scholarly journals Identification of an Metabolic Related Risk Signature Predicts Prognosis in Cervical Cancer and Correlates With Immune Infiltration

2021 ◽  
Vol 9 ◽  
Author(s):  
Chunliang Shang ◽  
Jiaming Huang ◽  
Hongyan Guo
Keyword(s):  
Cervical Cancer ◽  
Metabolic Pathways ◽  
Lipid Droplets ◽  
Immune Status ◽  
Cox Regression ◽  
Metabolic Reprogramming ◽  
Immune Microenvironment ◽  
Cox Regression Analysis ◽  
Metabolic Gene Expression ◽  
Related Risk

The tumor metabolic reprogramming contributes to the progression and prognosis of cervical cancer (CC). However, the potential remodeling mechanisms of tumor metabolism in the immune microenvironment of CC remain largely unknown. In this study, we first performed microarray analysis to identify differential metabolic gene expression. A novel 5-metabolic-related genes (MRGs) signature comprising P4HA1, P4HA2, ABL2, GLTP, and CYP4F12 was established to better predict prognosis of CC using LASSO-Cox regression analysis. This signature could reveal the metabolic features and monitor the immune status of tumor microenvironment (TME). Among them, P4HA2 was significantly upregulated in CC tissues and negatively correlated with CD8+T cells. Knockdown of P4HA2 inhibited lipid droplets (LDs) accumulation and cancer cells invasion. Moreover, P4HA2 knockdown significantly suppressed PD-L1 expression. This study provides a new and feasible method for evaluating the prognosis of CC and explores the potential value to navigate metabolic pathways to enhance anti-tumor immunity and immunotherapy.

scholarly journals Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Hu Qian ◽  
Ting Lei ◽  
Yihe Hu ◽  
Pengfei Lei
Keyword(s):  
Lipid Metabolism ◽  
Bone Remodeling ◽  
Immune Status ◽  
Risk Model ◽  
Cox Regression ◽  
Survival Prediction ◽  
Molecular Subgroups ◽  
Immune Microenvironment ◽  
Cox Regression Analysis ◽  
Lipid Metabolism Genes

ObjectivesOsteosarcoma was the most popular primary malignant tumor in children and adolescent, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past 35 years. This study aims to explore the role of lipid metabolism in the development and diagnosis of osteosarcoma.MethodsClinical information and corresponding RNA data of osteosarcoma patients were downloaded from TRGET and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the tumor immune microenvironment (TIME) and immune status of the identified subgroups. Functional analyses including GO, KEGG, GSVA and GSEA analyses were conducted to elucidate the underlying mechanisms. Prognostic risk model was constructed using LASSO algorithm and multivariate Cox regression analysis.ResultsTwo molecular subgroups with significantly different survival were identified. Better prognosis was associated with high immune score, low tumor purity, high abundance of immune infiltrating cells and relatively high immune status. GO and KEGG analyses revealed that the DEGs between the two subgroups were mainly enriched in immune- and bone remodeling-associated pathways. GSVA and GSEA analyses indicated that, lipid catabolism downregulation and lipid hydroxylation upregulation may impede the bone remodeling and development of immune system. Risk model based on lipid metabolism related genes (LMRGs) showed potent potential for survival prediction in osteosarcoma. Nomogram integrating risk model and clinical characteristics could predict the prognosis of osteosarcoma patients accurately.ConclusionExpression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately.

scholarly journals A lipid metabolism-related genes prognosis biomarker associated with the tumor immune microenvironment in colorectal carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chao Yang ◽  
Shuoyang Huang ◽  
Fengyu Cao ◽  
Yongbin Zheng
Keyword(s):  
Lipid Metabolism ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Risk Groups ◽  
Clinical Parameter ◽  
Gene Expression Omnibus ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment

