scholarly journals Identification and validation of autophagy-related lncRNA prognostic signature for breast cancer

2020 ◽  
Author(s):  
Xiaoying Li ◽  
Feng Jin ◽  
Yang Li
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Prognostic Signature ◽  
Roc Curve Analysis

Abstract Background: Long noncoding RNAs (lncRNAs) are emerging as crucial regulators to the development of breast cancer and are involved in controlling autophagy. LncRNAs are also widely known as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy-related lncRNAs with prognostic value in breast cancer. Methods: A coexpression network of autophagy-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA) was constructed. Univariate and multivariate Cox proportional hazards analyses were used to identify an autophagy risk model with prognostic value. Kaplan-Meier analysis, univariate and multivariate Cox regressionanalyses and time-dependent receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation were conducted to analyze the risk model.Results: In this study, autophagy-related lncRNAs in breast cancer were identified. We evaluated the prognostic value of these autophagy-related lncRNAs and eventually obtained a prognostic risk model consisting of 11 autophagy-related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2-DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further verified as a novel independent prognostic factor for breast cancer patients based on the calculated risk score. Moreover, based on the risk model, the low risk and high risk groups displayed different autophagy and oncogenic statues. Conclusions: These findings suggested that the risk model of the 11 autophagy-related lncRNAs has significant prognostic value for breast cancer and might be a promising prognostic signature and therapeutic targets in clinical practice.

scholarly journals A novel autophagy-related genes prognostic risk model and validation of autophagy-related oncogene VPS35 in breast cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaoying Li ◽  
Yu Cao ◽  
Xinmiao Yu ◽  
Feng Jin ◽  
Yang Li
Keyword(s):  
Breast Cancer ◽  
Prognostic Factor ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Critical Role ◽  
The Cancer Genome Atlas ◽  
Migration And Invasion ◽  
Breast Cancer Patients ◽  
Vacuolar Protein

Abstract Background Accumulating evidence implies that autophagy plays a critical role in breast cancer development and progression. It is crucial to screen out autophagy-related encoding genes (ARGs) with prognostic value in breast cancer and reveal their biological properties in the aggressiveness of breast cancer. Methods Univariate and multivariate Cox proportional hazards analyses were used to identify a prognostic risk model of ARGs from The Cancer Genome Atlas (TCGA). Kaplan–Meier analysis, univariate and multivariate Cox regression analyses and receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Western blot and immunohistochemistry (IHC) were conducted to assess the expression of VPS35 (one of ARGs in risk model). CCK8, Colony formation assay, Transwell migration/invasion assays and autophagy flux assay were used to confirm biological function of VPS35 in breast cancer. Results In this study, the prognostic risk model consisting of six ARGs (VPS35, TRIM21, PRKAB2, RUFY4, MAP1LC3A and LARP1) in breast cancer were identified. The risk model was further verified as a novel independent prognostic factor for breast cancer patients. We also clarified that vacuolar protein sorting-associated protein 35 (VPS35), one of ARGs in the risk model, was upregulated in breast cancer samples and cell lines. VPS35 overexpression was correlated with more aggressive phenotype of breast cancer and indicated worse prognosis in both progression-free survival and overall survival analyses. Meanwhile, VPS35 knockdown inhibited breast cancer cell proliferation, migration and invasion, suggesting that VPS35 promoted the progression of breast cancer. VPS35 silence also influenced autophagy process, indicating that VPS35 was essential for autophagy completion. Conclusion Taken together, the six ARGs risk model has a remarkably prognostic value for breast cancer. Among them, VPS35 might exert as a significant oncogenic and prognostic factor for breast cancer and could be a promising autophagy-related therapeutic target in clinical practice.

5012 ORAL Prognostic Value of TWIST1 Expression in Breast Cancer Patients

2011 ◽  
Vol 47 ◽  
pp. S333
Author(s):  
M. Riaz ◽  
A. Sieuwerts ◽  
M. Look ◽  
M. Smid ◽  
J. Foekens ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Prognostic Value ◽  
Breast Cancer Patients ◽  
Twist1 Expression

scholarly journals Comparing the Prognostic Value of PTEN and Akt Expression with the Mitotic Activity Index in Adjuvant Chemotherapy-Treated Node-Negative Breast Cancer patients aged <55 years

2007 ◽  
Vol 29 (1) ◽  
pp. 25-35
Author(s):  
Emiel A. M. Janssen ◽  
Håvard Søiland ◽  
Ivar Skaland ◽  
Einar Gudlaugson ◽  
Kjell H. Kjellevold ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Cycle ◽  
Cancer Patients ◽  
Mitotic Activity ◽  
Invasive Breast Cancer ◽  
Prognostic Value ◽  
Activity Index ◽  
Breast Cancer Patients ◽  
Node Negative ◽  
Mitotic Activity Index

