Prognostic Biomarker For Breast Cancer Research Articles

Radiomics Analysis of Contrast-Enhanced Breast MRI for Optimized Modelling of Virtual Prognostic Biomarkers in Breast Cancer.

Breast cancer clinical stage and nodal status are the most clinically significant drivers of patient management, in combination with other pathological biomarkers, such as estrogen receptor (ER), progesterone receptor or human epidermal growth factor receptor 2 (HER2) receptor status and tumor grade. Accurate prediction of such parameters can help avoid unnecessary intervention, including unnecessary surgery. The objective was to investigate the role of magnetic resonance imaging (MRI) radiomics for yielding virtual prognostic biomarkers (ER, HER2 expression, tumor grade, molecular subtype, and T-stage). Patients with primary invasive breast cancer who underwent dynamic contrast-enhanced (DCE) breast MRI between July 2013 and July 2016 in a single center were retrospectively reviewed. Age, N-stage, grade, ER and HER2 status, and Ki-67 (%) were recorded. DCE images were segmented and Haralick texture features were extracted. The Bootstrap Lasso feature selection method was used to select a small subset of optimal texture features. Classification of the performance of the final model was assessed with the area under the receiver operating characteristic curve (AUC). Median age of patients (n = 209) was 49 (21-79) years. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the model for differentiating N0 vs N1-N3 was: 71%, 79%, 76%, 74%, 75% [AUC = 0.78 (95% confidence interval (CI) 0.72-0.85)], N0-N1 vs N2-N3 was 81%, 59%, 24%, 95%, 62% [AUC = 0.74 (95% CI 0.63-0.85)], distinguishing HER2(+) from HER2(-) was 79%, 48%, 34%, 87%, 56% [AUC = 0.64 (95% CI 0.54-0.73)], high nuclear grade (grade 2-3) vs low grade (grades 1) was 56%, 88%, 96%, 29%, 61% [AUC = 0.71 (95% CI 0.63-0.80)]; and for ER (+) vs ER(-) status the [AUC=0.67 (95% CI 0.59-0.76)]. Radiomics performance in distinguishing triple-negative vs other molecular subtypes was [0.60 (95% CI 0.49-0.71)], and Luminal A [0.66 (95% CI 0.56-0.76)]. Quantitative radiomics using MRI contrast texture shows promise in identifying aggressive high grade, node positive triple negative breast cancer, and correlated well with higher nuclear grades, higher T-stages, and N-positive stages.

Abstract 5212: Prognostic biomarkers for breast cancer: Metabolic and inflammatory pathways

Abstract Objective: To assess the prognostic significance of molecular biomarkers, particularly metabolic and Inflammatory pathways using samples from a prospective study of breast cancer patients undergoing adjuvant radiotherapy (RT). Methods: We used ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) for measuring urine metabolites. Following normalization to osmolality, log transformation, and imputation of missing values, we used Welch’s two-sample t-test to identify biochemicals that differed significantly by progression status. We measured pre- and post-RT plasma high-sensitivity C-reactive protein (hsCRP) levels. Progression-free survival (PFS) was calculated from the date of diagnosis to the date of disease progression or last follow-up. Univariable and multivariable Cox proportional hazards regression models assessed the associations between hsCRP and PFS. Results: In 120 breast cancer patients, carbohydrate metabolism, branch-chain amino acid (BCAA) metabolism, phosphatidylcholine metabolism, arginine metabolism, oxidative stress-related metabolites, androgenic steroids, and nucleotide metabolism had the most notable differences in patients with progression versus progression-free cases. In 488 breast cancer patients, RT significantly increased plasma hsCRP levels, and post-RT hsCRP levels were significantly higher in deceased vs. alive patients (p=0.031). In multivariable models, hsCRP had a prognostic value for all-stage and stage 0-II patients. Conclusions: In our study of breast cancer patients receiving adjuvant RT, multiple metabolite biomarkers and post-RT plasma CRP levels were associated with a worse prognosis. BCAAs and fatty acids regulate cell metabolism by affecting mitochondrial function and inflammation signals. Thus, our findings suggest a potential role of metabolic and inflammatory pathways in breast cancer prognosis. These molecules may have potential utility as prognostic biomarkers for breast cancer patients undergoing adjuvant RT and targets for intervention. Keywords: breast cancer, metabolomics, inflammation, prognosis Citation Format: Alexandra N. McMahon, Cristiane Takita, Jean L. Wright, Isildinha M. Reis, Eunkyung Lee, George Yang, Jennifer J. Hu. Prognostic biomarkers for breast cancer: Metabolic and inflammatory pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5212.

