scholarly journals Bioinformatics prediction and experimental verification identify MAD2L1 and CCNB2 as diagnostic biomarkers of rhabdomyosarcoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tian Xia ◽  
Lian Meng ◽  
Zhijuan Zhao ◽  
Yujun Li ◽  
Hao Wen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Stage ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Clinical Staging ◽  
Soft Tissue Tumour ◽  
Hub Genes ◽  
Expression Levels ◽  
The Relationship ◽  
Differential Genes

Abstract Background Rhabdomyosarcoma (RMS) is a malignant soft-tissue tumour. In recent years, the tumour microenvironment (TME) has been reported to be associated with the development of tumours. However, the relationship between the occurrence and development of RMS and TME is unclear. The purpose of this study is to identify potential tumor microenvironment-related biomarkers in rhabdomyosarcoma and analyze their molecular mechanisms, diagnostic and prognostic significance. Methods We first applied bioinformatics method to analyse the tumour samples of 125 patients with rhabdomyosarcoma (RMS) from the Gene Expression Omnibus database (GEO). Differential genes (DEGs) that significantly correlate with TME and the clinical staging of tumors were extracted. Immunohistochemistry (IHC) was applied to validate the expression of mitotic arrest deficient 2 like 1 (MAD2L1) and cyclin B2 (CCNB2) in RMS tissue. Then, we used cell function and molecular biology techniques to study the influence of MAD2L1 and CCNB2 expression levels on the progression of RMS. Results Bioinformatics results show that the RMS TME key genes were screened, and a TME-related tumour clinical staging model was constructed. The top 10 hub genes were screened through the establishment of a protein–protein interaction (PPI) network, and then Gene Expression Profiling Interactive Analysis (GEPIA) was conducted to measure the overall survival (OS) of the 10 hub genes in the sarcoma cases in The Cancer Genome Atlas (TCGA). Six DEGs of statistical significance were acquired. The relationship between these six differential genes and the clinical stage of RMS was analysed. Further analysis revealed that the OS of RMS patients with high expression of MAD2L1 and CCNB2 was worse and the expression of MAD2L1 and CCNB2 was related to the clinical stage of RMS patients. Gene set enrichment analysis (GSEA) revealed that the genes in MAD2L1 and CCNB2 groups with high expression were mainly related to the mechanism of tumour metastasis and recurrence. In the low-expression MAD2L1 and CCNB2 groups, the genes were enriched in the metabolic and immune pathways. Immunohistochemical results also confirmed that the expression levels of MAD2L1 (30/33, 87.5%) and CCNB2 (33/33, 100%) were remarkably higher in RMS group than in normal control group (0/11, 0%). Moreover, the expression of CCNB2 was related to tumour size. Downregulation of MAD2L1 and CCNB2 suppressed the growth, invasion, migration, and cell cycling of RMS cells and promoted their apoptosis. The CIBERSORT immune cell fraction analysis indicated that the expression levels of MAD2L1 and CCNB2 affected the immune status in the TME. Conclusions The expression levels of MAD2L1 and CCNB2 are potential indicators of TME status changes in RMS, which may help guide the prognosis of patients with RMS and the clinical staging of tumours.

scholarly journals Bioinformatics Prediction and Experimental Verification Identify MAD2L1 and CCNB2 as Diagnostic Biomarkers of Rhabdomyosarcoma

2021 ◽  
Author(s):  
Tian Xia ◽  
Lian Meng ◽  
Zhijuan Zhao ◽  
Yujun Li ◽  
Hao Wen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Clinical Stage ◽  
Gene Set Enrichment Analysis ◽  
Clinical Staging ◽  
Control Group ◽  
Soft Tissue Tumour ◽  
Hub Genes ◽  
Expression Levels ◽  
Differential Genes

