Breast Cancer Samples Research Articles

Detection of BRCA1and BRCA2 mutation for Breast Cancer in Sample of Iraqi Women above 40 Years

Breast cancer is the commonest cancer affecting women worldwide. Different studies have dealt with the etiological factors of that cancer aiming to find a way for early diagnosis and satisfactory therapy. The present study clarified the relationship between genetic polymorphisms of BRCA1 & BRCA2 genes and some etiological risk factors among breast cancer patients in Iraq. This investigation was carried out on 25 patients (all were females) who were diagnosed as breast cancer patients attended AL-Kadhemya Teaching Hospital in Baghdad and 10 apparently healthy women were used as a control, all women (patients and control) aged above 40 years. The Wizard Promega kit was used for DNA isolation from breast patients and normal individuals. By this method suitable quantities of DNA approximately (50 µl) with purity ranged from (1.7-1.9) were obtained from 100-200µg of fresh biopsy which had been taken from women breast patients. The extracted DNA was successfully used in amplification of BRCA1 & BRCA2 genes by PCR and some mutation were detected. The outcome of genetic analysis indicated that the percentage of 185delAG mutation was 16 (4 patients) whereas, the percentage of 5382insC mutation was 32 (8patients) in BRCA1 gene and the third mutation 6174delT in BRCA2 present in 3 patients only (12%). The study demonstrated that the frequency of BRCA1 mutation (48%) was higher than BRCA2 (12%) in this sample of Iraqi women with breast cancer.

Profiling mRNA and miRNA expression variations associated with cyclin-dependent kinase pathway in the low-grade luminal early breast cancer.

Luminal A and B subtypes of breast tumors have fluctuated in proliferation rates, which arise from cell cycle dysregulation in cancer. Besides, microRNAs can regulate various cell processes through integration with mRNA. miRNAs that target the cell cycle are significant because of their prediction capability of prognosis. The objective of this study is to discover the integration between miRNA-mRNA and miRNA-miRNA related to cyclin-dependent kinase. Thirty-four pairs of human primary breast cancer and tumor margin samples from luminal breast cancer patients were investigated to assess the expression levels of CCND1, E2F1, miR-124, miR-503, miR-449a, and miR-449b. Afterward, the expression levels of mRNAs and miRNAs were investigated by real-time PCR. Statistical analysis was conducted to compare the expression levels between breast cancer and corresponding normal tissues. The protein expressions of E2F1 and CCND1 were verified by western blotting. Further, the correlation between mRNAs and miRNAs was calculated. E2F1 was significantly increased in both luminal A and B patients, while CCND1 was upregulated only in luminal B. Significant differences in all miRNAs were detected in both luminal A and B biopsy specimens (p < 0.0001). The correlation analysis revealed a positive strong correlation between miR-124 and E2F1 in luminal A patient. Moreover, the correlation test confirmed the ability of miR-449a to increase the CCND1 gene in luminal B subtypes. Also, miRNA correlation exhibited the miRNA-miRNA interaction in luminal breast cancer. This study demonstrated the novel miRNA-mRNA and miRNA-miRNA interactions, providing new insights into the molecular integration in luminal A and B patients. The authors propose that this research could contribute to introducing valuable biomarkers for luminal cancerous cells.

Deciphering MOSPD1's impact on breast cancer progression and therapeutic response.

