RHOA and mDia1 Promotes Apoptosis of Breast Cancer Cells Via a High Dose of Doxorubicin Treatment

Identification of novel membrane markers in circulating tumor cells of mesenchymal state in breast cancer

Cancer metastasis is a major cause of cancer-related deaths worldwide. The ability to detect and monitor circulating tumor cells (CTCs) offers a promising approach to early detection and management of metastasis. Although studies on epithelial markers for CTC detection are actively underway, the discovery of mesenchymal markers has not been studied sufficiently. In this study, we developed a new pipeline to identify membrane markers in CTCs of mesenchymal state in breast cancer based on expression profiles of the 310 CTC samples. From the total CTC samples, only CTC samples in the mesenchymal state were collected by employing hierarchical clustering. In samples belonging to the mesenchymal state, we calculated the correlation coefficients between 1995 membrane genes and ZEB2, which was determined as the key mesenchymal signature, allowing the 84 positively correlated genes. Furthermore, to ensure clinical significance, Kaplan-Meier analysis were performed on the 124 breast cancer patients, resulting in the 14 genes predicting prognosis. By exploring genes commonly identified in the both analyses, F11R and PTGIR were characterized as membrane markers in CTCs of mesenchymal state in breast cancer, which were evaluated by enriched terms, literature evidence, and relevant molecular pathways. We expect that the results will be helpful to more effective strategies for metastasis management.

The Impact of Polymorphisms in ATP-Binding Cassette Transporter Genes on Anthracycline-Induced Early Cardiotoxicity in Patients with Breast Cancer

BACKGROUND

Cardiac side effects associated with anthracycline-based treatment may seriously compromise the prognosis of patients with breast cancer (BC). Evidence shows that genes that operate in drug metabolism can influence the risk of anthracycline-induced cardiotoxicity (AIC). ATP-binding cassette (ABC) transporters could serve as one of the potential biomarkers for AIC risk stratification. We aimed to determine the link between single-nucleotide polymorphisms (SNPs) in several ABC genes (ABCB1 rs1045642, ABCC1 rs4148350, ABCC1 rs3743527) and cardiotoxicity.

METHODS

The study included 71 patients with BC, who were treated with doxorubicin-based chemotherapy. Two-dimensional echocardiography and speckle-tracking echocardiography were performed. AIC was defined as a new decrease of 10 percentage points in the left ventricular ejection fraction (LVEF). SNPs in ABCB1 and ABCC1 genes were evaluated using real-time PCR.

RESULTS

After a cumulative dose of 236.70 mg/m² of doxorubicin, 28.2% patients met the criteria of AIC. Patients who developed AIC had a larger impairment in left ventricular systolic function compared to those who did not develop AIC (LVEF: 50.20 ± 2.38% vs. 55.41 ± 1.13%, p < 0.001; global longitudinal strain: -17.03 ± 0.52% vs. -18.40 ± 0.88%, p < 0.001). The ABCC1 rs4148350 TG genotype was associated with higher rates of cardiotoxicity (TG vs. GG OR = 8.000, 95% CI = 1.405-45.547, p = 0.019).

CONCLUSIONS

The study showed that ABCC1 rs4148350 is associated with AIC and could be a potential biomarker to assess the risk of treatment side effects in patients with BC.

Doxorubicin and Quercetin Double Loading in Modified MCM-41 Lowered Cardiotoxicity in H9c2 Cardioblast Cells In Vitro

BACKGROUND

One of the therapeutic limitations of the use of doxorubicin (DOX) as an anticancer drug is its cardiotoxicity. Its hydrophilicity also causes difficulties in achieving sustained release. The simultaneous delivery with the well-known natural antioxidant quercetin could ameliorate its cardiotoxicity. Thus, the main aim of this work is to study the potential of carboxylated and non-carboxylated mesoporous silica MCM-41 nanoparticles for double loading of the hydrophilic doxorubicin hydrochloride and hydrophobic quercetin (Q) in one nanocarrier with a modified release pattern to reduce the cardiotoxic side effects of doxorubicin in vitro.

METHODS

The methods included the modification of MCM-41, single and double loading of modified and non-modified MCM-41, physicochemical characterization, in vitro release tests and kinetic study, and in vitro cell viability studies.

