Computational and systems oncology最新文献

Early-onset breast cancer presents in patients typically under the age of 40, while very early-onset breast cancer is usually viewed as breast cancer occurring before the age of 35. Early-onset breast cancer demonstrates specific molecular properties and has worse outcomes compared to its late-onset breast cancer counterpart. Furthermore, the global burden of early-onset breast cancer, mortality rates, and incidence are on an upward trajectory on a global scale, highlighting the importance of gaining a better comprehension of this disease. This study aims to examine the global burden and incidence of early-onset breast cancer and a myriad of risk factors that contribute to the development of this cancer. Furthermore, the study will dissect the early-onset breast cancer patient knowledge, attitudes, and outcomes, in addition to aspects about genetic testing, disparities, diagnosis, and treatment. By advancing our understanding and knowledge of the molecular and clinical properties of early-onset breast cancer, the scientific community can lay the groundwork for improving patient experiences, outcomes, and therapy.

Resistance to chemotherapy, which is demonstrated in almost every patient with advanced-stage lung cancer (ALC), underscores an urgent need to unravel the underlying molecular mechanisms and identify novel strategies to overcome drug resistance. In the present study, an attempt was made to identify epigenetic targets and modulators that can be exploited to reverse chemotherapeutic resistance in ALC. We performed an integrative analysis to identify epigenetically regulated key genes involved in drug resistance using clinical data from the “TCGA-LUAD” project. Transcriptomic and epigenetic analysis of 71 advanced-stage samples, compared with normal samples, revealed 8532 unique differentially expressed genes (DEGs) in ALC (5752 upregulated and 2779 downregulated genes) and 8313 differentially methylated genes (DMGs) (5816 hypermethylated and 2497 hypomethylated). A total of 143 methylation-driven drug resistance-related genes (mDRGs) were identified through the intersection of DEGs, DMGs, and drug-resistant genes in cancer. By correlating DMGs observed in ALC with crucial genes responsible for drug resistance, 10 hub genes, namely, FGFR2, BDNF, GFRA1, AGTR1, ENO1, GATA2, NTRK3, CXCL12, MSX1, and FGF2, were identified, which are supposed to be associated with the development of lung cancer and therapeutic resistance as well. Functional enrichment analysis revealed that mDRGs were mainly involved in the MAPK signaling pathway, Ras signaling pathway, chemokine signaling pathway, ErbB signaling pathway, and GPCR downstream signaling. Finally, the study identified three key genes, namely, AGTR1, NTRK3, and GFRA1, which can predict the survival of lung cancer patients as well as provide novel mechanisms of drug resistance in ALC. The findings were further validated using GEO datasets (GSE81089 and GSE66836) and were found to be consistent.

As the original member of the huge family of growth factor receptors, the epidermal growth factor receptor (EGFR) has shown inherent tyrosine kinase activity. In addition to its prototypic ligand, EGF, it is activated by TGF-α. EGFR and its ligand contribute to the pathogenesis of colorectal cancer and resistance to targeted therapy. The novel genome editing system, CRISPR/Cas9 has facilitated precise editing of oncogenic loci. This technique has been used in the context of colorectal cancer to either down-regulate EGFR signaling or amend/induce certain mutations affecting the response to tyrosine kinase inhibitors. This review summarizes the application of the mentioned technique in modulation of EGFR signaling and related pathways in the colorectal cancer. Moreover, we uniquely focused on compiling and interpreting results from CRISPR/Cas9 loss-of-function screens that directly investigate resistance to EGFR inhibition in colorectal cancer models. We also analyzed how these screens have identified key genes and pathways—within and beyond the canonical EGFR cascade—that drive resistance, providing a novel, gene-centric perspective on this critical clinical problem.

Cancer progresses when cancer cells selectively bind to inhibitory receptors on a T cell surface, downregulating tumor immune response. One standard-of-care strategy to combat this process is immune checkpoint blockade. Immune checkpoint blockade occurs when a therapeutic agent binds to, and inhibits, inhibitory receptors on a T cell surface, such that immune stimulation is favored when T cells and cancer cells interact. However, many cancers fail to respond to immune checkpoint blockade treatments. Here we explore a whole-tumor and an individual cell-focused model system to test expected outcomes of blockade perturbations in tumor-immune interactions. We first observe a transition point at which patients become more likely to reach “remission” or “stable disease” as a terminal state, and a “progressive disease” state is less likely. We propose a physical, agent-based framework for testing blockade strategies at the cellular level. This offers valuable guidance for blockade efficacy optimization in future development and design of therapeutic antibodies.

