Predictor Of Prognosis In Breast Cancer Research Articles

Exploring the role of aggrephagy-related signatures in immune microenvironment, prognosis, and therapeutic strategies of breast cancer.

Breast cancer (BC) is 1 of the most common malignant tumors among women globally. This study aimed to develop a prognostic signature based on aggrephagy-related genes (ARGs). Transcriptomic and clinical data for BC patients were downloaded from the cancer genome atlas and GEO databases. Differential expression analysis, univariate Cox proportional hazards regression and least absolute shrinkage and selection operator Cox regression were employed to construct a prognostic signature. Consensus clustering, evaluation of immune infiltration and drug sensitivity, and gene set enrichment analysis, and development of nomogram were performed. The expression of ARGs was validated using data from the Cancer Cell Line Encyclopedia and clinical samples. Eleven ARGs were abnormally expressed in BC, with 5 showing significant correlations with BC prognosis. Consensus clustering identified 2 molecular subtypes with distinct prognoses. A prognostic signature including 5 ARGs (VIM, TUBB1, TUBA3E, TUBA3D, TUBA1C) was developed, which showed high performance in predicting BC prognosis. The low-risk group showed enrichment in extracellular matrix organization and cell migration processes while chromosome separation was suppressed. Additionally, patients in this group also show activation in several signaling pathways including MAPK, PI3K-AKT, and cAMP pathways, whereas cell cycle and neutrophil extracellular trap formation were significantly inhibited. The signature was also associated with immune infiltration and drug sensitivity. A nomogram incorporating the risk signature, clinical stage and chemotherapy was constructed, demonstrating excellent performance in predicting prognosis. The expression of signature-related genes were validated in patients with BC. This study successfully constructed molecular subtypes and a prognostic signature based on ARGs in BC, and developed a nomogram.

Analysis of chromatin accessibility in peripheral blood mononuclear cells from patients with early-stage breast cancer.

Objective: This study was aimed at exploring a specific open region of chromatin in the peripheral blood mononuclear cells (PBMCs) of patients with breast cancer and evaluating its feasibility as a biomarker for diagnosing and predicting breast cancer prognosis. Methods: We obtained PBMCs from breast cancer patients and healthy people for the assay for transposase-accessible chromatin (ATAC) sequencing (n = 3) and obtained the GSE27562 chip sequencing data for secondary analyses. Through bioinformatics analysis, we mined the pattern changes for chromatin accessibility in the PBMCs of breast cancer patients. Results: A total of 1,906 differentially accessible regions (DARs) and 1,632 differentially expressed genes (DEGs) were identified via ATAC sequencing. The upregulated DEGs in the disease group were mainly distributed in the cells, organelles, and cell-intima-related structures and were mainly responsible for biological functions such as cell nitrogen complex metabolism, macromolecular metabolism, and cell communication, in addition to functions such as nucleic acid binding, enzyme binding, hydrolase reaction, and transferase activity. Combined with microarray data analysis, the following set of nine DEGs showed intersection between the ATAC and microarray data: JUN, MSL2, CDC42, TRIB1, SERTAD3, RAB14, RHOB, RAB40B, and PRKDC. HOMER predicted and identified five transcription factors that could potentially bind to these peak sites, namely NFY, Sp 2, GFY, NRF, and ELK 1. Conclusion: Chromatin accessibility analysis of the PBMCs from patients with early-stage breast cancer underscores its potential as a significant avenue for biomarker discovery in breast cancer diagnostics and treatment. By screening the transcription factors and DEGs related to breast cancer, this study provides a comprehensive theoretical foundation that is expected to guide future clinical applications and therapeutic developments.

