scholarly journals Identifying a Hypoxia-Related Long Non-Coding RNAs Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Pingfei Tang ◽  
Weiming Qu ◽  
Taoli Wang ◽  
Minji Liu ◽  
Dajun Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Targeted Therapy ◽  
High Risk ◽  
Clinical Outcomes ◽  
Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Testing Dataset ◽  
Non Coding Rnas

Abstract Background: Both hypoxia and long non-coding RNAs (lncRNAs) contribute to the tumor progression in hepatocellular carcinoma (HCC). We sought to establish a hypoxia-related lncRNA signature and explore its correlation with immunotherapy response in HCC.Materials and Methods: Hypoxia-related differentially expressed lncRNAs (HRDELs) were identified by conducting the differential gene expression analyses in GSE155505 and The Cancer Genome Atlas (TCGA)- liver hepatocellular carcinoma (LIHC) datasets. The HRDELs landscape in patients with HCC in TCGA-LIHC was dissected by an unsupervised clustering method. Patients in the TCGA-LIHC cohort were stochastically split into the training and testing dataset. The prognostic signature was developed using LASSO (least absolute shrinkage and selection operator) penalty Cox and multivariable Cox analyses. The tumor immune microenvironment was delineated by the single-sample gene set enrichment analysis (ssGSEA) algorithm. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied to evaluate the predictive value of the constructed signature in immunotherapeutic responsiveness.Results: A total of 55 HRDELs were identified through integrated bioinformatical analyses in GSE155505 and TCGA-LIHC. Patients in the TCGA-LIHC cohort were categorized into three HRDELs-specific clusters associated with different clinical outcomes. The prognostic signature involving five hypoxia-related lncRNAs (LINC00869, CAHM, RHPN1-AS1, MKLN1-AS, and DUXAP8) was constructed in the training dataset and then validated in the testing dataset and entire TCGA-LIHC cohort. The 5-years AUC of the constructed signature for prognostic prediction reaches 0.705 and is superior to that of age, AJCC stage, and histopathological grade. Patients with high-risk scores consistently had poorer overall survival outcomes than those with low-risk scores irrespective of other clinical parameters status. The low-risk group had more abundance in activated CD8+ T cell and activated B cell and were predicted to be more responsive to immunotherapy and targeted therapy than the high-risk group.Conclusion: We established a reliable hypoxia-related lncRNAs signature that could accurately predict the clinical outcomes of HCC patients and correlate with immunotherapy response and targeted drug sensitivity, providing new insights for immunotherapy and targeted therapy in HCC.

scholarly journals Prognosis-Predictive Signature and Nomogram Based on Autophagy-Related Long Non-coding RNAs for Hepatocellular Carcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Yu Jia ◽  
Yan Chen ◽  
Jiansheng Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Predictive Ability ◽  
Low Risk ◽  
Tnm Stage ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Risk Patients ◽  
Non Coding Rnas

Autophagy plays a vital role in hepatocellular carcinoma (HCC) pathogenesis. Long non-coding RNAs (lncRNAs) are considered regulators of autophagy, and the aim of the present study was to investigate the prognostic value of autophagy-related lncRNA (ARlncRNA) and develop a new prognostic signature to predict the 1-year and 3-year overall survival (OS) of HCC patients. Transcriptome and clinical survival information of HCC patients was obtained from The Cancer Genome Atlas database. A set of ARlncRNAs was identified by co-expression analysis, from which seven ARlncRNAs (AC005229.4, AL365203.2, AL117336.3, AC099850.3, ELFN1-AS1, LUCAT1, and AL031985.3) were selected for use as a predictive signature. Risk scores were derived for each patient, who were then divided into high-risk and low-risk groups according to the median risk value. The OS of high-risk patients was significantly lower than that of low-risk patients (P < 0.0001). The 1- and 3-year time-dependent ROC curves were used to evaluate the predictive ability of the risk score (AUC = 0.785 of 1 year, 0.710 of 3 years), and its predictive ability was found to be better than TNM stage. Moreover, the risk score was significantly, linearly related to pathological grade and TNM stage (P < 0.05). Overall, a novel nomogram to predict the 1-year and 3-year OS of HCC patients was developed, which shows good reliability and accuracy, for use in improved treatment decision-making.

scholarly journals Construction of a Ferroptosis-Related Nine-lncRNA Signature for Predicting Prognosis and Immune Response in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhijie Xu ◽  
Bi Peng ◽  
Qiuju Liang ◽  
Xi Chen ◽  
Yuan Cai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
High Risk ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Signature

