scholarly journals Identification of a prognostic ferroptosis-related lncRNA signature in the tumor microenvironment of lung adenocarcinoma

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yugang Guo ◽  
Zhongyu Qu ◽  
Dandan Li ◽  
Fanghui Bai ◽  
Juan Xing ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Proportional Hazards ◽  
Risk Model ◽  
Principal Component ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Poor Overall Survival

AbstractFerroptosis is closely linked to various cancers, including lung adenocarcinoma (LUAD); however, the factors involved in the regulation of ferroptosis-related genes are not well established. In this study, we identified and characterized ferroptosis-related long noncoding RNAs (lncRNAs) in LUAD. In particular, a coexpression network of ferroptosis-related mRNAs and lncRNAs from The Cancer Genome Atlas (TCGA) was constructed. Univariate and multivariate Cox proportional hazards analyses were performed to establish a prognostic ferroptosis-related lncRNA signature (FerRLSig). We obtained a prognostic risk model consisting of 10 ferroptosis-related lncRNAs: AL606489.1, AC106047.1, LINC02081, AC090559.1, AC026355.1, FAM83A-AS1, AL034397.3, AC092171.5, AC010980.2, and AC123595.1. High risk scores according to the FerRLSig were significantly associated with poor overall survival (hazard ratio (HR) = 1.412, 95% CI = 1.271–1.568; P < 0.001). Receiver operating characteristic (ROC) curves and a principal component analysis further supported the accuracy of the model. Next, a prognostic nomogram combining FerRLSig with clinical features was established and showed favorable predictive efficacy for survival risk stratification. In addition, gene set enrichment analysis (GSEA) revealed that FerRLSig is involved in many malignancy-associated immunoregulatory pathways. Based on the risk model, we found that the immune status and response to immunotherapy, chemotherapy, and targeted therapy differed significantly between the high-risk and low-risk groups. These results offer novel insights into the pathogenesis of LUAD, including the contribution of ferroptosis-related lncRNAs, and reveal a prognostic indicator with the potential to inform immunological research and treatment.

scholarly journals Comprehensive Analysis of the Immune Infiltrates of Pyroptosis in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhuolun Sun ◽  
Changying Jing ◽  
Xudong Guo ◽  
Mingxiao Zhang ◽  
Feng Kong ◽  
...  
Keyword(s):  
High Risk ◽  
Cell Carcinoma ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Kidney renal clear cell carcinoma (KIRC) has long been identified as a highly immune-infiltrated tumor. However, the underlying role of pyroptosis in the tumor microenvironment (TME) of KIRC remains poorly described. Herein, we systematically analyzed the prognostic value, role in the TME, response to ICIs, and drug sensitivity of pyroptosis-related genes (PRGs) in KIRC patients based on The Cancer Genome Atlas (TCGA) database. Cluster 2, by consensus clustering for 24 PRGs, presented a poor prognosis, likely because malignancy-related hallmarks were remarkably enriched. Additionally, we constructed a prognostic prediction model that discriminated well between high- and low-risk patients and was further confirmed in external E-MTAB-1980 cohort and HSP cohort. By further analyzing the TME based on the risk model, higher immune cell infiltration and lower tumor purity were found in the high-risk group, which presented a poor prognosis. Patients with high risk scores also exhibited higher ICI expression, indicating that these patients may be more prone to profit from ICIs. The sensitivity to anticancer drugs that correlated with model-related genes was also identified. Collectively, the pyroptosis-related prognosis risk model may improve prognostic information and provide directions for current research investigations on immunotherapeutic strategies for KIRC patients.

scholarly journals Expression Profile and Prognostic Values of SMC Family Members in HCC

2021 ◽  
Author(s):  
Wei Yan ◽  
Dan-dan Wang ◽  
He-da Zhang ◽  
Jinny Huang ◽  
Jun-Chen Hou ◽  
...  
Keyword(s):  
Gene Family ◽  
Expression Profile ◽  
Proportional Hazards ◽  
Wide Spectrum ◽  
Clinical Stage ◽  
Family Members ◽  
Enrichment Analysis ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas

