Complexity in cancer biology: is systems biology the answer?

System-level protein interaction network analysis and molecular dynamics study reveal interaction of ferulic acid with PTGS2 as a natural radioprotector

Ferulic acid is a crucial bioactive component of broccoli, wheat, and rice bran and is also an essential natural product that has undergone significant research. Ferulic acid's precise mode of action and effect on system-level protein networks have not been thoroughly investigated. An interactome was built using the STRING database and Cytoscape tools, utilizing 788 key proteins collected from PubMed literature to identify the ferulic acid-governed regulatory action on protein interaction network (PIN). The scale-free biological network of ferulic acid-rewired PIN is highly interconnected. We discovered 15 sub-modules using the MCODE tool for sub-modulization analysis and 153 enriched signaling pathways. Further, functional enrichment of top bottleneck proteins revealed the FoxO signaling pathway involved in enhancing cellular defense against oxidative stress. The selection of the critical regulatory proteins of the ferulic acid-rewired PIN was completed by performing analyses of topological characteristics such as GO term/pathways analysis, degree, bottleneck, molecular docking, and dynamics investigations. The current research derives a precise molecular mechanism for ferulic acid's action on the body. This in-depth in silico model would aid in understanding how ferulic acid origins its antioxidant and scavenging properties in the human body.Communicated by Ramaswamy H. Sarma.

Alteration of Lipid Metabolism in Prostate Cancer: Multifaceted Oncologic Implications

Cancer is increasingly recognized as an extraordinarily heterogeneous disease featuring an intricate mutational landscape and vast intra- and intertumor variability on both genetic and phenotypic levels. Prostate cancer (PCa) is the second most prevalent malignant disease among men worldwide. A single metabolic program cannot epitomize the perplexing reprogramming of tumor metabolism needed to sustain the stemness of neoplastic cells and their prominent energy-consuming functional properties, such as intensive proliferation, uncontrolled growth, migration, and invasion. In cancerous tissue, lipids provide the structural integrity of biological membranes, supply energy, influence the regulation of redox homeostasis, contribute to plasticity, angiogenesis and microenvironment reshaping, mediate the modulation of the inflammatory response, and operate as signaling messengers, i.e., lipid mediators affecting myriad processes relevant for the development of the neoplasia. Comprehensive elucidation of the lipid metabolism alterations in PCa, the underlying regulatory mechanisms, and their implications in tumorigenesis and the progression of the disease are gaining growing research interest in the contemporary urologic oncology. Delineation of the unique metabolic signature of the PCa featuring major aberrant pathways including de novo lipogenesis, lipid uptake, storage and compositional reprogramming may provide novel, exciting, and promising avenues for improving diagnosis, risk stratification, and clinical management of such a complex and heterogeneous pathology.

Capsaicin Alters the Expression of Genetic and Epigenetic Molecules In Hepatocellular Carcinoma Cell

Capsaicin is a natural product which is extracted from pepper and has the potential to be used in cancer treatment because of its anti- proliferative effects. The aim of the study was to determine the effect of capsaicin on the hepatocellular carcinoma cell proliferation and the expressions of related genetic markers as Ki-67, PI3K/AKT/mTOR and epigenetic markers as miR-126 and piR-Hep-1. The inhibitory concentration of capsaicin in HepG2 cells was determined. piR-Hep-1 and miR-126 expressions and Ki-67, PI3K, AKT and mTOR gene expressions were examined by RT-PCR. The inhibitory concentration of capsaicin for HepG2 cells was 200 nM and the decreased proliferation was observed at 24th hour. As epigenetic markers, an up regulation of miR-126 and down regulation of piR-Hep-1 expression were determined after treatment. Moreover, Ki-67, PI3K and mTOR gene expressions decreased while AKT gene expression increased after the treatment (p<0.001). According to the obtained data, capsaicin has an impact on proliferation both genetically and epigenetically. Furthermore, treatment of capsaicin effects miR-126 and piR-Hep-1 expressions which effect carcinogenesis in different way. Moreover, there are some clues which indicate that these two small non-coding RNA might affect each other and share the same target molecules post-transcriptionally.

A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines

Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model's predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific in silico models that can steer treatment decisions in the clinic.

