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.

Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study

BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system,ranking sixth in incidence and fourth in mortality worldwide.Since 42.5%of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type,the application of imaging diagnosis is restricted.AIM To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning(ML)algorithms and to evaluate their pre-dictive performance in clinical practice.METHODS Data of a total of 369 patients who underwent radical gastrectomy at the Depart-ment of General Surgery of Affiliated Hospital of Xuzhou Medical University(Xuzhou,China)from March 2016 to November 2019 were collected and retro-spectively analyzed as the training group.In addition,data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People’s Hospital(Jining,China)were collected and analyzed as the verifi-cation group.Seven ML models,including decision tree,random forest,support vector machine(SVM),gradient boosting machine,naive Bayes,neural network,and logistic regression,were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer.The ML models were established fo-llowing ten cross-validation iterations using the training dataset,and subsequently,each model was assessed using the test dataset.The models’performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.RESULTS Among the seven ML models,except for SVM,the other ones exhibited higher accuracy and reliability,and the influences of various risk factors on the models are intuitive.CONCLUSION The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer,which can aid in personalized clinical diagnosis and treatment.

Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.

Prognostic value of a nomogram model for postoperative liver metastasis of colon cancer

BACKGROUND Colon cancer is one of the most common malignant tumors of the digestive system.Liver metastasis after colon cancer surgery is the primary cause of death in patients with colon cancer.AIM To construct a novel nomogram model including various factors to predict liver metastasis after colon cancer surgery.METHODS We retrospectively analyzed 242 patients with colon cancer who were admitted and underwent radical resection for colon cancer in Zhejiang Provincial People’s Hospital from December 2019 to December 2022.Patients were divided into liver metastasis and non-liver metastasis groups.Sex,age,and other general and clinicopathological data(preoperative blood routine and biochemical test indexes)were compared.The risk factors for liver metastasis were analyzed using singlefactor and multifactorial logistic regression.A predictive model was then constructed and evaluated for efficacy.RESULTS Systemic inflammatory index(SII),C-reactive protein/albumin ratio(CAR),red blood cell distribution width(RDW),alanine aminotransferase,preoperative carcinoembryonic antigen level,and lymphatic metastasis were different between groups(P<0.05).SII,CAR,and RDW were risk factors for liver metastasis after colon cancer surgery(P<0.05).The area under the curve was 0.93 for the column-line diagram prediction model constructed based on these risk factors to distinguish whether liver metastasis occurred postoperatively.The actual curve of the column-line diagram predicting the risk of postoperative liver metastasis was close to the ideal curve,with good agreement.The prediction model curves in the decision curve analysis showed higher net benefits for a larger threshold range than those in extreme cases,indicating that the model is safer.CONCLUSION Liver metastases after colorectal cancer surgery could be well predicted by a nomogram based on the SII,CAR,and RDW.

Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports

BACKGROUND Development of distant metastasis(DM)is a major concern during treatment of nasopharyngeal carcinoma(NPC).However,studies have demonstrated im-proved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy.Therefore,precise prediction of metastasis in patients with NPC is crucial.AIM To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging(MRI)reports.METHODS This retrospective study included 792 patients with non-distant metastatic NPC.A total of 469 imaging variables were obtained from detailed MRI reports.Data were stratified and randomly split into training(50%)and testing sets.Gradient boosting tree(GBT)models were built and used to select variables for predicting DM.A full model comprising all variables and a reduced model with the top-five variables were built.Model performance was assessed by area under the curve(AUC).RESULTS Among the 792 patients,94 developed DM during follow-up.The number of metastatic cervical nodes(30.9%),tumor invasion in the posterior half of the nasal cavity(9.7%),two sides of the pharyngeal recess(6.2%),tubal torus(3.3%),and single side of the parapharyngeal space(2.7%)were the top-five contributors for predicting DM,based on their relative importance in GBT models.The testing AUC of the full model was 0.75(95%confidence interval[CI]:0.69-0.82).The testing AUC of the reduced model was 0.75(95%CI:0.68-0.82).For the whole dataset,the full(AUC=0.76,95%CI:0.72-0.82)and reduced models(AUC=0.76,95%CI:0.71-0.81)outperformed the tumor node-staging system(AUC=0.67,95%CI:0.61-0.73).CONCLUSION The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC.The number of metastatic cervical nodes was identified as the principal contributing variable.

