Methods for detecting lung cancer

Abstract

A biomarker panel for a serum test for detecting lung cancer, wherein the biomarker is selected from the group consisting of arginine, C18.2, decadienoylcarnitine (C10:2), LYSOC18.2, methionine, ornithine, PC32:2AA, PC36.0AA, PC36.0AE, putrescine, spermidine, spermine, and valine. Serum testing for diagnosing lung cancer may take into account smoking history.

Description

Technical Field

[0001] The present disclosure relates to methods for detecting cancer, and more particularly to methods for detecting lung cancer by measuring polyamine metabolites and other metabolites. Background Art

[0002] The polyamine pathway has been shown to be significantly upregulated in cancer cells. Spermidine / spermine N1-acetyltransferase (SSAT) is considered a key enzyme in this pathway and is highly regulated in all mammalian cells. While SSAT is present at very low concentrations in normal tissues, its levels are much higher in cancer cells. Therefore, as SSAT cellular levels increase, measurements of its enzymatic activity correlate with the presence and severity of cancer.

[0003] International Patent Application Publication No. WO 2016 / 205960 A1, published on December 29, 2016 in the name of BioMark Cancer Systems Inc., discloses a biomarker panel for a urine test for detecting lung cancer, wherein the biomarker panel detects a biomarker selected from the group consisting of DMA, C5:1, C10:1, ADMA, C5-OH, SDMA, and kynurenine, or a combination thereof. Also disclosed is a biomarker panel for a serum test for detecting lung cancer, wherein the biomarker panel detects a biomarker selected from the group consisting of valine, arginine, ornithine, methionine, spermidine, spermine, diacetylspermine, C10:2, PC aa C32:2, PC ae C36:0, and PC ae C44:5; and lysoPC a C18:2, or a combination thereof. Summary of the Invention

[0004] Disclosed herein is a biomarker panel for a serum test agent for detecting lung cancer, wherein the biomarker is selected from the group consisting of arginine, C18.2, decadienoylcarnitine (C10:2), LYSOC18.2, methionine, ornithine, PC32:2AA, PC36.0AA, PC36.0AE, putrescine, spermidine, spermine, and valine. Serum testing for diagnosing lung cancer can take into account smoking history.

[0005] This biomarker panel can be used to diagnose stage 1 lung cancer. This biomarker panel can be used to diagnose stage 2 lung cancer. This biomarker panel can be used to distinguish between stage 1 lung adenocarcinoma and stage 1 lung squamous cell carcinoma. This biomarker panel can be used to distinguish between stage 2 lung adenocarcinoma and stage 2 lung squamous cell carcinoma. This biomarker panel can be used to diagnose combined stage 1 and stage 2 lung adenocarcinoma. This biomarker panel can be used to diagnose combined stage 1 and stage 2 lung squamous cell carcinoma. This biomarker panel can be used to diagnose combined stage 1 lung adenocarcinoma and lung squamous cell carcinoma. This biomarker panel can be used to diagnose combined stage 2 lung adenocarcinoma and lung squamous cell carcinoma. This biomarker panel can be used to diagnose advanced stage 3b / 4 lung cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 Figure 3 is a variable importance in projection (VIP) plot of discriminant serum metabolites based on partial least squares discriminant analysis (PLS-DA), arranged in descending order of importance, showing the distinction between control patients and stage 1 lung cancer patients;

[0007] Figure 2 To include from Figure 1 Area under the receiver operating characteristic curve (AUROC) of the three most important serum metabolites for the VIP plot shown;

[0008] Figure 3 To include from Figure 1 AUROC curves of the six most important serum metabolites in the VIP plot;

[0009] Figure 4 To include smoking status and Figure 1 AUROC curves for the three most important serum metabolites of the VIP plot shown;

[0010] Figure 5 Smoking status, body mass index, and Figure 1 AUROC curves for the three most important serum metabolites of the VIP plot shown;

[0011] Figure 6 VIP plot of discriminant serum metabolites ranked in descending order of importance based on PLS-DA analysis, showing the distinction between control patients and stage 2 lung cancer patients;

