The p53 pathway in breast cancer

Investigating the Prognostic Role of Telomerase-Related Cellular Senescence Gene Signatures in Breast Cancer Using Machine Learning

Background: Telomeres and cellular senescence are critical biological processes implicated in cancer development and progression, including breast cancer, through their influence on genomic stability and modulation of the tumor microenvironment. Methods: This study integrated bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data to establish a gene signature associated with telomere maintenance and cellular senescence for prognostic prediction in breast cancer. Telomere-related genes (TEGs) and senescence-associated genes were curated from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A comprehensive machine learning framework incorporating 101 algorithmic combinations across 10 survival modeling approaches, including random survival forests and ridge regression, was employed to develop a robust prognostic model. Results: A set of 19 key telomere- and senescence-related genes was identified as the optimal prognostic signature. The model demonstrated strong predictive accuracy and was successfully validated in multiple independent cohorts. Functional enrichment analyses indicated significant associations with immune responses and aging-related pathways. Single-cell transcriptomic analysis revealed marked cellular heterogeneity, identifying distinct subpopulations (fibroblasts and immune cells) with divergent risk scores and biological pathway activity. Additionally, pseudo-time trajectory analysis and intercellular communication mapping provided insights into the dynamic evolution of the tumor microenvironment. Immunohistochemical (IHC) validation using data from the Human Protein Atlas confirmed differential protein expression between normal and tumor tissues for several of the selected genes, reinforcing their biological relevance and clinical utility. Conclusions: This study presents a novel 19-gene telomere- and senescence-associated signature with strong prognostic value in breast cancer. These findings enhance our understanding of tumor heterogeneity and may inform precision oncology approaches and future therapeutic strategies.

DeepWalk-Based Graph Embeddings for miRNA-Disease Association Prediction Using Deep Neural Network

Background: In recent years, micro ribonucleic acids (miRNAs) have been recognized as key regulators in numerous biological processes, particularly in the development and progression of diseases. As a result, extensive research has focused on uncovering the critical involvement of miRNAs in disease mechanisms to better comprehend the underlying causes of human diseases. Despite these efforts, relying solely on biological experiments to identify miRNA-disease associations is both time-consuming and costly, making it an impractical approach for large-scale studies. Methods: In this paper, we propose a novel DeepWalk-based graph embedding method for predicting miRNA-disease association (DWMDA). Using DeepWalk, we extracted meaningful low-dimensional vectors from the miRNA and disease networks. Then, we applied a deep neural network to identify miRNA-disease associations using the low-dimensional vectors of miRNAs and diseases extracted via DeepWalk. Results: An ablation study was conducted to assess the proposed graph embedding modules. Furthermore, DWMDA demonstrates exceptional performance in two major cancer case studies (breast and lung), with results based on statistically robust measures, further emphasizing its reliability as a method for identifying associations between miRNAs and diseases. Conclusions: We expect that our model will not only facilitate the accurate prediction of disease-associated miRNAs but also serve as a generalizable framework for exploring interactions among various biological entities.

TP53 mutations and MDM2 polymorphisms in breast and ovarian cancers: amelioration by drugs and natural compounds

Globally, breast and ovarian cancers are major health concerns in women and account for significantly high cancer-related mortality rates. Dysregulations and mutations in genes like TP53, BRCA1/2, KRAS and PTEN increase susceptibility towards cancer. Here, we discuss the impact of mutations in the key regulatory gene, TP53 and polymorphisms in its negative regulator MDM2 which are reported to accelerate cancer progression. Missense mutations, null mutations, transversions, transitions, and point mutations occurring in the TP53 gene can cause an increase in metastatic activity. This review discusses mutations occurring in exon regions of TP53, polymorphisms in MDM2 and their interaction with large ribosomal subunit protein (RPL) leading to cancer development. We also highlight the potential of small molecules e.g. p53 activators like XI-011, Tenovin-1, and Nutlin-3a for the treatment of breast and ovarian cancers. The therapeutic efficacy of natural compounds in amelioration of these two types of cancers is also discussed.

Combinatorial effects of cannabinoid receptor 1 and 2 agonists on characteristics and proteomic alteration in MDA-MB-231 breast cancer cells

Breast cancer is the most common cancer diagnosed in women worldwide. However, the effective treatment for breast cancer progression is still being sought. The activation of cannabinoid receptor (CB) has been shown to negatively affect breast cancer cell survival. Our previous study also reported that breast cancer cells responded to various combinations of CB1 and CB2 agonists differently. Nonetheless, the mechanism underlying this effect and whether this phenomenon can be seen in other cancer characteristics remain unknown. Therefore, this study aims to further elucidate the effects of highly selective CB agonists and their combination on triple-negative breast cancer proliferation, cell cycle progression, invasion, lamellipodia formation as well as proteomic profile of MDA-MB-231 breast cancer cells. The presence of CB agonists, specifically a 2:1 (ACEA: GW405833) combination, prominently inhibited colony formation and induced the S-phase cell cycle arrest in MDA-MB-231 cells. Furthermore, cell invasion ability and lamellipodia formation of MDA-MB-231 were also attenuated by the exposure of CB agonists and their 2:1 combination ratio. Our proteomic analysis revealed proteomic profile alteration in MDA-MB-231 upon CB exposure that potentially led to breast cancer suppression, such as ZPR1/SHC1/MAPK-mediated cell proliferation and AXL/VAV2/RAC1-mediated cell motility pathways. Our findings showed that selective CB agonists and their combination suppressed breast cancer characteristics in MDA-MB-231 cells. The exposure of CB agonists also altered the proteomic profile of MDA-MB-231, which could lead to cell proliferation and motility suppression.

Breast cancer genomic analyses reveal genes, mutations, and signaling networks

Breast cancer (BC) is the most commonly diagnosed cancer and the predominant cause of death in women. BC is a complex disorder, and the exploration of several types of BC omic data, highlighting genes, perturbations, signaling and cellular mechanisms, is needed. We collected mutational data from 9,555 BC samples using cBioPortal. We classified 1174 BC genes (mutated ≥ 40 samples) into five tiers (BCtier_I-V) and subjected them to pathway and protein‒protein network analyses using EnrichR and STRING 11, respectively. BCtier_I possesses 12 BC genes with mutational frequencies > 5%, with only 5 genes possessing > 10% frequencies, namely, PIK3CA (35.7%), TP53 (34.3%), GATA3 (11.5%), CDH1 (11.4%) and MUC16 (11%), and the next seven BC genes are KMT2C (8.8%), TTN (8%), MAP3K1 (8%), SYNE1 (7.2%), AHNAK2 (7%), USH2A (5.5%), and RYR2 (5.4%). Our pathway analyses revealed that the five top BC pathways were the PI3K-AKT, TP53, NOTCH, HIPPO, and RAS pathways. We found that BC panels share only seven genes. These findings show that BC arises from genetic disruptions evident in BC signaling and protein networks.

The Potential Compounds in Lansium parasiticum Leaf Extract for Breast Cancer Therapy: Metabolite Profiling, Pharmacological Network Analysis and In Silico Validation

OBJECTIVE

This study aims to identify the compounds found in Lansium parasiticum leaf extract (LPLE) and explain its activity in the context of breast cancer prevention and therapy using a pharmacological network approach and its validation in silico to understand the molecular mechanisms involved.

METHODS

Identification of compounds in LPLE is done using Liquid Chromatography Tandem Mass Spectrophotometry (LC-MS/MS). We also identified absorption and bioavailability profiles using ADMET software. Predictions about the molecular mechanisms of the anti-cancer compounds of LPLE were made through a network pharmacological approach involving devices such as Cytoscape 3.9.1, GeneCards, Disgenet, STRING 2.0.0, the Kyoto Encyclopedia of Genes and Genomes (KEGG) path, and SRplot. Interactions between potential compounds with TP53 receptors were analyzed using site-specific molecular docking, using PyRx Autodock Vina 9.0 and Biovia Discovery Studio.

RESULT

A total of 24 active compounds were successfully identified through LC-MS/MS. The results of the pharmacological network analysis of these compounds showed that there are four substances that have potential against the potential target gene of breast cancer, namely dihydrotestosterone with 8 target genes, Oxoberberine with 8 targets, Pregnenolone with 1 target gene, and Quercetine with 16 targets. The results of in silico validation revealed that the four compounds showed strong affinity to TP53, even higher than their original ligaments.

