Non-functional pituitary adenoma (NFPA) seriously affects hypothanamus-pituitary-target organ axis system, with a series of molecule alterations in the multiple levels of genome, transcriptome, proteome, and post-translational modifications, and those molecules mutually interact in a molecular-network system. Meta analysis coupled with IPA pathway-network program was used to comprehensively analyze nine sets of documented NFPA omics data, including NFPA quantitative transcriptomics data [280 differentially expressed genes (DEGs)], NFPA quantitative proteomics data [50 differentially expressed proteins (DEPs)], NFPA mapping protein data (218 proteins), NFPA mapping protein nitration data (9 nitroproteins and 3 non-nitrated proteins), invasive NFPA quantitative transriptomics data (346 DEGs), invasive NFPA quantitative proteomics data (57 DEPs), control mapping protein data (1469 proteins), control mapping protein nitration data (8 nitroproteins), and control mapping phosphorylation data (28 phosphoproteins). A total of 62 molecular-networks with 861 hub-molecules and 519 canonical-pathways including 54 cancer-related canonical pathways were revealed. A total of 42 hub-molecule panels and 9 canonical-pathway panels were identified to significantly associate with tumorigenesis. Four important molecular-network systems, including PI3K/AKT, mTOR, Wnt, and ERK/MAPK pathway-systems, were confirmed in NFPAs by PTMScan experiments with altered expression-patterns and phosphorylations. Nineteen high-frequency hub-molecules were also validated in NFPAs with PTMScan experiment with at least 2.5-fold changes in expression or phosphorylation, including ERK, ERK1/2, Jnk, MAPK, Mek, p38 MAPK, AKT, PI3K complex, p85, PKC, FAK, Rac, Shc, HSP90, NFκB Complex, histone H3, AP1, calmodulin, and PLC. Furthermore, mTOR and Wnt pathway-systems were confirmed in NFPAs by immunoaffinity Western blot analysis, with significantly decreased expression of PRAS40 and increased phosphorylation levels of p-PRAS40 (Thr246) in mTOR pathway in NFPAs compared to controls, and with the decreased protein expressions of GSK-3β and GSK-3β, significantly increased phosphorylation levels of p-GSK3α (Ser21) and p-GSK3β (Ser9), and increased expression level of β-catenin in Wnt pathway in NFPAs compared to controls. Those findings provided a comphrensive and large-scale pathway network data for NFPAs, and offer the scientific evidence for insights into the accurate molecular mechanisms of NFPA and discovery of the effective biomarkers for diagnosis, prognosis, and determination of therapeutic targets. Human non-functional pituitary adenoma (NFPA) is a common disease that occurs in the central regulatory organ pituitary, and seriously impacts on physiological functions and human health . CompareMolecular-network alterations are the hallmark in complex cancer disease , 7, 12. The present study collected all published omics data about NFPAs , includiDataset 1—NFPA quantitative transcriptomics data, including 280 differentially expressed genes (DEGs) (114 upregulated and 166 downregulated)  (21 upregulated and 29 downregulated)    Datgulated) . (ii) Dagulated) . (iii) Dproteins . (iv) Daproteins . (v) Datgulated) . (vi) Dagulated) . (vii) Dproteins . (viii) proteins . (ix) Daproteins .http://www.ingenuity.com). Briefly, the ID numbers of genes and proteins in each dataset were used as the identifiers, and input into the IPA analysis program with the Core analysis platform. For DEG and DEP data, the ID numbers and corresponding fold-change values were input simultaneously into the IPA analysis system to automatically search the matched genes/molecules, and generate a two-dimensional table that includes the matched and unmatched genes/proteins. Five subdatasets were automatically generated, including (i) All IDs that contained all input IDs, (ii) Unmapped IDs that were without the matched molecules in the IPA system, which did not enter the next-step pathway analysis, (iii) Mapped IDs that were the matched molecules with duplicated IDs, (iv) Network-eligible IDs that were the mapped IDs without duplicated IDs, and (v) Functions/Pathways/List-eligible IDs. For the duplicated IDs for the same gene/protein, the identifier with the highest fold-change was used in the pathway analysis; or, the first appeared gene/protein was used in the pathway analysis without an expression value such as mapping proteomic data, nitroprotein data, and phosphorylation data. The Network-eligible IDs were proceeded into the pathway network analysis with comparison of network-eligible molecules  with the IPA knowledge base (IPAKB); and IPAKB contains over 6 million scientific findings and over 800 canonical pathways  of the associations between the dataset and the canonical pathways in the IPAKB were measured with comparison of the number of use-specific genes/proteins of interest that participate in a given pathway to the total number of occurrences of these genes in all pathway annotations that are stored in the IPAKB. The Benjamini-Hochberg for multiple testing was used to calculate each p-value to determine the probability that the association between genes in the dataset and the canonical pathways in IPAKB was explained only by chance, with a statistical significance of p < 0.05. Each IPA analysis generated statistically significant networks, canonical pathways, biofunctions, and tox functions. A toxic pathway is defined as a canonical pathway that is significantly associated with toxicity lists that describe adaptive, defensive, or reparative responses to xenobiotic insult, and could be used to understand biological responses.Each dataset was analyzed with IPA analysis program  from different datasets together with the original gene/protein data were comprehensively analyzed in combination with literature-based bioinformatics and clinical features, to clarify molecular pathway-network alterations in NFPAs. Those common networks, canonical pathways, biofunctions, and tox functions derived from multiple datasets were important molecular events that occurred in NFPAs. Moreover, an important role of network is to find hub-molecules. All of those hub-molecules with at least five linked molecules among those networks identified from nine datasets were further analyzed to find hub-molecule panels. Each hub-molecule panel was further rationalized in NFPAs. Each canonical-pathway panel derived from nine datasets was also rationalized in NFPA biological processes.Pituitary adenoma tissue samples were obtained from Department of Neurosurgery, Xiangya Hospital, Central South University, and were approved by Xiangya Hospital Medical Ethics Committee of Central South University. Control pituitary glands were post-mortem tissues obtained from the Memphis Regional Medical Center, and were approved by University of Tennessee Health Science Center Internal Review Board (UTHSC-IRB). The written informed consent was obtained from each patient or the family of control pituitary subject, after full explanation of the purpose and nature of all used procedures. The tissues were removed during neurosurgery or autopsy, frozen immediately in liquid nitrogen, and stored (−80°C) until processed.n = 4) and control pituitaries (n = 4) (Pituitary tissue samples from NFPA patients ( (n = 4)  were anaAn amount (100 mg) of pituitary tissue samples were added in a volume (1 ml) of urea lysis buffer (20 mM 2-hydroxyethyl (HEPES), 9 M urea, 2.5 mM sodium pyrophosphate, 1 mM sodium orthovanadate, and 1 mM β-glycerophosphate, pH 8.0), and homogenized with refiner on the ice. The lysates were sonicated , and centrifuged . The supernatant was collected, and its protein concentration was measured with Bio-Rad 2-D Quant assay using bovine serum albumin (BSA) as standard. Each sample was mixed with the equal protein amount in NFPA group and in control group, respectively.Equal amount (10 mg/sample) of protein mixture  was reduced  with a final concentration of 4.5 mM dithiothreitol (DTT) in an incubator. After the solution was cooled on ice to room temperature, an appropriate volume (1 ml) of 100 mM iodoacetamide was added to 40 mg of protein extract, mixed well, and incubated . The reduced and alkalized samples were diluted (1:4) with 20 mM HEPES buffer at pH 8.0. The diluted samples were digested  with 10 μg/ml trypsin-TPCK  in 1 mM hydrochloric acid (HCl). After digestion, the tryptic peptides were acidified with 1% trifluoroacetic acid (TFA) to reach pH <3, and then stood on ice for 15 min to be precipitated. The acidified peptide solution was centrifuged , followed by desalination through a C18 Sep-Pak cartridge (Waters) and elution by 40% acetonitrile in 0.1% TFA. The eluted peptides were lyophilized.TM materials into a 10-μl pipette tip , passed with 50 μl 50% acetonitrile in 0.1% TFA , and followed by rinsing with 50 μl 0.1% TFA by centrifuging the tip . The peptides were eluted from the Stagetip through passing 10 μl 40% acetonitrile in 0.1% TFA and centrifuging . The eluted peptides were vacuum-dried.PTMScan Direct Multipathway V2.0  antibodyTM Protein Digestion Standard Mix 1; an overall quantity of 100 fmol (33 fmol per injection)] ahead of LC-MS/MS analysis on an Easy-nLC 1000 hyphened Q-ExactiveTM mass spectrometer. Peptides were separated by a linear gradient from 2 to 32% acetonitrile over 120 min. Both MS and MS/MS data were acquired in centroid mode. For precursor ion scan, resolution was set at 70,000 with an automatic gain control (AGC) target of 1 x 106, and scan range was from m/z 300 to 1,500. For product ion scan, resolution was set at 17,500 with AGC target of 1 x 105, and scan range was from m/z 200 to 2,000. The top 10 intensive precursor ions in each MS scan were selected for MS/MS analysis with normalized collision energy of 25.The PTMScan antibody-enriched peptides were resuspended in 12 μl of 0.125% formic acid for each sample, and separated through a reversed-phase C18 column (75 μm i.d. x 10 cm length) which packed into a PicoTip emitter (~8 μm i.d.) with a Magic C18 AQ (100 Å x 5 μm). Each sample was divided into two equal portions for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis to increase the number of identifications and perform analytical reproducibility. Each sample was spiked with a standard