Reduced Intensity Conditioning with Thiotepa and Fludarabine for Allogeneic Transplantation: Evidence for Low Toxicity and Long-Lasting Disease Control in MDS with Low/Intermediate-1 IPSS Score and in AML from MDS in Complete Remission.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 3285-3285
Author(s):  
Emilio Paolo Alessandrino ◽  
Luca Malcovati ◽  
Giorgio La Nasa ◽  
Paolo de Fabritiis ◽  
Massimo Bernardi ◽  
...  

Abstract This study investigated Thiotepa (TT) and Fludarabine (Fluda) as a preparative regimen for allogeneic peripheral stem cell transplantation in patients with myelodysplastic syndrome (MDS) or acute leukemia from MDS (MDS-AML) older than 50 or with comorbidities contraindicating standard conditioning. Patients were prepared with TT, given over 3 hours as an i.v. infusion at a dose of 10 mg/kg over two days (day -8 and day -7) and Fluda at the dose of 125 mg/m2 i.v. over five days ( from day -7 to day -3). Fresh or cryopreserved allogeneic peripheral stem cells were infused on day 0 or +1. Graft-versus-Host Disease (GvHD) prophylaxis consisted of cyclosporine A (CyA) at the dose of 1.5 mg/kg day as a continuous iv infusion from day -5 until engraftment. The CyA was then administered orally at the dose of 3 mg/kg twice a day. Doses were adjusted to maintain plasma level concentrations between 150–350 mg/dL. From day +60, in the absence of acute GvHD, the CyA was tapered down by 20% every 2 weeks until withdrawal. In addition, patients received methotrexate 10 mg/m2 on day +1, and 8 mg/m2 on days + 3, +6 and +11 after transplantation. At the time of transplantation, patients were classified in two risk groups (low vs high risk) according to IPSS score (low/intermediate-1 vs. intermediate-2/high) for MDS patients, and disease status (CR vs. not CR) for MDS-AML. Kaplan-Meier survival analysis was carried out to compare Overall Survival (OS), Transplant-Related Mortality (TRM) and probability of relapse. Fifty patients (29 males, 21 females) entered the study; the median age was 54 years (range 38–71). Sixteen MDS patients had a low/intermediate 1 score according to the International Prognostic Score System (IPSS), 16 had an intermediate 2/high IPSS score, 18 had MDS-AML. Thirty patients underwent transplantation as front-line therapy, 20 received one or more cycles of chemotherapy before transplant. Among the latter, nine with MDS-AML were in complete remission at the time of their transplant, while four were in a partial remission. The interval from diagnosis to transplantation ranged from 1 to 52 months (median value 11 months). Contraindications to a standard conditioning regimen were liver disease, hypertrophic cardiomyopathy secondary to hypertension or valvular stenosis, cardiac arrhythmia, diabetes mellitus, hypothyroidism, previous CNS bleeding, and a history of sepsis. All but one patient achieved engraftment, with full donor chimerism by day +30. Patients were followed up for a median time of 21 months (range 0.2–87). TRM at 1 and 2 years after transplantation was 25% and 33%; the 5-year probability of relapse was 27%. Twenty-six patients are alive in complete remission, and the 5-year OS is 50%. The 5-year OS was 73% and 28% in low- and high-risk patients respectively (p=0.002). TRM at 1 and 2 years after transplantation was 13% and 21% in the low-risk group and 39% and 45% in the high-risk group (p=0.046); the 5-year probability of relapse was 10% and 50% in the low- and high-risk group respectively (p=0.015). In a multivariate Cox regression, risk group retained a borderline significance (HR=2.6, p=0.07) when adjusted by age at transplantation (p=0.03) and interval from diagnosis to transplant (n.s.). The combination of Thiotepa and Fludarabine is an effective and well-tolerated conditioning regimen in patients with MDS or MDS-AML who are poor candidates for standard myeloablative transplantation, particularly in MDS patients with low/intermediate-1 IPSS score and MDS-AML patients in CR.