CONCLUSION

The study successfully identified the active compounds in Lansium parasiticum leaf extract (LPLE) that have potential in the prevention and treatment of breast cancer.

The p53/miRNA Axis in Breast Cancer

One of the main health issues in the modern world is cancer, with breast cancer (BC) as one of the most common types of malignancies. Different environmental and genetic risk factors are involved in the development of BC. One of the primary genes implicated in cancer development is the p53 gene, which is also known as the "gatekeeper" gene. p53 is involved in cancer development by interacting with numerous pathways and signaling factors, including microRNAs (miRNAs). miRNAs are small noncoding RNA molecules that regulate gene expression by binding to the 3' untranslated region of target mRNAs, resulting in their translational inhibition or degradation. If the p53 gene is mutated or degraded, it can contribute to the risk of BC by disrupting the expression of miRNAs. Similarly, the disruption of miRNAs causes the negative regulation of p53. Therefore, the p53/miRNA axis is a crucial pathway in the progression or prevention of BC, and understanding the regulation and function of this pathway may contribute to the development of new therapeutic strategies to help treat BC.

Integrating computational methods and i n vitro experimental validation reveals the pharmacological mechanism of Selaginella bryopteris (L.) Baker targeting major proteins in breast cancer

Breast cancer remains a significant global health challenge, necessitating the exploration of novel therapeutic options. The present study employs an integrated approach encompassing network pharmacology, molecular docking, molecular dynamics simulations, and in-vitro validation to investigate the potential of Selaginella bryopteris in breast cancer treatment. Initial network pharmacology analysis revealed different potential targets and pathways associated with breast cancer that could be modulated by S. bryopteris phytochemical constituents. Molecular docking and dynamics simulations further elucidated the stability and dynamics of protein-ligand complexes (lanaroflavone-EGFR and sequoiaflavone-CTNNB1). The in-vitro assays demonstrated the ability of S. bryopteris crude extract to inhibit cancer cell growth (IC50 - 78.34 μg/mL) migration and invasion, supporting the computational predictions. The integrated approach employed in the present study offers a robust framework for the systematic exploration of S. bryopteris in drug discovery as a promising candidate for breast cancer treatment.

EV-miRNAs from breast cancer patients of plasma as potential prognostic biomarkers of disease recurrence

Background

Extracellular vesicles (EVs), ubiquitously released by blood cells, facilitate intercellular communication. In cancer, tumor-derived EVs profoundly affect the microenvironment, promoting tumor progression and raising the risk of recurrence. These EVs contain miRNAs (EV-miRNAs), promising cancer biomarkers. Characterizing plasma EVs and identifying EV-miRNAs associated with breast cancer recurrence are crucial aspects of cancer research since they allow us to discover new biomarkers that are effective for understanding tumor biology and for being used for early detection, disease monitoring, or approaches to personalized medicine. This study aimed to characterize plasma EVs in breast cancer (BC) patients and identify EV-miRNAs associated with BC recurrence.

Methods

This retrospective observational study included 24 BC patients divided into recurrence (n= 11) and non-recurrence (n= 13) groups. Plasma EVs were isolated and characterized. Total RNA from EVs was analyzed for miRNA expression using NanoString's nCounter® miRNA Expression Assays panel. MicroRNA target prediction used mirDIP, and pathway interactions were assessed via Reactome.

Results

A stronger presence of circulating EVs was found to be linked with a less favorable prognosis (p = 0.0062). We discovered a distinct signature of EV-miRNAs, notably including miR-19a-3p and miR-130b-3p, which are significantly associated with breast cancer recurrence. Furthermore, miR-19a-3p and miR-130b-3p were implicated in the regulation of PTEN and MDM4, potentially contributing to breast cancer progression.A notable association emerged, indicating a high concentration of circulating EVs predicts poor prognosis (p = 0.0062). Our study found a distinct EV-miRNA signature involving miR-19a-3p and miR-130b-3p, strongly associated with disease recurrence. We also presented compelling evidence for their regulatory roles in PTEN and MDM4 genes, contributing to BC development.

Conclusion

This study revealed that increased plasma EV concentration is associated with BC recurrence. The prognostic significance of EVs is closely tied to the unique expression profiles of miR-19a-3p and miR-130b-3p. These findings underscore the potential of EV-associated miRNAs as valuable indicators for BC recurrence, opening new avenues for diagnosis and treatment exploration.

The Antioxidant Potential and Anticancer Activity of Halodule uninervis Ethanolic Extract against Triple-Negative Breast Cancer Cells

Natural remedies have been indispensable to traditional medicine practices for generations, offering therapeutic solutions for various ailments. In modern times, these natural products continue to play a pivotal role in the discovery of new drugs, especially for cancer treatment. The marine ecosystem offers a wide range of plants with potential anticancer activities due to their distinct biochemical diversity and adaptation to extreme situations. The seagrass Halodule uninervis is rich in diverse bioactive metabolites that bestow the plant with various pharmacological properties. However, its anticancer activity against invasive triple-negative breast cancer (TNBC) is still poorly investigated. In the present study, the phytochemical composition of an ethanolic extract of H. uninervis (HUE) was screened, and its antioxidant potential was evaluated. Moreover, the anticancer potential of HUE against MDA-MB-231 cells was investigated along with the possible underlying mechanisms of action. Our results showed that HUE is rich in diverse phytochemicals that are known for their antioxidant and anticancer effects. In MDA-MB-231 cells, HUE targeted the hallmarks of cancer, including cell proliferation, adhesion, migration, invasion, and angiogenesis. The HUE-mediated anti-proliferative and anti-metastatic effects were associated with the downregulation of the proto-oncogenic STAT3 signaling pathway. Taken together, H. uninervis could serve as a valuable source for developing novel drugs targeting TNBC.

IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

Investigating correlations between radiomic and genomic profiling in breast cancer (BC) molecular subtypes is crucial for understanding disease mechanisms and providing personalized treatment. We present a well-designed radiogenomic framework image-gene-gene set (IMAGGS), which detects multi-way associations in BC subtypes by integrating radiomic and genomic features. Our dataset consists of 721 patients, each of whom has 12 ultrasound (US) images captured from different angles and gene mutation data. To better characterize tumor traits, 12 multi-angle US images are fused using two distinct strategies. Then, we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm, deviating from the prevalent one-to-one relationship pattern observed in previous studies. Key radiomic and genomic features are screened using these associations. In addition, gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC subtypes. We further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and diseases. Taken together, IMAGGS provides a comprehensive characterization for diseases by associating imaging, genes, and gene sets, paving the way for biological interpretation of radiomics and development of targeted therapy.

Uncovering driver genes in breast cancer through an innovative machine learning mutational analysis method

Breast cancer has become a severe public health concern and one of the leading causes of cancer-related death in women worldwide. Several genes and mutations in these genes linked to breast cancer have been identified using sophisticated techniques, despite the fact that the exact cause of breast cancer is still unknown. A commonly used feature for identifying driver mutations is the recurrence of a mutation in patients. Nevertheless, some mutations are more likely to occur than others for various reasons. Sequencing analysis has shown that cancer-driving genes operate across complex networks, often with mutations appearing in a modular pattern. In this work, as a retrospective study, we used TCGA data, which is gathered from breast cancer patients. We introduced a new machine-learning approach to examine gene functionality in networks derived from mutation associations, gene-gene interactions, and graph clustering for breast cancer analysis. These networks have uncovered crucial biological components in critical pathways, particularly those that exhibit low-frequency mutations. The statistical strength of the clinical study is significantly boosted by evaluating the network as a whole instead of just single gene effects. Our method successfully identified essential driver genes with diverse mutation frequencies. We then explored the functions of these potential driver genes and their related pathways. By uncovering low-frequency genes, we shed light on understudied pathways associated with breast cancer. Additionally, we present a novel Monte Carlo-based algorithm to identify driver modules in breast cancer. Our findings highlight the significance and role of these modules in critical signaling pathways in breast cancer, providing a comprehensive understanding of breast cancer development. Materials and implementations are available at: [https://github.com/MahnazHabibi/BreastCancer].

