scholarly journals Prognostic and Therapeutic Value of Apolipoprotein A and a New Risk Scoring System Based on Apolipoprotein A and Adenosine Deaminase in Chronic Lymphocytic Leukemia

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoya Yun ◽  
Xiang Sun ◽  
Xinting Hu ◽  
Huimin Zhang ◽  
Zixun Yin ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Adenosine Deaminase ◽  
Scoring System ◽  
Prognostic Value ◽  
Therapeutic Potential ◽  
Lymphocytic Leukemia ◽  
Major Protein ◽  
Apolipoprotein A ◽  
Risk Scoring ◽  
Risk Scoring System

Lipid metabolism is related to lymphomagenesis, and is a novel therapeutic target in some hematologic tumors. Apolipoprotein A (ApoA), the major protein of high-density lipoprotein (HDL), plays a crucial role in lipid transportation and protecting against cardiovascular disease, and takes effect on anti-inflammation and anti-oxidation. It is correlated with the prognosis of some solid tumors. Yet, there is no investigation involving the role of ApoA plays in chronic lymphocytic leukemia (CLL). Our retrospective study focuses on the prognostic value of ApoA in CLL and its therapeutic potential for CLL patients. Herein, ApoA is a favorable independent prognostic factor for both overall survival (OS) and progression-free survival (PFS) of CLL patients. ApoA is negatively associated with β2-microglobulin (β2-MG) and advanced stage, which are poor prognostic factors in CLL. Age, Rai stage, ApoA, and adenosine deaminase (ADA) are included in a new risk scoring system named ARAA-score. It is capable of assessing OS and PFS of CLL patients. Furthermore, cell proliferation assays show that the ApoA-I mimetic L-4F can inhibit the proliferation of CLL cell lines and primary cells. In conclusion, ApoA is of prognostic value in CLL, and is a potential therapy for CLL patients. The ARAA-score may optimize the risk stratification of CLL patients.

scholarly journals PROGNOSTIC VALUE OF APOLIPOPROTEIN A IN CHRONIC LYMPHOCYTIC LEUKEMIA

2021 ◽  
Vol 39 (S2) ◽  
Author(s):  
X Yun ◽  
Y Zhang ◽  
X Sun ◽  
X Hu ◽  
H Zhang ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Prognostic Value ◽  
Lymphocytic Leukemia ◽  
Apolipoprotein A

scholarly journals Immune Score-based Molecular Subtypes and Signature Associated with Clinical Outcome in Hepatoblastoma

2021 ◽  
Vol 21 (9) ◽  
Author(s):  
Yongping Huang ◽  
Jinlong Yan ◽  
Ruiqi Liu ◽  
Guang Tang ◽  
Qi Dong ◽  
...  
Keyword(s):  
T Cells ◽  
Scoring System ◽  
Prognostic Value ◽  
Molecular Subtypes ◽  
Risk Group ◽  
Molecular Subtype ◽  
Gene Signature ◽  
Risk Scoring ◽  
Immune Score ◽  
Risk Scoring System

Background: This study aimed to identify genes related to the immune score of hepatoblastoma, examine the characteristics of the immune microenvironment of hepatoblastoma, and construct a risk scoring system for predicting the prognosis of hepatoblastoma. Methods: Through using the gene chip data of patients with hepatoblastoma with survival data in the ArrayExpress and GEO databases, the immune score of hepatoblastoma was calculated by the ESITIMATE algorithm, and the prognostic value of immune score in patients with hepatoblastoma was studied by the survival analysis. Genes related to the immune score were identified by the WGCNA algorithm. According to these genes, patients with hepatoblastoma were clustered unsupervised. Finally, the risk scoring system was constructed according to the immune score-related genes. Results: The immune score calculated by the ESTIMATE algorithm had a good prognostic value in patients with hepatoblastoma. Patients with high immune scores had better OS than those with low immune scores (P < 0.001). A total of 146 immune score-related genes were identified by WGCNA analysis, and univariate COX regression analysis indicated that 59 of the genes had prognostic value. According to the unsupervised clustering results of the 146 immune score-related genes, patients with hepatoblastoma could be divided into two subtypes with different prognoses, namely molecular subtype 1 and subtype 2, with molecular subtype 1 having a better prognosis. The immunocyte infiltration analysis results showed that the difference between the two subtypes was mainly in activated CD4 T cells, activated dendritic cells, CD56 bright natural killer cells, the macrophage, and regulatory T cells. According to the immune score-related genes, a risk scoring system was constructed based on a five-gene signature. After the cut-off value was determined, patients with hepatoblastoma were divided into a high-risk group and a low-risk group. The prognosis of the two groups was different. Conclusions: The immune score has a good prognostic value in patients with hepatoblastoma. Based on the different expression patterns of immune score-related genes, hepatoblastoma can be divided into two different prognostic molecular subtypes, showing different immunocyte infiltration patterns. The established risk scoring system based on a five-gene signature has a good predictive value in patients with hepatoblastoma.

