Simple parameters predicting extrahepatic recurrence after curative hepatectomy for hepatocellular carcinoma

AbstractExtrahepatic recurrence (EHR) after curative hepatectomy for hepatocellular carcinoma (HCC) is associated with a poor prognosis. We investigated the features of EHR and identified its predictive factors. This retrospective study included 398 treatment-naive patients who underwent curative hepatectomy for HCC at two tertiary hospitals. Multivariate Cox-regression analysis was performed to identify the variables associated with EHR. EHR was diagnosed in 94 patients (23.6%) over a median follow-up period of 5.92 years, most commonly in the lungs (42.6%). The 5-/10-year cumulative rates of HCC recurrence and EHR were 63.0%/75.6% and 18.1%/35.0%, respectively. The median time to EHR was 2.06 years. Intrahepatic HCC recurrence was not observed in 38.3% of patients on EHR diagnosis. On multivariate analysis, pathologic modified Union for International Cancer Control stage (III, IVa), surgical margin involvement, tumor necrosis, sum of tumor size > 7 cm, and macrovascular invasion were predictive factors of EHR. Four risk levels and their respective EHR rates were defined as follows: very low risk, 1-/5-year, 3.1%/11.6%; low risk, 1-/5-year, 12.0%/27.7%; intermediate risk, 1-/5-year, 36.3%/60.9%; and high risk, 1-year, 100.0%. Our predictive model clarifies the clinical course of EHR and could improve the follow-up strategy to improve outcomes.

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Clinical features of extrahepatic recurrence after curative hepatectomy for hepatocellular carcinoma: simple parameters predicting extrahepatic recurrence

10.21203/rs.3.rs-235985/v1 ◽

2021 ◽

Author(s):

Jae Hyun Yoon ◽

Won Jae Lee ◽

Sun Min Kim ◽

Kwang Tack Kim ◽

Hee Joon Kim ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Multivariate Analysis ◽

Cox Regression ◽

Low Risk ◽

Extrahepatic Recurrence ◽

Risk Levels ◽

Treatment Naïve ◽

Hcc Recurrence ◽

Curative Hepatectomy

Abstract Background Extrahepatic recurrence (EHR) after curative hepatectomy for hepatocellular carcinoma (HCC) is associated with a poor prognosis. We investigated the features of EHR and identified its predictive factors. Methods This retrospective study included 398 treatment-naive patients who underwent curative hepatectomy for HCC at two tertiary hospitals. Multivariate analysis via Cox-regression was performed to identify the variables associated with EHR. Results EHR was diagnosed in 94 patients (23.6%) over a median follow-up period of 5.92 years, most commonly in the lungs (42.6%). The 5-/10-year cumulative rates of HCC recurrence and EHR were 63.0%/75.6% and 18.1%/35.0%, respectively.. The median time to EHR was 2.06 years. Intrahepatic HCC recurrence was not observed in 38.3% of patients on EHR diagnosis. On multivariate analysis, bile duct invasion, tumor necrosis, sum of tumor size > 7 cm, macrovascular invasion, first recurrence free survival < 1 year, and serum alpha fetoprotein > 400 IU/mL during recurrence were predictive of EHR. Four risk levels and their respective EHR were defined: very low risk, 2-/5-year, 0.7%/14.2%; low risk, 2-/5-year, 6.4%/31.0%; intermediate risk, 2-/5-year, 21.9%/73.1%; and high risk, 2-/3-year, 70.8%/100.0%. Conclusion Our predictive model clarifies the clinical course of EHR and could improve the follow-up strategy to improve outcomes.

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Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

10.21203/rs.3.rs-17851/v1 ◽

2020 ◽

Author(s):

Li Liu ◽

She Tian ◽

Zhu Li ◽

Yongjun Gong ◽

Hao Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Principal Component ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

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Factors influencing early recurrence of hepatocellular carcinoma after curative resection

Journal of International Medical Research ◽

10.1177/0300060520945552 ◽

2020 ◽

Vol 48 (8) ◽

pp. 030006052094555

Author(s):

Yu Zhu ◽

Lingling Gu ◽

Ting Chen ◽

Guoqun Zheng ◽

Chao Ye ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Curative Resection ◽

