Cellular and molecular basis of therapeutic approaches to breast cancer

Bioinformatic analysis of neuropeptide related genes in patients diagnosed with invasive breast carcinoma

PURPOSE

Neuropeptide receptors are expressed in many malignancies. Effectors involved in the action mechanisms of HCRTR1, HCRTR2, NPY4R (PPYR1) may be related to breast cancer (BC). Genes encoding these receptors and PPY and PTPN11 genes were aimed to examine via bioinformatics tools in the BRCA cohort. To our knowledge, this is the first study in which these receptor genes and PP, which have not found much research in BC, are examined together with PTPN11 and analyzed comprehensively in large patient cohorts from public databases.

METHODS

cBioPortal was used for gene alteration analyses, GeneMania for association analyses with other genes, Kaplan-Meier Plotter for Overall Survival (OS) and Relapse Free Survival (RFS) analyses, UALCAN for methylation analyses, TIMER2.0 for expression analyses, The Human Protein Atlas database for expression validations, TIMER for immune infiltration analyses, WEKA 3.8.6 for diagnostic classification performances of the genes based on Random Forest Classifier and Enrichr-KG for Gene Ontology (GO) Biological Process (BP) and KEGG analysis.

RESULTS

19 (1.9 %) nucleotide changes were found in 996 cases. Missense mutation is most common. Decreased expression levels of the HCRTR1 gene were associated with shorter OS and RFS, but decreased expression levels of the PTPN11 gene were associated with longer OS and RFS. Decreased expression levels NPY4R (PPYR1) gene were associated with shorter RFS. Increased expression levels of HCRTR2 and PPY genes were associated with longer RFS. HCRTR1 and NPY4R (PPYR1) genes were statistically hypermethylated; conversely HCRTR2 and PPY genes were hypomethylated. There was no significant change in PTPN11 gene promoter methylation level. HCRTR1, NPY4R (PPYR1) and PTPN11 gene expressions were downregulated; conversely, HCRTR2 and PPY gene expressions upregulated. Weak correlations were observed between NPY4R (PPYR1) gene expression and CD4+ T Cell, Neutrophil, Dendritic Cell and between PTPN11 gene expression and CD8+ T Cell, Macrophage, Neutrophil, Dendritic Cell infiltrations. Area under the receiver operating characteristics curve values of the 10-fold cross-validation and by splitting the dataset in a ratio of 80:20 models were 0.930 and 0.963 respectively. HCRTR2 and HCRTR1 belong to regulation of cytosolic calcium ion concentration, cellular calcium ion homeostasis GO BPs.

CONCLUSION

In BC patients, increases in HCRTR2 and PPY gene expressions could be considered as positive prognostic factors. Decreases in HCRTR1 and NPY4R (PPYR1) gene expressions could be considered as negative prognostic factors. Decreased expression of PTPN11 gene may have a positive prognostic factor. Changes in existing genes are likely to be both a biomarker and therapeutic target for BC. However, experimental and clinical studies are needed to elucidate the mechanisms underlying these neuropeptide receptors in terms of breast carcinogenesis.

Identification of phytoestrogens as sirtuin inhibitor against breast cancer: Multitargeted approach

Despite progress in diagnosis and treatment strategies, breast cancer remains a primary risk to female health as indicated by second most cancer-deaths globally caused by this cancer. High risk mutation is linked to prognosis of breast cancer. Due to high resistance of breast cancer against current therapies, there is necessity of novel treatment strategies. Sirtuins are signaling proteins belonging to histone deacetylase class III family, known to control several cellular processes. Therefore, targeting sirtuins could be one of the approaches to treat breast cancer. Several plants synthesize phytoestrogens which exhibit structural and physiological similarities to estrogens and have been recognized to possess anticancer activity. In our study, we investigated several phytoestrogens for sirtuin inhibition by conducting molecular docking studies, and in-vitro studies against breast cancer cell lines. In molecular docking studies, we identified coumestrol possessing high binding energy with sirtuin proteins 1-3 as compared to other phytoestrogens. The molecular dynamic studies showed stable interaction of ligand and protein with higher affinity at sirtuin proteins 1-3 binding sites. In cell proliferation assay and colony formation assay using breast cancer cell lines (MCF-7 and MDAMB-231) coumestrol caused significant reduction in cell proliferation and number of colonies formed. Further, the flow cytometric analysis showed that coumestrol induces intracellular reactive oxygen species and the western blot analysis revealed reduction in the level of SIRT-1 expression in breast cancer cell lines. In conclusion, in-silico data and in-vitro studies suggest that the phytoestrogen coumestrol has sirtuin inhibitory activity against breast cancer.

