CN112489800A - Prognosis evaluation system for prostate cancer patient and application thereof - Google Patents
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Abstract
The invention provides a prognosis evaluation system for a prostate cancer patient and application thereof, and relates to the technical field of biomedicine. The evaluation system includes a system for detecting 14 immune-related signature gene sets of aDC, angiogenises, CSF1Response, GRANS, HCK, Lymphocyte-Specific activator, Neutrophils, NK CD56 bright cells, NK CD56 dim cells, Macrophages, Pro-inflamadory, Proliferation, Stromcell and ZMYND10 Metagene, and PCIPI. The invention overcomes the defects of the prior art, determines the correlation between the PCIPI and the relapse-free survival time (RFS) of the patient, tumor-infiltrated immune cells and mutation load by establishing the PCIPI, is helpful for explaining the potential mechanism of drug resistance generation of immunotherapy, and is convenient for guiding the clinical treatment of the prostate cancer patient.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to a prognosis evaluation system for a prostate cancer patient and application thereof.
Background
Prostate cancer is the second most common cancer in the global male population, and is also the fifth leading cause of death in men. However, currently available cancer classification systems do not accurately predict the prognosis of prostate cancer patients and do not guide clinicians to the treatment of patients. Therefore, we aimed to establish an evaluation system based on the behavior characteristics of immune cells/immune responses that can be used to predict the prognosis of patients and the response to immunotherapy.
The Prostate Cancer Immune Prognostic Index (PCIPI) is a promising biological typing system for assessing the recurrence-free survival of prostate cancer. Patients with high PCIPI may benefit from anti-PD-1/PD-L1 immunotherapy. Recently, more and more studies have found that Tumor Infiltrating Lymphocytes (TILs) are associated with the prognosis and progression of various cancers. Understanding the infiltration and activity of TILs in prostate cancer helps urologists/oncologists to understand patient prognosis and to develop a targeted immunotherapy regimen, so determining PCIPI is critical to clinical treatment of prostate cancer patients.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a prognosis evaluation system for a prostate cancer patient and application thereof, wherein the PCIPI is established to determine the correlation between the PCIPI and the relapse-free survival time (RFS) of the patient, tumor-infiltrated immune cells and mutation load, which is helpful for explaining the potential mechanism of drug resistance generation of immunotherapy and is convenient for guiding clinical treatment of the prostate cancer patient.
In order to achieve the above purpose, the technical scheme of the invention is realized by the following technical scheme:
a prognostic assessment system for prostate cancer patients, said assessment system comprising a system for detecting 14 sets of immune-related signatures of aDCs, angiogenises, CSF1Response, GRANS, HCK, Lymphocyte-Specific activator, Neutrophils, NK CD56 bright cells, NK CD56 dim cells, Macrophages, Pro-inflamamatory, promotion, Stromcell and ZMYND10 Metal.
Preferably, the assessment system comprises determining the prostate cancer immune prognosis index PCIPI by adding the NES scores of the 14 immune-related gene sets aDC, angiogenisis, CSF1Response, GRANS, HCK, Lymphocyte-Specific activator, Neutrophils, NK CD56 bright cells, NK CD56 dim cells, Macrophages, Pro-inflamadory, Proliferation, Stromcell, ZMYND10 Metal, i.e., the PCIPI is obtained by the formula
Wherein NESiIs directed toNormalized ssGSEA score for the ith immune signature.
Application of a prognosis evaluation system of a prostate cancer patient in predicting treatment efficacy of PD-1/PD-L1.
Preferably, the mode of use is a grouping of prostate cancer patients into the PCIPI-HIGH group and the PCIPI-LOW group by the PCIPI obtained in claim 2, wherein the PCIPI-HIGH group is determined to benefit more from anti-PD-1/PD-L1 immunotherapy than the PCIPI-LOW patient.
The application of a prognosis evaluation system of a prostate cancer patient in predicting recurrence-free survival after treatment of the prostate cancer patient.
Preferably, the mode of use is to classify prostate cancer patients into high-risk and low-risk by combining the PCIPI obtained in claim 2 with the clinical pathology, wherein high-risk prostate cancer patients are determined to have no recurrence with poor survival.
