A computer device for diagnosing mother cell plasmacytoid dendritic cell tumor based on PLD4 gene and application thereof

Abstract

The application discloses a computer device for diagnosing mother cell-like plasmacytic dendritic cell tumor based on PLD4 gene and application thereof. The application provides a computer device, which comprises a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to realize the following steps: inputting and storing standard data (content data of PLD4 from a non-mother cell-like plasmacytic dendritic cell tumor patient); receiving content data of PLD4 from a to-be-tested person; comparing, thereby determining and outputting whether the to-be-tested person is or is suspected to be a mother cell-like plasmacytic dendritic cell tumor patient. The application has important significance for the diagnosis and treatment of mother cell-like plasmacytic dendritic cell tumor.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, specifically to a computer device for diagnosing blastic plasmacytoid dendritic cell tumors based on the PLD4 gene and its application. Background Technology

[0002] Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematologic malignancy caused by plasmacytoid dendritic cell precursors. It typically presents as skin involvement, with blast cells expressing CD4, CD56, CD123, CD303, and TCL-1, but lacking specific surface markers for B cells, T cells, myeloid cells, monocytes, or NK cells. Nearly 90% of patients present with skin lesions, nodules, erythematous plaques, and ecchymosis-like changes. Initially, most patients do not develop bone marrow involvement; however, in later stages, over 70% of patients develop lymph node and bone marrow involvement, gradually transforming into leukemia and ultimately leading to death.

[0003] The diagnosis of BPDCN is mainly based on its clinical features, pathological features, and immunophenotype, and no specific molecular markers have yet been found. In actual clinical practice, the diagnosis of BPDCN also mainly relies on immunohistochemistry or flow cytometry to determine the specific immunophenotype. However, due to the phenotypic heterogeneity of BPDCN and its frequent difficulty in distinguishing it from acute monocytic leukemia (AML), exploring specific biomarkers remains a key point in the diagnosis of BPDCN.

[0004] Currently, there is no standard treatment for BPDCN, and the preferred approach is multi-drug intensive chemotherapy with all types of regimens. Unfortunately, the overall response rate for BPDCN patients with conventional treatment is less than 30%. Recently, Tagraxofusp (SL-401), a novel targeted drug based on the CD123 target, has been used to treat BPDCN, achieving an overall response rate as high as 90%. However, many patients still relapse after monotherapy, and the treatment outcome for relapsed / refractory BPDCN remains unsatisfactory. Furthermore, its severe adverse reaction, capillary leak syndrome, can be life-threatening. Therefore, finding new therapeutic targets and developing novel targeted drugs to improve the treatment of BPDCN remains urgent. Summary of the Invention

[0005] The purpose of this invention is to provide a computer device for diagnosing blastic plasmacytoid dendritic cell tumors based on the PLD4 gene and its application.

[0006] In a first aspect, the present invention claims protection for a computer device.

[0007] The computer device claimed in this invention includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to perform the following steps: (A1) Data input and storage: Input and store standard data as the judgment threshold, wherein the standard data is the content data of PLD4 in the test sample of the control group; the control group is a patient with non-blastocystic plasmacytoid dendritic cell tumor; (A2) Data reception: Receiving data on the PLD4 content in the test sample from the subject; (A3) Data comparison and result output: The PLD4 content data in the test sample from the subject is compared with the standard data. Based on the comparison results, it is determined and output whether the subject is or is suspected to be a patient with blastocystic plasmacytoid dendritic cell tumor.

[0008] Furthermore, in (A3), if the PLD4 content data in the test sample from the test subject is significantly higher than the standard data, the test result information "the test subject is or is suspected to be a patient with blastocystic plasmacytoid dendritic cell tumor" is output; otherwise, the test result information "the test subject is not or is not a candidate for blastocystic plasmacytoid dendritic cell tumor" is output.

[0009] Furthermore, step (A1) may also include the step of inputting and storing typical clinical symptom information of patients with blastocystic plasmacytoid dendritic cell tumors; correspondingly, step (A2) may also include the step of receiving clinical symptom information of the subject; and step (A3) may also include the step of comparing the clinical symptom information of the subject with typical clinical symptom information of patients with blastocystic plasmacytoid dendritic cell tumors stored in a computer.

[0010] Accordingly, in (A3), if the PLD4 content data in the test sample from the test subject is significantly higher than the standard data, and the clinical symptom information of the test subject is consistent with the typical clinical symptom information of a patient with blastocystic plasmacytoid dendritic cell tumor, then the test result information "the test subject is or is suspected to be a patient with blastocystic plasmacytoid dendritic cell tumor" is output; otherwise, the test result information "the test subject is not or is not a candidate for a patient with blastocystic plasmacytoid dendritic cell tumor" is output.

[0011] Typical clinical symptoms of patients with blastocystic plasmacytoid dendritic cell tumors include (or may include): solitary or generalized skin lesions presenting as nodules, masses, purple or reddish-brown infiltrates, rapidly progressing to multiple organs, including bone marrow, blood, lymph nodes, central nervous system (CNS), spleen, liver, lungs, and kidneys. The same applies below.

[0012] Secondly, this invention claims protection for a computer program product.

[0013] The computer program product claimed in this invention includes a computer program; when the computer program is executed by a processor, it performs the steps described in the first aspect above.

[0014] The computer program product may be a software product that primarily implements its solution through computer programs.

[0015] Thirdly, the present invention claims protection for a computer-readable storage medium.

