A prognostic risk scoring model for uveal melanoma and its application

Abstract

The present invention discloses a uveal melanoma prognostic risk scoring model and application thereof. Specifically, the present invention finds that SLC12A3, SLC12A9, MIR140, and HCP5 are significantly correlated with the prognosis of uveal melanoma patients. The present invention constructs a uveal melanoma prognostic risk scoring model based on these four genes for the first time, thereby obtaining a risk score. The clinical outcome of uveal melanoma can be predicted by the risk score, and the model has good clinical application prospects.

Description

Background Art

[0002] Biomarkers are defined as biochemical indicators that objectively measure and evaluate normal biological processes, pathological processes, or pharmacological responses to therapeutic interventions. Biomarkers, also known as molecular markers, can be used to observe the body's response to disease treatment. Finding potential biomarkers for cancer is of great significance to cancer research. Studies in recent years have shown that the application of biomarkers in cancer research mainly has the following three aspects, namely diagnosis, prognosis, and prediction. In terms of diagnosis, it is mainly manifested in the early diagnosis of cancer. In terms of prognosis, it refers to predicting the natural course of cancer and whether the patient's prognosis is good. The prediction aspect mainly refers to the patient's response to treatment. In 2011, the National Comprehensive Cancer Network (NCCN) of the United States divided biomarkers into four categories: diagnostic markers, prognostic markers, predictive markers, and combined diagnostic markers.

[0003] Uveal melanoma (UVM) is a rare eye disease, but it is the most common type of primary intraocular tumor. Uveal melanoma can occur at any age, with a low incidence in adolescents and a rapidly increasing incidence with age. Uveal melanoma originates from neuroectodermal melanocytes of the iris or choroid and ciliary body. The onset of uveal melanoma may be related to chromosomal mutations and gene changes, and may also be transformed from uveal nevus, but the specific cause remains unclear. In-depth research on the occurrence and development mechanism of uveal melanoma and the search for biomarkers of uveal melanoma are of great significance for the diagnosis of uveal melanoma. Summary of the invention

[0004] The purpose of the present invention is to provide a prognostic risk scoring model for uveal melanoma based on biomarkers of uveal melanoma. To achieve this purpose, the present invention adopts the following technical solutions:

[0005] On one hand, the present invention provides a uveal melanoma prognostic risk scoring model, wherein the uveal melanoma prognostic risk scoring model uses biomarker expression levels as input variables, and the biomarkers include SLC12A3, SLC12A9, MIR140 and / or HCP5.

[0006] In the present invention, the term "biomarker" means a compound, preferably a gene, which is differentially present (i.e., increased or decreased) in a biological sample from a subject or a group of subjects with a first phenotype (e.g., with a disease) compared to a biological sample from a subject or a group of subjects with a second phenotype (e.g., without a disease). The term "biomarker" generally refers to the presence / concentration / content of a gene or the presence / concentration / content of two or more genes. In the present invention, the biomarkers include SLC12A3, SLC12A9, MIR140 and / or HCP5. Biomarkers such as SLC12A3 (solute carrier family 12 member 3, gene ID: 6559), SLC12A9 (solute carrier family 12 member 9, gene ID: 56996), MIR140 (microRNA 140, gene ID: 406932), HCP5 (HLA complex P5, gene ID: 10866); including genes and proteins encoded by them and homologs, mutations, and isoforms thereof. The term encompasses full-length, unprocessed markers, as well as any form of markers derived from processing in cells. The term encompasses naturally occurring variants of the marker (e.g., splice variants or allelic variants). Gene IDs are available at https: / / www.ncbi.nlm.nih.gov / gene / .

[0007] The biomarkers may be differentially present at any level, but are generally present at a level that is increased by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, at least 160%, at least 170%, at least 180%, at least 190%, at least 200%, at least 210%, at least 220%, at least 230%, at least 240%, at least 250%, at least 260%, at least 270%, at least 280%, at least 290%, at least 300%, at least 310%, at least 320%, at least 330%, at least 340%, at least 350%, %, at least 130%, at least 140%, at least 150%, or more; or generally present at a level that is reduced by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% (i.e., absent).

[0008] Preferably, the biomarkers are differentially present at a level that is statistically significant (ie, p-value less than 0.05 and / or q-value less than 0.10, as determined using Welch's T-test or Wilcoxon's rank-sum Test).

[0009] Furthermore, the biomarkers include a combination of SLC12A3, SLC12A9, MIR140 and HCP5.

[0010] Furthermore, the uveal melanoma prognostic risk scoring model is constructed using multivariate Cox regression analysis.

[0011] Furthermore, the formula of the uveal melanoma prognostic risk scoring model is SlcMHscore=exp(-0.25982*MIR140+0.27088*HCP5+0.02578*SLC12A3+0.97102*SLC12A9-18.23654).

[0012] Another aspect of the present invention provides the use of biomarkers in constructing the above-mentioned uveal melanoma prognostic risk scoring model, wherein the biomarkers include SLC12A3, SLC12A9, MIR140 and / or HCP5.

[0013] Furthermore, the biomarkers include a combination of SLC12A3, SLC12A9, MIR140 and HCP5.