This research examines a cholera outbreak, a serious intestinal illness caused by a significant presence of harmful bacteria in the body. We developed a mathematical model to investigate how diseases spread following exposure to pathogens, emphasizing the emergence of symptoms. Initially, the model's predictions were consistent, but it later shifted to different mathematical equations, enhancing our understanding of the disease's molecular mechanisms. Our results indicate that the fixed-pattern model can both provide a biological explanation for the disorder's unpredictable patterns and reach a stable equilibrium. We backed up our conclusions with mathematical ideas that show how the system behaves over time, which will be essential for cholera research in the future. To gain a better understanding of the fundamental causes of the disease, we developed a particular technique called the RK-4 and Non-Standard Finite Difference scheme (NSFD) for the continuous model. This approach, which employs a variety of criteria to assess the stability of intervals with and without the presence of the disease under various conditions, facilitates a comprehensive analysis of the disease's dynamics. Researchers can learn crucial information about the disease's behavior and community effects because of this approach. The results of this study can be used to forecast the spread of various infectious diseases through theoretical and numerical analyses. By using this method, researchers can gain important insight into how diseases behave and how they might affect the affected communities. This study's theoretical and numerical analyses may help forecast how different infectious diseases will spread.

Brain tumors, though rare, are significant health risks, often reaching critical stages before diagnosis. Gliomas, classified as high grade (HGG) and low grade (LGG), require early detection to reduce mortality. While two-dimensional imaging has improved diagnostic techniques, three-dimensional imaging provides a more comprehensive view. This research introduces the Hierarchical Narrowing Multi-Deep Convolutional Neural Network (HNMD-CNN), a novel method for classifying brain tumors using 3D MRI images. The HNMD-CNN employs a hierarchical narrowing filtering strategy inspired by radiologists' models. Initially, large filters identify the tumor area and extract general features, followed by smaller filters to focus on specific tumor characteristics. This approach optimizes feature extraction and representation, improving diagnostic accuracy. We conducted extensive experiments using 3D MRI images from the BraTS2018 and BraTS2019 datasets, demonstrating the HNMD-CNN's ability to enhance convergence speed and classification accuracy without auxiliary algorithms. Our method achieved a remarkable classification accuracy of 99.93%, representing a significant advancement in 3D imaging for glioma classification. This work provides a powerful tool for early detection and accurate diagnosis of gliomas.

Cancer cell plasticity is the ability of tumor cells to switch phenotypes and is one of the predominant requisites of cancer cells capable of undergoing metastasis. Cancer cell plasticity is also recognized as one of the major contributors to intratumoral heterogeneity, a critical factor underlying the progression of malignant tumors, which is known to modify tumor response and induce resistance against various modes of therapy, thus posing a barrier to efficient cancer management. Cancer cell plasticity is acquired by the subversion of cell signaling pathways like mitogen-activated protein kinase pathway, phosphoinositide-3-kinase, signal transducer and activator of transcription 3, Wnt, Hedgehog and Notch as well as cellular programs such as epithelial to mesenchymal transition and phenotypic plasticity. This complex phenomenon has been studied in many cancer types like pancreatic cancer, colon cancer and breast cancer. This review will explore the current understanding we have in breast cancer on the intrinsic molecular mechanisms of cancer cell plasticity and the resistance to various types of cancer therapy that arise as a result of plasticity. We conclude by exploring the potential novel therapies that specifically target the pathways leading to plasticity and can be leveraged to treat patients living with the disease.

The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA-seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA-seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio-informatics. Finally, we propose potential directions for future research.

Biological systems across various length and time scales are noisy, including tissues. Why are biological tissues inherently chaotic? Does heterogeneity play a role in determining the physiology and pathology of tissues? How do physical and biochemical heterogeneity crosstalk to dictate tissue function? In this review, we begin with a brief primer on heterogeneity in biological tissues. Then, we take examples from recent literature indicating functional relevance of biochemical and physical heterogeneity and discuss the impact of heterogeneity on tissue function and pathology. We take specific examples from studies on epithelial tissues to discuss the potential role of inherent tissue heterogeneity in tumorigenesis.

Cancer is a life-threatening process that stems from genetic mutations in cells, which leads to the formation of tumors, and is a major cause of deaths in the United States, with secondary metastasis being a major driver of fatality. The development of an optimal metastatic environment is an essential process prior to tumor metastasis. This process, called pre-metastatic niche formation, involves the activation of resident fibroblast-like cells and macrophages. Tumor-mediated factors introduced to this environment transform resident cells that secrete additional growth factors and remodel the extracellular matrix, which is thought to promote tumor colonization and metastasis in the secondary environment. Furthermore, an important component of metastasis is the biological process of epithelial–mesenchymal transition, which can be exploited by cancer cells to change their phenotype, to migrate and proliferate as necessary. In this review, we discuss recent advances in the investigation of cancer growth and migration. Computational models that focus on biochemical signaling and multicellular dynamics are examined. Machine learning models and image analysis that classify cancer-related data are also explored. Through this review, we highlight advances in the study of important aspects of cancer and metastasis signaling and computational tools to study these dynamics.