Abstract Background: Rhabdomyosarcoma (RMS) is a malignant soft-tissue tumour. In recent years, the tumour microenvironment (TME) has been reported to be associated with the development of tumours. However, the relationship between the occurrence and development of RMS and TME is unclear. The purpose of this study is to identify potential tumor microenvironment-related biomarkers in rhabdomyosarcoma and analyze their molecular mechanisms, diagnostic and prognostic significance.Methods: We first applied bioinformatics method to analyse the tumour samples of 187 patients with rhabdomyosarcoma (RMS) from the Gene Expression Omnibus database (GEO). Then, we used cell function and molecular biology techniques to study the progress of RMS.Results: Bioinformatics results show that the RMS TME key genes were screened, and a TME-related tumour clinical staging model was constructed. The top 10 hub genes were screened through the establishment of a protein-protein interaction network, and then Gene Expression Profiling Interactive Analysis was conducted to measure the overall survival (OS) of the 10 hub genes in the sarcoma cases in The Cancer Genome Atlas (TCGA). Six differential genes of statistical significance were acquired. The correlation between these six differential genes and the clinical stage of RMS was analysed. Our data found that the expression levels of MAD2L1 and CCNB2 negatively correlated with the OS of RMS patients and positively correlated with the clinical stage of RMS patients. Immunohistochemical results also confirmed that the expression levels of MAD2L1 (30/33, 87.5%) and CCNB2 (33/33, 100%) were remarkably higher in RMS group than in normal control group (0/11, 0%). Moreover, the expression of CCNB2 was related to tumour size. Further gene set enrichment analysis revealed that the genes in MAD2L1 and CCNB2 groups with high expression were mainly related to the mechanism of tumour metastasis and recurrence. In the low-expression MAD2L1 and CCNB2 groups, the genes were enriched in the metabolic and immune pathways. Downregulation of MAD2L1 and CCNB2 suppressed the growth, invasion, migration, and cell cycling of RMS cells and promoted their apoptosis. The CIBERSORT immune cell fraction analysis indicated that the expression levels of MAD2L1 and CCNB2 affected the immune status in the TME. Conclusions: The expression levels of MAD2L1 and CCNB2 may help guide the prognosis of patients with RMS and the clinical staging of tumours.

Integrated Bioinformatical Analysis Identified HTRA4 As An Immune-Related Prognostic Biomarker In The Gastric Cancer Microenvironment

2021 ◽  
Author(s):  
Ce Ji ◽  
Jingwei Dai ◽  
Zhen Wang ◽  
Xiangchen Hu ◽  
Youwei Kou
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Histological Grade ◽  
Clinical Stage ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Signal Pathways ◽  
Activated T Cells ◽  
Expression Levels

Abstract HtrA serine peptidase 4 (HTRA4), which belongs to the AB family of proteases/chapterones, participates in multiple signal pathways and plays an important regulatory role in some malignancies; however, its role in the prognosis and immune infiltrates of gastric cancer (GC) remains unclear. HTRA4 expression was thus investigated in tumor tissues, and its association with immune infiltrates was assessed, to determine its prognostic roles in GC patients. Data for patients with GC was collected from three GEO datasets. This study has investigated if high expression levels of HTRA4 are associated with a poor prognosis in GC patients, especially in T2,T3,N1,NI&N2&N3,M1,stage3 and HER2+ subgroups. HTRA4 was positively correlated with CD8+ T cells, CD4+ memory activated T cells, gamma delta T cells, and M2 macrophages in the GC microenvironment. Gene Ontology and Gene set Enrichment Analysis showed that HTRA4 was enriched in some immune-related pathways. High expression levels of HTRA4 were significantly correlated with tumors in the T stage, N stage, and clinical stage, as well as those of histological grade. In summary, HTRA4 was found to play a vital role in the progression of GC and may be a predictive biomarker for the survival of GC patients.

KIAA0101 in Malignant Pleural Mesothelioma: a Potential Diagnostic and Prognostic Marker

2021 ◽  
Vol 24 ◽  
Author(s):  
Ping Lin ◽  
Yuean Zhao ◽  
Xiaoqian Li ◽  
Zongan Liang
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Malignant Pleural Mesothelioma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Pleural Mesothelioma ◽  
Hub Genes ◽  
Cox Regression Analysis

