scholarly journals 23. Development and Validation of a Risk Score for Post-transplant Lymphoproliferative Disorders among Solid Organ Transplant Recipients

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S15-S16
Author(s):  
Quenia dos Santos ◽  
Neval E Wareham ◽  
Amanda Mocroft ◽  
Allan Rasmussen ◽  
Finn Gustafsson ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Organ Transplant ◽  
High Risk Group ◽  
Low Risk ◽  
Solid Organ ◽  
Group Score ◽  
Barr Virus ◽  
Epstein Barr

Abstract Background Post-transplant lymphoproliferative disease (PTLD) is a well-recognized complication after transplant. This study aimed to develop and independently validate a risk score to predict PTLD among solid organ transplant (SOT) recipients (kidney, liver, lung and heart). Methods Poisson regression identified predictors of PTLD with the best fitting model selected for the risk score, where each predictor contributed with a risk coefficient to the risk score, dividing patients in high vs low risk of having a PTLD. Results For both cohorts, most of the patients were male, aged more than 16 years old, kidney recipients and with a low-risk pre-transplant Epstein-Barr Virus (EBV) IgG donor/recipient serostatus. The derivation cohort consisted of 2546 SOT transplanted at Rigshospitalet, Copenhagen between 2004-2019; 57 developed PTLD. Predictors of PTLD were high-risk pre-transplant Epstein-Barr Virus (EBV) IgG donor/recipient serostatus, and current plasma EBV DNA positive, abnormal hemoglobin and C-reactive protein levels. A positive EBV DNA was the strongest parameter for the PTLD risk score (figure 1), although the model was able to predict the risk of PTLD cases in both EBV positive and EBV negative individuals. Individuals in the high-risk group had almost 7 times higher incidence of PTLD compared to the low risk group (table 1). In the validation cohort of 1611 SOT recipients between 2008-2018 from University Hospital of Zürich, 24 developed PTLD. A similar seven times higher risk of PTLD was observed in the high-risk group compared to the low risk group (table 1). The discriminatory ability was also similar in derivation (Harrell’s C-statistic of 0.82 95%CI (0.76-0.88) and validation (0.82, 95% CI:0.72-0.92) cohorts. An explanation about how the risk for PTLD is calculated for the SOT recipients; in this example the risk of developing PTLD is calculated in the next 180 days Performance of the PTLD score in the derivation and validation cohorts (low-risk group: score<=17 points; high-risk group: score>17 points) Conclusion The risk score had a good discriminatory ability in both cohorts and helped to identify patients with higher risk of developing PTLD, so they can be monitored more often. This is the first risk-score developed and externally validated to predict risk of PTLD among SOT recipients. Disclosures All Authors: No reported disclosures

Adjuvant Chemotherapy Guidance for pT1-3N0-1 Breast Cancer Patients with HR+, HER2- subtype: a study based on SEER database

2021 ◽  
Author(s):  
juanjuan Qiu ◽  
Li Xu ◽  
Yu Wang ◽  
Jia Zhang ◽  
Jiqiao Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Rank Test ◽  
Breast Cancer Patients ◽  
Group Score

Abstract Background Although the results of gene testing can guide early breast cancer patients with HR+, HER2- to decide whether they need chemotherapy, there are still many patients worldwide whose problems cannot be solved well by genetic testing. Methods 144 735 patients with HR+, HER2-, pT1-3N0-1 breast cancer from the Surveillance, Epidemiology, and End Results database were included from 2010 to 2015. They were divided into chemotherapy (n = 38 392) and no chemotherapy (n = 106 343) group, and after propensity score matching, 23 297 pairs of patients were left. Overall survival (OS) and breast cancer-specific survival (BCSS) were tested by Kaplan–Meier plot and log-rank test and Cox proportional hazards regression model was used to identify independent prognostic factors. A nomogram was constructed and validated by C-index and calibrate curves. Patients were divided into high- or low-risk group according to their nomogram score using X-tile. Results Patients receiving chemotherapy had better OS before and after matching (p < 0.05) but BCSS was not significantly different between patients with and without chemotherapy after matching: hazard ratio (HR) 1.005 (95%CI 0.897, 1.126). Independent prognostic factors were included to construct the nomogram to predict BCSS of patients without chemotherapy. Patients in the high-risk group (score > 238) can get better OS HR 0.583 (0.507, 0.671) and BCSS HR 0.791 (0.663, 0.944) from chemotherapy but the low-risk group (score ≤ 238) cannot. Conclusion The well-validated nomogram and a risk stratification model was built. Patients in the high-risk group should receive chemotherapy while patients in low-risk group may be exempt from chemotherapy.

