Abstract:Loss of heterozygosity (LOH) on chromosome 10 frequently occurs in gliomas. Whereas genetic loci with allelic deletion often implicate tumor suppressor genes, a putative tumor suppressor Adducin3 (ADD3) mapped to chromosome 10q25.2 was found to be preferentially downregulated in high-grade gliomas compared with low-grade lesions. In this study, we unveil how the assessment of ADD3 deletion provides clinical significance in glioblastoma (GBM). By deletion mapping, we assessed the frequency of LOH in forty-three… Show more
“…Although the distance between PARG and SHLD2 is longer in the human genome (37 versus 2 MB), both genes are located on chromosome 10q, and recurrent arm-level deletions in this region have been reported in multiple cancer types, such as lymphomas, glioblastoma, and lung cancer. 66 , 67 , 68 , 69 , 70 …”
“…Although the distance between PARG and SHLD2 is longer in the human genome (37 versus 2 MB), both genes are located on chromosome 10q, and recurrent arm-level deletions in this region have been reported in multiple cancer types, such as lymphomas, glioblastoma, and lung cancer. 66 , 67 , 68 , 69 , 70 …”
“…Essential for actin cytoskeleton assembly, ADD3 deficiency in GBM cells triggered pro-angiogenic signaling, enhancing VEGFR expression in endothelial cells, which could have implications for angiogenesis in LGG [47]. Suggested as a tumor suppressor and survival predictor on chromosome 10q, ADD3 was valuable for prognostic assessments in LGG [48]. In kidney-related cancers, miR-126 has been recognized for its strong prognostic potential in clear cell renal cell carcinoma (ccRCC) [49], while miR-1271, markedly upregulated in ccRCC tissues, emerges as a significant marker for assessing disease severity [50].…”
Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus posing significant obstacles in multi-omics data integration. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation method and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks, then applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we design a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.
“…The top identified genes for the GBM/LGG cancer align with recent findings. For instance, the SDR39U1 was found as one of the prognostic factors in LGG using a multivariate Cox regression model; 21 ADD3 is predictor of shorter survival and has significant association with tumor grade; 22 DHX33 is discovered to be highly expressed in glioblastoma multiforme; 23 the EMP2 , one of the PMP22 gene families, is up-regulated in GBM/LGG; 24 SLC25A16 is a mitochondrial carrier protein that is highly suppressed in GBM/LGG; 25 ZNF286A is one of the zinc-finger proteins (an oncogene in GBM/LGG); 26 ENO2 is a highly expressed gene in GBM/LGG with high prognosis factor; 27 PAR s are a family of genes that are highly correlated with short survival in GBM/LGG. 28 Figure 4 Risk scores of genes in GBM/LGG cancer identified from the model The global risk score of the most influential genes computed with 5-fold cross-validation.…”
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