scholarly journals Identification of Tumor Microenvironment-Related Prognostic Biomarkers in Luminal Breast Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Yanyan Wang ◽  
Mingzhi Zhu ◽  
Feng Guo ◽  
Yi Song ◽  
Xunjie Fan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Prognostic Value ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Luminal Breast Cancer ◽  
Ppi Network ◽  
Cerna Network ◽  
Novel Biomarkers

Background: The tumor microenvironment (TME) has been reported to have significant value in the diagnosis and prognosis of cancers. This study aimed to identify key biomarkers in the TME of luminal breast cancer (BC).Methods: We obtained immune scores (ISs) and stromal scores (SSs) for The Cancer Genome Atlas (TCGA) luminal BC cohort from the online ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) portal. The relationships between ISs and SSs and the overall survival of luminal BC patients were assessed by the Kaplan-Meier method. The differentially expressed messenger RNAs (DEmRNAs) related to the ISs and SSs were subjected to functional enrichment analysis. Additionally, a competing endogenous RNA (ceRNA) network was constructed with differentially expressed microRNAs (DEmiRNAs) and long noncoding RNAs (DElncRNAs). Furthermore, a protein–protein interaction (PPI) network was established to analyze the DEmRNAs in the ceRNA network. Then, survival analysis of biomarkers involved in the ceRNA network was carried out to explore their prognostic value. Finally, these biomarkers were validated using the luminal BC dataset from the Gene Expression Omnibus (GEO) database.Results: The results showed that ISs were significantly associated with longer survival times of luminal BC patients. Functional enrichment analysis showed that the DEmRNAs were mainly associated with immune response, antigen binding, and the extracellular region. In the PPI network, the top 10 DEmRNAs were identified as hub genes that affected the TME of luminal BC. Finally, two DEmiRNAs, two DElncRNAs, and 17 DEmRNAs of the ceRNA network associated with the TME were shown to have prognostic value. Subsequently, the expression of 15 prognostic biomarkers was validated in one additional dataset (GSE81002). In particular, one lncRNA (GVINP1) and five mRNAs (CCDC69, DOCK2, IKZF1, JCHAIN, and NCKAP1L) were novel biomarkers.Conclusions: Our studies demonstrated that ISs were associated with the survival of luminal BC patients, and a set of novel biomarkers that might play a prognostic role in the TME of luminal BC was identified.

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.

scholarly journals Construction of a long noncoding RNA-based competing endogenous RNA network and prognostic signatures of left- and right-side colon cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ke-zhi Li ◽  
Yi-xin Yin ◽  
Yan-ping Tang ◽  
Long Long ◽  
Ming-zhi Xie ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Prognostic Value ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Molecular Characteristics ◽  
Prognostic Signature ◽  
Cerna Network ◽  
Colon Cancers ◽  
Cancer Tissues

Abstract Background Cancers located on the right and left sides of the colon have distinct clinical and molecular characteristics. This study aimed to explore the regulatory mechanisms of location-specific long noncoding RNAs (lncRNAs) as competing endogenous RNAs (ceRNAs) in colon cancer and identify potential prognostic biomarkers. Method Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) between right- and left-side colon cancers were identified by comparing RNA sequencing profiles. Functional enrichment analysis was performed for the DEGs, and a ceRNA network was constructed. Associations between DELs and patient survival were examined, and a DEL-based signature was constructed to examine the prognostic value of these differences. Clinical colon cancer tissues and Gene Expression Omnibus (GEO) datasets were used to validate the results. Results We identified 376 DELs, 35 DEMs, and 805 DEGs between right- and left-side colon cancers. The functional enrichment analysis revealed the functions and pathway involvement of DEGs. A ceRNA network was constructed based on 95 DEL–DEM–DEG interactions. Three DELs (LINC01555, AC015712, and FZD10-AS1) were associated with the overall survival of patients with colon cancer, and a prognostic signature was established based on these three DELs. High risk scores for this signature indicated poor survival, suggesting that the signature has prognostic value for colon cancer. Examination of clinical colon cancer tissues and GEO dataset analysis confirmed the results. Conclusion The ceRNA regulatory network suggests roles for location-specific lncRNAs in colon cancer and allowed the development of an lncRNA-based prognostic signature, which could be used to assess prognosis and determine treatment strategies in patients with colon cancer.

scholarly journals Identification and Validation of Immune-Related Prognostic Genes in the Tumor Microenvironment of Colon Adenocarcinoma