Synthesis and antiproliferative evaluation of novel 3,5,8-trisubstituted coumarins against breast cancer

Aim: This study focused on designing and synthesizing novel derivatives of 3,5,8-trisubstituted coumarin. Results: The synthesized compounds, particularly compound 5, exhibited significant cytotoxic effects on MCF-7 cells, surpassing staurosporine, and reduced toxicity toward MCF-10A cells, highlighting potential pharmacological advantages. Further, compound 5 altered the cell cycle and significantly increased apoptosis in MCF-7 cells, involving both early (41.7-fold) and late stages (33-fold), while moderately affecting necrotic signaling. The antitumor activity was linked to a notable reduction (4.78-fold) in topoisomerase IIβ expression. Molecular modeling indicated compound 5's strong affinity for EGFR, human EGF2 and topoisomerase II proteins. Conclusion: These findings highlight compound 5 as a multifaceted antitumor agent for breast cancer.

Daurisoline inhibits proliferation, induces apoptosis, and enhances TRAIL sensitivity of breast cancer cells by upregulating DR5

Daurisoline (DS) is an isoquinoline alkaloid that exerts anticancer activities in various cancer cells. However, the underlying mechanisms through which DS affects the survival of breast cancer cells remain poorly understood. Therefore, the present study was undertaken to investigate the potential anticancer effect of DS on breast cancer cells and reveal the mechanism underlying the enhanced tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-mediated apoptosis by DS. Cell counting kit-8 (CCK-8) and 5-ethynyl-2-deoxyuridine (EdU) assay were used to evaluate the ability of cell proliferation. Flow cytometry was selected to examine the cell cycle distribution. TUNEL assay was used to detect the cell apoptosis. The protein expression was measured by Western blot analysis. DS was found to reduce the cell viability and suppress the proliferation of MCF-7 and MDA-MB-231 cells by causing G1 phase cell cycle arrest. DS could trigger apoptosis by promoting the cleavage of caspase-8 and PARP. The phosphorylation of ERK, JNK, and p38MAPK was upregulated clearly following DS treatment. Notably, SP600125 (JNK inhibitor) pretreatment significantly abrogated DS-induced PARP cleavage. DS inactivated Akt/mTOR and Wnt/β-catenin signaling pathway and upregulated the expression of ER stress-related proteins. Additionally, DS amplified TRAIL-caused viability reduction and apoptosis in breast cancer cells. Mechanismly, DS upregulated the protein level of DR4 and DR5, and knockdown of DR5 attenuated the cotreatment-induced cleavage of PARP. Inhibition of JNK could block DS-induced upregulation of DR5. This study provides valuable insights into the mechanisms of DS inhibiting cell proliferation, triggering apoptosis, and enhancing TRAIL sensitivity of breast cancer cells.

Targeting human progesterone receptor (PR), through pharmacophore-based screening and molecular simulation revealed potent inhibitors against breast cancer

Breast cancer, the prevailing malignant tumor among women, is linked to progesterone and its receptor (PR) in both tumorigenesis and treatment responsiveness. Despite thorough investigation, the precise molecular mechanisms of progesterone in breast cancer remain unclear. The human progesterone receptor (PR) serves as an essential therapeutic target for breast cancer treatment, warranting the rapid design of small molecule therapeutics that can effectively inhibit HPR. By employing cutting-edge computational techniques like molecular screening, simulation, and free energy calculation, the process of identifying potential lead molecules from natural products has been significantly expedited. In this study, we employed pharmacophore-based virtual screening and molecular simulations to identify natural product-based inhibitors of human progesterone receptor (PR) in breast cancer treatment. High-throughput molecular screening of traditional Chinese medicine (TCM) and zinc databases was performed, leading to the identification of potential lead compounds. The analysis of binding modes for the top five compounds from both database provides valuable structural insights into the inhibition of HPR for breast cancer treatment. The top five hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as HPR inhibitors.

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen's Kappa value. These indicators highlight the model's proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information.

The role of the gut microbiome in gastrointestinal cancers

The gut microbiota present in the human digestive system is incredibly varied and is home to trillions of microorganisms. The gut microbiome is shaped at birth, while numerous genetic, dietary, and environmental variables primarily influence the microbiome composition. The importance of gut microbiota on host health is becoming more widely acknowledged. Digestion, intestinal permeability, and immunological and metabolism responses can all be affected by changes in the composition and function of the gut microbiota. There is mounting evidence that the microbial population's complex traits are important biomarkers and indicators of patient outcomes in cancer and its therapies. Numerous studies have demonstrated that changed commensal gut microorganisms contribute to the development and spread of cancer through various routes. Despite the ongoing controversy surrounding the gut microbiome and gastrointestinal cancer, accumulating evidence points to a potentially far more intricate connection than a simple cause-and-effect relationship. SIMPLE SUMMARY: Due to their high frequency and fatality rate, gastrointestinal cancers are regarded as a severe public health issue with complex medical and economic burdens. The gut microbiota may directly or indirectly interact with existing therapies like immunotherapy and chemotherapy, affecting how well a treatment works. The gut microbiome influences the immune response's activity, function, and development. Generally, certain gut bacteria impact the antitumor actions during cancer by creating particular metabolites or triggering T-cell responses. Yet, certain bacterial species have been found to promote cellular proliferation and metastasis in cancer, and comprehending these interactions in the context of cancer may help identify possible treatment targets. Notwithstanding the improvements in the field, additional research is still required to comprehend the underlying processes, examine the effects on existing therapies, and pinpoint certain bacteria and immune cells that can cause this interaction.