peptide mixture [MassPREPhomo sapiens FAST database . A mass accuracy of ±0.02 Da was used for product ions and ±5 ppm for precursor ions. Enzyme was selected as trypsin with at least one tryptic (K- or R-containing) terminus required and up to four miscleavages allowed per peptide. Carboxamidomethylation at cysteine residues was set as a fixed modification; and oxidation at methionine residues and the appropriate PTMs were set as variable modifications. All searches included reverse decoy database was used to value false discovery rates (FDR), and the linear discriminant module of core was screened with 5% FDR. Progenesis V4.1 (Waters Cooperation) and Skyline Version 3.1  were used to generate quantitative data and to extract the whole peal area of the corresponding peptide assignments. Extracted ion chromatograms of peptide ions with abundance variations between samples were manually assessed to make sure the accurate quantification in Skyline.SEQUEST and the core platform from Harvard University were used to evaluate MS/MS spectra. MS/MS data were used to search against Swiss-Prot t-test with p < 0.05 was used to determine statistically significant difference between NFPAs and controls.Equal amount of tissue lysates (20 μg) were mixed (v:v = 1:1) with 2x loading buffer that mixed 50 μl β-mercaptoethanol (β-ME) and 950 μl Laemmli sample buffer , boiled , and then chilled on the ice. The boiled protein samples were separated with 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) , transferred to a polyvinylidene fluoride (PVDF) membrane , and blocked  with 5% BSA in Tris buffered saline (TBS) containing 0.1% Tween 20 (TBST). The blocked proteins on the PVDF membrane were incubated  with primary antibodies, washed in TBST, and incubated  with anti-mouse or anti-rabbit HRP-conjugated secondary antibodies. Each primary or secondary antibody was prepared . The memp < 0.05 was used to determine statistically significant molecular-networks, and canonical pathways. For PTMScan experiments, 5% FDR with reverse decoy database search using Progenesis V4.1 (Waters Cooperation) and Skyline Version 3.1  was used to quantitatively determine a reliable peptide, protein, and phosphorylation. For Western blot analysis, Student t-test with p < 0.05 and at least repetition three times were used to determine statistically significant difference in each protein or phosphorylation in NFPAs relative to controls.For IPA analysis of multi-omics data, Benjamini-Hochberg for multiple testing with significance level of Nine sets of omics data  were inpIPA system compared and associated the network-eligible molecules  in each omics dataset with the large-scale IPAKB to link molecules with direct or indirect relationship into a biological function group, and further generate the complicated interactive molecular-network diagrams. This study identified 62 statistically significant molecular-networks from nine sets of omics data, including 15 networks from NFPA DEGs , 4 from To reveal the significance of overall variations in molecular networks from different datasets in NFPAs relative to controls, the frequencies of top functional items occurred among networks were counted and analyzed. The results revealed that top network-function items from control pituitary group were mainly related to essential biological processes of normal cell life, including RNA-transcriptional modifications, protein synthesis, carbohydrate metabolism, molecular transport, cell morphology, cell cycle, and cell growth and proliferation . WhereasEach network containing multiple node molecules  formed a web to participate in the biological functions. However, different node molecules in a given network did not equally contribute to the biological functions of that given network. Some node molecules in a network were in the hub position, namely hub molecule that connected with many other node molecules in a direct or indirect way, and played key roles in that network. Some node molecules were at the boundary position in a network, and interacted with a relatively less number of other node molecules; such a boundary node molecule could only contribute a relatively weak role in a network. Furthermore, some hub molecules were present in multiple networks that were derived from nine different datasets, which clearly revealed that different networks were interacted mutually in an NFPA biological system. Therefore, the detailed analysis of all hub-molecules in 62 networks from nine sets of omics datasets might reveal and discover key molecules, molecule-panels, and corresponding biological functions that operate in an NFPA biological system, which benefits the discovery of biomarkers for NFPAs.In this study, a hub-molecule was defined as a node molecule that directly or indirectly connected at least five other node molecules in a network. Thus, a total of 861 hub-molecules were identified from 62 networks, and the primary function annotation of each hub-molecule was obtained from UniProt annotation page, NCBI database, and extensive literature analysis . A total2+-related proteins , 25 from NFPA DEPs (Dataset 2), 89 from NFPA mapping proteins (Dataset 3), 29 from NFPA nitroproteins (Dataset 4), 30 from invasive NFPA DEGs (Dataset 5), 28 from invasive NFPA DEPs (Dataset 6), 174 form control mapping proteins (Dataset 7), 33 from control nitroproteins (Dataset 8), and 43 from control phosphoproteins (Dataset 9) –9.3. Of PTMScan® Direct test that combined immunoaffinity enrichment and LC-MS/MS was used to identify and quantify phosphorylated peptides/proteins within multiple key canonical pathways . This st). The total protein expression level of PRAS40 and its phosphorylation level at residue Thr246 in PRAS40 were the switch of mTORC1 pathway to decide the initiation of the downstream protein synthesis and metabolism enhancement , RNAs (transcriptome), proteins (proteome), metabolites (metabolome), and imaging features (radiome), and those different levels of molecules are mutually interacted. Multiomics data-based pathway network analysis benefits for the complete and comprehensive understanding of molecular mechanisms and discovery of reliable pathway-network-based biomarkers for pituitary adenomas. This study collected nine sets of omics data from different research groups in the world, including DEG and DEP data in NFPAs compared to controls, DEG and DEP data in invasive NFPAs compared to non-invasive NFPAs, nitroproteins in NFPAs, mapping proteins in NFPAs, and mapping proteins/nitroproteins/phosphoproteins in control pituitaries. Each omics data was performed by pathway-network analysis and related bioinformatics analysis, and hub-molecules were identified from pathway-networks. Those pathway networks derived from mapping proteins in NFPAs and mapping proteins/nitroproteins/phosphoproteins in controls were the base line data, which provided the reference for determination of reliable NFPA-related pathway-networks. Those pathway networks derived from DEGs and DEPs in NFPAs and invasive NFPAs, and nitroproteins in NFPAs, were directly associated with NFPAs pathophysiological changes. Thus, a total of 62 molecular networks and 861 hub-molecules were identified. According to the primary functions of 861 hub-molecules, 42 hub-molecule panels that were grouped into 16 functional categories were identified ; and 57 Based on multi-omics data, PTMScan experimental data, and immunoaffinity Western blot data of key hub-molecules within important signaling pathway systems, PI3K/Akt signaling pathways , and MAPK signaling pathways , were significantly associated with NFPA pathogenesis, and were further discussed in details below.-kinase signaling pathways, including receptor tyrosine kinases (RTKs) and G protein-coupled receptors (GPCRs), and its excessive activation often leads to genomic instability, tumor formation, progression, angiogenesis, and multidrug resistance , and mTOR complex 2 (mTORC2) . The corThere are three kinds of 4E-BPs, including 4E-BP1, 4E-BP2, and 4E-BP3 in mammals. In quiescent state, 4E-BPs tightly interact eIF4E to prevent the initiation of translation , 32. PhoThose findings clearly demonstrated that mTOR signaling, including mTORC1 and mTORC2 complexes, was involved in NFPA pathogenesis, including cell survival, protein synthesis, and metabolism.Wnt pathways are pivotal to modulate many cellular and physiological processes such as cell polarity, motility, adhesion, proliferation, survival, stem cells self-renewal, and tissue homeostasis, and include canonical and non-canonical Wnt pathways according to different ligands and downstream effectors  Figure .Canonical Wnt pathway mainly regulated the stability of β-catenin to modulate transcriptions of Wnt-targeted genes, and control cell fate, growth and differentiation . (i) β-c2+ pathway had been characterized  and phosphodiesterase (PDE), then invoked calcium-sensitive enzymes such as calcium–calmodulin-dependent kinase II (CaMK II) and PKC, which subsequently activated the corresponding transcription factor NFAT and CDC42 to cause a wide range of cellular effects, including cell adhesion, migration, inflammation, and tumorigenesis , G proteins, calmodulin/calcium, PKC, Src, and JNK . Among tcterized   family members were critical transcription factors, and were involved in numerous cellular processes including cancer . After cmTOR, Wnt, and NFκB signalings were dysregulated in NFPAs, and multiple molecules in these signaling pathways were further abnormally modulated in invasive relative to non-invasive NFPAs.BAD was a proapoptotic member in Bcl-2 family, and was phosphorylated by many upstream kinases to control apoptosis. BAD could inhibit Ras/MEK/ERK and JNK signaling, and EMT. Studies found that downregulated p-BAD and BAD in cancer cells promoted tumor invasion and migration , 51. TheMAPKs can regulate many biological processes, including proliferation, apoptosis, stress responses, and immune defense, and include four independent MAPK cascades: Extracellular signal-regulated kinase1/2 (ERK1/2) pathway , c-Jun N-terminal kinase (JNK) pathway, p38 pathway, and ERK5 pathway  Figure . In thisExtracellular signals were transducted to intracellular targets through phosphorylation cascade reactions in Ras-Raf-Mek-ERK1/2 