3.1 Study design
The design of the present study was illustrated in Fig.1. In the discovery phase, Huprot protein microarray was tested in 10 LC and 10 NC samples and 11 TAAbs were identified as selected TAAbs according to the selected criteria. ELISA was then applied to examine the level of selected TAAbs in samples from 212 LC and 212 NC in validation cohort 1 of validation phase, where 10 TAAbs were validated and a diagnostic model with 4 TAAbs and CEA was established. In the following validation cohort 2, 5 out of 10 TAAbs were further validated in 105 MPN and 105 BPN patients, and a model with TAAb and clinical characteristics was constructed for distinguishing malignant and benign PNs.
3.2 Candidate TAAs based on Huprot protein microarray
182 candidate TAAs were screened via protein microarray. The GO and KEGG analysis of 182 geneswas showed in Figure S1. We found thatthese genes were closely associated with terms in biological pathways through GO analysis, such as immune system process, biological adhesion, positive regulation of biological process and so on(Figure S1a). Besides, these genes revealed enrichment in KEGG analysis mainly relative to natural killer cell mediated cytotoxicity, glycosphingolipid biosynthesis, measles, cell adhesion molecules and so forth (Figure S1b).
Eleven TAAbs used for ELISA were selected based on the follow criterion. (a) FC>2, (b) The positive ratio of LC ≥50% while that of NC ≤10 %, (c) The positive ratio of LC - positive ratio of NC ≥60%. The detailed information of 11 TAAs (SARS, ZPR1, FAM131A, GGA3, PRKCZ, HDAC1, GOLPH3, NSG1, DAB1, CD84, EEA1) with FC>2, P<0.05 and positive ratio LC-NC≥60% were showed in Table S1. Figure S2 exhibited the specific SNR of each TAAbs in 20 individuals.
3.3 Autoantibodies against TAAs in validation cohort 1
As shown in Fig.2, the level of 10 TAAbs (anti-SARS, anti-ZPR1, anti-FAM131A, anti-GGA3, anti-PRKCZ, anti-HDAC1, anti-GOLPH3, anti-NSG1, anti-CD84 and anti-EEA1) showed a significant difference between LC patients and NC (P<0.05). Fig.3a-3j displayed the ROC analysis of 10 significant TAAbs in validation cohort 1. Anti-CD84 possessed the highest diagnostic ability with the AUC of 0.693 (0.643-0.744) while anti-HDAC1 yielded the lowest diagnostic ability with the AUC of 0.579 (0.524-0.634) for LC. The range of sensitivity and specificity were 8.96%-27.4% and 90.1%-99.5%, respectively (Table 3). The level of each TAAbs in different group was showed in Table S2. There were no significant difference among the expression of each TAAbs in LC patients with different characteristics (P<0.05).
3.4 Establishment and evaluation of the diagnostic model 1
In validation cohort 1, 320 samples (198 NC samples and 122 LC samples) with the result of CEA were selected for the construction of diagnostic model. The AUC of CEA was 0.654 (95%CI: 0.587-0.721). Based on these samples, 4 TAAbs panel (anti-ZPR1, anti-PRKCZ, anti-NSG1 and anti-CD84) built by forward logistic regression analysis could distinguish LC patients from NC with an AUC of 0.747 (95%CI: 0.691-0.802) (Fig.3k). When combined 4 TAAbs with CEA, the AUC of the diagnostic model 1 was up to 0.813 (95%CI: 0.762-0.864) with the sensitivity and specificity of 68.9% and 83.8% (Fig.3k, Table 4). The predicted possibility for diagnosis as LC was PRE (LC, 4TAAbs+CEA)=1/(1+EXP(-(-10.599×anti-ZPR1+4.033×anti-PRKCZ+4.700×anti-NSG1+7.531×anti-CD84+0.276×CEA-3.028))).
For the purpose of evaluating the diagnostic capability of model 1 in LC patients with different clinical character, LC patients were divided into different subgroups according to the clinical characteristics of tumor stage, pathological type, lymph node metastasis (LM) and distant metastasis (DM). Fig.4 and Table 4 demonstrated the diagnostic performance of the model in each subgroup. The model could distinguish LC patients from NC in each subgroup. No significant difference was discovered for the diagnostic capability of the model 1 among NSCLC, SCLC, DM+ and DM- patients (P>0.05, Fig.4a). To ensure the same specificity in all subgroups (cutoff: 0.392), the sensitivity of each subgroup varied from 47.8% to 73.6%, and the accuracy of each subgroup was from 78.1% to 82.6% (Table 4).