Abstract 3693: Molecular and functional characterization of circulating CK+/CD45+ cells in breast cancer

Abstract Background: Circulating tumor cells (CTCs) are considered a key element in metastatic progression and represent a validated prognostic biomarker in breast cancer (BC). CTCs in BC are normally defined as epithelial cells (positive for EPCAM or cytokeratin, CK) lacking the expression of the leukocyte marker CD45. Circulating cells expressing both EPCAM/CK and CD45 (dual positive, DPcells) have however been reported in BC but not investigated. It is believed that DPcells derive from the heterotypic cell fusion of tumor and immune cells, but this hypothesis has not been demonstrated yet. We recently reported, for the first time, the association of DPcells with worse survival in a cohort of 341 BC patients (pts) [Reduzzi et al. ASCO 2022] supporting their tumor origin. Here, we further investigated their malignancy and metastatic potential by molecular profiling and functional experiments. Methods: The study consisted of 2 components: A) Single CTCs and DPcells were enriched from the blood of BC patients with the CellSearch platform, and isolated with the DEPArray system. Single cells underwent whole genome amplification and lowpass whole genome sequencing for single cell copy number alteration (CNA) profiling; B) DPcells and CTCs were investigated in the blood of immunodeficient (NSG) and immunocompetent (FVB) mouse models using various cell lines (LM2, 4T1, MVT1). To test their metastatic potential, DPcells from the primary tumor of FVB donor mice were transplanted into recipient NSG mice, and the metastatic burden in the lungs and liver quantified, in comparison with control CD45- tumor cells. Results: We analyzed 73 DPcells and 18 CTCs collected from 16 BC pts: Aberrant genomes were observed in 28% and 93% of evaluable cells, respectively. When collected from the same pt, CTCs showed clonal CNA profiles, while DPcells were highly heterogeneous. In vivo, DPcells could only be detected in the blood of FVB mice but not in NSG mice. They were very rare, comprising of only about 3% of the total CTC count. When DPcells and control tumor cells were isolated from the primary tumor of 10 donor mice and transplanted into the tail vein of 10 recipient mice, we observed no significant differences in the metastatic colonization of the lungs and liver by the two population, indicating that DPcells do in fact possess metastatic ability, and to a similar extent as control cancer cells. Conclusions: DPcells can have an aberrant genome, supporting their malignancy. Contrary to CTCs, DPcells’ CNA profiles are not clonal and, in vivo, they can only be observed in immunocompetent models; both observations support the tumor/immune fusion hypothesis. Finally, DPcells show metastatic potential in mice, in line with the association with worse survival we previously observed in BC pts. Overall DPcells seem a new CTC subpopulation that should not be neglected anymore. More studies are needed to better understand their origin and phenotypic/molecular features. Citation Format: Carolina Reduzzi, Eleonora Nicolo, Lauren L. Ozimski, Marco Silvestri, Lorenzo Gerratana, Mara S. Serafini, Elisabetta Molteni, Nadia Bayou, Amanda K. Strickland, Huiping Liu, Vera Cappelletti, Nicola Aceto, Massimo Cristofanilli. Molecular and functional characterization of circulating CK+/CD45+ cells in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3693.

Identification and validation of DHCR7 as a diagnostic biomarker involved in the proliferation and mitochondrial function of breast cancer.

Energy metabolism has a complex intersection with pathogenesis and development of breast cancer (BC). This allows for the possibility of identifying energy-metabolism-related genes (EMRGs) as novel prognostic biomarkers for BC. 7-dehydrocholesterol reductase (DHCR7) is a key enzyme of cholesterol biosynthesis involved in many cancers, and in this paper, we investigate the effects of DHCR7 on the proliferation and mitochondrial function of BC. EMRGs were identified from the Gene Expression Omnibus (GEO) and MSigDB databases using bioinformatics methods. Key EMRGs of BC were then identified and validated by functional enrichment analysis, interaction analysis, weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, Cox analysis, and immune infiltration. Western blot, qRT-PCR, immunohistochemistry (IHC), MTT assay, colony formation assay and flow cytometry assay were then used to analyze DHCR7 expression and its biological effects on BC cells. We identified 31 EMRGs in BC. These 31 EMRGs and related transcription factors (TFs), miRNAs, and drugs were enriched in glycerophospholipid metabolism, glycoprotein metabolic process, breast cancer, and cell cycle. Crucially, DHCR7 was a key EMRG in BC identified and validated by WGCNA, LASSO regression and receiver operating characteristic (ROC) curve analysis. High DHCR7 expression was significantly associated with tumor immune infiltration level, pathological M, and poor prognosis in BC. In addition, DHCR7 knockdown inhibited cell proliferation, induced apoptosis and affected mitochondrial function in BC cells. DHCR7 was found to be a key EMRG up-regulated in BC cells. This study is the first to our knowledge to report that DHCR7 acts as an oncogene in BC, which might become a novel therapeutic target for BC patients.

