Development of an Oxidative Phosphorylation-Related and Immune Microenvironment Prognostic Signature in Uterine Corpus Endometrial Carcinoma

Background: As the fourth most common malignant tumors in women, uterine corpus endometrial carcinoma (UCEC) requires novel and reliable biomarkers for prognosis prediction to improve the overall survival. Oxidative phosphorylation (OXPHOS) is found to be strongly correlated with the progression of tumor. Here, we aimed to construct an OXPHOS-related and immune microenvironment prognostic signature to stratify UCEC patients for optimization of treatment strategies.Method: Prognosis-associated OXPHOS-related differentially expressed genes were identified by multivariable Cox regression from TCGA–UCEC cohort. Based on the candidate genes, an OXPHOS-related prognostic signature was constructed by the train set data and verified by the entire set. When integrated with relevant clinical characteristics, a nomogram was also created for clinical application. Through comparison of tumor microenvironment between different risk groups, the underlying mechanism of the model and the inner correlation between immune microenvironment and energy metabolism were further investigated.Results: An OXPHOS-related signature containing ATP5IF1, COX6B1, FOXP3, and NDUFB11 was constructed and had better predictive ability compared with other recently published signatures in UCEC. Patients with lower risk score showed higher immune cell infiltration, higher ESTIMATE score (p = 2.808E−18), lower tumor purity (p = 2.808E−18), higher immunophenoscores (IPSs) (p < 0.05), lower expression of mismatch repair (MMR) proteins (p < 0.05), higher microsatellite instability (MSI), lower expression of markers of N6-methyladenosine (m6A) mRNA methylation regulators, higher tumor mutation burden (TMB) (p = 1.278E−9), and more sensitivity to immune checkpoint blockade (ICB) (p < 0.001) and chemotherapy drugs, thus, possessing improved prognosis.Conclusion: An OXPHOS-related and immune microenvironment prognostic signature classifying EC patients into different risk subsets was constructed in our study, which could be used to predict the prognosis of patients and help to select a specific subset of patients who might benefit from immunotherapy and chemotherapy, thus, improving the overall survival rate of UCEC. These findings may contribute to the discovery of novel and robust biomarkers or target therapy in UCEC and give new insights into the molecular mechanism of tumorigenesis and progression of UCEC.

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Identifying immune subtypes of uterine corpus endometrial carcinoma and a four-paired-lncRNA signature with immune-related lncRNAs

Experimental Biology and Medicine ◽

10.1177/15353702211053588 ◽

2021 ◽

pp. 153537022110535

Author(s):

Nan Li ◽

Kai Yu ◽

Zhong Lin ◽

Dingyuan Zeng

Keyword(s):

Overall Survival ◽

Endometrial Carcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Immune Escape ◽

Enrichment Analysis ◽

Information Criterion ◽

Cox Regression Analysis ◽

Uterine Corpus ◽

Immune Related Genes

Uterine corpus endometrial carcinoma (UCEC) is the third most frequent gynecological malignancies in the female reproductive system. Long non-coding RNAs (lncRNAs) are closely involved in tumor progression. This study aimed to develop an immune subtyping system and a prognostic model based on lncRNAs for UCEC. Paired lncRNAs and non-negative matrix factorization were applied to identify immune subtypes. Enrichment analysis was conducted to assess functional pathways, immune-related genes, and cells. Univariate and multivariate Cox regression analysis were performed to analyze the relation between lncRNAs and overall survival (OS). A prognostic model was constructed and optimized by least absolute shrinkage and selection operator (LASSO) and Akaike information criterion (AIC). Two immune subtypes (C1 and C2) and four paired-prognostic lncRNAs closely associated with overall survival were identified. Some immune features, sensitivity of chemotherapy and immunotherapy, and the relation with immune escape showed variations between two subtypes. A nomogram established based on prognostic model and clinical features was effective in OS prediction. The immune subtyping system based on lncRNAs and the four-paired-lncRNA signature was predictive of UCEC prognosis and can facilitate personalized therapies such as immunotherapy or RNA-based therapy for UCEC patients.

