scholarly journals Construction and Validation of an Immune-Related lncRNA Prognosis Model for Thyroid Cancer

2021 ◽  
Author(s):  
Zheng Li ◽  
Hui Wang ◽  
Xia Deng ◽  
Jing Zhang ◽  
Wanyan Tang ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Correlation Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Principal Component ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Disease Stage ◽  
Clinical Samples ◽  
Prognosis Model

Abstract Background: Immune-related long noncoding RNAs (lncRNAs) play an important role in the development of cancer. This study aimed to identify immune-related lncRNAs in thyroid cancer (THCA) and to develop a prognostic model for THCA. Method: We downloaded immune-related gene sets from the Gene Set Enrichment Analysis (GSEA) website and obtained THCA gene expression and clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were then obtained by performing a correlation analysis on the expression of lncRNAs and immune-related genes. Prognostic model for THCA immune-related lncRNAs was developed though univariate Cox regression and multiple Cox regression analyses. We confirmed the results in clinical samples using quantitative real-time PCR. Results: A totally of 26 immune-related lncRNAs in THCA were obtained. Then we constructed a prognosis model composed of seven lncRNAs (LINC01614, AC017074.1, LINC01184, LINC00667, ACVR2B-AS1, AC090673.1 and LINC00900). Our model can be used as an independent prognostic factor. Principal component analysis displayed that the lncRNAs in the model can distinguish between high and low-risk groups. Clinical correlation analysis showed that the expression levels of AC090673.1 (P<0.05), LINC01184 (P<0.001), and LINC01614 (P<0.001) were related to disease stage, and LINC00900 (P<0.001) and LINC01614 (P<0.001) were related to T stage. We validated this model in cancer and paracancerous tissues from 24 THCA patients. Conclusion: We identified and experimentally validated seven immune-related lncRNAs that can serve as potential biomarkers for THCA prognosis.

scholarly journals Construction and Validation of an Immune-related lncRNA Prognosis Model in Thyroid Cancer

2021 ◽  
Author(s):  
Zheng Li ◽  
Hui Wang ◽  
Xia Deng ◽  
Jing Zhang ◽  
Wanyan Tang ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Correlation Analysis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Disease Stage ◽  
Clinical Samples ◽  
Cox Regression Analysis ◽  
Prognosis Model

Abstract Background A growing number of studies have shown that immune-related long non-coding RNAs (lncRNAs) play an important role in the development of cancer. The aim of this study was to identify immune-related lncRNAs in thyroid cancer (THCA) and to develop a prognostic model for THCA. Methods We downloaded immune-associated gene sets from the Gene Set Enrichment Analysis (GSEA) website and obtained THCA gene expression and clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were then obtained by performing a correlation analysis of the expression of lncRNAs and immune-related genes. Prognostic models for THCA immune-related lncRNAs were developed by univariate Cox regression and multiple Cox regression analysis. We confirmed the results in clinical samples using quantitative reverse transcription polymerase chain reaction (qRT-PCR).Results26 immune-related lncRNAs in THCA were obtained. Then we constructed a prognosis model composed of 7 lncRNAs (LINC01614, AC017074.1, LINC01184, LINC00667, ACVR2B-AS1, AC090673.1, LINC00900). Our model could be used as an independent prognostic factor. Principal component analysis displayed that the lncRNAs in the model can distinguish between high-risk and low-risk groups. Clinical correlation analysis showed that the expression levels of AC090673.1 (P<0.05), LINC01184 (P<0.001), LINC01614 (P<0.001) were related to disease stage, LINC00900 (P<0.001), LINC01614 (P<0.001) were related to the T stage of THCA. We validated this model in the cancer and paracancerous tissues from 24 THCA patients.ConclusionsWe identified seven immune-related lncRNAs as potential biomarkers for the prognosis of THCA.

scholarly journals BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Jin Zhou ◽  
Zheming Liu ◽  
Huibo Zhang ◽  
Tianyu Lei ◽  
Jiahui Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Early Stage ◽  
External Validation ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t -AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

Comprehensive Analysis of the Immune Effect of TGFβ1 in Colorectal Adenocarcinoma:A TGFβ1-related Prognostic Model of Tumor Microenvironment

2021 ◽  
Author(s):  
Yucheng Qian ◽  
Lina Zhang ◽  
Jihang Wen ◽  
Yanxia Mei ◽  
Jingsun Wei ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Survival Data ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Immune Checkpoints ◽  
Cox Regression Analysis