Background: The prognostic value of the PI3K/Akt/mTOR pathway and PTEN in invasive breast cancer (IBC) is controversial. Cell proliferation, especially the Mitotic Activity Index (MAI), is strongly prognostic in lymph node-negative (LNneg) invasive breast cancer. However, its prognostic value has not been compared with the value of Akt and PTEN expression. Material and Methods: Prognostic comparison of Her2Neu, p110alpha (PIK3CA), Akt, mTOR, PTEN, MAI and cell-cycle regulators in 125 LNneg patients aged <55 years with cyclophosphamide, methotrexate, and 5-fluorouracil (CMF)-based adjuvant systemic chemotherapy. Results: Twenty-one (17%) patients developed distant metastases = DMs (median follow-up: 134 months). p110alpha correlated (p = 0.01) with pAkt but only in PTEN-negatives; pAkt correlated (p = 0.02) with mTOR. PTEN-negativity correlated with high MAI, high grade and ER-negativity (p = 0.009). The MAI was the strongest prognosticator (Hazard Ratio = HR = 2.9, p = 0.01). Her2Neu/p110α/Akt/mTOR features have no additional prognostic value to the MAI. PTEN had additional value but only in MAI < 3 (39/125 = 31%; 8% DMs). 19/39 = 49% of the MAI < 3 patients have combined MAI < 3 / PTEN+ with 0% DMs, contrasting 15% DMs in MAI < 3 / PTEN− (p = 0.03). Conclusions: In T1−3N0M0 adjuvant CMF-treated breast cancer patients aged <55 years, MAI was the strongest survival predictor. The PI3K/Akt/mTOR pathway and cell-cycle regulator characteristics had no additional prognostic value, but PTEN has. Patients with combined MAI < 3 & PTEN-positivity had 100% survival. The small subgroup of MAI < 3 patients that died were PTEN-negative.

scholarly journals RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Lan-Xin Mu ◽  
You-Cheng Shao ◽  
Lei Wei ◽  
Fang-Fang Chen ◽  
Jing-Wei Zhang
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Impact ◽  
Immune Microenvironment ◽  
Model Based ◽  
Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p &lt; 0.001) and m6aRiskscore (p &lt; 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

scholarly journals Prognostic Significance of SQSTM1 in Breast Cancer: A Comprehensive Analysis

2020 ◽  
Author(s):  
Yang Liu ◽  
Qian Du ◽  
Dan Sun ◽  
Ruiying Han ◽  
Mengmeng Teng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Somatic Mutation ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Regulation Mechanism ◽  
Breast Cancer Patients ◽  
Gene Set ◽  
Multiple Datasets

Abstract Background: SQSTM1 (Sequestosome 1, p62) is degraded by activated autophagy and involved in the progression of in various types of cancers. However, the prognostic role and underlying regulation mechanism of SQSTM1 in the progression and development of breast cancer remain unclear.Methods: In this study, 1336 samples with available mRNA data from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database and 27 formalin fixation and paraffin embedding (FFPE) tissue samples from the First Affiliated Hospital of Xi’an Jiaotong University were collected to evaluate SQSTM1 expression in mRNA and protein levels. Kaplan–Meier and Cox regression were used for revealing prognostic value in three independent breast cancer independent datasets. Tumor Immune Estimation Resource (TIMER) database and Gene Set Variation Analysis (GSVA) was used to explore the relationship of SQSTM1 mRNA expression and immune infiltration level in breast cancer. Dysregulation mechanisms of SQSTM1 were also explored including copy number variation (CNV), somatic mutation, epigenetic alterations and other transcription and post-transcription level using multiple datasets. Finally, Gene Set Enrichment Analysis (GSEA) was constructed to elucidate functional regulating performance of SQSTM1 in breast cancer.Results: The results showed that mRNA and protein level of SQSTM1 were significantly elevated in breast cancer and receiver operating characteristic (ROC) curve showed that p62 may act as diagnostic biomarker. Lower expression of SQSTM1 predicted better outcome through multiple datasets. It was also found that SQSTM1 correlated with immune infiltrates in breast cancer. Moreover, CNV and methylation of SQSTM1 DNA was correlated with SQSTM1 dysregulation and act as prognostic factors for breast cancer patients. Yet, somatic mutation status of SQSTM1 didn’t show any prognostic relevance. We also identified diverse transcription factors that directly bound to SQSTM1 DNA and the miRNAs which may regulate SQSTM1 mRNA. Finally, functional enrichment analysis revealed that SQSTM1 is related to cell signal transduction, oxidative stress and autophagy in breast cancer.Conclusion: Our findings revealed that SQSTM1 plays a key role in the progression of breast cancer and might be a promising biomarker for the diagnosis and personalized treatment of breast cancer patients.

Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

2021 ◽  
Vol 16 ◽  
Author(s):  
Dongqing Su ◽  
Qianzi Lu ◽  
Yi Pan ◽  
Yao Yu ◽  
Shiyuan Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

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