Immunobiology. 2026 Aug 28;231(5):153239. doi: 10.1016/j.imbio.2026.153239. Online ahead of print.

ABSTRACT

Chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) frequently coexist, yet distinguishing inflammatory changes secondary to malignancy from primary pancreatitis remains challenging. Here, we present an integrated multi-omics analysis of spatially distinct pancreatic tissue compartments obtained from a single patient undergoing pancreaticoduodenectomy for PDAC, combining histopathology, transcriptomics, immunofluorescence, and meta-transcriptomic microbial profiling. Morphological assessment identified tumor tissue, adjacent CP, and normal pancreas. Transcriptomic profiling of macro-dissected samples revealed differential gene expression and enrichment of the classical complement pathway in the CP compartment, which was qualitatively supported by immunofluorescent detection of C1q and C3 along the ductal epithelium. Meta-transcriptomic analysis detected a limited number of bacterial taxa and no viral RNA; these findings were interpreted conservatively given the constraints of low-biomass tissue profiling and the single-patient design. Although causal inference and generalizability are limited by the single-patient design, this study demonstrates the feasibility of integrating pathology with transcriptomic and microbial analyses to generate a hypothesis-generating, multi-omics framework for exploring inflammatory-malignant interactions in pancreatic disease.

PMID:42679433 | DOI:10.1016/j.imbio.2026.153239

Clin Chim Acta. 2026 Mar 12;587:120973. doi: 10.1016/j.cca.2026.120973. Online ahead of print.

ABSTRACT

Hepatic fibrosis is a dynamic and progressive condition that can lead to cirrhosis and hepatocellular carcinoma (HCC) if left untreated. Appropriate assessment of the disease progression of fibrosis is critical for early intervention and individualized treatment regimens. Traditional biopsy techniques are invasive and prone to sampling errors, highlighting the need for less invasive predictive techniques. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as key regulators of hepatic fibrogenesis and as a possible biomarker for disease staging and prognosis. The emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized the comprehensive large-scale analysis of transcriptomic data, enhancing the identification of ncRNA biomarkers and predictive modeling. The AI-based algorithms have been found to be more precise in anticipating fibrosis progression by means of integrating multi-omics data, ncRNA interaction networks, and by improving non-invasive diagnostic tools. This review involves the analysis of AI and ncRNA research in hepatic fibrosis, highlighting recent discoveries, possible challenges, and future opportunities. We address the necessity of standardization of data and clinical validation, as well as discuss the role of AI in identifying biomarkers of ncRNA, predicting the stage of fibrosis and risk stratification. ncRNA analysis with AI has a tremendous potential of transforming the diagnostics and prognostics of hepatic fibrosis, enabling precision hepatology.

PMID:41831666 | DOI:10.1016/j.cca.2026.120973

Clin Chim Acta. 2026 Mar 12;587:120973. doi: 10.1016/j.cca.2026.120973. Online ahead of print.

ABSTRACT

Hepatic fibrosis is a dynamic and progressive condition that can lead to cirrhosis and hepatocellular carcinoma (HCC) if left untreated. Appropriate assessment of the disease progression of fibrosis is critical for early intervention and individualized treatment regimens. Traditional biopsy techniques are invasive and prone to sampling errors, highlighting the need for less invasive predictive techniques. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as key regulators of hepatic fibrogenesis and as a possible biomarker for disease staging and prognosis. The emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized the comprehensive large-scale analysis of transcriptomic data, enhancing the identification of ncRNA biomarkers and predictive modeling. The AI-based algorithms have been found to be more precise in anticipating fibrosis progression by means of integrating multi-omics data, ncRNA interaction networks, and by improving non-invasive diagnostic tools. This review involves the analysis of AI and ncRNA research in hepatic fibrosis, highlighting recent discoveries, possible challenges, and future opportunities. We address the necessity of standardization of data and clinical validation, as well as discuss the role of AI in identifying biomarkers of ncRNA, predicting the stage of fibrosis and risk stratification. ncRNA analysis with AI has a tremendous potential of transforming the diagnostics and prognostics of hepatic fibrosis, enabling precision hepatology.

PMID:41831666 | DOI:10.1016/j.cca.2026.120973