Introduction: Cancer Gene Networks

Network-based analysis of heterogeneous patient-matched brain and extracranial melanoma metastasis pairs reveals three homogeneous subgroups

Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome and methylome data followed by network-based impact propagation of patient-specific alterations. This innovative data analysis strategy allowed to predict potential impacts of patient-specific driver candidate genes on other genes and pathways. The patient-matched metastasis pairs clustered into three robust subgroups with specific downstream targets with known roles in cancer, including melanoma (SG1: RBM38, BCL11B, SG2: GATA3, FES, SG3: SLAMF6, PYCARD). Patient subgroups and ranking of target gene candidates were confirmed in a validation cohort. Summarizing, computational network-based impact analyses of heterogeneous metastasis pairs predicted individual regulatory differences in melanoma brain metastases, cumulating into three consistent subgroups with specific downstream target genes.

A method to improve the prediction performance of cancer-gene association by screening negative training samples through gene network data

This work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based on gene network and graph theory algorithms to improve the negative samples selection. Genetic data with low cancer correlation is used as negative training samples. After experimental verification, using the negative samples screened by this method to train the cancer gene classification model can improve prediction performance. The biggest advantage of this method is that it can be easily combined with other methods that focus on enhancing the quality of positive training samples. It has been demonstrated that significant improvement is achieved by combining this method with three state-of-the-arts cancer gene prediction methods.

Spectral Analysis Based on Hemodynamic Habitat Imaging Predicts Isocitrate Dehydrogenase Status and Prognosis in High-Grade Glioma

BACKGROUND

The intratumoral heterogeneity of high-grade gliomas (HGGs) is associated with isocitrate dehydrogenase (IDH) status and prognosis, which can be established by quantitative radioanalysis of spatial tumor habitats. Therefore, we designed a framework for tackling tumors based on spatial metabolism using the hemodynamic tissue signature (HTS), focusing on metabolic changes in tumor habitat to predict IDH status and assess prognosis in patients with HGG.

METHODS

Preoperative data for 121 patients with HGG with subsequent histologic confirmation of HGG were prospectively collected (January 2016 to December 2020). The HTS was mapped from the image data, chemical shift imaging voxels were selected from the HTS habitat as the region of interest, and the metabolic ratio of the HTS was calculated using weighted least square method fitting. The metabolic rate of the tumor enhancement area was used as a control to analyze the efficacy of each HTS metabolic rate in predicting the IDH status and prognosis of HGG.

RESULTS

Total choline (Cho)/total creatine and Cho/N-acetyl-aspartate showed significant differences between IDH-wildtype and IDH-mutant in high- and low-angiogenic enhanced tumor sites (P < 0.05); Cho/total creatine was an independent risk factor for prognosis of HGG patients in high-angiogenic enhanced tumor habitats, with significant differences in survival time between groups (P < 0.05). The metabolic ratio in the tumor enhanced area could not predict IDH status or evaluate prognosis.

CONCLUSIONS

Spectral analysis based on hemodynamic habitat imaging can clearly distinguish IDH mutations and the prognosis assessment is more accurate, rendering it superior to traditional spectral analysis in tumor enhancement areas.

Molecular biomarkers, network biomarkers, and dynamic network biomarkers for diagnosis and prediction of rare diseases

The difficulty of converting scientific research findings into novel pharmacological treatments for rare and life-threatening diseases is enormous. Biomarkers related to multiple biological processes involved in cell growth, proliferation, and disease occurrence have been identified in recent years with the development of immunology, molecular biology, and genomics technologies. Biomarkers are capable of reflecting normal physiological processes, pathological processes, and the response to therapeutic intervention; as such, they play vital roles in disease diagnosis, prevention, drug response, and other aspects of biomedicine. The discovery of valuable biomarkers has become a focal point of current research. Numerous studies have identified molecular biomarkers based on the differential expression/concentration of molecules (e.g., genes/proteins) for disease state diagnosis, characterization, and treatment. Although technological breakthroughs in molecular analysis platforms have enabled the identification of a large number of candidate biomarkers for rare diseases, only a small number of these candidates have been properly validated for use in patient treatment. The traditional molecular biomarkers may lose vital information by ignoring molecular associations/interactions, and thus the concept of network biomarkers based on differential associations/correlations of molecule pairs has been established. This approach promises to be more stable and reliable in diagnosing disease states. Furthermore, the newly-emerged dynamic network biomarkers (DNBs) based on differential fluctuations/correlations of molecular groups are able to recognize pre-disease states or critical states instead of disease states, thereby achieving rare disease prediction or predictive/preventative medicine and providing deep insight into the dynamic characteristics of disease initiation and progression.

Acquisition of Invasiveness by Breast Adenocarcinoma Cells Engages Established Hallmarks and Novel Regulatory Mechanisms

BACKGROUND/AIM

Proteomics of invasiveness opens a window on the complexity of the metastasis-engaged mechanisms. The extend and types of this complexity require elucidation.

MATERIALS AND METHODS

Proteomics, immunohistochemistry, immunoblotting, network analysis and systems cancer biology were used to analyse acquisition of invasiveness by human breast adenocarcinoma cells.

RESULTS

We report here that invasiveness network highlighted the involvement of hallmarks such as cell proliferation, migration, cell death, genome stability, immune system regulation and metabolism. Identified involvement of cell-virus interaction and gene silencing are potentially novel cancer mechanisms. Identified 6,113 nodes with 11,055 edges affecting 1,085 biological processes show extensive re-arrangements in cell physiology. These high numbers are in line with a similar broadness of networks built with diagnostic signatures approved for clinical use.

CONCLUSION

Our data emphasize a broad systemic regulation of invasiveness, and describe the network of this regulation.

Ectopic miR-975 induces CTP synthase directed cell proliferation and differentiation in Drosophila melanogaster

CTP synthase (CTPSyn) is an essential metabolic enzyme, synthesizing precursors required for nucleotides and phospholipids production. Previous studies have also shown that CTPSyn is elevated in various cancers. In many organisms, CTPSyn compartmentalizes into filaments called cytoophidia. In Drosophila melanogaster, only its isoform C (CTPSynIsoC) forms cytoophidia. In the fruit fly's testis, cytoophidia are normally seen in the transit amplification regions close to its apical tip, where the stem-cell niche is located, and development is at its most rapid. Here, we report that CTPSynIsoC overexpression causes the lengthening of cytoophidia throughout the entirety of the testicular body. A bulging apical tip is found in approximately 34% of males overexpressing CTPSynIsoC. Immunostaining shows that this bulged phenotype is most likely due to increased numbers of both germline cells and spermatocytes. Through a microRNA (miRNA) overexpression screen, we found that ectopic miR-975 concurrently increases both the expression levels of CTPSyn and the length of its cytoophidia. The bulging testes phenotype was also recovered at a penetration of approximately 20%. However, qPCR assays reveal that CTPSynIsoC and miR-975 overexpression each provokes a differential response in expression of a number of cancer-related genes, indicating that the shared CTPSyn upregulation seen in either case is likely the cause of observed testicular overgrowth. This study presents the first instance of consequences of miRNA-asserted regulation upon CTPSyn in D. melanogaster, and further reaffirms the enzyme's close ties to germline cells overgrowth.