Evaluation of apoptosis stimulating protein of TP53-1 (ASPP1/PPP1R13B) to predict therapy resistance and overall survival in acute myeloid leukemia (AML)

ASPP1 (PPP1R13B) belongs to a family of p53-binding proteins and enhances apoptosis by stimulation of p53-transactivation of selected proapoptotic target genes. It is preferentially expressed in hematopoietic stem cells (HSC) and together with p53 preserves the genomic integrity of the HSC pool. Consequently, dysfunction of ASPP1 has been associated with malignant transformation and development of acute lymphoblastic leukemias and lymphomas - whereas methylation of the promoter region is linked to reduced transcription and ultimately attenuated expression of ASPP1. The role of ASPP1 in AML is not known. We now show that impaired regulation of PPP1R13B contributes to the biology of leukemogenesis and primary therapy resistance in AML. PPP1R13B mRNA expression patterns thereby define a distinct prognostic profile - which is not reflected by the European leukemia net (ELN) risk score. These findings have direct therapeutic implications and we provide a strategy to restore ASPP1 protein levels using hypomethylating agents to sensitize cells towards proapoptotic drugs. Prospective clinical trials are warranted to investigate the role of ASPP1 (PPP1R13B) as a biomarker for risk stratification and as a potential therapeutic target to restore susceptibility to chemotherapy.

Emerging Role of Ferroptosis in Breast Cancer: Characteristics, Therapy, and Translational Implications for the Present and Future

Ferroptosis is a nonapoptotic, iron-dependent form of cell death that can be actuated in disease cells by expected improvements and manufactured specialists. Different studies have recently resurrected the role of this newly discovered cell death pathway and demonstrated its efficacy in treating breast cancer. Breast cancer is the most well-known type of cancer among women worldwide. Despite many years of research focusing on cell death in breast cancer, counting apoptosis, clinical treatment leftovers are difficult due to the high likelihood of recurrence. Ferroptosis is defined by a lack of lipid peroxide repair capacity by phospholipid hydroperoxides GPX4, accessibility of redox-active iron, and followed oxidation of polyunsaturated fatty acids acid-containing phospholipids signalling, amino acid and iron metabolism, ferritinophagy, epithelial-tomesenchymal transition, cell adhesion, and mevalonate and phospholipid biosynthesis can all be factors that influence ferroptosis susceptibility. Ferroptosis, an iron-dependent controlled cell death caused by excessive lipid peroxidation, has been entwined in breast cancer development and therapeutic response for the past decade. Advances in enhancing clinical drugs targeting ferroptosis are developing silver linings to treat breast cancer. Ferroptosis is influenced by metabolism and the expression of certain genes, making it a prospective therapeutic target for monitoring malignant growth and an appealing target for precision cancer medication disclosure. In the coming years, research into biomarkers to follow ferroptosis in patients with breast cancer and the course of events and the subsequent use of novel ferroptosis-based treatments will be captious. We present a fundamental analysis of the actual understanding of molecular mechanisms along with regulatory networks associated with ferroptosis, expected physiological functions in growth concealment, ferroptosis-associated differentially expressed genes, treatment targeting potential, and recent advances in the development of therapeutic strategies in this review.

In-vitro and in-vivo assessment of pH-responsive core-shell nanocarrier system for sequential delivery of methotrexate and 5-fluorouracil for the treatment of breast cancer

Breast cancer (BC) is one of the leading fatal diseases affecting females worldwide. Despite the presence of tremendous chemotherapeutic agents, the resistance emergence directs the recent research towards synergistic drugs' combination along with encapsulation inside biocompatible smart nanocarriers. Methotrexate (MTX) and 5-fluorouracil (Fu) are effective against BC and have sequential synergistic activity. In this study, a core-shell nanocarrier composed of mesoporous silica nanoparticles (MSN) as the core and zeolitic imidazolate framework-8 nano metal organic frameworks (ZIF-8 NMOF) as the shell was developed and loaded with Fu and MTX, respectively. The developed nanostructure; Fu-MSN@MTX-NMOF was validated by several characterization techniques and conferred high drugs' entrapment efficiency (EE%). In-vitro assessment revealed a pH-responsive drug release pattern in the acidic pH where MTX was released followed by Fu. The cytotoxicity evaluation indicated enhanced anticancer effect of the Fu-MSN@MTX-NMOF relative to the free drugs in addition to time-dependent fortified cytotoxic effect due to the sequential drugs' release. The in-vivo anticancer efficiency was examined using Ehrlich ascites carcinoma (EAC) animal model where the anticancer effect of the developed Fu-MSN@MTX-NMOF was compared to the sequentially administrated free drugs. The results revealed enhanced anti-tumor effect while maintaining the normal functions of the vital organs as the heart, kidney and liver.

Individualized causal discovery with latent trajectory embedded Bayesian networks

Bayesian networks have been widely used to generate causal hypotheses from multivariate data. Despite their popularity, the vast majority of existing causal discovery approaches make the strong assumption of a (partially) homogeneous sampling scheme. However, such assumption can be seriously violated, causing significant biases when the underlying population is inherently heterogeneous. To this end, we propose a novel causal Bayesian network model, termed BN-LTE, that embeds heterogeneous samples onto a low-dimensional manifold and builds Bayesian networks conditional on the embedding. This new framework allows for more precise network inference by improving the estimation resolution from the population level to the observation level. Moreover, while causal Bayesian networks are in general not identifiable with purely observational, cross-sectional data due to Markov equivalence, with the blessing of causal effect heterogeneity, we prove that the proposed BN-LTE is uniquely identifiable under relatively mild assumptions. Through extensive experiments, we demonstrate the superior performance of BN-LTE in causal structure learning as well as inferring observation-specific gene regulatory networks from observational data.

Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification

Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based GCN (AGCN), for breast cancer molecular subtype classification using messenger RNA expression, copy number variation and deoxyribonucleic acid methylation multi-omics data. In the extensive comparative studies, our AGCN models outperform state-of-the-art methods under different experimental conditions and both attention mechanisms and the graph convolution subnetwork play an important role in accurate cancer subtype classification. The layer-wise relevance propagation (LRP) algorithm is used for the interpretation of model decision, which can identify patient-specific important biomarkers that are reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of the GCN and attention mechanisms in multi-omics integrative analysis and the implement of the LRP algorithm can provide biologically reasonable insights into model decision.

The Potential of Eleutherine bulbosa in Inducing Apoptosis and Inhibiting Cell Cycle in Breast Cancer: A Network Pharmacology Approach and In Vitro Experiments

OBJECTIVE

The objective of this study was to evaluate the potential and mechanisms of phytochemicals in Eleutherine bulbosa (EBE) in inducing apoptosis and inhibiting the cell cycle in breast cancer through a network pharmacology approach and in vitro validation.

METHODS

This research employed a literature review approach to identify active anti-cancer compounds and utilized a network pharmacology approach to predict the mechanisms of action of EBE compounds in breast cancer. In addition, in vitro experiments were conducted using MTT method to evaluate the effects of EBE on the cytotoxicity of T47D cells, and the flow cytometry method was employed to determine the impact of EBE on apoptosis and the cell cycle.

RESULTS

The network pharmacology analysis revealed that EBE had an impact on 42 genes involved in breast cancer, including 23 important target genes implicated in the pathophysiology of breast cancer. Pathway analysis using the KEGG database showed a close association between EBE and crucial signaling pathways in breast cancer, including P53 signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, apoptosis and cell cycle. In vitro experiments demonstrated that EBE exhibited moderate anti-cancer activity. Furthermore, EBE demonstrated significant potential in inducing apoptosis in breast cancer cells, with a percentage of apoptotic cells reaching 93.6%. Additionally, EBE was observed to disrupt the cell cycle, leading to a significant increase in the sub G1 and S phases, and a significant decrease in the G2-M and G1 phases.

CONCLUSION

EBE has the potential to be an anti-cancer agent through various mechanisms, including apoptosis induction and cell cycle inhibition in breast cancer cells. These findings provide new insights into the potential of EBE as an alternative treatment for breast cancer.

