scholarly journals Identification of an eight-gene signature for survival prediction for patients with hepatocellular carcinoma based on integrated bioinformatics analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6548 ◽  
Author(s):  
Guo-jie Qiao ◽  
Liang Chen ◽  
Jin-cai Wu ◽  
Zhou-ri Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Bioinformatics Analysis ◽  
Gene Signature ◽  
Training Dataset ◽  
Rank Test ◽  
Survival Prediction ◽  
Gene Model ◽  
Cox Regression Analysis ◽  
Log Rank Test

Background Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related death worldwide. Despite recent advances in imaging techniques and therapeutic intervention for HCC, the low overall 5-year survival rate of HCC patients remains unsatisfactory. This study aims to find a gene signature to predict clinical outcomes in HCC. Methods Bioinformatics analysis including Cox’s regression analysis, Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analysis and the random survival forest algorithm were performed to mine the expression profiles of 553 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public database. Results We selected a signature comprising eight protein-coding genes (DCAF13, FAM163A, GPR18, LRP10, PVRIG, S100A9, SGCB, and TNNI3K) in the training dataset (AUC = 0.77 at five years, n = 332). The signature stratified patients into high- and low-risk groups with significantly different survival in the training dataset (median 2.20 vs. 8.93 years, log-rank test P < 0.001) and in the test dataset (median 2.68 vs. 4.24 years, log-rank test P = 0.004, n = 221, GSE14520). Further multivariate Cox regression analysis showed that the signature was an independent prognostic factor for patients with HCC. Compared with TNM stage and another reported three-gene model, the signature displayed improved survival prediction power in entire dataset (AUC signature = 0.66 vs. AUC TNM = 0.64 vs. AUC gene model = 0.60, n = 553). Stratification analysis shows that it can be used as an auxiliary marker for many traditional staging models. Conclusions We constructed an eight-gene signature that can be a novel prognostic marker to predict the survival of HCC patients.

Bioinformatics Analysis of The Expression of ATP Binding Cassette Transporters and The Screening of Regulatory Genes in PTEN/PI3K/Akt/mTOR Signaling Pathway in The Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Tingdan Zheng ◽  
Wuqi Song ◽  
Aiying Yang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Bioinformatics Analysis ◽  
Wilcoxon Test ◽  
Rank Test ◽  
Short Term ◽  
Term Survival ◽  
Log Rank Test ◽  
Short Term Survival

Abstract Objective Here we performed the Bioinformatics analysis on the data from The Cancer Genome Atlas (TCGA), in order to find the correlation between the expression of ATP Binding Cassette (ABC) Transporters’ genes and hepatocellular carcinoma (HCC) prognosis; Methods Transcriptome profiles and clinical data of HCC were obtained from TCGA database. Package edgeR was used to analyze differential gene expression. Patients were divided into low-ABC expression and high-ABC expression groups based on the median expression level of ABC genes in cancer. The overall survival and short-term survival (n= 341) of the two groups was analyzed using the log-rank test and Wilcoxon test; Results We found that ABC gene expression was correlated with the expression of PIK3C2B (p<0.001, ABCC1: r=0.27; ABCC10: r=0.57; ABCC4: r=0.20; ABCC5: r=0.28; ABCB9: r=0.17; ABCD1: r=0.21). All patients with low-ABC expression showed significantly increased overall survival. Significantly decreased overall survival (Log-rank test: p<0.05, Wilcoxon test: p<0.05) was found in patients with high expression of ABCC1 (HR=1.58), ABCD1 (HR=1.45), ABCC4 (HR=1.56), and ABCC5 (HR=1.64), while decreased short-term survival (Log-rank test: p>0.05, Wilcoxon test: p<0.05) was correlated with the increased expression of ABCC10 (HR=1.29), PIK3C2B (HR=1.29) and ABCB9 (HR=1.23); Conclusions Our findings indicate that the specific ABC gene expression correlates with the prognosis of HCC. Therefore, ABC expression profile could be a potential indicator for HCC patients.

