LU502762B1

LU502762B1 - Prognosis model of uterine corpus endometrial carcinoma based on cuproptosis-related lncrnas and its application in immunotherapy

Info

Publication number: LU502762B1
Application number: LU502762A
Authority: LU
Inventors: Li Song
Original assignee: Qilu Hospital Shandong Univ
Priority date: 2022-08-02
Filing date: 2022-09-06
Publication date: 2023-03-09
Also published as: CN116004815A

Abstract

Disclosed are a prognosis model of uterine corpus endometrial carcinoma (UCEC) based on cuproptosis-related lncRNAs and its application in immunotherapy, belonging to the field of biomedicine. The application discloses a product used for evaluating the prognosis of UCEC, comprising a reagent for detecting expression levels of biomarkers, where the biomarkers are cuproptosis-related lncRNAs, including AC103563.2, LINC01629, AL603832.1, AC080013.4, AC244517.7, AC025580.2, AC004596.1, AC243772.2, AC100861.1, AC083799.1, and AC013731.1. According to the application, the prognosis model is constructed and verified based on the above 11 cuproptosis-related lncRNAs, and which provides a strong theoretical basis for the prognosis and immunotherapy of patients with endometrial cancer.

Description

DESCRIPTION LU502762

PROGNOSIS MODEL OF UTERINE CORPUS ENDOMETRIAL CARCINOMA BASED

ON CUPROPTOSIS-RELATED LNCRNAS AND ITS APPLICATION IN

IMMUNOTHERAPY

TECHNICAL FIELD

The present application relates to the field of biomedicine, and in particular to a prognosis model of uterine corpus endometrial carcinoma based on cuproptosis-related IncRNAs and its application in immunotherapy.

BACKGROUND

Uterine corpus endometrial carcinoma (UCEC) is one of the three major malignant tumors of the female reproductive system, with an yearly increasing incidence accounting for 20% to 30% of the gynecologic malignancies, posing a serious threat to women's health. Most patients have already reached advanced stages by the time of diagnosis as a result of unconspicuous early symptoms of UCEC and the lack of effective screening tools and predictive models in clinical practice, in addition to that patients in progressive stages have high prevalence of distant metastases after treatment and low year-5 survival rate; therefore, it is important to clarify the molecular biological mechanisms related to UCEC pathogenesis so as to explore new therapeutic targets and improve prognosis of patients.

Copper is an important trace element in human body; however, free copper is highly toxic and barely detectable in cells under normal conditions; under a stable concentration, copper ensures a normal function of cellular metabolism, energy acquisition and signal transduction and other critical biological processes, while copper of excessive amounts is life-threatening; a recent study has shown that overload copper causes mitochondrial protein aggregation, resulting in a distinct cell death — cuproptosis; moreover, Tsvetkov P et al. discovered that copper is closely related to mitochondrial activity and that although copper does not have a large effect on mitochondrial respiration, copper toxicity is much enhanced in cells with active respiration, resulting in an increased production of lipid acylases, which on the one hand can alter the concentration of many metabolites, leading to acute lethal shock, and on the other hand, producing more aggregates that disrupt the smooth functioning of metabolic pathways and promote cell death. In this way, cuproptosis is introduced into novel therapy for cancer treatment.

As a type of non-coding RNA, long non-coding RNA (IncRNA) is gradually proved to be involved in the development and metastasis of various human cancers, and it can be used as a marker for prediction, diagnosis and prognosis of tumors owing to its good molecular stability.

As for treating UCEC, there are also available data demonstrating the importance of IncRNAs;

Xu et al. suggested that six IncRNAs (KIAA0087, RP11-50102, FAM212B-AS|], 500762

LOC102723552, RP11-140124 and RP11-600K 151) are major regulators of UCEC and they are closely involved in regulating the malignant phenotype of UCEC cells as well as in remodeling the tumor microenvironment (TME); nevertheless, no studies on UCEC prognosis and cuproptosis-related IncRNAs in the TME have been reported.

SUMMARY

With objectives of solving the problems existing in the prior art, the present application provides a prognosis model of uterine corpus endometrial carcinoma (UCEC) based on cuproptosis-related IncRNAs and its application in immunotherapy, where the prognosis model has good performance in distinguishing the prognosis of UCEC patients, which can be used to deliver accurate prognosis for UCEC patients.

