Evaluation of the Effect of Lymph Node Status on the Survival of Non-Small Cell Lung Cancer Patients With Brain Metastases: Applications of a Novel Grade Prognostic Assessment Score Model Involving N Stage

Molecular and metabolic orchestration of the lymphatic vasculature in physiology and pathology

Lymphangiogenesis refers to the generation of new lymphatic vessels from pre-existing ones. During development and particular adult states, lymphatic endothelial cells (LEC) undergo reprogramming of their transcriptomic and signaling networks to support the high demands imposed by cell proliferation and migration. Although there has been substantial progress in identifying growth factors and signaling pathways controlling lymphangiogenesis in the last decades, insights into the role of metabolism in lymphatic cell functions are just emerging. Despite numerous similarities between the main metabolic pathways existing in LECs, blood ECs (BEC) and other cell types, accumulating evidence has revealed that LECs acquire a unique metabolic signature during lymphangiogenesis, and their metabolic engine is intertwined with molecular regulatory networks, resulting in a tightly regulated and interconnected process. Considering the implication of lymphatic dysfunction in cancer and lymphedema, alongside other pathologies, recent findings hold promising opportunities to develop novel therapeutic approaches. In this review, we provide an overview of the status of knowledge in the molecular and metabolic network regulating the lymphatic vasculature in health and disease.

Integrative lymph node-mimicking models created with biomaterials and computational tools to study the immune system

The lymph node (LN) is a vital organ of the lymphatic and immune system that enables timely detection, response, and clearance of harmful substances from the body. Each LN comprises of distinct substructures, which host a plethora of immune cell types working in tandem to coordinate complex innate and adaptive immune responses. An improved understanding of LN biology could facilitate treatment in LN-associated pathologies and immunotherapeutic interventions, yet at present, animal models, which often have poor physiological relevance, are the most popular experimental platforms. Emerging biomaterial engineering offers powerful alternatives, with the potential to circumvent limitations of animal models, for in-depth characterization and engineering of the lymphatic and adaptive immune system. In addition, mathematical and computational approaches, particularly in the current age of big data research, are reliable tools to verify and complement biomaterial works. In this review, we first discuss the importance of lymph node in immunity protection followed by recent advances using biomaterials to create in vitro/vivo LN-mimicking models to recreate the lymphoid tissue microstructure and microenvironment, as well as to describe the related immuno-functionality for biological investigation. We also explore the great potential of mathematical and computational models to serve as in silico supports. Furthermore, we suggest how both in vitro/vivo and in silico approaches can be integrated to strengthen basic patho-biological research, translational drug screening and clinical personalized therapies. We hope that this review will promote synergistic collaborations to accelerate progress of LN-mimicking systems to enhance understanding of immuno-complexity.

Construction and validation of a nomogram for non small cell lung cancer patients with liver metastases based on a population analysis

Lung cancer is one of the most common malignancies in the United States, and the common metastatic sites in advanced non-small cell lung cancer (NSCLC) are bone, brain, adrenal gland, and liver, respectively, among which patients with liver metastases have the worst prognosis. We retrospectively analyzed 1963 patients diagnosed with NSCLC combined with liver metastases between 2010 and 2015. Independent prognostic factors for patients with liver metastases from NSCLC were identified by univariate and multivariate Cox regression analysis. Based on this, we developed a nomogram model via R software and evaluated the performance and clinical utility of the model by calibration curve, receiver operating characteristic curves, and decision curve analysis (DCA). The independent prognostic factors for NSCLC patients with liver metastases included age, race, gender, grade, T stage, N stage, brain metastases, bone metastases, surgery, chemotherapy, and tumor size. The area under the curve predicting OS at 6, 9, and 12 months was 0.793, 0.787, and 0.784 in the training cohort, and 0.767, 0.771, and 0.773 in the validation cohort, respectively. Calibration curves of the nomogram showed high agreement between the outcomes predicted by the nomogram and the actual observed outcomes, and the DCA further demonstrated the value of the clinical application of the nomogram. By analyzing the Surveillance, Epidemiology, and End Results database, we established and verified a prognostic nomogram for NSCLC patients with liver metastases, to personalize the prognosis of patients. At the same time, the prognostic nomogram has a satisfactory accuracy and the results are a guide for the development of patient treatment plans.

The effect of smoking on survival in lung carcinoma patients with brain metastasis: a systematic review and meta-analysis

The effects of smoking on survival in BM patients have yet to be reviewed and meta-analysed. However, previous studies have shown that smokers had a greater risk of dying from lung cancer compared to non-smokers. This meta-analysis, therefore, aimed to analyse the effects of cigarette smoking on overall survival (OS) and progression-free survival (PFS) in lung cancer BM patients. PubMed, Embase, Web of Science, Cochrane and Google Scholar were searched for comparative studies regarding the effects of smoking on incidence and survival in brain metastases patients up to December 2020. Three independent reviewers extracted overall survival (OS) and progression-free survival data (PFS). Random-effects models were used to pool multivariate-adjusted hazard ratios (HR). Out of 1890 studies, fifteen studies with a total of 2915 patients met our inclusion criteria. Amongst lung carcinoma BM patients, those who were smokers (ever or yes) had a worse overall survival (HR: 1.34, 95% CI 1.13, 1.60, I2: 72.1%, p-heterogeneity < 0.001) than those who were non-smokers (never or no). A subgroup analysis showed the association to remain significant in the ever/never subgroup (HR: 1.34, 95% CI 1.11, 1.63) but not in the yes/no smoking subgroup (HR: 1.30, 95% CI 0.44, 3.88). This difference between the two subgroups was not statistically significant (p = 0.91). Amongst lung carcinoma BM patients, smoking was associated with a worse OS and PFS. Future studies examining BMs should report survival data stratified by uniform smoking status definitions.