global regulatory network for functional annotation, illustrating the value of integrating epigenetic data for understanding complicated mechanisms. The cross-talking pathways of PIK3CB revealed that molecular functions of downregulated PIK3CB in AD were derived from apoptosis, axon guidance, and FoxO signaling pathway. The vulnerability of such pathways appears to be strikingly apparent at low PIK3CB levels, subsequently contributing to the onset of AD in a variety of pathogenic mechanisms , 77, 88. of PI3K . Future In aggregate, integrative genomic analysis is an effective approach to uncover pleiotropic roles of PIK3CB underlying AD development. Our findings lend strong support to the notion that low PIK3CB expression is involved in the pathogenesis of AD through apoptosis, axon guidance, and FoxO signaling pathway.