Assessment of MGP gene expression in cancer and contribution to prognosis

Caiado, Helena; Cancela, M. Leonor; Conceição, Natércia

doi:10.1016/j.biochi.2023.06.004

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…Additionally, in this manuscript, we investigated four differentially expressed genes: MUC1 [ 61 ], MGP [ 62 ], C1QA [ 63 ] and IGFBP5 [ 64 ] ( Figure 5D ). MUC1 encodes an adhesion protein that typically forms a protective mucous layer on the surface of epithelial cells.…”

Section: Resultsmentioning

confidence: 99%

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics

Lei,

Han,

Wang

et al. 2024

Briefings in Bioinformatics

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The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model’s attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.

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Section: Resultsmentioning

confidence: 99%

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics

Lei,

Han,

Wang

et al. 2024

Briefings in Bioinformatics

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“…Further examination of genes with pronounced boundary gradients revealed their biological significance in cancer progression. For instance, MGP, whose gene expression was found to be negatively associated with patients' overall survival, is a promising prognostic biomarker [38]. Similarly, increased CD24 expression in breast cancer tissues compared to normal tissues, as observed in TCGA, underscores its potential as a potential biomarker for prognosis [39].…”

Section: Spatial Wombling Enables the Identification Of Cliff Genesmentioning

confidence: 99%

Investigating spatial dynamics in spatial omics data with StarTrail

Chen,

Xiong,

Sun

et al. 2024

Preprint

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Spatial omics technologies revolutionize our view of biological processes within tissues. However, existing methods fail to capture localized, sharp changes characteristic of critical events (e.g. tumor development). Here, we present StarTrail, a novel gradient based method that powerfully defines rapidly changing regions and detects "cliff genes", genes exhibiting drastic expression changes at highly localized or disjoint boundaries. StarTrail, the first to leverage spatial gradients for spatial omics data, also quantifies directional dynamics. Across multiple datasets, StarTrail accurately delineates boundaries (e.g., brain layers, tumor-immune boundaries), and detects cliff genes that may regulate molecular crosstalk at these biologically relevant boundaries but are missed by existing methods. For instance, StarTrail precisely pinpointed the cancer-immune interface in a HER2+ breast cancer dataset, unveiled key cliff genes including a potential prognostic biomarker IGSF3, highlighting NK-, B-cell mediated immunity, and B cell receptor signaling pathways missed by all spatial variable gene methods attempted. StarTrail, filling important gaps in current literature, enables deeper insights into tissue spatial architecture.

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Nanogenosensors based on aptamers and peptides for bioelectrochemical cancer detection: an overview of recent advances in emerging materials and technologies

Kangarshahi,

Naghib

2024

Discov Appl Sci

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Electrochemical biosensors fabricated based on nucleic acids have shown great potential for cancer recognition because of their low cost, fast feedback, high sensitivity, and easy operation. This review will demonstrate the impression of recent advances and applications of electrochemical biosensors that are nucleic acid-based for cancer detection. We compare electrochemical biosensors formulated on nucleic acids with those formed on antibodies and highlight some examples of electrochemical biosensors developed on nucleic acids for cancer detection, such as biosensors that use DNA or RNA aptamers to detect prostate-specific antigens, microRNA-21, or carcinoembryonic antigens. We discuss the rewards and drawbacks of these biosensors and the challenges they face, such as stability, reproducibility, interference, and standardization. We also suggest some possible directions and opportunities for future research and development, such as developing novel nucleic acid recognition elements, exploring new transducer materials and configurations, designing new signal amplification strategies, integrating electrochemical biosensors with microfluidic devices or portable instruments, and evaluating electrochemical biosensors in clinical settings with actual samples from cancer patients or healthy donors. Overall, we believe that electrochemical biosensors that are nucleic acid-based offer an auspicious alternative to conventional methods for cancer detection and have great potential to contribute to early diagnosis and effective cancer treatment. Graphical Abstract

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Assessment of MGP gene expression in cancer and contribution to prognosis

Cited by 6 publications

References 21 publications

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics

Investigating spatial dynamics in spatial omics data with StarTrail

Nanogenosensors based on aptamers and peptides for bioelectrochemical cancer detection: an overview of recent advances in emerging materials and technologies

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