Application of LPCAT, phosphatidylcholine and lysophosphatidylcholine as markers in squamous cell carcinoma diagnosis

Abstract

The invention belongs to the field of biological medicine, and particularly relates to application of LPCAT, phosphatidylcholine and lysophosphatidylcholine as markers in squamous cell carcinoma diagnosis. The ratio of the phosphatidylcholine to the corresponding lysophosphatidylcholine (such as PC (16: 0 / 16: 0) / LPC (16: 0), PC (16: 0 / 20: 4) / LPC (20: 4) and LPCAT1). When the phosphatidylcholine ratio is independently used for diagnosing and distinguishing squamous cell carcinoma, AUC is 0.8015, and certain accuracy is achieved. When the LPCAT1 is used for distinguishing squamous cell carcinoma, AUC is 0.9195, and certain accuracy is achieved. The biomarker provided by the invention can be used for noninvasive diagnosis and distinguishing of squamous-cell carcinoma, overcomes the defects of squamous-cell carcinoma markers, and is expected to replace biopsy.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biological medicine, and particularly relates to application of LPCAT, phosphatidylcholine and lysophosphatidylcholine as markers in diagnosis of squamous cell carcinoma. BACKGROUND

[0002] Squamous cell carcinoma (SCC) is an epithelial tumor that originates from the surface of the skin or mucosa, and is named based on its origin site, such as skin, head and neck, esophagus, lung, cervix, and less common pancreas, thyroid, bladder and prostate. Despite the diverse anatomical sites, SCCs share common genetic mutations and squamous differentiation markers. The tumor suppressor TP53 is the most frequently mutated gene in SCCs. In addition, cell cycle regulators (CDKN2A, CDKN2B, CCND1, RB1), receptor tyrosine kinases (HRAS, KRAS, EGFR), squamous differentiation factors (NOTCH1, NOTCH2, SOX2), and epigenetic modifiers (KMT2C, KMT2D, NSD1) show varying degrees of somatic alterations in SCC subtypes, including esophageal squamous cell carcinoma (ESCC), head and neck squamous cell carcinoma (HNSCC), and lung squamous cell carcinoma (LUSC). However, these common mutations have not been translated into effective diagnostic markers or therapeutic strategies, which makes early detection crucial for improving prognosis. Early detection of SCC is extremely important because early SCC is potentially curable. In contrast, the survival rate of advanced tumors is significantly lower, about half of that of early cases. This highlights the urgent need to continuously explore and verify new biomarkers.

[0003] Metabolic abnormalities are one of the important characteristics of tumors, and an increasing number of studies have confirmed that small molecule metabolites can be used as new tumor markers. Specific metabolites, such as secondary bile acids and short-chain fatty acids, regulate tumor progression. For example, trimethylamine oxide (TMAO) promotes immune activation and enhances the response to immune checkpoint blockade in pancreatic cancer. Free GAGomes in urine or plasma can be used for early screening of up to 14 tumors. Succinate in tissues or body fluids can be used for typing or prognostic clues of SDH-deficient related tumors.

[0004] In the past, blood-based protein tumor marker detection has been widely used in early screening of tumors. However, how to use metabolites and proteins as multi-factors to provide more accurate and efficient methods for diagnosing squamous cell carcinoma is a problem that those skilled in the art are eager to solve. SUMMARY

[0005] The present application aims to provide a novel marker combination for squamous cell carcinoma diagnosis to solve the problem of insufficient sensitivity and specificity in the existing squamous cell carcinoma diagnosis technology. By detecting and analyzing the serum metabolite levels in a mouse squamous cell carcinoma model and squamous cell carcinoma patients, the present application finds that the expression level of lysophosphatidylcholine acyltransferase (LPCAT) is increased, and LPCAT catalyzes the conversion of lysophosphatidylcholine (LPC) to phosphatidylcholine (PC) in the presence of acyl-CoA. Therefore, the contents and ratios of PC and LPC are further analyzed to screen and verify novel markers.

[0006] The first aspect of the present application aims to provide the use of a reagent for detecting LPCAT, PC and LPC in the preparation of a product for diagnosing squamous cell carcinoma.

[0007] The second aspect of the present application aims to provide a method for constructing a model for squamous cell carcinoma diagnosis.

[0008] The third aspect of the present application aims to provide a system for squamous cell carcinoma diagnosis.

[0009] In order to achieve the above-mentioned objects of the present application, the technical solutions adopted by the present application are as follows:

[0010] The first aspect of the present application provides the use of a reagent for detecting A1) and / or A2) in the preparation of a product for diagnosing squamous cell carcinoma.

[0011] A1) lysophosphatidylcholine acyltransferase;

[0012] A2) phosphatidylcholine and lysophosphatidylcholine.

[0013] In some embodiments of the present application, the relevant information of lysophosphatidylcholine acyltransferase (LPCAT) is as follows: NCBI Gene: 79888; Ensembl: ENSG00000153395; UniProtKB / Swiss-Prot: Q8NF37.

