From RCD pathways to precision oncology: A spatially-aware approach in HCC

Abstract

This Letter to the Editor is being written to respond to the article by Yao et al. (2025), which explores the role of non-apoptotic regulatory cell death (RCD) pathways in hepatocellular carcinoma (HCC) and their application in molecular prognostication.

HCC is one of the most common primary liver cancers, which accounts for 600,000 deaths annually, with a prognosis of 500,000–1,000,000 new annual cases [1]; hence, it remains a major global health challenge. Despite the advent of immune checkpoint inhibitors (ICIs), therapeutic responses remain suboptimal. Many patients exhibit intrinsic or acquired resistance due to immunosuppressive tumor microenvironment (TME), impaired antigen presentation, tumor heterogeneity, and microbiome-driven influences [2].

Nonapoptotic RCD, which includes ferroptosis, pyroptosis, and necroptosis, was presented by Yao et al. as a new axis affecting tumor immune behavior and resistance mechanisms [3]. Weighted gene co-expression network (WGCNA) and nonnegative matrix factorization (NMF) were applied to stratify HCC patients into three subtypes based on RCD gene expression, with ramifications for survival, immune infiltration, and drug responsiveness. A six-gene prognostic model was proposed to differentiate high- and low-risk patients, providing a more dynamic alternative to static biomarkers [3, 4].

This study highlighted a significant advancement in functional stratification and precision oncology for HCC. However, by integrating spatial transcriptomics, the potential can be further amplified as this will allow anatomical mapping of gene expression within the TME. Offering a spatial lens through which to interpret molecular profiles, with site-specific risks, RCD-driven suppressive niches, and spatially distinct immune-excluded areas as discovered by this approach [5, 6]. For example, immune-cold tumor regions that usually escape detection can be visualized, targeted, and analyzed using site-adapted therapies.

Moreover, the synergy of spatial omics with AI-enhanced digital pathology holds promise for real-time, image-guided biomarker prediction and dynamic patient monitoring [7]. Such an integration opens a next-generation frontier for tailoring immunotherapies and predicting treatment response with unprecedented granularity.

We applaud the authors' efforts to connect clinical knowledge with computational biology, and we encourage future research to validate their model prospectively across diverse, multiethnic cohorts. Incorporating RCD-based stratification within spatially aware, AI-integrated frameworks could redefine prognostication and therapy personalization in HCC, ushering in a new era of precision oncology.

Haider Imran contributed to writing the manuscript and reviewed the manuscript. Zahra Ali Haque contributed to editing the manuscript and came up with the concept. Minhal Imaan contributed to writing the manuscript. Muhammad Aatir Aslam contributed to writing the manuscript.

The authors declare no conflict of interest.

The authors have nothing to report.