Progressive kidney cancer automatic scoring and classifying method based on non-DCCD subtype marker

Abstract

The invention discloses an automatic grading and classifying method for progressive kidney cancer based on a non-DCCD subtype marker, which comprises the following steps: taking an ACSM2A gene, a GATM gene and an LRP2 gene as marker genes for expressing renal clear cell carcinoma tissue samples, and performing DAB color development, nuclear staining, dehydration mounting and image scanning operation on each renal clear cell carcinoma tissue sample to obtain an immunohistochemical staining image; obtaining the staining intensity score of the immunohistochemical staining image of each marker gene antibody corresponding to each renal clear cell carcinoma tissue sample, and identifying the renal clear cell carcinoma tissue sample; performing image segmentation and cell counting on the immunohistochemical staining image to obtain a positive cell proportion score corresponding to each renal clear cell carcinoma tissue sample; classifying the renal clear cell carcinoma tissue sample as a progressive renal clear cell carcinoma patient or a non-progressive renal clear cell carcinoma based on the non-DCCD score; according to the invention, rapid and accurate screening of progressive renal clear cell carcinoma patients is realized, the realization mode is simple, and the cost is low.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics, and in particular to an automatic scoring and classification method for progressive renal cancer based on non-DCCD subtype markers. Background Art

[0002] As one of the most common malignancies of the urinary system, kidney cancer (RCC) has a high mortality rate. The most common pathological type of RCC is clear cell renal cell carcinoma (ccRCC), characterized by prominent abnormal lipid accumulation. Most patients are initially diagnosed at an early stage. In clinical practice, the standard treatment for early-stage ccRCC is surgical resection followed by passive observation. However, approximately one-third of surgically treated patients experience disease progression, such as recurrence or metastasis, resulting in a 5-year survival rate of less than 15%. Early-stage RCC patients account for 80% of those initially diagnosed. Although complete surgical resection is the preferred treatment for early-stage localized RCC, approximately one-third of patients experience disease progression, such as recurrence or metastasis, after surgery, resulting in a severely poor prognosis. This suggests that the current TNM staging system, while prioritizing tumor size, overlooks the differentiation characteristics of the tumor cells themselves, leading to disease progression in patients with early, clinically advanced disease during passive surveillance.

[0003] Invention patent CN117238369A has disclosed the detection of the relative expression levels of IM2-related genes and IM4-related genes in renal clear cell carcinoma tissue samples from renal cancer patients using sequencing, nucleic acid membrane strips, chips, or kit methods. Based on this, combined with the ssGSEA algorithm, the ssGSEA scores of the IM2- and IM4-related gene sets were calculated for all renal clear cell carcinoma tissue samples, namely IM2_ssGSEA and IM4_ssGSEA, respectively. When IM4_ssGSEA-IM2_ssGSEA>-0.2, the patient from which the sample was derived was defined as a "de-clear cell differentiation patient" and recorded as the IM4 type. When IM4_ssGSEA-IM2_ssGSEA≤-0.2, the patient from which the sample was derived was defined as a "non-de-clear cell differentiation patient" and recorded as the IM2 type. Among them, patients with the IM4 subtype have very little lipid accumulation but a significantly worse prognosis, while patients with the IM2 subtype have significant lipid accumulation but a good prognosis.

[0004] Although sequencing technology can achieve accurate classification of patients with renal clear cell carcinoma, its high price and poor timeliness limit its promotion and application. In addition, not all IM2 type patients do not have patients with advanced renal clear cell carcinoma. Therefore, it is necessary to further identify advanced renal clear cell carcinoma and non-advanced renal clear cell carcinoma in IM2 type renal cancer patients in order to prevent and treat advanced renal clear cell carcinoma. Summary of the Invention

[0005] The purpose of the present invention is to provide an automatic scoring and classification method for progressive renal cancer based on non-DCCD subtype markers to solve the technical problem that sequencing technology in the existing technology can achieve accurate typing of patients with renal clear cell carcinoma, but its high price and poor timeliness limit its promotion and application, and not all IM2 type patients do not have patients with progressive renal clear cell carcinoma.

[0006] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0007] An automatic scoring and classification method for advanced renal cancer based on non-DCCD subtype markers comprises the following steps:

[0008] Step 100: ACSM2A, GATM, and LRP2 genes are used as marker genes for renal clear cell carcinoma tissue samples expressing the non-de-clear cell differentiation subtype, and each renal clear cell carcinoma tissue sample is subjected to DAB color development, nuclear staining, dehydration, mounting, and image scanning to obtain an immunohistochemical staining image;

[0009] Step 200: Obtain staining intensity scores of immunohistochemical staining images corresponding to ACSM2A, GATM, and LRP2 antibody immunostaining for each renal clear cell carcinoma tissue sample;

[0010] Step 300: performing image segmentation and cell counting on the immunohistochemical staining image corresponding to each renal clear cell carcinoma tissue sample to obtain a positive cell ratio score corresponding to each renal clear cell carcinoma tissue sample;

[0011] Step 400: Multiply the staining intensity score and the positive cell proportion score of each clear cell renal cell carcinoma tissue sample as the IRS score of the ACSM2A, GATM, and LRP2 markers corresponding to each clear cell renal cell carcinoma tissue sample; take the sum of the IRS scores of the above markers corresponding to each clear cell renal cell carcinoma tissue sample as the non-DCCD score; and classify the clear cell renal cell carcinoma tissue sample as an advanced clear cell renal cell carcinoma patient sample or a non-advanced clear cell renal cell carcinoma sample based on the non-DCCD score.

