P959The specific characteristics and independent predictors of no-reflow phenomenon, development of a clinically-adaptable risk estimation system

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
F Banfi-Bacsardi ◽  
Z Ruzsa ◽  
A Lux ◽  
I Edes ◽  
L Molnar ◽  
...  
Keyword(s):  
Risk Score ◽  
Roc Analysis ◽  
Independent Predictor ◽  
Troponin T ◽  
Risk Estimation ◽  
Survival Curve ◽  
Risk Groups ◽  
Independent Factor ◽  
No Reflow ◽  
Estimation System

Abstract Introduction No-reflow (NR) phenomenon occurs, when myocardial perfusion is not re-established despite opening the coronary artery during percutaneous coronary intervention (PCI). Purpose Our aim was to identify no-reflow specific characteristics, its independent predictors, and to develop a clinically-adaptable risk score. Methods We have analysed 4085 patient data from two Hungarian cardiovascular centres. We included all STEMI/NSTEMI patients underwent PCI (n=3187). 158 patients treated with papaverine/adenosine formed NR group, while 3029 patients were in control (C) group. Anamnestic parameters, laboratory and operation data were compared. Statistical analysis was carried out with Mann-Whitney-, Fisher test, binary logistic regression and Kaplan Meier survival curve. Based on our results, we designed a risk estimation system, checking its applicability with ROC analysis. Results As for NR-specific characteristics, malignant ventricular arrhythmias (11% vs. 4%, p=0,0031; NR-C consequently) and complications (21% vs. 11%, p=0,064) showed their vulnerability. The increment of glucose (8,1 vs. 7,1 mmol/l, p=0,004), WBC (12,08 vs. 10,5 G/l, p=0,001), CRP (12,46 vs. 7,67 mg/l, p=0,051) and LDL levels (3,34 vs. 3,13 mmol/l, p=0,059) supported the pathomechanism of NR. Higher biomarker levels (troponinT: 2040 vs. 510,5 ng/ml; CK-MB: 100,4 vs. 63,65 U/l, p<0,0001) indicated severe perfusion disturbance. Tendency was seen in higher BMI (28,65 vs. 28,03 kg/m2, p=0,12). STEMI dominated in NR (83 vs. 59%, p<0,0001). Lower platelet level (213,3 vs. 228 G/l, p=0,107) and single vessel disease (46 vs. 25%, p=0,0042) characterized NR. 30-day survival was significantly different (85,1 vs. 93,54%, p<0,0001). The mortality rate of NR in STEMI was 69,7% (69,7% vs. 7,94%, p<0,0001) and in NSTEMI 3,7% (3,7% vs. 4,32%). From the significant differences, CRP was the independent predictor of NR (OR: 1,011, p=0,004; pro 1 mg/l change). Examining STEMI/NSTEMI separately, in STEMI CRP was the independent predictor (OR: 1,0092, p=0,036). In NSTEMI LDL (OR: 4,23, p=0,021) was the independent factor. In the risk score, the following 8 parameters were included: BMI>28 kg/m2, glucose>8 mmol/l, WBC>12 G/l, CK-MB>100 U/l, hs troponin T>2000 ng/ml, CRP>12 mg/l, LDL>3,3 mmol/l, STEMI (yes/no), thus maximum 8 points could be reached. Low (0–1 points, 5–20%), moderate (2–5 points, 55–70%) and high risk groups (6–8 points, 41–11%) were formed. Supervising the model with ROC analysis: AUC=0,69, p=0,0026, which indicates its ability to discriminate effectively between different risk levels of NR. Conclusions The specific characteristics of NR group were identified, from which CRP was the independent predictor - as well as in STEMI, while in NR-NSTEMI LDL was the independent factor. With the elaborated risk estimation system –using anamnestic and routine laboratory parameters– NR could be predicted and unsuccessful PCI could be reduced, resulting in positive therapeutic consequences.

