Expression Pattern and Prognostic Significance of Chemokines in Breast cancer: An Integrated Bioinformatics Analysis

Prognostic significance and expression pattern of glucose related genes in breast cancer: A comprehensive computational biology approach

Breast cancer (BC) is the most common type of malignancy globally and the main reason why women die from tumours. The Warburg effect, a characteristic of tumor, describes how most solid tumour cells acclimatize to their diverse surroundings by relying heavily on aerobic glycolysis for production of energy. In addition to producing key metabolic intermediates that are crucial for the production of macromolecules, which enable cancer cell division, invasiveness, and drug resistance, the transformed energy metabolism also supplies tumor cells with ATP for cellular energy. Here, we evaluated the expression profile, prognostic significance, and clinical relevance of glucose-related genes in BC using a bioinformatic approach. To clarify the significance of glucose-related genes in the development of breast tumours, we also performed a functional enrichment investigation of deregulated genes using the STRING and KEGG portal. The study depicted that of the 61 genes examined, 8 genes had a fold change Collapse

Key Words

• Bioinformatics
• Breast cancer
• Deregulation
• Glucose metabolism
• Prognosis
• Survival analysis
• Warburg effect

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MESH Headings Collapse

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Affiliation(s)

Bader Alshehri Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Almajmaah 11952, Saudi Arabia

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Targeting breast cancer stem cells through retinoids: A new hope for treatment

Breast cancer is a complex and diverse disease accounting for nearly 30% of all cancers diagnosed in females. But unfortunately, patients develop resistance to the existing chemotherapeutic regimen, resulting in approximately 90% treatment failure. With over half a million deaths annually, it is imperative to explore new therapeutic approaches to combat the disease. Within a breast tumor, a small sub-population of heterogeneous cells, with a unique ability of self-renew and differentiation and responsible for tumor formation, initiation, and recurrence are referred to as breast cancer stem cells (BCSCs). These BCSCs have been identified as one of the main contributors to chemoresistance in breast cancer, making them an attractive target for developing novel therapeutic strategies. These cells exhibit surface biomarkers such as CD44+, CD24-/LOW, ALDH, CD133, and CD49f phenotypes. Higher expression of CD44+ and ALDH activity has been associated with the formation of tumors in various cancers. Moreover, the abnormal regulation of signaling pathways, including Hedgehog, Notch, β-catenin, JAK/STAT, and P13K/AKT/mTOR, leads to the formation of cancer stem cells, resulting in the development of tumors. The growing drug resistance in BC is a significant challenge, highlighting the need for new therapeutic strategies to combat this dreadful disease. Retinoids, a large group of synthetic derivatives of vitamin A, have been studied as chemopreventive agents in clinical trials and have been shown to regulate various crucial biological functions including vision, development, inflammation, and metabolism. On a cellular level, the retinoid activity has been well characterized and translated and is known to induce differentiation and apoptosis, which play important roles in the outcome of the transformation of tissues into malignant. Retinoids have been investigated extensively for their use in the treatment and prevention of cancer due to their high receptor-binding affinity to directly modulate gene expression programs. Therefore, in this study, we aim to summarize the current understanding of BCSCs, their biomarkers, and the associated signaling pathways. Retinoids, such as Adapalene, a third-generation retinoid, have shown promising anti-cancer potential and may serve as therapeutic agents to target BCSCs.

Elucidation of interleukin-19 as a therapeutic target for breast cancer by computational analysis and experimental validation