The invention provides a prognosis evaluation system for prostate cancer patients and application thereof, and compared with the prior art, the invention has the advantages that:
(1) the invention discloses the correlation between PCIPI and RFS, tumor-infiltrated immune cells and mutation load of prostate cancer patients, and is helpful for explaining the potential mechanism of drug resistance generation of immunotherapy;
(2) the PCIPI can be used as a reliable typing system for evaluating RFS of a prostate cancer patient to guide the prognosis clinical treatment of the prostate cancer patient;
(3) the PCIPI typing system can effectively predict the RFS outcome of patients, in addition, the patients of the PCIPI-HIGH subgroup are likely to benefit from anti-PD-1/PD-L1 immunotherapy, and the combination of the PCIPI typing system and clinical pathological characteristics can improve the prediction efficacy of RFS and help promote the individualized treatment of prostate cancer patients.
The attached drawings of the specification:
FIGS. 1-14 are schematic diagrams of the Meta assay of the present invention showing that 14 of 8 cohorts had immunological markers (P.ltoreq.0.01) associated with patient relapse free survival;
FIG. 15 is a graph of the relationship between the PCIPI system constructed with 14 immune-related markers of the present invention and the survival rate without recurrence of prostate cancer patients, wherein: (A) the flow chart shows the overall design of the current study; survival analysis showed, TCGA-PRAD; (B) AHMU-PC, (C) difference in survival of subgroups of high PCIPI and low PCIPI patients in GSE116918(D) and GSE46602(E) cohorts; (F) meta-analysis showed the overall prognostic value of PCIPI in 8 cohorts; (G) the heatmap shows the Normalized Enrichment Score (NES) for immune characteristics associated with recurrence-free survival for 14 of the TCGA-PRAD and AHMU-PC cohorts; correlation of PCIPI and ImmuneScore in TCGA-PRAD cohort (H) and AHMU-PC cohort (I); correlation of PCIPI with TumourPurity in TCGA-PRAD study group (J) and AHMU-PC study group (K); (L) correlation between PCIKPI and immune indices in the MSKCC, GSE70770, GSE46602 and GSE116918 cohorts; (M) correlation between PCIPI and TILs (tumor infiltrating immune cells) infiltration in TCGA-PRAD cohort; (N-O) a significantly enriched signal pathway in the PCIPI-HIGH subgroup;
FIG. 16 is a bar graph of Normalized Enrichment Score (NES) of 14 immune markers in the TCGA-PRAD and AHMU-PC cohorts of the present invention;
FIG. 17 is a schematic representation of the differential infiltration of immune cells by the PCIPI of the present invention: (A) differential infiltration of 28 immune cells in 7 cohorts of PCIPI-HIGH and PCIPI-LOW subgroups; (B) differential infiltration of 7 specific immune cells in the AHMU-PC cohort; (C) immunohistochemical staining verified the differential infiltration of immune cells in the AHMU-PC cohort;
figure 18 is a schematic representation of significant correlation between PCIPI and immune checkpoint expression and immunotherapy efficacy of the invention: (A) correlation between PCIPI and 66 immune checkpoint expression; (B) increased expression of PD-L1 was observed in the PCIPI-HIGH group; (C) PD-1/PD-L1 treatment is more effective for patients with PCIPI-HIGH group;
FIG. 19 is a schematic diagram showing the difference in the distribution of Copy Number Variation (CNV) of the gene of the present invention between PCIPI-HIGH and PCIPI-LOW sub-groups: increased amplification and deletion of CNV was found at both arm ends (a) and intersection (B); mutations in the genes of PCIPI-HIGH (C) and PCIPI-LOW (D) groups were observed; (E) differences in PCIPI in TP53 wild-type and mutant subgroups; (F) distribution of mutation sites in the PCIPI-High and PCIPI-LOW groups. PCIPI, immune prognostic index for prostate cancer;
FIG. 20 is a schematic representation of the ability of PCIPIs of the invention to distinguish between molecular subtypes and to achieve accurate prognosis: (A) alluviation plots of PCIPI subtype and different immune subtypes; (B) a Kaplan-Meier curve of the recurrence-free survival of four immune subtypes; (C) pcipii distinguishes C3-immunized patients into high-risk and low-risk subgroups; (D) a significant increase in infiltration of Th1 and Th17 cells was observed in the PCIPI-HIGH group; (E) alluviation plots of PCIPI subtypes, LumA, LumB and basic molecule subtypes; (F) the LumB-PCIPI-HIGH subgroup had the lowest recurrence-free survival rate;