[0016] The present invention claims a computer-readable storage medium having a computer program stored thereon; said computer program, when executed by a processor, performs the steps described in the first aspect above.

[0017] The computer-readable storage medium refers to a carrier for storing data, which may be magnetic tape, disk, floppy disk, optical disk, magneto-optical disk, ROM, PROM, VCD, DVD, hard disk, flash memory, USB flash drive, CF card, SD card, MMC card, SM card, Memory Stick, or xD card, etc.

[0018] Fourthly, the present invention claims protection for a system for screening whether a subject is or is suspected of having blastic plasmacytoid dendritic cell tumor.

[0019] The system claimed in this invention for screening whether a candidate is or is suspected of having blastocystic plasmacytoid dendritic cell tumor may include: (B1) A substance capable of detecting the content of PLD4 in a test sample; (B2) Device; the device is the computer device described in the first aspect above.

[0020] Fifthly, the present invention claims a method for screening a subject using a computer to determine whether the subject is or is suspected of having blastic plasmacytoid dendritic cell tumor.

[0021] The method for screening a subject using a computer to determine whether they are or are suspected of having blastocystic plasmacytoid dendritic cell tumors, as claimed in this invention, may include: (C1) Data input and storage: Input and store standard data as the threshold for judgment, wherein the standard data is the content data of PLD4 in the test sample of the control group; the control group is a patient with non-blastocystic plasmacytoid dendritic cell tumor; (C2) Data reception: Receive data on the PLD4 content in the test sample from the subject; (C3) Data Comparison and Result Output: The PLD4 content data in the test sample from the subject is compared with the standard data. Based on the comparison results, the result is used to determine and output whether the subject is or is suspected of having blastic plasmacytoid dendritic cell tumor. If the PLD4 content in the test sample from the test subject is significantly higher than the standard data, the test result information "the test subject is or is suspected to be a patient with blastocystic plasmacytoid dendritic cell tumor" will be output; otherwise, the test result information "the test subject is not or is not a candidate for blastocystic plasmacytoid dendritic cell tumor" will be output.

[0022] Furthermore, step (C1) may include inputting and storing typical clinical symptom information of patients with blastocystic plasmacytoid dendritic cell tumors; correspondingly, step (C2) may include receiving clinical symptom information of the subject; and step (C3) may include comparing the clinical symptom information of the subject with typical clinical symptom information of patients with blastocystic plasmacytoid dendritic cell tumors stored in a computer.

[0023] Accordingly, in step (C3), if the PLD4 content data in the test sample from the test subject is significantly higher than the standard data, and the clinical symptom information of the test subject is consistent with the typical clinical symptom information of a patient with blastocystic plasmacytoid dendritic cell tumor, then the test result information "the test subject is or is suspected to be a patient with blastocystic plasmacytoid dendritic cell tumor" is output; otherwise, the test result information "the test subject is not or is not a candidate for a patient with blastocystic plasmacytoid dendritic cell tumor" is output.

[0024] Sixthly, the present invention claims protection for any of the following applications: (D1) The use of a substance capable of detecting the content of PLD4 in a test sample in the preparation of a product for screening whether a subject is or is suspected of having blastic plasmacytoid dendritic cell tumor; (D2) The use of substances capable of detecting the content of PLD4 in test samples in the preparation of products for distinguishing or assisting in the distinction between patients with blastocystic plasmacytoid dendritic cell tumors and controls; (D3) The use of substances that can reduce the expression and / or activity of PLD4 in the preparation of products for the treatment of patients with blastic plasmacytoid dendritic cell tumors.

[0025] In the aforementioned relevant aspects, the test sample may be lesion tissue (such as skin lesion tissue), peripheral blood, or bone marrow.

[0026] In the aforementioned related aspects, PLD4 may be a PLD4 protein or a gene encoding a PLD4 protein (such as DNA or mRNA). The amino acid sequence of the PLD4 protein is shown in SEQ ID No. 1. The nucleotide sequence of the gene encoding the PLD4 protein is shown in SEQ ID No. 2 (where positions 113-1633 are the CDS sequence).

[0027] In the aforementioned relevant aspects, the control group may be a healthy person, a patient with cutaneous squamous cell carcinoma (CSCC), a patient with basal cell carcinoma (BCC), a patient with NK / T-cell lymphoma (NKTL), a patient with diffuse large B-cell lymphoma (DLBCL), a patient with mantle cell lymphoma (MCL), a patient with Langerhans histiocytosis (LCH), a patient with mycosis fungoides (MF), a patient with melanoma, a patient with psoriasis (PsO), or a patient with atopic dermatitis (AD). Accordingly, the subjects to be tested can be selected from patients with blastocystic plasmacytoid dendritic cell tumors, healthy individuals, patients with cutaneous squamous cell carcinoma (CSCC), basal cell carcinoma (BCC), NK / T-cell lymphoma (NKTL), diffuse large B-cell lymphoma (DLBCL), mantle cell lymphoma (MCL), Langerhans histiocytosis (LCH), mycosis fungoides (MF), melanoma, psoriasis (PsO), and / or atopic dermatitis (AD).

[0028] In the aforementioned aspects, the substance used to detect the content of PLD4 in the test sample may be a reagent that can specifically bind to PLD4 and indicate its content, such as an antibody.

