scholarly journals Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xingyu Chen ◽  
Hua Lan ◽  
Dong He ◽  
Zhanwang Wang ◽  
Runshi Xu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Signaling Pathways ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Clinical Importance ◽  
Clinical Samples ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Cluster A

Ovarian cancer (OC) is one of the most lethal gynecologic malignant tumors. The interaction between autophagy and the tumor immune microenvironment has clinical importance. Hence, it is necessary to explore reliable biomarkers associated with autophagy-related genes (ARGs) for risk stratification in OC. Here, we obtained ARGs from the MSigDB database and downloaded the expression profile of OC from TCGA database. The k-means unsupervised clustering method was used for clustering, and two subclasses of OC (cluster A and cluster B) were identified. SsGSEA method was used to quantify the levels of infiltration of 24 subtypes of immune cells. Metascape and GSEA were performed to reveal the differential gene enrichment in signaling pathways and cellular processes of the subtypes. We found that patients in cluster A were significantly associated with higher immune infiltration and immune-associated signaling pathways. Then, we established a risk model by LASSO Cox regression. ROC analysis and Kaplan-Meier analysis were applied for evaluating the efficiency of the risk signature, patients with low-risk got better outcomes than those with high-risk in overall survival. Finally, ULK2 and GABARAPL1 expression was further validated in clinical samples. In conclusion, Our study constructed an autophagy-related prognostic indicator, and identified two promising targets in OC.

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 A Novel Prognostic Biomarker ARRDC2 has Potential to Define Tumor Microenvironment in Ovarian Cancer

2021 ◽  
Author(s):  
Mengjun Zhang ◽  
Yunduo Liu ◽  
Yuan Liu ◽  
Siyu Hou ◽  
Hao Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cell Signaling ◽  
Signaling Pathways ◽  
Immune Microenvironment ◽  
Poor Overall Survival ◽  
Protein Levels ◽  
Diagnosis And Prognosis ◽  
Tumor Immune Microenvironment ◽  
Cell Signaling Pathways

Abstract Purpose: The abnormal expression of α-arrestin protein family plays a key regulatory role in the occurrence and development of many cancers, including colorectal cancer and cervical cancer, and is inseparable from changes in the tumor immune microenvironment. However, the role of ARRDC2, an important member of this family, in the malignant biological process of ovarian cancer (OC) has not been reported, and its role in the change of the immune microenvironment is also unknown.Methods: In this study, HPA, TCGA, GEO and other databases were used to explore the role of ARRDC2 in the diagnosis and prognosis assessment of ovarian cancer. Then, GO, KEGG analysis and GSEA analysis of the biological processes and cell signaling pathways that ARRDC2 may be involved in activated or inhibited. In addition, the TIMER and TISIDB database were used to conduct in-depth research on the role of ARRDC2 in the change of the immune microenvironment of ovarian cancer. Finally, the CMAP database explored and screened drugs that may be used for treatment.Results: There were significant differences between OC and ARRDC2 mRNA and protein levels. High ARRDC2 expression level is associated with poor overall survival and can be used as an independent prognostic factor. Interestingly, ARRDC2 expression is positively correlated with B cells, Neutrophils, Dendritic cells and CD8+ T cells, signifying that ARRDC2 may be related to infiltration of immune cells. ARRDC2 and its co-expressed genes are enriched in cell signaling pathways related to the immune system. Finally, we explored two possible drugs for the treatment of ovarian cancer.Conclusion: The differentially expressed ARRDC2 may be a potential prognostic and diagnostic indicator and can be used as a novel biomarker for exploring the immune microenvironment of ovarian cancer.

scholarly journals Prognostic Risk Model and Tumor Immune Environment Modulation of m5C-Related LncRNAs in Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Hao Yuan ◽  
Jinhui Liu ◽  
Li Zhao ◽  
Pengfei Wu ◽  
Guosheng Chen ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Risk Model ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Regulatory Gene ◽  
Ductal Adenocarcinoma ◽  
Entire Group ◽  
Immune Microenvironment ◽  
Significant Difference ◽  
Tumor Immune Microenvironment

