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Candidate genes and pathways associated with brain metastasis from lung cancer compared with lymph node metastasis.

ABSTRACT: Brain metastasis from lung cancer (BMLC) is one of the common types of metastasis associated with poor prognosis. The aim of the present study was to elucidate the underlying molecular mechanisms of BMLC. The mRNA microarray dataset GSE18549 was downloaded from the Gene Expression Omnibus database. The Limma package of R was used to screen the differentially expressed genes (DEGs). Based on the DAVID database, functional and pathway enrichment analyses of DEGs were performed. Furthermore, the protein-protein interaction (PPI) network was predicted using the STRING database and visualized with Cytoscape software. In addition, hub genes and significant modules were selected based on the network. A total of 190 DEGs with log2|(fold change)|>1, including 129 significantly downregulated DEGs and 61 upregulated DEGs, were obtained. Gene Ontology functional enrichment analysis indicated that downregulated DEGs were mainly associated with 'immune response', 'cell activation' and 'leukocyte activation', while the upregulated DEGs were involved in 'DNA repair' and 'viral process'. Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that the downregulated DEGs were mainly enriched in 'chemokine signaling pathway', whereas the upregulated DEGs were associated with 'oocyte meiosis'. Based on the PPI network, 9 hub genes were selected, namely tumor necrosis factor, C-C motif chemokine ligand (CCL) 2, CD34, vascular cell adhesion molecule 1, CD48, CD27, CCL19, C-X-C motif chemokine receptor 6 and C-C motif chemokine receptor 2. The present study sheds light on the molecular mechanisms of BMLC and may provide molecular targets and diagnostic biomarkers for BMLC.

SUBMITTER: Zhao X

PROVIDER: S-EPMC6614716 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Candidate genes and pathways associated with brain metastasis from lung cancer compared with lymph node metastasis.

Zhao Xuelian X Wang Nan N Chidanguro Tungamirai T Gu Huanyu H Li Yi Y Cao Huiru H Wen Pushuai P Ren Fu F

Experimental and therapeutic medicine 20190626 2

Brain metastasis from lung cancer (BMLC) is one of the common types of metastasis associated with poor prognosis. The aim of the present study was to elucidate the underlying molecular mechanisms of BMLC. The mRNA microarray dataset GSE18549 was downloaded from the Gene Expression Omnibus database. The Limma package of R was used to screen the differentially expressed genes (DEGs). Based on the DAVID database, functional and pathway enrichment analyses of DEGs were performed. Furthermore, the pr ...[more]

PMID: 31363372

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Project description:Radiomics studies to predict lymph node (LN) metastasis has only focused on either primary tumor or LN alone. However, combining radiomics features from multiple sources may reflect multiple characteristic of the lesion thereby increasing the discriminative performance of the radiomic model. Therefore, the present study intends to evaluate the efficiency of integrative nomogram, created by combining clinical parameters and radiomics features extracted from gross tumor volume (GTV), peritumoral volume (PTV) and LN, for the preoperative prediction of LN metastasis in clinical cT1N0M0 adenocarcinoma. A primary cohort of 163 patients (training cohort, 113; and internal validation cohort, 50) and an external validation cohort of 53 patients with clinical stage cT1N0M0 were retrospectively included. Features were extracted from three regions of interests (ROIs): GTV; PTV (5.0 mm around the tumor) and LN on pre-operative contrast enhanced computed tomography (CT). LASSO logistic regression method was used to build radiomic signatures. Multivariable regression analysis was used to build a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The discriminative performance of nomogram was validated both internally and externally. The radiomic signatures using the features of GTV, PTV and LN showed a good ability in predicting LN metastasis with an area under the curve (AUC) of 0.74 (95% CI 0.60-0.88), 0.72 (95% CI 0.57-0.87) and 0.64 (95% CI 0.48-0.80) respectively in external validation cohort. The integration of different signature together further increases the discriminatory ability: GTV + PTV (GPTV): AUC 0.75 (95% CI 0.61-0.89) and GPTV + LN: AUC 0.76 (95% CI 0.61-0.91) in external validation cohort. An integrative nomogram of clinical parameters and radiomic features demonstrated further increase in discriminatory ability with AUC of 0.79 (95% CI 0.66-0.93) in external validation cohort. The nomogram showed good calibration. Decision curve analysis demonstrated that the radiomic nomogram was clinically useful. The integration of information from clinical parameters along with CT radiomics information from GTV, PTV and LN was feasible and increases the predictive performance of the nomogram in predicting LN status in cT1N0M0 adenocarcinoma patients suggesting merit of information integration from multiple sources in building prediction model.

Dataset Information

Candidate genes and pathways associated with brain metastasis from lung cancer compared with lymph node metastasis.

Publications

Candidate genes and pathways associated with brain metastasis from lung cancer compared with lymph node metastasis.

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