scholarly journals SNX10 and PTGDS are associated with the progression and prognosis of cervical squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Regression Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Curve Analysis ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis

Abstract Background Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC. Methods We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes. Results Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P < 0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression while SNX10 presented high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency (AUC = 0.738). GSEA analysis demonstrated that the two genes were correlated with the chemokine signaling pathway (P < 0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p = 0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients (P = 0.007 and 0.003). Conclusions PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients.

scholarly journals SNX10 and PTGDS are Associated with the Progression and Prognosis of Cervical Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Curve Analysis ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background: Cervical cancer (CC) is an important cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes.Results: Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P<0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency(AUC= 0.738). GSEA showed that the two genes were associated with the chemokine signaling pathway(P<0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p=0.037). Cox regression analysis showed that SNX10 (P=0.007;HR=1.424;95%CI:1.103-1.838) and PTGDS (P=0.003;HR=0.802;95%CI:0.693-0.928) were independent prognostic indicators for OS among CC patients. Conclusions: PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients..

scholarly journals SNX10 and PTGDS are Associated with the Progression and Prognosis of Cervical Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Curve Analysis ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes. Results: Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P<0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency(AUC=0.738). GSEA showed that the two genes were associated with the chemokine signaling pathway(P<0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p=0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients(P=0.007 and 0.003). Conclusions: PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients.

scholarly journals SNX10 and PTGDS are Associated with the Progression and Prognosis of Cervical Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Curve Analysis ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes. Results: Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P<0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency(AUC= 0.738). GSEA showed that the two genes were associated with the chemokine signaling pathway(P<0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p=0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients(P=0.007 and 0.003). Conclusions: PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients..

scholarly journals Identification of Two Novel Genes SNX10 and PTGDS with Cervical Squamous Cell Carcinoma Prognosis

2020 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Regression Analysis ◽  
Cox Regression ◽  
Trichostatin A ◽  
Expression Profiles ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the therapeutic targets of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen DEGs between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by STRING and selected hub modules via MCODE. CMap was used to find molecules with therapeutic potential for CC. We also validated hub genes in GEO datasets, GEPIA, immunohistochemistry. Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also done to dig out the significance of hub genes. Results: Functional analysis revealed that DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 prominent proteins, with CDK1 having the strongest association with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC. Eight genes were screened out through GEPIA. Of them, only PTGDS and SNX10 have not been reported in CC related articles. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. They can distinguish CC and normal tissue and have good diagnostic efficiency. GSEA showed that the two genes were associated with the chemokine signaling pathway. TCGA methylation analysis showed that patients with low-expressed and hyper-methylated PTGDS had a bad prognosis than the patients with high-expressed and hypo-methylated PTGDS. Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients. Conclusions: In conclusion, PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes could be used to develop new target treatments for CC.

Prognostic value of two novel genes SNX10 and PTGDS for cervical squamous cell carcinoma: an integrated bioinformatic analysis

2020 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Regression Analysis ◽  
Cox Regression ◽  
Trichostatin A ◽  
Expression Profiles ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the therapeutic targets of CC. Methods We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen DEGs between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by STRING and selected hub modules via MCODE. CMap was used to find molecules with therapeutic potential for CC. We also validated hub genes in GEO datasets, GEPIA, immunohistochemistry. Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also done to dig out the significance of hub genes. Results Functional analysis revealed that DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 prominent proteins, with CDK1 having the strongest association with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC. Eight genes were screened out through GEPIA. Of them, only PTGDS and SNX10 have not been reported in CC related articles. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. They can distinguish CC and normal tissue and have good diagnostic efficiency. GSEA showed that the two genes were associated with the chemokine signaling pathway. TCGA methylation analysis showed that patients with low-expressed and hyper-methylated PTGDS had a bad prognosis than the patients with high-expressed and hypo-methylated PTGDS. Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients. Conclusions PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes could be used to develop new target treatments for CC.

scholarly journals Identification of 4-Genes Model in Papillary Renal Cell Tumor Microenvironment Based on Comprehensive Analysis

2021 ◽  
Author(s):  
Liang Luo ◽  
Haiyi Zhou ◽  
Hao Su
Keyword(s):  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Renal Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
Ppi Network ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Venn Diagrams ◽  
Kaplan Meier

Abstract Background: The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC. Methods: PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. 323 samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression were conducted. Cross genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan–Meier analysis to identify the correlations between genes and overall survival (OS). Finally, univariate and multivariate cox regression analysis were employed to construct survival model and predict prognosis. Results: We found immune/stromal score was correlated with T pathological grade and PRCC subtypes. 989 differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Conclusions: Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.

