A CIBMTR Prognostic Model for Progression-Free Survival (PFS) After Autologous Hematopoietic Cell Transplantation (AHCT) for Relapsed or Refractory Hodgkin Lymphoma (HL)

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 499-499 ◽  
Author(s):  
Theresa Hahn ◽  
Philip L. McCarthy ◽  
Jeanette Carreras ◽  
Mei-Jie Zhang ◽  
Hillard M. Lazarus ◽  
...  

Abstract Abstract 499 AHCT is standard therapy for relapsed or refractory HL. Published prognostic models for HL patients based on factors measured at the time of AHCT have been limited by small sample sizes. HL prognostic models based on information from diagnosis may be difficult to use for AHCT outcomes since diagnostic information is often not available to the tertiary transplant center or the tests were not uniformly performed by multiple referring physicians. Our goal was to develop a new prognostic model for PFS post-AHCT based on factors available at time of AHCT. We analyzed a cohort of 728 relapsed or refractory HL patients receiving an AHCT between 1996–2007, reported to the CIBMTR by 162 centers, who had complete data for all significant factors previously reported in prognostic models. Patient characteristics at diagnosis: 40% male, 52% stage III-IV, 57% B symptoms, 34% extranodal disease. Patient characteristics at AHCT: median (range) age 33 (7–74) years; 74% KPS≥90 pre-AHCT; 40% had ≥3 prior chemotherapy regimens; 36% chemo-sensitive relapse 27% CR2, 19% PR1, 12% chemo-resistant relapse, 6% primary refractory/resistant; median (range) time from diagnosis to AHCT 22 (3–368) months. Histologic types were: 74% nodular sclerosis, 14% mixed cellularity, 7% lymphocyte rich, 1% lymphocyte depleted, 4% other/unknown. High dose therapy regimens were primarily BEAM (71%) or CBV (13%). For the entire cohort, 3-year estimates of PFS and OS were 60% and 73%, respectively. Multivariate models for treatment failure (1-PFS) were built using a forward step-wise procedure with p<0.05 to enter the model. The following variables were considered: number of prior chemotherapy regimens; KPS; histology; B symptoms at diagnosis; disease status at AHCT; chemo-sensitivity at AHCT; serum LDH at AHCT; extranodal involvement any time prior to AHCT; size of largest mass prior to AHCT; time from diagnosis to AHCT. A random subset of patients was used for model development (n=337) and the model was validated in the remaining cases (n= 391). The final model is shown in the TableRisk FactorRR (95% CI)PScore# of prior chemotherapy regimens: (3,4,5) vs (0,1,2)1.80 (1.31–2.47)0.00032Extranodal involvement any time prior to AHCT: Yes vs No1.77 (1.24–2.53)0.00182KPS prior to AHCT: 0–80% vs 90–100%1.47 (1.04–2.07)0.02751HL chemo-sensitivity at AHCT: Resistant vs Sensitive1.45 (1.01–2.07)0.04401 Patients were assigned a risk group based on the prognostic score: High risk, (score = 4, 5, or 6); Intermediate risk, (score = 1, 2, or 3); and Low risk, (score = 0). Figure 1 shows the PFS curves for the model development, model verification and combined groups, respectively. This CIBMTR Prognostic Model identifies patients at low, intermediate and high risk for treatment failure (progression or death). These risk groups discriminate patients with good post-AHCT outcomes and those who may benefit from other therapies, such as allogeneic HCT. Prospective evaluation of different treatment strategies based on this prognostic model are needed on a national or international level. Disclosures: Hahn: Novartis: stock. Montoto:Genentech: Research Funding; Roche: Honoraria.

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4791-4791
Author(s):  
Pradnya D Patil ◽  
Lisa Rybicki ◽  
Donna Abounader ◽  
Hien K. Liu ◽  
Brian T. Hill ◽  
...  

