Correlations Between the Characteristics of Alternative Splicing Events, Prognosis, and the Immune Microenvironment in Breast Cancer

ObjectiveAlternative splicing (AS) is the mechanism by which a few genes encode numerous proteins, and it redefines the concept of gene expression regulation. Recent studies showed that dysregulation of AS was an important cause of tumorigenesis and microenvironment formation. Therefore, we performed a systematic analysis to examine the role of AS in breast cancer (Breast Cancer, BrCa) progression.MethodsThe present study included 993 BrCa patients from The Cancer Genome Atlas (TCGA) database in the genome-wide analysis of AS events. We used differential and prognostic analyses and found differentially expressed alternative splicing (DEAS) events and independent prognostic factors related to patients’ overall survival (OS) and disease-free survival (DFS). We divided the patients into two groups based on these AS events and analyzed their clinical features, molecular subtyping and immune characteristics. We also constructed a splicing factor (SF) regulation network for key AS events and verified the existence of AS events in tissue samples using real-time quantitative PCR.ResultsA total of 678 AS events were identified as differentially expressed, of which 13 and 10 AS events were independent prognostic factors of patients’ OS and DFS, respectively. Unsupervised clustering analysis based on these prognostic factors indicated that the Cluster 1 group had a better prognosis and more immune cell infiltration. SFs were significantly related to the expression of AS events, and AA-RPS21 was significantly upregulated in tumors.ConclusionAlternative splicing expands the mechanism of breast cancer progression from a new perspective. Notably, alternative splicing may affect the patient’s prognosis by affecting the infiltration of immune cells. Our research provides important guidance for subsequent studies of AS in breast cancer.

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Systematic Profiling of Diagnostic and Prognostic Value of Autophagy-Related Genes for Sarcoma Patients

10.21203/rs.3.rs-76971/v1 ◽

2020 ◽

Author(s):

Yuanhe Wang ◽

Jianyi Li ◽

Cheng Shao ◽

Xiaojie Tang ◽

Yukun Du ◽

...

Keyword(s):

Tumor Microenvironment ◽

Immune Cells ◽

Disease Free Survival ◽

Independent Set ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Immune Checkpoints ◽

Systematic Analysis

Abstract Background: Autophagy-related genes (ARGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of ARGs and their clinical significance in sarcoma patients is lacking.Methods: Gene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed ARGs (DEARGs) were determined by matching the DEG and HADb gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEARGs was conducted, and associations with tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for disease-free survival (DFS), were established and validated in an independent set. Results: In total, 84 DEIRGs and two clusters were identified. TME scores, five immune checkpoints, and several types of immune cells were found to be significantly different between twp clusters. Two prognostic signatures incorporating DEARGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.818 and 0.636 for the OS and DFS nomograms, respectively.Conclusion: This comprehensive analyses of the ARG landscape in sarcoma showed novel ARGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

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Aberrant alternative splicing in breast cancer

Journal of Molecular Cell Biology ◽

10.1093/jmcb/mjz033 ◽

2019 ◽

Vol 11 (10) ◽

pp. 920-929 ◽

Cited By ~ 5

Author(s):

Quan Yang ◽

Jinyao Zhao ◽

Wenjing Zhang ◽

Dan Chen ◽

Yang Wang

Keyword(s):

Breast Cancer ◽

Alternative Splicing ◽

Cancer Progression ◽

Gene Expression Regulation ◽

Cancer Therapeutics ◽

Cancer Drug ◽

Splicing Factors ◽

Cancer Drug Resistance ◽

The Relationship

Abstract Alternative splicing is critical for human gene expression regulation, which plays a determined role in expanding the diversity of functional proteins. Importantly, alternative splicing is a hallmark of cancer and a potential target for cancer therapeutics. Based on the statistical data, breast cancer is one of the top leading causes of cancer-related deaths in women worldwide. Strikingly, alternative splicing is closely associated with breast cancer development. Here, we seek to provide a general review of the relationship between alternative splicing and breast cancer. We introduce the process of alternative splicing and its regulatory role in cancers. In addition, we highlight the functions of aberrant alternative splicing and mutations of splicing factors in breast cancer progression. Moreover, we discuss the role of alternative splicing in cancer drug resistance and the potential of being targets for cancer therapeutics.

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Systematic profiling of diagnostic and prognostic value of autophagy-related genes for sarcoma patients

BMC Cancer ◽

10.1186/s12885-020-07596-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yuanhe Wang ◽

Jianyi Li ◽

Cheng Shao ◽

Xiaojie Tang ◽

Yukun Du ◽

...