Abstract Background: The structural maintenance of chromosome (SMC) gene family, comprising 6 members, is involved in a wide spectrum of biological functions in many types of human cancers. However, there is little research on the expression profile and prognostic values of SMC genes in hepatocellular carcinoma (HCC). Based on updated public resources and integrative bioinformatics analysis, we tried to determine the value of SMC gene expression in predicting the risk of developing HCC. Methods and materials: The expression data of SMC family members were obtained from The Cancer Genome Atlas (TCGA). The prognostic values of SMC members and clinical features were identified. A gene set enrichment analysis (GSEA) was conducted to explore the mechanism underlying the involvement of SMC members in liver cancer. The associations between tumor immune infiltrating cells (TIICs) and the SMC family members were evaluated using the Tumor Immune Estimation Resource (TIMER) database. Results: Our analysis demonstrated that mRNA downregulation of SMC genes was common alteration in HCC patients. SMC1A, SMC2, SMC3, SMC4, SMC6 were upregulated in HCC. Upregulation of SMC2, SMC3 and SMC4, along with clinical stage, were associated with a poor HCC prognosis based on the results of univariate and multivariate Cox proportional hazards regression analyses. SMC2, SMC3 and SMC4 are also related to tumor purity and immune infiltration levels of HCC. The GSEA results indicated that SMC members participate in multiple biological processes underlying tumorigenesis. Conclusion: This study comprehensively analyzed the expression of SMC gene family members in patients with HCC. This can provide insights for further investigation of the SMC family members as potential targets in HCC and suggest that the use of SMC inhibitor targeting SMC2, SMC3 and SMC4 may be an effective strategy for HCC therapy.

scholarly journals Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Liang-Hao Zhang ◽  
Long-Qing Li ◽  
Yong-Hao Zhan ◽  
Zhao-Wei Zhu ◽  
Xue-Pei Zhang
Keyword(s):  
Bladder Cancer ◽  
Stromal Cells ◽  
Molecular Mechanisms ◽  
Proportional Hazards ◽  
Enrichment Analysis ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Validation Set

BackgroundIdentify immune-related gene pairs (IRGPs) signature related to the prognosis and immunotherapeutic efficiency for bladder cancer (BLCA) patients.Materials and MethodsOne RNA-seq dataset (The Cancer Genome Atlas Program) and two microarray datasets (GSE13507 and GSE31684) were included in this study. We defined these cohorts as training set to construct IRGPs and one immunotherapy microarray dataset as validation set. Identifying BLCA subclasses based on IRGPs by consensus clustering. The Lasso penalized Cox proportional hazards regression model was used to construct prognostic signature and potential molecular mechanisms were analyzed.ResultsThis signature can accurately predict the overall survival of BLCA patients and was verified in the immunotherapy validation set. IRGP-signatures can be used as independent prognostic risk factor in various clinical subgroups. Use the CIBERSORT algorithm to assess the abundance of infiltrating immune cells in each sample, and combine the results of the gene set enrichment analysis of a single sample to explore the differences in the immune microenvironment between IRPG signature groups. According to the results of GSVA, GSEA, and CIBERSORT algorithm, we found that IRGP is strikingly positive correlated with tumor microenvironment (TME) stromal cells infiltration, indicating that the poor prognosis and immunotherapy might be caused partly by enrichment of stromal cells. Finally, the results from the TIDE analysis revealed that IRGP could efficiently predict the response of immunotherapy in BLCA.ConclusionThe novel IRGP signature has a significant prognostic value for BLCA patients might facilitate personalized for immunotherapy.

scholarly journals Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24

2021 ◽  
Author(s):  
Roshan A. Karunamuni ◽  
Minh-Phuong Huynh-Le ◽  
Chun C. Fan ◽  
Wesley Thompson ◽  
Asona Lui ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
African Ancestry ◽  
Prostate Cancer Risk ◽  
Model Performance ◽  
Principal Component ◽  
Cox Proportional Hazards ◽  
Risk Scores ◽  
Improve Model ◽  
Improve Model Performance ◽  
Improve Health

AbstractWe previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ. Genotypic data were obtained from PRACTICAL consortium for 6,253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with performance of PHS46+African. A calibration factor (CF) was formulated using estimated Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC. CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our dataset with 1000 Genomes, we identified statistically significant associations between continental and calibration groupings. In conclusion, we identified PCs within 8q24 SNP window that were strongly associated with performance of PHS46+African. Further research to improve clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry

scholarly journals Construction of a Novel Signature and Prediction of the Immune Landscape in Soft Tissue Sarcomas Based on N6-Methylandenosine-Related LncRNAs