Abstract The assessment of pre-transplant comorbidities is crucial for risk-stratification and is a tool to guide clinical decisions in hematological malignancy patients (pts) undergoing evaluation for stem cell transplantation (SCT). The HCT-CI scale is commonly used to identify high risk patients pre-transplant as it is highly predictive of non-relapse mortality (NRM), severity of graft versus host disease and survival after allogeneic SCT (Sorror et al, Blood 2005; Sorror et al, Blood 2014). However, its role in ASCT remains undefined. In a Center for International Blood and Marrow Transplant Research analysis, HCT-CI score of ≥3 was prognostic for higher NRM and overall mortality in ASCT patients (Sorror et al, Biol. Blood Marrow Transplant 2015), but other single institution studies have failed to confirm this observation in lymphoma patients (Jaglowski et al, Bone Marrow Transplant 2014; Dahi et al, Biol. Blood Marrow Transplant 2014; Hosing et al, Ann. Oncol. 2008). No study has correlated HCT-CI with psychosocial functioning in the setting of ASCT. We conducted a retrospective study of 350 patients with Hodgkin (N=70) and non-Hodgkin Lymphoma (N=280) who underwent ASCT at our institution from January 2009 to June 2015. Based on their HCT-CI score, patients were categorized into low risk (score 0, N=90), intermediate risk (score 1-2, N=123) and high risk (score ≥3, N=137). Psychosocial Assessment of Candidates for Transplantation (PACT) scale (0-4: 0 being poor candidate for procedure and 4 being excellent candidate) (Foster et al. BMT 2009) was used for pre-transplant psychosocial risk assessment and was available for 235 pts. We analyzed the impact of HCT-CI on transplant outcomes and its correlation with PACT scores. Our cohort was predominantly male (63%), and Caucasian (93%) with a median age of 55 years (range 20-78). The majority of the pts (96%) had good performance status with an ECOG of 0-1. The primary diagnosis was NHL in 80%, with mostly advanced stage disease (80%), and no B symptoms (93%). Median time from diagnosis to ASCT was 16 months with 75% of the pts having received ≤2 prior therapies. The median annual income based on zip code was $49406 (range $18753-127312). Disease status prior to transplant was CR/PR in 93% of the subjects. Patient and disease characteristics were comparable among the 3 HCT-CI risk groups. Higher HCT-CI risk category was associated with a lower median household income (p=0.012), higher LDH (p=0.004), more days of apheresis (p=0.026) and lower CD34+ dose x106/kg (0.046). In relation to PACT scores, higher HCT-CI was associated with poor mental health (p<0.001), decreased coping skills (p<0.001), unhealthy lifestyle habits/sedentary life (p<0.001), decreased compliance with medications/medical advice (p=0.014) and inadequate medical/transplant knowledge (p=0.017) and lower final PACT score (p<0.001). Median follow up was 35 months with 100 observed deaths, of which 72 were attributed to relapse. The 5 year estimated relapse rate, NRM, relapse free survival (RFS) and overall survival (OS) in our cohort were 42%, 11%, 49% and 62% respectively. On univariate analysis, there was no significant difference between high vs. low/intermediate HCT-CI scores on 30 day readmission rates (OR 1.24, p=0.61), 100 day mortality (OR 1.12, p=0.86), incidence of secondary malignancy (HR 0.41, p=0.17), relapse rate (HR 0.92, p=0.64), relapse mortality (HR 1.35, p=0.20), NRM (HR 0.86, p=0.71), OS (HR 1.2, p=0.37) or RFS (HR 0.98, p=0.92). Though not statistically significant, the intermediate risk group was noted to have higher 100 day mortality and NRM compared to the low and high risk groups. To our knowledge, this is first study to correlate pre-transplant HCT-CI with PACT scores in lymphoma pts who underwent ASCT. Higher HCT-CI was associated with lower socioeconomic status, poor mental health and coping skills, unhealthy lifestyle habits, decreased medical/transplant knowledge and compliance. HCT-CI did not predict survival in our cohort. Further studies are needed to investigate the association between psychosocial risk factors and HCT-CI and define their combined utility in pre-transplant risk assessment in ASCT patients. Table Patient characteristics and HCT-CI risk categories Table. Patient characteristics and HCT-CI risk categories Figure Impact of HCT-CI on OS in ASCT patients Figure. Impact of HCT-CI on OS in ASCT patients Disclosures No relevant conflicts of interest to declare.