Exploring the repurposing potential of telmisartan drug in breast cancer: an in-silico and in-vitro approach

Anticancer drug resistance is one of the biggest hurdles in the treatment of breast cancer. Drug repurposing is a viable option fordeveloping novel medical treatment strategies since this method is more cost-efficient and rapid. Antihypertensive medicines have recently been found to have pharmacological features that could be used to treat cancer, making them effective candidates for therapeutic repurposing. The goal of our research is to find a potent antihypertensive drug that can be repurposed as adjuvant therapy for breast cancer. In this study, virtual screening was performed using a set of Food and Drug Administration (FDA)-approved antihypertensive drugs as ligands with selected receptor proteins (EGFR, KRAS, P53, AGTR1, AGTR2, and ACE) assuming these proteins are regarded to have a significant role in hypertension as well as breast cancer. Further, our in-silico results were further confirmed by an in-vitro experiment (cytotoxicity assay). All the compounds (enalapril, atenolol, acebutolol, propranolol, amlodipine, verapamil, doxazosin, prazosin, hydralazine, irbesartan, telmisartan, candesartan, and aliskiren) showed remarkable affinity towards the target receptor proteins. However, maximum affinity was displayed by telmisartan. Cell-based cytotoxicity study of telmisartan in MCF7 (breast cancer cell line) confirmed the anticancer effect of telmisartan. IC50 of the drug was calculated to be 7.75 µM and at this concentration, remarkable morphological alterations were observed in the MCF7 cells confirming its cytotoxicity in breast cancer cells. Based on both in-silico and in-vitro studies, we can conclude that telmisartan appears to be a promising drug repurposing candidate for the therapeutic treatment of breast cancer.

Expression of NORAD correlates with breast cancer aggressiveness and protects breast cancer cells from chemotherapy

The recently discovered human lncRNA NORAD is induced after DNA damage in a p53-dependent manner. It plays a critical role in the maintenance of genomic stability through interaction with Pumilio proteins, limiting the repression of their target mRNAs. Therefore, NORAD inactivation causes chromosomal instability and aneuploidy, which contributes to the accumulation of genetic abnormalities and tumorigenesis. NORAD has been detected in several types of cancer, including breast cancer, which is the most frequently diagnosed and the second-leading cause of cancer death in women. In the present study, we confirmed upregulated NORAD expression levels in a set of human epithelial breast cancer cell lines (MDA-MB-231, MDA-MB-436, and MDA-MB-468), which belong to the most aggressive subtypes (triple-negative breast cancer). These results are in line with previous data showing that high NORAD expression levels in basal-like tumors were associated with poor prognosis. Here, we demonstrate that NORAD downregulation sensitizes triple-negative breast cancer cells to chemotherapy, through a potential accumulation of genomic aberrations and an impaired capacity to signal DNA damage. These results show that NORAD may represent an unexploited neoadjuvant therapeutic target for chemotherapy-unresponsive breast cancer.

Role of homeobox d10 gene targeted signaling pathways in cancers

Homeobox D10 (HOXD10) is a transcription factor from the homeobox gene family that controls cell differentiation and morphogenesis throughout development.Due to their functional interaction, changes in HOXD10 gene expression might induce tumors. This narrative review focuses on how and why the dysregulation in the signaling pathways linked with HOXD10 contributes to the metastatic development of cancer. Organ development and tissue homeostasis need highly conserved homeotic transcription factors from homeobox (HOX) genes. Their dysregulation disrupts regulatory molecule action, causing tumors. The HOXD10 gene is upregulated in breast, gastric, hepatocellular, colorectal, bladder, cholangiocellular carcinoma and prostate cancer. Tumor signaling pathways are affected by HOXD10 gene expression changes. This study examines HOXD10-associated signaling pathway dysregulation, which may alter metastatic cancer signaling. In addition, the theoretical foundations that alter HOXD10-mediated therapeutic resistance in malignancies has been presented. New cancer therapy methods will be simpler to develop with the newly discovered knowledge. This review showed that HOXD10 may be a tumor suppressor gene and a new cancer treatment target signaling pathway.

A review of the mechanisms of anti-cancer activities of some medicinal plants-biochemical perspectives

Cancer is a disease resulting in unbridled growth of cells due to dysregulation in the balance of cell populations. Various management procedures in handling cases of cancer are not without their adverse side effects on the normal cells. Medicinal plants/herbs have been in use in the management of various ailments, including cancer, for a long time. Medicinal plants have been credited with wide safety margins, cost effectiveness, availability and diverse activities. This study reviewed various mechanisms of anti-cancer activities of some medicinal plants from a biochemical perspective. The mechanisms of anti-cancer activities of plant compounds addressed in this article include induction of apoptosis, anti-angiogenic effects, anti-metastasis, inhibition of cell cycle, inhibition of DNA destruction and effects on key enzymes, cytotoxic and anti-oxidant effects. The anti-cancer activities of some of the plants involve more than one mechanism.

Vaccines in Breast Cancer: Challenges and Breakthroughs

Breast cancer is a problem for women's health globally. Early detection techniques come in a variety of forms ranging from local to systemic and from non-invasive to invasive. The treatment of cancer has always been challenging despite the availability of a wide range of therapeutics. This is either due to the variable behaviour and heterogeneity of the proliferating cells and/or the individual's response towards the treatment applied. However, advancements in cancer biology and scientific technology have changed the course of the cancer treatment approach. This current review briefly encompasses the diagnostics, the latest and most recent breakthrough strategies and challenges, and the limitations in fighting breast cancer, emphasising the development of breast cancer vaccines. It also includes the filed/granted patents referring to the same aspects.

Effect of Borrelia burgdorferi on the Expression of miRNAs in Breast Cancer and Normal Mammary Epithelial Cells

Breast cancer is one of the leading causes of death in women worldwide. Recent studies have demonstrated that inflammation due to infections with microorganisms could play a role in breast cancer development. One of the known human pathogens, Borrelia burgdorferi, the causative agent of Lyme disease, has been shown to be present in various types of breast cancer and is associated with poor prognosis. We reported that B. burgdorferi can invade breast cancer cells and affect their tumorigenic phenotype. To better understand the genome-wide genetic changes caused by B. burgdorferi, we evaluated the microRNA (miRNA or miR) expression profiles of two triple-negative breast cancer cell lines and one non-tumorigenic mammary cell line before and after B. burgdorferi infection. Using a cancer-specific miRNA panel, four miRNAs (miR-206, 214-3p, 16-5p, and 20b-5p) were identified as potential markers for Borrelia-induced changes, and the results were confirmed by quantitative real-time reverse transcription (qRT-PCR). Among those miRNAs, miR-206 and 214 were the most significantly upregulated miRNAs. The cellular impact of miR-206 and 214 was evaluated using DIANA software to identify related molecular pathways and genes. Analyses showed that the cell cycle, checkpoints, DNA damage-repair, proto-oncogenes, and cancer-related signaling pathways are mostly affected by B. burgdorferi infection. Based on this information, we have identified potential miRNAs which could be further evaluated as biomarkers for tumorigenesis caused by pathogens in breast cancer cells.

Dysregulation of P53 in breast cancer: Causative factors and treatment strategies

One of the most prevalent cancers impacting women worldwide is breast cancer. Although there are several risk factors for breast cancer, the p53 gene's function has recently received much attention. The "gatekeeper" gene, or p53, is sometimes referred to as such since it is crucial in controlling cell proliferation and preventing the development of malignant cells. By identifying DNA damage and initiating cellular repair processes, p53 usually functions as a tumor-suppressor. But p53 gene alterations can result in a lack of function, allowing cells to divide out of control and perhaps triggering the onset of cancer. Various factors, such as mutation genes, signaling pathways, and hormones, can dysregulate P53 proteins and cause breast cancer. A promising strategy for individualized cancer treatment involves focusing on p53 mutations in breast cancer. While numerous techniques, including gene therapy and small compounds, have shown promise, further study is required to create safe and efficient treatments to target p53 mutations in breast cancer successfully.

Aspergillus Carneus metabolite Averufanin induced cell cycle arrest and apoptotic cell death on cancer cell lines via inducing DNA damage

Cancer is one of the leading causes of death worldwide, accounting for nearly 10 million deaths in 2020. Current treatment methods include hormone therapy, γ-radiation, immunotherapy, and chemotherapy. Although chemotherapy is the most effective treatment, there are major obstacles posed by resistance mechanisms of cancer cells and side-effects of the drugs, thus the search for novel anti-cancer compounds, especially from natural sources, is crucial for cancer pharmaceutics research. One natural source worthy of investigation is fungal species. In this study, the cytotoxicity of 5 metabolic compounds isolated from filamentous fungus Aspergillus Carneus. Arugosin C, Averufin, Averufanin, Nidurifin and Versicolorin C were analyzed using NCI-SRB assay on 10 different cell lines of breast cancer, ovarian cancer, glioblastoma and non-tumorigenic cell lines. Averufanin showed highest cytotoxicity with lowest IC₅₀ concentrations especially on breast cancer cells. Therefore, Averufanin was further investigated to enlighten cell death and molecular mechanisms of action involved. Cell cycle analysis showed increase in SubG1 phase suggesting apoptosis induction which was further confirmed by Annexin V and Caspase 3/7 Assays. H2A.X staining revealed accumulation of DNA damage in cells treated with Averufanin and finally western blot analysis validated DNA damage response and downstream effects of Averufanin treatment in various signaling pathways. Consequently, this study shows that Averufanin compound induces cell cycle arrest and cell death via apoptosis through causing DNA damage and can be contemplated and further explored as a new therapeutic strategy in breast cancer.