A Four-gene Autophagy-related Prognostic Signature and Its Association With Immune Landscape in Lung Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Lumeng Luo ◽  
Minghe Lv ◽  
Xuan Li ◽  
Tiankui Qiao ◽  
Kuaile Zhao ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Immune Suppression ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
High Risk Group ◽  
Low Risk

Abstract Background: Recent advances in immune checkpoint inhibitors (ICIs) have dramatically changed the therapeutic strategy against lung squamous cell carcinoma (LUSC). In the era of immunotherapy, effective biomarkers to better predict outcomes and inform treatment decisions for patients diagnosed with LUSC are urgently needed. We hypothesized that immune contexture of LUSC is potentially dictated by tumor intrinsic events, such as autophagy. Thus, we attempted to construct an autophagy-related risk signature and examine its prediction value for immune phenotype in LUSC.Method: The expression profile of LUSC was obtained from the cancer genome atlas (TCGA) database and the profile of autophagy-related genes (ARGs) was extracted. The survival‑related ARGs (sARGs) was screened out through survival analyses. Random forest was performed to select the sARGs and construct a prognostic risk signature based on these sARGs. The signature was further validated by receiver operating characteristic (ROC) analysis and Cox regression. GEO dataset was used as an independent testing dataset. Patients were divided into high-risk and low-risk group based on the risk score. Then, gene set enrichment analysis (GSEA) was conducted between the two groups. The Single-Sample GSEA (ssGSEA) was introduced to quantify the relative infiltration of immune cells. The correlations between risk score and several main immune checkpoints were examined. And the ESTIMATE algorithm was used to calculate the estimate/immune/stromal scores of the LUSC. Results: Four ARGs (CFLAR, RGS19, PINK1 and CTSD) with the most significant prognostic values were enrolled to construct the risk signature. Patients in high-risk group had better prognosis than the low-risk group (P < 0.0001 in TCGA; P < 0.01 in GEO) and considered as an independent prognosis factor. We also found that high-risk group indicated an immune-suppression status and had higher levels of infiltrating regulatory T cells and macrophages, which are correlated with worse outcome. Besides, risk score showed a significantly positive correlation with the expression of PD-1 and CTLA4, as well as estimate score and immune score.Conclusion: This study established a novel autophagy-related four-gene prognostic risk signature, and the autophagy-related scores are associated with immune landscape of LUSC, with higher score indicating a stronger immune-suppression status.

scholarly journals Identification of a Prognostic Signature Associated With the Homeobox Gene Family for Bladder Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Bingqi Dong ◽  
Jiaming Liang ◽  
Ding Li ◽  
Wenping Song ◽  
Jinbo Song ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Gene Family ◽  
Risk Score ◽  
Risk Group ◽  
Homeobox Genes ◽  
Homeobox Gene ◽  
High Risk Group ◽  
Low Risk ◽  
Prognostic Signature

Background: Bladder cancer (BLCA) is a common malignant tumor of the genitourinary system, and there is a lack of specific, reliable, and non-invasive tumor biomarker tests for diagnosis and prognosis evaluation. Homeobox genes play a vital role in BLCA tumorigenesis and development, but few studies have focused on the prognostic value of homeobox genes in BLCA. In this study, we aim to develop a prognostic signature associated with the homeobox gene family for BLCA.Methods: The RNA sequencing data, clinical data, and probe annotation files of BLCA patients were downloaded from the Gene Expression Omnibus database and the University of California, Santa Cruz (UCSC), Xena Browser. First, differentially expressed homeobox gene screening between tumor and normal samples was performed using the “limma” and robust rank aggregation (RRA) methods. The mutation data were obtained with the “TCGAmutation” package and visualized with the “maftools” package. Kaplan–Meier curves were plotted with the “survminer” package. Then, a signature was constructed by logistic regression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using “clusterProfiler.” Furthermore, the infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, the performance of the signature was evaluated by receiver-operating characteristic (ROC) curve and calibration curve analyses.Results: Six genes were selected to construct this prognostic model: TSHZ3, ZFHX4, ZEB2, MEIS1, ISL1, and HOXC4. We divided the BLCA cohort into high- and low-risk groups based on the median risk score calculated with the novel signature. The overall survival (OS) rate of the high-risk group was significantly lower than that of the low-risk group. The infiltration levels of almost all immune cells were significantly higher in the high-risk group than in the low-risk group. The average risk score for the group that responded to immunotherapy was significantly lower than that of the group that did not.Conclusion: We constructed a risk prediction signature with six homeobox genes, which showed good accuracy and consistency in predicting the patient’s prognosis and response to immunotherapy. Therefore, this signature can be a potential biomarker and treatment target for BLCA patients.