[0012] Figure 7 To include from Figure 6 AUROC curves for the two most important serum metabolites of the VIP plot shown;

[0013] Figure 8 To include from Figure 6 AUROC curves of the seven most important serum metabolites of the VIP plot shown;

[0014] Figure 9 To include smoking status and Figure 6 AUROC curves for the three most important serum metabolites of the VIP plot shown;

[0015] Figure 10 Smoking status, body mass index, and Figure 6 AUROC curves for the three most important serum metabolites of the VIP plot shown;

[0016] Figure 11 VIP plot of discriminant serum metabolites ranked in descending order of importance based on PLS-DA analysis, which shows the distinction between stage 1 lung adenocarcinoma patients and stage 1 lung squamous cell carcinoma patients;

[0017] Figure 12 To include from Figure 11 AUROC curves for the four most important serum metabolites of the VIP plot shown;

[0018] Figure 13 To include smoking status and Figure 11 AUROC curves for the four most important serum metabolites of the VIP plot shown;

[0019] Figure 14 VIP plot of discriminant serum metabolites ranked in descending order of importance based on PLS-DA analysis, showing the distinction between stage 2 lung adenocarcinoma patients and stage 2 squamous cell lung cancer patients;

[0020] Figure 15 To include from Figure 14 AUROC curves for the four most important serum metabolites of the VIP plot shown;

[0021] Figure 16 To include from Figure 14 AUROC curves of the seven most important serum metabolites of the VIP plot shown;

[0022] Figure 17 To include smoking status and Figure 14 AUROC curves for the four most important serum metabolites of the VIP plot shown;

[0023] Figure 18 This is the principal component analysis (PCA) plot of patients in groups 1 to 9 and control patients;

[0024] Figure 19Another PCA plot for patients in groups 1 to 9 and control patients;

[0025] Figure 20 Partial least squares discriminant analysis (PLS-DA) plots of patients in groups 1 to 9 and control patients;

[0026] Figure 21 Dendrogram of samples from patients in groups 1 to 9 and control patients;

[0027] Figure 22 This is the PCA plot of patients in groups 1 to 9 and control patients after data were eliminated;

[0028] Figure 23 This is another PCA plot of patients in groups 1 to 9 and control patients after data were eliminated;

[0029] Figure 24 This is the PLS-DA graph of patients in groups 1 to 9 and control patients after data were eliminated;

[0030] Figure 25 This is another PLS-DA plot of patients in groups 1 to 9 and control patients after data were eliminated;

[0031] Figure 26 This is a dendrogram of samples from patients in groups 1 to 9 and control patients after data were eliminated;

[0032] Figure 27 The figure shows the AUROC curve of smoking duration for all lung cancer patients (groups 1 to 6) and control patients;

[0033] Figure 28 The AUROC curve includes cigarette consumption (pack / year) for all lung cancer patients (Groups 1-6) and control patients;

[0034] Figure 29 AUROC curves for smoking status (yes / no) of all lung cancer patients (groups 1-6) and control patients;

[0035] Figure 30 The AUROC curve includes the age of all lung cancer patients (Group 1-Group 6) and control patients;

[0036] Figure 31 AUROC curves for BMI of all lung cancer patients (Groups 1 to 6) and control patients;

[0037] Figure 32 The AUROC curves include the gender of all lung cancer patients (Groups 1-6) and control patients;

[0038] Figure 33 AUROC curves for metabolites in total lung cancer patients (groups 1 to 6) and control patients only;

[0039] Figure 34A and Figure 34B Data distribution and concentration ranges of metabolites for total lung cancer patients (Groups 1-6) and control patients are shown;

[0040] Figure 35 PCA plot of combined stage 1 and stage 2 lung adenocarcinoma patients and control patients;

[0041] Figure 36 AUROC curves for metabolites in patients with stage 1 and 2 lung adenocarcinoma combined and controls are included only;

[0042] Figure 37 AUROC curves for metabolites and BMI in patients with combined stage 1 and 2 lung adenocarcinoma and controls are included;

