Molecular insights into clinical trials for immune checkpoint inhibitors in colorectal cancer:Unravelling challenges and future directions

Colorectal cancer(CRC)is a complex disease with diverse etiologies and clinical outcomes.Despite considerable progress in development of CRC therapeutics,challenges remain regarding the diagnosis and management of advanced stage metastatic CRC(mCRC).In particular,the five-year survival rate is very low since mCRC is currently rarely curable.Over the past decade,cancer treatment has significantly improved with the introduction of cancer immunotherapies,specifically immune checkpoint inhibitors.Therapies aimed at blocking immune checkpoints such as PD-1,PD-L1,and CTLA-4 target inhibitory pathways of the immune system,and thereby enhance anti-tumor immunity.These therapies thus have shown promising results in many clinical trials alone or in combination.The efficacy and safety of immunotherapy,either alone or in combination with CRC,have been investigated in several clinical trials.Clinical trials,including KEYNOTE-164 and CheckMate 142,have led to Food and Drug Administration approval of the PD-1 inhibitors pembrolizumab and nivolumab,respectively,for the treatment of patients with unresectable or metastatic microsatellite instability-high or deficient mismatch repair CRC.Unfortunately,these drugs benefit only a small percentage of patients,with the benefits of immunotherapy remaining elusive for the vast majority of CRC patients.To this end,primary and secondary resistance to immunotherapy remains a significant issue,and further research is necessary to optimize the use of immunotherapy in CRC and identify biomarkers to predict the response.This review provides a comprehensive overview of the clinical trials involving immune checkpoint inhibitors in CRC.The underlying rationale,challenges faced,and potential future steps to improve the prognosis and enhance the likelihood of successful trials in this field are discussed.

Osteocyte Egln1/Phd2 links oxygen sensing and biomineralization via FGF23

Osteocytes act within a hypoxic environment to control key steps in bone formation.FGF23,a critical phosphate-regulating hormone,is stimulated by low oxygen/iron in acute and chronic diseases,however the molecular mechanisms directing this process remain unclear.Our goal was to identify the osteocyte factors responsible for FGF23 production driven by changes in oxygen/iron utilization.Hypoxia-inducible factor-prolyl hydroxylase inhibitors(HIF-PHI)which stabilize HIF transcription factors,increased Fgf23 in normal mice,as well as in osteocyte-like cells;in mice with conditional osteocyte Fgf23 deletion,circulating i FGF23 was suppressed.An inducible MSC cell line(‘MPC2’)underwent FG-4592 treatment and ATACseq/RNAseq,and demonstrated that differentiated osteocytes significantly increased HIF genomic accessibility versus progenitor cells.Integrative genomics also revealed increased prolyl hydroxylase Egln1(Phd2)chromatin accessibility and expression,which was positively associated with osteocyte differentiation.In mice with chronic kidney disease(CKD),Phd1-3 enzymes were suppressed,consistent with FGF23 upregulation in this model.Conditional loss of Phd2 from osteocytes in vivo resulted in upregulated Fgf23,in line with our findings that the MPC2 cell line lacking Phd2(CRISPR Phd2-KO cells)constitutively activated Fgf23 that was abolished by HIF1αblockade.In vitro,Phd2-KO cells lost iron-mediated suppression of Fgf23 and this activity was not compensated for by Phd1 or-3.In sum,osteocytes become adapted to oxygen/iron sensing during differentiation and are directly sensitive to bioavailable iron.Further,Phd2 is a critical mediator of osteocyte FGF23 production,thus our collective studies may provide new therapeutic targets for skeletal diseases involving disturbed oxygen/iron sensing.

