Research Papers:

The effect of HMGB1 on the clinicopathological and prognostic features of non-small cell lung cancer

Anlin Feng _, Zhenbo Tu and Bingjiao Yin

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Oncotarget. 2016; 7:20507-20519. https://doi.org/10.18632/oncotarget.7050

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Anlin Feng1,*, Zhenbo Tu2,*, Bingjiao Yin1

1Department of Immunology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China, 430030

2Department of Immunology, Wuhan University School of Basic Medical Sciences, Wuhan, Hubei, People’s Republic of China, 430071

*These authors have contributed equally to this work

Correspondence to:

Bingjiao Yin, e-mail: [email protected]

Keywords: HMGB1, NSCLC, biomarker, prognosis, ADC

Received: September 30, 2015     Accepted: January 20, 2016     Published: January 28, 2016


Several studies have assessed the diagnostic and prognostic values of high mobility group protein box 1 (HMGB1) expression in non-small cell lung cancer (NSCLC), but these results remain controversial. The purpose of this study was to perform a meta-analysis of the gene microarray analyses of datasets from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to evaluate the association of HMGB1 expression with the clinicopathological and prognostic features of patients with NSCLC. Furthermore, we investigated the underlying molecular mechanisms by bioinformatics analysis. Twenty relevant articles involving 2651 patients were included in this meta-analysis; the HMGB1 expression in NSCLC tissues was significantly higher than that in the healthy non-cancer control tissues. We also found an indication by microarray analysis and meta-analysis that HMGB1 expression was associated with the cancer TNM Staging System. In terms of prognostic features, a survival analysis from KM-Plotter tool revealed that the high HMGB1 expression group exhibited poorer survival in lung adenocarcinoma (ADC) and overall NSCLC patients. The survival and disease-free analyses from TCGA datasets also showed that HMGB1 mainly affected the development of patients with ADC. Therefore, we focused on how HMGB1 affected the prognosis and development of ADC using bioinformatics analyses and detected that the mitogen-activated protein kinases (MAPK), apoptosis and cell cycle signaling pathways were the key pathways that varied during HMGB1 up-regulation in ADC. Moreover, various genes such as PLCG2, the phosphatidylinositol-4, 5-bisphosphate 3-kinase superfamily (PI3Ks), protein kinase C (PKC) and DGKZ were selected as hub genes in the gene regulatory network. Our results indicated that HMGB1 is a potential biomarker to predict progression and survival of NSCLC, especially of ADC types.

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