Identification of MAD2L1 and BUB1B as Potential Biomarkers Associated with Progression and Prognosis of Ovarian Cancer.

Ovarian cancer develops insidiously and is frequently diagnosed at advanced stages. Screening for ovarian cancer is an effective strategy for reducing mortality. This study aimed to investigate the molecular mechanisms underlying the development of ovarian cancer and identify novel tumor biomarkers for the diagnosis and prognosis of ovarian cancer. Three databases containing gene expression profiles specific to serous ovarian cancer (GSE18520, GSE12470, and GSE26712) were acquired. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were analyzed for the differentially expressed gene (DEGs). The protein-protein interaction (PPI) network was constructed using the STRING database. The pivotal genes in the PPI network were screened using the Cytoscape software. Survival curve analysis was performed using a Kaplan-Meier Plotter. The cancer genome atlas and Gene Expression Omnibus databases were used to find the relationship between Hub gene and serous ovarian cancer. PCR and immunohistochemistry were used to detect the expression of Hub gene in serous ovarian cancer tissues and cells. Downstream pathways of the candidate tumor marker genes were predicted using Gene Set Enrichment Analysis. In this study, 252 DEGs were screened for pathway enrichment. 20 Hub genes were identified. Survival analysis suggested that Aurka, Bub1b, Cenpf, Cks1b, Kif20a, Mad2l1, Racgap1, and Ube2c were associated with the survival of patients with serous ovarian cancer. MAD2L1 and BUB1B levels were significantly different in serous ovarian cancer at different stages. Finally, Mad2l1 was found to play a role in the cell cycle, oocyte meiosis, and ubiquitin-mediated proteolysis. Meanwhile, Bub1b may play a role in the cell cycle, ubiquitin-mediated proteolysis, and spliceosome processes. Mad2l1 and Bub1b could be used as markers to predict ovarian carcinogenesis and prognosis, providing candidate targets for the diagnosis and treatment of serous ovarian cancer.

Identification and validation of IRF6 related to ovarian cancer and biological function and prognostic value.

BACKGROUND: Ovarian cancer (OC) is a severe gynecological malignancy with significant diagnostic and therapeutic challenges. The discovery of reliable cancer biomarkers can be used to adjust diagnosis and improve patient care. However, serous OC lacks effective biomarkers. We aimed to identify novel biomarkers for OC and their pathogenic causes. METHODS: The present study used the differentially expressed genes (DEGs) obtained from the "Limma" package and WGCNA modules for intersection analysis to obtain DEGs in OC. Three hub genes were identified-claudin 3 (CLDN3), interferon regulatory factor 6 (IRF6), and prostasin (PRSS8)-by searching for hub genes through the PPI network and verifying them in GSE14407, GSE18520, GSE66957, and TCGA + GTEx databases. The correlation between IRF6 and the prognosis of OC patients was further confirmed in Kaplan-Miller Plotter. RT-qPCR and IHC confirmed the RNA and protein levels of IRF6 in the OC samples. The effect of IRF6 on OC was explored using transwell invasion and scratch wound assays. Finally, we constructed a ceRNA network of hub genes and used bioinformatics tools to predict drug sensitivity. RESULTS: The joint analysis results of TCGA, GTEx, and GEO databases indicated that IRF6 RNA and protein levels were significantly upregulated in serous OC and were associated with OS and PFS. Cell function experiments revealed that IRF6 knockdown inhibited SKOV3 cell proliferation, migration and invasion. CONCLUSION: IRF6 is closely correlated with OC development and progression and could be considered a novel biomarker and therapeutic target for OC patients.

Comparative analysis between high-grade serous ovarian cancer and healthy ovarian tissues using single-cell RNA sequencing.

Introduction: High-grade serous ovarian cancer (HGSOC) is the most common histological subtype of ovarian cancer, and is associated with high mortality rates. Methods: In this study, we analyzed specific cell subpopulations and compared different gene functions between healthy ovarian and ovarian cancer cells using single-cell RNA sequencing (ScRNA-seq). We delved deeper into the differences between healthy ovarian and ovarian cancer cells at different levels, and performed specific analysis on endothelial cells. Results: We obtained scRNA-seq data of 6867 and 17056 cells from healthy ovarian samples and ovarian cancer samples, respectively. The transcriptional profiles of the groups differed at various stages of ovarian cell development. A detailed comparison of the cell cycle, and cell communication of different groups, revealed significant differences between healthy ovarian and ovarian cancer cells. We also found that apoptosis-related genes, URI1, PAK2, PARP1, CLU and TIMP3, were highly expressed, while immune-related genes, UBB, RPL11, CAV1, NUPR1 and Hsp90ab1, were lowly expressed in ovarian cancer cells. The results of the ScRNA-seq were verified using qPCR. Discussion: Our findings revealed differences in function, gene expression and cell interaction patterns between ovarian cancer and healthy ovarian cell populations. These findings provide key insights on further research into the treatment of ovarian cancer.

