Circular RNA-EPHB4 As A Potential Biotarget In Colorectal Cancer: A Preliminary Analysis

The Modulator of Apoptosis 1 (MOAP-1) protein, a crucial regulator of apoptosis, has recently attracted considerable interest in oncology research. Its potential as a therapeutic target in bladder, breast, and non-small cell lung cancer (NSCLC) offers intriguing new therapeutic approaches. This comprehensive review consolidated work on MOAP-1's role in cancer biology, notably as a therapeutic target. A systematic review was conducted by searching digital databases for studies. Based on inclusion and exclusion criteria, ten articles were thoroughly assessed. Emerging evidence links MOAP-1 to bladder cancer chemosensitivity and MOAP-1-dependent chemosensitization of breast cancer mediated by phenylquinazoline derivatives. In addition, apoptosis in breast cancer cells was induced by α-mangostin, a potential therapeutic drug targeting MOAP-1 and BCL-XL interaction. Studies showed MOAP-1's importance in NSCLC, with its upregulation in the cancer cells as a potential treatment. This review also highlights the role of miR-25 as a cellular regulator of MOAP-1 and its implication for MOAP-1-mediated therapeutics. The intricate control of MOAP-1 in cancer highlights the need for more research to understand its function and regulation across cancer types completely. MOAP-1's tumor suppressor activity suggests it could be a therapeutic target, as evidenced by current research findings, which is likely to spur further research into MOAP-1-targeted cancer treatments. This systematic review fills a gap in the literature by bringing together a variety of study findings to promote a deeper understanding of MOAP-1 and its therapeutic potential.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Therapeutic Targeting of MOAP-1 in Cancer: A Systematic Review of Current Approaches and Future Directions

Koong,

Look,

Abdullah

et al. 2023

“…Deep learning, which involves using artificial neural networks structured in layers to enable learning, has found application in cancer research [15][16][17] and other medical fields such as dental research and molecular biology [18][19][20][21][22][23][24] . The study includes two deep learning models: SDAE and VAE, to validate and assess the feature subsets selected by SVM-RFE in 2.4.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Support Vector Machine – Recursive Feature Elimination for Feature Selection on Multi-omics Lung Cancer Data

Azman,

A Samah,

Lin

et al. 2023

Biological data obtained from sequencing technologies is growing exponentially. Multi-omics data is one of the biological data that exhibits high dimensionality, or more commonly known as the curse of dimensionality. The curse of dimensionality occurs when the dataset contains many features or attributes but with significantly fewer samples or observations. The study focuses on mitigating the curse of dimensionality by implementing Support Vector Machine – Recursive Feature Elimination (SVM-RFE) as the selected feature selection method in the lung cancer (LUSC) multi-omics dataset integrated from three single omics dataset comprising genomics, transcriptomics and epigenomics, and assess the quality of the selected feature subsets using SDAE and VAE deep learning classifiers. In this study, the LUSC datasets first undergo data pre-processing, including checking for missing values, normalization, and removing zero variance features. The cleaned LUSC datasets are then integrated to form a multi-omics dataset. Feature selection was performed on the LUSC multi-omics data using SVM-RFE to select several optimal feature subsets. The five smallest feature subsets (FS) are used in classification using SDAE and VAE neural networks to assess the quality of the feature subsets. The results show that all 5 VAE models can obtain an accuracy and AUC score of 1.000, while only 2 out of 5 SDAE models (FS 1000 & 4000) can do so. 3 out of 5 SDAE models have an AUC score of 0.500, indicating zero capability in separating the binary class labels. The study concludes that a fine-tuned supervised learning VAE model has better capability in classification tasks compared to SDAE models for this specific study. Additionally, 1000 and 4000 are the two most optimal feature subsets selected by the SVM-RFE algorithm. The SDAE and VAE models built with these feature subsets achieve the best classification results.

“…MicroRNA and DNA methylation biomarkers also contribute to diagnostic precision, with dysregulated miRNA expression patterns and epigenetic alterations serving as indicators of cancer types and prognostic factors [8][9][10][11][12] . Apart from the standard biomarkers, emerging evidence suggests the involvement of circular RNAs (circRNAs) in the regulation of cancer cell proliferation, invasion, angiogenesis, and metastasis [13,14] . The integration of these molecular biomarkers into cancer diagnostics enhances early detection, subtyping, prognosis assessment, and personalized treatment strategies, ultimately improving the clinical management and outcomes for individuals affected by breast cancer and beyond.…”

Section: Introductionmentioning

confidence: 99%

Identification of Potential Biomarkers and Metabolic Changes in the Serum of Breast Lump Patients Among Kelantanese Based on 1H NMR Metabolomics

Zulkefly,

Wan Azman,

Omar

et al. 2023

This case-control study, conducted at the Hospital USM BestARi unit, aimed to identify the serum metabolic fingerprint among individuals with breast lumps and healthy controls, and to discover potential biomarkers. Serum samples from healthy controls, benign breast lump patients, and malignant breast lump patients were analyzed using proton nuclear magnetic resonance spectroscopy (1H NMR). A multivariate data analysis approach was employed, with the OPLS-DA and clustered heat map techniques effectively differentiating between the three groups. The study revealed significant metabolite variations across the groups and proposed D-glucose, glycerol, and glycine as potential biomarkers for breast cancer diagnosis. Metabolic pathways such as alanine, aspartate, glutamate metabolism, and glycine, serine, and threonine metabolism were implicated. The metabolomics approach coupled with multivariate analysis successfully identified key metabolites leading to group separation and suggested altered metabolic pathways. However, further research and integration with other ‘omics’ technologies are necessary for clinical translation.