scholarly journals Pyroptosis-related Prognosis Model, Immunocyte Infiltration Characterization, and Competing Endogenous RNA Network of Glioblastoma

2021 ◽  
Author(s):  
Min-Rui Ding ◽  
Yan-Jie Qu ◽  
Xiao Peng ◽  
Jin-Fang Chen ◽  
Meng-Xue Zhang ◽  
...  
Keyword(s):  
Immune Response ◽  
Risk Score ◽  
Noncoding Rna ◽  
Prognostic Markers ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
Prognosis Model

Abstract Background: Glioblastoma (GBM) has a high incidence rate, invasive growth, and easy recurrence, and the current therapeutic effect is less than satisfying. Pyroptosis plays an important role in morbidity and progress of GBM. Meanwhile, the tumor microenvironment (TME) is involved in the progress and treatment tolerance of GBM. In the present study, we analyzed prognosis model, immunocyte infiltration characterization, and competing endogenous RNA (ceRNA) network of GBM on the basis of pyroptosis-related genes (PRGs).Methods: The transcriptome and clinical data of 155 patients with GBM and 120 normal subjects were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). Lasso (Least absolute shrinkage and selection operator) Cox expression analysis was used in predicting prognostic markers, and its predictive ability was tested using a nomogram. A prognostic risk score formula was constructed, and CIBERSORT, ssGSEA algorithm, Tumor IMmune Estimation Resource (TIMER), and TISIDB database were used in evaluating the immunocyte infiltration characterization and tumor immune response of differential risk samples. A ceRNA network was constructed with Starbase, mirtarbase, and lncbase, and the mechanism of this regulatory axis was explored using Gene Set Enrichment Analysis (GSEA). Results: Five PRGs (CASP3, NLRP2, TP63, GZMB, and CASP9) were identified as the independent prognostic biomarkers of GBM. Prognostic risk score formula analysis showed that the low-risk group had obvious survival advantage compared with the high-risk group, and significant differences in immunocyte infiltration and immune related function score were found. In addition, a ceRNA network of messenger RNA (CASP3, TP63)–microRNA (hsa-miR-519c-5p)–long noncoding RNA (GABPB1-AS1) was established. GSEA analysis showed that the regulatory axis played a considerable role in the extracellular matrix (ECM) and immune inflammatory response.Conclusions: Pyroptosis and TME-related independent prognostic markers were screened in this study, and a prognosis risk score formula was established for the first time according to the prognosis PRGs. TME immunocyte infiltration characterization and immune response were assessed using ssGSEA, CIBERSORT algorithm, TIMER, and TISIDB database. Besides a ceRNA network was built up. This study not only laid foundations for further exploring pyroptosis and TME in improving prognosis of GBM, but also provided a new idea for more effective guidance on clinical immunotherapy to patients and developing new immunotherapeutic drugs.

scholarly journals Prognostic Value of A Risk Score Model Constructed By m6A Methylation Regulators In Breast Cancer

2021 ◽  
Author(s):  
Xiaowei Qiu ◽  
Qiaoli Zhang ◽  
Jingnan Xu ◽  
Xin Jiang ◽  
Xuewei Qi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Rna Methylation ◽  
Model Based ◽  
Prognosis Model ◽  
M6a Rna Methylation