[0012] As a preferred embodiment of the present invention, in step 100, the non-declear cell differentiation subtype renal clear cell carcinoma gene set includes SLC10A2, CLVS2, KL, AVPR1B, NCR3LG1, SLC5A12, SLC5A10, MGAM, SLC13A1, FBXL16, PANK1, TINAG, TRHDE, SERPINA6, MIOX, CHRM3, GAREM1, WDR72, LARGE2, CDHR2, ZDHHC11B, VIL1, KLHDC7A, TRPM3, PTGER3, SMTNL2, TMEM72, TMEM27, ADSSL1, A1CF, TMEM1 74, TMEM125, TMEM38B, HSD11B2, RGS5, STK32B, TRIM15, TRIM10, SLC17A1, SLC17A3, SLC17A4, ERICH5, SLC16A9, CREB3L3, EDNRB, NAT8, CYP2J2, HHLA2, A CSM2A, ACSM2B, KCNJ3, PKHD1, PAIP2B, EMCN, EMX2, SLC47A1, ENTPD2, ENAM, ACE2, ADH6, PTH1R, PTH2R, LRRC19, ESPN, NPR3, RAB3IP, KCNJ15, ALPL, CYP4A 22, CYP4A11, COL23A1, FRAS1, CABP1, AOC1, COL25A1, SCGN, CERKL, APOM, FREM2, AQP7, AQP1, MYRFL, SLCO4C1, ASPG, ASPA, EHHADH, FBP1, FRMD3, GRIK3, SLC3A1, SLC51A, SLC5A8, SLC5A1, CSDC2, GIPC2, SLC6A3, SLC22A12, SLC22A11, SLC22A24, DIRAS2, FMO1, AMN, ABCG2, SLC2A2, STUM, LGALS2, CA4, CGN, CL EC18C, CLEC18B, CLEC18A, DDC, BDNF, SLC16A12, ANKS4B, SYT9, GHR, SLC39A5, FTCD, BHMT, G6PC, FUT6, KHK, SPON1, DOC2A, SMIM24, GABRB3, HMGCS2, PLG, SCN4B, BBOX1, GATM, GDF6, PDGFD, LIN7A, TLL1, ANXA13, PCK1, GJB1, AGMAT, TPPP, ZNF711, ACADL, ZNF385B, BTNL9, PHYHIPL, IQSEC3, CD36, AGTR1, PKLR,CES3, FAM196B, SLC22A6, SLC22A2, GSTA1, GLYATL1, C1orf210, ALDOB, CIB4, SLC27A2, AGXT2, SLC26A9, PDZK1, SLC6A19, SLC6A18, SLC6A13, SLC6A12, CMBL, S LC25A48, ATP2B2, SULT1C4, CLDN10, HAO2, CLCN5, CUBN, PEG10, LRP2, CYS1, ALDH6A1, UPB1, MASP1, FCAMR, ALDH1L1, ADCY5, GPAT3, SLC28A1, HRH2, MAP7, MAPT. ,

[0013] As a preferred embodiment of the present invention, in step 100, the method for obtaining the immunohistochemical staining image corresponding to each renal clear cell carcinoma tissue sample is as follows:

[0014] Each of the renal clear cell carcinoma tissue samples is wax-solidified, and the renal clear cell carcinoma tissue samples are serially sectioned;

[0015] Antigen retrieval was performed on the sections, and endogenous peroxidase activity of the sections was blocked;

[0016] All sections of renal clear cell carcinoma tissue samples were incubated with anti-ACSM2A primary antibody, anti-GATM primary antibody, and anti-LRP2 primary antibody respectively;

[0017] Each renal clear cell carcinoma tissue sample after primary antibody incubation was performed using HRP-conjugated polymer secondary antibody;

[0018] After incubation with the secondary antibody, the sections of each renal clear cell carcinoma tissue sample were subjected to DAB color development, nuclear staining, dehydration, sealing, and scanning to obtain immunohistochemical staining images.

[0019] As a preferred embodiment of the present invention, in step 200, the staining intensity scores of the immunohistochemical staining images corresponding to the multiple sections of each renal clear cell carcinoma tissue sample are obtained using Image J or Fiji software, and the average of the staining intensity scores of the multiple sections of each renal clear cell carcinoma tissue sample is used as the staining intensity score of the renal clear cell carcinoma tissue sample;

[0020] The staining intensity score range is set to 0-3, with the staining intensity score of no positive staining being set to 0, the staining intensity score of light yellow being set to 1, the staining intensity score of brownish yellow being set to 2, and the staining intensity score of tan being set to 3.