RNA methylation modification is a key process in epigenetics that regulates posttranscriptional gene expression. With advances in next-generation sequencing technology, 5-methylcytosine (m5C) modification has also been found in multiple RNAs. Long non-coding RNAs (lncRNAs) were proved to have a key role in cancer progression and closely related to the tumor immune microenvironment. Thus, based on the PDAC patients’ clinical information and genetic transcriptome data from the TCGA database, we performed a detailed bioinformatic analysis to establish a m5C-related lncRNA prognostic risk model for PDAC patients and discovered the relationship between the risk model and PDAC immune microenvironment. Pearson correlation coefficient analysis was applied to conduct a m5C regulatory gene and m5C-related lncRNA co-expression network. Expression of m5C-related lncRNAs screened by univariate regression analysis with prognostic value showed a significant difference between pancreatic cancer and normal tissues. The least absolute shrinkage and selection operator (LASSO) Cox regression method was applied to determine an 8-m5C-related lncRNA prognostic risk model. We used principal component analysis to indicate that the risk model could distinguish all the samples clearly. The clinical nomogram also accurately predicted 1-, 1.5-, 2-, and 3-year survival time among PDAC patients. Additionally, this risk model was validated in the entire group and sub-test groups using KM analysis and ROC analysis. Combined with the clinical characteristics, the risk score was found to be an independent factor for predicting the survival of PDAC patients. Furthermore, the association between the risk model and tumor immune microenvironment was evaluated via the ESTIMATE R package and CIBERSORT method. Consequently, the results indicated that immune cells were associated with m5C-related lncRNA risk model scores and had different distribution in the high- and low-risk groups. Based on all these analyses, the m5C-related lncRNA risk model could be a reliable prognostic tool and therapeutic target for PDAC patients.

scholarly journals Identification of Hypoxia Signature to Assess the Tumor Immune Microenvironment and Predict Prognosis in Patients with Ovarian Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Chunyan Wei ◽  
Xiaoqing Liu ◽  
Qin Wang ◽  
Qipei Li ◽  
Min Xie
Keyword(s):  
Ovarian Cancer ◽  
Risk Model ◽  
Risk Group ◽  
Plasmacytoid Dendritic Cell ◽  
Gene Signature ◽  
Immune Checkpoints ◽  
Immune Microenvironment ◽  
Cell Immunity ◽  
Tumor Immune Microenvironment ◽  
Hypoxia Signature

Background. The 5-year overall survival rate of ovarian cancer (OC) patients is less than 40%. Hypoxia promotes the proliferation of OC cells and leads to the decline of cell immunity. It is crucial to find potential predictors or risk model related to OC prognosis. This study aimed at establishing the hypoxia-associated gene signature to assess tumor immune microenvironment and predicting the prognosis of OC. Methods. The gene expression data of 378 OC patients and 370 OC patients were downloaded from datasets. The hypoxia risk model was constructed to reflect the immune microenvironment in OC and predict prognosis. Results. 8 genes (AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI) were included in the hypoxic gene signature. Patients in the high hypoxia risk group showed worse survival. Hypoxia signature significantly related to clinical features and may serve as an independent prognostic factor for OC patients. 2 types of immune cells, plasmacytoid dendritic cell and regulatory T cell, showed a significant infiltration in the tissues of the high hypoxia risk group patients. Most of the immunosuppressive genes (such as ARG1, CD160, CD244, CXCL12, DNMT1, and HAVCR1) and immune checkpoints (such as CD80, CTLA4, and CD274) were upregulated in the high hypoxia risk group. Gene sets related to the high hypoxia risk group were associated with signaling pathways of cell cycle, MAPK, mTOR, PI3K-Akt, VEGF, and AMPK. Conclusion. The hypoxia risk model could serve as an independent prognostic indicator and reflect overall immune response intensity in the OC microenvironment.

scholarly journals Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiang Yang ◽  
Shasha Hong ◽  
Xiaoyi Zhang ◽  
Jingchun Liu ◽  
Ying Wang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Killer Cell ◽  
Figo Stage ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Natural Killer Cell Cytotoxicity ◽  
Tumor Immune Microenvironment

BackgroundThe tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.Methods and ResultsThe stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.ConclusionThese findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.

scholarly journals N6-Methylandenosine-Related lncRNA Signature Is a Novel Biomarkers of Prognosis and Immune Response in Colon Adenocarcinoma Patients

2021 ◽  
Vol 9 ◽  
Author(s):  
Peiling Zhang ◽  
Guolong Liu ◽  
Lin Lu
Keyword(s):  
Clustering Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Data Set ◽  
Survival Status ◽  
Tumor Immune Microenvironment

BackgroundColon adenocarcinoma (COAD) is the most common type of colon cancer. To date, however, the prognostic values of m6A RNA methylation-related long non-coding RNAs (lncRNAs) in COAD are largely unknown.Materials and MethodsThe m6A-related lncRNAs were identified from The Cancer Genome Atlas (TCGA) data set. Univariate and multivariate Cox regression analyses were performed to explore the prognostic m6A-related lncRNAs. Consistent clustering analysis was performed to classify the COAD patients into different subgroups based on the expression of m6A-related lncRNAs. The potential biological functions as well as differences in the stemness index and tumor immune microenvironment between different subgroups were analyzed. The prognostic m6A-related lncRNAs were used to establish an m6A-related lncRNA risk model to predict prognosis and survival status.ResultsWe identified 31 m6A-associated lncRNAs with prognostic values from the TCGA data set. Based on the expression of prognostic m6A-associated lncRNAs, TCGA-COAD patients were classified into three clusters using consistent clustering analysis. There was a low correlation of tumor stemness between the three clusters but a significant correlation with the tumor immune microenvironment as well as the tumor mutational load. Thirty-one prognostic-related m6A-associated lncRNAs were used to construct a risk model, which was further determined by survival analysis, receiver operating characteristic (ROC) curve, and univariate and multifactor Cox analysis. The m6A-related risk model demonstrates good performance in predicting prognosis and survival status. The model-based high-risk group exhibited poorer overall survival (OS) compared with the low-risk group.ConclusionIn this study, we construct a risk model that consists of 31 m6A-related lncRNAs with independent prognostic values in COAD. Our study shows the critical roles of these 31 m6A-related lncRNAs in the tumor immune microenvironment, indicating the prospect of informing prognostic stratification and the development of immunotherapeutic strategies for COAD patients.

