SCRIB Is Involved in the Progression of Ovarian Carcinomas in Association with the Factors Linked to Epithelial-to-Mesenchymal Transition and Predicts Shorter Survival of Diagnosed Patients

Molecular biomarkers in prostate cancer tumorigenesis and clinical relevance

Prostate cancer (PCa) is the second most frequent type of cancer in men and assessing circulating tumor cells (CTCs) by liquid biopsy is a promising tool to help in cancer early detection, staging, risk of recurrence evaluation, treatment prediction and monitoring. Blood-based liquid biopsy approaches enable the enrichment, detection and characterization of CTCs by biomarker analysis. Hence, comprehending the molecular markers, their role on each stage of cancer development and progression is essential to provide information that can help in future implementation of these biomarkers in clinical assistance. In this review, we studied the molecular markers most associated with PCa CTCs to better understand their function on tumorigenesis and metastatic cascade, the methodologies utilized to analyze these biomarkers and their clinical significance, in order to summarize the available information to guide researchers in their investigations, new hypothesis formulation and target choice for the development of new diagnostic and treatment tools.

Scribble promotes fibrosis-dependent mechanisms of hepatocarcinogenesis by p53/PUMA-mediated glycolysis

BACKGROUNDS AND AIMS

Liver cancer is the sixth most common type of cancer and the fifth leading cause of cancer mortality worldwide. Scribble has been shown to function as a neoplastic tumor suppressor gene in most tumors. Our previous studies reported that down-regulation or mislocalization of Scribble was sufficient to initiate mammary tumorigenesis and NSCLC. Recently, it was reported that Scribble was highly expressed in hepatocellular carcinoma (HCC). We aim to study how it was up-regulated and the contradictory role of Scribble in HCC.

METHODS AND RESULTS

Using a mouse model of carbon tetrachloride (CCl4)-induced liver fibrosis system, we showed that Scribble was over-expressed and which may protect the mice against hepatic fibrosis. Unexpectedly, we found out the potential for Scribble to act as a tumor driver at the advanced stage of N-nitrosodiethylamine (DEN) plus CCl4 induced HCC mice model in vivo. In addition, we observed even higher expression of Scribble in HCC tumors harboring elevated levels of wild-type p53. Most importantly, nuclear translocated Scribble could interact with p53, which lead to enhanced stability and transcriptional activity of p53. Mechanistically, our data suggested that Scribble might drive HCC progression by promoting metabolic regulation of p53 through p53-upregulated modulator of apoptosis (PUMA)-mediated Warburg effect.

CONCLUSIONS

Our data identified the molecular basis of hepatic fibrosis-specific gene expression of polarity gene, such as Scribble. Interestingly, with the progression from fibrosis to cirrhosis to HCC, its nuclear translocation promoted a wild-type p53-mediated cancer metabolic switch and tumor progression in HCC. Taken together, we demonstrated that Scribble was up-regulated and served a protective role in liver fibrosis, while also apparently acting as a tumor driver in fibrosis-dependent hepatocarcinogenesis.

Afadin couples RAS GTPases to the polarity rheostat Scribble

AFDN/Afadin is required for establishment and maintenance of cell-cell contacts and is a unique effector of RAS GTPases. The biological consequences of RAS complex with AFDN are unknown. We used proximity-based proteomics to generate an interaction map for two isoforms of AFDN, identifying the polarity protein SCRIB/Scribble as the top hit. We reveal that the first PDZ domain of SCRIB and the AFDN FHA domain mediate a direct but non-canonical interaction between these important adhesion and polarity proteins. Further, the dual RA domains of AFDN have broad specificity for RAS and RAP GTPases, and KRAS co-localizes with AFDN and promotes AFDN-SCRIB complex formation. Knockout of AFDN or SCRIB in epithelial cells disrupts MAPK and PI3K activation kinetics and inhibits motility in a growth factor-dependent manner. These data have important implications for understanding why cells with activated RAS have reduced cell contacts and polarity defects and implicate AFDN as a genuine RAS effector.

Goudreault et al. investigate the role of Afadin downstream of RAS GTPases, substantiating this cell adhesion protein as a true RAS effector that couples its activation to cell polarity through the Scribble protein.

