BackgroundCancer treatment is based on tumor staging. Curative intent is only applied to localized tumors. Recent studies show that oligometastatic patients who have limited number of metastases may benefit from metastasis-directed local treatments to achieve long-term survival. However, mechanisms underlying oligometastatic to polymetastatic progression remains elusive.MethodsThe effects of miR-200c and Sec23a on tumor metastasis were verified both in vitro and in vivo. The secretome changes were detected by mass spectrometry.FindingsWe established a pair of homologous lung-metastasis derived oligometastatic and polymetastatic cell lines from human melanoma cancer cell line M14. Using the two cell lines, we have identified Sec23a, a gene target of miR-200c, suppresses miR-200c augmented oligometastatic to polymetastatic progression via its secretome. Firstly, miR-200c over-expression and Sec23a interference accelerated oligometastatic to polymetatic progression. Secondly, Sec23a functions downstream of miR-200c. Thirdly, mass spectrometric analysis of the secretory protein profile suggests that Sec23a-dependent secretome may impact metastatic colonization by modifying tumor microenvironment. Fourthly, the survival analysis using The Cancer Genome Atlas database shows Sec23a as a favorable prognostic marker for skin cutaneous melanoma, supporting the clinical relevance of our findings.InterpretationThe finding that Sec23a is a suppressor of oligometastatic to polymetastatic progression has clinical implications. First, it provides a new theoretical framework for the development of treatments that prevent oligometastasis to polymetastasis. Second, Sec23a may be used as a favorable prognostic marker for the selection of patients with stable oligometastatic disease for oligometastasis-based local therapies of curative intent.FundNational Natural Science Foundations of China.
The E2F family of transcription factors (E2Fs) consist of eight genes in mammals. These genes encode ten proteins that are usually classified as transcriptional activators or transcriptional repressors. E2Fs are important for many cellular processes, from their canonical role in cell cycle regulation to other roles in angiogenesis, the DNA damage response and apoptosis. A growing body of evidence demonstrates that cancer stem cells (CSCs) are key players in tumor development, metastasis, drug resistance and recurrence. This review focuses on the role of E2Fs in CSCs and notes that many signals can regulate the activities of E2Fs, which in turn can transcriptionally regulate many different targets to contribute to various biological characteristics of CSCs, such as proliferation, self-renewal, metastasis, and drug resistance. Therefore, E2Fs may be promising biomarkers and therapeutic targets associated with CSCs pathologies. Finally, exploring therapeutic strategies for E2Fs may result in disruption of CSCs, which may prevent tumor growth, metastasis, and drug resistance.
Lung cancer has the highest mortality rate due to late diagnosis and high incidence of metastasis. Cancer stem cells (CSCs) are a subgroup of cancer cells with self‐renewal capability similar to that of normal stem cells (NSCs). While CSCs may play an important role in cancer progression, mechanisms underlying CSC self‐renewal and the relationship between self‐renewal of the NSCs and CSCs remain elusive. The orphan nuclear receptor Nr5a2 is a transcriptional factor, and a regulator of stemness of embryonic stem cells and induced pluripotent stem cells. However, whether Nr5a2 regulates the self‐renewal of lung CSCs is unknown. Here, we showed the diagnostic and prognostic values of elevated Nr5a2 expression in human lung cancer. We generated the mouse LLC‐SD lung carcinoma CSC cellular model in which Nr5a2 expression was enhanced. Using the LLC‐SD model, through transient and stable siRNA interference of Nr5a2 expression, we provided convincing evidence for a regulatory role of Nr5a2 in the maintenance of lung CSC self‐renewal and stem cell properties in vitro. Further, using the syngeneic and orthotopic lung transplantation model, we elucidated augmented cancer biological properties associated with Nr5a2 promotion of LLC‐SD self‐renewal. More importantly, we revealed that Nr5a2’s regulatory role in promoting LLC‐SD self‐renewal is mediated by transcriptional activation of its direct target Nanog. Taken together, in this study, we have provided convincing evidence in vitro and in vivo demonstrating that Nr5a2 can induce lung CSC properties and promote tumorigenesis and progression through transcriptional up‐regulation of Nanog.
The dichotomy of cancer-regulatory genes into "oncogenes (OCGs)" and "tumorsuppressor genes (TSGs)" has greatly helped us in learning molecular details of tumor biology. SPDEF, known as the prostate-derived ETS factor, is reported to play a pivotal role in normal cell development and survival, which has also been endowed with dual characteristics in cancers. Breast cancer (BC) is a highly heterogeneous disease which becomes the leading reason for cancer-related fatality among women worldwide. The involvement of SPDEF in many aspects of BC has been postulated, whereas the mechanism governing the regulation of the pro-and anti-oncogenic activities of SPDEF in BC state remains poorly defined. In this review, we summarized SPDEF as the double agent involving in expression profiles, the regulatory mechanism in BC progression, as well as the role in diagnosis, treatment and prognosis of BC. The understanding of SPDEF duality has contributed to gain insight into the tumor biology and also add a new dimension to the new therapy targets for BC.
Artificial Intelligence (AI) is a branch of computer science that includes research in robotics, language recognition, image recognition, natural language processing, and expert systems. AI is poised to change medical practice, and oncology is not an exception to this trend. As the matter of fact, lung cancer has the highest morbidity and mortality worldwide. The leading cause is the complexity of associating early pulmonary nodules with neoplastic changes and numerous factors leading to strenuous treatment choice and poor prognosis. AI can effectively enhance the diagnostic efficiency of lung cancer while providing optimal treatment and evaluating prognosis, thereby reducing mortality. This review seeks to provide an overview of AI relevant to all the fields of lung cancer. We define the core concepts of AI and cover the basics of the functioning of natural language processing, image recognition, human-computer interaction and machine learning. We also discuss the most recent breakthroughs in AI technologies and their clinical application regarding diagnosis, treatment, and prognosis in lung cancer. Finally, we highlight the future challenges of AI in lung cancer and its impact on medical practice.
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