Motile Sperm Domain-Containing Protein 1 (MOSPD1) has been implicated in breast cancer (BC) pathophysiology, but its exact role remains unclear. This study aimed to assess MOSPD1 expression levels in BC versus normal tissues and investigate its diagnostic potential. MOSPD1 expression was analyzed in BC and normal tissues, with Receiver Operating Characteristic analysis for diagnostic evaluation. Validation was performed using immunohistochemistry. Functional studies included tumor growth assays, MOSPD1 suppression and overexpression experiments, and testing BC cell responses to anti-PD-L1 therapy. MOSPD1 expression was significantly higher in BC samples than normal tissues, correlating with poor clinical outcomes in BC patients. MOSPD1 suppression inhibited tumor growth, while overexpression accelerated it. Silencing MOSPD1 enhanced BC cell sensitivity to anti-PD-L1 therapy and decreased Th2 cell activity. In vivo experiments supported these findings, showing the impact of MOSPD1 on tumor growth and response to therapy. Elevated MOSPD1 levels in BC suggest its potential as a biomarker for adverse outcomes. Targeting MOSPD1, particularly with anti-PD-L1 therapy, may effectively inhibit BC tumor growth and modulate immune responses. This study emphasizes the significance of MOSPD1 in BC pathophysiology and highlights its promise as a therapeutic target.

A phenocopy signature of TP53 loss predicts response to chemotherapy

In preclinical studies, p53 loss of function impacts chemotherapy response, but this has not been consistently validated clinically. We trained a TP53-loss phenocopy gene expression signature from pan-cancer clinical samples in the TCGA. In vitro, the TP53-loss phenocopy signature predicted chemotherapy response across cancer types. In a clinical dataset of 3003 breast cancer samples treated with neoadjuvant chemotherapy, the TP53-loss phenocopy samples were 56% more likely to have a pathologic complete response (pCR), with a significant association between TP53-loss phenocopy and pCR in both ER positive and ER negative tumors. In an independent clinical validation in the I-SPY2 trial (N = 987), we confirmed the association with neoadjuvant chemotherapy pCR and found higher rates of chemoimmunotherapy response in TP53-loss phenocopy tumors compared to non-TP53-loss phenocopy tumors (64% vs. 28%). The TP53-loss phenocopy signature predicts chemotherapy response across cancer types in vitro, and in a proof-of-concept clinical validation is associated with neoadjuvant chemotherapy response across multiple clinical breast cancer cohorts.

Molecular correlates of social adversity in breast cancer.

48 Background: Neighborhood disadvantage (ND) is associated with shorter breast cancer (BCa) survival independent of individual, tumor, and treatment factors, suggesting more aggressive tumor biology in women from disadvantaged neighborhoods. This study evaluates how differential DNA methylation (DNAm), gene expression, and biologic pathways are wired with each other to delineate the regulatory landscape of ND on BCa. Methods: We leveraged 55 geocoded ER+/HER2- BCa samples from Hispanic White women enrolled in a prospective genomic-epidemiologic cohort study. Objective ND was evaluated using the Area Deprivation Index (ADI) and subjective ND was evaluated using the patient-reported Neighborhood Social Environment Adversity Survey and the Everyday Expanded Discrimination Survey. We analyzed the association between ND and (1) DNAm, (2) RNA transcription and miRNAs, and (3) blood concentration levels of DNAm co-factors (B12 and folate) from the same patient. To evaluate how the DNAm/expressed pathways are wired with each other to delineate the regulatory landscape of ND in these tumors, we performed correlation analyses among CpGs, genes, and miRNAs in the same topologically associated domain (TAD). We used Spearman correlation as our metric and adjusted the resulting p-values to FDR values. Results: The cohort was divided into low ND (ADI&lt;5; 31%) and a high ND (ADI&gt;=5; 69%). There were no significant differences between groups by age, race/ethnicity, genetic ancestry, BMI, smoking/alcohol, subtype, stage, or OncotypeDX scores. We discovered that high ND exhibits epigenetic deregulation of immune pathways, consistent with our findings of lower circulating B12 in high ND, suggesting activation of immune pathways through reduced systemic availability of methyl donors. RNA-seq identified higher tumor infiltration of immune cells in patients from high ND (e.g., Interferon alpha and gamma responses and inflammatory responses) consistent with aggressive biology. Estrogen response genes were downregulated suggesting that patients from high ND may develop estrogen-resistant tumors. Higher subjective ND scores discovered epigenetic changes on cell adhesion and calcium signaling genes, along with gene expression of E2F targets and cell cycle’s G2M checkpoint, indicative of a more proliferative/stemness signature. TAD analysis discovered significant developmental, immune, and signaling pathways when correlating DNAm with gene expression (e.g., estrogen response and EMT). Conclusions: In this multi-omic matched cohort, we discovered differentially methylated/expressed pathways, consistent with aggressive biology, wired with each other to identify a novel regulatory landscape of ND on BCa. These findings can inform interventions such as improved access to healthy nutrition (B12) in ND/food deserts or targeted stress-reduction interventions based on our survey findings to ultimately reverse aggressive tumor biology and reduce BCa disparities by ND.