Ferroptosis is an iron-dependent cell death process that plays important regulatory roles in the occurrence and development of cancers, including hepatocellular carcinoma (HCC). Moreover, the molecular events surrounding aberrantly expressed long non-coding RNAs (lncRNAs) that drive HCC initiation and progression have attracted increasing attention. However, research on ferroptosis-related lncRNA prognostic signature in patients with HCC is still lacking. In this study, the association between differentially expressed lncRNAs and ferroptosis-related genes, in 374 HCC and 50 normal hepatic samples obtained from The Cancer Genome Atlas (TCGA), was evaluated using Pearson’s test, thereby identifying 24 ferroptosis-related differentially expressed lncRNAs. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model were used to construct and validate a prognostic risk score model from both TCGA training dataset and GEO testing dataset (GSE40144). A nine-lncRNA-based signature (CTD-2033A16.3, CTD-2116N20.1, CTD-2510F5.4, DDX11-AS1, LINC00942, LINC01224, LINC01231, LINC01508, and ZFPM2-AS1) was identified as the ferroptosis-related prognostic model for HCC, independent of multiple clinicopathological parameters. In addition, the HCC patients were divided into high-risk and low-risk groups according to the nine-lncRNA prognostic signature. The gene set enrichment analysis enrichment analysis revealed that the lncRNA-based signature might regulate the HCC immune microenvironment by interfering with tumor necrosis factor α/nuclear factor kappa-B, interleukin 2/signal transducers and activators of transcription 5, and cytokine/cytokine receptor signaling pathways. The infiltrating immune cell subtypes, such as resting memory CD4(+) T cells, follicular helper T cells, regulatory T cells, and M0 macrophages, were all significantly different between the high-risk group and the low-risk group as indicated in Spearman’s correlation analysis. Moreover, a substantial increase in the expression of B7H3 immune checkpoint molecule was found in the high-risk group. Our findings provided a promising insight into ferroptosis-related lncRNAs in HCC and a personalized prediction tool for prognosis and immune responses in patients.

Contrast-enhanced ultrasound–based ultrasomics score: a potential biomarker for predicting early recurrence of hepatocellular carcinoma after resection or ablation

2021 ◽  
Author(s):  
Hui Huang ◽  
Si-min Ruan ◽  
Meng-fei Xian ◽  
Ming-de Li ◽  
Mei-qing Cheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Group ◽  
Early Recurrence ◽  
High Risk Group ◽  
Low Risk ◽  
Primary Hepatocellular Carcinoma ◽  
Contrast Enhanced Ultrasound ◽  
Combined Model ◽  
Contrast Enhanced

Objectives: This study aimed to construct a prediction model based on contrast-enhanced ultrasound (CEUS) ultrasomics features and investigate its efficacy in predicting early recurrence (ER) of primary hepatocellular carcinoma (HCC) after resection or ablation. Methods: This study retrospectively included 215 patients with primary HCC, who were divided into a developmental cohort (n = 139) and a test cohort (n = 76). Four representative images—grayscale ultrasound, arterial phase, portal venous phase and delayed phase —were extracted from each CEUS video. Ultrasomics features were extracted from tumoral and peritumoral area inside the region of interest. Logistic-regression was used to establish models, including a tumoral model, a peritumoral model and a combined model with additional clinical risk factors. The performance of the three models in predicting recurrence within 2 years was verified. Results: The combined model performed best in predicting recurrence within 2 years, with an area under the curve (AUC) of 0.845, while the tumoral model had an AUC of 0.810 and the peritumoral model one of 0.808. For prediction of recurrence-free survival, the 2 year cumulative recurrence rate was significant higher in the high-risk group (76.5%) than in the low-risk group (9.5%; p < 0.0001). Conclusion: These CEUS ultrasomics models, especially the combined model, had good efficacy in predicting early recurrence of HCC. The combined model has potential for individual survival assessment for HCC patients undergoing resection or ablation. Advances in knowledge: CEUS ultrasomics had high sensitivity, specificity and PPV in diagnosing early recurrence of HCC, and high efficacy in predicting early recurrence of HCC (AUC > 0.8). The combined model performed better than the tumoral ultrasomics model and peritumoral ultrasomics model in predicting recurrence within 2 years. Recurrence was more likely to occur in the high-risk group than in the low-risk group, with 2-year cumulative recurrence rates respectively 76.5% and 9.5% (p < 0.0001).

