scholarly journals Prognostic Impact of Autophagy and Immunity Related Biomarker for Uveal Melanoma

2021 ◽  
Author(s):  
Huibin Du ◽  
Yan He ◽  
Wei Lu ◽  
Qi Wan
Keyword(s):  
High Risk ◽  
Uveal Melanoma ◽  
Risk Model ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Impact ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis

Abstract Background: Autophagy and immunity related genes serve crucial roles in carcinogenesis, but little is known about the prognostic impact for uveal melanoma (UM).Methods: Autophagy related and immunity related genes (AIRGs) expression data of 80 UM patients were obtained from the cancer genome atlas project (TCGA) database. Next, univariate cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithms were applied to build a robust AIRGs signature in TCGA and validated in another two independent datasets. Besides, UM patients classified into two subgroups based on the risk model. Differences of clinical outcome, tumor microenvironment and the likelihood of chemotherapeutic response were further explored.Results: In total, a 4-AIRGs signature was constructed and validated in various datasets, which can robustly predict patients’ metastasis-free survival (MFS) and overall survival (OS) and is an independent prognostic factor in UM. The UM patients can be classified into high and low risk subgroups by applied risk score system. The high risk group have poor clinical outcomes, higher CD8+ T cell and macrophage immune-infiltrating and more sensitive to chemotherapies. In addition, Gene Set Enrichment Analysis (GSEA) analysis revealed that hallmark epithelial-mesenchymal transition and KRAS pathways are commonly enriched in high-risk expression phenotype.Conclusion: Thus, our findings provide a new clinical strategy for the accurate diagnosis and identify a novel prognostic autophagy and immunity associated biomarker for the treatment of uveal melanoma.

scholarly journals A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wanting Song ◽  
Yi Bai ◽  
Jialin Zhu ◽  
Fanxin Zeng ◽  
Chunmeng Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Critical Function ◽  
Mesenchymal Transition ◽  
Patient Prognosis

Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.

scholarly journals RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Lan-Xin Mu ◽  
You-Cheng Shao ◽  
Lei Wei ◽  
Fang-Fang Chen ◽  
Jing-Wei Zhang
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Impact ◽  
Immune Microenvironment ◽  
Model Based ◽  
Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

scholarly journals Identification of Epithelial–Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

2021 ◽  
Vol 9 ◽  
Author(s):  
Bo Xiao ◽  
Liyan Liu ◽  
Zhuoyuan Chen ◽  
Aoyu Li ◽  
Pingxiao Wang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

scholarly journals Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoyu Deng ◽  
Qinghua Bi ◽  
Shihan Chen ◽  
Xianhua Chen ◽  
Shuhui Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

scholarly journals AC099850.3 Promotes Cell Proliferation and is One of Five Costimulatory Molecule-Related LncRNAs That Predict Overall Survival of Liver Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Meimei Liu ◽  
Qiong Fang ◽  
Yanping Huang ◽  
Jin Zhou ◽  
Qi Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Proliferation ◽  
Risk Model ◽  
Cox Regression ◽  
Costimulatory Molecule ◽  
Costimulatory Molecules ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Liver Hepatocellular Carcinoma

Abstract Background: Extensive research has revealed that costimulatory molecules play central roles in mounting anti-tumor immune responses and long non‐coding RNA (lncRNA) is an important regulatory factor in the development of various cancers. However, their roles in liver hepatocellular carcinoma (HCC) remain unexplored. In this study, we aimed to explore costimulatory molecule-related lncRNAs in HCC and construct a prognostic signature to predict prognosis and improve clinical outcomes with HCC patients.Methods: The data we used for bioinformatics analysis were downloaded from The Cancer Genome Atlas database. Costimulatory molecules were obtained from the known literature. The R software, SPSS and GraphPad Prism were used for mapping and statistical analysis.Results: A five costimulatory molecule-related lncRNAs based risk model was initially constructed through lasso and Cox regression analysis. Moreover, multivariate regression suggested that the risk score was a significant prognostic risk factor in HCC. Samples in high- and low-risk groups exhibited significantly different in gene set enrichment analysis and immune infiltration analysis. Importantly, we found that the AC099850.3 were significantly related to cell proliferation in HCC according to the colony formation and CCK8 assays.Conclusion: In summary, we first identified and validated a novel costimulatory molecule-related survival model and we found that AC099850.3 is closely associated with clinical stage and could remarkably facilitate cell proliferation in HCC, making it potential to be a novel therapeutic target.

