A Novel Four-Gene Signature Associated With Immune Checkpoint for Predicting Prognosis in Lower-Grade Glioma

The overall survival of patients with lower grade glioma (LGG) varies greatly, but the current histopathological classification has limitations in predicting patients’ prognosis. Therefore, this study aims to find potential therapeutic target genes and establish a gene signature for predicting the prognosis of LGG. CD44 is a marker of tumor stem cells and has prognostic value in various tumors, but its role in LGG is unclear. By analyzing three glioma datasets from Gene Expression Omnibus (GEO) database, CD44 was upregulated in LGG. We screened 10 CD44-related genes via protein–protein interaction (PPI) network; function enrichment analysis demonstrated that these genes were associated with biological processes and signaling pathways of the tumor; survival analysis showed that four genes (CD44, HYAL2, SPP1, MMP2) were associated with the overall survival (OS) and disease-free survival (DFS)of LGG; a novel four-gene signature was constructed. The prediction model showed good predictive value over 2-, 5-, 8-, and 10-year survival probability in both the development and validation sets. The risk score effectively divided patients into high- and low- risk groups with a distinct outcome. Multivariate analysis confirmed that the risk score and status of IDH were independent prognostic predictors of LGG. Among three LGG subgroups based on the presence of molecular parameters, IDH-mutant gliomas have a favorable OS, especially if combined with 1p/19q codeletion, which further confirmed the distinct biological pattern between three LGG subgroups, and the gene signature is able to divide LGG patients with the same IDH status into high- and low- risk groups. The high-risk group possessed a higher expression of immune checkpoints and was related to the activation of immunosuppressive pathways. Finally, this study provided a convenient tool for predicting patient survival. In summary, the four prognostic genes may be therapeutic targets and prognostic predictors for LGG; this four-gene signature has good prognostic prediction ability and can effectively distinguish high- and low-risk patients. High-risk patients are associated with higher immune checkpoint expression and activation of the immunosuppressive pathway, providing help for screening immunotherapy-sensitive patients.

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The Landscape of Glycosyltransferase Gene Signature for Overall Survival Prediction in Hepatocellular Carcinoma

10.21203/rs.3.rs-114747/v1 ◽

2020 ◽

Author(s):

Qiang Cai ◽

Shizhe Yu ◽

Jian Zhao ◽

Duo Ma ◽

Long Jiang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Risk Score ◽

Prognostic Value ◽

Cox Regression ◽

Risk Groups ◽

Gene Signature ◽

Low Risk ◽

Regression Analyses ◽

Risk Patients

Abstract Background: Hepatocellular carcinoma (HCC) is heterogeneous disease occurring in the background of chronic liver diseases. The role of glycosyltransferase (GT) genes have recently been the focus of research associating with the development of tumors. However, the prognostic value of GT genes in HCC remains not elucidated. This study aimed to demonstrate the GT genes related to the prognosis of HCC through bioinformatics analysis.Methods: The GT genes signatures were identified from the training set of The Cancer Genome Atlas (TCGA) dataset using univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then, we analyzed the prognostic value of GT genes signatures related to the overall survival (OS) of HCC patients. A prognostic model was constructed, and the risk score of each patient was calculated as formula, which divided HCC patients into high- and low-risk groups. Kaplan-Meier (K-M) and Receiver operating characteristic (ROC) curves were used to assess the OS of HCC patients. The prognostic value of GT genes signatures was further investigated in the validation set of TCGA database. Univariate and multivariate Cox regression analyses were performed to demonstrate the independent factors on OS. Finally, we utilized the gene set enrichment analysis (GSEA) to annotate the function of these genes between the two risk categories. Results: In this study, we identified and validated 4 GT genes as the prognostic signatures. The K-M analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 4 gene signatures could predict OS for 3-, 5-, and 7-year in patients with HCC, revealing the prognostic ability of these gene signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for HCC. Functional analysis further revealed that immune-related pathways were enriched, and immune status in HCC were different between the two risk groups.Conclusion: In conclusion, a novel GT genes signature can be used for prognostic prediction in HCC. Thus, targeting GT genes may be a therapeutic alternative for HCC.