To achieve the above objectives, the present application provides the following technical schemes: a product for evaluating prognosis of UCEC, including a reagent for detecting biomarkers in terms of expression levels, where the biomarkers are cuproptosis-related IncRNAs selected from a group of AC103563.2, LINCO1629, AL603832.1, AC080013.4, AC244517.7, AC025580.2,

ACO004596.1, AC243772.2, AC100861.1, AC083799.1, and ACO13731.1.

Optionally, the reagent includes a primer set for detecting the cuproptosis-related IncRNAs, and the primer set has nucleotide sequence as shown in SEQ ID NO: 1 - 22.

The present application also provides a prognosis model of UCEC, where the prognosis model takes the expression levels of the biomarkers as input variables and calculates a risk scoring as follows: risk scoring = (0.39436 x expression level of AC103563.2) +(0.10874 x expression level of

LINCO1629) + (0.5615 x expression level of AL603832.1) + (0.10148 x expression level of

AC080013.4) + (0.31082 x expression level of AC002116.2) + (0.38386 x expression level of

AC002306.1) - (1.45378 x expression level of AC004596.11) + (1.3714 x expression level of

AC243772.2) +(0.58447 x expression level of AC100861.1) - (0.05297 x expression level of

ACO083799.1) - (0.62657 x expression level of ACO13731.1).

The present application also provides a system for assessing prognostic risk of UCEC; including a calculating unit, where the calculating unit calculates the risk score by using the prognosis model of UCEC; optionally, the system includes a detecting unit, where the detecting unit is arranged for detecting the expression levels of the biomarkers; optionally, the system includes an information acquiring unit, where the information acquiring unit is used to acquire detecting information of a subject, and the detecting information include$,502762 the expression levels of the biomarkers; optionally, the system includes an evaluation unit, where the evaluation unit is used to evaluate the risk of the prognosis of endometrial cancer of the subject based on a calculated result of the calculating unit and deliver rationalized treatment recommendations; and optionally, the system includes a result displaying unit, where the result displaying unit is used to display a conclusion obtained by the evaluation unit.

The present application further provides an application of the product or the prognosis model of

UCEC or the system for assessing prognostic risk of UCEC in screening immunotherapy drugs of UCEC.

Optionally, the patients with UCEC of high risk is suitable for treatment with ABT888, cisplatin, doxorubicin, etoposide, paclitaxel, PD.173074, sorafenib and/or gemcitabine; while the patients

UCEC of low risk is suitable for treatment with docetaxel, lapatinib and/or metformin.

The present application discloses the following technical effects: according to the present application, a prognosis model consisting of 11 cuproptosis-related

IncRNAs (AC103563.2, LINCO1629, AL603832.1, AC080013.4, AC244517.7, AC025580.2,

AC004596.1, AC243772.2, AC100861.1, AC083799.1, and ACO13731.1) is constructed and verified by single factor regression, Lasso regression and multi-factor regression analysis; as for risk scoring, the value of area under curve (AUC) in year-1, year-2 and year-3 are 0.826, 0.816 and 0.786, respectively, indicating that the prognosis model based on 11 genes related to prognosis of the present application has good performance in distinguishing the prognosis of

UCEC patients, as well as accurate prediction of prognosis of UCEC patients; after risk stratification of patients according to clinical and pathological features, patients are divided into low-risk group and high-risk group, and it is further found that the overall survival rate of low-risk group is higher than that of high-risk group; and results of Gene Set Enrichment

Analysis (GSEA) show that Notch signaling pathway, wingless (Wnt) signaling pathway, vascular endothelial growth factor (VEGF) signaling pathway, and mammalian target of rapamycin (mTOR) are significantly abundant in low-risk group, with relatively more immune cells show close relation to the low-risk group, and almost all immune checkpoints are activated in low-risk groups, such as CD44, CD200, TNFSF14, CD40LG, CTLA-4 and TNFRSF14, indicating that patients in low-risk groups are more sensitive to immunotherapy; besides, it is further revealed by applying ESTIMATE R package that immune scoring and estimated scoring are remarkably higher in the low-risk group than those of patients in the high-risk group, which allows the assessment of the clinical effectiveness of chemotherapy and immunotherapy;

the relationship between cuproptosis-related IncRNAs and UCEC is clarified to a greater extent,502762 by the present application, which provides a strong theoretical basis for exploring new targets for treating UCEC, especially for the method of immunotherapy.

BRIEF DESCRIPTION OF THE FIGURES

In order to more clearly explain the embodiments of the present application or the technical schemes in the prior art, the following will briefly introduce the figures that need to be used in the embodiments. Obviously, the figures in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other figures can be obtained according to these figures without any creative effort.

