Dataset Information

Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma.

ABSTRACT: The present study aimed to improve the understanding of non-uterine leiomyosarcoma (NULMS) prognostic genes through system biology approaches. This cancer is heterogeneous and rare. Moreover, gene interaction networks have not been reported in NULMS yet. The datasets were obtained from the public gene expression databases. Seven co-expression modules were identified from 5000 most connected genes; using weighted gene co-expression network analysis. Using Cox regression, the modules showed favorable (HR?=?0.6, 95% CI?=?0.4-0.89, P?=?0.0125), (HR?=?0.65, 95% CI?=?0.44-0.98, P?=?0.04) and poor (HR?=?1.55, 95% CI?=?1.06-2.27, P?=?0.025) prognosis to the overall survival (OS) (time?=?3740 days). The first one was significant in multivariate HR estimates (HR?=?0.4, 95% CI?=?0.28-0.69, P?=?0.0004). Enriched genes through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) revealed significant immune-related pathways; suggesting immune cell infiltration as a favorable prognostic factor. The most significant protective genes were ICAM3, NCR3, KLRB1, and IL18RAP, which were in one of the significant modules. Moreover, genes related to angiogenesis, cell-cell adhesion, protein glycosylation, and protein transport such as PYCR1, SRM, and MDFI negatively affected the OS and were found in the other related module. In conclusion, our analysis suggests that NULMS might be a good candidate for immunotherapy. Moreover, the genes found in this study might be potential candidates for targeted therapy.

SUBMITTER: Darzi M

PROVIDER: S-EPMC7840729 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma.

Darzi Mohammad M Gorgin Saeid S Majidzadeh-A Keivan K Esmaeili Rezvan R

Scientific reports 20210127 1

The present study aimed to improve the understanding of non-uterine leiomyosarcoma (NULMS) prognostic genes through system biology approaches. This cancer is heterogeneous and rare. Moreover, gene interaction networks have not been reported in NULMS yet. The datasets were obtained from the public gene expression databases. Seven co-expression modules were identified from 5000 most connected genes; using weighted gene co-expression network analysis. Using Cox regression, the modules showed favora ...[more]

PMID: 33504899

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Project description:BackgroundWhile large-scale genomic analyses symbolize a precious attempt to decipher the molecular foundation of uterine leiomyosarcoma (ULMS), bioinformatics results associated with the occurrence of ULMS based totally on WGCNA and CIBERSORT have not yet been reported. This study aimed to screen the hub genes and the immune cell infiltration pattern in ULMS by bioinformatics methods.MethodsFirstly, the GSE67463 dataset, including 25 ULMS tissues and 29 normal myometrium (NL) tissues, was downloaded from the public database. The differentially expressed genes (DEGs) were screened by the 'limma' package and hub modules were identified by weighted gene co-expression network analysis (WGCNA). Subsequently, gene function annotations were performed to investigate the biological role of the genes from the intersection of two groups (hub module and DEGs). The above genes were calculated in the protein-protein interaction (PPI) network to select the hub genes further. The hub genes were validated using external data (GSE764 and GSE68295). In addition, the differential immune cell infiltration between UL and ULMS tissues was investigated using the CIBERSORT algorithm. Finally, we used western blot to preliminarily detect the hub genes in cell lines.ResultsWGCNA analysis revealed a green-yellow module possessed the highest correlation with ULMS, including 1063 genes. A total of 172 DEGs were selected by thresholds set in the 'limma' package. The above two groups of genes were intersected to obtain 72 genes for functional annotation analysis. Interestingly, it indicated that 72 genes were mainly involved in immune processes and the Neddylation pathway. We found a higher infiltration of five types of cells (memory B cells, M0-type macrophages, mast cells activated, M1-type macrophages, and T cells follicular helper) in ULMS tissues than NL tissues, while the infiltration of two types of cells (NK cells activated and mast cells resting) was lower than in NL tissues. In addition, a total of five genes (KDR, CCL21, SELP, DPT, and DCN) were identified as the hub genes. Internal and external validation demonstrated that the five genes were over-expressed in NL tissues compared with USML tissues. Finally, the correlation analysis results indicate that NK cells activated and mast cells activated positively correlated with the hub genes. However, M1-type macrophages had a negative correlation with the hub genes. Moreover, only the DCN may be associated with the Neddylation pathway.ConclusionA series of evidence confirm that the five hub genes and the infiltration of seven types of immune cells are related to USML occurrence. These hub genes may affect the occurrence of USML through immune-related and Neddylation pathways, providing molecular evidence for the treatment of USML in the future.

