Breast Cancer Research Articles

Integrated pan-cancer analysis and experimental verification of the roles of meiotic nuclear divisions 1 in breast cancer

IntroductionThe aberrant up-regulation of meiotic nuclear division 1 (MND1) in somatic cells is considered as one of the driving factors of oncogenesis, whereas its expression and role in breast invasive cancer (BRCA) remain unclear. Hence, this study embarked on a comprehensive evaluation of MND1 across various cancers and identified its roles in BRCA. MethodsBased on publicly available databases, including but not limited to UCSC Xena, TCGA, GTEx, GEO, STRING, GeneMANIA, and CancerSEA, we evaluated the expression patterns, genomic features, and biological functions of MND1 from a pan-cancer viewpoint and delved into the implications of MND1 in the prognosis and treatment of BRCA. Further molecular biology experiments were undertaken to identify the role of MND1 in proliferation, migration, and apoptosis in BRCA cells. ResultsElevated levels of MND1 were notably observed in a wide array of tumor types, especially in BRCA, COAD, HNSC, LIHC, LUAD, LUSC, STAD, and UCEC. Elevated MND1 expression was markedly associated with shortened OS in several tumors, including BRCA (HR=1.52 [95%CI, 1.10-2.09], P=0.011). The up-regulation of MND1 in BRCA was validated in external cohorts and clinical samples. Survival analyses demonstrated that elevated MND1 expression was associated with decreased survival for patients with BRCA. Co-expressed genes of MND1 were identified, and subsequent pathway analyses based on significantly associated genes indicated that MND1 plays key roles in DNA replication, cell cycle regulation, and DNA damage repair. The observed abnormal elevation and activation of MND1 led to increased proliferation and migration, along with decreased apoptosis in BRCA cells. ConclusionsMND1 emerges as a promising biomarker for diagnostic and therapeutic targeting in various cancers, including BRCA. The abnormal up-regulation and activation of MND1 are linked to carcinogenesis and poor prognosis among BRCA patients, which may be attributed to its involvement in HR-dependent ALT, warranting further scrutiny.

Axillary clearance and chemotherapy rates in ER+HER2− breast cancer: secondary analysis of the SENOMAC trial

Randomized trials have shown that axillary clearance (AC) can safely be omitted in patients with sentinel lymph node-positive breast cancer. At the same time, de-escalation of chemotherapy in postmenopausal patients with ER+HER2- breast cancer may depend on detailed axillary nodal stage. The aim of this pre-specified secondary analysis of the SENOMAC trial was to investigate whether the choice of axillary staging affected the proportion of patients receiving adjuvant chemotherapy, and recurrence-free survival(RFS). Proportion receiving adjuvant chemotherapy was calculated according to AC or sentinel lymph node biopsy (SLNB) only, menopausal status, and region of inclusion, for 2168 patients with clinically node-negative ER+HER2- breast cancer and 1-2 sentinel lymph node macrometastases included in the SENOMAC trial. In premenopausal patients, 514 out of 615 patients (83.6%) received adjuvant chemotherapy with no significant difference between randomization arms. In postmenopausal patients, the proportion receiving chemotherapy varied considerably by region and country (36.0-82.4%). In Denmark, where 194 out of 539 postmenopausal patients (36.0%) received adjuvant chemotherapy, rates differed significantly between the AC and the SLNB only arm (41.3% vs 31.4%, p=0.019). After a median follow-up of 44.88 months for Danish postmenopausal patients, no significant difference was seen in 5-year RFS, which was 91% (85.6%-96.6%) for the SLNB only and 90.9% (86.3%-95.6%) for the AC arm (p=0.42). When omitting axillary clearance, and thus reducing the risk of long-term arm morbidity, potential under-treatment of postmenopausal patients with ER+HER2- breast cancer may require the development of new predictive and imaging tools. Swedish Research Council, Swedish Cancer Society, Nordic Cancer Union, Swedish Breast Cancer Association.

Integrating Bioinformatics and Drug Sensitivity Analyses to Identify Molecular Characteristics Associated with Targeting Necroptosis in Breast Cancer and their Clinical Prognostic Significance.