2021 ◽  
Vol 8 ◽  
Author(s):  
Li Zhang ◽  
Xianzhe Tang ◽  
Jia Wan ◽  
Xianghong Zhang ◽  
Tao Zheng ◽  
...  
Keyword(s):  
High Risk ◽  
Soft Tissue ◽  
Cox Regression ◽  
Soft Tissue Sarcomas ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Consensus Clustering ◽  
Immune Microenvironment

Background: N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) are critically involved in cancer development. However, the roles and clinical significance of m6A-related lncRNAs in soft tissue sarcomas (STS) are inconclusive, thereby warranting further investigations.Methods: Transcriptome profiling data were extracted from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). Consensus clustering was employed to divide patients into clusters and Kaplan–Meier analysis was used to explore the prognostic differences between the subgroups. Gene set enrichment analysis (GSEA) was conducted to identify the biological processes and signaling pathways associated with m6A-Related lncRNAs. Finally, patients were randomly divided into training and validation cohorts, and least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to establish the m6A-related lncRNA-based risk signature.Results: A total of 259 STS patients from TCGA-SARC dataset were enrolled in our study. Thirteen m6A-Related lncRNAs were identified to be closely related to the prognosis of STS patients. Patients were divided into two clusters, and patients in cluster 2 had a better overall survival (OS) than those in cluster 1. Patients in different clusters also showed differences in immune scores, infiltrating immune cells, and immune checkpoint expression. Patients were further classified into high-risk and low-risk subgroups according to risk scores, and high-risk patients were found to have a worse prognosis. The receiver operating characteristic (ROC) curve indicated that the risk signature displayed excellent performance at predicting the prognosis of patients with STS. Further, the risk signature was remarkably connected with the immune microenvironment and chemosensitivity in STS.Conclusion: Our study demonstrated that m6A-related lncRNAs were significantly associated with prognosis and tumor immune microenvironment and could function as independent prognosis-specific predictors in STS, thereby providing novel insights into the roles of m6A-related lncRNAs in STS.

scholarly journals Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma

2020 ◽  
Vol 21 (22) ◽  
pp. 8479
Author(s):  
Zhaodong Li ◽  
Fangyuan Qi ◽  
Fan Li
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Proportional Hazards ◽  
Risk Model ◽  
Expression Profiles ◽  
Clinical Stage ◽  
Gene Signature ◽  
Risk Scores ◽  
Survival Prediction ◽  
Signature Genes

Accumulating evidence indicates that the reliable gene signature may serve as an independent prognosis factor for lung adenocarcinoma (LUAD) diagnosis. Here, we sought to identify a risk score signature for survival prediction of LUAD patients. In the Gene Expression Omnibus (GEO) database, GSE18842, GSE75037, GSE101929, and GSE19188 mRNA expression profiles were downloaded to screen differentially expressed genes (DEGs), which were used to establish a protein-protein interaction network and perform clustering module analysis. Univariate and multivariate proportional hazards regression analyses were applied to develop and validate the gene signature based on the TCGA dataset. The signature genes were then verified on GEPIA, Oncomine, and HPA platforms. Expression levels of corresponding genes were also measured by qRT-PCR and Western blotting in HBE, A549, and PC-9 cell lines. The prognostic signature based on eight genes (TTK, HMMR, ASPM, CDCA8, KIF2C, CCNA2, CCNB2, and MKI67) was established, which was independent of other clinical factors. The risk model offered better discrimination between risk groups, and patients with high-risk scores tended to have poor survival rate at 1-, 3- and 5-year follow-up. The model also presented better survival prediction in cancer-specific cohorts of age, gender, clinical stage III/IV, primary tumor 1/2, and lymph node metastasis 1/2. The signature genes, moreover, were highly expressed in A549 and PC-9 cells. In conclusion, the risk score signature could be used for prognostic estimation and as an independent risk factor for survival prediction in patients with LUAD.

scholarly journals Identification and Validation of a Ferroptosis-Related Long Non-coding RNA Signature for Predicting the Outcome of Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhiyuan Zheng ◽  
Qian Zhang ◽  
Wei Wu ◽  
Yan Xue ◽  
Shuhan Liu ◽  
...  
Keyword(s):  
Immune Response ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Information Criteria ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Regression Analyses ◽  
Cox Regression Analysis ◽  
Kaplan Meier

