scholarly journals NF-κB-Related Metabolic Gene Signature Predicts the Prognosis and Immunotherapy Response in Gastric Cancer

2022 ◽  
Vol 2022 ◽  
pp. 1-30
Author(s):  
Qiuxiang Chen ◽  
Xiaojing Du ◽  
Sunkuan Hu ◽  
Qingke Huang
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Cancer Metabolism ◽  
Gene Signature ◽  
Sufficient Evidence ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Immune Infiltration ◽  
Related Risk ◽  
Cluster 2

Background. Sufficient evidence indicated the crucial role of NF-κB family played in gastric cancer (GC). The novel discovery that NF-κB could regulate cancer metabolism and immune evasion greatly increased its attraction in cancer research. However, the correlation among NF-κB, metabolism, and cancer immunity in GC still requires further improvement. Methods. TCGA, hTFtarget, and MSigDB databases were employed to identify NF-κB-related metabolic genes (NFMGs). Based on NFMGs, we used consensus clustering to divide GC patients into two subtypes. GSVA was employed to analyze the enriched pathway. ESTIMATE, CIBERSORT, ssGSEA, and MCPcounter algorithms were applied to evaluate immune infiltration in GC. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict patients’ response to immunotherapy. We also established a NFMG-related risk score by using the LASSO regression model and assessed its efficacy in TCGA and GSE62254 datasets. Results. We used 27 NFMGs to conduct an unsupervised clustering on GC samples and classified them into two clusters. Cluster 1 was characterized by high active metabolism, tumor mutant burden, and microsatellite instability, while cluster 2 was featured with high immune infiltration. Compared to cluster 2, cluster 1 had a better prognosis and higher response to immunotherapy. In addition, we constructed a 12-NFMG (ADCY3, AHCY, CHDH, GUCY1A2, ITPA, MTHFD2, NRP1, POLA1, POLR1A, POLR3A, POLR3K, and SRM) risk score. Followed analysis indicated that this risk score acted as an effectively prognostic factor in GC. Conclusion. Our data suggested that GC subtypes classified by NFMGs may effectively guide prognosis and immunotherapy. Further study of these NFMGs will deepen our understanding of NF-κB-mediated cancer metabolism and immunity.

scholarly journals PD-L1 and Immune Infiltration of m6A RNA Methylation Regulators and Its miRNA Regulators in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yingxue Lin ◽  
Yinhui Yao ◽  
Ying Wang ◽  
Lingdi Wang ◽  
Haipeng Cui
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Rna Methylation ◽  
Immune Infiltration ◽  
Significant Difference ◽  
M6a Rna Methylation

Background. The aim of this study was to systematically evaluate the relationship between the expression of m6A RNA methylation regulators and prognosis in HCC. Methods. We compared the expression of m6A methylation modulators and PD-L1 between HCC and normal in TCGA database. HCC samples were divided into two subtypes by consensus clustering of data from m6A RNA methylation regulators. The differences in PD-L1, immune infiltration, and prognosis between the two subtypes were further compared. The LASSO regression was used to build a risk score for m6A modulators. In addition, we identified miRNAs that regulate m6A regulators. Results. We found that fourteen m6A regulatory genes were significantly differentially expressed between HCC and normal. HCC samples were divided into two clusters. Of these, there are higher PD-L1 expression and poorer overall survival (OS) in cluster 1. There was a significant difference in immune cell infiltration between cluster 1 and cluster 2. Through the LASSO model, we obtained 12 m6A methylation regulators to construct a prognostic risk score. Compared with patients with a high-risk score, patients with a low-risk score had upregulated PD-L1 expression and worse prognosis. There was a significant correlation between risk score and tumor-infiltrating immune cells. Finally, we found that miR-142 may be the important regulator for m6A RNA methylation in HCC. Conclusion. Our results suggest that m6A RNA methylation modulators may affect the prognosis through PD-L1 and immune cell infiltration in HCC patients. In addition, the two clusters may be beneficial for prognostic stratification and improving immunotherapeutic efficacy.

scholarly journals Angiogenesis-Related Gene Expression Signatures Predicting Prognosis in Gastric Cancer Patients

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3685
Author(s):  
Haoyu Ren ◽  
Jiang Zhu ◽  
Haochen Yu ◽  
Alexandr Bazhin ◽  
Christoph Westphalen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Clinical Stage ◽  
External Validation ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Related Gene ◽  
Angiogenesis Related Gene