Breast cancer accounts for over 1.8 million new cases worldwide annually, and prompt diagnosis and treatment are imperative to prevent mortality. Necroptosis, a form of programmed cell death, is thought to be a critical pathway for cancer cell apoptosis, yet, its relationship with breast cancer progression and molecular mechanisms remains largely unexplored. Our study aims to investigate the molecular characteristics and clinical prognostic value of necroptosis-related genes in breast cancer using a comprehensive approach that involves integrated bioinformatics analysis along with drug sensitivity assessment. Transcriptional, clinical, and tumor mutation burden (TMB) data related to breast cancer from the TCGA and GEO databases were integrated, and the necroptosis gene set was downloaded from the GSEA website for analysis. The screening conditions were set as adjusted p < 0.05 and |log2FC(fold change)| > 0.585 to screen for differential expression genes related to breast cancer necroptosis. Survival prognosis analysis was conducted on breast cancer necroptosis genes. Further analysis was conducted on prognosis-related necroptosis genes, including immune infiltration analysis and GO/KEGG enrichment analysis, to explore the potential biological functions and signaling pathway mechanisms of breast cancer necroptosis genes. Drug sensitivity screening was conducted on the prognosis-related necroptosis to identify potential drugs that target the promotion of necroptosis gene expression, and ultimately, single-gene analysis was performed on the core target genes obtained. Through integrated bioinformatics analysis, 29 differentially expressed mRNAs related to BRCA-Necroptosis were identified, including 18 upregulated mRNAs and 11 downregulated mRNAs. In addition, single-factor analysis of differential genes showed that the expression of HSPA4, PLK1, TNFRSF1B, FLT3, and LEF1 was closely related to BRCA survival prognosis. Based on the expression of these genes, a breast cancer prognosis model was constructed, and it was found that the area under the curve (AUC) of the curve of the risk genes for necroptosis was the largest, indicating that these genes have a certain clinical predictive significance for the occurrence and prognosis of BRCA. Additionally, there were significant differences in clinical characteristics of BRCA patients with different necroptosis gene expressions. Furthermore, GSVA and immune infiltration analysis revealed that Necroptosis-DEGs mainly affect the occurrence and progression of BRCA by participating in immune functions such as APC co-inhibition, APC costimulation, CCR, checkpoint, as well as infiltrating immune cells such as B cells naive, plasma cells, and T cells CD8. Moreover, the necroptosis gene group column chart indicated a 1-year survival rate of 0.979, a 3-year survival rate of 0.883, and a 5-year survival rate of 0.774. The necroptosis gene group and column chart are important indicators for evaluating BRCA prognosis. Finally, drug sensitivity screening of BRCA-Necroptosis genes showed that compounds such as A- 770041, AC220, AP-24534, Bexarotene, and BMS-509744 have certain effects as potential targeted drugs for the treatment of BRCA necroptosis genes. Necroptosis genes are implicated in the pathogenesis and progression of breast cancer and are thought to impact the prognosis and response to drug treatments in individuals with BRCA. Consequently, understanding the role of these genes in breast cancer may aid in identifying more precise and efficacious therapeutic targets.

Ballet after breast cancer: A qualitative evaluation of a novel 16-week ballet intervention for women after breast cancer