RESULTS

Doxorubicin and quercetin were successfully double-loaded with encapsulation efficiency (EE) of 43 ± 4.1% and 37 ± 4.5%, respectively, in native MCM-41. The post-synthetic carboxylation led to 49 ± 4.3% EE (DOX) and 36 ± 4.0% (Q) and double lowering of the cardiotoxicity on H9c2 (IC₅₀ = 5.96 µm). Sustained release profiles over 72 h were achieved.

CONCLUSIONS

A successful procedure was proposed for the efficient double loading of a hydrophilic drug and a hydrophobic drug. The carboxy-modified double-loaded nanosystems demonstrate a decreased in vitro cardiotoxicity of doxorubicin and can be considered as a potential chemotherapeutic formulation.

Establishing a Proteomics-Based Signature of AKR1C3-Related Genes for Predicting the Prognosis of Prostate Cancer

Aldo-keto reductase family 1 member C3 (AKR1C3) plays an important role in prostate cancer (PCa) progression, particularly in castration-resistant prostate cancer (CRPC). It is necessary to establish a genetic signature associated with AKR1C3 that can be used to predict the prognosis of PCa patients and provide important information for clinical treatment decisions. AKR1C3-related genes were identified via label-free quantitative proteomics of the AKR1C3-overexpressing LNCaP cell line. A risk model was constructed through the analysis of clinical data, PPI, and Cox-selected risk genes. Cox regression analysis, Kaplan-Meier (K-M) curves, and receiver operating characteristic (ROC) curves were used to verify the accuracy of the model, and two external datasets were used to verify the reliability of the results. Subsequently, the tumor microenvironment and drug sensitivity were explored. Moreover, the roles of AKR1C3 in the progression of PCa were verified in LNCaP cells. MTT, colony formation, and EdU assays were conducted to explore cell proliferation and drug sensitivity to enzalutamide. Migration and invasion abilities were measured using wound-healing and transwell assays, and qPCR was used to assess the expression levels of AR target genes and EMT genes. CDC20, SRSF3, UQCRH, INCENP, TIMM10, TIMM13, POLR2L, and NDUFAB1 were identified as AKR1C3-associated risk genes. These risk genes, established using the prognostic model, can effectively predict the recurrence status, immune microenvironment, and drug sensitivity of PCa. Tumor-infiltrating lymphocytes and several immune checkpoints that promote cancer progression were higher in high-risk groups. Furthermore, there was a close correlation between the sensitivity of PCa patients to bicalutamide and docetaxel and the expression levels of the eight risk genes. Moreover, through in vitro experiments, Western blotting confirmed that AKR1C3 enhanced SRSF3, CDC20, and INCENP expression. We found that PCa cells with a high expression of AKR1C3 have high proliferation ability and high migration ability and were insensitive to enzalutamide. AKR1C3-associated genes had a significant role in the process of PCa, immune responses, and drug sensitivity and offer the potential for a novel model for prognostic prediction in PCa.

Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach

Brain tumors affect the normal functioning of the brain and if not treated in time these cancerous cells may affect the other tissues, blood vessels, and nerves surrounding these cells. Today, a large population worldwide is affected by the precarious disease of the brain tumor. Healthy tissues of the brain are suspected to be damaged because of tumors that become the most significant reason for a large number of deaths nowadays. Therefore, their early detection is necessary to prevent patients from unfortunate mishaps resulting in loss of lives. The manual detection of brain tumors is a challenging task due to discrepancies in appearance in terms of shape, size, nucleus, etc. As a result, an automatic system is required for the early detection of brain tumors. In this paper, the detection of tumors in brain cells is carried out using a deep convolutional neural network with stochastic gradient descent (SGD) optimization algorithm. The multi-classification of brain tumors is performed using the ResNet-50 model and evaluated on the public Kaggle brain-tumor dataset. The method achieved 99.82% and 99.5% training and testing accuracy, respectively. The experimental result indicates that the proposed model outperformed baseline methods, and provides a compelling reason to be applied to other diseases.