Interleukin 19 (IL-19) is a cytokine produced by monocytes and belongs to the family of IL-10. The IL-19 protein stimulates fibronectin (FN) expression and assembly, metastasis, and cell division in breast cancer (BC) cells. IL-19, which is connected to breast pathogenesis and has an autocrine action in BC cells, is a key predictor of prognosis for many tumour forms, including breast cancer. Augmented IL-19 expression has been related to poorer clinical outcomes for patients with BC and directly enhances proliferation and migration while also serving as a microenvironment for tumour formation. The main aim of our study was to examine the expression profile, functional role, and prognostic significance of interleukin-19 in BC pathogenesis and also to find out the molecular mechanism of IL-19 in BC. In this work, we used the various computational approach and tools, to evaluate the expression profile and prognostic implication of IL-19 in BC and discover the role of IL-19 in BC pathogenesis. IL-19 was shown to be highly upregulated in BC as compared to other interleukins. Also, its levels were highly overexpressed in liminal BC patients, mostly in 3rd stage groups under the age group of 21-40 years. IL-19 levels were increased in BC and elevated expression of IL-19 was examined to have worse overall survival (OS). The KEGG analysis and gene ontology of IL-19 depict that IL-19 is significantly augmented in cytokine activity and receptor-ligand activity and also in the JAK-STAT signaling pathway. Moreover, IL-19 showed a high correlation with IL20RA, as later is involved with the JAK-STAT signaling pathway. The in-vivo and in-vitro studies have also reflected that upregulation of IL-19 enhances tumor development and affects clinical outcomes in BC patients through several pathways including the JAK TAT signalling pathway. Overall, our study indicates that IL-19 increases tumour growth and that inhibiting it in addition to standard treatments will greatly improve BC patient's therapeutic responses.

CXCL12 and CXCR4 as Potential Early Biomarkers for Luminal A and Luminal B Subtypes of Breast Cancer

Purpose

Breast cancer is the most common type of malignancy in women. Factors that increase the risk of occurrence include chronic inflammation, with chemokines as its mediators. Therefore, the purpose of the present study was to determine the diagnostic utility of CXCL12 and CXCR4 as modern tumor markers in patients with early-stage luminal A and luminal B subtype of breast cancer and also to compare the results with the routinely used marker - CA 15-3.

Patients and Methods

The study included 100 patients with early breast cancer of luminal A and B subtypes, 50 women with benign breast lesion and 50 healthy women. The levels of CXCL12 and CXCR4 concentrations were determined by enzyme-linked immunosorbent assay (ELISA), comparative marker CA 15-3 - by electrochemiluminescence method (ECLIA).

Results

Concentrations of CXCL12 were significantly lower, while CXCR4 and CA 15-3 - significantly higher among patients with early-stage breast cancer than healthy women. CXCL12 also showed lower concentrations among fibroadenoma patients in comparison to healthy women, while CXCR4 - lower concentrations among fibroadenoma patients than cancer group. CXCL12 showed significantly higher values of sensitivity (79%), specificity (82%), positive predictive value (89.72%), negative predictive value (80%), diagnostic accuracy (80%) and diagnostic power (AUC = 0.8196) in the whole breast cancer group compared to the CA 15-3 marker (58%; 72%; 80.56%; 46.15%, 62.67%, 0.6434, resp.). Analysis of combined parameters resulted in increased sensitivity, negative predictive value and power of the test with a slight decrease in positive predictive value and a more significant decrease in specificity, reaching the best values for the three-parameter test CXCL12+CXCR4+CA15-3 (96%; 85.71%; AUC = 0.8812; 78.69%; 48%, resp.).

Conclusion

The results indicate the preliminary usefulness of CXCL12 and CXCR4 as early biomarkers in the diagnosis of breast cancer, especially in the combined panel with CA 15-3.

Taxifolin Inhibits Breast Cancer Growth by Facilitating CD8+ T Cell Infiltration and Inducing a Novel Set of Genes including Potential Tumor Suppressor Genes in 1q21.3