[0014] In some embodiments of the present application, the reagent for detecting lysophosphatidylcholine acyltransferase includes a reagent for detecting lysophosphatidylcholine acyltransferase at the gene level or protein level.

[0015] In some embodiments of the present application, the reagent for detecting lysophosphatidylcholine acyltransferase at the protein level is selected from one or more detection methods in the following group: chemiluminescence method, immunofluorescence method, protein chip method, protein mass spectrometry method, immunohistochemical method, patch tracing method based on labeling technology, Western blotting method, enzyme-linked immunosorbent assay.

[0016] In some embodiments of the application, the reagent for detecting lysophosphatidylcholine acyltransferase from the genetic level is selected from one or more reagents for detection methods of the group consisting of high-throughput sequencing, DNA / RNA-in situ hybridization, digital PCR, fluorescent quantitative PCR, gene chip.

[0017] In some embodiments of the application, the reagent for detecting phosphatidylcholine and lysophosphatidylcholine is selected from reagents in the following methods: chromatography, mass spectrometry, nuclear magnetic resonance.

[0018] In some embodiments of the application, the phosphatidylcholine (PC) comprises:

[0019] PC (16:0 / 16:0), 1,2-dihexadecanoyl-sn-glycero-3-phosphocholine, with a Cas number of: 63-89-8.

[0020] The lysophosphatidylcholine (LPC) comprises:

[0021] LPC (16:0), LPC (20:4), LPC (18:1), (LPC (14:0 / 0:0), LPC (0:0 / 20:4 (5Z, 8Z, 11Z, 14Z)), LPC (20:4), LPC (0:0 / 18:0), LPC (18:0 / 0:0), LPC (O-16:0 / 0:0).

[0022] Wherein, the information of each phosphatidylcholine (PC) and lysophosphatidylcholine (LPC) is as follows:

[0023] PC (16:0 / 16:0), 1,2-dihexadecanoyl-sn-glycero-3-phosphocholine, with a Cas number of: 63-89-8.

[0024] PC (16:0 / 20:4), 2-arachidonoyl-1-palmitoyl-sn-glycero-3-phosphocholine, with a Cas number of: 35418-58-7.

[0025] PC (P-16:0 / 0:0), 1-(1Z-hexadecenyl)-sn-glycero-3-phosphocholine, with a PubChem CID of: 10917802.

[0026] PC (18:1 (6Z) / 18:1 (6Z)), 1,2-diethylpropyl vinyl-sn-glycero-3-phosphocholine, with a PubChem CID of: 24778922.

[0027] PC (18:0 / 14:0), 1-stearoyl-2-myristoylphosphatidylcholine, with the Cas number 20664-02-2.

[0028] PC (18:0), 1-acetyl-2-hexadecanoyl-sn-glycero-3-phosphocholine, with the PubChem CID 18631343.

[0029] PC (16:0 / 18:0), 1-palmitoyl-2-stearoyl-sn-glycero-3-phosphocholine, with the Cas number 59403-51-9.

[0030] PC (O-16:0 / 20:5 (5Z,8Z,11Z,14Z,17Z)), 1-O-hexadecyl-2-eicosapentaenoyl-sn-glycero-3- phosphocholine, with the Cas number 132196-28-2.

[0031] LPC (16:0), 1-palmitoyl-sn-glycero-3-phosphocholine, with the Cas number 17364-16-8.

[0032] LPC (20:4), 1-((8Z,11Z,14Z,17Z)-eicosatetraenoyl)-sn-glycero-3-phosphocholine, with the PubChem CID 53480469.

[0033] LPC (18:1), 1-(9Z-octadecanoyl)-sn-glycero-3-phosphocholine, with the Cas number 19420-56-5.

[0034] LPC (14:0 / 0:0), 1-myristoyl-sn-glycero-3-phosphocholine, with the Cas number 20559-16-4.

[0035] LPC (0:0 / 20:4 (5Z,8Z,11Z,14Z), 2-arachidonoyl-sn-glycero-3-phosphocholine, with the Cas number 67341-29-1.

[0036] LPC (20:4), 1-eicosa-8,11,14,17-tetraenoyl glycerol 3-phosphocholine, with the PubChem CID 138162370.

[0037] LPC (0:0 / 18:0), 2-stearoyl-sn-glycero-3-phosphocholine, with the Cas number 4421-58-3.

[0038] LPC (18:0 / 0:0), 1-stearoyl-sn-glycero-3-phosphocholine, with the Cas number: 19420-57-6.

[0039] LPC (O-16:0 / 0:0), 1-O-hexadecyl-sn-glycero-3-phosphocholine, with the Cas number: 52691-62-0.

[0040] In some embodiments of the present application, when the expression level of the lysophosphatidylcholine acyltransferase is higher than the reference level, it is judged that the risk of squamous cell carcinoma is increased.

