A biomarker of renal cancer with venous tumor thrombus and application thereof

Abstract

The application provides a biomarker of renal cancer with venous cancer embolus and application thereof, and relates to the technical field of bioinformatics.The biomarker of renal cancer with venous cancer embolus comprises serum amyloid A.The biomarker can improve the treatment effect: by revealing the action mechanism of SAA in the treatment of renal cancer embolus, the application provides a basis for optimizing a neoadjuvant target immune combined treatment scheme, and improves the treatment effect.The biomarker can reduce treatment drug resistance: by regulating SAA and a related signal path, the application can enhance the sensitivity of tumor cells to target immune treatment, and reduce the occurrence of drug resistance.The biomarker has important clinical application value: the application provides a new biomarker and a treatment target for the clinical treatment of renal cancer embolus patients, and has important clinical application value.The detection of SAA can be used as an important index for evaluating treatment efficacy and predicting drug resistance, guiding clinical treatment decisions, and improving the treatment effect and life quality of patients.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics, and in particular to a biomarker for renal cell carcinoma with venous tumor thrombus and its application. Background Technology

[0002] Renal cell carcinoma with venous tumor thrombus presents with significant surgical challenges, high risks, and a high rate of perioperative complications and mortality. Currently, preoperative neoadjuvant targeted immunotherapy is considered an effective means to reduce tumor thrombus grade, surgical difficulty, and perioperative complications. However, some patients respond poorly to neoadjuvant therapy, limiting treatment efficacy. Therefore, exploring the specific mechanisms influencing treatment response, particularly key factors in the tumor microenvironment, is crucial for improving treatment outcomes and reducing drug resistance.

[0003] Although existing studies have explored the application of neoadjuvant targeted immunotherapy in renal cell carcinoma tumor thrombi, research on the interactions of various cell populations in the tumor microenvironment and their impact on treatment response is limited. In particular, the role of serum amyloid A (SAA) in the tumor microenvironment and its influence on targeted immunotherapy resistance remains insufficiently understood. Current research mainly focuses on the role of SAA as an acute-phase protein in the inflammatory response, while its specific functions in the tumor microenvironment and its interaction mechanisms with immune cells remain unclear. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a biomarker for renal cell carcinoma with venous tumor thrombus and its application. The technical solution is as follows:

[0005] A biomarker for renal cell carcinoma with venous tumor thrombus includes serum amyloid A.

[0006] Application of serum amyloid A as a biomarker in the preparation of products for early diagnosis, risk assessment, prognostic prediction and / or treatment selection of renal cell carcinoma with venous tumor thrombus.

[0007] Optionally, the early diagnosis, risk assessment, prognostic prediction, and / or treatment selection for renal cell carcinoma with venous tumor thrombus in the subject includes:

[0008] The serum amyloid A level in a sample from the subject was measured and compared with a reference value to enable early diagnosis, risk assessment, prognosis prediction, and / or selection of treatment options for the subject.

[0009] A product for early diagnosis, risk assessment, prognostic prediction, and / or treatment selection of renal cell carcinoma with venous tumor thrombus in subjects, the product comprising reagents, kits, and / or detection devices for detecting serum amyloid A levels in samples from subjects.

[0010] Optionally, the sample is derived from a blood sample of the subject.

[0011] Optionally, the kit includes:

[0012] (i) A reagent for detecting an effective amount of serum amyloid A in the sample;

[0013] (ii) Optionally, at least one substance selected from the group consisting of: container packaging, additives, solutions, buffer solutions, negative controls, positive controls, or instructions.

[0014] A system for early diagnosis, risk assessment, prognostic prediction, and / or treatment selection of renal cell carcinoma with venous tumor thrombus, the system comprising:

[0015] (1) A first device for collecting and / or receiving data on serum amyloid A levels in subject samples;

[0016] (2) A second device for analyzing the data to perform early diagnosis, risk assessment, prognosis prediction and / or treatment selection for the subject with renal cell carcinoma with venous tumor thrombus;

[0017] The first device includes the product described above;

[0018] And / or, the analysis includes comparing the serum amyloid A level in the sample with a reference value.