[0021] As a preferred embodiment of the present invention, in step 300, the immunohistochemical staining image of each renal clear cell carcinoma tissue sample is subjected to machine learning using Image J or Fiji software;

[0022] The positive cell percentages in the immunohistochemical staining images corresponding to the renal clear cell carcinoma tissue samples were calculated respectively, and the positive cell percentage scores corresponding to multiple sections of each renal clear cell carcinoma tissue sample were obtained. The average of the positive cell percentage scores of multiple sections of each renal clear cell carcinoma tissue sample was used as the positive cell percentage score of the corresponding indicator staining of the renal clear cell carcinoma tissue sample.

[0023] As a preferred embodiment of the present invention, when the positive cell ratio is 0-5%, the positive cell ratio score is 0;

[0024] When the positive cell ratio is 6%-25%, the positive cell ratio score is 1;

[0025] When the positive cell percentage is 26%-50%, the positive cell percentage score is 2;

[0026] When the positive cell percentage is 51%-75%, the positive cell percentage score is 3;

[0027] When the positive cell ratio was greater than 75%, the positive cell ratio score was 4.

[0028] As a preferred embodiment of the present invention, in step 400, the IRS scores of the immunohistochemical images corresponding to the ACSM2A, GATM, and LRP2I antibody staining in each renal clear cell carcinoma tissue sample are calculated respectively;

[0029] The sum of the IRS scores of all immunohistochemical images corresponding to ACSM2A, GATM, and LRP2 antibody staining in each renal clear cell carcinoma tissue sample was calculated, i.e., the non-DCCD score;

[0030] Based on the numerical range of the non-DCCD score, renal clear cell carcinoma tissue samples were classified as samples of patients with advanced renal clear cell carcinoma or samples of non-advanced renal clear cell carcinoma.

[0031] As a preferred embodiment of the present invention, renal clear cell carcinoma tissue samples with a non-DCCD score value range lower than 5 (2, 12) are used as samples of patients with advanced renal clear cell carcinoma;

[0032] The RCC tissue samples with non-DCCD score values ​​higher than 16 (12, 22) were considered as non-progressive RCC patient samples.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention performs a series of immunohistochemical marker staining on renal clear cell carcinoma tissue samples to be identified and classified (divided into non-progressive or progressive), determines a staining intensity score based on the color of the immunohistochemical marker staining image corresponding to the non-DCCD subtype of each renal clear cell carcinoma tissue sample, and determines a positive cell proportion score based on the positive cell proportion of the immunohistochemical marker staining image corresponding to each renal clear cell carcinoma tissue sample, uses the sum of the IRS score consisting of the staining intensity score and the positive cell proportion score as the DCCD score, and uses the DCCD score as a standard for screening progressive renal clear cell carcinoma, thereby achieving rapid and accurate screening of progressive renal clear cell carcinoma patients among non-DCCD subtype patients, and the implementation method is simple and low-cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0036] Figure 1 A schematic diagram of a process for an automatic scoring and classification method for progressive renal cancer provided by an embodiment of the present invention;

[0037] Figure 2 Immunohistochemical staining of a renal clear cell carcinoma tissue sample for non-DCCD subtype markers provided in an embodiment of the present invention;

[0038] Figure 3 Schematic diagram of processing immunohistochemical staining images when calculating staining intensity provided by an embodiment of the present invention

[0039] Figure 4 A schematic diagram of processing an immunohistochemical staining image when calculating positive cells provided by an embodiment of the present invention;

[0040] Figure 5 This is a statistical graph of IRS scores of immunohistochemical markers of non-DCCD subgroups according to an embodiment of the present invention; DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, the present invention provides an automatic scoring and classification method for progressive renal cell carcinoma based on non-DCCD subtype markers. The present invention aims to disclose a method for screening potential progressive renal clear cell carcinoma, achieve early screening of corresponding patients, and provide a theoretical basis for clinical stratified diagnosis and treatment of renal clear cell carcinoma and personalized precision treatment of patients, comprising the following steps:

[0043] Step 100: ACSM2A, GATM, and LRP2 genes are used as marker genes for renal clear cell carcinoma tissue samples expressing non-declear cell differentiation (non-DCCD) subtypes, and each renal clear cell carcinoma tissue sample is subjected to DAB development, nuclear staining, dehydration, mounting, and image scanning to obtain an immunohistochemical staining image;

[0044] Step 200: Obtain staining intensity scores of immunohistochemical staining images corresponding to ACSM2A, GATM, and LRP2 antibody immunostaining for each renal clear cell carcinoma tissue sample;

[0045] Step 300: performing image segmentation and cell counting on the immunohistochemical staining image corresponding to each renal clear cell carcinoma tissue sample to obtain a positive cell ratio score corresponding to each renal clear cell carcinoma tissue sample;

[0046] Step 400: Multiply the staining intensity score and the positive cell proportion score of each clear cell renal cell carcinoma tissue sample as the IRS score of the ACSM2A, GATM, and LRP2 markers corresponding to each clear cell renal cell carcinoma tissue sample; take the sum of the IRS scores of the above markers corresponding to each clear cell renal cell carcinoma tissue sample as the non-DCCD score; and classify the clear cell renal cell carcinoma tissue sample as an advanced clear cell renal cell carcinoma patient sample or a non-advanced clear cell renal cell carcinoma sample based on the non-DCCD score.

