Prognostic Value In Breast Cancer Research Articles

Tripartite Motif-Containing 2, a Glutamine Metabolism-Associated Protein, Predicts Poor Patient Outcome in Triple-Negative Breast Cancer Treated with Chemotherapy

Background: Breast cancer (BC) remains heterogeneous in terms of prognosis and response to treatment. Metabolic reprogramming is a critical part of oncogenesis and a potential therapeutic target. Glutaminase (GLS), which generates glutamate from glutamine, plays a role in triple-negative breast cancer (TNBC). However, targeting GLS directly may be difficult, as it is essential for normal cell function. This study aimed to determine potential targets in BC associated with glutamine metabolism and evaluate their prognostic value in BC. Methods: The iNET model was used to identify genes in BC that are associated with GLS using RNA-sequencing data. The prognostic significance of tripartite motif-containing 2 (TRIM2) mRNA was assessed in BC transcriptomic data (n = 16,575), and TRIM2 protein expression was evaluated using immunohistochemistry (n = 749) in patients with early-stage invasive breast cancer with long-term follow-up. The associations between TRIM2 expression and clinicopathological features and patient outcomes were evaluated. Results: Pathway analysis identified TRIM2 expression as an important gene co-expressed with high GLS expression in BC. High TRIM2 mRNA and TRIM2 protein expression were associated with TNBC (p < 0.01). TRIM2 was a predictor of poor distant metastasis-free survival (DMFS) in TNBC (p < 0.01), and this was independent of established prognostic factors (p < 0.05), particularly in those who received chemotherapy (p < 0.05). In addition, TRIM2 was a predictor of shorter DMFS in TNBC treated with chemotherapy (p < 0.01). Conclusions: This study provides evidence of an association between TRIM2 and poor patient outcomes in TNBC, especially those treated with chemotherapy. The molecular mechanisms and functional behaviour of TRIM2 and the functional link with GLS in BC warrant further exploration using in vitro models.

Gene Expression and Prognostic Value of NADPH Oxidase Enzymes in Breast Cancer.

NADPH oxidase enzymes (NOX) are involved in all stages of carcinogenesis, but their expression levels and prognostic value in breast cancer (BC) remain unclear. Thus, we aimed to assess the expression and prognostic value of NOX enzymes in BC samples using online databases. For this, mRNA expression from 290 normal breast tissue samples and 1904 BC samples obtained from studies on cBioPortal, Kaplan-Meier Plotter, and The Human Protein Atlas were analyzed. We found higher levels of NOX2, NOX4, and Dual oxidase 1 (DUOX1) in normal breast tissue. NOX1, NOX2, and NOX4 exhibited higher expression in BC, except for the basal subtype, where NOX4 expression was lower. DUOX1 mRNA levels were lower in all BC subtypes. NOX2, NOX4, and NOX5 mRNA levels increased with tumor progression stages, while NOX1 and DUOX1 expression decreased in more advanced stages. Moreover, patients with low expression of NOX1, NOX4, and DUOX1 had lower survival rates than those with high expression of these enzymes. In conclusion, our data suggest an overexpression of NOX enzymes in breast cancer, with certain isoforms showing a positive correlation with tumor progression.

Identification of a hypoxia-suppressed lncRNA RAMP2-AS1 in breast cancer

Hypoxia is a critical feature of solid tumors and exerts crucial roles in cancers, including breast cancer. However, the detailed relationship between lncRNA-miRNA-mRNA triple network and hypoxia in breast cancer is still indistinct. In this study, a series of in silico analyses and online databases or tools were employed to establish a hypoxia-related lncRNA-miRNA-mRNA network in breast cancer based on competing endogenous RNA mechanism at single-cell resolution. RAMP2-AS1 was, eventually, identified as the most potential lncRNA, which was significantly negatively associated with hypoxia in breast cancer. Compared with normal controls, RAMP2-AS1 was markedly downregulated in breast cancer. Moreover, survival analysis revealed favorable prognostic values of RAMP2-AS1 in total or in specific clinicopathological breast cancer patients. Next, miR-660-5p, miR-2277-5p and miR-1301-3p, upregulated and possessed poor prognostic values in breast cancer, were identified as three potential downstream miRNAs of RAMP2-AS1. Then, the most potential downstream hypoxia-related genes (ATM and MYH11) of RAMP2-AS1/miRNA axis in breast cancer were screened out. Intriguingly, in vitro experiments confirmed that RAMP2-AS1 was a hypoxia-suppressed lncRNA and miR-660-5p/ATM was a potential downstream axis of RAMP2-AS1 in breast cancer. Collectively, our current data elucidated a key hypoxia-suppressed lncRNA RAMP2-AS1 and its possible miRNA-mRNA regulatory mechanism in breast cancer.