[0019] Application of serum amyloid A-specific antibody in the preparation of drugs for treating renal cell carcinoma with venous tumor thrombus.

[0020] A drug for treating renal cell carcinoma with venous tumor thrombus includes a serum amyloid A-specific antibody.

[0021] Optionally, it may also include axitinib and / or a PD1 antibody.

[0022] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0023] 1. Improved Treatment Efficacy: By elucidating the mechanism of action of SAAs in the treatment of renal cell carcinoma tumor thrombi, this study provides a basis for optimizing neoadjuvant targeted immunotherapy regimens and improving treatment outcomes. Clarifying the role of SAAs in the tumor microenvironment and their impact on treatment response helps in developing individualized treatment strategies, thereby improving patient response rates and survival rates.

[0024] 2. Reducing Treatment Resistance: Clarifying the role of SAAs in drug resistance will help develop new treatment strategies to reduce resistance. By regulating SAAs and their related signaling pathways, it may be possible to enhance the sensitivity of tumor cells to targeted immunotherapy and reduce the occurrence of resistance.

[0025] 3. Clinical Application Value: It provides new biomarkers and therapeutic targets for the clinical treatment of patients with renal cell carcinoma tumor thrombi, possessing significant clinical application value. SAA detection can serve as an important indicator for assessing treatment efficacy and predicting drug resistance, guiding clinical treatment decisions, and improving patient treatment outcomes and quality of life. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1A This is a graph showing the changes in the maximum diameter of the tumor (top) and the height of the tumor thrombus (bottom) before and after neoadjuvant targeted immunotherapy; Figure 1B This is a graph of cohort clinical baseline data; Figure 1C This is a diagram showing the overall cell clustering in the queue;

[0028] Figure 1D This is a diagram of typical genes from different cell groups; Figure 1E This is a graph comparing the differences in cell subset abundance before and after treatment based on the overall efficacy.

[0029] Figure 2A It is a UMAP diagram showing different tumor cell subsets; Figure 2B This is a diagram showing the marker genes (left) and transcription factor activities (right) of different tumor cell subpopulations; Figure 2C This is a diagram showing the pseudo-temporal analysis of tumor cell subpopulations using the Monole3 (top) and RNA Velocity (bottom) algorithms;

[0030] Figure 3A This is a forest plot showing the prognostic outcomes of different tumor cell subsets in the TCGA-KIRC cohort; Figure 3B This is a KM plot showing the prognostic results of tumor cell subsets with high and low SAA expression in the IMmotion150 cohort; Figure 3C This is a KM plot showing the prognostic results of tumor cell subsets with high and low SAA expression in the JAVELN101 cohort;

[0031] Figure 4A This is a UMAP diagram showing different subsets of neutrophils; Figure 4B This is a diagram of the marker genes of different neutrophil subsets; Figure 4C This is a diagram representing the key pathways of different neutrophil subsets;

[0032] Figure 4DThis is a graph showing the relative abundance of different neutrophil subsets in different treatment response groups; Figure 4E MMP9 is a subtype of neutrophils. + Cumulative distribution of feature scores (above) and VEGFA+SPP+(below). Figure 4F This is a diagram showing the pseudo-time series analysis of neutrophil subsets using the Monole3 (left) and RNA Velocity (right) algorithms;

[0033] Figure 5A This is a schematic diagram of the drug application strategy in a mouse orthotopic transplantation model of renal cell carcinoma (Renca cells = mouse renal cell carcinoma cells); Figure 5B These are representative images of tumors in a Renca orthotopic transplantation mouse model after drug treatment (n=5); Figure 5C This is a graph comparing the tumor weight of Renca orthotopic model mice after drug treatment using statistical analysis. Figure 5D This is a graph comparing the tumor volume of Renca orthotopic model mice after drug treatment using statistical analysis. Figure 5E This is a graph showing the statistical analysis and comparison of the body weight of Renca orthotopic model mice after drug treatment;

