Screening prognosis-related lncRNAs based on WGCNA to establish a new risk score for predicting prognosis in patients with hepatocellular carcinoma

Abstract Background: Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods: In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in the The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso‐penalized Cox regression analysis and nomogram model were used to establish new risk scoring system and predict the prognosis of patients with liver cancer. Results: A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso‐penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Conclusions: In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC, and the establishment of new risk scoring system and nomogram model provide the new perspective for predicting the prognosis of HCC.

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Screening Prognosis-Related lncRNAs Based on WGCNA to Establish a New Risk Score for Predicting Prognosis in Patients with Hepatocellular Carcinoma

Journal of Immunology Research ◽

10.1155/2021/5518908 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Xueliang Zhou ◽

Mengmeng Dou ◽

Zaoqu Liu ◽

Dechao Jiao ◽

Zhaonan Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Scoring System ◽

Normal Tissue ◽

Cox Regression ◽

Cox Regression Analysis ◽

Cerna Network ◽

Kaplan Meier ◽

Risk Scoring ◽

Risk Scoring System

Background. Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods. In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso-penalized Cox regression analysis and nomogram model were used to establish a new risk scoring system and predict the prognosis of patients with liver cancer. The expression of survival-related DE lncRNAs was verified by qRT-PCR. Results. A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs, and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso-penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Finally, the correlation between the risk score and immune cell infiltration and gene set enrichment analysis were determined. Conclusions. In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC and the establishment of the new risk scoring system and nomogram model provides the new perspective for predicting the prognosis of HCC.

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Integrated Analysis of lncRNA-Mediated ceRNA Network Reveals a Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2020.602542 ◽

2020 ◽

Vol 11 ◽

Author(s):

Jian-Rong Sun ◽

Chen-Fan Kong ◽

Kun-Min Xiao ◽

Jia-Lu Yang ◽

Xiang-Ke Qu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Tnm Stage ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Cox Regression Analysis ◽

Cerna Network ◽

Kaplan Meier ◽

Risk Patients

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.

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Identification of a Pyroptosis-Related lncRNA Risk Model for Predicting Prognosis and Immune Response in Colon Adenocarcinoma

10.21203/rs.3.rs-1234682/v1 ◽

2022 ◽

Author(s):

Yuying Tan ◽

Liqing Lu ◽

Xujun Liang ◽

Yongheng Chen

Keyword(s):

Regression Analysis ◽

Risk Model ◽

Cox Regression ◽

Malignant Tumors ◽

Colon Adenocarcinoma ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Immune Infiltration ◽

Cerna Network ◽

Kaplan Meier

Abstract Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors and diagnosed at an advanced stage with poor prognosis in the world. Pyroptosis is involved in the initiation and progression of tumors. This research focused on constructing a pyroptosis-related ceRNA network to generate a reliable risk model for risk prediction and immune infiltration analysis of COAD.Methods: Transcriptome data, miRNA-sequencing data and clinical information were downloaded from the TCGA database. Firstly, differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) were identified to construct a pyroptosis-related ceRNA network. Secondly, a pyroptosis-related lncRNA risk model was developed applying univariate Cox regression analysis and least absolute shrinkage and selection operator method (LASSO) regression analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to functionally annotate RNAs contained in the ceRNA network. In addition, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox regression, and nomogram were applied to validate this risk model. Finally, the relationship of this risk model with immune cells and immune checkpoint blockade (ICB) related genes were analyzed.Results: Totally 5373 DEmRNAs, 1159 DElncRNAs and 355 DEmiRNAs were identified. A pyroptosis-related ceRNA regulatory network containing 132 lncRNAs, 7miRNAs and 5 mRNAs was constructed and a ceRNA-based pyroptosis-related risk model including 11 lncRNAs was built. Tumor tissues were classified into high- and low- risk groups according to the median risk score. Kaplan-Meier analysis showed that the high-risk group had a shorter survival time; ROC analysis, independent prognostic analysis and nomogram further indicated the risk model was a significant independent prognostic factor which had excellent ability to predict patients’ risk. Moreover, immune infiltration analysis indicated that the risk model was related to immune infiltration cells (i.e., B cells naïve, T cells follicular helper, Macrophages M1, etc.) and ICB-related genes (i.e., PD-1, CTLA4, HAVCR2, etc).Conclusions: This pyroptosis-related lncRNA risk model possessed good prognostic value and the ability to predict the outcome of ICB immunotherapy in COAD.

