Quantitative Prediction of Steatosis in Patients with Non-Alcoholic Fatty Liver by Means of Hepatic MicroRNAs Present in Serum and Correlating with Hepatic Fat

Cluster-Partial Least Squares (c-PLS) regression analysis: Application to miRNA and metabolomic data

BACKGROUND

Biomedicine and biological research frequently involve analyzing large datasets generated by high-throughput technologies like genomics, transcriptomics, miRNomics, and metabolomics. Pathway analysis is a common computational approach used to understand the impact of experimental conditions, phenotypes, or interventions on biological pathways and networks. This involves statistical analysis of omic data to identify differentially expressed variables and mapping them onto predefined pathways. Analyzing such datasets often requires multivariate techniques to extract meaningful insights such as Partial Least Squares (PLS). Variable selection strategies like interval-PLS (iPLS) help improve understanding and predictive performance by identifying informative variables or intervals. However, iPLS is suboptimal to treat omic data such as metabolic or miRNA profiles, where features cannot be distributed along a continuous dimension describing their relationships as in e.g., vibrational or nuclear magnetic resonance spectroscopy.

RESULTS

This study introduces a novel variable selection approach called cluster PLS (c-PLS) that aims to assess the joint impact of variable groups selected based on biological characteristics (such as miRNA-regulated metabolic pathway or lipid classes) on the predictive performance of a multivariate model. The usefulness of c-PLS is shown using miRNomic and metabolomic datasets obtained from the analysis of 24 liver tissue biopsies collected in the frame of a clinical study of steatosis.

SIGNIFICANCE AND NOVELTY

Results obtained show that c-PLS enables analyzing the effect of biologically relevant variable clusters, facilitating the identification of biological processes associated with the independent variable, and the prioritization of the biological factors influencing model performance, thereby improving the understanding of the biological factors driving model predictions. While the strategy is tested for the evaluation of PLS models, it could be extended to other linear and non-linear multivariate models.

Serum microRNA Levels as a Biomarker for Diagnosing Non-Alcoholic Fatty Liver Disease in Chinese Colorectal Polyp Patients

Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases and its prevalence is increasing worldwide. It is reported that NAFLD is associated with colorectal polyps. Since identifying NAFLD in its early stages could prevent possible disease progression to cirrhosis and decrease the risk of HCC by early intervention, patients with colorectal polyp may thus be considered a target group for screening NAFLD. This study aimed to investigate the potential of serum microRNAs (miRNAs) in identifying NAFLD for colorectal polyp patients. Serum samples were collected from 141 colorectal polyp patients, of which 38 had NAFLD. The serum level of eight miRNAs was determined by quantitative PCR and delta Ct values of different miRNA pairs which were compared between NAFLD and control groups. A miRNA panel was formulated from candidate miRNA pairs by multiple linear regression model and ROC analysis was performed to evaluate its diagnostic potential for NAFLD. Compared to the control group, the NAFLD group showed significantly lower delta Ct values of miR-18a/miR-16 (6.141 vs. 7.374, p = 0.009), miR-25-3p/miR-16 (2.311 vs. 2.978, p = 0.003), miR-18a/miR-21-5p (4.367 vs. 5.081, p = 0.021) and miR-18a/miR-92a-3p (8.807 vs. 9.582, p = 0.020). A serum miRNA panel composed of these four miRNA pairs significantly identified NAFLD in colorectal polyp patients with an AUC value of 0.6584 (p = 0.004). The performance of the miRNA panel was further improved to an AUC value of 0.8337 (p < 0.0001) when polyp patients with other concurrent metabolic disorders were removed from the analysis. The serum miRNA panel is a potential diagnostic biomarker for screening NAFLD in colorectal polyp patients. This serum miRNA test could be performed for colorectal polyp patients for early diagnosis and for prevention of the disease from progressing into more advanced stages.