AMPD1 Is Associated With the Immune Response and Serves as a Prognostic Marker in HER2-Positive Breast Cancer

BackgroundAlthough immunotherapy has been used in the treatment of metastatic triple negative breast cancer (TNBC), its therapeutic influence on human epidermal growth factor receptor 2 (HER2)-positive subtype remains controversial. It is therefore imperative to find biomarkers that can predict the immune response in HER2+ BC.MethodsESTIMATE was utilized to compute the ImmuneScore and StromalScore from data obtained from TCGA database, and differentially expressed genes (DEGs) were identified. In addition, univariate Cox regression was used to assess candidate genes such as AMPD1, CD33, and CCR5. Gene set enrichment analysis (GSEA) was used to further understand AMPD1-associated pathways. Moreover, immunohistochemical analyses were performed to further reveal the relationship among AMPD1, CD4 and CD8 genes.ResultsThe expression of AMPD1 was markedly associated with disease outcome and tumor-infiltrating immune cells (TICs). In addition, AMPD1 was associated with lymph node status, age and the expression of PD-L1 and PD-L2. High AMPD1 expression was linked to longer overall survival (OS). Upregulated expression of AMPD1 correlated with the enrichment of immune-related signaling pathways. In addition, immunohistochemical analyses demonstrated a co-expression profile among AMPD1, CD4 and CD8 genes.ConclusionsTaken together, our data demonstrated that AMPD1 might serve as a novel biomarker for predicting the immune response and disease outcome in HER2+ BC.

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Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

Pathology & Oncology Research ◽

10.3389/pore.2021.600727 ◽

2021 ◽

Vol 27 ◽

Author(s):

Aoshuang Qi ◽

Mingyi Ju ◽

Yinfeng Liu ◽

Jia Bi ◽

Qian Wei ◽

...

Keyword(s):

Breast Cancer ◽

Antigen Processing ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Gene Set ◽

Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

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

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

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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Establishment and Validation of a Novel Metabolism-related Gene Signature for Predicting Overall Survival of Patients with Breast Cancer

10.21203/rs.3.rs-222322/v1 ◽

2021 ◽

Author(s):

Jian Li ◽

Yang Liu ◽

Fei Liu ◽

Qiang Tian ◽

Baojiang Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Treatment Strategies ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Current Status ◽

Lasso Regression ◽

Related Gene

Abstract It is well known that Breast cancer is a heterogeneous disease.Although the current recurrence and mortality rate have been greatly improved, many people still suffer relapse and metastasis.Metabolic reprograming is currently considered to be a new hallmark of cancer.Therefore,in this study, we comprehensively analyzed the prognostic effect of metabolic-related gene signatures in breast cancer and its relationship with the immune microenvironment.We constructed a novel metabolic-related gene signature containing 6 genes to distinguish between high and low risk groups by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression, and validated its robustness and accuracy through multiple databases.The metabolic gene signature may be an independent risk factor for BC both in the training and the testing set,the nomogram has a moderately accurate performance,and the C index was 0.757 and 0.728 respectively.The signature can reveal metabolic characteristics based on gene set enrichment analysis and at the same time monitor the status of TME.This gene signature can be used as a promising independent prognostic marker for BC patients, and can indicate the current status of TME, providing more clues for exploring new diagnostic and treatment strategies.

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Overexpression of SKA3 correlates with poor prognosis in female early breast cancer

PeerJ ◽

10.7717/peerj.12506 ◽

2021 ◽

Vol 9 ◽

pp. e12506

Author(s):

Yue Zhong ◽

Zhenjie Zhuang ◽

Peiju Mo ◽

Mandi Lin ◽

Jiaqian Gong ◽

...