P9 Novel use of CHA2DS2VASC score to select patients to undergo repeat atrial fibrillation screening

2020 ◽  
Vol 41 (Supplement_1) ◽  
Author(s):  
W Sun ◽  
B P Y Yan
Keyword(s):  
Atrial Fibrillation ◽  
High Risk ◽  
Clinical Outcomes ◽  
Cross Validation ◽  
Risk Group ◽  
Curve Analysis ◽  
Low Risk ◽  
Decision Curve Analysis ◽  
Risk Patients

Abstract Background We have previously demonstrated unselected screening for atrial fibrillation (AF) in patients ≥65 years old in an out-patient setting yielded 1-2% new AF each time screen-negative patients underwent repeated screening at 12 to 18 month interval. Selection criteria to identify high-risk patients for repeated AF screening may be more efficient than repeat screening on all patients. Aims This study aimed to validate CHA2DS2VASC score as a predictive model to select target population for repeat AF screening. Methods 17,745 consecutive patients underwent 24,363 index AF screening (26.9% patients underwent repeated screening) using a handheld single-lead ECG (AliveCor) from Dec 2014 to Dec 2017 (NCT02409654). Adverse clinical outcomes to be predicted included (i) new AF detection by repeated screening; (ii) new AF clinically diagnosed during follow-up and (ii) ischemic stroke/transient ischemic attack (TIA) during follow-up. Performance evaluation and validation of CHA2DS2VASC score as a prediction model was based on 15,732 subjects, 35,643 person-years of follow-up and 765 outcomes. Internal validation was conducted by method of k-fold cross-validation (k = n = 15,732, i.e., Leave-One-Out cross-validation). Performance measures included c-index for discriminatory ability and decision curve analysis for clinical utility. Risk groups were defined as ≤1, 2-3, or ≥4 for CHA2DS2VASC scores. Calibration was assessed by comparing proportions of actual observed events. Results CHA2DS2VASC scores achieved acceptable discrimination with c-index of 0.762 (95%CI: 0.746-0.777) for derivation and 0.703 for cross-validation. Decision curve analysis showed the use of CHA2DS2VASC to select patients for rescreening was superior to rescreening all or no patients in terms of net benefit across all reasonable threshold probability (Figure 1, left). Predicted and observed probabilities of adverse clinical outcomes progressively increased with increasing CHA2DS2VASC score (Figure 1, right): 0.7% outcome events in low-risk group (CHA2DS2VASC ≤1, predicted prob. ≤0.86%), 3.5% intermediate-risk group (CHA2DS2VASC 2-3, predicted prob. 2.62%-4.43%) and 11.3% in high-risk group (CHA2DS2VASC ≥4, predicted prob. ≥8.50%). The odds ratio for outcome events were 4.88 (95%CI: 3.43-6.96) for intermediate-versus-low risk group, and 17.37 (95%CI: 12.36-24.42) for high-versus-low risk group.  Conclusion Repeat AF screening on high-risk population may be more efficient than rescreening all screen-negative individuals. CHA2DS2VASC scores may be used as a selection tool to identify high-risk patients to undergo repeat AF screening. Abstract P9 Figure 1

scholarly journals Racial Differences in the Biochemical Effects of Stress in Pregnancy

2020 ◽  
Vol 17 (19) ◽  
pp. 6941
Author(s):  
Paris Ekeke ◽  
Dara D. Mendez ◽  
Toby D. Yanowitz ◽  
Janet M. Catov
Keyword(s):  
High Risk ◽  
Racial Differences ◽  
Chronic Stress ◽  
Inflammatory Cytokines ◽  
Maternal Education ◽  
Risk Group ◽  
Medical Center ◽  
Low Risk ◽  
Risk Scores ◽  
Area Deprivation

Prenatal stress has been linked to preterm birth via inflammatory dysregulation. We conducted a cross-sectional study on female participants who delivered live, singleton infants at University of Pittsburgh Medical Center Magee Women’s Hospital. Participants (n = 200) were stratified by cumulative risk scores using a combination of individual factors (maternal education, diabetes, hypertension, smoking, relationship status, obesity, depression) and neighborhood deprivation scores. We hypothesized that inflammatory cytokines levels differ by risk group and race. Multiplex analyses of IL-6, IL-8, IL-10, IL-13 and TNF-alpha were run. We found that Black birthing people had more risk factors for chronic stress and had lower levels of IL-6 compared to White birthing people. When stratified by risk group and race, low-risk Black birthing people had lower levels of IL-6 compared to low-risk White birthing people, and high-risk Black birthing people had lower levels of IL-8 compared to high-risk White birthing people. Higher area deprivation scores were associated with lower IL-6 levels. Our results suggest that the relationship between chronic stress and inflammatory cytokines is modified by race. We theorize that Black birthing people encounter repetitive stress due to racism and social disadvantage which may result in stress pathway desensitization and a blunted cytokine response to future stressors.