[0034] Figure 6 It uses the ELISA method to detect baseline (pre-treatment) plasma levels in patients undergoing targeted immunotherapy for renal cell carcinoma. Detailed Implementation

[0035] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0036] This invention aims to elucidate the role of serum amyloid A (SAA) in renal cell carcinoma tumor thrombi by in-depth research into its effect on drug resistance during neoadjuvant targeted immunotherapy, thereby providing new strategies for improving treatment efficacy and reducing drug resistance. Specific objectives include:

[0037] 1. To investigate the association between baseline serum amyloid A (SAA) levels and the response to neoadjuvant targeted immunotherapy to renal cell carcinoma tumor thrombus.

[0038] 2. To investigate the specific mechanism by which high expression of SAA in tumor cells may mediate poor drug response in some patients undergoing neoadjuvant targeted immunotherapy for renal cell carcinoma tumor thrombi.

[0039] 3. To verify the interaction between tumor cells that highly express SAA and immune cells and its impact on treatment resistance.

[0040] Example 1

[0041] Technical methods

[0042] Human subjects: This study has been approved by the Ethics Review Committee of the General Hospital of the Chinese People's Liberation Army (Approval No.: S2017-100-01). Informed consent for the collection of clinical data was given to all participating patients.

[0043] Single-cell RNA sequencing (scRNA-seq)

[0044] 1. Cell preparation

[0045] After collection, tissues were washed on ice with RPMI 1640 and dissociated using a multi-tissue dissociation kit (#130-110-203, Miltenyi, Germany). DNase treatment varied depending on the viscosity of the homogenate. After removing red blood cells (#130-094-183, Miltenyi, Germany), the tissues were analyzed using a fluorescence analyzer (…). Cell counts and viability were assessed using Rigel S2 and AO / PI assays. Dead and fragmented cells were optionally removed (#130-090-101 / #130-109-398, Miltenyi, Germany). Cells were washed in RPMI 1640 and then resuspended in 1×PBS + 0.04% BSA at a concentration of 1×10⁻⁶. 6 Cells / ml

[0046] 2. Library construction and sequencing

[0047] Single-cell RNA-Seq library usage Cells were prepared using the MM kit (#K00301-0401, SeekGene, China). Cells were loaded into 170,000-well microarrays and sedimented. Unsedimented cells were removed, and cell barcode beads (CBBs) were injected and sedimented in a magnetic field. Cells were lysed to release RNA, which was captured by CBBs and reverse transcribed at 37°C for 20 min. Exonuclease I treatment removed unused primers. Barcode cDNA was hybridized with random primers to form second-strand DNA. This DNA was denatured, purified, amplified, and cleaned. Sequencing adapters and sample indexes were added by indexed PCR. The library was cleaned using SPRI beads, quantified by quantitative PCR (KAPA Biosystems KK4824), and then sequenced at PE150 reads on an Illumina NovaSeq 6000.

[0048] 3. scRNA-seq data processing and clustering

[0049] Raw Fastq sequencing data from each sample were processed using SeekSoulTools software and aligned with the human hg38 reference genome to generate a comprehensive gene expression count matrix. Subsequently, the inventors performed basic quality control (QC) procedures using Seurat (version 4.1.0). Cell quality was assessed based on three key metrics: (1) total UMI count (library size) per cell between 300 and 30,000; (2) the number of genes detected between 100 and 6,000; and (3) the percentage of mitochondrial genes less than 50%. Notably, considering the low transcript count in neutrophils, the inventors adjusted the acceptable range for the number of genes detected in neutrophils to 100–6,000. Next, scDblFinder (version 1.8.0) was used for each sample to remove potential double cells. After quality control, a total of 184,972 cells from 30 human samples (18 patients) were retained for downstream analysis.