[0043] Figure 38A and Figure 38B Data distribution and concentration ranges of metabolites are shown for the combined stage 1 and 2 lung adenocarcinoma patients and control patients;

[0044] Figure 39 This is the PCA diagram of the combined stage 1 and stage 2 lung squamous cell carcinoma patients and control patients;

[0045] Figure 40 AUROC curves for metabolites in patients with stage 1 and 2 lung squamous cell carcinoma combined with control patients are included;

[0046] Figure 41 AUROC curves for metabolites and BMI in patients with stage 1 and 2 lung squamous cell carcinoma combined with controls;

[0047] Figure 42 AUROC curves for metabolites and smoking in patients with combined stage 1 and stage 2 squamous cell lung cancer and control patients;

[0048] Figure 43A and Figure 43B Data distribution and concentration ranges of metabolites for combined stage 1 and stage 2 squamous cell lung cancer patients and control patients are shown;

[0049] Figure 44 PCA plot of stage 3b / 4 NSCLC patients (group 5) and control patients;

[0050] Figure 45 is the AUROC curve including metabolites only for stage 3b / 4 NSCLC lung cancer patients (group 5) and control patients;

[0051] Figure 46 AUROC curves for metabolites and BMI in patients with stage 3b / 4 NSCLC (Group 5) and control patients;

[0052] Figure 47 AUROC curves for metabolites and smoking status in patients with stage 3b / 4 NSCLC (Group 5) and controls;

[0053] Figure 48A and Figure 48B The data distribution and concentration range of metabolites for stage 3b / 4 NSCLC lung cancer patients (Group 5) and control patients are shown;

[0054] Figure 49 PCA plot of combined stage 1 (adenocarcinoma and squamous cell carcinoma) lung cancer patients and control patients;

[0055] Figure 50 PLS-DA plots for combined stage 1 (adenocarcinoma and squamous cell carcinoma) lung cancer patients and control patients;

[0056] Figure 51 AUROC curves for metabolites, cigarette consumption, and smoking duration in patients with combined stage 1 (adenocarcinoma and squamous cell carcinoma) lung cancer and controls.

[0057] Figure 52 AUROC curves for metabolites, cigarette consumption, smoking duration, and BMI in patients with combined stage 1 (adenocarcinoma and squamous cell carcinoma) lung cancer and controls are included;

[0058] Figure 53 AUROC curves for metabolites and smoking status in combined stage 1 (adenocarcinoma and squamous cell carcinoma) lung cancer patients and controls.

[0059] Figure 54 AUROC curves for metabolites in stage 1 (adenocarcinoma and squamous cell carcinoma) lung cancer patients and control patients only;

[0060] Figure 55 AUROC curves for metabolites and BMI in combined stage 1 (adenocarcinoma and squamous cell carcinoma) lung cancer patients and control patients.

[0061] Figure 56 PCA plot of combined stage 2 (adenocarcinoma and squamous cell carcinoma) lung cancer patients and control patients;

[0062] Figure 57 PLS-DA plots for combined stage 2 (adenocarcinoma and squamous cell carcinoma) lung cancer patients and control patients;

[0063] Figure 58AUROC curves for metabolites in patients with combined stage 2 (adenocarcinoma and squamous cell carcinoma) lung cancer and control patients are included;

[0064] Figure 59 AUROC curves for metabolites and BMI in patients with combined stage 2 (adenocarcinoma and squamous cell carcinoma) lung cancer and controls are included;

[0065] Figure 60 AUROC curves for metabolites and smoking duration in combined stage 2 (adenocarcinoma and squamous cell carcinoma) lung cancer patients and control patients;

[0066] Figure 61 AUROC curves for metabolites and cigarette consumption in patients with combined stage 2 (adenocarcinoma and squamous cell carcinoma) lung cancer and controls were included; and

[0067] Figure 62 Figure 2 shows the AUROC curves for metabolites and smoking status in patients with combined stage 2 (adenocarcinoma and squamous cell carcinoma) lung cancer and controls. DETAILED DESCRIPTION

