Elevated CDK5R1 predicts worse prognosis in hepatocellular carcinoma based on TCGA data

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with rapid progression, high recurrence rate and poor prognosis. The objective of our investigation was to explore the prognostic value of CDK5R1 in HCC. Methods: The raw data of HCC raw data were downloaded from The Cancer Genome Atlas (TCGA) database. The Wilcoxon signed-rank test, Kruskal-Wallis test and logistic regression were applied to investigate the relevance between the CDK5R1 expression and clinicopathologic characteristics in HCC. Kaplan-Meier and Cox regression analysis were employed to examine the association between clinicopathologic features and survival. Gene set enrichment analysis (GSEA) was applied to annotate the biological function of CDK5R1. Results: CDK5R1 was highly expressed in HCC tissues. The high expression of CDK5R1 in HCC tissues was significantly associated with tumor status (P=0.00), new tumor event (P=0.00), clinical stage (P=0.00), topography (P=0.00). Elevated CDK5R1 had significant correlation with worse overall survival (OS) (P=7.414e−04), disease-specific survival (DSS) (P=5.642e−04), disease-free interval (DFI) (P=1.785e−05), and progression-free interval (PFI) (P=2.512e−06). Besides, univariate and multivariate Cox regression analysis uncovered that increased CDK5R1 can independently predict adverse OS (P=0.037, hazard ratio [HR]=1.7 (95% CI [1.0-2.7])), DFI (P=0.007, hazard ratio [HR]=3.0 (95% CI [1.4-6.7])), PFI (P=0.007, hazard ratio [HR]=2.8 (95% CI [1.3-5.9])). GSEA disclosed that notch signaling pathway and non-small cell lung cancer were prominently enriched in CDK5R1 high expression phenotype. Conclusions: Increased CDK5R1 may act as a promising independent prognostic factor of poor survival in HCC.

Download Full-text

Expression of LOX Suggests Poor Prognosis in Gastric Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.718986 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jinfeng Zhu ◽

Chen Luo ◽

Jiefeng Zhao ◽

Xiaojian Zhu ◽

Kang Lin ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Poor Prognosis ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

High Expression ◽

Expression Level ◽

Cox Regression Analysis

Background: Lysyl oxidase (LOX) is a key enzyme for the cross-linking of collagen and elastin in the extracellular matrix. This study evaluated the prognostic role of LOX in gastric cancer (GC) by analyzing the data of The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset.Methods: The Wilcoxon rank-sum test was used to calculate the expression difference of LOX gene in gastric cancer and normal tissues. Western blot and immunohistochemical staining were used to evaluate the expression level of LOX protein in gastric cancer. Kaplan-Meier analysis was used to calculate the survival difference between the high expression group and the low expression group in gastric cancer. The relationship between statistical clinicopathological characteristics and LOX gene expression was analyzed by Wilcoxon or Kruskal-Wallis test and logistic regression. Univariate and multivariate Cox regression analysis was used to find independent risk factors affecting the prognosis of GC patients. Gene set enrichment analysis (GSEA) was used to screen the possible mechanisms of LOX and GC. The CIBERSORT calculation method was used to evaluate the distribution of tumor-infiltrating immune cell (TIC) abundance.Results: LOX is highly expressed in gastric cancer tissues and is significantly related to poor overall survival. Wilcoxon or Kruskal-Wallis test and Logistic regression analysis showed, LOX overexpression is significantly correlated with T-stage progression in gastric cancer. Multivariate Cox regression analysis on TCGA and GEO data found that LOX (all p < 0.05) is an independent factor for poor GC prognosis. GSEA showed that high LOX expression is related to ECM receptor interaction, cancer, Hedgehog, TGF-beta, JAK-STAT, MAPK, Wnt, and mTOR signaling pathways. The expression level of LOX affects the immune activity of the tumor microenvironment in gastric cancer.Conclusion: High expression of LOX is a potential molecular indicator for poor prognosis of gastric cancer.

Download Full-text

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.