Increasing evidence indicates that angiogenesis is crucial in the development and progression of gastric cancer (GC). This study aimed to develop a prognostic relevant angiogenesis-related gene (ARG) signature and a nomogram. The expression profile of the 36 ARGs and clinical information of 372 GC patients were extracted from The Cancer Genome Atlas (TCGA). Consensus clustering was applied to divide patients into clusters 1 and 2. Least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to identify the survival related ARGs and establish prognostic gene signatures, respectively. The Asian Cancer Research Group (ACRG) (n = 300) was used for external validation. Risk score of ARG signatures was calculated, and a prognostic nomogram was developed. Gene set enrichment analysis of the ARG model risk score was performed. Cluster 2 patients had more advanced clinical stage and shorter survival rates. ARG signatures carried prognostic relevance in both cohorts. Moreover, ARG-risk score was proved as an independent prognostic factor. The predictive value of the nomogram incorporating the risk score and clinicopathological features was superior to tumor, lymph node, metastasis (TNM) staging. The high-risk score group was associated with several cancer and metastasis-related pathways. The present study suggests that ARG-based nomogram could serve as effective prognostic biomarkers and allow a more precise risk stratification.

scholarly journals Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3343
Author(s):  
Mingjun Zheng ◽  
Junyu Long ◽  
Anca Chelariu-Raicu ◽  
Heather Mullikin ◽  
Theresa Vilsmaier ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Malignant Tumors ◽  
Biological Response ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Serous Ovarian Cancer ◽  
Lasso Regression ◽  
Cancer Genome Atlas

(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d’Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.

scholarly journals m6A-Related lncRNA to Develop Prognostic Signature and Predict the Immune Landscape in Bladder Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zuwei Li ◽  
Yuwu Li ◽  
Weizhang Zhong ◽  
Peiyuan Huang
Keyword(s):  
Bladder Cancer ◽  
T Cells ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Risk Score ◽  
Immune Cell ◽  
Clinical Stage ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Cluster 2

Abnormal m6A methylation plays a significant role in cancer progression. Increasingly, researchers have focused on developing lncRNA signatures to evaluate the prognosis of cancer patients. The specific function of m6A-related lncRNAs in the prognosis of bladder cancer patients and the immune microenvironment of bladder cancer remains elusive. Herein, we performed a comprehensive analysis of m6A-related lncRNA prognostic values and their association with the immune microenvironment in bladder cancer using the TCGA dataset. A total of 9 m6A-related lncRNAs were dramatically correlated with overall survival outcomes in bladder cancer. Two molecular subtypes (cluster 1 and cluster 2) were identified by consensus clustering for 9 m6A-related prognostic lncRNAs. Cluster 1 was significantly correlated with poor prognosis, advanced clinical stage, higher PD-L1 expression, a higher ESTIMATEScore and immuneScore, and distinct immune cell infiltration. GSEA revealed the enrichment of apoptosis and the JAK-STAT signaling pathway in cluster 2. A prognostic risk score was constructed using 9 m6A-related prognostic lncRNAs, which functioned as an independent prognostic factor for bladder cancer. Moreover, bladder cancer patients in the low-risk score group had a higher pN stage, pT stage, and clinical stage and a lower tumor grade and immuneScore. The risk score was correlated with the infiltration levels of certain immune cells, including B cells, plasma cells, follicular helper T cells, regulatory T cells, resting NK cells, neutrophils, M0 macrophages, M1 macrophages, and M2 macrophages. Collectively, our study elucidated the important role of m6A-related lncRNAs in the prognosis of bladder cancer patients and in the bladder cancer immune microenvironment. The results suggest that the components of the m6A-related prognostic lncRNA signature might serve as a crucial mediator of the immune microenvironment in bladder cancer, representing promising therapeutic targets for improving immunotherapeutic efficacy.

scholarly journals Exploring an RNA binding proteins-associated prognostic model for Gastric Cancer

2020 ◽  
Author(s):  
Ming Liu ◽  
Jiayi Xie ◽  
Xiaobei Luo ◽  
Yaxin Luo ◽  
Side Liu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Roc Curves ◽  
Low Risk ◽  
Lasso Regression

Abstract Background: Gastric cancer (GC) is one of the most prevalent malignant cancers around the world. Given that abnormal RNA binding proteins (RBPs) are involved in the tumorigenesis, we aimed to explore the potential value of RBPs-associated genes in gastric cancer.Methods: RNA-seq and clinical data were retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed RBPs genes were screened. GO and KEGG pathway enrichment analyses were implemented to elucidate the roles of RBPs in GC. The protein-protein interaction (PPI) networks of RBPs were carried out, and the hub genes were determined by MCODE built in Cytoscape. The TCGA-STAD dataset was randomly divided into training and testing groups. A prognostic signature including five RBPs was developed within the training cohort after Cox regression and Lasso regression analyses. We used Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves to evaluate the capacity of the model in both groups. Then, a nomogram based on hub RBPs expression was established. Gene Set Enrichment Analysis was performed between the high-risk and low-risk group.Results: A total of 166 up-regulated RBPs and 130 down-regulated RBPs were identified. Via Cox regression and Lasso regression analysis within the training group, five hub RBPs (RNASE1, SETD7, BOLL, PPARGC1B, MSI2) were screened and the prognostic model was constructed. The risk score was calculated and gastric cancer patients were divided into high-risk and low-risk groups. In multivariate analysis, risk score was still an independent prognostic indicator (HR = 1.80, 95% CI = 1.45-2.22, P < 0.01). Patients with low risk had favorable survival rate in both training and testing group compared to those at high risk (P < 0.001). The areas under the ROC curves (AUC) of the prognostic model are 0.718 in the training cohort and 0.651 in the testing cohort. The hub RBPs-based nomogram model exhibited excellent ability to predict the OS of GC. GSEA illustrated that several cancer-related signaling pathways were enriched in patients with a high-risk score.Conclusions: This study discovered a five RBPs signature which might provide a potential prognostic value to GC patients.