[0068] Serum samples collected from 60 control patients and 197 lung cancer patients were analyzed using a combination of direct injection mass spectrometry and reversed-phase LC-MS / MS. The p180 kit was obtained from Applied Biosystems / MDS Sciex (850 Lincoln Center Drive, Foster City, CA 94404, USA). Tandem mass spectrometry was used in combination for the targeted identification and quantification of up to 180 different endogenous metabolites including amino acids, acylcarnitines, biogenic amines, glycerophospholipids, sphingolipids, and sugars. Table 1 shows the clinical characteristics of the control patients and lung cancer patients.

[0069] Table 1: Clinical characteristics of control patients and lung cancer patients.

[0070]

[0071]

[0072] The following metabolites were analyzed in serum samples: valine, putrescience, MTA, arginine, ornithine, spermidine, spermine, diacetylspermine, methionine, decaenoylcarnitine (C10:2), PC aa C32:2, PC aa C36:0, PC ae C36:0, and lysoPC a C18:2. Metabolites with more than 20% missing values were removed across all groups. The high number of missing values resulted from values below the detection limit. Two metabolites, MTA and diacetylspermine, were removed due to high missing values. If the missing value was less than 20%, the missing value was imputed as half the minimum value for that metabolite. A total of 13 metabolites were analyzed.

[0073] The method used combines derivatization and extraction of the analytes with selective mass spectrometric detection using multiple reaction monitoring (MRM) pairs. Isotopically labeled internal standards and other internal standards are incorporated into p180 kit filter plate for metabolite quantification. The p180 kit contains 96 deep-well plates and filter plates with sealing tape, as well as reagents and solvents for preparing the plate-based assay. p180 kit provided), use The first 14 wells in the p180 kit. The protocol described in the p180 kit user manual was used. All serum samples were analyzed using the p180 kit.

[0074] Serum samples were thawed on ice and vortexed and centrifuged at 2750 × g for 5 minutes at 4°C. 10 μL of each serum sample was added to the center of the filter of the upper 96-well kit plate and dried under a stream of nitrogen. 20 μL of 5% phenyl isothiocyanate solution was then added for derivatization. The filter point was then dried again using an evaporator. Metabolite extraction was then achieved by adding 300 μL of methanol containing 5 mM ammonium acetate. The extract was obtained by centrifugation into the lower 96-deep-well plate. The extract was then used The MS running solvent in the p180 kit was used for the dilution step.

[0075] In the API4000 equipped with a solvent delivery system Mass spectrometry analysis was performed on a tandem mass spectrometer. Serum samples were delivered to the mass spectrometer by direct injection (DI) or liquid chromatography. TM software( p180 kit) to control the entire analytical workflow, from sample registration to automated calculation of metabolite concentrations and data export to other data analysis programs. Targeted analysis strategies were used to quantitatively screen for known small molecule metabolites using multiple reaction monitoring, neutral loss, and precursor ion scanning. The results were analyzed using MetaboAnalyst (www.metaboanalyst.com) and ROCCET ( www.roccet.ca ) for statistical analysis.

[0076] Figure 1 Figure 2 shows the variable projection importance index (VIP) plot of the most discriminative serum metabolites based on partial least squares discriminant analysis (PLS-DA) in descending order of importance, which shows the distinction between control patients and stage 1 lung cancer patients. A VIP score higher than 1.6 indicates that the metabolite is very significant. Table 2 shows the results from Figure 1 T-test statistics of the VIP plots for the discrimination of serum metabolites.

[0077] Table 2: T-test statistics of serum metabolites for discriminating stage 1 lung cancer.

