Hypoxia-Related Long Non-Coding RNA Signature Predicts the Prognosis of Breast Cancer

Abstract Background: Breast cancer(BC) is the most common tumour in women. Hypoxia stimulates metastasis in cancer and is linked to poor patient prognosis.Methods: We screened prognostic-related lncRNAs(Long Non-Coding RNAs) from the Cancer Genome Atlas (TCGA) data and constructed a prognostic signature based on hypoxia-related lncRNAs in BC.Results: We identified 21 differentially expressed lncRNAs associated with BC prognosis. Kaplan Meier survival analysis indicated a significantly worse prognosis for the high-risk group(P<0.001). Moreover, the ROC-curve (AUC) of the lncRNAs signature was 0.700, a performance superior to other traditional clinicopathological characteristics. Gene set enrichment analysis (GSEA) showed many immune and cancer-related pathways and in the low-risk group patients. Moreover, TCGA revealed that functions including activated protein C (APC)co-inhibition, Cinnamoyl CoA reductase(CCR),check-point pathways, cytolytic activity, human leukocyte antigen (HLA), inflammation-promotion, major histocompatibility complex(MHC) class1, para-inflammation, T cell co-inhibition, T cell co-stimulation, and Type Ⅰ and Ⅱ Interferons (IFN) responses were significantly different in the low-risk and high-risk groups. Immune checkpoint molecules such as ICOS, IDO1, TIGIT, CD200R1, CD28, PDCD1(PD-1), were also expressed differently between the two risk groups. The expression of m6A-related mRNA indicated that YTHDC1, RBM15, METTL3, and FTO were significantly between the high and low-risk groups.Additionally, immunotherapy in patients with BC from the low-risk group yielded a higher frequency of clinical responses to anti-PD-1/PD-L1 therapy or a combination of anti-PD-1/PD-L1and anti-CTLA4 therapies.Except for lapatinib, the results also show that a high-risk score is related to a higher half-maximal inhibitory concentration (IC50) of chemotherapy drugs.Conclusion: A novel hypoxia-related lncRNAs signature may serve as a prognostic model for BC.

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A New Ferroptosis-Related lncRNA Signature Predicts the Prognosis of Bladder Cancer Patients

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.699804 ◽

2021 ◽

Vol 9 ◽

Author(s):

Mei Chen ◽

Zhenyu Nie ◽

Yan Li ◽

Yuanhui Gao ◽

Xiaohong Wen ◽

...

Keyword(s):

High Risk ◽

Immune Status ◽

Risk Group ◽

Enrichment Analysis ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Clinicopathological Variables

Background: Ferroptosis is closely related to the occurrence and development of cancer. An increasing number of studies have induced ferroptosis as a treatment strategy for cancer. However, the predictive value of ferroptosis-related lncRNAs in bladder cancer (BC) still need to be further elucidated. The purpose of this study was to construct a predictive signature based on ferroptosis-related long noncoding RNAs (lncRNAs) to predict the prognosis of BC patients.Methods: We downloaded RNA-seq data and the corresponding clinical and prognostic data from The Cancer Genome Atlas (TCGA) database and performed univariate and multivariate Cox regression analyses to obtain ferroptosis-related lncRNAs to construct a predictive signature. The Kaplan-Meier method was used to analyze the overall survival (OS) rate of the high-risk and low-risk groups. Gene set enrichment analysis (GSEA) was performed to explore the functional differences between the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the predictive signature and immune status. Finally, the correlation between the predictive signature and the treatment response of BC patients was analyzed.Results: We constructed a signature composed of nine ferroptosis-related lncRNAs (AL031775.1, AL162586.1, AC034236.2, LINC01004, OCIAD1-AS1, AL136084.3, AP003352.1, Z84484.1, AC022150.2). Compared with the low-risk group, the high-risk group had a worse prognosis. The ferroptosis-related lncRNA signature could independently predict the prognosis of patients with BC. Compared with clinicopathological variables, the ferroptosis-related lncRNA signature has a higher diagnostic efficiency, and the area under the receiver operating characteristic curve was 0.707. When patients were stratified according to different clinicopathological variables, the OS of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the high-risk group. ssGSEA showed that the predictive signature was significantly related to the immune status of BC patients. High-risk patients were more sensitive to anti-PD-1/L1 immunotherapy and the conventional chemotherapy drugs sunitinib, paclitaxel, cisplatin, and docetaxel.Conclusion: The predictive signature can independently predict the prognosis of BC patients, provides a basis for the mechanism of ferroptosis-related lncRNAs in BC and provides clinical treatment guidance for patients with BC.

