An Efficient Technique for One-Dimensional Fractional Diffusion EquationModel for Cancer Tumor

This study intends to examine the analytical solutions to the resulting one-dimensional differential equation of acancer tumor model in the frame of time-fractional order with the Caputo-fractional operator employing a highlyefficient methodology called the q-homotopy analysis transform method.So,the preferred approach effectivelyfound the analytic series solution of the proposed model.The procured outcomes of the present frameworkdemonstrated that this method is authentic for obtaining solutions to a time-fractional-order cancer model.Theresults achieved graphically specify that the concerned paradigm is dependent on arbitrary order and parametersand also disclose the competence of the proposed algorithm.

Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

BACKGROUND Endometrial cancer(EC)is a common gynecological malignancy that typically requires prompt surgical intervention;however,the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes.Previous studies have highlighted the prognostic potential of circulating tumor DNA(ctDNA)monitoring for minimal residual disease in patients with EC.AIM To develop and validate an optimized ctDNA-based model for predicting shortterm postoperative EC recurrence.METHODS We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model,which was validated on 143 EC patients operated between 2020 and 2021.Prognostic factors were identified using univariate Cox,Lasso,and multivariate Cox regressions.A nomogram was created to predict the 1,1.5,and 2-year recurrence-free survival(RFS).Model performance was assessed via receiver operating characteristic(ROC),calibration,and decision curve analyses(DCA),leading to a recurrence risk stratification system.RESULTS Based on the regression analysis and the nomogram created,patients with postoperative ctDNA-negativity,postoperative carcinoembryonic antigen 125(CA125)levels of<19 U/mL,and grade G1 tumors had improved RFS after surgery.The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis,calibration curves,and DCA methods,highlighting its high accuracy and clinical utility.Furthermore,using the nomogram,the patients were successfully classified into three risk subgroups.CONCLUSION The nomogram accurately predicted RFS after EC surgery at 1,1.5,and 2 years.This model will help clinicians personalize treatments,stratify risks,and enhance clinical outcomes for patients with EC.

Prognostic value and predictive model of tumor markers in stageⅠtoⅢgastric cancer patients

BACKGROUND Preoperative serum tumor markers have been widely used in the diagnosis and treatment of gastric cancer patients.However,few studies have evaluated the prognosis of gastric cancer patients by establishing statistical models with multiple serum tumor indicators.AIM To explore the prognostic value and predictive model of tumor markers in stage I and III gastric cancer patients.METHODS From October 2018 to April 2020,a total of 1236 patients with stage I to III gastric cancer after surgery were included in our study.The relationship between serum tumor markers and clinical and pathological data were analyzed.We established a statistical model to predict the prognosis of gastric cancer based on the results of COX regression analysis.Overall survival(OS)was also compared across different stages of gastric cancer.RESULTS The deadline for follow-up was May 31,2023.A total of 1236 patients were included in our study.Univariate analysis found that age,clinical stage,T and N stage,tumor location,differentiation,Borrmann type,size,and four serum tumor markers were prognostic factors of OS(P<0.05).It was shown that clinical stage,tumor size,alpha foetoprotein,carcinoembryonic antigen,CA125 and CA19-9(P<0.05)were independent prognostic factors for OS.According to the scoring results obtained from the statistical model,we found that patients with high scores had poorer survival time(P<0.05).Furthermore,in stage I patients,the 3-year OS for scores 0-3 ranged from 96.85%,95%,85%,and 80%.In stage II patients,the 3-year OS for scores 0-4 were 88.6%,76.5%,90.5%,65.5%and 60%.For stage III patients,3-year OS for scores 0-6 were 70.9%,68.3%,64.1%,50.9%,38.4%,18.5%and 5.2%.We also analyzed the mean survival of patients with different scores.For stage I patients,the mean OS was 55.980 months.In stage II,the mean OS was 51.550 months.The mean OS for stage III was 39.422 months.CONCLUSION Our statistical model can effectively predict the prognosis of gastric cancer patients.

