Platelet RNA enables accurate detection of ovarian cancer:an intercontinental,biomarker identification study

Platelets are reprogrammed by cancer via a process called education,which favors cancer development.The transcriptional profile of tumor-educated platelets(TEPs)is skewed and therefore practicable for cancer detection.This intercontinental,hospital-based,diagnostic study included 761 treatment-naive inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers(China,n=3;Netherlands,n=5;Poland,n=1)between September 2016 and May 2019.The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese(VC1 and VC2)and the European(VC3)validation cohorts collectively and independently.Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets.The AUCs for TEPs in the combined validation cohort,VC1,VC2,and VC3 were 0.918(95%CI 0.889-0.948),0.923(0.855-0.990),0.918(0.872-0.963),and 0.887(0.813-0.960),respectively.Combination of TEPs and CA125 demonstrated an AUC of 0.922(0.889-0.955)in the combined validation cohort;0.955(0.912-0.997)in VC1;0.939(0.901-0.977)in VC2;0.917(0.824-1.000)in VC3.For subgroup analysis,TEPs exhibited an AUC of o.858,0.859,and 0.920 to detect early-stage,borderline,non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis.TEPs had robustness,compatibility,and universality for preop.erative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities,heterogeneous histoiogical subtypes,and early-stage ovarian cancer.However,these observations warrant prospective validations in a larger population beforeclinicalutilities.

Platelet RNA signature independently predicts ovarian cancer prognosis by deep learning neural network model

Dear Editor,Platelets are circulating anucleate cytoplasmic fragments of megakaryocytes and characterized by their functions in wound healing and vascular integrity maintenance.Increasing evidence highlights the extensive reciprocal signaling interactions between platelets and tumor cells(Haemmerle et al.,2018).Tumor cells activate and aggregate platelets to sustain proliferation(Cho et al.,2012),resist apoptosis,and promote metastasis(Haemmerle et al.,2017).

hTFtarget:A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets

Transcription factors(TFs)as key regulators play crucial roles in biological processes.The identification of TF-target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression.The accumulated data of chromatin immunoprecipitation sequencing(ChIP-seq)provide great opportunities to discover the TF-target regulations across different conditions.In this study,we constructed a database named hTFtarget,which integrated huge human TF target resources(7190 ChIP-seq samples of 659 TFs and high-confidence binding sites of 699 TFs)and epigenetic modification information to predict accurate TF-target regulations.hTFtarget offers the following functions for users to explore TF-target regulations:(1)browse or search general targets of a query TF across datasets;(2)browse TF-target regulations for a query TF in a specific dataset or tissue;(3)search potential TFs for a given target gene or noncoding RNA;(4)investigate co-association between TFs in cell lines;(5)explore potential coregulations for given target genes or TFs;(6)predict candidate TF binding sites on given DNA sequences;(7)visualize ChIP-seq peaks for different TFs and conditions in a genome browser.hTFtarget provides a comprehensive,reliable and user-friendly resource for exploring human TF-target regulations,which will be very useful for a wide range of users in the TF and gene expression regulatiol community.hTFtarget is available at bttp://bioinfo.life.hust.edu.cn/hTFtarget.