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Identification of TNFRSF4 as a Diagnosis and Prognosis Biomarker Associated with Immune Microenvironment in Uterine Corpus Endometrial Carcinoma

10.21203/rs.3.rs-488368/v1 ◽

2021 ◽

Author(s):

Heng Ma ◽

Penghui Feng ◽

Shuangni Yu ◽

Ruiqin Han ◽

Zaixin Guo ◽

...

Keyword(s):

T Cells ◽

Endometrial Carcinoma ◽

Molecular Mechanisms ◽

Cox Regression ◽

Functional Enrichment ◽

Prognostic Biomarkers ◽

Clinical Samples ◽

Immune Microenvironment ◽

Uterine Corpus ◽

Diagnosis And Prognosis

Abstract BackgroundThe interaction between tumor microenvironment (TME) and tumors offers various targets in mounting anti-tumor immunotherapies. However, the diagnostic and prognostic biomarkers in uterine corpus endometrial carcinoma (UCEC) are still limited. Here, we aimed to analyze the TME features and identify novel prognostic biomarkers for UCEC. MethodsESTIMATE, CIBERSORT, protein-protein interaction (PPI) network, univariate Cox regression, and functional enrichment analysis were performed to identify immune- and survival-related hub genes as well as possible molecular mechanisms. The limma package and the deconvolution algorithm were adopted to estimate the tumor-infiltrating immune cells (TICs) abundance and their relationship with the target gene. Tissue microarrays (TMAs) of UCEC were evaluated to validate protein expression of the identified immune markers, including TNFRSF4, CD4, and CD8. The receiver operating characteristic (ROC) curve was used to determine the efficacy of TNFRSF4 in diagnosing UCEC. ResultsTwo genes, TNFRSF4 and S1PR4, were screened out from 386 intersection differential expression gene (DEGs) shared by ImmuneScore and StromalScore in UCEC. Highlighted by TNFRSF4, we found that it was not only positively correlated with the TICs (mainly CD4+ T cells, CD8+ T cells, and Tregs) but significantly related to diagnosis and prognosis in patients of UCEC, both verified by data from the TCGA database and clinical samples. ConclusionsCollectively, TNFRSF4 could serve as a high-profile biomarker to robustly predict immune microenvironment, clinical diagnosis and prognosis for UCEC.

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Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma

Cancer Cell International ◽

10.1186/s12935-020-01560-w ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Cankun Zhou ◽

Chaomei Li ◽

Fangli Yan ◽

Yuhua Zheng

Keyword(s):

Endometrial Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Immune Gene ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Uterine Corpus ◽

Immune Genes

Abstract Background Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis particularly at an advanced stage. Herein, this study aims to construct prognostic markers of UCEC based on immune-related genes to predict the prognosis of UCEC. Methods We analyzed expression data of 575 UCEC patients from The Cancer Genome Atlas database and immune genes from the ImmPort database, which were used for generation and validation of the signature. We constructed a transcription factor regulatory network based on Cistrome databases, and also performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using the Cox regression analysis. We then constructed and verified a prognostic signature. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content. Results The immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic signature revealed a ten-gene prognostic signature, comprising of PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC. This signature showed a strong prognostic ability in both the training and testing sets and thus can be used as an independent tool to predict the prognosis of UCEC. In addition, levels of B cells and neutrophils were significantly correlated with the patient’s risk score, while the expression of ten genes was associated with immune cell infiltrates. Conclusions In summary, the ten-gene prognostic signature may guide the selection of the immunotherapy for UCEC.

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Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.788431 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jinhui Liu ◽

Rui Geng ◽

Sheng Yang ◽

Fang Shao ◽

Zihang Zhong ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Network Analysis ◽

Endometrial Carcinoma ◽

Risk Score ◽

Immune Status ◽

Low Risk ◽

Prognostic Signature ◽

Uterine Corpus ◽

Risk Patients

BackgroundUterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC.MethodsCIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan–Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy.ResultsRed module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs.ConclusionWe developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

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Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma

10.21203/rs.3.rs-16901/v3 ◽

2020 ◽

Author(s):

Cankun Zhou ◽

Chaomei Li ◽

Fangli Yan ◽

Yuhua Zheng

Keyword(s):

Endometrial Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Immune Gene ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Uterine Corpus ◽