ObjectivesOur evaluation of the pilot study- “Ballet after breast cancer”, demonstrated that a classical ballet program delivered online and/or face-to-face is feasible and acceptable, with promise for improving health and wellbeing following breast cancer. The present study aimed to explore participant experiences of the 16-week ballet intervention, and their perceptions of the future potential of ballet in the context of breast cancer. DesignQualitative analysis of focus group discussions. MethodsIntervention participants were invited to take part in online focus groups to discuss: Perceived changes resulting from ballet participation, content of the ballet classes, delivery of the ballet program, suitability of ballet for future implementation during/after active cancer treatment. Themes were generated using an inductive approach, and feedback/recommendations for future implementation of “Ballet after breast cancer” were summarised. ResultsSeventeen women previously treated for breast cancer (54.4 ​± ​11.9 years; 54.2 ​± ​43.5 months post-diagnosis) took part in one of three focus groups. Themes generated through discussion included: Embracing normal; Connection to others; Acceptance without judgement; Engagement; Learning and progress. Participants described perceived improvements in physical, psychosocial, and cognitive wellbeing, and identified some limitations of online attendance compared to face-to-face. The personal attributes and experience of the ballet instructor were identifed as key determinants of program engagement and enjoyment. Participants reported a preference for classes specifically for people with lived experience of breast cancer. Recommendations to improve future ballet programs during/after breast cancer treatment included: Hybrid delivery model; Opportunities to socialise with classmates; Access to additional learning resources. ConclusionsParticipants offered novel and valuable insight into their experiences of the “Ballet after breast cancer” program, providing guidance for future delivery of ballet across the cancer continuum.

Diagnostic Efficacy of Multimodal Ultrasound for Mass and Non-Mass Enhancements in Dynamic Contrast-Enhanced MRI of Idiopathic Granulomatous Mastitis and Breast Cancer

PurposeIdiopathic granulomatous mastitis (IGM) poses diagnostic challenges due to its diverse clinical and radiological presentations, often mimicking malignancies. This study aimed to assess the diagnostic efficacy of multimodal ultrasound for mass and non-mass enhancements in Dynamic Contrast-Enhanced MRI (DCE-MRI) of IGM and breast cancer. MethodsA retrospective analysis involved patients confirmed histopathologically with IGM and BC. All patients underwent conventional ultrasound (C-US), ultrasound elastography (UE), contrast-enhanced ultrasound (CEUS), and DCE-MR examinations. Blind experienced radiologists assessed imaging findings. Diagnostic accuracy, sensitivity, and specificity were calculated for mass and non-mass enhancements. ResultsFor mass enhancements (ME), multimodal ultrasound demonstrated strong efficacy (AUC = 0.8651, 95% CI: 0.7431 to 0.9871), exhibiting high sensitivity (83.3%) and specificity (92.4%) in differentiating IGM from breast cancer. However, for non-mass enhancements (NME), multimodal ultrasound showed limited accuracy (AUC = 0.6306) with lower sensitivity (65.6%) and specificity (81.2%) in distinguishing between IGM and breast cancer. ConclusionMultimodal ultrasound displayed good diagnostic efficacy for mass enhancements in DCE-MRI for idiopathic granulomatous mastitis and breast cancer, while for non-mass enhancement patterns, DCE-MRI remains the most valuable radiological modality for comprehensively assessing this condition's complexities.

Enhancing breast cancer diagnosis: a comparative analysis of feature selection techniques

Breast cancer is a significant contributor to female mortality, emphasizing the importance of early detection. Predicting breast cancer accurately remains a complex challenge within medical data analysis. Machine learning (ML) algorithms offer valuable assistance in decision-making and diagnosis using medical data. Numerous research studies highlight the effectiveness of ML techniques in improving breast cancer prediction. Feature selection plays a pivotal role in data preprocessing, eliminating irrelevant and redundant features to minimize feature count and improve classification accuracy. This study focuses on optimizing breast cancer diagnostics through feature selection methods, specifically genetic algorithms (GA) and particle swarm optimization (PSO). The research involves a comparative analysis of these methods and the application of a diverse set of ML classification techniques, including logistic regression (LR), support vector machine (SVM), decision tree (DT), and ensemble methods like random forest (RF), AdaBoost, and gradient boosting (GB), using a breast cancer dataset. The models' performance is subsequently evaluated using various performance metrics. The experimental findings illustrate that PSO achieved the highest average accuracy, reaching 99.6% when applied to AdaBoost, while GA attained an accuracy rate of 99.5% when employed with both AdaBoost and RF.