scholarly journals Genomics and prognosis analysis of epithelial-mesenchymal transition in colorectal cancer patients

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zizhen Zhang ◽  
Sheng Zheng ◽  
Yifeng Lin ◽  
Jiawei Sun ◽  
Ning Ding ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Related Gene ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis

Abstract Background The epithelial-mesenchymal transition (EMT) plays a pivotal role in various physiological processes, such as embryonic development, tissue morphogenesis, and wound healing. EMT also plays an important role in cancer invasion, metastasis, and chemoresistance. Additionally, EMT is partially responsible for chemoresistance in colorectal cancer (CRC). The aim of this research is to develop an EMT-based prognostic signature in CRC. Methods RNA-seq and microarray data, together with clinical information, were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. A total of 244 differentially expressed EMT-related genes (ERGs) were obtained by comparing the expression between normal and tumor tissues. An EMT-related signature of 11 genes was identified as crucially related to the overall survival (OS) of patients through univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and Cox regression analysis. Finally, we established a clinical nomogram to predict the survival possibility of CRC patients by integrating clinical characteristics and the EMT-related gene signature. Results Two hundred and forty-four differentially expressed ERGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that EMT-related signaling pathway genes were highly related to CRC. Kaplan-Meier analysis revealed that the 11-EMT signature could significantly distinguish high- and low-risk patients in both TCGA and GEO CRC cohorts. In addition, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusion We developed a novel EMT-related gene signature for the prognosis prediction of CRC patients, which could improve the individualized outcome prediction in CRC.

scholarly journals Identification of a Ubiquitination-Related Gene Risk Model for Predicting Survival in Patients With Pancreatic Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Hao Zuo ◽  
Luojun Chen ◽  
Na Li ◽  
Qibin Song
Keyword(s):  
Pancreatic Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Transcriptional Level ◽  
Cox Regression Analysis

Pancreatic cancer is known as “the king of cancer,” and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.

scholarly journals Comprehensive Analysis to Identify the Epithelial–Mesenchymal Transition-Related Immune Signatures as a Prognostic and Therapeutic Biomarkers in Hepatocellular Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Guozhi Wu ◽  
Yuan Yang ◽  
Yu Zhu ◽  
Yemao Li ◽  
Zipeng Zhai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Cancer Genome ◽  
Mesenchymal Transition ◽  
Immune Genes

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with the high rates of the morbidity and mortality due to the lack of the effective prognostic model for prediction.Aim: To construct a risk model composed of the epithelial–mesenchymal transition (EMT)-related immune genes for the assessment of the prognosis, immune infiltration status, and chemosensitivity.Methods: We obtained the transcriptome and clinical data of the HCC samples from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) databases. The Pearson correlation analysis was applied to identify the differentially expressed EMT-related immune genes (DE-EMTri-genes). Subsequently, the univariate Cox regression was introduced to screen out the prognostic gene sets and a risk model was constructed based on the least absolute shrinkage and selection operator-penalized Cox regression. Additionally, the receiver operating characteristic (ROC) curves were plotted to compare the prognostic value of the newly established model compared with the previous model. Furthermore, the correlation between the risk model and survival probability, immune characteristic, and efficacy of the chemotherapeutics were analyzed by the bioinformatics methods.Results: Six DE-EMTri-genes were ultimately selected to construct the prognostic model. The area under the curve (AUC) values for 1-, 2-, and 3- year were 0.773, 0.721, and 0.673, respectively. Stratified survival analysis suggested that the prognosis of the low-score group was superior to the high-score group. Moreover, the univariate and multivariate analysis indicated that risk score [hazard ratio (HR) 5.071, 95% CI 3.050, 8.432; HR 4.396, 95% CI 2.624, 7.366; p < 0.001] and stage (HR 2.500, 95% CI 1.721, 3.632; HR 2.111, 95% CI 1.443, 3.089; p < 0.001) served as an independent predictive factors in HCC. In addition, the macrophages, natural killer (NK) cells, and regulatory T (Treg) cells were significantly enriched in the high-risk group. Finally, the patients with the high-risk score might be more sensitive to cisplatin, doxorubicin, etoposide, gemcitabine, and mitomycin C.Conclusion: We established a reliable EMTri-genes-based prognostic signature, which may hold promise for the clinical prediction.