[0014] The present invention also provides a uveal melanoma prognosis risk assessment system, the system comprising a calculation unit, and the calculation unit calculates the risk score using the above-mentioned prognosis risk assessment model.

[0015] Furthermore, the system also includes a detection unit, which is used to detect the expression level of biomarkers, and the biomarkers include SLC12A3, SLC12A9, MIR140 and / or HCP5.

[0016] Further, the biomarkers include a combination of SLC12A3, SLC12A9, MIR140, and HCP5;

[0017] Furthermore, the system also includes an information acquisition unit, which is used to execute an operation of acquiring detection information of the subject, and the detection information includes the expression level of the biomarker.

[0018] Furthermore, the system also includes an evaluation unit, which is used to judge the risk of prognosis of uveal melanoma of the subject according to the calculation result of the calculation unit and give reasonable prevention and treatment suggestions.

[0019] Furthermore, the system further comprises a result display unit, and the result display unit is used to display the conclusion obtained by the evaluation unit.

[0020] Furthermore, the result display unit displays the result by screen display, voice broadcast or printing.

[0021] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the aforementioned uveal melanoma prognosis risk scoring model.

[0022] It should be understood that the "system", "device" and "unit" used herein are a method for distinguishing different components, elements, parts, parts or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0023] Those skilled in the art will appreciate that the present invention may be implemented as a device, method, or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: it may be complete hardware, it may be complete software (including firmware, resident software, microcode, etc.), or it may be a combination of hardware and software, generally referred to herein as a "unit" or "system". In addition, in some embodiments, the present invention may also be implemented in the form of a computer program product in one or more computer-readable media, which may contain computer-readable program code.

[0024] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0025] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0026] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0027] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0028] The present invention also provides the use of a reagent for detecting biomarkers in a sample in the preparation of a product for the prognosis diagnosis of uveal melanoma, wherein the biomarkers include SLC12A3, SLC12A9, MIR140 and / or HCP5.

[0029] Furthermore, the biomarkers include a combination of SLC12A3, SLC12A9, MIR140 and HCP5.

[0030] In the present invention, the term "sample" or "test sample" refers to a biological sample obtained or derived from a target individual, and the source of the biological sample can be a fresh, frozen and / or preserved organ or tissue sample or a solid tissue generated from a biopsy or primer; blood or any blood component. The term "sample" or "test sample" includes biological samples that have been manipulated in any way after they are obtained, such as by reagent treatment, stabilization, or enrichment for certain components (such as proteins or polynucleotides), or embedded in a semi-solid or solid matrix for sectioning purposes.

[0031] Furthermore, the reagent includes a reagent capable of detecting the expression level of the mRNA of the biomarker.

[0032] Furthermore, the expression level of the mRNA of the detection marker is measured using any one of the methods selected from polymerase chain reaction, real-time fluorescence quantitative reverse transcription polymerase chain reaction, reverse transcription polymerase chain reaction, competitive polymerase chain reaction, nuclease protection analysis, in situ hybridization, nucleic acid microarray, Northern blotting or DNA chip.

[0033] Furthermore, the reagent includes a reagent capable of detecting the expression level of the protein encoded by the biomarker.

[0034] Furthermore, the expression level of the protein encoded by the detection marker is measured using any one of multiple proximity extension analysis, enzyme-linked immunosorbent assay, radioimmunoassay, sandwich analysis, protein blotting, immunoprecipitation, immunohistochemical staining, fluorescent immunoassay, enzyme substrate development, antigen-antibody aggregation, fluorescence-activated cell sorting, mass spectrometry, MRM assay, an assay using a set of multiple amine-specific stable isotope reagents, or protein chip measurement.

[0035] Further, the reagent comprises:

[0036] Primers or probes that specifically bind to the biomarker gene;

[0037] An antibody, peptide, aptamer or compound that specifically binds to the biomarker protein.

[0038] The term "primer" as used herein is a chain of a short nucleic acid sequence that recognizes a target gene sequence, which includes a pair of forward and reverse primers. Specifically, the "primer" includes a pair of primers that provide specific and sensitive analysis results. Primers are believed to provide a high degree of specificity when used to amplify a target gene sequence, but they do not cause amplification of non-target sequences that are inconsistent or complementary to the target gene sequence.

[0039] The term "probe" as used herein refers to a substance that specifically binds to a target to be detected in a sample. Through the binding, the probe can determine the presence of the target in the sample. As long as it is commonly used in the art, any probe can be used in the present disclosure. In particular, the probe can be PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA or DNA, most preferably PNA. Specifically, the probe is a biological material that can come from an organism or can be synthesized in vitro or is a mimetic thereof. For example, the probe can be an enzyme, protein, antibody, microorganism, animal or plant cell or organ, neuron, DNA or RNA. DNA can include cDNA, genomic DNA and oligonucleotides. Similarly, genomic RNA, mRNA and oligonucleotides can fall within the scope of RNA. Examples of proteins include antibodies, antigens, enzymes and peptides.