FIG. 21 is a limiting mean survival curve (RMS) of the PCIPI and integrated IPICPS of the present invention: points in the graph represent RMS times for the respective PCIPI and IPICPS; the RMS curve showed a greater slope in all IPICPS cohorts, indicating that patient survival can be better predicted using IPICPS; the C-index for PCIPI and IPICPS is also shown in the figure; the P-value represents the difference in C-index between the two models.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
establishment of PCIPI:
(1) collection of patients and their clinical characteristics:
collecting formalin-fixed paraffin-embedded tissues of 69 patients who receive radical prostate cancer therapy at urology surgery (AHMU-PC cohort) of the first subsidiary hospital of the university of medical science, Anhui, and acquiring pathological information of each patient from an electronic medical record, and performing regular follow-up, wherein the follow-up endpoint is biochemical recurrence (BCR) which is defined as 6-13 weeks after radical prostate cancer therapy, the level of Prostate Specific Antigen (PSA) of the patient is increased and is more than 0.2ng/mL, and the level is maintained at a higher level in follow-up examination; and all the procedures involved and tested were performed according to the declaration of helsinki II and approved by the ethical committee of the first subsidiary hospital of the university of medical science of Anhui (approval number: PJ2019-09-11) and clinical and follow-up information and gene expression profiles thereof were obtained from public databases for 1,307 prostate cancer patients, including 495 patients in the cohort of prostate cancer genome atlas (TCGA-PRAD), 140 patients in the Stalon-Katelin cancer centre (MSKCC), 248 patients in GSE116918, 203 patients in GSE70770, 79 patients in GSE25136, 36 patients in GSE46602 and 106 patients in GSE54460, the demographic and clinical characteristics of the different datasets are shown in Table 1 below:
table 1: sequencing platform and source of different queue data
HTSeq-Count:high-throughput sequencing
(2) RNA extraction and sequencing:
the Neasy FFPE kit used for RNA extraction was produced by qiagen r, germany, and total RNA was extracted from FFPE samples according to the experimental method in its specification; the quality of the extracted RNA was assessed by Nanodrop (OD260/280, Thermo Fisher Scientific, MA, USA) and further analyzed by Agilent 2100 bioanalyzer (Agilent, CA, USA); after obtaining the total RNA, firstly removing the ribosome RNA in the total RNA, and then cracking the total RNA into a short segment of 250-300 bp; and using these short-fragment RNAs as templates and random oligonucleotides as primers to synthesize the first strand of cDNA; subsequently, the second strand of the cDNA is synthesized using dNTPs (dUTP, dATP, dGTP and dCTP) as a starting material by the action of DNA polymerase I; after purification, the double stranded cDNA was end-repaired and ligated with the Poly-A tail for sequencing. Using AMPure XP magnetic beads to sort out a cDNA fragment of 200 bp; the second strand of the U-containing cDNA was degraded using USER enzyme and subjected to PCR amplification to obtain a gene library. After a gene library is obtained, carrying out primary quantification by using a Qubit 2.0, diluting the gene library to 1.5ng/uL, and then detecting the size of an insert fragment of the library by using an Agilent 2100 bioanalyzer; after the library is checked, according to the effective concentration and the data output requirement of the gene library, carrying out Illumina PE150 sequencing after merging; then, the final expression matrix is obtained through the processes of preprocessing, reading and comparing, quality control, transcriptome reconstruction, expression quantification and the like of the original data.
(3) Data preprocessing of gene expression profiles:
the data from the 8 cohort studies incorporated above were de-batched and standardized. The expression profiles of all genes will be displayed as transcript values Per Kilobase Per Million fragments (TPM). Meanwhile, the genes with TPM value less than 1 are all rejected.
