scholarly journals Five crucial prognostic-related autophagy genes stratified female breast cancer patients aged 40-60 years

2020 ◽  
Author(s):  
Xiaolong Li ◽  
Hengchao Zhang ◽  
Jingjing Liu ◽  
Ping Li ◽  
Yi Sun
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Survival Curve ◽  
Predictive Ability ◽  
Female Breast Cancer ◽  
Clinicopathological Parameters ◽  
Risk Level ◽  
Lasso Regression ◽  
Breast Cancer Patients ◽  
Auc Value

Abstract Background: Autophagy is closely related to the progression of breast cancer.The aim of this study is to establish a prognostic-related model comprised of hub autophagy-genes(AGs) to assess patitents prognosis. Simultaneously, the model can guide clinicians to make up individualized strategies and stratify patients aged 40-60 years based on risk level.Methods: The hub AGs were identified through univariate COX regression and LASSO regression. The functions and alterations of these selected AGs were analyzed as well.Moreover,the multivariate COX regression and correlation analysis between hub AGs and clinicopathological parameters were done. Results: Totally,33 prognostic-related AGs were obtained from the univariate COX regression(P<0.05).SERPINA1, HSPA8, HSPB8, MAP1LC3A, and DIRAS3 were identified to constitute the prognostic model by the LASSO regression. The survival curve of patients in high-risk and in low-risk group was statistically significant(P<0.05).The 3-year and 5-year ROC displayed that their AUC value reached 0.762 and 0.825,respectively. Stage and riskscore were independent risk factors relevant about prognosis.RB1CC1, RPS6KB1, and BIRC6 were identified as the most predominant mutant genes. It was found that AGs were mainly involved in regulating the endopeptidases synthesis and played important roles in ErbB signal pathway. SERPIN1, riskscore were closely related to stage(P<0.05); HSPA8, riskscore were closely related to T staging(P<0.05); HSPB8 was closely related to N staging(P<0.05). Conclusions: Our prognostic model had relatively robust predictive ability on prognosis for patients aged 40-60 years.If stage was added into the prognostic model, the predictive ability would be more powerful.

scholarly journals Five Crucial Prognostic-Related Autophagy Genes Stratified Female Breast Cancer Patients Aged 40-60 Years

2020 ◽  
Author(s):  
Xiaolong Li ◽  
Hengchao Zhang ◽  
Jingjing Liu ◽  
Ping Li ◽  
Yi Sun
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Survival Curve ◽  
Predictive Ability ◽  
Clinicopathological Parameters ◽  
Risk Level ◽  
Lasso Regression ◽  
Breast Cancer Patients

Abstract Background : Autophagy is closely related to the progression of breast cancer. The aim at this study is to establish a prognostic-related model comprised of hub autophagy-genes (AGs ) to assess patient prognosis. Simultaneously, the model can guide clinicians to make up individualized strategies and stratify patients aged 40-60 years based on risk level. Methods : The hub AGs were identified with univariate COX regression and LASSO regression. The functions and alterations of these selected AGs were analyzed as well. Moreover, the multivariate COX regression and correlation analysis between hub AGs and clinicopathological parameters were done. Results : Totally, 33 prognostic-related AGs were obtained from the univariate COX regression (P<0.05 ) . SERPINA1 , HSPA8 , HSPB8 , MAP1LC3A , and DIRAS3 were identified to constitute the prognostic model by the LASSO regression . The survival curve of patients in high-risk and in low-risk group was statistically significant (P<0.05 ) . The 3-year and 5-year ROC displayed that their AUC value reached 0.762 and 0.825 , respectively . Stage and risk score were independent risk factors relevant about prognosis . RB1CC1 , RPS6KB1 , and BIRC6 were identified as the most predominant mutant genes . It was found that AGs were mainly involved in regulating the endopeptidases synthesis and played important roles in ErbB signal pathway . SERPIN1 , risk score were closely related to stage (P<0.05 ) ; HSPA8, risk score were closely related to T stag (P<0.05 ) ; HSPB8 was closely related to N stag (P<0.05 ). Conclusions : Our prognostic model had relatively robust predictive ability on prognosis for patients aged 40-60 years. If stage was added into 3 the prognostic model, the predictive ability would be more powerful.