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Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma

PeerJ ◽

10.7717/peerj.8312 ◽

2020 ◽

Vol 8 ◽

pp. e8312 ◽

Cited By ~ 5

Author(s):

Kai Xiao ◽

Qing Liu ◽

Gang Peng ◽

Jun Su ◽

Chao-Ying Qin ◽

...

Keyword(s):

High Risk ◽

Risk Score ◽

Prognostic Biomarker ◽

Risk Group ◽

Gene Signature ◽

Low Risk ◽

Low Grade ◽

Lower Grade ◽

Lower Grade Glioma ◽

Robust Likelihood

Background Lower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lower-grade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contribute to distinguish potential malignancy in low-grade glioma, to guide clinical adoption of more rational and effective treatments. Methods The RNA-seq data for LGG was downloaded from UCSC Xena and the Chinese Glioma Genome Atlas (CGGA). By a robust likelihood-based survival model, least absolute shrinkage and selection operator regression and multivariate Cox regression analysis, we developed a three-gene signature and established a risk score to predict the prognosis of patient with LGG. The three-gene signature was an independent survival predictor compared to other clinical parameters. Based on the signature related risk score system, stratified survival analysis was performed in patients with different age group, gender and pathologic grade. The prognostic signature was validated in the CGGA dataset. Finally, weighted gene co-expression network analysis (WGCNA) was carried out to find the co-expression genes related to the member of the signature and enrichment analysis of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, time-dependent receiver operating characteristic curves of our model and other models are constructed. Results In this study, a three-gene signature (WEE1, CRTAC1, SEMA4G) was constructed. Based on the model, the risk score of each patient was calculated with LGG (low-risk vs. high-risk, hazard ratio (HR) = 0.198 (95% CI [0.120–0.325])) and patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Furthermore, the model was validated in the CGGA dataset. Lastly, by WGCNA, we constructed the co-expression network of the three genes and conducted the enrichment of GO and KEGG. Our study identified a three-gene model that showed satisfactory performance in predicting the 1-, 3- and 5-year survival of LGG patients compared to other models and may be a promising independent biomarker of LGG.

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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 Ferroptosis and Pyroptosis Molecular Subtype-Related Signature Applicable for Prognosis and Immune Microenvironment Estimation in Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.761839 ◽

2021 ◽

Vol 9 ◽

Author(s):

Junyu Huo ◽

Jinzhen Cai ◽

Ge Guan ◽

Huan Liu ◽

Liqun Wu

Keyword(s):

High Risk ◽

Risk Score ◽

Risk Group ◽

Molecular Subtype ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Type I ◽

Immune Microenvironment ◽

Risk Patients

Background: Due to the heterogeneity of tumors and the complexity of the immune microenvironment, the specific role of ferroptosis and pyroptosis in hepatocellular carcinoma (HCC) is not fully understood, especially its impact on prognosis.Methods: The training set (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) based on the prognosis-related genes associated with ferroptosis and pyroptosis. The intersecting differentially expressed genes (DEGs) among C1, C2, and C3 were used in univariate Cox and LASSO penalized Cox regression analysis for the construction of the risk score. The median risk score served as the unified cutoff to divide patients into high- and low-risk groups.Results: Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC. As an independent prognostic indicator, the risk score could be applicable for patients with different clinical features tested by subgroup (n = 26) survival analysis. In the high-risk patients with a lower infiltration abundance of activated B cells, activated CD8 T cells, eosinophils, and type I T helper cells and a higher infiltration abundance of immature dendritic cells, the cytolytic activity, HLA, inflammation promotion, and type I IFN response in the high-risk group were weaker. The TP53 mutation rate, TMB, and CSC characteristics in the high-risk group were significantly higher than those in the low-risk group. Low-risk patients have active metabolic activity and a more robust immune response. The high- and low-risk groups differed significantly in histology grade, vascular tumor cell type, AFP, new tumor event after initial treatment, main tumor size, cirrhosis, TNM stage, BCLC stage, and CLIP score.Conclusion: The ferroptosis and pyroptosis molecular subtype-related signature identified and validated in this work is applicable for prognosis prediction, immune microenvironment estimation, stem cell characteristics, and clinical feature assessment in HCC.

