Analysis of Breast Cancer Based on the Dysregulated Network

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer

INTRODUCTION

Cross-talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross-talk in colorectal cancer (CRC) development at both the pathway and gene levels.

METHODS

In this study, we integrated the gene expression data with domain knowledge to construct state-dependent pathway cross-talk networks. The significance of the genes involved in pathway cross-talk was assessed by analyzing their association with cancer hallmarks, disease-gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression.

RESULTS

Cross-talk was observed between immune-related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial-mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high-risk from low-risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways.

CONCLUSIONS

This study highlights the crucial role of pathway cross-talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules.

maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server.

Cellular and Exosomal MicroRNAs: Emerging Clinical Relevance as Targets for Breast Cancer Diagnosis and Prognosis

Breast cancer accounts for the highest cancer cases globally, with 12% of occurrences progressing to metastatic breast cancer with a low survival rate and limited effective early intervention strategies augmented by late diagnosis. Moreover, a low concentration of prognostic and predictive markers hinders disease monitoring. Circulating and exosomal microRNAs (miRNAs) have recently shown a considerable interplay in breast cancer, standing out as effective diagnostic and prognostic markers. The primary functions are as gene regulatory agents at the genetic and epigenetic levels. An array of dysregulated miRNAs stimulates cancer-promoting mechanisms, activating oncogenes and controlling tumor-suppressing genes and mechanisms. Exosomes are vastly studied extracellular vesicles, carrying, and transporting cargo, including noncoding RNAs with premier roles in oncogenesis. Translocation of miRNAs from the circulation to exosomes, with RNA-binding proteins in stress-induced conditions, has shown significant cooperation in function to promote breast cancer. This review examines cellular and exosomal miRNA biogenesis and loading, the clinical implications of their dysregulation, their function in diagnosis, prognosis, and prediction of breast cancer, and in regulating cancer signaling pathways. The influence of cellular and exosomal miRNAs presents clinical significance on breast cancer diagnosis, subtyping, staging, prediction, and disease monitoring during treatment, hence a potent marker for breast cancer.

Detecting early-warning signals for influenza by dysregulated dynamic network biomarkers

As a dynamical system, complex disease always has a sudden state transition at the tipping point, which is the result of the long-term accumulation of abnormal regulations. This paper proposes a novel approach to detect the early-warning signals of influenza A (H3N2 and H1N1) outbreaks by dysregulated dynamic network biomarkers (dysregulated DNBs) for individuals. The results of cross-validation show that our approach can detect early-warning signals before the symptom appears successfully. Unlike the traditional DNBs, our dysregulated DNBs are anchored and very few, which is essential for disease early diagnosis in clinical practice. Moreover, the genes of dysregulated DNBs are significantly enriched in the influenza-related pathways. The source code of this paper can be freely downloaded from https://github.com/YanhaoHuo/dysregulated-DNBs.git.

Patterns of Immunohistochemical Expression of P53, BCL2, PTEN, and HER2/neu Tumor Markers in Specific Breast Cancer Lesions

Objective

This study aimed to associate the expression of P53, BCL2, PTEN, and HER2/neu tumor markers in specific breast cancer lesions.

Methods

This study analyzed the immunohistochemical expression of P53, BCL2, PTEN, and HER2/neu tumor markers for 306 patients who presented with lesions. Tissue blocks and patients' identification data were retrieved from the department of pathology, AL Madinah Almonwarah hospital, Al Madinah, UAE.

Results

Of the 306 patients, 104 had benign lesions and 202 had malignancy (including 194 females and 6 males). Most females were presented with invasive ductal carcinoma (IDC), followed by infiltrating ductal carcinoma, and invasive lobular carcinoma (ILC), representing 70%, 23.2%, and 3.7%, respectively. Positive P53, BCL2, PTEN, and HER2 were identified in 20.8%, 11.9%, 91%, and 18.3%, respectively.

Conclusion

: The expression of P53, BCL2, PTEN, and HER2/neu tumor markers among Saudi patients with breast cancer is relatively similar in many parts of the world.