[0029] In this invention, PLD4 serves as a BPDCN tumor marker and therapeutic target, obtained through single-cell transcriptome sequencing screening. The specific steps include: (1) Experiments were conducted using bone marrow, peripheral blood, and skin lesion tissue samples from newly diagnosed, untreated BPDCN patients and corresponding normal control samples. Single cells were isolated from the obtained samples, and cell quality control, standardization, dimensionality reduction, and clustering were performed on the obtained single-cell sequencing data. Finally, cell type annotation was performed.

[0030] (2) Specific tumor cell subsets were identified in the patient’s bone marrow, peripheral blood, skin lesion tissue and corresponding normal control cell subsets, and their differential gene expression and GSEA pathway enrichment were analyzed. Screening of tumor cell-specific high-expression genes revealed that PLD4 was a gene specifically highly expressed in tumor cells from different tissue sources, and its expression level was higher than that of the previously reported BPDCN marker gene CD123 (IL3RA).

[0031] (3) The expression of PLD4 was verified by performing multiple immunofluorescence experiments on tissue sections of BPDCN patients and control groups (other common skin tumors, cutaneous lymphomas, inflammatory skin diseases and healthy controls), and statistical analysis was performed.

[0032] Furthermore, in step (1): The specific samples included: bone marrow, peripheral blood, and skin lesion tissue samples from one BPDCN patient; bone marrow samples from three healthy controls; peripheral blood samples from three healthy controls; and skin tissue samples from six healthy controls. The cell quality control involved removing cells with a total UMI (Unique Molecular Identifier) ​​count of less than 500, less than 200 genes, and more than 10,000 genes. Cells with a mitochondrial gene count higher than 10% or a red blood cell count higher than 10% were also removed. Cells predicted as twin cells by Python Scrublet (v0.2) were also excluded.

[0033] The standardization is performed using the LogNormalize method in the Seurat package.

[0034] The dimensionality reduction is achieved by using the FindVariableGenes function in the Seurat package to select the top 2000 highly variable genes, and then using principal component analysis to reduce the dimensionality of the highly variable genes to a number of principal components.

[0035] The clustering was performed based on 30 principal components, using the FindNeighbors and FindClusters functions from the Seurat package, with a resolution of 0.8.

[0036] The cell type annotation is performed by calculating the differential marker genes for each cell cluster using the FindAllMarkers function in the Seurat package.

[0037] Furthermore, in step (2): The differential gene expression analysis was performed as follows: The likelihood ratio test using the "FindMarkers" function was used to analyze differentially expressed genes (DEGs) between tumor cells and benign cells. A value of |log2FC| > 0.25 and an adjusted p-value < 0.05 was considered statistically significant. After excluding ribosome-related genes, the top 20 genes were selected based on |log2FC| from highest to lowest for further analysis. Enrichment analysis of the gene set was performed using GSEA software (version 1.10).

[0038] Furthermore, in step (3): The multiplex immunofluorescence assay involved collecting skin lesions from BPDCN patients and skin tissues from control groups (common skin tumors, cutaneous lymphomas, inflammatory skin diseases, and healthy controls) for fluorescent staining, calculating the expression differences of PLD4 in different samples, and observing the co-localization of PLD4 and CD123 in BPDCN patient samples.

[0039] The method for calculating the expression differences of PLD4 in different tissues was to perform integral calculations using the image analysis module of the Akoya multispectral imaging system. Statistical analysis was performed using GraphPad Prism v8.0 software. A two-tailed independent samples Student's t-test was used to analyze the significant differences between the two groups.

[0040] This invention employs high-resolution single-cell RNA sequencing technology to meticulously analyze tumor cell subsets from different tissue origins, identifying PLD4 as a potential tumor marker and therapeutic target for blastocystic plasmacytoid dendritic cell tumors (BPDCNs). This finding was validated using multiplex immunofluorescence techniques, providing a more reliable basis for the clinical diagnosis and precise targeted therapy of BPDCNs. This invention is of significant importance for the diagnosis and treatment of blastocystic plasmacytoid dendritic cell tumors. Attached Figure Description

[0041] Figure 1 This is a computer flowchart illustrating the screening process for blastic plasmacytoid dendritic cell tumors according to the present invention.

[0042] Figure 2 This section presents the overall clinical characteristics of patients with BPDCN. A represents the skin manifestations of BPDCN patients; B represents the pathological images and typical immunohistochemical findings of skin biopsies from BPDCN patients, showing diffuse lymphocytic infiltration in the dermis, with immunohistochemical positivity for CD123, CD163, CD56, CD43, and Ki67; C represents the pathological biopsy image of the right inguinal lymph nodes; D represents the pathological biopsy image of the bone marrow; and E represents CT images showing pathological lymphadenopathy in the chest, abdomen, and pelvis, with the spleen and liver exhibiting suspicious BPDCN features.

[0043] Figure 3This study presents the results of single-cell transcriptome mapping and tumor cell subset identification in bone marrow of BPDCN patients and healthy controls (HC). A shows UMAP clustering visualization of bone marrow cell subsets from BPDCN patients and three healthy controls, indicating they are mainly composed of 10 cell subsets. B shows the proportion of cells in each sample within each cell subset. C shows inferCNV analysis of tumor cell subsets with other benign cell subsets. D shows a dotplot illustrating the expression of marker genes that identify each cell subset type. E shows a heatmap illustrating the expression of the Top 20 genes (pct2≤0.1, |log2FC|>0.25, P<0.05) significantly upregulated and downregulated in each cell subset after differential gene expression analysis between tumor cells (clusters 2 and 7) and all normal cells. F shows a bubble plot illustrating the significantly upregulated signaling pathways in GSEA pathway enrichment analysis.

