3D bioprinting of tumor models and potential applications

Cancer is the most common cause of human mortality and has created an unbridgeable health gap due to its unrestrained aberrant proliferation,rapid growth,metastasis,and high heterogeneity.Conventional two-dimensional cell culture and animal models for tumor diagnostic and therapeutic studies have extremely low clinical translation rates due to their intrinsic limitations.Appropriate tumor models are therefore required for cancer research.Engineered human three-dimensional(3D)cancer models stand out for their ability to better replicate the spatial organization,cellular resources,and microenvironmental features(e.g.,hypoxia,necrosis,and delayed proliferation)of actual human tumors.Further,the fabrication of these models can be achieved by an emerging technology known as 3D bioprinting,which allows for the fabrication of living structures by precisely regulating the spatial distribution of cells,biomolecules,and matrix components.The aim of this paper is to review the current technologies and bioinks associated with 3D bioprinted cancer models for glioma,breast,liver,intestinal,cervical,ovarian,and neuroblastoma,as well as,advances in the applications of 3D bioprinted-based tumor models in the fields of tumor microenvironment,tumor vascularization,tumor stem cells,tumor resistance and therapeutic drug screening,tumorimmunotherapy,and precision medicine.

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.

Construction and application of a three-dimensional vascular variation-based nephrometry scoring system for completely endophytic renal tumors

Background:Completely endophytic renal tumors(CERT)pose significant challenges due to their anatomical complexity and loss of visual clues about tumor location.A facile scoring model based on three-dimensional(3D)reconstructed images will assist in better assessing tumor location and vascular variations.Methods:In this retrospective study,80 patients diagnosed with CERT were included.Forty cases underwent preoperative assessment using 3D reconstructed imaging(3D-Cohort),while the remaining 40 cases were assessed using two-dimensional imaging(2D-Cohort).Vascular variations were evaluated by ascertaining the presence of renal arteries>1,prehilar branching arteries,and arteries anterior to veins.The proposed scoring system,termed RAL,encompassed three critical components:(R)adius(maximal tumor diameter in cm),(A)rtery(occurrence of arterial variations),and(L)ocation relative to the polar line.Comparison of the RAL scoring system was made with established nephrometry scoring systems.Results:A total of 48(60%)patients exhibited at least one vascular variation.In the 2D-Cohort,patients with vascular variations experienced significantly prolonged operation time,increased bleeding volume,and extended warm ischemia time compared with those without vascular variations.Conversely,the presence of vascular vari-ations did not significantly affect operative parameters in the 3D-Cohort.Furthermore,the 2D-Cohort demon-strated a notable decline in both short-and long-term estimated glomerular filtration rate(eGFR)changes com-pared with the 3D-Cohort,a trend consistent across patients with warm ischemia time≥25 min and those with vascular variations.Notably,the 2D-Cohort exhibited a larger margin of normal renal tissue compared with the 3D-Cohort.Elevated RAL scores correlated with larger tumor size,prolonged operation time,extended warm is-chemia time,and substantial postoperative eGFR decrease.The RAL scoring system displayed superior predictive capabilities in assessing postoperative eGFR changes compared with conventional nephrometry scoring systems.Conclusions:Our proposed 3D vascular variation-based nephrometry scoring system offers heightened proficiency in preoperative assessment,precise prediction of surgical complexity,and more accurate evaluation of postoper-ative renal function in CERT 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.

Primary Malignant Renal Tumors in Infancy and Childhood: CT Appearances

Objective： To investigate the imaging manifestation of primary malignant renal tumor with CT. Methods： Forty-three cases of surgically and pathologically confirmed primary malignant renal tumor were retrospectively reviewed. Un-contrast and contrast CT was performed in all 43 patients in which 15 patients received MRI examination. Results： The residual normal renal tissue of 29 cases out of 34 cases of Wilms＇ tumor was enhanced and manifested ＂crescent sign＂ or ＂ring sign＂. Four cases of malignant rabdoid tumor （RTK） manifested as large mass with notable necrosis and subcapsular fluid collection; Two cases of clear cell sarcoma （CCS） showed metastases to the skull which could indicate the diagnosis; Renal cell carcinoma （RCC） （n=3） showed calcification in 1 case. Conclusion： CT can precisely delineate the location, size, extent of involvement, imaging characteristics and metastases of renal tumor, which can provide information necessary to the clinical staging, therapy planning and prognosis of the tumors.

