scholarly journals A Coagulation-Related Gene-Based Prognostic Model for Invasive Ductal Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Li ◽  
Jiajia Du ◽  
Yanhong Wang ◽  
Hongyan Jia
Keyword(s):  
Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Prognostic Model ◽  
Ductal Carcinoma ◽  
Risk Group ◽  
Metastatic Breast ◽  
Related Gene ◽  
Training Set ◽  
Gene Set ◽  
Validation Set

Background: Invasive ductal carcinoma (IDC) is the most common type of metastatic breast cancer. Due to the lack of valuable molecular biomarkers, the diagnosis and prognosis of IDC remain a challenge. A large number of studies have confirmed that coagulation is positively correlated with angiogenesis-related factors in metastatic breast cancer. Therefore, the purpose of this study was to construct a COAGULATION-related genes signature for IDC using the bioinformatics approaches.Methods: The 50 hallmark gene sets were obtained from the molecular signature database (MsigDB) to conduct Gene Set Variation Analysis (GSVA). Gene Set Enrichment Analysis (GSEA) was applied to analyze the enrichment of HALLMARK_COAGULATION. The COAGULATION-related genes were extracted from the gene set. Then, Limma Package was used to identify the differentially expressed COAGULATION-related genes (DECGs) between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) samples in GSE26340 data set. A total of 740 IDC samples from The Cancer Genome Atlas (TCGA) database were divided into a training set and a validation set (7:3). The univariate and multivariate Cox regression analyses were performed to construct a risk signature, which divided the IDC samples into the high- and low-risk groups. The overall survival (OS) curve and receiver operating characteristic (ROC) curve were drawn in both training set and validation set. Finally, a nomogram was constructed to predict the 1-, 2-, 3-, 4-, and 5-year survival rates of IDC patients. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic genes.Results: The “HALLMARK_COAGULATION” was significantly activated in IDC. There was a significant difference in the clinicopathological parameters between the DCIS and IDC patients. Twenty-four DECGs were identified, of which five genes (SERPINA1, CAPN2, HMGCS2, MMP7, and PLAT) were screened to construct the prognostic model. The high-risk group showed a significantly lower survival rate than the low-risk group both in the training set and validation set (p=3.5943e-06 and p=0.014243). The risk score was demonstrated to be an independent predictor of IDC prognosis. A nomogram including risk score, pathological_stage, and pathological_N provided a quantitative method to predict the survival probability of 1-, 2-, 3-, 4-, and 5-year in IDC patients. The results of decision curve analysis (DCA) further demonstrated that the nomogram had a high potential for clinical utility.Conclusion: This study established a COAGULATION-related gene signature and showed its prognostic value in IDC through a comprehensive bioinformatics analysis, which may provide a potential new prognostic mean for patients with IDC.

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 A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Peijie Chen ◽  
Yuting Gao ◽  
Si Ouyang ◽  
Li Wei ◽  
Min Zhou ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Training Set ◽  
Non Coding Rnas ◽  
Validation Set ◽  
Immune Related Genes

Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups.Methods: We downloaded the gene expression profiles and clinical data of 227 patients from The Cancer Genome Atlas database and extracted immune-related lncRNAs. Cox regression analysis was used to pick out the predictive lncRNAs. The risk score of each patient was calculated based on the expression level of lncRNAs and regression coefficient (β), and a prognostic model was constructed. The overall survival (OS) of different risk groups was analyzed and compared by the Kaplan–Meier method. To analyze the distribution of immune-related genes in each group, principal component analysis and Gene set enrichment analysis were carried out. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data was performed to explore the immune microenvironment.Results: Patients were divided into training set and validation set. Five immune-related lncRNAs (H1FX-AS1, AL441992.1, USP30-AS1, AP001527.2, and AL031123.2) were selected for the construction of the prognostic model. Patients in the training set were divided into high-risk group with longer OS and low-risk group with shorter OS (p = 0.004); meanwhile, similar result were found in validation set (p = 0.013), combination set (p &lt; 0.001) and patients with different tumor stages. This model was further confirmed in 56 cervical cancer tissues by Q-PCR. The distribution of immune-related genes was significantly different in each group. In addition, the immune score and the programmed death-ligand 1 expression of the low-risk group was higher.Conclusions: The prognostic model based on immune-related lncRNAs could predict the prognosis and immune status of cervical cancer patients which is conducive to clinical prognosis judgment and individual treatment.

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.

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 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.

Prognostic significance of cytoplasmic SOX9 in invasive ductal carcinoma and metastatic breast cancer

2011 ◽  
Vol 236 (2) ◽  
pp. 145-155 ◽  
Author(s):  
Geetika Chakravarty ◽  
Krzysztof Moroz ◽  
Nick M Makridakis ◽  
Shelby Alaine Lloyd ◽  
Sarah E Galvez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Prognostic Significance ◽  
Metastatic Breast

scholarly journals Construction and Validation of a Prognostic Model for the Assessment of Postoperative Overall Survival of Patients with Metaplastic Breast Cancer: Based on a Retrospective Large Data Analysis and Chinese Multicenter Study