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease's influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue's nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework's higher accuracy and precision rate.

Recent Perspectives on Cardiovascular Toxicity Associated with Colorectal Cancer Drug Therapy

Cardiotoxicity is a well-known adverse effect of cancer-related therapy that has a significant influence on patient outcomes and quality of life. The use of antineoplastic drugs to treat colorectal cancers (CRCs) is associated with a number of undesirable side effects including cardiac complications. For both sexes, CRC ranks second and accounts for four out of every ten cancer deaths. According to the reports, almost 39% of patients with colorectal cancer who underwent first-line chemotherapy suffered cardiovascular impairment. Although 5-fluorouracil is still the backbone of chemotherapy regimen for colorectal, gastric, and breast cancers, cardiotoxicity caused by 5-fluorouracil might affect anywhere from 1.5% to 18% of patients. The precise mechanisms underlying cardiotoxicity associated with CRC treatment are complex and may involve the modulation of various signaling pathways crucial for maintaining cardiac health including TKI ErbB2 or NRG-1, VEGF, PDGF, BRAF/Ras/Raf/MEK/ERK, and the PI3/ERK/AMPK/mTOR pathway, resulting in oxidative stress, mitochondrial dysfunction, inflammation, and apoptosis, ultimately damaging cardiac tissue. Thus, the identification and management of cardiotoxicity associated with CRC drug therapy while minimizing the negative impact have become increasingly important. The purpose of this review is to catalog the potential cardiotoxicities caused by anticancer drugs and targeted therapy used to treat colorectal cancer as well as strategies focused on early diagnosing, prevention, and treatment of cardiotoxicity associated with anticancer drugs used in CRC therapy.

Advancements in clinical aspects of targeted therapy and immunotherapy in breast cancer

Breast cancer is the second leading cause of death for women worldwide. The heterogeneity of this disease presents a big challenge in its therapeutic management. However, recent advances in molecular biology and immunology enable to develop highly targeted therapies for many forms of breast cancer. The primary objective of targeted therapy is to inhibit a specific target/molecule that supports tumor progression. Ak strain transforming, cyclin-dependent kinases, poly (ADP-ribose) polymerase, and different growth factors have emerged as potential therapeutic targets for specific breast cancer subtypes. Many targeted drugs are currently undergoing clinical trials, and some have already received the FDA approval as monotherapy or in combination with other drugs for the treatment of different forms of breast cancer. However, the targeted drugs have yet to achieve therapeutic promise against triple-negative breast cancer (TNBC). In this aspect, immune therapy has come up as a promising therapeutic approach specifically for TNBC patients. Different immunotherapeutic modalities including immune-checkpoint blockade, vaccination, and adoptive cell transfer have been extensively studied in the clinical setting of breast cancer, especially in TNBC patients. The FDA has already approved some immune-checkpoint blockers in combination with chemotherapeutic drugs to treat TNBC and several trials are ongoing. This review provides an overview of clinical developments and recent advancements in targeted therapies and immunotherapies for breast cancer treatment. The successes, challenges, and prospects were critically discussed to portray their profound prospects.

Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer

OBJECTIVE

This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer.

METHODS

Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treatment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were measured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (∆) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predictive value of each parameter.

RESULTS

∆V, ∆TTP and ∆PI were independent predictors of pCR. The CEUS-ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone.

CONCLUSION

The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.

Aptamers Versus Vascular Endothelial Growth Factor (VEGF): A New Battle against Ovarian Cancer

Cancer is one of the diseases that causes a high mortality as it involves unregulated and abnormal cell growth proliferation that can manifest in any body region. One of the typical ovarian cancer symptoms is damage to the female reproductive system. The death rate can be reduced through early detection of the ovarian cancer. Promising probes that can detect ovarian cancer are suitable aptamers. Aptamers, i.e., so-called chemical antibodies, have a strong affinity for the target biomarker and can typically be identified starting from a random library of oligonucleotides. Compared with other probes, ovarian cancer targeting using aptamers has demonstrated superior detection effectiveness. Various aptamers have been selected to detect the ovarian tumor biomarker, vascular endothelial growth factor (VEGF). The present review highlights the development of particular aptamers that target VEGF and detect ovarian cancer at its earliest stages. The therapeutic efficacy of aptamers in ovarian cancer treatment is also discussed.