Molecular Pathways Related to Sulforaphane as Adjuvant Treatment: A Nanomedicine Perspective in Breast Cancer

Because cancer is a multifactorial disease, it is difficult to identify the specific agents responsible for the disease's progression and development, but lifestyle and diet have been shown to play a significant role. Diverse natural compounds are demonstrating efficacy in the development of novel cancer therapies, including sulforaphane (1-isothiocyanate-4-(methylsulfinyl)butane), a compound found in broccoli and other cruciferous vegetables that promotes key biological processes such as apoptosis, cell cycle arrest, autophagy, and suppression of key signalling pathways such as the PI3K/AKT/mTOR pathway in breast cancer cells. However, one of the primary challenges with sulforaphane treatment is its low solubility in water and oral bioavailability. As a consequence, several investigations were conducted using this component complexed in nanoparticles, which resulted in superior outcomes when combined with chemotherapy drugs. In this study, we discuss the properties and benefits of sulforaphane in cancer therapy, as well as its ability to form complexes with nanomolecules and chemotherapeutic agents that synergize the antitumour response in breast cancer cells.

Assessment of Central Sensitization in Breast Cancer Survivors: Convergent Validity and Use of the Central Sensitization Inventory (CSI) and Its Short-Form as a Clustering Tool

The Central Sensitization Inventory (CSI) measurement properties in patients having nonspecific, noncancer pain are well-established. However, studies examining the reliability and validity of either the CSI or the Central Sensitization Inventory short-form version (CSI-9) in breast cancer survivors (BCS) are scarce. The purpose was to evaluate convergent validity and internal consistency of the CSI and CSI-9. Additionally, the relevance of a new cluster calculator using the CSI was explored. The cross-sectional multi-center study included 65 BCS and 37 healthy volunteers. Patients filled out multiple questionnaires assessing pain, number of painful areas, anxiety, depression and quality of life. The relevance of a cluster calculator was explored by known-group comparisons and boxplot description. All hypotheses were formulated before data analysis. The majority of hypotheses on the correlations between the CSI or CSI-9 and other health outcomes were confirmed (22 out of 27). The CSI and CSI-9 have excellent (α = 0.92) and good (α = 0.86) internal consistency, respectively. The CSI cluster calculator might be an interesting tool to use to have a patient's overall condition snapshot. Generally, the study findings support the construct validity and internal consistency of the CSI, which underline the use of this self-reported instrument in BCS. The CSI-9 shows promising results, but should be further evaluated.

In Silico Approaches: A Way to Unveil Novel Therapeutic Drugs for Cervical Cancer Management

Cervical cancer (CC) is the fourth most common pathology in women worldwide and presents a high impact in developing countries due to limited financial resources as well as difficulties in monitoring and access to health services. Human papillomavirus (HPV) is the leading cause of CC, and despite the approval of prophylactic vaccines, there is no effective treatment for patients with pre-existing infections or HPV-induced carcinomas. High-risk (HR) HPV E6 and E7 oncoproteins are considered biomarkers in CC progression. Since the E6 structure was resolved, it has been one of the most studied targets to develop novel and specific therapeutics to treat/manage CC. Therefore, several small molecules (plant-derived or synthetic compounds) have been reported as blockers/inhibitors of E6 oncoprotein action, and computational-aided methods have been of high relevance in their discovery and development. In silico approaches have become a powerful tool for reducing the time and cost of the drug development process. Thus, this review will depict small molecules that are already being explored as HR HPV E6 protein blockers and in silico approaches to the design of novel therapeutics for managing CC. Besides, future perspectives in CC therapy will be briefly discussed.

Cholesterol Levels Affect the Performance of AuNPs-Decorated Thermo-Sensitive Liposomes as Nanocarriers for Controlled Doxorubicin Delivery

Stimulus-responsive liposomes (L) for triggering drug release to the target site are particularly useful in cancer therapy. This research was focused on the evaluation of the effects of cholesterol levels in the performance of gold nanoparticles (AuNPs)-functionalized L for controlled doxorubicin (D) delivery. Their interfacial and morphological properties, drug release behavior against temperature changes and cytotoxic activity against breast and ovarian cancer cells were studied. Langmuir isotherms were performed to identify the most stable combination of lipid components. Two mole fractions of cholesterol (3.35 mol% and 40 mol%, L1 and L2 series, respectively) were evaluated. Thin-film hydration and transmembrane pH-gradient methods were used for preparing the L and for D loading, respectively. The cationic surface of L allowed the anchoring of negatively charged AuNPs by electrostatic interactions, even inducing a shift in the zeta potential of the L2 series. L exhibited nanometric sizes and spherical shape. The higher the proportion of cholesterol, the higher the drug loading. D was released in a controlled manner by diffusion-controlled mechanisms, and the proportions of cholesterol and temperature of release media influenced its release profiles. D-encapsulated L preserved its antiproliferative activity against cancer cells. The developed liposomal formulations exhibit promising properties for cancer treatment and potential for hyperthermia therapy.