scholarly journals Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

2021 ◽  
Author(s):  
Chen-jie Qiu ◽  
Xue-bing Wang ◽  
Zi-ruo Zheng ◽  
Chao-zhi Yang ◽  
Kai Lin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Cell Infiltration

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.

scholarly journals A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoxia Tong ◽  
Xiaofei Qu ◽  
Mengyun Wang
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Score ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Rank Test ◽  
Score Model

BackgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.MethodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.ResultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P &lt; 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P &lt; 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P&lt; 0.001) and ulceration (P&lt; 0.001).ConclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.

scholarly journals Risk scoring model to predict recurrence in locally advanced breast cancer (LABC) treated with neoadjuvant chemotherapy (NACT).

2019 ◽  
Vol 5 (suppl) ◽  
pp. 98-98
Author(s):  
Sushma Agrawal ◽  
Prabhakar Mishra ◽  
Punita Lal ◽  
Gaurav Agarwal ◽  
Amit Agarwal ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Risk Group ◽  
Locally Advanced ◽  
High Risk Group ◽  
Low Risk ◽  
Scoring Model ◽  
Group Score ◽  
Risk Scoring ◽  
Independent Risk Factors

98 Background: Complete response (CR) to NACT portends favorable long term outcomes in LABC. There is a need for a tool to risk categorise patients for recurrence risk (RR), so that intensification of treatment can be offered to women with high risk of recurrence. Methods: A prospectively maintained database of LABC (between January 2007 to December 2012), who received NACT followed by definitive surgery, radiotherapy and endocrine therapy in endocrine sensitive disease was retrospectively analyzed for clinico-pathological and treatment factors affecting disease free survival (DFS). A risk scoring model was developed on the basis of beta coefficients of identified independent risk factors for DFS. Results: The incidence of loco-regional relapse was 8% and that of distant metastases was 32% in a dataset of 206 patients at a median follow-up of 47 months (IQR 24-62 mo). The independent risk factors for recurrence were index T stage [HR 1.8 (0.9-3.6)], N stage [HR 1.7 (0.4 – 4.7)], grade [HR 1.8 (0.8-4.2)], age less than and more than 40 years [HR 1.6 (0.4-0.9)], pathologic CR [HR 4.3 (1.7- 10.7)], intrinsic subtype [HR 2.2 (1.3-3.7)], and type of surgery (BCS vs MRM) [HR 2.2 (1.3-3.6)]. The ROC of the model for the prediction of recurrence was 0.67 (95 % CI: 0.61-0.75). The results of this model were validated by dividing the population into 3 risk groups: low risk (score less than 12), intermediate risk group (score between 13-15), high risk group (score 16 or more). The chances of recurrence are 16% versus 34% versus 57% in low, intermediate and high risk group respectively. Presence of three risk factors implies low risk, five intermediate and more than five high risk. Conclusions: The risk scoring model developed by us predicts RR and can be used for selecting patients for treatment intensification in high risk category.

scholarly journals A Ferroptosis and Pyroptosis Molecular Subtype-Related Signature Applicable for Prognosis and Immune Microenvironment Estimation in Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Junyu Huo ◽  
Jinzhen Cai ◽  
Ge Guan ◽  
Huan Liu ◽  
Liqun Wu
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Molecular Subtype ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Type I ◽  
Immune Microenvironment ◽  
Risk Patients

