Evaluation of the Prognostic Value of Long Noncoding RNAs in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSC) is the most common type of lung cancer accounting for 40% to 51%. Long noncoding RNAs (lncRNAs) have been reported to play a significant role in the invasion, migration, and proliferation of lung cancer tissue cells. However, systematic identification of lncRNA signatures and evaluation of the prognostic value for LUSC are still an urgent problem. In this work, LUSC RNA-seq data were collected from TCGA database, and the limma R package was used to screen differentially expressed lncRNAs (DElncRNAs). In total, 216 DElncRNAs were identified between the LUSC and normal samples. lncRNAs associated with prognosis were calculated using univariate Cox regression analysis. The overall survival (OS) prognostic model containing 10 lncRNAs and the disease-free survival (DFS) prognostic model consisting of 11 lncRNAs were constructed using a machine learning-based algorithm, systematic LASSO-Cox regression analysis. We found that the survival rate of samples in the high-risk group was lower than that in the low-risk group. Results of ROC curves showed that both the OS and DFS risk score had better prognostic effects than the clinical characteristics, including age, stage, gender, and TNM. Two lncRNAs (LINC00519 and FAM83A-AS1) that were commonly identified as prognostic factors in both models could be further investigated for their clinical significance and therapeutic value. In conclusion, we constructed lncRNA prognostic models with considerable prognostic effect for both OS and DFS of LUSC.

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Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021333 ◽

2021 ◽

Vol 18 (5) ◽

pp. 6709-6723

Author(s):

Xin Yu ◽

◽

Jun Liu ◽

Ruiwen Xie ◽

Mengling Chang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Prognostic Model ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Lung Squamous Cell Carcinoma ◽

Risk Groups ◽

Low Risk ◽

Immune Cell Infiltration ◽

Cox Regression Analysis

<abstract> <sec><title>Objective</title>We aimed to construct a novel prognostic model based on N6-methyladenosine (m6A)-related autophagy genes for predicting the prognosis of lung squamous cell carcinoma (LUSC). </sec> <sec><title>Methods</title>Gene expression profiles and clinical information of Patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) database. In addition, m6A- and autophagy-related gene profiles were obtained from TCGA and Human Autophagy Database, respectively. Pearson correlation analysis was performed to identify the m6A-related autophagy genes, and univariate Cox regression analysis was conducted to screen for genes associated with prognosis. Based on these genes, LASSO Cox regression analysis was used to construct a prognostic model. The corresponding prognostic score (PS) was calculated, and patients with LUSC were assigned to low- and high-risk groups according to the median PS value. An independent dataset (GSE37745) was used to validate the prognostic ability of the model. CIBERSORT was used to calculate the differences in immune cell infiltration between the high- and low-risk groups. </sec> <sec><title>Results</title>Seven m6A-related autophagy genes were screened to construct a prognostic model: <italic>CASP4</italic>, <italic>CDKN1A</italic>, <italic>DLC1</italic>, <italic>ITGB1</italic>, <italic>PINK1</italic>, <italic>TP63</italic>, and <italic>EIF4EBP1</italic>. In the training and validation sets, patients in the high-risk group had worse survival times than those in the low-risk group; the areas under the receiver operating characteristic curves were 0.958 and 0.759, respectively. There were differences in m6A levels and immune cell infiltration between the high- and low-risk groups. </sec> <sec><title>Conclusions</title>Our prognostic model of the seven m6A-related autophagy genes had significant predictive value for LUSC; thus, these genes may serve as autophagy-related therapeutic targets in clinical practice. </sec> </abstract>

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Identification and Validation of a Seven m6A-related lncRNAs Signature Predicting Prognosis of Ovarian Cancer

10.21203/rs.3.rs-915700/v1 ◽

2021 ◽

Author(s):

Yan Li ◽

Xiaoying Wang ◽

Yue Han ◽

Xun Li

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Correlation Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Pearson Correlation ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Pearson Correlation Analysis

