Prognosis-associated methylation subtypes in endometrial carcinoma patients and the role of magnetic nanoparticles in gene extraction

2021 ◽  
Vol 11 (8) ◽  
pp. 1288-1298
Author(s):  
Liang Wang ◽  
Fengxia Xue
Keyword(s):  
Endometrial Cancer ◽  
Magnetic Nanoparticles ◽  
Endometrial Carcinoma ◽  
High Throughput Sequencing ◽  
Risk Groups ◽  
Vital Role ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Tumor Subtypes

Endometrial cancer is one of the most common gynecological malignancies, and DNA methylation plays a vital role in its occurrence and development. In this study, we collected the relevant data on endometrial cancer from the Cancer Genome Atlas database and UCSC website. By screening and processing the data, we obtained 410 samples and 16,381 methylation sites. Endometrial carcinoma can be divided into seven molecular subtypes using consensus clustering method. Based on the analysis of the differences among subtypes, the methylation degree of different sites was obtained, and the prognosis model of methylation sites was established. Based on the median value of the train group, the train and test groups were divided into high and low-risk groups. The survival between the high and low-risk groups was different. It also showed that this model can predict the survival of patients, with better accuracy. In conclusion, the tumor subtypes based on methylation sites can provide a better guidance for treatment, relapse, and prognosis of endometrial cancer. In this study, magnetic nanoparticles can be used to extract genomic DNA and total RNA due to their paramagnetism and biocompatibility, then transcriptome high-throughput sequencing was performed. It may serve as potential cancer immune biomarker targets for developing future oncological treatments.

scholarly journals Immune-Related Matrisomes are Potential Biomarkers to Predict the Prognosis and Immune Microenvironment of Glioma Patients Through Bioinformatics Analysis and Experimental Verification

2021 ◽  
Author(s):  
Hao Yu ◽  
Minjie Wang ◽  
Xuan Wang ◽  
Xiaobing Jiang
Keyword(s):  
Characteristic Curve ◽  
Immunohistochemical Analysis ◽  
Risk Groups ◽  
Vital Role ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Immune Microenvironment ◽  
Kaplan Meier

Abstract Purpose The extracellular matrix (ECM) plays a vital role in the progression and metastasis of glioma and is an important part of the tumor microenvironment. However, there are few studies on the overall role of the ECM in the glioma immune microenvironment. This study aimed to analyze the prognosis of matrisomes in patients with glioma. Methods Overall, 676 glioma patients in The Cancer Genome Atlas (TCGA) database were divided into the low, moderate, and high immune infiltration groups. Immune-related matrisomes differentially expressed among the three groups were analyzed, and a risk signature was established. Eight immune-related matrisomes were screened, namely, LIF, LOX, MMP9, S100A4, SRPX2, SLIT1, SMOC1, and TIMP1. Kaplan Meier analysis, operating characteristic curve (ROC) analysis and nomogram were constructed to analyze the relationships between risk signatures and the prognosis of glioma patients. Results The risk signature was significantly correlated with the overall survival (OS) of glioma patients. Both high- and low-risk signatures were also associated with some immune checkpoints. In addition, analysis of somatic mutations and anti-PD1/L1 immunotherapy responses in the high- and low-risk groups showed that the high-risk group had worse prognosis and a higher response to anti-PD1/L1 immunotherapy. qPCR and immunohistochemical analysis showed that LIF, LOX, MMP9, S100A4, SRPX2 and TIMP1 were highly expressed in glioma, while SLITI1 and SMOC1 were low expressed in glioma. Conclusions Matrisomes play a vital role in the complex immune microenvironment of gliomas. Our analysis of immune-related matrisomes can improve understanding of the characteristics of the glioma immune microenvironment and provide important clues for glioma immunotherapy in the future.

PET Parameters are Useful in Predicting Endometrial Cancer Risk Classes and Prognosis

2020 ◽  
Author(s):  
Adnan Budak ◽  
Emrah Beyan ◽  
Abdurrahman Hamdi Inan ◽  
Ahkam Göksel Kanmaz ◽  
Onur Suleyman Aldemir ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Myometrial Invasion ◽  
Risk Groups ◽  
Total Sample ◽  
Tumor Stage ◽  
Low Risk ◽  
Tumor Diameter ◽  
Grade 3 ◽  
Fdg Pet Ct