FIG. 1 illustrates identifications of survival-related Cullin-RING Ligases (CRLs) in uterine corpus endometrial carcinoma (UCEC), where FIG. 1A shows CRLs that related to the prognosis of UCEC, FIG. 1B is a forest plot of top 20 survival-related CRLs, FIG. 1C is a regulation map showing a relationship between cuproptosis gene and 51 survival-related CRLs, FIG. 1D shows

Lasso regression curves of CRLs, and FIG. 1E shows results of 10-fold cross-validation of variables selection by Lasso.

FIG. 2 shows predictions of prognosis model against high-risk group and low-risk group, where

FIG. 2A shows a heatmap of 11 CRLs in two risk groups, FIG. 2B shows a distribution of risk scores, FIG. 2C illustrates survival duration and clinical endpoints, FIG. 2D shows Kaplan-Meier (K-M) curves of overall survivals (OS) of patients in high-risk group and low-risk group, FIG. 2E shows time-dependent receiver operating characteristic (ROC) curves predicted by the model, and FIG. 2F illustrates a relationship between risk scoring and clinical features.

FIG. 3 shows an Nom diagram and calibration curves, where FIG. 3A shows the Nom diagram, and FIG. 3B illustates calibration curves of survival rates of year-1, year-3 and year-5.

FIG. 4 shows expression levels of CRLs differentially expressed in UCEC tissues and paracancerous tissues verified by RT-qPCR, including AC083799.1, AL603832.1, AC243772.2,

ACO013731.1, AC004596.1, AC080013.4, and AC002116.2.

FIG. 5 shows differences of gene set enrichment analysis (GSEA) enriching Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, where FIG. SA illustrates enrichment pathways of high-risk group, and FIG. 5B shows enrichment pathways of low-risk group.

FIG. 6 illustrates relationships of risk scoring between immune cells and immune function, where FIG. 6A shows differences of immune cell infiltration between the two risk groups, FIG. 6B shows single-sample GSEA (ssGSEA) scoring of immune cells in two risk groups, and FIG. 6C shows immune function scoring of the two risk groups.

FIG. 7 illustrates immune microenvironments of high-risk group and low-risk group, where FIG.

7A shows difference in terms of expression at immune checkpoints between tow risk groups, and5p2762

FIG. 7B shows interstitial scoring, immune scoring and evaluation scoring of the two risk groups.

FIG. 8 illustrates correlations of the risk scoring of common chemotherapeutic drugs, targeted drugs and immunotherapy drugs between IC50, where the drugs include ABT888, cisplatin, doxorubicin, etoposide, paclitaxel, PD.173074, sorafenib, gemcitabine, docetaxel, and lapatinib.

DESCRIPTION OF THE INVENTION

Now, various exemplary embodiments of the present application will be described in detail. This detailed description should not be taken as a limitation of the present application, but should be understood as a more detailed description of some aspects, characteristics and embodiments of the present application.

It should be understood that the terms mentioned in the present application are only used to describe specific embodiments, and are not used to limit the present application. In addition, for the numerical range in the present application, it should be understood that each intermediate value between the upper limit and the lower limit of the range is also specifically disclosed.

Every smaller range between any stated value or the intermediate value within the stated range and any other stated value or the intermediate value within the stated range is also included in the present application. The upper and lower limits of these smaller ranges can be independently included or excluded from the range.

Unless otherwise stated, all technical and scientific terms used herein have the same meanings commonly understood by those of ordinary skill in the field to which this application relates.

Although the present application only describes preferred methods and materials, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present application. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and/or materials related to the documents. In case of conflict with any incorporated documents, the contents of this specification shall prevail.

Without departing from the scope or spirit of the present application, it is obvious to those skilled in the art that many modifications and changes can be made to the specific embodiments of the present specification. Other embodiments obtained from the description of the present application will be obvious to the skilled person. The description and embodiment of that application are only exemplary.

As used in this paper, the terms "comprising", "including", "having" and "containing" are all open terms, meaning including but not limited to.