Immune Genes

Abstract Background: Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis particularly at an advanced stage. Herein, this study aims to construct prognostic markers of UCEC based on immune-related genes to predict the prognosis of UCEC.Methods: We analyzed expression data of 575 UCEC patients from The Cancer Genome Atlas database and immune genes from the ImmPort database, which were used for generation and validation of the signature. We constructed a transcription factor regulatory network based on Cistrome databases, and also performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using the Cox regression analysis. We then constructed and verified a prognostic signature. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content.Results: The immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic signature revealed a ten-gene prognostic signature, comprising of PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC. This signature showed a strong prognostic ability in both the training and testing sets and thus can be used as an independent tool to predict the prognosis of UCEC. In addition, levels of B cells and neutrophils were significantly correlated with the patient's risk score, while the expression of ten genes was associated with immune cell infiltrates.Conclusions: In summary, the ten-gene prognostic signature may guide the selection of the immunotherapy for UCEC.

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Identification of the Prognostic Signature Associated With Tumor Immune Microenvironment of Uterine Corpus Endometrial Carcinoma Based on Ferroptosis-Related Genes

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.735013 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jinhui Liu ◽

Yichun Wang ◽

Huangyang Meng ◽

Yin Yin ◽

Hongjun Zhu ◽

...

Keyword(s):

High Risk ◽

Endometrial Carcinoma ◽

Immune Checkpoint ◽

Cox Regression ◽

Expression Profiles ◽

Risk Groups ◽

Low Risk ◽

Immune Microenvironment ◽

Cox Regression Analysis ◽

Uterine Corpus

Background: Uterine corpus endometrial carcinoma (UCEC) is the sixth most common cancer worldwide. Ferroptosis plays an important role in malignant tumors. However, the study of ferroptosis in the endometrial carcinoma remains blank.Methods: First, we constructed a ferroptosis-related signature based on the expression profiles from The Cancer Genome Atlas database. Then, patients were divided into the high-risk and low-risk groups based on this signature. The signature was evaluated by Kaplan–Meier analysis and receiver operating characteristic (ROC) analysis. We further investigated the relationship between this signature and immune microenvironment via CIBERSORT algorithm, ImmuCellAI, MAF, MSI sensor algorithm, GSEA, and GDSC.Results: This signature could be an independent prognostic factor based on multivariate Cox regression analysis. GSEA revealed that this signature was associated with immune-related phenotype. In addition, we indicated the different status of immune infiltration and response to the immune checkpoint between low-risk and high-risk groups. Patients in the low-risk group were more likely to present with a higher expression of immune checkpoint molecules and tumor mutation burden. Meanwhile, the low-risk patients showed sensitive responses to chemotherapy drugs.Conclusion: In summary, the six ferroptosis-related genes signature could be used in molecular subgrouping and accurately predict the prognosis of UCEC.

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A Transcription Factor Signature Predicts Prognosis of Patients with Adrenocortical Carcinoma

10.21203/rs.3.rs-178087/v1 ◽

2021 ◽

Author(s):

Jianyu Zhao ◽

Bo Liu ◽

Xiaoping Li

Keyword(s):

Overall Survival ◽

High Risk ◽

Risk Score ◽

Adrenocortical Carcinoma ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Prognostic Signature ◽

Abdominal Masses ◽

Cox Analysis

Abstract Background: Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for prediction of survival of ACC patients.Methods: The gene expression profile for ACC patients were downloaded from TCGA and GEO datasets. The univariate Cox analysis was applied to identify survival-related TFs and the LASSO Cox regression was conducted to construct the TF signature. The multivariate analysis was used to reveal the independent prognostic factors.Results: We identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6 using the univariate Cox analysis and LASSO Cox regression. The risk score based on the TF-signature could classify patients into low- and high-risk group. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival compared to the low-risk patients. ROC curves showed that the prognostic signature predicted the overall survival of ACC patients with good sensitivity and specificity. Furthermore, the TF-risk score was an independent prognostic factor.Conclusion: Taken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients.