Synergistic anticancer efficacy of polydatin and sorafenib against the MCF-7 breast cancer cell line via inhibiting of PI3K/AKT/mTOR pathway and reducing resistance to treatment

Polydatin (PD), a glucoside derivative of resveratrol, has been investigated for its potential to mitigate sorafenib (SOF) side effects and combat multidrug resistance in cancer treatment. The study evaluated its mechanism of action for inhibiting the protein kinase B/mTOR pathway in promoting breast cancer proliferation. The combined PD and SOF have synergistic effects with a combination index (CI) < 1 in the liver (HepG2) and breast (MCF-7) cancer cell lines. Molecular docking studies were conducted to analyze interactions of PD& SOF with protein kinases as well as apoptotic and multidrug resistance proteins, including AKT1, PI3K, mTOR, Apaf-1, and ABCB1 in MCF-7 cells. Experimental validation through real-time PCR confirmed. PD has a strong binding affinity, particularly with AKT1 (-56 kcal/mol) and ABCB1 (-27.16 kcal/mol), a gene associated with multidrug resistance. These interactions were linked to anti-proliferative anti-angiogenic effects and reduced resistance to treatment, demonstrating PD has potential therapeutic benefits. Furthermore, PD combined with SOF induced apoptosis, inhibited cell growth, and arrested MCF-7 cells in the sub-G1 phase with increased intracellular ROS. This was accompanied by reduced expression of AKT1 and ABCB1 genes, reinforcing the anticancer efficacy of PD/SOF combination therapy. In conclusion, the findings suggest that PD/SOF could serve as a promising anticancer treatment strategy, warranting further investigation for potential clinical applications and mechanistic studies in vivo.

Integrating the Symptom Experience in Patients with Stage I-III Breast Cancer in China: A Qualitative Study

PurposeTo develop an in-depth understanding of the meaning of symptoms in the context of how women with stage I-III breast cancer in China coped with the effects of primary and adjuvant therapies for breast cancer. MethodA qualitative descriptive approach was used. A purposive sample of women diagnosed with stage I-III breast cancer were recruited from the “Be Resilient to Breast Cancer” study between November 2023 and March 2024. Data was collected from in person interviews using a semi-structured interview guide. Interviews were audio-recorded and transcribed verbatim. The framework analysis method was used to generate codes and themes. ResultsA sample of 17 women with breast cancer agreed to participate. The average age was 50.1 years (SD=8.45), and the majority (65%) had stage III. The overarching theme was Confronting Physical and Psychological Symptoms. The four themes explaining the experience were Changed Identity, Uncertainty, Finding Meaning and Seeking Support and Solace. Changed Identity and Uncertainty reflected the challenges of coping with multiple symptoms from the treatment. The themes of Finding Meaning and Seeking Support and Solace captured how women adapted a positive perspective to cope with the experience. ConclusionsThis study contributed to the evidence of the integration of the symptom experience in coping with breast cancer treatment in the context of a collectivist Chinese culture. It enhanced the understanding of the physical and psychological symptom experience of curative intent breast cancer therapy and offered insight into how women from China cope in early survivorship.

Radiotherapy and Increased Risk of Second Primary Cancers in Breast Cancer Survivors: An Epidemiological and Large Cohort Study

BackgroundRadiotherapy (RT) for breast cancer (BC) may raise the risk of second primary cancers (SPCs), a relationship inadequately studied. MethodsWe analyzed 248268 female BC patients from 9 SEER registries, 1988-2018 , identifying SPCs >5 years after initial treatment, comparing SPC risks between RT and non-RT cohorts using Fine-Gray and Poisson regressions. ResultsOf all participants, 55.4% received surgery and RT. The RT group had a higher SPC incidence, with excess incidence significantly dropped from 6.9% in 1990 to 0.2% in 2012. The 30-year SPC incidence was 24.69% in the RT cohort and 18.11% in the NRT cohort. RT increased the risk of SPCs(HR, 1.29 [95%CI,1.26-1.33];P<0.001), BC(HR, 1.58[1.52-1.64];P<0.001), cancer of respiratory system(HR, 1.21[1.13-1.30];P=0.013), skin cancer(HR, 1.26[1.10-1.44];P<0.001), leukemia(HR, 1.30[1.11-1.54];P=0.001), soft tissue cancer(HR, 1.78[1.34-2.37]; P<0.001), and eye & orbit cancer(HR, 2.21[1.02-4.80];P=0.044), except for reducing the risk of multiple myeloma (HR 0.76). Notably, RT-related risks(RR) for BC declined with increasing age and the year of BC diagnosed, increased with longer latency, but the dynamic RR for cancer of respiratory system presented the almost opposite trends. The RT cohort had higher standardized incidence ratios than the NRT cohort and general population. Although 15-year overall survival was similar between RT and NRT cohorts, SPC presence significantly lowered 30-year survival from 35.64% to 23.90%. ConclusionsRT might increase susceptibility to SPC in breast, respiratory system, skin, soft tissue, eye and orbit, and leukemia in BC survivors. Efforts should be made to timely diagnose SPCs based on their specific patterns to improve patient’s quality of life.