Abstract ColorColorectal cancer is one of the most common cancer worldwide. Recently, tumor microenvironment (TME), especially its remoulding , is thought to control the colorectal cancer genesis and progression. In this study, we use ESTIMATEscore to make out the proportion of immune and stromal components in colorectal adenocarcinoma (CRA) samples from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were found by COX regression analysis and protein-protein interaction (PPI) network, among which TGFβ1 was supposed to be a prognosis factor and tumor environment indicator. Continuous analysis showed that TGFβ1 expression is positively correlated with lymph node metastasis (N stage) but negatively correlated with survival. Gene Set Enrichment Analysis (GSEA) revealed that the genes of the high-expression TGFβ1 group were mainly enriched in immune-related activities. Cluster analysis divided the samples into 2 subgroups. 24 HLA-related genes and 8 immune checkpoint genes were found upregulated in the high immunity group as well as TGFβ1, which suggests the possibility of novel therapies targeting immune checkpoints combined with TGFβ1. Tumor-infiltrating immune cell (TIC) profile of CRA patients was described by CIBERSORT analysis. Further analysis showed that the infiltration of Tregs and Neutrophils were positively correlated with TGFβ1 high expression. Then 3 TGFβ1-related genes were picked out to construct a prognostic model, which matches the survival data well.

scholarly journals Exploring the Pyroptosis-related Genes of Breast Cancer and their Effects on Tumor Immune Microenvironment

2021 ◽  
Author(s):  
Congli Jia ◽  
Fu Yang ◽  
Ruining Li
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Abstract Background: Breast cancer （BC） is the most common cancer among women, with high rates of metastasis and recurrence. Some studies have confirmed that pyroptosis is an immune-related programmed cell death. However, the correlation between the expression of pyroptosis-related genes in BC and its prognosis remains unclear. Methods: In this study, we identified 38 pyroptosis-related genes that were differentially expressed between BC and normal tissues. The prognostic value of each pyroptosis-related gene was evaluated using patient data from The Cancer Genome Atlas (TCGA). The Cox regression method was performed to establish a prognostic model for 16-gene signature, classifying all BC patients in the TCGA database into a low-or high-risk group. Results: The survival rate of BC patients in the high-risk group was significantly lower than that in the low-risk group (P＜0.01). Prognostic model is independent prognostic factor for BC patients compared to clinical features. Single sample gene set enrichment analysis (ssGSEA) showed a decrease for immune cells and immune function in the high-risk group. Conclusions: Pyroptosis-related genes influence the tumor immune microenvironment and can predict the prognosis of BC.

scholarly journals Significance of m 6A Methyltransferase‑Related lncRNAs in the Prognosis and Immunotherapy of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Lili Li ◽  
Rongrong Xie ◽  
Qichun Wei
Keyword(s):  
Hepatocellular Carcinoma ◽  
Correlation Analysis ◽  
Immune Cells ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Regression Analyses ◽  
Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) methyltransferase, has been proved to act as an oncogene in several human cancers. However, little is known about its relationship with the long non-coding RNAs (lncRNAs) that remains elusive in HCC.Methods: We comprehensively integrated gene expression data acquired from 371 HCC and 50 normal tissues in The Cancer Genome Atlas (TCGA) database. Differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRs) analysis and univariate regression & Kaplan-Meier (K-M) analysis was performed to identify m6A methyltransferase‑related lncRNAs that were related to overall survival (OS). m6A methyltransferase‑related lncRNA signature was constructed using the Least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Furthermore, Cox regression analysis was applied to identify independent prognostic factors in HCC. The signature was validated in an internal validation set. Finally, the correlation analysis between gene signature and immune cells infiltration was also investigated via single-sample Gene Set Enrichment Analysis (ssGSEA) and immunotherapy response was calculated through Tumor Immune Dysfunction and Exclusion (TIDE) algorithm.Results: A total of 21 m6A methyltransferase-related lncRNAs were screened out according to Spearman correlation analysis with the immune score (|R| > 0.3, P < 0.05). We selected 3 prognostic lncRNAs to construct m6A methyltransferase-related lncRNA signature through univariate and LASSO Cox regression analyses. The univariate and multivariate Cox regression analyses demonstrated that the lncRNAs signature was a robust independent prognostic factor in OS prediction with high accuracy. The GSEA also suggested that the m6A methyltransferase-related lncRNAs were involved in the immune-related biological processes and pathways which were very well-known in the context of HCC tumorigenesis. Besides, we found that the lncRNAs signature was strikingly correlated with the tumor microenvironment (TME) immune cells infiltration and expression of critical immune checkpoints. Finally, results from the TIDE analysis revealed that the m6A methyltransferase-related lncRNAs could efficiently predict the clinical response of immunotherapy in HCC.Conclusion: Together, our study screened potential prognostic m6A methyltransferase related lncRNAs and established a novel m6A methyltransferase-based prognostic model of HCC, which not only provides new potential prognostic biomarkers and therapeutic targets but also deepens our understanding of tumor immune microenvironment status and lays a theoretical foundation for immunotherapy.