SIRT3 Expression Predicts Overall Survival and Neoadjuvant Chemosensitivity in Triple-Negative Breast Cancer.

The Sirtuin (SIRT) family consists of seven evolutionary conserved NAD-dependent deacetylases that play important roles in various cancers, including breast cancer (BC). SIRTs expression has been reported to have prognostic value in BC, but these studies used limited sample size and yielded inconsistent conclusions. This study evaluated the association of SIRT3 and other SIRT family members with survival and neoadjuvant chemotherapy outcomes. BC patients' data was obtained from the TCGA-BRCA, METABRIC and GEO databases, comprising 4336 samples. SIRTs expression and overall survival (OS) were analyzed using Kaplan-Meier analysis and Cox proportional hazards regression. SIRT3 expression levels were compared between pathologic complete response (pCR) and non-pCR groups after neoadjuvant chemotherapy in triple-negative breast cancer (TNBC). Protein-protein interaction networks were constructed using the STRING database. Gene set enrichment analysis (GSEA) was performed to explore potential functions of SIRT3. Through systematic analysis of SIRTs expression and OS of BC using three independent cohorts: TCGA-BRCA, METABRIC and GSE16446, we found that high SIRT3 expression was significantly associated with worse OS in TNBC in the TCGA-BRCA cohort, which was validated in the METABRIC and GSE16446 cohorts. SIRT3 expression was correlated with BC subtypes and American Joint Committee on Cancer (AJCC) T stage, but not with age-at-diagnosis, race, or tumor stage. Moreover, TNBC patients with higher SIRT3 expression had lower pCR rates after neoadjuvant chemotherapy (p = 6.40e-03) and SIRT3 expression was significantly lower in the pCR group than in the non-pCR group in TNBC (p = 4.2e-03). GSEA indicated that SIRT3 was involved in drug-related pathways such as oxidative phosphorylation, metabolism of xenobiotics by cytochrome P450, and drug metabolism. Our study suggests that SIRT3 is a potential biomarker for both OS and neoadjuvant chemosensitivity in TNBC. It may also assist in selecting suitable candidates and treatment options for TNBC patients.

Integrated Bioinformatics Analysis to Identify a Novel Four-Gene Prognostic Model of Breast Cancer and Reveal Its Association with Immune Infiltration.

Liquid-liquid phase separation (LLPS) impact immune signaling in cancer and related genes have shown prognostic value in breast cancer (BRCA). However, the crosstalk between LLPS and immune infiltration in BRCA remain unclear. Therefore, we aimed to develop a novel prognostic model of BRCA related to LLPS and immune infiltration. BRCA-related, liquid-liquid phase separation (LLPS)-related genes, and differentially expressed genes (DEGs) were identified using public databases. Mutation and drug sensitivity analyses were performed using Gene Set Cancer Analysis database. Univariate cox regression and LASSO Cox regression were used for the construction and verification of prognostic model. Kaplan-Meier analysis was performed to evaluate overall survival (OS). Gene set variation analysis was conducted to analyze key pathways. CIBERSORT was used to assess immune infiltration and its correlation with prognostic genes was determined through Pearson analysis. A total of 6056 BRCA-associated genes, 3775 LLPS-associated genes, and 4049 DEGs, resulting in 314 overlapping genes. Twenty-eight prognostic genes were screened, and some of them were mutational and related to drug sensitivity Subsequently, a prognostic model comprising L1CAM, EVL, FABP7, and CST1 was built. Patients in high-risk group had shorter OS than those in low-risk group. The infiltrating levels of CD8+ T cells, macrophages M0, macrophages M2, dendritic cells activated, and mast cells resting was altered in high-risk group of breast cancer patients compared to low-risk group. L1CAM, EVL, FABP7, and CST1 were related to these infiltrating immune cells. L1CAM, EVL, FABP7, and CST1 were potential diagnostic biomarkers and therapeutic targets for BRCA.

