Proteomic analysis of human cervical adenocarcinoma mucus to identify potential protein biomarkers

Patient materials

Cervical mucus samples were obtained from three patients with normal cervices (Ctrl), three patients with endocervical adenocarcinoma (EA), and three patients with adenocarcinoma in situ of the cervix (AIS) who had a total hysterectomy at the Nanjing Maternity and Child Health Care Hospital Affiliated to Nanjing Medical University. The three normal cervical mucus samples acted as the control. All samples were stored at −80 °C and an initial histopathological diagnosis was conducted. The cancerous cervical mucus and normal cervical mucus were used for proteomic analyses. This study was approved by the ethical committee of Women’s Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital ((2019) KY-040). All participants voluntarily agreed to participate in this study and signed an informed consent.

Protein extraction

Protein extraction was performed on the nine cervical mucus samples. Each sample was mixed and homogenized with a protein lysate (7 M Urea, 2 M Thiourea, 4% SDS, 40 mM Tris–HCl, pH 8.5, 1 mM Phenylmethanesulfonyl fluoride (PMSF), 2 mM Ethylene Diamine Tetraacetic Acid (EDTA)) on ice for 5 min. Dithiothreitol (DTT) (Solarbio, 428F0422) was added to a final concentration of 10 mM, followed by an ice bath ultrasound for 15 min and centrifuging at 13,000g at 4 °C for 20 min. After ultracentrifugation, supernatants of each samples were collected to a new centrifugal tube. Cold acetone was added into the centrifugal tube at four times the volume and was left to stand overnight at −20 °C. The protein precipitation was collected and left to air dry. A total of 8 M urea/100 mM tetraethyl-ammonium bromide (TEAB) (BCBC6216; Sigma–Aldrich, St. Louis, MO, USA) (pH 8.0) solution was added to re-dissolve the protein and then DTT was added to the final concentration of 10 mM and the solution was immersed in water at 56 °C for 30 min for the reduction reaction. Iodoacetamide (IAM, Aladdin, J1513091) was added to the final concentration of 55 mM and placed at room temperature in the dark for 30 min to produce an alkylation reaction. The Bradford method was used to measure protein concentrations.

iTRAQ labeling and peptide fractionation

The extracted proteins were digested by trypsin to obtain the corresponding peptides, which were desalted by the Durashell C18 column (5 ms, 100 A, 4.6 × 250 mm) (Agela, Tianjin, China) and vacuum dried. The peptides were dissolved with 0.5 M TEAB and labeled using the itraq-8 standard kit (SCIEX, Shanghai, China) according to the manufacturer’s instructions. The samples were labeled and mixed and the mixed peptides were then graded and separated using the Ultimate 3000 HPLC system (DINOEX; Thermo, Waltham, MA, USA). The separation of peptides was achieved by increasing acetonitrile (CAN) concentration under alkaline conditions. The flow rate was 1 ml/min with one tube collected per minute. A total of 42 secondary fractions were collected and combined into 12 fractions, which were desalinated and vacuum-dried on the Strata-X column. All analyses were completed by Wuhan Genecreate Biological Engineering Co. LTD. (Wuhan, China).

Protein identification and quantification

Proteins were identified by the Proteinpilot™ V4.5 search engine. At least one unique peptide section of each protein line and the unused score was believed to be no more than 1.3 (just above 95%). The reliability of each identified peptide and its protein quantification was considered for protein quantification.

Liquid chromatography-mass spectrometry analysis

Mass spectrometry data were collected using the TripleTOF 5600+ liquid/mass coupling system (SCIEX, Shanghai, China). Polypeptide samples were dissolved in 2% acetonitrile/0.1% formic acid and analyzed using the TripleTOF 5600+ mass spectrometer. The peptide solution was added to the C18 capture column (5 ms, 100 ms × 20 mm) and eluted on the C18 analysis column (3 ms, 75 ms × 150 mm) for 90 min with a flow rate of 300 nL/min. The two mobile phases were buffer A (2% acetonitrile, 0.1% formic acid, 98% H₂O) and buffer B (98% acetonitrile, 0.1% formic acid, 2% H₂O). The primary mass spectrometry for information dependent acquisition (IDA) was scanned at 250 ms ion accumulation time and a secondary mass spectrometry of 30 precursor ions was collected at 50 ms ion accumulation time. The MS1 spectrum was collected in the range of 350–1,500 m/z, and the MS2 spectrum was collected in the range of 100–1,500 m/z. The dynamic exclusion time for precursor ions was set at 15 s.

Functional annotation

Gene ontology (GO) functional annotation analysis was performed for all identified proteins and the GO functions of the cellular component, biological process, and molecular function corresponding to all proteins were analyzed. Detailed information can be found at http://www.geneontology.org. COG analysis and KEGG analysis were used to further determine the function of the proteins.