Immunohistochemical expression of CD44 in invasive breast carcinoma

Background: In India, breast cancer forms the most common malignancy, accounting for 28.2% of all female cancers. Breast cancer consists of phenotypically diverse group of diseases. Due to different relapse abilities, doubling times, and different infiltrative capacities, it is important to identify potential biomarkers that could be used to screen high-risk patient and predict breast cancer prognosis in conjunction with classical pathological parameters. Aims and Objectives: The present study was conducted to evaluate immunohistochemical expression of CD44 in breast carcinoma and to correlate CD44 expression with other clinicopathological parameters and molecular classification of carcinoma breast. Materials and Methods: The present study was a prospective descriptive study conducted on 100 cases of carcinoma breast diagnosed on modified radical mastectomy specimens that were submitted to the Department of Pathology, Pt. B. D. Sharma, PGIMS, Rohtak, Haryana, over the period of 2 years. Results: Out of 100, 64 cases (64%) were CD44 positive. Out of these 64 cases, 9 (9%) cases were 1+, 34 (34%) cases were 2+, and 21 (21%) cases showed 3+ positive expression of CD44 in tumor cells. Loss of CD44 expression was seen in 36 (36%) cases. A statistically significant association of CD44 was seen with multifocality of the tumor, negative estrogen receptor/progesterone receptor (ER/PR) expression, and molecular subtypes. No statistically significant association of CD44 was seen with age, side of tumor, lymph node metastasis, lymphovascular invasion, ductal carcinoma in situ and Ki67, and HER2neu expression. Conclusion: A high CD44 expression in breast carcinoma correlates with aggressive tumor characteristics such as multifocality, negative ER/PR expression, and belonging to the basal subtype which is associated with poor prognosis. Conversely, low CD44 expression is linked to Luminal A and Luminal B subtypes which have good prognosis. These indicate the role of CD44 as a prognostic biomarker in the carcinoma breast.

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes.

Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating single-cell transcriptomic data with machine learning. We identified 44 significantly expressed NKRGs involved in cytokine and T cell-related functions. Using 101 machine learning algorithms, the Lasso + RSF model showed the highest predictive accuracy with nine key NKRGs. We explored cell-to-cell communication using CellChat, assessed immune-related pathways and tumour microenvironment with gene set variation analysis and ssGSEA, and observed immune components by HE staining. Additionally, drug activity predictions identified potential therapies, and gene expression validation through immunohistochemistry and RNA-seq confirmed the clinical applicability of NKRGs. The nomogram showed high concordance between predicted and actual survival, linking higher tumour purity and risk scores to a reduced immune score. This NKRG-based model offers a novel approach for risk assessment and personalized treatment in BC, enhancing the potential of precision medicine.

Prognostic Value and Immune Signatures of Anoikis-related Genes in Breast Cancer.

From databases of the Cancer Genome Atlas (TCGA) and GSE42568, transcriptome data of breast cancer patients was obtained. Then, anoikis-related genes (ANRGs) were identified and constructed a risk score system. As a threshold value, the median risk score was used to stratify patients into low-risk and high-risk groups. Kaplan-Meier analysis was then conducted to evaluate the prognostic ability of the risk score system, which was validated using GSE7390. Furthermore, we identified potential enrichment of function and tumor immune infiltration in the model. Finally, the biological functions of a risk gene (EPB41L4B) in breast cancer were investigated through in vitro experiments. We constructed a risk score system via 9 prognosis ANRGs (CXCL2, EPB41L4B, SLC7A5, SFRP1, SDC1, BHLHE41, SPINT1, KRT15, and CD24). The Kaplan-Meier analysis showed that both TCGA-BRCA (training set) and GSE7390 (testing set) patients with high-risk status had significantly worse survival outcomes. In addition, the calibration plots were in good agreement with the prognosis prediction. Breast cancer patients with immunosuppressive microenvironment could be screened using risk groups since risk scores were correlated negatively with ESTIMATE score, tumor-infiltration lymphocytes, immune checkpoints, and chemotactic factors. Furthermore, cellular viability and cell migration of cancerous breast cells were inhibited and apoptosis was promoted by down-regulation of EPB41L4B gene expression. Based on ANRGs, a 9-gene prognostic model could be developed to predict breast cancer prognosis; moreover, patients of the high-risk group were in an immunosuppressed tumor microenvironment.

