scholarly journals Polygenic risk scores predict diabetic complications and their response to therapy

2019 ◽  
Author(s):  
J. Tremblay ◽  
M. Haloui ◽  
F. Harvey ◽  
R. Tahir ◽  
F.-C. Marois-Blanchet ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Risk Group ◽  
Intensive Therapy ◽  
Cardiovascular Death ◽  
Response To Therapy ◽  
Low Risk ◽  
Number Needed To Treat ◽  
Genome Wide Association Studies ◽  
Increased Risk

AbstractType 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction can lead to timely intervention and better outcomes. Through summary statistics of meta-analyses of published genome-wide association studies performed in over 1.2 million of individuals, we combined 9 PRS gathering genomic variants associated to cardiovascular and renal diseases and their key risk factors into one logistic regression model, to predict micro- and macrovascular endpoints of diabetes. Its clinical utility in predicting complications of diabetes was tested in 4098 participants with diabetes of the ADVANCE trial followed during a period of 10 years and replicated it in three independent non-trial cohorts. The prediction model adjusted for ethnicity, sex, age at onset and diabetes duration, identified the top 30% of ADVANCE participants at 3.1-fold increased risk of major micro- and macrovascular events (p=6.3×10−21 and p=9.6×10−31, respectively) and at 4.4-fold (p=6.8×10−33) increased risk of cardiovascular death compared to the remainder of T2D subjects. While in ADVANCE overall, combined intensive therapy of blood pressure and glycaemia decreased cardiovascular mortality by 24%, the prediction model identified a high-risk group in whom this therapy decreased mortality by 47%, and a low risk group in whom the therapy had no discernable effect. Patients with high PRS had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. This novel polygenic prediction model identified people with diabetes at low and high risk of complications and improved targeting those at greater benefit from intensive therapy while avoiding unnecessary intensification in low-risk subjects.

The Glasgow whipple risk score to predict pancreas-specific complications after pancreaticoduodenectomy.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 394-394
Author(s):  
Lavanniya Kumar Palani Velu ◽  
Vishnuvardhan Chandrabalan ◽  
Ross Carter ◽  
Colin McKay ◽  
Donald McMillan ◽  
...  
Keyword(s):  
High Risk ◽  
Serum Amylase ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Prolonged Stay ◽  
Increased Risk ◽  
Clinically Significant ◽  
Serum Crp

394 Background: Pancreas-specific complications (PSC), comprising postoperative pancreatic fistula, post-pancreatectomy haemorrhage, and intra-abdominal collections, are drivers of morbidity following pancreaticoduodenectomy (PD). Intra-operatively derived pancreatic gland texture is a major determinant of postoperative PSC. We have previously demonstrated that a postoperative day 0 (PoD0) serum amylase ≥ 130 IU/L is an objective surrogate of pancreatic texture, and is associated with PSC. We sought to refine the PSC risk prediction model by including serial measurements of serum C-reactive protein (CRP). Methods: 230 consecutive patients undergoing PD between 2008 and 2014 were included in the study. Routine serum investigations, including amylase and CRP were performed from the pre-operative day. Receiver operating characteristic (ROC) curve analysis was used to identify a threshold value of serum CRP associated with clinically significant PSC. Results: 95 (41.3%) patients experienced a clinically significant PSC. ROC analysis identified post-operative day 2 (PoD2) serum CRP of 180 mg/L as the optimal threshold (P=0.005) associated with clinically significant PSC, a prolonged stay in critical care (P =0.032), and a relaparotomy (P = 0.045). Patients with a PoD0 serum amylase ≥ 130 IU/L who then developed a PoD2 serum CRP ≥ 180 mg/L had a higher incidence of postoperative complications. Patients were categorised into high, intermediate and low risk groups based on PoD0 serum amylase and PoD2 serum CRP. Patients in the high risk group (PoD0 serum amylase ≥ 130 IU/L and PoD2 serum CRP ≥ 180 mg/l) had significantly higher incidence of PSC, a return to theatre, prolonged lengths stay (all P≤ 0.05) and a four-fold increase in perioperative mortality compared patients in the intermediate and low risk groups (7 deaths in the high risk group versus 2 and nil in the intermediate and low risk groups respectively). Conclusions: A high risk profile, defined as PoD0 serum amylase ≥ 130 IU/L and PoD2 serum CRP ≥ 180 mg/l, should raise the clinician’s awareness of the increased risk of clinically significant PSC and a complicated postoperative course following pancreaticoduodenectomy.

