Shifting from static to dynamic: the prognostic value of the surgical stress response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a formidable global health challenge. While radical resection offers the most promising treatment for a cure, the persistently high rates of postoperative recurrence highlight the critical need for precise, individualized prognostic biomarkers. In recent years, the interplay between the host immune-inflammatory system and tumor biology has taken center stage in oncology. Integrated staging models and nomograms have proven increasingly superior to traditional staging by incorporating systemic factors to accurately predict individual survival (1). Specifically, the systemic immune-inflammation index (SII), integrating platelet, neutrophil, and lymphocyte counts, has been widely recognized as a robust prognostic indicator in these comprehensive models (2). However, traditional markers like the baseline SII are inherently static. This static approach overlooks a profound physiological event: the surgical intervention itself.

In the recent issue of the Journal of Gastrointestinal Oncology, Zou and colleagues present a compelling study that challenges this static paradigm (3). They introduce a novel metric, the postoperative-to-preoperative SII ratio (ΔSII), to capture the dynamic magnitude of the surgical stress response. In their retrospective analysis of 244 patients undergoing curative-intent resection for HCC, the authors demonstrated that a high ΔSII (>6.839) on postoperative day 1 (POD1) is a powerful, independent predictor of poor overall survival (OS) and recurrence-free survival (RFS). Strikingly, the prognostic accuracy of the ΔSII significantly outperformed the conventional preoperative SII.

The physiological implications of this elevated ΔSII are particularly intriguing. Surgical trauma is not merely a mechanical event; it triggers a cascade of systemic inflammation and concurrent immunosuppression (4). The dramatic surge in SII observed in the high-ΔSII group reflects a dysregulated, maladaptive immune response. This early postoperative window, characterized by profound neutrophilia and lymphopenia, creates a favorable environment for circulating tumor cells to evade immune surveillance and establish micrometastases. By capturing this specific window, the ΔSII serves as a quantifiable surrogate for a patient’s vulnerability to surgically induced oncological progression.

From a surgical perspective, the study addresses a critical viewpoint: the relationship between the extent of surgical trauma and the inflammatory response. The authors found that a high ΔSII was strongly associated with more extensive procedures, including major hepatectomy and open surgical approaches. However, their stratified survival analysis revealed a profound insight: patients with a low ΔSII who underwent open surgery exhibited vastly superior survival compared to those with a high ΔSII undergoing laparoscopic surgery. Recent high-impact studies utilizing preoperative nomograms have repeatedly highlighted that physiological reserve and the anticipated surgical stress are as critical as tumor burden in determining survival (5-7). The ΔSII corroborates this, indicating that while surgical trauma influences the inflammatory response, it is the patient’s inherent, maladaptive inflammatory phenotype that ultimately dictates the oncological outcome.

Looking forward, the comparative analysis of ΔSII across different surgical modalities—namely open, conventional laparoscopic, and robotic-assisted surgery—emerges as a highly compelling future perspective. As robotic surgery continues to gain attention in hepato-biliary-pancreatic surgery, offering unprecedented precision and potentially minimizing tissue trauma even further than conventional laparoscopy, ΔSII could serve as an objective, quantitative benchmark for evaluating surgical invasiveness. Integrated predictive models are increasingly being used to guide treatment allocation for HCC (8). By systematically tracking dynamic indices like ΔSII, we can better deduce how the subtle differences in surgical trauma among these modalities translate into long-term oncological prognoses, thereby refining these integrated models to guide the optimal selection of surgical approaches for individual patients.

Beyond refining surgical techniques, the identification of a high-ΔSII cohort highlights a critical clinical challenge: developing effective interventions to actively improve the prognosis of these high-risk patients. Because a high ΔSII reflects a state of acute postoperative immunosuppression and dysregulated inflammation, this specific group may be the ideal target for adjuvant therapies aimed at bolstering anti-tumor immunity. In this context, the perioperative application of immune checkpoint inhibitors (ICIs) could represent a potential therapeutic option (9). Active investigation into how ICIs might counteract surgically induced immunological exhaustion will be essential. This approach could be the key to preventing early recurrence, effectively translating the ΔSII from a purely prognostic biomarker into a guide for targeted therapeutic intervention.

While the integration of the ΔSII into a nomogram provides a highly practical clinical tool, the study is not without limitations. The cohort is predominantly composed of hepatitis B virus (HBV)-related HCC patients from a single institution. External validation across diverse etiologies is a mandatory next step. Furthermore, measuring the inflammatory response solely on POD1, while practical, may not fully encapsulate the dynamic resolution of inflammation. Future prospective studies tracking serial perioperative indices will be essential to identify the most prognostic temporal window.

In conclusion, Zou et al. have made a highly valuable contribution by shifting the focus from static baseline characteristics to the dynamic physiological realities of the perioperative period. The ΔSII concept bridges the gap between surgical technique, postoperative immunology, and tumor biology. Through the continued refinement of minimally invasive surgical skills and targeted perioperative immunomodulation, we may soon be able to blunt the ΔSII spike, transforming a period of extreme physiological vulnerability into an opportunity for improved oncological control.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Gastrointestinal Oncology. The article did not undergo external peer review.

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0279/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Beumer BR, Buettner S, Galjart B, et al. Systematic review and meta-analysis of validated prognostic models for resected hepatocellular carcinoma patients. Eur J Surg Oncol 2022;48:492-9. [Crossref] [PubMed]
2. Chen KL, Qiu YW, Yang M, et al. Prognostic value of preoperative systemic immune-inflammation index/albumin for patients with hepatocellular carcinoma undergoing curative resection. World J Gastroenterol 2024;30:5130-51. [Crossref] [PubMed]
3. Zou Y, Jin M, Luo M, et al. ΔSII-based nomogram for prognosis prediction after radical resection for hepatocellular carcinoma. J Gastrointest Oncol 2026;17:22. [Crossref] [PubMed]
4. Tohme S, Simmons RL, Tsung A. Surgery for Cancer: A Trigger for Metastases. Cancer Res 2017;77:1548-52. [Crossref] [PubMed]
5. Wang YY, Xiang BD, Ma L, et al. Development and Validation of a Nomogram to Preoperatively Estimate Post-hepatectomy Liver Dysfunction Risk and Long-term Survival in Patients With Hepatocellular Carcinoma. Ann Surg 2021;274:e1209-17. [Crossref] [PubMed]
6. Berardi G, Morise Z, Sposito C, et al. Development of a nomogram to predict outcome after liver resection for hepatocellular carcinoma in Child-Pugh B cirrhosis. J Hepatol 2020;72:75-84. [Crossref] [PubMed]
7. Chan AWH, Zhong J, Berhane S, et al. Development of pre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection. J Hepatol 2018;69:1284-93. [Crossref] [PubMed]
8. Han JW, Lee SK, Kwon JH, et al. A Machine Learning Algorithm Facilitates Prognosis Prediction and Treatment Selection for Barcelona Clinic Liver Cancer Stage C Hepatocellular Carcinoma. Clin Cancer Res 2024;30:2812-21. [Crossref] [PubMed]
9. Qin S, Chen M, Cheng AL, et al. Atezolizumab plus bevacizumab versus active surveillance in patients with resected or ablated high-risk hepatocellular carcinoma (IMbrave050): a randomised, open-label, multicentre, phase 3 trial. Lancet 2023;402:1835-47. [Crossref] [PubMed]