Cox Regression ◽

Postoperative Recurrence ◽

Early Recurrence ◽

Tumor Diameter ◽

Cox Regression Analysis ◽

Factors Influencing ◽

Linear Manner ◽

Hcc Recurrence

Objective To identify the factors influencing early recurrence in patients with hepatocellular carcinoma (HCC) after curative resection. Methods Clinical data for 99 patients with HCC undergoing curative resection were analyzed. The clinicopathological factors influencing early recurrence were analyzed by Cox regression. Results Twenty-five of 99 patients (25.3%) suffered from early recurrence. There were significant differences between patients with and without recurrence in terms of tumor diameter, tumor capsular integrity, and preoperative alpha fetoprotein level. Cox regression analysis revealed that a tumor diameter >2.6 cm and preoperatively increased total bilirubin (TBL) level were risk factors for postoperative recurrence, while tumor capsular integrity had a protective effect on postoperative recurrence. After adjusting for preoperative TBL level and tumor capsular integrity, the risk of HCC recurrence was markedly increased in line with increasing tumor diameter in a non-linear manner. Conclusion Tumor diameter >2.6 cm and preoperatively increased TBL level are associated with a higher risk of early recurrence after curative resection in patients with HCC, while tumor capsular integrity is associated with a lower risk of early recurrence.

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Development and validation of a robust immune-related risk signature for hepatocellular carcinoma

10.21203/rs.3.rs-16346/v1 ◽

2020 ◽

Author(s):

Zaoqu Liu ◽

Dechao Jiao ◽

Xueliang Zhou ◽

Yuan Yao ◽

Zhaonan Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Functional Enrichment ◽

Differentially Expressed ◽

Risk Genes ◽

Cox Regression Analysis ◽

Related Risk

Abstract Background: A growing amount of evidence has suggested immune-related genes (IRGs) play a key role in the development of hepatocellular carcinoma (HCC). However, there have been no investigations proposing a reliable prognostic signature in terms of tumor immunology. This study aimed to develop a robust signature based on IRGs in HCC.Methods: A total of 597 HCC patients were enrolled. The TCGA database was utilized for discovery, and the ICGC database was utilized for validation. Multiple algorithms (including univariate Cox, LASSO, and multivariate Cox regression) were performed to identify key prognostic IRGs and establish an immune-related risk signature. Bioinformatics analysis and R soft tools were utilized to annotate underlying biological functions. Results: A total of 1416 differentially expressed mRNAs (DEMs) were screened in the TCGA cohort, of which 90 were differentially expressed IRGs (DEIRGs). Using univariate Cox regression analysis, we identified 33 prognostically relevant DEIRGs. Using LASSO regression and multivariate Cox regression analysis, we extracted 8 optimal DEIRGs (APLN, CDK4, CXCL2, ESR1, IL1RN, PSMD2, SEMA3F, and SPP1) to construct a risk signature with the ability to distinguish cases as having a high or low risk of unfavorable prognosis in the TCGA cohort, and the signature was verified in the ICGC cohort. The signature was prognostically significant in all stratified cohorts and was deemed an independent prognostic factor for HCC. We also built a nomogram with good performance by combining the signature with clinicopathological factors to increase the accuracy of predicting HCC prognosis. By investigating the relationship of the risk score and 8 risk genes from our signature with clinical traits, we found that the aberrant expression of the immune-related risk genes is correlated with the development of HCC. Moreover, the high-risk group was higher than the low-risk group in terms of tumor mutation burden (TMB), immune cell infiltration, and the expression of immune checkpoints (PD-1, PD-L1, and CTLA-4), and functional enrichment analysis indicated the signature enriched an intensive immune phenotype.Conclusions: This study developed a robust immune-related risk signature and built a predictive nomogram that reliably predict overall survival in HCC, which may be helpful for clinical management and personalized immunotherapy decisions.

