Seminars in cancer biology最新文献

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer that is characterised by a prominent collagenous stromal reaction/desmoplasia surrounding tumour cells. Pancreatic stellate cells (PSCs) are responsible for the production of this stroma and have been shown to facilitate PDAC progression. Recently, extracellular vesicles (EVs), in particular, small extracellular vesicles (exosomes) have been a topic of interest in the field of cancer research for their emerging roles in cancer progression and diagnosis. EVs act as a form of intercellular communication by carrying their molecular cargo from one cell to another, regulating functions of the recipient cells. Although the knowledge of the bi-directional interactions between the PSCs and cancer cells that promote disease progression has advanced significantly over the past decade, studies on PSC-derived EVs in PDAC are currently rather limited. This review provides an overview of PDAC, pancreatic stellate cells and their interactions with cancer cells, as well as the currently known role of extracellular vesicles derived from PSCs in PDAC progression.

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.

The prevalence of obesity has reached pandemic levels worldwide, leading to a lower quality of life and higher health costs. Obesity is a major risk factor for noncommunicable diseases, including cancer, although obesity is one of the major preventable causes of cancer. Lifestyle factors, such as dietary quality and patterns, are also closely related to the onset and development of obesity and cancer. However, the mechanisms underlying the complex association between diet, obesity, and cancer remain unclear. In the past few decades, microRNAs (miRNAs), a class of small non-coding RNAs, have been demonstrated to play critical roles in biological processes such as cell differentiation, proliferation, and metabolism, highlighting their importance in disease development and suppression and as therapeutic targets. miRNA expression levels can be modulated by diet and are involved in cancer and obesity-related diseases. Circulating miRNAs can also mediate cell-to-cell communications. These multiple aspects of miRNAs present challenges in understanding and integrating their mechanism of action. Here, we introduce a general consideration of the associations between diet, obesity, and cancer and review the current knowledge of the molecular functions of miRNA in each context. A comprehensive understanding of the interplay between diet, obesity, and cancer could be valuable for the development of effective preventive and therapeutic strategies in future.

Declining thymic functions associated either with old age (i.e., age-related thymic involution), or with acute involution as a result of stress, infectious disease, or cytoreductive therapies (e.g., chemotherapy/radiotherapy), have been associated with cancer development. A key mechanism underlying such increased cancer risk is the thymus-dependent debilitation of adaptive immunity, which is responsible for orchestrating immunoediting mechanisms and tumor immune surveillance. In the past few years, a blooming set of evidence has intriguingly linked obesity with cancer development and progression. The majority of such studies has focused on obesity-driven chronic inflammation, steroid/sex hormone and adipokine production, and hyperinsulinemia, as principal factors affecting the tumor microenvironment and driving the development of primary malignancy. However, experimental observations about the negative impact of obesity on T cell development and maturation have existed for more than half a century. Here, we critically discuss the molecular and cellular mechanisms of obesity-driven thymic involution as a previously underrepresented intermediary pathology leading to cancer development and progression. This knowledge could be especially relevant in the context of childhood obesity, because impaired thymic function in young individuals leads to immune system abnormalities, and predisposes to various pediatric cancers. A thorough understanding behind the molecular and cellular circuitries governing obesity-induced thymic involution could therefore help towards the rationalized development of targeted thymic regeneration strategies for obese individuals at high risk of cancer development.

Tumor cells evolve in tumor microenvironment composed of multiple cell types. Among these, endothelial cells (ECs) are the major players in tumor angiogenesis, which is a driver of tumor progression and metastasis. Increasing evidence suggests that ECs also contribute to tumor progression and metastasis as they modify their phenotypes to differentiate into mesenchymal cells through a process known as endothelial-mesenchymal transition (EndoMT). This plasticity of ECs is mediated by various cytokines, including transforming growth factor-β (TGF-β), and modulated by other stimuli depending on the cellular contexts. Recent lines of evidence have shown that EndoMT is involved in various steps of tumor progression, including tumor angiogenesis, intravasation and extravasation of cancer cells, formation of cancer-associated fibroblasts, and cancer therapy resistance. In this review, we summarize current updates on EndoMT, highlight the roles of EndoMT in tumor progression and metastasis, and underline targeting EndoMT as a potential therapeutic strategy.

The family of mammalian E2F transcription factors (E2Fs) comprise of 8 members (E2F1-E2F8) classified as activators (E2F1-E2F3) and repressors (E2F4-E2F8) primarily regulating the expression of several genes related to cell proliferation, apoptosis and differentiation, mainly in a cell cycle-dependent manner. E2F activity is frequently controlled via the retinoblastoma protein (pRb), cyclins, p53 and the ubiquitin-proteasome pathway. Additionally, genetic or epigenetic changes result in the deregulation of E2F family genes expression altering S phase entry and apoptosis, an important hallmark for the onset and development of cancer. Although studies reveal E2Fs to be involved in several human malignancies, the mechanisms underlying the role of E2Fs in oral cancer lies nascent and needs further investigations. This review focuses on the role of E2Fs in oral cancer and the etiological factors regulating E2Fs activity, which in turn transcriptionally control the expression of their target genes, thus contributing to cell proliferation, metastasis, and drug/therapy resistance. Further, we will discuss therapeutic strategies for E2Fs, which may prevent oral tumor growth, metastasis, and drug resistance.

Transcription factors (TFs) are indispensable for the modulation of various signaling pathways associated with normal cell homeostasis and disease conditions. Among cancer-related TFs, FOXM1 is a critical molecule that regulates multiple aspects of cancer cells, including growth, metastasis, recurrence, and stem cell features. FOXM1 also impact the outcomes of targeted therapies, chemotherapies, and immune checkpoint inhibitors (ICIs) in various cancer types. Recent advances in cancer research strengthen the cancer-specific role of FOXM1, providing a rationale to target FOXM1 for developing targeted therapies. This review compiles the recent studies describing the pivotal role of FOXM1 in promoting metastasis of various cancer types. It also implicates the contribution of FOXM1 in the modulation of chemotherapeutic resistance, antitumor immune response/immunotherapies, and the potential of small molecule inhibitors of FOXM1.

Tumors consist of different genotypically distinct subpopulations—or subclones—of cells. These subclones can influence neighboring clones in a process called “clonal interaction.” Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models—in coordination with cell culture and animal model experiments—have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical—and often surprising—roles of clonal interactions in human cancers.