Association of Preoperative Prognostic Nutritional Index and Postoperative Acute Kidney Injury in Patients Who Underwent Hepatectomy for Hepatocellular Carcinoma

Various biological indicators are reportedly associated with postoperative acute kidney injury (AKI) in the surgical treatment of hepatocellular carcinoma (HCC). However, only a few studies have evaluated the association between the preoperative prognostic nutritional index (PNI) and postoperative AKI. This study evaluated the association of the preoperative PNI and postoperative AKI in HCC patients. We retrospectively analyzed 817 patients who underwent open hepatectomy between December 2007 and December 2015. Multivariate regression analysis was performed to evaluate the association between the PNI and postoperative AKI. Additionally, we evaluated the association between the PNI and outcomes such as postoperative renal replacement therapy (RRT) and mortality. Cox regression analysis was performed to assess the risk factors for one-year and five-year mortality. In the multivariate analysis, high preoperative PNI was significantly associated with a lower incidence of postoperative AKI (odds ratio (OR): 0.92, 95% confidence interval (CI): 0.85 to 0.99, p = 0.021). Additionally, diabetes mellitus and the use of synthetic colloids were significantly associated with postoperative AKI. PNI was associated with postoperative RRT (OR: 0.76, 95% CI: 0.60 to 0.98, p = 0.032) even after adjusting for other potential confounding variables. In the Cox regression analysis, high PNI was significantly associated with low one-year mortality (Hazard ratio (HR): 0.87, 95% CI: 0.81 to 0.94, p < 0.001), and five-year mortality (HR: 0.93, 95% CI: 0.90–0.97, p < 0.001). High preoperative PNI was significantly associated with a lower incidence of postoperative AKI and low mortality. These results suggest that the preoperative PNI might be a predictor of postoperative AKI and surgical prognosis in HCC patients undergoing open hepatectomy.

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The relationship between prognostic nutritional index and treatment response in patients with metastatic renal cell cancer

Journal of Oncology Pharmacy Practice ◽

10.1177/1078155219883004 ◽

2019 ◽

Vol 26 (5) ◽

pp. 1110-1116 ◽

Cited By ~ 1

Author(s):

Hatime Arzu Yasar ◽

Kadriye Bir Yucel ◽

Cagatay Arslan ◽

Gokhan Ucar ◽

Serdar Karakaya ◽

...

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Treatment Response ◽

Renal Cell Cancer ◽

Renal Cell ◽

Cox Regression ◽

Cell Cancer ◽

Prognostic Nutritional Index ◽

Cox Regression Analysis ◽

Nutritional Index

Introduction and aim To investigate the effect of the prognostic nutritional index on treatment response and survival in patients with metastatic renal cell cancer. Methods We retrospectively analyzed the treatment modalities; the demographic, clinical and pathological features of 396 patients with RCC and prognostic nutritional index. Based on the median value, patients were grouped as having low and high prognostic nutritional index values. Kaplan-Meier method was used for survival analysis, and Cox-regression analysis was used for multivariate analysis. Results The median overall survival was 39 months (95% CI 26.1–51.8), 28 months (95% CI 17.9–38) and 7 months (95% CI 4.7–9.2) in patients with favorable, intermediate and poor International Metastatic Renal Cell Carcinoma Database Consortium risk group, respectively. The difference between the groups was statistically significant (p < 0001). Overall survival was 11 months (95% CI 7.5–14.5) in the low-prognostic nutritional index (prognostic nutritional index ≤38.5) group, and 41 months (95% CI 30.5–51.4) in the high prognostic nutritional index (prognostic nutritional index >38.5) group (p < 0.001). In Cox regression analysis, Eastern Cooperative Oncology Group performance score (HR: 2.5), time to systemic treatment (HR: 1.7) and prognostic nutritional index (HR: 1.8) were associated with overall survival. Conclusion In patients with renal cell cancer, prognostic nutritional index is closely related to survival and has prognostic significance.