[0047] Invention patent CN117238369A has disclosed the use of sequencing, nucleic acid membrane strips, chips, or kits to detect the relative expression levels of IM2-related genes and IM4-related genes in renal clear cell carcinoma tissue samples from renal cancer patients, thereby classifying renal clear cell carcinoma into type IM2 (patients with obvious lipid droplet accumulation, i.e., clear cell differentiation) and type IM4 (patients with very few lipid droplets, i.e., de-clear cell differentiation). Type IM4 represents patients at high risk of early postoperative tumor recurrence and progression.

[0048] That is to say, invention patent CN117238369A is mainly used to identify IM4 type "de-clear cell differentiation patients" among renal cancer patients. However, it is necessary to further identify the progressive ccRCC and non-progressive ccRCC among IM4 type patients and IM2 type patients, so as to screen patients with progressive clear cell renal cell carcinoma early and prevent it in advance. Although IM2 type patients have a significantly better prognosis, it does not mean that there are no patients with progressive clear cell renal cell carcinoma among IM2 type patients. Therefore, it is necessary to conduct secondary identification and classification of IM2 type patients to further identify patients with progressive clear cell renal cell carcinoma among IM2 type patients.

[0049] This embodiment performs a series of immunohistochemical marker staining on renal clear cell carcinoma tissue samples to be identified and classified (divided into non-progressive or progressive), determines a staining intensity score based on the color of the immunohistochemical marker staining image corresponding to the non-DCCD subtype of each renal clear cell carcinoma tissue sample, and determines a positive cell proportion score based on the proportion of positive cells in the immunohistochemical marker staining image corresponding to each renal clear cell carcinoma tissue sample, uses the sum of the IRS score consisting of the staining intensity score and the positive cell proportion score as the DCCD score, and uses the DCCD score as a standard for screening progressive renal clear cell carcinoma, thereby achieving rapid and accurate screening of progressive renal clear cell carcinoma patients among non-DCCD subtype patients, and the implementation method is simple and low-cost.

[0050] The gene set expressed in non-declear cell differentiated (non-DCCD) subtype renal clear cell carcinoma tissue samples included SLC10A2, CLVS2, KL, AVPR1B, NCR3LG1, SLC5A12, SLC5A10, MGAM, SLC13A1, FBXL16, PANK1, TINAG, TRHDE, SERPINA6, MIOX, CHRM3, GAREM1, WDR72, LARGE2, CDHR2, ZDHHC11B, VIL1, KLHDC7A, TRPM3, PTGER3, SMTNL2, TMEM72, TMEM27, ADSSL1, A1CF, TMEM174, and TMEM1. 25, TMEM38B, HSD11B2, RGS5, STK32B, TRIM15, TRIM10, SLC17A1, SLC17A3, SLC17A4, ERICH5, SLC16A9, CREB3L3, EDNRB, NAT8, CYP2J2, HHLA2, ACSM2A, A CSM2B, KCNJ3, PKHD1, PAIP2B, EMCN, EMX2, SLC47A1, ENTPD2, ENAM, ACE2, ADH6, PTH1R, PTH2R, LRRC19, ESPN, NPR3, RAB3IP, KCNJ15, ALPL, CYP4A22, CYP4 A11, COL23A1, FRAS1, CABP1, AOC1, COL25A1, SCGN, CERKL, APOM, FREM2, AQP7, AQP1, MYRFL, SLCO4C1, ASPG, ASPA, EHHADH, FBP1, FRMD3, GRIK3, SLC3A1, SLC51A, SLC5A8, SLC5A1, CSDC2, GIPC2, SLC6A3, SLC22A12, SLC22A11, SLC22A24, DIRAS2, FMO1, AMN, ABCG2, SLC2A2, STUM, LGALS2, CA4, CGN, CLEC18C, CLEC18B, CLEC18A, DDC, BDNF, SLC16A12, ANKS4B, SYT9, GHR, SLC39A5, FTCD, BHMT, G6PC, FUT6, KHK, SPON1, DOC2A, SMIM24, GABRB3, HMGCS2, PLG, SCN4B , BBOX1, GATM, GDF6, PDGFD, LIN7A, TLL1, ANXA13, PCK1, GJB1, AGMAT, TPPP, ZNF711, ACADL, ZNF385B, BTNL9, PHYHIPL, IQSEC3, CD36, AGTR1, PKLR, CES3,FAM196B, SLC22A6, SLC22A2, GSTA1, GLYATL1, C1orf210, ALDOB, CIB4, SLC27A2, AGXT2, SLC26A9, PDZK1, SLC6A19, SLC6A18, SLC6A13, SLC6A12, CMBL, SLC2 5A48, ATP2B2, SULT1C4, CLDN10, HAO2, CLCN5, CUBN, PEG10, LRP2, CYS1, ALDH6A1, UPB1, MASP1, FCAMR, ALDH1L1, ADCY5, GPAT3, SLC28A1, HRH2, MAP7, MAPT. ,