Predicting gene signature in breast cancer patients with multiple machine learning models

AimsThe aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models.MethodsIn this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO).ResultsWe identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K–Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples.ConclusionsOur findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers.

Identification of a PANoptosis-related prognostic model in triple-negative breast cancer, from risk assessment, immunotherapy, to personalized treatment

BackgroundTriple-negative breast cancer is a breast cancer subtype characterized by its challenging prognosis, and establishing prognostic models aids its clinical treatment. PANoptosis, a recently identified type of programmed cell death, influences tumor growth and patient outcomes. Nonetheless, the precise impact of PANoptosis-related genes on the prognosis of triple-negative breast cancer has yet to be determined. MethodsClinical information for the triple-negative breast cancer samples was collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases, while 19 PANoptosis-related genes were sourced from previous studies. We first categorized PANoptosis-related subtypes and determined the differentially expressed genes between them. Subsequently, we developed and validated a PANoptosis-associated predictive model using LASSO and Cox multivariate regression analyses. Statistical evaluations were conducted using R software, and the mRNA expression levels of the genes were quantified using real-time PCR. ResultsUsing consensus clustering analysis, we divided triple-negative breast cancer patients into two clusters based on PANoptosis-related genes and identified 1054 differentially expressed genes between these clusters. Prognostic-related genes were subsequently selected to re-cluster patients, validating their predictive ability. A prognostic model was then constructed based on four genes: BTN2A2, CACNA1H, PIGR, and S100B. The expression and enriched cell types of these genes were examined and the expression levels were validated in vitro. Furthermore, the model was validated, and a nomogram was created to enhance personalized risk assessment. The risk score, proven to be an independent prognostic indicator for triple-negative breast cancer, showed a positive correlation with both age and disease stage. Immune infiltration and drug sensitivity analyses suggested appropriate therapies for different risk groups. Mutation profiles and pathway enrichment were analyzed, providing insights into potential therapeutic targets. ConclusionA PANoptosis-related prognostic model was successfully developed for triple-negative breast cancer, offering a novel approach for predicting patient prognosis and guiding treatment strategies.

Feature-based detection of breast cancer using convolutional neural network and feature engineering.

Breast cancer (BC) is a prominent cause of female mortality on a global scale. Recently, there has been growing interest in utilizing blood and tissue-based biomarkers to detect and diagnose BC, as this method offers a non-invasive approach. To improve the classification and prediction of BC using large biomarker datasets, several machine-learning techniques have been proposed. In this paper, we present a multi-stage approach that consists of computing new features and then sorting them into an input image for the ResNet50 neural network. The method involves transforming the original values into normalized values based on their membership in the Gaussian distribution of healthy and BC samples of each feature. To test the effectiveness of our proposed approach, we employed the Coimbra and Wisconsin datasets. The results demonstrate efficient performance improvement, with an accuracy of 100% and 100% using the Coimbra and Wisconsin datasets, respectively. Furthermore, the comparison with existing literature validates the reliability and effectiveness of our methodology, where the normalized value can reduce the misclassified samples of ML techniques because of its generality.

MammOnc-DB, an integrative breast cancer data analysis platform for target discovery.