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

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.

scholarly journals Identification of the Novel Methylated Genes’ Signature to Predict Prognosis in INRG High-Risk Neuroblastomas

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhichao Liu ◽  
Changchun Li
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Survival Time ◽  
Median Survival ◽  
Median Survival Time ◽  
Test Group ◽  
Functional Enrichment ◽  
Rank Test ◽  
Cox Regression Analysis ◽  
Log Rank Test

Background. Neuroblastomas are the most frequent extracranial pediatric solid tumors. The prognosis of children with high-risk neuroblastomas has remained poor in the past decade. A powerful signature is required to identify factors associated with prognosis and improved treatment selection. Here, we identified a strong methylation signature that favored the earlier diagnosis of neuroblastoma in patients. Methods. Gene methylation (GM) data of neuroblastoma patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) were analyzed using a multivariate Cox regression analysis (MCRA) and univariate Cox proportional hazards regression analysis (UCPHRA). Results. The methylated genes’ signature consisting of eight genes (NBEA, DDX28, TMED8, LOC151174, EFNB2, GHRHR, MIMT1, and SLC29A3) was selected. The signature divided patients into low- and high-risk categories, with statistically significant survival rates (median survival time: 25.08 vs. >128.80 months, log-rank test, P < 0.001 ) in the training group, and the validation of the signature’s risk stratification ability was carried out in the test group (log-rank test, P < 0.01 , median survival time: 30.48 vs. >120.36 months). The methylated genes’ signature was found to be an independent predictive factor for neuroblastoma by MCRA. Functional enrichment analysis suggested that these methylated genes were related to butanoate metabolism, beta-alanine metabolism, and glutamate metabolism, all playing different significant roles in the process of energy metabolism in neuroblastomas. Conclusions. The set of eight methylated genes could be used as a new predictive and prognostic signature for patients with INRG high-risk neuroblastomas, thus assisting in treatment, drug development, and predicting survival.

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

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.

Prognostic impact of LDH and HCG levels in marker-positive seminomas.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 392-392 ◽  
Author(s):  
Christoph Alexander Seidel ◽  
Gedske Daugaard ◽  
Tim Nestler ◽  
Alexey Tryakin ◽  
Christian Daniel Fankhauser ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Serum Levels ◽  
Rank Test ◽  
Prognostic Impact ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Cox Regression Analysis ◽  
Oncological Outcomes ◽  
Log Rank Test

392 Background: The prognostic impact of LDH and HCG serum levels in marker positive metastatic seminoma patients is uncertain. This analysis evaluated the association between LDH and HCG levels with oncological outcomes in this patient population. Methods: Seminoma patients with elevated HCG levels were retrospectively analyzed. After stratification according to tumor marker levels pre- and post-orchiectomy, outcomes of subgroups were compared using log-rank test and cox-regression analysis. Study endpoints were cancer specific- (CSS) and recurrence-free survival (RFS). Results: In total, 429 HCG-positive metastatic seminoma patients (stage II n=291; stage III n=138) diagnosed between 1981 and 2018 were included. LDH + HCG levels ranged from 124 U/l to 8833 U/l (median: 619; IQR: 955) + 2 IU/l to 283,782 IU/l (median: 20; IQR: 63) pre- and from 107 U/l to 8650 U/l (median: 324; IQR: 481) + 0 IU/l to 36700 IU/l post-orchiectomy (median: 30; IQR: 121), respectively. Five-year CSS and RFS rates were 90% and 79%, respectively. Patients with LDH levels pre-orchiectomy <1.5 UNL (n=142) had a 5-year CSS (RFS) rate of 97% (88%), compared to 86% (81%) for ≥1.5 to 3 UNL (n=40), 83% (77%) for >3 to 5 UNL (n=44) and 83% (72%) for >5 UNL (n=44) (CSS p <0.001; RFS p=0.142). Concerning LDH levels post-orchiectomy this stratification was not significant but patients with LDH levels ≥3 UNL (n=77) displayed an impaired prognosis associated with a 5-year CSS (RFS) rate of 85% (79%) compared to 94% (82%) for levels <3 UNL (n=186) (CSS p=0.025; RFS p=0.447). Patients with HCG levels ≥2000 IU/l (n=17) pre- but not post-orchiectomy had a 5-year CSS (RFS) rate of 73% (60%) compared to 94% (79%) for patients with HCG levels <2000 IU/l (n=855) (CSS p=0.09; RFS p=0.04). In cox-regression analysis LDH ≥1.5 UNL (p=0.037; HR 3.32, CI95%1.08-10.26) and HCG levels ≥2000 IU/l (p=0.044; HR 3.69, 95%CI1.04-13.13) pre-orchiectomy were confirmed as prognostic factors for CSS. Conclusions: LDH levels inversely correlate with survival outcomes, suggesting ≥1.5 UNL pre- and ≥3 UNL post-orchiectomy as potential cut-off values for further risk assessment. Patients with extensive HCG elevations may represent an unfavorable subgroup concerning RFS and CSS, but only few patients were affected.