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O63: POST-OPERATIVE ACUTE KIDNEY INJURY IN PATIENTS UNDERGOING MAJOR GASTROINTESTINAL SURGERY— AN OBSERVATIONAL COHORT STUDY AND EXTERNAL VALIDATION OF A PROGNOSTIC MODEL

British Journal of Surgery ◽

10.1093/bjs/znab117.063 ◽

2021 ◽

Vol 108 (Supplement_1) ◽

Author(s):

Keyword(s):

Acute Kidney Injury ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Cox Regression ◽

Gastrointestinal Surgery ◽

Kidney Injury ◽

One Year ◽

Postoperative Aki

Abstract Introduction Acute kidney injury (AKI) is well-recognised as a significant cause of morbidity and mortality. Due to limited evidence on the longer-term implications, this study aimed to explore the association of postoperative AKI one-year survival and renal function in patients undergoing major gastrointestinal and liver surgery. Method Patients undergoing major gastrointestinal surgery in the prospective Outcomes of Kidney Injury after Surgery (OAKS) study across UK and Ireland were followed up at one-year postoperatively. The primary outcome was survival at 1-year and secondary outcomes included the composite “Major Adverse Kidney Events” outcome at day 365 (MAKE-365), with respective multivariable Cox-regression and logistic regression analysis performed. Result Of 62.2% of OAKS patients (n=3,575/5,745) with 1-year follow-up, there were no significant differences compared to those without follow-up. Among the follow-up cohort, 8.0% (n=269) patients died. On univariate analysis, patients experiencing 7-day postoperative AKI had a significantly higher hazard of death between 30 to 365 days postoperatively (HR: 2.10, 95% CI: 1.50-2.94, p<0.001) compared to patients who did not. This persisted on multivariable Cox-regression (HR: 1.67, 95% CI: 1.17-2.40, p=0.005). Furthermore, 9.1% (n=305) patients met the MAKE-365 endpoint. Multilevel logistic regression analysis demonstrated that the MAKE-365 endpoint was independently associated with both stage 1 (OR: 1.78, 95% CI: 1.22-2.61, p=0.003) and stage 2-3 7-day postoperative AKI (OR: 6.13, 95% CI: 3.97-9.45, p<0.001). Conclusion Post-operative AKI is associated with significantly higher rate of 1-year mortality and MAKE-365 endpoints. Improved monitoring of these patients may be warranted to identify and facilitate potential avenues for intervention Take-home message Post-operative AKI is associated with significantly higher rate of 1-year mortality. Hence, early detection and improved monitoring of patients with AKI with improve long-term outcomes of these patients.

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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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Identification of Immune-Related Prognostic mRNA and lncRNA in Patients with Hepatocellular Carcinoma

Journal of Oncology ◽

10.1155/2022/5313149 ◽

2022 ◽

Vol 2022 ◽

pp. 1-16

Author(s):

Dan Chen ◽

Xiaoting Li ◽

Hui Li ◽

Kai Wang ◽

Xianghua Tian

Keyword(s):

Hepatocellular Carcinoma ◽

Flow Cytometry ◽

T Cells ◽

Regression Analysis ◽

Survival Analysis ◽

Immune Cells ◽

Cox Regression ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Immune Related Genes

Background. As the most common hepatic malignancy, hepatocellular carcinoma (HCC) has a high incidence; therefore, in this paper, the immune-related genes were sought as biomarkers in liver cancer. Methods. In this study, a differential expression analysis of lncRNA and mRNA in The Cancer Genome Atlas (TCGA) dataset between the HCC group and the normal control group was performed. Enrichment analysis was used to screen immune-related differentially expressed genes. Cox regression analysis and survival analysis were used to determine prognostic genes of HCC, whose expression was detected by molecular experiments. Finally, important immune cells were identified by immune cell infiltration and detected by flow cytometry. Results. Compared with the normal group, 1613 differentially expressed mRNAs (DEmRs) and 1237 differentially expressed lncRNAs (DElncRs) were found in HCC. Among them, 143 immune-related DEmRs and 39 immune-related DElncRs were screened out. These genes were mainly related to MAPK cascade, PI3K-AKT signaling pathway, and TGF-beta. Through Cox regression analysis and survival analysis, MMP9, SPP1, HAGLR, LINC02202, and RP11-598F7.3 were finally determined as the potential diagnostic biomarkers for HCC. The gene expression was verified by RT-qPCR and western blot. In addition, CD4 + memory resting T cells and CD8 + T cells were identified as protective factors for overall survival of HCC, and they were found highly expressed in HCC through flow cytometry. Conclusion. The study explored the dysregulation mechanism and potential biomarkers of immune-related genes and further identified the influence of immune cells on the prognosis of HCC, providing a theoretical basis for the prognosis prediction and immunotherapy in HCC patients.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

10.21203/rs.3.rs-97188/v1 ◽

2020 ◽

Author(s):

