Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis

Abstract Background This study intended to determine important genes related to the prognosis and recurrence of breast cancer. Methods Gene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed. Results Based on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors. Conclusion ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer.

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Identification of a 9-Gene Prognostic signature for breast cancer

10.21203/rs.3.rs-26949/v1 ◽

2020 ◽

Author(s):

Zelin Tian ◽

Jianing Tang ◽

Xing Liao ◽

Qian Yang ◽

Yumin Wu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Overall Survival ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Disease Free Survival ◽

Gene Signature ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Breast cancer (BRCA) is the most common cancer among women worldwide and results in the second leading cause of woman cancer death.Methods This study sought to develop a prognostic gene signature to predict the prognosis of patients with BRCA. Studies were performed using the genome-wide data of BRCA patients from the Gene Expression Omnibus dataset (GSE20685, GSE42568, GSE20711, GSE88770). Univariate COX regression analysis was used to determine the association between gene expression levels and overall survival(OS) in each dataset. Taking P value < 0.05 as the inclusion criterion, the common genes in all datasets were selected as prognostic genes, and a 9-gene prognostic signature was developed.Results The Kaplan-Meier survival curve was constructed using log-rank test to assess survival differences. The overall survival of patients in the low-risk group was significantly higher than that in the high-risk group. ROC analysis showed that this 9-gene signature showed good diagnostic efficiency both in overall survival(OS) and disease free survival(DFS). The 9-gene signature was further validated using GSE16446 dataset. In addition, multiple Cox regression analysis showed that this 9-gene signature was an independent risk factor. Finally, we established a nomogram that integrates conventional clinicopathological features and 9-gene signature. The analysis of the calibration plots showed that the nomogram has good performance.Conclusions This study has developed a reliable 9-gene prognostic signature, which is of great value in predicting the prognosis of BRCA and will help to make personalized treatment decisions for patients at different risk score.

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Regulation of the Estrogen-Inducible Gene Expression Profile by the Breast Cancer Susceptibility Gene BRCA1

Endocrinology ◽

10.1210/en.2004-0409 ◽

2005 ◽

Vol 146 (4) ◽

pp. 2031-2047 ◽

Cited By ~ 30

Author(s):

Jingwen Xu ◽

Saijun Fan ◽

Eliot M. Rosen

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Gene Expression Profile ◽

Expression Profile ◽

Cancer Susceptibility ◽

Susceptibility Gene ◽

Breast Cancer Susceptibility ◽

Breast Cancer Susceptibility Gene ◽

Cancer Susceptibility Gene ◽

Inducible Gene

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Gene expression profile by inhibiting Raf-1 protein kinase in breast cancer cells

International Journal of Molecular Medicine ◽

10.3892/ijmm.17.3.457 ◽

2006 ◽

Cited By ~ 1

Author(s):

Rajshree Mewani ◽

Song Tian ◽

Bihua Li ◽

Malika Danner ◽

Theresa Carr ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Protein Kinase ◽

Cancer Cells ◽

Gene Expression Profile ◽

Expression Profile ◽

Breast Cancer Cells

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Gene expression profile in breast cancer comprising predictive markers for metastatic risk

Genetics and Molecular Research ◽

10.4238/2015.september.21.3 ◽

2015 ◽

Vol 14 (3) ◽

pp. 10929-10936 ◽

Cited By ~ 4

Author(s):

S. Sirirattanakul ◽

P. Wannakrairot ◽

T. Tencomnao ◽

R. Santiyanont

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Gene Expression Profile ◽

Expression Profile ◽

Predictive Markers ◽

Metastatic Risk

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Prognostic value of metabolic activity of the psoas muscle evaluated by preoperative 18F-FDG PET-CT in breast cancer: a retrospective cross-sectional study

BMC Cancer ◽

10.1186/s12885-021-08886-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Keunyoung Kim ◽

In-Joo Kim ◽

Kyoungjune Pak ◽

Taewoo Kang ◽

Young Mi Seol ◽

...