2020 ◽  
Author(s):  
Jiaxing Lin ◽  
Jieping Yang ◽  
Xiao Xu ◽  
Yutao Wang ◽  
Meng Yu ◽  
...  

Abstract Background: Bladder cancer is the tenth most common cancer globally, but existing biomarkers and prognostic models are limited. Method: In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used the least absolute shrinkage and selection operator regression to construct a prognostic Cox model. Kaplan-Meier analysis, receiver operating characteristic curve, and univariate / multivariate Cox analysis were used to evaluate the prognostic model for the four cohorts. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model. Results: A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The 11-genes model could stratify the risk of patients in all five cohorts, and the prognosis was worse in the group with a high-risk score. The area under the curve values of the five cohorts in the first year are all greater than 0.65. Furthermore, this model's predictive ability is stronger than that of age, gender, grade, and T stage. Through the weighted co-expression network analysis, the gene module related to the model was found, and the key genes in this module were mainly enriched in the tumor microenvironment. B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst. Conclusion: The proposed eleven-genes model is a promising biomarker for estimating overall survival in bladder cancer. This model can be used to stratify the risk of bladder cancer patients, which is beneficial to the realization of individualized treatment.


2020 ◽  
Author(s):  
Jiaxing Lin ◽  
Jieping Yang ◽  
Xiao Xu ◽  
Yutao Wang ◽  
Meng Yu ◽  
...  

Abstract Background: Bladder cancer is the tenth most common cancer globally, but existing biomarkers and prognostic models are limited. Method: In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used the least absolute shrinkage and selection operator regression to construct a prognostic Cox model. Kaplan-Meier analysis, receiver operating characteristic curve, and univariate / multivariate Cox analysis were used to evaluate the prognostic model for the four cohorts. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model.Results: A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The 11-genes model could stratify the risk of patients in all five cohorts, and the prognosis was worse in the group with a high-risk score. The area under the curve values of the five cohorts in the first year are all greater than 0.65. Furthermore, this model's predictive ability is stronger than that of age, gender, grade, and T stage. Through the weighted co-expression network analysis, the gene module related to the model was found, and the key genes in this module were mainly enriched in the tumor microenvironment. B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst.Conclusion: The proposed eleven-genes model is a promising biomarker for estimating overall survival in bladder cancer. This model can be used to stratify the risk of bladder cancer patients, which is beneficial to the realization of individualized treatment.


2021 ◽  
Author(s):  
Chen-jie Qiu ◽  
Xue-bing Wang ◽  
Zi-ruo Zheng ◽  
Chao-zhi Yang ◽  
Kai Lin ◽  
...  

Abstract Background: The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.


2021 ◽  
Author(s):  
Chen-jie Qiu ◽  
Xue-bing Wang ◽  
Zi-ruo Zheng ◽  
Chao-zhi Yang ◽  
Kai Lin ◽  
...  

Abstract Background: With the development of genomics, ferroptosis has been determined to be highly important in cancer. The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas (TCGA) database, we employed univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox analysis to establish the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus (GEO) datasets and tissue samples obtained from our center were utilized to validate the prognostic model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The KM curve indicated that the overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The nomogram showed that the prognostic model was the most important element. Gene set enrichment analysis (GSEA) identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. GSE57495, GSE62452 and 88 pancreatic cancer tissues from our center were utilized to validate the prognostic model. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.


Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1215-1215
Author(s):  
Theresa Hahn ◽  
Philip L. McCarthy ◽  
Jeanette Carreras ◽  
Mei-Jie Zhang ◽  
Hillard M. Lazarus ◽  
...  

