Biology subjects

Sheng, V.

Publications and source records attributed to Sheng, V..

3 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

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↗