A multidimensional platform of patient-derived tumors identifies drug susceptibilities for clinical lenvatinib resistance

Lenvatinib,a second-generation multi-receptor tyrosine kinase inhibitor approved by the FDA for first-line treatment of advanced liver cancer,facing limitations due to drug resistance.Here,we applied a multidimensional,high-throughput screening platform comprising patient-derived resistant liver tumor cells(PDCs),organoids(PDOs),and xenografts(PDXs)to identify drug susceptibilities for conquering lenvatinib resistance in clinically relevant settings.Expansion and passaging of PDCs and PDOs from resistant patient liver tumors retained functional fidelity to lenvatinib treatment,expediting drug repurposing screens.Pharmacological screening identified romidepsin,YM155,apitolisib,NVP-TAE684 and dasatinib as potential antitumor agents in lenvatinib-resistant PDC and PDO models.Notably,romidepsin treatment enhanced antitumor response in syngeneic mouse models by triggering immunogenic tumor cell death and blocking the EGFR signaling pathway.A combination of romidepsin and immunotherapy achieved robust and synergistic antitumor effects against lenvatinib resistance in humanized immunocompetent PDX models.Collectively,our findings suggest that patient-derived liver cancer models effectively recapitulate lenvatinib resistance observed in clinical settings and expedite drug discovery for advanced liver cancer,providing a feasible multidimensional platform for personalized medicine.

Drug resistance and new therapies in colorectal cancer

Colorectal cancer(CRC) is often diagnosed at an advanced stage when tumor cell dissemination has taken place. Chemo-and targeted therapies provide only a limited increase of overall survival for these patients. The major reason for clinical outcome finds its origin in therapy resistance. Escape mechanisms to both chemo-and targeted therapy remain the main culprits. Here, we evaluate major resistant mechanisms and elaborate on potential new therapies. Amongst promising therapies is α-amanitin antibodydrug conjugate targeting hemizygous p53 loss. It becomes clear that a dynamic interaction with the tumor microenvironment exists and that this dictates therapeutic outcome. In addition, CRC displays a limited response to checkpoint inhibitors, as only a minority of patients with microsatellite instable high tumors is susceptible. In this review, we highlight new developments with clinical potentials to augment responses to checkpoint inhibitors.

Insights for hepatitis C virus related hepatocellular carcinoma genetic biomarkers: Early diagnosis and therapeutic intervention

The current review explores the role of emerging molecular contributing factors in liver carcinogenesis on top of hepatitis C virus(HCV). Here we will try to discuss the role genetic and epigenetic factors in pathogenesis of hepatocellular carcinoma. Understanding the role of these factors will help in discovering the mystery of liver carcinogenesis on top of chronic HCV infection. Moreover, use of the studied molecular factors will provide the hepatologists with tailored diagnostic promising biomarkers and flatten the way for establishment of emerging molecular treatment based on exploring the molecular subscription of this aggressive liver cancer.

Considering calcium-binding proteins in invertebrates: multi-functional proteins that shape neuronal growth

Calcium is a critical second messenger molecule in all cells and is vital in neurons for synaptic transmission.Given this importance,calcium ions are tightly controlled by a host of molecular players including ion channels,sensors,and buffering proteins.Calcium can act directly by binding to signaling molecules or calcium’s effects can be indirect,for example by altering nuclear histones.

Acid–base Homeostasis and Implications to the Phenotypic Behaviors of Cancer

Acid–base homeostasis is a fundamental property of living cells,and its persistent disruption in human cells can lead to a wide range of diseases.In this study,we conducted a computational modeling analysis of transcriptomic data of 4750 human tissue samples of 9 cancer types in The Cancer Genome Atlas(TCGA)database.Built on our previous study,we quantitatively estimated the average production rate of OHby cytosolic Fenton reactions,which continuously disrupt the intracellular pH(pHi)homeostasis.Our predictions indicate that all or at least a subset of 43 reprogrammed metabolisms(RMs)are induced to produce net protons(H+)at comparable rates of Fenton reactions to keep the pHi stable.We then discovered that a number of wellknown phenotypes of cancers,including increased growth rate,metastasis rate,and local immune cell composition,can be naturally explained in terms of the Fenton reaction level and the induced RMs.This study strongly suggests the possibility to have a unified framework for studies of cancerinducing stressors,adaptive metabolic reprogramming,and cancerous behaviors.In addition,strong evidence is provided to demonstrate that a popular view that Na+/H+exchangers along with lactic acid exporters and carbonic anhydrases are responsible for the intracellular alkalization and extracellular acidification in cancer may not be justified.

