Introducing Albumin and Interleukin 6 As Common Critical Dysregulated Proteins Between Migraine and Gliosarcoma

Introduction: It is reported that migraine may be risk factor of brain cancers. Since the one of the best ways to assess this possible relationship is molecular mechanism study, here the common central dysregulated proteins between these diseases are investigated via network analysis. Methods: The dysregulated proteins of migraine and gliosarcoma are extracted from STRING database and interacted via Cytoscape software to form two separate networks. Central nodes of the networks are compared to find the common central differentially expressed proteins. First neighbors of the common central proteins are studied. Results: Numbers of 11 hub-bottlenecks for each one of migraine and gliosarcoma cancer networks were identified. Albumin and interleukin 6 as common differentially expressed central proteins were introduced. KNG1, VEGFA, and NF1 the first neighbors of ALB-IL6 were connected to the central nodes of networks of the two studied diseases. Conclusion: Network analysis revealed that ALB and IL6 are common central dysregulate proteins between migraine and brain cancers. There are crucial proteins among the first neighbors of ALB and IL6 which are connected to migraine and gliosarcom.

Neuroscience meets cancer: networks and neuronal input to brain tumors

The nervous system with its complex organizational features and functions is well-known for its impressive ability to process information and drive countless biological processes. It has come to the surprise of many that the nervous system can also be intimately involved in an unwelcome area of human life: the initiation and progression of cancer. For brain tumors, the parallels to neurodevelopment and nervous system function can be found on multiple levels. First, cancer cells of incurable gliomas interconnect with long cellular extensions to a large communicating multicellular network. Second, indirect and direct neuronal input can generate, activate, and control brain tumor growth. Third, it is becoming increasingly clear that those features not only drive brain tumor progression but also the notorious resistance of these tumors against standard antitumor therapies. Remarkably, these recent insights have already generated novel ideas for better antitumor therapies.

Patient participation in cancer network governance: a six-year case study

Background Patient participation in decision-making has become a hallmark of responsive healthcare systems. Cancer networks in many countries have committed to involving people living with and beyond cancer (PLC) at multiple levels. However, PLC participation in network governance remains highly variable for reasons that are poorly understood. This study aims to share lessons learned regarding mechanisms that enable PLC participation in cancer network governance. Methods This multiple case study, using a qualitative approach in a natural setting, was conducted over six years in three local cancer networks within the larger national cancer network in Quebec (Canada), where PLC participation is prescribed by the Cancer Directorate. Data were collected from multiple sources, including individual and focus group interviews (n = 89) with policymakers, managers, clinicians and PLC involved in national and local cancer governance committees. These data were triangulated and iteratively analysed according to a framework based on functions of collaborative governance in the network context. Results We identify three main mechanisms that enable PLC participation in cancer network governance: (1) consistent emphasis on patient-centred care as a network objective; (2) flexibility, time and support to translate mandated PLC representation into meaningful participation; and (3) recognition of the distinct knowledge of PLC in decision-making. The shared vision of person-centred care facilitates PLC participation. The quality of participation improves through changes in how committee meetings are conducted, and through the establishment of a national committee where PLC can pool their experience, develop skills and establish a common voice on priority issues. PLC knowledge is especially valued around particular challenges such as designing integrated care trajectories and overcoming barriers to accessing care. These three mechanisms interact to enable PLC participation in governance and are activated to varying extents in each local network. Conclusions This study reveals that mandating PLC representation on governance structures is a powerful context element enabling participation, but that it also delineates which governance functions are open to influence from PLC participation. While the activation of mechanisms is context dependent, the insights from this study in Quebec are transferable to cancer networks in other jurisdictions seeking to embed PLC participation in decision-making.

The United Kingdom Thyroid Multi-Disciplinary Team; a National Survey of Services and Comparison to Guidelines.

Key Points: 1. We have identified ambiguity in the current guidance on thyroid MDT’s, and have also found nationwide variation in compliance with this. 2. We recommend: a. All surgeons undertaking thyroid surgery should complete a minimum of 20 thyroid procedures per year, and this should also form part of surgeons’ annual appraisal. b. All surgeons should contribute data to UKRETS (unless prevented by local legal frameworks) and this should form part of surgeons’ annual appraisal and be audited by individual MDT’s and regional cancer networks. c. Thyroid MDT’s should be held weekly where possible, with a minimum frequency of fortnightly. d. The core membership of a thyroid MDT (stand alone and joint) should include thyroid surgeons, specialist radiology, endocrinology, nuclear medicine, nurse specialists, histopathology +/- cytology and clinical oncology.

Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers

The complexity of cancer has always been a huge issue in understanding the source of this disease. However, by appreciating its complexity, we can shed some light on crucial gene associations across and in specific cancer types. In this study, we develop a general framework to infer relevant gene biomarkers and their gene-to-gene associations using multiple gene co-expression networks for each cancer type. Specifically, we infer computationally and biologically interesting communities of genes from kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma data sets of The Cancer Genome Atlas (TCGA) database. The gene communities are extracted through a data-driven pipeline and then evaluated through both functional analyses and literature findings. Furthermore, we provide a computational validation of their relevance for each cancer type by comparing the performance of normal/cancer classification for our identified gene sets and other gene signatures, including the typically-used differentially expressed genes. The hallmark of this study is its approach based on gene co-expression networks from different similarity measures: using a combination of multiple gene networks and then fusing normal and cancer networks for each cancer type, we can have better insights on the overall structure of the cancer-type-specific network.

Loss of Long Distance Co-Expression in Lung Cancer

Lung cancer is one of the deadliest, most aggressive cancers. Abrupt changes in gene expression represent an important challenge to understand and fight the disease. Gene co-expression networks (GCNs) have been widely used to study the genomic regulatory landscape of human cancer. Here, based on 1,143 RNA-Seq experiments from the TCGA collaboration, we constructed GCN for the most common types of lung tumors: adenocarcinoma (TAD) and squamous cells (TSCs) as well as their respective control networks (NAD and NSC). We compared the number of intra-chromosome (cis-) and inter-chromosome (trans-) co-expression interactions in normal and cancer GCNs. We compared the number of shared interactions between TAD and TSC, as well as in NAD and NSC, to observe which phenotypes were more alike. By means of an over-representation analysis, we associated network topology features with biological functions. We found that TAD and TSC present mostly cis- small disconnected components, whereas in control GCNs, both types have a giant trans- component. In both cancer networks, we observed cis- components in which genes not only belong to the same chromosome but to the same cytoband or to neighboring cytobands. This supports the hypothesis that in lung cancer, gene co-expression is constrained to small neighboring regions. Despite this loss of distant co-expression observed in TAD and TSC, there are some remaining trans- clusters. These clusters seem to play relevant roles in the carcinogenic processes. For instance, some clusters in TAD and TSC are associated with the immune system, response to virus, or control of gene expression. Additionally, other non-enriched trans- clusters are composed of one gene and several associated pseudo-genes, as in the case of the FTH1 gene. The appearance of those common trans- clusters reflects that the gene co-expression program in lung cancer conserves some aspects for cell maintenance. Unexpectedly, 0.48% of the edges are shared between control networks; conversely, 35% is shared between lung cancer GCNs, a 73-fold larger intersection. This suggests that in lung cancer a process of de-differentiation may be occurring. To further investigate the implications of the loss of distant co-expression, it will become necessary to broaden the investigation with other omic-based approaches. However, the present approach provides a basis for future work toward an integrative perspective of abnormal transcriptional regulatory programs in lung cancer.