[0040] The term "antisense" as used herein refers to an oligomer having a nucleotide base sequence and a subunit-subunit backbone that allows the antisense oligomer to hybridize to a target sequence in RNA through Watson-Crick base pairing to form an RNA:oligomer heteroduplex nucleic acid molecule in the target sequence.

[0041] The term "antibody" as used herein is well known in the art and refers to a specific immunoglobulin for an antigenic site. The antibody in the present invention refers to an antibody that specifically binds to the marker protein of the present invention, and the antibody can be manufactured according to conventional methods in the art. The form of the antibody includes polyclonal antibodies or monoclonal antibodies, antibody fragments (such as Fab, Fab', F(ab')2 and Fv fragments), single-chain Fv (scFv) antibodies, multispecific antibodies (such as bispecific antibodies), monospecific antibodies, monovalent antibodies, chimeric antibodies, humanized antibodies, human antibodies, fusion proteins comprising the antigen binding site of the antibody, and any other modified immunoglobulin molecules comprising the antigen binding site, as long as the antibody exhibits the desired biological binding activity.

[0042] The term "peptide" as used herein has the ability to highly bind to a target substance and does not denature during heat treatment / chemical treatment. Moreover, due to its small size, it can be used as a fusion protein by attaching it to other proteins. Specifically, because it can be specifically attached to a polymer protein chain, it can be used as a diagnostic kit and a drug delivery substance.

[0043] The term "aptamer" as used herein refers to a polynucleotide composed of a specific type of single-stranded nucleic acid (DNA, RNA or modified nucleic acid), which has a stable tertiary structure and has the property of being able to bind to a target molecule with high affinity and specificity. As described above, since aptamers can specifically bind to antigenic substances like antibodies, but are more stable than proteins and have a simple structure, and are composed of polynucleotides that are easy to synthesize, they can be used instead of antibodies.

[0044] Furthermore, the product includes a chip, a test kit, a test paper or a high-throughput sequencing platform.

[0045] In the present invention, the kit comprises reagents for detecting the biomarkers in the sample as described above. For example, the kit can be an RT-PCR kit, a DNA chip kit, an ELISA kit, a protein chip kit, a rapid kit, or an MRM (multiple reaction monitoring) kit.

[0046] For example, the diagnostic kit may further include the necessary elements for reverse transcription polymerase chain reaction. The RT-PCR kit includes a pair of primers specific for the gene encoding marker protein. Each primer is a nucleotide with a sequence specific for the nucleotide sequence of the gene, and its length may be about 7 to 50bp, more particularly about 10 to 39bp. In addition, the kit may further include a primer specific for the nucleotide sequence of a control gene. In addition, the RT-PCR kit may include a test tube or suitable vessel, a reaction buffer (different pH values ​​and magnesium concentrations), deoxynucleotides (dNTPs), enzymes (such as Taq polymerase and reverse transcriptase), deoxyribonuclease inhibitors, ribonuclease inhibitors, DEPC-water and sterile water.

[0047] In addition, the diagnostic kit of the present disclosure may include elements necessary for operating a DNA chip. The DNA chip kit may include a substrate bound to a gene or cDNA or an oligonucleotide equivalent to a fragment thereof, and reagents, agents and enzymes for constructing a fluorescently labeled probe. In addition, the substrate may include a control gene or cDNA or an oligonucleotide equivalent to a fragment thereof.

[0048] In some embodiments, the kit of the present disclosure may include elements necessary for performing ELISA. The ELISA kit may include antibodies specific for proteins. The antibodies have high selectivity and affinity for marker proteins, no cross-reactivity with other proteins, and may be monoclonal antibodies, polyclonal antibodies, or recombinant antibodies. In addition, the ELISA kit may include antibodies specific for control proteins. In addition, the ELISA kit may further include reagents capable of detecting the bound antibodies, for example, labeled second antibodies, chromophores, enzymes (e.g., conjugated with antibodies), and substrates thereof, or substances capable of binding the antibodies.

[0049] The present invention also provides the use of biomarkers in constructing the above-mentioned uveal melanoma prognosis risk assessment system, wherein the biomarkers include SLC12A3, SLC12A9, MIR140 and / or HCP5.

[0050] Furthermore, the biomarkers include a combination of SLC12A3, SLC12A9, MIR140 and HCP5.

[0051] The present invention has the following advantages and beneficial effects:

[0052] The present invention provides for the first time a prognostic risk scoring model for uveal melanoma, which detects the expression levels of SLC12A3, SLC12A9, MIR140 and HCP5 in samples of uveal melanoma patients and performs a comprehensive score as an evaluation parameter. A high score indicates that the prognosis of uveal melanoma patients is poor. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Figure 1 is an analysis result diagram of the effect of SLC12 family genes on the clinical outcome of uveal melanoma, wherein Figure A is a forest diagram of the effect of SLC12 family gene expression levels on OS and DSS; Figure B is a diagram showing the result of Kaplan-Meier analysis of OS and DSS based on SLC12A3 expression levels; Figure C is a diagram showing the result of Kaplan-Meier analysis of OS and DSS based on SLC12A9 expression levels; Figure D is a diagram showing the result of multivariate Cox proportional hazard regression analysis of SLC12A3 and SLC12A9 with other clinical characteristics; Figure E is a heat map of the correlation of SLC12 family genes; Figure F is a heat map based on SLC12A3 and SLC12A9 grouping; Figure G is a Kaplan-Meier survival curve of OS and DSS based on SLC12A3 and SLC12A9 grouping;