Background: Due to the heterogeneity of tumors and the complexity of the immune microenvironment, the specific role of ferroptosis and pyroptosis in hepatocellular carcinoma (HCC) is not fully understood, especially its impact on prognosis.Methods: The training set (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) based on the prognosis-related genes associated with ferroptosis and pyroptosis. The intersecting differentially expressed genes (DEGs) among C1, C2, and C3 were used in univariate Cox and LASSO penalized Cox regression analysis for the construction of the risk score. The median risk score served as the unified cutoff to divide patients into high- and low-risk groups.Results: Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC. As an independent prognostic indicator, the risk score could be applicable for patients with different clinical features tested by subgroup (n = 26) survival analysis. In the high-risk patients with a lower infiltration abundance of activated B cells, activated CD8 T cells, eosinophils, and type I T helper cells and a higher infiltration abundance of immature dendritic cells, the cytolytic activity, HLA, inflammation promotion, and type I IFN response in the high-risk group were weaker. The TP53 mutation rate, TMB, and CSC characteristics in the high-risk group were significantly higher than those in the low-risk group. Low-risk patients have active metabolic activity and a more robust immune response. The high- and low-risk groups differed significantly in histology grade, vascular tumor cell type, AFP, new tumor event after initial treatment, main tumor size, cirrhosis, TNM stage, BCLC stage, and CLIP score.Conclusion: The ferroptosis and pyroptosis molecular subtype-related signature identified and validated in this work is applicable for prognosis prediction, immune microenvironment estimation, stem cell characteristics, and clinical feature assessment in HCC.

Treatment stratification in B-cell PTLD after solid organ transplantation (SOT) by international prognostic index (IPI) and response to rituximab: Interim results from the PTLD-2 trial.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 8045-8045
Author(s):  
Ralf Ulrich Trappe ◽  
Christian Koenecke ◽  
Martin H. Dreyling ◽  
Christiane Pott ◽  
Ulrich Duehrsen ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Risk Stratification ◽  
B Cell ◽  
Primary Endpoint ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Solid Organ ◽  
Very High

8045 Background: The PTLD-1 trials have established risk-stratified sequential treatment of B-cell PTLD. After rituximab induction, patients (pts) in complete remission (25 %) received rituximab consolidation, while all others received R-CHOP. The PTLD-2 trial tests modified risk-stratification including clinical risk factors. These are the results of the 2nd scheduled interim analysis (40/60 planned pts). Methods: The prospective, multicenter phase II PTLD-2 trial (NCT02042391) enrols treatment-naïve adult SOT recipients with CD20-positive PTLD. Key exclusion criteria are CNS involvement, ECOG > 2, pregnancy, and severe organ dysfunction or severe, active infection. Treatment consists of rituximab (1400 mg SC; first application 375 mg/m2 IV) on days 1, 8, 15 and 22. After restaging, pts in CR as well as those in PR with ≤ 2 IPI risk factors at diagnosis (low-risk group) continue with four three-weekly courses of rituximab. Most other pts (high-risk group) receive 4 cycles of R-CHOP-21, while thoracic SOT recipients who progress under rituximab (very-high-risk group) receive six cycles of alternating R-CHOP-21 and R-DHAOx. The primary endpoint (event-free survival in the low-risk group) is not analyzed here. Secondary endpoints presented here are response and overall response (ORR) by computed tomography, overall survival (OS), time to progression (TTP) and treatment-related mortality (TRM) overall and by risk group. Results: 40 pts were recruited at 12 centers (2015 – 2019). 21/40 were kidney, 11 lung, 4 liver, 3 heart, and 1 liver/kidney transplant recipients. Median age was 54 years. 38/40 PTLD were monomorphic and 15/40 EBV-associated. 38 pts were evaluated for response at interim staging: 13 were allocated to the low-risk, 17 to the high-risk and 8 to the very-high-risk group. ORR was 28/30 (93 %, CR: 16/30 [53 %]). With a median follow-up of 1.9 years, the 1-year/3-year Kaplan-Meier (KM) estimates of TTP and OS in the intention-to-treat population (40 pts) were 85 %/80 % and 70 %/70 %, respectively. In the low-risk group, the 2-year KM estimate of OS was 100 %. The frequency of infections (all grades) was 50 %, and TRM occurred in 3/40 pts (8 %). Conclusions: One third of enrolled pts were treated in the low-risk group and the recruitment goal for evaluation of the primary endpoint will likely be reached. Interim efficacy and toxicity data with rituximab SC and modified risk-stratification are encouraging despite the inclusion of 35 % thoracic SOT recipients. Clinical trial information: NCT02042391 .

