Multi-Scale Agent-Based Multiple Myeloma Cancer Modeling and the Related Study of the Balance between Osteoclasts and Osteoblasts

Screening and Analysis of Core Genes for Osteoporosis Based on Bioinformatics Analysis and Machine Learning Algorithms

Objective

This study aimed to identify osteoporosis-related core genes using bioinformatics analysis and machine learning algorithms.

Methods

mRNA expression profiles of osteoporosis patients were obtained from the Gene Expression Profiles (GEO) database, with GEO35958 and GEO84500 used as training sets, and GEO35957 and GSE56116 as validation sets. Differential gene expression analysis was performed using the R software "limma" package. A weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and modular genes of osteoporosis. Kyoto Gene and Genome Encyclopedia (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were performed on the differentially expressed genes. LASSO, SVM-RFE, and RF machine learning algorithms were used to screen for core genes, which were subsequently validated in the validation set. Predicted microRNAs (miRNAs) from the core genes were also analyzed, and differential miRNAs were validated using quantitative real-time PCR (qPCR) experiments.

Results

A total of 1280 differentially expressed genes were identified. A disease key module and 215 module key genes were identified by WGCNA. Three core genes (ADAMTS5, COL10A1, KIAA0040) were screened by machine learning algorithms, and COL10A1 had high diagnostic value for osteoporosis. Four core miRNAs (has-miR-148a-3p, has-miR-195-3p, has-miR-148b-3p, has-miR-4531) were found by intersecting predicted miRNAs with differential miRNAs from the dataset (GSE64433, GSE74209). The qPCR experiments validated that the expression of has-miR-195-3p, has-miR-148b-3p, and has-miR-4531 was significantly increased in osteoporosis patients.

Conclusion

This study demonstrated the utility of bioinformatics analysis and machine learning algorithms in identifying core genes associated with osteoporosis.

Agent-based approaches for biological modeling in oncology: A literature review

CONTEXT

Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions.

METHODOLOGY

This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023.

RESULTS

Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.

The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous drug resistant disease

Multiple myeloma (MM) is an osteolytic malignancy that is incurable due to the emergence of treatment resistant disease. Defining how, when and where myeloma cell intrinsic and extrinsic bone microenvironmental mechanisms cause relapse is challenging with current biological approaches. Here, we report a biology-driven spatiotemporal hybrid agent-based model of the MM-bone microenvironment. Results indicate MM intrinsic mechanisms drive the evolution of treatment resistant disease but that the protective effects of bone microenvironment mediated drug resistance (EMDR) significantly enhances the probability and heterogeneity of resistant clones arising under treatment. Further, the model predicts that targeting of EMDR deepens therapy response by eliminating sensitive clones proximal to stroma and bone, a finding supported by in vivo studies. Altogether, our model allows for the study of MM clonal evolution over time in the bone microenvironment and will be beneficial for optimizing treatment efficacy so as to significantly delay disease relapse.

Computational analysis of adhesion between a cancer cell and a white blood cell in a bifurcated microvessel

BACKGROUND AND OBJECTIVE

Cancer is one of the diseases caused by irregular and uncontrolled growth of cells and their propagation into various parts of the body. The motion and adhesion of cancer cells in a blood vessel is a critical step in tumor progression that depends on some vascular parameters such as vessel branching. In this study, effect of microvessel branching on the bonds between a cancer cell and a white blood has been investigated as compared to an analogous problem in a straight vessel.

METHODS

The analysis is performed using finite elements and fluid-structure interaction methods. Moreover, the equations for adhesion of the cancer cell to white blood cell are coded in MATLAB for calculating forces between them and the code is coupled directly and simultaneously with the COMSOL software. For fluid-structure interaction analysis, it is assumed that the properties of the blood and the cells are homogeneous and the fluid is incompressible and Newtonian. Cancer cell is modeled as a rigid body and white blood cell is assumed as linear elastic.

RESULTS

The results show that although the geometry of the vessel does not affect on the separation distance of cancer cell considerably, but at the area near a bifurcation, high fluctuations in cancer cell velocity is occurred due to increasing in number of bond formation between the cancer cell and the white blood cell. Accordingly, it can be predicted that higher concentration of adhered particles occurs near the bifurcations. Moreover, shear stress at the point of contact of the cancer cell and the white blood cell in the branched vessel is greater than that in the straight path. In addition to, the probability of breaking of the bond between the cancer cell and the white blood cell increases in the branched vessel.

CONCLUSIONS

Through consideration in the adhesion charts of this study along with observations from medical interventions such as drug delivery to cancer patients, considerable developments on the treatment or prevention of cancer metastasis may be achieved.