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A Robust Signature Based on Autophagy-Associated LncRNAs for Predicting Prognosis in Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2020/3858373 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Mi Zhou ◽

Weihua Shao ◽

Haiyun Dai ◽

Xin Zhu

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Risk Group ◽

Surgical Margin ◽

Training Group ◽

High Risk Group ◽

Low Risk ◽

Validation Group ◽

Cox Regression Analysis

Objective. To construct a predictive signature based on autophagy-associated lncRNAs for predicting prognosis in lung adenocarcinoma (LUAD). Materials and Methods. Differentially expressed autophagy genes (DEAGs) and differentially expressed lncRNAs (DElncRNAs) were screened between normal and LUAD samples at thresholds of ∣log2Fold Change∣>1 and P value < 0.05. Univariate Cox regression analysis was conducted to identify overall survival- (OS-) associated DElncRNAs. The total cohort was randomly divided into a training group (n=229) and a validation group (n=228) at a ratio of 1 : 1. Multivariate Cox regression analysis was used to build prognostic models in the training group that were further validated by the area under curve (AUC) values of the receiver operating characteristic (ROC) curves in both the validation and total cohorts. Results. A total of 30 DEAGs and 2997 DElncRNAs were identified between 497 LUAD tissues and 54 normal tissues; however, only 1183 DElncRNAs were related to the 30 DEAGs. A signature consisting of 13 DElncRNAs was built to predict OS in lung adenocarcinoma, and the survival analysis indicated a significant OS advantage of the low-risk group over the high-risk group in the training group, with a 5-year OS AUC of 0.854. In the validation group, survival analysis also indicated a significantly favorable OS for the low-risk group over the high-risk group, with a 5-year OS AUC of 0.737. Univariate and multivariate Cox regression analyses indicated that only positive surgical margin (vs negative surgical margin) and high-risk group (vs low-risk group) based on the predictive signature were independent risk factors predictive of overall mortality in LUAD. Conclusions. This study investigated the association between autophagy-associated lncRNAs and prognosis in LUAD and built a robust predictive signature of 13 lncRNAs to predict OS.

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Predictive Factors and Oncologic Outcome of Downgrade to Pathologic Gleason Score 6–7 after Radical Prostatectomy in Patients with Biopsy Gleason Score 8–10

Journal of Clinical Medicine ◽

10.3390/jcm8040438 ◽

2019 ◽

Vol 8 (4) ◽

pp. 438 ◽

Cited By ~ 1

Author(s):

Doo Chung ◽

Jong Lee ◽

Hyeok Goh ◽

Dong Koh ◽

Min Kim ◽

...

Keyword(s):

Regression Analysis ◽

Radical Prostatectomy ◽

Gleason Score ◽

Predictive Factors ◽

Cox Regression ◽

Specific Antigen ◽

Surgical Margin ◽

Positive Surgical Margin ◽

Cox Regression Analysis ◽

Pathologic Features

Gleason score (GS) 8–10 is associated with adverse outcomes in prostate cancer (PCa). However, biopsy GS (bGS) may be upgraded or downgraded post-radical prostatectomy (RP). We aimed to investigate predictive factors and oncologic outcomes of downgrade to pathologic GS (pGS) 6–7 after RP in PCa patients with bGSs 8–10. We retrospectively reviewed clinical data of patients with bGS ≥ 8 undergoing RP. pGS downgrade was defined as a pGS ≤ 7 from bGS ≥ 8 post-RP. Univariate and multivariate cox regression analysis, logistic regression analysis, and Kaplan–Meier curves were used to analyze pGS downgrade and biochemical recurrence (BCR). Of 860 patients, 623 and 237 had bGS 8 and bGS ≥ 9, respectively. Post-RP, 332 patients were downgraded to pGS ≤ 7; of these, 284 and 48 had bGS 8 and bGS ≥ 9, respectively. Prostate-specific antigen (PSA) levels; clinical stage; and adverse pathologic features such as extracapsular extension, seminal vesicle invasion and positive surgical margin were significantly different between patients with pGS ≤ 7 and pGS ≥ 8. Furthermore, bGS 8 (odds ratio (OR): 0.349, p < 0.001), PSA level < 10 ng/mL (OR: 0.634, p = 0.004), and ≤cT3a (OR: 0.400, p < 0.001) were identified as significant predictors of pGS downgrade. pGS downgrade was a significant positive predictor of BCR following RP in patients with high bGS (vs. pGS 8, hazard radio (HR): 1.699, p < 0.001; vs. pGS ≥ 9, HR: 1.765, p < 0.001). In addition, the 5-year BCR-free survival rate in patients with pGS downgrade significantly differed from that in patients with bGS 8 and ≥ 9 (52.9% vs. 40.7%, p < 0.001). Among patients with bGS ≥ 8, those with bGS 8, PSA level < 10 ng/mL, and ≤cT3a may achieve pGS downgrade after RP. These patients may have fewer adverse pathologic features and show a favorable prognosis; thus we suggest that active treatment is needed in these patients. In addition, patients with high-grade bGS should be managed aggressively, even if they show pGS downgrade.