Download Full-text

A Novel Pyroptosis-related Prognostic Model for Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.770301 ◽

2021 ◽

Vol 9 ◽

Author(s):

Qianqian Wu ◽

Sutian Jiang ◽

Tong Cheng ◽

Manyu Xu ◽

Bing Lu

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Model ◽

Cox Regression ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Prognostic Biomarkers ◽

Cox Regression Analysis ◽

Public Data

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor because of its significant heterogeneity and complicated molecular pathogenesis. Novel prognostic biomarkers are urgently needed because no effective and reliable prognostic biomarkers currently exist for HCC patients. Increasing evidence has revealed that pyroptosis plays a role in the occurrence and progression of malignant tumors. However, the relationship between pyroptosis-related genes (PRGs) and HCC patient prognosis remains unclear. In this study, 57 PRGs were obtained from previous studies and GeneCards. The gene expression profiles and clinical data of HCC patients were acquired from public data portals. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a risk model using TCGA data. Additionally, the risk model was further validated in an independent ICGC dataset. Our results showed that 39 PRGs were significantly differentially expressed between tumor and normal liver tissues in the TCGA cohort. Functional analysis confirmed that these PRGs were enriched in pyroptosis-related pathways. According to univariate Cox regression analysis, 14 differentially expressed PRGs were correlated with the prognosis of HCC patients in the TCGA cohort. A risk model integrating two PRGs was constructed to classify the patients into different risk groups. Poor overall survival was observed in the high-risk group of both TCGA (p < 0.001) and ICGC (p < 0.001) patients. Receiver operating characteristic curves demonstrated the accuracy of the model. Furthermore, the risk score was confirmed as an independent prognostic indicator via multivariate Cox regression analysis (TCGA cohort: HR = 3.346, p < 0.001; ICGC cohort: HR = 3.699, p < 0.001). Moreover, the single-sample gene set enrichment analysis revealed different immune statuses between high- and low-risk groups. In conclusion, our new pyroptosis-related risk model has potential application in predicting the prognosis of HCC patients.

Download Full-text

Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.618976 ◽

2021 ◽

Vol 11 ◽

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Gene Set Enrichment ◽

Cox Regression Analysis ◽

Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

Download Full-text

KIAA0101 in Malignant Pleural Mesothelioma: a Potential Diagnostic and Prognostic Marker

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210707105634 ◽

2021 ◽

Vol 24 ◽

Author(s):

Ping Lin ◽

Yuean Zhao ◽

Xiaoqian Li ◽

Zongan Liang

Keyword(s):

Gene Expression ◽

Regression Analysis ◽

Malignant Pleural Mesothelioma ◽

Cox Regression ◽

Expression Profiles ◽

Gene Set Enrichment Analysis ◽

High Expression ◽

Pleural Mesothelioma ◽

Hub Genes ◽

Cox Regression Analysis

Background: Currently, there are no reliable diagnostic and prognostic markers for malignant pleural mesothelioma (MPM). The objective of this study was to identify hub genes that could be helpful for diagnosis and prognosis in MPM by using bioinformatics analysis. Materials and Methods: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA), LASSO regression analysis, Cox regression analysis, and Gene Set Enrichment Analysis (GSEA) were performed to identify hub genes and their functions. Results: A total of 430 up-regulated and 867 downregulated genes in MPM were identified based on the GSE51024 dataset. According to the WGCNA analysis, differentially expressed genes were classified into 8 modules. Among them, the pink module was most closely associated with MPM. According to genes with GS > 0.8 and MM > 0.8, six genes were selected as candidate hub genes (NUSAP1, TOP2A, PLOD2, BUB1B, UHRF1, KIAA0101) in the pink module. In the LASSO model, three genes (NUSAP1, PLOD2, and KIAA0101) were identified with non-zero regression coefficients and were considered hub genes among the 6 candidates. The hub gene-based LASSO model can accurately distinguish MPM from controls (AUC = 0.98). Moreover, the high expression level of KIAA0101, PLOD2, and NUSAP1 were all associated with poor prognosis compared to the low level in Kaplan–Meier survival analyses. After further multivariate Cox analysis, only KIAA0101 (HR = 1.55, 95% CI = 1.05-2.29) was identified as an independent prognostic factor among these hub genes. Finally, GSEA revealed that high expression of KIAA0101 was closely associated with 10 signaling pathways. Conclusion: Our study identified several hub genes relevant to MPM, including NUSAP1, PLOD2, and KIAA0101. Among these genes, KIAA0101 appears to be a useful diagnostic and prognostic biomarker for MPM, which may provide new clues for MPM diagnosis and therapy.