Breast cancer (BCa) is one of the most common malignancies among women worldwide. It is a complex disease that is characterized by morphological and molecular heterogeneity. In the early stages of the disease, most BCa cases are treatable, particularly hormone receptor-positive and HER2-positive tumors. Unfortunately, triple-negative BCa and metastases to distant organs are largely untreatable with current medical interventions. Recent advances in sequencing and proteomic technologies have improved our understanding of the molecular changes that occur during breast cancer initiation and progression. In this era of precision medicine, researchers and clinicians aim to identify subclass-specific BCa biomarkers and develop new targets and drugs to guide treatment. Although vast amounts of omics data including single cell sequencing data, can be accessed through public repositories, there is a lack of user-friendly platforms that integrate information from multiple studies. Thus, to meet the need for a simple yet effective and integrative BCa tool for multi-omics data analysis and visualization, we developed a comprehensive BCa data analysis platform called MammOnc-DB (http://resource.path.uab.edu/MammOnc-Home.html), comprising data from more than 20,000 BCa samples. MammOnc-DB was developed to provide a unique resource for hypothesis generation and testing, as well as for the discovery of biomarkers and therapeutic targets. The platform also provides pre- and post-treatment data, which can help users identify treatment resistance markers and patient groups that may benefit from combination therapy.

SNHG14 promotes triple-negative breast cancer cell proliferation, invasion, and chemoresistance by regulating the ERK/MAPK signaling pathway.

The functional role and molecular mechanisms of small-nucleolar RNA host gene 14 (SNHG14) in triple-negative breast cancer (TNBC) progression remain unclear. The expression levels of SNHG14 in breast cancer samples and cell lines were determined using real-time quantitative polymerase chain reaction. Cell proliferation, migration, and invasion abilities were detected using MTS and transwell assays. By RNA sequencing, differentially expressed genes were identified between the SNHG14 siRNA and the negative control group. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were used to predict the targets and pathways regulated by SNHG14. pRAF, pMEK, and pERK expression were measured by western blot. The xenograft model was constructed to access the biological function of SNHG14 in vivo. A minimal patient-derived xenograft model was established to evaluate the sensitivity to chemotherapy drugs. Our data indicated that SNHG14 expression was increased in TNBC tissues and cell lines. SNHG14 knockdown attenuated the proliferation, migration, and invasion abilities of TNBC cells both in vivo and in vitro. High SNHG14 expression was associated with lymph node metastasis and a high Ki67 index. The targets of SNHG14 were mainly enriched in the MAPK signaling pathway. pRAF, pMEK, and pERK expression were downregulated after being transfected with SNHG14 siRNA. Compared with the negative control group, the expression of CACNA1I, DUSP8, FGF17, FGFR4, FOS, PDGFRB, and DDIT3 was increased, and the expression of MKNK1 was decreased in the SNHG14 siRNA group. Minimal patient-derived xenograft model demonstrated that knockdown of SNHG14 enhanced the sensitivity to Docetaxel in vivo. Compared with the DMSO group, the proliferation of Docetaxel-resistant MDA-MB-231 cells was decreased in Dabrafenib, PD184352, and FR180204 treatment groups. SNHG14 knockdown inhibits TNBC progression by regulating the ERK/MAPK signaling pathway, which provides evidence for SNHG14 as a potential target for TNBC therapy.