Exploration of SERPINA family functions and prognostic value in breast cancer based on transcriptome and in vitro analysis.

Breast cancer poses a significant risk to women worldwide, yet specific role of SERPINA gene family in breast cancer remains unclarified. Data were collected from online databases. SERPINA family gene expression was presented, and prognosis value was evaluated. Multi-omics methods were employed to explore the SERPINA-related biological processes, followed by comprehensive analyses of their roles in breast cancer. Single-cell data were analyzed to characterize the SERPINA family gene expression in different cell clusters. We selected SERPINA5 as the target gene. Via pan-cancer analysis, SERPINA5 was also investigated in various cancers. The experimental validation was conducted in MDA-MB-231 cell line eventually. SERPINA family showed differential expression in breast cancer, which were mainly expressed in myeloid cells, epithelial cells, and dendritic cells. SERPINA5 expression was upregulated in breast cancer, which was associated with a better prognosis. Immune infiltration illustrated the positive correlativity between SERPINA5 intensity and eosinophilic recruitment. Pan-cancer analysis indicated the function of SERPINA5 as a potential biomarker in other cancers. Finally, experimental validation demonstrated that SERPINA5 contributes to lower invasion and metastatic potential of breast cancer cells. With bioinformatics analysis, the significant role SERPINA family genes functioned in breast cancer was comprehensively explored, with SERPINA5 emerging as a key gene in suppressing breast cancer progression.

Mutational and expressional association of the PIK3CA gene with the risk of breast cancer in the Pakistani population.

PI3K pathway is a very important pathway that is reported to be involved in breast cancer. Mutation ofPI3Kand p110 alpha-catalytic subunit of phosphatidylinositol 3-kinase (PIK3CA) is of high predictive and prognostic values in breast cancer. The purpose of the current study was to screen the hotspot mutations of thePIK3CAgene i.e. rs2677760, rs3806685, rs121913273 & rs121913279 along with expressional analysis ofPI3KandPIK3CAgenes in breast cancer female patients. For mutational analysis, TaqMan assay & Sanger sequencing were performed while for expressional analysis real-time PCR was carried out. Mutant allele C of rs2677760 was observed to be high in postmenopausal patients. The frequency of mutant allele G of rs3806685 was significantly high in breast cancer patients. All diseased and control samples were of wild type for the hotspot rs121913273 and rs121913279 with allele G for rs121913273 and A for rs121913279. Expression of the PI3K was high in breast cancer tissue samples as compared to the adjacent controls. While the expression ofthe thePIK3CAgene was significantly high in premenopausal breast cancer patients. It was concluded that the mutant allele C of rs2677760 might have some sort of association with the menopausal status and it could be used as a diagnostic marker in post-menopausal women if studied further. Mutant allele G of rs3806685 was also found to be associated with breast cancer. While multiallelic rs121913273 and rs121913279 showed a different trend for the studied population. For expressional analysis, PI3Kshowed over-expression in the cases while PIK3CAgene expression was observed to be significantly associated with pre-menopausal status.

ANO6 is a reliable prognostic biomarker and correlates to macrophage polarization in breast cancer.

To investigate the value of Anoctamin 6 (ANO6) in breast cancer (BC) by analyzing its expression, prognostic impact, biological function, and its association with immune characteristics. We initially performed the expression and survival analyses, followed by adopting restricted cubic spline to analyze the nonlinear relationship between ANO6 and overall survival (OS). Stratified and interaction analyses were conducted to further evaluate its prognostic value in BC. Next, we performed enrichment analyses to explore the possible pathways regulated by ANO6. Finally, the correlations between ANO6 and immune characteristics were analyzed to reveal its role in immunotherapy. Lower ANO6 expression was observed in BC than that in the normal breast group, but its overexpression independently predicted poor OS among BC patients (P < .05). Restricted cubic spline analysis revealed a linear relationship between ANO6 and OS (P-Nonlinear > 0.05). Interestingly, menopause status was an interactive factor in the correlation between ANO6 and OS (P for interaction = 0.016). Additionally, ANO6 was involved in stroma-associated pathways, and its elevation was significantly linked to high stroma scores and macrophage polarization (P < .05). Moreover, ANO6 was notably correlated with immune checkpoint expression levels, and scores of tumor mutation burden and microsatellite instability (all P < .05). ANO6 was an independent prognostic factor for BC, and might be a potential target for the BC treatment. Besides, ANO6 might affect BC progression via the regulation of stroma-related pathways and macrophage polarization.