Background: Currently, there are no reliable diagnostic and prognostic markers for malignant pleural mesothelioma (MPM). The objective of this study was to identify hub genes that could be helpful for diagnosis and prognosis in MPM by using bioinformatics analysis. Materials and Methods: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA), LASSO regression analysis, Cox regression analysis, and Gene Set Enrichment Analysis (GSEA) were performed to identify hub genes and their functions. Results: A total of 430 up-regulated and 867 downregulated genes in MPM were identified based on the GSE51024 dataset. According to the WGCNA analysis, differentially expressed genes were classified into 8 modules. Among them, the pink module was most closely associated with MPM. According to genes with GS > 0.8 and MM > 0.8, six genes were selected as candidate hub genes (NUSAP1, TOP2A, PLOD2, BUB1B, UHRF1, KIAA0101) in the pink module. In the LASSO model, three genes (NUSAP1, PLOD2, and KIAA0101) were identified with non-zero regression coefficients and were considered hub genes among the 6 candidates. The hub gene-based LASSO model can accurately distinguish MPM from controls (AUC = 0.98). Moreover, the high expression level of KIAA0101, PLOD2, and NUSAP1 were all associated with poor prognosis compared to the low level in Kaplan–Meier survival analyses. After further multivariate Cox analysis, only KIAA0101 (HR = 1.55, 95% CI = 1.05-2.29) was identified as an independent prognostic factor among these hub genes. Finally, GSEA revealed that high expression of KIAA0101 was closely associated with 10 signaling pathways. Conclusion: Our study identified several hub genes relevant to MPM, including NUSAP1, PLOD2, and KIAA0101. Among these genes, KIAA0101 appears to be a useful diagnostic and prognostic biomarker for MPM, which may provide new clues for MPM diagnosis and therapy.

scholarly journals Bioinformatics study on genes related to a high-risk postoperative recurrence of lung adenocarcinoma

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110180
Author(s):  
Xiao Lin ◽  
Meng Zhou ◽  
Zehong Xu ◽  
Yusheng Chen ◽  
Fan Lin
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Candidate Genes ◽  
Postoperative Recurrence ◽  
High Expression ◽  
Ppi Network ◽  
Hub Genes ◽  
Expression Levels

In this study, we aimed to screen out genes associated with a high risk of postoperative recurrence of lung adenocarcinoma and investigate the possible mechanisms of the involvement of these genes in the recurrence of lung adenocarcinoma. We identify Hub genes and verify the expression levels and prognostic roles of these genes. Datasets of GSE40791, GSE31210, and GSE30219 were obtained from the Gene Expression Omnibus database. Enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the screened candidate genes using the DAVID database. Then, we performed protein–protein interaction (PPI) network analysis through the database STRING. Hub genes were screened out using Cytoscape software, and their expression levels were determined by the GEPIA database. Finally, we assessed the relationships of Hub genes expression levels and the time of survival. Forty-five candidate genes related to a high-risk of lung adenocarcinoma recurrence were screened out. Gene ontology analysis showed that these genes were enriched in the mitotic spindle assembly checkpoint, mitotic sister chromosome segregation, G2/M-phase transition of the mitotic cell cycle, and ATP binding, etc. KEGG analysis showed that these genes were involved predominantly in the cell cycle, p53 signaling pathway, and oocyte meiosis. We screened out the top ten Hub genes related to high expression of lung adenocarcinoma from the PPI network. The high expression levels of eight genes (TOP2A, HMMR, MELK, MAD2L1, BUB1B, BUB1, RRM2, and CCNA2) were related to short recurrence-free survival and they can be used as biomarkers for high risk of lung adenocarcinoma recurrence. This study screened out eight genes associated with a high risk of lung adenocarcinoma recurrence, which might provide novel insights into researching the recurrence mechanisms of lung adenocarcinoma as well as into the selection of targets in the treatment of the disease.

scholarly journals APLN: A potential novel biomarker for cervical cancer

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110113
Author(s):  
Yusha Chen ◽  
Xiaoqian Lin ◽  
Jinwen Zheng ◽  
Jiancui Chen ◽  
Huifeng Xue ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Clinical Stage ◽  
Mapk Signaling ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Receptor Interaction ◽  
Primary Therapy ◽  
Advanced Clinical Stage

Apelin (APLN) is recently demonstrated a direct association with many malignant diseases. However, its effects on cervical cancer remain unclear. This study therefore aims to evaluate the association between APLN expression and cervical cancer using publicly available data from The Cancer Genome Atlas (TCGA). The Pearson χ2 test and Fish exact test, as well as logistic regression, were used to evaluate the relationship between clinicopathological factors in cervical cancer and the expression of APLN. Additionally, the Cox regression and Kaplan-Meier methods were conducted to analyze the Overall Survival (OS) of cervical cancer patients in TCGA. Finally, gene set enrichment analysis (GSEA) was performed to establish its biological functions. High expression of APLN in cervical cancer was significantly associated with a more advanced clinical stage (OR = 1.91 (1.21–3.05) for Stage II, Stage III, and Stage IV vs Stage I, p = 0.006). Additionally, it was associated with poor outcome after primary therapy (OR = 2.14 (1.03–4.59) for Progressive Disease (PD), Stable Disease (SD), and Partial Response (PR) vs Complete Remission (CR), p = 0.045) and high histologic grade (OR = 1.67 (1.03–2.72) for G3 and G4 vs G1 and G2, p = 0.037). Moreover, multivariate analysis showed that high expression of APLN was associated with a shorter OS. GSEA demonstrated that six KEGG pathways, including PPAR signaling, ECM-receptor interaction, focal adhesion, MAPK signaling, TGF-beta signaling, and Gap junction pathways were differentially enriched in the high expression APLN phenotype. The recent study suggests that APLN plays an important role in the progression of cervical cancer and might be a promising prognostic biomarker of the disease.

