We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
20 resultsShowing papers similar to Endoplasmic Reticulum Stress-related Classification for Prognosis Prediction in Hepatocellular Carcinoma
ClearEndoplasmic Reticulum Stress-Related Signature for Predicting Prognosis and Immune Features in Hepatocellular Carcinoma
Researchers developed a four-gene endoplasmic reticulum stress-based prognostic model for hepatocellular carcinoma using bioinformatics approaches, finding that higher risk scores correlated with advanced tumor stage, HBV infection, and worse survival outcomes. The model also predicted differences in immune cell infiltration profiles, suggesting potential utility for guiding immunotherapy decisions.
Identification and validation of novel signature associated with hepatocellular carcinoma prognosis using Single-cell and WGCNA analysis
This study identified a novel gene signature associated with hepatocellular carcinoma using TCGA datasets and validated key molecular targets with potential prognostic and therapeutic significance. The findings advance understanding of the molecular mechanisms driving liver cancer progression.
Acute Endoplasmic Reticulum Stress Induces Inflammation Reaction, Complement System Activation, and Lipid Metabolism Disorder of Piglet Livers: A Proteomic Approach
Researchers used piglet liver models to show that acute endoplasmic reticulum stress triggers a cascade of inflammation, complement system activation, and lipid metabolism disruption, providing proteomic insights relevant to understanding stress-related liver disease mechanisms.
Correlation Between Tumor Differentiation and Biomarkers in Hepatocellular Carcinoma: Implications for Early Diagnosis and Treatment
Researchers examined the correlation between tumor differentiation levels and biomarker expression in patients with hepatocellular carcinoma. The study found significant associations between tumor grade and certain biomarker levels, suggesting these markers may have potential value for early diagnosis and treatment planning in liver cancer.
Environmental PET-microplastic exposure and risk of non-alcoholic fatty liver disease: An integrated computational toxicology and multi-omics study
Researchers used computational toxicology and machine learning to identify six key genes linking PET microplastic exposure to non-alcoholic fatty liver disease (NAFLD), with the model achieving high diagnostic accuracy and molecular docking suggesting that PET-derived chemicals may directly bind to proteins controlling liver fat metabolism.
Early Predictor Tool of Disease Using Label-Free Liquid Biopsy-Based Platforms for Patient-Centric Healthcare
Algorithmic analysis of patient-derived cell clusters from liquid biopsy samples was combined with tumor models to develop an early disease prediction tool applicable across cancer types. The approach offers a label-free, non-invasive method for early cancer detection that could supplement or reduce reliance on conventional tissue biopsy.
Exposure to microplastics and liver oncogenesis: A comprehensive review on molecular mechanisms and pathogenic pathways
Researchers reviewed mechanisms by which microplastic exposure may promote liver cancer, identifying oxidative stress, mitochondrial dysfunction, inflammatory signaling, and epigenetic disruption as key pathways, while noting that microplastics can also carry heavy metals and organic pollutants that synergistically amplify hepatotoxic and carcinogenic risk.
Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis
Researchers used machine learning and weighted gene co-expression network analysis on three public genomic datasets to identify biomarkers for diffuse large B-cell lymphoma prognosis. They identified key hub genes and constructed a risk model that could predict patient survival, though this study is not directly related to microplastics.
Mitochondrial Quality Control and Metabolic Reprogramming in Hepatocellular Carcinoma: Implications for Immunotherapy and Treatment Resistance
Scientists reviewed research showing that liver cancer cells damage the tiny energy factories (mitochondria) inside immune cells, making it harder for the body's natural defenses to fight the cancer. When immune cells can't get enough energy, they become "exhausted" and stop working properly against tumors. The researchers suggest that targeting these energy problems in cells could help improve cancer treatments and make immunotherapy work better for liver cancer patients.
Exploring the prognostic implications of PET microplastic degradation products in colorectal cancer: insights from an integrated computational analysis on glucocorticoid pathway–mediated mechanisms
Combining network toxicology, machine learning, and molecular docking, this study found that PET plastic degradation products ethylene glycol and terephthalic acid may influence colorectal cancer prognosis through 43 shared genes linked to TNF/IL-17 signaling and glucocorticoid-mediated metabolic pathways.
Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy
Researchers developed an ensemble learning model using rapid Raman spectroscopy to classify gastric cancer cell lines without staining or culturing, achieving high accuracy for automated cell line identification.
Exploring the prognostic implications of PET microplastic degradation products in colorectal cancer: insights from an integrated computational analysis on glucocorticoid pathway–mediated mechanisms
Researchers used network toxicology, machine learning, and molecular docking to investigate how PET degradation products—ethylene glycol and terephthalic acid—affect colorectal cancer prognosis through the glucocorticoid signaling pathway. The analysis identified 43 shared target genes, suggesting that PET breakdown products may worsen colorectal cancer outcomes by dysregulating glucocorticoid-mediated anti-inflammatory and cell survival signals.
BRCC36 Deubiquitinates HMGCR to Regulate the Interplay Between Ferroptosis and Pyroptosis
This study uncovered a molecular switch (an enzyme called BRCC36) that controls whether liver cancer cells die by ferroptosis or pyroptosis, two different forms of programmed cell death. While not directly about microplastics, ferroptosis has been identified as one of the ways nanoplastics damage cells in recent studies. Understanding how cells regulate ferroptosis could help explain why some tissues are more vulnerable to nanoplastic-induced damage than others.
The Effect of Plastic-Related Compounds on Transcriptome-Wide Gene Expression on CYP2C19-Overexpressing HepG2 Cells
Researchers examined how plastic-related compounds affect gene expression in liver cells overexpressing the drug-metabolizing enzyme CYP2C19, revealing transcriptome-wide changes that suggest plasticizers and additives may disrupt hepatic metabolic pathways.
Syringodium isoetifolium Fosters an Antioxidant Defense System, Modulates Glycolytic Enzymes and Protects Membrane Integrity in DEN-induced Hepatocellular Carcinoma in Albino Wistar Rats
This paper is not about microplastics; it investigates the anti-cancer properties of Syringodium isoetifolium seagrass extract in a rat model of liver cancer, finding reduced tumor growth and restored liver tissue architecture.
Exploring the prognostic implications of PET microplastic degradation products in colorectal cancer: insights from an integrated computational analysis on glucocorticoid pathway–mediated mechanisms
This computational study investigated how PET microplastic degradation products affect colorectal cancer prognosis, identifying 43 genes linking ethylene glycol and terephthalic acid exposure to cancer pathogenesis via chronic inflammation mediated through TNF/IL-17 and glucocorticoid metabolic pathways.
Characterization of Microplastics in Human Gastric Cancer and Control Tissues and Analysis of Associated Genetic Features
Researchers detected and characterized microplastics in human gastric cancer tissue and adjacent healthy tissue, finding significantly higher microplastic concentrations in cancer tissue, and used transcriptome sequencing to explore potential molecular mechanisms linking microplastic exposure to gastric cancer development.
Nanoplastic propels diet-induced NAFL to NASH via ER-mitochondrial tether-controlled redox switch
Researchers investigated how nanoplastic exposure may accelerate the progression of diet-induced fatty liver conditions in animal models. The study found that nanoplastics disrupted the connections between the endoplasmic reticulum and mitochondria, triggering oxidative stress responses that worsened liver inflammation and damage.
Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using Daphnia magna Transcriptomic Profiles
Researchers used transcriptomic profiles from Daphnia magna to train machine learning models capable of identifying and predicting toxicity from 22 organic pollutants in aquatic environments. The models outperformed conventional toxicity tests by providing mechanistic insights into how specific contaminants affect gene expression pathways.
Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review
Researchers systematically reviewed animal and human trial data on two diabetes drugs to assess how well preclinical tests predicted real-world liver toxicity, finding that standard animal studies failed to flag troglitazone's serious liver risks — while in vitro (lab-based) cell testing showed clear differences in the two drugs' toxic profiles. The findings argue for using mechanistic lab data earlier in drug development to prevent harm that animal tests alone may miss.