[0041] In some embodiments of the present application, when the ratio of the phosphatidylcholine to the lysophosphatidylcholine is higher than the reference level, it is judged that the risk of squamous cell carcinoma is increased.

[0042] In some embodiments of the present application, the reference level is a healthy person level.

[0043] In some embodiments of the present application, the ratio of the phosphatidylcholine to the lysophosphatidylcholine includes any one of B1) to B15):

[0044] B1) PC (16:0 / 16:0) / LPC (16:0);

[0045] B2) PC (16:0 / 20:4) / LPC (16:0);

[0046] B3) PC (16:0 / 20:4) / LPC (20:4);

[0047] B4) PC (P-16:0 / 0:0) / LPC (16:0);

[0048] B5) PC (O-16:0 / 20:5 (5Z, 8Z, 11Z, 14Z, 17Z)) / LPC (O-16:0 / 0:0);

[0049] B6) PC (18:1 (6Z) / 18:1 (6Z)) / LPC (18:1);

[0050] B7) PC (18:0 / 14:0) / LPC (0:0 / 18:0);

[0051] B8) PC (18:0 / 14:0) / LPC (18:0 / 0:0);

[0052] B9) PC (16:0 / 18:0) / LPC (0:0 / 18:0);

[0053] B10) PC (16:0 / 18:0) / LPC (16:0);

[0054] B11) PC (16:0 / 18:0) / LPC (18:0 / 0:0);

[0055] B12) PC (18:0 / 14:0) / LPC (14:0 / 0:0);

[0056] B13) PC (16:0 / 20:4) / LPC (0:0 / 20:4 (5Z, 8Z, 11Z, 14Z));

[0057] B14) PC (18:0) / LPC (0:0 / 18:0);

[0058] B15) PC (18:0) / LPC (18:0 / 0:0).

[0059] In some embodiments of the present application, the product comprises a detection kit, a detection chip or a detection test strip.

[0060] In some embodiments of the present application, the detection sample of the product is serum, plasma, a cell sample or a tissue sample.

[0061] In some embodiments of the present application, the ratio of lysophosphatidylcholine acyltransferase, phosphatidylcholine and lysophosphatidylcholine can be used for diagnostic prediction.

[0062] Specifically, LPCAT1 can be combined with PC (18:0) / LPC (18:0 / 0:0), LPCAT1 with PC (16:0 / 20:4) / LPC (20:4), and LPCAT1 with PC (16:0 / 20:4) / LPC (16:0).

[0063] In a second aspect of the present application, a method for constructing a model for diagnosing squamous cell carcinoma is provided, comprising the following steps:

[0064] Constructing a model based on the results of A1) and / or A2);

[0065] A1) lysophosphatidylcholine acyltransferase;

[0066] A2) phosphatidylcholine and lysophosphatidylcholine.

[0067] In some embodiments of the present application, the construction algorithm of the model comprises at least one of logistic regression, linear discriminant analysis, support vector machine, random forest and recursive partitioning tree.

[0068] In some embodiments of the present application, the ratio of phosphatidylcholine and lysophosphatidylcholine comprises any one of B1) to B15):

[0069] B1) PC (16:0 / 16:0) / LPC (16:0);

[0070] B2) PC (16:0 / 20:4) / LPC (16:0);

[0071] B3) PC (16:0 / 20:4) / LPC (20:4);

[0072] B4) PC (P-16:0 / 0:0) / LPC (16:0);

[0073] B5) PC (O-16:0 / 20:5 (5Z, 8Z, 11Z, 14Z, 17Z)) / LPC (O-16:0 / 0:0);

[0074] B6) PC (18:1 (6Z) / 18:1 (6Z)) / LPC (18:1);

[0075] B7) PC (18:0 / 14:0) / LPC (0:0 / 18:0);

[0076] B8) PC (18:0 / 14:0) / LPC (18:0 / 0:0);

[0077] B9) PC (16:0 / 18:0) / LPC (0:0 / 18:0);

[0078] B10) PC (16:0 / 18:0) / LPC (16:0);

[0079] B11) PC (16:0 / 18:0) / LPC (18:0 / 0:0);

[0080] B12) PC (18:0 / 14:0) / LPC (14:0 / 0:0);

[0081] B13) PC (16:0 / 20:4) / LPC (0:0 / 20:4 (5Z, 8Z, 11Z, 14Z));

[0082] B14) PC (18:0) / LPC (0:0 / 18:0);

[0083] B15) PC (18:0) / LPC (18:0 / 0:0).

[0084] In a third aspect of the present application, there is provided a system for diagnosing squamous cell carcinoma, the system comprising a computing device for diagnosing squamous cell carcinoma based on the detection results of at least one of A1) to A2).

[0085] A1) lysophosphatidylcholine acyltransferase;

[0086] A2) phosphatidylcholine and lysophosphatidylcholine.