Related Study on the Nm23-H1 Gene Expression in the Model of Ovarian Carcinoma Cell Lines with High Frequent Metastasis

Objective: To select the ovarian carcinoma cell lines with high frequent metastasis and study the association between nm23-H1 gene expression and metastasis of ovarian carcinoma. Methods: Each ovarian cancer cell line was transplanted subcutaneously into the flank of nude mice, and the metastatic behavior was evaluated by counting lung tumor foci at different time points. The metastatic tumors were cultured in vitro, then substrain was established and transplanted subcutaneously three times. The RNA level of nm23 in 8 human ovarian cancer cell lines were examined by northern-blot. Results: Of the 8 human ovarian cancer cell lines, 4 had high requent metastatic potentiality. The expression of nm23 RNA in human ovarian cancer cells was inversely related to metastatic behavior in the experimental animals (r=0.96, P=0.0001). Conclusion: The difference of the tendency of metastasis which was determined by genetic and molecular levels was significant among different type of cell lines and subtypes. The expression of nm23 mRNA in human ovarian carcinomas was correlated closely with the reduced metastatic behavior in experimental animals and may serve as a sensitive prognostic indicator for ovarian cancer.

Prediction of tumor biological characteristics in different colorectal cancer liver metastasis animal models using^(18)F-FDG and^(18)F-FLT

Background: Positron emission tomography(PET) is a noninvasive method to characterize different metabolic activities of tumors, providing information for staging, prognosis, and therapeutic response of patients with cancer. The aim of this study was to evaluate the feasibility of18F-fludeoxyglucose(18F-FDG) and 3’-deoxy-3’-18F-fluorothymidine(18F-FLT) PET in predicting tumor biological characteristics of colorectal cancer liver metastasis.Methods: The uptake rate of18F-FDG and18F-FLT in SW480 and SW620 cells was measured via an in vitro cell uptake assay. The region of interest was drawn over the tumor and liver to calculate the maximum standardized uptake value ratio(tumor/liver) from PET images in liver metastasis model. The correlation between tracer uptake in liver metastases and VEGF, Ki67 and CD44 expression was evaluated by linear regression.Results: Compared to SW620 tumor-bearing mice, SW480 tumor-bearing mice presented a higher rate of liver metastases. The uptake rate of18F-FDG in SW480 and SW620 cells was 6.07% ± 1.19% and2.82% ± 0.15%, respectively(t = 4.69, P = 0.04); that of18F-FLT was 24.81% ± 0.45% and 15.57% ± 0.66%, respectively(t = 19.99, P < 0.001). Micro-PET scan showed that all parameters of FLT were significantly higher in SW480 tumors than those in SW620 tumors. A moderate relationship was detected between metastases in the liver and18F-FLT uptake in primary tumors(r = 0.73, P = 0.0019).18F-FLT uptake was also positively correlated with the expression of CD44 in liver metastases(r = 0.81, P = 0.0049).Conclusions: The uptake of18F-FLT in metastatic tumor reflects different biological behaviors of colon cancer cells.18F-FLT can be used to evaluate the metastatic potential of colorectal cancer in nude mice.

In vitro three-dimensional cancer metastasis modeling:Past,present,and future

Metastasis is the leading cause of most cancer deaths, as opposed to dysregulated cell growth of the primary tumor. Molecular mechanisms of metastasis have been studied for decades and the findings have evolved our understanding of the progression of malignancy. However, most of the molecular mechanisms fail to address the causes of cancer and its evolutionary origin, demonstrating an inability to find a solution for complete cure of cancer. After being a neglected area of tumor biology for quite some time, recently several studies have focused on the impact of the tumor microenvironment on cancer growth. The importance of the tumor microenvironment is gradually gaining attention, particularly from the per- spective of biophysics. In vitro three-dimensional （3-D） metastatic models are an indispensable platform for investigating the tumor microenvironment, as they mimic the in vivo tumor tissue. In 3-D metastatic in vitro models, static factors such as the mechanical properties, biochemical factors, as well as dynamic factors such as cell-cell, cell-ECM interactions, and fluid shear stress can be studied quantitatively. With increasing focus on basic cancer research and drug development, the in vitro 3-D models offer unique advantages in fundamental and clinical biomedical studies.