scholarly journals Five crucial prognostic-related autophagy genes stratified female breast cancer patients aged 40–60 years

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiaolong Li ◽  
Hengchao Zhang ◽  
Jingjing Liu ◽  
Ping Li ◽  
Yi Sun
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Survival Curve ◽  
Predictive Ability ◽  
Risk Scores ◽  
Clinicopathological Parameters ◽  
Risk Level ◽  
Lasso Regression

Abstract Background Autophagy is closely related to the progression of breast cancer. The aim at this study is to establish a prognostic-related model comprised of hub autophagy genes (AGs) to assess patient prognosis. Simultaneously, the model can guide clinicians to make up individualized strategies and stratify patients aged 40–60 years based on risk level. Methods The hub AGs were identified with univariate COX regression and LASSO regression. The functions and alterations of these selected AGs were analyzed as well. Moreover, the multivariate COX regression and correlation analysis between hub AGs and clinicopathological parameters were done. Results Totally, 33 prognostic-related AGs were obtained from the univariate COX regression (P < 0.05). SERPINA1, HSPA8, HSPB8, MAP1LC3A, and DIRAS3 were identified to constitute the prognostic model by the LASSO regression. The survival curve of patients in the high-risk and low-risk groups was statistically significant (P < 0.05). The 3-year and 5-year ROC displayed that their AUC value reached 0.762 and 0.825, respectively. Stage and risk scores were independent risk factors relevant to prognosis. RB1CC1, RPS6KB1, and BIRC6 were identified as the most predominant mutant genes. It was found that AGs were mainly involved in regulating the endopeptidases synthesis and played important roles in the ErbB signal pathway. SERPIN1, risk score was closely related to the stage (P < 0.05); HSPA8, risk score were closely related to T stag (P < 0.05); HSPB8 was closely related to N stag (P < 0.05). Conclusions Our prognostic model had the relatively robust predictive ability on prognosis for patients aged 40–60 years. If the stage was added into the prognostic model, the predictive ability would be more powerful.

scholarly journals A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

2021 ◽  
Author(s):  
Boxuan Liu ◽  
Yun Zhao ◽  
Shuanying Yang
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.

scholarly journals Study of Gene Expression of Programmed Cell Death Ligand 1 (PD-L1) in Breast Cancer Patients

2021 ◽  
Vol 107 (1_suppl) ◽  
pp. 2-2
Author(s):  
H Gadelrab ◽  
M Mokhtar ◽  
H Morsy ◽  
M Elnaggar
Keyword(s):  
Breast Cancer ◽  
Cell Death ◽  
Programmed Cell Death ◽  
Cancer Patients ◽  
Therapeutic Response ◽  
Female Breast Cancer ◽  
Clinicopathological Parameters ◽  
Breast Cancer Patients ◽  
Expression Levels ◽  
Female Breast

Introduction: Breast cancer is the most frequently occurring cancer among females and the second most common cancer overall. Programmed Cell Death Ligand 1 (PD-L1) plays an important role in blocking ‘cancer-immunity cycle’ and is considered as a major inhibitory pathway. The aim of the present study was to clarify the alterations of expression of PD-L1 in peripheral blood mononuclear cytes (PBMCs) of female breast cancer patients and analyze its association with clinico-pathological criteria as well as therapeutic response. Materials and Methods: The study was conducted on 45 female breast cancer patients and 45 female controls. Blood samples were collected followed by PBMCs isolation, total RNA extraction, reverse transcription and finally, quantitative polymerase chain reaction (qPCR) using SYBR Green DNA binding dye. Expression levels of PD-L1 were calculated and then compared with clinicopathological parameters of the patients in addition to initial therapeutic response. Results: A significant difference was detected for PD-L1 expression levels in breast cancer patients compared to controls. A significant association with age, metastatic breast cancer, estrogen receptor (ER) negative status as well as high concentrations of cancer antigen 15-3 (CA15-3) was detected. On the other hand, no significant association was recognized with tumor size, lymph nodal status, histopathological type, grade, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER-2) status, triple negative, among de novo and recurrent metastatic patients and for the number of metastatic sites as well as the therapeutic response. Conclusions: This study paves the way of the use of PD-L1 as a noninvasive prognostic and diagnostic biomarker for poor prognosis of breast cancer.