DNA Aptamers against Vaccinia-Related Kinase (VRK) 1 Block Proliferation in MCF7 Breast Cancer Cells

Vaccinia-related kinase (VRK) 1 is a serin/threonine kinase that plays an important role in DNA damage response (DDR), phosphorylating some proteins involved in this process such as 53BP1, NBS1 or H2AX, and in the cell cycle progression. In addition, VRK1 is overexpressed in many cancer types and its correlation with poor prognosis has been determined, showing VRK1 as a new therapeutic target in oncology. Using in vitro selection, high-affinity DNA aptamers to VRK1 were selected from a library of ssDNA. Selection was monitored using the enzyme-linked oligonucleotide assay (ELONA), and the selected aptamer population was cloned and sequenced. Three aptamers were selected and characterized. These aptamers recognized the protein kinase VRK1 with an affinity in the nanomolar range and showed a high sensibility. Moreover, the treatment of the MCF7 breast cell line with these aptamers resulted in a decrease in cyclin D1 levels, and an inhibition of cell cycle progression by G1 phase arrest, which induced apoptosis in cells. These results suggest that these aptamers are specific inhibitors of VRK1 that might be developed as potential drugs for the treatment of cancer.

Quantitative Proteomic Approach Reveals Altered Metabolic Pathways in Response to the Inhibition of Lysine Deacetylases in A549 Cells under Normoxia and Hypoxia

Growing evidence is showing that acetylation plays an essential role in cancer, but studies on the impact of KDAC inhibition (KDACi) on the metabolic profile are still in their infancy. Here, we analyzed, by using an iTRAQ-based quantitative proteomics approach, the changes in the proteome of KRAS-mutated non-small cell lung cancer (NSCLC) A549 cells in response to trichostatin-A (TSA) and nicotinamide (NAM) under normoxia and hypoxia. Part of this response was further validated by molecular and biochemical analyses and correlated with the proliferation rates, apoptotic cell death, and activation of ROS scavenging mechanisms in opposition to the ROS production. Despite the differences among the KDAC inhibitors, up-regulation of glycolysis, TCA cycle, oxidative phosphorylation and fatty acid synthesis emerged as a common metabolic response underlying KDACi. We also observed that some of the KDACi effects at metabolic levels are enhanced under hypoxia. Furthermore, we used a drug repositioning machine learning approach to list candidate metabolic therapeutic agents for KRAS mutated NSCLC. Together, these results allow us to better understand the metabolic regulations underlying KDACi in NSCLC, taking into account the microenvironment of tumors related to hypoxia, and bring new insights for the future rational design of new therapies.

Microproteomic sample preparation

Multiple applications of proteomics in life and health science, pathology and pharmacology, require handling size-limited cell and tissue samples. During proteomic sample preparation, analyte loss in these samples arises when standard procedures are used. Thus, specific considerations have to be taken into account for processing, that are summarised under the term microproteomics (μPs). Microproteomic workflows include: sampling (e.g., flow cytometry, laser capture microdissection), sample preparation (possible disruption of cells or tissue pieces via lysis, protein extraction, digestion in bottom-up approaches, and sample clean-up) and analysis (chromatographic or electrophoretic separation, mass spectrometric measurements and statistical/bioinformatic evaluation). All these steps must be optimised to reach wide protein dynamic ranges and high numbers of identifications. Under optimal conditions, sampling is adapted to the studied sample types and nature, sample preparation isolates and enriches the whole protein content, clean-up removes salts and other interferences such as detergents or chaotropes, and analysis identifies as many analytes as the instrumental throughput and sensitivity allow. In the suggested review, we present and discuss the current state in μP applications for processing of small number of cells (cell μPs) and microscopic tissue regions (tissue μPs).