[0054] Figure 2Figure 1 is an analysis of the effect of overexpression of SLC12A3 and / or SLC12A9 on the proliferation of uveal melanoma cells, wherein Figure A is a negative control result of immunostaining, Figure B is a SLC12A9 immunostaining result of UVM melanoma tissue, Figure C is a SLC12A3 immunostaining result of UVM melanoma tissue, Figure D is a Western blotting analysis of the protein level of SLC12A9 in overexpressing cells C918 and MUM2C, and Figure E is a Western blotting analysis of the protein level of SLC12A9 in overexpressing cells C918 and MUM2C. Figure blotting analysis of the protein level of SLC12A3 in overexpressing cells C918 and MUM2C, Figure F is a growth curve of C918 after SLC12A9 overexpression, Figure G is a growth curve of C918 after SLC12A3 overexpression, Figure H is a growth curve of MUM2C after SLC12A9 overexpression, Figure I is a growth curve of MUM2C after SLC12A3 overexpression, Figure J is a clone formation analysis of C918 cells overexpressing SLC12A9 and its quantitative results, Figure K is a clone formation analysis of C918 cells overexpressing SLC12A3 and its quantitative results, Figure L is a clone formation analysis of MUM2C cells overexpressing SLC12A9 and its quantitative results, Figure M is a clone formation analysis of MUM2C cells overexpressing SLC12A3 and its quantitative results;

[0055] Figure 3 Figure 1 is an analysis result diagram of the effect of knocking down SLC12A3 and SLC12A9 on the proliferation of uveal melanoma cells, wherein Figure A is an analysis result diagram of the knockdown efficiency of SLC12A3 or SLC12A9 by specific siRNA, Figure B is a growth curve diagram of C918 cells after knocking out SLC12A3 and / or SLC12A9, Figure C is a growth curve diagram of MUM2C cells after knocking out SLC12A3 and / or SLC12A9, Figure D is a clone formation analysis result diagram of C918 cells after knocking out SLC12A3 and / or SLC12A9; Figure E is a quantitative analysis diagram of clone formation of C918 cells after knocking out SLC12A3 and / or SLC12A9, and Figure F is Figure 1 is a graph showing the results of clone formation analysis of MUM2C cells after knocking out SLC12A3 and / or SLC12A9, Figure G is a quantitative graph showing the clone formation analysis of MUM2C cells after knocking out SLC12A3 and / or SLC12A9, Figure H is a graph showing the results of EDU staining of C918 cells after knocking out SLC12A3 and / or SLC12A9, Figure I is a graph showing the results of EDU staining quantitative analysis of C918 cells after knocking out SLC12A3 and / or SLC12A9, Figure J is a graph showing the results of EDU staining of MUM2C cells after knocking out SLC12A3 and / or SLC12A9, and Figure K is a graph showing the results of EDU staining quantitative analysis of MUM2C cells after knocking out SLC12A3 and / or SLC12A9;

[0056] Figure 4 Figure 3 is an experimental result diagram analyzing the superiority and robustness of the ceRNA-related risk model in predicting survival, wherein Figure A is a flowchart of ceRNA network construction, Figure B is a Kaplan-Meier survival curve diagram of OS and DSS for patients with high and low expression of MIR140 or HCP5, Figure C is a diagram of the distribution of risk scores, survival status of patients and molecular expression patterns, Figure D is a Kaplan-Meier analysis of the prognostic risk score model in the TCGA cohort and a 3- and 5-year timeROC curve diagram, Figure E is a Kaplan-Meier analysis of the prognostic risk score model in the GSE22138 cohort and a 3- and 5-year timeROC curve diagram, and Figure F is a Kaplan-Meier analysis of the prognostic risk score model in the GSE84976 cohort and a 3- and 5-year timeROC curve diagram.

[0057] Figure 5 The figures are the time-dependent ROC analysis results of SLC12A3 and / or SLC12A9 in predicting the prognosis of uveal melanoma, wherein Figure A is the time-dependent ROC analysis graph of SLC12A3, Figure B is the time-dependent ROC analysis graph of SLC12A9, and Figure C is the time-dependent ROC analysis graph of SLC12A3 and SLC12A9. DETAILED DESCRIPTION

[0058] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. The following examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. The experimental methods in the examples where specific conditions are not specified are usually carried out under conventional conditions or under conditions recommended by the manufacturer.

[0059] Experimental methods

[0060] 1. UVM patient cohort acquisition

[0061] The Tumor Genome Atlas (TCGA) transcriptional profiles (mRNA, miRNA, lncRNA) and clinical data of 80 UVM patients were obtained from the UCSC Xena database (https: / / xena.ucsc.edu / ). Three formalin-fixed paraffin-embedded (FFPE) UVM samples were excluded from this study. Two independent UVM cohorts with microarray expression profiles and clinical data were downloaded from the GEO database, with accession numbers GSE84976 (n=28) and GSE22138 (n=63).