Individual and Co-Expression Patterns of FAM83H and SCRIB at Diagnosis Are Associated with the Survival of Colorectal Carcinoma Patients

Background: FAM83H is important in teeth development; however, an increasing number of reports have indicated a role for it in human cancers. FAM83H is involved in cancer progression in association with various oncogenic molecules, including SCRIB. In the analysis of the public database, there was a significant association between FAM83H and SCRIB in colorectal carcinomas. However, studies evaluating the association of FAM83H and SCRIB in colorectal carcinoma have been limited. Methods: The clinicopathological significance of the immunohistochemical expression of FAM83H and SCRIB was evaluated in 222 colorectal carcinomas. Results: The expressions of FAM83H and SCRIB were significantly associated in colorectal carcinoma tissue. In univariate analysis, the nuclear expressions of FAM83H and SCRIB and the cytoplasmic expression of SCRIB were significantly associated with shorter survival of colorectal carcinomas. The nuclear expressions of FAM83H and SCRIB and the cytoplasmic expression of SCRIB were independent indicators of shorter cancer-specific survival in multivariate analysis. A co-expression pattern of nuclear FAM83H and cytoplasmic SCRIB predicted shorter cancer-specific survival (p < 0.001) and relapse-free survival (p = 0.032) in multivariate analysis. Conclusions: This study suggests that FAM83H and SCRIB might be used as prognostic markers of colorectal carcinomas and as potential therapeutic targets for colorectal carcinomas.

Designing of disruptor molecules to restrain the protein-protein interaction network of VANG1/SCRIB/NOS1AP using fragment-based drug discovery techniques

Governing protein-protein interaction networks are the cynosure of cell signaling and oncogenic networks. Multifarious processes when aligned with one another can result in a dysregulated output which can result in cancer progression. In the current research, one such network of proteins comprising VANG1/SCRIB/NOS1AP, which is responsible for cell migration, is targeted. The proteins are modeled using in-silico approaches, and the interaction is visualized utilizing protein-protein docking. Designing drugs for the convoluted protein network can serve as a challenging task that can be overcome by fragment-based drug designing, a recent game-changer in the computational drug discovery strategy for protein interaction networks. The model is exposed to the extraction of hotspots, also known as the restrained regions for small molecular hits. The hotspot regions are subjected to a library of generated fragments, which are then recombined and rejoined to develop small molecular disruptors of the macromolecular assemblage. Rapid screening methods using pharmacokinetic tools and 2D interaction studies resulted in four molecules that could serve the purpose of a disruptor. The final validation is executed by long-range simulations of 100 ns and exploring the stability of the complex using several parameters leading to the emergence of two novel molecules VNS003 and VNS005 that could be used as the disruptors of the protein assembly VANG1/SCRIB/NOS1AP. Also, the molecules were explored as single protein targets approbated via molecular docking and 100 ns molecular dynamics simulation. This concluded VNS003 as the most suitable inhibitor module capable of acting as a disruptor of a macromolecular assembly as well as acting on individual protein chains, thus leading to the primary hindrance in the formation of the protein interaction complex.

Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models

Bone is the most common site of distant metastasis from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Such bone metastases (BM) cause many painful skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia, with adverse effects on life quality. Many bone-targeting agents developed based on the current understanding of BM onset's molecular mechanisms dull these adverse effects. However, only a few studies investigated potential predictors of high risk for developing BM, despite such knowledge being critical for early interventions to prevent or delay BM. This work proposes a computational network-based pipeline that incorporates a ML/DL component to predict BM development. Based on the proposed pipeline we constructed several machine learning models. The deep neural network (DNN) model exhibited the highest prediction accuracy (AUC of 92.11%) using the top 34 featured genes ranked by betweenness centrality scores. We further used an entirely separate, "external" TCGA dataset to evaluate the robustness of this DNN model and achieved sensitivity of 85%, specificity of 80%, positive predictive value of 78.10%, negative predictive value of 80%, and AUC of 85.78%. The result shows the models' way of learning allowed it to zoom in on the featured genes that provide the added benefit of the model displaying generic capabilities, that is, to predict BM for samples from different primary sites. Furthermore, existing experimental evidence provides confidence that about 50% of the 34 hub genes have BM-related functionality, which suggests that these common genetic markers provide vital insight about BM drivers. These findings may prompt the transformation of such a method into an artificial intelligence (AI) diagnostic tool and direct us towards mechanisms that underlie metastasis to bone events.