Abstract Background and aim Lipid metabolic reprogramming is considered to be a new hallmark of malignant tumors. The purpose of this study was to explore the expression profiles of lipid metabolism-related genes (LMRG) in colorectal cancer (CRC). Methods The lipid metabolism statuses of 500 CRC patients from the Cancer Genome Atlas (TCGA) and 523 from the Gene Expression Omnibus (GEO GSE39582) database were analyzed. The risk signature was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression. Results A novel four-LMRG signature (PROCA1, CCKBR, CPT2, and FDFT1) was constructed to predict clinical outcomes in CRC patients. The risk signature was shown to be an independent prognostic factor for CRC and was associated with tumour malignancy. Principal components analysis demonstrated that the risk signature could distinguish between low- and high-risk patients. There were significantly differences in abundances of tumor-infiltrating immune cells and mutational landscape between the two risk groups. Patients in the low-risk group were more likely to have higher tumor mutational burden, stem cell characteristics, and higher PD-L1 expression levels. Furthermore, a genomic-clinicopathologic nomogram was established and shown to be a more effective risk stratification tool than any clinical parameter alone. Conclusions This study demonstrated the prognostic value of LMRG and showed that they may be partially involved in the suppressive immune microenvironment formation.

scholarly journals Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Xiaohong - Liu ◽  
Qian - Xu ◽  
Zi-Jing - Li ◽  
Bin - Xiong
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Metabolic Reprogramming ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract BackgroundMetabolic reprogramming is an important hallmark in the development of malignancies. Numerous metabolic genes have been demonstrated to participate in the progression of hepatocellular carcinoma (HCC). However, the prognostic significance of the metabolic genes in HCC remains elusive. MethodsWe downloaded the gene expression profiles and clinical information from the GEO, TCGA and ICGC databases. The differently expressed metabolic genes were identified by using Limma R package. Univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) Cox regression analysis were utilized to uncover the prognostic significance of metabolic genes. A metabolism-related prognostic model was constructed in TCGA cohort and validated in ICGC cohort. Furthermore, we constructed a nomogram to improve the accuracy of the prognostic model by using the multivariate Cox regression analysis.ResultsThe high-risk score predicted poor prognosis for HCC patients in the TCGA cohort, as confirmed in the ICGC cohort (P < 0.001). And in the multivariate Cox regression analysis, we observed that risk score could act as an independent prognostic factor for the TCGA cohort (HR (hazard ratio) 3.635, 95% CI (confidence interval)2.382-5.549) and the ICGC cohort (HR1.905, 95%CI 1.328-2.731). In addition, we constructed a nomogram for clinical use, which suggested a better prognostic model than risk score.ConclusionsOur study identified several metabolic genes with important prognostic value for HCC. These metabolic genes can influence the progression of HCC by regulating tumor biology and can also provide metabolic targets for the precise treatment of HCC.

scholarly journals Identification and Validation of a Glycolysis Related Metabolic Reprogramming Gene Signature for Predicting Survival in Colon Cancer Patients

2021 ◽  
Author(s):  
Gang Liu ◽  
Xiaowang WU ◽  
Jian Chen
Keyword(s):  
Colon Cancer ◽  
Prediction Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Gene Signature ◽  
Metabolic Reprogramming ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Prognosis Model ◽  
The Relationship

Abstract Background Colon cancer (CC) is one of the most common gastrointestinal malignant tumors with high mortality rate. Because of malignancy and easily metastasis feather, and limited treatments, the prognosis of CC remains poor. Glycolysis is a metabolic process of glucose in anoxic environments which is an important way to provide energy for tumor. The role of glycolysis in CC largely remains unknown and is necessary to be explored. Method In our study, we analyzed glycolysis related genes expression in CC, patients gene expression and corresponding clinical data were downloaded from GEO dataset, glycolysis related genes sets were collected from Msigdb. Through COX regression analysis, prognosis model based on glycolysis-related genes was established. The efficacy of gene model was tested by Survival analysis, ROC analysis and PCA analysis. Furthermore, the relationship between risk scores and clinical characteristic was researched. Results Our findings identified 13 glycolysis related genes (NUP107, SEC13, ALDH7A1, ALG1, CHPF, FAM162A, FBP2, GALK1, IDH1, TGFA, VLDLR, XYLT2 and OGDHL) consisted prognostic prediction model with relative high accuracy. The relationship between prediction model and clinical feathers were specifically studied, results showed age > 65years, TNM III-IV, T3-4, N1-3, M1 and high-risk score were independent prognostic risk factors with poorer prognosis. Finally, model genes were significantly expressed and EMT were activated in CC patients. Conclusion This study provided a new aspect to advance our understanding in the potential mechanism of glycolysis in CC.