[0050] For each sample, the inventors used the SCTransform() function in Seurat to perform regularized negative binomial regression to standardize UMI counts while regressing the percentage of mitochondrial genes. Subsequently, principal component analysis (PCA) was performed on the scaled data for linear dimensionality reduction, preserving principal components. To avoid batch effects between samples and experiments, anchor-based data integration was applied to appropriate batches based on experimental dates. Next, the inventors used the Louvain algorithm for iterative clustering of cells with an initial resolution of 0.8. This process helped identify thirteen major cell clusters, which were then validated using known cell lineage-specific marker genes. The data was visualized using the UniformManifold Approximation and Projection (UMAP) method. The inventors further used the same method to identify subpopulations within each major cell cluster. To obtain different fine-grained clustering results, the inventors adjusted the resolution parameter of the Louvain algorithm to the range of 0.3 to 0.9 in each sub-clustering step. Therefore, by combining the top-ranked differentially expressed genes (DEGs) with previously reported biologically relevant genes, the identity of the resulting cell clusters was determined, ensuring comprehensive and accurate annotation.

[0051] 4. Identification and analysis of malignant cells

[0052] In samples from the same patient, single-cell CNVs were detected using the CopyKAT R package (version 1.1.0) by establishing a T-cell pool as a reference normal cell population. Subsequently, predicted aneuploid cells were classified as "malignant cells". Furthermore, the CNV score was defined as the sum of the absolute values ​​of the CNVs predicted by CopyKAT.

[0053] After SCTransform normalization (regressing mitochondrial read percentage and cell cycle fraction) and Harmony integration (grouping variables by sample), malignant cell subpopulations were subjected to unsupervised Seurat clustering. The inventors applied the "FindAllMarkers" function in Seurat to identify specific genes for each cell subpopulation, a process based on the "MAST" method, with sample identity as a latent variable. The final cluster annotations were derived from marker genes, gene regulatory modules, and enriched pathways. Furthermore, the inventors used the scMetabolism R package (version 0.2.1) and the KEGG database to quantify the activity of tumor cell metabolic pathways.

[0054] 5. Identification of marker genes

[0055] The inventors applied the "FindAllMarkers" function in Seurat to identify specific genes for each cell subpopulation, a process based on the "MAST" method, with sample identity as a latent variable. To select marker genes, the inventors chose the top 50 cell-specific marker genes based on their average expression levels.

[0056] 6. Gene set enrichment analysis

[0057] Based on scRNA-seq data, multiple gene signature scores were calculated. For each gene signature, a score was assigned to each cell using the "AddModuleScore_UCell" function in the UCell R package (version 1.3.1) with default settings. For batch RNA-seq data, the signature scores of the gene set in the sample were evaluated using the "ssGSEA" method in the GSVA R package (version 1.44.5) based on normalized gene expression.

[0058] Use the "fora" function in the fgsea R package (version 1.16.0) to perform an analysis of overrepresentation of the gene set.

[0059] The fgsea R package (version 1.20.0) is used to assess differential enrichment of gene sets between two cell subpopulations under two different conditions. For comparing a cell type under two conditions, the log2 FC value of all genes is calculated using the "FindMarkers" function in Seurat, which serves as the ranking metric.

[0060] Hallmark92, KEGG93, Reactome94, and the Gene Ontology (GO) gene set were obtained from the msigdbr R package (version 7.4.1) and the MsigDB website (http: / / software.broadinstitute.org / gsea / msigdb).

[0061] 7. Trajectory Analysis

[0062] Using the "differentialGeneTest" function, the inventors identified genes with q values ​​less than 0.05 for cell sorting in pseudo-time analysis. Once cell trajectories were established, the inventors applied Branch Expression Analysis Modeling (BEAM) to identify genes exhibiting differential expression between trajectory branches. For those genes that were significantly branch-dependent (adjusted p-value less than 0.05), the inventors used the "plot_genes_branched_heatmap" function to visualize their expression patterns in their pseudo-time.

[0063] The lineage developmental trajectories of tumor cells and neutrophils were analyzed using the R package Monocle3 (version 1.0.0). Count matrices containing gene expression profiles of tumor cells and neutrophils, along with UMAP embeddings obtained during sub-clustering, were integrated into a single "CellDataSet" object. Cells were then partitioned using the "cluster_cells" function, and the trajectory plots were inferred using the "learn_graph" function. The pseudo-time for each individual cell was calculated using the "order_cells" function.