[0044] Figure 4 This study presents the single-cell transcriptome atlases and tumor cell subset identification results of peripheral blood mononuclear cells (PBMCs) from BPDCN patients and healthy controls (HC). A shows the UMAP clustering visualization of PBMC cell subsets from BPDCN patients and three healthy controls, indicating that they are mainly composed of 17 cell subsets. B shows the proportion of cells in each cell subset for each sample. C shows the inferCNV analysis of tumor cell subsets with other benign cell subsets. D shows the expression of marker genes identifying each cell subset type using UMAP. E shows the expression of the Top 20 significantly upregulated and downregulated genes in each cell subset after differential gene expression analysis between tumor cells (cluster 11) and all normal cells. F shows the significantly upregulated signaling pathways in the GSEA pathway enrichment analysis.

[0045] Figure 5 This study presents the results of single-cell transcriptomic atlases and tumor cell subset identification in skin tissues from BPDCN patients and healthy controls (HC). A shows UMAP clustering visualization of bone marrow cell subsets from BPDCN patients and 6 healthy controls, indicating they are primarily composed of 19 cell subsets. B shows the proportion of cells in each sample within each cell subset. C is a dotplot displaying the expression of marker genes identifying each cell subset type. D shows the re-clustering of T cells and monocytes in skin tissue. E is a heatmap showing the expression of the top 20 significantly upregulated and downregulated genes in each cell subset after differential gene expression analysis between tumor cells (cluster8) and all other normal cells. F is a bubble chart showing the significantly upregulated signaling pathways in GSEA pathway enrichment analysis.

[0046] Figure 6 PLD4 can serve as a diagnostic biomarker and potential intervention target for BPDCN. A is a Venn diagram showing a specific, significantly upregulated gene common to tumor cells from bone marrow (BM), PBMC, and skin lesions; B shows the expression levels of PLD4 and CD123 in tumor cells from different tissue origins; C is a heatmap showing the expression profiles of 117 genes in tumor cells from three different tissue origins (the expression levels of these genes were at least log2FC ≥ 1.5 in at least one tissue); D is multiplex immunofluorescence showing the expression levels of PLD4 and CD123 in some BPDCN patients and some controls (squamous cell carcinoma of the skin (CSCC), basal cell carcinoma (BCC), NK / T-cell lymphoma (NKTL), Langerhans histiocytosis (LH)). Expression and co-localization of CD123 and PLD4 in skin lesions of patients with LCH, mycosis fungoides (MF), melanoma, psoriasis (PsO), and healthy controls (HC); E represents the statistical analysis of relative protein levels of CD123 and PLD4 in skin lesions of BPDCN patients and controls (squamous cell carcinoma (CSCC), basal cell carcinoma (BCC), NK / T-cell lymphoma (NKTL), diffuse large B-cell lymphoma (DLBCL), mantle cell lymphoma (MCL), Langerhans histiocytosis (LCH), mycosis fungoides (MF), melanoma, psoriasis (PsO), atopic dermatitis (AD), and healthy controls (HC)). ****P < 0.0001. Detailed Implementation

[0047] The present invention will now be described in further detail with reference to specific embodiments. The given embodiments are merely illustrative of the invention and not intended to limit its scope. The embodiments provided below can serve as a guide for further improvements by those skilled in the art and do not constitute a limitation on the invention in any way.

[0048] Unless otherwise specified, the experimental methods used in the following examples are conventional methods, performed according to the techniques or conditions described in the literature in this field or according to the product instructions. Unless otherwise specified, the materials and reagents used in the following examples are commercially available.

[0049] Figure 1 This is a computer flowchart illustrating the screening process for blastic plasmacytoid dendritic cell tumors according to the present invention.

[0050] In step S1, standard data for determining the threshold is input and stored. The standard data is the PLD4 content data in the test sample of the control group; the control group is a patient with non-blastocystic plasmacytoid dendritic cell tumor. In step S2, the content data of PLD4 in the test sample from the subject is received; In step S3, the PLD4 content data in the test sample from the test subject is compared with the standard data. Based on the comparison result, it is determined and output whether the test subject is or is suspected of having blastocystic plasmacytoid dendritic cell tumor. If the PLD4 content data in the test sample from the test subject is higher than the standard data, the test result information "the test subject is or is suspected of having blastocystic plasmacytoid dendritic cell tumor" is output; otherwise, the test result information "the test subject is not or is not a candidate for blastocystic plasmacytoid dendritic cell tumor" is output.

[0051] The BPDCN patients involved in the following examples have typical clinical symptoms: isolated or generalized skin lesions, manifested as nodules, masses, purple or reddish-brown infiltrates, which can rapidly spread to multiple organs, including bone marrow, blood, lymph nodes, central nervous system (CNS), spleen, liver, lungs and kidneys.