Prognostic analyses of genes associated with anoikis in breast cancer.

Breast cancer (BRCA) is the most diagnosed cancer worldwide and is responsible for the highest cancer-associated mortality among women. It is evident that anoikis resistance contributes to tumour cell metastasis, and this is the primary cause of treatment failure for BRCA. However, anoikis-related gene (ARG) expression profiles and their prognostic value in BRCA remain unclear. In this study, a prognostic model of ARGs based on The Cancer Genome Atlas (TCGA) database was established using a least absolute shrinkage and selection operator analysis to evaluate the prognostic value of ARGs in BRCA. The risk factor graph demonstrated that the low-risk group had longer survival than the high-risk group, implying that the prognostic model had a good performance. We identified 11 ARGs that exhibited differential expression between the two risk groups in TCGA and Gene Expression Omnibus databases. Through Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes enrichment analyses, we revealed that the screened ARGs were associated with tumour progression and metastasis. In addition, a protein-protein interaction network showed potential interactions among these ARGs. Furthermore, gene set enrichment analysis suggested that the Notch and Wnt signalling pathways were overexpressed in the high-risk group, and gene set variation analysis revealed that 38 hallmark genes differed between the two groups. Moreover, Kaplan-Meier survival curves and receiver operating characteristic curves were used to identify five ARGs (CD24, KRT15, MIA, NDRG1, TP63), and quantitative polymerase chain reaction was employed to assess the differential expression of these ARGs. Univariate and multivariate Cox regression analyses were then performed for the key ARGs, with the best prediction of 3 year survival. In conclusion, ARGs might play a crucial role in tumour progression and serve as indicators of prognosis in BRCA.

Exosomal prognostic biomarkers predict metastatic progression and survival in breast cancer patients

Abstract Objectives This study aims to comprehensively evaluate extracellular vesicle (EV)-based biomarkers circulating in body fluids with significant prognostic value in breast cancer (BrCa). Methods We systematically searched WOS, PubMed, and Scopus databases on 14 February 2023 for studies indicating overall survival(OS), progression/disease/event-free survival(PFS/DFS/EFS), and metastatic progression. We computed univariate(UHR) or multivariate adjusted(AHR) hazard ratios, and AUC values for all prognostic EV-based biomarkers of blood-origin using random effect model and Stata 16.0 software. Subgroup analysis was conducted for positive and negative prognostic factors. Results Twenty-one articles comprising twenty-six studies and 3,423 patients satisfied the inclusion criteria. EV-based negative biomarkers indicated low OS(UHR=2.31, CI=1.77–3.03, I2=60.12 %, p&lt;0.001); worse DFS/PFS/EFS(UHR=3.91, CI=2.82–5.43, I2=19.08 %, p=0.24); increased risk for metastasis(pooled AUC=0.91). Out of 56 EV-based biomarkers that have been previously described, we identified PD-L2, sHLA-G, exo-XIST, and miR4800 as the best predictors of OS of BrCa patients. Expression levels of miR155, Annexin-A2, sHLA-G, PD-L2, miR1246, PSMA and the biomarkers constructing the EVP-panel hold significant potential to be combined in a prognostic-panel predicting DFS/PFS/EFS of BrCa patients. PD-L2 and sHLA-G standing out as leading biomarkers in both OS and DFS highlights the importance of immune system evasion for patient survival. In addition, we suggest that reinforcement with additional RNA biomarkers could significantly increase the metastatic prediction power of the previously described EVDX-panel. Conclusions This meta-analysis provides an overview of the liquid biopsy-based EV-biomarkers associated with OS, DFS, and metastatic progression of BrCa for the first time. Prognostic efficiency of the proposed panels should be further investigated before transition to clinical use.