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Proliferation-, estrogen-, and T-cell-related metagenes to predict outcome after adjuvant/neoadjuvant chemotherapy for operable breast cancer in the ECTO trial.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.1014 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. 1014-1014 ◽

Cited By ~ 1

Author(s):

Giampaolo Bianchini ◽

Vera Cappelletti ◽

Maurizio Callari ◽

Maria Luisa Carcangiu ◽

Wolfgang Eiermann ◽

...

Keyword(s):

Breast Cancer ◽

T Cell ◽

High Risk ◽

Risk Group ◽

Molecular Subtype ◽

Expression Profiles ◽

Risk Groups ◽

Operable Breast Cancer ◽

Low Risk ◽

Phase Iii

1014 Background: Predicting recurrence in operable breast cancer (BC) despite optimal chemotherapy would be relevant to new drug development and tailored treatments. Methods: A large series (n=3,154) of public Affymetrix gene-expression profiles (GEP) was used to define prognostic/predictive metagenes in different BC subtypes. In ER+/HER2- a proliferation and an ER-related metagene were combined to predict low, intermediate and high risk of recurrence. In TN and in HER2+ a T cell metagene was used to predict low, intermediate and high risk (higher expression associated with lower risk). The metagenes were validated in patients enrolled in the phase III ECTO trial (Gianni L. JCO 2009) and treated with the same taxane-anthracycline-CMF regimen as neoadjuvant or adjuvant therapy before endocrine therapy if indicated. The outcome was distant event free survival (DEFS). Results: 283 good quality GEPs were obtained (neoadjuvant n=121; adjuvant n=162) from 464 retrospectively collected samples. Median follow-up was 8.9 years. In ER+/HER2- tumors the 10-yrs DEFS was 92.3, 81.2 and 66.6% in low, intermediate and high risk groups, respectively [high vs low HR 4.38 (1.01-19.1) p=.048] according to proliferation and ER-related metagenes. In HER2+ and TN subgroup the 10-yrs DEFS was 97.2, 75.6 and 78.8% in low, intermediate and high risk groups, respectively [high vs low HR 8.73 (1.09-69.8) p=.041]. In TN tumors, the pCR rate was 20% in the high and 61.5% in the low risk group. By combining the predicted risk group in each molecular subtype the 10-yrs DEFS was 95.3, 79.2 and 71.5% in low (24.2%), intermediate (42.7%) and high (33.1%) risk group, respectively [logrank p=0.003; high vs low HR 6.22 (1.87-20.6) p=.002]. ER, PGR, Ki67 and lymphocyte infiltration (LI) by IHC underperformed compared to genomic predictors. Conclusions: BC patients at higher risk of relapse despite optimal standard treatment can be identified who should be spared ineffective and toxic therapy and considered for investigational new strategies. In TN and HER2+, high T cell metagene and to a lesser extent LI are prognostic/predictive and associated with an extremely low risk of DEFS after chemotherapy.

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Identification of an Immune-Related Signature Predicting Survival Risk and Immune Microenvironment in Gastric Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.687473 ◽

2021 ◽

Vol 9 ◽

Author(s):

Shuang Dai ◽

Tao Liu ◽

Xiao-Qin Liu ◽

Xiao-Ying Li ◽

Ke Xu ◽

...

