scholarly journals Systematic profiling of diagnostic and prognostic value of autophagy-related genes for sarcoma patients

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuanhe Wang ◽  
Jianyi Li ◽  
Cheng Shao ◽  
Xiaojie Tang ◽  
Yukun Du ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Immune Cells ◽  
Disease Free Survival ◽  
Independent Set ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Immune Checkpoints ◽  
Systematic Analysis

Abstract Background Autophagy-related genes (ARGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of ARGs and their clinical significance in sarcoma patients is lacking. Methods Gene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed ARGs (DEARGs) were determined by matching the DEG and HADb gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEARGs was conducted, and associations with tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for disease-free survival (DFS), were established and validated in an independent set. Results In total, 84 DEARGs and two clusters were identified. TME scores, five immune checkpoints, and several types of immune cells were found to be significantly different between two clusters. Two prognostic signatures incorporating DEARGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.818 and 0.747 for the OS and DFS nomograms, respectively. Conclusion This comprehensive analyses of the ARG landscape in sarcoma showed novel ARGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

scholarly journals Systematic Profiling of Diagnostic and Prognostic Value of Autophagy-Related Genes for Sarcoma Patients

2020 ◽  
Author(s):  
Yuanhe Wang ◽  
Jianyi Li ◽  
Cheng Shao ◽  
Xiaojie Tang ◽  
Yukun Du ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Immune Cells ◽  
Disease Free Survival ◽  
Independent Set ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Immune Checkpoints ◽  
Systematic Analysis

Abstract Background: Autophagy-related genes (ARGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of ARGs and their clinical significance in sarcoma patients is lacking.Methods: Gene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed ARGs (DEARGs) were determined by matching the DEG and HADb gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEARGs was conducted, and associations with tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for disease-free survival (DFS), were established and validated in an independent set. Results: In total, 84 DEIRGs and two clusters were identified. TME scores, five immune checkpoints, and several types of immune cells were found to be significantly different between twp clusters. Two prognostic signatures incorporating DEARGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.818 and 0.636 for the OS and DFS nomograms, respectively.Conclusion: This comprehensive analyses of the ARG landscape in sarcoma showed novel ARGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

scholarly journals Identification of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

scholarly journals Identification of Tumor Microenvironment-Related Genes in Kidney Renal Clear Cell Carcinoma Patients via Bioinformatic Analysis

2021 ◽  
Author(s):  
Rongjiong Zheng ◽  
Yaosen SHao ◽  
Mingming Wang ◽  
Yeli Tang ◽  
Meiling Hu
Keyword(s):  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment

Abstract BackgroundTumor microenvironment has been implicated in the development and progression of cancers. However, the prognostic significance of tumor microenvironment-related genes in kidney renal clear cell carcinoma (KIRC) remains unclear. MethodsIn this study, we obtained and analyzed gene expression profiles from The Cancer Genome Atlas database. Stromal and immune scores were calculated based on the ESTIMATE algorithm. ResultsIn the discovery series of 537 patients, we identified a list of differentially expressed genes which was significantly associated with prognosis in KIRC patients. Protein-protein interaction networks and functional enrichment analysis were both performed, indicating that these identified genes were related to the immune response. ConclusionsThe tumor microenvironment-related genes could serve as the potential biomarkers for KIRC.

scholarly journals Mining TCGA Database for Tumor Microenvironment-Related Genes of Prognostic Value in Hepatocellular Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Zhenfeng Deng ◽  
Jilong Wang ◽  
Banghao Xu ◽  
Zongrui Jin ◽  
Guolin Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Malignant Tumors ◽  
Public Access ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Protein Protein Interaction ◽  
Potential Association ◽  
Poor Outcomes

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies. Recent studies reveal that tumor microenvironment (TME) components significantly affect HCC growth and progression, particularly the infiltrating stromal and immune cells. Thus, mining of TME-related biomarkers is crucial to improve the survival of patients with HCC. Public access of The Cancer Genome Atlas (TCGA) database allows convenient performance of gene expression-based analysis of big data, which contributes to the exploration of potential association between genes and prognosis of a variety of malignancies, including HCC. The “Estimation of STromal and Immune cells in MAlignant Tumors using Expression data” algorithm renders the quantification of the stromal and immune components in TME possible by calculating the stromal and immune scores. Differentially expressed genes (DEGs) were screened by dividing the HCC cohort of TCGA database into high- and low-score groups according to stromal and immune scores. Further analyses of functional enrichment and protein-protein interaction networks show that the DEGs are mainly involved in immune response, cell adhesion, and extracellular matrix. Finally, seven DEGs have significant association with HCC poor outcomes. These genes contain FABP3, GALNT5, GPR84, ITGB6, MYEOV, PLEKHS1, and STRA6 and may be candidate biomarkers for HCC prognosis.

scholarly journals Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiarong Yi ◽  
Wenjing Zhong ◽  
Haoming Wu ◽  
Jikun Feng ◽  
Xiazi Zouxu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Key Genes