The breast cancer biomarkers associated with the development of the disease; an in-silico-based study

Introduction: Breast cancer (BC) is among the top causes of mortality among women worldwide. Identifying genes by differential expression associated with the development of the disease helps us to better understanding the molecular mechanisms of BC. Objectives: Our study used in-silico analysis to identify hub genes could trigger the development of BC. Materials and Methods: We identified GSE38959 and GSE45827 for differentially expressed genes (DEGs) in the Gene Expression Omnibus (GEO) database, with an adjusted P&lt;0.05. In both sets, logFC ≥ 2 and logFC ≤ -2 were observed in the DEGs that express themselves within cases and normal BC samples. A comparison was then performed, detecting two common datasets of DEGs using the GEO2R tool. Pathways were elucidated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology databases. Thereafter, protein-protein interactions (PPIs) were analyzed using Cytoscape and Gephi. Finally, a GEPIA analysis was conducted to validate the target genes. Results: Using the GEO, 322 common DEGs were identified and 65 hub genes as PPIs. The DEGs were enriched in functions associated with cell division, chromosomes, centromeric regions, microtubule binding, and the cell cycle based on the gene ontology (GO) and KEGG pathways analysis. The expression of 6 genes, CDK1, CCNB1, TOP2A, CXCL12, IGF1, and KIT, represented statistically significant values when the normal and tumor samples were compared via GEPIA analysis. Conclusion: This study introduced six genes (CDK1, CCNB1, TOP2A, CXCL12, IGF1, and KIT) with high expression significantly, which could act as a biomarker for BC development (P&lt;0.05 for all genes). Further comprehensive experimental in vivo studies are needed to describe their role in BC.

Differential MYC and PROM1 mRNA isoform expression in breast invasive carcinoma as biomarkers for subtyping and prognosis

BackgroundCancer stem cells are a subpopulation of tumor cells with the ability to self-renew; evidence suggests that cancer stem cells are responsible for recurrence, metastasis, and resistance to therapy. MYC and CD133 (PROM1 gene) are clinical biomarkers for cancer stem cells, and their dysregulation is involved in the progression of many cancers. Alternative splicing of these genes may contribute to cancer stem cell differentiation. MethodsTranscriptional and clinical data of PROM1 and MYC mRNA isoforms in breast cancer samples were downloaded from the TCGA Splicing Variants Database site, a web-tool to explore mRNA alternative-splicing based on TCGA samples. Data include RSEM isoform expression, clinical sample types, survival data, and clinical receptor expression. Breast cancer subtypes (luminal A, luminal B, Her2 positive, triple negative) were assigned on the basis of estrogen, progesterone, and HER2 expression. ResultsExpression of MYC isoforms uc003ysh.1 and uc003ysi.3 was significantly greater in triple-negative breast cancer compared with all other breast cancer subtypes (P < .001). Isoform uc003ysi.3 was associated with greater 5-year survival in luminal A breast cancer (hazard ratio, 0.79; 95% confidence interval, 0.65–0.96; P = .02). PROM1 isoforms uc003gop.2, uc003goq.3, uc003gos.2, and uc003gou.2 were expressed greatest in triple-negative breast cancer (P < .001). PROM1 isoform uc003gou.2 was associated with better 5-year survival in luminal A breast cancer (hazard ratio, 0.79; 95% confidence interval, 0.65–0.97; P = .02). ConclusionsMYC and PROM1 isoforms are differentially expressed in breast cancer subtypes. Certain isoforms confer better survival prognosis. Further work should be done to study alternative splicing in cancer stem cells.

Transparent precision: Explainable AI empowered breast cancer recommendations for personalized treatment

&lt;p&gt;Breast cancer stands as a prevalent global concern, prompting extensive research into its origins and personalized treatment through Artificial Intelligence (AI)-driven precision medicine. However, AI's black box nature hinders result acceptance. This study delves into Explainable AI (XAI) integration for breast cancer precision medicine recommendations. Transparent AI models, fuelled by patient data, enable personalized treatment recommendations. Techniques like feature analysis and decision trees enhance transparency, fostering trust between medical practitioners and patients. This harmonizes AI's potential with the imperative for clear medical decisions, propelling breast cancer care within the precision medicine era. This research work is dedicated to leveraging clinical and genomic data from samples of metastatic breast cancer. The primary aim is to develop a machine learning (ML) model capable of predicting optimal treatment approaches, including but not limited to hormonal therapy, chemotherapy, and anti-HER2 therapy. The objective is to enhance treatment selection by harnessing advanced computational techniques and comprehensive data analysis. A decision tree model developed here for the prediction of suitable personalized treatment for breast cancer patients achieves 99.87% overall prediction accuracy. Thus, the use of XAI in healthcare will build trust in doctors as well as patients.&lt;/p&gt;

Evaluation of MYC Oncogene Expression in Human Breast Cancer and Its Relationship with ACSL4 and Lipin-1 Expression.