Simple Linear Model of Tumor Growth in a Changing Environment

In an environment that is neither static nor in equilibrium, but is dynamic and changing, the kinetics of the reactions that cause the growth of a tumor, which depend on the state of the evolving environment, cannot be parametrized in terms of constant rates. We propose a simple model for describing the growth on an untreated tumor in such environments, which is characterized by a minimal number of parameters and is generalizable to include the effects of various types of therapies. In the simplest version that we consider here, it consists of a linear equation with a time-dependent growth rate, which we interpret as the coupling of the system with a dynamic environment. A complete solution is given in terms of the integral of the growth rate. The essential features of the general solution are illustrated with a few examples, and comparison is made with the models that have been proposed to describe recent data.

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.

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.

Hepatocellular carcinoma mouse models:Hepatitis B virusassociatedhepatocarcinogenesis and haploinsufficienttumor suppressor genes

The multifactorial and multistage pathogenesis of hepatocellular carcinoma(HCC)has fascinated a wide spectrum of scientists for decades.While a number of major risk factors have been identified,their mechanistic roles in hepatocarcinogenesis still need to be elucidated.Many tumor suppressor genes(TSGs)have been identified as being involved in HCC.These TSGs can be classified into two groups depending on the situation with respect to allelic mutation/loss in the tumors:the recessive TSGs with two required mutated alleles and the haploinsufficient TSGs with one required mutated allele.Hepatitis B virus(HBV)is one of the most important risk factors associated with HCC.Although mice cannot be infected with HBV due to the narrow host range of HBV and the lack of a proper receptor,one advantage of mouse models for HBV/HCC research is the numerous and powerfulgenetic tools that help investigate the phenotypic effects of viral proteins and allow the dissection of the dose-dependent action of TSGs.Here,we mainly focus on the application of mouse models in relation to HBV-associated HCC and on TSGs that act either in a recessive or in a haploinsufficient manner.Discoveries obtained using mouse models will have a great impact on HCC translational medicine.

Effects of Lvy noise and immune delay on the extinction behavior in a tumor growth model

The combined effects of Ltvy noise and immune delay on the extinction behavior in a tumor growth model are explored, The extinction probability of tumor with certain density is measured by exit probability. The expression of the exit probability is obtained using the Taylor expansion and the infinitesimal generator theory. Based on numerical calculations, it is found that the immune delay facilitates tumor extinction when the stability index α〈 1, but inhibits tumor extinction when the stability index α 〉 1. Moreover, larger stability index and smaller noise intensity are in favor of the extinction for tumor with low density. While for tumor with high density, the stability index and the noise intensity should be reduced to promote tumor extinction.

3D tumor model biofabrication

Animal models have been extensively used in cancer pathology studies and drug discovery.These models,however,fail to reflect the complex human tumor microenvironment and do not allow for high-throughput drug screening in more human-like physiological conditions.Three-dimensional(3D)cancer models present an alternative to automated high-throughput cancer drug discovery and oncology.In this review,we highlight recent technology innovations in building 3D tumor models that simulate the complex human tumor microenvironment and responses of patients to treatment.We discussed various biofabrication technologies,including 3D bioprinting techniques developed for characterizing tumor progression,metastasis,and response to treatment.

Establishment of an orthotopic transplantation tumor model of hepatocellular carcinoma in mice

AIM:To improve the outcome of orthotopic transplantation in a mouse model,we used an absorbable gelatin sponge(AGS) in nude mice to establish an orthotopic implantation tumor model.METHODS:MHCC-97L hepatocellular carcinoma(HCC)cells stably expressing the luciferase gene were injected into the subcutaneous region of nude mice.One week later,the ectopic tumors were harvested and transplanted into the left liver lobe of nude mice.The AGS was used to establish the nude mouse orthotopic implantation tumor model.The tumor suppressor gene,paired box gene 5(PAX5),which is a tumor suppressor in HCC,was transfected into HCC cells to validate the model.Tumor growth was measured by bioluminescence imaging technology.Semi-quantitative reverse transcription polymerase chain reaction(RT-PCR) and histopathology were used to confirm the tumorigenicity of the implanted tumor from the MHCC-97L cell line.RESULTS:We successfully developed an orthotopic transplantation tumor model in nude mice with the use of an AGS.The success rate of tumor transplantation was improved from 60% in the control group to 100% in the experimental group using AGS.The detection of fluorescent signals showed that tumors grew in all live nude mice.The mice were divided into 3 groups:AGS-,AGS+/PAX5-and AGS+/PAX5 +.Tumor size was significantly smaller in PAX5 transfected nude mice compared to control mice(P < 0.0001).These fluorescent signal results were consistent with observations made during surgery.Pathologic examination further confirmed that the tissues from the ectopic tumor were HCC.Results from RT-PCR proved that the HCC originated from MHCC-97L cells.CONCLUSION:Using an AGS is a convenient and efficient way of establishing an indirect orthotopic liver transplantation tumor model with a high success rate.