2021 ◽  
Author(s):  
Ge Wang ◽  
Xin Ren ◽  
Mengmeng Wang ◽  
Xiaomin Sun ◽  
Yongsheng Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Survival Rate ◽  
Prognostic Model ◽  
Seer Database ◽  
Training Set ◽  
Metaplastic Breast Cancer ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Purpose: Surgery is an important treatment for patients with metaplastic breast cancer (MBC). This study used prognostic clinicopathological factors to establish a model for predicting overall survival (OS) in patients with MBC. Methods: Patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with MBC from 2010–2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell's consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. Results: We divided 1044 patients from the SEER database randomly into a training set (n=732) and validation set (n=312) in a 7:3 ratio. Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the 7th edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. The DCA showed that the nomogram is more clinically useful and applicable. Conclusions: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans. Abstract Background: Surgery is an important treatment for patients with metaplastic breast cancer (MBC). This study used prognostic clinicopathological factors to establish a model for predicting overall survival (OS) in patients with MBC. Methods： Patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with MBC from 2010–2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell's consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. Results: We divided 1044 patients from the SEER database randomly into a training set (n=732) and validation set (n=312) in a 7:3 ratio. Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the 7th edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. The DCA showed that the nomogram is more clinically useful and applicable. Conclusions: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans.

scholarly journals Construction of Prognostic Risk Prediction Model of Oral Squamous Cell Carcinoma Based on Nine Survival-Associated Metabolic Genes

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhen-Dong Huang ◽  
Yang-Yang Yao ◽  
Ting-Yu Chen ◽  
Yi-Fan Zhao ◽  
Chao Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Clinical Characteristics ◽  
Prognostic Model ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Training Set ◽  
Metabolic Genes ◽  
Validation Set

The aim was to investigate the independent prognostic factors and construct a prognostic risk prediction model to facilitate the formulation of oral squamous cell carcinoma (OSCC) clinical treatment plan. We constructed a prognostic model using univariate COX, Lasso, and multivariate COX regression analysis and conducted statistical analysis. In this study, 195 randomly obtained sample sets were defined as training set, while 390 samples constituted validation set for testing. A prognostic model was constructed using regression analysis based on nine survival-associated metabolic genes, among which PIP5K1B, NAGK, and HADHB significantly down-regulated, while MINPP1, PYGL, AGPAT4, ENTPD1, CA12, and CA9 significantly up-regulated. Statistical analysis used to evaluate the prognostic model showed a significant different between the high and low risk groups and a poor prognosis in the high risk group (P &lt; 0.05) based on the training set. To further clarify, validation sets showed a significant difference between the high-risk group with a worse prognosis and the low-risk group (P &lt; 0.05). Independent prognostic analysis based on the training set and validation set indicated that the risk score was superior as an independent prognostic factor compared to other clinical characteristics. We conducted Gene Set Enrichment Analysis (GSEA) among high-risk and low-risk patients to identify metabolism-related biological pathways. Finally, nomogram incorporating some clinical characteristics and risk score was constructed to predict 1-, 2-, and 3-year survival rates (C-index = 0.7). The proposed nine metabolic gene prognostic model may contribute to a more accurate and individualized prediction for the prognosis of newly diagnosed OSCC patients, and provide advice for clinical treatment and follow-up observations.

Expression of Let-7c in invasive ductal carcinoma of breast cancer and its clinical significance

2009 ◽  
Vol 29 (4) ◽  
pp. 400-403
Author(s):  
Shu-rong SHEN ◽  
Jun-yi SHI ◽  
Xian SHEN ◽  
Guan-li HUANG ◽  
Xiang-yang XUE
Keyword(s):  
Breast Cancer ◽  
Clinical Significance ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma

scholarly journals Hydronephrosis Caused by Metastatic Breast Cancer

2021 ◽  
pp. 378-385
Author(s):  
Hitoshi Sugimoto ◽  
Goshi Oda ◽  
Minato Yokoyama ◽  
Kumiko Hayashi ◽  
Maho Yoshino ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Urinary Tract ◽  
Renal Dysfunction ◽  
Ductal Carcinoma ◽  
Lobular Carcinoma ◽  
Cancer Recurrence ◽  
Metastatic Breast ◽  
Ureteral Stent ◽  
High Care ◽  
Unilateral Case

Breast cancer metastasizes mainly to organs such as bone, lung, and liver, whereas metastases to the peritoneum and urinary tract are rare. Metastasis to the peritoneum or urinary tract may result in renal dysfunction, infection, and painful hydronephrosis. In our hospital, 1,409 breast cancer surgeries were performed between January 2004 and December 2015, and 7 cases of hydronephrosis associated with recurrence were observed. The median age of patients was 69 years (57–79 years). The median time from surgery to diagnosis of hydronephrosis was 47 months (20–70 months). Histology was invasive ductal carcinoma (IDC) in 6 cases and invasive lobular carcinoma (ILC) in 1 case. There were 6 bilateral cases and 1 unilateral case of hydronephrosis. The causes were retroperitoneal metastasis in 5 cases and lymph node metastasis in 2 cases. The hydronephrosis was untreated in 2 cases, and treated with a ureteral stent in 2 cases, nephrostomy in 1 case, and nephrostomy due to ureteral stent failure in 2 cases. The median survival from the onset of hydronephrosis was 12 months (3–57 months). Although the probability of hydronephrosis in breast cancer recurrence was not high, care must be taken to avoid renal dysfunction, infection, or pain, which may require treatment.

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