Although the tumor microenvironment (TME) plays an important role in the development of many cancers, its roles in breast cancer, especially triple-negative breast cancer (TNBC), are not well studied. This study aimed to identify genes related to the TME and prognosis of TNBC. Firstly, we identified differentially expressed genes (DEG) in the TME of TNBC, using Expression data (ESTIMATE) datasets obtained from the Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues. Next, survival analysis was performed to analyze the relationship between TME and prognosis of TNBC, as well as determine DEGs. Genes showing significant differences were scored as alternative genes. A protein-protein interaction (PPI) network was constructed and functional enrichment analysis conducted using the DEG. Proteins with a degree greater than 5 and 10 in the PPI network correspond with hub genes and key genes, respectively. Finally, CCR2 and CCR5 were identified as key genes in TME and prognosis of TNBC. Finally, these results were verified using Gene Expression Omnibus (GEO) datasets and immunohistochemistry of TNBC patients. In conclusion, CCR2 and CCR5 are key genes in the TME and prognosis of TNBC with the potential of prognostic biomarkers in TNBC.

Mine TCGA Database for Tumor Microenvironment-Related Genes of Prognostic value in lung squamous cell carcinoma

2020 ◽  
Author(s):  
Xiao Chen ◽  
Rui Li ◽  
Yun-Hong Yin ◽  
Xiao Liu ◽  
Xi-Jia Zhou ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Cells ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes

Abstract Background: Tumor microenvironment (TME) plays a significant role in the development of cancer. However, the roles of TME in lung squamous cell carcinoma (LUSC) are not well studied. In our study, we aimed to identify differentially expressed tumor microenvironment-related genes as biomarker for predicting the prognosis of LUSC.Methods: We combined The Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissue using Expression data (ESTIMATE) datasets to identified differentially expressed genes in lung squamous cell carcinoma microenvironment. Then, functional enrichment analysis and protein-protein interaction (PPI) network were conducted. The top six genes in the PPI network were regarded as tumor microenvironment-related hub genes. Finally, the relationship between hub genes and tumor-infiltrating immune cells was deciphered using TIMER.Results: Our study revealed that immune and stromal scores are associated with specific clinicopathologic variables in LUSC. These variables include gender, age, distant metastasis and prognosis. In addition, a total of 874 upregulated and 72 downregulated genes were identified. Functional enrichment analysis demonstrated a correlation between DEGs and the tumor microenvironment, tumor immune cells differentiation and activation. C3AR1, CSF1R, CCL2, CCR1, TYROBP, CD14were selected as the hub genes. A positive correlation was obtained between the expression of hub genes and the abundance of six immune cells.Conclusions: The results of the present study showed that ESTIMATE algorithm-based stromal and immune scores may be a reference indicator of cancer prognosis. We identified five TME-related genes, which could be used to predict the prognosis of LUSC patients.

scholarly journals Tumor Microenvironment Analysis Identified Subtypes Associated With the Prognosis and the Tumor Response to Immunotherapy in Bladder Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Hongxian Zhang ◽  
Jiwen Song ◽  
Junqiang Dong ◽  
Zhuo Liu ◽  
Lixuan Lin ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Tumor Response ◽  
Checkpoint Inhibitors ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Genetic Characteristics ◽  
Signature Genes ◽  
Micro Rnas

Background: The efficiency of immune checkpoint inhibitors (ICIs) in bladder cancer (BLCA) treatment has been widely validated; however, the tumor response to ICIs was generally low. It is critical and urgent to find biomarkers that can predict tumor response to ICIs. The tumor microenvironment (TME), which may play important roles to either dampen or enhance immune responses, has been widely concerned.Methods: The cancer genome atlas BLCA (TCGA-BLCA) cohort (n = 400) was used in this study. Based on the proportions of 22 types of immune cells calculated by CIBERSORT, TME was classified by K-means Clustering and differentially expressed genes (DEGs) were determined. Based on DEGs, patients were classified into three groups, and cluster signature genes were identified after reducing redundant genes. Then TMEscore was calculated based on cluster signature genes, and the samples were classified to two subtypes. We performed somatic mutation and copy number variation analysis to identify the genetic characteristics of the two subtypes. Correlation analysis was performed to explore the correlation between TMEscore and the tumor response to ICIs as well as the prognosis of BLCA.Results: According to the proportions of immune cells, two TME clusters were determined, and 1,144 DEGs and 138 cluster signature genes were identified. Based on cluster signature genes, samples were classified into TMEscore-high (n = 199) and TMEscore-low (n = 201) subtypes. Survival analysis showed patients with TMEscore-high phenotype had better prognosis. Among the 45 differentially expressed micro-RNAs (miRNAs) and 1,033 differentially expressed messenger RNAs (mRNAs) between the two subtypes, 16 miRNAs and 287 mRNAs had statistically significant impact on the prognosis of BLCA. Furthermore, there were 94 genes with significant differences between the two subtypes, and they were enriched in RTK-RAS, NOTCH, WNT, Hippo, and PI3K pathways. The Tumor Immune Dysfunction and Exclusion (TIDE) score of TMEscore-high BLCA was statistically lower than that of TMEscore-low BLCA. Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of TMEscore and tumor mutation burden (TMB) is 0.6918 and 0.5374, respectively.Conclusion: We developed a method to classify BLCA patients to two TME subtypes, TMEscore-high and TMEscore-low, and we found TMEscore-high subtype of BLCA had a good prognosis and a good response to ICIs.