(4) Collection of immune cell features and Gene Set Enrichment Analysis (GSEA):
for analyzing immune related factors of prostate cancer patients, related genes which appear at least once are selected through literature search, and genes which appear repeatedly are combined to finally obtain 65 immune related characteristic genes. Gene Set Enrichment Analysis (GSEA) was performed using the R package "clusterirprofiler" and single sample gene set enrichment analysis (ssGSEA) was performed using the "GSVA" R package. Normalized Enrichment Scores (NES) were calculated for 65 immune-related features in each sample from the eight cohorts.
(5) Establishment of PCIPI:
the above 65 tumor-associated immunoinfiltration/immune response characteristic gene sets were searched and sorted. 14 significant sets of immune-related signatures were found by univariate Cox regression analysis and meta-analysis methods (all feature sets with P <0.01, as shown in FIGS. 1-14), and the immune-related signature sets were aDC, angiogenisis, CSF1Response, GRANS, HCK, Lymphocyte-Specific activator, Neutrophils, NK CD56 bright cells, NK CD56 dim cells, Macrophages, Pro-infllamation, Prolification, Stromcell and ZMYND10 metals, respectively. The PCIPI was obtained by adding Normalized Enrichment Scores (NES) of the 14 important immune-related signals described above, and was calculated as:
wherein, NESiIs the ssGSEA score normalized against the ith immune signature.
The PCIPI for each patient in the 8 study cohorts (total 1,376 cases) was obtained by the formula calculation as shown in tables 1-2 and fig. 15A:
table 2: clinical and pathological characterization of inclusion data
Wherein: rack of Gleason score: 3 in AHMU-PC, 4 in GSE70770, 2in MSKCC, 2in GSE25136, 8in GSE46602, 1in GSE 54460;
#Lack of PSA value：57in TCGA-PRAD，1in AHMU-PC，3in GSE70770,2in MSKCC，3in GSE54460；
meta-analysis indicated that high PCIPI is a risk factor for prostate cancer RFS [ risk ratio (HR) 1.86, 95% CI: 1.52-2.29, P <0.05, as in FIGS. 15B-F.
Example 2:
predictive testing of the PCIPI system in adjusting the main clinical pathology:
(1) screening the queues with the HR value larger than 1.5, and performing multivariate Cox regression analysis; it was found that in the four cohorts of TCGA-PRAD, AHMU-PC, GSE116918 and GSE70770, PCIPI is an independent prognostic factor for prostate cancer patients (as shown in Table 3 below);
(2) comparing the RFS outcomes of patients in different PCIPI subgroups using a ratio of restricted mean survival time (RMS), the ratio of the four data sets was between 0.63 and 0.89 (as shown in Table 4 below), i.e., the predicted results were more stable;
(3) in the TCGA-PRAD and AHMU-PC cohorts, NES increases in aDC, angiogenesis, macrophages, NK cells and pro-inflammatory signals in the PCIPI-HIGH (HIGH score) subgroup (P <0.05, shown in fig. 15G and fig. 16).
Table 3: PCIPI can be used as an independent predictor for recurrence-free survival rate prediction in 4 queues
Wherein: p < 0.05; PCIPI, a state cancer animal diagnostic index; HR: a hazard ratio; 95% CI: 95% confidential interva; PSA: protate-specific antigen.
Table 4: limiting mean time to live (RMS) time differences of PCIPI subpopulations in different queues
The results show that the newly defined PCIPI typing system can predict RFS of prostate cancer patients and reflect the local immune infiltration state of prostate tumor.