scholarly journals Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8128 ◽  
Author(s):  
Cheng Yue ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Differentially Expressed ◽  
Lasso Regression

Background Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

scholarly journals Development and Validation of a Novel Model for Predicting the 5-Year Risk of Type 2 Diabetes in Patients with Hypertension: A Retrospective Cohort Study

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xintian Cai ◽  
Qing Zhu ◽  
Ting Wu ◽  
Bin Zhu ◽  
Xiayire Aierken ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Model ◽  
Cox Regression ◽  
Mainland China ◽  
Cardiovascular Complications ◽  
Calibration Plot ◽  
Lasso Regression ◽  
Derivation Cohort ◽  
Auc Value

Background. Hypertension is now common in China. Patients with hypertension and type 2 diabetes are prone to severe cardiovascular complications and poor prognosis. Therefore, this study is aimed at establishing an effective risk prediction model to provide early prediction of the risk of new-onset diabetes for patients with a history of hypertension. Methods. A LASSO regression model was used to select potentially relevant features. Univariate and multivariate Cox regression analyses were used to determine independent predictors. Based on the results of multivariate analysis, a nomogram of the 5-year incidence of T2D in patients with hypertension in mainland China was established. The discriminative capacity was assessed by Harrell’s C-index, AUC value, calibration plot, and clinical utility. Results. After random sampling, 1273 and 415 patients with hypertension were included in the derivation and validation cohorts, respectively. The prediction model included age, body mass index, FPG, and TC as predictors. In the derivation cohort, the AUC value and C-index of the prediction model are 0.878 (95% CI, 0.861-0.895) and 0.862 (95% CI, 0.830-0.894), respectively. In the validation cohort, the AUC value and C-index of the prediction model were 0.855 (95% CI, 0.836-0.874) and 0.841 (95% CI, 0.817-0.865), respectively. The calibration plots demonstrated good agreement between the estimated probability and the actual observation. Decision curve analysis shows that nomograms are clinically useful. Conclusion. Our nomogram can be used as a simple, affordable, reasonable, and widely implemented tool to predict the 5-year T2D risk of hypertension patients in mainland China. This application helps timely intervention to reduce the incidence of T2D in patients with hypertension in mainland China.

scholarly journals A Novel Predictive Model Associated With Osteosarcoma Metastasis

2021 ◽  
Author(s):  
Han Zhang ◽  
Guanhong Chen ◽  
Xiajie Lyu ◽  
Tao Li ◽  
Rong Chun ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Cox Regression ◽  
High Reliability ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Metastasis Model ◽  
Non Coding Rnas ◽  
Novel Model

Abstract Background: Long non-coding RNAs (lncRNAs) have diverse roles in modulating gene expression on both transcriptional and translational aspects, whereas its role in the metastasis of osteosarcoma (OS) is unclear.Method: Expression and clinical data were downloaded from TARGET datasets. The OS metastasis model was established by seven lncRNAs screened by univariate cox regression, lasso regression and multivariate cox regression analysis. The area under receiver operating characteristic curve (AUC) values were used to evaluate the models.Results: The predictive ability of this model is extraordinary (1 year: AUC = 0.92, 95% Cl = 0.83–1.01; 3 years: AUC = 0.87, 95% Cl = 0.79–0.96; 5 years: AUC = 0.86, 95% Cl = 0.76–0.96). Patients in high group had poor survival compared to low group (p < 0.0001). “NOTCH_SIGNALING”, and “WNT_BETA_CATENIN_SIGNALING” were enriched via the GSEA analysis and dendritic cells resting were associated with the AL512422.1, AL357507.1 and AC006033.2 (p < 0.05).Conclusion: We constructed a novel model with high reliability and accuracy to predict the metastasis of OS patients based on seven prognosis-related lncRNAs.