[0064] 8. RNA rate analysis

[0065] To explore the possibility of cell transition between tumor cells and neutrophil subtypes, the inventors combined Velocyto (version 0.17.15) and Dynamo (version 1.4.0) to calculate and visualize the proportion of spliced / unspliced ​​transcripts in each tumor cell and neutrophil subtype, thereby inferring their respective maturation states. Briefly, the inventors first used Velocyto and the GENCODE GRCh38 genome annotation file to generate Loom files from the corresponding BAM files of all samples. Subsequently, the inventors employed Dynamo's standard workflow to calculate the RNA velocity value for each gene in each cell.

[0066] 9. Tissue enrichment analysis of cell subsets

[0067] To quantify the enrichment of cell types in various tissues, the inventors systematically compared the cell counts of the "resistant" and "responsive" groups within each cluster. This comparison was facilitated by calculating the Ro / e value.

[0068] Batch RNA sequencing and survival analysis

[0069] Survival analyses were performed in multiple patient cohorts receiving different treatments and endpoints, including the TCGAccRCC cohort, IMmotion150, and Javelin 101. For survival analyses of all batch RNA-seq samples based on scRNA-seq cell types, the inventors used the BayesPrism R package (version 2.0) to infer the cell type composition in the batch RNA-seq data. These compositions were then bisected according to the median value or the optimal cutoff point determined by the “surv_cutpoint” function in the survminer R package. Survival curves were generated using the Kaplan-Meier method, and p-values ​​were calculated using the log-rank test. A log-rank p-value less than 0.05 was considered statistically significant. Furthermore, hazard ratios (HRs) were derived from a univariate Cox proportional hazards model.

[0070] 10. Construction of an orthotopic mouse model of renal cell carcinoma and combination drug regimen

[0071] For the orthotopic renal cell carcinoma model, Renca cells (#CM-0568, Procell) (1×10⁶ cells per mouse) were used. 6( ) was suspended in 50 μl of Matrigel (#MG6248, LABLEAD) and implanted into the right kidney of 5-week-old BALB / c mice (Sinogene Co. Ltd., Beijing). Seven days later, the mice were randomly divided into four groups (n=5 per group): (1) intraperitoneal injection (ip) of IgG and oral perfusion (ig) of 1% CMC-Na (per mouse per day); (2) anti-Saa1 / 2 antibody (ip 5 μg / mouse, twice a week) and 1% CMC-Na (ig per mouse per day); (3) anti-PD1 antibody (ip 100 μg / mouse, every three days) and axitinib (ig 30 mg / kg / mouse, daily); (4) anti-Saa1 / 2 antibody (ip 5 μg / mouse, twice a week) plus anti-PD1 antibody (ip 100 μg / mouse, every three days) and axitinib (ig 30 mg / kg / mouse, daily). Anti-PD-1 antibody (#A2122, Selleck), IgG (#A2123, Selleck), and CMC-Na (#S6703, Selleck) were all purchased from Selleck. Anti-Saa1 / 2 was purchased from R&D Systems (#AF2948). On day 16, mice were sacrificed by carbon dioxide asphyxiation, and their body weight and kidney weight were recorded. Tumor volume = 6L*W*H / π. All procedures were performed according to the guidelines of the Animal Care and Use Committee of the General Hospital of the People's Liberation Army.

[0072] 11. Validation Experiment: Detection of Plasma SAA Levels

[0073] The SAA level in the plasma of patients undergoing targeted immunotherapy for renal cell carcinoma was detected using a human serum amyloid A (SAA) ELISA kit (JL10489, Jianglai Biotechnology). After plasma collection, samples were anticoagulated with EDTA and centrifuged at 1000×g for 15 minutes at 2-8℃ to separate the plasma. The plasma was then either tested or aliquoted and stored at -80℃. For testing, 100 μL of plasma sample and standards were added to each well of a pre-coated ELISA plate. Biotin-labeled antibody and enzyme conjugate were added sequentially. After incubation and washing, TMB substrate was used for color development. The absorbance (OD value) was measured at 450 nm. The SAA concentration in the sample was calculated using a standard curve to assess treatment-related inflammatory responses.