Abstract Aim We investigate the role of preoperative PET parameters to determine risk classes and prognosis of endometrial cancer (EC). Methods We enrolled 81 patients with EC who underwent preoperative F-18 FDG PET/CT. PET parameters (SUVmax, SUVmean, MTV, TLG), grade, histology and size of the primary tumor, stage of the disease, the degree of myometrial invasion (MI), and the presence of lymphovascular invasion (LVI), cervical invasion (CI), distant metastasis (DM) and lymph node metastasis (LNM) were recorded. The relationship between PET parameters, clinicopathological risk factors and overall survival (OS) was evaluated. Results The present study included 81 patients with EC (mean age 60). Of the total sample, 21 patients were considered low risk (endometrioid histology, stage 1A, grade 1 or 2, tumor diameter < 4 cm, and LVI negative) and 60 were deemed high risk. All of the PET parameters were higher in the presence of a high-risk state, greater tumor size, deep MI, LVI and stage 1B-4B. MTV and TLG values were higher in the patients with non-endometrioid histology, CI, grade 3 and LNM. The optimum cut-off levels for differentiating between the high and low risk patients were: 11.1 for SUVmax (AUC = 0.757), 6 for SUVmean (AUC = 0.750), 6.6 for MTV(AUC = 0.838) and 56.2 for TLG(AUC = 0.835). MTV and TLG values were found as independent prognostic factors for OS, whereas SUVmax and SUVmean values were not predictive. Conclusions The PET parameters are useful in noninvasively differentiating between risk groups of EC. Furthermore, volumetric PET parameters can be predictive for OS of EC.

scholarly journals Comprehensive Analysis of The Significance of Hypoxia-Related Genes in Ovarian Cancer

2021 ◽  
Author(s):  
Wancheng Zhao ◽  
Lili Yin
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Mesenchymal Transition ◽  
Principal Component ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Mesenchymal Transition ◽  
Cancer Genome Atlas ◽  
Pca Algorithm

Abstract Background: Hypoxia-related genes have been reported to play important roles in a variety of cancers. However, their roles in ovarian cancer (OC) have remained unknown. The aim of our research was to explore the significance of hypoxia-related genes in OC patients.Methods: In this study, 15 hypoxia-related genes were screened from The Cancer Genome Atlas (TCGA) database to group the ovarian cancer patients using the consensus clustering method. Principal component analysis (PCA) was performed to calculate the hypoxia score for each patient to quantify the hypoxic status. Results: The OC patients from TCGA-OV dataset were divided into two distinct hypoxia statuses (cluster.A and cluster.B) based on the expression level of the 15 hypoxia-related genes. Most hypoxia-related genes were expressed more highly in the cluster.A group than in the cluster.B group. We also found that patients in the cluster.A group exhibited higher expression of immune checkpoint-related genes, epithelial-mesenchymal transition-related genes, and immune activation-related genes, as well as elevated immune infiltrates. PCA algorithm indicated that patients in the cluster.A group had higher hypoxia scores than that in in the cluster.B group.Conclusions: In summary, our research elucidated the vital role of hypoxia-related genes in immune infiltrates of OC. Our investigation of hypoxic status may be able to improve the efficacy of immunotherapy for OC.

scholarly journals Effects of Hypoxia in Intestinal Tumors on Immune Cell Behavior in the Tumor Microenvironment

2020 ◽  
Author(s):  
Luping Zhang ◽  
Shaokun Wang ◽  
Yachen Wang ◽  
Weidan Zhao ◽  
Yingli Zhang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Groups ◽  
Low Risk ◽  
Computational Method ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Behavior

Abstract Background: Imbalanced nutritional supply and demand in the tumor microenvironment often leads to hypoxia. The subtle interaction between hypoxia and immune cell behavior plays an important role in tumor occurrence and development. However, the functional relationship between hypoxia and the tumor microenvironment remains unclear. Therefore, we aimed to investigate the effect of hypoxia on the intestinal tumor microenvironment.Method: We extracted the names of hypoxia-related genes from the Gene Set Enrichment Analysis (GSEA) database and screened them for those associated with the prognosis of colorectal cancer, with the final list including ALDOB, GPC1, ALDOC, and SLC2A3. Using the sum of the expression levels of these four genes, provided by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and the expression coefficients, we developed a hypoxia risk score model. Using the median risk score value, we divided the patients in the two databases into high- and low-risk groups.GSEA was used to compare the enrichment differences between the two groups.We used the CIBERSORT computational method to analyze immune cell infiltration.Finally,the correlation between these five genes and hypoxia was analyzed. Result: The prognosis of the two groups differed significantly, with a higher survival rate in the low-risk group than in the high-risk group.We found that the different risk groups were enriched by immune-related and inflammatory pathways. We identified activated CD4 memory T cells and M0 macrophages in TCGA and GEO databases and found that CCL2/4/5, CSF1, and CX3CL1 contributed toward the increased infiltration rate of these immune cell types. Finally, we observed a positive correlation between the five candidate genes’ expression and the risk of hypoxia, with significant differences in the level of expression of each of these genes between patient risk groups.Conclusion: Overall, our data suggest that hypoxia is associated with the prognosis and rate of immune system infiltration in patients with colorectal cancer. This finding may improve immunotherapy for colorectal cancer.