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Academic Centers Are Associated with Improved Survival Outcomes in High Risk Diffuse Large B-Cell Lymphoma Patients

Blood ◽

10.1182/blood-2018-99-115578 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 4747-4747

Author(s):

Daniel A. Ermann ◽

Victoria Vardell Noble ◽

Avyakta Kallam ◽

James O. Armitage

Keyword(s):

Overall Survival ◽

High Risk ◽

Median Survival ◽

Risk Score ◽

B Cell Lymphoma ◽

Risk Groups ◽

Low Risk ◽

Survival Outcomes ◽

Intermediate Risk ◽

Hazard Ratios

Abstract Background: Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma, and is characterized as a heterogenous disease associated with varying outcomes. The International Prognostic Index (IPI) has been the standard for baseline prognostic assessment in these patients. In this study we aimed to determine the impact of treatment facility (academic versus non-academic centers) on overall survival outcomes in DLBCL patients stratified by IPI score risk groups, with a focus on high risk disease as this is associated with poorer outcomes. Methods: The 2018 National Cancer Database (NCDB) was utilized for patients diagnosed with DLBCL between 2004-2015. Patients were then stratified based on IPI risk score from low to high risk. Four risk groups were formed: low (0-1), low-intermediate (2), high-intermediate (3), and high (4-5). Overall survival was calculated using Kaplan-Meyer analysis with bivariate cox proportional hazard ratios to compare survival by facility type (academic or community centers) within these risk groups. Results: A total of 160,137 patients were identified. Of these cases 31.8% were classified as low risk, 21.9% were low-intermediate risk, 22.2% were high-intermediate risk, and 24% were high risk. 59.3% of patients were treated at a community center and 40.7% were treated at academic centers. Treatment at academic centers was associated with a significantly improved overall survival (OS) for each risk category. Median survival (in months) for high risk IPI score DLBCL was 47.9 months in community and 61.1 months in academic centers (p<.0001). Median survival for high-intermediate risk score was 48.3 months in community and 87.3 months in academic centers (p<.0001). Median survival for low-intermediate score was 90.3 months in community and 122.8 months in academic centers (p<.0001). Median survival for low risk score was 132 months in community and 148 months in academic centers (p<.0001). Hazard ratios for academic center versus community center for high risk, high-intermediate, low-intermediate and low risk are 0.768, 0.71, 0.848 and 0.818 respectively (p<.0001). Conclusions: Facility type is significantly associated with improved survival outcomes across all IPI based risk groups for DLBCL. This benefit is especially significant in higher risk disease where positive outcomes are less common, suggesting treatment at academic centers may be particularly beneficial in these patients. Some of the possible reasons for this difference may include provider experience, increased access to resources, and opportunity for clinical trials. Further investigations into the factors contributing to such disparities should be done to help standardize care and improve outcomes. Disclosures No relevant conflicts of interest to declare.

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Outcome of Patients with Lower-Risk Myelodysplastic Syndrome (MDS) After Azacitidine (AZA) Treatment Failure.

Blood ◽

10.1182/blood.v120.21.2815.2815 ◽

2012 ◽

Vol 120 (21) ◽

pp. 2815-2815

Author(s):

Asmita Mishra ◽

Jeffrey E Lancet ◽

Najla H Al Ali ◽

Eric Padron ◽

Viet Q. Ho ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Myelodysplastic Syndrome ◽

