scholarly journals Coexpressed Genes That Promote the Infiltration of M2 Macrophages in Melanoma Can Evaluate the Prognosis and Immunotherapy Outcome

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Kexin Yan ◽  
Yutao Wang ◽  
Yuxiu Lu ◽  
Zhangyong Yan
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Essential Genes ◽  
Assessment Model ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
M2 Macrophages ◽  
Cox Regression Analysis ◽  
Coexpression Module ◽  
Coexpressed Genes

Purpose. To improve immunotherapy efficacy for melanoma, a coexpression network and key genes of M2 macrophages in melanoma were explored. A prognostic risk assessment model was established for M2-related coexpressed genes, and the role of M2 macrophages in the immune microenvironment of melanoma was elucidated. Method. We obtained mRNA data from melanoma and peritumor tissue samples from The Cancer Genome Atlas-skin cutaneous melanoma (TCGA-SKCM). Then, we used CIBERSORT to calculate the proportion of M2 macrophage cells. A coexpression module most related to M2 macrophages in TCGA-SKCM was determined by analyzing the weighted gene coexpression network, and a coexpression network was established. After survival analysis, factors with significant results were incorporated into a Cox regression analysis to establish a model. The model’s essential genes were analyzed using functional enrichment, GSEA, and subgroup and total carcinoma. Finally, external datasets GSE65904 and GSE78220 were used to verify the prognostic risk model. Results. The yellow-green module was the coexpression module most related to M2 macrophages in TCGA-SKCM; NOTCH3, DBN1, KDELC2, and STAB1 were identified as the essential genes that promoted the infiltration of M2 macrophages in melanoma. These genes are concentrated in antigen treatment and presentation, chemokine, cytokine, the T cell receptor pathway, and the IFN-γ pathway. These factors were analyzed for survival, and factors with significant results were included in a Cox regression analysis. According to the methods, a model related to M2-TAM coexpressed gene was established, and the formula was risk   score = 0.25 ∗ NOTCH 3 + 0.008 ∗   DBN 1 − 0.031 ∗ KDELC 2 − 0.032 ∗ STAB 1 . The new model was used to perform subgroup evaluation and external queue validation. The results showed good prognostic ability. Conclusion. We proposed a Cox proportional hazards regression model associated with coexpression genes of melanoma M2 macrophages that may provide a measurement method for generating prognosis scores in patients with melanoma. Four genes coexpressed with M2 macrophages were associated with high levels of infiltration of M2 macrophages. Our findings may provide significant candidate biomarkers for the treatment and monitoring of melanoma.

scholarly journals Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

2020 ◽  
Vol 19 ◽  
pp. 153303382098417
Author(s):  
Ting-ting Liu ◽  
Shu-min Liu
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Small Gtpase ◽  
Functional Enrichment ◽  
Clinical Correlation ◽  
Cox Regression Analysis ◽  
Prognostic Analysis ◽  
Key Genes

Objective: The incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC). Methods: This study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes. Results: Of the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation. Conclusions: In this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

scholarly journals Identification of a Ferroptosis-Related LncRNA Signature as a Novel Prognosis Model for Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Lu Lu ◽  
Le-Ping Liu ◽  
Qiang-Qiang Zhao ◽  
Rong Gui ◽  
Qin-Yu Zhao
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Lung Adenocarcinoma ◽  
Tumor Cells ◽  
Risk Prediction ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Prediction Model ◽  
Functional Enrichment ◽  
Cox Regression Analysis

Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic.

scholarly journals Development of A Vitamin-related Gene Signature to Predict the Immune Characteristics and Prognosis of Glioma

2020 ◽  
Author(s):  
Qing Zhang ◽  
Qingyu Liang ◽  
Gefei Guan ◽  
Wen Cheng ◽  
Lianhe Yang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
The Body ◽  
Functional Enrichment ◽  
Molecular Signature ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Related Risk ◽  
Tcga Dataset ◽  
Genome Atlas