Keyword(s):

Gastric Cancer ◽

T Cell ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Effector Memory ◽

Immune Microenvironment

Background: Tumor immune microenvironment plays a vital role in tumorigenesis and progression of gastric cancer (GC), but potent immune biomarkers for predicting the prognosis have not been identified yet.Methods: At first, RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) were mined to identify an immune-risk signature using least absolute shrinkage and selection operator (LASSO) regression and multivariate stepwise Cox regression analyses. Furthermore, the risk score of each sample was calculated, and GC patients were divided into high-risk group and low-risk group based on their risk scores. Subsequently, the performance of this signature, including the correlation with overall survival (OS), clinical features, immune cell infiltration, and immune response, has been tested in GC data from TCGA database and Gene Expression Omnibus (GSE84437), respectively.Results: An immune signature composed of four genes (MAGED1, ACKR3, FZD2, and CTLA4) was constructed. The single sample gene set enrichment analysis (ssGSEA) indicated that activated CD4+/CD8+ T cell, activated dendritic cell, and effector memory CD8+ T cell prominently increased in the low-risk group, showing relatively high immune scores and low stromal scores. Further GSEA analysis indicated that TGF-β, Ras, and Rap1 pathways were activated in the high-risk group, while Th17/Th1/Th2 differentiation, T cell receptor and PD-1/PD-L1 checkpoint pathways were activated in the low-risk group. Low-risk patients presented higher tumor mutation burden (TMB) and expression of HLA-related genes. The immune-associated signature showed an excellent predictive ability for 2-, 3-, and 5-year OS in GC.Conclusion: The immune-related prognosis model contributes to predicting the prognosis of GC patients and providing valuable information about their response to immunotherapy using integrated bioinformatics methods.

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A Novel Signature Based on Pyroptosis-Related Genes for Predicting Breast Cancer

10.21203/rs.3.rs-521609/v1 ◽

2021 ◽

Author(s):

Shuang Shen ◽

Xin Chen ◽

Rui Qu ◽

Youming Guo ◽

Yingying Su ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Independent Predictive Factor ◽

Tumour Immunity

Abstract Background: Breast cancer (BC) surpassed lung cancer as the most frequent malignant tumour in women. In recent years, pyroptosis has revealed itself as an inflammatory form of programmed cell death. However, it is unclear as to the expression of genes associated with pyroptosis in BC and its relationship to prognosis. Results: In this study, we identified 31 pyroptosis regulators that are differentially expressed between BC and normal breast. The differently expressed genes (DEG) allow BC patients to be divided into three subtypes. Through single-factor and multi-factor COX regression and the application of least absolute contraction and selection operator (LASSO) Cox regression method, the survival prognostic value of each gene related to pyroptosis in The Cancer Genome Atlas (TCGA) cohort was evaluated, and a 4-gene signature was constructed. BC patients of the TCGA cohort are divided into low-risk or high-risk groups by risk score. The survival of the low-risk group was significantly higher than the high-risk group (P <0.001). Using the median risk score from the TCGA cohort, BC patients from the Gene Expression Omnibus (GEO) cohort were divided into two risk sub-groups and similar conclusions were drawn. In combination with clinicopathological characteristics, the risk score is an independent predictive factor of OS in BC patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) indicated that the high-risk group's immune genes were enriched and immune status was reduced. Conclusions: In conclusion, pyroptosis-related genes are important for tumour immunity and can be used to predict the prognosis of BC.

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Exploring the Pyroptosis-related Genes of Breast Cancer and their Effects on Tumor Immune Microenvironment

10.21203/rs.3.rs-1049643/v1 ◽

2021 ◽

Author(s):

Congli Jia ◽

Fu Yang ◽

Ruining Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment

Abstract Background: Breast cancer （BC） is the most common cancer among women, with high rates of metastasis and recurrence. Some studies have confirmed that pyroptosis is an immune-related programmed cell death. However, the correlation between the expression of pyroptosis-related genes in BC and its prognosis remains unclear. Methods: In this study, we identified 38 pyroptosis-related genes that were differentially expressed between BC and normal tissues. The prognostic value of each pyroptosis-related gene was evaluated using patient data from The Cancer Genome Atlas (TCGA). The Cox regression method was performed to establish a prognostic model for 16-gene signature, classifying all BC patients in the TCGA database into a low-or high-risk group. Results: The survival rate of BC patients in the high-risk group was significantly lower than that in the low-risk group (P＜0.01). Prognostic model is independent prognostic factor for BC patients compared to clinical features. Single sample gene set enrichment analysis (ssGSEA) showed a decrease for immune cells and immune function in the high-risk group. Conclusions: Pyroptosis-related genes influence the tumor immune microenvironment and can predict the prognosis of BC.