scholarly journals A radiogenomics application for prognostic profiling of endometrial cancer

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Erling A. Hoivik ◽  
Erlend Hodneland ◽  
Julie A. Dybvik ◽  
Kari S. Wagner-Larsen ◽  
Kristine E. Fasmer ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Treatment Strategies ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Tumor Segmentation ◽  
Poor Survival ◽  
Cancer Genome Atlas ◽  
Automated Machine Learning ◽  
Magnetic Resonance Imaging Mri

AbstractPrognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.

scholarly journals Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Chen Jin ◽  
Rui Li ◽  
Tuo Deng ◽  
Jialiang Li ◽  
Yan Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Prediction Ability ◽  
Cox Regression Analysis

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

scholarly journals Hypoxia-Related Long Non-Coding RNA Signature Predicts the Prognosis of Breast Cancer

2021 ◽  
Author(s):  
Jinlong Huo ◽  
Shuang Shen ◽  
Chen Chen ◽  
Rui Qu ◽  
Youming Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Cytolytic Activity ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas

Abstract Background: Breast cancer(BC) is the most common tumour in women. Hypoxia stimulates metastasis in cancer and is linked to poor patient prognosis.Methods: We screened prognostic-related lncRNAs(Long Non-Coding RNAs) from the Cancer Genome Atlas (TCGA) data and constructed a prognostic signature based on hypoxia-related lncRNAs in BC.Results: We identified 21 differentially expressed lncRNAs associated with BC prognosis. Kaplan Meier survival analysis indicated a significantly worse prognosis for the high-risk group(P<0.001). Moreover, the ROC-curve (AUC) of the lncRNAs signature was 0.700, a performance superior to other traditional clinicopathological characteristics. Gene set enrichment analysis (GSEA) showed many immune and cancer-related pathways and in the low-risk group patients. Moreover, TCGA revealed that functions including activated protein C (APC)co-inhibition, Cinnamoyl CoA reductase(CCR),check-point pathways, cytolytic activity, human leukocyte antigen (HLA), inflammation-promotion, major histocompatibility complex(MHC) class1, para-inflammation, T cell co-inhibition, T cell co-stimulation, and Type Ⅰ and Ⅱ Interferons (IFN) responses were significantly different in the low-risk and high-risk groups. Immune checkpoint molecules such as ICOS, IDO1, TIGIT, CD200R1, CD28, PDCD1(PD-1), were also expressed differently between the two risk groups. The expression of m6A-related mRNA indicated that YTHDC1, RBM15, METTL3, and FTO were significantly between the high and low-risk groups.Additionally, immunotherapy in patients with BC from the low-risk group yielded a higher frequency of clinical responses to anti-PD-1/PD-L1 therapy or a combination of anti-PD-1/PD-L1and anti-CTLA4 therapies.Except for lapatinib, the results also show that a high-risk score is related to a higher half-maximal inhibitory concentration (IC50) of chemotherapy drugs.Conclusion: A novel hypoxia-related lncRNAs signature may serve as a prognostic model for BC.

scholarly journals Identification of an Immune-Related Risk Signature Correlates With Immunophenotype and Predicts Anti-PD-L1 Efficacy of Urothelial Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Pengju Li ◽  
Shihui Hao ◽  
Yongkang Ye ◽  
Jinhuan Wei ◽  
Yiming Tang ◽  
...  
Keyword(s):  
High Risk ◽  
Urothelial Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Related Risk ◽  
Metastatic Urothelial Cancer