Topological and system-level protein interaction network (PIN) analyses to deduce molecular mechanism of curcumin

Curcumin is an important bioactive component of turmeric and also one of the important natural products, which has been investigated extensively. The precise mode of action of curcumin and its impact on system level protein networks are still not well studied. To identify the curcumin governed regulatory action on protein interaction network (PIN), an interectome was created based on 788 key proteins, extracted from PubMed literatures, and constructed by using STRING and Cytoscape programs. The PIN rewired by curcumin was a scale-free, extremely linked biological system. MCODE plug-in was used for sub-modulization analysis, wherein we identified 25 modules; ClueGo plug-in was used for the pathway’s enrichment analysis, wherein 37 enriched signalling pathways were obtained. Most of them were associated with human diseases groups, particularly carcinogenesis, inflammation, and infectious diseases. Finally, the analysis of topological characteristic like bottleneck, degree, GO term/pathways analysis, bio-kinetics simulation, molecular docking, and dynamics studies were performed for the selection of key regulatory proteins of curcumin-rewired PIN. The current findings deduce a precise molecular mechanism that curcumin might exert in the system. This comprehensive in-silico study will help to understand how curcumin induces its anti-cancerous, anti-inflammatory, and anti-microbial effects in the human body.

Bioprinting of in vitro tumor models for personalized cancer treatment: a review

Studying biological characteristics of tumors and evaluating the treatment effects require appropriate in vitro tumor models. However, the occurrence, progression, and migration of tumors involve spatiotemporal changes, cell-microenvironment and cell-cell interactions, and signal transmission in cells, which makes the construction of in vitro tumor models extremely challenging. In the past few years, advances in biomaterials and tissue engineering methods, especially development of the bioprinting technology, have paved the way for innovative platform technologies for in vitro cancer research. Bioprinting can accurately control the distribution of cells, active molecules, and biomaterials. Furthermore, this technology recapitulates the key characteristics of the tumor microenvironment and constructs in vitro tumor models with bionic structures and physiological systems. These models can be used as robust platforms to study tumor initiation, interaction with the microenvironment, angiogenesis, motility and invasion, as well as intra- and extravasation. Bioprinted tumor models can also be used for high-throughput drug screening and validation and provide the possibility for personalized cancer treatment research. This review describes the basic characteristics of the tumor and its microenvironment and focuses on the importance and relevance of bioprinting technology in the construction of tumor models. Research progress in the bioprinting of monocellular, multicellular, and personalized tumor models is discussed, and comprehensive application of bioprinting in preclinical drug screening and innovative therapy is reviewed. Finally, we offer our perspective on the shortcomings of the existing models and explore new technologies to outline the direction of future development and application prospects of next-generation tumor models.

The predictive and prognostic value of low skeletal muscle mass for dose-limiting toxicity and survival in head and neck cancer patients receiving concomitant cetuximab and radiotherapy

Purpose

This study aims to investigate the predictive value of low skeletal muscle mass (SMM) for cetuximab dose-limiting toxicity (DLT) and its prognostic value in head and neck squamous cell carcinoma (HNSCC) patients treated with concomitant cetuximab and radiotherapy.

Methods

Patients diagnosed with HNSCC and treated with primary or adjuvant concomitant cetuximab and radiotherapy were included. Clinical and demographic variables were retrospectively retrieved and SMM was measured at the level of the third cervical vertebra using pre-treatment diagnostic computed tomography or magnetic resonance imaging. An optimal cut-off value for low SMM was determined based on the lowest log-likelihood associated with cetuximab DLT. A multivariate linear regression model was used to determine predictive factors for cetuximab DLT. The prognostic value of low SMM for disease-free and overall survival was analyzed using Kaplan–Meier curves.

Results

The optimal cut-off value for low SMM as a predictor of cetuximab DLT was an LSMI ≤ 45.2 cm²/m². Of the 91 included patients, 74.7% had low SMM and 30.8% experienced cetuximab DLT. At multivariate analysis, low SMM had no predictive value for DLT (OR 0.83; 95% CI 0.27–2.56; p = 0.74). The Kaplan–Meier curve demonstrated that patients with low SMM had significantly lower overall survival (Log Rank χ² = 5.87; p = 0.02).