Treatment Failure ◽

Lower Risk ◽

Median Overall Survival ◽

Unmet Need ◽

Risk Groups ◽

Low Risk ◽

Risk Patients

Abstract Abstract 2815 Background: Azacitidine has emerged as the standard of care for treatment of higher risk MDS based upon results of the AZA-001 study. Several groups reported poor outcomes after AZA failure in patients with int-2 or high risk International Prognostic Scoring System (IPSS) risk groups with a median overall survival (OS) ranging from 4–8 months (mo). In the USA, AZA is approved for all FAB types and risk groups and is as first or second line therapy for anemia after erythroid stimulating agents in low /int-1 risk non-deletion 5q MDS and is the treatment of choice for thrombocytopenia. The outcome of patients with lower risk myelodysplastic syndrome (MDS) after AZA failure has not been characterized. We report our experience in a large cohort of low/int-1 (lower risk) MDS patients after AZA failure. Methods: Patients were identified through the Moffitt Cancer Center (MCC) MDS database. Individual charts were reviewed and relevant clinical data was extracted. Patients with low or intermediate-1 (int-1) risk disease as defined by IPSS who had received AZA treatment were identified. These patients were also risk stratified based on Global MD Anderson Score (MDAS). The primary objective was to estimate OS in these patients after AZA failure. AZA failure was defined as failure to respond after 4 or more treatment courses, loss of response, or disease progression while on therapy. All responses were defined according to the International Working Group (IWG) 2006 criteria. The Kaplan–Meier method was used to estimate median overall survival. Results: Two hundred eighty MDS patients with low/int-1 IPSS risk who had received AZA treatment were identified. Most patients (81%) were greater than 60 years of age (median, 69 years), and 90% of AZA treated patients were RBC transfusion dependent. Refractory cytopenia with multilineage dysplasia (RCMD) was the most common WHO subtype (44%), and 81% of patients had good risk cytogenetics (Table-1). The median time from MDS diagnosis to AZA treatment was 12.3 months; median number of AZA cycles received was six. At the time of AZA treatment, 241 patients (86 %) were risk stratified as int-1 versus 39 patients (14 %) who were stratified as low risk IPSS. The IWG 2006 responses to AZA treatment included 4% CR (n=10 ), 1% marrow CR (n=2), 4% PR (n=10), 27% Hematological improvement (HI) (n=75), whereas 52% (n=146) had stable disease with no HI (n=146) and 10% had progressive disease (n=10); 6 patients (2%) died on therapy, and responses were missing in 2 patients (<1%). The overall best response (HI or better) was 36%. The median OS for the entire cohort after AZA failure was 18.5 months (95% CI [13.5–23.5 mo], Figure 1A). The median OS for patients with low risk IPSS disease from time of AZA failure was 46 months versus 15 mo for int-1 patients (p<0.005, Figure 1B). When utilizing MDAS, median OS was 33.3 months for low risk patients, 21 months for int-1, 11 months for int-2, and 7.5 months for poor risk patients (p=0.005). Conclusions: To our knowledge this is the first report describing the outcome of lower risk MDS patients after AZA treatment failure. Outcome is particularly poor for those patients with int-1 risk MDS, with a median OS of 15 mo. Global MDAS identified patients upstaged to int-2 or high risk with less than one year OS. There is unmet need for effective novel therapies for lower risk MDS patients after AZA failure. Disclosures: List: Celgene: Consultancy. Komrokji:Celgene: Speakers Bureau.

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Identification of Iron Metabolism-Related Genes as Prognostic Indicators for Lower-Grade Glioma

Frontiers in Oncology ◽

10.3389/fonc.2021.729103 ◽

2021 ◽

Vol 11 ◽

Author(s):

Shenbin Xu ◽

Zefeng Wang ◽

Juan Ye ◽

Shuhao Mei ◽

Jianmin Zhang

Keyword(s):

Overall Survival ◽

High Risk ◽

Risk Score ◽

Iron Metabolism ◽

Risk Group ◽

High Risk Group ◽

Lower Grade ◽

Who Grade ◽

Lower Grade Glioma ◽

Score Model

Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6‐methylguanine‐DNA methyl‐transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of > 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P < 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.