Sirtuin1-p53: a potential axis for cancer therapy

Sirtuin1 (SIRT1) is a conserved nicotinamide adenine dinucleotide (NAD⁺)-dependent histone deacetylase that plays key roles in a range of cellular events, including the maintenance of genome stability, gene regulation, cell proliferation, and apoptosis. P53 is one of the most studied tumor suppressors and the first identified non-histone target of SIRT1. SIRT1 deacetylates p53 in a NAD⁺-dependent manner and inhibits its transcriptional activity, thus exerting action on a series of pathways related to tissue homeostasis and various pathological states. The SIRT1-p53 axis is thought to play a central role in tumorigenesis. Although SIRT1 was initially identified as a tumor promoter, evidence now indicates that SIRT1 may also act as a tumor suppressor. This seemingly contradictory evidence indicates that the functionality of SIRT1 may be dictated by different cell types and intracellular localization patterns. In this review, we summarize recent evidence relating to the interactions between SIRT1 and p53 and discuss the relative roles of these two molecules with regards to cancer-associated cellular events. We also provide an overview of current knowledge of SIRT1-p53 signaling in tumorigenesis. Given the vital role of the SIRT1-p53 pathway, targeting this axis may provide promising strategies for the treatment of cancer.

Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity

Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample.

Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios.

Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases.

Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.

The role of truncated p53 isoforms in the DNA damage response

The tumour suppressor p53 is activated following genotoxic stress and regulates the expression of target genes involved in the DNA damage response (DDR). The discovery that p53 isoforms alter the transcription of p53 target genes or p53 protein interactions unveiled an alternative DDR. This review will focus on the role p53 isoforms play in response to DNA damage. The expression of the C-terminally truncated p53 isoforms may be modulated via DNA damage-induced alternative splicing, whereas alternative translation plays an important role in modulating the expression of N-terminally truncated isoforms. The DDR induced by p53 isoforms may enhance the canonical p53 DDR or block cell death mechanisms in a DNA damage- and cell-specific manner, which could contribute to chemoresistance in a cancer context. Thus, a better understanding of the involvement of p53 isoforms in the cell fate decisions could uncover potential therapeutic targets in cancer and other diseases.

Lessons from other fields of medicine, Part 1: Breast cancer

Through the understanding of multiple etiologies, pathologies, and disease progression trajectories, breast cancer shifted historically from a singular malignancy of the breast to a complex of molecular/biological entities, translating into individualized disease-modifying treatments. As a result, this led to various de-escalations of treatment compared with the gold standard in the era preceding systems biology: radical mastectomy. Targeted therapies have minimized morbidity from the treatments and mortality from the disease. Biomarkers further individualized tumor genetics and molecular biology to optimize treatments targeting specific cancer cells. Landmark discoveries in breast cancer management have evolved through histology, hormone receptors, human epidermal growth factor, single-gene prognostic markers, and multigene prognostic markers. Relevant to the reliance on histopathology in neurodegenerative disorders, histopathology evaluation in breast cancer can serve as a marker of overall prognosis rather than predict response to therapies. This chapter reviews the successes and failures of breast cancer research through history, with focus on the transition from a universal approach for all patients to divergent biomarker development and individualized targeted therapies, discussing future areas of growth in the field that may apply to neurodegenerative disorders.

Transcriptomic profiles-based approach to decode the role of miR-122 in triple negative breast cancer

miR-122 has been considered both as tumor suppressor miRNA and oncomiR in breast tumor phenotypes. However, the role of miR-122 in triple-negative breast cancer (TNBC) is still unknown. In this study, the clinical value of miR-122 was used to describe the transcriptomic landscape of TNBC tumors obtained from The Cancer Genome Atlas database. Low expression levels of miR-122 were associated with poor overall survival (OS) of TNBC patients than those with higher expression levels of miR-122. We identified gene expression profiles in TNBC tumors expressed lower or higher miR-122. Gene coexpression networks analysis revealed gene modules and hub genes specific to TNBC tumors with low or high miR-122 levels. Gene ontology and KEGG pathways analysis revealed that gene modules in TNBC with gain of miR-122 were related to cell cycle and DNA repair, while in TNBC with loss of miR-122 were enriched in cell cycle, proliferation, apoptosis and activation of cell migration and invasion. The expression of hub genes distinguished TNBC tumors with gain or loss of miR-122 from normal breast tissues. Furthermore, high levels of hub genes were associated with better OS in TNBC patients. Interestingly, the gene coexpression network related to loss of miR-122 were enriched with target genes of miR-122, but this did not observed in those with gain of miR-122. Target genes of miR-122 are oncogenes mainly associated with cell differentiation-related processes. Finally, 75 genes were identified exclusively associated to loss of miR-122, which are also implicated in cell differentiation. In conclusion, miR-122 could act as tumor suppressor by controlling oncogenes in TNBC.

Systems and in vitro pharmacology profiling of diosgenin against breast cancer

Aim: The purpose of this study was to establish a mode of action for diosgenin against breast cancer employing a range of system biology tools and to corroborate its results with experimental facts. Methodology: The diosgenin-regulated domains implicated in breast cancer were enriched in the Kyoto Encyclopedia of Genes and Genomes database to establish diosgenin-protein(s)-pathway(s) associations. Later, molecular docking and the lead complexes were considered for molecular dynamics simulations, MMPBSA, principal component, and dynamics cross-correlation matrix analysis using GROMACS v2021. Furthermore, survival analysis was carried out for the diosgenin-regulated proteins that were anticipated to be involved in breast cancer. For gene expression analyses, the top three targets with the highest binding affinity for diosgenin and tumor expression were examined. Furthermore, the effect of diosgenin on cell proliferation, cytotoxicity, and the partial Warburg effect was tested to validate the computational findings using functional outputs of the lead targets. Results: The protein-protein interaction had 57 edges, an average node degree of 5.43, and a p-value of 3.83e-14. Furthermore, enrichment analysis showed 36 KEGG pathways, 12 cellular components, 27 molecular functions, and 307 biological processes. In network analysis, three hub proteins were notably modulated: IGF1R, MDM2, and SRC, diosgenin with the highest binding affinity with IGF1R (binding energy -8.6 kcal/mol). Furthermore, during the 150 ns molecular dynamics (MD) projection run, diosgenin exhibited robust intermolecular interactions and had the least free binding energy with IGF1R (-35.143 kcal/mol) compared to MDM2 (-34.619 kcal/mol), and SRC (-17.944 kcal/mol). Diosgenin exhibited the highest cytotoxicity against MCF7 cell lines (IC₅₀ 12.05 ± 1.33) µg/ml. Furthermore, in H₂O₂-induced oxidative stress, the inhibitory constant (IC₅₀ 7.68 ± 0.51) µg/ml of diosgenin was lowest in MCF7 cell lines. However, the reversal of the Warburg effect by diosgenin seemed to be maximum in non-cancer Vero cell lines (EC₅₀ 15.27 ± 0.95) µg/ml compared to the rest. Furthermore, diosgenin inhibited cell proliferation in SKBR3 cell lines more though. Conclusion: The current study demonstrated that diosgenin impacts a series of signaling pathways, involved in the advancement of breast cancer, including FoxO, PI3K-Akt, p53, Ras, and MAPK signaling. Additionally, diosgenin established a persistent diosgenin-protein complex and had a significant binding affinity towards IGF1R, MDM2, and SRC. It is possible that this slowed down cell growth, countered the Warburg phenomenon, and showed the cytotoxicity towards breast cancer cells.

A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients

Background

Sentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals about 40-75% of patients without non-sentinel lymph node (NSLN) metastasis after ALND. Unnecessary ALND increases the risk of complications and detracts from quality of life. In this study, we expect to develop a nomogram based on genotypic and clinicopathologic factors to predict the risk of NSLN metastasis in SLN-positive Chinese women breast cancer patients.

