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An Individualized Immune Prognostic Index is a Superior Predictor of Survival of Hepatocellular Carcinoma.

ABSTRACT: BACKGROUND The tumor microenvironment is largely orchestrated by the immune cells. Considerable evidence has shown their excellent clinicopathological application value in assessment of clinical outcomes and immunotherapy efficacy. Hence, a moderate, individualized prognostic signature based on immune cells that can estimate prognosis and reflect the immune microenvironment in hepatocellular carcinoma (HCC) patients is greatly needed. MATERIAL AND METHODS Here, we systematically analyzed the expression differences and survival prediction value of tumor infiltrating immune cells by analyzing 638 HCC patients from 3 public cohorts, including 2 microarray datasets and 1 RNA sequencing dataset. CIBERSORT software, a computational algorithm, was used to calculate the relative levels of immune cells. Three immune microenvironment subtypes were defined via ConsensuClusterPlus package. Univariate and multivariate survival analyses were used to develop an individualized immune prognostic index based on immune cell pairs. RESULTS Notably, HCC patients with higher immune signatures score, utterly appreciable, suffered inferior prognosis (hazard ratio=2.742; 95% confidence interval: 1.887-3.983; P.

SUBMITTER: Wang X

PROVIDER: S-EPMC7285951 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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An Individualized Immune Prognostic Index is a Superior Predictor of Survival of Hepatocellular Carcinoma.

Wang Xiaodong X Wu Yuquan Y Wen Dongyue D Wu Lin-Yong LY Zhao Yujia Y He Yun Y Yang Hong H

Medical science monitor : international medical journal of experimental and clinical research 20200531

BACKGROUND The tumor microenvironment is largely orchestrated by the immune cells. Considerable evidence has shown their excellent clinicopathological application value in assessment of clinical outcomes and immunotherapy efficacy. Hence, a moderate, individualized prognostic signature based on immune cells that can estimate prognosis and reflect the immune microenvironment in hepatocellular carcinoma (HCC) patients is greatly needed. MATERIAL AND METHODS Here, we systematically analyzed the exp ...[more]

PMID: 32527991

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Project description:Liver hepatocellular carcinoma (LIHC) is the most prevalent primary cancer of the liver, and immune-related genes (IRGs) regulate its development. So far, there is still no precise biomarker that predicts response to immunotherapy in LIHC. Therefore, this research seeks to identify immunogenic prognostic biomarkers and explore potential predictors for the efficacy of anti-PD-1/PD-L1 therapies in LIHC. The clinical data and gene expression profiles of patients diagnosed with LIHC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Moreover, IRGs were obtained from the ImmPort database. We discovered 35 IRGs that were differentially expressed between LIHC tissues and corresponding normal tissues. Through univariate Cox regression analysis, eight prognostic differentially expressed IRGs (PDEIRGs) were identified. Further, three optimal PDEIRGs (BIRC5, LPA, and ROBO1) were identified and used to construct a prognostic risk signature of LIHC patients via multivariate Cox regression analysis. The signature was validated by ROC curves. Subsequently, based on gene set enrichment analysis (GSEA) analysis, two out of the three optimal PDEIRGs (BIRC5 and LPA) were significantly enriched in the mismatch repair (MMR) pathway. Moreover, the two PDEIRGs (BIRC5 and LPA) were significantly correlated with the expression of genes related to mismatch repair (MLH1, MSH2, MSH6, and PMS2). Furthermore, correlations between the two PDEIRGs (BIRC5 and LPA) and immune checkpoints of cancer treatment (such as CTLA4, PD-1, and PD-L1) were demonstrated. Hyperprogressive disease (HPD) is a novel pattern of tumor progression which has a close relationship with immune checkpoint inhibitors (ICIs) utilization. MDM2 family amplification might promote the HPD phenomenon. Finally, we found a positive regulatory relationship between HPD related gene (MDM2) and BIRC5. Notably, MDM2 can either interact directly with BIRC5 or indirectly via downstream transcription factors of BIRC5. Overall, our study uncovered a novel 3-immune-related prognostic genes in LIHC.

Project description:Background: Immune checkpoint inhibitor (ICI) therapy has been proved to be a promising therapy to many types of solid tumors. However, effective biomarker for estimating the response to ICI therapy and prognosis of hepatocellular carcinoma (HCC) patients remains underexplored. The aim of this study is to build a novel immune-related prognostic index based on transcriptomic profiles. Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to identify immune-related hub genes that are differentially expressed in HCC cohorts. Next, univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to detect hub genes associated to overall survival (OS). To validate the immune-related prognostic index, univariate and multivariate Cox regression analysis were performed. CIBERSORT and ESTIMATE were used to explore the tumor microenvironment and immune infiltration level. Results: The differential expression analysis detected a total of 148 immune-related genes, among which 25 genes were identified to be markedly related to overall survival in HCC patients. LASSO analysis yielded 10 genes used to construct the immune-related gene prognostic index (IRGPI), by which a risk score is computed to estimate low vs. high risk indicating the response to ICI therapy and prognosis. Further analysis confirmed that this immune-related prognostic index is an effective indicator to immune infiltration level, response to ICI treatment and OS. The IRGPI low-risk patients had better overall survival (OS) than IRGPI high-risk patients on two independent cohorts. Moreover, we found that IRGPI high-risk group was correlated with high TP53 mutation rate, immune-suppressing tumor microenvironment, and these patients acquired less benefit from ICI therapy. In contrast, IRGPI-low risk group was associated with low TP53 and PIK3CA mutation rate, high infiltration of naive B cells and T cells, and these patients gained relatively more benefit from ICI therapy.