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Hypoxia-Related lncRNA Correlates With Prognosis and Immune Microenvironment in Lower-Grade Glioma

Frontiers in Immunology ◽

10.3389/fimmu.2021.731048 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shengchao Xu ◽

Lu Tang ◽

Zhixiong Liu ◽

Chengke Luo ◽

Quan Cheng

Keyword(s):

High Risk ◽

Risk Score ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitors ◽

Checkpoint Inhibitors ◽

Pearson Correlation ◽

Cell Counting ◽

Immune Checkpoints ◽

Lower Grade ◽

Lower Grade Glioma

BackgroundHypoxia-related genes are demonstrated to correlate with the prognosis of various cancers. However, the role of hypoxia-related long non-coding RNAs (HRLs) in lower-grade glioma (LGG) remains unclear.MethodsA total of 700 LGG samples were extracted from TCGA and CGGA databases. Pearson correlation analysis was used to identify HRLs. Lasso analysis was adopted to construct the HRL signature. TIDE algorithm was used to predict responses to immune checkpoint inhibitors. Cell proliferation was estimated by cell counting kit-8 assay, colony formation assay, and EdU assay.ResultsWe identified 340 HRLs and constructed a novel risk signature composed of 19 HRLs. The risk score exhibited potent value in predicting the prognosis of LGG patients and was significantly associated with the prognosis of LGG patients. Moreover, HRL signature could distinguish patients with similar expression levels of immune checkpoints and might predict the efficacy of immune checkpoint inhibitors. Additionally, hypoxia-related pathways and immune pathways were enriched in high-risk group, and high risk score indicated low tumor purity and high immune infiltration. Two major HRLs, LINC00941 and BASP1-AS1, could significantly affect the proliferation of glioma cells.ConclusionsOur study constructed a novel HRL signature that could predict the prognosis and immunotherapy response of LGG patients. HRLs could be novel biomarkers to predict the prognosis of LGG patients and potential targets for LGG treatment.

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Statin initiations and QRISK2 scoring in UK general practice: a THIN database study

British Journal of General Practice ◽

10.3399/bjgp17x693485 ◽

2017 ◽

Vol 67 (665) ◽

pp. e881-e887 ◽

Cited By ~ 15

Author(s):

Samuel Finnikin ◽

Ronan Ryan ◽

Tom Marshall

Keyword(s):

Primary Care ◽

High Risk ◽

Risk Score ◽

Risk Groups ◽

Low Risk ◽

Intermediate Risk ◽

Cvd Risk ◽

Nice Guidelines ◽

High Risk Patients ◽

Risk Patients

BackgroundStatin prescribing should be based on cardiovascular disease (CVD) risk, but evidence suggests overtreatment of low-risk groups and undertreatment of high-risk groups.AimTo investigate the relationship between CVD risk scoring in primary care and initiation of statins for the primary prevention of CVD, and the effect of changes to the National Institute for Health and Care Excellence (NICE) guidance in 2014.Design and settingHistorical cohort study using UK electronic primary care records.MethodA cohort was created of statin-naïve patients without CVD between 1 January 2000 and 31 December 2015. CVD risk scores (calculated using QRISK2 available from 2012) and statin initiations were identified. Rates of CVD risk score recording were calculated and relationships between CVD risk category (low-, intermediate-, and high-risk: <10%, 10–19.9%, and ≥20% 10-year CVD risk) and statin initiation were analysed.ResultsA total of 1.4 million patients were identified from 248 practices. Of these, 151 788 had a recorded CVD risk score since 2012 (10.67%) and 217 860 were initiated on a statin (15.31%). Among patients initiated on a statin after 2012, 27.1% had a documented QRISK2 score: 2.7% of low-risk, 13.8% of intermediate-risk, and 35.0% of high-risk patients were initiated on statins. Statin initiation rates halved from a peak in 2006. After the 2014 NICE guidelines, statin initiation rates declined in high-risk patients but increased in intermediate-risk patients.ConclusionMost patients initiated on statins had no QRISK2 score recorded. Most patients at high risk of CVD were not initiated on statins. One in six statin initiations were to low-risk patients indicating significant overtreatment. Initiations of statins in intermediate-risk patients rose after NICE guidelines were updated in 2014.

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Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.788431 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jinhui Liu ◽

Rui Geng ◽

Sheng Yang ◽

Fang Shao ◽

Zihang Zhong ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Network Analysis ◽

Endometrial Carcinoma ◽

Risk Score ◽

Immune Status ◽

Low Risk ◽

Prognostic Signature ◽

Uterine Corpus ◽

Risk Patients

BackgroundUterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC.MethodsCIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan–Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy.ResultsRed module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs.ConclusionWe developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

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