Advancing proteomics and machine learning in the clinic: an editorial on “Noninvasive proteomic biomarkers for alcohol-related liver disease”

HepatoBiliary Surg Nutr 2022;11(5):762-765 | https://dx.doi. org/10.21037/hbsn-22-390 Alcohol-associated liver disease (ALD) is increasingly prevalent throughout the world (1). It remains the leading cause of liver-related morbidity and mortality worldwide and contributes to approximately 6% of global deaths (2). ALD encompasses a wide range of hepatic pathologies, including steatosis, steatohepatitis, fibrosis, and cirrhosis (3). Given the severe lack of therapeutic interventions available to reverse the progression of ALD, it is currently the leading indication for liver transplantation (4). Advancements within the field are needed to identify diagnostic biomarkers for early detection and to develop therapeutics to ameliorate hepatic dysfunction and pathologies associated with ALD. The consequence of alcohol toxicity and resulting biochemical perturbations in hepatocytes include disruptions in genetic, proteomic, and metabolic processes, which are largely driven by alcohol metabolism, oxidative stress, and chronic inflammation (5,6). Given the lack of therapeutic options, identifying biological mechanisms and novel targets remains a key focus in the field. Because the liver coordinates whole-body homestasis, hepatic dysfunction and disease progression also influence extrahepatic tissues, in part, via circulating factors in the blood (3). Considering these factors, utilizing high sensitivity approaches to profile proteomic signatures more thoroughly in various compartments of the body are required to address current gaps in knowledge. Importantly, through the novel characterization of clinical biopsies and samples, researchers can discover altered proteomic and metabolomic pathways which will aid in the development of therapies to ameliorate ALD (7,8). In their article in Nature Medicine, Niu et al. provided an innovative approach profiling paired liver tissue and plasma samples through mass spectrometry (MS)-based proteomics in conjunction with clinical characterization of a large patient cohort of 596 individuals consisting of patients that were alcohol misusers, had early-stage asymptomatic ALD, or were healthy controls (9). In an effort to identify novel diagnostic biomarkers, Niu et al. identified key markers from pathways related to fibrosis, inflammation, and steatosis consistent with other studies (Figure 1) (10-12). Here, they made use of a cutting-edge open-source interactive data exploration tool to identify biomarker panels associated with clinical presentation. Most importantly, the authors developed a machine learning pipeline to deeply examine their plasma proteomics dataset, yielding an exceptional platform for future innovation in classifying diagnostic, prognostic, and therapeutic features in a timely manner related to the clinical presentation of ALD. The robustness of the current study, combined with the identification of novel biomarkers associated with ALD provides an exciting example of the ongoing integration of proteomics analyses within the clinic. One drawback of OMIC-based clinical analysis is the lack of standardized rigorous methodology for translating high-dimensional and meaningful data that can influence clinical outcomes. While providing targets for future Editorial