Aim: To study a more accurate quantification of hepatic fibrosis which would provide clinically useful information for monitoring the progression of chronic liver disease.
Methods: Using a cDNA microarray containing over 22000 clones, we analyzed the gene-expression profiles of non-cancerous liver in 74 patients who underwent hepatic resection. We calculated the ratio of azan-stained: total area, and determined the morphologic fibrosis index (MFI), as a mean of 9 section-images. We used the MFI as a reference standard to evaluate our method for assessing liver fibrosis.
Results: We identified 39 genes that collectively showed a good correlation (r > 0.50) between gene-expression and the severity of liver fibrosis. Many of the identified genes were involved in immune responses and cell signaling. To quantify the extent of liver fibrosis, we developed a new genetic fibrosis index (GFI) based on gene-expression profiling of 4 clones using a linear support vector regression analysis. This technique, based on a supervised learning analysis, correctly quantified the various degrees of fibrosis in both 74 training samples (r = 0.76, 2.2% vs 2.8%, P < 0.0001) and 12 independent additional test samples (r = 0.75, 9.8% vs 8.6%, P < 0.005). It was far better in assessing liver fibrosis than blood markers such as prothrombin time (r = -0.53), type IV collagen 7s (r = 0.48), hyaluronic acid (r = 0.41), and aspartate aminotransferase to platelets ratio index (APRI) (r = 0.38).
Conclusion: Our cDNA microarray-based strategy may help clinicians to precisely and objectively monitor the severity of liver fibrosis.