Simultaneous diagnosis marker, screening method and application of lupus erythematosus and colorectal cancer

Abstract

This invention discloses a diagnostic biomarker, screening method, and application for simultaneous diagnosis of systemic lupus erythematosus (SLE) and colorectal cancer. The diagnostic biomarker is a combination of the genes DNASE1L3, PTPN14, SELENBP1, and ECRG4. The screening method includes data collection and processing, differentially expressed gene screening, small molecule drug screening, characteristic gene screening, immune cell infiltration assessment, diagnostic nomogram model construction, and verification of diagnostic gene expression levels. This invention facilitates the simultaneous diagnosis of SLE and colorectal cancer by constructing a specific biomarker combination of DNASE1L3, PTPN14, SELENBP1, and ECRG4, improving the efficiency of early identification of both diseases. Furthermore, the nomogram model for diagnosing SLE and colorectal cancer based on DNASE1L3, PTPN14, SELENBP1, and ECRG4 aids in the early screening and prognostic assessment of both diseases.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology and relates to a diagnostic biomarker, screening method and application for simultaneous diagnosis of lupus erythematosus and colorectal cancer. Background Technology

[0002] Systemic lupus erythematosus (SLE) is a chronic, systemic, autoimmune inflammatory connective tissue disease. The global incidence of SLE is approximately 1.5-11 per 100,000 people-years, with a prevalence ranging from 13-7713.5 per 100,000 people. Colorectal cancer (CRC) ranks third in incidence and second in mortality among malignant tumors, accounting for 9.6% of new malignant tumor cases and 9.3% of all cancer deaths. Genetic factors, poor diet and lifestyle, and inflammatory bowel disease are important risk factors for colorectal cancer. Recent studies have shown a significant association between SLE and tumor development, with multiple large cohort studies finding a significantly increased risk of malignant tumors in SLE patients. Furthermore, related evidence suggests that anti-tumor drugs (such as capecitabine and fluorouracil) can induce SLE.

[0003] Several potential mechanisms may explain the link between lupus and colorectal cancer. First, persistent and severe chronic inflammation is a common risk factor for both SLE and CRC. Lupus, characterized by immune dysfunction, releases pro-inflammatory cytokines that provide a pro-tumor inflammatory microenvironment for colorectal cancer development. For example, IL-6 exerts anti-apoptotic and pro-tumorigenic effects by activating the JAK / STAT3, RAS / MAPK, and PI3K / AKT signaling pathways. In lupus patients, a large number of autoantigens can activate Toll-like receptors, promoting inflammatory responses through the MyD88 / NF-κB signaling pathway and driving the transformation of precancerous cells into cancer cells. Furthermore, lupus treatments may increase the risk of tumorigenesis.

[0004] Numerous studies have demonstrated a dynamic, bidirectional link between autoimmune diseases and tumors: disruption of immune tolerance leads to systemic lupus erythematosus (SLE), while dysregulation of immune surveillance promotes tumorigenesis. Although the immune systems in these two diseases exhibit different functional characteristics, their underlying mechanisms share commonalities. Patients with SLE and cancer not only suffer from a heavy disease burden and poor prognosis, but their treatment outcomes and long-term survival are also closely related to their autoimmune status. Therefore, the shared pathogenic mechanisms and therapeutic targets of SLE and CRC have significant scientific and clinical value, providing new methods for early identification, clinical intervention, and the development of individualized treatment strategies. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a diagnostic biomarker, screening method, and application for simultaneous diagnosis of lupus erythematosus and colorectal cancer, which effectively solves the problems in the prior art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A diagnostic biomarker for both lupus erythematosus and colorectal cancer, wherein the diagnostic biomarker is a combination of the genes DNASE1L3, PTPN14, SELENBP1, and ECRG4.

[0008] Optionally,

[0009] The DNASE1L3 primer sequence

[0010] Forward:TGGTTGAGGTCTACACGGACGT;

[0011] Reverse:GTCAGTCCTCAAGCGGATGTTC.