Keyword(s):

Breast Cancer ◽

Metabolic Activity ◽

Cox Regression ◽

Surrogate Marker ◽

Disease Free Survival ◽

Psoas Muscle ◽

Cox Regression Analysis ◽

Standardized Uptake Values ◽

The Mean ◽

Receptor Negativity

Abstract Background This study aimed to evaluate the potential of metabolic activity of the psoas muscle measured by 18F-fluorodeoxyglucose positron emission tomography-computed tomography to predict treatment outcomes in patients with resectable breast cancer. Methods The medical records of 288 patients who had undergone surgical resection for stages I–III invasive ductal carcinoma of the breast between January 2014 and December 2014 in Pusan National University Hospital were reviewed. The standardized uptake values (SUVs) of the bilateral psoas muscle were normalized using the mean SUV of the liver. SUVRmax was calculated as the ratio of the maximum SUV of the average bilateral psoas muscle to the mean SUV of the liver. SUVRmean was calculated as the ratio of the mean SUV of the bilateral psoas muscle to the mean SUV of the liver. Results Univariate analyses identified a higher T stage, higher N stage, estrogen receptor negativity, progesterone receptor negativity, human epidermal growth factor receptor 2 positivity, triple-negative breast cancer, mastectomy (rather than breast-conserving surgery), SUVRmean > 0.464, and SUVRmax > 0.565 as significant adverse factors for disease-free survival (DFS). Multivariate Cox regression analysis revealed that N3 stage (hazard ratio [HR] = 5.347, P = 0.031) was an independent factor for recurrence. An SUVRmax > 0.565 (HR = 4.987, P = 0.050) seemed to have a correlation with shorter DFS. Conclusions A higher SUVRmax of the psoas muscle, which could be a surrogate marker of insulin resistance, showed strong potential as an independent prognostic factor for recurrence in patients with resectable breast cancer.

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Identification of a Novel Gene Model-Based Homologous Recombination Deficiency Score to Improve Survival Prediction of TNBC.

10.21203/rs.3.rs-472194/v1 ◽

2021 ◽

Author(s):

Wenxiang Zhang ◽

Bolun Ai ◽

Xiangyi Kong ◽

Xiangyu Wang ◽

Jie Zhai ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Homologous Recombination ◽

Risk Score ◽

Cox Regression ◽

Roc Curves ◽

Cox Regression Analysis ◽

Homologous Recombination Deficiency ◽

Ajcc Stage ◽

Kaplan Meier

Abstract Background Triple-negative breast cancer (TNBC) is a specific histological type of breast cancer with a poor prognosis, early recurrence, which lacks durable chemotherapy responses and effective targeted therapies. We aimed to construct an accurate prognostic risk model based on homologous recombination deficiency (HRD) - gene expression profiles for improving prognosis prediction of TNBC. Methods Triple-negative breast cancer RNA sequencing data and sample clinical information were downloaded from the breast invasive carcinoma (BRCA) cohort in the Cancer Genome Atlas (TCGA) database. Combined with the HRD database, tumor samples were divided into two sets. We screened differentially expressed genes (DEGs) and then identified HRD-related prognostic genes using weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identifying key prognostic genes. Risk scores were calculated and compared with HRD score, Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. GSE103091 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to independently verify the prognosis of the risk score. A nomogram was constructed and revealed by time-dependent ROC curves to guide clinical practice. Results We found that HRD tumor samples (HRD score > = 42) in TNBC patients were associated with poor overall survival (p = 0.027). We identified a total of 147 differential genes including 203 up-regulated and 213 down-regulated genes, among which 29 were prognosis-related genes. Through the LASSO method, 6 key prognostic genes ((MUCL1, IVL, FAM46C, CHI3L1, PRR15L, and CLEC3A) were selected and a 6-gene risk score was constructed. We found risk score was negatively associated with homologous recombination deficiency (HRD) scores (r = -0.22, p = 0.019). Compared with the low-risk group, Kaplan-Meier survival analysis shows that the high-risk group has an obvious poorer prognosis (P < 0.0001). Finally, we integrated the risk score model and clinical factors of TNBC (AJCC-stage, HRD score, T stage, and N stage) to construct a compound nomogram. Time-dependent ROC curves showed the risk score performed better in 1-, 3- and 5-year survival predictions compared with AJCC-stage. Conclusions Based on HRD gene expression data, our six HRD-related gene signature and nomogram could be practical and reliable tools for predicting OS in patients with TNBC.

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