Dataset Information

A novel risk signature for predicting brain metastasis in patients with lung adenocarcinoma.

ABSTRACT:

Background

Brain metastasis (BM) are a devastating consequence of lung cancer. This study was aimed to screen risk factors for predicting BM.

Methods

Using an in vivo BM preclinical model, we established a series of lung adenocarcinoma (LUAD) cell subpopulations with different metastatic ability. Quantitative proteomics analysis was used to screen and identify the differential protein expressing map among subpopulation cells. Q-PCR and Western-blot were used to validate the differential proteins in vitro. The candidate proteins were measured in frozen LUAD tissue samples (n = 81) and validated in an independent TMA cohort (n = 64). A nomogram establishment was undertaken by performing multivariate logistic regression analysis.

Results

The quantitative proteomics analysis, qPCR and Western blot assay implied a five-gene signature that might be key proteins associated with BM. In multivariate analysis, the occurrence of BM was associated with age ≤ 65 years, high expressions of NES and ALDH6A1. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI, 0.881-0.988) in the training set. The validation set showed a good discrimination with an AUC of 0.719 (95% CI, 0.595-0.843).

Conclusions

We have established a tool that is able to predict occurrence of BM in LUAD patients. Our model based on both clinical information and protein biomarkers will help to screen patient in high-risk population of BM, so as to facilitate preventive intervention in this part of the population.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC10708939 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Publications

A novel risk signature for predicting brain metastasis in patients with lung adenocarcinoma.

Zhao Yanyan Y Gu Shen S Li Lingjie L Zhao Ruping R Xie Shujun S Zhang Jingjing J Zhou Rongjing R Tu Linglan L Jiang Lei L Zhang Shirong S Ma Shenglin S

Neuro-oncology 20231201 12

<h4>Background</h4>Brain metastasis (BM) are a devastating consequence of lung cancer. This study was aimed to screen risk factors for predicting BM.<h4>Methods</h4>Using an in vivo BM preclinical model, we established a series of lung adenocarcinoma (LUAD) cell subpopulations with different metastatic ability. Quantitative proteomics analysis was used to screen and identify the differential protein expressing map among subpopulation cells. Q-PCR and Western-blot were used to validate the differ ...[more]

PMID: 37379245

Similar Datasets

Project description:BackgroundLung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature.MethodsIn this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients.ResultsWe identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels.ConclusionOur identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

Dataset Information

A novel risk signature for predicting brain metastasis in patients with lung adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

A novel risk signature for predicting brain metastasis in patients with lung adenocarcinoma.

Similar Datasets

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