[0012] Optionally,

[0013] The SELENBP1 primer sequence

[0014] Forward:TTGGAGATCCGCTTCCTGCACA;

[0015] Reverse:GGATCACCTTCTCCACTGACCA.

[0016] Optionally,

[0017] The ECRG4 primer sequence

[0018] Forward:CCAGCAGTTTCTCTACATGGGC;

[0019] Reverse:GCAGAGTCTTCATCATAGTGACG.

[0020] Optionally,

[0021] The PTPN14 primer sequence

[0022] Forward:AGTGTGGTGAGCACTACTCGGA;

[0023] Reverse:CTACACACGCTGCCATTGGTGA.

[0024] A screening method, wherein the method is the screening method for the simultaneous diagnostic markers of lupus erythematosus and colorectal cancer as described above, includes:

[0025] S1. Obtain tumor tissue and normal tissue data of colorectal cancer patients, sequencing data of systemic lupus erythematosus and normal samples from the database. The data includes clinical information and gene expression profile matrix.

[0026] S2. Analyze differentially expressed genes between tumor tissues and normal tissues of colorectal cancer patients, and between systemic lupus erythematosus and normal samples;

[0027] S3. Based on the cMAP database, analyze the association between common differentially expressed genes (DEGs) and small molecule therapeutic drugs;

[0028] S4. Based on univariate Cox regression, LASSO regression and SVM-RFE methods, four prognostic genes that contribute most to the diagnosis of colorectal cancer and systemic lupus erythematosus were screened.

[0029] S5 and four prognostic genes were uploaded to the NetworkAnalyst platform. An association network was constructed using an integrative interaction database to analyze the regulatory relationships between key genes and transcription factors and miRNAs.

[0030] S6. Based on the CIBERSORT algorithm, analyze the immune infiltration patterns of colorectal cancer and systemic lupus erythematosus datasets, and analyze the association between four prognostic genes and infiltrating immune cells based on Spearman correlation coefficient.

[0031] S7. Based on four prognostic genes, a nomogram model for diagnosing colorectal cancer and systemic lupus erythematosus was constructed, and the diagnostic efficacy was analyzed.

[0032] S8 and RT-qPCR were used to verify the expression levels of four prognostic genes in multiple pairs of colorectal cancer and adjacent normal tissues, as well as the expression levels in systemic lupus erythematosus and control group PBMCs.

[0033] Optionally, S2 includes using the pheatmap package to draw heatmaps to present the expression patterns of commonly differentially expressed genes in the two datasets, performing GO and KEGG enrichment analyses based on the DAVID database, constructing a protein-protein interaction network through the STRING database, and assessing potential functional associations between genes.

[0034] One application is the application of the above-mentioned diagnostic biomarkers for both lupus erythematosus and colorectal cancer, wherein the genes of the diagnostic biomarkers are used to construct a nomogram diagnostic model for colorectal cancer and a nomogram diagnostic model for lupus erythematosus.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. This invention constructs a specific biomarker combination consisting of DNASE1L3, PTPN14, SELENBP1 and ECRG4, which facilitates the simultaneous diagnosis of systemic lupus erythematosus and colorectal cancer, and improves the efficiency of early identification of the two diseases.

[0037] 2. A nomogram model for diagnosing systemic lupus erythematosus (SLE) and colorectal cancer, based on DNASE1L3, PTPN14, SELENBP1, and ECRG4, is helpful for early screening and prognostic assessment of SLE and colorectal cancer. Attached Figure Description

[0038] Figure 1 This is a flowchart of the screening process according to an embodiment of the present invention;

[0039] Figure 2 Volcano plot of differentially expressed genes in colorectal cancer and systemic lupus erythematosus datasets;

[0040] Figure 3 Venn diagram and protein interaction network diagram of common differentially expressed genes in colorectal cancer and systemic lupus erythematosus;

[0041] Figure 4 A diagram illustrating the expression patterns of differentially expressed genes shared by colorectal cancer and systemic lupus erythematosus;

[0042] Figure 5 Diagrams for GO biological process analysis, cellular component analysis, molecular function analysis, and KEGG pathway analysis;

[0043] Figure 6 This is a graph showing the association between commonly differentially expressed genes and small molecule therapeutic drugs.