scholarly journals A Novel Ferroptosis-Related Genes Model for Prognosis Prediction of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Fei Li ◽  
Dongcen Ge ◽  
Shu-lan Sun
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Prediction Model ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background. Ferroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. The aim of this study is to investigate the relationship between ferroptosis and the prognosis of lung adenocarcinoma (LUAD).Methods. RNA-seq data was collected from the LUAD dataset of The Cancer Genome Altas (TCGA) database. We used ferroptosis-related genes as the basis, and identify the differential expression genes (DEGs) between cancer and paracancer. The univariate Cox regression analysis were used to screen the prognostic-related genes. We divided the patients into training and validation sets. Then, we screened out key genes and built a 5 genes prognostic prediction model by the applications of the least absolute shrinkage and selection operator (LASSO) 10-fold cross-validation and the multi-variate Cox regression analysis. We divided the cases by the median value of risk score and validated this model in the validation set. Meanwhile, we analyzed the somatic mutations, and estimated the score of immune infiltration in the high- and low-risk groups, as well as performed functional enrichment analysis of DEGs.Results. The result revealed that the high-risk score triggered the worse prognosis. The maximum area under curve (AUC) of the training set and the validation set of in this study was 0.7 and 0.69. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of cases with survival time of 1, 3 and 5 years are 0.698, 0.71 and 0.73. In addition, the mutation frequency of patients in the high-risk group was higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results.Conclusion. This study constructed a novel LUAD prognosis prediction model base on 5 ferroptosis-related genes, which can provide a prognostic evaluation tool for the clinical therapeutic decision.

Construction and validation of an eight-gene risk prediction model for stage II-IIIA lung adenocarcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e20553-e20553
Author(s):  
Jianchun Duan ◽  
Hua Bai ◽  
Yiting Sun ◽  
Fei Gai ◽  
Shenya Tian ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Lung Adenocarcinoma ◽  
Risk Prediction ◽  
Risk Group ◽  
Survival Outcome ◽  
Risk Prediction Model ◽  
Low Risk ◽  
Gene Set

e20553 Background: Clinical characters cannot precisely evaluate long-term survival of patients with resectable lung adenocarcinoma. Genomics studies of lung adenocarcinoma (LUAD) have advanced our understanding of LUAD's biology. Thus, genomics-based robust models predicting survival outcome for patients with operatable LUAD needs to be investigated. Here, we aimed to identify new gene signatures to construct a risk prediction model via integrating Omics data from The Cancer Genome Atlas (TCGA) to better evaluate the long-term clinical outcome of LUAD patients. Methods: A cohort of one hundred and eighty-nine stage II-IIIA lung adenocarcinoma cases receiving tumor resection were screened out and downloaded from TCGA database. Tumor samples without survival information and genes with low or no expression were removed. Genes associated with cancer and immune were further narrowed down using a Master Panel Gene Set (Amoydx). Lasso-Cox regression analysis was used to screen gene-survival outcome, and then a risk prediction model was established. LUAD cases were divided into high-risk or low-risk groups as per the scores, to assess differential expressed genes and pathways. Results: A total of 8 most survival outcome related genes (CLEC7A, PAX5, XCR1, KRT7, PLCG1, DKK1, CLEC10A, IKZF3) were identified after Lasso-Cox regression analysis and used for model construction. The overall survival (OS), progression-free survival (PFS) and disease-free survival (DFS) from the subgroups within the high- and low-risk groups were assessed and showed significant prolonged in low-risk group, the hazard ratio (HR) of OS was 2.72 (95%CI: 2.04-3.61, P = 5.91e-12) in high-risk group. Hierarchical clustering analysis, gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) revealed that genes involved in immune responses were significantly suppressed in high-risk group, while as genes involved in antioxidative metabolism were activated, which gave us a hint that immune-metabolism interaction might play a vital role in determining the distal survival outcome of LUAD. Conclusions: Our risk prediction model enables precise evaluation of long-term survival for patients with LUAD. Further, it provides a novel and comprehensive understanding of biological impacts on LUAD prognosis, which offers new insights for future development of precise diagnostic and therapeutic approaches.[Table: see text]