EVALUATION ON FACTORS INFLUENCING LIVER CANCER METASTASIS AFTER LIVER SURGERY BY A MOUSE MODEL

Objective: To evaluate the influence of surgical trauma on liver cancer metastasis. Methods: A mouse model of experimental liver cancer metastasis was established by subcapsule injecting hepatoma ascites tumor cells (H22) into spleen of NIH mice. Simple intrasplenic inoculation, with sham operation, partial hepatectomy, total occlusion of hepatic blood inflow and blood loss and re-perfusion were performed and metastatic effects were observed. Results: There were significant higher metastasis-augmenting effects in sham operation and partial hepatectomy groups. Compared with no-blood transfusion, blood transfusion group was found to be potent to increase intrahepatic metastases. But, neither inhibition nor enhancement with total occlusion of hepatic blood inflow for 20 and 30 minutes was seen. Conclusions: Surgical trauma, especially partial hepatectomy and blood transfusion, are involved in enhancing metastasis, but total occlusion of hepatic blood inflow is not responsible for enhanced liver metastasis in the experimental metastasis model.

ESTABLISHMENT OF VISIBLE ANIMAL METASTASIS MODELS FOR HUMAN NASOPHARYNGEAL CARCINOMA BASED ON A FAR-RED FLUORESCENT PROTEIN

Background and aims:The spectral properties of enhanced greenfluorescent protein(EGFP)used in current visualizable animal models for nasopharyngeal carcinoma(NPC)result in a limited imaging depth.Far-redfluorescent proteins have optimal spectral wavelengths that allow deep tissue penetration,thus are well-suited for the imaging of tumor growth and metastases in live animals.This study aims to establish an imageable animal model of NPC using far-redfluorescent proteins.Methods:Eukaryotic expression vectors of far-redfluorescent proteins,mLumin and Katushka S158A,were separately transfected into 5-8F NPC cells,and cell lines stably expressing the far-redfluorescent proteins were obtained.These cells were intraperitoneally or intravenously injected into mice,and their tumorigenic and metastatic potential were examined throughfluorescence imaging.Finally,factors affecting their tumorigenic ability were further assessed through testing side population(SP)cells proportion byflow cytometry.Results:NPC cell line with high tumorigenicity and metastasis(5-8F-mL2)was screened out,which stably expressed far-redfluorescent protein.Intraperitoneal and intravenous injection of 5-8F-mL2 cells resulted in an abdomen metastasis model and a lung metastasis model.In addition,NPC cell line without tumorigenicity(5-8F-Katushka S158A)was screened out.The percentage of SP cells between 5-8F-mL2 and 5-8F-Katushka S158A was found different,suggesting that the SP cell proportion may play a key role in the determination of cell tumorigenic ability.Conclusion:We successfully established animal models for NPC with high tumorigenicity and metastasis using a super-bright far-redfluorescent protein.Owing to the super-brightness and excellent wavelength parameters,these models may be applied as useful tools for intuitive and efficient monitoring of tumor growth and metastasis,as well as assessing the efficacy of nasopharyngeal cancer drugs.

Predicting lymph node metastasis in colorectal cancer:An analysis of influencing factors to develop a risk model

BACKGROUND Colorectal cancer(CRC)is a significant global health issue,and lymph node metastasis(LNM)is a crucial prognostic factor.Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC.However,the prediction of LNM is challenging and depends on various factors such as tumor histology,clinicopathological features,and molecular characteristics.The most reliable method to detect LNM is the histopathological examination of surgically resected specimens;however,this method is invasive,time-consuming,and subject to sampling errors and interobserver variability.AIM To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue.METHODS This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021.A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors.RESULTS The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables.The model achieved high accuracy(0.86),sensitivity(0.81),specificity(0.87),positive predictive value(0.66),negative predictive value(0.94),area under the curve for the receiver operating characteristic(0.91),and a low Brier score(0.10).The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds,indicating good calibration and clinical utility.CONCLUSION The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC.This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables,demonstrating superior performance and clinical applicability compared to existing models.The study provides new insights into the potential of deep learning to extract valuable information from tumor histology,in turn,improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice.

