The MIDA quantitative mortality risk score: Prognostic model in floppy mitral valves

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

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Identification of a two-gene prognostic model associated with cytolytic activity for colon cancer

Cancer Cell International ◽

10.1186/s12935-021-01782-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiaoye Jiang ◽

Zhongxiang Jiang ◽

Lichun Xiang ◽

Xuenuo Chen ◽

Jiao Wu ◽

...

Keyword(s):

Colon Cancer ◽

Risk Score ◽

Immune Checkpoint ◽

Prognostic Model ◽

Quantitative Polymerase Chain Reaction ◽

Cytolytic Activity ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Aberrant Expression ◽

Immune Activity

Abstract Background Increasing evidence has shown that cytolytic activity (CYT) is a new immunotherapy biomarker that characterises the antitumour immune activity of cytotoxic T cells and macrophages. In this study, we established a prognostic model associated with CYT. Methods A prognostic model based on CYT-related genes was developed. Furthermore, aberrant expression of genes of the model in colon cancer (CC) was identified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) assays. Next, the correlation between the model and T-cell infiltration in the CC microenvironment was analysed. The Tumour Immune Dysfunction and Exclusion (TIDE) algorithm and subclass mapping were used to predict clinical responses to immune checkpoint inhibitors. Results In total, 280 of the 1418 genes were differentially expressed based on CYT. A prognostic model (including HOXC8 and MS4A2) was developed based on CYT-related genes. The model was validated using the testing set, the whole set and a Gene Expression Omnibus (GEO) cohort (GSE41258). Gene set enrichment analysis (GSEA) and other analyses showed that the levels of immune infiltration and antitumour immune activation in low-risk-score tumours were greater than those in high-risk-score tumours. CC patients with a low-risk-score showed more promise in the response to anti-immune checkpoint therapy. Conclusions Overall, our model may precisely predict the overall survival of CC and reflect the strength of antitumour immune activity in the CC microenvironment. Furthermore, the model may be a predictive factor for the response to immunotherapy.

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MIDA mortality risk score in patients undergoing percutaneous edge-to-edge mitral repair

European Heart Journal ◽

10.1093/ehjci/ehaa946.1965 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

R Kavsur ◽

C Iliadis ◽

C Metze ◽

M Spieker ◽

V Tiyerili ◽

...

Keyword(s):

Mitral Valve ◽

Mitral Valve Repair ◽

Risk Score ◽

Patient Selection ◽

Mortality Risk ◽

Left Ventricular ◽

Left Ventricular Ejection ◽

Valve Repair ◽

Ventricular Ejection Fraction

Abstract Background Recent studies indicate that careful patient selection is key for the percutaneous edge-to-edge repair via MitraClip procedure. The MIDA Score represents a useful tool for patient selection and is validated in patients with degenerative mitral regurgitation (MR). Aim We here assessed the potential benefit of the MIDA Score for patients with functional or degenerative MR undergoing edge-to-edge mitral valve repair via the MitraClip procedure. Methods In the present study, we retrospectively included 520 patients from three Heart Centers undergoing MitraClip implantation for MR. All parameters of the MIDA Score were available in these patients, consisting of the 7 variables age, symptoms, atrial fibrillation, left atrial diameter, right ventricular systolic pressure, left-ventricular end-systolic diameter, left ventricular ejection fraction. According to the median MIDA-Score of 9 points, patients were stratified in to a high and a low MIDA Score group and association with all-cause mortality was evaluated. Moreover, MR was assessed in echocardiographic controls in 370 patients at discharge, 279 patients at 3-months and 222 patients at 12 months after MitraClip implantation. Results During 2-years follow-up after MitraClip implantation, 69 of 291 (24%) patients with a high MIDA Score and 25 of 229 (11%) patients with a low MIDA Score died. Kaplan-Meier analysis and log rank test showed inferior rates of death in patients with a low score (p<0.001) and multivariate cox regression revealed an odds ratio of 0.54 (0.31–0.95; p=0.032) regarding 2-year survival in this group. Moreover, one point increase in the MIDA Score was associated with a 1.18-fold increase in the risk for mortality (1.02–1.36; p=0.025). Comparing patients with a high MIDA Score and patients with a low score, post-procedural residual moderate/severe MR tended to be more frequent in patients with a high MIDA Score at discharge (53% vs 43%; p=0.061), 3-months (50% vs 40%; p=0.091) and significantly at 12-months follow-up (52% vs 37%; p=0.029). Conclusion The MIDA Mortality Risk Score remained its predictive ability in patients with degenerative or function MR undergoing transcatheter edge-to-edge mitral valve repair. Moreover, a high MIDA score was associated with a higher frequency of post-procedural residual moderate/severe MR, indicating a lower effectiveness of this procedure in these patients. Funding Acknowledgement Type of funding source: None

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Validation of the Zwolle score for selection of very low-risk STEMI patients treated with primary angioplasty

European Heart Journal ◽

10.1093/ehjci/ehaa946.1840 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

A Cordero ◽

B Cid ◽

P Monteiro ◽

J.M Garcia-Acuna ◽

M Rodriguez-Manero ◽

...