Abstract Background: Long non-coding RNAs (lncRNAs) play an important role in angiogenesis, immune response, inflammatory response and tumor development and metastasis. m6 A (N6 - methyladenosine) is one of the most common RNA modifications in eukaryotes. The aim of our research was to investigate the potential prognostic value of m6A-related lncRNAs in ovarian cancer (OC).Methods: The data we need for our research was downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Pearson correlation analysis between 21 m6A regulators and lncRNAs was performed to identify m6A-related lncRNAs. Univariate Cox regression analysis was implemented to screen for lncRNAs with prognostic value. A least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses was used to further reduct the lncRNAs with prognostic value and construct a m6A-related lncRNAs signature for predicting the prognosis of OC patients. Results: 275 m6A-related lncRNAs were obtained using pearson correlation analysis. 29 m6A-related lncRNAs with prognostic value was selected through univariate Cox regression analysis. Then, a seven m6A-related lncRNAs signature was identified by LASSO Cox regression. Each patient obtained a riskscore through multivariate Cox regression analyses and the patients were classified into high-and low-risk group using the median riskscore as a cutoff. Kaplan-Meier curve revealed that the patients in high-risk group have poor outcome. The receiver operating characteristic curve revealed that the predictive potential of the m6A-related lncRNAs signature for OC was powerful. The predictive potential of the m6A-related lncRNAs signature was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the m6A-related lncRNAs signature was an independent prognostic factor for OC patients. Moreover, a nomogram based on the expression level of the seven m6A-related lncRNAs was established to predict survival rate of patients with OC. Finally, a competing endogenous RNA (ceRNA) network associated with the seven m6A-related lncRNAs was constructed to understand the possible mechanisms of the m6A-related lncRNAs involed in the progression of OC.Conclusions: In conclusion, our research revealed that the m6A-related lncRNAs may affect the prognosis of OC patients and identified a seven m6A-related lncRNAs signature to predict the prognosis of OC patients.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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Development and Validation of a Ferroptosis-Related Gene Signature and Nomogram for Predicting the Prognosis of Esophageal Squamous Cell Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.697524 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jiecheng Ye ◽

Yining Wu ◽

Heyuan Cai ◽

Li Sun ◽

Wanying Deng ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Squamous Cell ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Clinical Variable ◽

Cox Regression Analysis

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor with high mortality and poor prognosis. Ferroptosis is a newly discovered form of cell death induced by iron-catalyzed excessive peroxidation of polyunsaturated fatty acids (PUFAs). However, the prognostic value of ferroptosis-related genes (FRGs) for ESCC remains unclear. Based on the ESCC dataset from the Gene Expression Omnibus (GEO) database, we identified 39 prognostic FRGs through univariate Cox regression analysis. After LASSO regression and multivariate Cox regression analyses, a multigene signature based on 10 prognostic FRGs was constructed and successfully divided ESCC patients into two risk groups. Patients in the low-risk group showed a significantly better prognosis than patients in the high-risk group. In addition, we combined the risk score with clinical predictors to construct a nomogram for ESCC. The predictive ability of the nomogram was further verified by ROC curves and calibration plots in both the training and validation sets. The predictive power of the nomogram was demonstrated to be better than that of either the risk score or clinical variable alone. Furthermore, functional analysis revealed that the 10-FRG signature was mainly associated with ferroptosis, differentiation and immune response. Connectivity map analysis identified potential compounds capable of targeting FRGs in ESCC. Finally, we demonstrated the prognostic value of SRC gene in ESCC using the clinical samples and found that SRC inhibition sensitized ESCC cells to ferroptosis inducers by in vitro experiments. In conclusion, we identified and verified a 10-FRG prognostic signature and a nomogram, which provide individualized prognosis prediction and provide insight into potential therapeutic targets for ESCC.

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Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes

Anti-Cancer Agents in Medicinal Chemistry ◽

10.2174/1871520621666210329100052 ◽

2021 ◽

Vol 21 ◽

Author(s):

Dawei Zhou ◽

Junchen Wan ◽

Jiang Luo ◽

Yuhao Tao

Keyword(s):

Regression Analysis ◽

High Risk ◽

Liver Cancer ◽

Cancer Patients ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Primary Liver Cancer ◽

Risk Groups ◽

Cox Regression Analysis

Background: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and prognostic value of autophagy genes in liver cancer. Objective: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer to establish a prognostic model of primary liver cancer based on autophagy genes. Results: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. To optimize the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes to establish a prognostic model. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO), and performed multi-factor cox regression analysis on the 9 selected genes to construct a prognostic model. The risk score of each patient was calculated based on 4 genes(BIRC5, HSP8, SQSTM1, and TMEM74) which participated in the establishing of the model, then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was the independent prognostic factors (HR=1.872, 95%CI=1.544-2.196, P<0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. Combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer. Conclusion: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and revealed part of the molecular mechanism of liver cancer autophagy. At the same time, the certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.