[0051] This embodiment specifically uses non-DCCD subtype markers of ACSM2A, GATM, and LRP2 as the basis for identifying advanced clear cell renal cell carcinoma. All clear cell renal cell carcinoma tissue samples are incubated with anti-ACSM2A (1:250, Abcam, ab181204), anti-GATM (1:150, Abcam, ab119269), and anti-LRP2 (1:5000, Abcam, ab309086) primary antibodies. The sample images are scanned to form immunohistochemical staining images. Among them, the expression of ACSM2A, GATM, and LRP2 in advanced and non-advanced ccRCC is significantly different. Based on the staining intensity score and positive cell proportion score of the immunohistochemical staining image of each indicator, a non-DCCD score can be obtained to further identify advanced ccRCC patient samples.

[0052] Therefore, the method for obtaining the immunohistochemical staining image corresponding to each renal clear cell carcinoma tissue sample in this embodiment is as follows:

[0053] Each renal clear cell carcinoma tissue sample was wax-cured and serially sectioned. Specifically, the renal clear cell carcinoma tissue sample was fixed in 4% paraformaldehyde for 24-48 hours, dehydrated using gradient ethanol (50%-100% ethanol, 30 minutes per level), and cleared twice in xylene (60 minutes each). After wax immersion, the sample was infiltrated in 60°C paraffin wax in stages (low melting point wax → high melting point wax) for a total of approximately 6-8 hours. After embedding using a fully automatic embedding machine, the sample was serially sectioned to a thickness of 3-4 μm. After baking and other anti-slicing treatments, immunohistochemical staining was performed.

[0054] The sections were subjected to antigen repair and endogenous peroxidase activity was blocked. Specifically, after dewaxing with xylene and gradient ethanol, EDTA alkaline repair solution (pH 9.0) was used for antigen repair, and 3% H2O2 was used to block endogenous peroxidase activity.

[0055] Sections of each renal clear cell carcinoma tissue sample were incubated with anti-ACSM2A, anti-GATM, and anti-LRP2 primary antibodies, respectively, at 4°C. Anti-ACSM2A (1:250, Abcam, ab181204), anti-GATM (1:150, Abcam, ab119269), and anti-LRP2 (1:5000, Abcam, ab309086) were used in this study. Other brands of antibodies can be tested and standards established in actual testing.

[0056] Each renal clear cell carcinoma tissue sample after primary antibody incubation was performed using HRP-conjugated polymer secondary antibody;

[0057] After incubation with the secondary antibody, the sections of each renal clear cell carcinoma tissue sample were subjected to DAB color development, nuclear staining, dehydration, sealing, and scanning to obtain immunohistochemical staining images.

[0058] In order to identify samples of patients with advanced or non-advanced renal clear cell carcinoma among renal cancer patients, this embodiment selects the ACSM2A gene, GATM gene, and LRP2 gene from the IM2-related gene set as non-DCCD subtype markers. By identifying the expression levels of ACSM2A, GATM, and LRP2 in each renal clear cell carcinoma tissue sample, it is determined whether the renal clear cell carcinoma tissue sample is a renal clear cell carcinoma tissue sample. This method of identifying renal clear cell carcinoma tissue samples is simpler, faster, and more convenient.

[0059] Specifically, in this embodiment, the specific implementation method for identifying whether each renal clear cell carcinoma tissue sample contains the ACSM2A gene, the GATM gene, and the LRP2 gene is as follows:

[0060] Obtain the immunohistochemical staining images corresponding to each renal clear cell carcinoma tissue sample. After incubating the renal clear cell carcinoma tissue sample with anti-ACSM2A primary antibody, anti-GATM primary antibody, and anti-LRP2 primary antibody, the obtained immunohistochemical staining images are as follows: Figure 2 As shown in Figure 2, for samples from patients with non-DCCD subtypes, the positive cells in the immunohistochemical staining images were dark and wide in range ( Figure 2 Left), while the immunohistochemical staining images of DCCD subtype renal clear cell carcinoma tissue samples were lightly stained and had a small range ( Figure 2(Right image) Therefore, by scoring the immunohistochemical staining intensity of ACSM2A, GATM, and LRP2 expression levels, it is possible to distinguish renal clear cell carcinoma tissue samples with the DCCD subtype from renal clear cell carcinoma tissue samples with the non-DCCD subtype.

[0061] In clinical studies, the overall survival and disease-free survival of patients with non-DCCD subtype renal cancer were significantly better than those of DCCD patients. Multiple group tests found that the expression levels of ACSM2A gene, GATM gene, and LRP2 gene in renal clear cell carcinoma tissue samples of patients with non-DCCD subtype renal cancer were significantly higher than those of patients with DCCD subtype renal cancer. Therefore, the immunohistochemical staining images of ACSM2A gene, GATM gene, and LRP2 gene in non-DCCD subtype renal clear cell carcinoma tissue samples had a wider coloring range and darker coloring, while the immunohistochemical staining images of DCCD subtype renal clear cell carcinoma tissue samples had a smaller coloring range and lighter coloring.