Abstract 14486: Validation of the Intermountain Risk Score and Get With the Guidelines-Heart Failure Score in Predicting 30 Day and 1 Year Mortality

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Erik K Engelsgjerd ◽  
Benjamin D Horne ◽  
Catherine P Benziger
Keyword(s):  
Heart Failure ◽  
High Risk ◽  
Risk Score ◽  
Patient Population ◽  
Complete Blood Count ◽  
Reference Group ◽  
Risk Estimation ◽  
Risk Groups ◽  
Get With The Guidelines ◽  
Age And Sex

Background: The Intermountain Risk Score (IMRS) predicts mortality in heart failure (HF) patients utilizing common, inexpensive tests in conjunction with patient age and sex. IMRS has not been validated for Essentia Health (EH)’s patient population nor compared to various pre-existing scores in this population. Methods: Individuals were selected from the American Heart Association’s Get With The Guidelines®- HF (GWTG-HF) registry as patients to evaluate in a retrospective study. This patient population consists of consecutive inpatients age ≥18 admitted with a HF diagnosis at EH from 7/2017 through 6/2019. IMRS was derived using common HF laboratory measures (complete blood count and basic metabolic profile), age, and sex. EH is a large rural health care system in MN, WI, and ND. Results: A total of 703 individuals (mean age: 74.20, 44.38% female) were studied. The 30-day IMRS predicted 30 day mortality for both sexes (Females N=312: OR=1.19 (95% confidence interval: 1.08, 1.32) per +1, p<0.001; Males N=391: OR=1.23 (1.12, 1.36) per +1, p<0.001). The 1-year IMRS predicted 1-year mortality (Females: OR=1.14 (1.06, 1.23) per +1, p<0.001; Males: OR=1.28 (1.18, 1.38) per +1, p<0.001). Using sex-specific cut offs for IMRS, females had better 30-day risk stratification in moderate to high risk groups compared to low risk reference group (Mod-risk: OR=5.90 (1.35, 25.85), p=0.018; high-risk: OR=9.63 (2.12, 43.72), p=0.003) vs. men (Mod-risk: OR=1.52 (0.61, 3.79), p=0.37; high risk: OR=4.53 (1.92, 10.65), p<0.001). The GWTG-HF risk score was significant for 30-day (OR=1.29 (1.16, 1.44) per +1, p<0.001) and 1-year mortality (OR=1.23 (1.13, 1.35) per +1, p<0.001) but was only calculated in 292 patients (41.5%). Conclusions: The Intermountain Risk Score is a useful predictor of 30-day and 1-year mortality for heart failure patients in a large rural healthcare system. The GWTG-HF risk score also predicts 30-day and 1-year mortality but is often not calculated due to not all variables being collected at each hospital visit (i.e. ejection fraction). The IMRS risk score uses simple variables that most patients have on admission and future study to evaluate cost-effectiveness as a prospective tool for initial risk estimation is warranted.

Abstract 12923: Development of a Novel Biomarker-based Risk Score for Heart Failure in Patients With Diabetes

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
David D Berg ◽  
Stephen D Wiviott ◽  
Benjamin M Scirica ◽  
Erica Goodrich ◽  
Deepak L Bhatt ◽  
...  
Keyword(s):  
Heart Failure ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Troponin T ◽  
Absolute Risk ◽  
Sglt2 Inhibitor ◽  
Risk Groups ◽  
Sglt2 Inhibitors ◽  
Baseline Risk

Introduction: Heart failure (HF) is a prognostically important complication of T2DM, the risk of which can be reduced by SGLT2 inhibitors. Clinical factors and circulating biomarkers of myocardial injury and hemodynamic stress predict risk of hosp for HF (HHF) in pts with T2DM. Aim: We aimed to develop and validate a biomarker-based risk score for HHF in pts with T2DM and to assess whether this score can identify high-risk pts with T2DM who have the greatest reduction in HHF risk with SGLT2 inhibition. Methods: Blood samples were prospectively collected from pts enrolled in SAVOR-TIMI 53 and DECLARE-TIMI 58 at randomization; high-sensitivity troponin T (hsTnT) and N-terminal B-type natriuretic peptide (NT-proBNP) were measured. We derived a risk score in 6106 pts with T2DM in the placebo arm of SAVOR-TIMI 53. Candidate variables (n=27) were assessed using Cox regression. The strongest indicators of HHF risk (based on Wald χ;2 values) were selected for inclusion and assigned integer weights. We externally validated the score in 7251 T2DM pts in the placebo arm of DECLARE-TIMI 58. Hazard ratios (HR) and absolute risk reductions (ARR) in HHF with the SGLT2 inhibitor dapagliflozin were assessed by baseline risk group. Results: The strongest independent indicators of HHF risk were NT-proBNP and hsTnT concentrations, and prior HF (each p<0.001). A risk score using these 3 variables identified a strong gradient of HHF risk (p-trend <0.001) in both the derivation and validation cohorts, with c-indices of 0.87 and 0.84, respectively. HRs with dapagliflozin were similar across risk groups (p-int = 0.64); however, ARRs were greater in those at higher baseline risk (p-trend <0.001), with high- (10-13 points) and very high-risk (14+ points) pts having 3.2% and 4.4% ARRs in KM estimates of HHF at 4 yrs, respectively ( Fig ). Conclusions: A novel biomarker-based risk score for HHF in pts with T2DM identifies pts at higher risk of HHF who derive greater absolute benefit from SGLT2 inhibitors.