[0052] Example 1: Application of PLD4 gene as a marker and therapeutic target for blastic plasmacytoid dendritic cell tumors. This invention utilizes single-cell RNA sequencing to efficiently and practically screen PLD4 as a tumor marker and potential intervention target for BPDCN. Its main steps include: (1) Single-cell sequencing was performed on bone marrow, peripheral blood and skin tissue of BPDCN patients and healthy controls to obtain raw sequencing data; (2) Perform quality control, dimensionality reduction, clustering, and visualization processing on the raw data; (3) Based on the differentially expressed genes and their respective specific expressed genes among different cell subpopulations, as well as inferCNV analysis, tumor cell subpopulations in single-cell data from different tissues were finally screened out. (4) Based on indicators such as pct2, log2FC, and P value, specific differentially expressed genes of tumor cells from different tissue sources were obtained, and the intersection was taken. Finally, PLD4 was identified as a tumor marker and potential intervention target for BPDCN.

[0053] (5) Multiple immunofluorescence was performed on tissue samples from BPDCN patients and control groups to further verify the potential of PLD4 as a tumor marker for BPDCN, and the sensitivity of PLD4 and CD123 was compared.

[0054] I. Preparation of single-cell suspension Bone marrow, peripheral blood, and skin lesion tissue were collected from one newly diagnosed, untreated patient with BPDCN (detailed clinical characteristics are available in [link to clinical data]). Figure 2), 3 bone marrow samples from healthy controls, 3 peripheral blood samples from healthy controls, and 6 skin tissue samples from healthy controls were used to prepare single-cell suspensions. The specific experimental steps are as follows: 1. Preparation of human skin single-cell suspension Fresh skin tissue was collected, washed with pre-chilled HBSS, and then soaked in Dispase II to separate the epidermis and dermis overnight at 4°C. After separating the epidermis, it was washed with pre-chilled HBSS and digested in 0.05% Trypsin-EDTA preheated at 37°C for 30 min, inverting and mixing several times every 3 min during digestion. After digestion, digestion was terminated with Trypsin Neutralizing Solution (5% resin-chelated serum prepared with HBSS), and the cells were mixed several times by pipetting and then passed through a 70 μm cell sieve. The cells were centrifuged at 400 g for 5 min, the supernatant was discarded, and the cells were resuspended in HBSS. After separating the dermis, it was washed with pre-chilled HBSS, placed in digestion working solution (a mixture of Collagenase P and DNase I in a certain ratio, prepared in serum-free DMEM high-glucose medium), and cut into small pieces. Sufficient digestion working solution was added, and the cells were incubated at 37°C for 0.5 h–1 h. Mix thoroughly by pipetting, pass through a 70 μm cell sieve, and add an equal volume of DMEM high-glucose medium containing 10% FBS. Centrifuge at 400 g for 5 min, discard the supernatant, and resuspend the cells in HBSS. Combine the epidermal and dermal resuspensions, centrifuge at 400 g for 5 min, discard the supernatant, and resuspend the cells in HBSS for single-cell sequencing.

[0055] 2. Extraction of human peripheral blood PBMCs and bone marrow cells Take 5 mL of peripheral blood or bone marrow from each sample and dilute with an equal volume of PBS. Add to lymphocyte separation medium: In a new 15 mL centrifuge tube, add 5 mL of lymphocyte separation medium, then carefully add the diluted blood on top of the lymphocyte separation medium (lymphocyte separation medium: diluted blood = 1:2). Centrifuge at 1000 g for 22 min, acceleration 7, deceleration 1. Carefully aspirate the cells from the cloud layer and add them to a new centrifuge tube. Add PBS to approximately 5 mL of the aspirated cloud layer cells. Centrifuge at 700 g for 7 min, acceleration 9, deceleration 9. Discard the supernatant directly, then add 5 mL of PBS on top of the cell pellet; no resuspending of cells is necessary. Centrifuge at 500 g for 5 min, acceleration 9, deceleration 9. Resuspend the cells in PBS for single-cell sequencing.

[0056] II. Single-cell sequencing Library construction was then performed using the 10X Genomics single-cell sequencing library preparation kit. Cellbarcode and UMI were used to label each cell and each mRNA within the cell, respectively. Sequencing was then performed using an Illumina sequencer to obtain the raw FastQ sequence file. The specific experimental steps are as follows: (1) Prepare the Master Mix. Add 36.3 μL of Master Mix to each of the 8-tube PCR apparatus. Count the single-cell suspension using a cell counting chamber, and dilute the skin tissue single-cell suspension to the optimal concentration (1000 cells / μL) with PBS containing 10% FBS. Determine the final cell capture count to be 6000 cells. According to the cell suspension volume calculation table provided in the 10xGenomics manual, take 9.9 μL of cell suspension + 28.8 μL of PBS containing 10% FBS to prepare 38.7 μL of cell suspension.

[0057] (2) Chromium Next GEM Chip K Sample Loading: Add 50% glycerol to unused wells of the chip, 70 μL to well 1, 50 μL to well 2, and 45 μL to well 3. For each sample, add 38.7 μL of cell suspension to 36.3 μL of Master Mix in an 8-tube container, and gently pipette to mix, preparing a 75 μL suspension. Add 70 μL of suspension to the first row of blank wells, being careful not to generate air bubbles. Prepare Gel Beads, place them on a shaker, and shake vigorously for 30 seconds. Then, aspirate 50 μL of Gel Beads and add them to the second row of wells (corresponding to the first row), being careful not to generate air bubbles, and wait for 30 seconds. Add 45 μL of Partitioning Oil to the corresponding well in the third row. Place a 10× Gasket on the chip, ensuring the gasket holes are aligned with the chip wells.