[0062] 2. Differentially expressed genes (DEG) analysis

[0063] Two-tailed unpaired Mann-Whitney U test was used to screen differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs) and lncRNAs (DElncRNAs) among different groups. Only mRNAs expressed in at least 80% of the samples were retained.

[0064] Screening criteria for DEmiRNAs and DElncRNAs: log2 (fold change)>1 (upregulation) or <-1 (downregulation), FDR<0.05.

[0065] DEmRNAs screening criteria: log2 (fold change)>2 (upregulation) or <-2 (downregulation), FDR<0.05.

[0066] 3. Functional enrichment analysis

[0067] GO and KEGG pathway enrichment analysis was performed using the R package clusterProfile (18) to identify significantly enriched GO biological process (BP) terms and KEGG pathways. Then, the R package GOplot was used to calculate z scores to determine whether the enriched terms or pathways were more likely to be decreased (negative values) or increased (positive values). Gene set enrichment analysis software (http: / / software.broadinstitute.org / gsea / index.jsp) was used to analyze the gene sets that were enriched or overexpressed in different subtypes.

[0068] 4. Computational quantification of the tumor immune microenvironment

[0069] A gene profile associated with 16 immune cell subsets was obtained from Charoentong's study (Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017; 18(1): 248-62.) The relative abundance of 16 immune cell subsets in the tumor immune microenvironment of each patient was quantified using single-sample gene set enrichment analysis (ssGSEA) with a specific gene set in R package.

[0070] 5. Network Analysis

[0071] The Pearson correlation coefficient was used to measure the co-expression relationship between genes, and gene pairs with Pearson's|r|>0.2 and p<0.05 were used to construct the gene co-expression network, which was visualized after removing repeated edges and self-loops. The molecular complex detection (MCODE) algorithm was used to generate highly connected gene modules, with a node score cutoff of 0.1 and other parameters set to default values.

[0072] The dysregulated competing endogenous RNA (ceRNA) network is constructed based on the ceRNA hypothesis as follows:

[0073] 1) Retrieve human miRNA-SLC12A3 / A9 and miRNA-lncRNAs interactions from ENCORI, TargetScan (release 8.0), and mirDIP;

[0074] 2) The expression correlations between DEmiRNAs and SLC12A3 / A9, DEmiRNAs and DElncRNAs, and DElncRNAs and SLC12A3 / A9 were measured using Pearson’s correlation coefficient;

[0075] 3) SLC12A3 / A9-DEmiRNA-DElncRNAs triplets that were positively correlated with DElncRNA-SLC12A3 / A9 (Pearson's r>0.2) and downregulated by the same DEmiRNA (Pearson's r<-0.2) were selected as ceRNA triplets. These ceRNA triplets were integrated into a ceRNA network.

[0076] 6. Cell culture and gene knockout

[0077] Uveal melanoma cell lines C918 and MUM2C were cultured in DMEM (Gibco) supplemented with 10% fetal bovine serum and antibiotics in a 5% CO 2 , cultured at 37°C. In the siRNA study, cells were cultured in 12-well plates until about 50% confluence and then lysed with LipoJet TM Reagent (SignaGEN) was used to transfect 40 pmol of siRNA. siRNA was designed and synthesized by Shanghai Gene Pharmaceutical Co., Ltd., China, and the sequence is as follows:

[0078] si-NC: 5'-UUCUCCGAACGUGUCACGUTT (SEQ ID NO. 1);

[0079] si-SLC12A9: 5'-GCATTGGGCTCATGTTCTA (SEQ ID NO. 2);

[0080] si-SLC12A3: 5'-GCCCACAUAUGAGCACUAUTT (SEQ ID NO. 3).

[0081] 7. Gene overexpression using lentiviral infection

[0082] SLC12A3 overexpression lentivirus (NM_001126108) was purchased from GeneCare Gene Co., Ltd. (Shanghai, China), while SLC12A9 overexpression lentivirus (NM_020246) was purchased from Yijin Biotechnology Co., Ltd. (Guangzhou, China). One day before infection, C918 cells were cultured in 12-well plates to approximately 50% confluency. Then, 10 μL of SLC12A3 or SLC12A9 overexpression lentivirus (2 × 10 8 TU / ml) was added to DMEM without FBS for about 6 hours, and the cells were cultured in complete medium. 72 hours after infection, SLC12A3 or SLC12A9 protein was analyzed by Western blotting.

[0083] 8. Western blotting

[0084] UVM cells were washed with PBS and lysed with RIPA (Biyuntian, China) on ice for 30 min. Equal amounts of protein lysates were separated by SDS-PAGE and transferred to PVDF membranes, blocked with 5% skim milk for 2 h at room temperature, and then incubated with primary antibodies overnight at 4 °C. Primary antibodies included: anti-SLC12A9 antibody (1:1000, HuaBio CHINA), anti-SLC12A3 antibody (1:1000, Abcam, ab233401), or anti-GAPDH antibody (1:4000, Cell Signaling Technology, 5174S). Primary antibodies were identified with fluorescein-conjugated secondary antibodies (LI-COR) within 2 h at room temperature, and blots were analyzed using the odysseyCLx system (LI-COR), and bands were quantitatively densitometrically determined using ImageJ software.