[0062] In the TCGA-KIRC (clear cell renal cell carcinoma) public database, higher ACSM2A, GATM, and LRP2 gene expression scores (based on RNA levels) were associated with significantly better overall survival and disease-free survival, indicating the importance of evaluating ACSM2A, GATM, and LRP2 expression levels for screening non-progressive renal cell carcinoma subtypes.

[0063] When performing immunohistochemical staining of renal tumor samples, the intensity of positive cell staining for the corresponding indicator depends on the antigen content and distribution density, the labeling method, and its sensitivity. The greater the antigen content and the higher the distribution density of the corresponding indicator, the stronger the positive staining result obtained with the corresponding indicator antibody. The staining intensity of the nucleus / cytoplasm / cell membrane (different indicators may have different cellular localizations) ranges from no staining (only the blue nucleus is visible) to light yellow to brownish yellow to tan.

[0064] In step 200, the staining intensity scores of the immunohistochemical staining images corresponding to multiple sections of each renal clear cell carcinoma tissue sample are obtained using the IHC Profiler plug-in of Image J or Fiji software, and the average of the staining intensity scores of the multiple sections of each renal clear cell carcinoma tissue sample is used as the staining intensity score of the renal clear cell carcinoma tissue sample.

[0065] The staining intensity score range is set to 0-3, the staining intensity score of no positive staining is set to 0, the staining intensity score of light yellow is set to 1, the staining intensity score of brown is set to 2, and the staining intensity score of tan is set to 3.

[0066] like Figure 3As shown in the figure, the IHC Profiler plug-in of Image J software can automatically evaluate the average gray value of positive cells and the percentage of positive area after importing immunohistochemical staining images, and output the results of four staining intensity scores. The basic principle of this plug-in is to first perform color deconvolution on the input immunohistochemical staining image, separate the blue negative area and the yellow (brown) positive area, and determine the staining type (cytoplasm / nuclear staining) according to the cellular localization of the indicator. Then, the gray value of positive cells and the proportion of positive area are calculated respectively, and the staining intensity score results are output, which represent negative (0 points) - weak positive (1 point) - positive (2 points) - strong positive (3 points).

[0067] In step 300, machine learning is performed on the immunohistochemical staining image corresponding to each renal clear cell carcinoma tissue sample using the Trainable_Weka_Segmentation plug-in of Image J or Fiji software.

[0068] The positive cells in the immunohistochemical staining images corresponding to each renal clear cell carcinoma tissue sample were calculated respectively, and the positive cell proportion scores corresponding to multiple sections of each renal clear cell carcinoma tissue sample were obtained. The average value of the positive cell proportion scores of multiple sections of each renal clear cell carcinoma tissue sample was used as the positive cell proportion score of the renal clear cell carcinoma tissue sample.

[0069] When the positive cell proportion is 0-5%, the positive cell proportion score is 0;

[0070] When the positive cell ratio is 6%-25%, the positive cell ratio score is 1;

[0071] When the positive cell percentage is 26%-50%, the positive cell percentage score is 2;

[0072] When the positive cell percentage is 51%-75%, the positive cell percentage score is 3;

[0073] When the positive cell ratio was greater than 75%, the positive cell ratio score was 4.

[0074] The IRS score table formed by combining the positive cell proportion score and the staining intensity score is shown below.

[0075]

[0076] For the scoring of the positive cell percentage, the stained image can be machine-learned and segmented using the Trainable_Weka_Segmentation plug-in of Image J or Fiji software, and then the cells can be counted. The positive cell percentage can be calculated and scored according to the criteria of 0-5% cells being positive (0 points), 6-25% cells being positive (1 point), 26%-50% cells being positive (2 points), 51%-75% cells being positive (3 points), and >75% cells being positive (4 points). Figure 4 shown.

[0077] In step 400, the IRS scores of the immunohistochemical staining images corresponding to each clear cell renal cell carcinoma tissue sample incubated with anti-ACSM2A primary antibody, anti-GATM primary antibody, and anti-LRP2 primary antibody are obtained. The sum of the IRS scores of the immunohistochemical staining images corresponding to each clear cell renal cell carcinoma tissue sample is calculated, i.e., the non-DCCD score, such as Figure 5 shown.

[0078] Based on the numerical range of the non-DCCD score, renal clear cell carcinoma tissue samples were classified as samples of patients with advanced renal clear cell carcinoma or samples of non-advanced renal clear cell carcinoma.

[0079] The renal clear cell carcinoma tissue samples with a non-DCCD score value range lower than 5 (2, 12) were regarded as samples of patients with advanced renal clear cell carcinoma;

[0080] Each clear cell renal cell carcinoma tissue sample with a non-DCCD score value range higher than 16 (12, 22) was considered a non-progressive clear cell renal cell carcinoma patient sample.

[0081] The sum of the IRS scores of non-DCCD subtype markers (ACSM2A, GATM, LRP2) was calculated, i.e., the non-DCCD score. For samples from patients with progressive ccRCC, the value range was less than 5 (2, 12); for samples from patients with non-progressive ccRCC, the value range was greater than 16 (12, 22).