Keyword(s):

Breast Cancer ◽

Early Breast Cancer ◽

Poor Prognosis ◽

Cox Regression ◽

Molecular Subtype ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Chi Square ◽

Exact Test ◽

Chi Square Test

Background Spindle and kinetochore associated complex subunit 3 (SKA3) plays an important role in tumorigenesis and the progression of various tumors. But the relationship between SKA3 and early breast cancer remains unclear. The study aimed to explore the prognostic significance of SKA3 in breast cancer. Methods In the study, SKA3 expression was initially assessed using the Oncomine database and The Cancer Genome Atlas database (TCGA). Then, we presented validation results for RT-qPCR (quantitative reverse transcription PCR) and ELISA (enzyme-linked immunosorbent assay). The relationship between clinical characteristics and SKA3 expression was assessed by Chi-square test and Fisher’s exact test. Kaplan–Meier method and Cox regression analysis were conducted to evaluate the prognostic value of SKA3. Gene set enrichment analysis (GSEA) was performed to screen biological pathways using the TCGA dataset. Besides, single sample gene set enrichment analysis (ssGSEA) was utilized to identify immune infiltration cells about SKA3. Results SKA3 mRNA was expressed at high levels in breast cancer tissues compared with normal tissues. Chi-square test and Fisher’s exact test showed SKA3 expression was related to age, tumor (T) classification, node (N) classification, tumor-node-metastasis (TNM) stage, estrogen receptor (ER), progesterone receptor (PR), molecular subtype, and race. RT-qPCR results showed that SKA3 expression was overexpressed in ER, PR status, and molecular subtype in Chinese people. Kaplan–Meier curves implicated that high SKA3 expression was related to a poor prognosis in female early breast cancer patients. Cox regression models showed that high SKA3 expression could be used as an independent risk factor for female early breast cancer. Four signaling pathways were enriched in the high SKA3 expression group, including mTORC1 signaling pathway, MYC targets v1, mitotic spindle, estrogen response early. Besides, the SKA3 expression level was associate with infiltrating levels of activated CD4 T cells and eosinophils in breast cancer. Conclusion High SKA3 expression correlates with poor prognosis and immune infiltrates in breast cancer. SKA3 may become a biomarker for the prognosis of breast cancer.

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A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.569318 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiaoping Li ◽

Jishang Chen ◽

Qihe Yu ◽

Hui Huang ◽

Zhuangsheng Liu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

High Risk ◽

Cox Regression ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Risk Scoring

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

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Bioinformatic Analysis of Immune Significance of RYR2 Mutation in Breast Cancer

BioMed Research International ◽

10.1155/2021/8072796 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Zhiquan Xu ◽

Ling Xiang ◽

Rong Wang ◽

Yongfu Xiong ◽

He Zhou ◽

...

Keyword(s):

Breast Cancer ◽

Immune Response ◽

T Cells ◽

Immune Cell ◽

Cox Regression ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Specific Gene ◽

Clinical Prognosis

Background. Currently, immunotherapy is widely used for breast cancer (BC) patients, and tumor mutation burden (TMB) is regarded as a valuable independent predictor of response to immunotherapy. However, specific gene mutations and their relationship with TMB and tumor-infiltrating immune cells in BC are not fully understood. Methods. Comprehensive bioinformatic analyses were performed using data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Survival curves were analyzed via Kaplan-Meier analysis. Univariate and multivariate Cox regression analyses were used for prognosis analysis. Gene set enrichment analysis (GSEA) was performed to explore regulatory mechanisms and functions. The CIBERSORT algorithm was used to calculate the tumor-infiltrating immune cell fractions. Results. We analyzed somatic mutation data of BC from TCGA and ICGC datasets and found that 19 frequently mutated genes were reported in both cohorts, namely, SPTA1, TTN, MUC17, MAP3K1, CDH1, FAT3, SYNE1, FLG, HMCN1, RYR2 (ryanodine receptor 2), GATA3, MUC4, PIK3CA, KMT2C, TP53, PTEN, ZFHX4, MUC16, and USH2A. Among them, we observed that RYR2 mutation was significantly associated with higher TMB and better clinical prognosis. Moreover, GSEA revealed that RYR2 mutation-enriched signaling pathways were related to immune-associated pathways. Furthermore, based on the CIBERSORT algorithm, we found that RYR2 mutation enhanced the antitumor immune response by enriching CD8+ T cells, activated memory CD4+ T cells, and M1 macrophages. Conclusion. RYR2 is frequently mutated in BC, and its mutation is related to increased TMB and promotes antitumor immunity; thus, RYR2 may serve as a valuable biomarker to predict the immune response.