activation pattern. The ectopic activation of this signaling could result in tumorigenesis, progression, and metastasis; and Ras-Raf-Mek-ERK1/2 signaling can interact with Ras-PI3K-Akt signaling to amplify signal regulation range and mutually modulate tumorigenesis . (i) In p38 signaling primarily participated in responses to environmental stress, and also answers to immune response and inflammation. p38 MAPKs included four homologous members p38α, p38β, p38γ, and p38δ. p38α and p38β were ubiquitously expressed in human body, whereas p38γ and p38δ were restricted expressed in muscle, and lung and kidney, respectively . Upon stThis present study used the systems biology opinion to find out the key pathological mechanisms commonly existing in NFPA through Meta analysis. The IPA system was used to further analyze different omics data of NFPA and extract important data such as molecular networks, canonical signaling pathways, and high-frequency hub molecules closely related to the occurrence and development of NFPAs, providing effective data to promote individualized precise treatment. Many studies have shown that targeting some high-frequency hub-molecules in Table 1 has achieved good results in other types of pituitary adenomas. For example, TGFβ1 is used as a novel therapeutic target to treat resistant prolactinomas ; the micPituitary adenoma is a common pituitary disease, with a series of molecule alterations in DNAs (genome), RNAs (transcriptome), proteins (proteome), metabolites (metabolome), and imaging characteristics (radiome) that resulted from exogenous and endogenous carcinogens, and those molecules associate mutually and function in a molecular network system. Molecular network-based molecule pattern has important scientific merits in clarification of molecular mechanisms and discovery of effective biomarkers and therapeutic targets for pituitary adenomas. This study used the Meta-analysis strategy and integratively analyzed all documented NFPA omics data  with IPA pathway network program. A total of 62 molecular-networks and 519 canonical-pathways were revealed with statistical significance from nine sets of NFPA omics data. A total of 861 hub-molecules were derived from those molecular-networks and were classified into 42 hub-molecule panels to associate with pituitary tumorigenesis. A total of 139 canonical-pathways were found from at least two sets of omics data, which generated 68 cancer-related canonical-pathways to obviously associate with the occurrence and development of tumor, and of them 54 canonical pathways involved in any DEGs or DEPs were divided into 9 canonical-pathway panels according to the similar cellular functions and biological processes. Those molecular networks, hub molecules, hub-molecule panels, canonical pathways, and canonical-pathway panels formed the overall pathway-network characterization of NFPAs. The important pathway-networks and hub-molecules were further validated and in-depth studied with PTMScan experiments and immunoaffinity Western blot analysis to quantify the alterations in the protein expressions and specific phosphorylation status. Comprehensive analysis of all data including multi-omics data, PTMScan experimental data, and immunoaffinity Western blot data revealed several important signaling pathway systems that operate in NFPA biological system, including PI3K/Akt signaling pathways , and MAPK signaling pathways . These findings are the solid scientific evidence and molecular targets to discover molecular-network-based biomarkers and effective therapeutic targets for the accurate diagnosis and treatment of the different types and different development stages of NFPAs. Here we specially emphasize that, NFPAs are very complex diseases, involving a series of molecule changes and pathway-network changes. One should change our thinking and working models from mono-targeting pharmacological treatment concept to the multi-targeting pharmacological treatment concept, from mono-molecule biomarker to multi-molecule-panel biomarker for insights into its molecular mechanism, patient stratification, diagnosis, and prognostic assessment. This present multi-omics data exactly offer the scientific data for multi-targeting pharmacological treatment concept and multi-molecule-panel biomarker for pituitary adenomas including NFPAs.All datasets generated for this study are included in the article/Pituitary adenoma tissue samples were obtained from Department of Neurosurgery, Xiangya Hospital, Central South University, and were approved by Xiangya Hospital Medical Ethics Committee of Central South University. Control pituitary glands were post-mortem tissues obtained from the Memphis Regional Medical Center, and were approved by University of Tennessee Health Science Center Internal Review Board (UTHSC-IRB). The written informed consent was obtained from each patient or the family of control pituitary subject, after full explanation of the purpose and nature of all used procedures.YL analyzed the documented omics data, IPA-mined result data and PTMScan experimental result data, performed Western blot experiments, and wrote partial manuscript draft. ML and TC participated in Western blot experiments. XiaoZ participated in the critical revision. XianZ conceived the concept, designed the entire project, collected the documented omics data, performed IPA analysis, designed and coordinated the entire experiments, designed and wrote manuscript, critically revised the manuscript, trained YL regarding omics, systems biology, molecular networks, and pathway analysis, and was responsible for its financial supports and the corresponding works. All authors approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Ulcerative colitis (UC) is a chronic idiopathic inflammatory bowel disease whose treatment strategies remain unsatisfactory. This study aims to investigate the mechanisms of Quyushengxin formula acting on UC based on network pharmacology.  Ingredients of the main herbs in Quyushengxin formula were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Absorption, distribution, metabolism, and excretion properties of all ingredients were evaluated for screening out candidate bioactive compounds in Quyushengxin formula. Weighted ensemble similarity algorithm was applied for predicting direct targets of bioactive ingredients. Functional enrichment analyses were performed for the targets. In addition, compound-target network, target-disease network, and target-pathway network were established via Cytoscape 3.6.0 software.  A total of 41 bioactive compounds in Quyushengxin formula were selected out from the TCMSP database. These bioactive compounds were predicted to target 94 potential proteins by weighted ensemble similarity algorithm. Functional analysis suggested these targets were closely related with inflammatory- and immune-related biological progresses. Furthermore, the results of compound-target network, target-disease network, and target-pathway network indicated that the therapeutic effects of Quyushengxin on UC may be achieved through the synergistic and additive effects.  Quyushengxin may act on immune and inflammation-related targets to suppress UC progression in a synergistic and additive manner. Ulcerative colitis (UC) is a chronic and progressive immunologically mediated disease causing consecutive mucosal inflammation of the colon , 2. The UC therapy is aimed to reduce the recurrent rate, as well as improve the life quality and minimize drug-related adverse events. Basic therapies for UC are determined based on the severity of symptoms, which are often thought as step-up approaches. To date, 5-aminosalycilates (5-ASAs) have been the mainstay for treatment of mild-to-moderate UC . Though Panax ginseng C.A. Mey. , Astragalus membranaceus (Fisch) Bunge, Pulsatilla chinensis (Bge.) Regel, and Coptis chinensis Franch. Our clinical practice demonstrated Quyushengxin formula could relieve the clinical symptoms in active stage and suppress the inflammatory reaction of UC patients and could be used for treating mild-to-moderate UC [To date, an increasing number of traditional Chinese herbal compounds are successfully used for treating UC with less side effects, such as Gegen Qinlian decoction , Jianpi erate UC . AlthougTraditional Chinese medicine- (TCM-) oriented network pharmacology provides us a novel way to unveil the molecular mechanisms of TCM through pharmacokinetic evaluation, network/pathway analysis, and target prediction , 19. In http://lsp.nwu.edu.cn) is a systems pharmacology platform of Chinese herbal medicines that captures the relationships between drugs, targets, and diseases [X was the molecular descriptors of herbal ingredients and Y showed the average molecular properties of all molecules in the DrugBank database (http://www.drugbank.ca/).Traditional Chinese Medicine Systems Pharmacology Database  algorithm , as follhttp://www.uniprot.org/) for annotation and normalization.Weighted ensemble similarity (WES) algorithm was applied for predicting direct targets of the bioactive compounds via a large scale of drug target relationships . Those thttp://www.genome.jp/kegg/) pathways of the candidate targets of bioactive compounds were predicted via the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database [P < 0.05 as the criterion for significance.Gene Ontology- (GO-) biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes  [http://bidd.nus.edu.sg/group/cjttd/) [https://www.pharmgkb.org/) [Target-related diseases were predicted by integrating multisource databases, including Comparative Toxicogenomics Database  , Therape/cjttd/) , and Phakb.org/) .Three kinds of networks in this study were established using Cytoscape 3.6.0 software : compounPanax ginseng C.A. Mey. , Astragalus membranaceus (Fisch) Bunge, Pulsatilla chinensis (Bge.) Regel, and Coptis chinensis Franch. After retrieving from TCMSP, 190, 87, 57, and 48 ingredients were obtained for these four herbs, respectively. Based on the criteria of OB ≥ 30%, DL ≥ 0.18, and 4 ≤ HL ≤ 8, 41 potential bioactive compounds, including quercetin, ursolic acid, kaempferol, β-sitosterol, and rutin, were sifted out , rutin (degree = 15), 7-O-methylisomucronulatol (degree = 11), stigmasterol (degree = 10), and isorhamnetin (degree = 