Methods

This retrospective study collected data from 1,879 women breast cancer patients enrolled from multiple centers. Genotypic features contain 96 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, therapy and prognosis. SNP genotyping was identified by the quantitative PCR detection platform. The genetic features were divided into two clusters by the mutational stability. The normalized polygenic risk score (PRS) was used to evaluate the combined effect of each SNP cluster. Recursive feature elimination (RFE) based on linear discriminant analysis (LDA) was adopted to select the most useful predictive features, and RFE based on support vector machine (SVM) was used to reduce the number of SNPs. Multivariable logistic regression models (i.e., nomogram) were built for predicting NSLN metastasis. The predictive abilities of three types of model (based on only clinicopathologic information, the integrated clinicopathologic and all SNPs information, and integrated clinicopathologic and significant SNPs information) were compared. Internal and external validations were performed and the area under ROC curves (AUCs) as well as a series of evaluation indicators were assessed.

Results

229 patients underwent SLNB followed by ALND and without any neo-adjuvant therapy, 79 among them (34%) had a positive axillary NSLN metastasis. The LDA-RFE identified the characteristics including lymphovascular invasion, number of positive SLNs, number of negative SLNs and two SNP clusters as significant predictors of NSLN metastasis. Furthermore, the SVM-RFE selected 29 significant SNPs in the prediction of NSLN metastasis. In internal validation, the median AUCs of the clinical and all SNPs combining model, the clinical and 29 significant SNPs combining model, and the clinical model were 0.837, 0.795 and 0.708 respectively. Meanwhile, in external validation, the AUCs of the three models were 0.817, 0.815 and 0.745 respectively.

Conclusion

We present a new nomogram by combining genotypic and clinicopathologic factors to achieve higher sensitivity and specificity comparing with traditional clinicopathologic factors to predict NSLN metastasis in Chinese women breast cancer. It is recommended that more validations are required in prospective studies among different patient populations.

Anti-cancer drug molecules targeting cancer cell cycle and proliferation

Cancer, a vicious clinical burden that potentiates maximum fatality for humankind, arises due to unregulated excessive cell division and proliferation through an eccentric expression of cell cycle regulator proteins. A set of evolutionarily conserved machinery controls the cell cycle in an extremely precise manner so that a cell that went through the cycle can produce a genetically identical copy. To achieve perfection, several checkpoints were placed in the cycle for surveillance; so, errors during the division were rectified by the repair strategies. However, irreparable damage leads to exit from the cell cycle and induces programmed cell death. In comparison to a normal cell, cancer cells facilitate the constitutive activation of many dormant proteins and impede negative regulators of the checkpoint. Extensive studies in the last few decades on cell division and proliferation of cancer cells elucidate the molecular mechanism of the cell-cycle regulators that are often targeted for the development of anti-cancer therapy. Each phase of the cell cycle has been regulated by a unique set of proteins including master regulators Cyclins, and CDKs, along with the accessory proteins such as CKI, Cdc25, error-responsive proteins, and various kinase proteins mainly WEE1 kinases, Polo-like kinases, and Aurora kinases that control cell division. Here in this chapter, we have analytically discussed the role of cell cycle regulators and proliferation factors in cancer progression and the rationale of using various cell cycle-targeting drug molecules as anti-cancer therapy.

p53 Mutation as Plausible Predictor for Endocrine Resistance Therapy in Luminal Breast Cancer

Endocrine therapy resistance in Luminal Breast Cancer is a significant issue to be tackled, but currently, no specific biomarker could be used to anticipate this event. p53 mutation is widely known as one of Breast Cancer's most prominent genetic alterations. Its mutation could generate various effects in Estrogen Receptor and Progesterone Receptor molecular works, tangled in events leading to the aggravation of endocrine therapy resistance. Hence the possibility of p53 mutation utilization as an endocrine therapy resistance predictive biomarker is plausible. The purpose of this review is to explore the latest knowledge of p53 role in Estrogen Receptor and Progesterone Receptor molecular actions, thus aggravating the Endocrine Therapy resistance in Luminal Breast Cancer, from which we could define possibilities and limitations to utilize p53 as the predictive biomarker of endocrine therapy resistance in Luminal Breast Cancer.

Alterations in the p53 isoform ratio govern breast cancer cell fate in response to DNA damage

Our previous studies have shown that p53 isoform expression is altered in breast cancer and related to prognosis. In particular, a high ∆40p53:p53α ratio is associated with worse disease-free survival. In this manuscript, the influence of altered Δ40p53 and p53α levels on the response to standard of care DNA-damaging agents used in breast cancer treatment was investigated in vitro. Our results revealed that a high Δ40p53:p53α ratio causes cells to respond differently to doxorubicin and cisplatin treatments. Δ40p53 overexpression significantly impairs the cells' sensitivity to doxorubicin through reducing apoptosis and DNA damage, whereas Δ40p53 knockdown has the opposite effect. Further, a high Δ40p53:p53α ratio inhibited the differential expression of several genes following doxorubicin and promoted DNA repair, impairing the cells' canonical response. Overall, our results suggest that the response of breast cancer cells to standard of care DNA-damaging therapies is dependent on the expression of p53 isoforms, which may contribute to outcomes in breast cancer.

S, Torabi M, Mohammadi M, Ranjbar H. Evaluation of P53 protein expression in gingival tissues of patients with chronic periodontitis by immunohistochemistry methods

OBJECTIVE

Periodontitis is one of the most important periodontal diseases that can be affected by many factors. Although the mechanism of periodontitis development is not yet fully understood, previous studies suggest that apoptosis may be one of the pathological factors that can affect the process of the disease by destroying old and damaged cells. Low expression of P53 protein is one of the reasons for delaying cell death that allows damaged cells to survive longer and gives more time for the chance of mutations and pathogenesis. Because of the important role of P53 in gingival cells of patients with chronic periodontitis, the objective of our study is to evaluate the P53 protein expression in gingival tissues of patients with chronic periodontitis by immunohistochemistry methods.

MATERIALS AND METHODS

In this cross-sectional study, 35 patients with severe to moderate chronic periodontitis (loss of attachment ≥3 mm, probing depth ≥5 mm) with no treatment and 25 people who were healthy for periodontal problems were examined. Gingival biopsies from marginal and attached gingiva were obtained, prepared, and mounted on slides. Then, the expression of P53 on each slide was evaluated by optic microscopy after using P53 antibodies and staining with hematoxylin-eosin (immunohistochemistry method). Data were analyzed using independent t-test, Mann-Whitney U-test, and Spearman correlation test using SPSS Statistics version 18.0.

RESULTS

The mean ages of participants in the case and control groups were 37.58 and 32.09, respectively. Our results showed that the expression of P53 was not significant in periodontitis compared to the control group (p > .05). Also, gender could not affect the expression of P53 in both groups (p > .05), and there was no significant relationship between age and P53 gene incidence.

CONCLUSION

Chronic periodontitis has no significant effect on P53 expression, so changes in apoptosis due to P53 expression in periodontitis are not significant.

Identification of miRNA biomarkers for breast cancer by combining ensemble regularized multinomial logistic regression and Cox regression

BACKGROUND

Breast cancer is one of the most common cancers in women. It is necessary to classify breast cancer subtypes because different subtypes need specific treatment. Identifying biomarkers and classifying breast cancer subtypes is essential for developing appropriate treatment methods for patients. MiRNAs can be easily detected in tumor biopsy and play an inhibitory or promoting role in breast cancer, which are considered promising biomarkers for distinguishing subtypes.

RESULTS

A new method combing ensemble regularized multinomial logistic regression and Cox regression was proposed for identifying miRNA biomarkers in breast cancer. After adopting stratified sampling and bootstrap sampling, the most suitable sample subset for miRNA feature screening was determined via ensemble 100 regularized multinomial logistic regression models. 124 miRNAs that participated in the classification of at least 3 subtypes and appeared at least 50 times in 100 integrations were screened as features. 22 miRNAs from the proposed feature set were further identified as the biomarkers for breast cancer by using Cox regression based on survival analysis. The accuracy of 5 methods on the proposed feature set was significantly higher than on the other two feature sets. The results of 7 biological analyses illustrated the rationality of the identified biomarkers.

CONCLUSIONS

The screened features can better distinguish breast cancer subtypes. Notably, the genes and proteins related to the proposed 22 miRNAs were considered oncogenes or inhibitors of breast cancer. 9 of the 22 miRNAs have been proved to be markers of breast cancer. Therefore, our results can be considered in future related research.