Inhibition of PRMT5 by market drugs as a novel cancer therapeutic avenue

Market drugs,suchas Foodand Drug Administration(FDA)or European Medicines Agency(EMA)-approved drugs for specific indications provide opportunities for repurposing for newer therapeutics.This potentially saves resources invested in clinical trials that verify drug safety and tolerance in humans prior to alternative indication approval.Protein arginine methyltransferase 5(PRMT5)overexpression has been linked to promoting the tumor phenotype in several cancers,including pancreatic ductal adenocarcinoma(PDAC),colorectal cancer(CRC),and breast cancer(BC),making PRMT5 an important target for cancer therapy.Previously,we showed that PRMT5-mediated methylation of the nuclear factor(NF)-kB,partially contributes to its constitutive activation observed in cancers.In this study,we utilized an AlphaLiSA-based high-throughput screening method adapted in our lab,and identified one FDA-approved drug,Candesartan cilexetil(Can,used in hypertension treatment)and one EMA-approved drug,Cloperastine hydrochloride(Clo,used in cough treatment)that had significant PRMT5-inhibitory activity,and their anti-tumor properties were validated using cancer phenotypic assays in vitro.Furthermore,PRMT5 selective inhibition of methyltransferase activity was confirmed by reduction of both NF-kB methylation and its subsequent activation upon drug treatment.Using in silico prediction,we identified critical residues on PRMT5 targeted by these drugs that may interfere with its enzymatic activity.Finally,Clo and Can treatment have exhibited marked reduction in tumor growth in vivo.Overall,we provide basis for pursuing repurposing Clo and Can as anti-PRMT5 cancer therapies.Our study offers potential safe and fast repurposing of previously unknown PRMT5 inhibitors into clinical practice.

Role of post-translational modification of the Y box binding protein 1 in human cancers

Y box binding protein-1(YBX1)belongs to a DNA-and RNA-binding family of transcription factors,containing the highly conserved cold shock domain(CSD).YBX1 is involved in a number of cellular functions including transcription,translation,DNA damage repair etc.,and it is upregulated during times of environmental stress.YBX1 is localized in both the cytoplasm and the nucleus.There,its nuclear translocation is observed in a number of cancers and is associated with poor prognosis and disease progression.Additionally,YBX1 expression is upregulated in a variety of cancers,pointing towards its role as a potential oncogene.Under certain circumstances,YBX1 also promotes the expression of multidrug resistance 1(MDR1)gene,which is involved in the development of drug resistance.Thus,it is critical to understand the mechanism of YBX1 regulation and its downstream effects on promoting cancer development.A number of recent studies have highlighted the mechanisms of YBX1 regulation.Mass spectrometric analyses have reported several post-translational modifications that possibly play an important role in modulating YBX1 function.Phosphorylation is the most widely occurring post-translational modification in YBX1.In vivo analyses of sites like S102 and more recently,S165 illustrate the relationship of post-translational regulation of YBX1 in promoting cell proliferation and tumor growth.This review provides a comprehensive and up-to-date account of post-translational modifications identified in YBX1.This knowledge is a key in allowing us to better understand the mechanism of YBX1 regulation,which will aid in development of novel therapeutic strategies to target YBX1 in many types of cancer in the future.

Photo-activated microtubule targeting drugs: Advancing therapies for colorectal cancer

Over the years immunotherapy has demonstrably improved the field of cancer treatment.However,achieving long-term survival for colorectal cancer(CRC)patients remains a significant unmet need.Combination immunotherapies incor-porating targeted drugs like MEK or multi-kinase inhibitors have offered some palliative benefit.Nevertheless,substantial gaps remain in the current therapeutic armamentarium for CRC.In recent years,there has been a surge of interest in exploring novel treatment strategies,including the application of light-activated drugs in conjunction with optical devices.This approach holds promise for achie-ving localized and targeted delivery of cytotoxic agents,such as microtubule-targeting drugs,directly to cancerous cells within the colon.