Prognostic values of the integrated model incorporating the volume of metastatic regional cervical lymph node and pretreatment serum Epstein-Barr virus DNA copy number in predicting distant metastasis in patients with N1 nasopharyngeal carcinoma

Background: According to the 7 th edition of the American Joint Committee on Cancer(AJCC) staging system, over50% of patients with nasopharyngeal carcinoma(NPC) have N1 disease at initial diagnosis. However, patients with N1 NPC are relatively under-researched, and the metastasis risk of this group is not well-stratified. This study aimed to evaluate the prognostic values of gross tumor volume of metastatic regional lymph node(GTVnd) and pretreatment serum copy number of Epstein-Barr virus(EBV) DNA in predicting distant metastasis of patients with N1 NPC, and to develop an integrated prognostic model that incorporates GTVnd and EBV DNA copy number for this group of patients.Methods: The medical records of 787 newly diagnosed patients with nonmetastatic, histologically proven N1 NPC who were treated at Sun Yat-sen University Cancer Center between November 2009 and February 2012 were analyzed. Computed tomography-derived GTVnd was measured using the summation-of-area technique. Blood samples were collected before treatment to quantify plasma EBV DNA. The receiver operating characteristic(ROC) curve analysis was used to evaluate the cut-off point for GTVnd, and the area under the ROC curve was used to assess the predicted validity of GTVnd. The survival rates were assessed by Kaplan-Meier analysis, and the survival curves were compared using a log-rank test. Multivariate analysis was conducted using the Cox proportional hazard regression model.Results: The 5-year distant metastasis-free survival(DMFS) rates for patients with GTVnd > 18.9 vs.≤ 18.9 mL were82.2% vs. 93.2%(P < 0.001), and for patients with EBV DNA copy number > 4000 vs. < 4000 copies/mL were 83.5% vs.93.9%(P < 0.001). After adjusting for GTVnd, EBV DNA copy number, and T category in the Cox regression model, both GTVnd > 18.9 mL and EBV DNA copy number > 4000 copies/mL were significantly associated with poor prognosis(both P < 0.05). According to combination of GTVnd and EBV DNA copy number, all patients were divided into low-,moderate-, and high-risk groups, with the 5-year DMFS rates of 96.1,87.4, and 73.8%, respectively(P < 0.001). Multivariate analysis confirmed the prognostic value of this model for distant metastatic risk stratification(hazard ratio [HR],4.17; 95% confidence interval [CI] 2.34-7.59; P < 0.001).Conclusions: GTVnd and serum EBV DNA copy number are independent prognostic factors for predicting distant metastasis in NPC patients with N1 disease. The prognostic model incorporating GTVnd and EBV DNA copy number may improve metastatic risk stratification for this group of patients.

Liver metastasis models of human colorectal carcinoma established in nude mice by orthotopic transplantation and their biologic characteristics

AIM To establish a liver metastasis model of human colorectal carcinoma in nude mice.METHODS Orthotopic transplantation of histologically intact colorectal tissues from patients into colorectal mucosa of nude mice. Tumorgenicity, invasion, metastasis and morphological characteristics of the transplanted tumors were studied by light microscopy, electron microscopy and immunohistochemistry.RESULTS Liver metastasis models of human colon carcinoma (HCA-HMN-1) and human rectal carcinoma (HRA-HMN-2) were established after screening from 34 colorectal carcinomas. They had been passaged in vivo for 18 and 21 generations respectively. There were lymphatic, hemotogenous and implanting metastasesis. CEA secretion was maintained after transplantation. The primary and liver metastatic tumors were similar to the original human carcinoma in histopathological and ultrastructural features, DNA content and chromosomal karyotype.CONCLUSION The liver metastasis models provide useful tools for the study of mechanism of metastasis and its treatment of human colorectal cancer.INTRUDUCTIONSome models of nude mice that fresh human colorectal carcinoma tissue or cells were successfully transplanted subcuteneously have been reported at home and abroad[1,2]. But until now there has been no report on a liver metastasis model of human colorectal carcinoma established by orthotopic transplantation in nude mice in China. Based on our previous models of human liver and pancreas carcinoma by orthotopic transplantation[3,4], we established liver metastasis models of colon and rectum carcinoma with a spontaneous metastasis rate of 100%.

Engineering 3D approaches to model the dynamic microenvironments of cancer bone metastasis

Cancer metastasis to bone is a three-dimensional(3D), multistep, dynamic process that requires the sequential involvement of three microenvironments, namely, the primary tumour microenvironment, the circulation microenvironment and the bone microenvironment. Engineered 3D approaches allow for a vivid recapitulation of in vivo cancerous microenvironments in vitro, in which the biological behaviours of cancer cells can be assessed under different metastatic conditions. Therefore, modelling bone metastasis microenvironments with 3 D cultures is imperative for advancing cancer research and anti-cancer treatment strategies. In this review, multicellular tumour spheroids and bioreactors, tissue engineering constructs and scaffolds, microfluidic systems and 3D bioprinting technology are discussed to explore the progression of the 3D engineering approaches used to model the three microenvironments of bone metastasis. We aim to provide new insights into cancer biology and advance the translation of new therapies for bone metastasis.