A model of five genes of tumor microenvironment predicts prognosis in Cholangiocarcinoma

Background:Cholangiocarcinoma(CCA)is highly malignant and has a poor prognosis has a high malignant degree and poor prognosis.The purpose of this study is to develop a new prognostic model based on genes related to the tumor microenvironment(TME).Methods:Derived from the discerned differentially expressed genes within The Cancer Genome Atlas(TCGA)dataset,this investigation employed the methodology of weighted gene co-expression network analysis(WGCNA)to ascertain gene co-expressed modules intricately linked to the Tumor Microenvironment(TME)among Cholangiocarcinoma(CCA)patients.The genes associated with prognosis,as identified through Cox regression analysis,were employed in the formulation of a predictive model.This model underwent validation,leading to the development of a risk score formula and nomogram.Concurrently,we validated the model’s reliability using data from CCA patients in the Gene Expression Omnibus(GEO)database(accession:GSE107943).Results:6139 DEGs were divided into 10 co-expressed gene modules using WGCNA.Among these,two modules(blue module with 832 genes and brown module with 1379 genes)showed high correlation with the TME.Five prognostic genes(BNIP3,COL4A3,SPRED3,CEBPB,PLOD2)were identified through Cox regression analysis,and a prognostic model and risk score formula were developed based on these genes.Risk score formula:Risk score=BNIP3×1.70520-COL4A3×2.39815+SPRED3×1.17936+CEBPB×0.40456+PLOD2×0.24785.Kaplan-Meier survival analysis revealed that the survival probabilities of the low-risk group were significantly higher than those of the high-risk group.Furthermore,the related evaluation indexes suggested that the model exhibited strong predictive ability.Conclusion:The prognostic model,based on five TME-related genes(BNIP3,COL4A3,SPRED3,CEBPB,PLOD2),could accurately assess the prognosis of CCA patients to aid in guiding clinical decisions.

Patient-derived xenograft model in colorectal cancer basic and translational research

Colorectal cancer(CRC)is one of the most popular malignancies globally,with 930000 deaths in 2020.The evaluation of CRC-related pathogenesis and the discovery of po-tential therapeutic targets will be meaningful and helpful for improving CRC treat-ment.With huge efforts made in past decades,the systematic treatment regimens have been applied to improve the prognosis of CRC patients.However,the sensitivity of CRC to chemotherapy and targeted therapy is different from person to person,which is an important cause of treatment failure.The emergence of patient-derived xenograft(PDX)models shows great potential to alleviate the straits.PDX models possess similar genetic and pathological characteristics as the features of primary tu-mors.Moreover,PDX has the ability to mimic the tumor microenvironment of the original tumor.Thus,the PDX model is an important tool to screen precise drugs for individualized treatment,seek predictive biomarkers for prognosis supervision,and evaluate the unknown mechanism in basic research.This paper reviews the recent advances in constructed methods and applications of the CRC PDX model,aiming to provide new knowledge for CRC basic research and therapeutics.

Spontaneous xenogeneic GvHD in Wilms'tumor Patient-Derived xenograft models and potential solutions

Severely immunocompromised NOD.Cg-PrkdcIl2rg(NOG)mice are among the ideal animal recipients for generation of human cancer models.Transplantation of human solid tumors having abundant tumor-i nfiltrating lymphocytes(TILs)can induce xenogeneic graft-versus-host disease(xGvHD)following engraftment and expansion of the TILs inside the animal body.Wilms’tumor(WT)has not been recognized as a lymphocyte-predominant tumor.However,3 consecutive generations of NOG mice bearing WT patient-derived xenografts(PDX)xenotransplanted from a single donor showed different degrees of inflammatory symptoms after transplantation before any therapeutic intervention.In the initial generation,dermatitis,auto-amputation of digits,weight loss,lymphadenopathy,hepatitis,and interstitial pneumonitis were observed.Despite antibiotic treatment,no response was noticed,and thus the animals were prematurely euthanized(day 47 posttransplantation).Laboratory and histopathologic evaluations revealed lymphoid infiltrates positively immunostained with anti-human CD3 and CD8 antibodies in the xenografts and primary tumor,whereas no microbial infection or lymphoproliferative disorder was found.Mice of the next generation that lived longer(91 days)developed sclerotic skin changes and more severe pneumonitis.Cutaneous symptoms were milder in the last generation.The xenografts of the last 2 generations also contained TILs,and lacked lymphoproliferative transformation.The systemic immunoinflammatory syndrome in the absence of microbial infection and posttransplant lymphoproliferative disorder was suggestive of xGvHD.While there are few reports of xGvHD in severely immunodeficient mice xenotransplanted from lymphodominant tumor xenografts,this report for the first time documented serial xGvHD in consecutive passages of WT PDX-bearing models and discussed potential solutions to prevent such an undesired complication.