Keyword(s):

Tumor Microenvironment ◽

Immune Cells ◽

Disease Free Survival ◽

Independent Set ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Immune Checkpoints ◽

Systematic Analysis

Abstract Background Autophagy-related genes (ARGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of ARGs and their clinical significance in sarcoma patients is lacking. Methods Gene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed ARGs (DEARGs) were determined by matching the DEG and HADb gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEARGs was conducted, and associations with tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for disease-free survival (DFS), were established and validated in an independent set. Results In total, 84 DEARGs and two clusters were identified. TME scores, five immune checkpoints, and several types of immune cells were found to be significantly different between two clusters. Two prognostic signatures incorporating DEARGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.818 and 0.747 for the OS and DFS nomograms, respectively. Conclusion This comprehensive analyses of the ARG landscape in sarcoma showed novel ARGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

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Identification of Prognostic Alternative Splicing Signature in Triple-negative breast cancer

10.21203/rs.3.rs-17500/v1 ◽

2020 ◽

Author(s):

Shuqi Zhou ◽

Shu Guan ◽

Xuemei Lv ◽

Rong Ma ◽

Yifan Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Alternative Splicing ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Pathway Enrichment Analysis ◽

Clear Trend ◽

Systematic Analysis

Abstract Background: Alternative splicing (AS) is a pervasive and vital mechanism involved in the progression of various cancer. Studies confirm the importance of prognostic value of AS events in tumor patients, but systematic analysis of AS in triple-negative breast cancer (TNBC) is still lacking. Methods: Information from 115 TNBC patients from the Cancer Genome Atlas (TCGA) database were extracted. And we performed a comprehensive analysis of whole-genome AS events with corresponding clinical information to evaluate the roles of seven AS patterns. Prognostic analyses were performed with predictive models and splicing network built for TNBC patients. Results: Among 28,744 mRNA AS events in 20,353 genes, we detected 1,428 AS in 138 important survival genes related to the overall survival of TNBC patients event. Through functional and pathway enrichment analysis, we found that these genes are involved in ubiquitin-mediated proteolytic pathways. At 1800 days overall survival, the area under the ROC curve for prognostic signatures was 0.8. It shows that this model is very effective in differentiating patient prognosis. The use of Spearman's test to establish a potential regulatory network between survival-related AS events and abnormal SF indicates a clear trend in the role of SF in TNBC. Conclusions: In summary, we established a reliable and powerful TNBC prognostic signature. A splicing network that could be its underlying mechanism was discovered. Keywords: Alternative splicing; Triple-negative breast cancer; Prognosis; Splicing factor

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Iron-Bound Lipocalin-2 from Tumor-Associated Macrophages Drives Breast Cancer Progression Independent of Ferroportin

Metabolites ◽

10.3390/metabo11030180 ◽

2021 ◽

Vol 11 (3) ◽

pp. 180

Author(s):

Christina Mertens ◽

Matthias Schnetz ◽

Claudia Rehwald ◽

Stephan Grein ◽

Eiman Elwakeel ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Progression ◽

Tumor Cells ◽

Cancer Progression ◽

Lung Metastases ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Lipocalin 2 ◽

Tumor Associated Macrophages

Macrophages supply iron to the breast tumor microenvironment by enforced secretion of lipocalin-2 (Lcn-2)-bound iron as well as the increased expression of the iron exporter ferroportin (FPN). We aimed at identifying the contribution of each pathway in supplying iron for the growing tumor, thereby fostering tumor progression. Analyzing the expression profiles of Lcn-2 and FPN using the spontaneous polyoma-middle-T oncogene (PyMT) breast cancer model as well as mining publicly available TCGA (The Cancer Genome Atlas) and GEO Series(GSE) datasets from the Gene Expression Omnibus database (GEO), we found no association between tumor parameters and Lcn-2 or FPN. However, stromal/macrophage-expression of Lcn-2 correlated with tumor onset, lung metastases, and recurrence, whereas FPN did not. While the total iron amount in wildtype and Lcn-2−/− PyMT tumors showed no difference, we observed that tumor-associated macrophages from Lcn-2−/− compared to wildtype tumors stored more iron. In contrast, Lcn-2−/− tumor cells accumulated less iron than their wildtype counterparts, translating into a low migratory and proliferative capacity of Lcn-2−/− tumor cells in a 3D tumor spheroid model in vitro. Our data suggest a pivotal role of Lcn-2 in tumor iron-management, affecting tumor growth. This study underscores the role of iron for tumor progression and the need for a better understanding of iron-targeted therapy approaches.