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The novel immune-related genes predict the prognosis of patients with hepatocellular carcinoma

Scientific Reports ◽

10.1038/s41598-021-89747-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lunxu Li ◽

Shilin Xia ◽

Xueying Shi ◽

Xu Chen ◽

Dong Shang

Keyword(s):

Hepatocellular Carcinoma ◽

Network Analysis ◽

Risk Score ◽

Cox Regression ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prediction Ability ◽

Cox Regression Analysis ◽

Immune Related Genes

AbstractHepatocellular carcinoma (HCC) is one of the main causes of cancer deaths globally. Immunotherapy is becoming increasingly important in the cure of advanced HCC. Thus it is essential to identify biomarkers for treatment response and prognosis prediction. We searched publicly available databases and retrieved 465 samples of genes from The Cancer Genome Atlas (TCGA) database and 115 tumor samples from Gene Expression Omnibus (GEO). Meanwhile, we used the ImmPort database to determine the immune-related genes as well. Weighted gene correlation network analysis, Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to identify the key immune related genes (IRGs) which are closely related to prognosis. Gene set enrichment analysis (GSEA) was implemented to explore the difference of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway between Immune high- and low-risk score groups. Finally, we made a prognostic nomogram including Immune-Risk score and other clinicopathologic factors. A total of 318 genes from prognosis related modules were identified through weighted gene co-expression network analysis (WGCNA). 46 genes were strongly linked to prognosis after univariate Cox analysis. We constructed a seven genes prognostic signature which showed powerful prediction ability in both training cohort and testing cohort. 16 significant KEGG pathways were identified between high- and low- risk score groups using GSEA analysis. This study identified and verified seven immune-related prognostic biomarkers for the patients with HCC, which have potential value for immune modulatory and therapeutic targets.

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Identification and validation of hub genes predicting prognosis of hepatocellular carcinoma

Digestive Surgery ◽

10.1159/000520893 ◽

2021 ◽

Author(s):

Ziyan Chen ◽

Haitao Yu ◽

Lijun Wu ◽

Sina Zhang ◽

Zhihui Lin ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Model ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Histological Grade ◽

High Risk Group ◽

Low Risk ◽

Hub Genes ◽

Cox Regression Analysis

Introduction: Selecting the hub genes associated with hepatocellular carcinoma (HCC) to construct a COX regression model for predicting prognosis in HCC patients. Methods: Using HCC patient data from the ICGC and TCGA databases, screened for 40 core genes highly correlated with histological grade of HCC. Univariate and multivariate COX regression analysis were performed on the genes highly associated with HCC prognosis and the model was established. The expression of those genes was measured by immunohistochemistry in 110 HCC patients who underwent the surgery in The First Affiliated Hospital of Wenzhou Medical University. The survival of HCC patients was analyzed by the Kaplan-Meier method. Results: Eight genes (CDC45, CENPA, MCM10, MELK, CDC20, ASF1B, FANCD2 and NCAPH) were correlated with prognosis, and the same result was observed in 110 HCC patients. Using the regression model, the HCC patients in the training set were classified as high- and low-risk groups. The overall survival (OS) of patients in the high-risk group was shorter than that in the low-risk group, the same results were obtained in verification set. Conclusion: This study found that the risk model according to these eight genes can be used as a predictor of prognosis in HCC. These genes may become alternative biomarkers and therapeutic targets and provide new therapeutic strategies for HCC.