Validation of a Predictive Model for Indentifying An Increased Risk for Thromboembolism in Children with Acute Lymphoblastic Leukemia: Results of a Multicenter Cohort Study

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 524-524
Author(s):  
Silke Flege ◽  
Lesley Mitchell ◽  
Gili Kenet ◽  
Christine Heller ◽  
Michael Fruhwald ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
High Risk ◽  
Predictive Model ◽  
Risk Group ◽  
Lymphoblastic Leukemia ◽  
Low Risk ◽  
Carrier Status ◽  
Central Venous ◽  
Increased Risk ◽  
Genetic Abnormalities

Abstract Children with acute lymphoblastic leukemia (ALL) are at increased risk for venous thromboembolism (VTE), however, not all children experience a VTE. Developing a predictive model for determining children at increased risk would be beneficial in targeting interventional studies to only high risk groups. A recent meta-analysis of studies in VTE in children with ALL identified four potential risk factors: treatment with Escherichia coli asparaginase (CASP), concomitant use of steroids, presence of central venous lines and thrombophilic genetic abnormalities. As VTE in childhood ALL is well recognized as serious clinical problem and due to the lack of studies on prevention, the standard of practice varies and some centres use enoxaparin prophylaxis for these children. However, the risks and benefits of the intervention are unknown. The aim of the study was to develop a simple model for predicting ALL-chemotherapy-associated VTE using baseline clinical and laboratory variables, and to evaluate, on an explorative basis, the increasing off-label use of enoxaparin for VTE prophylaxis in ALL children. For development of the risk model the predictive variables were scored as follows: treatment with CASP (5000–10000/m2) in combination with prednisone or dexamethasone, presence of central venous lines, thrombophilic genetic abnormalities, e.g. positive family history for VTE or identification of a single thrombophilic trait (1 point each), or carrier status of combined thrombophilic traits (2 points). A definition of VTE risk by score was low (1–2) and high (□ 3). The risk score was than prospectively validated in an independent cohort of 136 newly recruited patients enrolled into the German database. Seven patients were excluded (lost to follow-up n=2; death n=2, secondary malignancy, VTE before ALL-onset, infant < 12 months of age: each n=1). The cumulative VTE rates at 3.5 months in the validation cohorts were 3.6% (95% CI 1%–9%) in the low-risk group (4 of 112), and 47% (95%CI 23%–72%) in the high-risk category (8 of 17). In multivariate analysis [Cox regression] the high risk group was significantly associated with VTE when compared to the low risk group even after adjusting for age at ALL-onset, duration of CASP administration, steroid administered (prednisone/dexamethasone), and presence or absence of enoxaparin prophylaxis [hazard/95%CI: 4.16/1.13–15.34]. The negative predictive value for VTE was 96.3% [95%CI: 92.9–99.8]. Early enoxaparin prophylaxis reduced the absolute VTE-risk about 60% [95%CI: 23–96]. Therefore, the model can identify ALL-children with an increased risk for symptomatic VTE.

scholarly journals Predictive ability of the osteoporosis self-assessment tool for assessing the risk of osteoporosis

2019 ◽  
Keyword(s):  
High Risk ◽  
Assessment Tool ◽  
Risk Group ◽  
Osteoporotic Fractures ◽  
High Risk Group ◽  
Low Risk ◽  
Moderate Risk ◽  
Self Assessment ◽  
Number Of Patients ◽  
Increased Risk