Abstract Background: Vitamins not only play a pivotal role in maintaining homeostasis of the body, but also have complex impacts on the occurrence and progression of tumors. However, the effects of vitamins on glioma and the underlying mechanism have not been fully elucidated. Methods: Vitamin -related genes were extracted from the Molecular Signature Database v7.1 (MSigDB). The overlapping overall survival (OS)-related genes in The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and GSE16011 cohorts screened out by univariate COX regression analysis were utilized to construct a risk model based on the TCGA cohort via random survival forest analysis and multivariate COX regression analysis. The powerful prognostic predictive potential of the vitamin-related risk signature was verified by Kaplan–Meier survival analysis and receiver operating characteristic (ROC) analysis in the three datasets. The ssGSEA method of the GSVA package was used for functional enrichment and immune cell component analyses. ESTIMATE score analysis was used for auxiliary analysis of glioma immune characteristics. A nomogram was constructed and assessed based on the TCGA dataset.Results: The vitamin-related six-gene (POSTN, IRX5, EEF2, RAB27A, MDM2, and ENO1) risk signature constructed based on the TCGA dataset accurately predicted the outcomes of glioma patients and credibly distinguished between different levels and molecular subtypes of glioma in the TCGA, CGGA, and GSE16011 cohorts. Gliomas with high risk scores exhibited high immune scores, low tumor purity, and immunosuppressive features. The nomogram constructed by combining the vitamin-related risk signature and clinicopathological factors precisely predicted the 1-, 3-, and 5-year OS of glioma patients.Conclusions: Our study revealed that the vitamin-related six-gene risk signature, as an independent prognostic factor, could accurately distinguish the grade, molecular subtype, and immune characteristics of glioma.

scholarly journals Predictive Proteomic Signature for the Prognosis of Cervical Cancer

2021 ◽  
Author(s):  
Xiaoyu Ji ◽  
Guangdi Chu ◽  
Jinwen Jiao ◽  
Teng Lv ◽  
Yulong Chen ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Incidence And Mortality

Abstract Objective: Cervical cancer (CC) is one of the most common types of malignant female cancer, and its incidence and mortality are not optimistic. Protein panels can be a powerful prognostic factor for many types of cancer. The purpose of our study was to investigate a proteomic panel to predict survival of patients with common CC. Methods and results: The protein expression and clinicopathological data of CC were downloaded from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA) database, respectively. We selected the prognosis-related proteins (PRPs) by univariate Cox regression analysis and found that the results of functional enrichment analysis were mainly related to apoptosis. We used Kaplan–Meier(K-M) analysis and multivariable Cox regression analysis further to screen PRPs to establish a prognostic model, including BCL2, SMAD3, and 4EBP1-pT70. The signature was verified to be independent predictors of OS by Cox regression analysis and the Area Under Curves. Nomogram and subgroup classification were established based on the signature to verify its clinical application. Furthermore, we looked for the co-expressed proteins of three-protein panel as potential prognostic proteins.Conclusion: A proteomic signature independently predicted OS of CC patients, and the predictive ability was better than the clinicopathological characteristics. This signature can help improve prediction for clinical outcome and provides new targets for CC treatment.

Development and validation of a robust immune-related risk signature for hepatocellular carcinoma

2020 ◽  
Author(s):  
Zaoqu Liu ◽  
Dechao Jiao ◽  
Xueliang Zhou ◽  
Yuan Yao ◽  
Zhaonan Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Risk Genes ◽  
Cox Regression Analysis ◽  
Related Risk

Abstract Background: A growing amount of evidence has suggested immune-related genes (IRGs) play a key role in the development of hepatocellular carcinoma (HCC). However, there have been no investigations proposing a reliable prognostic signature in terms of tumor immunology. This study aimed to develop a robust signature based on IRGs in HCC.Methods: A total of 597 HCC patients were enrolled. The TCGA database was utilized for discovery, and the ICGC database was utilized for validation. Multiple algorithms (including univariate Cox, LASSO, and multivariate Cox regression) were performed to identify key prognostic IRGs and establish an immune-related risk signature. Bioinformatics analysis and R soft tools were utilized to annotate underlying biological functions. Results: A total of 1416 differentially expressed mRNAs (DEMs) were screened in the TCGA cohort, of which 90 were differentially expressed IRGs (DEIRGs). Using univariate Cox regression analysis, we identified 33 prognostically relevant DEIRGs. Using LASSO regression and multivariate Cox regression analysis, we extracted 8 optimal DEIRGs (APLN, CDK4, CXCL2, ESR1, IL1RN, PSMD2, SEMA3F, and SPP1) to construct a risk signature with the ability to distinguish cases as having a high or low risk of unfavorable prognosis in the TCGA cohort, and the signature was verified in the ICGC cohort. The signature was prognostically significant in all stratified cohorts and was deemed an independent prognostic factor for HCC. We also built a nomogram with good performance by combining the signature with clinicopathological factors to increase the accuracy of predicting HCC prognosis. By investigating the relationship of the risk score and 8 risk genes from our signature with clinical traits, we found that the aberrant expression of the immune-related risk genes is correlated with the development of HCC. Moreover, the high-risk group was higher than the low-risk group in terms of tumor mutation burden (TMB), immune cell infiltration, and the expression of immune checkpoints (PD-1, PD-L1, and CTLA-4), and functional enrichment analysis indicated the signature enriched an intensive immune phenotype.Conclusions: This study developed a robust immune-related risk signature and built a predictive nomogram that reliably predict overall survival in HCC, which may be helpful for clinical management and personalized immunotherapy decisions.