BackgroundFerroptosis is a recently recognized type of programmed cell death that is involved in the biological processes of various cancers. However, the mechanism of ferroptosis in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the role of ferroptosis-associated long non-coding RNAs (lncRNAs) in LUAD and to establish a prognostic model.MethodsWe downloaded ferroptosis-related genes from the FerrDb database and RNA sequencing data and clinicopathological characteristics from The Cancer Genome Atlas. We randomly divided the data into training and validation sets. Ferroptosis-associated lncRNA signatures with the lowest Akaike information criteria were determined using COX regression analysis and the least absolute shrinkage and selection operator. The risk scores of ferroptosis-related lncRNAs were calculated, and patients with LUAD were assigned to high- and low-risk groups based on the median risk score. The prognostic value of the risk scores was evaluated using Kaplan–Meier curves, Cox regression analyses, and nomograms. We then explored relationships between ferroptosis-related lncRNAs and the immune response using gene set enrichment analysis (GSEA).ResultsTen ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan–Meier and Cox regression analyses confirmed that the risk scores were independent predictors of LUAD outcome in the training and validation sets (all P &lt; 0.05). The area under the curve confirmed that the signatures could determine the prognosis of LUAD. The predictive accuracy of the established nomogram model was verified using the concordance index and calibration curve. The GSEA showed that the 10 ferroptosis-related lncRNAs might be associated with tumor immune response.ConclusionWe established a novel signature involving 10 ferroptosis-related lncRNAs (LINC01843, MIR193BHG, AC091185.1, AC027031.2, AL021707.2, AL031667.3, AL606834.1, AC026355.1, AC124045.1, and AC025048.4) that can accurately predict the outcome of LUAD and are associated with the immune response. This will provide new insights into the development of new therapies for LUAD.

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Author(s):  
Yinglian Pan ◽  
LiPing Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Risk Groups ◽  
Cox Proportional Hazards ◽  
Low Risk ◽  
Patient Choice ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment

Abstract Background: Ovarian cancer (OV) is the most common type of primary female reproductive cancer. BRCA1/2 gene is an important biomarker for evaluating the risk of OV, breast cancer and other related tumors and influences patient choice of individualized treatment. A powerful signature to predict OV prognosis and improve treatment personalization is urgently needed. This study aimed to identify a novel OV-related lncRNA prognostic biomarker.Methods: A Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from The Cancer Genome Atlas (TCGA) database. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was assessed, and the sensitivity and specificity of the prediction model were determined.Results: The signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as a criterion for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The 3-year overall survival (OS) rates for the high- and low-risk groups were approximately 38% and 100%, respectively. Chemotherapy treatment survival rates indicated that high-risk groups had significantly shorter OS rates with adjuvant chemotherapy than the low-risk groups. The OS of 1-, 3- and 5- years were 100%, 40%, and 15% in the high-risk groups respectively. The survival rate of the high-risk group declined rapidly after two years of OA chemotherapy treatment. In addition, multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development.Conclusion: In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with in BRCA1/2 mutations to predict prognosis and chemotherapy efficiency.

scholarly journals Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Chuanchuan Zhan ◽  
Zichu Wang ◽  
Chao Xu ◽  
Xiao Huang ◽  
Junzhou Su ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.

scholarly journals Identification and validation of autophagy-related lncRNA prognostic signature for breast cancer

2020 ◽  
Author(s):  
Xiaoying Li ◽  
Feng Jin ◽  
Yang Li
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Prognostic Signature ◽  
Roc Curve Analysis

Abstract Background: Long noncoding RNAs (lncRNAs) are emerging as crucial regulators to the development of breast cancer and are involved in controlling autophagy. LncRNAs are also widely known as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy-related lncRNAs with prognostic value in breast cancer. Methods: A coexpression network of autophagy-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA) was constructed. Univariate and multivariate Cox proportional hazards analyses were used to identify an autophagy risk model with prognostic value. Kaplan-Meier analysis, univariate and multivariate Cox regressionanalyses and time-dependent receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation were conducted to analyze the risk model.Results: In this study, autophagy-related lncRNAs in breast cancer were identified. We evaluated the prognostic value of these autophagy-related lncRNAs and eventually obtained a prognostic risk model consisting of 11 autophagy-related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2-DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further verified as a novel independent prognostic factor for breast cancer patients based on the calculated risk score. Moreover, based on the risk model, the low risk and high risk groups displayed different autophagy and oncogenic statues. Conclusions: These findings suggested that the risk model of the 11 autophagy-related lncRNAs has significant prognostic value for breast cancer and might be a promising prognostic signature and therapeutic targets in clinical practice.

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