[0085] 9. Cell growth curve analysis

[0086] Cell Counting Kit-8 (CCK-8, Beyotime Biotechnology Co., Ltd.) was used to analyze cell viability. 1,000 melanoma cells were cultured in a 96-well culture plate, and 10uL of CCK-8 working solution was added at 0h, 24h, 48h, 78h, and 96h, and the optical density at 450nm was measured 2 hours later.

[0087] 10. EdU incorporation method

[0088] BeyoClick with Alexa Fluor 594 TM EdU cell proliferation kit was purchased from Bio-Technology (Shanghai, China). Cells were cultured in 6-well plates and incubated in 10 μmol / L EdU working solution at 37 °C for 2 h. Then, cells were fixed in 4% polyformaldehyde for 15 min at room temperature and incubated with 0.3% Triton X-100 for 15 min in the dark. After the nuclei were stained with DAPI for 5 min at room temperature, the cells were observed under an inverted fluorescence microscope.

[0089] 11. Immunohistochemistry (IHC) staining

[0090] Tissue specimens were subjected to immunohistochemical analysis and the slice thickness was 5 μm. Dewaxed with xylene, hydrated with a series of graded alcohol washes, antigen heat retrieval was performed using EDTA 100°C for 20 min and rinsed in PBS. Antibodies included: anti-SLC12A9 antibody (1:200, HuaBio, CHINA), anti-SLC12A3 antibody (1:200, ab233401, Abcam, USA). Negative controls were processed in parallel using the same protocol, but the primary antibody was omitted, and images were captured using a Leica DM750 automated microscope system.

[0091] 12. Statistical analysis

[0092] All analyses were performed in R version 3.6. Statistical differences in continuous variables between the two groups were determined by using the two-tailed Wilcoxon rank-sum test. Each experiment was repeated three times, and the results are expressed as mean ± standard deviation (SD). The Student's t test was used to evaluate the statistical significance between the experimental and control groups. Univariate and multivariate Cox proportional hazard regression models were established using the coxph function of the R package survival to examine the effects of specific factors and clinical variables on patient survival. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. The Kaplan-Meier method and log-ranch test were used to evaluate the survival differences between the groups. Correlation analysis was performed using the R package “Hmisc”, and correlation matrix visualization was performed using the R package “corrplot”. The performance of the prognostic model was evaluated by plotting the time-dependent receiver operating characteristic (timeROC) curve using the R package “timeROC”, and the area under the curve (AUC) was calculated. p < 0.05 was considered statistically significant.

[0093] Example 1 Correlation between SLC12A3 and SLC12A9 expression levels and prognosis of UVM patients

[0094] In order to evaluate the relationship between SLC12s and the prognosis of UVM patients, the present invention performed univariate Cox regression analysis in the TCGA-UVM cohort. The results showed that high expression of SLC12A3 and SLC12A9 was significantly associated with poor overall survival (OS) (SLC12A3: HR = 1.16, 95% CI = 1.03-1.29, P = 0.01; SLC12A9: HR = 2.36, 95% CI = 1.14-4.88, P = 0.02), and disease-specific survival (DSS) (SLC12A3: HR = 1.16, 95% CI = 1.03-1.31, P = 0.01; SLC12A9: HR = 2.55, 95% CI = 1.20-5.45, P = 0.02), while other SLC12 family members did not show significant correlation (such as Figure 1 A).

[0095] Survival analysis also showed that the 77 UVM patients could be divided into a high-risk group and a low-risk group with significant differences in OS and DSS according to the expression levels of SLC12A3 and SLC12A9 (Log-Rank P < 0.05). Figure 1In the multivariate Cox regression analysis of gender, age, and stage, SLC12A3 and SLC12A9 were still significantly correlated with OS (SLC12A3: HR = 1.20, 95% CI = 1.1-1.4, P = 0.005; SLC12A9: HR = 2.44, 95% CI = 1.10-5.70, P = 0.038), and were still significantly correlated with DSS (SLC12A3: HR = 1.20, 95% CI = 1.04-1.40, P = 0.009; SLC12A9: HR = 2.70, 95% CI = 1.10-6.50, P = 0.031) (as shown in Figure 1B and 1C). Figure 1 D), indicating that SLC12A3 and SLC12A9 are independent prognostic factors affecting clinical outcomes. The present invention uses time-dependent ROC analysis to compare the predictive ability of patients in 3 years, such as Figure 5 As shown in the figure, the 3-year AUC of SLC12A3 was 0.68; the 3-year AUC of SLC12A9 was 0.67, and the 3-year AUC of SLC12A3 and SLC12A9 combined to predict prognosis was 0.74. The above results indicate that SLC12A3 and SLC12A9 have good prognostic prediction ability in UVM patients.