2012 ◽  
Vol 72 (12) ◽  
pp. 1920-1926 ◽  
Author(s):  
Lotte Arwen van de Stadt ◽  
Birgit I Witte ◽  
Wouter H Bos ◽  
Dirkjan van Schaardenburg

ObjectiveTo predict the development of arthritis in anticyclic citrullinated peptide antibodies and/or IgM rheumatoid factor positive (seropositive) arthralgia patients.MethodsA prediction rule was developed using a prospective cohort of 374 seropositive arthralgia patients, followed for the development of arthritis. The model was created with backward stepwise Cox regression with 18 variables.Results131 patients (35%) developed arthritis after a median of 12 months. The prediction model consisted of nine variables: Rheumatoid Arthritis in a first degree family member, alcohol non-use, duration of symptoms <12 months, presence of intermittent symptoms, arthralgia in upper and lower extremities, visual analogue scale pain ≥50, presence of morning stiffness ≥1 h, history of swollen joints as reported by the patient and antibody status. A simplified prediction rule was made ranging from 0 to 13 points. The area under the curve value (95% CI) of this prediction rule was 0.82 (0.75–0.89) after 5 years. Harrell's C (95% CI) was 0.78 (0.73–0.84). Patients could be categorised in three risk groups: low (0–4 points), intermediate (5–6 points) and high risk (7–13 points). With the low risk group as a reference, the intermediate risk group had a hazard ratio (HR; 95% CI) of 4.52 (2.42–8.77) and the high risk group had a HR of 14.86 (8.40–28.32).ConclusionsIn patients presenting with seropositive arthralgia, the risk of developing arthritis can be predicted. The prediction rule that was made in this patient group can help (1) to inform patients and (2) to select high-risk patients for intervention studies before clinical arthritis occurs.


2020 ◽  
Author(s):  
Kui Wu ◽  
Yongjie Shui ◽  
Wenzheng Sun ◽  
Sheng Lin ◽  
Haowen Pang

Abstract Objective This study aimed to develop and validate the combination of radiomic features and clinical characteristics that can predict patient survival in HCC with PVTT treated with SBRT. Materials and Methods The prediction model was developed in a primary cohort of 70 patients with HCC and PVTT treated with SBRT, using data acquired between December 2015 and June 2017. The radiomic features were extracted from computed tomography (CT) scans. A least absolute shrinkage and selection operator regression model was used to build the radiomic feature. Multivariate Cox-regression hazard models were created for analyzing survival outcomes and the radiomic features and clinical characteristics were presented with a nomogram. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate the model. Participants were divided into a high-risk group and a low-risk group based on the radiomic features. Results A total of seven radiomic features and five clinical characteristics were extracted for survival analysis. A combination of the radiomic features and clinical characteristics resulted in better performance for the estimation of overall survival (OS) [AUC = 0.859 (CI: 0.770–0.948)] than that with clinical characteristics alone [AUC = 0.761 (CI: 0.641–0.881)]. These patients were divided into high-risk and low-risk groups according to the radiomic features. Conclusion This study demonstrated that a nomogram of combined radiomic features and clinical characteristics can be conveniently used to facilitate individualized preoperative prediction of OS in patients with HCC with PVTT before SBRT.


2021 ◽  
Author(s):  
Fang Wen ◽  
Xiaoxue Chen ◽  
Wenjie Huang ◽  
Shuai Ruan ◽  
Suping Gu ◽  
...  

Abstract Background: The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global, so it is of great significance to predict the survival time of GC patients. Ferroptosis and iron-metabolism make a critical impact on tumor development and are closely linked to the treatment of cancer and the prognosis of patients. However, the predictive value of the genes involved in ferroptosis and iron-metabolism in GC and their effects on immune microenvironment remain to be further clarified.Methods: In this study, the RNA sequence information and general clinical indicators of GC patients were acquired from the public databases. We first systematically screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) based on the LASSO-penalized Cox regression analysis. The 6-gene prognostic risk model was established in the TCGA cohort and the GC patients were separated into the high- and the low-risk groups through the risk score median value. GEO cohort was used for verification. The expression of PRDEGs was verified by quantitative QPCR.Results: Our study demonstrated that patients in the low-risk group had a higher survival probability compared with those in high-risk group. In addition, univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prediction parameter. The ROC curve analysis and nomogram manifested that the risk model had the high predictive ability and was more sensitive than general clinical features. Furthermore, compared with the high-risk group, the low-risk group had higher TMB and a longer 5-year survival period. In the immune microenvironment of GC, there were also differences in immune function and highly infiltrated immune cells between the two risk groups.Conclusions: The prognostic risk model based on the six genes associated with ferroptosis and iron-metabolism has a good performance for predicting the prognosis of patients with GC. The treatment of cancer by inducing tumor ferroptosis or mediating tumor iron-metabolism, especially combined with immunotherapy, provides a new possibility for individualized treatment of GC patients.