[0044] Figure 7 Here is a structural diagram of a small molecule therapeutic drug;

[0045] Figure 8 Feature gene screening diagrams for univariate Cox regression, LASSO regression, and SVM-RFE algorithm;

[0046] Figure 9 Feature gene screening diagram and feature gene regulatory network analysis diagram for LASSO and SVM-RFE algorithms;

[0047] Figure 10 This is a graph assessing immune cell infiltration in the GSE39582 dataset.

[0048] Figure 11 A graph assessing immune cell infiltration from the GSE61635 dataset;

[0049] Figure 12 Nodal plot model for colorectal cancer diagnosis;

[0050] Figure 13 ROC curves, violin plots, DCA curves, and calibration curves for a nomogram model of colorectal cancer diagnosis;

[0051] Figure 14 A nomogram model for the diagnosis of systemic lupus erythematosus;

[0052] Figure 15 ROC curves, violin plots, DCA curves, and calibration curves for a nomogram model of systemic lupus erythematosus diagnosis;

[0053] Figure 16 This is a graph showing gene expression levels. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figure 1 This invention discloses a diagnostic biomarker for simultaneous diagnosis of lupus erythematosus and colorectal cancer, wherein the diagnostic biomarker is a combination of genes DNASE1L3, PTPN14, SELENBP1 and ECRG4.

[0056] For a better understanding of the simultaneous diagnostic marker for lupus erythematosus and colorectal cancer proposed in this application, please refer to [link to relevant documentation]. Figure 1 This application also discloses a screening method, including

[0057] S1. Obtain tumor tissue and normal tissue data of colorectal cancer patients, sequencing data of systemic lupus erythematosus and normal samples from the database. The data includes clinical information and gene expression profile matrix.

[0058] S2. Analyze differentially expressed genes between tumor tissues and normal tissues of colorectal cancer patients, and between systemic lupus erythematosus and normal samples;

[0059] S3. Based on the cMAP database, analyze the association between common differentially expressed genes (DEGs) and small molecule therapeutic drugs;

[0060] S4. Based on univariate Cox regression, LASSO regression and SVM-RFE methods, four prognostic genes that contribute most to the diagnosis of colorectal cancer and systemic lupus erythematosus were screened.

[0061] S5 and four prognostic genes were uploaded to the NetworkAnalyst platform. An association network was constructed using an integrative interaction database to analyze the regulatory relationships between key genes and transcription factors and miRNAs.

[0062] S6. Based on the CIBERSORT algorithm, analyze the immune infiltration patterns of colorectal cancer and systemic lupus erythematosus datasets, and analyze the association between four prognostic genes and infiltrating immune cells based on Spearman correlation coefficient.

[0063] S7. Based on four prognostic genes, a nomogram model for diagnosing colorectal cancer and systemic lupus erythematosus was constructed, and the diagnostic efficacy was analyzed.

[0064] S8 and RT-qPCR were used to verify the expression levels of four prognostic genes in multiple pairs of colorectal cancer and adjacent normal tissues, as well as the expression levels in systemic lupus erythematosus and control group PBMCs.

[0065] Experimental example:

[0066] RNA expression matrices from the GSE39582 dataset were downloaded from the GEO database. This dataset contains gene expression profiles from tumor samples of 566 colorectal cancer patients and 19 normal samples. Expression profiling was performed on the GPL570 platform, and data acquisition was completed using the "GEOquery" package. The resulting matrix is ​​a normalized matrix after log2 transformation. The GSE61635 dataset from peripheral blood mononuclear cells of systemic lupus erythematosus (SLE) patients was also downloaded from the GEO database. This dataset contains 99 SLE samples and 30 control samples. All data were processed using RStudio software.

[0067] Differentially expressed genes in the GSE39582 and GSE61635 datasets were screened using the "limma" package, and visualized using volcano plots. The screening criteria were set as |Log2(fold change)|>1 and a corrected p-value ≤0.05. Subsequently, Venn plots were used to display 58 commonly differentially expressed genes in the colorectal cancer and systemic lupus erythematosus datasets.