Download Full-text

Ferroptosis-Related lncRNAs Signature is a Prognostic Factor in Hepatocellular Carcinoma: A Study Based on TCGA Data

10.21203/rs.3.rs-847334/v1 ◽

2021 ◽

Author(s):

Xiangjin Hu ◽

Sailun Wang ◽

Jia Guo ◽

Fang Xiong ◽

Jun Lv

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Signaling Pathway ◽

Cox Regression ◽

Pearson Correlation ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Sphingolipid Metabolism ◽

Immune Microenvironment ◽

Cox Regression Analysis

Abstract Background Hepatocellular carcinoma (HCC) is the most common type of liver cancer and is the fourth leading cause of cancer-related death worldwide. Ferroptosis is a form of iron-dependent programmed cell death, and is characterized by intracellular accumulation of reactive oxygen species (ROS). Long non-coding RNAs (lncRNAs), as valuable prognostic factors for HCC patients, play a vital role in regulating ferroptosis. Methods RNA-sequencing datasets and ferroptosis-related genes were retrieved from The Cancer Genome Atlas (TCGA) database and the Molecular Signature Database. we performed Pearson correlation analysis between the lncRNAs and ferroptosis-related genes, and subsequently used regression analysis (univariate Cox analysis, multivariate Cox regression analysis, and Lasso regression analysis) to screen the ferroptosis-related lncRNAs with prognostic value in HCC, the prognostic ferroptosis-related lncRNAs signature (FRLS) was finally constructed. In addition, we reevaluated the model in terms of survival, clinical characteristics, and immune microenvironment. Results Univariate Cox regression analysis revealed 34 differently expressed ferroptosis-related lncRNAs related to the prognosis of HCC. Among them, 12 ferroptosis-related lncRNAs (LUCAT1, LINC01224, THUMPD3-AS1, AC116025.2, LINC00942, SNHG10, AC131009.1, POLH-AS1, MKLN1-AS, LINC01138, LNCSRLR, AL031985.3) were regarded as independent prognosis predictors of HCC, and were incorporated into the construction of the prognostic FRLS. Patients were divided into two groups based on the prognostic FRLS. Kaplan–Meier survival plot showed that patients in the high-risk groups exhibited shorter overall survival (OS) than those in low-risk groups (P < 0.001). Compared with clinical data, the area under curve (AUC) values of the risk factors, decision curve analysis (DCA), the AUC values of different years and multivariate Cox regression suggested that the signature had better predictive power. Gene set enrichment analysis (GSEA) revealed the potential pathways of 12 ferroptosis-related lncRNAs, including sphingolipid-metabolism, mTOR signaling pathway, notch signaling pathway, homologous recombination, endocytosis, cell cycle, etc. Immune microenvironment including tumor-infiltrating immune cells, immune-related functions, checkpoint-related genes and N6-methyladenosine (m6A)-related mRNA were also significantly different between the two risk groups. Conclusions This study constructed 12 FRLS for HCC patients to predict survival, which may provide promising targets for the therapy of HCC.

