scholarly journals Metabolic syndrome related gene signature predicts the prognosis of patients with pancreatic ductal carcinoma. A novel link between metabolic dysregulation and pancreatic ductal carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weiyang Cai ◽  
Wenming Bao ◽  
Shengwei Chen ◽  
Yan Yang ◽  
Yanyan Li
Keyword(s):  
T Cells ◽  
High Risk ◽  
Cox Regression ◽  
Ductal Carcinoma ◽  
Risk Groups ◽  
Cancer Genome ◽  
Low Risk ◽  
Pancreatic Ductal Carcinoma ◽  
Blue Module ◽  
Geo Database

Abstract Background Pancreatic cancer is one of the most common malignancies worldwide. In recent years, specific metabolic activities, which involves the development of tumor, caused wide public concern. In this study, we wish to explore the correlation between metabolism and progression of tumor. Methods A retrospective analysis including 95 patients with pancreatic ductal adenocarcinoma (PDAC) and PDAC patients from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and The Gene Expression Omnibus (GEO) database were involved in our study. Multivariate Cox regression analysis was used to construct the prognosis model. The potential connection between metabolism and immunity of PDAC was investigated through a weighted gene co-expression network analysis (WGCNA). 22 types of Tumor-infiltrating immune cells (TIICs) between high-risk and low-risk groups were estimated through CIBERSORT. Moreover, the potential immune-related signaling pathways between high-risk and low-risk groups were explored through the gene set enrichment analysis (GSEA). The role of key gene GMPS in developing pancreatic tumor was further investigated through CCK-8, colony-information, and Transwell. Results The prognostic value of the MetS factors was analyzed using the Cox regression model, and a clinical MetS-based nomogram was established. Then, we established a metabolism-related signature to predict the prognosis of PDAC patients based on the TCGA databases and was validated in the ICGC database and the GEO database to find the distinct molecular mechanism of MetS genes in PDAC. The result of WGCNA showed that the blue module was associated with risk score, and genes in the blue module were found to be enriched in the immune-related signaling pathway. Furthermore, the result of CIBERSORT demonstrated that proportions of T cells CD8, T cells Regulatory, Tregs NK cells Activated, Dendritic cells Activated, and Mast cells Resting were different between high-risk and low-risk groups. These differences are potential causes of different prognoses of PDAC patients. GSEA and the protein–protein interaction network (PPI) further revealed that our metabolism-related signature was significantly enriched in immune‐related biological processes. Moreover, knockdown of GMPS in PDAC cells suppressed proliferation, migration, and invasion of tumor cells, whereas overexpression of GMPS performed oppositely. Conclusion The results shine light on fundamental mechanisms of metabolic genes on PDAC and establish a reliable and referable signature to evaluate the prognosis of PDAC. GMPS was identified as a potential candidate oncogene with in PDAC, which can be a novel biomarker and therapeutic target for PDAC treatment.

scholarly journals Metabolic Syndrome Related Gene Signature Predicts the Prognosis of Patients With Pancreatic Ductal Carcinoma. A Novel Link Between Metabolic Dysregulation and Pancreatic Ductal Carcinoma.

2021 ◽  
Author(s):  
Weiyang Cai ◽  
Wenming Bao ◽  
Shengwei Chen ◽  
Yan Yang ◽  
Yan-yan Li
Keyword(s):  
T Cells ◽  
High Risk ◽  
Cox Regression ◽  
Ductal Carcinoma ◽  
Risk Groups ◽  
Low Risk ◽  
Pancreatic Ductal Carcinoma ◽  
Potential Candidate ◽  
Blue Module ◽  
Geo Database