[0074] Results Analysis

[0075] Based on the applicant's joint, first domestic multicenter clinical trial of targeted immunotherapy combined with neoadjuvant therapy for tumor thrombus (No. ChiCTR2000030405), the inventors prospectively collected puncture samples from 9 cases before neoadjuvant therapy for renal cell carcinoma tumor thrombus and surgical samples from 13 cases after treatment, and performed single-cell transcriptome sequencing. Results on the maximum tumor diameter and tumor thrombus length before and after treatment are shown in [reference needed]. Figure 1A Significant changes were observed in the treatment response group, while no significant differences were observed in the treatment resistance group. The samples included in the analysis and their corresponding clinicopathological features are shown below. Figure 1B A total of 184,972 cells were included in the analysis after quality control. Results are shown in [link to analysis]. Figure 1C Cells were divided into 13 subpopulations based on hypervariable genes and classic markers. See the results below. Figure 1D Furthermore, by comparing the overall response with significant differences in cell subpopulations before and after treatment, the inventors found that neutrophils were significantly enriched in the drug-resistant group regardless of whether treatment was initiated or completed. (See attached results.) Figure 1E .

[0076] Given the crucial role of tumor cells in drug resistance and the immunosuppressive microenvironment, an unsupervised clustering algorithm was first used to subdivide the tumor cell population into 11 subpopulations, named Tumor_01 to Tumor_11. Figure 2A Annotation depends on marker genes (see results). Figure 2B (Left) Gene regulatory modules defined for each cluster using SCENIC (see results) Figure 2B (Right) The inventors identified 11 renal tumor cell states, including Tumor_01_Stress (HSP90AA1), Tumor_02_IGFBP5 (IGFBP5), Tumor_03_SERPINE1 (SERPINE1), Tumor_04_LDHA (LDHA, TPI1, PKM), Tumor_05_SAA1 (SAA1, SAA2), Tumor_06_LOX_MT1X (LOX, MT1X), Tumor_07_CCL2 (CCL2), Tumor_08_MHCII (HLA-DRA), Tumor_09_MMP7 (MMP7), Tumor_10_NAT8 (NAT8), and Tumor_11_NLGN1 (NLGN1). The inventors discovered significant intratumoral heterogeneity in tumor cell subpopulations. To further characterize tumor evolutionary trajectories, they performed pseudo-temporal analysis using Monole3 and RNA Velocity algorithms. The analysis revealed that the tumor cell subpopulation exhibits a typical differentiation trajectory from its initial state of Tumor_10_NAT8. (See attached results). Figure 2CThat is, LOX+MT1X+Tumor can be converted to Tumor_03_SERPINE1. It is worth noting that SAA+Tumor can also be converted to Tumor_03_SERPINE1 and also to Tumor_07_CCL2.

[0077] Different tumor subpopulations may be associated with different prognoses and treatment responses. The inventors analyzed the TCGA-KIRC cohort using a deconvolution algorithm and found that patients with renal cell carcinoma whose tumor cell subpopulation highly expresses SAA have a worse prognosis. (See attached results). Figure 3A SAA was consistently observed in renal cell carcinoma cohorts treated with combination TKIs and anti-PD1 therapy (IMmotion150 and JAVELIN_Renal_101). + High tumor cell abundance was associated with poorer progression-free survival; see results below. Figure 3C In summary, the inventors have discovered that tumor cells within the primary lesion and the tumor embolus exhibit significant heterogeneity, with different populations displaying unique progression patterns and differentiation trajectories. A subpopulation of tumor cells highly expressing SAA may be associated with treatment response.