[0058] (3) Run the 10×Genomics machine: Click the pop-up button on the machine to eject the tray. Place the assembled chip on the tray, push it into the machine, and click the play button. Proceed to the next step immediately after running the program for approximately 18 minutes.

[0059] (4) Transferring GEMs (water-in-oil structure): Place an 8-tube on ice. Open the chip box and slowly draw out 100 μL of GEMs (the GEMs should have a uniform and opaque appearance) from the bottom of the No. 3 recovery port, and quickly place them into the 8-tube.

[0060] (5) GEM-RT incubation followed by purification: At room temperature, add 125 μL of Recovery Agent (after GEM-RT incubation) to each sample, without shaking, and wait for 2 min. The resulting two-phase mixture consists of an oil phase (pink) and an aqueous phase (clear). Slowly aspirate 125 μL of the pink oil phase from the bottom of the 8-tube strip and discard.

[0061] (6) Prepare Dynabeads Cleanup Mix and Elution Solution I.

[0062] (7) cDNA amplification: Prepare cDNA Amplification Mix. Vortex and centrifuge briefly. Add 65 μL of cDNA Amplification Mix to 35 μL of sample (after GEM-RT purification). Set the pipette to 90 μL, gently pipette to mix, and centrifuge briefly. Incubate in a PCR instrument according to the corresponding procedure, and immediately proceed to the next experiment.

[0063] (8) cDNA purification: Vortex SPRIselect reagent, add 60 μL to each sample, and adjust the pipette to 140 μL, gently pipetting and mixing 15 times. Incubate at room temperature for 5 min. Place on a magnetic rack (High) until the liquid becomes clear. Discard the supernatant. Add 200 μL of 80% ethanol to the particles and wait 30 sec. Discard the ethanol. Repeat the steps, washing twice in total. Centrifuge briefly and place on a magnetic rack (Low). Remove residual ethanol and air dry for 2 min. Remove the magnetic rack, add 45.5 μL of Buffer EB, and pipette and mix 15 times. Incubate at room temperature for 2 min. Place the 8-tube on a magnetic rack (High) until the liquid becomes clear. Transfer 45 μL of sample to a new 8-tube.

[0064] (9) Quality control and sequencing: The samples underwent cDNA quality control, library construction, and subsequent sequencing. The amplified cDNA and the final library were quality controlled using a high-sensitivity DNA kit on an Agilent Bio-Analyzer. The library was then constructed and sequenced on a NovaSeq 6000 (Illumina) at a depth of approximately 350M-1200M per sample to ensure an average of about 50,000 reads per cell.

[0065] III. Data Processing Subsequently, CellRanger 2.0 was used to perform sequence alignment, identify cell barcodes and UMIs (unique molecular identifiers) in the sequences, and count each UMI for each cell to obtain the gene expression matrix for each cell.

[0066] Subsequent data processing was performed using Seurat 3.0. First, low-quality cells and genes were filtered out. Cells with fewer than 500 UMI (Unique Molecular Identifier) ​​genes, fewer than 200 genes, and more than 10,000 genes were removed. Cells with a mitochondrial gene count higher than 10% or a red blood cell count higher than 10% were also deleted. Cells predicted as twin cells by Python Scrublet (v0.2) were also excluded. Next, the expression matrix was standardized (using the LogNormalize method in the Seurat package), highly heterogeneous genes were identified, and Principal Component Analysis (PCA) was used for linear dimensionality reduction (using the FindVariableGenes function in the Seurat package to select the top 2000 highly variable genes, and then reducing the dimensionality of these genes to several principal components using principal component analysis). The FindClusters function was used to calculate clusters of the top 30 principal components at optimal resolution. UniformManifold Approximation and... The Projection (UMAP) algorithm performs further nonlinear dimensionality reduction and displays all cells on a two-dimensional graph, such as Figure 3 China A Figure 4 China A Figure 5 As shown in Figure A, the mapping of bone marrow, peripheral blood, and skin cells on a UMAP two-dimensional map is presented. Furthermore, the sample components and proportions within each cell subpopulation are analyzed, such as... Figure 3 B, Figure 4 B, Figure 5 As shown in B.

[0067] Based on differentially expressed genes and their specific gene expression among different cell subpopulations, and using inferCNV analysis (using the inferCNV R package to estimate gene expression levels in each chromosomal region), tumor cells (specifically expressing genes such as CD123, TCL1A, IRF8, and PLD4, and exhibiting more copy number variations) were further screened from each cell group, thereby identifying clusters 2 and 7 in bone marrow cells and cluster 11 in PBMCs as tumor cells. Figure 3 C and D Figure 4In the skin lesion tissue cells, T cells and monocytes were identified based on differentially expressed genes among different cell subpopulations and their respective specific expressed genes. Figure 5 (C), and then re-clustered them, identifying cluster 8 as tumor cells ( Figure 5 (D).

[0068] Differentially expressed genes (DEGs) in tumor cells and benign cells of bone marrow, PBMCs, and skin tissues were analyzed using the Likelihood-ratio test in FindMarkers. |log2FC| > 0.25 and adjusted P < 0.05 were considered statistically significant. After excluding ribosome-related genes, the top 20 genes were selected for further analysis based on pct2 ≤ 0.1 and |log2FC| from highest to lowest. Figure 3 E, Figure 4 E, Figure 5 (E). Enrichment analysis of the gene set was performed using GSEA software (version 1.10). Figure 3 China F, Figure 4 China F, Figure 5 (FC). Here, log2FC refers to the logarithm of the fold change in expression levels, base 2. It is a commonly used statistical indicator. FC stands for Fold change. The color intensity represents the average expression level.