Prognostic value of FOXA1 in patients with Т1–2N0M0 breast cancer

Background. New biomarkers of breast cancer are actively being studied to used them for breast cancer prognosis and develop optimal approaches to therapy. FOXA1 is one of these factors whose prognostic value for breast cancer has been analyzed in several studies.Aim. To evaluate the prognostic value of FOXA1 in patients with T1–2N0M0 breast cancer.Materials and methods. We measured FOXA1 expression in tumor cells of patients with hormone-dependent and hormone-independent breast cancer using the method developed by us (patent No. RU2748716C1 dated 03.06.2020). We performed immunohistochemical examination of tumor tissue using specific antibodies for staining. The Allred scale was used for results interpretation (to assess the number of cells with stained nuclei and the intensity of their staining). Then the total Allred score was calculated and used as a prognostic parameter. Patients with the Allred score between 4 and 8 had a favorable prognosis. We followed up patients in the groups and evaluated their survival. A univariate analysis was used to assess the risk of lethal outcome depending on the level of FOXA1 expression.Results. In 75 % of patients with hormone-dependent and 82 % of patients with hormone-independent breast cancer (stages T1–2, N0 in both groups), the prognostic parameter measured using the Allred scale ranged between 4 and 8 points; their 10-year overall survival was 93 % and 92 %, respectively.Conclusion. Our findings suggest high accuracy of our method for breast cancer prognosis. It can be used for breast cancer prognosis in early stages (T1–2, N0) and expands the range of techniques for breast cancer prognosis.

Analysis of tumor infiltrating CD4+ and CD8+ CDR3 sequences reveals shared features putatively associated to the anti-tumor immune response.

Tumor-infiltrating lymphocytes (TILs) have predictive and prognostic value in breast cancer (BC) and exert a protective function against tumor growth, indicating that it is susceptible to treatment using adoptive cell transfer of TILs or T cell receptor (TCR)-based therapies. TCR can be used to identify naturally tumor-reactive T cells, but little is known about the differences in the TCR repertoires of CD4+ and CD8+ TILs. TCR high-throughput sequencing was performed using TILs derived from the initial cultures of 11 BC biopsies and expanded and sorted CD4+ and CD8+ TILs as well as using PBMCs from healthy donors expanded and sorted using the same methodology. Physicochemical TCR differences between T cell subsets were observed, as CD4+ TILs presented larger N(D)Nnt TRB sequences and with a higher usage of positively charged residues, although only the latest was also observed in peripheral T cells from healthy individuals. Moreover, in CD4+ TILs, a more restricted TCR repertoire with a higher abundance of similar sequences containing certain amino acid motifs was observed. Some differences between CD4+ and CD8+ TCRs were intrinsic to T cell subsets as can also be observed in peripheral T cells from healthy individuals, while other were only found in TILs samples and therefore may be tumor-driven. Notably, the higher similarity among CD4+ TCRs suggests a higher TCR promiscuity in this subset.

Predicting the immune microenvironment and prognosis with a anoikis - related signature in breast cancer.

Tumor heterogeneity is widely recognized as a crucial factor impacting the prognosis of breast cancer (BC) patients. However, there remains an insufficient understanding of the underlying impact of anoikis on the prognosis of BC patients. The researchers utilized the TCGA-BRCA dataset to screen and analyze the differentially expressed genes of anoikis-related genes (ARGs) in BC and normal breast tissue. Prognostic gene signatures were established through univariate, LASSO, and multivariate Cox regression analyses. These signatures were evaluated using Kaplan-Meier curve and receiver operating characteristic (ROC) analyses, resulting in the development of an anoikis-related index (ACI). The training dataset was TCGA-BRCA, while METABRIC and GSE96058 were used for external validation. Additionally, nomograms were developed by combining risk scores and clinical parameters, enabling gene set enrichment analysis (GSEA) and tumor immunoassay. Furthermore, an exploration of small molecule compounds was conducted to identify potential therapeutic benefits. A six-gene anoikis-related signature was constructed, which divided BC patients into high- and low-ACI groups based on median ACI scores. The ACI accurately predicted prognosis and acted as an independent prognostic factor for BC patients. Patients in the high-ACI group exhibited poorer overall survival (OS) across all cohorts and showed more severe clinical manifestations compared to the low-ACI group. The study also explored the potential impacts of anoikis on immune cells infiltrating tumors, immune checkpoints, growth factors, and cytokine levels. Additionally, the potential implications of anoikis in BC immunotherapy were discussed, along with highlighting small molecule compounds that could offer therapeutic benefits. Anoikis was found to hold significant prognostic value in breast cancer, providing a novel approach for managing patients with different prognoses and implementing more precise immunotherapy strategies.

