Serum endostatin levels are associated with diffusion capacity and with tuberous sclerosis- associated lymphangioleiomyomatosis

The development for emerging biomarkers of lymphangioleiomyomatosis

Lymphangioleiomyomatosis (LAM) is a rare, slowly progressing, low-grade metastatic tumor primarily affecting women. Currently, vascular endothelial growth factor-D (VEGF-D) is the only validated diagnostic biomarker, enabling diagnosis of LAM without the need for lung biopsy in appropriate clinical settings. However, VEGF-D concentrations are normal in about 30% of patients, rendering it insufficient for diagnosing all cases of LAM. There remains a need to identify more non-invasive, safe, sensitive, and specific biomarkers associated with LAM. Therefore, it is imperative to explore novel non-invasive, safe, and specific diagnostic methods for LAM. This article aims to review biomarkers associated with LAM, including potential biomarkers newly discovered or showing advancements in classical biomarkers widely used in LAM, and discuss their application in LAM diagnosis, assessment of disease severity, prediction of treatment response, and prognosis. （LAM） 、，。，-D （VEGF-D） ， LAM。， 30% VEGF-D ， LAM 。 LAM 、、。，、 LAM 。 LAM ， LAM ， LAM 、、。.

Lymphangioleiomyomatosis: circulating levels of FGF23 and pulmonary diffusion

OBJECTIVE

Lymphangioleiomyomatosis (LAM) is a rare, destructive disease of the lungs with a limited number of determinants of disease activity, which are a critical need for clinical trials. FGF23 has been implicated in several chronic pulmonary diseases. We aimed to determine the association between serum FGF23 levels and pulmonary function in a cohort of patients with LAM.

METHODS

This was a descriptive single-center study in which subjects with LAM and controls with unreported lung disease were recruited. Serum FGF23 levels were measured in all subjects. Clinical data, including pulmonary function testing, were retrospectively obtained from electronic medical records of LAM subjects. Associations between FGF23 levels and clinical features of LAM were explored via nonparametric hypothesis testing.

RESULTS

The sample comprised 37 subjects with LAM and 16 controls. FGF23 levels were higher in the LAM group than in the control group. In the LAM group, FGF23 levels above the optimal cutoff point distinguished 33% of the subjects who had nondiagnostic VEGF-D levels. Lower FGF23 levels were associated with impaired DLCO (p = 0.04), particularly for those with isolated diffusion impairment with no other spirometric abnormalities (p = 0.04).

CONCLUSIONS

Our results suggest that FGF23 is associated with pulmonary diffusion abnormalities in LAM patients and elicit novel mechanisms of LAM pathogenesis. FGF23 alone or in combination with other molecules needs to be validated as a biomarker of LAM activity in future clinical research.

Lymphangioleiomyomatosis: Searching for potential biomarkers

Background

Vascular endothelial growth factor-D (VEGF-D) is the most commonly used biomarker for diagnosing lymphangioleiomyomatosis (LAM). However, lung biopsy is often necessary as well; therefore, defining new biomarkers for LAM is crucial. The aim of this study was to describe the diagnostic accuracy of a variety of biomarkers.

Methods

We assessed 13 analytes in serum related to extracellular matrix remodeling, lymphatic involvement and angiogenesis in a cohort of patients with LAM, comparing them with patients with other cystic lung diseases (OCLD) and healthy women. A scoring method based on the cut-point of each VEGF-D and metalloproteinase-2 (MMP-2) was used to evaluate the diagnostic performance of the marker combination.

Results

A total of 97 subjects were recruited: 59 (61%) LAM patients, 18 (19%) OCLD patients, and 20 (20%) healthy female controls. MMP-2 was the only extracellular matrix remodeling biomarker able to differentiate LAM patients from OCLD and healthy patients. Serum MMP-2 was higher in LAM patients [median 578 (465-832) ng/ml] than in patients with OCLD and healthy controls [medians 360 (314-546) and 427 (365-513) ng/ml, respectively (p < 0.0001)]. The area under ROC curve (AUC) of MMP-2 was 0.785 and that of VEGF-D 0.815 (p = 0.6214). The sensitivity/specificity profiles of each biomarker (54/92% for MMP-2, 59/95% for VEGF-D) yielded a composite score (-6.36 + 0.0059 × VEGF-D + 0.0069 × MMP-2) with higher accuracy than each component alone (AUC 0.88 and sensitivity/specificity 79/87%).

Conclusion

Combining MMP-2 and VEGF-D may increase diagnostic accuracy for LAM.

The role of angiogenesis in melanoma: Clinical treatments and future expectations

The incidence of melanoma has increased rapidly over the past few decades, with mortality accounting for more than 75% of all skin cancers. The high metastatic potential of Melanoma is an essential factor in its high mortality. Vascular angiogenic system has been proved to be crucial for the metastasis of melanoma. An in-depth understanding of angiogenesis will be of great benefit to melanoma treatment and may promote the development of melanoma therapies. This review summarizes the recent advances and challenges of anti-angiogenic agents, including monoclonal antibodies, tyrosine kinase inhibitors, human recombinant Endostatin, and traditional Chinese herbal medicine. We hope to provide a better understanding of the mechanisms, clinical research progress, and future research directions of melanoma.

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis

BACKGROUND

Lymphangioleiomyomatosis (LAM) is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to identify clusters of associated features which could be used to stratify patients and predict outcomes in individuals.

PATIENTS AND METHODS

Using unsupervised machine learning we generated patient clusters using data from 173 women with LAM from the UK and 186 replication subjects from the US National Heart, Lung, and Blood Institute (NHLBI) LAM registry. Prospective outcomes were associated with cluster results.

RESULTS

Two- and three-cluster models were developed. A three-cluster model separated a large group of subjects presenting with dyspnoea or pneumothorax from a second cluster with a high prevalence of angiomyolipoma symptoms (p=0.0001) and tuberous sclerosis complex (TSC) (p=0.041). Patients in the third cluster were older, never presented with dyspnoea or pneumothorax (p=0.0001) and had better lung function. Similar clusters were reproduced in the NHLBI cohort. Assigning patients to clusters predicted prospective outcomes: in a two-cluster model the future risk of pneumothorax was 3.3 (95% CI 1.7-5.6)-fold greater in cluster 1 than cluster 2 (p=0.0002). Using the three-cluster model, the need for intervention for angiomyolipoma was lower in clusters 2 and 3 than cluster 1 (p<0.00001). In the NHLBI cohort, the incidence of death or lung transplant was much lower in clusters 2 and 3 (p=0.0045).

CONCLUSIONS

Machine learning has identified clinically relevant clusters associated with complications and outcome. Assigning individuals to clusters could improve decision making and prognostic information for patients.