[0069] The intersection of specifically upregulated genes shared by tumor cells derived from bone marrow, PBMCs, and skin lesions was analyzed, revealing five genes: PLD4, CD123 (IL3RA), FAM129C, LAMP5, and TCL1A (IL3RA). Figure 6 (A); Simultaneously, the expression levels of PLD4 and CD123 were compared in tumor cells from different tissue sources. Heatmaps showed the expression profiles of 117 genes in tumor cells from three different tissue sources (the expression levels of these genes were at least log2FC≥1.5 in at least one tissue). It was found that in tumor cells from bone marrow, peripheral blood, and skin tissues, the expression level of PLD4 was higher than that of CD123. Figure 6 (B and C in the middle).

[0070] IV. Multiplex Immunofluorescence Detection The expression of PLD4 and CD123 in skin lesions from 7 patients with BPDCN and controls (3 cases of cutaneous squamous cell carcinoma (CSCC), 4 cases of basal cell carcinoma (BCC), 3 cases of NK / T-cell lymphoma (NKTL), 2 cases of diffuse large B-cell lymphoma (DLBCL), 2 cases of mantle cell lymphoma (MCL), 3 cases of Langerhans histiocytosis (LCH), 4 cases of mycosis fungoides (MF), 3 cases of melanoma, 5 cases of psoriasis (PsO), 3 cases of atopic dermatitis (AD), and 20 healthy controls (HC)) was further validated using multiplex immunofluorescence. The specific experimental steps for multiplex immunofluorescence sample processing are as follows: (1) Baking the slides: Incubate the tissue slides at 75℃ for 30 min to prevent them from falling off.

[0071] (2) Dewaxing: The paraffin slices were dewaxed in two stages of xylene solution for 5 minutes each time.

[0072] (3) Rehydration: Rehydrate in 100%, 95%, 90%, 85% and 75% alcohol and distilled water in sequence, for 5 minutes each.

[0073] (4) Antigen retrieval: The slide was immersed in citrate buffer at pH 6.0 and autoclaved for 2 min, rinsed with tap water and cooled, and washed twice with PBS for 5 min each time.

[0074] (5) Blocking: Use a histochemical pen to circle the area on each slide and add 200 μL of 5% BSA. Treat at room temperature for 30 min to block.

[0075] (6) Primary antibody incubation: Discard the blocking solution, add 200 μL of primary antibody (abcam product) diluted with 1% BSA to each slice, incubate overnight at 4°C or 2 h at 37°C, wash 3 times with PBS for 5 min each time.

[0076] (7) Secondary antibody incubation: Add 1 drop of secondary antibody reagent to each slide, incubate at 37°C for 30 min, wash with PBS 3 times, 5 min each time.

[0077] (8) Fluorescence amplification signal: 1) Remove residual washing solution from the slide; 2) Use a pipette to add 100 μL of 1× dye working solution (diluted 1:100 with signal amplification solution) to the slide, immersing the sample area; 3) Incubate at room temperature with shaking for 10 min. 4) Wash the slide with PBS, immersing at room temperature for 5 min, repeat 3 times. 5) Antigen retrieval, allowing to cool naturally to room temperature. 6) Wash the slide once with sterile water, immersing in PBS for 5 min. 7) After single staining, add subsequent staining (starting from the blocking step, repeating several rounds of experiments for each antibody staining). 8) Note: After each round of staining, the staining status can be confirmed with a fluorescence microscope. Be careful to cover the sample with PBS to prevent the slide from drying out. 9) Add 1× DAPI working solution to the sample, immersing the sample area, and incubate at room temperature for 5 min. Wash the slide with PBS 3 times, 5 min each time. Then add anti-fluorescence quenching mounting medium, seal with a coverslip, avoiding air bubbles. For long-term storage, seal the edges of the coverslip with clear nail polish. 10) Review the slides: Observe and analyze the stained tissue slides under a fluorescence microscope.

[0078] Statistical analysis was performed using GraphPad Prism v8.0 software. A two-tailed independent samples Student's t-test was used to analyze the significant differences between the two groups.

[0079] The results showed that both PLD4 and CD123 were widely and highly expressed in BPDCN patients, while they were less expressed or not expressed at all in the control group, with significant statistical differences. PLD4 and CD123 also showed high colocalization. Figure 6 (D and E in the middle).

[0080] Conclusion: PLD4 can serve as a tumor marker and potential therapeutic target for BPDCN.

[0081] The present invention has been described in detail above. Those skilled in the art will recognize that the invention can be practiced in a wide range of ways with equivalent parameters, concentrations, and conditions without departing from its spirit and scope, and without requiring unnecessary experiments. While specific embodiments have been provided, it should be understood that further modifications can be made to the invention. In summary, according to the principles of the invention, this application is intended to include any changes, uses, or improvements to the invention, including changes made using conventional techniques known in the art that depart from the scope disclosed herein.

Claims

1. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to perform the following steps: (A1) Data input and storage: Input and store standard data as the judgment threshold, wherein the standard data is the content data of PLD4 in the test sample of the control group; the control group is a patient with non-blastocystic plasmacytoid dendritic cell tumor; (A2) Data reception: Receiving data on the PLD4 content in the test sample from the subject; (A3) Data comparison and result output: The PLD4 content data in the test sample from the subject is compared with the standard data. Based on the comparison results, it is determined and output whether the subject is or is suspected to be a patient with blastocystic plasmacytoid dendritic cell tumor.

