EMDIP: An Entropy Measure to Discover Important Proteins in PPI networks

NUF2 Promotes Breast Cancer Development as a New Tumor Stem Cell Indicator

Multiple new subtypes of breast cancer (BRCA) are identified in women each year, rendering BRCA the most common and rapidly expanding form of cancer in females globally. NUF2 has been identified as a prognostic factor in various human cancers, regulating cell apoptosis and proliferation. However, its role in BRCA prognosis has not been clarified. This study explored the role of NUF2 in breast cancer development and prognosis using informatic analysis combined with in vivo intracellular studies. Through the online website TIMER, we evaluated the transcription profile of NUF2 across a variety of different cancer types and found that NUF2 mRNA was highly expressed in BRCA patients. Its transcription level was found to be related to the subtype, pathological stage, and prognosis of BRCA. The R program analysis showed a correlation of NUF2 with cell proliferation and tumor stemness in the BRCA patient samples. Subsequently, the association between the NUF2 expression level and immune cell infiltration was analyzed using the XIANTAO and TIMER tools. The results revealed that NUF2 expression was correlated with the responses of multiple immune cells. Furthermore, we observed the effect of NUF2 expression on tumor stemness in BRCA cell lines in vivo. The experimental results illuminated that the overexpression of NUF2 statistically upregulated the proliferation and tumor stemness ability of the BRCA cell lines MCF-7 and Hs-578T. Meanwhile, the knockdown of NUF2 inhibited the abilities of both cell lines, a finding which was verified by analyzing the subcutaneous tumorigenic ability in nude mice. In summary, this study suggests that NUF2 may play a key role in the development and progression of BRCA by affecting tumor stemness. As a stemness indicator, it has the potential to be one of the markers for the diagnosis of BRCA.

MAGEA11 as a STAD Prognostic Biomarker Associated with Immune Infiltration

Expression of MAGE family member A11 (MAGEA11) is upregulated in different tumors. However, in gastric cancer, the prognostic significance of MAGEA11 and its relationship with immune infiltration remain largely unknown. The expression of MAGEA11 in pan-cancer and the receiver operating characteristic (ROC) and survival impact of gastric cancer were evaluated by The Cancer Genome Atlas (TCGA). Whether MAGEA11 was an independent risk factor was assessed by Cox analysis. Nomograms were constructed from MAGEA11 and clinical variables. Gene functional pathway enrichment was obtained based on MAGEA11 differential analysis. The relationship between MAGEA11 and immune infiltration was determined by the Tumor Immunity Estimation Resource (TIMER) and the Tumor Immune System Interaction Database (TISIDB). Finally, MAGEA11-sensitive drugs were predicted based on the CellMiner database. The results showed that the expression of MAGEA11 mRNA in gastric cancer tissues was significantly higher than that in normal tissues. The ROC curve indicated an AUC value of 0.667. Survival analysis showed that patients with high MAGEA11 had poor prognosis (HR = 1.43, p = 0.034). In correlation analysis, MAGEA11 mRNA expression was found to be associated with tumor purity and immune invasion. Finally, drug sensitivity analysis found that the expression of MAGEA11 was correlated with seven drugs. Our study found that upregulated MAGEA11 in gastric cancer was significantly associated with lower survival and invasion by immune infiltration. It is suggested that MAGEA11 may be a potential biomarker and immunotherapy target for gastric cancer.

CCDC134 as a Prognostic-Related Biomarker in Breast Cancer Correlating With Immune Infiltrates

Background

The expression of Coiled-Coil Domain Containing 134(CCDC134) is up-regulated in different pan-cancer species. However, its prognostic value and correlation with immune infiltration in breast cancer are unclear. Therefore, we evaluated the prognostic role of CCDC134 in breast cancer and its correlation with immune invasion.

Methods

We downloaded the transcription profile of CCDC134 between breast cancer and normal tissues from the Cancer Genome Atlas (TCGA). CCDC134 protein expression was assessed by the Clinical Proteomic Cancer Analysis Consortium (CPTAC) and the Human Protein Atlas. Gene set enrichment analysis (GSEA) was also used for pathway analysis. Receiver operating characteristic (ROC) curve was used to differentiate breast cancer from adjacent normal tissues. Kaplan-Meier method was used to evaluate the effect of CCDC134 on survival rate. The protein-protein interaction (PPI) network is built from STRING. Function expansion analysis is performed using the ClusterProfiler package. Through tumor Immune Estimation Resource (TIMER) and tumor Immune System Interaction database (TISIDB) to determine the relationship between CCDC134 expression level and immune infiltration. CTD database is used to predict drugs that inhibit CCDC134 and PubChem database is used to determine the molecular structure of identified drugs.

Results

The expression of CCDC134 in breast cancer tissues was significantly higher than that of CCDC134 mRNA expression in adjacent normal tissues. ROC curve analysis showed that the AUC value of CCDC134 was 0.663. Kaplan-meier survival analysis showed that patients with high CCDC134 had a lower prognosis (57.27 months vs 36.96 months, P = 2.0E-6). Correlation analysis showed that CCDC134 mRNA expression was associated with tumor purity immune invasion. In addition, CTD database analysis identified abrine, Benzo (A) Pyrene, bisphenol A, Soman, Sunitinib, Tetrachloroethylene, Valproic Acid as seven targeted therapy drugs that may be effective treatments for seven targeted therapeutics. It may be an effective treatment for inhibiting CCDC134.

Conclusion

In breast cancer, upregulated CCDC134 is significantly associated with lower survival and immune infiltrates invasion. Our study suggests that CCDC134 can serve as a biomarker of poor prognosis and a potential immunotherapy target in breast cancer. Seven drugs with significant potential to inhibit CCDC134 were identified.

A Survey of Information Entropy Metrics for Complex Networks

Information entropy metrics have been applied to a wide range of problems that were abstracted as complex networks. This growing body of research is scattered in multiple disciplines, which makes it difficult to identify available metrics and understand the context in which they are applicable. In this work, a narrative literature review of information entropy metrics for complex networks is conducted following the PRISMA guidelines. Existing entropy metrics are classified according to three different criteria: whether the metric provides a property of the graph or a graph component (such as the nodes), the chosen probability distribution, and the types of complex networks to which the metrics are applicable. Consequently, this work identifies the areas in need for further development aiming to guide future research efforts.