[0087] In some embodiments of the present application, the system further comprises any one or more of the following:

[0088] C1) a detection result collection device, which can also be referred to as a detection result input device, and which can specifically be one or more of a mouse, a keyboard, a touch screen display, one or more buttons, one or more switches, one or more triggers, and the like;

[0089] C2) a diagnostic result output device, which can also be referred to as a diagnostic result display device, and which can specifically be one or more of a liquid crystal display (LCD), a light-emitting diode (LED) display, a plasma display, a projection display, a touch screen display, and the like;

[0090] C3) a diagnostic result transmission device, which can transmit the result of distinguishing whether the subject is a high-risk or low-risk population to an information communication terminal device that can be consulted by the patient or medical staff.

[0091] The present application has the following beneficial effects:

[0092] The present application discloses biomarkers for diagnosing and distinguishing squamous cell carcinoma, and the biomarkers include one or more of the following: a ratio of phosphatidylcholine to corresponding lysophosphatidylcholine (such as PC(16:0 / 20:4) / LPC(20:4)), and LPCAT1. When the ratio of phosphatidylcholine is used alone to diagnose and distinguish squamous cell carcinoma, the AUC is 0.8015, which has a certain accuracy. When tissue LPCAT1 is used to distinguish squamous cell carcinoma, the AUC is 0.837; when macrophage-specific LPCAT1 is used to distinguish squamous cell carcinoma, the AUC is 0.9195, which has a certain accuracy. When serum phosphatidylcholine ratio PC 18:0 / LPC(18:0 / 0:0) and tissue LPCAT1 are used in combination to diagnose and distinguish squamous cell carcinoma, the AUC is 0.816; when serum phosphatidylcholine ratio prediction probability LPCAT1+PC(16:0 / 20:4) / LPC(20:4) and tissue LPCAT1 are used in combination to diagnose and distinguish squamous cell carcinoma, the AUC is 0.959; and when serum phosphatidylcholine ratio PC(16:0 / 20:4) / LPC(16:0) and tissue LPCAT1 are used in combination to diagnose and distinguish squamous cell carcinoma, the AUC is 1. The biomarkers provided by the present application can be used for non-invasive diagnosis and distinction of squamous cell carcinoma, overcome the shortcomings of squamous cell carcinoma markers, and are expected to replace biopsy and can be developed into diagnostic reagents or kits. BRIEF DESCRIPTION OF DRAWINGS

[0093] The present application will be further described below in conjunction with the drawings and examples, wherein:

[0094] Figure 1 The ratio change result of PC / LPC in the mouse SCC tumorigenic model.

[0095] Figure 2 The immunohistochemical result of LPCAT1 in the mouse SCC tumorigenic model. Figure 3 The expression amount statistical result of LPCAT1 in the mouse SCC tumorigenic model.

[0096] Figure 4 The ROC curve result of LPCAT1 in the mouse SCC tumorigenic model.

[0097] Figure 5 The verification result of LPCAT1 in the clinical verification sample, wherein A is the result of verification of the expression amount of LPCAT1 increasing with tumor development in the network single cell database, B is the ROC curve result when LPCAT1 is used as a diagnostic marker in the network single cell database, C is an example of the immunohistochemical detection result of LPCAT1 of a clinical patient (selected from 6 cases); D is the expression amount statistical result of LPCAT1 of a clinical patient; E is the ROC curve result when LPCAT1 is used as a diagnostic marker in the clinical patient verification sample. Figure 6 The ratio change result of PC / LPC in the clinical verification sample.

[0098] Figure 7 The ROC curve result of different PC / LPC ratios (AUC is greater than 0.7).

[0099] Figure 8 The ROC curve result of different PC / LPC ratios (AUC is less than 0.7).

[0100] Figure 9 The ROC verification result of combined detection. DETAILED DESCRIPTION

[0101] The concept and technical effects of the present application will be described below in conjunction with the embodiments for a clear and complete description, so as to fully understand the purposes, features and effects of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0102] The present application experiment was approved by the Ethics Committee of Sun Yat-sen University, with the ethics number SYSU-LS-IACUC-2024-0113. All human sample collections comply with the ethical standards of the Ethics Committee of Sun Yat-sen University, with the ethics number SYSKY-2025-037-01.

[0103] Example 1 Screening of candidate biomarkers in an animal model

[0104] 1. Construction of mouse SCC tumorigenic model

[0105] Sample collection

[0106] C57BL / 6 background mice were used in this study. At the age of 6 weeks, the skin microbiota was removed by ultrasonic brushing and alcohol cleaning at the T0 time point to achieve standardization of the microbial composition between experimental groups. The mice were shaved on the back, and DMBA (7,12-dimethylbenz(a)anthracene) was applied once on the shaved skin (25 pg). After 1 week, the mice were applied with TPA (Phorbol ester) every other week on the local skin (6 pg) for 20 weeks. During the experiment, the tumor burden was evaluated by counting and image recording every week. The tumor surface microbiota was sampled using a cotton swab method, and serum samples were obtained by suborbital venous blood collection. To ensure the exclusion of interference factors, the time and space sampling method was adopted in the screening stage, and blood samples and skin swabs were collected at four stages of health (T0), precancerous lesions (T1), primary tumors (T2), and advanced tumors (T3).