The Association Between Breast Cancer Predisposing Genetic Variants and Multifocal, Multicentric Breast Cancer.

Breast-conserving surgery is often discouraged in BRCA gene carriers with early onset breast cancer. The genetic variant carrier breast cancers are more likely to be multifocal or multicentric (MFMC). This retrospective study includes newly diagnosed patients with breast cancer undergoing genetic testing between 2010 and 2021 within the Johns Hopkins Regional Health System. After excluding patients who received neoadjuvant chemotherapy or stage IV breast cancers, patients were divided into two groups: those who tested positive for a variant recognized by the National Comprehensive Cancer Network as predisposing the patient to breast cancer (ATM, BRCA1, BRCA2, CHEK2, NF1, PALB2, RAD51C, RAD51D, and TP53) and those who tested negative. Pathologic features of the tumors were compared, focusing on evidence for MFMC disease, defined as more than one malignant foci more than 5mm apart. Among the 282 eligible cases, 69 (24%) were positive for a genetic variant. The variant carriers were younger at diagnosis (p < 0.001), more likely to have invasive ductal carcinoma (p = 0.03), more likely to have undergone mastectomy (p = 0.03), and more likely to have a grade 3 cancer (p = 0.003). Variant carriers were not more likely to have MFMC disease (28% vs. 22%, p = 0.4). A positive genetic variant was not a predictor of MFMC within the entire cohort [odds ratio (OR):1.3, 95% confidence interval (CI) 0.6-2.6, p = 0.5). Genetic variant carrier cancers are not more likely to be MCMF than sporadic cancers.

Is there an association between mastitis and breast cancer? a retrospective cohort study from Germany.

The aim of the study was to explore the association between mastitis and subsequent breast cancer. This retrospective cohort study included women aged ≥ 18years with an initial mastitis diagnosis from 315 office-based gynecologists in Germany between January 2005 and December 2021. Women without mastitis were matched to women with mastitis using propensity score matching based on age, index year, average yearly consultation frequency during the follow-up period, and coexisting diseases such as obesity, benign mammary dysplasia, hypertrophy of the breast, unspecified lump of breast, and other disorders of the breast. The 10-year cumulative incidence of breast cancer for the mastitis-cohort and non-mastitis-cohort was studied with Kaplan-Meier curves using the log-rank test. The association between mastitis and breast cancer was studied separately for four age groups with univariable Cox regression analyses. In the follow-up period of 7months to 10years after the index date, 2.9% of mastitis patients and 2.4% of matched non-mastitis patients were diagnosed with breast cancer. A Cox regression analysis revealed a significant association between mastitis and subsequent breast cancer (HR: 1.37; 95% CI: 1.11-1.70). According to the age-stratified analyses, a strong and significant association was only observed in the age group > 50years (HR: 1.73; 95% 1.25-2.40). The findings of our retrospective cohort study support an association between mastitis and subsequent breast cancer diagnoses in women aged > 50years. The pathophysiological basis and possibility of confounders however requires further investigation.