Example 3:
further assessment whether PCIPI indicates immunoinfiltrates in prostate cancer patients:
(1) assessing the correlation of the PCIPI identified above with the Immune Score and tumor purity as defined by Yoshihara et al, with PCIPI significantly positively correlated with Immune Score (all P <0.001, as in FIG. 15H-I) and negatively correlated with tumor purity (all P <0.001, as in FIG. 15J-K) in the TCGA-PRAD and AHMU-PC groups; similar results were obtained in the MSKCC, GSE70770, GSE46602 and GSE116918 queues (see fig. 15L);
(2) TILs infiltration abundance was confirmed by HE staining, confirming that these degrees of cellular infiltration correlated positively with PCIPI scores in the TCGA-PRAD cohort (P <0.001, as in fig. 15M);
(3) GSEA analysis was also performed to compare two groups of PCIPI-HIGH and PCIPI-LOW (LOW score) in the TCGA-PRAD cohort, and significant enrichment of chemokine signaling pathways (FIG. 15N), B cell receptor signaling pathways (FIG. 15O), T cell receptor signaling pathways, etc. in the PCIPI-HIGH subgroup was found;
(4) comparing the infiltration of 28 immune cells in the PCIPI-HIGH group and the PCIPI-LOW group; as shown in fig. 17A, infiltration abundance of most immune cells in 7 cohorts of PCIPI-HIGH group was HIGH;
(5) comparing the infiltration of immune cells between PCIPI-HIGH and PCIPI-LOW subgroups in the AHMU-PC cohort, a significant increase in infiltration of CD8+ T cells, macrophages, Th1 cells and Th17 cells was observed for the PCIPI-HIGH group (all P <0.05, fig. 17B);
(6) to verify the infiltration of different immune cells, formalin fixed paraffin-embedded (FFPE) tissue sections were used to evaluate specific markers in tissues from the AHMU-PC cohort from 10 PCIPI-HIGH patients and 10 PCIPI-LOW patients (see fig. 17C); the PCIPI-HIGH group is highly enriched in CD8 positive cells (P ═ 0.0271) and CD163 positive (macrophage specific marker) immune cells (P ═ 0.0208).
In addition, there was an increased tendency for staining with IFN- γ (P. 0.1635) and IL-17A (P. 0.7113) in the PCIPI high group compared to the PCIPI low group.
Example 4:
validation that the PCIPI typing system can be used as a predictor to assess whether patients benefit from anti-PD-1/PD-L1 immunotherapy:
(1) immune checkpoint blockade therapies have been approved by the Food and Drug Administration (FDA) for the treatment of a variety of tumors. In the present invention, the association between PCIPI and expression of 66 immune checkpoints was evaluated, as shown in fig. 18A, PCIPI is positively correlated with expression of most immune checkpoints in the 7 cohorts, in particular PD-L1(CD 274);
(2) positive correlation of PD-L1 with PCIPI was observed in 5 out of the 7 cohorts in the above evaluation, in these 5 cohorts mRNA expression was higher for the PCIPI-HIGH subgroup PD-L1 than for the PCIPI-LOW subgroup (TCGA-PRAD: P < 0.001; GSE 116918: P ═ 0.004; GSE 70770: P < 0.001; kcmscc: P < 0.001; AHMU-PC: P ═ 0.066, fig. 18B);
(3) the response of the PCIPI cohort to anti-PD-1/PD-L1 and anti-CTAL-4 immunotherapy in the five cohorts described above was predicted and the results showed that the PCIPI-HIGH subgroup of patients better benefited from anti-PD-1/PD-L1 immunotherapy compared to the PCIPI-LOW subgroup of patients (all five cohorts, Bonferroni corrected P <0.05, fig. 18C).
The above results indicate that the PCIPI typing system can be used as a predictor to assess whether patients benefit from anti-PD-1/PD-L1 immunotherapy.
Example 5:
copy Number Variation (CNV) and Somatic mutation (somatization) of tumor tissues affect immune activation/suppression and patient response to immunotherapy:
(1) the PCIPI-HIGH subgroup of patients had higher levels of CNV variation in both Arm-level (FIG. 19A) and Focal-level (FIG. 19B); among the frequently mutated genes, TP53 (16%), SPOP (11%), TTN (10%), KMT2D (7%) and FOXA1 (6%) were the most common 5 mutated genes in the PCIPI-HIGH group (as in fig. 19C), while SPOP (11%), TTN (11%), TP53 (7%), FOXA1 (7%) and SPTA1 (5%) were the most mutated 5 genes in the PCIPI-LOW group (as in fig. 19D).
(2) The mutation of the TP53 gene was significantly different between the two groups (16% vs.7%, fisher exact test P0.0017); in addition, patients with the TP53 mutation had higher PCIPI values than patients without the TP53 mutation (P ═ 0.034, fig. 19E); and most of the mutations were located in the DNA binding region of TP53 (FIG. 19F).
The above results indicate that the TP53 mutation is closely related to the immune microenvironment activated by prostate cancer, highlighting the potential role of TP53 in modulating the immunotherapeutic response.