Abstract Background: N6-methyladenosine (m6A) methylation modification can affect the tumorigenesis, progression, and metastasis of breast cancer (BC). Up to now, a prognostic model based on m6A methylation regulators for BC is still lacking. This study aimed to construct an accurate prediction prognosis model by m6A methylation regulators for BC patients.Methods: After processing of The Cancer Genome Atlas (TCGA) datasets, the differential expression and correlation analysis of m6A RNA methylation regulators were applied. Next, tumor samples were clustered into different groups and clinicopathologic features in different clusters were explored. By univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analysis, m6A regulators with prognostic value were identified to develop a prediction model. Furthermore, we constructed and validated a predictive nomogram to predict the prognosis of BC patients.Results: 19 m6A related genes were extracted and 908 BC patients enrolled from TCGA dataset. After univariate Cox and LASSO analysis, 3 m6A RNA methylation regulators (YTHDF3, ZC3H13 and HNRNPC) were selected to establish the prognosis model based on median risk score (RS) in training and validation cohort. With the increasing of RS, the expression levels of YTHDF3 and ZC3H13 were individually elevated, while the HNRNPC expressed decreasingly. By survival analysis and Receiver Operating Characteristic (ROC) curve, we found that the overall survival (OS) of high-risk group was significantly shorter than that of the low-risk group based on Kaplan-Meier (KM) analysis in each cohort. Univariate and multivariate analysis identified the RS, age, and pathological stage are independent prognostic factors. A nomogram was constructed to predict 1- and 3-year OS and the calibration plots validate the performance. The C-index of nomogram reached 0.757 (95% CI:0.7-0.814) in training cohort and 0.749 (95% CI:0.648-0.85) in validation cohort, respectively.Conclusions: We successfully constructed a predictive prognosis model by m6A RNA methylation regulators. These results indicated that the m6A RNA methylation regulators are potential therapeutic targets of BC patients.

scholarly journals Development a Prognostic Model Integrating lncRNA/ mRNA novel Biomarkers Identified by Bioinformatics Analysis and Experiments in Breast Cancer

2021 ◽  
Author(s):  
Jinrong Wei ◽  
Qianshu Dou ◽  
Futing Ba ◽  
Guo-Qin Jiang
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Breast Cancers ◽  
Rt Pcr ◽  
Cerna Network ◽  
Prognosis Model

Abstract Purpose: The purpose of this study is to established a prognosis model based on the expression profiles of lncRNAs and mRNAs for breast cancers.Methods: Single Variable Cox Proportional Risk Regression analysis and difference analysis were applied to screen survival-related and differently expressed lncRNAs and mRNAs between tumor and normal tissues from TCGA data. GO and KEGG analysis were applied for top 30 survival-related genes. LncRNA/mRNA co-expressed network was constructed based on correlation analysis. LASSO analysis and Multivariate Stepwise Cox Regression analysis were applied to establish the prognosis model. RT-PCR experiments were applied to verify the correctness of the analysis results. Relative components of the TME in breast cancers with high and low risk groups were analysed by xCell and Cox proportional risk regression analysis. The ceRNA network was constructed by calculating the Pearson correlation coefficient (PCC) for miRNA-mRNA and miRNA-lncRNA using paired miRNA, mRNA, and lncRNA expression profile data.Results:Venn diagrams showed that there were 60 genes and 54 lncRNAs that were differently expressed and related with survival. Through lncRNA/mRNA co-expressed network construction, 19 lncRNA and 16 mRNA hub genes were gained. The genes were then included in LASSO and multivariate Cox proportional hazard regression analysis, and finally, 3 lncRNAs (LINC01497, LINC02766, LINC02528) and 2 mRNAs (C20orf85, CST1) were selected as prognosis predictive genes. According to the median risk score of the 5 candidates, patients were divided into high-risk group and low-risk group. The results of RT-PCR were consistent with the analysis results. The proportions of Adipocytes, Endothelial cells, HSCs, Fibroblasts were significantly lower in low risk score tissues compared with the high risk score tissues, while the proportions of M1 macrophages, MSCs, Th2 cells were significantly higher. A lncRNA-miRNA-mRNA ceRNA network containing 3 lncRNAs, 2 mRNAs, and 158 miRNAs was finally constructed, preliminarily revealed a proper mechanism of the 5 molecules playing important roles in breast cancer progression and prognosis prediction.Conclusion: We found that LINC01497, LINC02766, LINC02528 and C20orf85, CST1 may serve as a powerful prognostic tool to optimize the prognosis evaluation system of breast cancer.

scholarly journals Identification of Novel Survival Related lncRNA-miRNA-mRNA Competing Endogenous RNA Network Associated with Immune Infiltration in Colorectal Cancer