Immune checkpoint inhibitor (ICI) treatment has been used to treat advanced urothelial cancer. Molecular markers might improve risk stratification and prediction of ICI benefit for urothelial cancer patients. We analyzed 406 cases of bladder urothelial cancer from The Cancer Genome Atlas (TCGA) data set and identified 161 messenger RNAs (mRNAs) as differentially expressed immunity genes (DEIGs). Using the LASSO Cox regression model, an eight-mRNA-based risk signature was built. We validated the prognostic and predictive accuracy of this immune-related risk signature in 348 metastatic urothelial cancer (mUC) samples treated with anti-PD-L1 (atezolizumab) from IMvigor210. We built an immune-related risk signature based on the eight mRNAs: ANXA1, IL22, IL9R, KLRK1, LRP1, NRG3, SEMA6D, and STAP2. The eight-mRNA-based risk signature successfully categorizes patients into high-risk and low-risk groups. Overall survival was significantly different between these groups, regardless if the initial TCGA training set, the internal TCGA testing set, all TCGA set, or the ICI treatment set. The hazard ratio (HR) of the high-risk group to the low-risk group was 3.65 (p &lt; 0.0001), 2.56 (p &lt; 0.0001), 3.36 (p &lt; 0.0001), and 2.42 (p = 0.0009). The risk signature was an independent prognostic factor for prediction survival. Moreover, the risk signature was related to immunity characteristics. In different tumor mutational burden (TMB) subgroups, it successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome. Our eight-mRNA-based risk signature is a stable biomarker for urothelial cancer and might be able to predict which patients benefit from ICI treatment. It might play a role in precision individualized immunotherapy.

scholarly journals ­­­An Integrated Autophagy-Related Gene Signature Predicts Prognosis in Human Endometrial Cancer

2020 ◽  
Author(s):  
Jun Zhang ◽  
Ziwei Wang ◽  
Rong Zhao ◽  
Lanfen An ◽  
Xing Zhou ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Signaling Pathways ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Endometrial Tissue ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Cancer Tissue ◽  
Protein Levels

Abstract Background: Globally, endometrial cancer is the fourth most common malignant tumor in women and the number of women being diagnosed is increasing. Tumor progression is strongly related to the cell survival-promoting functions of autophagy. We explored the relationship between endometrial cancer prognoses and the expression of autophagy genes using human autophagy databases.Methods: The Cancer Genome Atlas was used to identify autophagy related genes (ARGs) that were differentially expressed in endometrial cancer tissue compared to healthy endometrial tissue. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were referenced to identify important biological functions and signaling pathways related to these differentially expressed ARGs. A prognostic model for endometrial cancer was constructed using univariate and multivariate Cox, and Least Absolute Shrinkage and Selection Operator regression analysis. Endometrial cancer patients were divided into high- and low-risk groups according to risk scores. Survival and receiver operating characteristic (ROC) curves were plotted for these patients to assess the accuracy of the prognostic model. Using immunohistochemistry the protein levels of the genes associated with risk were assessed.Results: We determined 37 ARGs were differentially expressed between endometrial cancer and healthy tissues. These genes were enriched in the biological processes and signaling pathways related to autophagy. Four ARGs (CDKN2A, PTK6, ERBB2 and BIRC5) were selected to establish a prognostic model of endometrial cancer. Kaplan–Meier survival analysis suggested that high-risk groups have significantly shorter survival times than low-risk groups. The area under the ROC curve indicated that the prognostic model for survival prediction was relatively accurate. Immunohistochemistry suggested that among the four ARGs the protein levels of CDKN2A, PTK6, ERBB2, and BIRC5 were higher in endometrial cancer than healthy endometrial tissue.Conclusions: Our prognostic model assessing four ARGs (CDKN2A, PTK6, ERBB2, and BIRC5) suggested their potential as independent predictive biomarkers and therapeutic targets for endometrial cancer.

Expression patterns and a prognostic model of m6A-associated regulators in prostate adenocarcinoma

2020 ◽  
Vol 14 (18) ◽  
pp. 1717-1731
Author(s):  
Song Ou-Yang ◽  
Ji-Hong Liu ◽  
Qin-Zhang Wang
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Prostate Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Clinical Biomarkers

Aim: To study the expression patterns and prognostic value of the m6A-associated regulators in prostate adenocarcinoma (PRAD). Materials & methods: The mRNA expression and clinical data were downloaded from ‘The Cancer Genome Atlas database’. The m6A-associated variants were downloaded from m6AVar database, and combined with 14 common m6A regulators for subsequent analysis. One-way analysis of variance, univariate Cox regression analysis and least absolute shrinkage and selection operator algorithm were successively applied to obtain the ultimate regulators and prognostic model. Finally, consensus clustering, protein–protein interaction (PPI) and enrichment analysis were performed. Result: Nine regulators were obtained. PRAD patients could be classified into two risk groups and subclasses with significant survival differences by the prognostic model and consensus clustering, respectively. Conclusion: All these nine regulators were related to prognosis in PRAD, and could be used as clinical biomarkers.

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