[0078] metabolites p-value FDR Spermine 6.20E-06 8.06E-05 Valine 0.0081354 0.044501 LYSOC18.2 0.010578 0.044501 C10.2 0.013692 0.044501 PC36.0AA 0.024106 0.062676 C18.2 0.057366 0.12429 PC36.0AE 0.10719 0.19906 Ornithine 0.19421 0.29725 Spermidine 0.20579 0.29725 Arginine 0.25549 0.33213 Methionine 0.58547 0.69191 Putrescine 0.85106 0.92198 PC32.2AA 0.96471 0.96471

[0079] Use from Figure 1 A logistic regression model was established to predict the probability of developing stage 1 lung cancer using the three serum metabolites identified in the VIP diagram shown, using the following formula: logit(P) = log(P / (1-P)) = 0.217-1.241×spermine-0.598×LYSOC18.22-0.817×C10.2. Figure 2 The area under the receiver operating characteristic curve (AUROC) generated by the formula is shown.

[0080] Use from Figure 1 Another logistic regression model was established using the six serum metabolites identified in the VIP plot shown to predict the probability of developing stage 1 lung cancer, with the following formula: logit(P) = log(P / (1-P)) = 0.243-1.131×spermine-0.62×LYSOC18.2-0.92×C10.2+0.642×valine-0.825×PC36.0AA+0.573×C18.2. Figure 3 The AUROC curve generated by this formula is shown.

[0081] Figure 4 shows the AUROC curve generated by the logistic regression model for Figure 1The three serum metabolites identified in the VIP plot shown are used to predict the probability of developing stage 1 lung cancer taking into account smoking status, and the formula is as follows: logit(P) = log(P / (1-P)) = 0.207 + 0.32 × smoking status - 1.18 × spermine - 0.472 × LYSOC18.2 - 0.724 × C10.2.

[0082] Figure 5 Shown is the AUROC curve generated by the logistic regression model used to predict the probability of stage 1 lung cancer using three serum metabolites and taking into account smoking status and body mass index (BMI), with the following formula: logit(P)=log(P / (1-P))=0.215-1.279×spermine-0.42×LYSOC18.2-0.748×C10.2+0.507×BMI+0.294×smoking status.

[0083] Figure 6 The VIP plot of the most discriminative serum metabolites based on PLS-DA analysis, ranked in descending order of importance, shows the distinction between control patients and stage 2 lung cancer patients. A VIP score higher than 1.6 indicates that the metabolite is very significant. Table 3 shows the results from Figure 6 T-test statistics of the VIP plots for the discrimination of serum metabolites.

[0084] Table 3: T-test statistics of serum metabolites for discriminating stage 2 lung cancer.

[0085] metabolites p-value FDR Spermine 3.42E-09 4.45E-08 LYSOC18.2 7.60E-08 4.94E-07 PC36.0AA 0.00012008 0.00052034 PC36.0AE 0.0032963 0.010713 Valine 0.014762 0.03838 C10.2 0.029034 0.062906 Ornithine 0.071816 0.13337 C18.2 0.10904 0.17719 Spermidine 0.15233 0.22003 Arginine 0.38045 0.49458 PC32.2AA 0.611 0.72209 Putrescine 0.70261 0.76116 Methionine 0.91798 0.91798

[0086] Use from Figure 6 A logistic regression model was established using the two serum metabolites identified in the VIP plot shown to predict the probability of developing stage 2 lung cancer, using the following formula: logit(P) = 0.088-1.728×spermine-1.484×LYSOC18.2. Figure 7 The AUROC curve generated by this formula is shown.

[0087] Use from Figure 6 Another logistic regression model was established using the seven serum metabolites identified in the VIP plot shown to predict the probability of developing stage 2 lung cancer, with the following formula: logit(P) = log(P / (1-P)) = 0.172-1.647×spermine-1.346×LYSOC18.2-1.521×PC36.0AA+0.215×PC36.0AE+0.563×valine-0.358×C10.2+0.757×ornithine. Figure 8 The AUROC curve generated by this formula is shown.

[0088] Figure 9shows the AUROC curve generated by the logistic regression model for Figure 6 The three serum metabolites identified in the VIP plot shown are used to predict the probability of developing stage 2 lung cancer taking into account smoking status, and the formula is as follows: logit(P) = log(P / (1-P)) = -0.107-1.903×spermine+0.632×smoking status-0.882×LYSOC18.2-1.549×PC36.0AA.