N6-methyladenosine-related lncRNAs as a biomarker for predicting the prognosis and immune microenvironment of clear cell renal cell carcinoma

2021 ◽  
Author(s):  
Yuqin Qiu ◽  
Xiaogang Wang ◽  
Zhenjia Fan ◽  
Shanhui Zhang ◽  
Jinchang Huang ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Model ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Cell Renal Cell Carcinoma ◽  
Cluster 2

Abstract Background: Patients with advanced clear cell renal cell carcinoma (ccRCC) have a poor prognosis and lack effective prognostic biomarkers. This study uses bioinformatics analysis to identify N6-methyladenosine-related lncRNAs (m6A-related lncRNAs) as new prognostic biomarkers for ccRCC.Methods: Gene expression data and related clinical information of ccRCC patients were extracted from the Cancer Genome Atlas Database. m6A-related lncRNAs were obtained by co-expression analysis. Univariate Cox regression analysis was performed on these lncRNAs to find the prognostic-related m6A-related lncRNAs, and consensus clustering analysis was performed. The prognostic signature was screened by LASSO regression and a prognostic model was constructed. The predictive performance of the prognostic model was evaluated and validated by survival analysis and ROC curve analysis, etc. In addition, we also systematically analyzed the expression of immune checkpoints and immune cell infiltration in ccRCC patients.Results: First, 27 m6A-related lncRNAs associated with prognosis were identified, which were significantly differentially expressed between tumor and normal tissues. Consensus clustering analysis indicated that cluster 2 was associated with poor prognosis, low stromal score, high expression of PD-1, PD-L1, CTLA-4, LAG-3, TIM-3, TIGIT, and immunosuppressive cell infiltration. The signaling pathways related to tumor progression, drug resistance, and angiogenesis and biological processes related to protein methylation and phosphatidic acid metabolism are significantly enriched in cluster 2. Subsequently, LASSO regression analysis was used to construct a prognostic risk model based on 7 m6A-related lncRNAs signature, which can be used as an independent prognostic indicator. After a series of analyses, it was shown that this model had good sensitivity and specificity, can predict the prognosis of patients with different clinical stratifications and was associated with the progression of ccRCC. The expression levels of immune checkpoints were significantly increased in high-risk patients, and there was a certain correlation between the risk score and immune cell infiltration. Conclusions: In summary, we constructed and validated a risk model that can independently predict the prognosis of ccRCC patients and reflect the immune microenvironment based on m6A-related lncRNAs; the model is conducive to the screening of biomarkers of ccRCC prognosis and may have the potential to reflect the response of ccRCCs to immunotherapy.

scholarly journals Identification and Validation of a Novel Pyroptosis-Related Gene Signature for Prognosis Prediction in Soft Tissue Sarcoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Lin Qi ◽  
Ruiling Xu ◽  
Lu Wan ◽  
Xiaolei Ren ◽  
WenChao Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Risk Score ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Survival Difference ◽  
Related Risk

Soft tissue sarcoma (STS) represents an uncommon and heterogenous group of malignancies, and poses substantial therapeutic challenges. Pyroptosis has been demonstrated to be related with tumor progression and prognosis. Nevertheless, no studies exist that delineated the role of pyroptosis-related genes (PRGs) in STS. In the present study, we comprehensively and systematically analyzed the gene expression profiles of PRGs in STS. The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were utilized to identify differentially expressed PRGs. In total, 34 PRGs were aberrantly expressed between STS and normal tissues. Several PRGs were validated with RT-qPCR. Consensus clustering analysis based on PRGs was conducted to divide STS patients into two clusters, and significant survival difference was observed between two distinct clusters (p = 0.019). Differentially expressed genes (DEGs) were identified between pyroptosis-related clusters. Based on the least absolute shrinkage and selection operator (LASSO) COX regression analysis, the pyroptosis-related gene signature with five key DEGs was constructed. The high pyroptosis-related risk score group of TCGA cohort was characterized by poorer prognosis (p &lt; 0.001), with immune infiltration and function significantly decreased. For external validation, STS patients from Gene Expression Omnibus (GEO) were grouped according to the same cut-off point. The survival difference between two risk groups of GEO cohort was also significant (p &lt; 0.001). With the combination of clinical characteristics, pyroptosis-related risk score was identified to serve as an independent prognostic factor for STS patients. In conclusion, this study provided a comprehensive overview of PRGs in STS and the potential role in prognosis, which could be an important direction for future studies.