[0107] 2. 16S sequencing

[0108] The MagPure DNA Extraction Kit KF B (MAGEN, China) was used to extract DNA from mouse fecal samples according to the manufacturer's instructions. The 2x Phanta Max premix enzyme (VAZYME, China) was used for library construction, and the bacterial 16S rRNA gene V3-V4 variable region was amplified by PCR (using forward / reverse primers). The resulting PCR products were purified by DNA magnetic beads. Subsequently, the final double-stranded library product was denatured to generate a single-stranded library. Single-stranded circular DNA was generated by a circularization reaction. The phi29 polymerase was used for rolling circle amplification of the circular library to generate DNA nanoballs (DNBs) containing multiple copies of the original library molecules. The DNBs were loaded into a patterned nanometer array and sequenced on the DNBSEQ-G400 platform.

[0109] The raw sequencing data was quality controlled using fqtools (v2.3) and cutadapt (v2.6). After filtering, high-quality clean reads were obtained, and readfq (v1.0) was used for processing. FLASH software (v1.2.11) was used for tag assembly, and then OTUs were generated based on 97% sequence similarity by Usearch (v7.0.1090). Taxonomic annotation of OTU representative sequences was completed by RDP Classifier (v2.2), and the sequence consistency threshold was set to 0.6. To identify microbial markers between different groups, the linear discriminant analysis effect size (LEfSe) algorithm was used to analyze the relative abundance of microbial communities within the Galaxy framework.

[0110] 3. Serum sample LC-MS analysis

[0111] Non-targeted metabolomics detection was performed using an Orbitrap Exploris 480 mass spectrometer coupled with liquid chromatography-tandem mass spectrometry (LC-MS / MS). Data acquisition covered both positive and negative ion modes to ensure comprehensive metabolite coverage. Raw data were processed by Met4DX (v2.2.0), including peak extraction, peak alignment, and retention time correction. Met4DX results were imported into the MetDNA2 web platform for compound identification and metabolite annotation. Data were standardized using the probability quotient normalization method (PQN) to calculate relative peak areas; QC-RLSC (quality control sample robust LOESS signal correction) was used to correct batch effects; compounds with a coefficient of variation (CV) > 30% in all quality control (QC) samples were excluded; data quality was evaluated by QC sample reproducibility, including chromatogram overlap, PCA analysis, peak number, and peak response intensity differences.

[0112] 4. Differential metabolite screening

[0113] Differential metabolites were screened based on the VIP values of the first two principal components of the PLS-DA (partial least squares discriminant analysis) model, combined with fold change and univariate analysis of the Student's t-test. The criteria were as follows: VIP > 1 for the first two PLS-DA components; fold change > 1.2 or < 0.83; p-value < 0.05.

[0114] 5. Joint analysis results

[0115] Mfuzz R package was used to cluster the changes in the microbiota over time, and the relationship between different clusters and time was analyzed. Based on the fact that tumor-related bacteria must be positively correlated with tumor generation over time, the bacterial group 6 was screened. Spearman correlation analysis was performed between all bacteria in group 6 and metabolites to find the highest correlation between Clostridium polysaccharolyticum and LPC (negative correlation), and the synthesis product PC of LPC was also found in the positively correlated metabolites. Further analysis of all phosphatidylcholines and lysophosphatidylcholines showed that there was a conversion of LPC to PC with tumor generation. Based on the carbon chain length, the ratio of PC / LPC corresponding to the same carbon chain was used as a marker for tumor generation. Protein verification related to this conversion process showed that LPCAT1 is a metabolic enzyme that promotes this conversion and can also be used as a marker for tumor generation.

[0116] The results of the change in the PC / LPC ratio in mice are shown in Figure 1 .

[0117] Validation of the candidate markers in a mouse model

[0118] 1. Experimental methods

[0119] Mouse model: FVB background mice were used, same as in Example 1, and DMBA-TPA chemical method was used to induce tumorigenesis, and the experiment was stopped when tumors appeared, and mouse tumor skin and normal skin samples were collected.

[0120] LPCAT1 immunohistochemical detection: Roche / Ventana Discovery Ultra automatic stainer was used to complete the tissue section staining. The section was baked at 60°C for 20 minutes, and 3 times of deparaffinization cycle (8 minutes each time) was performed, and high pH buffer was used for antigen repair. Endogenous peroxidase activity was blocked using Inhibitor CM (Roche / Ventana). LPCAT1 primary antibody (Proteintech) was used for detection, and immunohistochemical staining was completed by horseradish peroxidase (HRP) method. After scanning the section, ImageJ software (v1.54g) was used to quantitatively analyze the expression of LPCAT1, and the ratio of LPCAT1 antibody positive staining area to total tissue area was taken as the parameter for ROC analysis.