Conclusion

Low SMM is highly prevalent in HNSCC patients treated with concomitant cetuximab and radiotherapy. Low SMM has no predictive value for cetuximab DLT in HNSCC patients. Low SMM is probably not a prognostic factor for overall survival in highly selected HNSCC patients treated with concomitant cetuximab and radiotherapy and unfit for platin-based chemotherapy.

ING5 inhibits cancer aggressiveness by inhibiting Akt and activating p53 in prostate cancer

Prostate cancer (PCa) is one of the most common types of cancer in men. In several recent studies, chromosomal deletions in the q arm of chromosome 2, where ING5 resides within, have been identified in various cancer types including PCa. In this study, we investigate the role of ING5 as a tumor suppressor in PCa. We examined the expression level of ING5 in tissue samples and cell lines using quantitative real-time polymerase chain reaction and western blot analysis. We tested the in vitro tumor suppressor potential of ING5 in PC3 and LNCaP cells stably overexpressing it using cell viability, colony formation, migration, invasion, and apoptosis assays. We then investigated the effects of ING5 on the Akt and p53 signaling using western blot analysis. We show that ING5 is significantly downregulated in PCa tumor tissue samples and cell lines compared with the corresponding controls. In vitro assays demonstrate that ING5 effectively suppresses proliferative, clonogenic, migratory, and invasive potential and induce apoptosis in PCa cells. ING5 may potentially exert its anti-tumor potential by inhibiting AKT and inducing p53 signaling pathways. Our findings demonstrate that ING5 possesses tumor suppressor roles in vitro, pointing its importance during the prostatic carcinogenesis processes.

Investigation of Precise Molecular Mechanistic Action of Tobacco-Associated Carcinogen `NNK´ Induced Carcinogenesis: A System Biology Approach

Cancer is the second deadliest disease listed by the WHO. One of the major causes of cancer disease is tobacco and consumption possibly due to its main component, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). A plethora of studies have been conducted in the past aiming to decipher the association of NNK with other diseases. However, it is strongly linked with cancer development. Despite these studies, a clear molecular mechanism and the impact of NNK on various system-level networks is not known. In the present study, system biology tools were employed to understand the key regulatory mechanisms and the perturbations that will happen in the cellular processes due to NNK. To investigate the system level influence of the carcinogen, NNK rewired protein–protein interaction network (PPIN) was generated from 544 reported proteins drawn out from 1317 articles retrieved from PubMed. The noise was removed from PPIN by the method of modulation. Gene ontology (GO) enrichment was performed on the seed proteins extracted from various modules to find the most affected pathways by the genes/proteins. For the modulation, Molecular COmplex DEtection (MCODE) was used to generate 19 modules containing 115 seed proteins. Further, scrutiny of the targeted biomolecules was done by the graph theory and molecular docking. GO enrichment analysis revealed that mostly cell cycle regulatory proteins were affected by NNK.

Network spectra for drug-target identification in complex diseases: new guns against old foes

The fundamental understanding of altered complex molecular interactions in a diseased condition is the key to its cure. The overall functioning of these molecules is kind of jugglers play in the cell orchestra and to anticipate these relationships among the molecules is one of the greatest challenges in modern biology and medicine. Network science turned out to be providing a successful and simple platform to understand complex interactions among healthy and diseased tissues. Furthermore, much information about the structure and dynamics of a network is concealed in the eigenvalues of its adjacency matrix. In this review, we illustrate rapid advancements in the field of network science in combination with spectral graph theory that enables us to uncover the complexities of various diseases. Interpretations laid by network science approach have solicited insights into molecular relationships and have reported novel drug targets and biomarkers in various complex diseases.