10).Candidate targets of the 41 bioactive compounds were predicted via WES algorithm. A total of 367 potential targets for these 41 bioactive compounds were obtained. After removing the overlapping targets, 94 candidate proteins were reserved. Then, C-T network was built by Cytoscape 3.6.0 which contains 367 connections between 41 compounds and corresponding 94 candidate targets . The degThe degree of the candidate targets was also calculated and displayed in To further validate whether biological processes enriched by candidate targets as mentioned above were correlated with UC, GO-BP enrichment analysis was performed via DAVID. The top 20 significant GO-BP terms are shown in Target-related diseases were predicted by mapping them to integrating multisource databases, including CTD, TTD, and PharmGKB. A T-D network consisting of 90 targets and 4 kinds of diseases was built . The fouKEGG pathway enrichment analysis was performed for the 94 targets, and T-P network was built. Results displayed that 79 targets could be further mapped to 78 pathways, including “mTOR signaling pathway,” “T-cell receptor signaling pathway,” “JAK-STAT signaling pathway,” and “FOXO signaling pathway” . The aveBy combing the networks above, a compound-target-function module network was built, which included 140 nodes  and 653 edges .To further unveil the multi-targets mechanisms of Quyushengxin formula in the treatment of UC, an integrated “UC-related pathway” was established according to the key pathways from the T-P network analysis. UC-related pathways as shown in TCM has the advantages of high treatment efficacy and low treatment cost and side effect in the treatment of several diseases, including UC in China for several thousands of years –34. Afteα production, and IL-8 production [in vitro and in vivo [α, IL-1β, IL-6, intercellular adhesion molecule- (ICAM-) 1, and vascular cell adhesion molecule- (VCAM-) 1 induced by LPS [β-sitosterol  and rutin  were shared with significant anti-inflammatory activity [Previous reports showed that the TCMSP-based method was reliable for screening out bioactive compounds of TCM for treatment of thrombosis , gastricoduction , 41 to e in vivo . Kaempfed by LPS . In addiactivity , 45. Aboβ) [Eight of the 94 targets have degree larger than 10 in the C-T network, including ESR1, PTGS2, NOS2, PTGS1, PPARG, NOS3, ESR2, and KCNH2. ESR1 was targeted by 34 compounds, which contributed to T-cell-mediated autoimmune inflammation by promoting T-cell activation and proliferation . Besidesβ) . These rIn this study, 94 targets were utilized to perform T-P network analysis, and the results showed that 79 targets could be further mapped to 78 pathways. Meanwhile, numerous pathways mapped by multiple targets might be the main factors for UC progression. Four pathways including “T-cell receptor signaling pathway,” “FOXO signaling pathway,” “JAK-STAT signaling pathway,” and “mTOR signaling pathway” were closely associated with immune and inflammatory effects. T-cell receptors play significant role in function of T cells and formation of the immunological synapse, and they connected T cells and the antigen-presenting cells . T-cell Nevertheless, limitations in this study could never be neglected. First, results in this study were mainly based on known chemical components in Quyushengxin, related targets, and pathways in UC. With the development of science and technology, new components in Quyushengxin, as well as new targets and pathways in UC will be further discovered, which will supply us with more theoretical evidences for further elucidation of underlying mechanisms of UC pathology. Second, the interaction relationships of the nodes in the networks, such as the action type, e.g., activation, inhibition, binding, and catalysis, and the action effect, e.g., positive, negative, and unspecified, are not investigated due to lack of these data. Third, due to the complex interaction between TCM and the human body, many of its acting mechanisms still needed to be further elucidated via pharmacokinetic test and other experiments.In short, network pharmacology analysis of Quyushengxin showed that 41 bioactive components of Quyushengxin may act on 94 immune and inflammation-related targets to suppress UC progression in a synergistic and additive manner, which may provide us with a new starting point for a more detailed knowledge of mechanisms of UC pathogenesis.


Background and Aims: ALPPS , a novel 2-staged hepatectomy, dramatically accelerates liver regeneration and thus enables extensive liver tumor resection. The signaling networks underlying the ALPPS-induced accelerated regeneration process are largely unknown.Methods: We performed transcriptome profiling (TP) of liver tissue obtained from a mouse model of ALPPS, standard hepatectomy (68% model), and additional control surgeries . We also performed TP using human liver biopsies (n = 5) taken from the occluded lobe and the future liver remnant (FLR) during the first step of ALPPS surgery (4–5 h apart). We used Oncofinder computational tools, which covers 378 ISPs, for unsupervised, unbiased quantification of ISP activity.Results: Gene expression cluster analysis revealed an ALPPS specific signature: the IGF1R Signaling Pathway , the ILK Pathway (Induced cell proliferation), and the IL-10 Pathway (Stability determination) were significantly enriched, whereas the activity of the Interferon Pathway (Transcription) was reduced (p < 0.05). Further, the PAK- and ILK-associated ISPs were activated at an earlier time point, reflecting significant acceleration of liver regeneration (p < 0.001). These pathways, which were also recovered in human liver biopsies, control cell growth and proliferation, inflammatory response, and hypoxia-related processes.Conclusions: ALPPS is not a straightforward addition of portal vein ligation (PVL) plus transection—it is more. The early stages of normal and accelerated liver regeneration are clearly discernible by a significantly increased and earlier activation of a small number of signaling pathways. Compounds mimicking these responses may help to improve the ALPPS method and further reduce the hospitalization time of the patient. Associating Liver Partition and Portal vein ligation for Staged hepatectomy)  is performed. In the second step, the isolated, deportalized liver is removed, leaving behind a tumor-free, hypertrophic liver remnant  . In the  remnant –6. The m remnant , 7.The biological processes underlying liver regeneration processes are complex. They involve (thyroid) hormones , cytokinIhh), a secreted ligand important for fetal development, is a crucial mediator of the regenerative acceleration triggered by ALPPS surgery  was performed as described earlier  in mice was performed as described by Lehmann . In shorMice liver tissue was collected at different time points after surgery (as indicated throughout the manuscript). Human liver tissue specimens (biopsies) were obtained from the Department of Visceral and Transplant Surgery, University Hospital Zurich with the approval of the local ethics committees  and written consent of all patients . An equimolar RNA pool was prepared from liver tissue and/or biopsies using DNA Column Clean-up . Then, 1 μg of total RNA was used for library preparation according to the Illumina TruSeq stranded mRNA sample preparation protocol (Illumina). The resulting mRNA library was sequenced on an Illumina Hiseq 2,500 sequencer . Sequenced reads were aligned to the mouse and human (hg19) reference genome with TopHat (version 2.0.10) 27 by using the -G (GTF file of Ensembl release 75) option. Furthermore, the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1)28 with hg19 GTF (Ensembl release 75). All data have been deposited to the European Nucleotide Archive (ENA) under the accession code Raw reads were filtered by quality >30 score through FASTX toolkit and then trimmed at 5′ and 3′ in order to remove index and adapter. Only the remaining reads were used for alignment with the human genome assembly (GRCh37), where we employed TopHat v2.0.14 .https://www.slideshare.net/elsavonlicy/pathway-mapreferenceguide) was used to determine structures of intracellular pathways for OncoFinder  in the sample to the mean value for the control group. The Boolean flag of BTIF  equals to zero when the CNR value has passed simultaneously the two criteria that demark the significantly perturbed expression level from essentially normal. First, the expression level for the sample lies within the tolerance interval, where p > 0.05. Second, the value of CNR differs from 1 considerably, CNR 0.66 or CNR 1.5. The discrete value of ARR (activator / repressor role) reflects the functional role of a protein n in the pathway . We used miRTarBase for predicting targets of miRNAs and assessed the influence of miRNAs on ISPs using our method MiRImpact  suite  was used. Individual-sample gene set enrichment scores were calculated using Gene Set Variation Analysis with the GSVA R package, version 1.26.0. All gene set enrichment analyses were performed within the R statistical programming environment, version 3.4.3.Cell Cycle-pathway (metaphase-anaphase) 8 and 12 h after ALPPS surgery (ATM Pathway (G2_M Checkpoint Arrest), the HGF Pathway (Cell cycle progression), the EMT-associated hypoxia pathways, and the IL-10 Pathway (Stability determination). In addition, the three branches of the TGFβ Pathway and 10 branches of the ILK Pathway were significantly increased 4 h after ALPPS surgery  profiles were established using the normalized gene expression levels of liver-expressed genes with the OncoFinder algorithm. First, we analyzed the activation status of 378 ISPs. Using the PAS values, we built hierarchical clustering heat maps with Euclidean distance and average linkage for all groups , the Growth Hormone Pathway (Gene expression), and the IL-2 Pathway (Actin reorganization). The ALPPS procedure was marked by three unique upregulated pathways , whereas the PVL procedure was by twenty unique ISPs  and two late groups (≥8 h post OP). The dendrogram presentation revealed that ALPPS samples isolated 4 h after surgery grouped together with all late samples of PVL and ALPPS (≥8 h post OP), indicating an accelerated biological response after the ALPPS procedure . PrincipMmu-miR 466i-3p and mmu-miR 466i-5p were exclusively expressed 1 h after ALPPS surgery, whereas mmu-miR 675-3p and mmu-miR 675-5p were exclusively observed 12 h after ALPPS surgery. In addition, mmu-miR 3470a and mmu-miR-3470b were detected 4 h after ALPPS surgery, whereas they first appeared in the PVL samples 8 h post-surgery.Our dataset contained 73 differentially expressed long non-coding (lnc)RNA transcripts, of which 60 were identified as pre-miRNAs . Assuminn = 5). Again, we built ISPs with normalized RNAseq expression data using the OncoFinder software and plotted the PAS values of the 35 most affected ISPs (STAT3 Pathway [G1_to_S_Cell_Cycle_Progression and the STAT3 Pathway (Anti-Apoptosis)], the JAK-STAT Pathway (Gene_Expression_via_MYC), the IL-10 Pathway (Stability determination), the Estrogen Pathway (Vasodilatation), and the Akt-Signaling Pathway (Regulation of Na+transport). The five most repressed pathways were identified as the Glucocorticoid Receptor Signaling Pathway (Cell_Cycle_Arrest), the Glucocorticoid Receptor Signaling Pathway (Histone_Deacetylation), the EGF Pathway (Rab5_Regulation_Pathway), the IGF1R Signaling Pathway (Glucose_Uptake), and the BRCA1 Pathway (Base_Excision_Repair). A comprehensive list of all affected pathways, including the PAS values, can be found in the IGF1R Signaling Pathway  and the IL-10 Pathway (Stability determination)]. In contrast, the ILK Pathway (Induced cell proliferation) and the IFN main pathway were not significantly affected. In the human samples, we also retrieved the ISPs that were highly and significantly enriched in ALPPS when compared to standard hepatectomy, including the ATM Pathway (G2_M Checkpoint Arrest), the HGF Pathway (Cell cycle progression), the EMT-associated hypoxia pathways, and the ensuing activation of the HIF1Alpha pathway via Jun_CREB3 . Previously, we reported that the rocedure . We here studies . Importaer ALPPS .IGF1R Signaling Pathway , the ILK Pathway (Induced cell proliferation), and the IL-10 Pathway (Stability determination) were significantly enriched, whereas the activity of the Interferon Pathway (Transcription) was reduced. The IGF1R signaling pathway for cell survival has been associated with early wound healing and liver cell proliferation via the IGF1R/IRS1/ERK axis and activation of the cyclins A1 and D1. Disrupting hepatic IGF1R signaling has been shown to significantly impair hepatocyte proliferation in a mouse model of liver regeneration, and IGFBP-1 null mutants show abnormal liver regeneration , which is also a characteristic feature of ALPPS, may reflect the above findings, since ILK and PAK1 cross-regulate each other and together regulate cytoskeletal dynamics. A function for PAKs has also been established in cell cycle progression in cancers, in which PAKs are overexpressed or hyper-activated  is a PI3-kinase-dependent, serine/threonine protein kinase that interacts with the cytoplasmic domains of both β1 and β3 integrins, possesses kinase activity, and controls an assortment of biological activities . The widctivated .IFN Main Pathway, which clearly demarcated ALPPS from standard hepatectomy. The majority of the affected ISPs demonstrated a low but detectable activity after normal liver regeneration. After ALPPS surgery, however, the activities of these ISPs were dramatically increased. Of particular interest are the four branches of the hypoxia-induced pathway, which are 8.2-fold higher in ALPPS than in standard hepatectomy. Hypoxia signaling has been implicated in regulating the transition that is necessary to produce the extracellular matrix but also to initiate the regenerative capacity of EMT-like liver cells , and the ATM-pathway (G2-M_Checkpoint_Arrest) are significantly enriched after the ALPPS procedure. At the molecular level, one of the key mediators of regenerative responses is the secreted cytokine transforming growth factor-β (TGFβ) (IL-10-pathway (stability_determination) is again recovered. Relative to normal hepatectomy, the activity of the ATM main pathway and its specific branch for G2-M checkpoint arrest were significantly increased 4 h post ALPPS surgery. The G2-M DNA damage checkpoint is an important cell cycle checkpoint ensuring that cells do not initiate mitosis before repairing damaged DNA after replication. Cells that have a defective G2-M checkpoint enter mitosis before repairing their DNA, leading to death after cell division.Further, three branches of the β (TGFβ) . The samMmu-miR 466i-3p and mmu-miR 466i-5p were uniquely expressed immediately after ALPPS surgery, indicating they may underlie the initiation of the accelerated regeneration process by deactivating the mRNAs of cell cycle inhibitors. Indeed, miR 466i-3p and mmu-miR 466i-5p intervene with many targets of ISPs that keep the cells committed and prevent them from preparing for cell cycle entry, including the PAK Pathway (Actin_Organization), the p38 pathway (Actin-Cytoskeleton_reorganization), the MAPK signaling pathways, and the Jnk Pathway (Gene expression Apoptosis Inflammation_Tumorigenesis_Cell-Migration) , thus enhancing PI3-Kinase/Akt signaling and fatty acid metabolism. The downregulation of PTEN may fuel liver growth after hepatectomy due to increased β-oxidation  are short, single-stranded RNAs that modify gene expression at the post-transcriptional level and are heavily involved in the spatiotemporal control of gene expression during the entire process of liver regeneration. The number, nature and level of expressed miRNAs profoundly depends on the organism studied, the activation status and microenvironment of cells, as well as other unidentified factors. In rats and mice, several miRNAs have been linked to liver regeneration, including miR19a, miR21, and miR214 . Here, wion) see . LikewisComparing the human data to the murine data, we observed that they have several ISPs in common and that the direction of change is the same, indicating our ALPPS model reflects the major changes of accelerated regeneration in humans at the molecular level. That not all ALPPS-specific ISPs observed in mice are retrieved in human liver tissue may reflect distinct kinetics of the liver regeneration process: sufficient regeneration in our mice model is achieved 2 days post-surgery, while it takes 7–10 days in humans. Still, the data signify that our murine ALPPS model—despite differences—may be useful to gain insight into the molecular background underlying ALPPS-induced accelerated liver regeneration in humans. Considering the close match between ISPs in mice and humans, deeper exploration of our ALPPS model may disclose essential leads to the development of potential therapeutic strategies targeting specific ISPs, in particular those that counteract responses of the immune system.We realize that our results require further biological validation, since RNAseq data do not give insights into protein expression changes or activities of proteins belonging to a specific pathway. Still, our study provides a comprehensive framework of the signaling pathways involved in normal and accelerated liver regeneration, which is now available for further exploration.The datasets generated for this study can be found in the ENA, PRJEB15593.The studies involving human participants were reviewed and approved by Cantonal Ethics Committee, Stampfenbachstrasse 121, 8090 Zürich Switzerland. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Veterinary Office Zürich, Zollstrasse 20 8090 Zürich, Switzerland.PB: experimental design, fund raising, data analysis, and writing. MSc: experimental design, patients, and writing. LF, MSo, and AB: data analysis. ML and EK: animals and experiments. RG and BH: fund raising, consultancy, and animal studies. P-AC: fund raising, study design, patients, and consultancy.AB was employed by company OmicsWay Corp., Walnut, CA, United States and Oncobox ltd., Moscow, Russia. MSo was employed by company OmicsWay Corp., Walnut, CA, United States. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Evidence of familial inheritance in non-medullary thyroid cancer (NMTC) has accumulated over the last few decades. However, known variants account for a very small percentage of the genetic burden. Here, we focused on the identification of common pathways and networks enriched in NMTC families to better understand its pathogenesis with the final aim of identifying one novel high/moderate-penetrance germline predisposition variant segregating with the disease in each studied family. We performed whole genome sequencing on 23 affected and 3 unaffected family members from five NMTC-prone families and prioritized the identified variants using our Familial Cancer Variant Prioritization Pipeline (FCVPPv2). In total, 31 coding variants and 39 variants located in upstream, downstream, 5′ or 3′ untranslated regions passed FCVPPv2 filtering. Altogether, 210 genes affected by variants that passed the first three steps of the FCVPPv2 were analyzed using Ingenuity Pathway Analysis software. These genes were enriched in tumorigenic signaling pathways mediated by receptor tyrosine kinases and G-protein coupled receptors, implicating a central role of PI3K/AKT and MAPK/ERK signaling in familial NMTC. Our approach can facilitate the identification and functional validation of causal variants in each family as well as the screening and genetic counseling of other individuals at risk of developing NMTC. Thyroid cancer is the most common endocrine malignancy with an age adjusted incidence of 0.5-20/100,000 persons per year . The famDICER1 syndrome [Approximately 90–95% of all thyroid cancers are non-medullary thyroid cancers (NMTC) [syndrome . Known sRET proto-oncogene to the development of thyroid cancer, the genetic causes for FNMTC remain largely unknown. Over the years, studies seeking genetic factors predisposing to NMTC have been performed using linkage analysis, candidate gene sequencing and recently also whole genome sequencing. These studies have suggested several genes as potential NMTC-predisposing genes, including, FOXE1, SRGAP1, TITF-1/NKX2.1, SRRM2, and HABP2 [Unlike the case of familial medullary thyroid cancer, in which there is extensive evidence linking germline point mutations in the nd HABP2 ,9,10,11.nd HABP2 . Nonethend HABP2 . TherefoThe