A signature of Ten Chromodomain Helicase DNA (CHD) Genes Related LncRNAs Predicts Overall Survival in Gastric Cancer Patients

2021 ◽  
Author(s):  
xiaodong Wang ◽  
yaxian Li ◽  
yida Lu ◽  
Yongxiang Li
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Mutations ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Limma Package ◽  
Prognosis Model ◽  
Gastric Cancer Patients

Abstract Background: Gastric cancer (GC) is one of the most common tumors in the world and is often found at an advanced stage. Gene mutations and changes in lncRNA expression often led to the occurrence of GC. In this study, we explored the function of Chromodomain Helicase DNA (CHD) genes and a multi-gene prognostic model was established for its co-expressed lncRNA.Methods: We used the "Limma" package of R software to analyze the expression differences of CHDs genes. Use Kaplan-Meier curve to plot the relationship between risk score and patient survival time. Receiver operating characteristic (ROC) curve was used to describe the reliability of the model. The data used is obtained from The Cancer Genome Atlas (TCGA) database and CellMiner database. Results: We found that CHDs gene mutations had significant statistical differences with tumor mutation load and were involved in biological functions such as DNA winding and transcription. 10 CHDs gene-related lncRNAs were used to establish a prognosis model for GC patients. Univariate and multivariate Cox regression analysis proved that risk score could be an independent prognostic factor. Conclusion: In our study, we developed a novel prognostic model using 10 CHDs-related lncRNAs to predict individualized survival in patients with GC.

scholarly journals Comprehensive analysis of competitive endogenous RNA associated with immune infiltration in lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenjie Chen ◽  
Wen Li ◽  
Zhenkun Liu ◽  
Guangzhi Ma ◽  
Yunfu Deng ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Mast Cells ◽  
Correlation Analysis ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cerna Network ◽  
Monocytes And Macrophages ◽  
Competitive Endogenous Rna

AbstractTo identify the prognostic biomarker of the competitive endogenous RNA (ceRNA) and explore the tumor infiltrating immune cells (TIICs) which might be the potential prognostic factors in lung adenocarcinoma. In addition, we also try to explain the crosstalk between the ceRNA and TIICs to explore the molecular mechanisms involved in lung adenocarcinoma. The transcriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) database, and the hypergeometric correlation of the differently expressed miRNA-lncRNA and miRNA-mRNA were analyzed based on the starBase. In addition, the Kaplan–Meier survival and Cox regression model analysis were used to identify the prognostic ceRNA network and TIICs. Correlation analysis was performed to analysis the correlation between the ceRNA network and TIICs. In the differently expressed RNAs between tumor and normal tissue, a total of 190 miRNAs, 224 lncRNAs and 3024 mRNAs were detected, and the constructed ceRNA network contained 5 lncRNAs, 92 mRNAs and 10 miRNAs. Then, six prognostic RNAs (FKBP3, GPI, LOXL2, IL22RA1, GPR37, and has-miR-148a-3p) were viewed as the key members for constructing the prognostic prediction model in the ceRNA network, and three kinds of TIICs (Monocytes, Macrophages M1, activated mast cells) were identified to be significantly related with the prognosis in lung adenocarcinoma. Correlation analysis suggested that the FKBP3 was associated with Monocytes and Macrophages M1, and the GPI was obviously related with Monocytes and Macrophages M1. Besides, the LOXL2 was associated with Monocytes and Activated mast cells, and the IL22RA1 was significantly associated with Monocytes and Macrophages M1, while the GPR37 and Macrophages M1 was closely related. The constructed ceRNA network and identified Monocytes, Macrophages M1 and activated Mast cells are all prognostic factors for lung adenocarcinoma. Moreover, the crosstalk between the ceRNA network and TIICs might be a potential molecular mechanism involved.