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Identification and Prognostic Value Exploration of Radiotherapy Sensitivity-Associated Genes in Non-Small-Cell Lung Cancer

BioMed Research International ◽

10.1155/2021/5963868 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Qing Ma ◽

Kai Geng ◽

Ping Xiao ◽

Lili Zeng

Keyword(s):

Lung Cancer ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Small Cell ◽

Small Cell Lung ◽

Cox Regression Analysis ◽

Nsclc Patients

Background. Non-small-cell lung cancer (NSCLC) is a prevalent malignancy with high mortality and poor prognosis. The radiotherapy is one of the most common treatments of NSCLC, and the radiotherapy sensitivity of patients could affect the individual prognosis of NSCLC. However, the prognostic signatures related to radiotherapy response still remain limited. Here, we explored the radiosensitivity-associated genes and constructed the prognostically predictive model of NSCLC cases. Methods. The NSCLC samples with radiotherapy records were obtained from The Cancer Genome Atlas database, and the mRNA expression profiles of NSCLC patients from the GSE30219 and GSE31210 datasets were obtained from the Gene Expression Omnibus database. The Weighted Gene Coexpression Network Analysis (WGCNA), univariate, least absolute shrinkage and selection operator (LASSO), multivariate Cox regression analysis, and nomogram were conducted to identify and validate the radiotherapy sensitivity-related signature. Results. WGCNA revealed that 365 genes were significantly correlated with radiotherapy response. LASSO Cox regression analysis identified 8 genes, including FOLR3, SLC6A11, ALPP, IGFN1, KCNJ12, RPS4XP22, HIST1H2BH, and BLACAT1. The overall survival (OS) of the low-risk group was better than that of the high-risk group separated by the Risk Score based on these 8 genes for the NSCLC patients. Furthermore, the immune infiltration analysis showed that monocytes and activated memory CD4 T cells had different relative proportions in the low-risk group compared with the high-risk group. The Risk Score was correlated with immune checkpoints, including CTLA4, PDL1, LAG3, and TIGIT. Conclusion. We identified 365 genes potentially correlated with the radiotherapy response of NSCLC patients. The Risk Score model based on the identified 8 genes can predict the prognosis of NSCLC patients.

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Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2021/5564040 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Fanbo Qin ◽

Junyong Zhang ◽

Jianping Gong ◽

Wenfeng Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Prognostic Value ◽

Prognostic Model ◽

Cox Regression ◽

Cancer Genome ◽

Clinical Parameters ◽

Cox Regression Analysis ◽

Model Based

Background. Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients. Methods. Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database. Results. A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients. Conclusion. The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

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The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma

Scientific Reports ◽

10.1038/s41598-021-00364-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Dankun Luo ◽

Wenchao Yao ◽

Qiang Wang ◽

Qiu Yang ◽

Xuxu Liu ◽

...

Keyword(s):

Regression Analysis ◽

Prognostic Model ◽

Invasive Carcinoma ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis ◽

Pathway Enrichment

AbstractLong non-coding RNA (lncRNA) is a prognostic biomarker for many types of cancer. Here, we aimed to study the prognostic value of lncRNA in Breast Invasive Carcinoma (BRCA). We downloaded expression profiles from The Cancer Genome Atlas (TCGA) datasets. Subsequently, we screened the differentially expressed genes between normal tissues and tumor tissues. Univariate Cox, LASSO regression, and multivariate Cox regression analysis were used to construct a lncRNA prognostic model. Finally, a nomogram based on the lncRNAs model was developed, and weighted gene co-expression network analysis (WGCNA) was used to predict mRNAs related to the model, and to perform function and pathway enrichment. We constructed a 6-lncRNA prognostic model. Univariate and multivariate Cox regression analysis showed that the 6-lncRNA model could be used as an independent prognostic factor for BRCA patients. We developed a nomogram based on the lncRNAs model and age, and showed good performance in predicting the survival rates of BRCA patients. Also, functional pathway enrichment analysis showed that genes related to the model were enriched in cell cycle-related pathways. Tumor immune infiltration analysis showed that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group. In general, the 6-lncRNA prognostic model and nomogram could be used as a practical and reliable prognostic tool for invasive breast cancer.

