搜索结果：Biochimica et biophysica acta. Reviews on cancer

Cancer data, particularly cancer incidence and mortality, are fundamental to understand the cancer burden, to set targets for cancer control and to evaluate the evolution of the implementation of a cancer control policy. However, the complexity of data collection, classification, validation and processing result in cancer incidence figures often lagging two to three years behind the calendar year. In response, national or regional population-based cancer registries (PBCRs) are increasingly interested in methods for forecasting cancer incidence. However, in many countries there is an additional difficulty in projecting cancer incidence as regional registries are usually not established in the same year and therefore cancer incidence data series between different regions of a country are not harmonised over time. This study addresses the challenge of forecasting cancer incidence with incomplete data at both regional and national levels. To achieve this, we propose the use of multivariate spatio-temporal shared component models that jointly model mortality data and available cancer incidence data. We evaluate the performance of these multivariate models using lung cancer incidence dat

// Weidong Li 1,3,* , Shakir M. Saud 2,3,* , Matthew R. Young 3 , Guohong Chen 4 , Baojin Hua 1 1 Department of Oncology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China 2 Nutritional Science Research Group, Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA 3 Basic Research Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, USA 4 Department of Urinary Surgery, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China * These authors have contributed equally to this work Correspondence to: Baojin Hua, email: // Guohong Chen, email: // Keywords : AMP activated kinase, cancer, prevention, treatment Received : February 02, 2015 Accepted : February 26, 2015 Published : March 20, 2015 Abstract AMP-activated protein kinase (AMPK) is an important mediator in maintaining cellular energy homeostasis. AMPK is activated in response to a shortage of energy. Once activated, AMPK can promote ATP production and regulate metabolic energy. AMPK is a known target for treating metabolic syndrome and type-2 diabetes; however, recently AMPK is emerging as a possible metabolic tumor suppressor and target for cancer prevention and treatment. Recent epidemiological studies indicate that treatment with metformin, an AMPK activator reduces the incidence of cancer. In this article we review the role of AMPK in regulating inflammation, metabolism, and other regulatory processes with an emphasis on cancer, as well as, discuss the potential for targeting AMPK to treat various types of cancer. Activation of AMPK has been found to oppose tumor progression in several cancer types and offers a promising cancer therapy. This review evaluates the evidence linking AMPK with tumor suppressor function and analyzes the molecular mechanisms involved. AMPK activity opposes tumor development and progression in part by regulating inflammation and metabolism.

Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones.

We present a general computational theory of cancer and its developmental dynamics. The theory is based on a theory of the architecture and function of developmental control networks which guide the formation of multicellular organisms. Cancer networks are special cases of developmental control networks. Cancer results from transformations of normal developmental networks. Our theory generates a natural classification of all possible cancers based on their network architecture. Each cancer network has a unique topology and semantics and developmental dynamics that result in distinct clinical tumor phenotypes. We apply this new theory with a series of proof of concept cases for all the basic cancer types. These cases have been computationally modeled, their behavior simulated and mathematically described using a multicellular systems biology approach. There are fascinating correspondences between the dynamic developmental phenotype of computationally modeled {\em in silico} cancers and natural {\em in vivo} cancers. The theory lays the foundation for a new research paradigm for understanding and investigating cancer. The theory of cancer networks implies that new diagnostic methods

Nanorobots are a promising development in targeted drug delivery and the treatment of neurological disorders, with potential for crossing the blood-brain barrier (BBB). These small devices leverage advancements in nanotechnology and bioengineering for precise navigation and targeted payload delivery, particularly for conditions like brain tumors, Alzheimer's disease, and Parkinson's disease. Recent progress in artificial intelligence (AI) and machine learning (ML) has improved the navigation and effectiveness of nanorobots, allowing them to detect and interact with cancer cells through biomarker analysis. This study presents a new reinforcement learning (RL) framework for optimizing nanorobot navigation in complex biological environments, focusing on cancer cell detection by analyzing the concentration gradients of surrounding biomarkers. We utilize a computer simulation model to explore the behavior of nanorobots in a three-dimensional space with cancer cells and biological barriers. The proposed method uses Q-learning to refine movement strategies based on real-time biomarker concentration data, enabling nanorobots to autonomously navigate to cancerous tissues for targeted drug d

Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29\% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. In this paper, we explore the potential of enhancing PCa gland segmentation using a novel MRI modality called synthetic correlated diffusion imaging (CDI$^s$). We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment prostate glands from a 200 CDI$^s$ patient cohort. We find that SegResNet achieved superior segmentation performance with a Dice-Sorensen coefficient (DSC) of $76.68 \pm 0.8$. Notably, the Attention U-Net, while slightly less accurate (DSC $74.82 \pm 2.0$), offered a favorable balance between accuracy and computational efficiency. Our findings demonstrate the potential of deep learning models in improving prostate gland segmentation using CDI$^s$ to enhance PCa management and clinical support.

Genetic alteration is the driving force for thyroid tumorigenesis and progression, based upon which novel approaches to the management of thyroid cancer can be developed. A recent important genetic finding in thyroid cancer is the oncogenic T1799A transversion mutation of BRAF (the gene for the B-type Raf kinase, BRAF). Since the initial report of this mutation in thyroid cancer 2 years ago, rapid advancements have been made. BRAF mutation is the most common genetic alteration in thyroid cancer, occurring in about 45% of sporadic papillary thyroid cancers (PTCs), particularly in the relatively aggressive subtypes, such as the tall-cell PTC. This mutation is mutually exclusive with other common genetic alterations, supporting its independent oncogenic role, as demonstrated by transgenic mouse studies that showed BRAF mutation-initiated development of PTC and its transition to anaplastic thyroid cancer. BRAF mutation is mutually exclusive with RET/PTC rearrangement, and also displays a reciprocal age association with this common genetic alteration in thyroid cancer. The T1799A BRAF mutation occurs exclusively in PTC and PTC-derived anaplastic thyroid cancer and is a specific diagnostic marker for this cancer when identified in cytological and histological specimens. This mutation is associated with a poorer clinicopathological outcome and is a novel independent molecular prognostic marker in the risk evaluation of thyroid cancer. Moreover, preclinical and clinical evaluations of the therapeutic value of novel specific mitogen-activated protein kinase pathway inhibitors in thyroid cancer are anticipated. This newly discovered BRAF mutation may prove to have an important impact on thyroid cancer in the clinic.

Non-prescriptional use of medicinal herbs among cancer patients is common around the world. The alleged anti-cancer effects of most herbal extracts are mainly based on studies derived from in vitro or in vivo animal experiments. The current information suggests that these herbal extracts exert their biological effect either through cytotoxic or immunomodulatory mechanisms. One of the active compounds responsible for the immune effects of herbal products is in the form of complex polysaccharides known as beta-glucans. beta-glucans are ubiquitously found in both bacterial or fungal cell walls and have been implicated in the initiation of anti-microbial immune response. Based on in vitro studies, beta-glucans act on several immune receptors including Dectin-1, complement receptor (CR3) and TLR-2/6 and trigger a group of immune cells including macrophages, neutrophils, monocytes, natural killer cells and dendritic cells. As a consequence, both innate and adaptive response can be modulated by beta-glucans and they can also enhance opsonic and non-opsonic phagocytosis. In animal studies, after oral administration, the specific backbone 1-->3 linear beta-glycosidic chain of beta-glucans cannot be digested. Most beta-glucans enter the proximal small intestine and some are captured by the macrophages. They are internalized and fragmented within the cells, then transported by the macrophages to the marrow and endothelial reticular system. The small beta-glucans fragments are eventually released by the macrophages and taken up by other immune cells leading to various immune responses. However, beta-glucans of different sizes and branching patterns may have significantly variable immune potency. Careful selection of appropriate beta-glucans is essential if we wish to investigate the effects of beta-glucans clinically. So far, no good quality clinical trial data is available on assessing the effectiveness of purified beta-glucans among cancer patients. Future effort should direct at performing well-designed clinical trials to verify the actual clinical efficacy of beta-glucans or beta-glucans containing compounds.