The promises and challenges of patient-derived tumor organoids in drug development and precision oncology

In the era of precision medicine,cancer researchers and oncologists are eagerly searching for more realistic,cost effective,and timely tumor models to aid drug development and precision oncology.Tumor models that can faithfully recapitulate the histological and molecular characteristics of various human tumors will be extremely valuable in increasing the successful rate of oncology drug development and discovering the most efficacious treatment regimen for cancer patients.Two‐dimensional(2D)cultured cancer cell lines,genetically engineered mouse tumor(GEMT)models,and patient‐derived tumor xenograft(PDTX)models have been widely used to investigate the biology of various types of cancers and test the efficacy of oncology drug candidates.However,due to either the failure to faithfully recapitulate the complexity of patient tumors in the case of 2D cultured cancer cells,or high cost and untimely for drug screening and testing in the case of GEMT and PDTX,new tumor models are urgently needed.The recently developed patient‐derived tumor organoids(PDTO)offer great potentials in uncovering novel biology of cancer development,accelerating the discovery of oncology drugs,and individualizing the treatment of cancers.In this review,we will summarize the recent progress in utilizing PDTO for oncology drug discovery.In addition,we will discuss the potentials and limitations of the current PDTO tumor models.

Patient-derived non-small cell lung cancer xenograft mirrors complex tumor heterogeneity

Objective:Patient-derived xenograft(PDX)models have shown great promise in preclinical and translational applications,but their consistency with primary tumors in phenotypic,genetic,and pharmacodynamic heterogeneity has not been well-studied.This study aimed to establish a PDX repository for non-small cell lung cancer(NSCLC)and to further elucidate whether it could preserve the heterogeneity within and between tumors in patients.Methods:A total of 75 surgically resected NSCLC specimens were implanted into immunodeficient NOD/SCID mice.Based on the successful establishment of the NSCLC PDX model,we compared the expressions of vimentin,Ki67,EGFR,and PD-L1 proteins between cancer tissues and PDX models using hematoxylin and eosin staining and immunohistochemical staining.In addition,we detected whole gene expression profiling between primary tumors and PDX generations.We also performed whole exome sequencing(WES)analysis in 17 first generation xenografts to further assess whether PDXs retained the patient heterogeneities.Finally,paclitaxel,cisplatin,doxorubicin,atezolizumab,afatininb,and AZD4547 were used to evaluate the responses of PDX models to the standard-of-care agents.Results:A large collection of serially transplantable PDX models for NSCLC were successfully developed.The histology and pathological immunohistochemistry of PDX xenografts were consistent with the patients’tumor samples.WES and RNA-seq further confirmed that PDX accurately replicated the molecular heterogeneities of primary tumors.Similar to clinical patients,PDX models responded differentially to the standard-of-care treatment,including chemo-,targeted-and immuno-therapeutics.Conclusions:Our established PDX models of NSCLC faithfully reproduced the molecular,histopathological,and therapeutic characteristics,as well as the corresponding tumor heterogeneities,which provides a clinically relevant platform for drug screening,biomarker discovery,and translational research.

Translational pancreatic cancer research:a comparative study on patient-derived xenograft models

AIM To assess the viability of orthotopic and heterotopic patient-derived pancreatic cancer xenografts implanted into nude mice.METHODS This study presents a prospective experimental analytical follow-up of the development of tumours in mice upon implantation of human pancreatic adenocarcinoma samples. Specimens were obtained surgically from patients with a pathological diagnosis of pancreatic adenocarcinoma. Tumour samples from pancreatic cancer patients were transplanted into nude mice in three different locations(intraperitoneal, subcutaneous and pancreatic). Histological analysis(haematoxylin-eosin and Masson's trichrome staining) and immunohistochemical assessment of apoptosis(TUNEL), proliferation(Ki-67), angiogenesis(CD31) and fibrogenesis(α-SMA) were performed. When a tumour xenograft reached the target size, it was reimplanted in a new nude mouse. Three sequential tumour xenograft generations were generated(F1, F2 and F3).RESULTS The overall tumour engraftment rate was 61.1%. The subcutaneous model was most effective in terms of tissue growth(69.9%), followed by intraperitoneal(57.6%) and pancreatic(55%) models. Tumour development was faster in the subcutaneous model(17.7 ± 2.6 wk) compared with the pancreatic(23.1 ± 2.3 wk) and intraperitoneal(25.0 ± 2.7 wk) models(P = 0.064). There was a progressive increase in the tumour engraftment rate over successive generations for all three models(F1 28.1% vs F2 71.4% vs F3 80.9%, P < 0.001). There were no significant differences in tumour xenograft differentiation and cell proliferation between human samples and the three experimental models among the sequential generations of tumour xenografts. However, a progressive decrease in fibrosis, fibrogenesis, tumour vascularisation and apoptosis was observed in the three experimental models compared with the human samples. All three pancreatic patient-derived xenograft models presented similar histological and immunohistochemical characteristics.CONCLUSION In our experience, the faster development andgreatest number of viable xenografts could make the subcutaneous model the best option for experimentation in pancreatic cancer.