Impact of NDUFAF6 on breast cancer prognosis: linking mitochondrial regulation to immune response and PD-L1 expression

BackgroundBreast cancer is a major global health concern, and there is a continuous search for novel biomarkers to predict its prognosis. The mitochondrial protein NDUFAF6, previously studied in liver cancer, is now being investigated for its role in breast cancer. This study aims to explore the expression and functional significance of NDUFAF6 in breast cancer using various databases and experimental models.MethodsWe analyzed breast cancer samples from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Human Protein Atlas (HPA) databases, supplemented with immunohistochemistry (IHC) staining to assess NDUFAF6 expression. A breast cancer cell xenograft mouse model was used to evaluate tumor growth, apoptosis, and NDUFAF6 expression. Survival probabilities were estimated through Kaplan–Meier plots and Cox regression analysis. A Protein–Protein Interaction (PPI) network was constructed, and differentially expressed genes related to NDUFAF6 were analyzed using GO, KEGG, and GSEA. The relationship between NDUFAF6 expression, immune checkpoints, and immune infiltration was also evaluated.ResultsNDUFAF6 was found to be overexpressed in breast cancer patients and in the xenograft mouse model. Its expression correlated with worse clinical features and prognosis. NDUFAF6 expression was an independent predictor of breast cancer outcomes in both univariate and multivariate analyses. Functionally, NDUFAF6 is implicated in several immune-related pathways. Crucially, NDUFAF6 expression correlated with various immune infiltrating cells and checkpoints, particularly promoting PD-L1 expression by inhibiting the NRF2 signaling pathway.ConclusionThe study establishes NDUFAF6 as a potential prognostic biomarker in breast cancer. Its mechanism of action, involving the inhibition of NRF2 to upregulate PD-L1, highlights its significance in the disease's progression and potential as a target for immunotherapy.

The presence of autoantibodies as a potential prognostic biomarker for breast cancer.

The presence of autoantibodies is closely associated with the occurrence and development of cancer. Autoantibodies can be used as biomarkers for early breast cancer diagnosis. However, the relationship between autoantibodies and the prognosis of breast cancer patients remains elusive. This retrospective study aimed to investigate the correlation between the presence of autoantibodies and outcomes in breast cancer patients. This study included a total of 155 patients from People's Hospital of Henan University (Zhengzhou, China) who were diagnosed with breast cancer from January 2017 to December 2021. The enrolled patients' clinicopathological features were collected, and 88 patients were ultimately involved in the analysis. Univariate and multivariate Cox regression analyses were performed to search for the risk factors related to the poor prognosis of breast cancer patients. The association between the presence of autoantibodies and patients' survival was analysed using Kaplan-Meier curves. After screening, there were 38 autoantibody-positive cases and 50 autoantibody-negative cases. Breast cancer patients with autoantibody-positive had a 57% lower risk of death compared with autoantibody-negative patients. Multivariate Cox regression analysis indicated that the presence of autoantibody could be a potential prognostic predictor for breast cancer, independent of age, histological grade, pathological classification, clinical stage, and the expression levels of hormonal receptors. In addition, autoantibody-positive breast cancer patients had longer progression-free survival and overall survival compared with autoantibody-negative cases. Positive autoantibody was found as an independent biomarker of better prognosis in breast cancer patients, providing a new strategy for the prognostic assessment of breast cancer patients.

NDRG1 is a prognostic biomarker in breast cancer and breast cancer brain metastasis

AbstractBrain metastases are secondary brain tumours characterised by their aggressive nature and poor prognosis. Breast cancer is one of the most common primary tumours in women to spread to the brain. A lack of biomarkers predicting likely spread to the brain and limited therapeutic interventions represents major areas of clinical unmet need. We investigated N‐myc downregulated gene‐1 (NDRG1) as a clinically relevant biomarker in breast cancer brain metastasis patients. NDRG1 expression was investigated using immunohistochemistry on tissue microarrays of two clinical cohorts: (i) brain metastatic breast cancers (n = 48) and brain metastases (n = 64; including a subset of 39 patient‐matched breast and brain metastasis cases) and (ii) unselected primary breast cancers (n = 336). NDRG1 was highly expressed in breast‐to‐brain metastases, as well as in high‐grade primary breast cancers. High NDRG1 expression and also an absence of expression were associated with worse survival outcomes in both breast cancer and breast cancer brain metastasis patients. This establishes NDRG1 as a ‘Goldilocks’ protein, where too much or too little has a negative effect on survival. We pose that this accounts for its previous categorisation as both tumour suppressor and oncoprotein. Additionally, a shift in NDRG1 localisation with a gain of nuclear expression was seen at the brain metastasis stage. Significant survival benefit in cases expressing cytoplasmic NDRG1 was observed, whereas NDRG1 localisation in the nucleus showed a clear association with poorer survival. In vitro analyses revealed that hypoxic stress significantly elevated NDRG1 expression and resulted in its nuclear localisation. Our findings suggest NDRG1 expression and subcellular localisation are clinically relevant biomarkers for poor prognosis in breast cancer and breast cancer brain metastasis.