scholarly journals Expression of LOX Suggests Poor Prognosis in Gastric Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Jinfeng Zhu ◽  
Chen Luo ◽  
Jiefeng Zhao ◽  
Xiaojian Zhu ◽  
Kang Lin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Expression Level ◽  
Cox Regression Analysis

Background: Lysyl oxidase (LOX) is a key enzyme for the cross-linking of collagen and elastin in the extracellular matrix. This study evaluated the prognostic role of LOX in gastric cancer (GC) by analyzing the data of The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset.Methods: The Wilcoxon rank-sum test was used to calculate the expression difference of LOX gene in gastric cancer and normal tissues. Western blot and immunohistochemical staining were used to evaluate the expression level of LOX protein in gastric cancer. Kaplan-Meier analysis was used to calculate the survival difference between the high expression group and the low expression group in gastric cancer. The relationship between statistical clinicopathological characteristics and LOX gene expression was analyzed by Wilcoxon or Kruskal-Wallis test and logistic regression. Univariate and multivariate Cox regression analysis was used to find independent risk factors affecting the prognosis of GC patients. Gene set enrichment analysis (GSEA) was used to screen the possible mechanisms of LOX and GC. The CIBERSORT calculation method was used to evaluate the distribution of tumor-infiltrating immune cell (TIC) abundance.Results: LOX is highly expressed in gastric cancer tissues and is significantly related to poor overall survival. Wilcoxon or Kruskal-Wallis test and Logistic regression analysis showed, LOX overexpression is significantly correlated with T-stage progression in gastric cancer. Multivariate Cox regression analysis on TCGA and GEO data found that LOX (all p < 0.05) is an independent factor for poor GC prognosis. GSEA showed that high LOX expression is related to ECM receptor interaction, cancer, Hedgehog, TGF-beta, JAK-STAT, MAPK, Wnt, and mTOR signaling pathways. The expression level of LOX affects the immune activity of the tumor microenvironment in gastric cancer.Conclusion: High expression of LOX is a potential molecular indicator for poor prognosis of gastric cancer.

scholarly journals Identification of Inflammatory Genes, Pathways, and Immune Cells in Necrotizing Enterocolitis of Preterm Infant by Bioinformatics Approaches

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lili Zhang ◽  
Lizhen Sun ◽  
Mingli Wu ◽  
Jie Huang
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Rna Sequencing ◽  
Necrotizing Enterocolitis ◽  
Immune Cells ◽  
Gastrointestinal Disease ◽  
Gene Set Enrichment Analysis ◽  
High Morbidity ◽  
Expression Levels ◽  
Gene Expression Levels

Background. Necrotizing enterocolitis (NEC) is one of the most serious gastrointestinal disease-causing high morbidity and mortality in premature infants. However, the underlying mechanism of the pathogenesis of NEC is still not fully understood. Methods. RNA sequencing of intestinal specimens from 9 NEC and 5 controls was employed to quantify the gene expression levels. RNA sequencing was employed to quantify the gene expression levels. DESeq2 tool was used to identify the differentially expressed genes. The biological function, pathways, transcription factors, and immune cells dysregulated in NEC were characterized by gene set enrichment analysis. Results. In the present study, we analyzed RNA sequencing data of NECs and controls and revealed that immune-related pathways were highly activated, while some cellular responses to external stimuli-related pathways were inactivated in NEC. Moreover, B cells, macrophages M1, and plasma cells were identified as the major cell types involved in NEC. Furthermore, we also found that inflammation-related transcription factor genes, such as STAT1, STAT2, and IRF2, were significantly activated in NEC, further suggesting that these TFs might play critical roles in NEC pathogenesis. In addition, NEC samples exhibited heterogeneity to some extent. Interestingly, two subgroups in the NEC samples were identified by hierarchical clustering analysis. Notably, B cells, T cells, Th1, and Tregs involved in adaptive immune were predicted to highly infiltrate into subgroup I, while subgroup II was significantly infiltrated by neutrophils. The heterogeneity of immune cells in NEC indicated that both innate and adaptive immunes might induce NEC-related inflammatory response. Conclusions. In summary, we systematically analyzed inflammation-related genes, signaling pathways, and immune cells to characterize the NEC pathogenesis and samples, which greatly improved our understanding of the roles of inflammatory responses in NEC.