Inhibition of never in mitosis A (NIMA)-related kinase-4 reduces survivin expression and sensitizes cancer cells to TRAIL-induced cell death

The tumor necrosis factor-related apoptosis inducing ligand (TRAIL) preferentially induces apoptosis in cancer cells. However, many tumors are resistant to TRAIL-induced apoptosis, and resistance mechanisms are not fully understood. To identify novel regulatory molecules of TRAIL resistance, we screened a siRNA library targeting the human kinome, and NEK4 (NIMA-related kinase-4) was identified. Knockdown of NEK4 sensitized TRAIL-resistant cancer cells and in vivo xenografts to cell death. In contrast, over expression of NEK4 suppressed TRAIL-induced cell death in TRAIL-sensitive cancer cells. In addition, loss of NEK4 resulted in decrease of the anti-apoptotic protein survivin, but an increase in apoptotic cell death. Interestingly, NEK4 was highly upregulated in tumor tissues derived from patients with lung cancer and colon cancer. These results suggest that inhibition of NEK4 sensitizes cancer cells to TRAIL-induced apoptosis by regulation of survivin expression.

Therapeutic target discovery using Boolean network attractors: improvements of kali

In a previous article, an algorithm for identifying therapeutic targets in Boolean networks modelling pathological mechanisms was introduced. In the present article, the improvements made on this algorithm, named kali, are described. These improvements are (i) the possibility to work on asynchronous Boolean networks, (ii) a finer assessment of therapeutic targets and (iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system, such as a Boolean network, are associated with the phenotypes of the modelled biological system. Given a logic-based model of pathological mechanisms, kali searches for therapeutic targets able to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing their likeliness. kali is illustrated on an example network and used on a biological case study. The case study is a published logic-based model of bladder tumorigenesis from which kali returns consistent results. However, like any computational tool, kali can predict but cannot replace human expertise: it is a supporting tool for coping with the complexity of biological systems in the field of drug discovery.

System Biology Approach to Identify Potential Receptor for Targeting Cancer and Biomolecular Interaction Studies of Indole[2,1-a]Isoquinoline Derivative as Anticancerous Drug Candidate Against it

Cancer is a public health concern which is spreading throughout the world. Different approaches have been employed to combat this disease. System biology approach has been used to understand the molecular mechanisms of drugs targeting cancer cell's receptor which have opened-up a window to develop effective drugs for it. We have demonstrated biomolecular interaction studies using the rational drug design of indole[2,1-a]isoquinoline derivative as a potent inhibitor against identified cancerous protein PIK3CA -a catalytic sub-unit of PI3K family protein-and compared its affinity with FDA approved drugs for receptors such as dactolisib, idelalisib, and several others such afatinib, avastin, ceritinib and crizotinib, etc.; by docking against potential receptor to set a cutoff value for our screening. Isoquinolines are small alkaloids with a vast variety of substitution depending upon their biogenetic pattern. Isoquinoline derivatives have been reported for their antimalarial, antibacterial, antifungal and anticancerous activities. The results obtained from the present studies conclude that membrane protein is an efficient drug that can be used to target cancer. Moreover, comparative study with ADMET prediction concludes that isoquinoline can be a potent drug for cancer treatment.

Modeling the dynamics of chromosomal alteration progression in cervical cancer: A computational model

Computational modeling has been applied to simulate the heterogeneity of cancer behavior. The development of Cervical Cancer (CC) is a process in which the cell acquires dynamic behavior from non-deleterious and deleterious mutations, exhibiting chromosomal alterations as a manifestation of this dynamic. To further determine the progression of chromosomal alterations in precursor lesions and CC, we introduce a computational model to study the dynamics of deleterious and non-deleterious mutations as an outcome of tumor progression. The analysis of chromosomal alterations mediated by our model reveals that multiple deleterious mutations are more frequent in precursor lesions than in CC. Cells with lethal deleterious mutations would be eliminated, which would mitigate cancer progression; on the other hand, cells with non-deleterious mutations would become dominant, which could predispose them to cancer progression. The study of somatic alterations through computer simulations of cancer progression provides a feasible pathway for insights into the transformation of cell mechanisms in humans. During cancer progression, tumors may acquire new phenotype traits, such as the ability to invade and metastasize or to become clinically important when they develop drug resistance. Non-deleterious chromosomal alterations contribute to this progression.

Assessing the carcinogenic potential of low-dose exposures to chemical mixtures in the environment: the challenge ahead

Low-dose exposures to common environmental chemicals that are deemed safe individually may be combining to instigate carcinogenesis, thereby contributing to the incidence of cancer. This risk may be overlooked by current regulatory practices and needs to be vigorously investigated.