According to the application, clinical information and RNA-seq sequencing data of UCEC patients are downloaded from a database of The Cancer Genome Atlas (TCGA), differençe,502762

IncRNAs related to cuproptosis are screened by using Pearson correlation and wilcox analysis, then a prognosis risk model of uterine corpus endometrial carcinoma (UCEC) patients is established and verified by univariate Cox regression, Lasso regression and multivariate Cox regression analysis; the established risk model is investigated in terms of clinical value by

Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves, then, independent prognostic factors are screened and a prognosis model based on clinical parameters and risk scores (RS) is established accordingly, meanwhile, correlations between risk score and immune cell infiltration, immune checkpoint molecules and chemotargeted drug sensitivity are analyzed respectively; the specific test is designed as follows: 1 Experimental design 1.1 Data collection

Data including RNA sequences of 522 UCEC tumors and 35 paracancerous tissues is downloaded form a database of The Cancer Genome Atlas (TCGA), value of fragments per kilobase of exon model per million mapped fragments (FPKM) is used to identify the expressed

IncRNAs; the clinical information of 526 patients is searched form survival analysis, including age, gender, survival status and follow-up days, etc. 1.2 Selection of survival-related CRL cuproptosis genes are selected with reference to cuproptosis gene set and relevant literatures, and Pearson correlation analysis is performed on all IncRNAs in UCEC samples against cuproptosis genes, where some IncRNAs (satisfying |coefficient|}>0.3, P value < 0.001) are regarded as CRLs; a IncRNA-mRNA network is developed by "igraph" using R software package, and mutual regulation relationship between cuproptosis gene and CRL is therefore disclosed; "limma" of R package is used to compare the UCEC tumor and paracancerous tissue in terms of differential expression of CRLs with a standard of |Log2(FC)|>1; 1.3 Establishment and verification of prognosis model the survival-related CRL is determined by univariate Cox regression analysis (P < 0.05); all

IncRNAs, whether significantly associated with UCEC prognosis or not, are shown in a volcano plot, and a top 20 survival-related CRLs are shown in a forest plot; "ggplot2" and "ggplot2" of R package are used to regulatecuproptosis gene and survival-related CRLs, followed by Lasso and multi-Cox regression analysis so as to establish a prognosis model, where the risk score of

UCEC patients is calculated according to the following formula:

Risk score =%[coef(IncRNA(XExp(IncRNA)]; all UCEC samples are divided into low-risk group and high-risk group; overall survivals (OS) of both risk groups are evaluated by the K-M curve generated by "Survival Rate" of R packaggi,502762 values of area under curve (AUC) in year-1, year-2 and year-3 of ROC curve are calculated to evaluate the model in terms of accuracy; besides, correlations of risk scoring between age, gender and living conditions are analyzed respectively. 1.4 Gene set enrichment analysis (GSEA) "clusterProfiler", "limma", "org Hs.e.g.db" and "enrichplot" of R software package are used to identify the enrichment pathways of high-risk group and low-risk group (statistically significant if P < 0.05); the enrichment pathways are revealed by GSEA 4.2.2; 1.5 Construction of Nom diagram stratified survival analysis is conducted to investigate the cuproptosis in terms of applicability according to the risk scoring and clinicopathological features (such as age and grade); the Nom diagram 1s developed given to that there are independent prognostic factors of year-1, year-3 and year-5 OS of UCEC patients with R package "rms"; then calibration curve is plotted using

Hosmer-Lemeshow so as to illustrate that the actual results are consistent with predicted results; 1.6 RNA isolation and RT-qPCR

TRIzol reagent (Invitrogen; Thermo Fisher Scientific) is used to extract total ribonucleic acid (RNA) from UCEC frozen tissues; then PrimeScript RT kit (Takara) is used to reverse transcribe

RNA into cDNA (a reaction system of 3,000 nanograms per 10 milliliters (uL)) according to specification of manufacturer; then in a 10 pL reaction system of StepOne™ PCR amplifier (Applied Biosystems, USA) and SYBR green (TAKARA, Japan), glyceraldehyde 3-phosphate dehydrogenase (GAPDH) is used as endogenous control for RT-qPCR, where the PCR includes there stages of conditions: holding stage (95 degree Celsius (°C), 30 seconds (s)); 40 cycles of

PCR stage (95°C, 5 s, and 60°C, 34 s); and melting curve stage (95°C, 15 s, 60°C, 1 minute (min) and 95°C, 15 s); sequences of applied primer is shown in the following table (SEQ ID NO: 1-22):

ST Primers weed Ter RTgPCR Luso2762

Primer Sequence (943) :

LINOOL629 Fo GCGAACAGCCTATAAAGAAAATGG

LE MC AGAATOCUICOTTIGTA paces à Fi OTGGACCTOGAAGTOOGGT rc Fe OGCGQUAAGATA TOAGAGSG : acosoniza RE ATAAGGEAGTIOGAGEEAG vue Ri ONCAAGOATTICAGATATAGAACT