A Retrospective Analysis: A Novel Index Predicts Survival and Risk-Stratification for Bone Destruction in 419 Newly Diagnosed Multiple Myelomas

Objective

Multiple myeloma (MM) patients with bone destruction are difficult to restore, so it is of great clinical significance to further explore the factors affecting MM bone destruction.

Methods and results

This study retrospectively analyzed 419 cases with MM. Multiple linear regression analysis showed that those MM patients with a higher concentration of Ca²⁺ in serum, higher positive rate of CD138 immuno-phenotype and advanced in stage with 13q34 deletion in cytogenetics would be more prone to bone destruction, while total bile acid (TBA) and kappa chain isotope negatively correlated with bone destruction in MM patients. The Kaplan-Meier analysis indicated that Ca²⁺, serum β2-microglobulin (β2-MG), hemoglobin (HGB), creatinine (CREA), uric acid (UA) and age correlated with the survival of bone destruction in MM patients. Cox regression analysis further showed that the independent prognostic factors of β2-MG and CREA had a higher risk for early mortality in bone destruction patients. Moreover, an index was calculated based on β2-MG and globulin (GLB) to white blood cell (WBC) ratio to predict the poor survival of bone destruction patients.

Conclusion

We provide a novel marker to predict the prognosis of myeloma patients using routine examination method instead of bone marrow aspiration, and provide a reference for clinical evaluation.

Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution

Cancer is a complex, dynamic disease that despite recent advances remains mostly incurable. Inter- and intratumoral heterogeneity are generally considered major drivers of therapy resistance, metastasis, and treatment failure. Recent advances in high-throughput experimentation have produced a wealth of data on tumor heterogeneity and researchers are increasingly turning to mathematical modeling to aid in the interpretation of these complex datasets. In this mini-review, we discuss three important classes of approaches for modeling cellular dynamics within heterogeneous tumors: agent-based models, population dynamics, and multiscale models. An important new focus, for which we provide an example, is the role of intratumoral cell-cell interactions.

Correlations of DKK1 with pathogenesis and prognosis of human multiple myeloma

OBJECTIVE

Human multiple myeloma (MM) is a kind of common tumor in middle-aged and elderly people, in which the osteolytic lesion is formed mainly through inhibiting osteoblast (OB) differentiation and promoting osteoclast (OC) differentiation. Dickkopf-1 (DKK1) is a soluble Wnt inhibitor, which has an important correlation with the pathogenesis of human MM. Therefore, the correlations of DKK1 with pathogenesis and prognosis of human MM were investigated in this study.

METHODS

The DKK1 expression in tissues and serum of myeloma patients was detected via immunohistochemistry and enzyme-linked immunosorbent assay (ELISA). Correlation between DKK1 expression and survival time of patients was analyzed via Kaplan-Meier analysis. To further study the mechanism of DKK1 expression in pathogenesis and prognosis of human MM, MM cells were treated with DKK1 neutralizing antibody (BHQ880) or transfected with DKK1-small-interfering ribonucleic acid (siRNA) to study its effects on OB differentiation, osteocalcin level, β-catenin and interleukin-6 (IL-6) secretion. Moreover, the effect of DKK1-siRNA transfection on the activity of U266 cells was detected via methyl thiazolyl tetrazolium (MTT) assay.

RESULTS

The DKK1 expression in tissues and serum of myeloma patients was significantly higher than that in control group (p< 0.01). In terms of survival time, the median survival time (45 months) in patients with low DKK1 expression was significantly longer than that in patients with high DKK1 expression (only 22 months). The DKK1 neutralizing antibody (BHQ880) and DKK1-siRNA significantly reduced the DKK1 level in MM cells, promoted the OB differentiation, increased the osteocalcin deposition, promoted the β-catenin expression and decreased the IL-6 expression and β-catenin phosphorylation. DKK1-siRNA could also reduce the proliferative activity of MM cells.

CONCLUSION

DKK1 is closely related to the pathogenesis and prognosis of human MM, which might be a potential biomarker for the diagnosis of MM.

Advances in computational approaches in identifying synergistic drug combinations

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.

A new conceptual framework for the therapy by optimized multidimensional pulses of therapeutic activity. The case of multiple myeloma model

We developed simulation methodology to assess eventual therapeutic efficiency of exogenous multiparametric changes in a four-component cellular system described by the system of ordinary differential equations. The method is numerically implemented to simulate the temporal behavior of a cellular system of multiple myeloma cells. The problem is conceived as an inverse optimization task where the alternative temporal changes of selected parameters of the ordinary differential equations represent candidate solutions and the objective function quantifies the goals of the therapy. The system under study consists of two main cellular components, tumor cells and their cellular environment, respectively. The subset of model parameters closely related to the environment is substituted by exogenous time dependencies - therapeutic pulses combining continuous functions and discrete parameters subordinated thereafter to the optimization. Synergistic interaction of temporal parametric changes has been observed and quantified whereby two or more dynamic parameters show effects that absent if either parameter is stimulated alone. We expect that the theoretical insight into unstable tumor growth provided by the sensitivity and optimization studies could, eventually, help in designing combination therapies.