scholarly journals Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9621
Author(s):  
Shanliang Zhong ◽  
Huanwen Chen ◽  
Sujin Yang ◽  
Jifeng Feng ◽  
Siying Zhou
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Cox Regression ◽  
Risk Group ◽  
Breast Cancer Survival ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Breast Cancer Patients ◽  
Prognostic Signature

We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade breast cancer. In conclusion, our 6-ARG signature was of significance in predicting of overall survival of patients with breast cancer. EIF4EBP1 may be a key ARG associated with breast cancer survival.

scholarly journals Integrated analysis of the functions and prognostic values of RNA-binding proteins in neuroblastoma

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260876
Author(s):  
Jun Yang ◽  
Jiaying Zhou ◽  
Cuili Li ◽  
Shaohua Wang
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Risk Model ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Differentially Expressed ◽  
Lasso Regression

Background Neuroblastoma (NB) is the most common solid tumor in children. NB treatment has made significant progress; however, given the high degree of heterogeneity, basic research findings and their clinical application to NB still face challenges. Herein, we identify novel prognostic models for NB. Methods We obtained RNA expression data of NB and normal nervous tissue from TARGET and GTEx databases and determined the differential expression patterns of RNA binding protein (RBP) genes between normal and cancerous tissues. Lasso regression and Cox regression analyses identified the five most important differentially expressed genes and were used to construct a new prognostic model. The function and prognostic value of these RBPs were systematically studied and the predictive accuracy verified in an independent dataset. Results In total, 348 differentially expressed RBPs were identified. Of these, 166 were up-regulated and 182 down-regulated RBPs. Two hubs RBPs (CPEB3 and CTU1) were identified as prognostic-related genes and were chosen to build the prognostic risk score models. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using proportional hazards regression model. A five gene prognostic model: Risk score = (-0.60901*expCPEB3)+(0.851637*expCTU1) was built. Based on this model, the overall survival of patients in the high-risk subgroup was lower (P = 2.152e-04). The area under the curve (AUC) of the receiver-operator characteristic curve of the prognostic model was 0.720 in the TARGET cohort. There were significant differences in the survival rate of patients in the high and low-risk subgroups in the validation data set GSE85047 (P = 0.1237e-08), with the AUC 0.730. The risk model was also regarded as an independent predictor of prognosis (HR = 1.535, 95% CI = 1.368–1.722, P = 2.69E-13). Conclusions This study identified a potential risk model for prognosis in NB using Cox regression analysis. RNA binding proteins (CPEB3 and CTU1) can be used as molecular markers of NB.

scholarly journals Identification of Prognostic Genes in Neuroblastoma in Children by Weighted Gene Coexpression Network Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jun Yang ◽  
Ying Zhang ◽  
Jiaying Zhou ◽  
Shaohua Wang
Keyword(s):  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Lasso Regression ◽  
Regression Analyses ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Background. Neuroblastoma is a malignant neuroendocrine tumor from the sympathetic nervous system, the most common extracranial tumor in children. Identifying potential prognostic markers of neuroblastoma can provide clues for early diagnosis, recurrence, and treatment. Methods. RNA sequence data and clinical features of 147 neuroblastomas were obtained from the TARGET (Therapeutically Applicable Research to Generate Effective Treatments project) database. Application weighted gene coexpression network analysis (WGCNA) was used to construct a free-scale gene coexpression network, to study the interrelationship between its potential modules and clinical features, and to identify hub genes in the module. We performed Lasso regression and Cox regression analyses to identify the three most important genes and develop a new prognostic model. Data from the GSE85047 cohort verified the predictive accuracy of the prognostic model. Results. 14 coexpression modules were constructed using WGCNA. Brown coexpression modules were found to be significantly associated with disease survival status. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analyses using the Cox proportional hazards regression model. Finally, we constructed a three-gene prognostic model: risk score = (0.003812659 ∗ CKB) + (−0.152376975 ∗ expDST) + (0.032032815 ∗ expDUT). The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group ( P = 1.225 e − 06 ). The risk model was also regarded as an independent predictor of prognosis (HR = 1.632; 95% CI = 1.391–1.934; P < 0.001 ). Conclusion. Our study constructed a neuroblastoma coexpressing gene module and identified a prognostic potential risk model for prognosis in neuroblastoma.

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