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A Transcription Factor Signature Predicts Prognosis of Patients with Adrenocortical Carcinoma

10.21203/rs.3.rs-151903/v1 ◽

2021 ◽

Author(s):

Jianyu Zhao ◽

Bo Liu ◽

Xiaoping Li

Keyword(s):

Overall Survival ◽

High Risk ◽

Cancer Progression ◽

Risk Score ◽

Adrenocortical Carcinoma ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Prognostic Signature ◽

Abdominal Masses

Abstract Background: Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for prediction of survival of ACC patients.Results: We identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6 using the univariate Cox analysis and LASSO Cox regression. The risk score based on the TF-signature could classify patients into low- and high-risk group. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival compared to the low-risk patients. ROC curves showed that the prognostic signature predicted the overall survival of ACC patients with good sensitivity and specificity. Furthermore, the TF-risk score was an independent prognostic factor.Conclusion: Taken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients.

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A novel comprehensive immune-related gene signature as a promising survival predictor for head and neck squamous cell carcinoma

10.21203/rs.3.rs-26402/v1 ◽

2020 ◽

Author(s):

Ruihua Fang ◽

Lin Chen ◽

Jing Liao ◽

Jierong Luo ◽

Chenchen Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Head And Neck ◽

Cox Regression ◽

Gene Signature ◽

Related Gene ◽

Prognostic Signature ◽

Immune Microenvironment ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Gene

Abstract Background: Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. Growing evidence has demonstrated that the immune system plays a crucial role in the development and progression of HNSCC. The goal of our study was to develop an immune-related signature for accurately predicting the survival of HNSCC patients. Methods: Gene expression profiles were established from a total of 546 HNSCC and normal tissues to establish a training set and 83 HNSCC tissues for a validation set. Differentially expressed prognostic immune genes were identified by univariate Cox regression analysis and a corresponding network of differentially expressed transcription factors (TFs) were identified using Cytoscape. The immune-related gene signature was established and validated by univariate Cox regression analysis, least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. In addition, the prognostic value of the immune-related signature was analyzed by survival and Cox regression analysis. Finally, the correlation between the immune-related signature and the immune microenvironment was established.Results: In this study, the TF-mediated network revealed that Foxp3 plays a central role in the regulatory mechanism of most immune genes. A prognostic signature based on 10 immune-related genes, which divided patients into high and low risk groups, was developed and successfully validated using two independent databases. Our prognostic signature was significantly related to worse survival and predicted prognosis in patients with different clinicopathological factors. A nomogram including clinical characteristics was also constructed for accurate prediction. Furthermore, it was determined that our prognostic signature may act as an independent factor for predicting the survival of HNSCC patients. ROC analysis also revealed that our signature had superior predictive value compared with TNM stage. As for the immune microenvironment, our signature showed a positive correlation with activated mast cells and M0 macrophages, a negative correlation with Tregs, and immune checkpoint molecules PD-1 and CLTA-4. Conclusions: Our study established an immune-related gene signature, which not only provides a promising biomarker for survival prediction, but may be evaluated as an indicator for personalized immunotherapy in patients with HNSCC.

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Prognostic Nomogram Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma

Oxidative Medicine and Cellular Longevity ◽

10.1155/2021/9978206 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Yang Li ◽

Rongrong Sun ◽

Rui Li ◽

Yonggang Chen ◽

He Du

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Regulatory Mechanism ◽

Cox Regression ◽

Gene Signature ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, the function and application of circRNAs in lung adenocarcinoma (LUAD) are still unknown. In this study, we constructed a circRNA-associated competitive endogenous RNA (ceRNA) network to investigate the regulatory mechanism of LUAD procession and further constructed a prognostic signature to predict overall survival for LUAD patients. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network. Based on the TargetScan prediction tool and Pearson correlation coefficient, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs, and 49 DEmRNAs. GO and KEGG enrichment indicated that the ceRNA network might be involved in the regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate Cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using the LASSO method in the TCGA-LUAD training cohort. By applying the signature, patients could be categorized into the high-risk or low-risk subgroups with significant survival differences (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009 ). The prognostic performance was confirmed in an independent GEO cohort (GSE42127, HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004 ). Multivariate Cox regression analysis proved that the three-gene signature was an independent prognostic factor. Combining the three-gene signature with clinical characters, a nomogram was constructed. The primary and external verification C -indexes were 0.717 and 0.716, respectively. The calibration curves for the probability of 3- and 5-year OS showed significant agreement between nomogram predictions and actual observations. Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and further constructed a useful prognostic signature to guide personalized treatment of LUAD patients.

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