2020 ◽  
Author(s):  
Jianxin Li ◽  
Ting Han ◽  
Xin Wang ◽  
Yinchun Wang ◽  
Qingqiang Yang
Keyword(s):  
Colorectal Cancer ◽  
Regulatory Network ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Competing Endogenous Rna ◽  
Expression Levels ◽  
Go Annotation ◽  
Immune Infiltration ◽  
Cerna Network

Abstract Background: Increasing studies have reported that long noncoding RNAs (lncRNAs) play critical roles in the initiation and progression of carcinogenesis. However, the underlying regulatory mechanisms of lncRNA related competing endogenous RNA (ceRNA) network in colorectal cancer (CRC) are not fully understood.Methods: Dysregulated microRNAs (miRNAs) in CRC samples were screened from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. After that, the key miRNAs were filtered out through a comprehensive assessment of their expression levels and prognostic values. Subsequently, the targeted downstream mRNAs and upstream lncRNAs of the key miRNAs were predicted by using multiple bioinformatic databases. A ceRNA network was constructed by using Cytoscape, and the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on this network using the DAVID database. Ultimately, expression levels and prognostic values of the lncRNAs and mRNAs were evaluated, and a survival related ceRNA network was constructed and visualized by using Cytoscape. In addition, the Gene Set Enrichment Analysis (GSEA) software package was employed to identify the pathways in which this survival related ceRNA network was enriched. Furthermore, correlations of ceRNA network with immune infiltration level were estimated by the Tumor Immune Estimation Resource (TIMER) databases.Results: In total, 28 dysregulated miRNAs were obtained, and two of them were identified as key miRNAs based on expression levels and prognostic values analyses. Subsequently, a total of three upstream lncRNAs and 309 downstream mRNAs were predicted by using bioinformatic tools, and two key lncRNAs and eight key mRNAs were identified by expression and survival analysis. A ceRNA regulatory network associated with the prognosis of CRC patients was constructed. Furthermore, GSEA analysis indicated the possible association of key mRNAs with CRC onset and progression. Importantly, immune infiltration analysis revealed that the ceRNA network was remarkably associated with infiltration abundance of multiple immune cells and expression levels of immune checkpoints.Conclusions: We constructed a survival related ceRNA regulatory network in human CRC, NEAT1 and XIST are potential prognostic factors that affect CRC onset and progression by targeting miR-195-5p.

scholarly journals Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Weige Zhou ◽  
Shijing Zhang ◽  
Hui-biao Li ◽  
Zheyou Cai ◽  
Shuting Tang ◽  
...  
Keyword(s):  
Risk Score ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Lasso Regression

There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and multivariate Cox regression were used to develop an autophagy-related lncRNA signature. A risk score based on the signature was established, and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of autophagy-related lncRNAs was visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Ten prognostic autophagy-related lncRNAs (AC027307.2, AC068580.3, AL138756.1, CD27-AS1, EIF3J-DT, LINC01011, LINC01063, LINC02381, AC073896.3, and SNHG16) were identified to be significantly different, which made up an autophagy-related lncRNA signature. The signature divided patients with colon adenocarcinoma into the low-risk group and the high-risk group. A risk score based on the signature was a significantly independent factor for the patients with colon adenocarcinoma (HR=1.088, 95%CI=1.057−1.120; P<0.001). Additionally, the ten lncRNAs were significantly enriched in autophagy process, metabolism, and tumor classical pathways. In conclusion, the ten autophagy-related lncRNAs and their signature might be molecular biomarkers and therapeutic targets for the patients with colon adenocarcinoma.

scholarly journals A Glycolysis-Based Long Non-coding RNA Signature Accurately Predicts Prognosis in Renal Carcinoma Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Honghao Cao ◽  
Hang Tong ◽  
Junlong Zhu ◽  
Chenchen Xie ◽  
Zijia Qin ◽  
...  
Keyword(s):  
Clinical Significance ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Kaplan Meier

