Prognostic value of combined inflammatory and nutritional biomarkers in HCC within the Milan criteria after hepatectomy

Decoding the Prognostic Significance and Therapeutic Implications of Inflammation-Based Scores in Hepatocellular Carcinoma: A Comprehensive Review

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, posing a significant global health challenge with an increasing incidence. In recent years, multiple staging systems and scores have been proposed, emphasising the necessity for the development of precise prognostic tools. The well-documented etiological relationship between chronic inflammation and carcinogenesis has prompted researchers to explore novel prognostic markers associated with the inflammatory status of HCC patients. This review summarises the current data about inflammation-based scores in the context of HCC. We discuss established scores like the Glasgow Prognostic Score (GPS), modified GPS (mGPS) and the neutrophil-to-lymphocyte ratio (NLR) and others not as extensively studied, examining their utility in predicting survival outcomes and treatment response in HCC patients. Furthermore, we explore emerging scores, including the prognostic nutritional index (PNI) and other lymphocyte-based scores, assessing their potential in refining risk stratification and guiding therapeutic decisions in the era of precision medicine. As research progresses and these scores undergo further refinement and integration into the evolving landscape of HCC management, they carry significant potential for improving patient outcomes.

Inflammation-related prognostic markers in resected hepatocellular carcinoma

Hepatocellular carcinoma is usually detected late and therapeutic options are unsatisfactory. Despite marked progress in patient care, HCC remains among the deadliest cancers world-wide. While surgical resection remains a key option for early-stage HCC, the 5-year survival rates after surgical resection are limited. One reason for limited outcomes is the lack of reliable prognostic biomarkers to predict HCC recurrence. HCC prognosis has been shown to correlate with different systemic and pathological markers which are associated with patient survival and HCC recurrence. Liver inflammatory processes offer a large variety of systemic and pathological markers which may be exploited to improve the reliability of prognosis and decision making of liver surgeons and hepatologists. The following review aims to dissect the potential tools, targets and prognostic meaning of inflammatory markers in patients with resectable HCC. We analyze changes in circulant cellular populations and assess inflammatory biomarkers as a surrogate of impaired outcomes and provide an overview on predictive gene expression signatures including inflammatory transcriptional patterns, which are representative of poor survival in these patients.

A novel nomogram and risk classification system based on inflammatory and immune indicators for predicting prognosis of pancreatic cancer patients with liver metastases

BACKGROUND

The study determined to construct a novel predictive nomogram to access the prognosis of pancreatic cancer patients with liver metastases (PCLM).

METHODS

Medical records included clinical and laboratory variables were collected. The patients were randomly divided into training and validation cohort. First, in the training cohort, the optimal cutoff value of SII, PNI, NLR, PLR were obtained. Then the survival analysis evaluated the effects of above indices on OS. Next, univariate and multivariate analyses were used to identify the independent factors of OS. Moreover, a nomogram was constructed based on LASSO cox analysis. Additionally, the predictive efficacy of the nomogram was evaluated by ROC curve and calibration curve in the training and validation cohort. Finally, a risk stratification system based on the nomogram was performed.

RESULTS

A total of 472 PCLM patients were enrolled in the study. The optimal cutoff values of SII, PNI, PLR and NLR were 372, 43.6, 285.7143 and 1.48, respectively. By combing SII and PNI, named coSII-PNI, we divided the patients into three groups. The Kaplan-Meier curves demonstrated above indices were correlated with OS. Univariate and multivariate analyses found the independent prognostic factors of OS. Through LASSO cox analysis, coSII-PNI, PNI, NLR, CA199, CEA, chemotherapy and gender were used to construct the nomogram. Lastly, the ROC curve and calibration curve demonstrated that the nomogram can predict prognosis of PCLM patients. Significant differences were observed between high and low groups.

CONCLUSIONS

The nomogram based on immune, inflammation, nutritional status and other clinical factors can accurately predict OS of PCLM patients.