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Identification of Biomarkers of Hepatocellular Carcinoma Gene Prognosis Based on Immune-Related lncRNA Signature of Transcriptome Data

10.21203/rs.3.rs-57303/v1 ◽

2020 ◽

Author(s):

Andi Ma ◽

Yukai Sun ◽

Racheal O. Ogbodu ◽

Ling Xiao ◽

Haibing Deng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis

Abstract Background: It is well known that long non-coding RNAs (lncRNAs) play a vital role in cancer. We aimed to explore the prognostic value of potential immune-related lncRNAs in hepatocellular carcinoma (HCC). Methods: Validated the established lncRNA signature of 343 patients with HCC from The Cancer Genome Atlas (TCGA) and 81 samples from Gene Expression Omnibus (GEO). Immune-related lncRNAs for HCC prognosis were evaluated using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. LASSO analysis was performed to calculate a risk score formula to explore the difference in overall survival between high- and low-risk groups in TCGA, which was verified using GEO, Gene Ontology (GO), and pathway-enrichment analysis. These analyses were used to identify the function of screened genes and construct a co-expression network of these genes. Results: Using computational difference algorithms and lasso Cox regression analysis, the differentially expressed and survival-related immune-related genes (IRGs) among patients with HCC were established as five novel immune-related lncRNA signatures (AC099850.3, AL031985.3, PRRT3-AS1, AC023157.3, MSC-AS1). Patients in the low‐risk group showed significantly better survival than patients in the high‐risk group ( P = 3.033e−05). The signature identified can be an effective prognostic factor to predict patient survival. The nomogram showed some clinical net benefits predicted by overall survival. In order to explore its underlying mechanism, several methods of enrichment were elucidated using Gene Set Enrichment Analysis. Conclusion: Identifying five immune-related lncRNA signatures has important clinical implications for predicting patient outcome and guiding tailored therapy for patients with HCC with further prospective validation.

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Predictive factors for recurrent febrile seizures in children

Paediatrica Indonesiana ◽

10.14238/pi52.6.2012.317-23 ◽

2012 ◽

Vol 52 (6) ◽

pp. 317

Author(s):

Pengekuten Marudur ◽

Elisabeth Herini ◽

Cahya Dewi Satria

Keyword(s):

Predictive Factors ◽

Febrile Seizure ◽

Cox Regression ◽

Febrile Seizures ◽

Cox Regression Analysis ◽

First Degree Relatives ◽

History Of ◽

First Recurrence ◽

First Time

Background One􀁡third of children who experience febrile seizureshave a recurrence, '\.Vith rates of75% in the first year, and 90% mthinthe second year following the first febrile seizure. Predictive factorsfor recurrent febrile seizures have been reported in studies from othercountries, but there have been few of these studies in Indonesia.Objective To determine predictive factors for the recurrence offebrile seizures in children.Methods Children w i t h first􀁡time febrile seizures wereprospectively followed up, for at least 12 months. Subjects wererecruited consecutively from August 2008 to April 20 1 0 from twohospitals in Yogyakarta and one hospital in Klaten. We monitoredrecurrences of febrile seizure by telephone or home visits to parentsevery 3 months. Time to first recurrence of febrile seizures wasanalyzed using the Cox regression model.Results T here were 196 children v,ith first􀁡time febrile seizures whocompleted the follow up. Recurrent seizures were observed in 56children (28.6%). Me811 follow up time was 21.7 (SD 6.6) months.Temperature of <40"C at the time of seizure (RR=2.29, 95%CI 135to 3.89, P=0.OO2), history of febrile seizures in first􀁡degree relatives(RR=330, 95%CI 1.25 to 8.08, P<O.OOl), age at first febrile seizureof <12 months (RR􀁢2.40, 95%CI 1.42 to 4.06, P􀁢O.OOI) andduration of fever before the seizure of:51 hour (RR=4.62, 95%CI:1.35 to 15.80, P=0.015) were significantly associated v,ith recurrenceof febrile seizures. Furthermore, Cox regression analysis revealedthat the age of < 12 months, history of febrile seizures in first􀁡degreerelatives and temperature of < 40" C were significantpredictive factorsfor the recurrence of febrile seizures.Conclusion Age at first seizure of < 12 months, history of febrileseizures in first􀁡degree relatives, and seizure v,ith temperature of<40"C were independent predictive factors for recurrent febrileseizures in children. [Paediatr lndones. 2012;52:317,23].

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