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Pathological and genetic aggressiveness of left-sided breast cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e12579 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e12579-e12579

Author(s):

Yara Abdou ◽

Mariko Asaoka ◽

Kazuaki Takabe

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Enrichment Analysis ◽

Tumor Grade ◽

Mitotic Rate ◽

Cytolytic Activity ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Gene Sets ◽

Significant Difference

e12579 Background: Breast Cancer in women consistently occurs more frequently in the left breast, with the ratio of left to right sided breast cancer cases ranging from 1.05 to 1.26. In spite of the difference in frequency, prior studies have failed to show any significant difference in clinical characteristics between left sided and right sided cancer. Methods: Genomic and clinical features were collected from The Cancer Genome Atlas breast cancer project. LVI status, mitotic rate, nuclear score and tubular score were collected from pathology reports in TIES client 5.8. Fisher's exact test was used for group comparison and survival analysis was performed with Cox regression. Cytolytic activity (CYT) indicates anti-cancer immune response and was quantified from gene expression data. Hallmark gene-sets were used for gene set enrichment analysis (GSEA). Results: Among the 1081 women with unilateral invasive breast cancer, 561 had tumor on the left side compared to 520 on the right. Our results didn’t show any significant differences between left and right side with regards to tumor location, histology, race, and tumor characteristics including stage, tumor size, nodal status and receptor status. No statistical significant differences were observed in mitotic rate, LVI status and tubular score, however, the tumor grade was significantly higher in the left side. Moreover, there were no significant differences in mutation count, CYT and overall survival between both sides. GSEA revealed cell-cycle related gene sets like G2M checkpoint, Mitotic spindle, E2F targets and MYC targets which were significantly enriched in left sided tumor. Furthermore, out of the 865 genes which were highly expressed on the left side, we identified specific genes including BRCA1, BRCA2, BRIP1, CHEK2, FANCC, PALB2, TP53 and MSH6 which are associated primarily with breast cancer genesis and mostly have established clinical management guidelines. Conclusions: Our results suggest a more aggressive nature to left sided breast cancer with a higher pathological grade perhaps requiring more aggressive treatment. Such a hypothesis needs further study to confirm or refute its validity. If confirmed, it may have a major impact with regard to biology of breast cancer and its subsequent management.

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Prominin-1 (PROM1) Significantly Correlated With Bone Metastasis and Influenced Prognosis in Breast Cancer

10.21203/rs.3.rs-951336/v1 ◽

2021 ◽

Author(s):

Yi-nan Wu ◽

Cheng-Cheng Yu ◽

Kai-min Hu ◽

Su-zhan Zhang

Keyword(s):

Breast Cancer ◽

Bone Metastasis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Significant Prognostic Factor ◽

Related Factors ◽

Protein Protein Interaction ◽

Kaplan Meier ◽

Chemokine Ligand

Abstract BackgroundThis study aimed to explore the important biomarker associated with bone metastasis (BM) in breast cancer (BRCA).MethodsThe GSE175692 dataset was used to detect significant differential expressed genes (DEGs) between BRCA samples with or without BM, and important pathways were then explored. Further, we constructed protein-protein interaction (PPI) network on GEGs and filtered 5 vital nodes. Through performing cox regression, Kaplan-Meier analysis, nomogram, ROC curve, and risk score model, significant prognostic factor was gradually identified. Finally, gene set enrichment analysis (GSEA) analysis was performed to reveal the potential mechanism.ResultsTotally, 74 DEGs were detected, which mainly correlated with infectious disease, signaling molecules and interaction. The 5 important DEGs were filtered, and cox regression further showed that prominin-1 (PROM1) and C-C Motif Chemokine Ligand 2 (CCL2) were prognosis-related factors. A negative correlation was observed between the expression of these 2 genes and the overall survival of metastatic BRCA patients. Especially, PROM1 presented a better prognostic performance on the survival probability of patients. Prognosis verification analysis also confirmed the significant influence of PROM1 on patient’s survival. In addition, we found that PROM1 expression was related to the distant metastasis-free survival in BRCA. Finally, GSEA analysis revealed that PROM1 was negatively related to IGF1 and mTOR pathways involved in BRCA metastasis. ConclusionPROM1 was identified as an important DEGs associated with BRCA bone metastasis. It acted as a vital prognostic biomarker involved in BRCA metastasis, which may be due to the negative regulation of IGF1 and mTOR pathways.

