Interacting partners of FEN1 and its role in the development of anticancer therapeutics

Prediction of transient and permanent protein interactions using AI methods

Protein-protein interactions (PPIs) can be classified as permanent or transient interactions based on their stability or lifetime. Understanding the precise details of such protein interactions will pave the way for the discovery of inhibitors and for understanding the nature and function of PPIs. In the present work, 43 relevant physicochemical, geometrical and structural features were calculated for a curated dataset from the literature, comprising of 402 protein-protein complexes of permanent and transient categories, and 5 different Supervised Machine Learning models were developed with Scikit-learn to predict transient and permanent PPI. Additionally, deep learning method with Artificial Neural Network was also performed using Tensor Flow and Keras. Predicted models achieved accuracy ranging from 76.54% to 82.71% and k-NN has achieved the highest accuracy. Detailed analysis of these methods revealed that Interface areas such as Percent interface accessible area, Interface accessible area and Total interface area and the parameters defining the shape of the PPI interface such as Planarity, Eccentricity and Circularity are the most discriminating factors between these two categories. The present method could serve as an effective tool to understand the mechanism of protein association and to predict the transient and permanent interactions, which could supplement the costly and time-consuming experimental techniques.

A highly sensitive homogeneous electrochemiluminescence biosensor for flap endonuclease 1 based on branched hybridization chain reaction amplification and ultrafiltration separation

A sensitive homogeneous electrochemiluminescence (ECL) biosensor for flap endonuclease 1 (FEN1) detection was developed by combining highly sensitive ECL detection, high efficiency of branched hybridization chain reaction (BHCR) amplification, a convenient homogeneous strategy, and simple ultrafiltration separation. Magnetic beads were first modified with well-designed double flap DNAs containing 5'-flaps. In the presence of FEN1, the 5'-flap can be cleaved, and a large amount of single-stranded DNA can be produced, which can be separated easily from the double-flap DNA-modified beads by a magnet. Then, the cleaved 5'-flap can be used to initiate BHCR amplification to produce a large amount of long-strand dsDNA. Ru(phen)₃²⁺ can insert dsDNA to form Ru-dsDNAs, which can be easily separated from the main solution through ultrafiltration. The ECL signal from the separated Ru-dsDNAs has a good linear relationship with the logarithm of the FEN1 concentration ranging from 6.5 × 10^-2 ∼ 6.5 × 10³ U/L with a detection limit of 2.2 × 10^-2 U/L. The proposed biosensor was used to evaluate FEN1 activity in real samples with satisfactory results.

The prognostic significance of Flap Endonuclease 1 (FEN1) in breast ductal carcinoma in situ

Background

Impaired DNA repair mechanism is one of the cancer hallmarks. Flap Endonuclease 1 (FEN1) is essential for genomic integrity. FEN1 has key roles during base excision repair (BER) and replication. We hypothesised a role for FEN1 in breast cancer pathogenesis. This study aims to assess the role of FEN1 in breast ductal carcinoma in situ (DCIS).

Methods

Expression of FEN1 protein was evaluated in a large (n = 1015) well-characterised cohort of DCIS, comprising pure (n = 776) and mixed (DCIS coexists with invasive breast cancer (IBC); n = 239) using immunohistochemistry (IHC).

Results

FEN1 high expression in DCIS was associated with aggressive and high-risk features including higher nuclear grade, larger tumour size, comedo type necrosis, hormonal receptors negativity, higher proliferation index and triple-negative phenotype. DCIS coexisting with invasive BC showed higher FEN1 nuclear expression compared to normal breast tissue and pure DCIS but revealed significantly lower expression when compared to the invasive component. However, FEN1 protein expression in DCIS was not an independent predictor of local recurrence-free interval.

Conclusion

High FEN1 expression is linked to features of aggressive tumour behaviour and may play a role in the direct progression of DCIS to invasive disease. Further studies are warranted to evaluate its mechanistic roles in DCIS progression and prognosis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06271-y.

Systematic Analysis of the Transcriptome Profiles and Co-Expression Networks of Tumour Endothelial Cells Identifies Several Tumour-Associated Modules and Potential Therapeutic Targets in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third most common cause of cancer-related death, with tumour associated liver endothelial cells being thought to be major drivers in HCC progression. This study aims to compare the gene expression profiles of tumour endothelial cells from the liver with endothelial cells from non-tumour liver tissue, to identify perturbed biologic functions, co-expression modules, and potentially drugable hub genes that could give rise to novel therapeutic targets and strategies. Gene Set Variation Analysis (GSVA) showed that cell growth-related pathways were upregulated, whereas apoptosis induction, immune and inflammatory-related pathways were downregulated in tumour endothelial cells. Weighted Gene Co-expression Network Analysis (WGCNA) identified several modules strongly associated to tumour endothelial cells or angiogenic activated endothelial cells with high endoglin (ENG) expression. In tumour cells, upregulated modules were associated with cell growth, cell proliferation, and DNA-replication, whereas downregulated modules were involved in immune functions, particularly complement activation. In ENG⁺ cells, upregulated modules were associated with cell adhesion and endothelial functions. One downregulated module was associated with immune system-related functions. Querying the STRING database revealed known functional-interaction networks underlying the modules. Several possible hub genes were identified, of which some (for example FEN1, BIRC5, NEK2, CDKN3, and TTK) are potentially druggable as determined by querying the Drug Gene Interaction database. In summary, our study provides a detailed picture of the transcriptomic differences between tumour and non-tumour endothelium in the liver on a co-expression network level, indicates several potential therapeutic targets and presents an analysis workflow that can be easily adapted to other projects.