BackgroundThe prognosis of renal cell carcinoma (RCC) varies greatly among different risk groups, and the traditional indicators have limited effect in the identification of risk grade in patients with RCC. The purpose of our study is to explore a glycolysis-based long non-coding RNAs (lncRNAs) signature and verify its potential clinical significance in prognostic prediction of RCC patients.MethodsIn this study, RNA data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate cox regression displayed six significantly related lncRNAs (AC124854.1, AC078778.1, EMX2OS, DLGAP1-AS2, AC084876.1, and AC026401.3) which were utilized in construction of risk score by a formula. The accuracy of risk score was verified by a series of statistical methods such as receiver operating characteristic (ROC) curves, nomogram and Kaplan-Meier curves. Its potential clinical significance was excavated by gene enrichment analysis.ResultsKaplan-Meier curves and ROC curves showed reliability of the risk score to predict the prognosis of RCC patients. Stratification analysis indicated that the risk score was independent predictor compare to other traditional clinical parameters. The clinical nomogram showed highly rigorous with index of 0.73 and precisely predicted 1-, 3-, and 5-year survival time of RCC patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene set enrichment analysis (GSEA) depicted the top ten correlated pathways in both high-risk group and low-risk group. There are 6 lncRNAs and 25 related mRNAs including 36 lncRNA-mRNA links in lncRNA-mRNA co-expression network.ConclusionThis research demonstrated that glycolysis-based lncRNAs possessed an important value in survival prediction of RCC patients, which would be a potential target for future treatment.

scholarly journals Comprehensive Analysis of Competing Endogenous RNA Network Focusing on Long Noncoding RNA Involved in Cirrhotic Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuli Zhang ◽  
Dinggui Chen ◽  
Miaomiao Yang ◽  
Xianfeng Qian ◽  
Chunmei Long ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Noncoding Rna ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Competing Endogenous Rna ◽  
Regulatory Pathways ◽  
Qrt Pcr ◽  
Cerna Network

The role of long noncoding RNAs- (lncRNAs-) associated competing endogenous RNA (ceRNA) in the field of hepatocellular carcinoma (HCC) biology is well established, but the involvement of lncRNAs competing interactions in the progression of liver cirrhosis to HCC is still unclear. We aimed to explore the differential expression profiles of lncRNAs, microRNAs (miRNA), and messenger RNAs (mRNAs) to construct a functional ceRNA network in cirrhotic HCC. The lncRNA, miRNA, and mRNA expression datasets were obtained from Gene Expression Omnibus and The Cancer Genome Atlas. Based on miRanda and TargetScan, the HCC-specific ceRNA network was constructed to illustrate the coexpression regulatory relationship of lncRNAs, miRNAs, and mRNAs. The potential prognostic indicators in the network were confirmed by survival analysis and validated by qRT-PCR. A total of 74 lncRNAs, 36 intersection miRNAs, and 949 mRNAs were differentially expressed in cirrhotic HCC samples compared with cirrhosis samples. We constructed a ceRNA network, including 47 lncRNAs, 35 miRNAs, and 168 mRNAs. Survival analysis demonstrated that 2 lncRNAs (EGOT and SERHL), 4 miRNAs, and 40 mRNAs were significantly associated with the overall survival of HCC patients. Two novel regulatory pathways, EGOT-miR-32-5p-XYLT2 axis and SERHL-miR-1269a/miR-193b-3p-BCL2L1/SYK/ARNT/CHST3/LPCAT1 axis, were built up and contribute to the underlying mechanism of HCC pathogenesis. The higher-expressed SERHL was associated with a higher risk of all-cause death. The expressions of SERHL-miR-1269a-BCL2L1 were significantly different using qRT-PCR in vitro studies. lncRNAs EGOT and SERHL might serve as effective prognostic biomarkers and potential therapeutic targets in cirrhotic HCC treatment.

scholarly journals Identification of an EMT-related lncRNA signature in glioma

2020 ◽  
Author(s):  
Chuming Tao ◽  
Qing Hu ◽  
Haitao Luo ◽  
Peng Wang ◽  
Zewei Tu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
World Health ◽  
Competing Endogenous Rna ◽  
Mesenchymal Transition ◽  
High Risk Patients ◽  
Risk Patients ◽  
Genome Atlas

