ABSTRACT Breast cancer affects approximately one in every eight women globally, as reported by the World Health Organization. Among all female malignant tumors, breast cancer has the highest fatality rate, surpassed only by lung cancer. Advanced stages of this disease are almost always fatal, and despite the advancements in surgical techniques and carefully designed chemotherapy regimens, relapse remains nearly inevitable. Although there are various chemical therapies for the treatment of breast cancer that can destroy the tumor or inhibit its growth, they often come with a variety of side‐effects. The polyphenolic compound quercetin can be found in numerous food plants. Previous research strongly suggests quercetin's potential as a cancer therapy. Various types of cancer cell lines have demonstrated that quercetin can induce apoptosis and suppress the proliferation of cancer cells. Therefore, we conducted an integrative bioinformatics analysis to identify molecular signatures, signaling pathways, and lncRNA‐associated gene relationships potentially related to quercetin‐associated transcriptional changes in breast cancer. The identified candidates were subsequently explored using protein–protein interaction and public regulatory databases. These findings are intended to generate hypotheses regarding potential lncRNA‐associated mechanisms rather than establish direct causal quercetin–lncRNA interactions. We found several genes involved in different metabolic pathways in relation to breast cancer and quercetin treatment.
ABSTRACT Inherited factors account for a large share of breast cancer susceptibility, yet the biological consequences of most risk variants are still poorly understood. To address this gap, we studied 175 breast cancer risk variants confirmed by genome‐wide association studies and gathered the genes that lie near them. Two complementary gene sets were assembled. The first collected every gene immediately flanking each risk variant, regardless of distance. The second kept only the closest gene together with genes tagged by variants in strong linkage disequilibrium within a 100 kb interval. Across both sets, the clearest biological signal pointed to androgen receptor‐mediated signaling. Pathway mapping additionally implicated the Wnt/β‐catenin and Hedgehog cascades, and this pattern was most evident in the flanking‐gene set. With HaploReg, we annotated correlated variants at each locus and recovered features that mark active regulation. Enrichment testing returned a 4.8‐fold excess of DNase I hypersensitivity in a breast cancer cell line and a 15.7‐fold excess of enhancer motifs in a stem cell line. Together, these results indicate that breast cancer risk markers and their correlated variants influence the activity of neighboring genes and converge on a small number of signaling pathways central to mammary biology.
ABSTRACT Cadherin 1 ( CDH1 ) encodes E‐cadherin, a key epithelial adhesion molecule traditionally associated with tumor suppression and epithelial–mesenchymal transition (EMT). However, its roles across cancers remain incompletely understood, particularly within multilayer regulatory contexts involving genomic, epigenetic, transcriptional, and immune mechanisms. CDH1 expression, survival associations, EMT‐correlated gene profiles ( VIM , SNAI1 , and ZEB1 ), immune infiltration patterns, immune checkpoint correlations ( PDCD1 , CD274 , and CTLA4 ), promoter methylation, and genomic alterations were assessed across five epithelial cancers, breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), lung adenocarcinoma (LUAD), ovarian serous cystadenocarcinoma (OV), and stomach adenocarcinoma (STAD). Cross‐platform analysis was performed using The Cancer Genome Atlas (TCGA)/Genomic Data Commons (GDC) datasets, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), UALCAN, TIMER, KM Plotter, cBioPortal, and g:Profiler. CDH1 was overexpressed but showed variable prognostic significance; TCGA/GDC analyses were nonsignificant, whereas KM Plotter showed better survival in COAD, LUAD, and STAD, worse survival in BRCA, and no association in OV. Classic inverse relationships between CDH1 and VIM or ZEB1 were evident only in STAD, and SNAI1 showed no consistent association. Immune infiltration patterns were tumor‐specific, ranging from cytotoxic T‐cell dominance in LUAD to macrophage‐rich profiles in OV; immune checkpoint correlations were similarly context‐dependent. Cancer‐specific enrichment revealed distinct trafficking‐ and adhesion‐related profiles. Promoter methylation patterns varied by cancer, whereas genomic alterations of CDH1 were rare. CDH1 does not function as a universal epithelial or EMT marker across epithelial cancers. Instead, its associations with EMT, immune contexture, methylation, and prognosis are context‐dependent, supporting a model of CDH1 as a heterogeneous regulator of epithelial plasticity. These findings challenge single‐function interpretations and support cancer‐specific CDH1 evaluation in translational research.