scholarly journals Exploration of the Key Proteins in the Normal-Adenoma-Carcinoma Sequence of Colorectal Cancer Evolution Using In-Depth Quantitative Proteomics

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Yin Zhang ◽  
Chun-Yuan Li ◽  
Wei Ge ◽  
Yi Xiao
Keyword(s):  
Colorectal Cancer ◽  
Quantitative Proteomics ◽  
Prognostic Value ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Disease Free Survival ◽  
Gene Set Enrichment Analysis ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
Proteomic Data

Purpose. In most cases, the carcinogenesis of colorectal cancer (CRC) follows the normal-adenoma-carcinoma (N-A-C) sequence. In this study, we aimed to identify the key proteins in the N-A-C sequence. Methods. Differentially expressed proteins (DEPs) in normal, adenoma, and carcinoma tissues were identified using the Tandem Mass Tag- (TMT-) based quantitative proteomics approach. The landscape of proteomic variation in the N-A-C sequence was explored using gene set enrichment analysis (GSEA) and Proteomaps. Key proteins in the N-A-C sequence were identified, verified, and validated based on our proteomic data, external proteomic data, and external transcriptomic data in the ProteomeXchange, CPTAC, GEO, and TCGA databases. The prognostic value of the key proteins in our database was evaluated by univariate and multivariate Cox regression analysis. The effects of the key proteins on adenoma organoids and colorectal cancer cells were explored in functional studies. Results. Based on our proteomic profiles, we identified 1,294 DEPs between the carcinoma (CG) and normal (NG) groups, 919 DEPs between the adenoma group (AG) and NG, and 1,030 DEPs between the CG and AG. Ribosome- and spliceosome-related pathways were mainly enriched in the N-A process. Extracellular matrix- and epithelial-mesenchymal transition- (EMT-) related pathways were mainly enriched in the A-C process. RRP12 and SERPINH1 were identified, verified, and validated as candidate key proteins in the N-A and A-C processes, respectively. Furthermore, RRP12 and SERPINH1 knockdown impeded the viability and proliferation of adenoma organoids. SERPINH1 was validated as a risk factor for disease-free survival (DFS) based on the TCGA and our database, whereas RRP12 did not show prognostic value. SERPINH1 knockdown was accompanied by EMT-related protein variation, increased apoptosis, and reduced proliferation, invasion, and migration of CRC cells in vitro. Conclusions. RRP12 and SERPINH1 may play an important role in the N-A and A-C processes, respectively. Furthermore, SERPINH1 showed favorable prognostic value for DFS in CRC patients. We speculate that SERPINH1 might promote not only the A-C process but also the development of CRC.

scholarly journals DKK2 Impairs Tumor Immunity Infiltration and Correlates with Poor Prognosis in Pancreatic Ductal Adenocarcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Jianyu Yang ◽  
Yongsheng Jiang ◽  
Ruizhe He ◽  
Wei Liu ◽  
Minwei Yang ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Tumor Immunity ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Gene Set Enrichment Analysis ◽  
Cellular Migration ◽  
Ductal Adenocarcinoma ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most devastating cancer types despite the improvement of modern medicine. In our present study, we found that dickkopf-related protein 2 (DKK2) shares a higher expression in PDAC compared with adjacent pancreas tissue in tissue microarray. In addition, an elevated expression of DKK2 predicts poorer prognosis of patients and positively correlated with poor tumor differentiation. Multivariate Cox regression analysis was also performed and confirmed that the expression of DKK2 is an independent prognostic factor in PDAC. A high expression of DKK2 correlates with cell migration and epithelial mesenchymal transition based on gene set enrichment analysis (GSEA) while knockdown of DKK2 in PDAC cells resulted in impaired cellular migration. Furthermore, GSEA predicts negative correlation between tumor immunity invasion and DKK2 expression. We then confirmed these results and demonstrated that a higher expression of DKK2 imparts the recruitment of CD8+ T cells. Our work suggested that DKK2 imparts tumor immune evasion and is associated with poor prognosis in pancreatic ductal adenocarcinoma.

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