Integration of Network Biology and Imaging to Study Cancer Phenotypes and Responses

Machine learning based identification of hub genes in renal clear cell carcinoma using multi-omics data

Renal cell carcinoma is one of the most universal urinary system cancers in the world. The most common renal cell carcinoma subtype is renal clear cell carcinoma. It is usually associated with high rates of metastasis and mortality. Therefore, finding effective therapeutic targets and prognostic molecular markers is of great significance to improve the early diagnosis rate and prognostic accuracy of renal clear cell carcinoma. In this work, we successfully identified six hub genes that are closely related to the occurrence, development and prognosis of renal clear cell carcinoma and proposed three new potential prognostic markers, namely ATP4B, AC144831.1 and Tfcp2l1 through differentially expressed genes (DEGs) analysis, GO functional enrichment and KEGG pathway analysis, WGCNA analysis, and survival analysis. In addition, we established machine learning models to predict the occurrence of tumors through the gene expression data of patients. It is expected that the results of this study can provide reference value for the treatment of renal clear cell carcinoma.

Identification of co-expression hub genes for ferroptosis in kidney renal clear cell carcinoma based on weighted gene co-expression network analysis and The Cancer Genome Atlas clinical data

Renal clear cell carcinoma (KIRC) is one of the most common tumors worldwide and has a high mortality rate. Ferroptosis is a major mechanism of tumor occurrence and development, as well as important for prognosis and treatment of KIRC. Here, we conducted bioinformatics analysis to identify KIRC hub genes that target ferroptosis. By Weighted gene co-expression network analysis (WGCNA), 11 co-expression-related genes were screened out. According to Kaplan Meier's survival analysis of the data from the gene expression profile interactive analysis database, it was identified that the expression levels of two genes, PROM2 and PLIN2, are respectively related to prognosis. In conclusion, our findings indicate that PROM2 and PLIN2 may be effective new targets for the treatment and prognosis of KIRC.

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas

Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.

M-CSF, IL-6, and TGF-β promote generation of a new subset of tissue repair macrophage for traumatic brain injury recovery

Traumatic brain injury (TBI) leads to high mortality rate. We aimed to identify the key cytokines favoring TBI repair and found that patients with TBI with a better outcome robustly increased concentrations of macrophage colony-stimulating factor, interleukin-6, and transforming growth factor-β (termed M6T) in cerebrospinal fluid or plasma. Using TBI mice, we identified that M2-like macrophage, microglia, and endothelial cell were major sources to produce M6T. Together with the in vivo tracking of mCherry+ macrophages in zebrafish models, we confirmed that M6T treatment accelerated blood-borne macrophage infiltration and polarization toward a subset of tissue repair macrophages that expressed similar genes as microglia for neuroprotection, angiogenesis and cell migration. M6T therapy in TBI mice and zebrafish improved neurological function while blocking M6T-exacerbated brain injury. Considering low concentrations of M6T in some patients with poor prognostic, M6T treatment might repair TBI via generating a previously unidentified subset of tissue repair macrophages.

Optical and magnetic resonance imaging approaches for investigating the tumour microenvironment: state-of-the-art review and future trends

The tumour microenvironment (TME) strongly influences tumorigenesis and metastasis. Two of the most characterized properties of the TME are acidosis and hypoxia, both of which are considered hallmarks of tumours as well as critical factors in response to anticancer treatments. Currently, various imaging approaches exist to measure acidosis and hypoxia in the TME, including magnetic resonance imaging (MRI), positron emission tomography and optical imaging. In this review, we will focus on the latest fluorescent-based methods for optical sensing of cell metabolism and MRI as diagnostic imaging tools applied both in vitro and in vivo. The primary emphasis will be on describing the current and future uses of systems that can measure intra- and extra-cellular pH and oxygen changes at high spatial and temporal resolution. In addition, the suitability of these approaches for mapping tumour heterogeneity, and assessing response or failure to therapeutics will also be covered.

The Sustained Induction of c-MYC Drives Nab-Paclitaxel Resistance in Primary Pancreatic Ductal Carcinoma Cells

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with limited and, very often, ineffective medical and surgical therapeutic options. The treatment of patients with advanced unresectable PDAC is restricted to systemic chemotherapy, a therapeutic intervention to which most eventually develop resistance. Recently, nab-paclitaxel (n-PTX) has been added to the arsenal of first-line therapies, and the combination of gemcitabine and n-PTX has modestly prolonged median overall survival. However, patients almost invariably succumb to the disease, and little is known about the mechanisms underlying n-PTX resistance. Using the conditionally reprogrammed (CR) cell approach, we established and verified continuously growing cell cultures from treatment-naïve patients with PDAC. To study the mechanisms of primary drug resistance, nab-paclitaxel-resistant (n-PTX-R) cells were generated from primary cultures and drug resistance was verified in vivo, both in zebrafish and in athymic nude mouse xenograft models. Molecular analyses identified the sustained induction of c-MYC in the n-PTX-R cells. Depletion of c-MYC restored n-PTX sensitivity, as did treatment with either the MEK inhibitor, trametinib, or a small-molecule activator of protein phosphatase 2a. IMPLICATIONS: The strategies we have devised, including the patient-derived primary cells and the unique, drug-resistant isogenic cells, are rapid and easily applied in vitro and in vivo platforms to better understand the mechanisms of drug resistance and for defining effective therapeutic options on a patient by patient basis.

Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers

Drawing on concepts from experimental biology, computer science, informatics, mathematics and statistics, systems biologists integrate data across diverse platforms and scales of time and space to create computational and mathematical models of the integrative, holistic functions of living systems. Endocrine-related cancers are well suited to study from a systems perspective because of the signaling complexities arising from the roles of growth factors, hormones and their receptors as critical regulators of cancer cell biology and from the interactions among cancer cells, normal cells and signaling molecules in the tumor microenvironment. Moreover, growth factors, hormones and their receptors are often effective targets for therapeutic intervention, such as estrogen biosynthesis, estrogen receptors or HER2 in breast cancer and androgen receptors in prostate cancer. Given the complexity underlying the molecular control networks in these cancers, a simple, intuitive understanding of how endocrine-related cancers respond to therapeutic protocols has proved incomplete and unsatisfactory. Systems biology offers an alternative paradigm for understanding these cancers and their treatment. To correctly interpret the results of systems-based studies requires some knowledge of how in silico models are built, and how they are used to describe a system and to predict the effects of perturbations on system function. In this review, we provide a general perspective on the field of cancer systems biology, and we explore some of the advantages, limitations and pitfalls associated with using predictive multiscale modeling to study endocrine-related cancers.

Manganese-Enhanced Magnetic Resonance Imaging as a Diagnostic and Dispositional Tool after Mild-Moderate Blast Traumatic Brain Injury

Traumatic brain injury (TBI) caused by explosive munitions, known as blast TBI, is the signature injury in recent military conflicts in Iraq and Afghanistan. Diagnostic evaluation of TBI, including blast TBI, is based on clinical history, symptoms, and neuropsychological testing, all of which can result in misdiagnosis or underdiagnosis of this condition, particularly in the case of TBI of mild-to-moderate severity. Prognosis is currently determined by TBI severity, recurrence, and type of pathology, and also may be influenced by promptness of clinical intervention when more effective treatments become available. An important task is prevention of repetitive TBI, particularly when the patient is still symptomatic. For these reasons, the establishment of quantitative biological markers can serve to improve diagnosis and preventative or therapeutic management. In this study, we used a shock-tube model of blast TBI to determine whether manganese-enhanced magnetic resonance imaging (MEMRI) can serve as a tool to accurately and quantitatively diagnose mild-to-moderate blast TBI. Mice were subjected to a 30 psig blast and administered a single dose of MnCl2 intraperitoneally. Longitudinal T1-magnetic resonance imaging (MRI) performed at 6, 24, 48, and 72 h and at 14 and 28 days revealed a marked signal enhancement in the brain of mice exposed to blast, compared with sham controls, at nearly all time-points. Interestingly, when mice were protected with a polycarbonate body shield during blast exposure, the marked increase in contrast was prevented. We conclude that manganese uptake can serve as a quantitative biomarker for TBI and that MEMRI is a minimally-invasive quantitative approach that can aid in the accurate diagnosis and management of blast TBI. In addition, the prevention of the increased uptake of manganese by body protection strongly suggests that the exposure of an individual to blast risk could benefit from the design of improved body armor.

Effects of altered ephrin-A5 and EphA4/EphA7 expression on tumor growth in a medulloblastoma mouse model

Background

Members of the Eph/ephrin gene families act as key regulators of cerebellar development during embryogenesis. Aberrant signaling of Eph family of receptor tyrosine kinases and their ephrin ligands has also been implicated in human cancers. Medulloblastoma is an aggressive primitive neuroectodermal tumor that originates from granule neuron precursors in the cerebellum. Previous studies have suggested a role for the ephrin-A5 ligand and its receptors, EphA4 and EphA7, in granule cell-precursor formation and in guiding cell migration. In the present study, we investigated the effects of genetic loss of ephrin-A5, EphA4, and EphA7 on the spatiotemporal development of medulloblastoma tumors in the context of the smoothened transgenic mouse model system.

Findings

Radiographic magnetic resonance imaging (MRI) was performed to monitor tumor growth in a genetically engineered mouse model of medulloblastoma. Tumor tissue was harvested to determine changes in the expression of phosphorylated Akt by Western blotting. This helped to establish a correlation between genotype and/or tumor size and survival. Our in vivo data establish that in ND2-SmoA1 transgenic mice, the homozygous deletion of ephrin-A5 resulted in a consistent pattern of tumor growth inhibition compared to their ephrin-A5 wild-type littermate controls, while the loss of EphA4/EphA7 failed to produce consistent effects versus EphA4/EphA7 wild-type mice. A positive correlation was evident between tumor size, p-Akt, and proliferating cell nuclear antigen (PCNA) expression in our transgenic mouse model system, regardless of genotype.

Conclusions

Taken together, our findings underscore the importance of targeting specific members of the Eph/ephrin families in conjunction with the Akt pathway in order to inhibit medulloblastoma tumor growth and progression.