Disturbances in lipid metabolism are one of the hallmarks of cancer cells. Fatty acid synthesis and oxidation play a crucial role in the proliferation, growth, and survival of cancer cells. Several enzymes are involved in lipid metabolism. MYC is an oncogene and plays various regulatory roles in lipid metabolism. This study aimed to evaluate MYC expression and its association with the expressions of Lipin-1, ACSL4 (enzymes involved in lipid metabolism), in pairs of breast cancer (BC) and adjacent normal tissues to further understand the MYC influence on metabolic regulation. Fifty-five pairs of samples of BC and noncancerous adjacent tissues were utilized in the present study to analyze MYC, Lipin-1, and ACSL4 by quantitative real-time polymerase chain reaction. Further, the expression of Lipin-1 and ACSL4 proteins and a number of other clinicopathologically relevant variables were studied employing immunohistochemistry staining. MYC expression was substantially higher in BC tissues than in adjacent normal tissues, according to our findings. This upregulation was positively correlated with tumor size and stage. Although MYC expression was not correlated with that of ACSL4 and Lipin-1 expression, this may result from the complex metabolic changes that occur when cells become malignant. Although further research is required to assess MYC's impact on tumor metabolic regulation, the correlations seen here between MYC, the pathological stage, and tumor size may indicate its prognostic significance in BC. Hence, it may be considered as a potential therapeutic target for further studies.

The Assessment of Female Breast Cancer Tumors Using Some Biomarkers in Attendees of Federal Medical Centre, Yenagoa, Bayelsa State, Nigeria

Breast cancer has remained a major public health issue globally but predominantly affects women in developing nations. The overall survival rate of Nigerian women with breast cancer is low and patients with early breast cancer tend to have better survival than those with advanced distic basis and pathology. These changes are leading to revisions in the management of the disease with a positive impact on prognosis.The current research was done to describe the epidemiological and histopathological features of breast cancer amongst females attending the Federal Medical Center, Yenagoa, Bayelsa State, Nigeria. In this retrospective study, 178 breast specimens were used, spanned from 2010 to 2022. Results presented an age at diagnosis lower to the Western population and invasive ductal carcinoma as the main histological type. In this study, the prevalence of breast cancer amongst females attending Federal Medical Center, Yenagoa, Bayelsa State, Nigeria was analyzed using Hematoxylin and Eosin staining techniques on breast cancer samples collected over the study period. The pathophysiology of breast cancer is not very clear; however, some risk factors are. While some common risk factors are advancing age and the female gender, genetic mutations—namely BRCA 1 and 2—account for only about 10% of breast cancers. The role played by biomarkers, such as estrogen receptors, progesterone receptors, and human epidermal growth factor receptors, in the detection and management of patients with breast cancer. Human breast cancer is known to be dependent on sexual hormones for its growth, as it is derived from breast tissue that normally responds to endogenous hormones. Despite improvements in early detection and therapy, Breast Cancer remains one of the major burdens to healthcare systems and societies.

High NTRK2 protein expression levels may be associated with poorer prognosis of breast cancer patients.