[0068] Please see Figure 2 The expression patterns of differentially expressed genes in the GSE39582 and GSE61635 datasets are shown. In the GSE39582 dataset, 1967 differentially expressed genes were identified in colorectal cancer tissues and control tissues, of which 1087 genes were upregulated and 880 genes were downregulated. In the GSE61635 dataset, 721 differentially expressed genes were found in peripheral blood mononuclear cells from systemic lupus erythematosus patients and healthy controls, including 474 upregulated genes and 247 downregulated genes.

[0069] To further explore the key genes shared by lupus and colorectal cancer, please refer to [link / reference needed]. Figure 3 Venn diagram analysis was used to identify 58 common differentially expressed genes, and protein-protein interaction networks were employed to reveal the interactions between the proteins encoded by these genes. Please refer to [link / reference]. Figure 4A heatmap was used to illustrate the expression patterns of differentially expressed genes in the GSE39582 and GSE61635 cohorts. Please refer to [link / reference]. Figure 5 GO biological process analysis showed that these common differentially expressed genes were mainly enriched in processes such as tissue regeneration, regulation of exogenous apoptosis signaling pathways under ligand-free conditions, and cell senescence. GO cell component analysis showed that differentially expressed genes were mainly located in structures such as tertiary granule compartments, RNA polymerase II transcriptional regulatory complexes, and membrane anchoring components. GO molecular function analysis indicated that differentially expressed genes were significantly enriched in ATPase-coupled intramembrane lipid transport activity, peroxidase activity, and peroxidase-associated oxidoreductase activity. KEGG pathway analysis showed that differentially expressed genes were mainly involved in signaling pathways such as glutathione metabolism, chemokine signaling pathways, and thyroid hormone synthesis.

[0070] Please see Figure 6 This study analyzed the association between commonly differentially expressed genes and small molecule therapeutics using the cMAP database system. Thirty-one upregulated differentially expressed genes were imported into the cMAP platform, and the top 10 compounds with the highest enrichment scores were screened. The chemical structures of these small molecule compounds were then obtained from the ChemSpider database and visualized using ChemDraw software. Ultimately, 10 candidate small molecule compounds were identified, including treprostacyclin, IKK-2 inhibitors, PRL-3 inhibitor-I, idebenone, aresstatin, GW-843682X, BRD-K15107389, AT-9283, pyrimethamine, and MAZ-51. These compounds primarily act on mechanisms such as phosphatase inhibition, VEGFR blockade, and calcium channel regulation. (See [link to relevant documentation]). Figure 7 The chemical structures of 10 candidate small molecule compounds are shown.

[0071] Please see Figure 8 This study employed three machine learning methods—univariate Cox regression, LASSO regression, and SVM-RFE algorithm—to screen for characteristic biomarkers of colorectal cancer and systemic lupus erythematosus (SLE). First, univariate Cox regression analysis was performed on differentially expressed genes using the "survival" package to screen for 15 prognostic-related genes. Then, a LASSO regression model was constructed using the "glmnet" package, and the optimal hyperparameter λ was determined through 5-fold cross-validation. The regression coefficients were compressed to zero to eliminate weakly correlated features, identifying 9 and 10 characteristic genes from the GSE39582 and GSE61635 datasets, respectively. Simultaneously, the SVM-RFE algorithm was executed using the "mlbench" and "caret" packages, iteratively eliminating the least important eigenvectors to complete feature selection, identifying 14 and 13 characteristic genes from the GSE39582 and GSE61635 datasets, respectively. (See also...) Figure 9Finally, the intersection of the LASSO and SVM-RFE algorithms in identifying characteristic genes in the two diseases was used to obtain four common characteristic genes: DNASE1L3, SELENBP1, ECRG4, and PTPN14, which were used for subsequent analysis. Network analysis based on the NetworkAnalyst database showed that these four characteristic genes are regulated by transcription factors such as SRF, YY1, and IRF2, as well as miRNAs such as hsa-mir-335-5p, hsa-mir-27a-3p, and hsa-mir-124-3p.