[0096] The present invention measured the expression correlation between SLC12 family members and found that there was no significant correlation between SLC12A3 and SLC12A9 (e.g. Figure 1 E). Further study was conducted to determine whether the combined use of SLC12A3 and SLC12A9 had a synergistic effect on the prognosis of patients. All patients were divided into four risk groups according to the expression levels of SLC12A3 and SLC12A9. Figure 1 As shown in G, there were significant differences in OS and DSS among the four risk groups (OS: log-rank p = 0.015; DSS: log-rank p = 0.016). Patients with high expression of SLC12A3 and SLC12A9 (referred to as A3 H A9 H The OS and DSS were the worst in the A3 group, while patients with low expression of SLC12A3 and SLC12A9 (referred to as A3 L A9 L The OS and DSS of the 24-week follow-up group were the longest. These findings suggest that the combined effect of SLC12A3 and SLC12A9 in predicting prognosis is better than that of a single marker.

[0097] Example 2 Effect of overexpression of SLC12A3 and / or SLC12A9 on proliferation of uveal melanoma cells

[0098] The present invention uses immunohistochemical methods to analyze the expression of SLC12A3 and SLC12A9 in uveal melanoma tissue. Immunohistochemical staining showed that SLC12A3 and SLC12A9 were lowly expressed in normal retinal adjacent tissues and highly expressed in uveal melanoma tissues (such as Figure 2 AC). In order to study the functional role of SLC12A3 and SLC12A9 in uveal melanoma, the present invention used lentiviral infection to overexpress SLC12A3 and SLC12A9 in uveal melanoma cell lines C918 and MUM2C. Western blotting results showed that the protein levels of SLC12A3 and SLC12A9 were increased in the overexpressed cell lines (as shown in Figure 2 D and Figure 2 E). The cell growth curve showed that compared with the EGFP control group, overexpression of SLC12A3 and SLC12A9 could increase cell proliferation activity (as shown in Figure 2 In addition, overexpression of SLC12A3 and SLC12A9 also accelerated the colony formation of uveal melanoma cells (as shown in Figure FI). Figure 2 These results clearly demonstrate that both SLC12A3 and SLC12A9 are highly expressed in uveal melanoma and promote uveal melanoma cell proliferation.

[0099] Example 3 Effect of knockdown of SLC12A3 and SLC12A9 on proliferation of uveal melanoma cells

[0100] The present invention knocks down SLC12A3 or SLC12A9 in uveal melanoma cell lines, or knocks down SLC12A3 and SLC12A9 simultaneously, and finds that si-SLC12A3 (referred to as si-A3) and si-SLC12A9 (referred to as si-A9) can effectively knock down SLC12A3 and SLC12A9, respectively (such as Figure 3 The cell growth curve showed that knocking down SLC12A3 and SLC12A9 could inhibit the proliferation of C918 cells (as shown in Figure 2A). Figure 3 Knockdown of both SLC12A3 and SLC12A9 in C918 cells had a stronger inhibitory effect on cell proliferation (as shown in Figure 2B). Figure 3 B). In order to further confirm this result in other uveal melanoma cell lines, the present invention also knocked down SLC12A3 and SLC12A9 in MUM2C cells, and the cell growth curve also showed that the proliferation of cells with SLC12A3 and SLC12A9 knocked down was inhibited ( Figure 3C). Consistent with the cell proliferation data, knockdown of SLC12A3 and SLC12A9 reduced colony formation in C918 and MUM2C cells, whereas simultaneous knockdown of SLC12A3 and SLC12A9 had a synergistic effect ( Figure 3 DG). In addition, compared with NC cells, in C918 (e.g. Figure 3 H and Figure 3 I) and MUM2C cells ( Figure 3 In J and K), after SLC12A3 or SLC12A9 gene knockdown, the proportion of cells positive for EdU staining decreased (EdU marks proliferating cells in the S phase).

[0101] Example 4 Construction of the prognostic risk score model for the SLC12A3 / A9-MIR140-HCP5 axis

[0102] In order to further study the regulatory mechanism of SLC12A3 / A9 involved in uveal melanoma, the present invention constructed a dysregulated SLC12A3 / A9-miRNA-lncRNA ceRNA network, such as Figure 4 As shown in A. The results showed that lncRNA HCP5 may act as a ceRNA to regulate the expression of SLC12A3 and SLC12A9 by competing for shared MIR140.

[0103] In addition, survival analysis of the TCGA-UVM cohort also showed that low expression of MIR140 and high expression of HCP5 were significantly associated with poor OS and DSS ( Figure 4 B), suggesting that the SLC12A3 / A9-MIR140-HCP5ceRNA regulatory axis may serve as a new prognostic biomarker and potential therapeutic target for UVM patients. In order to determine the prognostic value of the SLC12A3 / A9-MIR140-HCP5 axis, the present invention used regression analysis of the TCGA-UVM cohort to establish a prognostic risk score model for the SLC12A3 / A9-MIR140-HCP5 axis exp(-0.25982*MIR140+0.27088*HCP5+0.02578*SLC12A3+0.97102*SLC12A9-18.23654) (abbreviated as SlcMHscore), and divided 77 patients into a high-risk group (n=38) and a low-risk group (n=39). The survival rates of the two groups of patients were significantly different (HR=4.61, 95%CI 1.77-12, log-rank p<0.001). The time-dependent ROC (time-dependent ROC) of the ceRNA risk model to predict 3-year and 5-year survival rates is shown in the following analysis results. Figure 4D, the AUCs were 0.81 and 0.82, respectively. Two external UVM cohorts were used for independent validation, and the AUCs for predicting 3-year and 5-year survival rates were both higher than 0.7 ( Figure 4 E and Figure 4 F). Kaplan-Meier curve showed that at the optimal cut-off point, patients with high SlcMHscore had a lower survival rate than those with low SlcMHscore (GSE22138: HR = 2.19, 95% CI = 1.10-4.36, log-rank P = 0.022; GSE84976: HR = 15.61, 95% CI = 3.40-71.66, log-rank P < 0.001) ( Figure 4 E and Figure 4 F).