[0121] 2. Experimental results

[0122] The immunohistochemical results of LPCAT1 in mice are shown in Figure 2 , the expression results are shown in Figure 3 , and the ROC curve is shown in Figure 4 .

[0123] Example 3: Verification of candidate markers in clinical samples

[0124] 1. Sample collection

[0125] Patient inclusion criteria:

[0126] All patients enrolled must voluntarily participate and sign the informed consent form.

[0127] (1) Experimental group (tumor patients)

[0128] Diagnosis confirmation: patients newly diagnosed as squamous cell carcinoma (SCC) by histopathological examination.

[0129] Clinical staging: patients of different stages who have not received any anti-tumor treatment (including surgery, radiotherapy, chemotherapy, immunotherapy) to ensure that the molecular state of the tissue sample has not been changed by treatment.

[0130] Age and gender: between 18 and 75 years old, gender unrestricted.

[0131] (2) Control group (non-tumor patients) matching conditions: The control group should be matched with the experimental group in terms of age (± 5 years), gender, and other basic characteristics.

[0132] Source selection: Patients who seek medical treatment due to benign lesions (such as pigmented nevi, keloids, benign hyperplasia) and need surgical resection. The relatively normal tissue adjacent to the lesion is taken as control.

[0133] Health status: No history of any malignant tumor.

[0134] Patient exclusion criteria:

[0135] Individuals meeting any of the following conditions will be excluded:

[0136] (1) Combined with other malignant tumors: current or past history of any malignant tumor other than the target SCC (for the experimental group).

[0137] (2) Severe dysfunction of major organs: severe abnormalities in heart, lung, liver, and kidney function that may affect metabolic status.

[0138] (3) Systemic metabolic diseases and special medication history: severe metabolic diseases such as uncontrolled diabetes (HbA1c > 7.5%), hyperthyroidism or hypothyroidism, autoimmune diseases, etc.; or recent (within 3 months before enrollment) long-term use of systemic corticosteroids, immunosuppressants, or metabolic modulators.

[0139] (4) Acute and chronic infections and inflammation: definite active bacterial, viral, or fungal infection within 4 weeks before enrollment, or presence of uncontrolled chronic inflammatory diseases (such as rheumatoid arthritis, inflammatory bowel disease).

[0140] (5) Sample quality and data integrity: patients who cannot obtain sufficient high-quality tissue or blood samples, or have incomplete clinical data that cannot be effectively analyzed.

[0141] Based on the above criteria, a total of 17 healthy volunteers and 33 patients diagnosed with squamous cell carcinoma were collected, and their whole blood samples were placed in anticoagulant blood collection tubes. Centrifugation was performed at 8000 rpm for 10 minutes in a centrifuge, and the upper plasma was transferred to an EP tube. Dry ice was used to transport the samples to the laboratory for sample pretreatment.

[0142] Sample information is shown in Table 1.

[0143] Table 1

[0144]

[0145] 2. Sample pretreatment

[0146] Add 100 μL of serum to 4 times the amount of pre-cooled methanol (pre-cooled overnight at -20°C), vortex for 5 min, and place at -20°C to precipitate proteins for 30 min; centrifuge at 14000 rpm for 10 min at 4°C; transfer 420 μL of the supernatant to a 1.5 mL EP tube; freeze-dry to dryness at 4°C; store at -80°C before sample delivery; before instrument analysis, add 100 μL of reconstitution solvent (80% acetonitrile aqueous solution containing 5 μg / mL internal standard), vortex for 1 min, centrifuge at 14000 rpm for 6 min at room temperature, and transfer 90 μL of the supernatant to a 2 mL mass spectrometer vial containing an inner tube for analysis.

[0147] QC Sample: When reconstituted samples, 30 μL of each sample is mixed to form a quality control sample, which is used for instrument system balancing and data calibration.

[0148] 3. Mass spectrometry detection

[0149] Untargeted metabolomics assays were performed using an Orbitrap Exploris 480 mass spectrometer combined with liquid chromatography-tandem mass spectrometry (LC-MS / MS). Data acquisition included both positive and negative ion modes to ensure comprehensive metabolite coverage. Raw data were processed using Met4DX (v2.2.0), including peak extraction, peak alignment, and retention time correction. Met4DX results were imported into the MetDNA2 network platform for compound identification and metabolite annotation. Data standardization was performed using probability quotient normalization (PQN) to calculate relative peak areas; batch effects were corrected using QC-RLSC (quality control sample robust LOESS signal correction); compounds with a coefficient of variation (CV) >30% in all quality control (QC) samples were removed; data quality was assessed through QC sample reproducibility, including chromatographic overlap, PCA analysis, peak number, and peak response intensity differences.

