Resurgence of phosphotyrosine binding domains: Structural and functional properties essential for understanding disease pathogenesis

Padarti, Akhil; Abou‐Fadel, Johnathan; Zhang, Jun

doi:10.1016/j.bbagen.2021.129977

Cited by 4 publications

(1 citation statement)

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“…It is made biomarkers, providing insights into the progression of the disease. Finally, elevated CCM protein levels in tumors night suggest their viability as biomarkers across multiple cancers [49][50][51][52][53][54][55][56][57][58][59]. Both main types of progesterone receptors (nPRs and mPRs) might serve as potential biomarkers for genetically influenced diverse cancer subtypes [60][61][62][63][64][65][66][67][68].…”

Section: Introductionmentioning

confidence: 99%

Identification of Cholangiocarcinoma (CCA) Subtype-Specific Biomarkers

Croft,

Gao,

Quintanar

et al. 2023

Preprint

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Liver cancer ranks sixth globally in diagnoses and second in cancer-related deaths. Cholangiocarcinoma (CCA), a relatively rare cancer originating from bile duct epithelium, constitutes 2% of all cancers, with increasing occurrences in Westerns. Incidence is influenced by inflammation, genetics, risk factors, and regional disparities, with higher rates in the Eastern hemisphere. Diagnostic and prognostic biomarkers are pivotal for effective cancer prevention and management. Recent research explores serum proteins for non-invasive CCA diagnosis and proposes targeted receptor approaches for therapeutics.This study aims to identify these biomarkers via bioinformatics analysis of public datasets, focusing on CCA patient transcriptomes to uncover gene biomarkers linked to age and survival. Pathway analysis reveals functions and pathways associated with these biomarkers. Additionally, the study employs the ESM-TFpredict machine learning model to predict transcription factors (TFs) using protein sequence data.Leveraging publicly available data enhances our understanding of liver cancer’s molecular profiles and clinical relevance, particularly concerning CCA, This study integrates bioinformatics analysis, transcriptomic exploration, and machine learning to unveil a novel set of potential diagnostic and prognostic biomarkers for CCA.

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