Essentiality of CTNNB1 in Malignant Transformation of Human Embryonic Stem Cells under Long-Term Suboptimal Conditions

PKI: A bioinformatics method of quantifying the importance of nodes in gene regulatory network via a pseudo knockout index

BACKGROUND

Gene regulatory network (GRN) is a model that characterizes the complex relationships between genes and thereby provides an informatics environment to measure the importance of nodes. The evaluation of important nodes in a GRN can effectively refer to their functional implications severing as key players in particular biological processes, such as master regulator and driver gene. Currently, it is mainly based on network topological parameters and focuses only on evaluating a single node individually. However, genes and products play their functions by interacting with each other. It is worth noting that the effects of gene combinations in GRN are not simply additive. Key combinations discovery is of significance in revealing gene sets with important functions. Recently, with the development of single-cell RNA-sequencing (scRNA-seq) technology, we can quantify gene expression profiles of individual cells that provide the potential to identify crucial nodes in gene regulations regarding specific condition, e.g., stem cell differentiation.

RESULTS

In this paper, we propose a bioinformatics method, called Pseudo Knockout Importance (PKI), to quantify the importance of node and node sets in a specific GRN structure using time-course scRNA-seq data. First, we construct ordinary differential equations to approach the gene regulations during cell differentiation. Then we design gene pseudo knockout experiments and define PKI score evaluation criteria based on the coefficient of determination. The importance of nodes can be described as the influence on the ODE system of removing variables. For key gene combinations, PKI is derived as a combinatorial optimization problem of quantifying the in silico gene knockout effects.

CONCLUSIONS

Here, we focus our analyses on the specific GRN of embryonic stem cells with time series gene expression profile. To verify the effectiveness and advantage of PKI method, we compare its node importance rankings with other twelve kinds of centrality-based methods, such as degree and Latora closeness. For key node combinations, we compare the results with the method based on minimum dominant set. Moreover, the famous combinations of transcription factors in induced pluripotent stem cell are also employed to verify the vital gene combinations identified by PKI. These results demonstrate the reliability and superiority of the proposed method.

Down-regulated FST expression is involved in the poor prognosis of triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is more commonly associated with young patients, featuring high histological grade, visceral metastasis, and distant recurrence. Follistatin (FST) is a secreted extracellular regulatory protein that antagonizes TGF-β superfamily such as activin and TGF-β related superfamily such as bone morphogenetic protein (BMP). The implication of FST in the proliferation, angiogenesis, and metastasis of solid tumors documents good or poor outcome of patients with BC. However, the role of FST in TNBC remains unclear.

Methods

Data of 935 patients with breast cancer (BC) were extracted from TCGA. Case–control study, Kaplan–Meier, uni-multivariate COX, and ROC curve were utilized to investigate the relationship between FST expression and the clinical characteristics and prognosis of BC. Functional studies were used to analyze the effect of FST expression on proliferation, apoptosis, migration, and invasion of TNBC cell lines. Bioinformatic methods such as volcanoplot, GO annd KEGG enrichment, and protein–protein interactions (PPI) analyses were conducted to further confirm the different roles of FST in the apoptotic pathways among mesenchymal and mesenchymal stem-like cells of TNBC.

Results

Data from TCGA showed that low FST expression correlated with poor prognosis (for univariate analysis, HR = 0.47, 95% CI: 0.27–0.82, p = 0.008; for multivariate analysis, HR = 0.40, 95% CI: 0.21–0.75, p = 0.004). Low FST expression provided high predicted value of poor prognosis in TNBC amongst BCs. FST knockdown promoted the proliferation, migration and invasion of BT549 and HS578T cell lines. FST inhibited the apoptosis of mesenchymal cells by targeting BMP7.

Conclusions

Low FST expression is associated with poor prognosis of patients with TNBC. FST expressions exhibit the anisotropic roles of apoptosis between mesenchymal and mesenchymal stem-like cells but promote the proliferation, migration, invasion in both of two subtypes of TNBC in vitro. FST may be a subtype-heterogeneous biomarker for monitoring the progress of TNBC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01977-x.