Prioritizing predictive biomarkers for gene essentiality in cancer cells with mRNA expression data and DNA copy number profile

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.

Potential biomarkers in endometrial cancer: a narrative review

CONTEXT

Every year, approximately 0.4 million women suffer from endometrial cancer (EC) worldwide and it has become the most common gynecological malignancy. Almost 66% of EC cases are diagnosed at an early stage and can be cured by performing surgery while those at an advanced stage turns out to be fatal. Biomarkers of endometrial cancer would be very valuable for screening of women who are at high risk and in detecting the chance of recurrence of disease.

OBJECTIVE

The current article has reviewed studies published on expression of biomarkers and susceptibility to EC.

METHODS

Google Scholar and PubMed were used as searching platforms and we have majorly considered the literature from last 10 years.

RESULTS

Potential biomarkers of EC identified from various studies were summarised.