[0150] 4. LPCAT1 detection

[0151] Detection method: The method was the same as for mouse tissue staining. Immunohistochemical staining was performed using LPCAT1 antibody, followed by microscopic photography and statistical analysis of the images using ImageJ. Experimental results are as follows: Figure 5 As shown, A represents the results of the online single-cell database validation showing the increasing expression level of LPCAT1 with tumor development; B represents the ROC curve results when LPCAT1 is used as a diagnostic marker in the online single-cell database; C represents an example of LPCAT1 immunohistochemical detection results in clinical patients (6 cases selected); D represents the statistical results of LPCAT1 expression in clinical patients; and E represents the ROC curve results when LPCAT1 is used as a diagnostic marker in the clinical patient validation samples.

[0152] The network data comes from three SCI papers with literature numbers (PMIDs) 34044317, 38857913 and 38519500, corresponding to sample numbers GSE172577, GSE226620 and HRA004648 respectively. After merging, a total of 43 patient samples were obtained, including healthy tissues, precancerous lesions, primary tumors and metastatic recurrent tumors. In the network database sample, the area under the curve of LPCAT1 as a diagnostic standard reached 0.9195, with good marker detection performance.

[0153] In the clinical sample, the area under the curve of LPCAT1 as a diagnostic marker reached 0.8367, with good marker detection performance.

[0154] 5. Phosphatidylcholine and lysophosphatidylcholine analysis

[0155] Detection method: The level of non-targeted metabolites by mass spectrometry is calculated according to the carbon chain, and the ratio of PC to LPC containing the same carbon chain is calculated. The results of non-tumor patients and tumor patients are compared, and the fold change is calculated. And select each PC / LPC ratio for ROC statistical analysis.

[0156] The experimental results are shown in Figure 6 , and the PC / LPC ratio is increased in tumor patients. Further, different PC / LPC ratios are selected for ROC curve analysis, and the results are shown in Figure 7 , 8 .

[0157] Example 4 Joint detection effect

[0158] 1. Experimental method

[0159] In the above-mentioned clinical sample patient cohort, 7 non-tumor patients and 7 tumor patients were collected, and the whole blood samples were subjected to the above-mentioned sample pretreatment and mass spectrometry analysis to obtain the metabolite content. At the same time, the non-tumor tissue blocks and tumor tissue blocks removed by surgery from the patients were subjected to paraffin embedding treatment and section tissue chemical staining, and the content of LPCAT1 was detected by the above-mentioned method.

[0160] SPSS 31.0 was used for data analysis, and Logistic regression analysis was used to construct a risk diagnosis model. The Receiver Operating Characteristic Curve (ROC) was used to verify the predictive effect of the difference phosphatidylcholine ratio and the LPCAT1 positive value and the diagnostic model.

[0161] 2. Experimental results

[0162] The experimental results are shown in Figure 9As shown, the AUC was 0.816 when serum phosphatidylcholine ratio PC 18:0 / LPC (18:0 / 0:0) and tissue LPCAT1 were combined to diagnose and distinguish squamous cell carcinoma; the AUC was 0.959 when serum phosphatidylcholine ratio prediction probability LPCAT1+PC (16:0 / 20:4) / LPC (20:4) and tissue LPCAT1 were combined to diagnose and distinguish squamous cell carcinoma; the AUC was 1 when serum phosphatidylcholine ratio PC (16:0 / 20:4) / LPC (16:0) and tissue LPCAT1 were combined to diagnose and distinguish squamous cell carcinoma. The combined detection has good specificity and sensitivity.

[0163] The embodiments of the present application are described in detail above with reference to the drawings, but the present application is not limited to the above-described embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present application. Furthermore, the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

Claims

1. Use of a reagent for detecting A1) and / or A2) in the manufacture of a product for diagnosing squamous cell carcinoma. A1) lysophosphatidylcholine acyltransferase; A2) phosphatidylcholine and lysophosphatidylcholine.

2. The use according to claim 1, wherein: the reagent for detecting lysophosphatidylcholine acyltransferase comprises a reagent for detecting lysophosphatidylcholine acyltransferase at the genetic level or at the protein level; the reagent for detecting phosphatidylcholine and lysophosphatidylcholine is selected from the group consisting of reagents for chromatography, mass spectrometry, nuclear magnetic resonance; preferably, the reagent for detecting lysophosphatidylcholine acyltransferase at the protein level is selected from the group consisting of one or more detection methods selected from the group consisting of chemiluminescence, immunofluorescence, protein chip, protein mass spectrometry, immunohistochemistry, patch tracing based on labeling technology, Western blotting, enzyme-linked immunosorbent assay; preferably, the reagent for detecting lysophosphatidylcholine acyltransferase at the genetic level is selected from the group consisting of one or more detection methods selected from the group consisting of high-throughput sequencing, DNA / RNA-in situ hybridization, digital PCR, real-time quantitative PCR, gene chip.