Osteoporosis (OP) is a progressive metabolic bone disease caused by disturbed balance between bone formation and bone resorption. Osteoporotic fractures lead to a deterioration in the quality of patients’ life due to high morbidity and mortality, and the economic burden of osteoporotic fractures is expected to increase. Various tools have been developed to assess the risk of osteoporosis in the clinical practice. The Osteoporosis Self-Assessment Tool (OST) is used to predict osteoporosis and is suitable for self-assessment. The purpose of this study is to assess the ability of the OST score to predict the risk of OP. 180 postmenopausal women with a mean age of 61 ± 13 years (38-86 years) were included in the study. The OST score was evaluated using the formula: (body weight  age) × 0.2. Patients were divided into three groups according to the risk of OP: low risk (> -1), moderate risk (-1 to -4) and high risk (<-4). Based on the total lumbar spine T-score, measured by dual-energy X-ray absorptiometry (DEXA), the actual number of the women with OP was established. According to the OST score, 22 women were in the high risk group, 41 women in the moderate risk group, and 117 women in the low risk group. There was a correlation between the risk of OP calculated with OST and the number of patients with OP, established by DEXA measurement - with increased risk of OP, the number of the women with OP also increased (p = 0.000). The percentage of the women with osteoporosis is highest in the high risk group and lowest in the low risk group. In the high risk group, 95.5% of the women had a diagnosis of osteoporosis. These results demonstrate the good ability of OST score to predict the risk of OP in the Bulgarian population.

scholarly journals A Glycolysis-Related Five-Gene Signature Predicts Biochemical Recurrence-Free Survival in Patients With Prostate Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zijun Xu ◽  
Lijuan Xu ◽  
Liping Liu ◽  
Hai Li ◽  
Jiewen Jin ◽  
...  
Keyword(s):  
High Risk ◽  
Biochemical Recurrence ◽  
Risk Group ◽  
Predictive Biomarkers ◽  
Gene Signature ◽  
Low Risk ◽  
Free Survival ◽  
Training Cohort ◽  
Increased Risk ◽  
Robust Likelihood

Prostate cancer (PCa) is one of the most frequently diagnosed cancers in males worldwide. Approximately 25% of all patients experience biochemical recurrence (BCR) after radical prostatectomy (RP) and BCR indicates increased risk for metastasis and castration resistance. PCa patients with highly glycolytic tumors have a worse prognosis. Thus, this study aimed to explore glycolysis-based predictive biomarkers for BCR. Expression data and clinical information of PCa samples were retrieved from three publicly available datasets. One from The Cancer Genome Atlas (TCGA) dataset was used as the training cohort, and two from the Gene Expression Omnibus (GEO) dataset (GSE54460 and GSE70769) were used as validation cohorts. Using the training cohort, univariate Cox regression survival analysis, robust likelihood-based survival model, and stepwise multiply Cox analysis were sequentially applied to explore predictive glycolysis-related candidates. A five-gene risk score was then constructed based on the Cox coefficient as the following: (−0.8367*GYS2) + (0.3448*STMN1) + (0.3595*PPFIA4) + (−0.1940*KDELR3) + (0.4779*ABCB6). Receiver operating characteristic curve (ROC) analysis was used to identify the optimal cut-off point, and patients were divided into low risk and high risk groups. Kaplan–Meier analysis revealed that high risk group had significantly shorter BCR free survival time as compared with that in low risk group in training and validation cohorts. In conclusion, our data support the glycolysis-based five-gene signature as a novel and robust signature for predicting BCR of PCa patients.

scholarly journals Predicting long-term disability outcomes in patients with MS treated with teriflunomide in TEMSO

2017 ◽  
Vol 4 (5) ◽  
pp. e379 ◽  
Author(s):  
Maria Pia Sormani ◽  
Philippe Truffinet ◽  
Karthinathan Thangavelu ◽  
Pascal Rufi ◽  
Catherine Simonson ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Group ◽  
Low Risk ◽  
Clinical Relapse ◽  
Differential Rate ◽  
Close Monitoring ◽  
Intermediate Risk ◽  
Link Type ◽  
Increased Risk