Breast cancer identification using machine learning and hyperparameter optimization

Breast cancer identification can be analyzed through genomic analysis using gene expression data, one type of which is mRNA. This involves analyzing gene expression patterns of breast tissue samples to distinguish breast cancer from healthy tissue or to differentiate subtypes of different breast cancers. This research developed the right computational model for breast cancer classification using machine learning and hyperparameter optimization algorithms. The primary objective of this research is to utilize various machine learning algorithms to classify breast cancer based on gene expression and enhance the models developed in previous studies. This paper provides an extensive literature review of prior breast cancer classification research and offers new theoretical perspectives. This research used a problem-solving approach with conventional machine learning techniques, most notably the decision tree. It also evaluates other machine learning algorithms for comparison, including k-nearest neighbor, naïve bayes, random forest, extra tree classifier, and support vector machine. The evaluation process used classification reports that provide insight into the precision, recall, F1-score, and accuracy of each machine learning model. The evaluation results show that the performance of the decision tree algorithm model is superior and impressive, achieving 99.73% accuracy and a score of 1 for precision, recall, and F1-score.

Study of NALT1 and CTBP1-AS1 lncRNAs expression levels in triple-negative breast cancer patients in the Iranian population

BackgroundTriple-negative breast cancer (TNBC), an aggressive subtype with a poor prognosis, is notably difficult to treat. Emerging research highlights the significant roles of long non-coding RNAs (lncRNAs) in cancer biology. LncRNAs, such as NALT1 and CTBP1-AS1 are implicated in oncogenic processes; this study hypothesizes that NALT1 and CTBP1-AS1 are overexpressed in TNBC tissues compared to adjacent non-tumor tissues and may serve as biomarkers. Methods: One hundred pairs of tumor and adjacent non-tumor tissues were obtained from female patients with triple-negative breast cancer. After extracting RNA, cDNA synthesis was carried out for all samples. Quantitative real-time PCR (qRT-PCR) was employed to assess differential gene expression. Results: The expression of NALT1 (p-value <0.0001) and CTBP1-AS1 (p-value <0.0002) lncRNAs increased in TNBC tumor tissues in comparison to adjacent non-tumor tissues. A statistically positive correlation (ρ = 0.5844, p < 0.0001) was observed between the expression levels of NALT1 and CTBP1-AS1 in breast cancer patients. The ROC analysis indicated that NALT1 (AUC = 0.718, specificity = 61 %, sensitivity = 70 %) shows moderate potential and CTBP1-AS1 (AUC = 0.648, specificity = 65 %, sensitivity = 55 %) exhibits poor potential as a diagnostic biomarker for breast cancer. Conclusion: This study shows that NALT1 and CTBP1-AS1 lncRNAs are upregulated in TNBC tissues. Additionally, a positive correlation exists between their expression levels in breast cancer. Further research is needed to understand their mechanisms as molecular biomarkers.

Crocin Potentiates Anti-tumor Properties of 5-FU by Regulating Cell Proliferation and Tumor Necrosis in Breast Cancer

Introduction: Breast cancer is one of the most prevalent malignancies among women around the world. Crocus sativus, a natural food coloring and flavoring, has potent anti-tumor properties. The aim of the current study was to investigate the therapeutic potential of crocin, the main pharmacological active component of saffron, either alone or in combination with the standard chemotherapeutic treatment, 5-FU, in Breast cancer (BC) progression in both cellular and animal models. Material and Methods: MTT, Real-Time PCR, Western Blotting, Hematoxylin and eosin (H&amp;E) tissue staining were applied to determine the anti-tumor properties of crocin in in vitro and in vivo samples. Results: Our findings showed that crocin decreased breast cancer cell proliferation by suppressing cyclin D1 expression and Wnt/β-catenin signaling activation. Moreover, this molecule improved 5- FU anti-cancer activities by decreasing the tumor volume and weight, increasing tumor necrosis, and suppressing tumor inflammation in an animal model. Inflammation-associated anti-cancer activity of crocin is mediated by the down-regulation of pro-inflammatory genes, including IFN-γ and IL-1β, as well as inhibition of oxidative stress responses within the tumor environment. Conclusion: This is the first study demonstrating the potent anti-tumor properties of crocin against BC progression. Our results suggest that this effective and low-toxic molecule could be a promising agent for reducing BC tumor progression when administered either alone or in combination with standard treatment in breast cancer patients.