[0104] The preferred embodiments of the present application are described in detail above in conjunction with the accompanying drawings; however, the present application is not limited to the specific details in the above embodiments. Within the technical concept of the present application, a variety of simple modifications can be made to the technical solution of the present application, and these simple modifications all fall within the protection scope of the present application.

[0105] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this application will not further describe various possible combinations.

[0106] In addition, the various implementation modes of the present application may be arbitrarily combined, and as long as they do not violate the concept of the present application, they should also be regarded as the contents disclosed in the present application.

Claims

1. A uveal melanoma prognostic risk scoring model for predicting the prognosis of uveal melanoma, wherein the uveal melanoma prognostic risk scoring model uses biomarker expression levels as input variables, wherein the biomarkers are a combination of SLC12A3, SLC12A3A9, MIR140 and HCP5, and the formula of the uveal melanoma prognostic risk scoring model is SlcMHscore=exp(-0.25982*MIR140+0.27088*HCP5+0.02578*SLC12A3+0.97102*SLC12A9-18.23654).

2. The use of a biomarker in constructing the prognostic risk scoring model for uveal melanoma according to claim 1, characterized in that: The biomarkers are a combination of SLC12A3, SLC12A3A9, MIR140 and HCP5.

3. A prognostic risk assessment system for uveal melanoma, characterized in that: The system includes a calculation unit and a detection unit. The calculation unit calculates the risk score using the prognostic risk scoring model of claim 1. The detection unit is used to detect the expression level of the biomarker, and the biomarker is a combination of SLC12A3, SLC12A3A9, MIR140 and HCP5.

4. The uveal melanoma prognosis risk assessment system according to claim 3, characterized in that: The system further comprises an information acquisition unit, which is used to execute an operation of acquiring detection information of the subject, wherein the detection information comprises the expression level of the biomarker.

5. The uveal melanoma prognosis risk assessment system according to claim 3, characterized in that: The system also includes an evaluation unit, which is used to judge the risk of prognosis of uveal melanoma of the subject according to the calculation result of the calculation unit and give reasonable prevention and treatment suggestions.

6. The uveal melanoma prognosis risk assessment system according to claim 3, characterized in that: The system further comprises a result display unit, and the result display unit is used to display the conclusion obtained by the evaluation unit.

7. The uveal melanoma prognosis risk assessment system according to claim 6, characterized in that: The result display unit displays the result by screen display, voice broadcast or printing.

8. Use of a reagent for detecting biomarkers in a sample in the preparation of a product for the prognosis diagnosis of uveal melanoma, characterized in that: The biomarkers are a combination of SLC12A3, SLC12A3A9, MIR140 and HCP5.

9. The use according to claim 8, characterized in that: The reagents include reagents capable of detecting the expression level of mRNA of the biomarker.

10. The use according to claim 9, characterized in that: The expression level of the mRNA of the detection marker is measured using any one of the methods selected from polymerase chain reaction, nuclease protection analysis, in situ hybridization, nucleic acid microarray, Northern blotting or DNA chip.

11. The use according to claim 10, characterized in that: The polymerase chain reaction includes real-time fluorescence quantitative reverse transcription polymerase chain reaction, reverse transcription polymerase chain reaction, and competitive polymerase chain reaction.

12. The use according to claim 8, characterized in that: The reagents include reagents capable of detecting the expression level of the protein encoded by the biomarker.

13. The use according to claim 12, characterized in that: The expression level of the protein encoded by the detection marker is measured using any one of multiple proximity extension analysis, enzyme-linked immunosorbent assay, radioimmunoassay, sandwich analysis, protein blotting, immunoprecipitation, immunohistochemical staining, fluorescent immunoassay, enzyme substrate development, antigen-antibody aggregation, fluorescence-activated cell sorting, mass spectrometry, MRM assay, an assay using a set of multiple amine-specific stable isotope reagents, or protein chip measurement.

14. The use according to claim 8, characterized in that: The reagents include: Primers or probes that specifically bind to the biomarker gene; An antibody, peptide, aptamer or compound that specifically binds to the biomarker protein.

15. The use according to claim 8, characterized in that: The products include chips, test kits, test strips or high-throughput sequencing platforms.

16. Use of biomarkers in constructing the uveal melanoma prognosis risk assessment system according to claim 3, characterized in that: The biomarkers are a combination of SLC12A3, SLC12A3A9, MIR140 and HCP5.

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