Example 6:
firstly, the method comprises the following steps: predictive effect on RFS in prostate cancer patients
(1) After comparing RFS results for 4 immune subtype patients, the survival rate was found to be highest for the C3 subtype patients (P ═ 0.0026, fig. 20A-B).
(2) Further comparison of the C3 subtype revealed a significant deterioration in prognosis for patients with PCIPI-HIGH compared to those with PCIPI-LOW (P ═ 0.062, fig. 20C).
(3) It was found that HIGH infiltration of Th1 and Th17 cells was a hallmark of the C3 subtype, whereas in the C3 subtype of this study, infiltration of Th1 and Th17 cells was significantly higher in the PCIPI-HIGH subgroup than in the PCIPI-LOW subgroup (both P <0.05, fig. 20D).
(4) Comparing the PCIPI subgroup to the three PAM50 subgroups, the results indicated that the Basal subtype was associated with the high PCIPI subtype and the LumB subtype was associated with the low PCIPI subtype (fig. 20E); Kaplan-Meier survival analysis showed that patients with PCIPI-HIGH and LumB subtypes had the worst prognosis (P <0.001, FIG. 20F).
Taken together, the prognostic approach based on the combination of PCIPI of 6 pan-carcinoma immune subtypes with the PAM50 system improved the predictive effect on RFS in prostate cancer patients.
II, secondly: further improving the accuracy of prognosis prediction
(1) Combining the PCIPI with the clinical pathology variables included in the study based on the coefficients obtained from the multivariate Cox regression analysis; it was found that a continuous form of the clinical pathology of the Immune Prognostic Index (IPICPS) associated with PCIPI could significantly improve the predictive power of RFS (C-Index: TCGA-PRAD group: 0.731vs.0.598, P < 0.001; AHMU-PC group: 0.712vs.0.534, P < 0.001; MSKCC cohort: 0.850vs.0.605, P < 0.001; GSE46602 cohort: 0.770vs.0.631, P ═ 0.002; FIG. 21).
(2) Improvements in prediction performance were also observed in the GSE25136 and GSE70770 queues, however, there were no significant differences in the GSE116918 queues.
In conclusion, the prediction accuracy of the RFS of the prostate cancer patient can be obviously improved by combining the PCIPI system and clinical pathological features.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (6)
1. A prognostic assessment system for prostate cancer patients, comprising a system for detecting 14 sets of immune-related signatures from aDC, angiogenises, CSF1Response, GRANS, HCK, Lymphocyte-Specific activator, neutrophiles, NK CD56 bright cells, NK CD56 dim cells, Macrophages, Pro-inflimatory, Proliferation, Stromal cells and ZMYND10 gene.
2. The system of claim 1, wherein the system is configured to perform the following steps: the assessment system comprises determining the prostate cancer immune prognosis index PCIPI, wherein the PCIPI is obtained by adding NES scores of 14 immune-related gene sets aDC, angiogenises, CSF1Response, GRANS, HCK, Lymphocyte-Specific activator, Neutrophils, NK CD56 bright cells, NK CD56 dim cells, Macrophages, Pro-inflamadory, Proliferation, Stromcell and ZMYND10 gene, namely the PCIPI is obtained by the formula
Wherein NESiIs the ssGSEA score normalized against the ith immune signature.
3. Application of a prognosis evaluation system of a prostate cancer patient in predicting treatment efficacy of PD-1/PD-L1.
4. Use of a prognostic assessment system for prostate cancer patients according to claim 3, characterized in that: the method of use is to group a group of prostate cancer patients into a PCIPI-HIGH group and a PCIPI-LOW group by the PCIPI obtained in claim 2, wherein the PCIPI-HIGH group is determined to benefit more from anti-PD-1/PD-L1 immunotherapy than the PCIPI-LOW patient.
5. The application of a prognosis evaluation system of a prostate cancer patient in predicting recurrence-free survival after treatment of the prostate cancer patient.
6. Use of the system for prognostic evaluation of prostate cancer patients according to claim 5, wherein the high risk prostate cancer patients are determined to have no recurrence but poor survival by combining the PCIPI obtained in claim 2 with the clinical pathology to classify prostate cancer patients at high risk and low risk.
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