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Development of a prognostic signature based on six immune related lncRNAs for hepatocellular carcinoma.

10.21203/rs.3.rs-16348/v1 ◽

2020 ◽

Author(s):

Ze-bing Song ◽

Guo-pei Zhang ◽

shaoqiang li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumor in the world which prognosis is poor. Therefore, a precise biomarker is needed to guide treatment and improve prognosis. More and more studies have shown that lncRNAs and immune response are closely related to the prognosis of hepatocellular carcinoma. The aim of this study was to establish a prognostic signature based on immune related lncRNAs for HCC.Methods: Univariate cox regression analysis was performed to identify immune related lncRNAs, which had negative correlation with overall survival (OS) of 370 HCC patients from The Cancer Genome Atlas (TCGA). A prognostic signature based on OS related lncRNAs was identified by using multivariate cox regression analysis. Gene set enrichment analysis (GSEA) and a competing endogenous RNA (ceRNA) network were performed to clarify the potential mechanism of lncRNAs included in prognostic signature. Results: A prognostic signature based on OS related lncRNAs (AC145207.5, AL365203.2, AC009779.2, ZFPM2-AS1, PCAT6, LINC00942) showed moderately in prognosis prediction, and related with pathologic stage (Stage I&II VS Stage III&IV), distant metastasis status (M0 VS M1) and tumor stage (T1-2 VS T3-4). CeRNA network constructed 15 aixs among differentially expressed immune related genes, lncRNAs included in prognostic signature and differentially expressed miRNA. GSEA indicated that these lncRNAs were involved in cancer-related pathways. Conclusion: We constructed a prognostic signature based on immune related lncRNAs which can predict prognosis and guide therapies for HCC.

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Effects of miRNA-140 on the Growth and Clinical Prognosis of SMMC-7721 Hepatocellular Carcinoma Cell Line

BioMed Research International ◽

10.1155/2021/6638915 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Cun-qing Kong ◽

Xing-cai Chen ◽

Guan-hua Qiu ◽

Jing-chen Liang ◽

Duo Wang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Expression Level ◽

Migration And Invasion ◽

Clinical Prognosis ◽

Cox Regression Analysis ◽

Qrt Pcr ◽

Kaplan Meier ◽

Hcc Cells

Background. A growing number of studies have suggested that microRNAs exert an essential role in the development and occurrence of multiple tumours and act as crucial regulators in various biological processes. However, the expression and function of miRNA-140 in hepatocellular carcinoma (HCC) cells are not yet adequately identified and manifested. Methods. The expression of miRNA-140 was determined in HCC tissues and adjacent nontumour tissues by quantitative real-time polymerase chain reaction (qRT-PCR). Kaplan–Meier survival analysis and Cox regression analysis were performed to explore the correlation between miRNA-140 expression level and the survival rate of patients with HCC. Additionally, overexpression experiments were conducted to investigate the biological role of miRNA-140 in HCC cells. Bioinformatics was used to predict the related target genes and pathways of miRNA-140. Results. QRT-PCR results signified that the expression level of miRNA-140 in HCC was lower than that of adjacent normal tissues ( P < 0.0001 ). Compared with the control group, the SMMC-7721 HCC cells in the miRNA-140 mimic group had a decrease in proliferation, migration, and invasion ( P < 0.05 ), whereas those in the miRNA-140 inhibitor group had an increase in proliferation, migration, and invasion ( P < 0.05 ). Cell cycle arrest occurred in the G0/1 phase. Prognosis analysis showed that the expression level of miRNA-140 was not related to the prognosis of HCC. Furthermore, the Kaplan–Meier test revealed that patients with lower miRNA-140 expression levels in liver cancer tissue had significantly shorter disease-free survival (DFS, P = 0.004 ) and overall survival (OS) times ( P = 0.010 ) after hepatectomy. Cox regression analysis further indicated that miRNA-140 was an independent risk factor that may affect the DFS ( P = 0.004 ) and OS times ( P = 0.014 ) of patients after hepatectomy. Our results suggested that miRNA-140 might be a crucial regulator involved in the HCC progression and is thus considered a potential prognostic biomarker and therapeutic target for HCC.