scholarly journals Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xiaoqing Yu ◽  
Jingsong Zhang ◽  
Rui Yang ◽  
Chun Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Single Sample ◽  
Survival Prediction ◽  
Prediction Ability ◽  
Cox Regression Analysis ◽  
Score Model ◽  
Function Enrichment

Objective. Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need. Methods. The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape. Results. Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions. Conclusion. This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.

scholarly journals AB0274 USE OF TNF-INHIBITORS BIOSIMILARS IN CHRONIC INFLAMMATORY ARTHRITIDES: A THREE-YEAR EXPERIENCE IN A LARGE MONOCENTRIC COHORT OF PATIENTS FROM THE NORTH-EAST ITALY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1435.2-1436
Author(s):  
D. Astorri ◽  
F. Ometto ◽  
L. Friso ◽  
B. Raffeiner ◽  
C. Botsios ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Disease Duration ◽  
Cox Regression ◽  
University Hospital ◽  
Rank Test ◽  
Cox Regression Analysis ◽  
Factors Associated ◽  
Drug Survival ◽  
Log Rank Test ◽  
Kaplan Meier

Background::In recent years several biosimilars (BS) of tumour necrosis factor inhibitors (TNF-i) were introduced. At the Padova University Hospital the first BS of etanercept (bsETN) was available in October 2016 and the BS of adalimumab (bsADA) was available in November 2018.Objectives:The objectives of the study were to evaluate the rate of bioriginator-biosimilar (BO-BS) switch in all patients with rheumatoid arthritis (RA), psoriatic arthritis (PSA) and axial spondiloarthritis (axSpA) in the cohort of the Padova University Hospital and to examine factors favouring BO-BS switch. Secondly, we investigated survival of BO-BS switch and BO treatment and factors associated with longer treatment survival.Methods:We considered all patients on ETN originator (boETN) treatment when the first bsETN was available (1st October 2016) and all patients on ADA originator (boADA) when bsADA was available (1st November 2018). Patients were followed until 30 August 2019 and were classified as BO-BS switchers if they underwent a switch from either boETN or boADA to BS during the follow-up, otherwise they were considered as continuing BO treatment. Factors associated with BO-BS switch were tested with a multivariable regression analysis. To test the survival of the BO-BS switch and of the BO treatment, Cox regression analysis was used including all variables achiving a p<0.10 in univariate analysis tested with Log-rank test and Kaplan-Meier curves.Results:Among 1208 patients (553 RA, 433 PSA, 215 axSpA), 560 (46.3%) patients switched to bsETN (391) or bsADA (169). Mean disease duration was 16 (14.2) years and mean duration of the bDMARD treatment was 96.3 (56.8) months. After adjustment for potential confounders, factors associated with BO-BS switch were a longer disease duration, a shorter duration of previous bDMARD treatments and diagnosis (Tab.1) RA patients had almost a 3 fold increased likelihood of being switched to BS compared to PSA and axSPA, while difference between PSA and axSPA was not significant.Following Cox regression analysis we observed a longer drug survival in BO-BS switchers compared to those continuing with BO (HR 1.38; 95% C.I. 1.2-1.58; p<0.001) (Fig. 1). A longer drug survival was also associated with a longer disease duration (.15years: HR 1.75; 95% C.I. 1.5-2; p<0.001), longer mean duration of previous bDMARDs (.5years: HR 4.1; 95% C.I. 3.5-4.7; p<0.001), and diagnosis (RA vs PSA: HR 1.22; 95% C.I. 1.02-1.47; p=0.030; RA vs axSpA: HR 0.89 95% C.I. 0.067-0.97; p=0.023; PSA vs axSpA: HR 0.66; 95% C.I. 0.57-0.77; p<0.001) (Fig 2).Figure 1.Kaplan-Meier curves for treatment survival, Log-rank test.Figure 2.Kaplan-Meier curves for treatment survival in all patients, Log-rank tesConclusion:BO-BS switch was undertaken in almost half of the patients. Patients with longer disease duration and longer bDMARD duration, were the most likely to be switched successfully to BS. BO-BS switching does not affect the survival of the treatment, indeed, it provides sustained effectiveness particularly if undertaken in patients with stable disease activity.Table 1.Factors associated with BO-BS switch, multivariate regression analysis.Disclosure of Interests:DAVIDE ASTORRI: None declared, Francesca Ometto: None declared, LARA FRISO: None declared, BERND RAFFEINER: None declared, Costantino Botsios: None declared, Andrea Doria Consultant of: GSK, Pfizer, Abbvie, Novartis, Ely Lilly, Speakers bureau: UCB pharma, GSK, Pfizer, Janssen, Abbvie, Novartis, Ely Lilly, BMS

