Biology subjects

Croft, J.

Publications and source records attributed to Croft, J..

5 recordsLinked to original sources

bioRxiv2023

Novel hepatocellular carcinomas (HCC) Subtype-Specific Biomarkers

IntroductionHepatocellular carcinoma (HCC), the most common form of liver cancer, is a global health concern and a leading cause of cancer-related deaths. HCC accounts for a significant portion of liver cancers and has low survival rates of 5% to 30%, especially for HCC patients with a survival rate of 15%. Early detection is challenging due to the absence of symptoms in the early stages. The complexity and molecular diversity of HCC contribute to its poor prognosis. Understanding its molecular subtypes and mechanisms is crucial for improved management. MethodsThe study utilized publicly available data to investigate the potential diagnostic and prognostic biomarkers for hepatocellular carcinoma (HCC) based on their transcript per million (TPM) expression levels. A dataset of 407 HCC patient profiles was analyzed for survival trends and gene expression patterns. ResultsThrough a comprehensive approach, over 900 potential prognostic candidates were identified. Further analysis narrowed down 647 prognostic and diagnostic candidate biomarkers. The study also explored the role of the CmPn signaling network in HCC, reaffirming that its components could act as prognostic markers. Additionally, the study-utilized machine learning to discover 102 transcription factors (TFs) associated with HCC, from candidate biomarkers. ConclusionsThe findings provide insights into the molecular basis of HCC and offer potential avenues for improved diagnosis, treatment, and patient outcomes. The expanded prognostic biomarker pool aids in pinpointing HCC-specific grading and staging biomarkers, facilitating targeted therapies for improved patient outcomes and survival rates

cancer biology↗

bioRxiv2023

Identification of Cholangiocarcinoma (CCA) Subtype-Specific Biomarkers

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 cancers 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.

systems biology↗

bioRxiv2023

Updated Biomarkers for TNBC in African vs. Caucasian American Women

IntroductionBreast cancer, especially triple-negative breast cancer (TNBC), is a significant concern in the US, being the most common cancer among women and the second leading cause of cancer-related deaths. TNBC lacks crucial receptors targeted in other breast cancer types, leading to a poor prognosis and limited treatment options due to its aggressive and heterogeneous nature. SignificanceAfrican American women (AAW) with TNBC face higher mortality rates and more aggressive disease compared to Caucasian American women (CAW). Despite efforts to find biomarkers specific to AAW and CAW with TNBC, limited sample availability and data resources have been obstacles. MethodsIn our study, we examined 237 candidate peptide biomarkers using publicly available data. Resultsidentify 23 unique prognostic biomarkers. These biomarkers accurately assess patient conditions based on race-specific gene expression patterns, holding potential to address racial disparities in TNBC treatment. ConclusionOverall, our research sheds light on how racial genetic profiles influence TNBC prognosis and treatment efficacy. The identified prognostic biomarkers pave the way for future studies addressing TNBC racial disparities and personalized treatment approaches.

genetics↗

bioRxiv2023

Whole-genome Omics delineates the function of CCM1 within the CmPn networks

IntroductionCerebral cavernous malformations (CCMs) are abnormal dilations of brain capillaries that increase the risk of hemorrhagic strokes. Mutations in the KRIT1, MGC4607, and PDCD10 genes cause CCMs, with mutations in CCM1 accounting for about 50% of familial cases. The disorder exhibits incomplete penetrance, meaning that individuals with CCM may appear normal initially, but once symptoms manifest, their brains have already suffered irreversible damage. Compromised blood-brain barrier (BBB) is crucial in regulating the flow of substances between the blood and the central nervous system, which can result in hemorrhagic CCMs. Progesterone and its derivatives have been studied for their impact on maintaining BBB integrity. CCM2 interacts with CCM1 and CCM3, forming the CCM signaling complex (CSC), which connects classic and non-classic progesterone signaling to establish the CmPn signaling network, vital in preserving BBB integrity. MethodsThe study aimed to explore the relationship between CCM1 and key pathways of the CmPn signaling network, utilizing a toolset comprising three mouse embryonic fibroblast lines (MEFs) with distinct CCM1 expression levels. Omics and systems biology analysis were performed to investigate Ccm1-mediated signaling within the CmPn signaling network. ResultsThe findings suggest that CCM1 plays a critical role in controlling cellular processes in response to different progesterone-mediated actions within CmPn/CmP signaling networks, partly by regulating gene transcription. This function is crucial for preserving the integrity of microvessels, indicating that targeting CCM1 could hold promise as a therapeutic approach for this condition.

molecular biology↗

bioRxiv2023

Blood prognostic biomarker signatures for hemorrhagic cerebral cavernous malformations (CCMs)

BackgroundCerebral cavernous malformations (CCMs) is a neurological disorder that causes enlarged intracranial capillaries in the brain, leading to an increased risk of hemorrhagic strokes, which is a leading cause of death and disability worldwide. ObjectivesThe current treatment options for CCMs are limited, highlighting the need for prognostic biomarkers to predict the risk of hemorrhagic events to better inform treatment decisions and identify future pharmacological targets. Eligibility CriteriaThe study is centered on a comparative proteomic analysis between hemorrhagic CCMs (HCs) and healthy controls, while excluding patients with non-hemorrhagic CCMs (NHCs) from the analysis due to the experimental design. Sources of EvidenceRecent research has identified several serum biomarkers and blood circulating biomarkers in a selected cohort of homogeneous CCM patients and animal models. MethodProteomic profiles from both human and mouse CCM models were examined, and pathway enrichment analyses were performed using three approaches (GO, KEGG, and DOSE). To account for multiple comparisons, t-tests were employed to evaluate differences. A p-value below 0.05 was deemed statistically significant. ResultsThe authors have developed the first panel of candidate biomarker signatures, featuring both etiological and prognostic markers in two distinct pathways. This panel of biomarker signatures demonstrates a robust correlation with the likelihood of hemorrhagic CCMs. ConclusionsThis groundbreaking biomarker panel paves the way for further investigation of potential blood biomarkers to determine the risk of hemorrhagic CCMs.

genomics↗