Lifestyle factors are responsible for a considerable portion of cancer incidence worldwide, but credible estimates from the World Health Organization and the International Agency for Research on Cancer (IARC) suggest that the fraction of cancers attributable to toxic environmental exposures is between 7% and 19%. To explore the hypothesis that low-dose exposures to mixtures of chemicals in the environment may be combining to contribute to environmental carcinogenesis, we reviewed 11 hallmark phenotypes of cancer, multiple priority target sites for disruption in each area and prototypical chemical disruptors for all targets, this included dose-response characterizations, evidence of low-dose effects and cross-hallmark effects for all targets and chemicals. In total, 85 examples of chemicals were reviewed for actions on key pathways/mechanisms related to carcinogenesis. Only 15% (13/85) were found to have evidence of a dose-response threshold, whereas 59% (50/85) exerted low-dose effects. No dose-response information was found for the remaining 26% (22/85). Our analysis suggests that the cumulative effects of individual (non-carcinogenic) chemicals acting on different pathways, and a variety of related systems, organs, tissues and cells could plausibly conspire to produce carcinogenic synergies. Additional basic research on carcinogenesis and research focused on low-dose effects of chemical mixtures needs to be rigorously pursued before the merits of this hypothesis can be further advanced. However, the structure of the World Health Organization International Programme on Chemical Safety ‘Mode of Action’ framework should be revisited as it has inherent weaknesses that are not fully aligned with our current understanding of cancer biology.

Prostate cancer metastasis-driving genes: hurdles and potential approaches in their identification

Metastatic prostate cancer is currently incurable. Metastasis is thought to result from changes in the expression of specific metastasis-driving genes in nonmetastatic prostate cancer tissue, leading to a cascade of activated downstream genes that set the metastatic process in motion. Such genes could potentially serve as effective therapeutic targets for improved management of the disease. They could be identified by comparative analysis of gene expression profiles of patient-derived metastatic and nonmetastatic prostate cancer tissues to pinpoint genes showing altered expression, followed by determining whether silencing of such genes can lead to inhibition of metastatic properties. Various hurdles encountered in this approach are discussed, including (i) the need for clinically relevant, nonmetastatic and metastatic prostate cancer tissues such as xenografts of patients’ prostate cancers developed via subrenal capsule grafting technology and (ii) limitations in the currently available methodology for identification of master regulatory genes.

Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps

Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless ‘geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies.

Applications of systems approaches in the study of rheumatic diseases

The complex interaction of molecules within a biological system constitutes a functional module. These modules are then acted upon by both internal and external factors, such as genetic and environmental stresses, which under certain conditions can manifest as complex disease phenotypes. Recent advances in high-throughput biological analyses, in combination with improved computational methods for data enrichment, functional annotation, and network visualization, have enabled a much deeper understanding of the mechanisms underlying important biological processes by identifying functional modules that are temporally and spatially perturbed in the context of disease development. Systems biology approaches such as these have produced compelling observations that would be impossible to replicate using classical methodologies, with greater insights expected as both the technology and methods improve in the coming years. Here, we examine the use of systems biology and network analysis in the study of a wide range of rheumatic diseases to better understand the underlying molecular and clinical features.

A “Ship in a Bottle” Strategy To Load a Hydrophilic Anticancer Drug in Porous Metal Organic Framework Nanoparticles: Efficient Encapsulation, Matrix Stabilization, and Photodelivery

An essential challenge in the development of nanosized metal organic framework (nanoMOF) materials in biomedicine is to develop a strategy to stabilize their supramolecular structure in biological media while being able to control drug encapsulation and release. We have developed a method to efficiently encapsulate topotecan (TPT, 1), an important cytotoxic drug, in biodegradable nanoMOFs. Once inside the pores, 1 monomers aggregate in a "ship in a bottle" fashion, thus filling practically all of the nanoMOFs' available free volume and stabilizing their crystalline supramolecular structures. Highly efficient results have been found with the human pancreatic cell line PANC1, in contrast with free 1. We also demonstrate that one- and two-photon light irradiation emerges as a highly promising strategy to promote stimuli-dependent 1 release from the nanoMOFs, hence opening new standpoints for further developments in triggered drug delivery.