A machine learning based framework for breast cancer prediction using biomarkers

&lt;p&gt;Breast cancer is the most frequent cancer in women and the second-leading cause of cancer-related deaths globally. The main problems in managing breast cancer are high heterogeneity and the formation of therapeutic resistance. White blood cells, omics and large Wisconsin diagnostic breast cancer datasets present the three-decade genomic revolution and advance the understanding of cellular function. The precision of cancer diagnosis has also increased over the past decades. High throughput sequencing, screening, and artificial intelligence technologies have significantly improved and increased the methodologies used for diagnosis, prognosis, and therapy. This paper follows several phases of breast cancer, studies datasets and evaluate many algorithms of machine learning (ML) used for analysis and feature selection i.e. k-means, similarity correlation, genetic algorithm, and principal component analysis, have been used to recognize the subset of proteins with the highest significance for breast cancer prediction by using different biomarkers. The best correlation, as determined by Pearson correlation, between copy number and protein is 0.014, and the accuracy achieved by the genetic algorithm is 93.5% using multi-omics datasets.&lt;/p&gt;

Beyond the Genome: Deciphering the Role of MALAT1 in Breast Cancer Progression.

The MALAT1, a huge non-coding RNA, recently came to light as a multifaceted regulator in the intricate landscape of breast cancer (BC) progression. This review explores the multifaceted functions and molecular interactions of MALAT1, shedding light on its profound implications for understanding BC pathogenesis and advancing therapeutic strategies. The article commences by acknowledging the global impact of BC and the pressing need for insights into its molecular underpinnings. It is stated that the core lncRNA MALAT1 has a range of roles in both healthy and diseased cell functions. The core of this review unravels MALAT1's multifaceted role in BC progression, elucidating its participation in critical processes like resistance, invasion, relocation, and proliferating cells to therapy. It explores the intricate mechanisms through which MALAT1 modulates gene expression, interacts with other molecules, and influences signalling pathways. Furthermore, the paper emphasizes MALAT1's clinical significance as a possible prognostic and diagnostic biomarker. Concluding on a forward-looking note, the review highlights the broader implications of MALAT1 in BC biology, such as its connections to therapy resistance and metastasis. It underscores the significance of deeper investigations into these intricate molecular interactions to pave the way for precision medicine approaches. This review highlights the pivotal role of MALAT1 in BC progression by deciphering its multifaceted functions beyond the genome, offering profound insights into its implications for disease understanding and the potential for targeted therapeutic interventions.

Phytochemical examination of Cistus laurifolius Extract and its Impact on Cytotoxicity, Apoptosis and Oxidative Stress in Colorectal and Breast Cancer Cell Lines

IntroductionColon and breast cancer are the most common types of cancer worldwide. This study investigatesd the anticancer properties of Cistus laurifolius L. leaf extract on breast cancer (MCF-7) and human colon cancer (Caco-2) cell lines. MethodsThe research involved meticulous collection, drying and processing of C. laurifolius leaves to extract bioactive compounds, subsequently analyzed for phenolic content using advanced LC-MS/MS technology. Cell viability of the extract on MCF-7 and Caco-2 cells was demonstrated by MTT test. Levels of critical apoptotic markers (Bad, Bax/Bcl-2, Bax/Bcl-xl, Caspase-9) and Total Antioxidant Capacity (TAC) and Total Oxidant Capacity (TOC), which affect the antioxidant system, were evaluated by the ELISA method. Identification of phenolic compounds, including quercetin and rutin, through target prediction analysis enriches our understanding of bioactive molecules. ResultsThe results of the study showed that C. laurifolius extract inhibited cell proliferation time and dose-dependent on Caco-2 and MCF-7 cells (P<0.05). TAC, Bax/Bcl-2 and Bax/Bcl-xl ratios in MCF-7 and Caco-2 cells increased in a dose-dependent manner compared to the control group. In MCF-7 cells, TAC (p<0.05; p<0.01), Bax/Bcl-2 (p<0.001; p<0.0001) and Bax-Bcl-xl (p<0.01) ratios increased at 24 hours compared to the control group. In Caco-2 cells, TAC (p<0.001), Bax/Bcl-2 ratios increased at 48 hours (p<0.05), while Bax-Bcl-xl ratios decreased (p<0.01; p<0.001) compared to the control group. ConclusionC. laurifolius leaf extracts emerge as a promising anticancer candidate, hindering cell proliferation and inducing apoptosis in colon and breast cancer cells. The classification of bioactive molecules may facilitate further clinical therapeutic interventions targeting colon and breast cancer.