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information

BackgroundIntra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer. MethodsDeep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262). ResultsWe observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22–3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03–3.3; p = 3.99 ×10−2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm). ConclusionWe developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer. SignificanceTranscriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.

Development of a risk model to predict prognosis in breast cancer based on cGAS-STING-related genes.

Background: Breast cancer (BRCA) is regarded as a lethal and aggressive cancer with increasing morbidity and mortality worldwide. cGAS-STING signaling regulates the crosstalk between tumor cells and immune cells in the tumor microenvironment (TME), emerging as an important DNA-damage mechanism. However, cGAS-STING-related genes (CSRGs) have rarely been investigated for their prognostic value in breast cancer patients. Methods: Our study aimed to construct a risk model to predict the survival and prognosis of breast cancer patients. We obtained 1087 breast cancer samples and 179 normal breast tissue samples from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) database, 35 immune-related differentially expression genes (DEGs) from cGAS-STING-related genes were systematically assessed. The Cox regression was applied for further selection, and 11 prognostic-related DEGs were used to develop a machine learning-based risk assessment and prognostic model. Results: We successfully developed a risk model to predict the prognostic value of breast cancer patients and its performance acquired effective validation. The results derived from Kaplan-Meier analysis revealed that the low-risk score patients had better overall survival (OS). The nomogram that integrated the risk score and clinical information was established and had good validity in predicting the overall survival of breast cancer patients. Significant correlations were observed between the risk score and tumor-infiltrating immune cells, immune checkpoints and the response to immunotherapy. The cGAS-STING-related genes risk score was also relevant to a series of clinic prognostic indicators such as tumor staging, molecular subtype, tumor recurrence, and drug therapeutic sensibility in breast cancer patients. Conclusion: cGAS-STING-related genes risk model provides a new credible risk stratification method to improve the clinical prognostic assessment for breast cancer.

A novel feedback regulated loop of circRRM2-IGF2BP1-MYC promotes breast cancer metastasis

BackgroundMetastasis is the leading cause of mortality in patients with breast cancer (BC). Studies demonstrate that circular RNAs (circRNAs) were involved in BC progression, while the molecular mechanisms remain largely unclear.MethodsThe microArray circRNA profiles were used to explore the differential expression circRNAs in BC and paracancerous normal tissues, and the quantitative reverse transcription-polymerase chain reaction was used to validate their expression level in clinical samples and cell lines. Nuclear/cytosolic fractionation and fluorescence in situ hybridization (FISH) assays were performed to examine circRRM2 (hsa_circ_0052582) subcellular location. The scratch wound healing and transwell assays were conducted to evaluate the impact of circRRM2 on BC cell migration and invasion. We predicted miRNAs that might bind with cricRRM2 and the downstream target genes using bioinformatics analysis and explored their expression levels and prognostic value in BC. FISH, RNA immunoprecipitation, Co-immunoprecipitation, Western blot, and rescue experiments were implemented to figure out circRRM2 function and underlying mechanisms in BC.ResultsThe present study revealed several aberrant circRNAs in BC tissues and observed that circRRM2 was upregulated in tumor tissues of 40 patients with BC. High circRRM2 was significantly associated with advanced N stage in patients with BC. Gain- and loss- of function experiments revealed that circRRM2 promoted the migration and invasion of cells and functioned as an oncogene in BC. Mechanism studies showed that circRRM2 competed with miR-31-5p/miR-27b-3p to upregulate the IGF2BP1 expression. Furthermore, IGF2BP1 upregulated the circRRM2 level via interacting with MYC, which functioned as the transcriptional factor of circRRM2. Thus, the positive feedback loop that was composed of circRRM2/IGF2BP1/MYC was identified.ConclusionThis study confirms that upregulated circRRM2 functions an oncogenic role in BC metastasis. The positive feedback loop of circRRM2/IGF2BP1/MYC enforces the circRRM2 expression, which might offer a potential target for BC treatment.