Xiaohong - Liu ◽

Qian - Xu ◽

Zi-Jing - Li ◽

Bin - Xiong

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Expression Profiles ◽

Prognostic Significance ◽

Metabolic Reprogramming ◽

Cox Regression Analysis ◽

Metabolic Genes

Abstract BackgroundMetabolic reprogramming is an important hallmark in the development of malignancies. Numerous metabolic genes have been demonstrated to participate in the progression of hepatocellular carcinoma (HCC). However, the prognostic significance of the metabolic genes in HCC remains elusive. MethodsWe downloaded the gene expression profiles and clinical information from the GEO, TCGA and ICGC databases. The differently expressed metabolic genes were identified by using Limma R package. Univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) Cox regression analysis were utilized to uncover the prognostic significance of metabolic genes. A metabolism-related prognostic model was constructed in TCGA cohort and validated in ICGC cohort. Furthermore, we constructed a nomogram to improve the accuracy of the prognostic model by using the multivariate Cox regression analysis.ResultsThe high-risk score predicted poor prognosis for HCC patients in the TCGA cohort, as confirmed in the ICGC cohort (P < 0.001). And in the multivariate Cox regression analysis, we observed that risk score could act as an independent prognostic factor for the TCGA cohort (HR (hazard ratio) 3.635, 95% CI (confidence interval)2.382-5.549) and the ICGC cohort (HR1.905, 95%CI 1.328-2.731). In addition, we constructed a nomogram for clinical use, which suggested a better prognostic model than risk score.ConclusionsOur study identified several metabolic genes with important prognostic value for HCC. These metabolic genes can influence the progression of HCC by regulating tumor biology and can also provide metabolic targets for the precise treatment of HCC.

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Construction and Validation of a Prognostic Risk Model Based on Autophagy-Related Genes in Hepatocellular Carcinoma Cells

10.21203/rs.3.rs-714025/v1 ◽

2021 ◽

Author(s):

Rui Feng ◽

Jian Li ◽

Weiling Xuan ◽

Hanbo Liu ◽

Dexin Cheng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Differential Expression ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Univariate Analysis ◽

Differential Expression Analysis ◽

Cox Regression Analysis ◽

Dismal Prognosis

Abstract Background Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer and the main cause of cancer mortality. Its high complexity and dismal prognosis bring dramatic difficulty to treatment. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into seeking novel biomarkers for HCC. Methods Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. GO and KEGG pathway analyses were conducted on these genes. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by ICGC validation set. Results Altogether, 42 autophagy-related differential genes were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model was constituted by 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, FKBP1A. Validation confirmed the accuracy and independence of this model in predicting HCC patient’s prognosis. Conclusion A total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.

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De Novo Hepatocellular Carcinoma in Hepatitis C-Related Cirrhosis: Are Advanced Glycation End Products a Key Driver?

Frontiers in Cellular and Infection Microbiology ◽

10.3389/fcimb.2021.662431 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ahmed Abdel-Razik ◽

Walaa Shabana ◽

Ahmed Mohamed El Nakib ◽

Mostafa Abdelsalam ◽

Ahmed Abdelwahab ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Hepatitis C ◽

Advanced Glycation End Products ◽

Cox Regression ◽

Diabetic Patients ◽

Advanced Glycation ◽

Cox Regression Analysis ◽

Glycation End Products ◽

End Products

Background and PurposeThe advanced glycation end products (AGEs) have been implicated in different diseases’ pathogenesis, but their role in hepatocellular carcinoma (HCC) is still a matter of debate. This study aims to investigate the association of AGEs with HCC development in patients with hepatitis C-related cirrhosis.MethodsOnly 153 of the 181 non-diabetic patients with cirrhosis were consecutively involved in this pilot cohort prospective study, along with 34 healthy control participants. Demographic characteristics, biochemical parameters, clinical data, and AGEs levels in all subjects at the starting point and every year after that for two years were assessed. Multivariable Cox regression analysis was used to settle variables that could predict HCC development within this period.ResultsHCC developed in 13 (8.5%) patients. Univariate Cox regression analysis reported that body mass index (P=0.013), homeostatic model assessment-insulin resistance (P=0.006), alpha-fetoprotein (P <0.001), and AGEs levels (P <0.001) were related to HCC development. After adjusting multiple confounders, the multivariable Cox regression model has revealed that AFP and AGEs were the powerful parameters related to the HCC occurrence (all P<0.05). AGEs at a cutoff value of more than 79.6 ng/ml had 100% sensitivity, 96.4% specificity, and 0.999 area under the curve (all P<0.001), using the receiver operating characteristic curve, for prediction of HCC development.ConclusionThis work suggests that AGEs are associated with an increased incidence of HCC, particularly in cirrhosis, which is encouraging in decreasing the risk of HCC in these patients.