SR GITAGTOCCOYTOTTGCTOG

WF TOTOGUCAGOAGGUAGAT EE

1.7 Relationship between risk scoring and immune cell infiltration

XCELL, TIMER, QUANTISEQ, McCounter, EPIC, CIBERSORT-ABS and CIBERSORT are used to evaluate the correlations between risk scoring and tumor infiltrating immune cells, the infiltration and function of immune cells in two risk groups are also compared; and tumor microenvironment (TME) scoring including matrix scoring, immune scoring and estimated scoring of the two risk groups are calculated by using "Estimate" of R software package; 1.8 Risk scoring of different response of chemotherapy and immunotherapy

IC50 values of 16 common drugs that can be used for UCEC treatment are calculated using "Prrophytic", "limma", "ggpub" and "ggplot2" of R package, whereby the response to chemotherapy, targeted and immunotherapy drugs of UCEC patients in high-risk and low-risk groups are predicted, and IC50 values of the two risk groups are analyzed by Wilcoxon signature ranking test in terms of difference; 2. Test results 2.1 Identification and characterization of CRLs in UCEC a total of 14,004 IncRNAs are extracted from the RNA map, where 1,265 IncRNAs are considered as CRLs as being related to cuproptosis genes; a mRNA-IncRNA co-expression network is established to determine the potential impact of cuproptosis, where 429 IncRNAs are out of balance in UCEC tissue, including 249 up-regulated IncRNAs and 180 down-regulated)5p2762

IncRNAs, as comparing to a normal organization; a first 100 differentially expressed CRLs are shown in the heatmap, whereby the tumor group can be effectively distinguished from the non-tumor group; 2.2 CRLs-based predictive prognosis model construction as can be seen from FIG. 1A, CRLs are related to the prognosis of UCEC; univariate Cox regression analysis is performed to screen survival-related CRLs, and a total of 51 survival-related CRLs are found with first 20 being shown in the forest plot (FIG. 1B); further, a regulation map (FIG. 1C) of cuproptosis gene and survival-related CRLs is plotted, from which it can be seen that most of the regulations are positive; then Lasso regression analysis is performed to exclude co-expressed CRLs and avoid over-fitting (FIG. 1D), and 20 CRLs are selected for further analysis, with results of 10-fold cross-validation of variable selection with

Lasso shown in FIG. 1F; subsequently, the prognosis model consisting of 11 CRLs is constructed by multiple Cox regression analysis, as shown in the following table:

Table 2 The prognosis model based on CRLs _ CRLs ~~ cof ~~ HR HR95L HROH Pvalue —

AC103563.2 0.394355726 1.483428148 1.20577855 1.825010962 0.000191648

LINCO1629 0.108742295 1.114875005 1.001021329 1.241678115 0.047868123

AL603832.1 0.561498716 1.753298228 1.223508655 2.512491157 0.002221547

ACO080013.4 0.101477415 1.106804921 1.043341757 1.174128347 0.000756398

AC002116.2 0.310820444 1.364544187 1.037986191 1.793839701 0.025940474

AC002306.1 0.383857809 1.4679367 1.085826781 1.98451373 0.012588085

ACO004596.1 -1.453778952 0.233685531 0.107143804 0.509678819 0.000258252

AC243772.2 1371403485 3.940877778 1.814047391 8.561252444 0.000531235

AC100861.1 0.58446631 1.794033273 1.308209019 2.460276102 0.000286382

ACO083799.1 -0.052970106 0.948408364 0.912466533 0.985765935 0.00720379 _ACOI3731.1 -0.626367499 0554425064 0289107549 0967896063 0045628663 the risk scores of UCEC patients are calculated according to the formula for calculating risk scores mentioned above; then the above 11 prognosis-related CRLs are used to construct the model and a formula for calculating the risk scoring based on its expression is obtained as follows: risk scoring = (0.39436 x expression level of AC103563.2) + (0.10874 x expression level of

LINCO1629) + (0.5615 x expression level of AL603832.1) +(0.10148 x expression level of

AC0O80013.4) + (0.31082 x expression level of AC002116.2) + (0.38386 x expression level of

AC243772.2) + (0.58447 x expression level of AC100861.1) - (0.05297 x expression level of

AC083799.1) - (0.62657 x expression level of AC013731.1); all UCEC patients are divided into a high-risk group (260 patients) and a low-risk group (261 patients) according to a median of th@,5p27g2 risk scoring; 2.3 Evaluation of prognosis model the prognosis model is evaluated by comparing the expression of 11 CRLs, the distribution of risk scoring and the survival status between low-risk group and high-risk group, and the results show that three CRLs (AC004596.1, AC083799.1 and AC013731.1) are down-regulated and the other eight CRLs are up-regulated (FIG. 2A) in the high-risk group, and it can be seen form the