Download Full-text

Upregulation of FGD6 Predicts Poor Prognosis in Gastric Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.672595 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jianmin Zeng ◽

Man Li ◽

Huasheng Shi ◽

Jianhui Guo

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Prognostic Factor ◽

Poor Prognosis ◽

Cox Regression ◽

Independent Prognostic Factor ◽

Gene Set Enrichment Analysis ◽

High Expression ◽

Cox Regression Analysis ◽

The Relationship

Background: The aim of this study was to investigate the prognostic significance of faciogenital dysplasia 6 (FGD6) in gastric cancer (GC).Methods: The data of GC patients from The Cancer Genome Atlas (TCGA) database were used for the primary study. Then, our data were validated by the GEO database and RuiJin cohort. The relationship between the FGD6 level and various clinicopathological features was analyzed by logistic regression and univariate Cox regression. Multivariate Cox regression analysis was used to evaluate whether FGD6 was an independent prognostic factor for survival of patients with GC. The relationship between FGD6 and overall survival time was explored by the Kaplan–Meier method. In addition, gene set enrichment analysis (GSEA) was performed to investigate the possible biological processes of FGD6.Results: The FGD6 level was significantly overexpressed in GC tissues, compared with adjacent normal tissues. The high expression of FGD6 was related to a high histological grade, stage, and T classification and poor prognosis of GC. Multivariate Cox regression analysis showed that FGD6 was an independent prognostic factor for survival of patients with GC. GSEA identified that the high expression of FGD6 was mainly enriched in regulation of actin cytoskeleton.Conclusion: FGD6 may be a prognostic biomarker for predicting the outcome of patients with GC.

Download Full-text

Contributions and Prognostic Values of N6-Methyladenosine RNA Methylation Regulators in Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2020.614566 ◽

2021 ◽

Vol 11 ◽

Author(s):

Li-Wen Qi ◽

Jian-Hui Jia ◽

Chen-Hao Jiang ◽

Jian-Ming Hu

Keyword(s):

Hepatocellular Carcinoma ◽

Rna Binding ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Biological Processes ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Free Interval ◽

Cancer Types

IntroductionThe methylation at position N6 of adenine is called N6-methyladenosine (m6A). This transcriptional RNA modification exerts a very active and important role in RNA metabolism and in other biological processes. However, the activities of m6A associated with malignant liver hepatocellular carcinoma (LIHC) are unknown and are worthy of study.Materials and MethodsUsing the data of University of California, Santa Cruz (UCSC), the expression of M6A methylation regulators in pan-cancer was evaluated as a screening approach to identify the association of M6A gene expression and 18 cancer types, with a specific focus on LIHC. LIHC datasets of The Cancer Genome Atlas (TCGA) were used to explore the expression of M6A methylation regulators and their clinical significance. Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were used to explore the underlying mechanism based on the evaluation of aberrant expression of m6A methylation regulators.ResultsThe expression alterations of m6A-related genes varied across cancer types. In LIHC, we found that in univariate Cox regression analysis, up-regulated m6A modification regulators were associated with worse prognosis, except for ZC3H13. Kaplan–Meier survival curve analysis indicated that higher expression of methyltransferase-like protein 3 (METTL3) and YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) genes related to the worse survival rate defined by disease-related survival (DSS), overall survival (OS), progression-free interval (PFI), and disease-free interval (DFI). Up-regulated m6A methylation regulator group (cluster2) obtained by consensus clustering was associated with poor prognosis. A six-gene prognostic signature established using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm performed better in the early (I + II; T1 + T2) stages than in the late (III + IV; T3 + T4) stages of LIHC. Using the gene signature, we constructed a risk score and found that it was an independent predictive factor for prognosis. Using GSEA, we identified processes involved in DNA damage repair and several biological processes associated with malignant tumors that were closely related to the high-risk group.ConclusionIn summary, our study identified several genes associated with m6A in LIHC, especially METTL3 and YTHDF1, and confirmed that a risk signature comprised of m6A-related genes was able to forecast prognosis.

Download Full-text

Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽

10.1186/s12885-021-08041-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lin Chen ◽

Yuxiang Dong ◽

Yitong Pan ◽

Yuhan Zhang ◽

Ping Liu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Validation Dataset ◽

Immune Gene ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

Download Full-text

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.

Download Full-text