[0082] In summary, patients with a non-DCCD score lower than 5 (2, 12) can be defined as patients with advanced clear cell renal cell carcinoma (i.e., advanced ccRCC); patients with a non-DCCD score higher than 16 (12, 22) can be defined as patients with non-progressive clear cell renal cell carcinoma (i.e., non-progressive ccRCC), thus achieving the classification and identification of patients with clear cell renal cell carcinoma and providing a theoretical basis for further precise management.

[0083] Furthermore, the non-DCCD score had a screening efficacy of AUC = 0.82 for both progressive and non-progressive ccRCC patient samples. AUC (Area Under Curve) refers to the area under the curve, defined as the area under the receiver operating characteristic curve and the coordinate axis, and ranges from 0.5 to 1. The closer the AUC is to 1.0, the higher the authenticity of the detection method; when it is equal to 0.5, the authenticity is the lowest and has no application value. The AUC calculation in this embodiment is implemented in R language, indicating that the non-DCCD score has high authenticity in screening both progressive and non-progressive ccRCC patient samples.

[0084] Not every DCCD subtype patient is a patient with advanced clear cell renal cell carcinoma, nor is there no patient with advanced clear cell renal cell carcinoma among non-DCCD subtype patients. Therefore, in this context, this embodiment further provides the method of screening out patients with potential advanced clear cell renal cell carcinoma and patients with non-advanced clear cell renal cell carcinoma from non-DCCD subtype patients, thereby forming a treatment plan in advance for patients with advanced clear cell renal cell carcinoma.

[0085] The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the scope of the present application. The scope of protection of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and scope of protection of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present application.

Claims

1. An automatic scoring and classification method for progressive renal cancer based on non-DCCD subtype markers, characterized in that: The following steps are involved: Step 100: ACSM2A, GATM, and LRP2 genes are used as marker genes for renal clear cell carcinoma tissue samples expressing the non-de-clear cell differentiation subtype, and each renal clear cell carcinoma tissue sample is subjected to DAB color development, nuclear staining, dehydration, mounting, and image scanning to obtain an immunohistochemical staining image; Step 200: Obtain staining intensity scores of immunohistochemical staining images corresponding to ACSM2A, GATM, and LRP2 antibody immunostaining for each renal clear cell carcinoma tissue sample; Step 300: performing image segmentation and cell counting on the immunohistochemical staining image corresponding to each renal clear cell carcinoma tissue sample to obtain a positive cell ratio score corresponding to each renal clear cell carcinoma tissue sample; Step 400: Multiply the staining intensity score and the positive cell proportion score of each clear cell renal cell carcinoma tissue sample as the IRS score of the ACSM2A, GATM, and LRP2 markers corresponding to each clear cell renal cell carcinoma tissue sample; take the sum of the IRS scores of the above markers corresponding to each clear cell renal cell carcinoma tissue sample as the non-DCCD score; and classify the clear cell renal cell carcinoma tissue sample as an advanced clear cell renal cell carcinoma patient sample or a non-advanced clear cell renal cell carcinoma sample based on the non-DCCD score.