2022 ◽  
Vol 12 ◽  
Author(s):  
Shenghua Pan ◽  
Tingting Tang ◽  
Yanke Wu ◽  
Liang Zhang ◽  
Zekai Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Tumor Microenvironment ◽  
Signaling Pathway ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Kaplan Meier

The tumor microenvironment (TME) has been shown to be involved in angiogenesis, tumor metastasis, and immune response, thereby affecting the treatment and prognosis of patients. This study aims to identify genes that are dysregulated in the TME of patients with colon adenocarcinoma (COAD) and to evaluate their prognostic value based on RNA omics data. We obtained 512 COAD samples from the Cancer Genome Atlas (TCGA) database and 579 COAD patients from the independent dataset (GSE39582) in the Gene Expression Omnibus (GEO) database. The immune/stromal/ESTIMATE score of each patient based on their gene expression was calculated using the ESTIMATE algorithm. Kaplan–Meier survival analysis, Cox regression analysis, gene functional enrichment analysis, and protein–protein interaction (PPI) network analysis were performed. We found that immune and stromal scores were significantly correlated with COAD patients’ overall survival (log rank p < 0.05). By comparing the high immune/stromal score group with the low score group, we identified 688 intersection differentially expressed genes (DEGs) from the TCGA dataset (663 upregulated and 25 downregulated). The functional enrichment analysis of intersection DEGs showed that they were mainly enriched in the immune process, cell migration, cell motility, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. The hub genes were revealed by PPI network analysis. Through Kaplan–Meier and Cox analysis, four TME-related genes that were significantly related to the prognosis of COAD patients were verified in GSE39582. In addition, we uncovered the relationship between the four prognostic genes and immune cells in COAD. In conclusion, based on the RNA expression profiles of 1091 COAD patients, we screened four genes that can predict prognosis from the TME, which may serve as candidate prognostic biomarkers for COAD.

scholarly journals Identification of Potential Target Genes in HER-2 Positive Breast Cancer by Bioinformatics Analysis.

2020 ◽  
Author(s):  
Song Wang ◽  
Yi Quan ◽  
Hongying Lyu ◽  
Jian Deng
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Her 2 ◽  
Positive Breast Cancer

Abstract Background: HER-2 positive breast cancer has a high risk of for relapse, metastasis and drug resistance, and is correlated with a poor prognosis. Thus, the study objective was to reveal target genes and key pathways in HER-2 subtype breast cancer. Methods: The gene expression dataset (GSE29431) was downloaded from the Gene Expression Omnibus database(GEO), and the differentially expressed genes (DEGs) were determined using LIMMA package in R software. Subsequently, Functional enrichment analysis were performed in ClusterProfiler package of R platform. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction (PPI) network of DEGs. Module analysis and target genes were identified by Cytoscape software. Further more, The influence of target genes on overall survival (OS) was assessed using the Kaplan-Meier plotter database.Results: The differential expression analysis revealed 96 genes were up-regulated while 407 genes were down-regulated in HER-2 positive breast cancer tissue compared to normal breast tissue. Functional enrichment analysis showed that the DEGs were mainly involved in regulation of lipid metabolic process, PPAR signaling pathway and PI3K-Akt signaling pathway. PPI network construction revealed a total of 199 nodes and 560 edges, and 12 target genes were identified by the highest value of degree. In addition, target genes were associated with worse overall prognosis, including NUSAP1, PTTG1, CEP55, TOP2A, CCNB1, CENPF, MELK, AURKA, UBE2C, BUB1B, KIF20A and RRM2.Conclusion: The present study identified 12 target genes associated with the development of HER-2 subtype breast cancer, which may help to provide new biomarkers and therapeutic targets.

scholarly journals Identification of key gene in colorectal cancer using bioinformatics analysis.

2020 ◽  
Author(s):  
Rongrong Xiao ◽  
Ping Wang ◽  
Tian Xia ◽  
Chun-Yi Li ◽  
Ting Jiang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Tumor Microenvironment ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes

Abstract Background Tumor microenvironment plays important roles in the development of cancer. The aim of our study was to examine the expression of genes in colorectal cancer and also to evaluate the association value between expression level of these genes and clinical features. Methods We combined The Cancer Genome Atlas (TCGA) datasets to identify differentially expressed genes in colon cancer. Using these differentially expressed genes, we constructed protein-protein interaction network and conducted functional enrichment analysis. Genes with degree beyond 10 in the PPI network were regarded as hub genes. Then, we verified of the expression of molecules in Oncomine datasets and conducted Kaplan-Meier curve and log-rank test and functional enrichment analysis on these hub genes. Finally, we analyzed the relationship clinicopathological features analysis with the key gene. Results There were 719 differentially expressed genes identified to be associated with colon cancer microenvironment. We screened out 10 hub genes by construction of PPI network. The functions of these hub genes were enriched in cytokine-cytokine receptor interaction, alcoholism and systemic lupus erythematosus, which provided further insight into the roles of these genes in the tumor microenvironment. GNG4, with the highest degrees in the PPI network, were highly exprepressed in metastasis(P = 9.5-05) ,N1(P = 0.0025) and N2(,0.037).It was a relationship with stage. It was significantly different between with stage I and IV, II and III, II and IV,III and IV (P = 0.0015,0.029,3.9-05,0.00074,0.01,respectively) Conclusions We identified GNG4 can be regarded as a prognostic biomarker in colon cancer.

scholarly journals Identification of Biomarkers with Therapeutic And prognostic Value in Skin Cutaneous Melanoma

2020 ◽  
Author(s):  
Guangwen Wang ◽  
Yonghuo Ling ◽  
Qianru Zhuang ◽  
Yingbang Li ◽  
Yunpeng Bai ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Cutaneous Melanoma ◽  
Prognostic Value ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Key Genes ◽  
Differential Genes

Abstract Background The morbidity and mortality of skin cutaneous melanoma (SKCM), the most deadly type of skin cancer, are on the rise worldwide. Through in-depth study of the tumor microenvironment (TME) of SKCM, this study further identified biomarkers with therapeutic and prognostic value. Methods The gene expression profiles and clinical data of SKCM patients were downloaded from The Cancer Genome Atlas database. Then we calculated the immune score and stromal score of patients with skin melanoma by using the estimate algorithm, and divided all patients into the high/low immune/stromal score groups and discussed the correlation between them and clinical characteristics.Then, limma R package was used to screen out the differential genes in the high/low immune/ stromal score groups, and the heat map of the differential genes was drawn. At the same time, the functional enrichment analysis of the differential genes was carried out. The protein‒protein interaction (PPI) network of different genes was constructed by using STRING and Cytoscape, and the key genes related to the prognosis of cutaneous melanoma were further selected. Finally, kinase target, co expression genes and immune infiltrating cells of key genes were discussed. Results Patients in the low-immune/stromal score group had poorer survival outcome. The immune and stromal scores are associated with specific clinicopathologic variables (age, tumor grade, tumor stage) in SKCM. In total, 914 DEGs (909 upregulated and 5 downregulated genes) were screened from the gene expression profiles of patients with high immune and stromal scores. Functional enrichment analysis demonstrated a correlation between DEGs and the tumor microenvironment, tumor immune response and RCC tumorigenesis.Kaplan-Meier survival curves showed that 15 out of the 914 identified tumor microenvironment related genes are involved in the prognosis of SKCM. Finally CXCR8 and CCR5 were selected as the hub genes. A positive correlation was obtained between the expression of CXCR8/CCR5 and the abundance of six immune cells. Conclusions We studied the tumor microenvironment of SKCM, and finally screened out the biomarkers with therapeutic and prognostic effects.

scholarly journals STAT1, IGF1, RAC1, and MDM2 Are Associated with Recurrence of Giant Cell Tumor of Bone

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Shuxin Chen ◽  
Zepeng Du ◽  
Bingli Wu ◽  
Huiyang Shen ◽  
Chunpeng Liu ◽  
...  
Keyword(s):  
Giant Cell Tumor ◽  
Giant Cell ◽  
Clinical Characteristics ◽  
Multivariate Logistic Regression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Small Gtpase ◽  
Cell Tumor ◽  
Functional Enrichment ◽  
Ppi Network

Background. In our previous study, mouse double minute 2 homolog (MDM2), insulin-like growth factor 1 (IGF1), signal transducer and activator of transcription 1 (STAT1), and Rac family small GTPase 1 (RAC1) were correlated with the recurrence of giant cell tumor of bone (GCT). The aim of this study is to use a large cohort study to confirm the involvement of these four genes in GCT recurrence. Methods. The expression of these four genes was detected and compared between GCT patients with or without recurrence. The correlation between the expression of these four genes and clinical characteristics was evaluated. Protein-protein interaction (PPI) network was constructed for functional enrichment analysis. Results. It showed that the expression levels of MDM2, IGF1, STAT1, and RAC1 in GCT patients with recurrence were significantly higher than those in GCT patients without recurrence (P<0.05). Multivariate logistic regression analysis suggested that several clinical characteristics may influence prognosis. A PPI network was constructed using the four genes as hub genes. Functional enrichment analysis showed that this network involves many important biological progress mediated by these four genes, including immune response. Conclusion. MDM2, IGF1, STAT1, and RAC1 are associated with GCT recurrence, which might serve as biomarkers for GCT recurrence.