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Construction and Comprehensive Analysis of a Stratification System Based on AGTRAP in Patients with Hepatocellular Carcinoma

Disease Markers ◽

10.1155/2021/6144476 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Li Wang ◽

Wenjun Zhang ◽

Tao Yang ◽

Le He ◽

Yunmei Liao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Differential Expression ◽

Topological Degree ◽

Cox Regression ◽

Ppi Network ◽

Hub Gene ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Stratification System

Background. With the development of sequencing technology, several signatures have been reported for the prediction of prognosis in patients with hepatocellular carcinoma (HCC). However, the above signatures are characterized by cumbersome application. Therefore, the study is aimed at screening out a robust stratification system based on only one gene to guide treatment. Methods. Firstly, we used the limma package for performing differential expression analysis on 374 HCC samples, followed by Cox regression analysis on overall survival (OS) and disease-free interval (PFI). Subsequently, hub prognostic genes were found at the intersection of the above three groups. In addition, the topological degree inside the PPI network was used to screen for a unique hub gene. The rms package was used to construct two visual stratification systems for OS and PFI, and Kaplan-Meier analysis was utilized to investigate survival differences in clinical subgroups. The ssGSEA algorithm was then used to reveal the relationship between the hub gene and immune cells, immunological function, and checkpoints. In addition, we also used function annotation to explore into putative biological functions. Finally, for preliminary validation, the hub gene was knocked down in the HCC cell line. Results. We discovered 6 prognostic genes (SKA1, CDC20, AGTRAP, BIRC5, NEIL3, and CDC25C) for constructing a PPI network after investigating survival and differential expression genes. According to the topological degree, AGTRAP was chosen as the basis for the stratification system, and it was revealed to be a risk factor with an independent prognostic value in Kaplan-Meier analysis and Cox regression analysis ( P < 0.05 ). In addition, we constructed two visualized nomograms based on AGTRAP. The novel stratification system had a robust predictive value for PFI and OS in ROC analysis and calibration curve ( P < 0.05 ). Meanwhile, AGTRAP upregulation was associated with T staging, N staging, M staging, pathological stage, grade, and vascular invasion ( P < 0.05 ). Notably, AGTRAP was overexpressed in tumor tissues in all pancancers with paired samples ( P < 0.05 ). Furthermore, AGTRAP was associated with immune response and may change immune microenvironment in HCC ( P < 0.05 ). Next, gene enrichment analysis suggested that AGTRAP may be involved in the biological process, such as cotranslational protein targeting to the membrane. Finally, we identified the oncogenic effect of AGTRAP by qRT-PCR, colony formation, western blot, and CCK-8 assay ( P < 0.05 ). Conclusion. We provided robust evidences that a stratification system based on AGTRAP can guide survival prediction for HCC patients.