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Construction of a Prognostic Model for Predicting Overall Survival of Patients with Colorectal Cancer

10.21203/rs.3.rs-26225/v1 ◽

2020 ◽

Author(s):

Shuwen Han ◽

Kefeng Ding

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Levulinic Acid ◽

Prognostic Model ◽

Cox Regression ◽

Aminolevulinic Acid ◽

Risk Group ◽

Prognostic Models ◽

Cox Regression Analysis

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies. The purpose of this study is to construct a prognostic model for predicting the overall survival (OS) in patients with CRC. Methods: The mRNA-seq and miRNA-seq data of colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) were downloaded from The Cancer Genome Atlas (TCGA) database. The differentially expressed RNAs (DE-RNAs) between tumor and normal tissues were screened. The Kaplan-Meier and univariate Cox regression analysis were used to screen the survival-related genes. Functional enrichment analysis of survival-related genes was conducted, followed by protein-protein interaction (PPI) analysis. Subsequently, the potential drugs targeting differentially expressed mRNAs (DE-mRNAs) were investigated. Multivariate Cox regression analysis was then conducted to screen the independent prognostic factors, and these genes were used to establish a prognostic model. A receiver operator characteristic (ROC) curve was constructed, and the area under the curve (AUC) value of ROC was calculated to evaluate the specificity and sensitivity of the model. Results: A total of 855 survival-related genes were screened. These genes were mainly enriched in Gene Ontology (GO) terms, such as methylation, synapse organization, and methyltransferase activity; and pathway analysis showed that these genes were significantly involved in N-Glycan biosynthesis and the calcium signaling pathway. PPI analysis showed that aminolevulinate dehydratase (ALAD) and cholinergic receptor muscarinic 2 (CHRM2) served vital roles in the development of CRC. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for CRC treatment. The prognostic models were built and the patients were divided into high-risk and low-risk groups based on the median of risk score (RS) as screening threshold. The OS for patients in the high-risk group was markedly shorter than that for patients in the low-risk group. Meanwhile, kazal type serine peptidase inhibitor domain 1 (KAZALD1), hippocalcin like 4 (HPCAL4), cadherin 8 (CDH8), synaptopodin 2 (SYNPO2), cyclin D3 (CCND3), and hsa_mir_26b may be independent prognostic factors that could be considered as therapeutic targets for CRC.Conclusion: We established prognostic models that could predict the OS for CRC patients and may assist clinicians in providing personalized and precision treatment in this patient population.Highlights:1. ALAD served a vital role in the development of CRC.2. CHRM2 played a role in CRC development by affecting the calcium signaling pathway.3. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for treating CRC.4. KAZALD1 and HPCAL4 were associated with the OS of CRC.5. CDH8, SYNPO2, CCND3, and hsa-mir-26b were closely related to the prognostic of CRC staging.

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Identification of an immune gene signature with prognostic value for patients with uterine corpus endometrial carcinoma

10.21203/rs.3.rs-16901/v1 ◽

2020 ◽

Author(s):

Cankun Zhou ◽

Chaomei Li ◽

Fangli Yan ◽

Yuhua Zheng

Keyword(s):

Regression Analysis ◽

Endometrial Carcinoma ◽

Prognostic Value ◽

Prognostic Model ◽

Immune Cell ◽

Cox Regression ◽

Gene Signature ◽

Immune Gene ◽

Cox Regression Analysis ◽

Immune Genes

Abstract Background: Uterine corpus endometrial carcinoma (UCEC) is a very common gynecological malignancy with a poor prognosis in the late stage. Therefore, the purpose of this study was to determine an immune-related gene signature that predicts the patients’ OS for UCEC. Methods: Based on TCGA, ImmPort, and Cistrome databases, the differential immune genes were screened and the TFs regulatory network was constructed. Functional enrichment and pathway analysis of differential immune genes were carried out. Prognostic value of 410 immune genes was analyzed by Cox regression analysis, a prognostic model was constructed, ROC curves were used to verify the accuracy of the model, and independent prognostic analysis was performed. Finally, the immune cell content was obtained by TIMER, and the correlation with the immune gene expression was evaluated by univariate Cox regression analysis. Results: It was found that the immune cell microenvironment and PI3K-Akt, MARK signaling pathways were involved in the development of UCEC. Based on the established prognostic model, ten-gene prognosis signature (PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, CBLC) for UCEC prognostic prediction were finally identified, and our study has shown that risk-score can be a powerful prognostic factor for UCEC, independent of other clinical factors. The levels of B cells and neutrophils may be significantly correlated with the patient's risk score. Conclusions: Our studies showed that the ten-gene prognosis signature had important clinical value for the prognosis of UCEC, which was helpful for individualized treatment and provided a new target for tumor immunotherapy.

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