Exploring CCL11 in breast cancer: unraveling its anticancer potential and immune modulatory effects involving the Akt-S6 signaling

BackgroundCCL11, a chemokine known for recruiting immune cells to the tumor microenvironment (TME), has an unclear role in the context of its expression, patient prognosis, and the presence of tumor-infiltrating immune cells (TILs) in breast cancer.MethodsThe expression of CCL11 in invasive breast cancer (BRCA) was analyzed using TCGA database. Survival curve and Cox regression analysis determined the potential of CCL11 as an independent prognostic indicator. GSEA performed functional analysis on genes related to CCL11. CIBERSORT algorithm quantified the infiltration level of immune cells with varying CCL11 expression. Lastly, the correlation between CCL11 expression and anticancer drug sensitivity was examined. Immunohistochemistry (IHC) and qRT-PCR confirmed CCL11 expression in clinical tissue samples. The anti-tumor efficacy of CCL11 was investigated using CCK-8, plate formation, transwell assay, and Western blot.ResultsCCL11 expression was elevated in BRCA tumor tissues compared to adjacent normal tissues. Recurrence-free survival (RFS) was longer in patients with high expression of CCL11. Enrichment and co-expression analyses revealed CCL11's association with numerous immune-related signaling pathways and genes. Validation studies confirmed high CCL11 expression in breast cancer tissues. In vitro experiments substantiated CCL11's anticancer effects in BRCA.ConclusionCCL11 expression correlates with immune cell infiltration in breast cancer, indicating its potential as a prognostic biomarker for BRCA.

CENPA knockdown restrains cell progression and tumor growth in breast cancer by reducing PLA2R1 promoter methylation and modulating PLA2R1/HHEX axis.

Breast cancer is a lethal malignancy affecting females worldwide. It has been reported that upregulated centromere protein A (CENPA) expression might indicate unfortunate prognosis and can function as a prognostic biomarker in breast cancer. This study aimed to investigate the accurate roles and downstream mechanisms of CENPA in breast cancer progression. CENPA protein levels in breast cancer tissues and cell lines were analyzed by Western blot and immunohistochemistry assays. We used gain/loss-of-function experiments to determine the potential effects of CENPA and phospholipase A2 receptor (PLA2R1) on breast cancer cell proliferation, migration, and apoptosis. Co-IP assay was employed to validate the possible interaction between CENPA and DNA methyltransferase 1 (DNMT1), as well as PLA2R1 and hematopoietically expressed homeobox (HHEX). PLA2R1 promoter methylation was determined using methylation-specific PCR assay. The biological capabilities of CENPA/PLA2R1/HHEX axis in breast cancer cells was determined by rescue experiments. In addition, CENPA-silenced MCF-7 cells were injected into mice, followed by measurement of tumor growth. CENPA level was prominently elevated in breast cancer tissues and cell lines. Interestingly, CENPA knockdown and PLA2R1 overexpression both restrained breast cancer cell proliferation and migration, and enhanced apoptosis. On the contrary, CENPA overexpression displayed the opposite results. Moreover, CENPA reduced PLA2R1 expression through promoting DNMT1-mediated PLA2R1 promoter methylation. PLA2R1 overexpression could effectively abrogate CENPA overexpression-mediated augment of breast cancer cell progression. Furthermore, PLA2R1 interacted with HHEX and promoted HHEX expression. PLA2R1 knockdown increased the rate of breast cancer cell proliferation and migration but restrained apoptosis, which was abrogated by HHEX overexpression. In addition, CENPA silencing suppressed tumor growth in vivo. CENPA knockdown restrained breast cancer cell proliferation and migration and attenuated tumor growth in vivo through reducing PLA2R1 promoter methylation and increasing PLA2R1 and HHEX expression. We may provide a promising prognostic biomarker and novel therapeutic target for breast cancer.