Modeling One Dimensional Two-Cell Model with Tumor Interaction Using Krylov Subspace Methods

A brain tumor occurs when abnormal cells grow, sometimes very rapidly, into an abnormal mass of tissue. The tumor can infect normal tissue, so there is an interaction between healthy and infected cell. The aim of this paper is to propose some efficient and accurate numerical methods for the computational solution of one-dimensional continuous basic models for the growth and control of brain tumors. After computing the analytical solution, we construct approximations of the solution to the problem using a standard second order finite difference method for space discretization and the Crank-Nicolson method for time discretization. Then, we investigate the convergence behavior of Conjugate gradient and generalized minimum residual as Krylov subspace methods to solve the tridiagonal toeplitz matrix system derived.

Role of cancer stem cell ecosystem on breast cancer metastasis and related mouse models

Breast cancer metastasis is responsible for most breast cancer-related deaths and is influenced by many factors within the tumor ecosystem,including tumor cells and microenvironment.Breast cancer stem cells(BCSCs)constitute a small population of cancer cells with unique characteristics,including their capacity for self-renewal and differentiation.Studies have shown that BCSCs not only drive tumorigenesis but also play a crucial role in promoting metastasis in breast cancer.The tumor microenvironment(TME),composed of stromal cells,immune cells,blood vessel cells,fibroblasts,and microbes in proximity to cancer cells,is increasingly recognized for its crosstalk with BCSCs and role in BCSC survival,growth,and dissemination,thereby influencing metastatic ability.Hence,a thorough understanding of BCSCs and the TME is critical for unraveling the mechanisms underlying breast cancer metastasis.In this review,we summarize current knowledge on the roles of BCSCs and the TME in breast cancer metastasis,as well as the underlying regulatory mechanisms.Furthermore,we provide an overview of relevant mouse models used to study breast cancer metastasis,as well as treatment strategies and clinical trials addressing BCSC-TME interactions during metastasis.Overall,this study provides valuable insights for the development of effective therapeutic strategies to reduce breast cancer metastasis.

A Prognostic Model Based on Colony Stimulating Factors-related Genes in Triple-negative Breast Cancer

Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells,playing an important role in the malignant progression of TNBC.This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes(CRGs),and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy.Methods We downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database.Through LASSO Cox regression analysis,we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score(CRRS).We further analyzed the correlation between CRRS and patient prognosis,clinical features,tumor microenvironment(TME)in both high-risk and low-risk groups,and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy.Results We identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model.Kaplan-Meier survival curves,time-dependent receiver operating characteristic curves,and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival,and the predictive ability of CRRS prognostic model was further validated using the GEO dataset.Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients.Moreover,patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil,ipatasertib,and paclitaxel.Conclusion We have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs,which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment.Moreover,the key genes within this model may represent potential molecular targets for future therapies of TNBC.

3D bioprinting of in vitro porous hepatoma models:establishment,evaluation,and anticancer drug testing