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yinglian Pan ◽  
Li Ping Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

Differences In Community and Academic Practice Patterns For Newly Diagnosed Myelodysplastic Syndromes (MDS) Patients In Minnesota: A Population Based Study

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2798-2798 ◽  
Author(s):  
Daniel Pease ◽  
Julie A Ross ◽  
Phuong L. Nguyen ◽  
Betsy Hirsch ◽  
Adina Cioc ◽  
...  
Keyword(s):  
High Risk ◽  
Supportive Care ◽  
Practice Patterns ◽  
Risk Group ◽  
Population Based ◽  
Low Risk ◽  
Risk Scores ◽  
First Year ◽  
Newly Diagnosed ◽  
Population Based Study

Abstract Introduction Expanding treatment options for MDS have changed therapeutic decision-making for clinicians. To better characterize therapeutic choices in newly diagnosed MDS, we report the practice patterns captured during the first year of MDS diagnosis for patients enrolled in our statewide population-based study. We highlight a comparison of treatment in community and academic centers. Methods Adults in Minnesota with MDS (AIMMS) is a statewide prospective population-based study conducted by the University of Minnesota (UMN), Mayo Clinic, and Minnesota Department of Health. Starting in April 2010, all newly diagnosed adult cases (ages 20+) of MDS were invited to participate. After patient enrollment, central review was performed consisting of independent hematopathology and cytogenetic review coupled with oncologist chart review assigning prognostic risk scores [International Prognostic Scoring System (IPSS) and IPSS-R (Revised)] and abstracting treatment exposures. All enrolled patients with one year follow-up were included in this analysis. Treatment was divided into supportive, active, transplant, or other. Supportive care included observation, growth factors, and transfusions. Active care included azacitidine, decitabine, lenalidomide, or 7+3 chemotherapy. Academic centers were defined as the UMN and Mayo Clinic; all other centers were designated as community based practices. Results The median patient age was 73 years, with 68% males. IPSS and IPSS-R risk scores were calculated for 100% and 97% of patients, respectively. Treatment choices stratified by IPSS risk group showed 89% low risk, 53% INT-1, 31% INT-2, and 13% high risk with supportive care; active and transplant strategies were utilized for 9% low risk, 44% INT-1, 64% INT-2, and 88% high risk. INT-1 in the community received 70% supportive treatment, in academic 35%. Active treatment for INT-1 was 30% in community and 45% in academic. Community INT-2 received supportive care in 45% of cases, in academic 23%. Transplants were limited to academic centers, with the highest rate in INT-2 at 34%. Among community diagnoses, 100% of high risk, 52% INT-2, 26% INT-1, and 13% low risk were referred to an academic center. Comparison of age <65 and 65+ years showed 83% of transplants occurred in those <65. INT-2/high risk group patients <65 received 95% active therapy or transplant, compared to 51% of those 65+. Discussion This prospective, population based study provides a well-defined patient cohort based on central review of pathologic and clinical data. Evaluation of practice patterns during the first year after diagnosis showed higher utilization of active and transplant treatment strategies as IPSS risk score increased. Further, compared to community, higher utilization occurred for patients at academic centers, suggesting more aggressive treatment in these settings. Age was also a predictor of treatment choice. In addition, referral patterns followed IPSS score. Whether these treatment differences are driven by patient preference and/or translate into improved disease control and decreased mortality requires continued prospective analysis and will be detailed in future reports. Disclosures No relevant conflicts of interest to declare.

scholarly journals A radiomics nomogram for prediction of overall survival  in hepatocellular carcinoma after hepatectomy

2020 ◽  
Author(s):  
Qinqin Liu ◽  
Jing Li ◽  
Fei Liu ◽  
Weilin Yang ◽  
Jingjing Ding ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Clinicopathological Factors ◽  
Training Cohort ◽  
Texture Parameters ◽  
Radiomics Signature