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Screening of DNA Damage Repair Genes Involved in the Prognosis of Triple-Negative Breast Cancer Patients Based on Bioinformatics

Frontiers in Genetics ◽

10.3389/fgene.2021.721873 ◽

2021 ◽

Vol 12 ◽

Author(s):

Nan Wang ◽

Yuanting Gu ◽

Jiangrui Chi ◽

Xinwei Liu ◽

Youyi Xiong ◽

...

Keyword(s):

Breast Cancer ◽

Regulatory Network ◽

Prognostic Model ◽

Triple Negative ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Regression Analyses ◽

Gene Set Enrichment ◽

Qrt Pcr

Background: Triple-negative breast cancer (TNBC) is a special subtype of breast cancer with poor prognosis. DNA damage response (DDR) is one of the hallmarks of this cancer. However, the association of DDR genes with the prognosis of TNBC is still unclear.Methods: We identified differentially expressed genes (DEGs) between normal and TNBC samples from The Cancer Genome Atlas (TCGA). DDR genes were obtained from the Molecular Signatures Database through six DDR gene sets. After the expression of six differential genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR), we then overlapped the DEGs with DDR genes. Based on univariate and LASSO Cox regression analyses, a prognostic model was constructed to predict overall survival (OS). Kaplan–Meier analysis and receiver operating characteristic curve were used to assess the performance of the prognostic model. Cox regression analysis was applied to identify independent prognostic factors in TNBC. The Human Protein Atlas was used to study the immunohistochemical data of six DEGs. The prognostic model was validated using an independent dataset. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analysis were performed by using gene set enrichment analysis (GSEA). Single-sample gene set enrichment analysis was employed to estimate immune cells related to this prognostic model. Finally, we constructed a transcriptional factor (TF) network and a competing endogenous RNA regulatory network.Results: Twenty-three differentially expressed DDR genes were detected between TNBC and normal samples. The six-gene prognostic model we developed was shown to be related to OS in TNBC using univariate and LASSO Cox regression analyses. All the six DEGs were identified as significantly up-regulated in the tumor samples compared to the normal samples in qRT-PCR. The GSEA analysis indicated that the genes in the high-risk group were mainly correlated with leukocyte migration, cytokine interaction, oxidative phosphorylation, autoimmune diseases, and coagulation cascade. The mutation data revealed the mutated genes were different. The gene-TF regulatory network showed that Replication Factor C subunit 4 occupied the dominant position.Conclusion: We identified six gene markers related to DDR, which can predict prognosis and serve as an independent biomarker for TNBC patients.

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How Different Are the Molecular Mechanisms of Nodal and Distant Metastasis in Luminal A Breast Cancer?

Cancers ◽

10.3390/cancers12092638 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2638

Author(s):

Petr Lapcik ◽

Anna Pospisilova ◽

Lucia Janacova ◽

Peter Grell ◽

Pavel Fabian ◽

...

Keyword(s):

Breast Cancer ◽

Lymph Node ◽

Distant Metastasis ◽

Molecular Mechanisms ◽

Molecular Subtypes ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Lymph Node Status ◽

Luminal A ◽

Primary Tumors

Lymph node status is one of the best prognostic factors in breast cancer, however, its association with distant metastasis is not straightforward. Here we compare molecular mechanisms of nodal and distant metastasis in molecular subtypes of breast cancer, with major focus on luminal A patients. We analyze a new cohort of 706 patients (MMCI_706) as well as an independent cohort of 836 primary tumors with full gene expression information (SUPERTAM_HGU133A). We evaluate the risk of distant metastasis, analyze targetable molecular mechanisms in Gene Set Enrichment Analysis and identify relevant inhibitors. Lymph node positivity is generally associated with NF-κB and Src pathways and is related to high risk (OR: 5.062 and 2.401 in MMCI_706 and SUPERTAM_HGU133A, respectively, p < 0.05) of distant metastasis in luminal A patients. However, a part (≤15%) of lymph node negative tumors at the diagnosis develop the distant metastasis which is related to cell proliferation control and thrombolysis. Distant metastasis of lymph node positive patients is mostly associated with immune response. These pro-metastatic mechanisms further vary in other molecular subtypes. Our data indicate that the management of breast cancer and prevention of distant metastasis requires stratified approach based on targeted strategies.

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