The diagnostic model owned a higher diagnostic ability in LC patients at advanced stage (AUC: 0.840, 95%CI: 0.788-0.892, Fig 4d) and with positive lymph node metastasis (AUC: 0.827, 95%CI: 0.769-0.885, Fig.4h) than LC patients at early stage (AUC：0.695, 95%CI: 0.570-0.820, Fig.4c) and with negative lymph node metastasis (AUC: 0.770, 95%CI: 0.677-0.863, Fig.4g). The model 1 showed no significant difference in diagnosing NSCLC patients (AUC: 0.818, 95%CI: 0.761-0.878, Fig.4e) compared with SCLC patients (AUC: 0.816, 95%CI: 0.718-0.915, Fig.4f), in DM+ patients (AUC: 0.808, 95%CI: 0.740-0.872, Fig.4j) compared with DM- patients (AUC: 0.816, 95%CI: 0.742-0.890, Fig.4i) from NC.
3.5 Autoantibodies against TAAs differentially expressed in MPN and BPN patients
Table 2 presented 12 nodular characteristic of CT in validation cohort 2. All of the CT characteristics were distributed differently between MPN and BPN (P<0.05) except for diameter, edge and emphysema.
In order to understand the performance of TAAbs in MPN and BPN, we selected 7 TAAbs (anti-SARS, anti-FAM131A, anti-PRKCZ, anti-GOLPH3, anti-NSG1, anti-CD84 and anti-EEA1) according to the criterion as P<0.05 and AUC>0.6 in the validation cohort 1 to be tested by using ELISA in 105 BPN patients and 105 MPN patients of validation cohort 2. The level of 5 TAAbs (anti-SARS, anti-GOLPH3, anti-NSG1, anti-CD84 and anti-EEA1) was significantly higher in MPN patients than that in BPN patients (P<0.05, Fig.5a). The AUC (95%CI) of 5 TAAbs was from 0.580 (95%CI: 0.503-0.657) to 0.630 (95%CI: 0.555-0.705) and the sensitivity was from 12.4% to 21.9% with a specificity over 90% (Fig.5b-5f, Table 3).
3.6 Establishment and evaluation of the model in discriminating MPN from BPN
In validation cohort 2, 5 significant TAAbs (anti-SARS, anti-GOLPH3, anti-NSG1, anti-CD84, anti-EEA1), 3 traditional biomarkers (CEA, CYFRA211 and CA125) and 9 nodular characteristics of CT (number, cavity, spicule sign, vascular notch sign, lobulation sign, spines, pleural indentation, mediastinal lymph node enlargement, calcification) were applied to establish model used for distinguishing MPN from BPN patients through logistic regression analysis. One hundred eighteen samples (60 MPN samples and 58 BPN samples) with the result of traditional biomarkers and CT were selected for the further research. Fig.5j presented that a model (model 2) with anti-EEA1, traditional biomarkers (CEA, CYFRA211 and CA125) and 3 nodular characteristics of CT (vascular notch sign, lobulation sign and mediastinal lymph node enlargement) could improve the discrimination capability compared to any single diagnostic method. The model 2 owned the AUC of 0.845 with the sensitivity and specificity of 58.3% and 96.6% for the discrimination of PN (Fig.5j, Table 5). The predicted possibility for discrimination as MPN was PRE (MPN, TAAbs+3CT+3biomarkers) = 1/(1+EXP(-(+4.497×anti-EEA1+1.264×vascular notch sign +0.921×lobulation sign +0.999×mediastinal lymph node enlargement +0.167×CEA+0.002×CA125+0.203×CYFRA21-1-3.860))).
60 MPN patients were layered by the clinical characteristics of tumor stage, nodular diameter, LM and DM. The discriminating performance of the model 2 for MPN patients with different characteristics was described in Fig.6 and Table 5. The model could discriminate MPN patients in each subgroup from BPN patients (Fig 6a). Furthermore, the AUC of model 2 in all subgroups ranged from 0.683 to 0.983 (Fig.6b-6j, Table5). The sensitivity of each subgroup was from 26.9% to 94.4% with the same specificity of 96.6% (Table 5). To ensure same specificity in all subgroups (cutoff: 0.392), the sensitivity varied from 47.8% to 73.6%. Moreover, the accuracy of each subgroup was from 78.1% to 82.6% (Table 5).
The model 2 exhibited a higher distinguishing ability in MPN patients at advanced stage (AUC: 0.942, 95%CI: 0.893-0.990), LM+ patients (AUC: 0.950, 95%CI: 0.910-0.999) and DM+ patients (AUC: 0.983, 95%CI: 0.952-1) than MPN patients at early stage (AUC: 0.713, 95% CI: 0.592-0.834), LM- MPN patients (AUC: 0.683, 95%CI: 0.545-0.820) and DM- MPN patients (AUC: 0.731, 95%CI: 0.615-0.848) (Fig.6c-6h, Table 5). The AUC of patients with more than or equal to 3cm in diameter (AUC: 0.938, 95%CI: 0.862-1) was higher than patients with less than 3 cm in diameter (AUC: 0.808, 95%CI: 0.722-0.894) (Fig.6i-6j, Table 5).