The Multifaceted Role of Signal Peptide-CUB-EGF Domain-Containing Protein (SCUBE) in Cancer

Evaluation of network-guided random forest for disease gene discovery

BACKGROUND

Gene network information is believed to be beneficial for disease module and pathway identification, but has not been explicitly utilized in the standard random forest (RF) algorithm for gene expression data analysis. We investigate the performance of a network-guided RF where the network information is summarized into a sampling probability of predictor variables which is further used in the construction of the RF.

RESULTS

Our simulation results suggest that network-guided RF does not provide better disease prediction than the standard RF. In terms of disease gene discovery, if disease genes form module(s), network-guided RF identifies them more accurately. In addition, when disease status is independent from genes in the given network, spurious gene selection results can occur when using network information, especially on hub genes. Our empirical analysis on two balanced microarray and RNA-Seq breast cancer datasets from The Cancer Genome Atlas (TCGA) for classification of progesterone receptor (PR) status also demonstrates that network-guided RF can identify genes from PGR-related pathways, which leads to a better connected module of identified genes.

CONCLUSIONS

Gene networks can provide additional information to aid the gene expression analysis for disease module and pathway identification. But they need to be used with caution and validation on the results need to be carried out to guard against spurious gene selection. More robust approaches to incorporate such information into RF construction also warrant further study.

Assessment of the levels of some prognostic biomolecules (galectins, ACE2, SCUBE1/2/3) in COVID-19 patients

Aim: Our aim was to investigate the differences between healthy people and COVID-19 patients in terms of some immunological biomolecules, especially including those related to the inflammation process. Materials & methods: A total of 180 participants (90 healthy controls and 90 COVID-19 patients) were included. The expression levels of eight different inflammation-related biomolecules were measured by the ELISA technique. Results: The mean levels of ACE2, ANG1-7, GAL3, GAL9, SCUBE1, SCUBE2 and SCUBE3 were elevated in COVID-19 patients when compared with healthy controls, while the mean level of GAL2 was lower in COVID-19 patients than controls. Conclusion: To understand the cytokine storm mechanism and related parameters, more detailed studies should be performed investigating more related biomolecules and related signaling pathways.

SCUBE2 as a Marker of Resistance to Taxane-based Neoadjuvant Chemotherapy and a Potential Therapeutic Target in Breast Cancer

Objective

Taxane-based neoadjuvant chemotherapy is the most common neoadjuvant approach in breast cancer, especially in human epidermal growth factor receptor 2 (HER2)-positive and triple-negative subtypes. However, chemoresistance is a problem in many patients, and success rates are low in estrogen receptor (ER)-positive breast cancer. The aim of this study was to identify predictive markers for resistance to taxane-based therapy, which may have a potential as therapeutic targets in breast cancer.

Materials and Methods

Three comprehensive breast cancer Gene Expression Omnibus datasets were analyzed to identify differentially expressed genes (DEGs) in breast cancer patients resistant to taxane-based neoadjuvant chemotherapy. Functional annotation clustering and enrichment analysis were performed on the DEGs list. A protein-protein interaction network was established with the upregulated genes. The predictive value and the differential expression of the central genes were validated in the extensive ROC Plotter database.

Results

Seventeen upregulated genes were found which were associated with resistance to taxane-based neoadjuvant therapy and high connectivity in the network analysis. ESR1, CCND1, and SCUBE2 emerged as the top three key genes associated with resistance. SCUBE2 displayed a high predictive power comparable to ESR1, and better than CCND1, the two commonly accepted markers. The predictive ability of SCUBE2 was higher in ER-positive and HER2-positive breast cancers.

Conclusion

These results suggest that SCUBE2 may be used as a predictive marker to guide decisions on neoadjuvant therapy. Emerging evidence about the role of SCUBE2 as a coreceptor involved in tumor progression and angiogenesis also suggests SCUBE2 as a potential therapeutic target. These points should be investigated in further studies.