2. The automatic scoring and classification method for progressive renal cancer based on non-DCCD subtype markers according to claim 1, characterized in that: In step 100, the gene set of non-declear cell differentiation subtype renal clear cell carcinoma includes SLC10A2, CLVS2, KL, AVPR1B, NCR3LG1, SLC5A12, SLC5A10, MGAM, SLC13A1, FBXL16, PANK1, TINAG, TRHDE, SERPINA6, MIOX, CHRM3, GAREM1, WDR72, LARGE2, CDHR2, ZDHHC11B, VIL1, KLHDC7A, TRPM3, PTGER3, SMTNL2, TMEM72, TMEM27, ADSSL1, A1CF, TMEM174, TMEM125, T MEM38B, HSD11B2, RGS5, STK32B, TRIM15, TRIM10, SLC17A1, SLC17A3, SLC17A4, ERICH5, SLC16A9, CREB3L3, EDNRB, NAT8, CYP2J2, HHLA2, ACSM2A, ACSM2B , KCNJ3, PKHD1, PAIP2B, EMCN, EMX2, SLC47A1, ENTPD2, ENAM, ACE2, ADH6, PTH1R, PTH2R, LRRC19, ESPN, NPR3, RAB3IP, KCNJ15, ALPL, CYP4A22, CYP4A11, C OL23A1, FRAS1, CABP1, AOC1, COL25A1, SCGN, CERKL, APOM, FREM2, AQP7, AQP1, MYRFL, SLCO4C1, ASPG, ASPA, EHHADH, FBP1, FRMD3, GRIK3, SLC3A1, SLC51A , SLC5A8, SLC5A1, CSDC2, GIPC2, SLC6A3, SLC22A12, SLC22A11, SLC22A24, DIRAS2, FMO1, AMN, ABCG2, SLC2A2, STUM, LGALS2, CA4, CGN, CLEC18C, CLEC18B , CLEC18A, DDC, BDNF, SLC16A12, ANKS4B, SYT9, GHR, SLC39A5, FTCD, BHMT, G6PC, FUT6, KHK, SPON1, DOC2A, SMIM24, GABRB3, HMGCS2, PLG, SCN4B, BBOX1, G ATM, GDF6, PDGFD, LIN7A, TLL1, ANXA13, PCK1, GJB1, AGMAT, TPPP, ZNF711, ACADL, ZNF385B, BTNL9, PHYHIPL, IQSEC3, CD36, AGTR1, PKLR, CES3, FAM196B,SLC22A6,SLC22A2,GSTA1,GLYATL1,C1orf210,ALDOB,CIB4,SLC27A2,AGXT2,SLC26A9,PDZK1,SLC6A19,SLC6A18,SLC6A13,SLC6A12,CMBL,SLC25A48,ATP2B2,SULT1C4,CLDN10,HAO2,CLCN5,CUBN,PEG10,LRP2,CYS1,ALDH6A1,UPB1,MASP1,FCAMR,ALDH1L1,ADCY5,GPAT3,SLC28A1,HRH2,MAP7,MAPT。, 3. The automatic scoring and classification method for progressive renal cancer based on non-DCCD subtype markers according to claim 1, characterized in that: In step 100, the method for obtaining the immunohistochemical staining image corresponding to each renal clear cell carcinoma tissue sample is as follows: Each of the renal clear cell carcinoma tissue samples is wax-solidified, and the renal clear cell carcinoma tissue samples are serially sectioned; Antigen retrieval was performed on the sections, and endogenous peroxidase activity of the sections was blocked; All sections of renal clear cell carcinoma tissue samples were incubated with anti-ACSM2A primary antibody, anti-GATM primary antibody, and anti-LRP2 primary antibody respectively; Each renal clear cell carcinoma tissue sample after primary antibody incubation was performed using HRP-conjugated polymer secondary antibody; After incubation with the secondary antibody, the sections of each renal clear cell carcinoma tissue sample were subjected to DAB color development, nuclear staining, dehydration, sealing, and scanning to obtain immunohistochemical staining images.

4. The automatic scoring and classification method for progressive renal cancer based on non-DCCD subtype markers according to claim 1, characterized in that: In step 200, the staining intensity scores of the immunohistochemical staining images corresponding to the multiple sections of each renal clear cell carcinoma tissue sample are obtained using Image J or Fiji software, and the average of the staining intensity scores of the multiple sections of each renal clear cell carcinoma tissue sample is used as the staining intensity score of the renal clear cell carcinoma tissue sample; The staining intensity score range is set to 0-3, with the staining intensity score of no positive staining being set to 0, the staining intensity score of light yellow being set to 1, the staining intensity score of brownish yellow being set to 2, and the staining intensity score of tan being set to 3.

5. The automatic scoring and classification method for progressive renal cancer based on non-DCCD subtype markers according to claim 4, characterized in that: In step 300, machine learning is performed on the immunohistochemical staining image of each renal clear cell carcinoma tissue sample using Image J or Fiji software; The positive cell percentages in the immunohistochemical staining images corresponding to the renal clear cell carcinoma tissue samples were calculated respectively, and the positive cell percentage scores corresponding to multiple sections of each renal clear cell carcinoma tissue sample were obtained. The average of the positive cell percentage scores of multiple sections of each renal clear cell carcinoma tissue sample was used as the positive cell percentage score of the corresponding indicator staining of the renal clear cell carcinoma tissue sample.

6. The automatic scoring and classification method for progressive renal cancer based on non-DCCD subtype markers according to claim 5, characterized in that: When the positive cell proportion is 0-5%, the positive cell proportion score is 0; When the positive cell ratio is 6%-25%, the positive cell ratio score is 1; When the positive cell percentage is 26%-50%, the positive cell percentage score is 2; When the positive cell percentage is 51%-75%, the positive cell percentage score is 3; When the positive cell ratio was greater than 75%, the positive cell ratio score was 4.

7. The automatic scoring and classification method for progressive renal cancer based on non-DCCD subtype markers according to claim 6, characterized in that: In step 400, the IRS scores of the immunohistochemical images corresponding to the ACSM2A, GATM, and LRP2 antibody staining in each renal clear cell carcinoma tissue sample are calculated respectively; Calculate the sum of the IRS scores of all immunohistochemical images corresponding to ACSM2A, GATM, and LRP2 antibody staining in each renal clear cell carcinoma tissue sample, i.e., the non-DCCD score; Based on the numerical range of the non-DCCD score, renal clear cell carcinoma tissue samples were classified as samples of patients with advanced renal clear cell carcinoma or samples of non-advanced renal clear cell carcinoma.

8. The automatic scoring and classification method for progressive renal cancer based on non-DCCD subtype markers according to claim 7, characterized in that: The renal clear cell carcinoma tissue samples with a non-DCCD score value range lower than 5 (2, 12) were regarded as samples of patients with advanced renal clear cell carcinoma; The RCC tissue samples with non-DCCD score values ​​higher than 16 (12, 22) were considered as non-progressive RCC patient samples.

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