Traditional tumor models do not tend to accurately simulate tumor growth in vitro or enable personalized treatment and are particularly unable to discover more beneficial targeted drugs.To address this,this study describes the use of threedimensional(3D)bioprinting technology to construct a 3D model with human hepatocarcinoma SMMC-7721 cells(3DP-7721)by combining gelatin methacrylate(GelMA)and poly(ethylene oxide)(PEO)as two immiscible aqueous phases to form a bioink and innovatively applying fluorescent carbon quantum dots for long-term tracking of cells.The GelMA(10%,mass fraction)and PEO(1.6%,mass fraction)hydrogel with 3:1 volume ratio offered distinct pore-forming characteristics,satisfactorymechanical properties,and biocompatibility for the creation of the 3DP-7721 model.Immunofluorescence analysis and quantitative real-time fluorescence polymerase chain reaction(PCR)were used to evaluate the biological properties of the model.Compared with the two-dimensional culture cell model(2D-7721)and the 3D mixed culture cell model(3DM-7721),3DP-7721 significantly improved the proliferation of cells and expression of tumor-related proteins and genes.Moreover,we evaluated the differences between the three culture models and the effectiveness of antitumor drugs in the three models and discovered that the efficacy of antitumor drugs varied because of significant differences in resistance proteins and genes between the three models.In addition,the comparison of tumor formation in the three models found that the cells cultured by the 3DP-7721 model had strong tumorigenicity in nude mice.Immunohistochemical evaluation of the levels of biochemical indicators related to the formation of solid tumors showed that the 3DP-7721 model group exhibited pathological characteristics of malignant tumors,the generated solid tumors were similar to actual tumors,and the deterioration was higher.This research therefore acts as a foundation for the application of 3DP-7721 models in drug development research.

Establishing prognostic models for intrahepatic cholangiocarcinoma based on immune cells

BACKGROUND Intrahepatic cholangiocarcinoma(ICC)is a malignant liver tumor that is challenging to treat and manage and current prognostic models for the disease are inefficient or ineffective.Tumor-associated immune cells are critical for tumor development and progression.The main goal of this study was to establish models based on tumor-associated immune cells for predicting the overall survival of patients undergoing surgery for ICC.AIM To establish 1-year and 3-year prognostic models for ICC after surgical resection.METHODS Immunohistochemical staining was performed for CD4,CD8,CD20,pan-cytokeratin(CK),and CD68 in tumors and paired adjacent tissues from 141 patients with ICC who underwent curative surgery.Selection of variables was based on regression diagnostic procedures and goodness-of-fit tests(PH assumption).Clinical parameters and pathological diagnoses,combined with the distribution of immune cells in tumors and paired adjacent tissues,were utilized to establish 1-and 3-year prognostic models.RESULTS This is an important application of immune cells in the tumor microenvironment.CD4,CD8,CD20,and CK were included in the establishment of our prognostic model by stepwise selection,whereas CD68 was not significantly associated with the prognosis of ICC.By integrating clinical data associated with ICC,distinct prognostic models were derived for 1-and 3-year survival outcomes using variable selection.The 1-year prediction model yielded a C-index of 0.7695%confidence interval(95%CI):0.65-0.87 and the 3-year prediction model produced a C-index of 0.69(95%CI:0.65-0.73).Internal validation yielded a C-index of 0.761(95%CI:0.669-0.853)for the 1-year model and 0.693(95%CI:0.642-0.744)for the 3-year model.CONCLUSION We developed Cox regression models for 1-year and 3-year survival predictions of patients with ICC who underwent resection,which has positive implications for establishing a more comprehensive prognostic model for ICC based on tumor immune microenvironment and immune cell changes in the future.

Computed tomography radiogenomics:A potential tool for prediction of molecular subtypes in gastric stromal tumor

BACKGROUND Preoperative knowledge of mutational status of gastrointestinal stromal tumors(GISTs)is essential to guide the individualized precision therapy.AIM To develop a combined model that integrates clinical and contrast-enhanced computed tomography(CE-CT)features to predict gastric GISTs with specific genetic mutations,namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions.METHODS A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio.The models were constructed using selected clinical features,conventional CT features,and radiomics features extracted from abdominal CE-CT images.Three models were developed:ModelCT sign,modelCT sign+rad,and model CTsign+rad+clinic.The diagnostic performance of these models was evaluated using receiver operating characteristic(ROC)curve analysis and the Delong test.RESULTS The ROC analyses revealed that in the training cohort,the area under the curve(AUC)values for model_(CT sign),model_(CT sign+rad),and modelCT_(sign+rad+clinic)for predicting KIT exon 11 mutation were 0.743,0.818,and 0.915,respectively.In the validation cohort,the AUC values for the same models were 0.670,0.781,and 0.811,respectively.For predicting KIT exon 11 codons 557-558 deletions,the AUC values in the training cohort were 0.667,0.842,and 0.720 for model_(CT sign),model_(CT sign+rad),and modelCT_(sign+rad+clinic),respectively.In the validation cohort,the AUC values for the same models were 0.610,0.782,and 0.795,respectively.Based on the decision curve analysis,it was determined that the model_(CT sign+rad+clinic)had clinical significance and utility.CONCLUSION Our findings demonstrate that the combined modelCT_(sign+rad+clinic)effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions.This combined model has the potential to be valuable in assessing the genotype of GISTs.