Combined proliferation and apoptosis index provides better risk stratification in breast cancer.

Breast cancer (BC) risk stratification is critical for predicting behaviour and guiding management decision-making. Despite the well-established prognostic value of cellular proliferation in BC, the interplay between proliferation and apoptosis remains to be defined. In this study, we hypothesised that the combined proliferation and apoptosis indices can provide a more accurate in-vivo growth rate measure and a precise prognostic predictor. Apoptotic and mitotic figures were counted in whole slide images (WSI) generated from haematoxylin and eosin-stained sections of 1545 BC cases derived from two well-defined BC cohorts. Counts were carried out visually within defined areas. There was a significant correlation between mitosis and apoptosis scores. High apoptotic counts were associated with features of aggressive behaviour, including high grade, high pleomorphism score and hormonal receptor negativity. Although the mitotic index (MI) and apoptotic index (AI) were independent prognostic indicators, the prognostic value was synergistically higher when combined. BCpatients with a high combined AI and MI had theshortest survival. Replacing the mitosis score with the mitosis-apoptosis index in the Nottingham grading system revealed that the modified grade with the new score had a higher significant association with BC-specific survival with a higher hazard ratio. Apoptotic figures count provides additional prognostic value in BC when combined with MI; such a combination can be implemented to assess the behaviour of BC and provides an accurate prognostic indicator. This can be considered when using artificial intelligence algorithms to assess proliferation in BC.

Abstract P2-21-04: The tumor immune microenvironment composition and prognostic value in breast cancer during pregnancy is dynamic during the gestation period

Abstract Introduction: Breast cancer (BC) during pregnancy (PrBC) is an uncommon malignancy characterized by a more aggressive clinical course compared to pregnancy-unrelated BC. Specific patterns of tumor-infiltrating lymphocytes (TILs) subpopulations have been observed in these patients, with significant prognostic roles. Previous studies demonstrated the varying histopathologic and prognostic profiles of PrBC by gestational age. However, the underlying immune landscape dynamics has never been investigated. Here, we sought to provide comprehensive insights into the association between gestational age at breast cancer diagnosis and tumor immune microenvironment (TIME) composition. Materials and Methods: A total of 110 PrBC were selected from our Institutional registry and categorized based on the trimester in which they were diagnosed. All cases were subjected to TILs profiling according to the International TILs Working Group recommendations. Immunohistochemistry for CD4, CD8, forkhead box P3 (FOXP3), and PD-L1 (clone 22C3) on a Dako Omnis platform was performed. Fisher’s and Chi-squared tests, multinomial logistic regression models, ROC curve, and survival analyses were performed. Results: The proportion of patients with high histologic grades incremented with the increase in gestational age (1st, n=24, 53%; 2nd, n=27, 69.2%; 3rd trimester, n=20, 87.0%; p=0.02). Neither breast cancer subtypes nor the hormone receptor (HR) and HER2 status changed significantly according to the pregnancy trimester. In HR+/HER2- subtype, the proportion of TILs+ tumors were higher in the early phases of pregnancy (1st, n=29, 100%; 2nd, n=17, 89.5%; 3rd trimester, n=9, 81.8%; p=0.04) imprinted by FOXP3 positivity where more FOXP3+ TILs were seen in the first months and decreased progressively (1st, n=10, 55.6%; 2nd, n=2, 11.8%; 3rd trimester, n=0, 0%; p&amp;lt; 0.01)). While in the triple negative breast cancer (TNBC) population, the proportion of PD-L1+ tumors (i.e. CPS&amp;gt;1) was significantly higher in the later stages of pregnancy (1st, n=2, 16.7%; 2nd, n=2, 18.2%; 3rd trimester, n=5, 71.4%; p=0.03). Patients who relapsed after a BC diagnosis during the 1st and 2nd trimesters lacked more frequently FOXP3+ and CD8+ cells, unlike those with no disease recurrence (n=21, 77.8% vs. n=17, 48.6%; p=0.02 and n=18, 66.7% vs. n=10, 28.6%; p&amp;lt; 0.01, respectively). Conclusions: TIME dynamics of PrBC are different according to the gestational age in both HR+ and TNBC PrBC. Our results suggest that immune tolerance events are likely to involve PrBC at later gestational age. Specific escape mechanisms (i.e., TILs and FOXP3 decrease in HR+ and PD-L1 expression in TNBC) might explain the aggressiveness of PrBC diagnosed during the later gestational age. Citation Format: Elham Sajjadi, Konstantinos Venetis, Mariia Ivanova, Marianna Noale, Concetta Blundo, Giovanna Scarfone, Eugenia Di Loreto, Stefano Ferrero, Stefania Maggi, Paolo Veronesi, Viviana Enrica Galimbreti, Giuseppe Viale, Fedro Alessandro A. Peccatori, Elena Guerini-Rocco, Nicola Fusco. The tumor immune microenvironment composition and prognostic value in breast cancer during pregnancy is dynamic during the gestation period [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-21-04.