identification of such predisposition genes could be of great value in the screening of individuals at risk of developing NMTCs as well as in the development of personalized adjuvant therapies based on the affected pathways. It has been observed that hereditary NMTC is characterized by early onset, a higher degree of aggressiveness and more frequent multifocal disease and recurrence compared with its sporadic counterpart . Thus, mHere we report the germline genomic landscape of five families with NMTC aggregation consistent with an autosomal dominant pattern of inheritance. The aim of the current study was to use whole genome sequencing (WGS) data to discern pathways affected in the FNMTC families to facilitate the identification of possible disease-causing high/moderate-penetrance germline variants in each family. With our results, we hope to facilitate genetic counseling and targeted therapy in these families and improve screening of other individuals at risk of developing NMTC.Blood samples were collected from the participants with informed consent following ethical guidelines approved by “Comitato Etico Indipendente dell ‘Azienda Ospedaliero-Universitaria di Bologna, Policlinico S. Orsola-Malpighi ” and “comité utiltative de protection des personnes dans la recherche biomédicale, Le centre de ute contre le cancer Léon-Bérard ”.Five families with NMTC aggregation consistent with an autosomal dominant pattern of inheritance were provided by the S. Orsola-Malpighi Hospital, Unit of Medical Genetics in Bologna, Italy. Samples from a total of 23 affected and 3 unaffected family members from the five families were submitted for WGS. Their respective pedigrees are shown in ® DNA Mini Kit  according to the manufacturer’s instructions.WGS for 23 cases and 3 controls was performed using Illumina-based small read sequencing after DNA was isolated from peripheral blood using the QIAamp Sequencing data was mapped to a reference human genome (assembly version Hs37d5) using BWA mem (version 0.7.8) and duplicates were removed using biobambam (version 0.0.148). Single nucleotide variants (SNVs) and indels were called from all the samples in a family together using Platypus (version 0.8.1). ANNOVAR, 1000 Genomes, dbSNP and ExAC (Exome Aggregation Consortium) were used in the annotation of variants as explained in detail in our previous paper [Variant evaluation was performed using the criteria of our in-house developed Familial Cancer Variant Prioritization Pipeline v2 (FCVPPv2) . This prThe variants were filtered based on pedigree data considering family members diagnosed with NMTC or micro-PTC as cases, benign nodules or goiter as potential variant carriers and unaffected members as controls. The probability of an individual being a Mendelian case or true control was considered. The general rule was that variants had to be present in all cases and absent from all controls.After filtering variants based on pedigree segregation, the CADD tool v1.3  was applNon-coding regions make up over 98% of the genome and possess millions of potentially regulatory elements and noncoding RNA genes. Hence it is crucial to analyze the potential pathogenic impact of such variants in a Mendelian disease. Putative miRNA targets at variant positions within 3′ untranslated regions (UTRs) and 1 kb downstream of transcription end sites were detected by scanning the entire dataset of the human miRNA target atlas from TargetScan 7.0  with theIn order to increase the confidence in variant calls and reduce the risk of false positives, we visually inspected the sequencing data of all short-listed variants for correctness using the Integrative Genomics Viewer  .http://www.qiagen.com/ingenuity; analysis date 08/04/2019) was used to perform a core analysis to identify relationships, mechanisms, functions, networks, and pathways relevant to the genes affected by variants that passed the mean allele frequency cut-off, fulfilled family-based segregation criteria, had CADD scores >10 and were not intergenic or intronic variants. Data were analyzed for all five families together. Top canonical pathways were identified from the IPA pathway library and ranked according to their significance to our input data. This significance was determined by p-values calculated using the right tailed Fisher’s exact test. These values indicated the probability of association of genes from the input dataset with the canonical pathway by random chance alone. Ratios were also calculated for each pathway by dividing the number of genes from the input dataset that map to the pathway by the total number of genes in that pathway. The ratios did not influence the ranking of the canonical pathways.IPA . These connections represent experimentally observed cause-effect relationships that relate to expression, transcription, activation, molecular modification and transport as well as binding events.https://string-db.org; v11, 19/01/2019).A protein-protein interaction network was generated for each of the prioritized candidates using STRING  [After applying the FCVPPv2, the number of potential pathogenic protein coding variants was reduced to 31. These variants are listed in response . EWSR1 g sarcoma , RET is t cancer , POT1 iss (AITD) .PCM1 variant is a 5′ UTR variant that our data showed to affect three transcription factor binding sites . Chromosomal aberrations involving this gene have been associated with PTC and a variety of hematological malignancies [P4HB gene which is known to be involved in the structural modification of the thyroglobulin precursor in hormone biogenesis [DAPL1 gene, shown to affect the binding sites of MAZR and Sp1, a potential tumor suppressor in thyroid cancer, by SNPnexus and Segway.FCVPPv2 also identified 14 upstream and 5′ UTR variants, which are shown in gnancies . The othogenesis . Both vaACVR1B and NLK. Mutations in the ACVR1B gene are associated with pancreatic cancer [Furthermore, 25 variants located downstream and in 3′ UTRs were shortlisted . Among tc cancer . The varc cancer . The corVariants prioritized by the FCVPPv2 were also present in pathways, networks, and disease categories shown to be significantly enriched in FNMTC by IPA.In order to identify key biological functions and signaling pathways affected in FNMTC, we filtered the variants according to pedigree segregation, CADD scores and location, excluding intronic and intergenic variants. The variants were in 339 genes, with 92, 122, 14, 72 and 39 genes coming from families 1-5 respectively. Of these genes, 210 gene IDs could be mapped by IPA and were part of the subsequent analysis . The rem−5), NMTC at position 120 (p = 6.39 × 10−5), differentiated thyroid cancer (DTC) at position 125 (p = 7.88 × 10−5) and PTC at position 148 (p = 2.16 × 10−5) (RET and TG), that are of particular interest in thyroid cancer.Of the top 150 diseases and bio functions, 123 were cancer-related with thyroid cancer at position 99 (p = 3.17 × 10 × 10−5) . There wWith the aim of evaluating the canonical pathway results to determine the most significant pathways in our dataset, we created a network of the top 18 overlapping canonical pathways . The thrWe conducted a network analysis using the IPA software to predict interacting molecular networks significant to our input-data and to evaluate genes with a central role in FNMTC . Since tTG from our prioritized shortlist.In coherence with the pathway analysis, the network analysis reinforces the importance of central perpetrators of GPCR and RTK mediated signaling  and their downstream effectors . Furthermore, Network 3 encompasses a number of genes related to thyroid metabolism including Since GPCR and RTK mediated signaling were highlighted in both pathway and network analyses, we propose a pathway to facilitate a general understanding of FNMTC at a molecular level .αs, Gαi/o, Gαq/11, and Gα12/13). Dimerization of receptor-tyrosine kinase (RTK) receptors can be induced by growth factors such as EGFR and GDNF, which results in the phosphorylation and subsequent activation of the receptor monomers. Receptor activation is linked to downstream signal transduction pathways like the MAPK signaling cascade and the PI3K/AKT system via adaptor proteins. Genes from our dataset that were present in these pathways as activators or regulators are highlighted in Activation of GPCR receptors can activate MAPK/ERK signaling as well as PI3K/AKT signaling via one of the four subclasses of G-proteins  were involved in the enrichment of MAPK/ERK and PI3K/AKT pathways. The MAPK pathway is the most frequently mutated signaling pathway in human cancer and is thus considered one of the most promising targets for cancer therapy. This pathway plays a central role in the induction of biological responses such as cell proliferation, differentiation, growth, migration and apoptosis [Other genes of interest shortlisted based on the pipeline (poptosis . Initiatpoptosis . Althougpoptosis . Overexppoptosis . A commopoptosis , thyroidpoptosis  and hairpoptosis .The importance of the PI3K/AKT pathway in thyroid cancer was first recognized when patients suffering from Cowden’s syndrome caused by a germline mutation in the PTEN gene were found to have FTC . PI3K acFurthermore, our findings are in line with recent studies on PTC tissues and PTC cell lines have implicated activation of MAPK/ERK and PI3K/AKT pathways in thyroid carcinogenesis ,54,55. IDICER1 as a candidate predisposing gene in familial Hodgkin lymphoma [Interpreting WGS data and selecting one out of millions of genetic variants as the cause of hereditary cancer is a daunting task and highlights the importance of the use of a standardized protocol like the FCVPPv2. We were able to prioritize 31 exonic and 39 non-coding potential cancer-predisposing variants using our family-based pipeline from which we hope to pinpoint one candidate gene for each family. The final selection and implication of one candidate gene predisposing to cancer in each family is beyond the scope of this paper as it will involve further steps including population screening and functional studies. In the present study, we decided to focus on the analysis of pathways that are enriched in familial NMTC to see how the variants prioritized using our pipeline fit into the general pathway analysis