[0072] Please see Figure 10 and Figure 11 The CIBERSORT package was used to estimate the proportions of 22 types of immune cells in patient and control samples. The ggplot2 package was used to present the immune cell composition of each sample as a stacked bar chart, and box plots were used to show the differences in immune cell proportions between the two groups. The corrplot package was used to analyze the interactions between infiltrating immune cells, and the pheatmap package was used to create correlation heatmaps. Spearman correlation coefficients were used to analyze the associations between four characteristic genes and infiltrating immune cells. Immune infiltration analysis showed that lupus and colorectal cancer share a common characteristic: a decreased proportion of resting memory CD4⁺ T cells and resting mast cells, while neutrophil and M0 macrophage infiltration is increased.

[0073] Furthermore, the expression levels of the four genes are closely related to the infiltration of various immune cells, including T cells, macrophages, neutrophils, and mast cells. This shared immune microenvironment characteristic provides a cellular basis for the comorbidity mechanism of the two diseases.

[0074] Please see Figure 12 A nomogram diagnostic model for colorectal cancer was constructed using the "rms" package, based on four characteristic genes. Risk scores were calculated for each patient and control individual using this model, and the model's ability to distinguish between colorectal cancer patients and controls was evaluated using ROC, calibration curves, and DCA curves. Please refer to [link to relevant documentation]. Figure 13 The results showed that the nomogram model achieved an AUC of 0.999 for diagnosing colorectal cancer. Risk scores were calculated for each sample based on the nomogram model, and violin plots showed that the risk score in the SLE group was significantly higher than that in the control group. Furthermore, DCA assessed the clinical "net benefit" of the nomogram model at different threshold probabilities and compared it with "all treatment" or "no treatment" strategies. The calibration curves further demonstrated that the predicted probabilities of the nomogram model were highly consistent with the actual probabilities.

[0075] Please see Figure 14A lupus nomogram diagnostic model was constructed using the "rms" package, based on four characteristic genes. Risk scores were calculated for each lupus patient and control individual using this model, and the model's ability to distinguish between SLE and the control population was evaluated using ROC, calibration curves, and DCA curves. Please refer to [link to relevant documentation]. Figure 15 The sensitivity and specificity of the nomogram model in diagnosing SLE were evaluated using ROC curves. The results showed that the AUC value of the nomogram model for diagnosing SLE was 0.993. Risk scores for each sample were calculated based on the nomogram model, and violin plots showed that the risk score in the SLE group was significantly higher than that in the control group. Furthermore, DCA (Discretionary Clinical Assessment) evaluated the clinical "net benefit" of the nomogram model at different threshold probabilities and compared it with "all treatment" or "no treatment" strategies. The calibration curves further demonstrated that the predicted probabilities of the nomogram model were highly consistent with the actual probabilities.

[0076] PBMCs from systemic lupus erythematosus (SLE) patients and normal controls were collected, and RNA was extracted and reverse transcribed using a kit. Microarrays containing 15 pairs of colorectal cancer samples and their matched adjacent normal cDNAs were obtained. qPCR was performed using a kit, and the relative expression levels of mRNA were calculated using the 2-ΔΔCt method, with β-actin as an internal control for standardization.

[0077] Table 1 Primer sequence list

[0078]

[0079] The relative expression levels of mRNA were calculated using the 2-ΔΔCt method. The expression levels of four genes (GSE39582, GSE61635), colorectal cancer tissue microarrays, and lupus PBMCs) were analyzed. Please refer to [link to relevant documentation]. Figure 16 The results showed that, compared with the control group, the mRNA expression levels of DNASE1L3, SELENBP1, and ECRG4 in colorectal cancer samples from the GSE39582 dataset and colorectal cancer tissue microarray were significantly reduced, while the mRNA expression level of PTPN14 was significantly increased. Similarly, the expression levels of four genes in lupus samples and control samples from datasets GSE61635 and PBMC were consistent with the above.