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Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay

Cancers ◽

10.3390/cancers12102772 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2772

Author(s):

Michael A. Jacobs ◽

Christopher B. Umbricht ◽

Vishwa S. Parekh ◽

Riham H. El Khouli ◽

Leslie Cope ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Risk Group ◽

Area Under The Curve ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Intermediate Risk ◽

Breast Cancer Patients ◽

Gene Assay

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p < 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10−3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine.

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A ferroptosis-associated gene signature for the prediction of prognosis and therapeutic response in luminal-type breast carcinoma

Scientific Reports ◽

10.1038/s41598-021-97102-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yang Peng ◽

Haochen Yu ◽

Yingzi Zhang ◽

Fanli Qu ◽

Zhenrong Tang ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Breast Carcinoma ◽

Risk Score ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Luminal Type

AbstractFerroptosis is a new form of regulated cell death (RCD), and its emergence has provided a new approach to the progression and drug resistance of breast cancer (BRCA). However, there is still a great gap in the study of ferroptosis-related genes in BRCA, especially luminal-type BRCA patients. We downloaded the mRNA expression profiles and corresponding clinical data of BRCA patients from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) databases. Then, we built a prognostic multigene signature with ferroptosis-related differentially expressed genes (DEGs) in the METABRIC cohort and validated it in the TCGA cohort. The predictive value of this signature was investigated in terms of the immune microenvironment and the probability of a response to immunotherapy and chemotherapy. The patients were divided into a high-risk group and a low-risk group according to the ferroptosis-associated gene signature, and the high-risk group had a worse overall survival (OS). The risk score based on the 10 ferroptosis-related gene-based signature was determined to be an independent prognostic predictor in both the METABRIC and TCGA cohorts (HR, 1.41, 95% CI, 1.14–1.76, P = 0.002; HR, 2.19, 95% CI, 1.13–4.26, P = 0.02). Gene set enrichment analysis indicated that the term “cytokine-cytokine receptor interaction” was enriched in the high-risk score subgroup. Moreover, the immune infiltration scores of most immune cells were significantly different between the two groups, the low-risk group was much more sensitive to immunotherapy, and six drugs might have potential therapeutic implications in the high-risk group. Finally, a nomogram incorporating a classifier based on the 10 ferroptosis-related genes, tumor stage, age and histologic grade was established. This nomogram showed favorable discriminative ability and could help guide clinical decision-making for luminal-type breast carcinoma.

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A ferroptosis-associated gene signature for the prediction of prognosis and therapeutic response in luminal-type breast carcinoma

10.21203/rs.3.rs-125141/v2 ◽

2021 ◽

Author(s):

Yang Peng ◽

Haochen Yu ◽

Yingzi Zhang ◽

Fanli Qu ◽

Zhenrong Tang ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Breast Carcinoma ◽

Risk Score ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Luminal Type

Abstract Background: Ferroptosis is a new form of regulated cell death (RCD), and its emergence has provided a new approach to the progression and drug resistance of breast cancer (BRCA). However, there is still a great gap in the study of ferroptosis-related genes in BRCA, especially luminal-type BRCA patients.Methods: We downloaded the mRNA expression profiles and corresponding clinical data of BRCA patients from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) databases. Then, we built a prognostic multigene signature with ferroptosis-related differentially expressed genes (DEGs) in the METABRIC cohort and validated it in the TCGA cohort. The predictive value of this signature was investigated in terms of mutations, copy number variations (CNVs), the immune microenvironment and the probability of a response to immunotherapy and chemotherapy.Findings: The patients were divided into a high-risk group and a low-risk group by the ferroptosis-associated gene signature, and the high-risk group had a worse overall survival (OS). The risk score based on the 10 ferroptosis-related gene-based signature was determined to be an independent prognostic predictor in both the METABRIC and TCGA cohorts (HR, 1.41, 95% CI, 1.14-1.76, P = 0.002; HR, 2.19, 95% CI, 1.13-4.26, P= 0.02). Gene set enrichment analysis indicated that the term “cytokine-cytokine receptor interaction” was enriched in the high-risk score subgroup. Moreover, the immune infiltration scores of most immune cells were significantly different between the two groups, and the low-risk group was much more sensitive to immunotherapy and six drugs might have potential therapeutic implications in high- risk group. In addition, we found that amplifications on chromosome 11 accompanied by the deletion of chromosome 1 were enriched in the high-risk subgroup. Finally, a nomogram incorporating a classifier based on the 10 ferroptosis-related genes, tumor stage, age and histologic grade was established. This nomogram showed a favorable discriminating ability and might contribute to clinical decision-making for luminal-type breast carcinoma.