Taxifolin inhibits breast cancer (BC) via novel mechanisms. In a syngeneic mouse BC model, taxifolin suppressed 4T-1 cell-derived allografts. RNA-seq of 4T-1 tumors identified 36 differentially expressed genes (DEGs) upregulated by taxifolin. Among their human homologues, 19, 7, and 2 genes were downregulated in BCs, high-proliferative BCs, and BCs with high-fatality risks, respectively. Three genes were established as tumor suppressors and eight were novel to BC, including HNRN, KPRP, CRCT1, and FLG2. These four genes exhibit tumor suppressive actions and reside in 1q21.3, a locus amplified in 70% recurrent BCs, revealing a unique vulnerability of primary and recurrent BCs with 1q21.3 amplification with respect to taxifolin. Furthermore, the 36 DEGs formed a multiple gene panel (DEG36) that effectively stratified the fatality risk in luminal, HER2+, and triple-negative (TN) equivalent BCs in two large cohorts: the METABRIC and TCGA datasets. 4T-1 cells model human TNBC cells. The DEG36 most robustly predicted the poor prognosis of TNBCs and associated it with the infiltration of CD8+ T, NK, macrophages, and Th2 cells. Of note, taxifolin increased the CD8+ T cell content in 4T-1 tumors. The DEG36 is a novel and effective prognostic biomarker of BCs, particularly TNBCs, and can be used to assess the BC-associated immunosuppressive microenvironment.

Adaptive Sparse Multi-Block PLS Discriminant Analysis: An Integrative Method for Identifying Key Biomarkers from Multi-Omics Data

With the growing use of high-throughput technologies, multi-omics data containing various types of high-dimensional omics data is increasingly being generated to explore the association between the molecular mechanism of the host and diseases. In this study, we present an adaptive sparse multi-block partial least square discriminant analysis (asmbPLS-DA), an extension of our previous work, asmbPLS. This integrative approach identifies the most relevant features across different types of omics data while discriminating multiple disease outcome groups. We used simulation data with various scenarios and a real dataset from the TCGA project to demonstrate that asmbPLS-DA can identify key biomarkers from each type of omics data with better biological relevance than existing competitive methods. Moreover, asmbPLS-DA showed comparable performance in the classification of subjects in terms of disease status or phenotypes using integrated multi-omics molecular profiles, especially when combined with other classification algorithms, such as linear discriminant analysis and random forest. We have made the R package called asmbPLS that implements this method publicly available on GitHub. Overall, asmbPLS-DA achieved competitive performance in terms of feature selection and classification. We believe that asmbPLS-DA can be a valuable tool for multi-omics research.

Identification and analysis of dysregulated fatty acid metabolism genes in breast cancer subtypes

Breast cancer is one of the most aggressive and lethal types of transformation among women. An anomaly of normal fatty acid metabolism is acknowledged as a critical trigger for malignant transformations including breast cancer, but the prospect of targeting fatty acid metabolism for the treatment of malignancy has remained unrecognized so far. It has been observed that specific fatty acid metabolism genes are involved in the commencement and development of breast cancer. These specific genes have also been observed to be related to different isotypes/molecular subtypes of breast cancer. The main purpose of this study was to scrutinize the prognostic significance, functional role, and expression pattern of fatty acid metabolism genes. In-Silico tools like TCGA BrCA, Gepia2, Ualcan Analysis, UCSC Xena, Kaplan-Meier plotter, Bc-gene EXminer, String, gene ontology, and KEGG databases, were used to assess the expression pattern of the fatty acid metabolism genes in breast cancer patients and also among the different molecular sub-types of breast cancer. Differential gene expression analysis revealed dysregulation of FABP4, FABP5, PLIN1, PLIN2, PLIN4, PLIN5, LPIN1, MGLL, PNPLA2, PNPLA7, ACSL1, and ACOX2 showing a fold change >  ± 1.5. Also, most of these genes show downregulation in Ualcan analysis of different isotypes/molecular subtypes of breast cancer. The study reveals that the screened genes i.e., FABP4, FABP5, PLIN1, PLIN2, PLIN4, PLIN5, LPIN1, MGLL, PNPLA2, PNPLA7, ACSL1, and ACOX2 can be used as biomarkers that reveal poor prognosis and may serve as therapeutic targets for the treatment of breast cancer.

