Quantitative proteomics identified circulating biomarkers in lung adenocarcinoma diagnosis

Abstract

Background: Lung cancer (LC) is a common malignant tumor with a high incidence and poor prognosis. Early LC could be cured, but the 5-year-survival rate for patients advanced is extremely low. Early screening of tumor biomarkers through plasma could allow more LC to be detected at an early stage, leading to a earlier treatment and a better prognosis.

Methods: This study was based on total proteomic analysis and parallel reaction monitoring validation of peripheral blood from 20 lung adenocarcinoma patients and 20 healthy individuals. Furthermore, differentially expressed proteins closely related to prognosis were analysed using Kaplan-Meier Plotter and receiver operating characteristic curve (ROC) curve analysis.

Results: The candidate proteins GAPDH and RAC1 showed the highest connectivity with other differentially expressed proteins between the lung adenocarcinoma group and the healthy group using STRING. Kaplan-Meier Plotter analysis showed that lung adenocarcinoma patients with positive ATCR2, FHL1, RAB27B, and RAP1B expression had observably longer overall survival than patients with negative expression (P < 0.05). The high expression of ARPC2, PFKP, PNP, RAC1 was observably negatively correlated with prognosis (P < 0.05). 17 out of 27 proteins showed a high area under the curve (> 0.80) between the lung adenocarcinoma and healthy plasma groups. Among those proteins, UQCRC1 had an area under the curve of 0.960, and 5 proteins had an area under the curve from 0.90 to 0.95, suggesting that these hub proteins might have discriminatory potential in lung adenocarcinoma, P < 0.05.

Conclusions: These findings provide UQCRC1, GAPDH, RAC1, PFKP have potential as novel biomarkers for the early screening of lung adenocarcinoma.

Keywords: Biomarkers; Early diagnosis; Lung adenocarcinoma; Proteomics; RAC1; UQCRC1.

Fig. 1

Global proteome characterization of LUAD and healthy plasma a The protocol of detailing experiments b Identification of quantifiable proteins from MS/MS spectra c Principal Component Analysis of quantified proteins, a complete separation of LUAD and NL groups d Pearson’s Correlation Coefficient of LUAD and NL groups. e volcano plot of all proteins in LUAD compared to NL

Fig. 2

Proteomic features of differentially expressed proteins in lung adenocarcinoma a Heatmap with hierarchical clustering of DEPs b The orange indicating high expression and green indicating low expression. Each line represents a protein, and each column represents a sample c GO analysis of DEPs. d subcellular localization prediction of DEPs e KOG categories of DEPs f KEGG enrichment analyses of DEPs

Fig. 3

Detection of targeted proteins by PRM a 35 proteins according to the adjusted P value are colored in blue (down-regulated) and yellow (up-regulated) b GO analysis of 27 DEPs detection by PRM c PPI network analysis was performed using the STRING11.0 online software. d the graph demonstrates individually the top 10 significant DEPs, P value < 0.05

Fig. 4

Kaplan–Meier survival analysis of LUAD group compared with NL group

Fig. 5

ROC curves of the best protein a The AUC values of six proteins in these datasets b The AUC values of the combination of six-protein

Fig. 6

Western blot analysis and immunohistochemistry staining of six proteins a Western blot of GP5, CCT7, UQCRC1, PGD, GAPDH, LDHA in healthy and LUAD plasmas, Ponceau S was used for quantification of total protein, b histogram of protein expression level calculated by line’s IntDen divide total protein per lane (µg). c Immunohistochemistry staining of GP5, CCT7, UQCRC1, PGD, GAPDH and LDHA in LUAD tissues and tumor-adjacent tissues; scale bar = 100 µm (magnification, × 200). (*P value < 0.05, **P value < 0.01)