Evaluation of MMP-13 and Micro RNA-138 as Prognostic Biomarkers for Breast Cancer in Egyptian women patients

BackgroundElevated serum levels of MMP-13 are linked to tumor growth and metastasis, while miR-138 dysregulation is observed in breast cancer cases. The aim of this study is to investigate the expression miR-138 and MMP-13 levels as potential biomarkers for prognosis of breast cancer. Patients and MethodIn this retrospective case-control study 119 female subjects were recruited and divided into three groups. MMP-13 level was measured using Enzyme linked Immunosorbent Assay (ELISA), while real time PCR technique was employed to quantify miR-138 expression. ResultsBoth non-metastatic and metastatic groups showed a significant higher levels of serum MMP-13 compared to other groups. MMP-13 levels are significantly increased among patients with advanced tumor size, lymph node metastasis and triple-negative breast cancer cases. An inverse significant association between MMP-13 levels and response to treatment was observed. Expression of miR-138 underwent significant down-regulation in breast cancer patients, strong statistically significant association was established between miR-138 expression and triple-negative breast cancer cases. A positive association between the increase in miR-138 expression and good response to treatment. The expression of miR-138 was inversely coined with the MMP-13 levels. ConclusionMMP-13 levels were significantly higher in breast cancer, especially in advanced cases, suggesting its role in promoting tumor invasion and metastasis. MiR-138 was down-regulated in breast cancer, especially in triple-negative breast cancer patients, rendering it a promising biomarker for triple-negative breast cancer. Modulation of miR-138 expression and MMP-13 levels may represent therapeutic targets for breast cancer.

Peritumoral Fat Content Identified Using Iterative Decomposition of Water and Fat with Echo Asymmetry and Least-squares Estimation (IDEAL) Correlates with Breast Cancer Prognosis.

Adipocytes around aggressive breast cancer (BC) are less lipid different from naive adipocytes (cancer-associated adipocytes, CAAs), and peritumoral edema caused by the release of cytokines from CAAs can conduce to decrease the peritumoral fat proportion. The purpose of this study was to correlate peritumoral fat content identified by using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) with lymph node metastasis (LNM) and recurrence-free survival (RFS) in BC patients and to compare with T2-weighted (T2WI) and diffusion-weighted images (DWI) analyses. This retrospective study consisted of 85 patients who were diagnosed with invasive carcinoma of breast and underwent breast MRI, including IDEAL before surgery. The scan time of fat fraction (FF) map imaging using IDEAL was 33s. Four regions of interest (ROIs), which are 5 mm from the tumor edge, and one ROI in the mammary fat of the healthy side were set on the FF map. Then average peritumoral FF values (TFF), average FF values on the healthy side (HFF), and peritumoral fat ratio (PTFR, which is defined as TFF/HFF) were calculated. Tumor apparent diffusion coefficient (ADC) values were measured on ADC map obtained by DWI. Peritumoral edema was classified into three grades based on the degree of signal intensity around the tumor on T2WI (T2 edema). The results of stepwise logistic regression analysis for four variables (TFF, PTFR, T2 edema, and ADC value) indicated that TFF and T2 edema were significant factors of LNM (p < 0.01). RFS was significantly associated with TFF (p = 0.016), and 47 of 49 (95.9%) patients with TFF more than 85.5% were alive without recurrence. Peritumoral fat content identified by using IDEAL is associated with LNM and RFS and may therefore be a useful prognostic biomarker for BC.

THEM6 is a prognostic biomarker for breast cancer and is associated with immune infiltration

To characterize the implications of lipid metabolism-related gene thioesterase superfamily member 6 (THEM6) in breast cancer. Several databases including The Cancer Genome Atlas (TCGA) were utilized for our meticulous bioinformatics analysis. We further performed qRT-PCR, immunoblotting and IHC assays to validate the expression of THEM6 in various breast cancer cells and tissues. In addition, we have carried out relevant functional experiments to explore the regulatory role of THEM6 in vitro. Lipid metabolism-related genes are independent factors for overall survival. According to several databases, THEM6 was significantly more expressed in cancerous tissues of breast invasive carcinoma (BRCA) compared to its paracancerous tissues. Furthermore, THEM6 overexpression was correlated with poorer overall survival of BRCA patients, serving as a separate prognostic factor for BRCA. Biological functional analyses revealed that THEM6 was associated with tumor progression and pathogenesis. Finally, we discovered that in BRCA, THEM6 expression was linked to multiple immune cell types. qRT-PCR and Western blotting experiments demonstrated a general upregulation of THEM6 expression in breast carcinoma cells. IHC showed that THEM6 was expressed in both breast cancer tissues and para-cancer tissues, but its expression level was significantly higher in carcinoma tissues. In vitro studies indicated that THEM6 increased proliferation, invasion, and inhibited apoptosis of breast carcinoma cells, while also affecting the cell cycle and promoting cancer progression. Furthermore, THEM6 may influence macrophage recruitment and polarization in the tumor microenvironment by regulating CCL2 secretion, which in turn affects macrophage recruitment in the tumor microenvironment. Our findings indicate that the overexpression of THEM6, which is linked to the development of breast cancer, is a predictor of a poor prognosis and has an impact on the degree of immune cell infiltration. Therefore, THEM6 has the potential to be a valuable target for BRCA.