Keyword(s):

Hospital Mortality ◽

Risk Score ◽

Mortality Risk ◽

Area Under The Curve ◽

Coronary Intervention ◽

Low Risk ◽

Individual Risk ◽

Care Organization ◽

Grace Score ◽

Selection Of

Abstract Background The Zwolle risk score was designed to stratify the actual in-hospital mortality risk of ST-elevation myocardial infarction (STEMI) patients treated with primary percutaneous coronary intervention (p-PCI) but, also, for decision-making related to patients location in an intensive care unit or not. Since the GRACE score continues being the gold-standard for individual risk assessment in STEMI in most institutions we assessed the specificity of both scores for in-hospital mortality. Methods We assessed the accuracy of Zwolle risk score for in-hospital mortality estimation as compared to the GRACE score in all patients admitted for STEMI in 3 tertitary hospitals. Patients with Zwolle risk score <3 would qualify as “low risk”, 3–5 as “intermediate risk” and ≥6 as “high risk”. Patients with GRACE score <140 were classified as low-risk. Specificity, sensitivity and classification were assessed by ROC curves and the area under the curve (AUC). Results We included 4,446 patients, mean age 64.7 (13.6) years, 24% women and 39% with diabetes. Mean GRACE score was 157.3 (4.9) and Zwolle was 2.8 (3.3). In-hospital mortality was 10.6% (471 patients). Patients who died had higher GRACE score (218.4±4.9 vs. 149.6±37.5; p<0.001) and Zwolle score (7.6±4.3 vs. 2.3±2.18; p<0.001); a statistically significant increase of in-hospital mortality risk, adjusted adjusted by age, gender and revascularization, was observed with both scores (figure). A total of 1,629 patients (40.0%) were classified as low risk by the GRACE score and 2,962 (66.6%) by the Zwolle score; in-hospital mortality was 1.6% and 2.7%, respectively. Moreover, the was a significant increase of in-hospital mortality rate according to Zwolle categories (2.7%; 13.0%; 41.6%)The AUC of both score was the same (p=0.49) but the specificity of GRACE score <140 was 43.1% as compared to 72.6% obtained by Zwolle score <3; patients accurately classified was also lower with the GRACE score threshold (48.8% vs. 73.7%). Conclusions Selection of low-risk STEMI patients treated with p-PCI based on the Zwolle risk score has higher specificity than the GRACE score and might be useful for the care organization in clinical practice. Funding Acknowledgement Type of funding source: None

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

BioData Mining ◽

10.1186/s13040-021-00261-y ◽

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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A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1871520621666210713112630 ◽

2021 ◽

Vol 24 ◽

Author(s):

Yongmei Wang ◽

Guimin Zhang ◽

Ruixian Wang

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Differential Expression ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Differential Expression Analysis ◽

Functional Enrichment ◽

Risk Scores ◽

Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

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An Immune Risk Score Predicts Survival of Patients with Myelodysplastic Syndrome

Blood ◽

10.1182/blood-2020-137349 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 33-34

Author(s):

Yang Liang ◽

Fang Hu ◽

Yu-Jun Dai ◽

Yun Wang ◽

Huan Li

Keyword(s):

Regression Analysis ◽

Myelodysplastic Syndrome ◽

Risk Score ◽

Prognostic Model ◽

Validation Cohort ◽

Gene Set Enrichment Analysis ◽

Type I ◽

Immune Gene ◽

Training Cohort ◽

Immune Score

Background: Myelodysplastic syndrome (MDS) was characterized as ineffective hematopoiesis, increased transformation to acute myeloid leukemia (AML), and accompanied by immune system dysfunction. However, the immune signature of MDS remains elusive. Methods: The clinical data (age, sex, international prognostic score system (IPSS), hemoglobin, blast, RBC transfusion dependence, and corresponding subject-level survival) as well as expression profiles of MDS (CD34+ cells) were obtained from Gene Expression Omnibus (GEO: GSE 58831; GSE 114922). A robust prognosis model of immune genes was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis. Survival analysis for prognostic model was carried out through the Kaplan-Meier curve and Log-rank test. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the accuracy of prognostic models. Immune score for different subtype were calculated further by single sample gene set enrichment analysis (ssGSEA). Result: A novel robust immune gene prognostic model indicate that subtype with lower risk score were longer overall survival (OS) than subtype with higher risk score in training cohort (Figure1 A, C). The model was further verified by the validation cohort (Figure1 B, D). The multivariate Cox regression analysis demonstrated the model was an independent prognostic factor for OS prediction with hazard ratios of 56.694 (95% CIs: 9.038−355.648), 3.009 (95% CIs: 1.042−8.692) both in train cohort and external validation cohort respectively (Figure1 G, H). The AUC of 5- year were 0.92 (95% CIs: 0.86 - 0.97) and 0.7 (95% CIs: 0.51 - 0.89) for OS respectively in training cohort and validation cohort (Figure1 E, F). Furthermore, ssGSEA showed higher risk score subtype was significantly associated with higher immune score of check point, human leukocyte antigen (HLA), T cell co-inhibition and type I interferon (IFN) response (Figure1 K-N), which indicating that the poor outcome might be caused by tumor-associated immune response dysfunction partly. Conclusion: We constructed a robust immune gene prognostic model, which have a potential prognostic value for MDS patients and may provide evidence for personalized immunotherapy. Figure Disclosures No relevant conflicts of interest to declare.

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Development of a Point-of-Care Mortality Risk Score for Hospitalized COVID-19 Patients

10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a2657 ◽

2021 ◽

Author(s):

E.J. Yoo ◽

B. Percha ◽

M. Tomlinson ◽

S. Pan ◽

M. Basist ◽

...

Keyword(s):

Risk Score ◽

Mortality Risk ◽

Point Of Care

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