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.

The construction of in vitro tumormodels based on 3D bioprinting

Cancer is characterized by a high fatality rate,complex molecular mechanism,and costly therapies.The microenvironment of a tumor consists of multiple biochemical cues and the interaction between tumor cells,stromal cells,and extracellular matrix plays a key role in tumor initiation,development,angiogenesis,invasion and metastasis.To better understand the biological features of tumor and reveal the critical factors of therapeutic treatments against cancer,it is of great significance to build in vitro tumor models that could recapitulate the stages of tumor progression and mimic tumor behaviors in vivo for efficient and patient-specific drug screening and biological studies.Since conventional tissue engineering methods of constructing tumor models always fail to simulate the later stages of tumor development due to the lack of ability to build complex structures and angiogenesis potential,three-dimensional(3D)bioprinting techniques have gradually found its applications in tumor microenvironment modeling with accurate composition and well-organized spatial distribution of tumor-related cells and extracellular components in the past decades.The capabilities of building tumor models with a large range of scale,complex structures,multiple biomaterials and vascular network with high resolution and throughput make 3D bioprinting become a versatile platform in bio-manufacturing aswell as inmedical research.In this review,wewill focus on 3D bioprinting strategies,design of bioinks,current 3D bioprinted tumor models in vitro classified with their structures and propose future perspectives.

Advances in prostate cancer research models:From transgenic mice to tumor xenografting models

The identification of the origin and molecular characteristics of prostate cancer(PCa)has crucial implications for personalized treatment.The development of effective treatments for PCa has been limited;however,the recent establishment of several transgenicmouse lines and/or xenografting models is better reflecting the disease in vivo.With appropriate models,valuable tools for elucidating the functions of specific genes have gone deep into prostate development and carcinogenesis.In the present review,we summarize a number of important PCa research models established in our laboratories(PSA-Cre-ERT2/PTEN transgenic mouse models,AP-OX model,tissue recombination-xenografting models and PDX models),which represent advances of translational models from transgenic mouse lines to human tumor xenografting.Better understanding of the developments of these models will offer new insights into tumor progression and may help explain the functional significance of genetic variations in PCa.Additionally,this understanding could lead to new modes for curing PCa based on their particular biological phenotypes.

Similarity on neural stem cells and brain tumor stem cells in transgenic brain tumor mouse models

Although it is believed that glioma is derived from brain tumor stem cells, the source and molecular signal pathways of these cells are still unclear. In this study, we used stable doxycycline-inducible transgenic mouse brain tumor models （c-myc/SV40Tag＋/Tet-on＋） to explore the malignant trans- formation potential of neural stem cells by observing the differences of neural stem cells and brain tumor stem cells in the tumor models. Results showed that chromosome instability occurred in brain tumor stem cells. The numbers of cytolysosomes and autophagosomes in brain tumor stem cells and induced neural stem cells were lower and the proliferative activity was obviously stronger than that in normal neural stem cells. Normal neural stem cells could differentiate into glial fibrillary acidic protein-positive and microtubule associated protein-2-positive cells, which were also negative for nestin. However, glial fibrillary acidic protein/nestin, microtubule associated protein-2/nestin, and glial fibrillary acidic protein/microtubule associated protein-2 double-positive cells were found in induced neural stem cells and brain tumor stem cells. Results indicate that induced neural stem cells are similar to brain tumor stem cells, and are possibly the source of brain tumor stem cells.

Non-Linear Mathematical Model of the Interaction between Tumor and Oncolytic Viruses

A mathematical modeling of tumor therapy with oncolytic viruses is discussed. The model consists of two coupled, deterministic differential equations allowing for cell reproduction and death, and cell infection. The model is one of the conceptual mathematical models of tumor growth that treat a tumor as a dynamic society of interacting cells. In this paper, we obtain an approximate analytical expression of uninfected and infected cell population by solving the non-linear equations using Homotopy analysis method (HAM). Furthermore, the results are compared with the numerical simulation of the problem using Matlab program. The obtained results are valid for the whole solution domain.

A Semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These segmented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radiologist.