Abstract Background Epithelial-mesenchymal transition (EMT) has been implicated in the invasion and progression of gliomas, the present study investigated EMT-related long noncoding (lnc)RNAs in gliomas.Methods Candidate lncRNAs screened from a glioma dataset in The Cancer Genome Atlas were used to construct EMT-related lncRNA coexpression networks in order to identify EMT-related lncRNAs; these were validated using the Chinese Glioma Genome Atlas (CGGA). Gene set enrichment analysis and principal component analysis (PCA) were used to annotate lncRNA functions. We established an EMT-related lncRNA signature composed of 9 lncRNAs (HAR1A, LINC00641, LINC00900, MIR210HG, MIR22HG, PVT1, SLC25A21-AS1, SNAI3-AS1, and SNHG18) that could distinguish between low- and high-risk glioma patients. The functions of the identified lncRNAs were evaluated by constructing a competing endogenous RNA network.Results The low-risk group had longer overall survival (OS) than the high-risk group (P<0.0001). High-risk patients also had no deletion on chromosome arms 1p and/or 19q, expressed wild-type isocitrate dehydrogenase, and had higher World Health Organization (WHO) tumor grade. The signature was an independent factor significantly associated with OS (P=0.041, hazard ratio=1.806). These findings were validated in the CGGA dataset. PCA also revealed differences in EMT status between low- and high-risk patients. Competing endogenous RNA network showed that complex lncRNA–microRNA–mRNA interactions potentially contributing to EMT progression in glioma. Conclusion Our results demonstrate that the EMT-related 9-lncRNA signature has prognostic value for glioma patients.

scholarly journals Construction of An Immune-Related CeRNA Network as a Prognostic Signature in Bladder Cancer

2020 ◽  
Author(s):  
Ran-ran Zhou ◽  
Hu Tian ◽  
Cheng Yang ◽  
Fan Peng ◽  
Hao-yu Yuan ◽  
...  
Keyword(s):  
Immune Response ◽  
Bladder Cancer ◽  
Risk Score ◽  
Prognostic Value ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
External Validation ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cerna Network

Abstract Background: Immunotherapy has been proved to be effective for bladder cancer (BLCA). However, the molecular network involved in BLCA tumor immune response remains unclear. This study aims to construct an immune-related ceRNA network and to identify the prognostic value. Methods: Based on The Cancer Genome Atlas (TCGA), we used single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network analysis (WGCNA) to determine immune-related mRNA, lncRNA and miRNA. Then least absolute shrinkage, and selection operator (LASSO) and Cox regression were performed to identify the mRNAs with high prognostic value, and accordingly, the risk score was calculated. Internal and external validation were performed both in TCGA and GSE13507 with Kaplan-Meier (KM) survival and Receiver Operating Characteristic (ROC) curve analysis. Using the immune-related mRNA, lncRNA and miRNA, a ceRNA network was established via MiRcode, starBase, miRDB, miRTarBase and TargetScan. Besides, we also explore the relationship between the risk score and immune cell infiltration via CIBERSORT algorithm. Results: 5 mRNAs (PCGF3, FASN, DPYSL2, TGFBI and NTF3) were ultimately identified, and KM survival analysis displayed the 5-mRNA risk signature could predict the prognosis of BLCA with high efficacy both in TCGA (p = 1.006e-13) and GSE13507 (p = 7.759e-04). Using miRNA targeting molecular prediction database, an immune-related ceRNA network, including 5 mRNAs, 24 miRNAs and 86 lncRNAs, was constructed. Memory B cells, activated dendritic cells, and regulatory T cells infiltration into tumors were negatively correlated with risk score, while the infiltration levels of macrophages M0, M1 and M2 were positively correlated with risk score. Conclusion: This study helped to better understand the molecular mechanisms of tumor immune response from the view of ceRNA hypothesis, and provided a novel prognostic signature for bladder cancer.

scholarly journals Long noncoding RNA LINC01291 promotes the aggressive properties of melanoma by functioning as a competing endogenous RNA for microRNA-625-5p and subsequently increasing IGF-1R expression