Abstract Abstract 1215 Poster Board I-237 AHCT is standard therapy for relapsed or refractory Hodgkin Lymphoma (HL). Prognostic risk score models for HL patients receiving AHCT aim to predict post transplant outcomes based on factors measured at the time of AHCT. We performed a comparison of 3 such models from Dana-Farber Cancer Institute (DFCI), Roswell Park Cancer Institute (RPCI) and University of Minnesota (UMinn) in an independent multicenter dataset of 597 relapsed or refractory HL patients receiving AHCT from 1996-2004, reported to the CIBMTR by 150 centers. Patient characteristics at diagnosis: 60% male, 52% stage III-IV, 59% B symptoms, 33% extranodal disease. Patient characteristics at AHCT: median (range) age 32 (7-74) years; 73% KPS≥90; 19% with extranodal disease; 39% had ≥3 prior chemotherapy regimens; 18% had resistant disease; median (range) time from diagnosis to AHCT 22 (3-238) months. High dose therapy regimens were primarily BEAM (72%) or CBV (12%) with 91% receiving peripheral blood stem cells. Progression free (PFS) and overall survival (OS) estimates at 3 years were 59% and 72%, respectively. The 3 prognostic models each measured 3 prognostic variables at AHCT that were combined into a prognostic score and assigned to a risk group (low, intermediate, high). The DFCI model risk factors were: chemo-resistant disease, KPS<90, ≥1 extranodal site; with corresponding risk groups low (0 factors), intermediate, (1 factor) and high (2-3 factors). The RPCI model risk factors were: chemo-resistant disease, KPS<90, ≥3 prior regimens with risk groups low (0-1 factor) and high (2-3 factors). The UMinn model risk factors were: chemo-resistant disease, B symptoms, not in CR at BMT with risk groups low (0-1 factor), intermediate (2 factors) and high (3 factors). Only 1 factor (chemo- resistant disease) was included in all 3 models. We quantified the predictive capabilities of the models using Brier score (B) and R2 for each model. Brier score as a function of time is a measure of the accuracy of the model calculated as the average deviation between the predicted probabilities and the actual outcome. R2 measures goodness of fit based on the observed vs. predicted difference in the regression model. A smaller Brier score and a larger R2 indicate better predictive performance. The models are compared in Table 1 with regards to prediction of 36 month PFS: Table 1 DFCI Model RPCI model UMinn model Brier Score 0.2344 0.2360 0.2394 R2 3.24% 2.57% 1.17% The high risk group PFS (Figure 1) was similar for the DFCI and RPCI models but the DFCI model separated a low and intermediate risk group which were not significantly different from each other. The UMinn model high risk group had a higher PFS than either of the other 2 models' high risk group and the intermediate group in this model was not significantly different from the high risk group. The relative incremental change in R2 was 26% higher for the DFCI than the RPCI model and 120% higher for the RPCI than the UMinn model. From the B and R2 values, the DFCI model had marginal superiority over RPCI model while both performed better than the UMinn model. Newer prognostic systems incorporating other prognostic variables are needed to distinguish lower and intermediate risk patients. Figure 1 Figure 1. Disclosures: No relevant conflicts of interest to declare.


2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Qi Feng ◽  
Margaret T. May ◽  
Suzanne Ingle ◽  
Ming Lu ◽  
Zuyao Yang ◽  
...  

Background. This study was designed to review the methodology and reporting of gastric cancer prognostic models and identify potential problems in model development. Methods. This systematic review was conducted following the CHARMS checklist. MEDLINE and EMBASE were searched. Information on patient characteristics, methodological details, and models’ performance was extracted. Descriptive statistics was used to summarize the methodological and reporting quality. Results. In total, 101 model developments and 32 external validations were included. The median (range) of training sample size, number of death, and number of final predictors were 360 (29 to 15320), 193 (14 to 9560), and 5 (2 to 53), respectively. Ninety-one models were developed from routine clinical data. Statistical assumptions were reported to be checked in only nine models. Most model developments (94/101) used complete-case analysis. Discrimination and calibration were not reported in 33 and 55 models, respectively. The majority of models (81/101) have never been externally validated. None of the models have been evaluated regarding clinical impact. Conclusions. Many prognostic models have been developed, but their usefulness in clinical practice remains uncertain due to methodological shortcomings, insufficient reporting, and lack of external validation and impact studies. Impact. Future research should improve methodological and reporting quality and emphasize more on external validation and impact assessment.