2. The computer device according to claim 1, characterized in that: In (A3), if the PLD4 content data in the test sample from the test subject is higher than the standard data, the test result information "the test subject is or is suspected to be a patient with blastocystic plasmacytoid dendritic cell tumor" is output; otherwise, the test result information "the test subject is not or is not a candidate for blastocystic plasmacytoid dendritic cell tumor" is output.

3. The computer device according to claim 1, characterized in that: In step (A1), the method further includes the step of inputting and storing typical clinical symptom information of patients with blastocystic plasmacytoid dendritic cell tumors; in step (A2), the method further includes the step of receiving clinical symptom information of the subject; and in step (A3), the method further includes the step of comparing the clinical symptom information of the subject with typical clinical symptom information of patients with blastocystic plasmacytoid dendritic cell tumors stored in a computer. Furthermore, in step (A3), if the PLD4 content data in the test sample from the test subject is higher than the standard data, and the clinical symptom information of the test subject is consistent with the typical clinical symptom information of a patient with blastocystic plasmacytoid dendritic cell tumor, then the test result information "the test subject is or is suspected to be a patient with blastocystic plasmacytoid dendritic cell tumor" is output; otherwise, the test result information "the test subject is not or is not a candidate for a patient with blastocystic plasmacytoid dendritic cell tumor" is output.

4. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by the processor, it performs the steps described in any one of claims 1-3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it performs the steps described in any one of claims 1-3.

6. A system for screening whether a candidate is or is suspected of having blastic plasmacytoid dendritic cell tumor, comprising: (B1) A substance capable of detecting the content of PLD4 in a test sample; (B2) Apparatus; The device is any one of the computer devices described in claims 1-3.

7. A method for screening a subject using a computer to determine whether they are or are suspected of having blastic plasmacytoid dendritic cell tumor, comprising: (C1) Data input and storage: Input and store standard data as the threshold for judgment, wherein the standard data is the content data of PLD4 in the test sample of the control group; the control group is a patient with non-blastocystic plasmacytoid dendritic cell tumor; (C2) Data reception: Receive data on the PLD4 content in the test sample from the subject; (C3) Data Comparison and Result Output: The PLD4 content data in the test sample from the subject is compared with the standard data. Based on the comparison results, the result is used to determine and output whether the subject is or is suspected of having blastic plasmacytoid dendritic cell tumor. If the PLD4 content in the test sample from the test subject is higher than the standard data, the test result information "the test subject is or is suspected to be a patient with blastocystic plasmacytoid dendritic cell tumor" will be output; otherwise, the test result information "the test subject is not or is not a candidate for blastocystic plasmacytoid dendritic cell tumor" will be output.

8. The method according to claim 7, characterized in that: In step (C1), the method further includes the step of inputting and storing typical clinical symptom information of patients with blastocystic plasmacytoid dendritic cell tumors; in step (C2), the method further includes the step of receiving clinical symptom information of the subject; and in step (C3), the method further includes the step of comparing the clinical symptom information of the subject with typical clinical symptom information of patients with blastocystic plasmacytoid dendritic cell tumors stored in a computer. Further, in step (C3), if the PLD4 content data in the test sample from the test subject is higher than the standard data, and the clinical symptom information of the test subject is consistent with the typical clinical symptom information of a patient with blastocystic plasmacytoid dendritic cell tumor, then the test result information "the test subject is or is suspected to be a patient with blastocystic plasmacytoid dendritic cell tumor" is output; otherwise, the test result information "the test subject is not or is not a candidate for a patient with blastocystic plasmacytoid dendritic cell tumor" is output.

9. Any of the following applications: (D1) The use of a substance capable of detecting the content of PLD4 in a test sample in the preparation of a product for screening whether a subject is or is suspected of having blastic plasmacytoid dendritic cell tumor; (D2) The use of substances capable of detecting the content of PLD4 in test samples in the preparation of products for distinguishing or assisting in the distinction between patients with blastocystic plasmacytoid dendritic cell tumors and controls; (D3) The use of substances that can reduce the expression and / or activity of PLD4 in the preparation of products for the treatment of patients with blastic plasmacytoid dendritic cell tumors.

10. A computer device, computer program product, computer-readable storage medium, system, or method according to any one of claims 1-9, characterized in that: The test sample is lesion tissue, peripheral blood, or bone marrow; and / or The PLD4 is the PLD4 protein or the gene encoding the PLD4 protein. and / or The control group consisted of healthy individuals, patients with squamous cell carcinoma of the skin, patients with basal cell carcinoma, patients with NK / T-cell lymphoma, patients with diffuse large B-cell lymphoma, patients with mantle cell lymphoma, patients with Langerhans histiocytosis, patients with mycosis fungoides, patients with melanoma, patients with psoriasis, or patients with atopic dermatitis. and / or The subjects were selected from patients with blastocystic plasmacytoid dendritic cell tumors, healthy individuals, patients with squamous cell carcinoma of the skin, patients with basal cell carcinoma, patients with NK / T-cell lymphoma, patients with diffuse large B-cell lymphoma, patients with mantle cell lymphoma, patients with Langerhans histiocytosis, patients with mycosis fungoides, patients with melanoma, patients with psoriasis, and / or patients with atopic dermatitis.

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