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 Identifcation of The Ferroptosis-Related Gene Signature In Placenta of Patients With Early-Onset Preeclampsia

2021 ◽  
Author(s):  
Nana Yang ◽  
Qianghua Wang ◽  
Biao Ding ◽  
Yinging Gong ◽  
Yue Wu ◽  
...  
Keyword(s):  
Iron Homeostasis ◽  
Molecular Mechanisms ◽  
Late Onset ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Network Analyses

Abstract Background: The accumulation of ROS resulting from upregulated levels of oxidative stress is commonly implicated in preeclampsia (PE). Ferroptosis is a novel form of iron-dependent cell death instigated by lipid peroxidation likely plays important role in PE pathogenesis. This study aims to investigate expression profiles and functions of the ferroptosis-related genes (FRGs) in early- and late-onset preeclampsia.Methods: The gene expression data and clinical information were downloaded from GEO database. The “limma” R package was used for screening differentially expressed genes. GO(Gene Ontology), Kyoto Encyclopedia of Genes and Genomes(KEGG) and protein protein interaction (PPI) network analyses were conducted to investigate the bioinformatics functions and molecular interactions of significantly different FRGs. Quantitative real-time reverse transcriptase PCR was used to verify the expression of hub FRGs in PE.Results: A total number of 4,215 DEGs were identified between EOPE and preterm cases and 3,356 DEGs were found between EOPE and LOPE subtypes. 20 significantly different FRGs were identified in EOPE, while only 3 in LOPE. Functional enrichment analysis revealed that the differentially expressed FRGs was mainly involved in EOPE and enriched in hypoxia- and iron-related pathways, such as response to hypoxia, iron homeostasis and iron ion binding process. The PPI network analysis and verification by RT-qPCR resulted in the identification of the following six interesting FRGs: FTH1, HIF1A, FTL, IREB2, MAPK8 and PLIN2. Conclusions: EOPE and LOPE owned distinct underlying molecular mechanisms and ferroptosis may be mainly implicated in pathogenesis of EOPE. Further studies are necessary for deeper inquiry into placental ferroptosis and its role in the pathogenesis of EOPE.

scholarly journals The Role of Circular RNA CDR1as/ciRS-7 in Regulating Tumor Microenvironment: A Pan-Cancer Analysis

Biomolecules ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 429 ◽  
Author(s):  
Zou ◽  
Zheng ◽  
Deng ◽  
Yang ◽  
Xie ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Signaling Pathway ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Circular Rna ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction

Circular RNA CDR1as/ciRS-7 functions as an oncogenic regulator in various cancers. However, there has been a lack of systematic and comprehensive analysis to further elucidate its underlying role in cancer. In the current study, we firstly performed a bioinformatics analysis of CDR1as among 868 cancer samples by using RNA-seq datasets of the MiOncoCirc database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), CIBERSORT, Estimating the Proportion of Immune and Cancer cells (EPIC), and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were applied to investigate the underlying functions and pathways. Functional enrichment analysis suggested that CDR1as has roles associated with angiogenesis, extracellular matrix (ECM) organization, integrin binding, and collagen binding. Moreover, pathway analysis indicated that it may regulate the TGF-β signaling pathway and ECM-receptor interaction. Therefore, we used CIBERSORT, EPIC, and the ESTIMATE algorithm to investigate the association between CDR1as expression and the tumor microenvironment. Our data strongly suggest that CDR1as may play a specific role in immune and stromal cell infiltration in tumor tissue, especially those of CD8+ T cells, activated NK cells, M2 macrophages, cancer-associated fibroblasts (CAFs) and endothelial cells. Generally, systematic and comprehensive analyses of CDR1as were conducted to shed light on its underlying pro-cancerous mechanism. CDR1as regulates the TGF-β signaling pathway and ECM-receptor interaction to serve as a mediator in alteration of the tumor microenvironment.

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