Identification of GZMA as a Potential Therapeutic Target Involved in Immune Infiltration in Breast Cancer by Integrated Bioinformatical Analysis.

Granzyme A (GZMA) is a potential prognostic target for various cancer types. However, its therapeutic significance in breast cancer with immune infiltration remains controversial. We analyzed GZMA expression and its prognostic value in breast cancer with immune cell infiltration. Data was obtained from patients with breast cancer registered at The Cancer Genome Atlas. A correlation was performed between GZMA expression and patient's clinicopathological features such as age, pathologic stage, metastasis stage, overall survival (OS), disease-specific survival (DSS), and progress free interval (PFI). Kaplan-Meier analyses and Cox proportional hazard regression model were used to examine the predictive significance of GZMA expression for breast cancer. The co-expression pattern of GZMA was assessed by the LinkedOmics web portal. The relationship between GZMA expression and immune cells was analyzed using the TIMER database. The correlation between GZMA and lymphocytes and immunomodulators was established with the TISIDB database. There was a lower GZMA expression in breast cancer tissue than in normal tissue. Interestingly, GZMA expression was associated with age, pathologic stage, and the Tumour, Node, and Metastasis stage. Overexpression of GZMA was also associated with better OS, DSS, and PFI. Based on the Cox regression analysis, GZMA was identified as an independent favorable prognostic factor for breast cancer. Our findings demonstrated a strong association between GZMA and T-cell checkpoints (PD-1, PD-L1, and cytotoxic T lymphocyte-associated antigen (CTLA-4)) in breast cancer. Moreover, we evaluated the interactions between GZMA expression and markers of dendritic and CD8+ T cells using quantitative immunofluorescence. We discovered that increased infiltration of dendritic and CD8+ T cells was associated with GZMA expression in breast cancer. GZMA expression is associated with a favorable prognosis in breast cancer and is significantly correlated with immune cell infiltration. GZMA may be considered a promising therapeutic target for patients with breast cancer.

Mining TCGA Database for Genes with Prognostic Value in Breast Cancer

The aim of the study was to use transcriptomics data to identify genes associated with advanced/aggressive breast cancer and their effect on survival outcomes. We used the publicly available The Cancer Genome Atlas (TCGA) database to obtain RNA sequence data from patients with less than five years survival (Poor Prognosis, n = 101), patients with greater than five years survival (Good Prognosis, n = 200), as well as unpaired normal tissue data (normal, n = 105). The data analyses performed included differential expression between groups and selection of subsets of genes, gene ontology, cell enrichment analysis, and survival analyses. Gene ontology results showed significantly reduced enrichment in gene sets related to tumor immune microenvironment in Poor Prognosis and cell enrichment analysis confirmed significantly reduced numbers of macrophages M1, CD8 T cells, plasma cells and dendritic cells in samples in the Poor Prognosis samples compared with Good Prognosis. A subset of 742 genes derived from differential expression analysis as well as genes coding for immune checkpoint molecules was evaluated for their effect on overall survival. In conclusion, this study may contribute to the better understanding of breast cancer transcriptomics and provide possible targets for further research and eventual therapeutic interventions.