scholarly journals Transcriptome analysis reveals the prognostic and immune infiltration characteristics of glycolysis and hypoxia in head and neck squamous cell carcinoma

2021 ◽  
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Wenli Li
Keyword(s):  
Squamous Cell Carcinoma ◽  
Risk Score ◽  
Predictive Ability ◽  
Gene Signature ◽  
Risk Scores ◽  
Combinatorial Screening ◽  
Immune Infiltration ◽  
Cancer Hallmarks ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Abstract Background This study aims to construct a new prognostic gene signature based on cancer hallmarks for patients with Head and neck squamous cell carcinoma (HNSCC). Method The transcriptome profiling data and hallmark gene sets in the Molecular Signatures Database was used to explore the cancer hallmarks most relevant to the prognosis of HNSCC patients. Differential gene expression analysis, weighted gene co-expression network analysis, univariate COX regression analysis, random forest algorithm and multiple combinatorial screening were used to construct the prognostic gene signature. The predictive ability of gene signature was verified in the TCGA HNSCC cohort as the training set and the GEO HNSCC cohorts (GSE41613 and GSE42743) as the validation sets, respectively. Moreover, the correlations between risk scores and immune infiltration patterns, as well as risk scores and genomic changes were explored. Results A total of 3391 differentially expressed genes in HNSCC were screened. Glycolysis and hypoxia were screened as the main risk factors for OS in HNSCC. Using univariate Cox analysis, 97 prognostic candidates were identified (P<0.05). Top 10 important genes were then screened out by random forest. Using multiple combinatorial screening, a combination with less genes and more significant P value was used to construct the prognostic gene signature (RNF144A, STC1, P4HA1, FMNL3, ANO1, BASP1, MME, PLEKHG2 and DKK1). Kaplan-Meier analysis showed that patients with higher risk scores had worse overall survival (p <0.001). The ROC curve showed that the risk score had a good predictive efficiency (AUC> 0.66). Subsequently, the predictive ability of the risk score was verified in the validation sets. Moreover, the two-factor survival analysis combining the cancer hallmarks and risk scores suggested that HNSCC patients with the high hypoxia or glycolysis & high risk-score showed the worst prognosis. Besides, a nomogram based on the nine-gene signature was established for clinical practice. Furthermore, the risk score was significantly related to tumor immune infiltration profiles and genome changes. Conclusion This nine-gene signature associated with glycolysis and hypoxia can not only be used for prognosis prediction and risk stratification, but also may be a potential therapeutic target for patients with HNSCC.

scholarly journals Development and Validation of a Novel Glycolysis-Related Risk Signature for Predicting Survival in Pancreatic Adenocarcinoma

2020 ◽  
Author(s):  
Ang Li ◽  
Sinan Hou ◽  
Jian Chen ◽  
Huimin Hou ◽  
Yanfang Jiang
Keyword(s):  
Regression Analysis ◽  
Pancreatic Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Related Risk

Abstract Background Pancreatic adenocarcinoma (PAAD) is one of the leading causes of cancer death worldwide. Through data mining, an increasing number of biomarkers have been identified for predicting survival of PAAD. However, the ability of single gene biomarkers to predict patient survival is still insufficient. This study aimed to develop a novel risk signature for predicting survival of PAAD. Methods mRNA expression profiling was performed in a large PAAD cohort (N = 177) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was analyzed to detect whether the gene sets showed statistically differences between PAAD and adjacent normal tissues. Univariate Cox regression analysis was used to analyze and identify genes related to overall survival (OS), then subjected to multivariable Cox regression to further confirm the prognostic genes and obtain the coefficients. The expression level of selected genes weighted by their coefficients through linearly combining, we constructed a risk score formula for prognostic prediction. The three-mRNA signature for survival prediction is validated by Kaplan–Meier curve analysis. Results We demonstrated that a set of three genes (KIF20A, CHST2, and MET) were significantly associated with OS. Based on this three-gene signature, 177 PAAD patients were classified into high-risk groups and low-risk groups using the median risk score as cut off value. Additionally, multivariate Cox regression analysis revealed that the three-gene signature had independent prognostic value. Conclusions To our best knowledge, we first develop a glycolysis-related risk signature for predicting survival of pancreatic adenocarcinoma. The findings provide insight into identification of patients with poor prognosis in PAAD and improve novel therapy targets for this disease.

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