FIG. 2B - FIG. 2D that the risk scoring of the high-risk group is obviously higher, where a higher risk scoring indicates an increased mortality; the survival rate of patients in low-risk group is significantly improved (P < 0.001); the predictive performance against risk scoring of UCEC patient is evaluated by ROC curve, and the year-1, year-2 and year-3 AUC values of risk scoring are 0.842, 0.814 and 0.759, respectively (FIG. 2E), indicating a good distinguishing performance for the prognosis of UCEC patients and an accurate prediction of prognosis of UCEC patients; besides, stratified survival analysis is also carried out in two groups of UCEC patients according to age, grade and survival status to reveal the relationship between risk scoring and prognosis of clinical pathological variables, and the results show that the risk scoring of patients over 65 years old, G3 group and death group are higher; the results also show that the risk scoring is positively correlated with clinicopathological variables (FIG. 2F); 2.4 Establishment of Nom model the Nom diagram is developed according to the clinicopathological factors such as age, grade and risk scoring to predict the survival rate of UCEC patients, so as to transform the characteristics of CRLs into clinical utility; the year-1, year-3 and year-5 OS are predicted to be 0.981, 0.916 and 0.885 respectively, and the consistency between the predicted results and the actual observed results is investigated by calibration curves, where a good correspondence is revealed by the calibration curves (see FIG. 3A: Nom diagram for predicting the operating system; FIG. 3B: calibration curves of year-1, year-3 and year-5 overall survivals); 2.5 Validation of the diagnostic and prognostic value of the proposed prognosis model by

RT-qPCR the expression levels of 11 CRLs constituting the prognosis modelare are verified by RT-qPCR in 20 UCEC tumor tissues and 10 paracancerous tissues, and the results show that six kinds of

IncRNAs, including AC083799.1, AL603832.1, AC243772.2, ACO13731.1, AC004596.1 and

AC002116.2, are significantly up-regulated in UCEC tumors as comparing to the paracancerous tissues, AC080013.4 is down-regulated in UCEC tumors (see FIG. 4), and the results are consistent with the results of TCGA database analysis; while the other four IncRNAs are expressed at very low levels in tumors and paracancerous tissues and are difficult to detect hy,5p2762

RT-qPCR; 2.6 TME characterized by prognosis model

GSEA software 1s used to analyze the difference of biological function between high-risk group and low-risk group; as shown in FIG. 5, several tumor and immune-related pathways, such as

Notch, Wnt, VEGF and mTOR signaling pathways, are significantly enriched in the high-risk group (FIG. 5A), while in the low-risk group, pathways enriched are mainly related to metabolism (FIG. 5B), suggesting that signal pathway plays an important role in the progress of

UCEC patients; in addition, the correlation between risk scoring and tumor infiltrating immune cells is studied through seven different softwares, with results shown in Figs. 6A-C, and it can be seen form the figures that more immune cells are negatively correlated with risk scoring, especially CD8+T cells, NK cells and T cell regulatory cells; then, ssGSEA is used to quantify the scores of different immune cells and functions, and it is found that the risk scoring is negatively correlated with CD8+T cells, dendritic cells (DC), immature dendritic cells (IDC), neutrophils, NK cells, tumor infiltrating lymphocytes (TIL), and helper T cells, while positively correlated with activated dendritic cells (ADC), some immune functions, such as T cell co-stimulation/co-inhibition, type II interferon response, HLA, cytolytic activity, checkpoint and

CCR, score higher in low-risk group than in high-risk group, while the functions of MHC class I and type I interferon responses score higher in high-risk groups; 2.7 TME characteristics between high-risk group and low-risk group by comparing the expression of different immune checkpoints in the two risk groups, it is found that several immune checkpoints are activated in the low risk group, including CTLA-4, CD44,

CD40/40LG, CD200, TNFSF14/15 and TNFRSF14 indicating that the immune function of the low-risk group is activated and patients are more sensitive to immunotherapy (FIG. 7A); TME scoring, including matrix scoring, immune scoring and estimated scoring, are calculated, and the calculated scoring show that the immune scoring and estimated scoring of patients in low-risk group are significantly higher than those in high-risk group (FIG. 7B); 2.8 Relationship between risk scoring and chemotherapy and immunotherapy response the relationships between risk scoring and IC50 values of 16 FDA-approved chemotherapy and immune drugs are respectively investigated, and the results show that patients in the high-risk groups are more sensitive to ABT888, cisplatin, doxorubicin, etoposide, paclitaxel, PD.173074, sorafenib and gemcitabine, while patients in the low-risk group are more sensitive to docetaxel, lapatinib and metformin (see FIG. 8), and there is no difference against other five drugs

(AG.014699, AZD 2281, cytarabine, sunitinib and mitomycin) between the two groups. LU502762

The above-mentioned embodiments only describe the preferred mode of the application, but do not limit the scope of the application. On the premise of not departing from the design spirit of the application, all kinds of modifications and improvements made by ordinary technicians in the field to the technical scheme of the application shall fall within the scope of protection determined by the claims of the application.