Abstract Background: Pancreatic cancer is one of the most common malignancies worldwide. In recent years, specific metabolic activities, which involves the development of tumor, caused wide public concern. In this study, we wish to explore the correlation between metabolism and progression of tumor.Methods: A retrospective analysis including 95 patients with pancreatic ductal adenocarcinoma (PDAC) and PDAC patients from TCGA, ICGA, and GEO database were involved in our study. Multivariate Cox regression analysis was used to construct the prognosis model. The potential connection between metabolism and immunity of PDAC was investigated through WGCNA. 22 types of TIICs between high-risk and low-risk groups were estimated through CIBERSORT. Moreover, the potential immune-related signaling pathways between high-risk and low-risk groups were explored through the GSEA. The role of key gene GMPS in developing pancreatic tumor was further investigated through CCK-8, colony-information, and Transwell. Results: The prognostic value of the MetS factors was analyzed using the Cox regression model, and a clinical MetS-based nomogram was established. Then, we established a metabolism-related signature to predict the prognosis of PDAC patients based on the TCGA databases and was validated in the ICGC database and the GEO database to find the distinct molecular mechanism of MetS genes in PDAC. The result of WGCNA showed that the blue module was associated with risk score, and genes in the blue module were found to be enriched in the immune-related signaling pathway. Furthermore, the result of CIBERSORT demonstrated that proportions of T cells CD8, T cells Regulatory, Tregs NK cells Activated, Dendritic cells Activated, and Mast cells Resting were different between high-risk and low-risk groups. These differences are potential causes of different prognoses of PDAC patients. GSEA and the protein–protein interaction network (PPI) further revealed that our metabolism-related signature was significantly enriched in immune‐related biological processes. Moreover, knockdown of GMPS in PDAC cells suppressed proliferation, migration, and invasion of tumor cells, whereas overexpression of GMPS performed oppositely.Conclusion: The results shine light on fundamental mechanisms of metabolic genes on PDAC and establish a reliable and referable signature to evaluate the prognosis of PDAC. GMPS was identified as a potential candidate oncogene with in PDAC, which can be a novel biomarker and therapeutic target for PDAC treatment.

scholarly journals Ferroptosis-Related lncRNA Signature Predicts Prognosis and Immunotherapy Efficacy in Cutaneous Melanoma

2022 ◽  
Author(s):  
Yujian Xu ◽  
Youbai Chen ◽  
Zehao Niu ◽  
Zheng Yang ◽  
Jiahua Xing ◽  
...  
Keyword(s):  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Immune Checkpoint ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk

Abstract Ferroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of our study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM. Ferroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed. Here, we identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups. Overall, our novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

scholarly journals Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongjie Chen ◽  
Hui Huang ◽  
Longjun Zang ◽  
Wenzhe Gao ◽  
Hongwei Zhu ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Low Risk ◽  
Ductal Adenocarcinoma ◽  
Prognostic Signature ◽  
Score Model

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

A Novel Risk Classification Model Predicts Overall Survival and Locoregional Surgery Benefit in Colorectal Patients with Distant Metastasis at the Initial Diagnosis

2020 ◽  
Author(s):  
Mo Chen ◽  
Tian-en Li ◽  
Pei-zhun Du ◽  
Junjie Pan ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Distant Metastasis ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Risk Classification ◽  
Low Risk ◽  
Classification Model ◽  
Intermediate Risk