Tumor protein P53 is believed to be involved in over half of human cancers cases, the prediction of malignancies plays essential roles not only in advance detection for cancer, but also in discovering effective prevention and treatment of cancer, till now there isn't approach be able in prediction the mutated in tumor protein P53 which is caused high ratio of human cancers like breast, Blood, skin, liver, lung, bladder etc. This research proposed a new approach for prediction pre-cancer via detection malignant mutations in tumor protein P53 using bioinformatics tools like FASTA, BLAST, CLUSTALW and TP53 databases worldwide. Implement and apply this new approach of prediction pre-cancer through mutations at tumor protein P53 shows an effective result when used more specific parameters/features to extract the prediction result that means when the user increase the number of filters of the results which obtained from the database gives more specific diagnosis and classify, addition that the detecting pre-cancer via prediction mutated tumor protein P53 will reduces a person's cancers in the future by avoiding exposure to toxins, radiation or monitoring themselves at older ages by chang

A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor reoccurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows to profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a whole-system understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.

Background Precise prediction of cancer types is vital for cancer diagnosis and therapy. Important cancer marker genes can be inferred through predictive model. Several studies have attempted to build machine learning models for this task however none has taken into consideration the effects of tissue of origin that can potentially bias the identification of cancer markers. Results In this paper, we introduced several Convolutional Neural Network (CNN) models that take unstructured gene expression inputs to classify tumor and non-tumor samples into their designated cancer types or as normal. Based on different designs of gene embeddings and convolution schemes, we implemented three CNN models: 1D-CNN, 2D-Vanilla-CNN, and 2D-Hybrid-CNN. The models were trained and tested on combined 10,340 samples of 33 cancer types and 731 matched normal tissues of The Cancer Genome Atlas (TCGA). Our models achieved excellent prediction accuracies (93.9-95.0%) among 34 classes (33 cancers and normal). Furthermore, we interpreted one of the models, known as 1D-CNN model, with a guided saliency technique and identified a total of 2,090 cancer markers (108 per class). The concordance of differential e

With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians and is considered a black-box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer-reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model-agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP mo

BACKGROUND: Cancer stem cells (CSCs) are subpopulations of cancer cells sharing similar characteristics as normal stem or progenitor cells such as self-renewal ability and multi-lineage differentiation to drive tumour growth and heterogeneity. Throughout the cancer progression, CSC can further be induced from differentiated cancer cells via the adaptation and cross-talks with the tumour microenvironment as well as a response from therapeutic pressures, therefore contributes to their heterogeneous phenotypes. Challengingly, conventional cancer treatments target the bulk of the tumour and are unable to target CSCs due to their highly resistance nature, leading to metastasis and tumour recurrence. MAIN BODY: This review highlights the roles of CSCs in tumour initiation, progression and metastasis with a focus on the cellular and molecular regulators that influence their phenotypical changes and behaviours in the different stages of cancer progression. We delineate the cross-talks between CSCs with the tumour microenvironment that support their intrinsic properties including survival, stemness, quiescence and their cellular and molecular adaptation in response to therapeutic pressure. An insight into the distinct roles of CSCs in promoting angiogenesis and metastasis has been captured based on in vitro and in vivo evidences. CONCLUSION: Given dynamic cellular events along the cancer progression and contributions of resistance nature by CSCs, understanding their molecular and cellular regulatory mechanism in a heterogeneous nature, provides significant cornerstone for the development of CSC-specific therapeutics.

Environmental and genetic mutations can transform the cells in a co-operating healthy tissue into an ecosystem of individualistic tumour cells that compete for space and resources. Various selection forces are responsible for driving the evolution of cells in a tumour towards more malignant and aggressive phenotypes that tend to have a fitness advantage over the older populations. Although the evolutionary nature of cancer has been recognised for more than three decades (ever since the seminal work of Nowell) it has been only recently that tools traditionally used by ecological and evolutionary researchers have been adopted to study the evolution of cancer phenotypes in populations of individuals capable of co-operation and competition. In this chapter we will describe game theory as an important tool to study the emergence of cell phenotypes in a tumour and will critically review some of its applications in cancer research. These applications demonstrate that game theory can be used to understand the dynamics of somatic cancer evolution and suggest new therapies in which this knowledge could be applied to gain some control over the evolution of the tumour.