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Identification of Five Immune-Related lncRNAs Predicting Survival and Tumor Microenvironment Characteristics in Breast Cancer

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6676692 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Ran Xiao ◽

Meng Yang ◽

Yuanyuan Tan ◽

Rumeng Ding ◽

Duolu Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Tumor Microenvironment ◽

Risk Model ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Clinical Samples ◽

Cox Regression Analysis

A common cancer in females, breast cancer (BRCA) mortality has been recently reduced; however, the prognosis of BRCA patients remains poor. This study attempted to develop prognostic immune-related long noncoding RNAs (lncRNAs) for BRCA and identify the effects of these lncRNAs on the tumor microenvironment (TME). Gene expression data from The Cancer Genome Atlas (TCGA) database were collected in order to select differentially expressed lncRNAs. Immune-related lncRNAs were downloaded from the ImmLnc database, where 316 immune-related lncRNAs were identified, 12 of which were found to be significantly related to the prognosis of BRCA patients. Multivariate cox regression analysis was then applied to construct prognostic immune-related lncRNAs as the risk model, including C6orf99, LINC00987, SIAH2-AS1, LINC01010, and ELOVL2-AS1. High-risk and low-risk groups were distinguished according to the median of immune-related risk scores. Accordingly, the overall survival (OS) in the high-risk group was observed to be shorter than that in the low-risk group. qRT-PCR analysis demonstrated that lncRNA expression levels in BRCA cell lines were in basic agreement with predictions except for LINC00987. By validating numerous clinical samples, lncRNA C6orf99 was shown to be highly expressed in the advanced stage, while LINC01010 and SIAH2-AS1 decreased in the advanced T-stage and M-stage. Moreover, the expression of LINC0098 was found to be significantly decreased among the groups (>50 years old). Gene set enrichment analysis (GSEA) was applied to analyze the cancer hallmarks and immunological characteristics of the high-risk and low-risk groups. Importantly, the TIMER database demonstrated that this immune-related lncRNA risk model for breast cancer is related to the infiltration of immune cells. In conclusion, the results indicated that five immune-related lncRNAs could be used as a prognostic model and may even accelerate immunotherapy for BRCA patients.

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Development of a novel three-lncRNA immune-related signature as a prognostic indicator for hepatocellular carcinoma

10.21203/rs.2.23831/v1 ◽

2020 ◽

Author(s):

Bo Hu ◽

Xiao-Bo Yang ◽

Xinting Sang

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Risk Group ◽

Principal Component ◽

Enrichment Analysis ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Testing Set

Abstract Background: Hepatocellular carcinoma (HCC) is one of the deadliest malignancies. Currently, there is still a lack of effective treatment. Our purpose was to develop an immune-related prognosis lncRNA signature with regard to HCC.Methods: A total of 14,142 lncRNAs and 331 immune genes were obtained from The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database to construct the immune-related lncRNAs co-expression networks. Moreover, the tumor samples collected from TCGA were randomized as training set and testing set, among which, the testing set and the entire set were used for verification. Subsequently, gene set enrichment analysis (GSEA) and principal component analysis (PCA) were employed for functional annotation.Results: An immune-related signature consisting of AC015908.3, AC068987.4 and AL365203.2 was identified among HCC patients. Under different conditions, patients in low-risk group exhibited longer overall survival (OS) than those in high-risk group (P < 0.001). Moreover, the as-constructed signature was an independent factor, which showed marked association with patient OS (P < 0.001, hazard ratio (HR) = 1.407). These findings were further validated in testing set and the entire set. Additionally, GSEA results revealed the different immune states between low-risk and high-risk groups. On the other hand, lncRNA-related mRNAs were also extracted to depict the networks.Conclusion: Our findings indicate that the three-lncRNA immune-related signature shows prognostic value for HCC.

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