The Effect of Aflatoxin B1 on Tumor-Related Genes and Phenotypic Characters of MCF7 and MCF10A Cells

The fungal toxin aflatoxin B1 (AB1) and its reactive intermediate, aflatoxin B1-8, 9 epoxide, could cause liver cancer by inducing DNA adducts. AB1 exposure can induce changes in the expression of several cancer-related genes. In this study, the effect of AB1 exposure on breast cancer MCF7 and normal breast MCF10A cell lines at the phenotypic and epigenetic levels was investigated to evaluate its potential in increasing the risk of breast cancer development. We hypothesized that, even at low concentrations, AB1 can cause changes in the expression of important genes involved in four pathways, i.e., p53, cancer, cell cycle, and apoptosis. The transcriptomic levels of BRCA1, BRCA2, p53, HER1, HER2, cMyc, BCL2, MCL1, CCND1, WNT3A, MAPK1, MAPK3, DAPK1, Casp8, and Casp9 were determined in MCF7 and MCF10A cells. Our results illustrate that treating both cells with AB1 induced cytotoxicity and apoptosis with reduction in cell viability in a concentration-dependent manner. Additionally, AB1 reduced reactive oxygen species levels. Phenotypically, AB1 caused cell-cycle arrest at G1, hypertrophy, and increased cell migration rates. There were changes in the expression levels of several tumor-related genes, which are known to contribute to activating cancer pathways. The effects of AB1 on the phenotype and epigenetics of both MCF7 and MCF10A cells associated with cancer development observed in this study suggest that AB1 is a potential risk factor for developing breast cancer.

Breast cancer in the era of precision medicine

Breast cancer is a heterogeneous disorder with different molecular subtypes and biological characteristics for which there are diverse therapeutic approaches and clinical outcomes specific to any molecular subtype. It is a global health concern due to a lack of efficient therapy regimens that might be used for all disease subtypes. Therefore, treatment customization for each patient depending on molecular characteristics should be considered. Precision medicine for breast cancer is an approach to diagnosis, treatment, and prevention of the disease that takes into consideration the patient's genetic makeup. Precision medicine provides the promise of highly individualized treatment, in which each individual breast cancer patient receives the most appropriate diagnostics and targeted therapies based on the genetic profile of cancer. The knowledge about the molecular features and development of breast cancer treatment approaches has increased, which led to the development of new targeted therapeutics. Tumor genomic profiling is the standard of care for breast cancer that could contribute to taking steps to better management of malignancies. It holds great promise for accurate prognostication, prediction of response to common systemic therapies, and individualized monitoring of the disease. The emergence of targeted treatment has significantly enhanced the survival of patients with breast cancer and contributed to reducing the economic costs of the health system. In this review, we summarized the therapeutic approaches associated with the molecular classification of breast cancer to help the best treatment selection specific to the target patient.

Investigation of the effects of overexpression of jumping translocation breakpoint (JTB) protein in MCF7 cells for potential use as a biomarker in breast cancer

Jumping translocation breakpoint (JTB) gene acts as a tumor suppressor or an oncogene in different malignancies, including breast cancer (BC), where it was reported as overexpressed. However, the molecular functions, biological processes and underlying mechanisms through which JTB protein causes increased cell growth, proliferation and invasion is still not fully deciphered. Our goal is to identify the functions of JTB protein by cellular proteomics approaches. MCF7 breast cancer cells were transfected with sense orientation of hJTB cDNA in HA, His and FLAG tagged CMV expression vector to overexpress hJTB and the expression levels were confirmed by Western blotting (WB). Proteins extracted from transfected cells were separated by SDS-PAGE and the in-gel digested peptides were analyzed by nano-liquid chromatography tandem mass spectrometry (nanoLC-MS/MS). By comparing the proteome of cells with upregulated conditions of JTB vs control and identifying the protein dysregulation patterns, we aim to understand the function of this protein and its contribution to tumorigenesis. Gene Set Enrichment Analysis (GSEA) algorithm was performed to investigate the biological processes and pathways that are associated with the JTB protein upregulation. The results demonstrated four significantly enriched gene sets from the following significantly upregulated pathways: mitotic spindle assembly, estrogen response late, epithelial-to-mesenchymal transition (EMT) and estrogen response early. JTB protein itself is involved in mitotic spindle pathway by its role in cell division/cytokinesis, and within estrogen response early and late pathways, contributing to discrimination between luminal and mesenchymal breast cancer. Thus, the overexpressed JTB condition was significantly associated with an increased expression of ACTNs, FLNA, FLNB, EZR, MYOF, COL3A1, COL11A1, HSPA1A, HSP90A, WDR, EPPK1, FASN and FOXA1 proteins related to deregulation of cytoskeletal organization and biogenesis, mitotic spindle organization, ECM remodeling, cellular response to estrogen, proliferation, migration, metastasis, increased lipid biogenesis, endocrine therapy resistance, antiapoptosis and discrimination between different breast cancer subtypes. Other upregulated proteins for overexpressed JTB condition are involved in multiple cellular functions and pathways that become dysregulated, such as tumor microenvironment (TME) acidification, the transmembrane transport pathways, glycolytic flux, iron metabolism and oxidative stress, metabolic reprogramming, nucleocytosolic mRNA transport, transcriptional activation, chromatin remodeling, modulation of cell death pathways, stress responsive pathways, and cancer drug resistance. The downregulated proteins for overexpressed JTB condition are involved in adaptive communication between external and internal environment of cells and maintenance between pro-apoptotic and anti-apoptotic signaling pathways, vesicle trafficking and secretion, DNA lesions repair and suppression of genes involved in tumor progression, proteostasis, redox state regulation, biosynthesis of macromolecules, lipolytic pathway, carbohydrate metabolism, dysregulation of ubiquitin-mediated degradation system, cancer cell immune escape, cell-to-cell and cell-to-ECM interactions, and cytoskeletal behaviour. There were no significantly enriched downregulated pathways.

Abrogating the Interaction Between p53 and Mortalin (Grp75/HSPA9/mtHsp70) for Cancer Therapy: The Story so far

p53 is a transcription factor that activates the expression of a set of genes that serve as a critical barrier to oncogenesis. Inactivation of p53 is the most common characteristic in sporadic human cancers. Mortalin is a differentially sub-cellularly localized member of the heat shock protein 70 family of chaperones that has essential mitochondrial and extra-mitochondrial functions. Elevated mortalin levels in multiple cancerous tissues and tumor-derived cell lines emphasized its key role in oncogenesis. One of mortalin’s major oncogenic roles is the inactivation of p53. Mortalin binds to p53 sequestering it in the cytoplasm. Hence, p53 cannot freely shuttle to the nucleus to perform its tumor suppressor functions as a transcription factor. This protein-protein interaction was reported to be cancer-specific, hence, a selective druggable target for a rationalistic cancer therapeutic strategy. In this review article, the chronological identification of mortalin-p53 interactions is summarized, the challenges and general strategies for targeting protein-protein interactions are briefly discussed, and information about compounds that have been reported to abrogate mortalin-p53 interaction is provided. Finally, the reasons why the disruption of this druggable interaction has not yet been applied clinically are discussed.

Effects of Inflammatory Response Genes on the Immune Microenvironment in Colorectal Cancer

Background: The close relationship between colorectal cancer and inflammation has been widely reported. However, the relationship between colorectal cancer and inflammation at the genetic level is not fully understood.

Method: From a genetic perspective, this study explored the relationship between inflammation-related genes and the immune microenvironment in colorectal cancer. We identified prognostic genes, namely CX3CL1, CCL22, SERPINE1, LTB4R, XCL1, GAL, TIMP1, ADIPOQ, and CRH, by using univariate and multivariate regression analyses. A risk scoring model for inflammatory response was established, and patients in The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database were divided into two groups: high risk group and low risk group.

Results: The analysis showed that the prognosis of the two groups was significantly different, and the low-risk group had a higher survival rate and longer survival time. Pathways related to apoptosis, inflammatory response, and hypoxia were significantly enriched as shown via Gene Set Enrichment Analysis (GSEA). Activated dendritic cell infiltration was found in both the TCGA and GEO databases, and the CCL21 gene played a significant role in the process of activated dendritic cell infiltration. CCL21 gene was also positively correlated with inflammatory response, and the gene expression and risk score were significantly different between the two groups.

Conclusion: In summary, inflammatory response has a direct impact on patients with colorectal cancer in the prognosis and immune infiltration and further research studies on the inflammatory response can help in advancing the development of immunotherapy for colorectal cancer.