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University</ApplicantName> <ApplicantNameLatin> QILU HOSPITAL OF SHANDONG UNIVERSITY</ApplicantNameLatin> <InventionTitle languageCode="zh">Prognosis model of uterine corpus endometrial carcinoma based on cuproptosis-related IncRNAs and its application in immunotherapy</InventionTitle> <SequenceTotalQuantity>22</SequenceTotalQuantity> <SequenceData sequencelDNumber="1"> <INSDSeq> <INSDSeq_length>24</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..24</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value>

</INSDQualifier> LU502762 <INSDQualifier id="q1"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>gcgaacagcctataaagaaaatgg</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="2"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q2">

<INSDQualifier_name>organism</INSDQualifier_name> LUS02762 <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>ggcagaatgcgtectctgta</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="3"> <INSDSeq> <INSDSeq_length>19</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..19</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q3"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> LUS02762 </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>ctggacctggaagtggggt</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="4"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q4"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value>

</INSDQualifier> LU502762 </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>tcactaagececcagetttc</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="5"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q5"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals>

</INSDFeature> LU502762 </INSDSeq_feature-table> <INSDSeq_sequence>tttcacatctgagggcagca</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="6"> <INSDSeq> <INSDSeq_length>19</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..19</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q6"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table>

<INSDSeq_sequence>ctgactccttcaaccgeca</INSDSeq_sequence> </INSDSeq> LUS02762 </SequenceData> <SequenceData sequencelDNumber="7"> <INSDSeq> <INSDSeq_length>19</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..19</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q7"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>cgcgggaagacatgagagg</INSDSeq_sequence> </INSDSeq> </SequenceData>

<SequenceData sequencelDNumber="8"> <INSDSeq> LUS02762 <INSDSeq_length>21</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..21</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q8"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>ccataaggcagttggacccag</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="9"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length>

<INSDSeq_moltype>DNA</INSDSeq_moltype> LUS02762 <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table>

<INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q9"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>aggggagaaaaggtgcgtag</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="10"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype>

<INSDSeq_division>PAT</INSDSeq_division> LUS02762 <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q10"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>aaacaagcaccctgactgge</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="11"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature>

<INSDFeature_key>source</INSDFeature_key> LUS02762 <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q11"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>gtctecactttggtecccag</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="12"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key>

<INSDFeature_location>1..20</INSDFeature_location> LU502762 <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q12"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>tcggggcaggatctgtctta</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="13"> <INSDSeq> <INSDSeq_length>23</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..23</INSDFeature_location> <INSDFeature_quals> <INSDQualifier>

<INSDQualifier_name>mol_type</INSDQualifier_name> LUS02762 <INSDQualifier_value>other DNA</INSDQualifier_value>

</INSDQualifier> <INSDQualifier id="q13"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>cccaaggattcagacacagaacc</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="14"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value>

</INSDQualifier> LU502762 <INSDQualifier id="q14"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>gttagtccecttgttgetgg</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="15"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q15">

<INSDQualifier_name>organism</INSDQualifier_name> LUS02762 <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>tgggcttttcctttcgacat</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="16"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q16"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> LUS02762 </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>ccaaaccaactgcctctgga</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="17"> <INSDSeq> <INSDSeq_length>18</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..18</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q17"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier>

</INSDFeature_quals> LU502762 </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>tgtgggcaggagggagac</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="18"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q18"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals>

</INSDFeature> LU502762 </INSDSeq_feature-table> <INSDSeq_sequence>gtcggagtcggaggaagaac</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="19"> <INSDSeq> <INSDSeq_length>22</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..22</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q19"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table>

<INSDSeq_sequence>ttgagccaaggcactgtaacta</INSDSeq_sequence> </INSDSeq> LUS02762 </SequenceData> <SequenceData sequencelDNumber="20"> <INSDSeq> <INSDSeq_length>24</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..24</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q20"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>gtagccaaagaaacaggaccttac</INSDSeq_sequence> </INSDSeq> </SequenceData>