ABSTRACT Breast cancer is still a serious problem in the world arena, where its early and prompt detection is the most important factor in improving patient prognosis and survival. The use of traditional diagnostic techniques, such as imaging (e.g., mammography and ultrasound) and subsequent histopathological examination, is the mainstay, which, however, is hindered by a variety of limitations and is less sensitive in certain instances (e.g., dense breast tissue). Computer‑aided diagnosis (CAD) systems based on machine learning (ML) and deep learning (DL) approaches have become revolutionary technologies to tackle these issues. The review is a comparative analysis of all the various methodologies involved in detecting breast cancer. It first analyzes the conventional imaging methods and their shortcomings. It then reviews the use of classical ML algorithms (e.g., support vector machines, random forests) mostly on structured clinical data. The main idea of the review is the popularity of DL to evaluate a number of medical images, such as histopathology slides, ultrasound, and mammograms, in particular, convolutional neural networks (CNNs). Finally, the review examines the next generation of hybrid approaches, such as ensemble learning, data fusion architectures, and combinations of multimodal data, such as genomics, pathomics, and metabolomics. This paper assesses the performance, strengths, and limitations of these coexisting strategies by synthesizing recent findings, noting that there are still some persistent challenges in terms of data scarcity, model interpretability, and clinical translation. The review has ended with a summary of the present situation and the future of breast cancer diagnostics.
ABSTRACT As population aging accelerates, the coexistence of cancer and Alzheimer's disease and related dementias (ADRD) is becoming increasingly common. While each condition independently contributes to mortality, national trends in cancer‐related deaths occurring alongside ADRD—and their demographic and geographic disparities—remain poorly defined. We conducted a retrospective population‐based analysis using mortality data from the CDC WONDER (1999–2020). Deaths listing both malignant neoplasms (ICD‐10: C00–C97) and ADRD (F01, F03, G30) were identified. Age‐adjusted mortality rates (AAMRs) per 100,000 population were calculated using the 2000 U.S. standard population. Temporal trends were assessed using joinpoint regression to estimate annual percent change (APC) with 95% confidence intervals. Stratified analyses by sex, race, region, and state were performed to evaluate disparities. A total of 89,514 deaths with concurrent cancer and ADRD were identified. The AAMR more than doubled from 2.0 per 100,000 in 1999 to 4.6 in 2020, peaking in 2012. Overall mortality increased significantly from 1999 to 2009 (APC 9.11%, p < 0.05), followed by stabilization. Sex‐specific analyses showed continued increases among females, whereas males demonstrated a significant decline after 2010. By race, Black individuals experienced a steeper early rise followed by a significant decline, while trends among White individuals plateaued after 2009. Regionally, the South exhibited a significant post‐2010 decline, whereas other regions stabilized. Marked state‐level variation was observed, with the highest rates in the Midwest and the lowest in the Southwest. Cancer mortality with concurrent ADRD increased substantially in the early 2000s, followed by divergent trends across demographic and geographic subgroups. Persistent disparities highlight the need for integrated oncologic and cognitive care strategies and targeted public health interventions addressing inequities in aging populations.
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.
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.
ABSTRACT 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.
ABSTRACT 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.
ABSTRACT 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.
ABSTRACT 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.
ABSTRACT The rapid advancement of medicine, fueled by big data and interdisciplinary research, has significantly transformed the landscape of computational oncology research. In this evolving environment, effective time management strategies are essential for researchers across diverse specializations. Computational oncology researchers typically pursue one of three primary career paths: clinical medicine, experimental biology, or computational/statistical research, each with its own set of challenges and time management practices. Clinicians, often constrained by patient care duties, struggle to balance research with clinical responsibilities. Experimental biologists face intensive laboratory workloads, while computational researchers enjoy more flexible schedules but encounter issues with software inefficiencies and remote collaboration challenges. Given these differences, optimizing time management requires a structured approach, including setting clear goals, balancing work with continuous learning, and prioritizing tasks effectively. Researchers must also cultivate complementary skills, such as scientific writing and secondary language acquisition, and leverage automation and collaboration to enhance productivity. Strategic partnerships across disciplines, integrating expertise from clinical practice, bioinformatics, and experimental sciences, can accelerate research progress and improve scientific rigor. In addition, maintaining emotional well‐being and aligning professional aspirations with personal interests contribute to sustained motivation and efficiency. In conclusion, effective time management is essential for maximizing productivity in computational oncology research. By adopting structured learning practices, prioritizing tasks wisely, fostering collaboration, and maintaining a healthy work‐life balance, researchers can navigate their demanding careers more efficiently. As medical science continues to evolve, strategic time management will play a crucial role in shaping the future of research and innovation.