Genome-wide identification and analysis of epithelial-mesenchymal transition-related RNA-binding proteins and alternative splicing in a human breast cancer cell line

Exploring the mechanism of breast cancer metastasis and searching for new drug therapeutic targets are still the focuses of current research. RNA-binding proteins (RBPs) may affect breast cancer metastasis by regulating alternative splicing (AS) during epithelial-mesenchymal transition (EMT). We hypothesised that during EMT development in breast cancer cells, the expression level of RBPs and the gene AS pattern in the cell were significantly changed on a genome-wide scale. Using GEO database, this study identified differentially expressed RBPs and differential AS events at different stages of EMT in breast cancer cells. By establishing the correlation network of differential RBPs and differential AS events, we found that RBM47, PCBP3, FRG1, SRP72, RBMS3 and other RBPs may regulate the AS of ITGA6, ADGRE5, TNC, COL6A3 and other cell adhesion genes. By further analysing above EMT-related RBPs and AS in breast cancer tissues in TCGA, it was found that the expression levels of ADAT2, C2orf15, SRP72, PAICS, RBMS3, APOBEC3G, NOA1, ACO1 and the AS of TNC and COL6A3 were significantly correlated with the prognosis of breast cancer patients. The expression levels of all 8 RBPs were significantly different in breast cancer tissues without metastasis compared with normal breast tissues. Conclusively, eight RBPs such as RBMS3 and AS of TNC and COL6A3 could be used as predictors of breast cancer prognosis. These findings need to be further explored as possible targets for breast cancer treatment.

Integrative analyses identified gap junction beta-2 as a prognostic biomarker and therapeutic target for breast cancer.

Increasing evidence has shown that connexins are involved in the regulation of tumor development, immune escape, and drug resistance. This study investigated the gene expression patterns, prognostic values, and potential mechanisms of connexins in breast cancer. We conducted a comprehensive analysis of connexins using public gene and protein expression databases and clinical samples from our institution. Connexin mRNA expressions in breast cancer and matched normal tissues were compared, and multiomics studies were performed. Gap junction beta-2 mRNA was overexpressed in breast cancers of different pathological types and molecular subtypes, and its high expression was associated with poor prognosis. The tumor membrane of the gap junction beta-2 mutated group was positive, and the corresponding protein was expressed. Somatic mutation and copy number variation of gap junction beta-2 are rare in breast cancer. The gap junction beta-2 transcription level in the p110α subunit of the phosphoinositide 3-kinase mutant subgroup was higher than that in the wild-type subgroup. Gap junction beta-2 was associated with the phosphoinositide 3-kinase-Akt signaling pathway, extracellular matrix-receptor interaction, focal adhesion, and proteoglycans in cancer. Furthermore, gap junction beta-2 overexpression may be associated with phosphoinositide 3-kinase and histone deacetylase inhibitor resistance, and its expression level correlated with infiltrating CD8+ T cells, macrophages, neutrophils, and dendritic cells. Gap junction beta-2 may be a promising therapeutic target for targeted therapy and immunotherapy and may be used to predict breast cancer prognosis.

Abstract PO1-07-11: Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets

Abstract Background: Intratumor heterogeneity (ITH) refers to variations observed among cancer cells within a single tumor, posing significant challenges in clinical practice due to its contribution to treatment resistance and unfavorable outcome. However, the inconvenience of multi-region biopsy and limitations of genomic sequencing based on limited tissue impede the practical detection of ITH. Consequently, there is an urgent need for a non-invasive method that comprehensively captures ITH from a holistic perspective of the entire tumor. Methods: We utilized a large multicenter dataset comprising dynamic contrast-enhanced magnetic resonance images from breast cancer patients (n = 1423) along with matched multiomics data (n = 468) to develop a non-invasive machine learning approach for measuring ITH. We extracted quantitative radiomic features from both the entire tumor and peritumor regions, with a specific focus on features associated with imaging heterogeneity. Using these radiomic features, we established an imaging ITH (IITH) model. The robustness of IITH evaluation was determined by assessing its correlation with genomic ITH (employing the mutant-allele tumor heterogeneity [MATH] algorithm) and pathological cellular ITH (analyzing variation in quantitative nuclear features extracted through digital pathology). Prognostic power was evaluated using Kaplan-Meier and multivariate Cox proportional hazards analyses. Integrated multiomics analyses were performed to investigate the molecular basis of different IITH subgroups. Results: We developed a non-invasive radiomic signature to quantify imaging ITH (IITH) in the FUSCC cohort. Breast cancer patients were retrospectively categorized into high and low IITH groups. Multivariate Cox analysis identified high IITH as an independent predictor of poor prognosis in breast cancer patients, even after adjusting for clinical risk factors such as tumor size, positive lymph nodes, clinical subtype and lymphovascular invasion status. These findings were further validated in the DUKE cohort, confirming the prognostic value of IITH. Moreover, we substantiated the robustness of IITH by demonstrating its association with genomic and pathological ITH. Multiomics analysis revealed the activation of oncogenic pathways and metabolic dysregulation in high-IITH tumors. Intriguingly, our investigation also highlighted ferroptosis as a vulnerability and potential therapeutic target in high-IITH tumors, which was further supported by evidence from the TCGA cohort. Conclusion: Radiomic-based assessment of ITH provides a non-invasive approach to comprehensively capture ITH and predict the prognosis of breast cancer patients. Targeting ferroptosis may hold promise as a treatment strategy for patients with high IITH. Citation Format: Guan-Hua Su, Yi Xiao, Chao You, Ren-Cheng Zheng, Shen Zhao, He Wang, Yi-Zhou Jiang, Ya-Jia Gu, Zhi-Ming Shao. Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-11.