2021 ◽  
Author(s):  
Lijun Wu ◽  
Ke Li ◽  
Wei Lin ◽  
Jianjiang Liu ◽  
Qiang Qi ◽  
...  
Keyword(s):  
Colony Formation ◽  
Noncoding Rna ◽  
Melanoma Cells ◽  
The Cancer Genome Atlas ◽  
Cell Counting ◽  
Tumor Xenograft ◽  
Luciferase Reporter ◽  
Migration And Invasion ◽  
Competing Endogenous Rna ◽  
Melanoma Tumor

AbstractStudies have confirmed the relationship between dysregulated long noncoding RNAs and melanoma pathogenesis. However, the regulatory functions of long intergenic non-protein coding RNA 1291 (LINC01291) in melanoma remain unknown. Therefore, we evaluated LINC01291 expression in melanoma and explored its roles in regulating tumor behaviors. Further, the molecular events via which LINC01291 affects melanoma cells were investigated. LINC01291 expression in melanoma cells was analyzed using The Cancer Genome Atlas database and quantitative real-time polymerase chain reaction. Functional assays, including the Cell Counting Kit-8 assay, colony formation assay, flow cytometry, cell migration and invasion assays, and tumor xenograft models, were used to examine LINC01291’s role in melanoma cells. Additionally, bioinformatics analysis, RNA immunoprecipitation, luciferase reporter assay, and western blotting were conducted to determine the tumor-promoting mechanism of LINC01291. LINC01291 was upregulated in melanoma tissues and cell lines. Following LINC01291 knockdown, cell proliferation, colony formation, migration, and invasion were diminished, whereas apoptosis was enhanced and the cell cycle was arrested at G0/G1. In addition, loss of LINC01291 decreased the chemoresistance of melanoma cells to cisplatin. Furthermore, LINC01291 interference inhibited melanoma tumor growth in vivo. Mechanistically, LINC01291 functions as a competing endogenous RNA by sponging microRNA-625-5p (miR-625-5p) in melanoma cells and maintaining insulin-like growth factor 1 receptor (IGF-1R) expression. Rescue experiments revealed that the roles induced by LINC01291 depletion in melanoma cells could be reversed by suppressing miR-625-5p or overexpressing IGF-1R. Our study identified the LINC01291/miR-625-5p/IGF-1R competing endogenous RNA pathway in melanoma cells, which may represent a novel diagnostic biomarker and an effective therapeutic target for melanoma.

scholarly journals Increased VEGF-A in solid type of lung adenocarcinoma reduces the patients’ survival

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Woon Yong Jung ◽  
Kyueng-Whan Min ◽  
Young Ha Oh
Keyword(s):  
Immune Response ◽  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Survival Rates ◽  
Treatment Strategies ◽  
Enrichment Analysis ◽  
Genetic Alterations ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Solid Type

AbstractThe histological classification of lung adenocarcinoma includes 5 types: lepidic, acinar, papillary, micropapillary and solid. The complex gene interactions and anticancer immune response of these types are not well known. The aim of this study was to reveal the survival rates, genetic alterations and immune activities of the five histological types and provide treatment strategies. This study reviewed the histological findings of 517 patients with lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database and classified them into five types. We performed gene set enrichment analysis (GSEA) and survival analysis according to the different types. We found six oncogenic gene sets that were higher in lung adenocarcinoma than in normal tissues. In the survival analysis of each type, the acinar type had a favorable prognosis, and the solid subtype had an unfavorable prognosis; however, the survival differences between the other types were not significant. Our study focused on the solid type, which had the poorest prognosis. The solid type was related to adaptive immune resistance associated with elevated CD8 T cells and high CD274 (encoding PD-L1) expression. In the pathway analyses, the solid type was significantly related to high vascular endothelial growth factor (VEGF)-A expression, reflecting tumor angiogenesis. Non-necrosis/low immune response affected by high VEGF-A was associated with worse prognosis. The solid type associated with high VEGF-A expression may contribute to the development of therapeutic strategies for lung adenocarcinoma.

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