scholarly journals Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongsheng He ◽  
Shengyin Liao ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

Phospholipase C Delta 3 inhibits apoptosis and promotes proliferation, migration, and invasion of thyroid cancer cells via Hippo pathway

2021 ◽  
Vol 53 (4) ◽  
pp. 481-491
Author(s):  
Lizhi Lin ◽  
Jialiang Wen ◽  
Bangyi Lin ◽  
Hao Chen ◽  
Adheesh Bhandari ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Phospholipase C ◽  
Cell Lines ◽  
Epithelial Mesenchymal Transition ◽  
Hippo Pathway ◽  
Suppressive Effect ◽  
The Cancer Genome Atlas ◽  
Disease Stage ◽  
Migration And Invasion ◽  
Mesenchymal Transition

Abstract In recent decades, the incidence of thyroid cancer (TC) has rapidly increased, leading us to explore the complex underlying mechanisms. We identified the gene Phospholipase C Delta 3 (PLCD3) as a potential oncogene in TC by conducting the whole transcriptome sequencing. Our study is to understand the oncogenic role of PLCD3 in TC. We verified the overexpression of PLCD3 in TC from The Cancer Genome Atlas, Gene Expression Omnibus databases, and a locally validated cohort. Clinical correlation analysis showed that PLCD3 expression was related to histological type, T stage, lymph node metastasis (LNM), and disease stage. The high expression of PLCD3 could be a distinguishing factor for TC and its LNM. The biological function was examined using small interfering RNA-transfected TC cell lines. Silenced PLCD3 could inhibit colony formation, migration, and invasion ability and promote apoptosis of TC cell lines. PLCD3 silencing reversed the epithelial-mesenchymal transition but induced the apoptotic progress. Further exploration revealed that PLCD3 might be associated with critical genes of the Hippo pathway. The expressions of RHOA, YAP1/TAZ, and their downstream targets were decreased significantly when PLCD3 was down-regulated. YAP1 overexpression rescued the tumor-suppressive effect caused by PLCD3 silencing. This study demonstrates that PLCD3 is an oncogene that supports tumorigenesis and progression in TC, and PLCD3 may be a potential target gene for TC treatment.

scholarly journals APLN: A potential novel biomarker for cervical cancer

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110113
Author(s):  
Yusha Chen ◽  
Xiaoqian Lin ◽  
Jinwen Zheng ◽  
Jiancui Chen ◽  
Huifeng Xue ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Clinical Stage ◽  
Mapk Signaling ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Receptor Interaction ◽  
Primary Therapy ◽  
Advanced Clinical Stage

Apelin (APLN) is recently demonstrated a direct association with many malignant diseases. However, its effects on cervical cancer remain unclear. This study therefore aims to evaluate the association between APLN expression and cervical cancer using publicly available data from The Cancer Genome Atlas (TCGA). The Pearson χ2 test and Fish exact test, as well as logistic regression, were used to evaluate the relationship between clinicopathological factors in cervical cancer and the expression of APLN. Additionally, the Cox regression and Kaplan-Meier methods were conducted to analyze the Overall Survival (OS) of cervical cancer patients in TCGA. Finally, gene set enrichment analysis (GSEA) was performed to establish its biological functions. High expression of APLN in cervical cancer was significantly associated with a more advanced clinical stage (OR = 1.91 (1.21–3.05) for Stage II, Stage III, and Stage IV vs Stage I, p = 0.006). Additionally, it was associated with poor outcome after primary therapy (OR = 2.14 (1.03–4.59) for Progressive Disease (PD), Stable Disease (SD), and Partial Response (PR) vs Complete Remission (CR), p = 0.045) and high histologic grade (OR = 1.67 (1.03–2.72) for G3 and G4 vs G1 and G2, p = 0.037). Moreover, multivariate analysis showed that high expression of APLN was associated with a shorter OS. GSEA demonstrated that six KEGG pathways, including PPAR signaling, ECM-receptor interaction, focal adhesion, MAPK signaling, TGF-beta signaling, and Gap junction pathways were differentially enriched in the high expression APLN phenotype. The recent study suggests that APLN plays an important role in the progression of cervical cancer and might be a promising prognostic biomarker of the disease.

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