ABSTRACT In Australia, health authorities have not implemented lung cancer screening but have explored its potential implementation through various inquiries. Evidence suggests that combining AI and CT scans for screening holds promise in terms of cost–benefit ratio, cost savings, improved healthcare outcomes, optimized resource allocation, and enhanced efficiency in lung cancer diagnosis by radiologists. Therefore, this study aimed to evaluate the cost‐effectiveness of an AI system for the initial annual lung cancer screening. In this retrospective analysis, a Markov model—built using data from published sources—was applied to estimate quality‐adjusted life years (QALYs) and lifetime costs for various diagnostic strategies. To ensure a thorough evaluation of model uncertainty and AI system expenses, the study applied both deterministic and probabilistic sensitivity analyses, capturing variability across diverse input scenarios. Using AI significantly reduces the cost of detecting lung cancer, from $150,000 to $300, and from €138,000 to €276. Simultaneously, there is a marginal increase in QALYs from 14.0 to 14.2. These findings claim that AI can reduce the cost of screening by 99.8% and increase the QALYs by 0.2. AI (reduced price) for lung cancer screening is more cost‐effective than the original CT cost scenario. Both methods reach full cost‐effectiveness, with AI (reduced cost) lowering mortality by 20% and standard CT achieving a 40% drop. Beyond 50% reduction, cost‐effectiveness wanes; the optimal range remains between 20% and 50%, where CT plus AI proves economically strong. Integrating AI with CT scans in lung cancer screening is a cost‐effective approach that provides superior value for money. Defining cost thresholds for AI can speed up its adoption in clinical practice, and further research is required to assess the combination of AI models with specialized radiologists and address uncertainties regarding AI system accuracy.
Abstract 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.
Abstract 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.
AbstractCancer 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.
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.
Ovarian cancer is commonly diagnosed in its late stages, and new treatment modalities are needed to improve patient outcomes and survival. We have recently established the synergistic effects of combination tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) and procaspase activating compound (PAC-1) therapies in granulosa cell tumours (GCT) of the ovary, a rare form of ovarian cancer, using a mathematical model of the effects of both drugs in a GCT cell line. Here, to understand the mechanisms of combined TRAIL and PAC-1 therapy, study the viability of this treatment strategy, and accelerate preclinical translation, we leveraged our mathematical model in combination with population pharmacokinetics (PopPK) models of both TRAIL and PAC-1 to expand a realistic heterogeneous cohort of virtual patients and optimize treatment schedules. Using this approach, we investigated treatment responses in this virtual cohort and determined optimal therapeutic schedules based on patient-specific pharmacokinetic characteristics. Our results showed that schedules with high initial doses of PAC-1 were required for therapeutic efficacy. Further analysis of individualized regimens revealed two distinct groups of virtual patients within our cohort: one with high PAC-1 elimination, and one with normal PAC-1 elimination. In the high elimination group, high weekly doses of both PAC-1 and TRAIL were necessary for therapeutic efficacy, however virtual patients in this group were predicted to have a worse prognosis when compared to those in the normal elimination group. Thus, PAC-1 pharmacokinetic characteristics, particularly clearance, can be used to identify patients most likely to respond to combined PAC-1 and TRAIL therapy. This work underlines the importance of quantitative approaches in preclinical oncology.
Abstract Misexpression and remodeling of the extracellular matrix is a canonical hallmark of cancer, although the extent of cancer‐associated aberrations in the genes coding for extracellular matrix (ECM) proteins and the consequences thereof are not well understood. In this study, we examined the alterations in core matrisomal genes across a set of nine cancers. These genes, especially the ones encoding for ECM glycoproteins (GP), were observed to be more susceptible to mutations than copy number variations across cancers. We classified the glycoprotein genes based on the ubiquity of their mutations across the nine cancer groups and estimated their evolutionary age using phylostratigraphy. To our surprise, the ECM glycoprotein genes commonly mutated across all cancers were predominantly unicellular in origin, whereas those commonly showing mutations in specific cancers evolved mostly during and after the unicellular‐multicellular transition. Pathway annotation for biological interactions revealed that the most pervasively mutated glycoprotein set regulated a larger set of inter‐protein interactions and constituted more cohesive interaction networks relative to the cancer‐specific mutated set. In addition, ontological prediction revealed the pervasively mutated set to be strongly enriched for basement membrane (BM) dynamics. Our results suggest that ancient unicellular‐origin ECM GP were canalized into playing critical tissue morphogenetic roles, and when disrupted through matrisomal gene mutations, associated with neoplastic transformation of a wide set of human tissues.
Cancer progression, including the development of intratumor heterogeneity, is inherently a spatial process. Mathematical models of tumor evolution may be a useful starting point for understanding the patterns of heterogeneity that can emerge in the presence of spatial growth. A commonly studied spatial growth model assumes that tumor cells occupy sites on a lattice and replicate into neighboring sites. Our R package SITH provides a convenient interface for exploring this model. Our efficient simulation algorithm allows for users to generate 3D tumors with millions of cells in under a minute. For visualizing the distribution of mutations throughout the tumor, SITH provides interactive graphics and summary plots. Additionally, SITH can produce synthetic bulk and single-cell DNA-seq datasets by sampling from the simulated tumor. A streamlined API makes SITH a useful tool for investigating the relationship between spatial growth and intratumor heterogeneity. SITH is a part of CRAN and can be installed by running install.packages("SITH") from the R console. See https://CRAN.R-project.org/package=SITH for the user manual and package vignette.