Expression Patterns of ER, PR, HER-2/neu and p53 in Association with Nottingham Tumour Grade in Breast Cancer Patients

Objectives: Recent molecular studies show that breast cancer (BC) is a heterogeneous disease, and several molecular changes may accumulate over time to influence treatment response. As a result, employing reliable molecular biomarkers to monitor these modifications may help deliver personalised treatment. However, this may be unrealistic in the resource-limited parts of the world. Thus, this study aimed to investigate the expression pattern of hormone receptors and p53 tumour suppressor using immunohistochemistry (IHC) in BC compared to the traditional tumour grade. Methods: In total, 205 cases were investigated, and the Modified Bloom-Richardson score system was adopted in grading the tumours. The tissue sections of the cases were stained with specific primary antibodies at dilutions of 1:60 for oestrogen receptors (ER) and progesterone receptors (PR), 1:350 for the human epidermal growth factor (HER-2/neu) and 1:50 for p53. Results: Invasive ductal carcinoma of no-specific type (n = 190, 92.7%) was predominant and grade II tumour (n = 146, 71.2%) was the most frequent. Hormone receptors ER (n = 127) and PR (n = 145) had 62.0% and 70.7% positive cases, respectively; 34.1% (n = 70) were positive for HER-2/ neu, while 76.1% (n = 156) were positive for p53. Significant associations between Nottingham grade and expression patterns of ER (P &lt;0.01), PR (P &lt;0.001), HER-2/neu (P &lt;0.001) and p53 (P = 0.001) were observed. Conclusion: Nottingham grade had a high degree of concordance with the patterns of expression of hormone receptors, HER- 2/neu and p53, suggesting that it may play an important role in connection with the predictive and prognostic biomarkers for BC. Keywords: Breast Cancer; HER-2/neu; Heterogeneity; Hormone Receptor; Oncoprotein p53.

PCMT1 knockdown attenuates malignant properties by globally regulating transcriptome profiles in triple-negative breast cancer cells.

As the most frequently diagnosed cancer in women, Breast cancer has high mortality and metastasis rate, especially triple-negative breast cancer (TNBC). As an oncogene, protein-L-isoaspartate (D-aspartate) O-methyltransferase (PCMT1) is a prognostic biomarker in breast cancer and is highly expressed, while its underlying functions remain unknown. In this study, we silenced PCTM1 in TNBC MDA-MB-231 cells by short hairpin RNA (shPCMT1) to investigate its cellular functions using cell proliferation, apoptosis, migration, and invasion experiments. Following this, the transcriptome sequencing (RNA-seq) experiment was conducted to explore the molecular targets of PCMT1, including differentially expressed genes (DEGs) and regulated alternative splicing events (RASEs). The results showed that shPCMT1 inhibited the proliferation, migration, and invasion of MDA-MB-231 cells. We obtained 1,084 DEGs and 2,287 RASEs between shPCMT1 and negative control (NC) groups through RNA-seq. The DEGs were significantly enriched in immune or inflammation response and cell adhesion-associated pathways, pathways associated with PCMT1 cellular function in cell migration. The RASE genes were enriched in cell cycle-associated pathways and were associated with the altered cell proliferation rate. We finally validated the changed expression and splicing levels of DEGs and RASEs. We found that 34 RNA binding protein (RBP) genes were dysregulated by shPCMT1, including NQO1, S100A4, EEF1A2, and RBMS2. The dysregulated RBP genes could partially explain how PCMT1 regulates the global transcriptome profiles. In conclusion, our study identified the molecular targets of PCMT1 in the TNBC cell line, expands our understanding of the regulatory mechanisms of PCMT1 in cancer progression, and provides novel insights into the progression of TNBC. The identified molecular targets are potential therapeutic targets for future TNBC treatment.

Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images

BackgroundSentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially for hormone receptor-positive, HER2-negative cases. However, invasive lymph node staging is increasingly omitted before therapy, and studies such as the randomised Intergroup Sentinel Mamma (INSEMA) trial address the potential for further de-escalation of axillary surgery. Therefore, it would be helpful to accurately predict the pretherapeutic sentinel status using medical images. MethodsUsing a ResNet 50 architecture pretrained on ImageNet and a previously successful strategy, we trained deep learning (DL)-based image analysis algorithms to predict sentinel status on hematoxylin/eosin-stained images of predominantly luminal, primary breast tumours from the INSEMA trial and three additional, independent cohorts (The Cancer Genome Atlas (TCGA) and cohorts from the University hospitals of Mannheim and Regensburg), and compared their performance with that of a logistic regression using clinical data only. Performance on an INSEMA hold-out set was investigated in a blinded manner. ResultsNone of the generated image analysis algorithms yielded significantly better than random areas under the receiver operating characteristic curves on the test sets, including the hold-out test set from INSEMA. In contrast, the logistic regression fitted on the Mannheim cohort retained a better than random performance on INSEMA and Regensburg. Including the image analysis model output in the logistic regression did not improve performance further on INSEMA. ConclusionsEmploying DL-based image analysis on histological slides, we could not predict SLN status for unseen cases in the INSEMA trial and other predominantly luminal cohorts.

MAF1 is a predictive biomarker in HER2 positive breast cancer.

RNA polymerase III transcription is pivotal in regulating cellular growth and frequently deregulated in various cancers. MAF1 negatively regulates RNA polymerase III transcription. Currently, it is unclear if MAF1 is universally deregulated in human cancers. Recently, MAF1 expression has been demonstrated to be altered in colorectal and liver carcinomas and Luminal B breast cancers. In this study, we analyzed clinical breast cancer datasets to determine if MAF1 alterations correlate with clinical outcomes in HER2-positive breast cancer. Using various bioinformatics tools, we screened breast cancer datasets for alterations in MAF1 expression. We report that MAF1 is amplified in 39% of all breast cancer sub-types, and the observed amplification co-occurs with MYC. MAF1 amplification correlated with increased methylation of the MAF1 promoter and MAF1 protein expression is significantly decreased in luminal, HER2-positive, and TNBC breast cancer subtypes. MAF1 protein expression is also significantly reduced in stage 2 and 3 breast cancer compared to normal and significantly decreased in all breast cancer patients, regardless of race and age. In SKBR3 and BT474 breast cancer cell lines treated with anti-HER2 therapies, MAF1 mRNA expression is significantly increased. In HER2-positive breast cancer patients, MAF1 expression significantly increases and correlates with five years of relapse-free survival in response to trastuzumab treatment, suggesting MAF1 is a predictive biomarker in breast cancer. These data suggest a role for MAF1 alterations in HER2-positive breast cancer. More extensive studies are warranted to determine if MAF1 serves as a predictive and prognostic biomarker in breast cancer.

The Role of MicroRNAs in Breast Cancer and the Challenges of Their Clinical Application.

MicroRNAs (miRNAs) constitute a subclass of non-coding RNAs that exert substantial influence on gene-expression regulation. Their tightly controlled expression plays a pivotal role in various cellular processes, while their dysregulation has been implicated in numerous pathological conditions, including cancer. Among cancers affecting women, breast cancer (BC) is the most prevalent malignant tumor. Extensive investigations have demonstrated distinct expression patterns of miRNAs in normal and malignant breast cells. Consequently, these findings have prompted research efforts towards leveraging miRNAs as diagnostic tools and the development of therapeutic strategies. The aim of this review is to describe the role of miRNAs in BC. We discuss the identification of oncogenic, tumor suppressor and metastatic miRNAs among BC cells, and their impact on tumor progression. We describe the potential of miRNAs as diagnostic and prognostic biomarkers for BC, as well as their role as promising therapeutic targets. Finally, we evaluate the current use of artificial intelligence tools for miRNA analysis and the challenges faced by these new biomedical approaches in its clinical application. The insights presented in this review underscore the promising prospects of utilizing miRNAs as innovative diagnostic, prognostic, and therapeutic tools for the management of BC.

Identification of potential diagnostic and prognostic biomarkers for breast cancer based on gene expression omnibus.

Breast cancer is regarded as a highly malignant neoplasm in the female population, posing a significant risk to women's overall well-being. The prevalence of breast cancer has been observed to rise in China, accompanied by an earlier age of onset when compared to Western countries. Breast cancer continues to be a prominent contributor to cancer-related mortality and morbidity among women, primarily due to its limited responsiveness to conventional treatment modalities. The diagnostic process is challenging due to the presence of non-specific clinical manifestations and the suboptimal precision of conventional diagnostic tests. There is a prevailing uncertainty regarding the most effective screening method and target populations, as well as the specificities and execution of screening programs. To identify diagnostic and prognostic biomarkers for breast cancer. Overlapping differentially expressed genes were screened based on Gene Expression Omnibus (GSE36765, GSE10810, and GSE20086) and The Cancer Genome Atlas datasets. A protein-protein interaction network was applied to excavate the hub genes among these differentially expressed genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, as well as gene set enrichment analyses, were conducted to examine the functions of these genes and their potential mechanisms in the development of breast cancer. For clarification of the diagnostic and prognostic roles of these genes, Kaplan-Meier and Cox proportional hazards analyses were conducted. This study demonstrated that calreticulin, heat shock protein family B member 1, insulin-like growth Factor 1, interleukin-1 receptor 1, Krüppel-like factor 4, suppressor of cytokine signaling 3, and triosephosphate isomerase 1 are potential diagnostic biomarkers of breast cancer as well as potential treatment targets with clinical implications. The screening of biomarkers is of guiding significance for the diagnosis and prognosis of the diseases.