Visualization analysis of research hotspots and trends on gastrointestinal tumor organoids

BACKGROUND Gastrointestinal tumor organoids serve as an effective model for simulating cancer in vitro and have been applied in basic biology and preclinical research.Despite over a decade of development and increasing research achievements in this field,a systematic and comprehensive analysis of the research hotspots and future trends is lacking.AIM To address this problem by employing bibliometric tools to explore the publication years,countries/regions,institutions,journals,authors,keywords,and references in this field.METHODS The literature was collected from Web of Science databases.CiteSpace-6.2R4,a widely used bibliometric analysis software package,was used for institutional analysis and reference burst analysis.VOSviewer 1.6.19 was used for journal cocitation analysis,author co-authorship and co-citation analysis.The‘online platform for bibliometric analysis(https://bibliometric.com/app)’was used to assess the total number of publications and the cooperation relationships between countries.Finally,we employed the bibliometric R software package(version R.4.3.1)in R-studio,for a comprehensive scientific analysis of the literature.RESULTS Our analysis included a total of 1466 publications,revealing a significant yearly increase in articles on the study of gastrointestinal tumor organoids.The United States(n=393)and Helmholtz Association(n=93)have emerged as the leading countries and institutions,respectively,in this field,with Hans Clevers and Toshiro Sato being the most contributing authors.The most influential journal in this field is Gastroenterology.The most impactful reference is"Long term expansion of epithelial organs from human colon,adenoma,adenocarcinoma,and Barrett's epithelium".Keywords analysis and citation burst analysis indicate that precision medicine,disease modeling,drug development and screening,and regenerative medicine are the most cutting-edge directions.These focal points were further detailed based on the literature.CONCLUSION This bibliometric study offers an objective and quantitative analysis of the research in this field,which can be considered as an important guide for next scientific research.

Lecithin-cholesterol acyltransferase is a potential tumor suppressor and predictive marker for hepatocellular carcinoma metastasis

BACKGROUND Hepatocellular carcinoma(HCC)is a major cause of cancer mortality worldwide,and metastasis is the main cause of early recurrence and poor prognosis.However,the mechanism of metastasis remains poorly understood.AIM To determine the possible mechanism affecting HCC metastasis and provide a possible theoretical basis for HCC treatment.METHODS The candidate molecule lecithin-cholesterol acyltransferase(LCAT)was screened by gene microarray and bioinformatics analysis.The expression levels of LCAT in clinical cohort samples was detected by quantitative realtime polymerase chain reaction and western blotting.The proliferation,migration,invasion and tumor-forming ability were measured by Cell Counting Kit-8,Transwell cell migration,invasion,and clonal formation assays,respectively.Tumor formation was detected in nude mice after LCAT gene knockdown or overexpression.The immunohistochemistry for Ki67,E-cadherin,N-cadherin,matrix metalloproteinase 9 and vascular endothelial growth factor were performed in liver tissues to assess the effect of LCAT on HCC.Gene set enrichment analysis(GSEA)on various gene signatures were analyzed with GSEA version 3.0.Three machine-learning algorithms(random forest,support vector machine,and logistic regression)were applied to predict HCC metastasis in The Cancer Genome Atlas and GEO databases.RESULTS LCAT was identified as a novel gene relating to HCC metastasis by using gene microarray in HCC tissues.LCAT was significantly downregulated in HCC tissues,which is correlated with recurrence,metastasis and poor outcome of HCC patients.Functional analysis indicated that LCAT inhibited HCC cell proliferation,migration and invasion both in vitro and in vivo.Clinicopathological data showed that LCAT was negatively associated with HCC size and metastasis(HCC size≤3 cm vs 3-9 cm,P<0.001;3-9 cm vs>9 cm,P<0.01;metastatic-free HCC vs extrahepatic metastatic HCC,P<0.05).LCAT suppressed the growth,migration and invasion of HCC cell lines via PI3K/AKT/mTOR signaling.Our results indicated that the logistic regression model based on LCAT,TNM stage and the serum level of α-fetoprotein in HCC patients could effectively predict high metastatic risk HCC patients.CONCLUSION LCAT is downregulated at translational and protein levels in HCC and might inhibit tumor metastasis via attenuating PI3K/AKT/mTOR signaling.LCAT is a prognostic marker and potential therapeutic target for HCC.