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Predictive Value of F-SIRI in Prognosis of Patients with Hepatocellular Carcinoma after Radical Hepatectomy

10.21203/rs.3.rs-651278/v1 ◽

2021 ◽

Author(s):

hu panyi ◽

Yongwei Zhang ◽

yeben qian

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Predictive Value ◽

Cox Regression ◽

Clinicopathological Characteristics ◽

Tumor Diameter ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

High Level ◽

The Relationship

Abstract Background: Objective to evaluate the predictive value of preoperative fibrinogen and systemic inflammatory response index (F-SIRI) in the prognosis of patients with hepatocellular carcinoma after radical hepatectomy. Methods: the clinical data of 298 patients with hepatocellular carcinoma who underwent surgery and confirmed by postoperative pathology in our hospital from January 2015 to December 2017 were retrospectively analyzed. The F-SIRI score was calculated according to FIB and SIRI data of peripheral blood. The relationship between F-SIRI score and clinicopathological characteristics was analyzed. The survival analysis was performed by Kaplan Meier method, Cox regression analysis was used to analyze the prognostic factors. Results: preoperative F-SIRI score was correlated with tumor diameter, FIB and SIRI (P<0.05), but not with age, gender, TNM stage and other clinical features (P>0.05). There were significant differences in the 5-year DFS rate and OS rate among patients with different preoperative F-SIRI scores(P<0.05); Cox regression analysis showed that preoperative tumor diameter, alpha fetoprotein level and F-SIRI score were independent predictors of DFS in patients with HCC (P< 0.05), while preoperative tumor diameter, ALB and F-SIRI score as independent predictors of OS (P<0.05). Conclusion: preoperative F-SIRI is an independent prognostic factor in patients with HCC after radical hepatectomy, with poor prognosis in patients with high level of F-SIRI.

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WAITING DAAS LIST MORTALITY IMPACT IN HCV CIRRHOTIC PATIENTS

Arquivos de Gastroenterologia ◽

10.1590/s0004-2803.201800000-76 ◽

2018 ◽

Vol 55 (4) ◽

pp. 343-345 ◽

Cited By ~ 1

Author(s):

Giovanni Faria SILVA ◽

Vanessa Gutierrez de ANDRADE ◽

Alecsandro MOREIRA

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Antiviral Agents ◽

Meld Score ◽

New Drugs ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Cirrhotic Patients ◽

Direct Antiviral Agents

ABSTRACT BACKGROUND: The infection for the hepatitis C virus (HCV) is a leading cause of liver-related morbidity and mortality through its evolution to liver cirrhosis, end-stage liver complications and hepatocellular carcinoma. Currently, the new drugs for the HCV infection, based on direct antiviral agents, have changed the outcomes in this setting. OBJECTIVE: To assess death incidence, during the wait for the treatment with the new drugs, and to analyze which independent variable (age, sex, ascite, HDA, albumin, α-fetoprotein, platelets and Meld score) had relation with death. METHODS: Prospective study with cirrhotic patients by HCV. Inclusion: cirrhotic patients by hepatic biopsy (METAVIR), clinic or image, detectable RNA (HCV). Exclusion: Other stages of hepatic fibrosis and hepatocellular carcinoma. Descriptive statistic in continue variables. Fisher Exact and Kaplan Meier and Cox Regression Analysis to assess the association of variables studied with death. P<0.05. RESULTS: A total of 129 patients were included. Of this, 73% were men. Mean age was 57.8±12.1, albumin of 3.5±0.6 mg/dL, platelets of 123.4±59.6 and Meld score of 10.59±3.56. The time of observation was 11.2±3.26 months, and the number of death 9/129 (6,9%). The Kaplan-Meier showed association between death with albumin lower than 2.9 (0.0006), MELD score higher than 15 (0.007) and α-fetoprotein higher than 40 ng/mL (<0.0001). Adjusted Cox Regression Analysis showed that α-fetoprotein higher than 40 ng/ml could be considered an independent risk for death. CONCLUSION: We conclude that, patients with advanced cirrhosis should be prioritized for treatment with direct antiviral agents.

