Label-free quantitative proteomics reveals aberrant expression levels of LRG, C9, FN, A1AT and AGP1 in the plasma of patients with colorectal cancer

Abstract

Background: Colorectal cancer (CRC) is one of the major causes of cancer-related death worldwide. Although commercial biomarkers of CRC are currently available, they are still lacking in terms of sensitivity and specificity; thus, searching for reliable blood-based biomarkers are important for the primary screening of CRC.

Methods: Plasma samples of patients with non-metastatic (NM) and metastatic (M) CRC and healthy controls were fractionated using MARS-14 immunoaffinity chromatography. The flow-through and elute fractions representing low- and high-abundant proteins, respectively, were analyzed by label-free quantitative proteomics mass spectrometry. The functional analysis of the proteins with greater than 1.5-fold differential expression level between the CRC and the healthy control groups were analyzed for their biological processes and molecular functions. In addition, the levels of plasma proteins showing large alterations in CRC patients were confirmed by immunoblotting using two independent cohorts. Moreover, receiver operating characteristic (ROC) curve analysis was performed for individual and combinations of biomarker candidates so as to evaluate the diagnostic performance of biomarker candidates.

Results: From 163 refined identifications, five proteins were up-regulated and two proteins were down-regulated in NM-CRC while eight proteins were up-regulated and three proteins were down-regulated in M-CRC, respectively. Altered plasma proteins in NM-CRC were mainly involved in complement activation, while those in M-CRC were clustered in acute-phase response, complement activation, and inflammatory response. Results from the study- and validation-cohorts indicate that the levels of leucine-rich alpha-2-glycoprotein-1(LRG), complement component C9 (C9), alpha-1-acid glycoprotein 1 (AGP1), and alpha-1-antitrypsin (A1AT) were statistically increased, while fibronectin (FN) level was statistically decreased in CRC patients compared to healthy controls, with most alterations found in a metastatic stage-dependent manner. ROC analysis revealed that FN exhibited the best diagnostic performance to discriminate CRC patients and healthy controls while AGP1 showed the best discrimination between the disease stages in both cohorts. The combined biomarker candidates, FN + A1AT + AGP1, exhibited perfect discriminatory power to discriminate between the CRC population and healthy controls whereas LRG + A1AT + AGP1 was likely to be the best panel to discriminate the metastatic stages in both cohorts.

Conclusions: This study identified and quantified distinct plasma proteome profiles of CRC patients. Selected CRC biomarker candidates including FN, LRG, C9, A1AT, and AGP1 may be further applied for screening larger cohorts including disease groups from other types of cancer or other diseases.

Keywords: Affinity chromatography; MARS-14; blood-based biomarkers; colorectal cancer; label-free quantitative proteomics.

Fig. 1

Infographic of plasma proteins identified by label-free quantitative proteomics in flow-through (A) and elute (D) fractions of pooled plasma from healthy controls, non-metastatic CRC patients and metastatic CRC patients separated by MARS-14 immunochromatography. Volcano plots of the protein expression levels in non-metastatic and metastatic CRC patients in comparison to those of the healthy controls in the flow-through (B and C) and elute (E and F) fractions, respectively. The x-axis represents log₂ fold changes of proteins and the y-axis represents -log₁₀p-value. The red and green dots indicate proteins with significantly different expression identified by log₂ fold change > 0.58 and < -0.58 (> 1.5-fold differential expression), and -log₁₀p-value greater than 1.3 (p-value < 0.05), respectively. The black dots indicate proteins which were not significantly altered between CRC patients and healthy control groups.

Fig. 2

The protein patterns and expression levels of LRG, S100A8, C9, FN, AGP1, and A1AT in crude plasma, MARS-14 flow-though (FT) and MARS-14 elute (EL) fractions of the pooled samples of healthy controls (HC), patients with non-metastatic CRC (NM) and patients with metastatic CRC (M). (A) Stain-free imaging of the gel displayed the pattern of total proteins from three groups (HC, NM, and M). Crude plasma (30 µg), flow-through (5 µg) and elute (5 µg) fractions of three groups were separated on 10% TGX stain-free FastCast. Total proteins were visualized by a stain-free imaging system. (B) Immunoblots of LRG, S100A8, C9, FN, AGP1, and A1AT. Values below immunoblots denote the ratio of each protein band intensity normalized by its total protein loading and compared to those of the healthy control.

Fig. 3

Scatter plots show relative expression levels of (A) LRG, (B) S100A8, (C) C9, (D) FN, (E) AGP1, and (F) A1AT from immunoblotting of individual plasma samples in the study-cohort and validation-cohort. All immunoblot results are provided in the supplementary data, Table S4 and S5. Green dots, Healthy control (HC); Red dots, Non-Metastatic CRC patients (NM); Blue dots, Metastatic CRC patients (M); Black dots, all CRC patients (CRC). Black lines represent the medians of samples in each group. Stars represent statistical significance calculated by non-parametric one-way ANOVA (Kruskal-Wallis) and Dunn’s multiple comparison test for the comparison among HC, NM and M groups; and non-parametric t-test (Mann-Whitney U test) for the comparison between HC and CRC groups. *, **, and *** represent p-value < 0.05, < 0.01, and < 0.001, respectively.

Fig. 4

ROC curves representing the diagnostic performance of biomarker candidates between CRC patients and healthy controls. The upper charts demonstrated ROC curves of each biomarker candidate in (A) the study-cohort and (C) the validation-cohort. The bottom charts demonstrated ROC curves of the combination sets in (B) the study-cohort and (D) the validation-cohort. Details of the area under the ROC curve (AUC) with 95% confidence interval (CI), sensitivity and specificity of each protein candidate and the most effective combinations were shown in the Table 4.

Fig. 5

ROC curves representing the diagnostic performance of biomarker candidates between CRC patients with non-metastatic and metastatic stages. The upper charts demonstrated ROC curves of each biomarker candidate in (A) the study-cohort and (C) the validation-cohort. The bottom charts demonstrated ROC curves of the combination sets in (B) the study-cohort and (D) the validation-cohort. Details of the area under the ROC curve (AUC) with 95% confidence interval (CI), sensitivity and specificity of each protein candidate and the most effective combinations were shown in the Table 5.