Previous research has shown that the role of neurotrophic receptor tyrosine kinase 2 (NTRK2) in breast cancer (BRCA) remains ambiguous. To help elucidate this, we conducted a retrospective study to investigate the relationship between NTRK2 protein expression and BRCA. The prognostic significance of NTRK2 protein expression patterns was assessed by performing immunohistochemistry assays on 131 BRCA tissues and 56 adjacent normal tissues in a retrospective study. Furthermore, the sensitivity to chemotherapeutic drugs was quantified by "pRRophetic" and the sensitivity to immunotherapy was estimated using The Cancer Immunome Atlas website. NTRK2 protein was expressed at significantly higher levels in BRCA samples compared with normal tissues. The data indicated that NTRK2 expression is an independent risk factor for BRCA patient prognosis. Additionally, the high NTRK2 group exhibited increased sensitivity to certain chemotherapy drugs and achieved higher scores for immune checkpoint blockade therapy compared with the low NTRK2 group. Our study demonstrated that higher NTRK2 protein expression is related to a less favorable prognosis in BRCA patients, as well as to enhanced sensitivity to specific chemotherapy and immunotherapy drugs.

Therapeutic Potential of PLK1, KIF4A, CDCA5, UBE2C, CDT1, SKA3, AURKB, and PTTG1 Genes in Triple-Negative Breast Cancer: Correlating Their Expression with Sensitivity to GSK 461364 and IKK 16 Drugs.

The treatment of triple-negative breast cancer (TNBC) has been associated with challenges due to the lack of expression of ER, PR, and HER2 receptors in tumor cells. This study aimed to identify genes with potential therapeutic targets in TNBC. Data from the cancer genome atlas regarding breast cancer (BC) were downloaded. After initial preprocessing, cancer samples were categorized into four groups: TNBC, HER2-positive, luminal A, and luminal B. Gene expression differences between these groups were calculated, focusing on genes that showed differential expression in TNBC. A protein-protein interaction network was conducted to identify hub genes among the candidate genes related to TNBC. The protein expression of candidate genes was assessed using immunohistochemistry data from the human protein atlas. Drug resistance and sensitivity associated with hub genes were identified using data from PharmacoDB. TNBC samples and the RT-qPCR method were used to confirm the results. Our findings revealed that eight genes, namely PLK1, KIF4A, CDCA5, UBE2C, CDT1, SKA3, AURKB, and PTTG1, had significant upregulation at the RNA level in TNBC subgroup compared to other subgroups and could be considered hub genes in TNBC. Compared to other subgroups, their expression level in TNBC samples had high sensitivity and specificity. RT-qPCR results also demonstrated a significant increase in levels of SKA3 and PTTG1 in the TNBC compared to healthy tissue and other subgroups. The protein expression of these genes was notably high in some BC samples. PharmacoDB data showed that some candidate genes were closely linked to drug sensitivity of GSK 461364 and IKK 16. The results of this study showed a significant increase in the expression level of PLK1, KIF4A, CDCA5, UBE2C, CDT1, SKA3, AURKB, and PTTG1 in TNBC compared to other BC subgroups. These genes show considerable promise as therapeutic targets for the TNBC subgroup.

Prognostic and Clinical Significance of the Proliferation Marker MCM7 in Breast Cancer.

Minichromosome maintenance complex component 7 (MCM7) plays an essential role in proliferation and DNA replication of cancer cells. However, the expression and prognostic significance of MCM7 in breast cancer (BC) remain to be defined. In this study, we aimed to evaluate the role of MCM7 in BC. We conducted immunohistochemistry staining of MCM7 in 1,156 operable early-stage BC samples and assessed MCM7 at the transcriptomic levels using publicly available cohorts (n = 13,430). MCM7 expression was evaluated and correlated with clinicopathological parameters including Ki67 labelling index and patient outcome. At the transcriptomic level, there was a significant association between high MCM7 mRNA levels and shorter patient survival in the whole cohort and in luminal BC class but not in the basal-like molecular subtype. High MCM7 protein expression was detected in 43% of patients and was significantly associated with parameters characteristic of aggressive tumour behaviour. MCM7 was independently associated with shorter survival, particularly in oestrogen receptor-positive (luminal) BC. MCM7 stratified luminal tumours with aggressive clinicopathological features into distinct prognostic groups. In endocrine therapy-treated BC patients, high MCM7 was associated with poor outcome, but such association disappeared with administration of adjuvant chemotherapy. Patients with high expression of Ki67 and MCM7 showed worst survival, while patients with double low expression BC showed the best outcome compared with single expression groups. The current findings indicate that MCM7 expression has a prognostic value in BC and can be used to identify luminal BC patients who can benefit from adjuvant chemotherapy.