2021 ◽  
Author(s):  
Tian Lan ◽  
Die Wu ◽  
Wei Quan ◽  
Donghu Yu ◽  
Sheng Li ◽  
...  

Abstract Background: Glioma is a fatal brain tumor characterized by invasive nature, rapidly proliferation and tumor recurrence. Despite aggressive surgical resection followed by concurrent radiotherapy and chemotherapy, the overall survival (OS) of Glioma patients remains poor. Ferroptosis is a unique modality to regulate programmed cell death and associated with multiple steps of tumorigenesis of a variety of tumors.Methods: In this study, ferroptosis-related genes model was identified by differential analysis and Cox regression analysis. GO, KEGG and GSVA analysis were used to detect the potential biological functions and signaling pathway. The infiltration of immune cells was quantified by Cibersort.Results: The patients’ samples are stratified into two risk groups based on 4-gene signature. High-risk group has poorer overall survival. The results of functional analysis indicated that the extracellular matrix-related biologic functions and pathways were enriched in high-risk group, and that the infiltration of immunocytes is different in two groups.Conclusion: In summary, a novel ferroptosis-related gene signature can be used for prognostic prediction in glioma. The filtered genes related to ferroptosis in clinical could be a potential extra method to assess glioma patients’ prognosis and therapeutic.


2021 ◽  
Vol 8 ◽  
Author(s):  
Ji Yin ◽  
Xiaohui Li ◽  
Caifeng Lv ◽  
Xian He ◽  
Xiaoqin Luo ◽  
...  

Background: Long non-coding RNA (lncRNA) plays a significant role in the development, establishment, and progression of head and neck squamous cell carcinoma (HNSCC). This article aims to develop an immune-related lncRNA (irlncRNA) model, regardless of expression levels, for risk assessment and prognosis prediction in HNSCC patients.Methods: We obtained clinical data and corresponding full transcriptome expression of HNSCC patients from TCGA, downloaded GTF files to distinguish lncRNAs from Ensembl, discerned irlncRNAs based on co-expression analysis, distinguished differentially expressed irlncRNAs (DEirlncRNAs), and paired these DEirlncRNAs. Univariate Cox regression analysis, LASSO regression analysis, and stepwise multivariate Cox regression analysis were then performed to screen lncRNA pairs, calculate the risk coefficient, and establish a prognosis model. Finally, the predictive power of this model was validated through the AUC and the ROC curves, and the AIC values of each point on the five-year ROC curve were calculated to select the maximum inflection point, which was applied as a cut-off point to divide patients into low- or high-risk groups. Based on this methodology, we were able to more effectively differentiate between these groups in terms of survival, clinico-pathological characteristics, tumor immune infiltrating status, chemotherapeutics sensitivity, and immunosuppressive molecules.Results: A 13-irlncRNA-pair signature was built, and the ROC analysis demonstrated high sensitivity and specificity of this signature for survival prediction. The Kaplan–Meier analysis indicated that the high-risk group had a significantly shorter survival rate than the low-risk group, and the chi-squared test certified that the signature was highly related to survival status, clinical stage, T stage, and N stage. Additionally, the signature was further proven to be an independent prognostic risk factor via the Cox regression analyses, and immune infiltrating analyses showed that the high-risk group had significant negative relationships with various immune infiltrations. Finally, the chemotherapeutics sensitivity and the expression level of molecular markers were also significantly different between high- and low-risk groups.Conclusion: The signature established by paring irlncRNAs, with regard to specific expression levels, can be utilized for survival prediction and to guide clinical therapy in HNSCC.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11074
Author(s):  
Jin Duan ◽  
Youming Lei ◽  
Guoli Lv ◽  
Yinqiang Liu ◽  
Wei Zhao ◽  
...  

Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 1237-1237
Author(s):  
Fotios V. Michelis ◽  
Rouslan Kotchetkov ◽  
Aamir Azeem ◽  
Rebecca M. Grunwald ◽  
Jieun Uhm ◽  
...  

Abstract Allogeneic hematopoietic cell transplantation (HCT) is a curative treatment option for acute myeloid leukemia (AML) when indicated. Numerous pre-transplant risk scores have been developed to predict post-transplant outcome, utilizing a variety of parameters. The purpose of this single-center study was to retrospectively develop and validate a prognostic score based on known significant pre-transplant variables for outcomes of 747 patients that underwent HCT for AML between 1978 and 2013. Median age of all patients at transplant was 44 years (range 17-71 years), 391 patients (52%) were female. HCT was performed in first complete remission (CR1) for 497 patients (67%) and in second complete remission (CR2) or advanced disease for 250 patients (33%). Donors were related for 538 patients (72%) and unrelated for 209 patients (28%). Peripheral blood stem cells (PBSC) were used as a graft source in 367 patients (49%). Myeloablative conditioning (MAC) was administered to 615 (82%) patients, 132 (18%) received reduced-intensity conditioning (RIC) regimens. HCT was performed over the time periods 1978-1990 (n=139), 1991-1999 (n=192), 2000-2006 (n=183) and 2007-2013 (n=233). Median follow-up of survivors was 90 months. Patients were assigned a combined score based on patient age, disease status and donor status. For disease status CR1, age <45 years and related donors, each parameter received a score 0. For disease status CR2 or advanced stage, age ≥45 and unrelated donors, each parameter received a score 1. Patients demonstrated a cumulative score of 0 (n=197), 1 (n=326), 2 (n=179) or 3 (n=45). All 747 patients were randomized into two groups, a test cohort (n=373) and a validation cohort (n=374). Univariate analysis for the test cohort demonstrated a favorable risk group with score 0 (n=92), an intermediate risk group of score 1-2 (n=255) and an unfavorable risk group with score 3 (n=26) with a 5-year overall survival (OS) of 61%, 35% and 20% respectively (p=0.0001)(Figure). Cumulative incidence of relapse (CIR) demonstrated a marginally significant difference between groups (p=0.05) with 5-year CIR 14%, 28% and 23% respectively. Non-relapse mortality (NRM) was marginally different (p=0.07) with 5-year NRM 27%, 39% and 54% respectively. Multivariable analysis of the test cohort for OS demonstrated that the presented scoring system remained significantly prognostic (p<0.0001) accounting for year of transplant as a continuous variable in the analysis (p=0.001, HR for transplant year 0.97, 95%CI 0.96-0.98). For OS, HR was 2.3 and 4.0 for the intermediate and high risk group respectively compared to favorable risk. For CIR, HR was 2.1 and 1.8 for intermediate and high risk patients respectively (p=0.05). For NRM, HR was 1.5 and 2.8 for intermediate and high risk patients respectively (p=0.01). Analysis of the validation cohort confirmed significant stratification for OS on univariate (p<0.0001, 5-year OS 68%, 36% and 30% for the three risk groups respectively) and multivariable analysis (p<0.0001). For CIR, no significant difference was seen on univariate (p=0.3, 5-year CIR 15%, 23% and 17% respectively) or multivariable analysis (p=0.3). For NRM, the validation cohort confirmed significant stratification between the risk groups on univariate (p<0.0001, 5-year NRM 19%, 42% and 48% respectively) and multivariable analysis (p<0.0001). In the presented study we developed and validated this readily calculable pre-transplant scoring system involving age, CR status and donor type which is highly prognostic for OS and NRM of patients undergoing allogeneic HCT for AML. We also demonstrated that the year transplant was performed over the last three decades had no influence on the prognostic significance of the scoring system. Figure 1 Figure 1. Disclosures Messner: Otsuka Pharmaceuticals Inc: Research Funding.


