Dataset Information

Integration of Inflammation-Immune Factors to Build Prognostic Model Predictive of Prognosis and Minimal Residual Disease for Hepatocellular Carcinoma.

ABSTRACT:

Background

Tumor recurrence after hepatectomy is high for hepatocellular carcinoma (HCC), and minimal residual disease (MRD) could be the underlying mechanism. A predictive model for recurrence and presence of MRD is needed.

Methods

Common inflammation-immune factors were reviewed and selected to construct novel models. The model consisting of preoperative aspartate aminotransferase, C-reactive protein, and lymphocyte count, named ACLR, was selected and evaluated for clinical significance.

Results

Among the nine novel inflammation-immune models, ACLR showed the highest accuracy for overall survival (OS) and time to recurrence (TTR). At the optimal cutoff value of 80, patients with high ACLR (> 80) had larger tumor size, higher Edmondson's grade, more vascular invasion, advanced tumor stage, and poorer survival than those with low ACLR (≤ 80) in the training cohort (5-year OS: 43.3% vs. 80.1%, P < 0.0001; 5-year TTR: 74.9% vs. 45.3%, P < 0.0001). Multivariate Cox analysis identified ACLR as an independent risk factor for OS [hazard ratio (HR) = 2.22, P < 0.001] and TTR (HR = 2.36, P < 0.001). Such clinical significance and prognostic value were verified in validation cohort. ACLR outperformed extant models, showing the highest area under receiver operating characteristics curve for 1-, 3-, and 5-year OS (0.737, 0.719, and 0.708) and 1-, 3-, and 5-year TTR (0.696, 0.650, and 0.629). High ACLR correlated with early recurrence (P < 0.001) and extremely early recurrence (P < 0.001). In patients with high ACLR, wide resection margin might confer survival benefit by decreasing recurrence (median TTR, 25.5 vs. 11.4 months; P = 0.037).

Conclusions

The novel inflammation-immune model, ACLR, could effectively predict prognosis, and the presence of MRD before hepatectomy and might guide the decision on resection margin for patients with HCC.

SUBMITTER: Xu X

PROVIDER: S-EPMC9213691 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Integration of Inflammation-Immune Factors to Build Prognostic Model Predictive of Prognosis and Minimal Residual Disease for Hepatocellular Carcinoma.

Xu Xin X Huang Ao A Guo De-Zhen DZ Wang Yu-Peng YP Zhang Shi-Yu SY Yan Jia-Yan JY Wang Xin-Yu XY Cao Ya Y Fan Jia J Zhou Jian J Fu Xiu-Tao XT Shi Ying-Hong YH

Frontiers in oncology 20220608

<h4>Background</h4>Tumor recurrence after hepatectomy is high for hepatocellular carcinoma (HCC), and minimal residual disease (MRD) could be the underlying mechanism. A predictive model for recurrence and presence of MRD is needed.<h4>Methods</h4>Common inflammation-immune factors were reviewed and selected to construct novel models. The model consisting of preoperative aspartate aminotransferase, C-reactive protein, and lymphocyte count, named ACLR, was selected and evaluated for clinical sign ...[more]

PMID: 35756674

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Project description:BackgroundCurrently, there are few studies on immune-related prognostic analysis of hepatocellular carcinoma (HCC). Our aim was to establish an immune-correlated prognostic model for HCC.MethodsImmune-associated cells were obtained from the scRNA-seq dataset (GSE149614) of HCC. Differentially expressed genes between normal and tumor cells from immune-associated cells and the immune-related genes from the ImmPort database were used to identify immune-related differentially expressed genes (IRDEGs). Subsequently, the risk model was established in the TCGA-LIHC cohort (n = 438) from the Cancer Genome Atlas (TCGA) database by using Kaplan-Meier (K-M) survival curve, univariate/multivariate Cox regression analysis. Subsequently, we further analyzed tumor immune microenvironment characteristics, somatic mutation, immune checkpoint and its ligand expression levels between high- and low-risk groups, as well as drug sensitivity prediction. ICGC cohort was set as the validation cohort. TCGA-LIHC cohort and three independent the Gene Expression Omnibus (GEO) datasets (GSE54236, GSE14520, and GSE64041) was used to verify IRDEGs expression, as well as PCR assays using clinical samples.ResultsThe IRDEGs was composed of 4 genes, namely B2M, SPP1, PPIA, and HRG. The 438 HCC patients were divided into high- and low-risk group. The high-risk group was associated with poor prognosis, including higher T stage, advanced pathological stages, less immune cell infiltration, higher TP53 mutation rate, the high expression of CTLA4 and HAVCR2. Besides, high-risk populations benefit from most chemotherapy drugs. Similarly, the performance of the risk model was validated in the ICGC. All four datasets (TCGA-LIHC cohort, GSE54236, GSE14520, and GSE64041) and clinical q-PCR results demonstrated that, compared with normal samples, the expressions of B2M and HRG were lower in tumor samples, and the expression of SPP1 was higher.ConclusionIn summary, the immune-related prognostic signature had a good predictive performance on prognosis and immunotherapy for HCC patients.

Project description:The systemic immune-inflammatory index (SII), calculated by (platelet count × neutrophil count)/lymphocyte count, is a novel biomarker with predive and prognostic value in numerous diseases. However, the relationship between SII and congestive heart failure (CHF) is not clear. This study aims to document the association of SII with the prevalence of CHF in the whole population and the long-term prognosis in CHF patients. This study included 57,500 participants in the National Health and Nutritional Examination Surveys, who were categorized into 3 categories based on their SII levels. A cross-sectional study was conducted to examine the relationship between SII and CHF prevalence in the whole population, followed by a prospective longitudinal study with a 5.4-year follow-up period for CHF patients to assess the predictive significance of SII for CHF. The main focus of the longitudinal study was on all-cause death as the primary outcome, with cardiovascular (CV) death as the secondary outcome. Associations were estimated using multivariate logistic regression and Cox proportional hazards models. The dose-response relationship was assessed with the restricted cubic spline (RCS) analysis. In the cross-sectional analysis, there were 1927 (3.35%) participants diagnosed with CHF. The high SII group showed a significantly higher prevalence of CHF than the low SII group (odds ratio (OR) 1.24, 95% confidence interval (CI): 1.05, 1.45). In the longitudinal analysis, 882 all-cause deaths including 379 CV deaths were collected among CHF patients, and high SII was associated with a significant increase in the risk of all-cause death (hazard ratio (HR) 1.44; 95% CI: 1.14, 1.81) and CV death (HR 1.31; 95% CI: 1.08, 1.58). RCS confirmed the positive correlation of SII with the prevalence of CHF in the whole population, as well as the mortality risk in CHF patients. This study is the first to reveal that high SII was related to a high prevalence of CHF and a poor prognosis in CHF patients. These findings underscore the potential role of SII in the prevention and management of CHF.

Dataset Information

Integration of Inflammation-Immune Factors to Build Prognostic Model Predictive of Prognosis and Minimal Residual Disease for Hepatocellular Carcinoma.

Background

Methods

Results

Conclusions

Publications

Integration of Inflammation-Immune Factors to Build Prognostic Model Predictive of Prognosis and Minimal Residual Disease for Hepatocellular Carcinoma.

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