In silico screening and identifying phytoconstituents of Withania somnifera as potent inhibitors of BRCA1 mutants: A therapeutic against breast cancer

Breast CAncer gene 1 (BRCA1) is an anti-oncogene that helps the cell repair damaged DNA and preserve genetic material. BRCA1 also acts as a cell growth suppressor and produces tumor suppressor gene (TSG) proteins, i.e., BRCA1 protein. Remarkably, BRCA1 mutations account for 90 % of hereditary breast cancer and a majority of hereditary ovarian cancer. Hence, we have considered three mutants of BRCA1 (R1699W, R1699Q, T1700A) in this study and adopted an in-silico approach to find the best possible phytochemical to inhibit these mutated proteins, enabling early breast cancer diagnosis. Perceiving the importance, many natural molecules from ancient medicinal plants are considered for molecular docking. Our findings suggest that though many molecules bind actively with the receptor's active site, the top three phytoconstituents (27-Deoxy-14-hydroxywithaferin A, Withacoagulin, Somniferanolide) of Withania somnifera, commonly known as Ashwagandha, have high binding affinities and suitable pharmacokinetic properties, making these natural compounds potential drug candidates. Further, molecular dynamics (MD) simulation and the binding free energy calculation show stability and thermodynamically favourable. We can, therefore, draw the conclusion that these lead compounds act as potential inhibitors against BRCA1. However, wet lab experiments and clinical trials are recommended to ascertain its efficacy, hence the development of novel BRCA1 inhibitors.

Developed improved lion optimization for breast cancer classification using histopathology images

Breast cancer, a prevalent kind of cancer, is a major health problem among women. Researchers recently achieved categorization effectiveness of breast cancer (BC) detection in histopathology picture database using convolutional neural networks (CNNs) of medical image processing. Although CNN method parameter settings were complex, employing breast cancer histopathological database (BCHD) data for categorization was valued as expensive. This research used uniform experimental design (UED) to solve these issues and improved lion optimization (ILO) breast cancer histopathology image categorization. To optimize the variables at UED-ILO, a regression method was employed. According to the experimental data, the proposed approach of UED-ILO (uniform experimental design based improved lion optimization) variable optimization provided a categorization accuracy rate of 84.41%. Finally, the proposed approach can effectively increase classification accuracy, with results that outperform others of an equivalent nature.

Machine learning algorithms for breast cancer analysis: performance and accuracy comparison

Breast cancer, a leading cause of cancer mortality among women, necessitates early detection to improve survival rates. Traditional diagnostics face accuracy and speed limitations, prompting this study to explore machine learning for enhanced diagnostics. We applied bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), naive Bayes, support vector machine (SVM), and random forest to the Breast Cancer Wisconsin dataset, implementing a thorough methodology involving data preprocessing, feature extraction, and model validation. BERT led in accuracy at 92.5%, showcasing advanced algorithms' potential in medical diagnostics, with random forest 90.6%, SVM 89.3%, LSTM 88.7%, and naive Bayes 85.2%; also showing promising results. The study underscores the importance of incorporating machine learning, especially BERT, into clinical decision-making, potentially revolutionizing breast cancer diagnostics by improving accuracy and efficiency. We recommend healthcare practitioners integrate these algorithms into their diagnostic processes. Future research should reeefine these algorithms and extend their application to enhance patient care further.