scholarly journals A Biomarker-Based Score for Risk of Hospitalization for Heart Failure in Patients With Diabetes

2021 ◽  
Author(s):  
David D. Berg ◽  
Stephen D. Wiviott ◽  
Benjamin M. Scirica ◽  
Thomas A. Zelniker ◽  
Erica L. Goodrich ◽  
...  
Keyword(s):  
Heart Failure ◽  
Risk Score ◽  
Cox Regression ◽  
Troponin T ◽  
Absolute Risk ◽  
High Sensitivity ◽  
Risk Groups ◽  
Risk Category ◽  
Clinical Factor ◽  
Patients With Diabetes

<b>Objective:</b> Heart failure (HF) is an impactful complication of type 2 diabetes mellitus (T2DM). We aimed to develop and validate a risk score for hospitalization for HF (HHF) incorporating biomarkers and clinical factor(s) in patients with T2DM. <p><b> </b></p> <p><b>Research Design and Methods:</b> We derived a risk score for HHF using clinical data, high-sensitivity troponin T (hsTnT), and N-terminal-pro-B-type natriuretic peptide (NT-proBNP) from 6,106 placebo-treated patients with T2DM in SAVOR-TIMI 53. Candidate variables were assessed using Cox regression. The strongest indicators of HHF risk were included in the score using integer weights. The score was externally validated in 7,251 placebo-treated patients in DECLARE-TIMI 58. The effect of dapagliflozin on HHF was assessed by risk category in DECLARE-TIMI 58.</p> <p> </p> <p><b>Results:</b> The strongest indicators of HHF risk were NT-proBNP, prior HF, and hsTnT (each p<0.001). A risk score using these 3 variables identified a gradient of HHF risk (p-trend<0.001) in the derivation and validation cohorts, with c-indices of 0.87 (95%CI, 0.84-0.89) and 0.84 (0.81-0.86), respectively. Whereas there was no significant effect of dapagliflozin vs. placebo on HHF in the low-risk group (hazard ratio [HR] 0.98[0.50-1.92]), dapagliflozin significantly reduced HHF in the intermediate-, high-, and very high-risk groups (HR 0.64[0.43-0.95], 0.63[0.43-0.94], and 0.72[0.54-0.96], respectively). Correspondingly, absolute risk reductions increased across these latter 3 groups: 1.0%(0.0%-1.9%), 3.0%(0.7%-5.3%), and 4.4%(-0.2%-8.9%) (p-trend<0.001).</p> <p> </p> <p><b>Conclusions:</b> We developed and validated a risk score for HHF in T2DM that incorporated NT-proBNP, prior HF, and hsTnT. The risk score identifies patients at higher risk of HHF who derive greater absolute benefit from dapagliflozin.</p> <br> <p> </p>

scholarly journals A Biomarker-Based Score for Risk of Hospitalization for Heart Failure in Patients With Diabetes

2021 ◽  
Author(s):  
David D. Berg ◽  
Stephen D. Wiviott ◽  
Benjamin M. Scirica ◽  
Thomas A. Zelniker ◽  
Erica L. Goodrich ◽  
...  
Keyword(s):  
Heart Failure ◽  
Risk Score ◽  
Cox Regression ◽  
Troponin T ◽  
Absolute Risk ◽  
High Sensitivity ◽  
Risk Groups ◽  
Risk Category ◽  
Clinical Factor ◽  
Patients With Diabetes