Proximity-Guaranteed DNA Machine for Accurate Identification of Breast Cancer Extracellular Vesicles.

Breast cancer is one of the most diagnosed cancers worldwide. Precise diagnosis and subtyping have important significance for targeted therapy and prognosis prediction of breast cancer. Herein, we design a proximity-guaranteed DNA machine for accurate identification of breast cancer extracellular vesicles (EVs), which is beneficial to explore the subtype features of breast cancer. In our design, two proximity probes are located close on the same EV through specific recognition of coexisting surface biomarkers, thus being ligated with the help of click chemistry. Then, the ligated product initiates the operation of a DNA machine involving catalytic hairpin assembly and clusters of regularly interspaced short palindromic repeats (CRISPR)-Cas12a-mediated trans-cleavage, which finally generates a significant response that enables the identification of EVs expressing both biomarkers. Principle-of-proof studies are performed using EVs derived from the breast cancer cell line BT474 as the models, confirming the high sensitivity and specificity of the DNA machine. When further applied to clinical samples, the DNA machine is shown to be capable of not only distinguishing breast cancer patients with special subtypes but also realizing the tumor staging regarding the disease progression. Therefore, our work may provide new insights into the subtype-based diagnosis of breast cancer as well as identification of more potential therapeutic targets in the future.

Prognostic and clinicopathological value of systemic inflammation response index (SIRI) in patients with breast cancer: a meta-analysis

Background Many studies have explored the value of the systemic inflammation response index (SIRI) in predicting the prognosis of patients with breast cancer (BC); however, their findings remain controversial. Consequently, we performed the present meta-analysis to accurately identify the role of SIRI in predicting BC prognosis. Methods PubMed, Embase, Cochrane Library, and Web of Science databases were comprehensively searched between their inception and February 10, 2024. The significance of SIRI in predicting overall survival (OS) and disease-free survival (DFS) in BC patients was analyzed by calculating pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Results Eight articles involving 2,997 patients with BC were enrolled in the present study. According to our combined analysis, a higher SIRI was markedly associated with dismal OS (HR = 2.43, 95%CI = 1.42–4.15, p < 0.001) but not poor DFS (HR = 2.59, 95%CI = 0.81–8.24, p = 0.107) in patients with BC. Moreover, based on the pooled results, a high SIRI was significantly related to T3–T4 stage (OR = 1.73, 95%CI = 1.40–2.14, p < 0.001), N1–N3 stage (OR = 1.61, 95%CI = 1.37–1.91, p < 0.001), TNM stage III (OR = 1.63, 95%CI = 1.34–1.98, p < 0.001), and poor differentiation (OR = 1.25, 95%CI = 1.02–1.52, p = 0.028). Conclusion According to our results, a high SIRI significantly predicted poor OS in patients with BC. Furthermore, elevated SIRI was also remarkably related to increased tumor size and later BC tumor stage. The SIRI can serve as a novel prognostic biomarker for patients with BC.