results. The IPA analysis of all genes already presented us with valuable data and there was a high involvement of genes prioritized using our pipeline in the top diseases and bio functions, canonical pathways and networks generated by IPA. Although IPA could give us a general idea of molecular pathways affected in the studied families, it is important to keep in mind that the analysis was conducted at a gene level and not at a variant level. The evaluation at a variant level is largely dependent on the pipeline and its subsequent steps as mentioned above. We have already successfully implemented this pipeline to identify lymphoma  and are In conclusion, WGS data analysis of five NMTC-prone families allowed us to prioritize 31 exonic and 39 non-coding variants from which we subsequently hope to identify one candidate gene per family. Furthermore, we were able to identify pathways and networks significant to our dataset, including important tumorigenic pathways such as MAPK/ERK and PI3K/AKT signaling pathways. The implication of previously reported tumorigenic signaling pathways and the presence of known tumor suppressor or oncogenes in these affected pathways show that the pathogenesis of FNMTC is in concordance with characteristic molecular mechanisms of cancer. The next steps will include selecting one candidate gene per family and validating it with the help of population screening and functional studies. We hope that our results can facilitate personalized therapy in the studied families and contribute to the screening of other individuals at risk of developing NMTC.


Transcriptomic data is often used to build statistical models which are predictive of a given phenotype, such as disease status. Genes work together in pathways and it is widely thought that pathway representations will be more robust to noise in the gene expression levels. We aimed to test this hypothesis by constructing models based on either genes alone, or based on sample specific scores for each pathway, thus transforming the data to a ‘pathway space’. We progressively degraded the raw data by addition of noise and examined the ability of the models to maintain predictivity.Models in the pathway space indeed had higher predictive robustness than models in the gene space. This result was independent of the workflow, parameters, classifier and data set used. Surprisingly, randomised pathway mappings produced models of similar accuracy and robustness to true mappings, suggesting that the success of pathway space models is not conferred by the specific definitions of the pathway. Instead, predictive models built on the true pathway mappings led to prediction rules with fewer influential pathways than those built on randomised pathways. The extent of this effect was used to differentiate pathway collections coming from a variety of widely used pathway databases.Prediction models based on pathway scores are more robust to degradation of gene expression information than the equivalent models based on ungrouped genes. While models based on true pathway scores are not more robust or accurate than those based on randomised pathways, true pathways produced simpler prediction rules, emphasizing a smaller number of pathways. A common objective in modern biological investigations is to use transcriptomic or other ‘omics’ data to develop statistical models predictive of a given phenotype, such as disease status or prognosis. The main goal of such studies is often to identify groups of genes, or signatures, which are associated with the desired outcome. However, data from all omics technologies are subject to a wide variety of technical noise and biological variation, which will degrade the performance of these models and limit the fidelity with which predictive signatures can be identified.In nature, genes and other biomolecules function together in pathways and it was proposed early in post-genomic science that grouping genes into pathways could circumvent the difficulties with noise suffered by gene focused analysis , 2. WhilPredictive models are usually built using gene expression levels as variables, hence such models are built in the ‘gene space’. However, the idea of predictive modelling can be combined with pathway analysis, so that predictive models are built instead in a ‘pathway space’  where eaAlthough we employ a type of pathway scoring, it is not our aim to introduce a new method for that purpose. The most common type of pathway analysis outputs a single score for each pathway (irrespective of the number of samples), which can be tested to determine pathways significantly associated with the outcome of interest . Our patThe coordinated action of genes in pathways naturally leads to the idea that pathways are ‘special’ collections of genes and thus models based on true pathways should be more predictive than those based on groups of unrelated genes. We therefore conjectured that performance of predictive models could be used to differentiate collections of real pathways from random collections of genes, thus addressing pathway significance. Surprisingly, we find that the predictive accuracy and robustness of models based on random gene sets can be similar to that of models based on pathways from databases. This counterintuitive result motivated us to examine other aspects of our models that could differentiate real and random pathway sets. We found that models based on true pathways are significantly simpler, in that they assign strong weights to fewer pathways, as compared to randomised versions of the same pathways. This yields an intrinsic “signature” to characterise different pathway collections such as those in existing databases.In order to assess the contribution of pathway information to the robustness of predictive models we defined pathway scores that combined the expression of genes in each pathway using PCA. We calculated a one component PCA model for each pathway and compiled the scores into a new matrix that represents the samples in a “pathway space” Fig. . SeparatWe examined the robustness of predictive models to degradation of the raw data. As more noise is added to the data, models whose accuracy declines slowly are deemed to be more robust than those whose accuracy declines quickly. Having predictive models both in pathway space and in the original gene space allowed us to study the contribution of pathway information to this type of robustness.The degradation profile of Fig. To investigate the influence of different components of the workflow on the predictive robustness, we replaced steps of the default workflow by alternative methods, one at a time. Firstly, we replaced the default degradation method by random permutation of the sample order for the degraded genes Fig. a, or by Aside from changes in the workflow, we also explored the robustness hypothesis on a second, unrelated, data set on leukaemia . The degHaving established the advantage of predictive models based on pathway scores, our next goal was to identify any underlying explanatory factors. Pathway databases reflect a corpus of existing biological knowledge and thus we hypothesised that the pathway composition, i.e. which genes are annotated to which pathway, has an impact on accuracy and robustness. We tested this hypothesis by creating databases of fake pathways which exactly replicated the size of the true pathways, as well as their overlap of shared genes. This approach is analogous to classical hypothesis testing in statistics, where a null distribution (here estimated by randomising pathway definitions) is used to assess the significance of an observed statistic (here accuracy and robustness). Surprisingly, the degradation profiles showed little or no difference between the true pathway set and the randomised versions Fig. . We alsoTo explore the unexpected similarity of predictive robustness for true and fake pathway sets, we investigated the contribution of pathways across all predictive models in more detail. We hypothesized that for true pathway sets typically only a small number of pathways would be clearly influential for prediction, and conversely with randomised pathways, a larger number of pathways would be required, each one with a smaller contribution to the prediction. Note that in the remainder of this study, we only examine models based on the full, non-degraded data.In PLS-DA models, the regression coefficients indicate the contribution of pathways to the model and therefore we compared the distribution of PLS-DA coefficients from true and fake pathway sets Fig. . For botThe large set of pathways used in this work is a compilation of many individual pathway databases. To investigate the nature of these collections, we applied the analysis of the previous section to each of the contributing databases. The results comparing real and fake pathways for each source database were summarised using the area between curves Fig. . A numbeEvidence for the advantage of prediction based on pathways over individual genes has remained conflicting, with comparable evidence for the advantage  and indiOur proposed “predictive robustness” measure assesses the ability of models to sustain predictive accuracy when the data is degraded. In this work, strong levels of perturbation to the original data resulted in lower predictive robustness of gene space models compared to that of pathway space models. Multiple controls suggested that these results are independent of the specific numerical methods or data sets used and thus the predictive robustness seems a more general property of a given pathway collection. Notably, the accuracy of pathway space models is not superior at low levels of degradation. However, this is not critical to our concept of predictive robustness, since it takes into account the maintenance of accuracy from zero to 100% degradation of the data.To build pathway based models we adopted a bottom-up approach that included the pathway information (the gene collections) within the input data of the models. This was implemented by aggregation of the gene expression with an unsupervised method (PCA) in order to reduce the chance of over fitting, often observed in gene expression modelling . Our finUsing randomised pathway sets we showed that the accuracy and