Identification of the Pyroptosis-related Signature for Predicting Prognosis in Cervical Cancer

2021 ◽  
Author(s):  
Cankun Zhou ◽  
Chaomei Li ◽  
Yuhua Zheng ◽  
Xiaochun Liu
Keyword(s):  
Cervical Cancer ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Experimental Studies ◽  
Risk Groups ◽  
Core Gene ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

Abstract Background: Cervical cancer (CC) is one of the most common malignancies in gynecology. There is still a lack of specific biomarkers for the diagnosis and prognosis of CC. Pyroptosis is one of the methods of programmed cell death, and its various components are related to the occurrence, invasion, and metastasis of tumors. However, the role of pyroptosis in CC has not yet been elucidated.Methods: This study focuses on the development of a prognostic signature associated with pyroptosis for CC patients using integrated bioinformatics to elucidate the relationship between the signature and the tumor microenvironment and immune response.Results: We identified a prognostic signature based on eight pyroptosis-related genes (PRGs), with better prognostic survival in the low-risk group (P<0.05) and AUC values greater than 0.7. The results of the multi-factor Cox regression analysis indicated that the signature could be used as an independent prognostic factor, and both the DCA and the Nomogram suggested that the prognostic signature had good predictive power. Interestingly, this prognostic signature can also be applied to multiple tumors. In addition, the tumor microenvironment and immune infiltration status were significantly different between high and low-risk groups (P<0. 05). The core gene GZMB was screened and the CC-associated GZMB/ miR-378a/TRIM52-AS1 regulatory axis was constructed.Conclusion: The study successfully established the prognostic signature based on eight PRGs and reflected their tumor microenvironment and immune infiltration. The GZMB/ miR-378a/TRIM52-AS1 regulatory axis may play an important regulatory role in the development of CC, and further experimental studies are needed to validate these results subsequently.

scholarly journals A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Mingqin Ge ◽  
Jie Niu ◽  
Ping Hu ◽  
Aihua Tong ◽  
Yan Dai ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

scholarly journals Development and validation of an immune-related prognostic signature in cervical cancer

2021 ◽  
Author(s):  
Rongjia Su ◽  
Chengwen Jin ◽  
Hualei Bu ◽  
Xiaoyun Wang ◽  
Menghua Kuang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Clinical Information ◽  
Risk Groups ◽  
Gene Signature ◽  
Cancer Prognosis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Gynecological Malignancy ◽  
Immune Infiltration

Abstract Background Cervical cancer is the fourth most frequently gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods Raw data and clinical information of cervical cancer samples were download from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results There were 8 immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing PD-L1 expression and PD-1 checkpoint pathway differences between high risk and low risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Conclusion The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

scholarly journals Molecular Characterization and Clinical Relevance of m6A RNA Methylation Regulators in Cervical Cancer

2020 ◽  
Author(s):  
Jin Chen ◽  
Ji He ◽  
Xiaolei Ma ◽  
Xia Guo
Keyword(s):  
Cervical Cancer ◽  
Clinical Relevance ◽  
Cox Regression ◽  
Critical Role ◽  
The Cancer Genome Atlas ◽  
Rna Modification ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
M6a Rna Methylation

