Identification of SLC7A11-AS1/SLC7A11 pair as a ferroptosis-related therapeutic target for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), a prevalent malignancy worldwide, poses significant challenges in terms of prognosis, necessitating innovative therapeutic approaches. Ferroptosis offers notable advantages over apoptosis, holding promise as a novel therapeutic approach for HCC complexities. Moreover, while the interaction between long non-coding RNAs (lncRNAs) and mRNAs is pivotal in various physiological and pathological processes, their involvement in ferroptosis remains relatively unexplored. In this study, we constructed a ferroptosis-related lncRNA-mRNA correlation network in HCC using Pearson correlation analysis. Notably, the SLC7A11-AS1/SLC7A11 pair, exhibiting high correlation, was identified. Bioinformatics analysis revealed a significant correlation between the expression levels of this pair and key clinical characteristics of HCC patients, including gender, pathology, Ishak scores and tumour size. And poor prognosis was associated with high expression of this pair. Functional experiments demonstrated that SLC7A11-AS1, by binding to the 3'UTR region of SLC7A11 mRNA, enhanced its stability, thereby promoting HCC cell growth and resistance to erastin- induced ferroptosis. Additionally, in vivo studies confirmed that SLC7A11-AS1 knockdown potentiated the inhibitory effects of erastin on tumour growth. Overall, our findings suggest that targeting the SLC7A11-AS1/SLC7A11 pair holds promise as a potential therapeutic strategy for HCC patients.

PDFll: Predictors of Disorder and Function of Proteins from the Language of Life.

The identification of intrinsically disordered proteins and their functional roles is largely dependent on the performance of computational predictors, necessitating a high standard of accuracy in these tools. In this context, we introduce a novel series of computational predictors, termed PDFll (Predictors of Disorder and Function of proteins from the Language of Life), which are designed to offer precise predictions of protein disorder and associated functional roles based on protein sequences. PDFll is developed through a two-step process. Initially, it leverages large-scale protein language models (pLMs), trained on an extensive dataset comprising billions of protein sequences. Subsequently, the embeddings derived from pLMs are integrated into streamlined, yet sophisticated, deep-learning models to generate predictions. These predictions notably surpass the performance of existing state-of-the-art predictors, particularly those that forecast disorder and function without utilizing evolutionary information.

CBGDA: a manually curated resource for gene-disease associations based on genome-wide CRISPR.

The field of understanding the association between genes and diseases is rapidly expanding, making it challenging for researchers to keep up with the influx of new publications and genetic datasets. Fortunately, there are now several regularly updated databases available that focus on cataloging gene-disease relationships. The development of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 system has revolutionized the field of gene editing, providing a highly efficient, accurate, and reliable method for exploring gene-disease associations. However, currently, there is no resource specifically dedicated to collecting and integrating the latest experimentally supported gene-disease association data derived from genome-wide CRISPR screening. To address this gap, we have developed the CRISPR-Based Gene-Disease Associations (CBGDA) database, which includes over 200 manually curated gene-disease association data derived from genome-wide CRISPR screening studies. Through CBGDA, users can explore gene-disease association data derived from genome-wide CRISPR screening, gaining insights into the expression patterns of genes in different diseases, associated chemical data, and variant information. This provides a novel perspective on understanding the associations between genes and diseases. What is more, CBGDA integrates data from several other databases and resources, enhancing its comprehensiveness and utility. In summary, CBGDA offers a fresh perspective and comprehensive insights into the research on gene-disease associations. It fills the gap by providing a dedicated resource for accessing up-to-date, experimentally supported gene-disease association data derived from genome-wide CRISPR screening. Database URL: http://cbgda.zhounan.org/main.

O-GlcNAcylation regulates the stability of transferrin receptor (TFRC) to control the ferroptosis in hepatocellular carcinoma cells.

Ferroptosis is an iron-dependent programmed cell death (PCD) enforced by lipid peroxidation accumulation. Transferrin receptor (TFRC), one of the signature proteins of ferroptosis, is abundantly expressed in hepatocellular carcinoma (HCC). However, post-translational modification (PTM) of TFRC and the underlying mechanisms for ferroptosis regulation remain less understood. In this study, we found that TFRC undergoes O-GlcNAcylation, influencing Erastin-induced ferroptosis sensitivity in hepatocytes. Further mechanistic studies found that Erastin can trigger de-O-GlcNAcylation of TFRC at serine 687 (Ser687), which diminishes the binding of ubiquitin E3 ligase membrane-associated RING-CH8 (MARCH8) and decreases polyubiquitination on lysine 665 (Lys665), thereby enhancing TFRC stability that favors labile iron accumulation. Therefore, our findings report O-GlcNAcylation on an important regulatory protein of ferroptosis and reveal an intriguing mechanism by which HCC ferroptosis is controlled by an iron metabolism pathway.

Identification of glycogene-based prognostic signature and validation of B3GNT7 as a potential biomarker and therapeutic target in breast cancer.

BACKGROUND: Breast cancer is the most common cancer worldwide, with the fifth highest mortality rate among all cancers and high risk of metastasis. However, potential biomarkers and molecular mechanisms underlying the stratification of breast cancer in terms of clinical outcomes remain to be investigated. Therefore, we aimed to find a novel prognostic biomarker and therapeutic target for breast cancer patients. METHODS: Unsupervised hierarchical clustering was used to perform comprehensive transcriptomic study of total 185 glycogenes in public datasets of breast cancer with clinicopathological and survival information. A glycogene-based signature for subtype classification was discovered using Limma packages, and relevance to four known molecular features was identified by GSVA. Experimental verification was performed and biological functions of B3GNT7 were characterized by quantitative RT-PCR, western blot, transwell assays, and lectin immunofluorescence staining in breast cancer cells. RESULTS: A 23-glycogene signature was identified for the classification of breast cancer. Among the 23 glycogenes, B3GNTs showed significantly positive associations with ER-/Her2- subtype in breast cancer patients (n = 2655). Overexpressed B3GNT7 were correlated with poor prognosis in breast cancer patients based on public datasets. B3GNT7 depletion inhibited cell proliferation, migration, and invasion, and decreased global fucosylation in MDA-MB-231 and HCC1937 breast cancer cells. CONCLUSIONS: Herein, we discovered a unique 23-gene signature for breast cancer patient glycogene-type classification. Among these genes, B3GNT7 was shown to be a potential biomarker for unfavorable outcomes and therapeutic target of breast cancer.