[0080] In some feasible approaches, PBMCs from systemic lupus erythematosus and normal controls were obtained from the First Affiliated Hospital of Zhengzhou University, the reverse transcription and qPCR kits were from Nanjing Novivenzan R433, and the microarrays of colorectal cancer samples and their matched adjacent normal cDNAs were from Shanghai Chipchao Biotechnology Co., Ltd.

[0081] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A diagnostic marker for simultaneous diagnosis of lupus erythematosus and colorectal cancer, characterized in that: The diagnostic markers are a combination of the genes DNASE1L3, PTPN14, SELENBP1, and ECRG4.

2. The diagnostic marker for simultaneous diagnosis of lupus erythematosus and colorectal cancer according to claim 1, characterized in that: The DNASE1L3 primer sequence Forward:TGGTTGAGGTCTACACGGACGT; Reverse:GTCAGTCCTCAAGCGGATGTTC.

3. The diagnostic marker for simultaneous diagnosis of lupus erythematosus and colorectal cancer according to claim 1, characterized in that: The SELENBP1 primer sequence Forward:TTGGAGATCCGCTTCCTGCACA; Reverse:GGATCACCTTCTCCACTGACCA.

4. The diagnostic marker for simultaneous diagnosis of lupus erythematosus and colorectal cancer according to claim 1, characterized in that: The ECRG4 primer sequence Forward:CCAGCAGTTTCTCTACATGGGC; Reverse:GCAGAGTCTTCATCATAGTGACG.

5. The diagnostic marker for simultaneous diagnosis of lupus erythematosus and colorectal cancer according to claim 1, characterized in that: The PTPN14 primer sequence Forward:AGTGTGGTGAGCACTACTCGGA; Reverse:CTACACACGCTGCCATTGGTGA.

6. A screening method, wherein the method is the screening method for simultaneous diagnostic markers of lupus erythematosus and colorectal cancer as described in any one of claims 1-5, characterized in that, include: S1. Obtain tumor tissue and normal tissue data of colorectal cancer patients, sequencing data of systemic lupus erythematosus and normal samples from the database. The data includes clinical information and gene expression profile matrix. S2. Analyze differentially expressed genes between tumor tissues and normal tissues of colorectal cancer patients, and between systemic lupus erythematosus and normal samples; S3. Based on the cMAP database, analyze the association between common differentially expressed genes (DEGs) and small molecule therapeutic drugs; S4. Based on univariate Cox regression, LASSO regression and SVM-RFE methods, four prognostic genes that contribute most to the diagnosis of colorectal cancer and systemic lupus erythematosus were screened. S5 and four prognostic genes were uploaded to the NetworkAnalyst platform. An association network was constructed using an integrative interaction database to analyze the regulatory relationships between key genes and transcription factors and miRNAs. S6. Based on the CIBERSORT algorithm, analyze the immune infiltration patterns of colorectal cancer and systemic lupus erythematosus datasets, and analyze the association between four prognostic genes and infiltrating immune cells based on Spearman correlation coefficient. S7. Based on four prognostic genes, a nomogram model for diagnosing colorectal cancer and systemic lupus erythematosus was constructed, and the diagnostic efficacy was analyzed. S8 and RT-qPCR were used to verify the expression levels of four prognostic genes in multiple pairs of colorectal cancer and adjacent normal tissues, as well as the expression levels in systemic lupus erythematosus and control group PBMCs.

7. The screening method according to claim 3, characterized in that: S2 includes using the pheatmap package to draw heatmaps to show the expression patterns of commonly differentially expressed genes in the two datasets, performing GO and KEGG enrichment analysis based on the DAVID database, constructing a protein-protein interaction network through the STRING database, and evaluating potential functional associations between genes.

8. An application, wherein the application is the application of the simultaneous diagnostic marker for lupus erythematosus and colorectal cancer as described in any one of claims 1-5, characterized in that: The genes of the diagnostic biomarkers are used to construct nomogram diagnostic models for colorectal cancer and lupus.

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