Association between estrogen receptor alpha and aryl hydrocarbon receptor gene polymorphisms in the prognosis of breast cancer in Egypt

Breast cancer is the most malignant tumor among women in the world. Single nucleotide polymorphisms (SNPs) might better predict breast cancer prognosis. PvuII (T/C substitution), XbaI (A/G substitution), and aryl hydrocarbon (AhR) (G/A substitution) were evaluated as possible genetic prognostic factors for breast cancer. The aim of the current study was to assess the relation between PvuII (rs2234693), XbaI (rs9340799), and aryl hydrocarbon receptor gene polymorphisms AhR (rs2066853) in breast cancer prognosis. This was a case-control study that included 120 breast cancer patients classified into two groups. The first group included 60 patients with good prognostic factors, and the second group included 60 patients with poor prognostic factors. Blood samples were taken from all study participants to perform the genotyping assay. We found that positive genotypes of PvuII, XbaI, and AhR polymorphisms were strongly associated with better prognostic factors for breast cancer patients, while negative genotypes of PvuII and XbaI were more and significantly prevalent in poor prognostic breast cancer patients. We conclude that PvuII T/C, XbaI G/A, and AhR G/A alleles may be prognostic for breast cancer progression. ‎

ЗАСТОСУВАННЯ ГЛИБОКИХ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ КЛАСИФІКАЦІЇ МУЛЬТИМОДАЛЬНИХ ДАНИХ

Multimodal data analysis is gaining attention in recent research. Pu Liang et al. (2023) provide a comprehensive overview on multimodal machine learning, highlighting its founda-tions, challenges and achievements in recent years. More problem-oriented works propose new methods and applications for multimodal ML, such a Ngiam et al. (2011) propose to use joint audio and video data to improve speech recognition accuracy; Sun, Wand and Li (2018) describe application of multimodal classification for breast cancer prognosis prediction; Mao et al. (2014) propose an architecture of multimodal recurrent network to generate text de-scription of images and so on. However, such works usually focus on the task itself and meth-ods therein, and not on integrating multimodal data processing into other software systems. The goal of this research is to propose a way to conduct multimodal data processing, specifically as a part of a digital twin systems, thus efficiency and near-real-time operation are required. The paper presents an approach to conduct parallel multimodal data classification, adapting to available computing power. The method is modular and scalable and intended for in digital twin application as a part of analysis and modeling tools. Later, the detailed example of such a software module is discussed. It uses multimodal data from open datasets to detect and classify the behavior of pets using deep learning mod-els. Videos are processed using two artificial neural networks: YOLOv3 object detection net-work to process individual frames of the video and a relatively simple convolutional network to classify sounds based on their frequency spectra. Constructed module uses a producer-consumer parallel processing pattern and allows processing 5 frames per second of a video on available hardware, which can be sufficiently improved by using GPU acceleration or more paralleled processing threads.

Correction for: A novel six-microRNA-based model to improve prognosis prediction of breast cancer.

Correction for: A novel six-microRNA-based model to improve prognosis prediction of breast cancer.

Independent Validation of the BRENDA-Score Breast Cancer Prognosis Prediction Tool In Chinese Patients

BackgroundThe BRENDA-Score was developed and used to predict the prognosis of patients with breast cancer (BC). This study was performed to validate the use of this tool in Chinese patients with primary invasive BC patients. MethodsPatients underwent surgery for BC from January 2009 to December 2016. Discrimination was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Calibrations were assessed by comparing predicted and observed 5-year and 10-year metastasis-free survival (MFS) in the overall cohort and patient subgroups. ResultsA total of 2029 BC patients were enrolled. Kaplan–Meier analysis revealed significant differences in MFS risk groups (log-rank test P < .01). ROC analysis showed good accuracy for 5-year MFS (AUC 0.779) and fair accuracy for 10-year MFS (AUC 0.728). The BRENDA-Score accurately predicted 5-year and 10-year MFS in the entire cohort and in all other predefined subgroups, except for the 5-year MFS in the subgroup aged<40 years, which was overestimated (differences between the predicted and observed MFS were 6.7%, P < .05). The 5-year MFS rates of ER- positive and ER-negative patients were 90.9% and 80.6%, respectively (P < .05). The 10-year MFS rates of ER-positive and ER-negative patients were 78.0% and 73.7%, respectively (P = .25). ConclusionsThe BRENDA-Score accurately predicted 5-year and 10-year MFS. The results showed good validity, transportability, and potential clinical value. However, the results for 5-years MFS should be interpreted carefully in patients aged <40 years. After 10 years the value of the ER as a prognostic factor was less important.