3. The use according to claim 1, wherein: the phosphatidylcholine (PC) comprises: PC (16:0 / 16:0), PC (16:0 / 20:4), PC (P-16:0 / 0:0), PC (18:1 (6Z) / 18:1 (6Z)), PC (18:0 / 14:0), PC (18:0), PC (16:0 / 18:0), PC (O-16:0 / 20:5 (5Z, 8Z, 11Z, 14Z, 17Z)); the lysophosphatidylcholine (LPC) comprises: LPC (16:0), LPC (20:4), LPC (18:1), LPC (14:0 / 0:0), LPC (0:0 / 20:4 (5Z, 8Z, 11Z, 14Z)), LPC (20:4), LPC (0:0 / 18:0), LPC (18:0 / 0:0), LPC (O-16:0 / 0:0).

4. The use according to claim 1, wherein: when the expression level of lysophosphatidylcholine acyltransferase is higher than the reference level, it is determined that the risk of squamous cell carcinoma is increased.

5. The use according to claim 3, wherein: when the ratio of phosphatidylcholine to lysophosphatidylcholine is higher than the reference level, it is determined that the risk of squamous cell carcinoma is increased; the ratio of phosphatidylcholine to lysophosphatidylcholine comprises any one of B1) to B15): B1) PC (16:0 / 16:0) / LPC (16:0); B2) PC (16:0 / 20:4) / LPC (16:0); B3) PC (16:0 / 20:4) / LPC (20:4); B4) PC (P-16:0 / 0:0) / LPC (16:0); B5) PC(O-16:0 / 20:5(5Z,8Z,11Z,14Z,17Z)) / LPC(O-16:0 / 0:0); B6) PC(18:1(6Z) / 18:1(6Z)) / LPC(18:1); B7) PC(18:0 / 14:0) / LPC(0:0 / 18:0); B8) PC(18:0 / 14:0) / LPC(18:0 / 0:0); B9) PC(16:0 / 18:0) / LPC(0:0 / 18:0); B10) PC(16:0 / 18:0) / LPC(16:0); B11) PC(16:0 / 18:0) / LPC(18:0 / 0:0); B12) PC(18:0 / 14:0) / LPC(14:0 / 0:0); B13) PC(16:0 / 20:4) / LPC(0:0 / 20:4(5Z,8Z,11Z,14Z)); B14) PC(18:0) / LPC(0:0 / 18:0); B15) PC(18:0) / LPC(18:0 / 0:0).

6. The use according to claim 1, wherein: the product comprises a detection kit, a detection chip or a detection test strip.

7. The use according to claim 6, wherein: the detection sample of the product is serum, plasma, a cell sample or a tissue sample.

8. A method for constructing a model for diagnosing squamous cell carcinoma, comprising the following steps: constructing a model based on the results of A1) and / or A2); A1) lysophosphatidylcholine acyltransferase; A2) phosphatidylcholine and lysophosphatidylcholine; Preferably, the construction algorithm of the model comprises at least one of logistic regression, linear discriminant analysis, support vector machine, random forest and recursive partitioning tree; Preferably, the ratio of phosphatidylcholine and lysophosphatidylcholine comprises at least one of B1) to B15): B1) PC(16:0 / 16:0) / LPC(16:0); B2) PC(16:0 / 20:4) / LPC(16:0); B3) PC(16:0 / 20:4) / LPC(20:4); B4) PC(P-16:0 / 0:0) / LPC(16:0); B5) PC(O-16:0 / 20:5(5Z,8Z,11Z,14Z,17Z)) / LPC(O-16:0 / 0:0); B6) PC(18:1(6Z) / 18:1(6Z)) / LPC(18:1); B7) PC(18:0 / 14:0) / LPC(0:0 / 18:0); B8) PC(18:0 / 14:0) / LPC(18:0 / 0:0); B9) PC(16:0 / 18:0) / LPC(0:0 / 18:0); B10) PC(16:0 / 18:0) / LPC(16:0); B11) PC(16:0 / 18:0) / LPC(18:0 / 0:0); ​ ​ B12) PC (18:0 / 14:0) / LPC (14:0 / 0:0); B13) PC (16:0 / 20:4) / LPC (0:0 / 20:4 (5Z, 8Z, 11Z, 14Z)); B14) PC (18:0) / LPC (0:0 / 18:0); B15) PC (18:0) / LPC (18:0 / 0:0).

9. A system for squamous cell carcinoma diagnosis, the system comprising a computing device that diagnoses squamous cell carcinoma based on the detection result of at least one of A1) to A2).

10. The system according to claim 9, characterized in that: the system further comprises one or more of C1) to C3): C1) a detection result collection device; C2) a diagnosis result output device; C3) a diagnosis result transmission device.

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