predictive robustness of the true pathways bears little or no relation to the gene membership of the pathways as dictated by current biological knowledge. This striking result provides an important insight into the use (and misuse) of predictive models for extracting information about the explanatory variables in the ‘omics’ sciences. It is often assumed that the model captures the true relationship between the measured variables and the outcome. In contrast, when models are assessed only on their prediction performance, a plausible connection between the predictors and the outcomes is not required. A mechanistic connection between the measured variables and outcomes is not guaranteed in predictive models of omics data even when machine learning methods combine efficiently information distributed across many variables , includiFor the second goal of quantifying the relevance of pathway definitions for multivariate models we proposed a randomisation method that creates fake mappings between genes and pathways. Across these models the contribution of the true and fake pathway sets to prediction rules, as revealed by regression coefficients, distributed differently, suggesting that true pathways are “special” collections. Specifically, the true pathway sets had significantly fewer pathways with high regression coefficients and an excess of pathways with low coefficients. These changes in coefficient distributions, summarised though the entropy and area between distribution curves, showed clear differences between different source databases , showing a potential application to pathway collections in general. A second dataset reproduced all the conclusions observed throughout this work. Interestingly a recent method for pathway discovery included lasso regularisation to force sparsity in predictive models of candidate pathways . This suThe long lasting debate about the predictive power of pathways suggests that the effect of pathways might be weak, or perhaps that the predictive accuracy might not be an adequate metric to assess the effect of pathways. From a modelling perspective, the predictive robustness is a novel approach that acknowledges the fact that real life datasets are degraded versions of the true biological signals. Our metrics based on predictive robustness and randomisation of pathway definition could offer a fresh viewpoint and help during tasks such automated pathway discovery and pathway curation.We propose the concept of predictive robustness as a new tool to assess predictive models. Predictive robustness assesses the strengths of pathways as predictors in the face of noisy data, as opposed to the outcomes of posthoc analysis of differential gene lists. We showed that pathway-based models achieve more robust predictions than gene-based models, irrespective of the data or exact workflow used. We also observed that use of real pathways does not confer a higher quality of models than randomised pathways, raising a warning regarding the interpretation of models based on predictive ability alone. However, we found that models based on true pathways are simpler, in that fewer pathways contribute strongly, when compared to those based on random collections of genes. While the current work assesses the robustness of collections of pathways, an intriguing next step in this area would be an approach which quantifies the predictive robustness of individual pathways or gene sets. This might then be used in assessing the quality of different pathway definitions.We employed two independent transcriptomic data sets. The first dataset derives from the carcinoGENOMICS (CG) project  intendedn samples by m genes can be transformed to a pathway space matrix of n samples by k pathways – see Fig. Our approach requires us to summarise the expression of all genes in each pathway by a score for every biological sample. Thus, gene space data matrix of Both the gene level and pathway level data were separately used to build multivariate models predictive of relevant outcomes: carcinogenicity class (3 classes) in the CG data and disease subtype (2 classes) for the leukaemia data. Models were built using Partial Least Squares-Discriminant Analysis (PLS-DA), k-Nearest Neighbour (kNN) and a linear support vector machines (SVM) and prediction accuracy measured using cross-validation. Accuracy was defined as the number of correctly classified samples divided by the total number of samples. In the case of the CG data, all samples from a given compound treatment were left out in the test set at each round. In the case of the Leukaemia data, balanced 2-fold cross-validation was used. The model complexity is given by the number of components in the case of PLS-DA, the number of neighbours in kNN and the soft threshold in SVM. The complexity was selected separately for gene and pathway models as follows: First the median predictive accuracy was computed for each complexity at each degradation level. Next the sum of the median accuracies across degradation levels was calculated for each model complexity. The optimal complexity was selected as the one which maximized this sum :The local robustness, di} and the trapezium rule. An approximate confidence interval for R was calculated by estimating the robustness for each of the 20 repeats and identifying the 5 and 95% percentiles of this sample. Note that the differences in predictive robustness between gene and pathway models were insensitive to the exact complexity of the models  was estimated using a histogram of b, based on 50 bins. The ABC statistic consisted of the sum of absolute areas enclosed between distributions of coefficients from each real pathway collection and the median distribution from all fake pathway sets.The distribution of PLS-DA regression coefficients in models based on true and fake pathway sets was characterised through measures of Shannon entropy and an area between curves (ABC) statistic. The entropy was calculated asAll data analysis was performed with R (version 2.7) using custom written scripts and the R packages “class”,“caret” and “e1071” for kNN, PLS-DA and SVM models, respectively.Additional file 1. Accompanying file with multiple figures that further support those findings described in the body of this article.


Mammalian sex determination  is largely controlled by genes, whereas sex differentiation (development of reproductive structures) is largely controlled by hormones. Work in the 20th century indicated that female external anatomy was a “default” pathway of development not requiring steroids, whereas male genital development required testicular testosterone plus dihydrotestosterone (DHT) made in genital skin according to a “classic” pathway. Recent work added the description of an alternative “backdoor” pathway of androgen synthesis discovered in marsupials. Unique “backdoor steroids” are found in human hyperandrogenic disorders, and genetic disruption of the pathway causes disordered male sexual development, suggesting it plays an essential role. O’Shaughnessy and colleagues now show that the principal human backdoor androgen is androsterone and provide strong evidence that it derives from placental progesterone that is metabolized to androsterone in nontesticular tissues. These studies are essential to understanding human sexual development and its disorders. Female external anatomy is the ‘default’ pathway of development, while male genital development requires testicular testosterone plus dihydrotestosterone made in genital skin. This Primer explores recent evidence that an additional 'backdoor' pathway to masculinization involves androsterone, derived from placental progesterone. Sexual selection and the importance of sexually dimorphic displays and reproductive structures were first considered by Darwin and Wallace . We now Clinical studies, studies of human tissues, and studies of conventional laboratory animals had converged to a classical model of androgen synthesis, both in the testis and in a zone of the primate adrenal cortex called the zona reticularis . CholestThe pioneering studies of Marilyn Renfree provided an animal model in which the accuracy of this pathway in the developing male could be tested: the Tammar wallaby –12. MarsAKR1C2 and AKR1C4 genes that encode the 3α-reductases that function uniquely in this pathway [Indirect data supporting the relevance of the backdoor pathway in human physiology are emerging. Studies measuring steroidal intermediates of the backdoor pathway by liquid chromatography coupled with tandem mass spectrometry have indicated that this pathway plays an important role in human hyperandrogenic disorders such as congenital adrenal hyperplasia caused by steroid 21-hydroxylase deficiency , the pol pathway . NeverthPLOS Biology, O’Shaughnessy and colleagues addressed the problem directly by measuring the plasma and tissue concentrations of steroids from the conventional and backdoor pathways in 42 midgestation human male fetuses [In the current issue of  fetuses . They foRemoving the testis from the center of fetal androgen synthesis is a novel, unexpected finding. The work of O’Shaughnessy and colleagues confirms the “two-androgen” model of male sexual development wherein testosterone is sufficient to stabilize and differentiate the Wolffian ducts and to promote penile growth, while DHT is needed to virilize the external genitalia. But the testis is no longer supreme: it is necessary but not sufficient to produce the androgenic steroids that are responsible for male sexual development.The study was limited to a modest number of fetuses at estimated gestational ages of 11–21 weeks, and concentrations of many of the analytes were near the lower limits of assay detection. Nevertheless, the specimens used by the authors are difficult to obtain, and the authors carefully attempted to control for many variables, including fetal sex. Future studies will need to address the flux of steroids in the various tissues along various steroidogenic steps. The unexpected results of this study will inspire additional work to unravel the proportions of androgens derived from the traditional and alternative pathways across gestation and their relative contributions to male sexual development in the normal fetus and in disorders of sex development.