2020 ◽  
Author(s):  
Ming Liu ◽  
Jiayi Xie ◽  
Xiaobei Luo ◽  
Yaxin Luo ◽  
Side Liu ◽  
...  

Abstract Background: Gastric cancer (GC) is one of the most prevalent malignant cancers around the world. Given that abnormal RNA binding proteins (RBPs) are involved in the tumorigenesis, we aimed to explore the potential value of RBPs-associated genes in gastric cancer.Methods: RNA-seq and clinical data were retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed RBPs genes were screened. GO and KEGG pathway enrichment analyses were implemented to elucidate the roles of RBPs in GC. The protein-protein interaction (PPI) networks of RBPs were carried out, and the hub genes were determined by MCODE built in Cytoscape. The TCGA-STAD dataset was randomly divided into training and testing groups. A prognostic signature including five RBPs was developed within the training cohort after Cox regression and Lasso regression analyses. We used Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves to evaluate the capacity of the model in both groups. Then, a nomogram based on hub RBPs expression was established. Gene Set Enrichment Analysis was performed between the high-risk and low-risk group.Results: A total of 166 up-regulated RBPs and 130 down-regulated RBPs were identified. Via Cox regression and Lasso regression analysis within the training group, five hub RBPs (RNASE1, SETD7, BOLL, PPARGC1B, MSI2) were screened and the prognostic model was constructed. The risk score was calculated and gastric cancer patients were divided into high-risk and low-risk groups. In multivariate analysis, risk score was still an independent prognostic indicator (HR = 1.80, 95% CI = 1.45-2.22, P < 0.01). Patients with low risk had favorable survival rate in both training and testing group compared to those at high risk (P < 0.001). The areas under the ROC curves (AUC) of the prognostic model are 0.718 in the training cohort and 0.651 in the testing cohort. The hub RBPs-based nomogram model exhibited excellent ability to predict the OS of GC. GSEA illustrated that several cancer-related signaling pathways were enriched in patients with a high-risk score.Conclusions: This study discovered a five RBPs signature which might provide a potential prognostic value to GC patients.


2020 ◽  
Author(s):  
liu jinhui ◽  
Li siyue ◽  
Gao feng ◽  
meng huangyang ◽  
Nie sipei ◽  
...  

Abstract Background: Endometrial cancer is the fourth most common cancer in women. The death rate for endometrial cancer has increased. Glycolysis of cellular respiration is a complex reaction and is the first step in most carbohydrate catabolism, which was proved to participate in tumors. Methods: We analyzed the sample data of over 500 patients from TCGA database. The bioinformatic analysis included GSEA, cox and lasso regression analysis to select prognostic genes, as well as construction of a prognostic model and a nomogram for OS evaluation. The immunohistochemistry staining, survival analysis and expression level validation were also performed. Maftools package was for mutation analysis. GSEA identified Glycolysis was the most related pathway to EC. Results: According to the prognostic model using the train set, 9 glycolysis-related genes including B3GALT6, PAM, LCT, GMPPB, GLCE, DCN, CAPN5, GYS2 and FBP2 were identified as prognosis-related genes. Based on nine gene signature, the EC patients could be classified into high and low risk subgroups, and patients with high risk score showed shorter survival time. Time-dependent ROC analysis and Cox regression suggested that the risk score predicted EC prognosis accurately and independently. Analysis of test and train sets yielded consistent results A nomogram which incorporated the 9‐mRNA signature and clinical features was also built for prognostic prediction. Immunohistochemistry staining and TCGA validation showed that expression levels of these genes do differ between EC and normal tissue samples. GSEA revealed that the samples of the low-risk group were mainly concentrated on Bile Acid Metabolism. Patients in the low-risk group displayed obvious mutation signatures compared with those in the high-risk group. Conclusion: This study found that the Glycolysis pathway is associated with EC and screened for hub genes on the Glycolysis pathway, which may serve as new target for the treatment of EC.


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