scholarly journals Development of a prognostic model based on immunogenomic landscape analysis of hepatocellular carcinoma

2021 ◽  
Author(s):  
ligong lu ◽  
Shaoqing Liu ◽  
Shengni Hua ◽  
Zhenlin Zhang ◽  
Meixiao Zhan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Risk Score ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Prognostic Indicators

Abstract Background Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, and the systematic exploration of its prognostic indicators is urgently needed. In this study, we obtained 12 IRGs for the construction of a risk score prediction model in HCC by bioinformatics analysis. Methods Differentially expressed genes were screened using the R software edgeR package. Functional enrichment analysis was performed through gene ontology analyses as well as the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Single factor and multi-factor Cox analysis were employed for survival analysis. We used the Timer software to examine the correlation between risk score and tumor-infiltrating immune cells. Results We identified 3,215 up-regulated and 1,044 down-regulated genes in HCC tissues based on a cohort from The Cancer Genome Atlas (TCGA). Differentially expressed immune-related genes (IRGs) and survival-associated IRGs were further identified. We also integrated multivariate Cox regression analyses to obtain 12 IRGs for the construction of a risk score prediction model, whose performance was verified using the Kaplan-Meier survival and receiver operating characteristic curve analyses. Our findings suggest that the risk score was associated with clinical characteristics and the infiltration of immune cells in HCC patients. Conclusions We obtained a risk score prediction model of 12 IRGs in HCC by bioinformatics analysis and confirmed its performance.

scholarly journals Construction and Prognostic Analysis of miRNA-mRNA Regulatory Network in Liver Metastasis from Colorectal Cancer

2020 ◽  
Author(s):  
Ruyun Cai ◽  
Qian Lu ◽  
Da Wang
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Liver Metastases ◽  
Immune Cells ◽  
Interaction Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Mesenchymal Transition ◽  
Prognostic Analysis

Abstract Background: Colorectal cancer (CRC) is one of the most common cancers in the world, and liver metastasis is the leading cause of colorectal cancer-related deaths. However, the mechanism of liver metastasis in CRC hasn’t been clearly elucidated.Methods: Three datasets from the Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEGs), which were subjected to functional enrichment analysis and protein-protein interaction analysis. Subsequently, mRNA-miRNA network was constructed and the associated DEGs and DEMs were performed for prognostic analysis. Finally, we did infiltration analysis of GAS1-associated immune cells. Results: We obtained 325 DEGs and 9 differentially expressed miRNAs (DEMs) between primary CRC and liver metastases. Enrichment analysis and protein-protein interactions (PPI) further revealed the involvement of DEGs in the formation of the inflammatory microenvironment and epithelial-mesenchymal transition (EMT) during the liver metastases process in CRC. Survival analysis demonstrated that low-expressed GAS1 as well as low-expressed hsa-miR-33b-5p was a favorable prognostic indicator of overall survival. Further exploration of GAS1 revealed that its expression was interrelated with the infiltration of immune cells in tumor tissues. Conclusions: In summary, DEGs, DEMs and their interactions found in liver metastasis of CRC may provide a basis for further understanding of the mechanism of CRC metastasis.

scholarly journals Comprehensive Analysis of Clinical Prognosis and Molecular Immune Characterization of TPM4 in Pancreatic Cancer

2021 ◽  
Author(s):  
Xue Zhou ◽  
Xiaowei Zhu ◽  
Junchao Yao ◽  
Xue Wang ◽  
Ning Wang
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Immune Cells ◽  
Enrichment Analysis ◽  
Positive Association ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Clinical Prognosis ◽  
Tumor Cell Migration

Abstract Pancreatic cancer (PC) is one of the most lethal human solid malignancies with devastating prognosis, making biomarker detection considerably important. Immune infiltrates in microenvironment is associated with patients’ survival in PC. The role of TPM4 (Tropomyosin 4) gene in PC has not been reported. Our study first identifies TPM4 expression and its potential biological functions in PC. The potential oncogenic roles of TPM4 was examined using the datasets of TCGA (The cancer genome atlas) and GEO (Gene expression omnibus). We investigated the clinical significance and prognostic value of TPM4 gene based on The Gene Expression Profiling Interactive Analysis (GEPIA) and survival analysis. TIMER and TISIDB databases were used to analyze the correlations between TPM4 gene and tumor-infiltrating immune cells. We found that the expression level of TPM4 was upregulated in PC malignant tissues with the corresponding normal tissues as controls. High TPM4 expression was correlated with the worse clinicopathological features and poor prognosis in PC cohorts. The positive association between TPM4 expression and tumor-infiltrating immune cells was identified in tumor microenvironment (TME). Moreover, functional enrichment analysis suggested that TPM4 might participate in cell adhesion and promote tumor cell migration. This is the first comprehensive study to disclose that TPM4 may serve as a novel prognostic biomarker associating with immune infiltrates and provide a potential therapeutic target for the treatment of PC.This study is not a clinical trial without the registration number.

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