Targeting aurora kinase a (AURKA) in cancer: molecular docking and dynamic simulations of potential AURKA inhibitors

The Aurora family of serine/threonine kinases in mammals are key regulators of mitotic progression and are commonly upregulated in human tumors. Since AURKA's increased expression has been linked to cancer, AURKA inhibitors could reduce AURKA expression and function as potent therapeutic drugs. The study's objective was to find and categorize inhibitors with a stronger affinity for AURKA. This study also aimed to identify AURKA's expression profile and prognostic significance across pan-cancers. We looked into therapeutic compounds that were structurally comparable to MK8745 for their potential to selectively inhibit AURKA. We used drug likeliness analysis, MD simulation studies to evaluate the therapeutic possibility of screened MK8745 analogues. AURKA was found to be strongly upregulated in several cancers and is linked to worse overall and relapse-free survival. The Molecular docking and dynamic analysis revealed two new MK8745 analogues to be potent AURKA inhibitors with higher binding affinities and stabilities than MK8745. Furthermore, MK8745 analogues are potential replacements for MK8745 because they have strong binding affinity, which is consistent with MDS results, and have appropriate ADMET properties. Through basic, clinical, and preclinical research, the identification of novel compounds may open the door for their prospective use in the prevention of cancer.

Adapalene inhibits the growth of triple-negative breast cancer cells by S-phase arrest and potentiates the antitumor efficacy of GDC-0941

Although advances in diagnostics and therapeutics have prolonged the survival of triple-negative breast cancer (TNBC) patients, metastasis, therapeutic resistance, and lack of targeted therapies remain the foremost hurdle in the effective management of TNBC. Thus, evaluation of new therapeutic agents and their efficacy in combination therapy is urgently needed. The third-generation retinoid adapalene (ADA) has potent antitumor activity, and using ADA in combination with existing therapeutic regimens may improve the effectiveness and minimize the toxicities and drug resistance. The current study aimed to assess the anticancer efficacy of adapalene as a combination regimen with the PI3K inhibitor (GDC-0941) in TNBC in vitro models. The Chou–Talalay’s method evaluated the pharmacodynamic interactions (synergism, antagonism, or additivity) of binary drug combinations. Flow cytometry, Western blotting, and in silico studies were used to analyze the mechanism of GDC–ADA synergistic interactions in TNBC cells. The combination of GDC and ADA demonstrated a synergistic effect in inhibiting proliferation, migration, and colony formation of tumor cells. Accumulation of reactive oxygen species upon co-treatment with GDC and ADA promoted apoptosis and enhanced sensitivity to GDC in TNBC cells. The findings indicate that ADA is a promising therapeutic agent in treating advanced BC tumors and enhance sensitivity to GDC in inhibiting tumor growth in TNBC models while reducing therapeutic resistance.

Targeting cyclin-dependent kinase 1 (CDK1) in cancer: molecular docking and dynamic simulations of potential CDK1 inhibitors

Cell cycle dysregulation is a characteristic hallmark of malignancies, which results in uncontrolled cell proliferation and eventual tumor formation. Cyclin-dependent kinase 1 (CDK1) is a member of the family of cell cycle regulatory proteins involved in cell cycle maintenance. Given that overexpression of CDK1 has been associated with cancer, CDK1 inhibitors may restore equilibrium to the skewed cell cycle system and operate as an effective therapeutic drug. This study aimed to identify and classify inhibitors having a higher affinity for CDK1 and also evaluate the expression pattern and prognostic relevance of CDK1 in a wide range of cancers. We investigated therapeutic molecules structurally similar to dinaciclib for their ability to inhibit CDK1 selectively. To assess the therapeutic potential of screened Dinaciclib analogs, we used drug likeliness analysis, molecular docking, and simulation analysis. CDK1 was found to be highly upregulated across several malignancies and is associated with poor overall and relapse-free survival. Molecular docking and dynamics evaluation identified two novel dinaciclib analogs as potent CDK1 inhibitors with high binding affinity and stability compared to dinaciclib. The results indicate that increased CDK1 expression is associated with decreased OS and RFS. Additionally, dinaciclib analogs are prospective replacements for dinaciclib since they exhibit increased binding affinity, consistent with MDS findings, and have acceptable ADMET qualities. The discovery of new compounds may pave the road for their future application in cancer prevention through basic, preclinical, and clinical research.