scholarly journals 5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Ke Wang ◽  
Weibo Zhong ◽  
Zining Long ◽  
Yufei Guo ◽  
Chuanfan Zhong ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Biochemical Recurrence ◽  
Prognostic Model ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Lasso Regression ◽  
Clinical Value ◽  
Cox Regression Analysis

The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p < 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA–protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, MAFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.

scholarly journals SCUBE3 serves as an independent poor prognostic factor in breast cancer

2021 ◽  
Author(s):  
Qin Huo ◽  
Xi He ◽  
Zhenwei Li ◽  
Fan Yang ◽  
Shengnan He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Prognostic Factor ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Functional Enrichment ◽  
High Expression ◽  
Poor Prognostic Factor ◽  
Cox Regression Analysis ◽  
Significant Difference

Abstract Background: Accumulating evidences indicate that the signal peptide-CUB-EGF-like domain-containing protein 3 (SCUBE3) plays a key role in the development and progression of many human cancers. However, the underlying mechanism and prognosis value of SCUBE3 in breast cancer are still unclear. Methods: The clinical data of 137 patients with breast cancer who underwent surgical resection in Taizhou Hospital of Zhejiang Province were retrospectively analyzed. We first conducted a comprehensive study on the expression pattern of SCUBE3 using the Tumor Immune Estimation Resource (TIMER) and UALCAN databases. In addition, the expression of SCUBE3 in breast tumor tissues was confirmed by immunohistochemistry. The protein-protein interaction analysis and functional enrichment analysis of SCUBE3 were analyzed using the STRING and Enrichr databases. Moreover, tissue microarray (TMA) was used to analyze the relationship between SCUBE3 expression levels and clinical-pathological parameters, such as histological type, grade, the status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2). We further supplemented and identified the above results using the UALCAN and bc-GenExMiner v4.4 databases from TCGA data. The correlation between the expression of SCUBE3 and survival was calculated by multivariate Cox regression analysis to investigate whether SCUBE3 expression may be an independent prognostic factor of breast cancer. Results: We found that the expression level of SCUBE3 was significantly upregulated in breast cancer tissue compared with adjacent normal tissues. The results showed that the distribution of breast cancer patients in the high expression group and the low expression group was significantly different in ER, PR, HER2, E-cadherin, and survival state (p < 0.05), but there was no significant difference in age, histologic grade, histologic type, tumor size, lymph node metastasis, TMN stage, subtypes, or recurrence (p > 0.05). In addition, the high expression of SCUBE3 was associated with relatively poor prognosis of ER- (p = 0.012), PR- (p = 0.029), HER2+ (p = 0.007). The multivariate Cox regression analysis showed that the hazard ratio (HR) was 2.80 (95 % CI: 1.20-6.51, p = 0.0168) in individuals with high SCUBE3 expression, and HR was increased by 1.86 (95 % CI: 1.06-3.25, p = 0.0300) for per 1-point increase of SCUBE3 expression.Conclusions: These findings demonstrate that the high expression of SCUBE3 indicates poor prognosis in breast cancer. SCUBE3 expression may serve as a potential diagnostic indicator of breast cancer.

scholarly journals Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis

2020 ◽  
Author(s):  
Peng Zhang ◽  
Aiyu Wang ◽  
Liming Dong ◽  
Xuefeng Zhang
Keyword(s):  
Prostate Cancer ◽  
Extracellular Matrix ◽  
Regression Analysis ◽  
Cell Membrane ◽  
Sensitivity And Specificity ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Prediction Ability ◽  
Tissue Samples ◽  
Cox Regression Analysis