<b>Objective:</b> Heart failure (HF) is an impactful complication of type 2 diabetes mellitus (T2DM). We aimed to develop and validate a risk score for hospitalization for HF (HHF) incorporating biomarkers and clinical factor(s) in patients with T2DM. <p><b> </b></p> <p><b>Research Design and Methods:</b> We derived a risk score for HHF using clinical data, high-sensitivity troponin T (hsTnT), and N-terminal-pro-B-type natriuretic peptide (NT-proBNP) from 6,106 placebo-treated patients with T2DM in SAVOR-TIMI 53. Candidate variables were assessed using Cox regression. The strongest indicators of HHF risk were included in the score using integer weights. The score was externally validated in 7,251 placebo-treated patients in DECLARE-TIMI 58. The effect of dapagliflozin on HHF was assessed by risk category in DECLARE-TIMI 58.</p> <p> </p> <p><b>Results:</b> The strongest indicators of HHF risk were NT-proBNP, prior HF, and hsTnT (each p<0.001). A risk score using these 3 variables identified a gradient of HHF risk (p-trend<0.001) in the derivation and validation cohorts, with c-indices of 0.87 (95%CI, 0.84-0.89) and 0.84 (0.81-0.86), respectively. Whereas there was no significant effect of dapagliflozin vs. placebo on HHF in the low-risk group (hazard ratio [HR] 0.98[0.50-1.92]), dapagliflozin significantly reduced HHF in the intermediate-, high-, and very high-risk groups (HR 0.64[0.43-0.95], 0.63[0.43-0.94], and 0.72[0.54-0.96], respectively). Correspondingly, absolute risk reductions increased across these latter 3 groups: 1.0%(0.0%-1.9%), 3.0%(0.7%-5.3%), and 4.4%(-0.2%-8.9%) (p-trend<0.001).</p> <p> </p> <p><b>Conclusions:</b> We developed and validated a risk score for HHF in T2DM that incorporated NT-proBNP, prior HF, and hsTnT. The risk score identifies patients at higher risk of HHF who derive greater absolute benefit from dapagliflozin.</p> <br> <p> </p>

scholarly journals A Peri-Implant Disease Risk Score for Patients with Dental Implants: Validation and the Influence of the Interval between Maintenance Appointments

2019 ◽  
Vol 8 (2) ◽  
pp. 252 ◽  
Author(s):  
Miguel de Araújo Nobre ◽  
Francisco Salvado ◽  
Paulo Nogueira ◽  
Evangelista Rocha ◽  
Peter Ilg ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Disease Risk ◽  
Disease Incidence ◽  
Risk Groups ◽  
Risk Profile ◽  
Moderate Risk ◽  
C Statistic ◽  
Disease Risk Score ◽  
Very High

Background: There is a need for tools that provide prediction of peri-implant disease. The purpose of this study was to validate a risk score for peri-implant disease and to assess the influence of the recall regimen in disease incidence based on a five-year retrospective cohort. Methods: Three hundred and fifty-three patients with 1238 implants were observed. A risk score was calculated from eight predictors and risk groups were established. Relative risk (RR) was estimated using logistic regression, and the c-statistic was calculated. The effect/impact of the recall regimen (≤ six months; > six months) on the incidence of peri-implant disease was evaluated for a subset of cases and matched controls. The RR and the proportional attributable risk (PAR) were estimated. Results: At baseline, patients fell into the following risk profiles: low-risk (n = 102, 28.9%), moderate-risk (n = 68, 19.3%), high-risk (n = 77, 21.8%), and very high-risk (n = 106, 30%). The incidence of peri-implant disease over five years was 24.1% (n = 85 patients). The RR for the risk groups was 5.52 (c-statistic = 0.858). The RR for a longer recall regimen was 1.06, corresponding to a PAR of 5.87%. Conclusions: The risk score for estimating peri-implant disease was validated and showed very good performance. Maintenance appointments of < six months or > six months did not influence the incidence of peri-implant disease when considering the matching of cases and controls by risk profile.

scholarly journals Usefulness of the PRECISE DAPT score as a predictor of ischaemic stroke after an acute coronary syndrome

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Gonzalez Ferrero ◽  
B.A.A Alvarez Alvarez ◽  
C.C.A Cacho Antonio ◽  
M.P.D Perez Dominguez ◽  
P.A.M Antunez Muinos ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Ischaemic Stroke ◽  
Independent Predictor ◽  
Multivariable Logistic Regression Analysis ◽  
Funding Source ◽  
Independent Factor ◽  
Prediction Ability ◽  
Coronary Syndrome ◽  
Grace Score