Abstract Background Hepatocellular carcinoma (HCC) is associated with dismal prognosis, and prediction of the prognosis of HCC can assist the therapeutic decisions. More and more studies showed that the texture parameters of images can reflect the heterogeneity of the tumor, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of the study was to investigate the prognostic value of computed tomography (CT) texture parameters for patients with HCC after hepatectomy, and try to develop a radiomics nomograms by combining clinicopathological factors with radiomics signature.Methods 544 eligible patients were enrolled in the retrospective study and randomly divided into training cohort (n=381) and validation cohort (n=163). The regions of interest (ROIs) of tumor is delineated, then the corresponding texture parameters are extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in training cohort, and the radiomics score (Rad-score) was generated. According to the cut-off value of the Rad-score calculated by the receiver operating characteristic (ROC) curve, the patients were divided into high-risk group and low-risk group. The prognosis of the two groups was compared and validated in the validation cohort. Univariate and multivariable analyses by COX proportional hazard regression model were used to select the prognostic factors of overall survival (OS). The radiomics nomogram for OS were established based on the radiomics signature and clinicopathological factors. The Concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram.Result 7 texture parameters associated with OS were selected in the training, and the radiomics signature was formulated based on the texture parameters. The patients were divided into high-risk group and low-risk group by the cut-off values of the Rad-score of OS. The 1-, 3- and 5-year OS rate was 71.0%, 45.5% and 35.5% in the high-risk group, respectively, and 91.7%, 82.1% and 78.7%, in the low-risk group, respectively, with significant difference (P <0.001). COX regression model found that Rad-score was an independent prognostic factor of OS. In addition, the radiomics nomogram was developed based on five variables: α‐fetoprotein (AFP), platelet lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and Rad-score. The nomograms displayed good accuracy in predicting OS (C-index=0.747) in the training cohort and was confirmed in the validation cohort (C-index=0.777). The calibration plots also showed an excellent agreement between the actual and predicted survival probabilities. The DAC indicated that the radiomics nomogram showed better clinical usefulness than the clinicopathologic nomogram.Conclusion The radiomics signature is potential biomarkers of the prognosis of HCC after hepatectomy. Radiomics nomogram that integrated radiomics signature can provide more accurate estimate of OS for patients with HCC after hepatectomy.

scholarly journals Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

2020 ◽  
Author(s):  
Hui Wang ◽  
Xiaoling Ma ◽  
Jinhui Liu ◽  
Yicong Wan ◽  
Yi Jiang ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Test Set ◽  
Gene Set

Abstract Background: Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.Methods: First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariat, LASSO, and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, gene set enrichment and somatic mutation analyses were also used for these prognostic autophagy-related genes. Results: A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (P<0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. Gene set enrichment analysis revealed high risk score was associated with tumor initiation and progression associated pathways.Conclusions: Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

scholarly journals A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Junli Wang ◽  
Qi Zhang ◽  
Fukang Shi ◽  
Dipesh Kumar Yadav ◽  
Zhengtao Hong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Group ◽  
Radical Resection ◽  
Tissue Microarrays ◽  
Gene Signature ◽  
High Risk Group ◽  
Prognostic Models ◽  
Risk Scores

Purpose: Hepatocellular carcinoma (HCC) is one of the most prevalent malignant diseases worldwide and has a poor prognosis. Gene-based prognostic models have been reported to predict the overall survival of patients with HCC. Unfortunately, most of the genes used in earlier prognostic models lack prospective validation and, thus, cannot be used in clinical practice.Methods: Candidate genes were selected from GEPIA (Gene Expression Profiling Interactive Analysis), and their associations with patients’ survival were confirmed by RT-PCR using cDNA tissue microarrays established from patients with HCC after radical resection. A multivariate Cox proportion model was used to calculate the coefficient of corresponding gene. The expression of seven genes of interest (MKI67, AR, PLG, DNASE1L3, PTTG1, PPP1R1A, and TTR) with two reference genes was defined to calculate a risk score which determined groups of different risks.Results: Our risk scoring efficiently classified patients (n = 129) with HCC into a low-, intermediate-, and high-risk group. The three groups showed meaningful distinction of 3-year overall survival rate, i.e., 88.9, 74.5, and 20.6% for the low-, intermediate-, and high-risk group, respectively. The prognostic prediction model of risk scores was subsequently verified using an independent prospective cohort (n = 77) and showed high accuracy.Conclusion: Our seven-gene signature model performed excellent long-term prediction power and provided crucially guiding therapy for patients who are not a candidate for surgery.

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