Abstract Background and Objective: There is significant heterogeneity between cellular composition and patient outcome in prostate cancer (PCa). Accumulating evidence shows that long noncoding RNAs (lncRNAs) possess great potential in the diagnosis and prognosis of PCa with biological and clinical significance. Therefore, this study aimed to construct an lncRNA-based signature to more accurately predict the prognosis of different PCa patients, so as to improve patient management and prognosis. Methods The Cancer Genome Atlas (TCGA) database was used to download RNA-seq expression data together with the clinical information of 499 PCa tissue samples as well as 52 corresponding non-carcinoma tissue samples. Differently expressed lncRNAs (DElncRNAs) were selected based on tumor tissues and non-carcinoma samples. Through univariate and multivariate Cox regression analysis, this study constructed a 4 lncRNAs-based prognosis nomogram for the classification and prediction of survival risk in patients with PCa. The receiver operating characteristic (ROC) curve was plotted for detecting and validating our prediction model sensitivity and specificity. In addition, univariate as well as multivariate Cox regression was conducted to examine whether the constructed lncRNA signature’s prediction ability was independent of additional clinicopathological variables (like age, Gleason score, N stage, T stage and M stage) among PCa cases. Possible biological functions for those prognostic lncRNAs were predicted through gene ontology (GO) together with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on those 4 protein-coding genes (PCGs) related to lncRNAs. Results A total of 451 differently expressed lncRNAs (DElncRNAs) related to the overall survival (OS) rate for PCa cases were screened from 3838 lncRNAs in the TCGA database. Four lncRNAs (HOXB-AS3, YEATS2-AS1, LINC01679, PRRT3-AS1) were extracted after univariate as well as multivariate COX regression analysis for classifying patients into high and low-risk groups by different OS rates. As suggested by ROC analysis, our proposed model showed high sensitivity and specificity. Independent prognostic capability of the model from other clinicopathological factors was indicated through further analysis. Based on functional enrichment, those action sites for prognostic lncRNAs were mostly located in the extracellular matrix and cell membrane, and their functions are mainly associated with the adhesion, activation and transport of the components across the extracellular matrix or cell membrane. Conclusion Our current study successfully identifies a novel four-lncRNA candidate, which can provide more convincing evidence for prognosis in addition to the traditional clinicopathological indicators to predict the PCa survival, and laying the foundation for offering potentially novel therapeutic treatment. Additionally, this study sheds more lights on the PCa-related molecular mechanisms.

scholarly journals Identification of the Prognostic LncRNA Biomarkers and Comprehensive Analysis of LncRNA-Mediated ceRNA Network for Uterine Corpus Endometrial Carcinoma

2021 ◽  
Author(s):  
Yiran Cai ◽  
Jin Cui ◽  
Huiqun Wu
Keyword(s):  
Regression Analysis ◽  
Endometrial Carcinoma ◽  
Noncoding Rna ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Uterine Corpus ◽  
Cerna Network

Abstract Background Given that long non-coding RNAs (lncRNAs) involved in the tumor initiation or progression of the endometrium and that competing endogenous RNA (ceRNA) plays an important role in increasingly more biological processes, lncRNA-mediated ceRNA is likely to function in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). Our present study aimed to explore the potential molecular mechanisms for the prognosis of UCEC through an lncRNA-mediated ceRNA network. Methods The transcriptome profiles and corresponding clinical profiles of UCEC dataset were retrieved from CPTAC and TCGA databases respectively. Differentially expressed genes (DEGs) in UCEC samples were identified via “Edge R” package. Then, an integrated bioinformatics analysis including functional enrichment analysis, tumor infiltrating immune cell(TIIC) analysis, Kaplan-Meier curve, Cox regression analysis were conducted to analyze the prognostic biomarkers. Results In the CPTAC dataset of UCEC, a ceRNA network comprised of 36 miRNAs, 123 lncRNAs and 124 targeted mRNAs was established, and 8 of 123 prognostic-related DElncRNAs(Differentially Expressed long noncoding RNA) were identified. While in the TCGA dataset, a ceRNA network comprised of 38 miRNAs, 83 lncRNAs and 110 targeted mRNAs was established, and 2 of 83 prognostic-related DElncRNAs were identified. After filtered by risk grouping and Cox regression analysis, 10 prognostic-related lncRNAs including LINC00443, LINC00483, C2orf48, TRBV11-2, MEG-8 were identified. In addition, 33 survival-related DEmRNAs(Differentially Expressed messager RNA) in two ceRNA networks were further validated in the HPA database. Finally, six lncRNA/miRNA/mRNA axes were established to elucidate prognostic regulatory roles in UCEC. Conclusion Several prognostic lncRNAs are identified and prognostic model of lncRNA-mediated ceRNA network is constructed, which promotes the understanding of UCEC development mechanisms and potential therapeutic targets.

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