Apoptotic effects of human amniotic fluid mesenchymal stem cells conditioned medium on human MCF-7 breast cancer cell line

Introduction

Breast cancer, as the most common malignancy among women, is shown to have a high mortality rate and resistance to chemotherapy. Research has shown the possible inhibitory role of Mesenchymal stem cells in curing cancer. Thus, the present work used human amniotic fluid mesenchymal stem cell-conditioned medium (hAFMSCs-CM) as an apoptotic reagent on the human MCF-7 breast cancer cell line.

Methods

Conditioned medium (CM) was prepared from hAFMSCs. After treating MCF-7 cells with CM, a number of analytical procedures (MTT, real-time PCR, western blot, and flow cytometry) were recruited to evaluate the cell viability, Bax and Bcl-2 gene expression, P53 protein expression, and apoptosis, respectively. Human fibroblast cells (Hu02) were used as the negative control. In addition, an integrated approach to meta-analysis was performed.

Results

The MCF-7 cells' viability was decreased significantly after 24 hours (P < 0.0001) and 72 hours (P < 0.05) of treatment. Compared with the control cells, Bax gene's mRNA expression increased and Bcl-2's mRNA expression decreased considerably after treating for 24 hours with 80% hAFMSCs-CM (P = 0.0012, P < 0.0001, respectively); an increasing pattern in P53 protein expression could also be observed. The flow cytometry analysis indicated apoptosis. Results from literature mining and the integrated meta-analysis showed that hAFMSCs-CM is able to activate a molecular network where Bcl2 downregulation stands in harmony with the upregulation of P53, EIF5A, DDB2, and Bax, leading to the activation of apoptosis.

Conclusion

Our finding demonstrated that hAFMSCs-CM presents apoptotic effect on MCF-7 cells; therefore, the application of hAFMSCs-CM, as a therapeutic reagent, can suppress breast cancer cells' viabilities and induce apoptosis.

Tumor cell intrinsic and extrinsic features predict prognosis in estrogen receptor positive breast cancer

Although estrogen-receptor-positive (ER+) breast cancer is generally associated with favorable prognosis, clinical outcome varies substantially among patients. Genomic assays have been developed and applied to predict patient prognosis for personalized treatment. We hypothesize that the recurrence risk of ER+ breast cancer patients is determined by both genomic mutations intrinsic to tumor cells and extrinsic immunological features in the tumor microenvironment. Based on the Cancer Genome Atlas (TCGA) breast cancer data, we identified the 72 most common genomic aberrations (including gene mutations and indels) in ER+ breast cancer and defined sample-specific scores that systematically characterized the deregulated pathways intrinsic to tumor cells. To further consider tumor cell extrinsic features, we calculated immune infiltration scores for six major immune cell types. Many individual intrinsic features are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. In addition, statistical learning models that integrated these features predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay (our optimized random forest model AUC = 0.841, Oncotype DX model AUC = 0.792, p = 0.04). As a proof-of-concept, our study indicates the great potential of genomic and immunological features in prognostic prediction for improving breast cancer precision medicine. The framework introduced in this work can be readily applied to other cancers.

Analyzing Association between Expression Quantitative Trait and CNV for Breast Cancer Based on Gene Interaction Network Clustering and Group Sparse Learning

Aims:

The occurrence and development of tumor is accompanied by the change of pathogenic gene expression. Tumor cells avoid the damage of immune cells by regulating the expression of immune related genes.

Background:

Tracing the causes of gene expression variation is helpful to understand tumor evolution and metastasis.

Objective:

Current gene expression variation explanation methods are confronted with several main challenges: low explanation power, insufficient prediction accuracy, and lack of biological meaning.

Method:

In this study, we propose a novel method to analyze the mRNA expression variations of breast cancers risk genes. Firstly, we collected some high-confidence risk genes related to breast cancer and then designed a rank-based method to preprocess the breast cancers copy number variation (CNV) and mRNA data. Secondly, to elevate the biological meaning and narrow down the combinatorial space, we introduced a prior gene interaction network and applied a network clustering algorithm to generate high density subnetworks. Lastly, to describe the interlinked structure within and between subnetworks and target genes mRNA expression, we proposed a group sparse learning model to identify CNVs for pathogenic genes expression variations.

Result:

The performance of the proposed method is evaluated by both significantly improved predication accuracy and biological meaning of pathway enrichment analysis.

Conclusion:

The experimental results show that our method has practical significance

A seven-lncRNA signature for predicting prognosis in breast carcinoma

Background

Long non-coding RNAs (lncRNAs) play an important part in tumorigenesis and cancer metastasis and can serve as a potential biosignature for cancer prognosis. However, the use of lncRNA signatures to predict survival in breast carcinoma is yet unreported.

Methods

The lncRNA expression profiles and homologous clinical data of 913 breast carcinoma samples from the Cancer Genome Atlas (TCGA), were analyzed to obtain 2,547 differentially expressed lncRNAs. Univariate Cox proportional risk regression was applied to both the training and testing datasets to screen the common prognostic lncRNAs. Potential prognostic LncRNAs were screened by multivariate Cox proportional risk regression in the training data set of the selected LncRNAs.

Results

Seven lncRNAs (LINC02037, MAPT-AS1, RP1-37C10.3, RP11-344E13.4, RP11-454P21.1, RP11-616M22.1, SPACA6P-AS) were prominently associated with overall survival. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves indicated that these indicators were sensitive and specific for survival prediction. The areas under the ROC curve of the seven-lncRNA signature in predicting 3- and 5-year survival rates were 0.771 and 0.780 respectively in the combined cohort. Furthermore, enrichment analysis revealed that these seven lncRNAs might participate multiple pathways related to tumorigenesis and prognosis.

Conclusions

The proposed seven-lncRNA signature could serve as a latent prognostic biomarker for survival prediction in patients with breast carcinoma.

Combined MEK/MDM2 inhibition demonstrates antitumor efficacy in TP53 wild-type thyroid and colorectal cancers with MAPK alterations

Most tumors with activating MAPK (mitogen-activated protein kinase) pathway alterations respond poorly to MEK inhibitors alone. Here, we evaluated combination therapy with MEK inhibitor selumetinib and MDM2 inhibitor KRT-232 in TP53 wild-type and MAPK altered colon and thyroid cancer models. In vitro, we showed synergy between selumetinib and KRT-232 on cell proliferation and colony formation assays. Immunoblotting confirmed p53 upregulation and MEK pathway inhibition. The combination was tested in vivo in seven patient-derived xenograft (PDX) models (five colorectal carcinoma and two papillary thyroid carcinoma models) with different KRAS, BRAF, and NRAS mutations. Combination therapy significantly prolonged event-free survival compared with monotherapy in six of seven models tested. Reverse-phase protein arrays and immunohistochemistry, respectively, demonstrated upregulation of the p53 pathway and in two models cleaved caspase 3 with combination therapy. In summary, combined inhibition of MEK and MDM2 upregulated p53 expression, inhibited MAPK signaling and demonstrated greater antitumor efficacy than single drug therapy in both in vitro and in vivo settings. These findings support further clinical testing of the MEK/MDM2 inhibitor combination in tumors of epithelial origin with MAPK pathway alterations.

Down-regulation of MEG3, PANDA and CASC2 as p53-related lncRNAs in breast cancer

TP53 encodes a major tumor suppressor protein which blocks carcinogenesis process in a variety of tissues including breast tissue. Expression and function of this gene is regulated by a number of long non-coding RNAs (lncRNAs) among them are PANDA, MEG3 and CASC2. We measured expression of TP53 and these transcripts in a cohort of Iranian breast cancer patients. Expression levels of TP53, MEG3, CASC2 and PANDA were significantly lower in tumoral samples compared with non-tumoral samples (Posterior mean differences =  -4.26, -1.66, -5.98 and -3.13, respectively; P values < 0.0001). Expression of CASC2 was higher in Her2 1+ cases compared with Her2 negative cases (Beta = 1.85, P value = 0.037). Expression levels of MEG3 and TP53 were lower in grade 2 samples compared with grade 1 (Beta = -1.86, P value = 0.006 and Beta = -2.24, P value = 0.003, respectively). There was no other significant association between expression of genes and clinical variables. CASC2 had the best performance among these genes with area under curve value of 0.78 and sensitivity and specificity values of 56.33% and 88.73%, respectively (P value < 0.0001). The current investigation supports the role of TP53-related lncRNAs in the pathogenesis of breast cancer.