scholarly journals A lipid metabolism-related genes prognosis biomarker associated with the tumor immune microenvironment in colorectal carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chao Yang ◽  
Shuoyang Huang ◽  
Fengyu Cao ◽  
Yongbin Zheng
Keyword(s):  
Lipid Metabolism ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Risk Groups ◽  
Clinical Parameter ◽  
Gene Expression Omnibus ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment

Abstract Background and aim Lipid metabolic reprogramming is considered to be a new hallmark of malignant tumors. The purpose of this study was to explore the expression profiles of lipid metabolism-related genes (LMRG) in colorectal cancer (CRC). Methods The lipid metabolism statuses of 500 CRC patients from the Cancer Genome Atlas (TCGA) and 523 from the Gene Expression Omnibus (GEO GSE39582) database were analyzed. The risk signature was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression. Results A novel four-LMRG signature (PROCA1, CCKBR, CPT2, and FDFT1) was constructed to predict clinical outcomes in CRC patients. The risk signature was shown to be an independent prognostic factor for CRC and was associated with tumour malignancy. Principal components analysis demonstrated that the risk signature could distinguish between low- and high-risk patients. There were significantly differences in abundances of tumor-infiltrating immune cells and mutational landscape between the two risk groups. Patients in the low-risk group were more likely to have higher tumor mutational burden, stem cell characteristics, and higher PD-L1 expression levels. Furthermore, a genomic-clinicopathologic nomogram was established and shown to be a more effective risk stratification tool than any clinical parameter alone. Conclusions This study demonstrated the prognostic value of LMRG and showed that they may be partially involved in the suppressive immune microenvironment formation.

scholarly journals A Novel Defined Risk Signature of the Ferroptosis-Related Genes for Predicting the Prognosis of Ovarian Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Ying Ye ◽  
Qinjin Dai ◽  
Shuhong Li ◽  
Jie He ◽  
Hongbo Qi
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Risk Model ◽  
Cox Regression ◽  
Ovarian Tissue ◽  
Gene Signature ◽  
High Risk Group ◽  
Cancer Prognosis ◽  
Consensus Clustering ◽  
Gynecologic Tumor

Ferroptosis is an iron-dependent, regulated form of cell death, and the process is complex, consisting of a variety of metabolites and biological molecules. Ovarian cancer (OC) is a highly malignant gynecologic tumor with a poor survival rate. However, the predictive role of ferroptosis-related genes in ovarian cancer prognosis remains unknown. In this study, we demonstrated that the 57 ferroptosis-related genes were expressed differently between ovarian cancer and normal ovarian tissue, and based on these genes, all OC cases can be well divided into 2 subgroups by applying consensus clustering. We utilized the least absolute shrinkage and selection operator (LASSO) cox regression model to develop a multigene risk signature from the TCGA cohort and then validated it in an OC cohort from the GEO database. A 5-gene signature was built and reveals a favorable predictive efficacy in both TCGA and GEO cohort (P < 0.001 and P = 0.03). The GO and KEGG analysis revealed that the differentially expressed genes (DEGs) between the low- and high-risk subgroup divided by our risk model were associated with tumor immunity, and lower immune status in the high-risk group was discovered. In conclusion, ferroptosis-related genes are vital factors predicting the prognosis of OC and could be a novel potential treatment target.

scholarly journals Identification of a Glycolysis-Related Gene Signature for Survival Prediction of Ovarian Cancer Patients

2020 ◽  
Author(s):  
Dai Zhang ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
Jia Yao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gynecological Cancer ◽  
Predictive Ability ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Test Sets

Abstract Background: Ovarian cancer (OV) is deemed as the most lethal gynecological cancer in women. The aim of this study was construct an effective gene prognostic model for OV patients.Methods: The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed in training and test sets.Results: Based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4), a gene risk signature was identified to predict the outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve of 0.709, 0.762, and 0.808 for 3-, 5- and 10-year survival, respectively. Similar results were found in the test sets, and the signature was also an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was constructed.Conclusion: Our study established a nine-GRG risk model and a nomogram to better perform on OV patients’ survival prediction. The risk model represents a promising and independent prognostic predictor for OV patients. Moreover, our study of GRGs could offer guidances for underlying mechanisms explorations in the future.

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