<SequenceData sequencelDNumber="21"> <INSDSeq> LUS02762 <INSDSeq_length>20</INSDSeq_length> <INSDSeq_moltype>DNA</INSDSeq_moltype> <INSDSeq_division>PAT</INSDSeq_division> <INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q21"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>gttttgaactggggcacatt</INSDSeq_sequence> </INSDSeq> </SequenceData> <SequenceData sequencelDNumber="22"> <INSDSeq> <INSDSeq_length>20</INSDSeq_length>

<INSDSeq_moltype>DNA</INSDSeq_moltype> LUS02762 <INSDSeq_division>PAT</INSDSeq_division>

<INSDSeq_feature-table> <INSDFeature> <INSDFeature_key>source</INSDFeature_key> <INSDFeature_location>1..20</INSDFeature_location> <INSDFeature_quals> <INSDQualifier> <INSDQualifier_name>mol_type</INSDQualifier_name> <INSDQualifier_value>other DNA</INSDQualifier_value> </INSDQualifier> <INSDQualifier id="q22"> <INSDQualifier_name>organism</INSDQualifier_name> <INSDQualifier_value>synthetic construct</INSDQualifier_value> </INSDQualifier> </INSDFeature_quals> </INSDFeature> </INSDSeq_feature-table> <INSDSeq_sequence>tgaactgcacaaccacctca</INSDSeq_sequence> </INSDSeq> </SequenceData> </ST26Sequencelisting>

Claims

CLAIMS LU502762

1. A product for evaluating a prognosis of uterine corpus endometrial carcinoma (UCEC), characterized in that the product comprises a reagent for detecting expression levels of biomarkers, and the biomarkers are cuproptosis-related IncRNAs, comprising AC103563.2, LINCO1629, AL603832.1, AC080013.4, AC244517.7, AC025580.2, AC004596.1, AC243772.2, AC100861.1, AC083799.1, and ACO13731.1.

2. The product according to claim 1, characterized in that the reagent comprises a primer set for detecting the cuproptosis-related IncRNAs, and the primer set has nucleotide sequence as shown in SEQ ID NO: 1 - 22.

3. A prognosis model of UCEC, characterized in that the expression levels of the biomarkers in claim 1 are used as an input variable, and a risk scoring is calculated as follows: risk scoring = (0.39436 x expression level of AC103563.2) + (0.10874 x expression level of LINCO1629) + (0.5615 x expression level of AL603832.1) +(0.10148 x expression level of AC080013.4) + (0.31082 x expression level of AC002116.2) + (0.38386 x expression level of AC002306.1) - (1.45378 x expression level of AC004596.11) + (1.3714 x expression level of AC243772.2) + (0.58447 x expression level of AC100861.1) - (0.05297 x expression level of ACO083799.1) - (0.62657 x expression level of ACO13731.1).

4. A system for assessing prognostic risk of UCEC, characterized by comprising a calculating unit, wherein the calculating unit calculates the risk score by using the prognosis model of UCEC according to claim 3.

5. The system for assessing prognostic risk of UCEC according to claim 4, characterized by further comprising a detecting unit for detecting the expression levels of the biomarkers according to claim 1.

6. The system for assessing prognostic risk of UCEC according to claim 4, characterized by further comprising an information acquiring unit, wherein the information acquiring unit is arranged to acquire detecting information of a subject, and the detecting information includes the expression levels of the biomarkers.

7. The system for assessing prognostic risk of UCEC according to claim 4, characterized by further comprising an evaluation unit, wherein the evaluation unit is used to evaluate the risk score of the prognosis of endometrial cancer of the subject based on a calculated result of the calculating unit and deliver rationalized treatment recommendations.

8. The system for assessing prognostic risk of UCEC according to claim 4, characterized by further comprising a result displaying unit for displaying a conclusion obtained by the evaluation unit.

9. An application of the product according to any one of claims 1-2, the prognosis model pf;502762 UCEC according to claim 3 or the system for assessing prognostic risk of endometrial cancer according to any one of claims 4 - 8 in screening immunotherapy drugs for endometrial cancer.

10. The application according to claim 9, characterized in that the drugs suitable for patients with endometrial cancer of high risk comprise ABT888, cisplatin, doxorubicin, etoposide, paclitaxel,

PD.173074, sorafenib and/or gemcitabine, and the drugs suitable for patients with endometrial cancer of low risk comprise docetaxel, lapatinib and/or metformin.