Prognostic value of inflammation-based indices in patients with resected hepatocellular carcinoma

Naples Prognostic Score is an Independent Prognostic Factor in Patients Undergoing Hepatectomy for Hepatocellular Carcinoma

Background

Nutritional and inflammatory status has been reported to be associated with the prognosis of hepatocellular carcinoma (HCC), but many studies did not include all biomarkers simultaneously. The present study aimed to determine the impact of Naples prognostic score (NPS) on the long-term survival in patients undergoing hepatectomy for HCC.

Methods

Patients with HCC after curative resection were eligible. Then, all patients were stratified into three groups according to the NPS. Clinical features and survival outcomes were compared among the three groups. Independent prognostic factors were determined by COX analysis. The time dependent receiver operating characteristic (ROC) curves were used to compare prognostic performance with other immunonutrition scoring systems.

Results

A total of 476 patients were enrolled eventually. Baseline characteristics showed that patients with higher NPS had a higher proportion of poor liver function and advanced tumor features. Accordingly, Kaplan-Meier survival curves showed that patients with higher NPS had a lower rate of overall survival (OS) and recurrence-free survival (RFS). Multivariable COX analysis demonstrated that NPS was an independent risk factor of OS (NPS group 2 vs 1: HR=1.958, 95% CI: 1.038-3.369, p = 0.038; NPS group 3 vs 1: HR=2.608, 95% CI: 1.358-5.008, p=0.004, respectively) and RFS (NPS group 2 vs 1: HR=2.014, 95% CI: 1.299-2-3.124, p=0.002; NPS group 3 vs 1: HR=2.002, 95% CI: 1.262-3.175, p=0.003, respectively). The time-dependent ROC curve showed that NPS was superior to other models in prognostic performance and discriminatory power for long-term survival (median AUC 0.675, 95% CI: 0.586-0.712, P < 0.05).

Conclusion

The NPS is a simple tool strongly associated with long-term survival in patients undergoing curative hepatectomy for HCC.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Preoperative alpha fetoprotein, total bilirubin, fibrinogen, albumin, and lymphocytes predict postoperative survival in hepatocellular carcinoma

AIMS

Our study focused on exploring the feasible prognostic laboratory parameters of HCC and establishing a score model to estimate individualized overall survival (OS) in HCC after resection.

METHODS

Four hundred and sixty-one patients with HCC who underwent hepatectomy between January 2010 and December 2017 was enrolled in this investigation. Cox proportional hazards model was conducted to analyze the prognostic value of laboratory parameters. The score model construction was based on the forest plot results. Overall survival was evaluated by Kaplan-Meier method and the log-rank test. The novel score model was validated in an external validation cohort from a different medical institution.

RESULTS

We identified that alpha fetoprotein (AFP), total bilirubin (TB), fibrinogen (FIB), albumin (ALB), and lymphocyte (LY) were independent prognostic factors. High AFP, TB, FIB (HR > 1, p < 0.05), and low ALB, LY (HR < 1, p < 0.05) were associated with the survival of HCC. The novel score model of OS based on these five independent prognostic factors achieved high C-index of 0.773 (95% confidence interval [CI]: 0.738-0.808), which was significantly higher than those of the single five independent factors (0.572-0.738). The score model was validated in the external cohort whose C-index was 0.7268 (95% CI: 0.6744-0.7792).

CONCLUSION

The novel score model we established was an easy-to-use tool which could enable individualized estimation of OS in patients with HCC who underwent curative hepatectomy.

An m6A-Related lncRNA Signature Predicts the Prognosis of Hepatocellular Carcinoma

Objective: The purpose of this study was to establish an N6-methylandenosine (m6A)-related long non-coding RNA (lncRNA) signature to predict the prognosis of hepatocellular carcinoma (HCC).

Methods: Pearson correlation analysis was used to identify m6A-related lncRNAs. We then performed univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct an m6A-related lncRNA signature. Based on the cutoff value of the risk score determined by the X-title software, we divided the HCC patients into high -and low-risk groups. A time-dependent ROC curve was used to evaluate the predictive value of the model. Finally, we constructed a nomogram based on the m6A-related lncRNA signature.

Results: ZEB1-AS1, MIR210HG, BACE1-AS, and SNHG3 were identified to comprise an m6A-related lncRNA signature. These four lncRNAs were upregulated in HCC tissues compared to normal tissues. The prognosis of patients with HCC in the low-risk group was significantly longer than that in the high-risk group. The M6A-related lncRNA signature was significantly associated with clinicopathological features and was established as a risk factor for the prognosis of patients with HCC. The nomogram based on the m6A-related lncRNA signature had a good distinguishing ability and consistency.

Conclusion: We identified an m6A-related lncRNA signature and constructed a nomogram model to evaluate the prognosis of patients with HCC.