scholarly journals Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Wei Ma ◽  
Fangkun Zhao ◽  
Xinmiao Yu ◽  
Shu Guan ◽  
Huandan Suo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Curve Analysis ◽  
Time Dependent ◽  
Training Set ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer. Methods We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses. Results A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group (p = 1.215e − 06 in the training set; p = 0.0069 in the validation set; p = 1.233e − 07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR = 1.432; 95% CI 1.204–1.702, p < 0.001), validation set (HR = 1.162; 95% CI 1.004–1.345, p = 0.044), and whole set (HR = 1.240; 95% CI 1.128–1.362, p < 0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. Conclusions We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

scholarly journals Identification of 4-genes model in papillary renal cell tumor microenvironment based on comprehensive analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang Luo ◽  
Haiyi Zhou ◽  
Hao Su
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Renal Cell ◽  
Cox Regression ◽  
Ppi Network ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Venn Diagrams ◽  
Kaplan Meier

Abstract Background The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC. Methods PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. Three hundred fifty-seven samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression was conducted. Intersect genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan–Meier analysis to identify the correlations between genes and overall survival (OS) and progression-free survival (PFS). Univariate and multivariate cox regression analysis were employed to construct survival model. Cibersort was used to predict the immune cell composition of high and low risk group. Combined nomograms were built to predict PRCC prognosis. Immune properties of PRCC were validated by The Cancer Immunome Atlas (TCIA). Results We found immune/stromal score was correlated with T pathological stages and PRCC subtypes. Nine hundred eighty-nine differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Finally, we constructed a prognostic nomogram which combined with influence factors. Conclusions Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.

scholarly journals Immune-Related LncRNAs as Predictors of Survival in Breast Cancer: A Prognostic Signature

2020 ◽  
Author(s):  
wei ma ◽  
fangkun zhao ◽  
xinmiao yu ◽  
shu guan ◽  
huandan suo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Curve Analysis ◽  
Time Dependent ◽  
Training Set ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background: Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancers development and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Methods: We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separatedinto training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate and multivariate Cox regression analyses. Results: A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group( p= 1.215e−06 in the training set; p =0.0069 in the validation set; p =1.233e−07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR= 1.432; 95% CI 1.204−1.702, p <0.001), validation set (HR= 1.162; 95% CI 1.004−1.345, p = 0.044), and whole set (HR=1.240; 95% CI 1.128−1.362, p <0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. Conclusions: We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

Global longitudinal strain and chronic kidney disease prognostic impact on acute coronary syndrome

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A.R Morgado Gomes ◽  
D Campos ◽  
C Saleiro ◽  
J Gameiro Lopes ◽  
J.P Sousa ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Roc Curve ◽  
Cox Regression ◽  
Left Ventricular ◽  
Curve Analysis ◽  
Left Ventricular Ejection ◽  
Roc Curve Analysis ◽  
Ventricular Ejection Fraction ◽  
Cox Regression Analysis ◽  
Coronary Syndrome

Abstract Background Impaired left ventricular ejection fraction (LVEF) and chronic kidney disease (CKD) have been associated with poorer outcomes in acute coronary syndrome (ACS). Increasing evidence on global left ventricular longitudinal strain (GLS) suggests superiority over left ventricular ejection fraction (LVEF) in risk stratification. Methods This study was based on a retrospective analysis of consecutive patients admitted to a Coronary Care Unit between 2009 and 2016. Baseline characteristics and echocardiographic parameters, including LVEF, were assessed. For each patient, a two-dimensional speckle tracking of the left ventricle was assessed and average GLS was calculated using 2, 3 and 4-chamber views. Blood creatinine was measured during hospital stay and used to estimate glomerular filtration rate (GFR) with Modification of Diet in Renal Disease (MDRD) equation. A cox regression analysis was performed to determine mortality prediction value of average GLS, LVEF and GFR in this population. Receiver operating characteristic (ROC) curve analysis was conducted and area under the curve (AUC) was estimated. Results A total of 85 patients (66.7±12.7 years old; 78.8% males) were enrolled. LVEF mean was 49.4±9.8% and average GLS was −16.0±4.0%. GFR median was 80.0±48.9 ml/min/1.73m2. In cox regression analysis, worse average GLS was associated with greater mortality (HR 0.721; 95% CI 0.599–0.867; P=0.001). GFR was inversely related to death (HR 0.967; 95% CI 0.944–0.991, P=0.008). In cox regression analysis using average GLS and GFR as covariates, both proved to be independent predictors of mortality (for average GLS, HR 0.748; 95% CI 0.610–0.918, P=0.005; for GFR, HR 0.974; 95% CI 0.949–0.999; P=0.044). The AUC of average GLS to predict mortality was 0.78 (P&lt;0.001, sensitivity 50.7% and specificity 100%) and for average GLS and GFR combined was 0.85 (P&lt;0.001, sensitivity 84.0% and specificity 77.8%). Although LVEF proved to be a mortality predictor, the AUC obtained by ROC curve analysis was inferior to average GLS, with statistical significance (P=0.043). Conclusions GLS and CKD proved to be independent predictors of mortality in ACS patients. GLS showed superiority when compared to LVEF in risk stratification and in the future it might replace LVEF. The model combining GLS and GFR emphasized the increased risk of CKD patients and how they should be seen as high-risk patients. ROC curve analysis Funding Acknowledgement Type of funding source: None

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