[0078] Recent studies have shown a close relationship between neutrophils and resistance to tumor drug therapy. The inventors observed an increased proportion of neutrophils in the drug-resistant group; the results are detailed below. Figure 1E This prompted the inventors to further analyze their composition and function. Through unsupervised clustering (see results...), Figure 4A Combining classic symbols (see results) Figure 4B The inventors identified seven distinct neutrophil subsets: Neu_01_CXCR2 (CXCR2), Neu_02_IL1B_CXCL8 (IL1B, CXCL8), Neu_03_Stress (HSP90AA1, HSPA1A), Neu_04_CCL4L2 (CCL4L2, CCL3L1), Neu_05_MMP9 (MMP9, S100A9), Neu_06_LDHA (LDHA, TPI1), and Neu_07_CD74 (CD74). Further pathway enrichment analysis was performed on these seven neutrophil subsets using the GO, KEGG, HALLMARK, and REACTOME datasets to characterize the specific functions of each subset. The results are detailed below. Figure 4C To further characterize the evolutionary trajectory of neutrophil immunosuppression, the inventors performed pseudo-temporal analysis using Monole3 and RNA Velocity algorithms. The inventors determined Neu_05_MMP9 as the initiating cell and Neu_06_LDHA as the terminal differentiation cell. The results are detailed below. Figure 4EFurthermore, the neutrophil subset exhibits a typical differentiation trajectory from its initial Neu_5_MMP9 state, namely, transformation into Neu_06_LDHA. (See the results below.) Figure 4F Interestingly, the Neu_06_LDHA subgroup was primarily found in patients in the NR group; see the results below. Figure 4D This study revealed pathways including enhanced glycolysis, regulation of epithelial cell migration, and negative regulation of lymphocyte activation. (See attached results). Figure 4C .

[0079] Based on these insights, the inventors hypothesize that specific inhibition of SAA in the renal cell carcinoma tumor microenvironment may enhance the efficacy of targeted combined immunotherapy for renal cell carcinoma. To verify this, the inventors established an orthotopic mouse renal cell carcinoma model and used an SAA-specific antibody in combination with axitinib and / or a PD1 antibody. For details of the drug administration model, please refer to [link to specific drug administration model]. Figure 5A Using SAA mouse monoclonal antibody combined with targeted immunotherapy drugs, the inventors found that SAA can enhance the effect of targeted immunotherapy. The tumor reduction effect of the three-drug combination was much greater than that of the two-drug combination of targeted immunotherapy. See the results below. Figures 5B to 5D Furthermore, no significant difference in body weight was observed among the various treatment groups, indicating that the combined medication had no adverse side effects. See the results below. Figure 5E .

[0080] This invention proposes serum amyloid A (SAA) as a potential biomarker in targeted-immunotherapy for renal cell carcinoma. Based on the results in Figures 1 to 5, the inventors hypothesize that tumor cells in treatment-resistant patients can secrete large amounts of SAA into the bloodstream. Therefore, detecting SAA expression levels before treatment may reflect the patient's therapeutic effect at an early stage. To verify this hypothesis, the inventors prospectively collected plasma samples from 19 patients with advanced renal cell carcinoma who received targeted-immunotherapy (9 in the treatment-resistant group and 10 in the treatment-sensitive group), and used enzyme-linked immunosorbent assay (ELISA) to quantitatively detect the SAA concentration in the patients' plasma before treatment. The results of this verification experiment showed that the plasma SAA concentration in the treatment-resistant group was significantly higher than that in the treatment-sensitive group (P<0.05). For specific data, please refer to [link to relevant data]. Figure 6 Experimental results showed a significant association between SAA concentration and treatment efficacy; that is, high pre-treatment plasma SAA levels indicated potential treatment resistance, while low levels suggested a possible response. This suggests that SAA could serve as a biomarker in targeted immunotherapy for renal cell carcinoma, used for early prediction of patient response.

[0081] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. Application of serum amyloid A-specific antibody in the preparation of drugs for treating renal cell carcinoma with venous tumor thrombus.

2. The application according to claim 1, characterized in that, The drugs mentioned for treating renal cell carcinoma with venous tumor thrombus also include axitinib and PD1 antibody.

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