RNA-binding protein LSM7 facilitates breast cancer metastasis through mediating alternative splicing of CD44

AimsThe RNA-binding protein LSM7 is essential for RNA splicing, acting as a key component of the spliceosome complex; however, its specific role in breast cancer (BC) has not been extensively investigated. Materials and methodsLSM7 expression in BC samples was evaluated through bioinformatics analysis and immunohistochemistry. The impact of LSM7 on promoting metastatic tumor characteristics was examined using transwell and wound healing assays, as well as an orthotopic xenograft model. Additionally, the involvement of LSM7 in alternative splicing of CD44 was explored via RNA immunoprecipitation and third-generation sequencing. The regulatory role of TCF3 in modulating LSM7 gene expression was further elucidated using luciferase reporter assays and chromatin immunoprecipitation. Key findingsOur findings demonstrate that LSM7 was significantly overexpressed in metastatic BC tissues and was associated with poor prognostic outcomes in patients with BC. LSM7 overexpression markedly increased the migratory and invasive capabilities of BC cells in vitro and significantly promoted spontaneous lung metastasis in vivo. Furthermore, RIP-seq analysis revealed that LSM7 binded to CD44 RNA, enhancing the expression of its alternatively spliced isoform CD44s, thereby driving BC metastasis and invasion. Additionally, the transcription factor TCF3 was found to activate LSM7 transcription by directly binding to its promoter. SignificanceIn summary, this study highlights the pivotal role of LSM7 in the production of the CD44s isoform and the promotion of breast cancer metastasis. Targeting the TCF3/LSM7/CD44s axis may offer a promising therapeutic strategy for breast cancer treatment.

C188-9 reduces patient-specific primary breast cancer cells proliferation at the low, clinic-relevant concentration

ObjectivesSTAT3 is a transcriptional activator of breast cancer oncogenes, suggesting that it could be a potential therapeutic target for breast cancer. Therefore, this study investigated the potential application of C188-9, a STAT3 signal pathway inhibitor, in the treatment of breast cancer through a novel pre-clinical platform with patient-specific primary cells (PSPCs).MethodsPSPCs were isolated from breast cancer samples obtained via biopsy or surgery from fifteen patient donors with their full acknowledgements. PSPCs were treated with C188-9 or other chemotherapeutic agents, and then analyzed with cell viability assay. Western blot assay and real-time quantitative PCR were also used to determine the expression and activity of STAT3 signaling pathway of corresponding PSPCs.ResultsC188-9 treatment at normal (experimental) concentration had valid inhibition on PSPCs proliferation. Meanwhile, treatment at a low (clinic-relevant) concentration of C188-9 for an extended period reduced cell viability of PSPCs still more than some of other traditional chemotherapy drugs. In addition, C188-9 decreased expression level of pSTAT3 in PSPCs from some, but not all patient samples. The treatment of C188-9 reduced cell viability of the breast cancer samples through inhibiting the STAT3 to C-myc signaling pathway.ConclusionsIn this study, we tested a novel drug C188-9 at a low, clinic-relevant concentration, together with several traditional chemotherapy agents. PSPCs from ten out of fifteen patient donors were sensitive to C188-9, while some of traditional chemotherapy agents failed. This finding suggested that C188-9 could have treatment effects only on those ten PSPC patient donors, indicating the future personalized utilization of PSPCs.