Abstract Introduction Ischaemic stroke (IS) risk after acute coronary syndrome is increasing. The aim of our study was to evaluate the stroke rate in a multicentre study and to determine the prediction ability of the PRECISE DAPT score, added to the prediction power of the GRACE score, already demonstrated. Methods This was a retrospective study, carried out in two centres with 5916 patients, with ACS discharged between 2011 and 2017 (median 66±13 years, 27.7% women). The primary endpoint was the occurrence of ischaemic stroke and its risk during follow up (median 5.5, IQR 2.6–7.0). Results A multivariable logistic regression analysis was made, where GRACE (HR 1.01, IC 95% 1.00–1.02) and PRECISE DAPT score (HR 1.03, IC 95% 1.01–1.05) were both an independent predictor of ischaemic stroke after ACS, in a model adjusted by age and AF, which was found to be the independent factor with highest risk (HR 1.67, IC 95% 1.09–2.55). Conclusions GRACE and PRECISE DAPT scores are ischaemic stroke predictors used during follow-up for patients after acute coronary syndrome. We should use both of them not only trying to predict ischaemic/haemorrhagic risk respectively but also as ischaemic stroke predictors. Figure 1. AUC Curves Funding Acknowledgement Type of funding source: None

Short-term risk stratification using CADILLAC risk score in patients with ST elevation myocardial infarction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Satou ◽  
H Kitahara ◽  
K Ishikawa ◽  
T Nakayama ◽  
Y Fujimoto ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
High Risk ◽  
Adverse Events ◽  
Risk Score ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Short Term ◽  
Recurrent Myocardial Infarction ◽  
St Elevation

Abstract Background The recent reperfusion therapy for ST-elevation myocardial infarction (STEMI) has made the length of hospital stay shorter without adverse events. CADILLAC risk score is reportedly one of the risk scores predicting the long-term prognosis in STEMI patients. Purpose To invenstigate the usefulness of CADILLAC risk score for predicting short-term outcomes in STEMI patients. Methods Consecutive patients admitted to our university hospital and our medical center with STEMI (excluding shock, arrest case) who underwent primary PCI between January 2012 and April 2018 (n=387) were enrolled in this study. The patients were classified into 3 groups according to the CADILLAC risk score: low risk (n=176), intermediate risk (n=87), and high risk (n=124). Data on adverse events within 30 days after hospitalization, including in-hospital death, sustained ventricular arrhythmia, recurrent myocardial infarction, heart failure requiring intravenous treatment, stroke, or clinical hemorrhage, were collected. Results In the low risk group, adverse events within 30 days were significantly less observed, compared to the intermediate and high risk groups (n=13, 7.4% vs. n=13, 14.9% vs. n=58, 46.8%, p&lt;0.001). In particular, all adverse events occurred within 3 days in the low risk group, although adverse events, such as heart failure (n=4), recurrent myocardial infarction (n=1), stroke (n=1), and gastrointestinal bleeding (n=1), were substantially observed after day 4 of hospitalization in the intermediate and high risk groups. Conclusions In STEMI patients with low CADILLAC risk score, better short-term prognosis was observed compared to the intermediate and high risk groups, and all adverse events occurred within 3 days of hospitalization, suggesting that discharge at day 4 might be safe in this study population. CADILLAC risk score may help stratify patient risk for short-term prognosis and adjust management of STEMI patients. Initial event occurrence timing Funding Acknowledgement Type of funding source: None

scholarly journals Identification of ferroptosis genes in immune infiltration and prognosis in thyroid papillary carcinoma using network analysis

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ruoting Lin ◽  
Conor E. Fogarty ◽  
Bowei Ma ◽  
Hejie Li ◽  
Guoying Ni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Carcinoma ◽  
Risk Score ◽  
Immune Cells ◽  
Survival Curve ◽  
Enrichment Analysis ◽  
Papillary Thyroid ◽  
Hub Genes ◽  
Cox Analysis ◽  
Cluster 2

Abstract Background Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analyse the gene expression profiles of the disease and established a new model for the correlation. Methods The thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 types of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated. Results We identify two subtypes in PTC based on the expression of ferroptosis related genes, with the proportion of cluster 1 significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. The mutations of Braf and Nras are detected as the major mutations of cluster 1 and 2, respectively. Subsequent analyses of TME immune cells infiltration indicated cluster 1 is remarkably richer than cluster 2. The risk score of THCA is in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data shows that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer. Conclusions Our study finds seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we construct the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

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