The Potentiality of Prostate-Specific Antigen as a Prognostic Biomarker in Breast Cancer.

Background Serum prostate-specific antigen (PSA) is a well-established marker that can be measured as an indicator for screening, diagnosing, and managing prostate cancer due to its advanced tissue specificity. Numerous studies have revealed that free PSA is the predominant molecular form of PSA in breast cancer cases. In contrast, total PSA is prevalent in benign breast tumor cases and healthy females. This case-control study aims to measure PSA levels among individuals with breast cancer in order to establish PSA as a prognostic biomarker. Methods The study involved 150 female subjects between the ages of 18 and 70 and was conducted between 2013 and 2014. The subjects were then categorized into three groups: those with malignant breast cancer, those with benign breast tumors, and the control group with no history of malignant or benign breast tumors. Participants were asked to complete a lifestyle questionnaire and interview using hospital medical records to establish past and pertinent patient medical history. These cases were acquired from the 7th of October Hospital's surgery department and Benghazi Central Hospital's oncology clinic in Libya. Sandwich-type ELISA's were used for PSA quantitation, while the Wilcoxon Rank-Sum test was used to identify statistically significant differences between total PSA and free PSA measurements within each patient group. Results This study did not reveal significant statistical differences in total PSA levels between breast cancer cases and control groups (p=0.200), or between breast cancer and fibroadenoma patients (p=0.472). However, there was a significant difference in F-PSA levels between breast cancer and fibroadenoma cases (p=0.0001). Neither total-PSA (p=0.200) nor F-PSA (p=0.262) levels showed significant differences between breast cancer cases and controls. This study paved the way for further investigations into PSA's role in breast cancer. Despite its limitations, it offers an opportunity to delve deeper into understanding PSA's potential role and use in breast cancer. Conclusion A comprehensive statistical analysis revealed a positive correlation between F-PSA levels and breast cancer diagnosis. The findings suggest that PSA may serve as a prognostic biomarker for breast cancer. This may contribute to improved customized treatment approaches, offering precise and accurate risk assessments, understanding breast cancer biology, and improving health outcomes for patients with breast cancer.

Identification of prognostic biomarkers of breast cancer based on the immune-related gene module

Breast cancer (BC) is highly malignant and its mortality rate remains high. The development of immunotherapy has gradually improved the prognosis and survival rate of patients. Therefore, identifying molecular markers concerned with BC immunity is of great importance for the treatment of this disease. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) was utilized as the training set while the BC expression dataset from the gene expression omnibus database was taken as the validation set here. Weighted gene co-expression network analysis combined with Pearson analysis and Tumor immune estimation resource (TIMER) was used to obtain immune cell-related hub gene module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on this module. Then, receiver operating characteristic curves combining Kaplan–Meier was used to evaluate the effectiveness of the model. Feature genes were screened and the independence of risk score was evaluated by univariate and multivariate Cox analyses. Differences in immune characteristics were analyzed via single-sample gene set enrichment analysis and CIBERSORT, and differences in gene mutation frequency were assessed via GenVisR analysis. Finally, the expression levels of prognostic feature genes in BC cells were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). In this study, cell immune-related gene modules in TCGA-BRCA were successfully excavated, and a five-gene (TNFRSF14, NFKBIA, DLG3, IRF2, and CYP27A1) prognostic model was established. The prognostic model could effectively forecast the prognosis and survival rate of BC patients. The result showed that human leukocyte antigen-related proteins and macrophage M2 scores were remarkably highly expressed in the high-risk group, whereas CD8+ T cells, natural killer cells, M1, and other anti-tumor cells were lowly expressed. The model could be used as an independent prognostic factor to predict the prognosis of BC patients. The results of qRT-PCR validation were consistent with the results in the database, that is, except DLG3, the other four feature genes were lowly expressed in BC. The five-gene model established in this study can predict the prognostic and immune mode of BC patients effectively, which is anticipated to become a feasible molecular target for BC therapy.