Liver Fibrosis: New Insights and Potential for Early Diagnosis

-


Chronic liver disease (CLD) is a major health problem worldwide, affecting around 1.5 billion people. It's often caused by things like viral hepatitis, excessive alcohol consumption and metabolic issues linked to fatty liver disease. When the liver is chronically damaged and inflamed, it can lead to fibrosis, a condition where excessive scar tissue builds up in the liver. This fibrosis can progress to cirrhosis, a severe form of liver damage, which increases the risk of liver cancer. Sadly, liver fibrosis is often diagnosed late, when the damage is quite advanced, due to a lack of clear symptoms and effective ways to identify those at high risk. This new study looks into the complex molecular processes involved in liver fibrosis and tries to find ways to diagnose it earlier using non-invasive methods.

What the researchers did: The study used a technique called proteo-transcriptomics, which looks at both the proteins and the genetic material (RNA) in the liver and blood. Researchers analysed liver tissue and plasma samples from 330 people, including 40 healthy individuals and 290 patients with varying stages of liver fibrosis due to different causes, including chronic viral infection, alcohol consumption, and metabolic dysfunction-associated steatotic liver disease (MASLD). The goal was to identify the specific molecular changes linked to advanced fibrosis and find potential biomarkers that could be used for diagnosis.

Key findings:
  • Changes in the liver: The study found that in advanced fibrosis, there's an increase in pathways related to the extracellular matrix (ECM), the material that surrounds cells, and pathways related to inflammation and immune response. In contrast, metabolic pathways involved in energy production were reduced in cirrhosis. Specific genes linked to liver cells involved in fibrosis, like hepatic stellate cells and myofibroblasts, were also found to be more active.
  • Cellular composition: Using single-cell deconvolution, a way of estimating cell types, the researchers found that the number of certain cells decreased, such as hepatocytes (the main liver cells) and some immune cells in advanced fibrosis, while others like scar-associated macrophages (SAMs) and certain types of T-cells increased.
  • Blood proteins: The study identified 132 proteins in the blood that were linked to advanced fibrosis. Some proteins increased as fibrosis got worse, while others changed at different stages. Many of the altered proteins were linked to inflammation and immune response, which highlights the role of inflammation in the progression of liver disease.
  • Biomarkers: The researchers created machine learning models using these protein changes to predict advanced fibrosis and cirrhosis. They found that the proteins neurofascin (NFASC) and growth differentiation factor 15 (GDF15) were particularly good at predicting advanced fibrosis. The model using these proteins performed better than the standard FIB-4 test, which is a commonly used blood test to evaluate liver fibrosis.
  • Etiology: Although there were some differences in gene expression in the liver depending on the cause of the fibrosis, the overall patterns were quite similar. In particular, genes related to host-virus interactions were more active in patients with viral hepatitis. Additionally, some proteins were changed in the plasma of patients with alcohol-related liver disease (ARLD) compared to those with chronic viral hepatitis (CVH), but not the corresponding liver mRNA expression levels.

What it means: This study provides several important insights into liver fibrosis:
  • It shows that the molecular changes associated with advanced fibrosis are complex, involving multiple pathways and cell types.
  • It identifies several blood proteins that can be used as biomarkers for the early diagnosis of advanced fibrosis and cirrhosis.
  • It demonstrates that machine learning can be used to combine multiple protein measurements for improved disease prediction.
  • The identification of NFASC and GDF15 as strong predictors of advanced fibrosis could lead to the development of more accurate non-invasive tests for liver fibrosis.

Limitations the researchers acknowledge some limitations to their study: The study mainly included patients with advanced fibrosis and cirrhosis, so the findings might not be as applicable to people with early stages of liver disease. Second, the protein analysis didn't capture all the proteins that could be important in liver fibrosis and third, the findings need to be validated in larger, more diverse populations, as the study participants were mainly from specific geographic regions.

In conclusion: This study significantly advances our understanding of the molecular processes involved in liver fibrosis, highlighting the importance of immune response and ECM remodeling. By combining different types of data and using machine learning techniques, the research team were able to identify circulating proteins that could help predict advanced fibrosis and cirrhosis with improved accuracy compared to existing methods. These findings offer hope for earlier diagnosis and potential for more effective treatments in the future. The research also highlights the complexity of the disease and indicates that more work is needed to understand the differences related to the causes of liver fibrosis. This study is a significant step towards improving the care of people with chronic liver disease and reducing the impact of this major health concern.
 
Additional information: Integrative proteo-transcriptomic characterization of advanced fibrosis in chronic liver disease across etiologies. Cell Reports Medicine (2025). 10.1016/j.xcrm.2025.101935

Location: Oxford, UK

Comments

Post a Comment

Popular posts from this blog

Unlocking insights: simplifying single-cell data with Strand’s scRNA portal

-
Image
Image credit:   https://scrna-curation.mystrand.org/ Simplifying single-cell data exploration with Strand’s scRNA portal In the rapidly advancing world of single-cell RNA sequencing (scRNA-seq), navigating complex datasets can be a major challenge. Strand’s scRNA Portal is designed to transform this challenge into an opportunity by harmonizing publicly available datasets, making disease-specific omics data accessible, actionable, and ready for discovery. Why strand’s scRNA portal was built 1) Focus on unmet needs in key diseases Starting with datasets with diseases with significant unmet clinical need: Inflammatory bowel disease :  Covering Ulcerative Colitis and Crohn’s Disease. Neurodegenerative disorders :  Including Alzheimer’s Disease, Parkinson’s Disease and Frontotemporal Dementia. 2)  The rise of scRNA-seq in aiding precision medicine:  Single-cell RNA sequencing provides unprecedented insights into cellular heterogeneity, which is pivotal for identifyi...
Read more

Understanding Your Immune System: A Lifespan Journey

-
Image
Image credit:   https://openai.com/index/dall-e/   Have you ever wondered how your immune system changes as you get older? It’s not just about catching colds, our immune system is always working hard to protect us and it’s constantly evolving throughout our lives. A new study has looked at how our immune system changes from birth right through to old age, providing some fascinating insights. What the study did : Scientists looked at blood samples from a large group of healthy people, ranging from newborns to those over 90 years old. They used advanced techniques to study the different types of immune cells in these samples. They looked at how these cells behave and change over time. This is called a "single-cell atlas," because they looked at each cell individually. Key findings Immune cells change over time: The study identified 25 different types of immune cells in the blood. The number of each type of cell changes throughout life. Some become more commo...
Read more

Cracking the code of itchy skin: A new way to diagnose and treat skin problems

-
Image
a : The ridgeline plots show the activity scores of different immune response types (Th1, Th2, Th17, type I interferon, neutrophil-related, macrophage-related, and eosinophil-related responses) in skin samples from different inflammatory skin diseases (Psoriasis, Atopic Dermatitis, Lichen Planus, Cutaneous Lupus Erythematosus, Neutrophilic Dermatosis, and Wells Syndrome). The solid lines mark the point where each response type is considered active. b : Box plots compare the immune response scores in skin samples across all these diseases for the "sentinel" (reference) biopsies. Each dot is one sample. The number of samples for each condition is listed: Psoriasis (25), Atopic Dermatitis (16), etc., along with healthy controls (8). c : Box plots show the immune response scores for "test" samples (a different set of biopsies). Sample numbers for each disease are also listed, like Psoriasis (8) and Atopic Dermatitis (15). b–c : In the box plots, the middle line shows th...
Read more