[0089] Figure 10 shows the AUROC curve generated by the logistic regression model for Figure 6 The three serum metabolites identified in the VIP plot shown are used to predict the probability of developing stage 2 lung cancer, taking into account smoking status and BMI, as follows: logit(P) = log(P / (1-P)) = -0.132-0.917×LYSOC18.2-1.91×spermine+0.661×smoking status-1.518×PC36.0AA-0.419×BMI.

[0090] Figure 11 The VIP plot showing the most discriminative serum metabolites in descending order of importance based on PLS-DA analysis shows the distinction between stage 1 lung adenocarcinoma patients and stage 1 lung squamous cell carcinoma patients. VIP scores above 1.6 indicate that the metabolite is highly significant. Table 4 shows the results from Figure 11 T-test statistics of the discriminant serum metabolites in VIP analysis.

[0091] Table 4: T-test statistics of serum metabolites for discriminating between stage 1 lung adenocarcinoma and stage 1 lung squamous cell carcinoma.

[0092] metabolites p-value FDR Ornithine 0.0096012 0.11067 Valine 0.02525 0.11067 C18.2 0.02554 0.11067 Methionine 0.035935 0.11679 Spermine 0.12507 0.32518 Putrescine 0.17277 0.37434 Arginine 0.24145 0.39447 C10.2 0.24696 0.39447 PC36.0AE 0.27309 0.39447 PC36.0AA 0.32467 0.42207 LYSOC18.2 0.57942 0.68477 PC32.2AA 0.87243 0.94513 Spermidine 0.971 0.971

[0093] Use from Figure 11 A logistic regression model was established using the four serum metabolites identified in the VIP diagram shown to predict the probability of developing stage 1 lung adenocarcinoma and stage 1 lung squamous cell carcinoma, using the following formula: logit(P) = logit(P) = log(P / (1-P)) = -1.074 + 0.588 × ornithine + 0.614 × C18.2 + 0.547 × valine + 0.141 × methionine. Figure 12 The AUROC curve generated by this formula is shown.

[0094] Use from Figure 11Another logistic regression model was established using the four serum metabolites identified in the VIP plot shown, and smoking history was taken into account to predict the probability of stage 1 lung adenocarcinoma and stage 1 lung squamous cell carcinoma, with the following formula: logit(P) = log(P / (1-P)) = 0.172-1.647×spermine-1.346×LYSOC18.2-1.521×PC36.0AA+0.215×PC36.0AE+0.563×valine-0.358×C10.2+0.757×ornithine. Figure 8 The AUROC curve generated by this formula is shown.

[0095] Figure 14 The VIP plot of the most discriminative serum metabolites in descending order of importance based on PLS-DA analysis shows the distinction between stage 2 lung adenocarcinoma patients and stage 2 lung squamous cell carcinoma patients. VIP scores above 1.6 indicate that the metabolite is very significant. Table 5 shows the results from Figure 14 T-test statistics of the discriminant serum metabolites in VIP analysis.

[0096] Table 5: T-test statistics of serum metabolites for discriminating between stage 2 lung adenocarcinoma and stage 2 lung squamous cell carcinoma.

[0097] metabolites p-value FDR Spermine 3.42E-09 4.45E-08 LYSOC18.2 7.60E-08 4.94E-07 PC36.0AA 0.00012008 0.00052034 PC36.0AE 0.0032963 0.010713 Valine 0.014762 0.03838 C10.2 0.029034 0.062906 Ornithine 0.071816 0.13337 C18.2 0.10904 0.17719 Spermidine 0.15233 0.22003 Arginine 0.38045 0.49458 PC32.2AA 0.611 0.72209 Putrescine 0.70261 0.76116 Methionine 0.91798 0.91798

[0098] Use from Figure 14 A logistic regression model was established using the four serum metabolites identified in the VIP plot shown to predict the probability of developing stage 2 lung adenocarcinoma and stage 2 lung squamous cell carcinoma, using the following formula: log(P / (1-P)) = -0.825 + 0.466 × spermidine + 0.662 × putrescine + 0.762 × valine - 0.406 × methionine. Figure 15 The AUROC curve generated by this formula is shown.