Project description:IntroductionColorectal cancer shows high incidence and mortality rates. Immune checkpoint blockade can be used to treat colorectal carcinoma (CRC); however, it shows limited effectiveness in most patients.MethodsTo identify patients who may benefit from immunotherapy using immune checkpoint inhibitors, we constructed an immune-related gene prognostic index (IRGPI) for predicting the efficacy of immunotherapy in patients with CRC. Transcriptome datasets and clinical information of patients with CRC were used to identify differential immune-related genes between tumor and para-carcinoma tissue. Using weighted correlation network analysis and Cox regression analysis, the IRGPI was constructed, and Kaplan-Meier analysis was used to evaluate its predictive ability. We also analyzed the molecular and immune characteristics between IRGPI high-and low-risk subgroups, performed sensitivity analysis of ICI treatment, and constructed overall survival-related receiver operating characteristic curves to validate the IRGPI. Finally, IRGPI genes and tumor immune cell infiltration in CRC model mice with orthotopic metastases were analyzed to verify the results.ResultsThe IRGPI was constructed based on the following 11 hub genes: ADIPOQ, CD36, CCL24, INHBE, UCN, IL1RL2, TRIM58, RBCK1, MC1R, PPARGC1A, and LGALS2. Patients with CRC in the high-risk subgroup showed longer overall survival than those in the low-risk subgroup, which was confirmed by GEO database. Clinicopathological features associated with cancer progression significantly differed between the high- and low-risk subgroups. Furthermore, Kaplan-Meier analysis of immune infiltration showed that the increased infiltration of naïve B cells, macrophages M1, and regulatory T cells and reduced infiltration of resting dendritic cells and mast cells led to a worse overall survival in patients with CRC. The ORC curves revealed that IRGPI predicted patient survival more sensitive than the published tumor immune dysfunction and rejection and tumor inflammatory signature.DiscussionThus, the low-risk subgroup is more likely to benefit from ICIs than the high-risk subgroup. CRC model mice showed higher proportions of Tregs, M1 macrophages, M2 macrophages and lower proportions of B cells, memory B cell immune cell infiltration, which is consistent with the IRGPI results. The IRGPI can predict the prognosis of patients with CRC, reflect the CRC immune microenvironment, and distinguish patients who are likely to benefit from ICI therapy.

Project description:Background: Lymphocytes were reported to play a significant part in host anticancer immune responses and influence tumour prognosis. Few studies have focused on the prognostic values of aspartate aminotransferase (AST) to lymphocyte ratio (ALRI), aspartate aminotransferase to platelet count ratio index (APRI) and systemic immune-inflammation index (SII) in hepatocellular carcinoma (HCC) treated with palliative treatments. Methods: Five hundred and ninety-eight HCC patients treated with palliative therapies were retrospectively analysed. We randomly assigned patients into the training cohort (429 patients) and the validation cohort I (169 patients). Receiver operating characteristic (ROC) curves were used to identify the best cut-off values for the ALRI, APRI and SII in the training cohort and the values were further validated in the validation cohort I. Correlations between ALRI and other clinicopathological factors were also analysed. A prognostic nomogram including ALRI was established. We validated the prognostic value of the ALRI, SII and APRI with two independent cohorts, the validation cohort II of 82 HCC patients treated with TACE and the validation cohort III of 150 HCC patients treated with curative resection. In the training cohort and all the validation cohorts, univariate analyses by the method of Kaplan-Meier and multivariate analysis by Cox proportional hazards regression model were carried out to identify the independent prognostic factors. Results: The threshold values of ALRI, APRI and SII were 86.3, 1.37 and 376.4 respectively identified by ROC curve analysis in the training cohort. Correlation analysis showed that ALRI>86.3 was greatly associated with higher rates of Child-Pugh B&C, portal vein tumor thrombosis (PVTT) and ascites (P < 0.05). Correspondingly, ALRI level of HCC patients with Child-Pugh B&C, PVTT and ascites was evidently higher than that of HCC patients with Child-Pugh A, without PVTT and without ascites (P < 0.001). In the training cohort and the validation cohort I, II, III, the OS of patients with ALRI >86.3 was obviously shorter than patients with ALRI ≤86.3 (P <0.001). We identified ALRI as an independent prognostic factor by univariate and multivariate analyses both in training Cohort (HR=1.481, P=0.004), validation cohort I (HR=1.511, P=0.032), validation cohort II (HR=3.166, P=0.005) and validation cohort III (HR=3.921, P=0.010). The SII was identified as an independent prognostic factor in training cohort (HR=1.356, P=0.020) and the validation cohort II (HR=2.678, P=0.002). The prognostic nomogram including ALRI was the best in predicting 3-month, 6-month, 1-year, 2-year survival And OS among TNM, ALRI, ALRI-TNM and nomogram. Conclusions: The ALRI was a novel independent prognostic index for the HCC patients treated with palliative treatments.

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An Individualized Immune Prognostic Index is a Superior Predictor of Survival of Hepatocellular Carcinoma.

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An Individualized Immune Prognostic Index is a Superior Predictor of Survival of Hepatocellular Carcinoma.

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