The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes and targeted treatment. Furthermore, numerous studies have highlighted the capability of graph neural networks (GNN) to model complex biological systems and capture non-linear interactions in high-throughput data. GNN has proven to be useful in leveraging multiple types of omics data, including prior biological knowledge from various sources, such as transcriptomics, genomics, proteomics, and metabolomics, to improve cancer classification. However, current works do not fully utilize the non-linear learning potential of GNN and lack of the integration ability to analyse high-throughput multi-omics data simultaneously with prior biological knowledge. Nevertheless, relying on limited prior knowledge in generating gene graphs might lead to less accurate classification due to undiscovered significant gene-gene interactions, which may require expert intervention and can be time-consuming. Hence, this study proposes a graph classification model called associative multi-omics graph embedding learning (AMOGEL) to effectively integrate multi-omics datasets and prior knowledge through GNN coupled with association rule mining (ARM). AMOGEL employs an early fusion technique using ARM to mine intra-omics and inter-omics relationships, forming a multi-omics synthetic information graph before the model training. Moreover, AMOGEL introduces multi-dimensional edges, with multi-omics gene associations or edges as the main contributors and prior knowledge edges as auxiliary contributors. Additionally, it uses a gene ranking technique based on attention scores, considering the relationships between neighbouring genes. Several experiments were performed on BRCA and KIPAN cancer subtypes to demonstrate the integration of multi-omics datasets (miRNA, mRNA, and DNA methylation) with prior biological knowledge of protein-protein interactions, KEGG pathways and Gene Ontology. The experimental results showed that the AMOGEL outperformed the current state-of-the-art models in terms of classification accuracy, F1 score and AUC score. The findings of this study represent a crucial step forward in advancing the effective integration of multi-omics data and prior knowledge to improve cancer subtype classification.

As the primary microtubule organizing center in animal cells, centrosome abnormalities are involved in human colon cancer.

To explore the role of centrosome-related genes (CRGs) in colon cancer.

CRGs were collected from public databases. Consensus clustering analysis was performed to separate the Cancer Genome Atlas cohort. Univariate Cox and least absolute shrinkage selection operator regression analyses were performed to identify candidate prognostic CRGs and construct a centrosome-related signature (CRS) to score colon cancer patients. A nomogram was developed to evaluate the CRS risk in colon cancer patients. An integrated bioinformatics analysis was conducted to explore the correlation between the CRS and tumor immune microenvironment and response to immunotherapy, chemotherapy, and targeted therapy. Single-cell transcriptome analysis was conducted to examine the immune cell landscape of core prognostic genes.

A total of 726 CRGs were collected from public databases. A CRS was constructed, which consisted of the following four genes: TSC1, AXIN2, COPS7A, and MTUS1. Colon cancer patients with a high-risk signature had poor survival. Patients with a high-risk signature exhibited decreased levels of plasma cells and activated memory CD4+ T cells. Regarding treatment response, patients with a high-risk signature were resistant to immunotherapy, chemotherapy, and targeted therapy. COPS7A expression was relatively high in endothelial cells and fibroblasts. MTUS1 expression was high in endothelial cells, fibroblasts, and malignant cells.

We constructed a centrosome-related prognostic signature that can accurately predict the prognosis of colon cancer patients, contributing to the development of individualized treatment for colon cancer.

Mitochondria play a fundamental role in cell survival and motility. Abnormalities in mitochondria are associated with carcinogenesis, especially with tumor metastasis. In this study, we explore the biological function of ATIP1, which is a mitochondrial-located isoform of angiotensin II AT2 receptor interacting proteins (ATIPs) in prostate cancer cells. The results showed that ATIP is downregulated in prostate cancer tissues and is negatively correlated with the disease-free survival rate of prostate cancer patients. Silencing of ATIP promotes mitochondrial fission and enhances tumor cell migration and invasion. Reconstitution of ATIP1 in ATIP-deficient cells significantly attenuates mitochondrial trafficking and tumor cell movement. Therefore, ATIP1 is a negative regulator of mitochondrial dynamics and tumor cell motility and is also a potential biomarker for predicting prostate cancer malignancy.

Tumor suppressors represent a critical line of defense against tumorigenesis. Their mechanisms of action and the pathways they are involved in provide important insights into cancer progression, vulnerabilities, and treatment options. Although nuclear and cytosolic tumor suppressors have been extensively investigated, relatively little is known about tumor suppressors localized within the mitochondria. However, recent research has begun to uncover the roles of these important proteins in suppressing tumorigenesis. Here, we review this newly developing field and summarize available information on mitochondrial tumor suppressors.