The Correlation between Morpho-Dynamic Contrast-Enhanced Mammography (CEM) Features and Prognostic Factors in Breast Cancer: A Single-Center Retrospective Analysis.

Breast cancer, a major contributor to female mortality globally, presents challenges in detection, prompting exploration beyond digital mammography. Contrast-Enhanced Mammography (CEM), integrating morphological and functional information, emerges as a promising alternative, offering advantages in cost-effectiveness and reduced anxiety compared to MRI. This study investigates CEM's correlation with breast cancer prognostic factors, encompassing histology, grade, and molecular markers. In a retrospective analysis involving 114 women, CEM revealed diverse lesion characteristics. Statistical analyses identified correlations between specific CEM features, such as spiculated margins and irregular shape, and prognostic factors like tumor grade and molecular markers. Notably, spiculated margins predicted lower grade and HER2 status, while irregular shape correlated with PgR and Ki-67 status. The study emphasizes CEM's potential in predicting breast cancer prognosis, shedding light on tumor behavior. Despite the limitations, including sample size and single-observer analysis, the findings advocate for CEM's role in stratifying breast cancers based on biological characteristics. CEM features, particularly spiculated margins, irregular shape, and enhancement dynamics, may serve as valuable indicators for personalized treatment decisions. Further research is crucial to validate these correlations and enhance CEM's clinical utility in breast cancer assessment.

A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis.

Breast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients. BRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the 'limma' package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function. Our study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells. Our 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.

Correlation Between Quantitative Parameters Obtained by Dual Energy Spectral CT and Prognostic Histopathological Factors and Biomarkers in Breast Cancer

Rationale and ObjectivesTo investigate the correlation between quantitative parameters obtained by dual energy spectral computed tomography (DESCT) and various histopathological factors and biomarkers associated with the prognosis of breast cancer. Materials and MethodsQuantitative parameters such as iodine content (IC), normalized IC (nIC), iodine enhancement (IE) and normalized IE (nIE) were measured on virtual monochromatic images and iodine mapping images obtained from DESCT in 116 female breast cancer patients. The relationship between these parameters and prognostic biomarkers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status and Ki67 levels, as well as the correlation with histological grade (HG), lymphovascular invasion (LVI), and metastatic axillary lymphadenopathy (LAP) were evaluated. ResultsER-negative tumors had significantly higher values of IC, nIC, IE, and nIE compared to ER-positive tumors. PR-negative tumors had significantly higher values of IE and nIEc compared to PR-positive tumors. HER2 overexpressed and Ki-67 high proliferation tumors showed significantly higher values of all quantitative parameters compared to HER2 negative and Ki-67 low proliferation tumors. All quantitative parameters were significantly higher in HG 3 tumors, tumors with detected LVI, and tumors with metastatic axillary LAP compared to low-grade tumors, LVI-negative tumors and tumors without metastatic axillary lymph nodes, respectively. ConclusionQuantitative parameters of IC and IE obtained from DESCT have shown potential for predicting prognosis in breast cancer patients. Higher values of these parameters have been found to correlate with poor prognostic biomarkers and histopathological features. These results suggest that quantitative DESCT imaging may offer an additional benefit in the noninvasive prediction of breast cancer prognosis.

Electrochemical aptasensor fabricated by anchoring recognition aptamers and immobilizing redox probes on bipolar silica nanochannel array for reagentless detection of carbohydrate antigen 15-3.