Abstract Background and aims: In this research, we aimed to construct a risk classification model to predict overall survival (OS) and locoregional surgery benefit in colorectal cancer (CRC) patients with distant metastasis.Methods: We selected a cohort consisting of 12741 CRC patients diagnosed with distant metastasis between 2010 and 2014, from the Surveillance, Epidemiology and End Results (SEER) database. Patients were randomly assigned into training group and validation group at the ratio of 2:1. Univariable and multivariable Cox regression models were applied to screen independent prognostic factors. A nomogram was constructed and assessed by the Harrell’s concordance index (C-index) and calibration plots. A novel risk classification model was further established based on the nomogram.Results: Ultimately 12 independent risk factors including race, age, marriage, tumor site, tumor size, grade, T stage, N stage, bone metastasis, brain metastasis, lung metastasis and liver metastasis were identified and adopted in the nomogram. The C-indexes of training and validation groups were 0.77 (95% confidence interval [CI] 0.73-0.81) and 0.75 (95% CI 0.72-0.78), respectively. The risk classification model stratified patients into three risk groups (low-, intermediate- and high-risk) with divergent median OS (low-risk: 36.0 months, 95% CI 34.1-37.9; intermediate-risk: 18.0 months, 95% CI 17.4-18.6; high-risk: 6.0 months, 95% CI 5.3-6.7). Locoregional therapies including surgery and radiotherapy could prognostically benefit patients in the low-risk group (surgery: hazard ratio [HR] 0.59, 95% CI 0.50-0.71; radiotherapy: HR 0.84, 95% CI 0.72-0.98) and intermediate risk group (surgery: HR 0.61, 95% CI 0.54-0.68; radiotherapy: HR 0.86, 95% CI 0.77-0.95), but not in the high-risk group (surgery: HR 1.03, 95% CI 0.82-1.29; radiotherapy: HR 1.03, 95% CI 0.81-1.31). And all risk groups could benefit from systemic therapy (low-risk: HR 0.68, 95% CI 0.58-0.80; intermediate-risk: HR 0.50, 95% CI 0.47-0.54; high-risk: HR 0.46, 95% CI 0.40-0.53).Conclusion: A novel risk classification model predicting prognosis and locoregional surgery benefit of CRC patients with distant metastasis was established and validated. This predictive model could be further utilized by physicians and be of great significance for medical practice.

scholarly journals The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Bufu Tang ◽  
Jinyu Zhu ◽  
Jie Li ◽  
Kai Fan ◽  
Yang Gao ◽  
...  
Keyword(s):  
High Risk ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Models ◽  
Integrated Analysis ◽  
Immune Microenvironment ◽  
Diagnostic Models

Abstract Background In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results Four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients. Graphical abstract

Breast Cancer Index and risk stratification in luminal subtypes: A trans-ATAC study.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 534-534
Author(s):  
Ivana Sestak ◽  
Yi Zhang ◽  
Catherine A. Schnabel ◽  
Jack M. Cuzick ◽  
Mitchell Dowsett
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Early Stage ◽  
Risk Groups ◽  
Low Risk ◽  
Luminal A ◽  
Prognostic Information ◽  
Prognostic Performance ◽  
Breast Cancer Index

534 Background: The Breast Cancer Index (BCI) is a gene-expression based signature that provides prognostic information for overall (0-10 years) and late (5-10 years) distant recurrence (DR) and prediction of extended endocrine benefit in hormone receptor positive (HR+) early stage breast cancer. The current analysis aims to further characterize, correlate and compare the prognostic performance of BCI in luminal subtypes based on immunohistochemical classification. Methods: 670 postmenopausal women with HR+, LN- disease from the TransATAC cohort were included in this analysis. Luminal A-like tumors (LumA) were identified as those with ER+ and/or PR+ and HER2 -, and Ki67 < 20% by IHC. All other tumors were classified as Luminal B-like (LumB) for this analysis. Primary endpoint was DR. Cox regression models were used to examine BCI prognostic performance according to luminal subtype, adjusting for the clinicopathological model Clinical Treatment Score (CTS). Results: 452 (67.5%) patients were classified as LumA and 218 (32.5%) as LumB. BCI was highly prognostic in LumA cancers (adjusted HR = 1.57 (1.23-1.96), P < 0.001, ΔLR-χ2= 14.09), but not in LumB tumors (adjusted HR = 1.20 (0.94-1.52, P = 0.14, ΔLR-χ2= 2.23). In LumA, 10-year DR risks in BCI intermediate and high risk groups were very similar (25.6% (16.4-38.6) and 25.3% (13.5-44.3), respectively) and significantly different from BCI low (3.9% (2.1-7.0); HR = 7.47 (3.50-15.96) and HR = 8.13 (3.27-20.23), respectively). In LumB, 10-year DR risks in BCI low and BCI intermediate risk groups (13.8% (6.8-26.9) and 14.6% (8.3-24.9), respectively) were very similar and significantly lower than for the BCI high (29.1% (20.0-41.1)). Lum subtyping was only prognostic in the BCI low risk group (LumA vs. LumB: HR = 4.27 (1.65-11.02)) but not in the other two BCI risk groups. Conclusions: BCI provided significant prognostic information in Lum A subtype. These results show that BCI intermediate and high risk had similar risk of DR in LumA tumors, while shared similarly low risk of DR as BCI-low in LumB tumors. Further evaluation is needed to elucidate the distinct mechanisms underlying each classification system.