scholarly journals Identification of the 11-lncRNA signatures associated with the prognosis of endometrial carcinoma

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042110065
Author(s):  
Jing Wan ◽  
Peigen Chen ◽  
Yu Zhang ◽  
Jie Ding ◽  
Yuebo Yang ◽  
...  
Keyword(s):  
High Risk ◽  
Endometrial Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Training Set ◽  
Lncrna Expression ◽  
Robust Likelihood

Endometrial carcinoma (EC) is the fourth most common cancer in women. Some long non-coding RNAs (lncRNAs) are regarded as potential prognostic biomarkers or targets for treatment of many types of cancers. We aim to screen prognostic-related lncRNAs and build a possible lncRNA signature which can effectively predict the survival of patients with EC. We obtained lncRNA expression profiling from the TCGA database. The patients were classified into training set and verification set. By performing Univariate Cox regression model, Robust likelihood-based survival analysis, and Cox proportional hazards model, we developed a risk score with the Cox co-efficient of individual lncRNAs in the training set. The optimum cut-off point was selected by ROC analysis. Patients were effectively divided into high-risk group and low-risk group according to the risk score. The OS of the low-risk patients was significantly prolonged compared with that of the high-risk group. At last, we validated this 11-lncRNA signature in the verification set and the complete set. We identified an 11-lncRNA expression signature with high stability and feasibility, which can predict the survival of patients with EC. These findings provide new potential biomarkers to improve the accuracy of prognosis prediction of EC.

scholarly journals Establishing a Prognostic Signature Based on Epithelial–Mesenchymal Transition-Related Genes for Endometrial Cancer Patients

2022 ◽  
Vol 12 ◽  
Author(s):  
Jinhui Liu ◽  
Guoliang Cui ◽  
Shuning Shen ◽  
Feng Gao ◽  
Hongjun Zhu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Epithelial Mesenchymal Transition ◽  
Risk Group ◽  
Checkpoint Inhibitors ◽  
High Risk Group ◽  
Low Risk ◽  
Clinical Samples ◽  
Chemotherapeutic Drugs ◽  
Mesenchymal Transition

BackgroundsEpithelial–mesenchymal transition (EMT) is a sequential process where tumor cells develop from the epithelial state to the mesenchymal state. EMT contributes to various tumor functions including initiation, propagating potential, and resistance to therapy, thus affecting the survival time of patients. The aim of this research is to set up an EMT-related prognostic signature for endometrial cancer (EC).MethodsEMT-related gene (ERG) expression and clinical data were acquired from The Cancer Genome Atlas (TCGA). The entire set was randomly divided into two sets, one for contributing the risk model (risk score) and the other for validating. Univariate and multivariate Cox proportional hazards regression analyses were applied to the training set to select the prognostic ERGs. The expression of 10 ERGs was confirmed by qRT-PCR in clinical samples. Then, we developed a nomogram predicting 1-/3-/5-year survival possibility combining the risk score and clinical factors. The entire set was stratified into the high- and low-risk groups, which was used to analyze the immune infiltrating, tumorigenesis pathways, and response to drugs.ResultsA total of 220 genes were screened out from 1,316 ERGs for their differential expression in tumor versus normal. Next, 10 genes were found to be associated with overall survival (OS) in EC, and the expression was validated by qRT-PCR using clinical samples, so we constructed a 10-ERG-based risk score to distinguish high-/low-risk patients and a nomogram to predict survival rate. The calibration plots proved the predictive value of our model. Gene Set Enrichment Analysis (GSEA) discovered that in the low-risk group, immune-related pathways were enriched; in the high-risk group, tumorigenesis pathways were enriched. The low-risk group showed more immune activities, higher tumor mutational burden (TMB), and higher CTAL4/PD1 expression, which was in line with a better response to immune checkpoint inhibitors. Nevertheless, response to chemotherapeutic drugs turned out better in the high-risk group. The high-risk group had higher N6-methyladenosine (m6A) RNA expression, microsatellite instability level, and stemness indices.ConclusionWe constructed the ERG-related signature model to predict the prognosis of EC patients. What is more, it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs.

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