In modern collider experiments, the quest to explore fundamental interactions between elementary particles has reached unparalleled levels of precision. Signatures from particle physics detectors are low-level objects (such as energy depositions or tracks) encoding the physics of collisions (the final state particles of hard scattering interactions). The complete simulation of them in a detector is a computational and storage-intensive task. To address this computational bottleneck in particle physics, alternative approaches have been developed, introducing additional assumptions and trade off accuracy for speed.The field has seen a surge in interest in surrogate modeling the detector simulation, fueled by the advancements in deep generative models. These models aim to generate responses that are statistically identical to the observed data. In this paper, we conduct a comprehensive and exhaustive taxonomic review of the existing literature on the simulation of detector signatures from both methodological and application-wise perspectives. Initially, we formulate the problem of detector signature simulation and discuss its different variations that can be unified. Next, we classify

There is a widening recognition that cancer cells are products of complex developmental processes. Carcinogenesis and metastasis formation are increasingly described as systems-level, network phenomena. Here we propose that malignant transformation is a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of carcinogenesis as a model of cellular learning. We describe the hallmarks of increased system plasticity of early, tumor initiating cells, such as increased noise, entropy, conformational and phenotypic plasticity, physical deformability, cell heterogeneity and network rearrangements. Finally, we argue that the large structural changes of molecular networks during cancer development necessitate a rather different targeting strategy in early and late phase of carcinogenesis. Plastic networks of early phase cancer development need a central hit, while rigid networks of late stage primary tumors or established metastases should be attacked by the network influence strategy, such as by edgetic, multi-target, or allo-network drugs. Cancer stem cells need special diagnosis and targeting, since their dormant and rapid

In this article, I put forward the idea that the neoplastic process (NP) has deep evolutionary roots and make specific predictions about the connection between cancer and the formation of the first embryo, which allowed for the evolutionary radiation of metazoans. My main hypothesis is that the NP is at the heart of cellular mechanisms responsible for animal morphogenesis and, given its embryological basis, also at the center of animal evolution. It is thus understood that NP-associated mechanisms are deeply rooted in evolutionary history and tied to the formation of the first animal embryo. In my consideration of these arguments, I expound on how cancer biology is perfectly intertwined with evolutionary biology. I describe essential cellular components of unicellular holozoans that served as a basis for the formation of the neoplastic functional module (NFM) and its subsequent exaptation, which brought forth two great biophysical revolutions within the first embryo. Finally, I examine the role of Physics in the modeling of the NFM and its contribution to morphogenesis to reveal the totipotency of the zygote.

DNA and RNA can fold into a variety of alternative conformations. In recent years, a particular nucleic acid structure was discussed to play a role in malignant transformation and cancer development. This structure is called a G-quadruplex (G4). G4 structure formation can drive genome instability by creating mutations, deletions and stimulating recombination events. The importance of G4 structures in the characterization of malignant cells was currently demonstrated in breast cancer samples. In this analysis a correlation between G4 structure formation and an increased intratumor heterogeneity was identified. This suggests that G4 structures might allow breast cancer stratification and supports the identification of new personalized treatment options. Because of the stability of G4 structures and their presence within most human oncogenic promoters and at telomeres, G4 structures are currently tested as a therapeutic target to downregulate transcription or to block telomere elongation in cancer cells. To date, different chemical molecules (G4 ligands) have been developed that aim to target G4 structures. In this review we discuss and compare G4 function and relevance for therapeutic approaches and their impact on cancer development for three cancer entities, which differ significantly in their amount and type of mutations: pancreatic cancer, leukemia and malignant melanoma. G4 structures might present a promising new strategy to individually target tumor cells and could support personalized treatment approaches in the future.

This study focuses on comparing deep learning methods for the segmentation and quantification of uncertainty in prostate segmentation from MRI images. The aim is to improve the workflow of prostate cancer detection and diagnosis. Seven different U-Net-based architectures, augmented with Monte-Carlo dropout, are evaluated for automatic segmentation of the central zone, peripheral zone, transition zone, and tumor, with uncertainty estimation. The top-performing model in this study is the Attention R2U-Net, achieving a mean Intersection over Union (IoU) of 76.3% and Dice Similarity Coefficient (DSC) of 85% for segmenting all zones. Additionally, Attention R2U-Net exhibits the lowest uncertainty values, particularly in the boundaries of the transition zone and tumor, when compared to the other models.