Timely, convenient, and efficient detection of carbohydrate antigen 15-3 (CA15-3) levels in serum holds significant importance in early screening, diagnostic assistance and prognosis prediction of breast cancer. The development of efficient and convenient electrochemical aptasensors with immobilized redox probes for label-free detection of CA15-3 is highly desirable. In this work, a bipolar silica nanochannel array film (bp-SNA) with two distinct functional domains including nanochannels and an outer surface was employed for the immobilization of recognition ligands and electrochemical redox probes, enabling the construction of a probe-integrated aptasensor for reagentless electrochemical detection of CA15-3. Cost-effective and readily available indium tin oxide (ITO) was used as the supporting electrode for sequential growth of a negatively charged inner layer (n-SNA) followed by a positively charged outer layer (p-SNA). The preparation process of bp-SNA is convenient. Functionalization of amino groups on the outer surface of bp-SNA was modified by aldehyde groups for covalent immobilization of recognition aptamers, further establishing the recognition interface. Within the nanochannels of bp-SNA, the electrochemical redox probe, tri (2,2'-dipyridyl) cobalt (II) (Co(bpy)3 2+) was immobilized, which experienced a dual effect of electrostatic attraction from n-SNA and electrostatic repulsion from p-SNA, resulting in high stability of the immobilized probes. The constructed aptasensor allowed for reagentless electrochemical detection of CA15-3 ranged from 0.001U/mL to 500 U/mL with a low detection limit (DL), 0.13mU/mL). The application of the constructed aptasensor for CA15-3 detection in fetal bovine serum was also validated. This sensor offers advantages of a simple and readily obtainable supporting electrode, easy bp-SNA fabrication, high probe stability and good stability.

Construction of a ferroptosis-based prognostic model for breast cancer helps to discriminate high/low risk groups and treatment priority.

Breast cancer is a common malignant tumor associated with high morbidity and mortality. The role of ferroptosis, a regulated form of cell death, in breast cancer development and prognosis remains unclear. This study aims to investigate the relationship between ferroptosis-related genes and breast cancer and develop a prognostic model. RNA-seq expression datasets and clinical samples of breast cancer patients were obtained from public databases. Immunity- and drug resistance-related data were integrated. A preliminary screening was performed, resulting in the identification of 73 candidate ferroptosis factors. Univariate Cox regression analysis was conducted to select 12 genes, followed by LASSO Cox regression analysis to construct a prognostic risk prediction model consisting of 10 ferroptosis-related genes. The model was further characterized by immune cell infiltration. The expression levels of ferroptosis-related genes were validated in human breast cancer cell lines, and immunohistochemical (IHC) analysis was conducted on cancer specimens to assess ferroptosis-related protein expression. The study identified 10 ferroptosis-related genes that were significantly associated with breast cancer prognosis. The constructed prognostic risk prediction model showed potential for predicting the prognostic value of these genes. In addition, the infiltration of immune cells was observed to be a characteristic of the model. The expression levels of ferroptosis-related genes were confirmed in human breast cancer cell lines, and IHC analysis provided evidence of ferroptosis-related protein expression in cancer specimens. This study provides a novel prognostic model for breast cancer, incorporating 10 ferroptosis-related genes. The model demonstrates the potential for predicting breast cancer prognosis and highlights the involvement of immune cell infiltration. The expression levels of ferroptosis-related genes and proteins further support the association between ferroptosis and breast cancer development.

Multi-modal fusion network with intra- and inter-modality attention for prognosis prediction in breast cancer

Accurate breast cancer prognosis prediction can help clinicians to develop appropriate treatment plans and improve life quality for patients. Recent prognostic prediction studies suggest that fusing multi-modal data, e.g., genomic data and pathological images, plays a crucial role in improving predictive performance. Despite promising results of existing approaches, there remain challenges in effective multi-modal fusion. First, albeit a powerful fusion technique, Kronecker product produces high-dimensional quadratic expansion of features that may result in high computational cost and overfitting risk, thereby limiting its performance and applicability in cancer prognosis prediction. Second, most existing methods put more attention on learning cross-modality relations between different modalities, ignoring modality-specific relations that are complementary to cross-modality relations and beneficial for cancer prognosis prediction. To address these challenges, in this study we propose a novel attention-based multi-modal network to accurately predict breast cancer prognosis, which efficiently models both modality-specific and cross-modality relations without bringing in high-dimensional features. Specifically, two intra-modality self-attentional modules and an inter-modality cross-attentional module, accompanied by latent space transformation of channel affinity matrix, are developed to successfully capture modality-specific and cross-modality relations for efficient integration of genomic data and pathological images, respectively. Moreover, we design an adaptive fusion block to take full advantage of both modality-specific and cross-modality relations. Comprehensive experiment demonstrates that our method can effectively boost prognosis prediction performance of breast cancer and compare favorably with the state-of-the-art methods.