Establishment,functional and genetic characterization of three novel patient-derived rectal cancer cell lines

AIM To establish patient-individual tumor models of rectal cancer for analyses of novel biomarkers, individual response prediction and individual therapy regimens.METHODS Establishment of cell lines was conducted by direct in vitro culturing and in vivo xenografting with subsequent in vitro culturing. Cell lines were in-depth characterized concerning morphological features, invasive and migratory behavior, phenotype, molecular profile including mutational analysis, protein expression, and confirmation of origin by DNA fingerprint. Assessment of chemosensitivity towards an extensive range of current chemotherapeutic drugs and of radiosensitivity was performed including analysis of a combined radioand chemotherapeutic treatment. In addition, glucose metabolism was assessed with 18 F-fluorodeoxyglucose(FDG) and proliferation with 18 F-fluorothymidine.RESULTS We describe the establishment of ultra-low passage rectal cancer cell lines of three patients suffering from rectal cancer. Two cell lines(HROC126, HROC284 Met) were established directly from tumor specimens while HROC239 T0 M1 was established subsequent to xenografting of the tumor. Molecular analysis classified all three cell lines as CIMP-0/non-MSI-H(sporadic standard) type. Mutational analysis revealed following mutational profiles: HROC126: APC^(wt), TP53^(wt), KRAS^(wt), BRAF^(wt), PTEN^(wt); HROC239 T0 M1: APC^(mut), P53^(wt), KRAS^(mut), BRAF^(wt), PTEN^(mut) and HROC284 Met: APC^(wt), P53^(mut), KRAS^(mut), BRAF^(wt), PTEN^(mut). All cell lines could be characterized as epithelial(EpCAM+) tumor cells with equivalent morphologic features and comparable growth kinetics. The cell lines displayed a heterogeneous response toward chemotherapy, radiotherapy and their combined application. HROC126 showed a highly radio-resistant phenotype and HROC284 Met was more susceptible to a combined radiochemotherapy than HROC126 and HROC239 T0 M1. Analysis of 18 F-FDG uptake displayed a markedly reduced FDG uptake of all three cell lines after combined radiochemotherapy. CONCLUSION These newly established and in-depth characterized ultra-low passage rectal cancer cell lines provide a useful instrument for analysis of biological characteristics of rectal cancer.

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.

Early changes of hepatic hemodynamics measured by functional CT perfusion in a rabbit model of liver tumor

BACKGROUND:Early detection and treatment of hepatocellular carcinoma is crucial to improving the patients’ survival.The hemodynamic changes caused by tumors can be serially measured using CT perfusion.In this study,we used a CT perfusion technique to demonstrate the changes of hepatic hemodynamics in early tumor growth,as a proof-of-concept study for human early hepatocellular carcinoma.METHODS:VX2 tumors were implanted in the liver of ten New Zealand rabbits.CT perfusion scans were made 1 week(early) and 2 weeks(late) after tumor implantation.Ten normal rabbits served as controls.CT perfusion parameters were obtained at the tumor rim,normal tissue surrounding the tumor,and control liver;the parameters were hepatic blood flow,hepatic blood volume,mean transit time,permeability of capillary vessel surface,hepatic arterial index,hepatic arterial perfusion and hepatic portal perfusion.Microvessel density and vascular endothelial growth factor were correlated.RESULTS:At the tumor rim,compared to the controls,hepatic blood flow,hepatic blood volume,permeability of capillary vessel surface,hepatic arterial index,and hepatic arterial perfusion increased,while mean transit time and hepatic portal perfusion decreased on both early and late scans(P<0.05).Hepatic arterial index increased(135%,P<0.05),combined with a sharp increase in hepatic arterial perfusion(182%,P<0.05) and a marked decrease in hepatic portal perfusion(-76%,P<0.05) at 2 weeks rather than at 1 week(P<0.05).Microvessel density and vascular endothelial growth factor showed significant linear correlations with hepatic blood flow,permeability of capillary vessel surface and hepatic arterial index,but not with hepatic blood volume or mean transit time.CONCLUSION:The CT perfusion technique demonstrated early changes of hepatic hemodynamics in this tumor model as proof-of-concept for early hepatocellular carcinoma detection in humans.