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Mining prognostic markers of Asian hepatocellular carcinoma patients based on the apoptosis-related genes

BMC Cancer ◽

10.1186/s12885-021-07886-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Junbin Yan ◽

Jielu Cao ◽

Zhiyun Chen

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Roc Curve ◽

Prognostic Model ◽

Cox Regression ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Cox Analysis ◽

Kaplan Meier Plotter

Abstract Background Apoptosis-related genes(Args)play an essential role in the occurrence and progression of hepatocellular carcinoma(HCC). However, few studies have focused on the prognostic significance of Args in HCC. In the study, we aim to explore an efficient prognostic model of Asian HCC patients based on the Args. Methods We downloaded mRNA expression profiles and corresponding clinical data of Asian HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The Args were collected from Deathbase, a database related to cell death, combined with the research results of GeneCards、National Center for Biotechnology Information (NCBI) databases and a lot of literature. We used Wilcoxon-test and univariate Cox analysis to screen the differential expressed genes (DEGs) and the prognostic related genes (PRGs) of HCC. The intersection genes of DEGs and PGGs were seen as crucial Args of HCC. The prognostic model of Asian HCC patients was constructed by least absolute shrinkage and selection operator (lasso)- proportional hazards model (Cox) regression analysis. Kaplan-Meier curve, Principal Component Analysis (PCA) analysis, t-distributed Stochastic Neighbor Embedding (t-SNE) analysis, risk score curve, receiver operating characteristic (ROC) curve, and the HCC data of ICGC database and the data of Asian HCC patients of Kaplan-Meier plotter database were used to verify the model. Results A total of 20 of 56 Args were differentially expressed between HCC and adjacent normal tissues (p < 0.05). Univariate Cox regression analysis showed that 10 of 56 Args were associated with survival time and survival status of HCC patients (p < 0.05). There are seven overlapping genes of these 20 and 10 genes, including BAK1, BAX, BNIP3, CRADD, CSE1L, FAS, and SH3GLB1. Through Lasso-Cox analysis, an HCC prognostic model composed of BAK1, BNIP3, CSE1L, and FAS was constructed. Kaplan-Meier curve, PCA, t-SNE analysis, risk score curve, ROC curve, and secondary verification of ICGC database and Kaplan-Meier plotter database all support the reliability of the model. Conclusions Lasso-Cox regression analysis identified a 4-gene prognostic model, which integrates clinical and gene expression and has a good effect. The expression of Args is related to the prognosis of HCC patients, but the specific mechanism remains to be further verified.

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The prognostic and diagnostic significance of the neutrophil-to-lymphocyte ratio in hepatocellular carcinoma: a prospective controlled study

British Journal of Cancer ◽

10.1038/s41416-021-01445-3 ◽

2021 ◽

Author(s):

Philip J. Johnson ◽

Sofi Dhanaraj ◽

Sarah Berhane ◽

Laura Bonnett ◽

Yuk Ting Ma

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Prognostic Factor ◽

Cox Regression ◽

Prognostic Models ◽

Controlled Study ◽

Cox Regression Analysis ◽

Neutrophil Lymphocyte Ratio ◽

Lymphocyte Ratio ◽

Significant Difference

Abstract Background The neutrophil–lymphocyte ratio (NLR), a presumed measure of the balance between neutrophil-associated pro-tumour inflammation and lymphocyte-dependent antitumour immune function, has been suggested as a prognostic factor for several cancers, including hepatocellular carcinoma (HCC). Methods In this study, a prospectively accrued cohort of 781 patients (493 HCC and 288 chronic liver disease (CLD) without HCC) were followed-up for more than 6 years. NLR levels between HCC and CLD patients were compared, and the effect of baseline NLR on overall survival amongst HCC patients was assessed via multivariable Cox regression analysis. Results On entry into the study (‘baseline’), there was no clinically significant difference in the NLR values between CLD and HCC patients. Amongst HCC patients, NLR levels closest to last visit/death were significantly higher compared to baseline. Multivariable Cox regression analysis showed that NLR was an independent prognostic factor, even after adjustment for the HCC stage. Conclusion NLR is a significant independent factor influencing survival in HCC patients, hence offering an additional dimension in prognostic models.

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