Ultrasmall Gold Nanoclusters-Enabled Fabrication of Ultrafine Gold Aerogels as Novel Self-Supported Nanozymes.

Metal aerogels represent an emerging type of functional porous materials with promising applications in diverse fields, but the fabrication of metal aerogels with specific structure and property still remains a challenge. Here, the authors report a new approach to fabricate metal aerogels by using ultrasmall metal nanoclusters (NCs) as functional building blocks. By taking D-penicillamine-stabilized gold NCs (AuNCs) with a diameter of 1.4 nm as an example, Au aerogels with ultrafine ligament size (3.5 nm) and good enzyme-mimic properties are synthesized. Detailed characterization shows that the obtained Au aerogels possess typical 3D self-supported porous network structure with high gold purity and surface area. Time-lapse spectroscopic and microscopic monitoring of the gelation process reveal that these ultrasmall AuNCs first grow into large nanoparticles before fusion into nanowire networks, during which both pH and the precursor concentration are identified to be the determining factor. Owing to their highly porous structure and abundant metal nodes, these self-supported Au aerogels display excellent peroxidase-like properties. This work provides a strategy for fabricating advanced metal aerogels by taking ultrasmall-sized metal NCs as building blocks, which also opens new avenues for engineering the structure and properties of metal aerogels for further advancing their applications.

Weak Interaction-Tailored Catalytic Interface of Ultrasmall Gold Nanoclusters as Enzyme Mimics for Enhanced Colorimetric Biosensing.

Gold nanoclusters (AuNCs) represent an emerging type of engineered nanomaterials with intrinsic enzymatic activity for both chemical and biological applications, but the catalytic activity of most reported AuNCs remains rather limited. Herein, we report a new, efficient strategy of promoting the peroxidase-mimic activity of AuNCs by tailoring their catalytic interfaces via small molecule-mediated weak interactions. Inspired by the presence of imidazole structures in many biocatalytic centers, we screened a series of imidazole-containing small molecules to evaluate their impact on the enzymatic activity of AuNCs. Through monitoring the absorbance change of 3,3',5,5'-tetramethylbenzidine, 1-methyl-2-imidazolecarboxaldehyde (MCA) was identified to possess the most significant effect on enhancing the peroxidase-mimic activity of glutathione-stabilized AuNCs (GSH-AuNCs) among all the examined molecules. Interestingly, the enhancement effect of MCA on the catalytic activity of these AuNCs was found to be highly reversible and can be switched on/off by simply adding MCA/dialysis treatment. Molecular dynamics simulations and further experimental analysis confirmed that these MCA molecules were adsorbed on the surface of GSH-AuNCs through weak non-covalent interactions. The underlying mechanism analysis suggested that the presence of MCA can efficiently promote the production of •OH in the GSH-AuNC system. As a proof of example, we then demonstrated that the presence of MCA can greatly increase the bioanalytical performance of AuNC-based peroxidase mimics, as evidenced by a 65-fold lower LOD for glucose detection of AuNCs@MCA than that using AuNCs only. Finally, the present system has been successfully applied for sensing the blood glucose level of both healthy people and diabetics with promising results.

In Situ Investigation on the Protein Corona Formation of Quantum Dots by Using Fluorescence Resonance Energy Transfer.

A fundamental understanding of nanoparticle-protein corona and its interactions with biological systems is essential for future application of engineered nanomaterials. In this work, fluorescence resonance energy transfer (FRET) is employed for studying the protein adsorption behavior of nanoparticles. The adsorption of human serum albumin (HSA) onto the surface of InP@ZnS quantum dots (QDs) with different chirality (d- and l-penicillamine) shows strong discernible differences in the binding behaviors including affinity and adsorption orientation that are obtained upon quantitative analysis of FRET data. Circular dichroism spectroscopy further confirms the differences in the conformational changes of HSA upon interaction with d- and l-chiral QD surfaces. Consequently, the formed protein corona on chiral surfaces may affect their following biological interactions, such as possible protein exchange with serum proteins plasma as well as cellular interactions. These results vividly illustrate the potential of the FRET method as a simple yet versatile platform for quantitatively investigating biological interactions of nanoparticles.