Phase tissue intercellular adhesion molecule-1 expression in nude mice human liver cancer metastasis model

AIM To study the phase cancer tissue intercellular adhesion molecule-1 (ICAM-1) expression of human cancer metastasis model in nude mice, and to analyze the relationship between ICAM-1 expression and the metastasis and recurrence of hepatocellular cancinoma (HCC).METHODS HCC tissues from liver cancer metastasis model in nude mice (LCI-D20) was orthotopically implanted, and ICAM-1 expression in HCC tissues at different growing time were detected by immunodot blot. Tumor size, intrahepatic and extrahepatic metastasis foci were observed by naked eyes and under light microscope.RESULTS ICAM-1 was positively correlated to the tumor growing time (r=0.88, P＜0.01) and tumor size r=0.5, P＜0.05). It was higher in metastatic HCC than in nonmetastatic HCC (8.24±0.95 vs 3.03±0.51, P＜0.01). ICAM-1 content in cancer tissues increased suddenly after metastasis occurred and then maintained in a high level. ICAM-1 was also higher in multimetastasis group than in monometastasis group (10.05±1.17 vs 5.48±0.49, P＜0.05).CONCLUSION Tissue ICAM-1 could predict not only the metastasis of human liver cancer metastasis model in nude mice early and sensitively, but also the metastasis degree. So tissue ICAM-1 may be a potential index indicating the status of metastasis of HCC patients.

Targeted Micellar Phthalocyanine for Lymph Node Metastasis Homing and Photothermal Therapy in an Orthotopic Colorectal Tumor Model

Tumor lymph node(LN)metastasis seriously affects the treatment prognosis.Studies have shown that nanoparticles with size of sub-50 nm can directly penetrate into LN metastases after intravenous administration.Here,we speculate through introducing targeting capacity,the nanoparticle accumulation in LN metastases would be further enhanced for improved local treatment such as photothermal therapy.Trastuzumabtargeted micelles(<50 nm)were formulated using a unique surfactantstripping approach that yielded concentrated phthalocyanines with strong near-infrared absorption.Targeted micellar phthalocyanine(T-MP)was an effective photothermal transducer and ablated HT-29 cells in vitro.A HER2-expressing colorectal cancer cell line(HT-29)was used to establish an orthotopic mouse model that developed metastatic disease in mesenteric sentinel LN.T-MP accumulated more in the LN metastases compared to the micelles conjugated with control IgG.Following surgical resection of the primary tumor,minimally invasive photothermal treatment of the metastatic LN with T-MP,but not the control micelles,extended mouse survival.Our findings demonstrate for the first time that targeted small-sized nanoparticles have potential to enable superior paradigms for dealing with LN metastases.

Establishment of nude mice model of human osteosarcoma cell MG63 with different potential of metastasis

Objective: To establish a nude mice model of human osteosarcoma lung metastasis. Methods: The growth of human osteosarcoma cell sublines M8 and M6 was determined by MTT assay. 2 × 107 cells were injected into the tail vein of nude mice. Mice were sacrificed started on week 4 after injection, and lung metastases were evaluated under both mac-roscopic and microscopic observation with HE staining. Results: The growth of low-metastatic subline M6 was lower than high-metastatic sublines M8. Seventeen mice after injected M8 had occurred lung metastases while only one mice had oc-curred in M6 group. Moreover, M8 cells within metastases were arrangement disorder with variable nuclear hyperchromasia. Conclusion: A mouse model for human osteosarcoma cancer lung metastasis can be established by injection different ability of metastasis MG63 cells into tail vein.