Statistics

The mean value of the ratio of repeated samples was normalized using the median as the difference multiple by the samples to be compared. The minimum P value of Student’s t test of the single sample of parewise comparisons between repeated samples was used as the significance difference of the samples. Differential proteins were screened according to fold change and P value. The proteins were considered to be statistically different when the difference of multiples was ≥1.5 (i.e., up-regulate ≥1.5 and down-regulate ≤0.67) with a P value < 0.05 after the significance statistical test.

Identification of proteins in cervical mucinous

A total of nine samples were divided into three groups to identify differently abundant proteins. Ctrl was comprised of samples from three healthy individuals, EA was comprised of samples from three invasive cervical adenocarcinoma individuals, and AIS had samples from three cervical adenocarcinoma in situ individuals. All samples were obtained from individuals with an average age of 41 years. The clinicopathological characteristics of these patients were summarized in Table 1. A diagnosis of endocervical adenocarcinoma was suggested by the cervical sample based on cytomorphological features. The postoperative pathological diagnosis showed no lymph node metastasis and immunohistochemical results directed the diagnosis and subsequent treatment. The corresponding pathological images were obtained for the nine samples and were stained by HE (Fig. 1A; Fig. S1).

The unique peptides only needed to be detected in one protein to identify the protein in the sample. The presence of the peptide uniquely determined the presence of the corresponding protein. The biordinate distribution of the unique peptides representing the unique proteins was identified (Fig. 1B). The abscissa is the number of unique peptides contained in the protein, and the left ordinate is the number of proteins corresponding to the abscissa. The right ordinate is the cumulative protein proportion corresponding to the abscissa. The results showed that there are 757 protein with at least 2 unique peptides, accounting for 78.12% of the total protein. The length distribution of the peptide was obtained by mass spectrometer (Fig. 1C). The average length of the peptides identified was 12.55, which was a reasonable peptide length. A protein with more peptide support makes the protein more reliable, therefore, protein coverage can indirectly reflect the overall accuracy of the identification results. Pie charts were used to represent the percentage of different proteins within the identification coverage (Fig. 1D). The identified range of proteins was 37.56% and proteins with coverage ≥20% accounted for 39.73% of the total protein. The average protein coverage was 21.90%.

The analysis of differential protein expression profile

Of the identified proteins, 237, 256, and 242 proteins were significantly differently expressed in the comparison group EA/Ctrl, AIS/Ctrl, and AIS/EA using iTRAQ quantitative analysis with the filtered threshold of up-regulation ≥1.5 or down-regulation ≤0.67, respectively. The number of significant protein differences was shown between two samples (Fig. 2A). The results of the relative quantitative analysis of the protein showed that the protein ratio distribution followed a normal distribution pattern. The distribution of the differential multiples of all quantifiable proteins was shown (Fig. 2B), where the x-coordinate represented the value of the differential multiples after logarithmic transformation in base 2. The part greater than 0 indicated the up-regulated expression, while the part less than 0 indicated the down-regulated expression. The hierarchical clustering heat map of the intersection of 98 differently significant proteins in the comparison groups visually aggregated samples/proteins with higher similarity (Fig. 2C). The data source is the log2 logarithmic value of the protein abundance ratio between the two comparison groups. Quantitative information of some significantly different proteins is shown in Table 2. Some of the differently expressed proteins were significantly expressed in the groups EA/Ctrl, AIS/Ctrl, and AIS/EA, including myeloperoxidase and APOA1, which are involved in the development of cancer (Cine et al., 2014; Castelao et al., 2015; Zadeh Fakhar et al., 2019).

Functional and pathway analysis of identified proteins

It is important to analyze the function of genes as more genomes are sequenced and to predict the function of unknown genes to guide further experiments. We provided the commonly used GO annotation and KEGG annotation results, as well as the COG annotation results to comprehensively reflect the function of proteins obtained from different databases to reveal the biological significance of proteins in various life activities. A total of 1,118 proteins were identified and functionally annotated (Fig. 3A). The annotation information for some proteins varied among the databases due to the limitations of the background annotation library. GO, COG, and KEGG annotated 1,056 proteins, 569 proteins, and 751 proteins, respectively. GO is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge (Gene Ontology Consortium, 2015). GO was used to annotate 1,056 proteins and statistical analysis was performed based on biological, cellular, and molecular processes (Figs. 3B–3D). We conducted independent functional annotation analysis on the up-regulated and down-regulated differently expressed proteins to better analyze the functions of differently expressed proteins. A total of 237 differently expressed proteins (98 significantly up-regulated and 139 significantly down-regulated proteins) in EA/Ctrl were used as examples to compare and analyze their GO annotation results. Functional notes were analyzed for groups AIS/Ctrl and AIS/EA, and the results are shown in a bar chart in Figs. 4A–4C.