Lung cancer: progression of heat shock protein 70 in association with flap endonuclease 1 protein

Lung cancer is one of the leading causes of cancer deaths worldwide and existing approaches are not enough to manage, and hence, it is important to concentrate on new drug strategies. This study was aimed to identify the interacting partner of Flap endonuclease 1 (FEN1) and its role in cancer treatment. We identified a new FEN1 interacting partner confirmed it as Heat Shock Protein 70 (HSP 70), and its effect on FEN1 expression, in vitro. Additionally, we found that the 5-Fluorouracil's (5-FU) function was significantly improved when used in combination with HSP 70 inhibitor (KNK 437). The findings are interesting, elucidating the synergistic mechanism between two compounds which helps to develop a novel management strategy for over-expressed FEN1 in the lung.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-020-02598-3.

Jianpi-yangwei decoction inhibits DNA damage repair in the drug resistance of gastric cancer by reducing FEN1 expression

Background

Flap Endonuclease 1(FEN1) has been considered as a new tumor marker in recent years and Jianpi Yangwei Decoction (JPYW) is a basic Traditional Chinese Medicine (TCM) for the treatment of gastric cancer. This study aimed to explore the role of FEN1-mediated DNA damage repair in the drug resistance of gastric cancer and the effect of JPYW on it by employing BGC823/5-Fu drug-resistant cell model.

Methods

The DNA repair efficiency of BGC823 and BGC823/5-Fu was compared intracellularly and extracellularly using an extrachromosomal assay system and the reconstituted base excision repair assay. By comparing gene and protein expression and identifying cell survival rates after knockdown or high expression of FEN1, the correlation between FEN1 high expression and 5-Fluorouracil (5-Fu) drug resistance was revealed. The effect of JPYW on DNA damage repair and FEN1 expression was observed by the degree of γ-H2AX phosphorylation in the cells, DNA repair efficiency and enzyme activity, et al.

Results

BGC823/5-Fu had a higher DNA repair efficiency than BGC823(P < 0.001), which proved to be both intracellular and extracellular. FEN1 was highly expressed in BGC823/5-Fu regardless of gene level(P < 0.001) or protein level. Furthermore, manipulating FEN1 altered the sensitivity of cancer cells to chemotherapeutic drug 5-Fu. Different concentrations of JPYW were used to investigate the inhibitory effect on the expression of FEN1 and DNA damage repair. JPYW inhibited DNA damage repair both intracellularly and extracellularly: the phosphorylation of γ-H2AX increased, with more DNA damage in the cells; the synthetic 8-oxo dG damage repair was reduced; and the ability of cell lysates to repair DNA damage decreased. The decrease of FEN1 expression in BGC823/5-Fu had a concentration dependent relationship with JYPW. In addition, JPYW inhibited the activity of FEN1 at the enzymatic level, as the amount of cut-off synthetic ³²p labeled DNA substrates were decreased.

Conclusion

FEN1 was highly expressed in drug-resistance gastric cancer cells BGC823/5-Fu, which leading to BGC823 resistant to (5-Fu) by acting on DNA damage repair. JPYW inhibited DNA damage repair and reversed 5-Fu drug resistance by reducing FEN1 expression and inhibiting FEN1 functional activity.

Identification of Key Genes Related to Lung Squamous Cell Carcinoma Using Bioinformatics Analysis

Lung squamous cell carcinoma (LUSC) is often diagnosed at the advanced stage with poor prognosis. The mechanisms of its pathogenesis and prognosis require urgent elucidation. This study was performed to screen potential biomarkers related to the occurrence, development and prognosis of LUSC to reveal unknown physiological and pathological processes. Using bioinformatics analysis, the lung squamous cell carcinoma microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs). Furthermore, PPI and WGCNA network analysis were integrated to identify the key genes closely related to the process of LUSC development. In addition, survival analysis was performed to achieve a prognostic model that accomplished good prediction accuracy. Three hundred and thirty–seven up–regulated and 119 down-regulated genes were identified, in which four genes have been found to play vital roles in LUSC development, namely CCNA2, AURKA, AURKB, and FEN1. The prognostic model contained 5 genes, which were all detrimental to prognosis. The AUC of the established prognostic model for predicting the survival of patients at 1, 3, and 5 years was 0.692, 0.722, and 0.651 in the test data, respectively. In conclusion, this study identified several biomarkers of significant interest for additional investigation of the therapies and methods of prognosis of lung squamous cell carcinoma.

Versatile Types of DNA-Based Nanobiosensors for Specific Detection of Cancer Biomarker FEN1 in Living Cells and Cell-Free Systems

Flap structure-specific endonuclease 1 (FEN1) is overexpressed in various types of human cancer cells and has been recognized as a promising biomarker for cancer diagnosis in the recent years. In order to specifically detect the abundance and activity of this cancer-overexpressed enzyme, different types of DNA-based nanodevices were created during our investigations. It is shown in our studies that these newly designed biosensors are highly sensitive and specific for FEN1 in living cells as well as in cell-free systems. It is expected that these nanoprobes could be useful for monitoring FEN1 activity in human cancer cells, and also for cell-based screening of FEN1 inhibitors as new anticancer drugs.