scholarly journals Development and Validation of a 7-Gene Prognostic Signature to Improve Survival Prediction in Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Zengyu Feng ◽  
Hao Qian ◽  
Kexian Li ◽  
Jianyao Lou ◽  
Yulian Wu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Disease Free Survival ◽  
Roc Curves ◽  
Gene Signature ◽  
Ductal Adenocarcinoma ◽  
Survival Prediction ◽  
Cox Regression Analysis

Background: Previous prognostic signatures of pancreatic ductal adenocarcinoma (PDAC) are mainly constructed to predict the overall survival (OS), and their predictive accuracy needs to be improved. Gene signatures that efficaciously predict both OS and disease-free survival (DFS) are of great clinical significance but are rarely reported.Methods: Univariate Cox regression analysis was adopted to screen common genes that were significantly associated with both OS and DFS in three independent cohorts. Multivariate Cox regression analysis was subsequently performed on the identified genes to determine an optimal gene signature in the MTAB-6134 training cohort. The Kaplan–Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were employed to assess the predictive accuracy. Biological process and pathway enrichment analyses were conducted to elucidate the biological role of this signature.Results: Multivariate Cox regression analysis determined a 7-gene signature that contained ASPH, DDX10, NR0B2, BLOC1S3, FAM83A, SLAMF6, and PPM1H. The signature had the ability to stratify PDAC patients with different OS and DFS, both in the training and validation cohorts. ROC curves confirmed the moderate predictive accuracy of this signature. Mechanically, the signature was related to multiple cancer-related pathways.Conclusion: A novel OS and DFS prediction model was constructed in PDAC with multi-cohort and cross-platform compatibility. This signature might foster individualized therapy and appropriate management of PDAC patients.

scholarly journals A Novel Five-gene Signature Predicts Overall Survival in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Zhigang Wang ◽  
Leyu Pan ◽  
Deliang Guo ◽  
Xiaofeng Luo ◽  
Jie Tang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Cox Regression ◽  
Gene Signature ◽  
Tnm Stage ◽  
Time Dependent ◽  
Quantitative Real Time Pcr ◽  
Cox Regression Analysis ◽  
Normal Tissues