2022 ◽  
Vol 2022 ◽  
pp. 1-27
Author(s):  
Wen Lv ◽  
Qi Yao

Background. Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant tumors that have been discovered so far, which makes the prognostic prediction difficult. The hypoxia, angiogenesis, and immunity-related genes (HAIRGs) are closely related to the development of liver cancer. However, the prognostic and treatment effect of hypoxia, angiogenesis, and immunity-related genes in HCC continues to be further clarified. Methods. The gene expression quantification data and clinical information in patients with liver cancer were downloaded from the TCGA database, and HAIRG signature was built by using the least absolute shrinkage and selection operator (LASSO) technique. Patient from the ICGC database validated the model. Then, tumor immune dysfunction and exclusion (TIDE) algorithm was applied to estimate the clinical response to immunotherapy and the sensitivity of drugs was evaluated by the half-maximal inhibitory concentration (IC50). Result. The HAIRGs were identified between the HCC patients and normal patients in the TCGA database. In univariate Cox regression analysis, seventeen differentially expressed genes (DEGs) were associated with overall survival (OS). An eight HAIRG signature model was constructed and was used to divide the patients into two groups according to the median value of the risk score base on the TCGA dataset. Patients in the high-risk group had a significant reduction in OS compared to those in the low-risk group ( P < 0.001 in the TCGA, P < 0.001 in the ICGC). For TCGA and ICGC databases of univariate Cox regression analyses, the risk score was used as an independent predictor of OS ( HR > 1 , P < 0.001 ). Functional analysis showed that the relevant immune pathways and immune responses were enriched, cellular component analysis showed that the immunoglobulin complex and other related substances were enriched, and immune status existed a difference in the high- and low-risk groups. Then, the tumor immune dysfunction and exclusion (TIDE) algorithm presented differences in immune response in the high- and low-risk groups ( P < 0.05 ), and based on drug sensitivity prediction, patients in the high-risk group were more sensitive to cisplatin compared to those in the low-risk group in both the TCGA and ICGC cohorts ( P < 0.05 ). Conclusions. HAIRG signature can be utilized for prognostic prediction in HCC, while it can be considered a prediction model for clinical evaluation of immunotherapy response and chemotherapy sensitivity in HCC.


2022 ◽  
Author(s):  
Yujian Xu ◽  
Youbai Chen ◽  
Zehao Niu ◽  
Zheng Yang ◽  
Jiahua Xing ◽  
...  

Abstract Ferroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of our study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM. Ferroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed. Here, we identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups. Overall, our novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.


2021 ◽  
Vol 12 ◽  
Author(s):  
Min Fu ◽  
Qiang Wang ◽  
Hanbo Wang ◽  
Yun Dai ◽  
Jin Wang ◽  
...  

BackgroundProstate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa.MethodsData were retrieved from the tumor Genome Atlas (TCGA) database and GSE46602 database for performing the least absolute shrinkage and selection operator (LASSO) cox regression model analysis. Immune related genes (IRGs) data were retrieved from ImmPort database.ResultsThe weighted gene co-expression network analysis (WGCNA) showed that nine functional modules are correlated with the biochemical recurrence of PCa, including 259 IRGs. Univariate regression analysis and survival analysis identified 35 IRGs correlated with the prognosis of PCa. LASSO Cox regression model analysis was used to construct a risk prognosis model comprising 18 IRGs. Multivariate regression analysis showed that risk score was an independent predictor of the prognosis of PCa. A nomogram comprising a combination of this model and other clinical features showed good prediction accuracy in predicting the prognosis of PCa. Further analysis showed that different risk groups harbored different gene mutations, differential transcriptome expression and different immune infiltration levels. Patients in the high-risk group exhibited more gene mutations compared with those in the low-risk group. Patients in the high-risk groups showed high-frequency mutations in TP53. Immune infiltration analysis showed that M2 macrophages were significantly enriched in the high-risk group implying that it affected prognosis of PCa patients. In addition, immunostimulatory genes were differentially expressed in the high-risk group compared with the low-risk group. BIRC5, as an immune-related gene in the prediction model, was up-regulated in 87.5% of prostate cancer tissues. Knockdown of BIRC5 can inhibit cell proliferation and migration.ConclusionIn summary, a risk prognosis model based on IGRs was developed. A nomogram comprising a combination of this model and other clinical features showed good accuracy in predicting the prognosis of PCa. This model provides a basis for personalized treatment of PCa and can help clinicians in making effective treatment decisions.


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