A Risk Scoring System for Predicting Overall Survival in Patients with Liver Cancer

2020 ◽  
Author(s):  
Haibei Xin ◽  
Guanxiong Zhang ◽  
Wei Zhou ◽  
Shanshan Li ◽  
Minfeng Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Liver Cancer ◽  
Scoring System ◽  
Risk Scoring ◽  
Risk Scoring System

A Novel Risk Scoring System for Predicting Primary Graft Dysfunction after Orthotopic Heart Transplantation

2020 ◽  
Vol 26 (10) ◽  
pp. S136-S137
Author(s):  
Syed Adeel Ahsan ◽  
Jasjit Bhinder ◽  
Syed Zaid ◽  
Parija Sharedalal ◽  
Chhaya Aggarwal-Gupta ◽  
...  
Keyword(s):  
Heart Transplantation ◽  
Scoring System ◽  
Orthotopic Heart Transplantation ◽  
Primary Graft Dysfunction ◽  
Graft Dysfunction ◽  
Risk Scoring ◽  
Risk Scoring System

scholarly journals Novel risk scoring system for metastatic renal cell carcinoma patients treated with cabozantinib

2021 ◽  
pp. 100393
Author(s):  
Dylan J. Martini ◽  
Meredith R. Kline ◽  
Yuan Liu ◽  
Julie M. Shabto ◽  
Bradley C. Carthon ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Scoring System ◽  
Risk Scoring ◽  
Risk Scoring System

scholarly journals Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 853
Author(s):  
Jee-Yun Kim ◽  
Jeong Yee ◽  
Tae-Im Park ◽  
So-Youn Shin ◽  
Man-Ho Ha ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Linear Regression ◽  
Parenteral Nutrition ◽  
Prediction Model ◽  
Scoring System ◽  
Prediction Equation ◽  
Icu Patients ◽  
Risk Scoring ◽  
Risk Scoring System

Predicting the clinical progression of intensive care unit (ICU) patients is crucial for survival and prognosis. Therefore, this retrospective study aimed to develop the risk scoring system of mortality and the prediction model of ICU length of stay (LOS) among patients admitted to the ICU. Data from ICU patients aged at least 18 years who received parenteral nutrition support for ≥50% of the daily calorie requirement from February 2014 to January 2018 were collected. In-hospital mortality and log-transformed LOS were analyzed by logistic regression and linear regression, respectively. For calculating risk scores, each coefficient was obtained based on regression model. Of 445 patients, 97 patients died in the ICU; the observed mortality rate was 21.8%. Using logistic regression analysis, APACHE II score (15–29: 1 point, 30 or higher: 2 points), qSOFA score ≥ 2 (2 points), serum albumin level < 3.4 g/dL (1 point), and infectious or respiratory disease (1 point) were incorporated into risk scoring system for mortality; patients with 0, 1, 2–4, and 5–6 points had approximately 10%, 20%, 40%, and 65% risk of death. For LOS, linear regression analysis showed the following prediction equation: log(LOS) = 0.01 × (APACHE II) + 0.04 × (total bilirubin) − 0.09 × (admission diagnosis of gastrointestinal disease or injury, poisoning, or other external cause) + 0.970. Our study provides the mortality risk score and LOS prediction equation. It could help clinicians to identify those at risk and optimize ICU management.

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