Searching Synergistic Dose Combinations for Anticancer Drugs

Recent development has enabled synergistic drugs in treating a wide range of cancers. Being highly context-dependent, however, identification of successful ones often requires screening of combinational dose on different testing platforms in order to gain the best anticancer effects. To facilitate the development of effective computational models, we reviewed the latest strategy in searching optimal dose combination from three perspectives: (1) mainly experimental-based approach; (2) Computational-guided experimental approach; and (3) mainly computational-based approach. In addition to the introduction of each strategy, critical discussion of their advantages and disadvantages were also included, with a strong focus on the current applications and future improvements.

Developing an Agent-Based Drug Model to Investigate the Synergistic Effects of Drug Combinations

The growth and survival of cancer cells are greatly related to their surrounding microenvironment. To understand the regulation under the impact of anti-cancer drugs and their synergistic effects, we have developed a multiscale agent-based model that can investigate the synergistic effects of drug combinations with three innovations. First, it explores the synergistic effects of drug combinations in a huge dose combinational space at the cell line level. Second, it can simulate the interaction between cells and their microenvironment. Third, it employs both local and global optimization algorithms to train the key parameters and validate the predictive power of the model by using experimental data. The research results indicate that our multicellular system can not only describe the interactions between the microenvironment and cells in detail, but also predict the synergistic effects of drug combinations.

Investigation of mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model and experimental optimization/validation

Herein, we have developed a novel approach to investigate the mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model, experimental optimization of key parameters and experimental data validation of the predictive power of the model. The advantages of this study are that the impact of mechanical stimulation on bone regeneration in a porous biodegradable CaP scaffold is considered, experimental design is used to investigate the optimal combination of growth factors loaded on the porous biodegradable CaP scaffold to promote bone regeneration and the training, testing and analysis of the model are carried out by using experimental data, a data-mining algorithm and related sensitivity analysis. The results reveal that mechanical stimulation has a great impact on bone regeneration in a porous biodegradable CaP scaffold and the optimal combination of growth factors that are encapsulated in nanospheres and loaded into porous biodegradable CaP scaffolds layer-by-layer can effectively promote bone regeneration. Furthermore, the model is robust and able to predict the development of bone regeneration under specified conditions.

Paeoniflorin inhibits proliferation and promotes apoptosis of multiple myeloma cells via its effects on microRNA‑29b and matrix metalloproteinase‑2

Multiple myeloma (MM) is a type of cancer characterized by the excessive proliferation of malignant plasma cells. In China, the incidence of MM has been increasing annually. Paeoniflorin exerts numerous functions, including coronary vessel expansion, and anti‑inflammation and anticancer activities. The present study aimed to investigate the effects of paeoniflorin on the proliferation and apoptosis of SKO‑007 MM cells, via its effects on the regulation of matrix metalloproteinase‑2 (MMP‑2) and microRNA (miR)‑29b. In the present study, an MTT assay was used to analyze the proliferation of SKO‑007 cells treated with paeoniflorin. Annexin V‑fluorescein isothiocyanate/propidium iodide apoptosis and caspase‑3 activation assays were used to detect the levels of cellular apoptosis. The expression levels of MMP‑2 and miR‑29b were detected using gelatin zymography and quantitative‑polymerase chain reaction, respectively. In addition, miR‑29b and anti‑miR‑29b plasmids were transfected into SKO‑007 cells, and the effects of paeoniflorin on cell proliferation and apoptosis were subsequently detected. The results of the present in vitro studies demonstrated that paeoniflorin was able to inhibit the proliferation of SKO‑007 cells in a dose‑ and time‑dependent manner. Furthermore, paeoniflorin effectively increased cell apoptosis, and augmented the activation of caspase‑3 and caspase‑9 in the SKO‑007 cells. The expression levels of MMP‑2 were suppressed following treatment of the SKO‑007 cells with paeoniflorin. In addition, paeoniflorin was able to induce the expression of miR‑29b. Notably, the results of the present study indicated that miR‑29b expression may control the expression of MMP‑2 in SKO‑007 cells. In conclusion, the present study demonstrated that paeoniflorin was able to inhibit cell proliferation and promote apoptosis of MM cells by suppressing the expression of MMP‑2, via the upregulation of miR‑29b.