scholarly journals Network analysis reveals the relationship among wood properties, gene expression levels and genotypes of naturalPopulus trichocarpaaccessions

2013 ◽  
Vol 200 (3) ◽  
pp. 727-742 ◽  
Author(s):  
Ilga Porth ◽  
Jaroslav Klápště ◽  
Oleksandr Skyba ◽  
Michael C. Friedmann ◽  
Jan Hannemann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Wood Properties ◽  
Expression Levels ◽  
The Relationship ◽  
Gene Expression Levels

Identification of Molecular Risk Score in Advanced Hodgkin Lymphoma: Integrating Tumor and Microenvironment Signatures.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3660-3660
Author(s):  
Beatriz Sánchez-Espiridión ◽  
Carlos Montalbán ◽  
Mónica García-Cosio ◽  
Jose García-Laraña ◽  
Javier Menarguez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Treatment Response ◽  
Hodgkin Lymphoma ◽  
Risk Score ◽  
Molecular Techniques ◽  
Gene Set Enrichment Analysis ◽  
Transcriptional Analysis ◽  
Rank Statistic ◽  
Rt Pcr ◽  
Expression Levels

Abstract Abstract 3660 Poster Board III-596 Introduction Despite the major advances in the treatment of classical Hodgkin Lymphoma (cHL) patients, around 30% to 40% of cases in advanced stages may relapse or die as result of the disease. Current predictive systems, based on clinical and analytical parameters, fail to identify accurately this significant fraction of patients with short failure-free survival (FFS). Transcriptional analysis has identified genes and pathways associated with clinical failure, but the biological relevance and clinical applicability of these data await further development. Robust molecular techniques for the identification of biological processes associated with treatment response are necessary for developing new predictive tools. Patients and Methods We used a multistep approach to design a quantitative RT-PCR-based assay to be applied to routine formalin-fixed, paraffin-embedded samples (FFPEs), integrating genes known to be expressed either by the tumor cells and their reactive microenvironment, and related with clinical response to adriamycin-based chemotherapy. First, analysis of 29 patient samples allowed the identification of gene expression signatures related to treatment response and outcome and the design of an initial RT-PCR assay tested in 52 patient samples. This initial model included 60 genes from pathways related to cHL outcome that had been previously identified using Gene Set Enrichment Analysis (GSEA). Second, we selected the best candidate genes from the initial assay based on amplification efficiency, biological significance and treatment response correlation to set up a novel assay of 30 genes that was applied to a large series of 282 samples that were randomly split and assigned to either estimation (194) or validation series (88). The results of this assay were used to design an algorithm, based on the expression levels of the best predictive genes grouped in pathways, and a molecular risk score was calculated for each tumor sample. Results Adequate RT-PCR profiles were obtained in 264 of 282 (93,6%) cases. Normalized expression levels (DCt) of individual genes vary considerably among samples. The strongest predictor genes were selected and included in a multivariate 10-gene model integrating four gene expression pathway signatures, termed CellCycle, Apoptosis, NF-KB and Monocyte, which are able to predict treatment response with an overall accuracy of 68.5% and 73.4% in the estimation and validation sets, respectively. Patients were stratified by their molecular risk score and predicted probabilities identified two distinct risk groups associated with clinical outcome in the estimation (5-year FFS probabilities 75.6% vs. 45.9%, log rank statistic p≈0.000) and validation sets (5-year FFS probabilities 71.4% vs. 43.5%, log rank statistic p<0.004). Moreover, this biological model is independent of and complementary to the conventional International Prognostic Score using multivariate Cox proportional hazards analysis. Conclusions We have developed a molecular risk algorithm that includes genes expressed by tumoral cells and their reactive microenvironment. This makes it possible to classify advanced cHL patients with different risk of treatment failure using a method that could be applied to routinely prepared tumor blocks. These results could pave the way for more individualized and risk-adapted treatment strategies of cHL patients, enabling subsets of patients to be identified who might benefit from alternative approaches Disclosures: No relevant conflicts of interest to declare.

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