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common challenges for public health worldwide. Due to its complex molecular and great heterogeneity, the effectiveness of existing HCC risk prediction models is unsatisfactory. Hence, more accurate prognostic models are pressingly needed. Materials and methods: Differentially expressed mRNAs (DEMs) between HCC and normal tissues were identified after downloading GSE1450 from gene omnibus (GEO) database. We randomly divided all patients into training and testing sets. Univariate Cox regression, lasso Cox regression and multivariable Cox regression analysis were used to constructed the prognostic gene signature in training set. Our study utilized Kaplan-Meier plot, time-dependent receiver operating characteristic (ROC), multivariable Cox regression analysis with clinical information, nomogram and decision curve analysis (DCA) to evaluate the predictive ability for overall survival of the novel gene signature in training, testing and whole sets. We also validated the prognostic capacity of the five-gene signature in an external validation set. The information of mutation of each gene was explored on cBioPortal online website. We performed gene set enrichment analysis (GSEA) to explore underlying mechanisms in the high and low risk group. Finally, quantitative real-time PCR was conducted to validate the expression tendency between 12 paired HCC and adjacent normal tissues. Results: Our study constructed a novel five-gene signature (CNIH4, SOX4, SPP1, SORBS2 and CCL19) for predicting overall survival of HCC. Time-dependent ROC curve indicated admirable ability in survival prediction in two datasets. Multivariable Cox regression analysis indicated that both this five-gene signature and TNM stage were two independent prognostic factors for overall survival of HCC patients. Combined with TNM stage clinical pathological parameters, the predictive capacity of nomogram had a decent improvement. The mutation of the five genes had no obvious variation. Plenty pathways were enriched by GSEA, including cell cycle and various metabolism. Furthermore, the mRNA levels of these five genes had significantly different expressions between HCC tissues and adjacent normal tissues by quantitative real-time PCR. Conclusions: A five-gene prognostic model and nomogram were constructed and validated for predicting prognostic of HCC patients. And the five-gene risk score with TNM stage models might help various HCC patients to customize individual therapies.

scholarly journals The lncRNAs RP1-261G23.7, RP11-69E11.4 and SATB2-AS1 are a novel clinical signature for predicting recurrent osteosarcoma

2020 ◽  
Vol 40 (1) ◽  
Author(s):  
Tang Ying ◽  
Jin-ling Dong ◽  
Cen Yuan ◽  
Peng Li ◽  
Qingshan Guo
Keyword(s):  
Regression Analysis ◽  
Signaling Pathway ◽  
Cox Regression ◽  
Wnt Signaling Pathway ◽  
Cox Proportional Hazards ◽  
Recurrence Time ◽  
Rank Test ◽  
Recurrence Rates ◽  
Cox Regression Analysis ◽  
Log Rank Test

Abstract Background: Osteosarcoma is the most common primary bone malignancy in children and adolescents. In order to find factors related to its recurrence, and thus improve recovery prospects, a powerful clinical signature is needed. Long noncoding RNAs (lncRNAs) are essential in osteosarcoma processes and development, and here we report significant lncRNAs to aid in earlier diagnosis of osteosarcoma. Methods: A univariate Cox proportional hazards regression analysis and a multivariate Cox regression analysis were used to analyze osteosarcoma patients’ lncRNA expression data from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET), a public database. Results: A lncRNA signature consisting of three lncRNAs (RP1-261G23.7, RP11-69E11.4 and SATB2-AS1) was selected. The signature was used to sort patients into high-risk and low-risk groups with meaningful recurrence rates (median recurrence time 16.80 vs. &gt;128.22 months, log-rank test, P&lt;0.001) in the training group, and predictive ability was validated in a test dataset (median 16.32 vs. &gt;143.80 months, log-rank test, P=0.006). A multivariate Cox regression analysis showed that the significant lncRNA was an independent prognostic factor for osteosarcoma patients. Functional analysis suggests that these lncRNAs were related to the PI3K-Akt signaling pathway, the Wnt signaling pathway, and the G-protein coupled receptor signaling pathway, all of which have various, important roles in osteosarcoma development. The significant 3-lncRNA set could be a novel prediction biomarker that could aid in treatment and also predict the likelihood of recurrence of osteosarcoma in patients.

Sign in / Sign up

Export Citation Format

Share Document