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Prognostic Value and Regulatory Network of Immune-Related Genes in Hepatocellular Carcinoma

10.21203/rs.3.rs-123214/v1 ◽

2020 ◽

Author(s):

Xiang Zhou ◽

Keying Zhang ◽

Fa Yang ◽

Chao Xu ◽

Jianhua Jiao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Risk Model ◽

Cox Regression ◽

Wilcoxon Test ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background: Hepatocellular carcinoma (HCC) is a disease with higher morbidity, mortality, and poor prognosis in the whole world. Understanding the crosslink between HCC and the immune system is essential for people to uncover a few potential and valuable therapeutic strategies. This study aimed to reveal the correlation between HCC and immune-related genes and establish a clinical evaluation model. Methods: We had analyzed the clinical information consisted of 373 HCC and 49 normal samples from the cancer genome atlas (TCGA). The differentially expressed genes (DEGs) were selected by the Wilcoxon test and the immune-related differentially expressed genes (IRDEGs) in DEGs were identified by matching DEGs with immune-related genes downloaded from the ImmPort database. Furthermore, the univariate Cox regression analysis and multivariate Cox regression analysis were performed to construct a prognostic risk model. Then, twenty-two types of tumor immune-infiltrating cells (TIICs) were downloaded from Tumor Immune Estimation Resource (TIMER) and were used to construct the correlational graphs between the TIICs and risk score by the CIBERSORT. Subsequently, the transcription factors (TFs) were gained in the Cistrome website and the differentially expressed TFs (DETFs) were achieved. Finally, the KEGG pathway analysis and GO analysis were performed to further understand the molecular mechanisms between DETFs and PDIRGs.Results: In our study, 5839 DEGs, 326 IRDEGs, and 31 prognosis-related IRDEGs (PIRDEGs) were identified. And 8 optimal PIRDEGs were employed to construct a prognostic risk model by multivariate Cox regression analysis. The correlation between risk genes and clinical characterizations and TIICs has verified that the prognostic model was effective in predicting the prognosis of HCC patients. Finally, several important immune-related pathways and molecular functions of the eight PIRDEGs were significantly enriched and there was a distinct association between the risk IRDEGs and TFs. Conclusion: The prognostic risk model showed a more valuable predicting role for HCC patients, and produced many novel therapeutic targets and strategies for HCC.

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Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Endoplasmic Reticulum Stress-Related Gene Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.641487 ◽

2021 ◽

Vol 11 ◽

Author(s):

Peng Liu ◽

Jinhong Wei ◽

Feiyu Mao ◽

Zechang Xin ◽

Heng Duan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Endoplasmic Reticulum ◽

Regression Analysis ◽

Endoplasmic Reticulum Stress ◽

Cox Regression ◽

Prognostic Biomarker ◽

Cancer Genome ◽

Survival Prediction ◽

Training Cohort ◽

Cox Regression Analysis

Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide and its incidence continues to increase year by year. Endoplasmic reticulum stress (ERS) caused by protein misfolding within the secretory pathway in cells and has an extensive and deep impact on cancer cell progression and survival. Growing evidence suggests that the genes related to ERS are closely associated with the occurrence and progression of HCC. This study aimed to identify an ERS-related signature for the prospective evaluation of prognosis in HCC patients. RNA sequencing data and clinical data of patients from HCC patients were obtained from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC). Using data from TCGA as a training cohort (n=424) and data from ICGC as an independent external testing cohort (n=243), ERS-related genes were extracted to identify three common pathways IRE1, PEKR, and ATF6 using the GSEA database. Through univariate and multivariate Cox regression analysis, 5 gene signals in the training cohort were found to be related to ERS and closely correlated with the prognosis in patients of HCC. A novel 5-gene signature (including HDGF, EIF2S1, SRPRB, PPP2R5B and DDX11) was created and had power as a prognostic biomarker. The prognosis of patients with high-risk HCC was worse than that of patients with low-risk HCC. Multivariate Cox regression analysis confirmed that the signature was an independent prognostic biomarker for HCC. The results were further validated in an independent external testing cohort (ICGC). Also, GSEA indicated a series of significantly enriched oncological signatures and different metabolic processes that may enable a better understanding of the potential molecular mechanism mediating the progression of HCC. The 5-gene biomarker has a high potential for clinical applications in the risk stratification and overall survival prediction of HCC patients. In addition, the abnormal expression of these genes may be affected by copy number variation, methylation variation, and post-transcriptional regulation. Together, this study indicated that the genes may have potential as prognostic biomarkers in HCC and may provide new evidence supporting targeted therapies in HCC.

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