Mohammadreza Akbarian Khorasgani
Qilu Hospital of Shandong University, ChinaPresentation Title:
Integrative immunometabolic profiling for predicting immune checkpoint inhibitor response in hepatocellular carcinoma: A retrospective–prospective multi-omics study
Abstract
Immune checkpoint Inhibitors (ICIs) have transformed the therapeutic landscape of Hepatocellular Carcinoma (HCC), yet treatment responses remain highly variable, and existing biomarkers such as PD-L1 expression offer limited predictive accuracy. Recent evidence highlights the central role of immunometabolic pathways—including T-cell exhaustion, metabolic reprogramming, mitochondrial dysfunction, and cytokine-driven immune regulation—in modulating ICI effectiveness within the tumor microenvironment. Integrating immune and metabolic indicators may therefore provide a more precise and biologically relevant framework for predicting treatment outcomes. To address this need, the present study aims to develop and validate a comprehensive immunometabolic predictive model for ICI response in HCC using a hybrid retrospective–prospective design.
In the retrospective arm (n = 60), key clinical variables (TNM stage, HBV status, BMI) were analyzed alongside immune markers such as PD-L1 and CD8 expression via Immunohistochemistry (IHC), and circulating cytokines including IFN-γ and IL-6 measured through ELISA. Metabolic indicators—Lactate Dehydrogenase (LDH), glucose, and triglycerides—were obtained from patient records and archived serum samples at Qilu Hospital. In the prospective arm (n = 15), fresh or cryopreserved tumor biopsies from patients undergoing ICI therapy were processed for RNA extraction, followed by cDNA synthesis and SYBR Green–based quantitative PCR to quantify expression of core immunometabolic genes including PD-L1, GLUT1, IDO1, CPT1A, TIGIT, and IFN-γ. Data integration employed multivariate logistic regression, Principal Component Analysis (PCA), and machine learning algorithms, with predictive robustness assessed through Receiver Operating Characteristic (ROC) curve analysis and k-fold cross-validation.
The study is expected to generate a clinically deployable biomarker panel with ≥85% sensitivity and specificity for distinguishing ICI responders from non-responders. It will also identify molecular signatures linking gene expression profiles to therapeutic outcomes and develop a Stratified Immunotherapy Guidance Flowchart to support personalized treatment decisions. This integrative immunometabolic framework holds strong translational potential in precision oncology and may serve as a platform for expanding predictive strategies to other solid tumors.
Biography
MohammadReza Akbarian Khorasgani is a senior medical intern currently completing his MBBS degree at Shandong University, where he is undergoing clinical training at Qilu Hospital, one of the leading tertiary medical centers in China. His academic focus spans cardiology, gastroenterology and interdisciplinary clinical research. Throughout his medical training, he has demonstrated strong commitment to scientific investigation and evidence-based medicine.
He has authored three peer-reviewed publications in international medical journals and has contributed to multiple clinical research projects involving immunometabolism, cancer prediction models and cardiovascular medicine. In addition, two of his research abstracts have been officially accepted for presentation at the International Conference on Biomedicine (ICB) 2025, reflecting the global recognition of his scientific work. His ongoing projects include the development of multi-omics predictive models for immunotherapy response, retrospective cohort studies in cardiology and gastrointestinal oncology, and machine-learning–based clinical decision tools.
He has authored three peer-reviewed publications in international medical journals and has contributed to multiple clinical research projects involving immunometabolism, cancer prediction models and cardiovascular medicine. In addition, two of his research abstracts have been officially accepted for presentation at the International Conference on Biomedicine (ICB) 2025, reflecting the global recognition of his scientific work. His ongoing projects include the development of multi-omics predictive models for immunotherapy response, retrospective cohort studies in cardiology and gastrointestinal oncology, and machine-learning–based clinical decision tools.
Beyond research, MohammadReza has participated actively in academic competitions, hospital-based quality improvement activities, and interdisciplinary collaborations with faculty and clinical supervisors. He aims to pursue advanced clinical training and residency abroad, with a long-term goal of contributing to precision medicine and translational research. His academic dedication, multicultural clinical experience, and growing research portfolio distinguish him as a promising young physician-scientist.
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