Objective:To predict long-term disability outcomes in TEMSO core (NCT00134563) and extension (NCT00803049) studies in patients with relapsing forms of MS treated with teriflunomide.Methods:A post hoc analysis was conducted in a subgroup of patients who received teriflunomide in the core study, had MRI and clinical relapse assessments at months 12 (n = 552) and 18, and entered the extension. Patients were allocated risk scores for disability worsening (DW) after 1 year of teriflunomide treatment: 0 = low risk; 1 = intermediate risk; and 2–3 = high risk, based on the occurrence of relapses (0 to ≥2) and/or active (new and enlarging) T2-weighted (T2w) lesions (≤3 or >3) after the 1-year MRI. Patients in the intermediate-risk group were reclassified as responders or nonresponders (low or high risk) according to relapses and T2w lesions on the 18-month MRI. Long-term risk (7 years) of DW was assessed by Kaplan-Meier survival curves.Results:In patients with a score of 2–3, the risk of 12-week–confirmed DW over 7 years was significantly higher vs those with a score of 0 (hazard ratio [HR] = 1.96, p = 0.0044). Patients reclassified as high risk at month 18 (18.6%) had a significantly higher risk of DW vs those in the low-risk group (81.4%; HR = 1.92; p = 0.0004).Conclusions:Over 80% of patients receiving teriflunomide were classified as low risk (responders) and had a significantly lower risk of DW than those at increased risk (nonresponders) over 7 years of follow-up in TEMSO. Close monitoring of relapses and active T2w lesions after short-term teriflunomide treatment predicts a differential rate of subsequent DW long term.ClinicalTrials.gov identifier:TEMSO, NCT00134563; TEMSO extension, NCT00803049.

scholarly journals High-Risk Additional Chromosomal Abnormalities in CML Herald Death By Blast Crisis Already at Low Blast Levels

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 666-666 ◽  
Author(s):  
Rüdiger Hehlmann ◽  
Astghik Voskanyan ◽  
Michael Lauseker ◽  
Markus Pfirrmann ◽  
Lida Kalmanti ◽  
...  
Keyword(s):  
High Risk ◽  
Research Funding ◽  
Chromosomal Abnormalities ◽  
Blast Crisis ◽  
Intensive Therapy ◽  
Chronic Phase ◽  
Response To Therapy ◽  
Low Risk ◽  
University Hospital ◽  
Additional Chromosomal Abnormalities

Background. The end phase or metamorphosis is one of the remaining challenges of chronic myeloid leukemia (CML) management. Blast crisis (BC) is a late marker. Earlier diagnosis may improve outcome. The detection of additional chromosomal abnormalities (ACA) at low blast levels might allow earlier treatment when outcome is better. Methods. We made use of 1536 Ph+CML-patients in chronic phase followed in the randomized CML study IV (Hehlmann et al, Leukemia 2017) for a median of 8.6 years. 1510 cytogenetically evaluable patients were analyzed for ACA and blast increase (Flow chart). According to impact on survival ACA were grouped into high-risk (+ 8; +Ph; i(17q); +17; +19 +21; 3q26; 11q23; -7; complex) and low-risk (all other). Prognosis with +8 alone was clearly better than with +8 accompanied by further abnormalities, but still worse than with low-risk ACA. +8 alone was therefore included in the high-risk group. The presence of high- and low-risk ACA was linked to 6 thresholds of blast increase (1%, 5%, 10%, 15%, 20%, and 30%) in a Cox proportional hazards model. Results. 139 patients (9.2%) displayed ACA at any time before BC diagnosis, 88 (5.8%) had high-risk and 51 (3.4%) low-risk ACA. ACA emerged after a median of 17 (0-133) months. 79 patients developed BC. 43 (61%) of 71 cytogenetically evaluable patients with BC had high-risk ACA. 3-year survival after emergence of high-risk ACA was 48%, after emergence of low-risk ACA 92%. At low blast levels (1-15%), high-risk ACA showed an increased hazard to die (ratios: 3.66 in blood; 6.84 in marrow) compared to no ACA in contrast to low-risk ACA. This effect was not observed anymore at blast increases to 20-30% (Figure). 38 patients with high-risk ACA died, 36 with known causes of death which were almost exclusively BC (n=26, 72%) and progression-related transplantation (n=8, 22%). Only 2 patients died of CML-unrelated causes. Conclusions. High-risk ACA herald death by BC already at low blast levels and may help to define CML end phase in a subgroup of patients at an earlier time than is possible with current blast thresholds. Cytogenetic monitoring is indicated when signs of progression surface and response to therapy is unsatisfactory. More intensive therapy may be indicated at emergence of high-risk ACA. Disclosures Hehlmann: Novartis: Research Funding. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Fabarius:Novartis: Research Funding. Krause:Siemens: Research Funding; Takeda: Honoraria; MSD: Honoraria; Gilead: Other: travel; Celgene Corporation: Other: Travel. Baerlocher:Novartis: Research Funding. Burchert:Novartis: Research Funding. Brümmendorf:Novartis: Consultancy, Research Funding; Janssen: Consultancy; Merck: Consultancy; Ariad: Consultancy; Pfizer: Consultancy, Research Funding; University Hospital of the RWTH Aachen: Employment. Hochhaus:Pfizer: Research Funding; Novartis: Research Funding; BMS: Research Funding; Incyte: Research Funding; MSD: Research Funding. Saussele:BMS: Honoraria, Research Funding; Incyte: Honoraria, Research Funding; Pfizer: Honoraria; Novartis: Honoraria, Research Funding. Baccarani:Novartis: Consultancy, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; Takeda: Consultancy.