[0099] Another logistic regression model was established using the seven most important serum metabolites to predict the probability of developing stage 2 lung adenocarcinoma and stage 2 lung squamous cell carcinoma, with the following formula: logit(P) = log(P / (1-P)) = -0.95 + 0.872×spermidine - 0.327×LYSOC18.2 - 2.125×PC36.0AA + 1.63×PC36.0AE + 1.068×valine + 0.445×C10.2 - 0.105×ornithine. Figure 16 The AUROC curve generated by this formula is shown.

[0100] Figure 17Shown is the AUROC curve generated by the logistic regression model used to predict the probability of stage 2 lung adenocarcinoma and stage 2 lung squamous cell carcinoma using the four most important serum metabolites and taking into account smoking status, with the following formula: logit(P) = log(P / (1-P)) = -0.941 + 0.361 × spermidine + 0.595 × putrescine + 0.787 × valine - 0.358 × methionine + 0.416 × smoking status.

[0101] The above description and Figures 1 to 17 The results shown indicate that 13 metabolites have been identified as putative biomarkers for lung cancer, namely, arginine, C18.2 decadienoylcarnitine (C10:2), LYSOC18.2, methionine, ornithine, PC32:2AA, PC36.0AA, PC36.0AE, putrescine, spermidine, spermine, and valine. These metabolites can be used in a biomarker panel to detect lung cancer.

[0102] Figures 18-26 Data preprocessing for Groups 1-9 patients and control patients is shown. Figures 27-32 The contribution of clinical factors is shown for the total lung cancer patients (Groups 1-6) and the control patients. Smoking appears to be the best clinical variable, especially the amount consumed. Figure 33-Figure 3 4 The metabolites of total lung cancer patients (Groups 1-6) and control patients were analyzed. Figure 33 The robustness of the analysis was demonstrated; using metabolites alone yielded an AUC score of 0.873, but including both metabolites and smoking (cigarette consumption) increased the AUC score to 0.967.

[0103] Figure 35-Figure 3 8 analyzed metabolites for diagnosis of patients with stage 1 and stage 2 lung adenocarcinoma.

[0104] Figure 39-Figure 4 3 analyzed the contribution of metabolites and clinical factors to the diagnosis of combined stage 1 and stage 2 squamous cell lung cancer patients. Figure 41 The results show that the combined use of metabolites and BMI to diagnose stage 1 and stage 2 squamous cell lung cancer patients achieved an AUC score of 0.922. After adding smoking, the AUC score was much higher than 0.97.

[0105] Figure 44-Figure 4 8 analyzed the contribution of metabolites and clinical factors for the diagnosis of stage 3b / 4 NSCLC lung cancer patients (group 5).

[0106] Figures 49-55 The contribution of metabolites and clinical factors to the diagnosis of combined stage 1 (adenocarcinoma and squamous cell carcinoma) lung cancer patients was analyzed.

[0107] Figures 56-62The contribution of metabolites and clinical factors to the diagnosis of combined stage 2 (adenocarcinoma and squamous cell carcinoma) lung cancer patients was analyzed.

[0108] Those skilled in the art will appreciate that many of the details provided above are merely examples and are not intended to limit the scope of the invention, which should be determined with reference to the following claims.

Claims

1. Use of a biomarker panel comprising PC36.0AA in the preparation of a serum test for detecting lung cancer, wherein the biomarker panel further comprises C18.2, spermine, LYSOC18.2, decadienoylcarnitine (C10:2) and valine, and wherein the lung cancer is stage 1 lung cancer.

2. Use of a biomarker panel comprising PC36.0AA in the preparation of a serum test for detecting lung cancer, wherein the biomarker panel further comprises C18.2, spermine, LYSOC18.2, decadienoylcarnitine (C10:2) and valine and at least one additional biomarker, wherein the at least one additional biomarker is selected from the group consisting of PC36.0AE and spermidine, and wherein the lung cancer is stage 2 lung cancer.

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