Distinct transcriptomic landscapes of cutaneous basal cell carcinomas and squamous cell carcinomas

The majority of non-melanoma skin cancer(NMSC)is cutaneous basal cell carcinoma(BCC)or squamous cell carcinoma(SCC),which are also called keratinocyte carcinomas,as both of them originate from keratinocytes.The incidence of keratinocyte carcinomas is over 5 million per year in the US,three-fold higher than the total incidence of all other types of cancer combined.While there are several reports on gene expression profiling of BCC and SCC,there are significant variations in the reported gene expression changes in different studies.One reason is that tumor-adjacent normal skin specimens were not included in many studies as matched controls.Furthermore,while numerous studies of skin stem cells in mouse models have been reported,their relevance to human skin cancer remains unknown.In this report,we analyzed gene expression profiles of paired specimens of keratinocyte carcinomas with their matched normal skin tissues as the control.Among several novel findings,we discovered a significant number of zinc finger encoding genes up-regulated in human BCC.In BCC,a novel link was found between hedgehog signaling,Wnt signaling,and the cilium.While the SCC cancerstem-cell gene signature is shared between human and mouse SCCs,the hair follicle stem-cell signature of mice was not highly represented in human SCC.Differential gene expression(DEG)in human BCC shares gene signature with both bulge and epidermal stem cells.We have also determined that human BCCs and SCCs have distinct gene expression patterns,and some of them are not fully reflected in current mouse models.

Levels of circulating follicular helper T cells,T helper 1 cells,and the prognostic significance of soluble form of CD40 ligand on survival in patients with alcoholic cirrhosis

Background:Excessive drinkers(ED)and patients with alcoholic liver disease(ALD)are several times more susceptible to bacterial and viral infections and have a decrease in antibody responses to vaccinations.Follicular helper T(TFH)cells are essential to select B cells in the germinal center and to produce antibodies.TFH cells express both a membrane-associated and a soluble form of CD40 ligand(sCD40L),in which the latter form is released to circulation upon T cell activation.The effect of alcohol on TFH cells has not been studied.Objectives:The goals of this study are to determine the levels of TFH and T helper 1(Th1)cells in ED and those with alcoholic cirrhosis(AC)when compared to healthy controls and to determine the prognostic significance of sCD40L in a cohort of patients with AC.Methods:Controls,ED,and those with AC were enrolled.Baseline demographic,laboratory tests,and peripheral blood mononuclear cells(PBMCs)were isolated and assessed via flow cytometry for TFH cells.In vitro study was performed to determine the ability of PBMCs to secrete interferon(IFN)-ɣupon stimulation.Serum sCD40L was also determined and its prognostic significance was tested in a cohort of AC patients.Results:The levels of circulating TFH(cTFH)cells were significantly lower in peripheral blood of subjects with ED and AC compared to controls(P<0.05).IFN-ɣsecretion from PBMCs upon stimulation was also lower in ED and those with cirrhosis.Serum sCD40L was significantly lower in ED and AC when compared to that in controls(P<0.0005).Its level was an independent predictor of mortality.Conclusions:Patients with AC had significantly lower level of cTFH and sCD40L.The level of sCD40L was an independent predictor of mortality in these patients.

Differential Splicing of Skipped Exons Predicts Drug Response in Cancer Cell Lines

Alternative splicing of pre-mRNA transcripts is an important regulatory mechanism that increases the diversity of gene products in eukaryotes.Various studies have linked specific transcript isoforms to altered drug response in cancer;however,few algorithms have incorporated splicing information into drug response prediction.In this study,we evaluated whether basal-level splicing information could be used to predict drug sensitivity by constructing doxorubicin-sensitivity classification models with splicing and expression data.We detailed splicing differences between sensitive and resistant cell lines by implementing quasi-binomial generalized linear modeling(QBGLM)and found altered inclusion of 277 skipped exons.We additionally conducted RNA-binding protein(RBP)binding motif enrichment and differential ex-pression analysis to characterize cis-and trans-acting elements that potentially influence doxorubicin response-mediating splicing alterations.Our results showed that a classification model built with skipped exon data exhibited strong predictive power.We discovered an association between differentially spliced events and epithelial-mesenchymal transition(EMT)and observed motif enrichment,as well as differential expression of RBFOX and ELAVL RBP family members.Our work demonstrates the potential of incorporating splicing data into drug response algorithms and the utility of a QBGLM approach for fast,scalable identification of relevant splicing differences between large groups of samples.

BrcaSeg:A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment.Here,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological images.The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)Program.BrcaSeg achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art methods.Using this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data.We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios.Gene Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues.Taken all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors.BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.