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Comparison between male and female breast cancer survival using propensity score matching analysis

Scientific Reports ◽

10.1038/s41598-021-91131-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Serena Scomersi ◽

Fabiola Giudici ◽

Giuseppe Cacciatore ◽

Pasquale Losurdo ◽

Stefano Fracon ◽

...

Keyword(s):

Breast Cancer ◽

Prognostic Factors ◽

Propensity Score ◽

Propensity Score Matching ◽

Cancer Survival ◽

Univariate Analysis ◽

Disease Free Survival ◽

Female Breast Cancer ◽

Lymph Node Status ◽

Propensity Score Matching Analysis

AbstractMale breast cancer (MBC) is a rare disease. The few studies on MBC reported conflicting data regarding survival outcomes compared to women. This study has two objectives: to describe the characteristics of a single-cohort of MBC and to compare overall survival (OS) and disease-free survival (DFS) between men and women using the propensity score matching (PSM) analysis. We considered MBC patients (n = 40) diagnosed between January 2004 and May 2019. Clinical, pathological, oncological and follow-up data were analyzed. Univariate analysis was performed to determine the prognostic factors on OS and DFS for MBC. We selected female patients with BC (n = 2678). To minimize the effect of the imbalance of the prognostic factors between the two cohorts, the PSM method (1:3 ratio) was applied and differences in survival between the two groups were assessed. The average age of MBC patients was 73 years. The 5-year OS and DFS rates were 76.7% and 72.2% respectively. The prognostic factors that significantly influenced OS and DFS were tumor size and lymph node status. After the PSM, 5 year-OS was similar between MBC and FBC (72.9% vs 72.3%, p = 0.70) while we found a worse DFS for MBC (72.2% vs 91.4%, p = 0.03). Our data confirmed previous reported MBC characteristics: we found a higher risk of recurrence in MBC compared to FMC but similar OS. MBC and FMC are different entities and studies are needed to understand its epidemiology and guide its management.

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Methylation of the NT5E Gene Is Associated with Poor Prognostic Factors in Breast Cancer

Diagnostics ◽

10.3390/diagnostics10110939 ◽

2020 ◽

Vol 10 (11) ◽

pp. 939

Author(s):

Young Ju Jeong ◽

Hoon Kyu Oh ◽

Hye Ryeon Choi ◽

Sung Hwan Park

Keyword(s):

Breast Cancer ◽

Prognostic Factors ◽

Methylation Status ◽

Prognostic Significance ◽

Disease Free Survival ◽

Receptor Expression ◽

Gene Methylation ◽

Clinicopathologic Characteristics ◽

Large Tumor ◽

The Mean

Cluster of differentiation (CD) 73, which is encoded by the NT5E gene, regulates production of immunosuppressive adenosine and is an emerging checkpoint in cancer immunotherapy. Despite the significance of CD73 in immuno-oncology, the roles of the NT5E gene methylation in breast cancer have not been well-defined yet. Therefore, we aimed to investigate the prognostic significance of the NT5E gene methylation in breast cancer. The DNA methylation status of the NT5E gene was analyzed using pyrosequencing in breast cancer tissues. In addition, the levels of inflammatory markers and lymphocyte infiltration were evaluated. The mean methylation level of the NT5E gene was significantly higher in breast cancer than in normal breast tissues. In the analysis of relevance with clinicopathologic characteristics, the mean methylation levels of the NT5E gene were significantly higher in patients with large tumor size, high histologic grade, negative estrogen receptor expression, negative Bcl-2 expression, and premenopausal women. There was no difference in disease-free survival according to the methylation status of the NT5E gene. We found that the NT5E gene methylation was related to breast cancer development and associated with poor prognostic factors in breast cancer. Our results suggest that the NT5E gene methylation has potential as an epigenetic biomarker in breast cancer.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.650499 ◽

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 < 0.001) and m6aRiskscore (p < 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.

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Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽

10.7717/peerj.7821 ◽

2019 ◽

Vol 7 ◽

pp. e7821 ◽

Cited By ~ 7

Author(s):

Xiaoming Zhang ◽

Jing Zhuang ◽

Lijuan Liu ◽

Zhengguo He ◽

Cun Liu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Prognostic Value ◽

Early Stage ◽

Expression Profiles ◽

Diagnostic Value ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

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Identification of a prognostic long non-coding RNA signature in breast cancer

10.21203/rs.3.rs-27286/v1 ◽

2020 ◽

Author(s):

Gaochen Lan ◽

Xiaoling Yu ◽

Yanna Zhao ◽

Jinjian Lan ◽

Wan Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Prognostic Biomarker ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

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