scholarly journals A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Kaixuan Guo ◽  
Cong Lai ◽  
Juanyi Shi ◽  
Zhuang Tang ◽  
Cheng Liu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Risk Score ◽  
Risk Group ◽  
Expression Profiles ◽  
Disease Free Survival ◽  
Glucose Consumption ◽  
Low Risk ◽  
Migration And Invasion

BackgroundProstate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa.MethodsDifferential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the “edgeR” package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells.ResultsFive glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. In vitro experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown.ConclusionThe risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR.

scholarly journals Seven Autophagy-Related lncRNAs Associated With Clinical Prognosis in Hepatocellular Carcinoma.

2021 ◽  
Author(s):  
Yaping Zhou ◽  
Liu Yang ◽  
Xiangxin Zhang ◽  
Xiaotong Zhao ◽  
Jianfeng Fu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Rate ◽  
High Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Prediction Model ◽  
Low Risk

Abstract Background: LncRNA may be involved in the occurrence, metastasis, and chemical reaction of hepatocellular carcinoma (HCC) through various pathways associated with autophagy. Therefore, it is urgent to reveal more autophagy-related lncRNAs, explore these lncRNAs' clinical significance, and find new targeted treatment strategies. Methods: In our study, RNA-seq and clinical data of normal and HCC patients were obtained from the TCGA database, and autophagy genes were obtained from the human autophagy database. Results: The risk prediction model containing seven autophagy-related lncRNAs was constructed. Overall survival (OS) curves show that the high-risk group patients significantly shorter than the low-risk group (P=2.292e-10), and the five years survival rate of the high-risk group (HR 0.286, 95%CI 0.199-0.411) is less than half of the low-risk group (HR 0.694, 95%CI 0.547-0.77). Univariate Cox regression indicated that risk score of the risk prediction model (P<0.001, 95%CI 1.210-1.389 ), T (P<0.001, 95%CI 1.443-2.287), and stage (P<0.001 ,95%CI 1.466-2.408 ) were independent prognostic indicators. However, only the risk score remains the independent prognostic indicator(P<0.001, 95%CI 1.197-1.400 ) based on the multivariate analysis. This risk model's prediction efficiency is significantly higher than other clinicopathological factors for 1-, 3- and 5-year survival rate prediction (AUC are 0.853, 0.794, and 0.764, respectively). Remarkably, the 7 autophagy-related lncRNAs may participate in Spliceosome, Cell cycle, RNA transport, DNA replication, and mRNA surveillance pathway and be related to the biological process of RNA splicing and mRNA splicing. Conclusion: In conclusion, the 7 autophagy-related lncRNAs might be promising prognostic and therapeutic targets for HCC.

Sign in / Sign up

Export Citation Format

Share Document