Predictability of polygenic risk score for progression to dementia and its interaction with APOEε4 in mild cognitive impairment

Background:The combinatorial efect of multiple genetic factors calculated as a polygenic risk score(PRS)has been studied to predict disease progression to Alzheimer’s disease(AD)from mild cognitive impairment(MCI).Previous studies have investigated the performance of PRS in the prediction of disease progression to AD by including and excluding single nucleotide polymorphisms within the region surrounding the APOE gene.These studies may have missed the APOE genotype-specifc predictability of PRS for disease progression to AD.Methods:We analyzed 732 MCI from the Alzheimer’s Disease Neuroimaging Initiative cohort,including those who progressed to AD within 5 years post-baseline(n=270)and remained stable as MCI(n=462).The predictability of PRS including and excluding the APOE region(PRS_(+APOE) and PRS_(−APOE))on the conversion to AD and its interaction with the APOEε4 carrier status were assessed using Cox regression analyses.Results:PRS_(+APOE)(hazard ratio[HR]1.468,95%CI 1.335-1.615)and PRS_(−APOE)(HR 1.293,95%CI 1.157-1.445)were both associated with a signifcantly increased risk of MCI progression to dementia.The interaction between PRS_(+APOE) and APOEε4 carrier status was signifcant with a P-value of 0.0378.The association of PRSs with the progression risk was stronger in APOEε4 non-carriers(PRS_(+APOE):HR 1.710,95%CI 1.244-2.351;PRS_(−APOE):HR 1.429,95%CI 1.182-1.728)than in APOEε4 carriers(PRS_(+APOE):HR 1.167,95%CI 1.005-1.355;PRS_(−APOE):HR 1.172,95%CI 1.020-1.346).Conclusions:PRS could predict the conversion of MCI to dementia with a stronger association in APOEε4 noncarriers than APOEε4 carriers.This indicates PRS as a potential genetic predictor particularly for MCI with no APOEε4 alleles.

SARS-CoV-2 spike protein induces IL-18-mediated cardiopulmonary inflammation via reduced mitophagy

Cardiopulmonary complications are major drivers of mortality caused by the SARS-CoV-2 virus.Interleukin-18,an inflammasomeinduced cytokine,has emerged as a novel mediator of cardiopulmonary pathologies but its regulation via SARS-CoV-2 signaling remains unknown.Based on a screening panel,IL-18 was identified amongst 19 cytokines to stratify mortality and hospitalization burden in patients hospitalized with COVID-19.Supporting clinical data,administration of SARS-CoV-2 Spike 1(S1)glycoprotein or receptor-binding domain(RBD)proteins into human angiotensin-converting enzyme 2(hACE2)transgenic mice induced cardiac fibrosis and dysfunction associated with higher NF-κB phosphorylation(pNF-κB)and cardiopulmonary-derived IL-18 and NLRP3 expression.IL-18 inhibition via IL-18BP resulted in decreased cardiac pNF-κB and improved cardiac fibrosis and dysfunction in S1-or RBD-exposed hACE2 mice.Through in vivo and in vitro work,both S1 and RBD proteins induced NLRP3 inflammasome and IL-18 expression by inhibiting mitophagy and increasing mitochondrial reactive oxygenation species.Enhancing mitophagy prevented Spike protein-mediated IL-18 expression.Moreover,IL-18 inhibition reduced Spike protein-mediated pNF-κB and EC permeability.Overall,the link between reduced mitophagy and inflammasome activation represents a novel mechanism during COVID-19 pathogenesis and suggests IL-18 and mitophagy as potential therapeutic targets.

N^6-Methyladenosine modification: a novel pharmacological target for anti-cancer drug development

N6-Methyladenosine(m6 A) modification is the most pervasive modification of human mRNA molecules. It is reversible via regulation of m6 A modification methyltransferase, demethylase and proteins that preferentially recognize m6 A modification as "writers", "erasers" and "readers", respectively. Altered expression levels of the m6 A modification key regulators substantially affect their function, leading to significant phenotype changes in the cell and organism. Recent studies have proved that the m6 A modification plays significant roles in regulation of metabolism, stem cell self-renewal, and metastasis in a variety of human cancers. In this review, we describe the potential roles of m6 A modification in human cancers and summarize their underlying molecular mechanisms. Moreover, we will highlight potential therapeutic approaches by targeting the key m6 A modification regulators for cancer drug development.& 2018 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