Abstract Background: RNA modification, such as methylation of N6 adenosine (m6A), plays a critical role in many biological processes. However, the role of m6A RNA modification in cervical cancer (CC) remains largely unknown. Methods: The present study systematically investigated the molecular signatures and clinical relevance of 20 m6A RNA methylation regulators (writers, erasers, readers) in CC. The mRNA expression and clinical significance of m6A-related genes were investigated using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cervical cancer cohort. Mutations, copy number variation (CNV), differential expression, gene ontology analysis and the construction of a mRNA-microRNA regulatory network were performed to investigate the underlying mechanisms involved in the abnormal expression of m6A-related genes. Results: We found inclusive genetic information alterations among the m6A regulators and that their transcript expression levels were significantly associated with cancer hallmark-related pathways activity, such as the PI3K-AKT signaling pathway, microRNAs in cancer and the focal adhesion pathway, which were significantly enriched. Moreover, m6A regulators were found to be potentially useful for prognostic stratification and we identified FMR1 and ZC3H13 as potential prognostic risk oncogenes by LASSO regression. The ROC curves of 3, 5 and 10 years were 0.685, 0.726 and 0.741, respectively. The specificity for 3, 5 and 10 years were 0.598, 0.631 and 0.833, the sensitivity were 0.707, 0.752 and 0.811, respectively. Conclusions: Multivariable Cox regression analysis revealed that the risk score is an independent prognostic marker and can be used to predict the clinical and pathological features of CC.

scholarly journals A  novel  comprehensive immune-related gene signature as a promising survival predictor for head and neck squamous cell carcinoma

2020 ◽  
Author(s):  
Ruihua Fang ◽  
Lin Chen ◽  
Jing Liao ◽  
Jierong Luo ◽  
Chenchen Zhang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Head And Neck ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Prognostic Signature ◽  
Immune Microenvironment ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Gene

Abstract Background: Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. Growing evidence has demonstrated that the immune system plays a crucial role in the development and progression of HNSCC. The goal of our study was to develop an immune-related signature for accurately predicting the survival of HNSCC patients. Methods: Gene expression profiles were established from a total of 546 HNSCC and normal tissues to establish a training set and 83 HNSCC tissues for a validation set. Differentially expressed prognostic immune genes were identified by univariate Cox regression analysis and a corresponding network of differentially expressed transcription factors (TFs) were identified using Cytoscape. The immune-related gene signature was established and validated by univariate Cox regression analysis, least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. In addition, the prognostic value of the immune-related signature was analyzed by survival and Cox regression analysis. Finally, the correlation between the immune-related signature and the immune microenvironment was established.Results: In this study, the TF-mediated network revealed that Foxp3 plays a central role in the regulatory mechanism of most immune genes. A prognostic signature based on 10 immune-related genes, which divided patients into high and low risk groups, was developed and successfully validated using two independent databases. Our prognostic signature was significantly related to worse survival and predicted prognosis in patients with different clinicopathological factors. A nomogram including clinical characteristics was also constructed for accurate prediction. Furthermore, it was determined that our prognostic signature may act as an independent factor for predicting the survival of HNSCC patients. ROC analysis also revealed that our signature had superior predictive value compared with TNM stage. As for the immune microenvironment, our signature showed a positive correlation with activated mast cells and M0 macrophages, a negative correlation with Tregs, and immune checkpoint molecules PD-1 and CLTA-4. Conclusions: Our study established an immune-related gene signature, which not only provides a promising biomarker for survival prediction, but may be evaluated as an indicator for personalized immunotherapy in patients with HNSCC.

scholarly journals Application of an Autophagy-Related Gene Prognostic Risk Model Based on TCGA Database in Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Huadi Shi ◽  
Fulan Zhong ◽  
Xiaoqiong Yi ◽  
Zhenyi Shi ◽  
Feiyan Ou ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Survival Prediction ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based

Background: Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature.Methods: ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability.Results: Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable.Conclusion: A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.

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