scholarly journals The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

2020 ◽  
Author(s):  
Jiansong Ji ◽  
Bufu Tang ◽  
Jinyu Zhu ◽  
Jie Li ◽  
Kai Fan ◽  
...  
Keyword(s):  
High Risk ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Models ◽  
Integrated Analysis ◽  
Immune Microenvironment ◽  
Diagnostic Models

Abstract Background : In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods : Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results : four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions: The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients.

scholarly journals The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

2020 ◽  
Author(s):  
Jiansong Ji ◽  
Bufu Tang ◽  
Jinyu Zhu ◽  
Jie Li ◽  
Kai Fan ◽  
...  
Keyword(s):  
High Risk ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Models ◽  
Integrated Analysis ◽  
Immune Microenvironment ◽  
Diagnostic Models

Abstract Background : In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods : Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results : four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions: The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients.

scholarly journals Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Kebing Huang ◽  
Xiaoyu Yue ◽  
Yinfei Zheng ◽  
Zhengwei Zhang ◽  
Meng Cheng ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Migration And Invasion ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Genome Atlas

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

scholarly journals Identification and Validation of m6A-Related lncRNA Signature as Potential Predictive Biomarkers in Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Wenchang Lv ◽  
Yichen Wang ◽  
Chongru Zhao ◽  
Yufang Tan ◽  
Mingchen Xiong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Score ◽  
Assessment Tool ◽  
Cox Regression ◽  
Screening Method ◽  
Predictive Biomarkers ◽  
Risk Groups ◽  
Low Risk ◽  
Main Challenge

The metastasis and poor prognosis are still regarded as the main challenge in the clinical treatment of breast cancer (BC). Both N6-methyladenosine (m6A) modification and lncRNAs play vital roles in the carcinogenesis and evolvement of BC. Considering the unknown association of m6A and lncRNAs in BC, this study therefore aims to discern m6A-related lncRNAs and explore their prognostic value in BC patients. Firstly, a total of 6 m6A-related lncRNAs were screened from TCGA database and accordingly constructed a prognostic-predicting model. The BC patients were then divided into high-risk and low-risk groups dependent on the median cutoff of risk score based on this model. Then, the predictive value of this model was validated by the analyses of cox regression, Kaplan-Meier curve, ROC curve, and the biological differences in the two groups were validated by PCA, KEGG, GSEA, immune status as well as in vitro assay. Finally, we accordingly constructed a risk prognostic model based on the 6 identified m6A-related lncRNAs, including Z68871.1, AL122010.1, OTUD6B-AS1, AC090948.3, AL138724.1, EGOT. Interestingly, the BC patients were divided into the low-risk and high-risk groups with different prognoses according to the risk score. Notably, the risk score of the model was an excellent independent prognostic factor. In the clinical sample validation, m6A regulatory proteins were differentially expressed in patients with different risks, and the markers of tumor-associated macrophages and m6A regulators were co-localized in high-risk BC tissues. This well-validated risk assessment tool based on the repertoire of these m6A-related genes and m6A-related lncRNAs, is of highly prognosis-predicting ability, and might provide a supplemental screening method for precisely judging BC prognosis.

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