PMTDS： a computational method based on genetic interaction networks for Precision Medicine Target-Drug Selection in cancer

Background： Precision medicine attempts to tailor the right therapy for the right patient. Recent progress in large- scale collection of patents＇ tumor molecular profiles in The Cancer Genome Atlas （TCGA） provides a foundation for systematic discovery of potential drug targets specific to different types of cancer. However, we still lack powerful computational methods to effectively integrate multiple omics data and protein-protein interaction network technology for an optimum target and drug recommendation for an individual patient. Methods： In this study, a computation method, Precision Medicine Target-Drug Selection （PMTDS） based on genetic interaction networks is developed to select the optimum targets and associated drugs for precision medicine style treatment of cancer. The PMTDS system includes three parts： a personalized medicine knowledgebase for each cancer type, a genetic interaction network-based algorithm and a single patient molecular profiles. The knowledgebase integrates cancer drugs, drug-target databases and gene biological pathway networks. The molecular profiles of each tumor consists of DNA copy number alteration, gene mutation, and tumor gene expression variation compared to its adjacent normal tissue. Results： The novel integrated PMTDS system is applied to select candidate target-drug pairs for 178 TCGA pancreatic adenocarcinoma （PDAC） tumors. The experiment results show known drug targets （EGFR, IGF1R, ERBB2, NRII2 and AKR1B1） of PDAC treatment are identified, which provides important evidence of the PMTDS algorithm＇s accuracy. Other potential targets PTK6, ATF, SYK are, also, recommended for PDAC. Further validation is provided by comparison of selected targets with, both, cell line molecular profiles from the Cancer Cell Line Encyclopedia （CCLE） and drug response data from the Cancer Therapeutics Response Portal （CTRP）. Results from experimental analysis of forty six individual pancreatic cancer samples show that drugs selected by PMTDS have more sample-specific efficacy than the current clinical PDAC therapies. Conclusions： A novelty target and drug priority algorithm PMTDS is developed to identify optimum target-drug pairs by integrating the knowledgebase base with a single patient＇s genomics. The PMTDS system provides an accurate and reliable source for target and off-label drug selection for precision cancer medicine.

TSUNAMI:Translational Bioinformatics Tool Suite for Network Analysis and Mining

Gene co-expression network(GCN)mining identifies gene modules with highly correlated expression profiles across samples/conditions.It enables researchers to discover latent gene/molecule interactions,identify novel gene functions,and extract molecular features from certain disease/condition groups,thus helping to identify disease bio-markers.However,there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis,as well as modules that may share common members.To address this need,we developed an online GCN mining tool package:TSUNAMI(Tools SUite for Network Analysis and MIning).TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data(microarray,RNA-seq,or any other numerical omics data),and then performs downstream gene set enrichment analysis for the identified modules.It has several features and advantages:1)a user-friendly interface and real-time co-expression network mining through a web server;2)direct access and search of NCBI Gene Expression Omnibus(GEO)and The Cancer Genome Atlas(TCGA)databases,as well as user-input gene ex-pression matrices for GCN module mining;3)multiple co-expression analysis tools to choose from,all of which are highly flexible in regards to parameter selection options;4)identified GCN modules are summarized to eigengenes,which are convenient for users to check their correlation with other clinical traits;5)integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools;and 6)visualization of gene loci by Circos plot in any step of the process.The web service is freely accessible through URL:https://biolearns.medicine.iu.edu/.Source code is available at https://github.com/huangzhii/TSUNAMI/.

MRHCA： a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network

Background： Gene co-expression and differential co-expression analysis has been increasingly used to study co- functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods： In this study, we develop a nonparametric approach to identify hub genes and modules in a large co- expression network with low computational and memory cost, namely MRHCA. Results： We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co- expression analysis to E. coil and TCGA cancer data, we have identified significant condition specific activated genes in E. coil and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion： Our analysis has demonstrated MRItCA can （i） deal with large association networks, （ii） rigorously assess statistical significance for hubs and module sizes, （iii） identify co-expression modules with low associations, （iv） detect small and significant modules, and （v） allow genes to be present in more than one modules, compared with existing methods.