A novel animal model for in vivo study of liver cancer metastasis

AIM:To establish an animal model with human hepatocyte-repopulated liver for the study of liver cancer metastasis.METHODS:Cell transplantation into mouse livers was conducted using alpha-fetoprotein(AFP)-producing hu-man gastric cancer cells(h-GCCs) and h-hepatocytes as donor cells in a transgenic mouse line expressing urokinase-type plasminogen activator(uPA) driven by the albumin enhancer/promoter crossed with a severe combined immunodeficient(SCID) mouse line(uPA/SCID mice).Host mice were divided into two groups(A and B).Group A mice were transplanted with h-GCCs alone,and group B mice were transplanted with h-GCCs and h-hepatocytes together.The replacement index(RI),which is the ratio of transplanted h-GCCs and h-hepatocytes that occupy the examined area of a histological section,was estimated by measuring h-AFP and h-albumin concentrations in sera,respectively,as well as by immunohistochemical analyses of h-AFP and human cytokeratin 18 in histological sections.RESULTS:The h-GCCs successfully engrafted,repopulated,and colonized the livers of mice in group A(RI = 22.0% ± 2.6%).These mice had moderately differentiated adenocarcinomatous lesions with disrupted glandular structures,which is a characteristics feature of gastric cancers.The serum h-AFP level reached 211.0 ± 142.2 g/mL(range,7.1-324.2 g/mL).In group B mice,the h-GCCs and h-hepatocytes independently engrafted,repopulated the host liver,and developed colonies(RI = 12.0% ± 6.8% and 66.0% ± 12.3%,respectively).h-GCC colonies also showed typical adenocarcinomatous glandular structures around the h-hepatocyte-colonies.These mice survived for the full 56 day-study and did not exhibit any metastasis of h-GCCs in the extrahepatic regions during the observational period.The mice with an h-hepatocyte-repopulated liver possessed metastasized h-GCCs and therefore could be a useful humanized liver animal model for studying liver cancer metastasis in vivo.CONCLUSION:A novel animal model of human liver cancer metastasis was established using the uPA/SCID mouse line.This model could be useful for in vivo testing of anti-cancer drugs and for studying the mechanisms of human liver cancer metastasis.

Monitoring of prostate cancer growth and metastasis using a PSA luciferase report plasmid in a mouse model

Objective:To construct a PSA luciferase report plasmid and monitor the growth and metastasis of prostate cancer after emasculation in SCID mice.Methods:PSA promoter sequence and luciferase gene were amplified by PCR and subsequently inserted into pZsCreen1-1 vector to construct pPSA-FL-Luc vector.LNCaP cells that were stably transfected with pPSA-FL-Luc were used to establish a SCID mouse xenograft model.Then,the growth and metastasis of prostate cancer were monitored via living imaging.Results:We successfully constructed a PSA luciferase piasmid,pPSA-FL-Luc.DHT enhanced lucifcrase activity in a concentration-dependent manner in 293 T cells with pPSA-FL-Luc transfection.Prostate cancer SCID mouse model was established with pPSA-FL-Luc transfected LNCaP cells.In tumor bearing mice with or without emasculation,pPSA-FL-Lue piasmid was applied to monitored tumor growth and metastasis based on bioluminescence imaging.Conclusions:We construct a pPSA-FL-Luc piasmid,which stably expresses luciferase and can be applied to monitor tumor development in a prostate SCID mouse model.

Inhibition of growth and metastasis of triple-negative breast cancer targeted by Traditional Chinese Medicine Tubeimu in orthotopic mice models

Objective： Triple-negative breast cancer（TNBC） is highly invasive and metastatic, which is in urgent need of transformative therapeutics. Tubeimu（TBM）, the rhizome of Bolbostemma paniculatum（Maxim.） Franquet, is one of the Chinese medicinal herbs used for breast diseases since the ancient times. The present study evaluated the efficacy, especially the anti-metastatic effects of the dichloromethane extract of Tubeimu（ETBM） on TNBC orthotopic mouse models and cell lines.Methods： We applied real-time imaging on florescent orthotopic TNBC mice model and tested cell migration and invasion abilities with MDA-MB-231 cell line. Digital gene expression sequencing was performed and Kyoto Encyclopedia of Genes and Genomes（KEGG） analysis applied to explore the pathways influenced by ETBM.Moreover, quantitative real-time polymerase chain reactions（q RT-PCR） and Western blot were delivered to confirm the gene expression changes.Results： ETBM exhibited noticeable control on tumor metastasis and growth of TNBC tumors with no obvious toxicity. In compliance with this, it also showed inhibition of cell migration and invasion in vitro. Its impact on the changed biological behavior in TNBC may be a result of decreased expression of integrin β1（ITGβ1）, integrin β8（ITGβ8） and Rho GTPase activating protein 5（ARHGAP5）, which disabled the focal adhesion pathway and caused change in cell morphology.Conclusions： This study reveals that ETBM has anti-metastatic effects on MDA-MB-231-GFP tumor and may lead to a new therapeutic agent for the integrative treatment of highly invasive TNBC.