Tumor enhancement ratio with unenhanced imaging is an independent prognostic factor for patients with hepatocellular carcinoma after transarterial chemoembolization

Objective To investigative whether the odds tumor enhancement ratio (OTER) on cross-sectional imaging is a prognostic factor for hepatocellular carcinoma after transarterial chemoembolization (TACE). Methods This study involved 126 patients who underwent TACE from May 2015 to March 2019. The signal intensity/Hounsfield units (HU) was measured by placing regions of interest on the tumor and surrounding liver in unenhanced and arterial-phase contrast-enhanced cross-sectional images. The OTER was calculated as follows: OTER = (HUTUMORart − HUTUMORun)/ (HULIVERart − HULIVERun). Univariate analysis was performed to determine the factors associated with overall survival (OS). Variables with a P value of <0.10 were included in the multivariate Cox regression analysis. Results The median OS was 757 days. Tumors with a peripheral location, small size, and low OTER had better OS than those with a central location, large size, and high OTER. OS did not differ according to the extent of tumor involvement or tumor enhancement pattern. The OTER, tumor location, and size were included in the multivariate Cox regression analysis. A low OTER was the predictor of better OS. Conclusion A high OTER is a risk factor for poor OS in patients undergoing TACE. This should be taken into consideration before the procedure.

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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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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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Idenfication of a novel signature based on mTORC1 pathway for hepatocellular carcinoma.

10.21203/rs.2.23745/v1 ◽

2020 ◽

Author(s):

Zhuomao Mo ◽

Shaoju Luo ◽

Zhirui Cao ◽

Hao Hu ◽

Ling Yu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Prognostic Factor ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Signal Pathway ◽

Cancer Genome ◽

Cox Regression Analysis

Abstract Background mTORC1 signal pathway play a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between mTORC1 signal pathway and HCC and establish the related genes signature. Methods HCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases. The genes to be included in mTORC1-assiociated signature were selected by performing univariate, multivariate Cox regression analysis and lasso regression analysis. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, a nomogram was established and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the six genes ( ETF1, GSR, SKAP2, HSPD1, CACYBP and PNP ). Under the grouping from signature, patients in the high- risk group showed worse survival than those in the low-risk group in both three datasets. The univariate and multivariate Cox regression analysis indicated that mTORC1 related signature can be the potential independent prognostic factor in HCC. Conclusion The mTORC1 associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC.

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An Autophagy-Related Gene-Based Prognostic Risk Signature for Hepatocellular Carcinoma: Construction and Validation

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/5770228 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

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 ◽

Primary Liver Cancer ◽

Univariate Analysis ◽

Differential Expression Analysis ◽

Cox Regression Analysis

Background. Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer. Treatment is dramatically difficult due to its high complexity and poor prognosis. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into novel biomarkers for HCC. Methods. Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. These genes were subjected to GO and KEGG pathway analyses. 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 the ICGC validation set. Results. To sum up, 42 differential genes relevant to autophagy 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 constituted 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, and FKBP1A. Validation confirmed the accuracy and independence of this model in predicting the 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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A Novel Signature Based On Mtorc1 Pathway In Hepatocellular Carcinoma

10.21203/rs.3.rs-18272/v1 ◽

2020 ◽

Author(s):

Zhuomao Mo ◽

Shuqiao Zhang ◽

Shijun Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

T Cells ◽

Regression Analysis ◽

Prognostic Factor ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Signal Pathway ◽

Cancer Genome ◽

Cox Regression Analysis

Abstract BackgroundmTORC1 signal pathway play a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between mTORC1 signal pathway and HCC and establish the related genes signature.MethodsHCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases. The genes to be included in mTORC1-assiociated signature were selected by performing univariate, multivariate Cox regression analysis and lasso regression analysis. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Moreover, the correlation between signature and immune cells infiltration was investigated. Furthermore, a nomogram was established and evaluated by C-index and calibration plot.ResultsThe signature was established with the six genes (ETF1, GSR, SKAP2, HSPD1, CACYBP and PNP). Under the grouping from signature, patients in the high- risk group showed worse survival than those in the low-risk group in both three datasets. The signature was found significantly associated with the infiltration of B cells, CD4+T-cells, CD8+T-cells, dendritic cells, macrophages and neutrophils. The univariate and multivariate Cox regression analysis indicated that mTORC1 related signature can be the potential independent prognostic factor in HCC. Finally, the nomogram involved age, gender, stage and signature have been established.ConclusionThe mTORC1 associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC. *These authors contributed equally.

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Prognostic factors in advanced seminoma in the era of etoposide- and cisplatin-based chemotherapy: Importance of pretreatment LDH-level

Journal of Clinical Oncology ◽

10.1200/jco.2009.27.15_suppl.e16038 ◽

2009 ◽

Vol 27 (15_suppl) ◽

pp. e16038-e16038

Author(s):

A. Tryakin ◽

M. Fedyanin ◽

A. Bulanov ◽

D. Titov ◽

G. Allakhverdiyeva ◽

...

Keyword(s):

Regression Analysis ◽

Prognostic Factors ◽

Prognostic Factor ◽

Cox Regression ◽

Univariate Analysis ◽

Testicular Tumor ◽

Progression Free Survival ◽

Risk Groups ◽

Cox Regression Analysis ◽

Metastatic Sites

e16038 Background: The commonly used IGCCCG classification probably underestimates other prognostic factors (tumor markers, stage) for advanced seminoma, which was shown later (Fossa S., 1997). Furthermore, in contrast to nonseminoma different cisplatin-based regimens have not been directly compared in this population. We performed an analysis to review the outcome and prognostic factors of patients (pts) with advanced seminoma treated in our center during the last two decades. Methods: From 1983 to 2005, 250 chemotherapy (CT)-naïve pts with advanced seminoma received induction platinum-based CT, which was divided as an “older” (76 pts) and “modern” (174 pts) one. “Older CT” included cyclophosphamide + cisplatin (46 pts), ifosfamide + carboplatin (12 pts), PVB (8 pts) and other regimens (10 pts). “Modern CT” contained BEP (26 pts) and EP (148 pts) regimens. 227 (91%) pts had primary testicular tumor, 241 (96%) pts belonged to IGCCCG good prognostic group. Median follow-up was 57 (range, 3–276) months for the pts who survived. Prognostic factors were analyzed in “modern CT” group. Progression-free survival (PFS) was an end-point for Cox‘ stepwise regression analysis. Results: “Modern CT” significantly improved PFS (5-years, 91% and 74%, p = 0.002) but not OS (5-years, 92% and 89%, p = 0.28), which could be explained by effective salvage CT. Univariate analysis revealed following factors as significant: number of metastatic sites, presence of pulmonary metastases, RPLN size, hCG level, and LDH level. Cox‘ regression analysis showed pre-CT LDH as the only prognostic factor for PFS (HR 7,6, 95% CI 1,6–36.3). Using cut-off 2 x upper limit of normal for LDH level, “modern CT” group can be divided into favorable (105 [60%] pts) and unfavorable (69 (40%) pts) groups with 5-years DFS 98% vs. 78% (HR 11.1, 95% CI 3.2–33.3) and 5-years OS 99% vs. 80% (HR 11.07, 95% CI 3.09–27.92), respectively. Conclusions: Comparing with older cisplatin-based regimens, the new ones (BEP or EP) improved PFS without significant influence on OS in pts with advanced seminoma. Pre-treatment LDH level is an important independent prognostic factor, which could help stratify pts better into risk groups. Further studies with risk-adapted policy in advanced seminoma are warranted. No significant financial relationships to disclose.

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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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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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