Project description:ObjectiveUterine leiomyosarcoma (ULMS) is the most common subtype of uterine sarcoma and is difficult to discern from uterine leiomyoma (ULM) preoperatively. The aim of the study was to determine the potential and significance of immune-related diagnostic biomarkers in distinguishing ULMS from ULM.MethodsTwo public gene expression profiles (GSE36610 and GSE64763) from the GEO datasets containing ULMS and ULM samples were downloaded. Differentially expressed genes (DEGs) were selected and determined among 37 ULMS and 25 ULM control samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Disease Ontology (DO) enrichment analyses as well as gene set enrichment analysis (GSEA). The candidate biomarkers were identified by least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analyses. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the biomarker expression levels and diagnostic value in ULMS were verified in the GSE9511 and GSE68295 datasets (12 ULMS and 10 ULM), and validated by immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in ULMS.ResultIn total, 55 DEGs were recognized via GO analysis, and KEGG analyses revealed that the DEGs were enriched in nuclear division, and cell cycle. The recognized DEGs were primarily implicated in non-small cell lung carcinoma and breast carcinoma. Gene sets related to the cell cycle and DNA replication were activated in ULMS. DPP6 and MFAP5 were distinguished as diagnostic biomarkers of ULMS (AUC = 0.957, AUC = 0.899, respectively), and they were verified in the GSE9511 and GSE68295 datasets (AUC = 0.983, AUC = 0.942, respectively). The low expression of DPP6 and MFAP5 were associated with ULMS. In addition, the analysis of the immune microenvironment indicated that resting mast cells were positively correlated with DPP6 and MFAP5 expression and that eosinophils and M0 macrophages were negatively correlated with DPP6 expression (P<0.05).ConclusionThese findings indicated that DPP6 and MFAP5 are diagnostic biomarkers of ULMS, thereby offering a novel perspective for future studies on the occurrence, function and molecular mechanisms of ULMS.

Project description:BackgroundGastric cancer (GC) is a major public health problem worldwide. In recent decades, the treatment of gastric cancer has improved greatly, but basic research and clinical application of gastric cancer remain challenges due to the high heterogeneity. Here, we provide new insights for identifying prognostic models of GC.MethodsWe obtained the gene expression profiles of GSE62254 containing 300 samples for training. GSE15459 and TCGA-STAD for validation, which contain 200 and 375 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules. We performed Lasso regression and Cox regression analyses to identify the most significant five genes to develop a novel prognostic model. And we selected two representative genes within the model for immunohistochemistry staining with 105 GC specimens from our hospital to verify the prediction efficiency. Moreover, we estimated the correlation coefficient between our model and immune infiltration using the CIBERSORT algorithm. The data from GSE15459 and TCGA cohort validated the robustness and predictive accuracy of this prognostic model.ResultsOf the 12 gene modules identified, 1,198 green-yellow module genes were selected for further analysis. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using the Cox proportional hazards regression model. Finally, we constructed a five gene prognostic model: Risk Score = [(-0.7547) * Expression (ARHGAP32)] + [(-0.8272) * Expression (KLF5)] + [1.09 * Expression (MAMLD1)] + [0.5174 * Expression (MATN3)] + [1.66 * Expression (NES)]. The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group (p = 6.503e-11). The risk model was also regarded as an independent predictor of prognosis (HR, 1.678, p < 0.001). The observed correlation with immune cells suggested that this risk model could potentially predict immune infiltration.ConclusionThis study identified a potential risk model for prognosis and immune infiltration prediction in GC using WGCNA and Cox regression analysis.

Project description:Glioma is the most common primary brain tumor and glioblastoma multiforme (GBM) is the most malignant brain glioma with the worst prognosis. T cell immune regulator 1 (TCIRG1) constitutes the V0a3 subunit of vacuolar ATPase (V-ATPase), and the function of V-ATPase in malignant tumors, such as breast cancer, melanoma and hepatocellular carcinoma, has been reported. However, the effect of the TCIRG1 subunit on GBM remains to be fully elucidated. mRNA levels of TCIRG1 in different cancer types and the corresponding normal tissues were extracted from the Oncomine and Tumor Immune Estimation Resource (TIMER) databases. The Gene Expression Omnibus (access number: GSE16011), the Chinese Glioma Genome Atlas and The Cancer Genome Atlas were used to investigate the mRNA level of TCIRG1 in glioma. Protein level validation in glioma was performed using western blotting. The Database for Annotation, Visualization and Integrated Discovery was used to analyze Gene Ontology (GO) categories for genes correlated with TCIRG1 in GBM. Protein-protein interaction (PPI) networks and module analyses were performed using Cytoscape software and the MCODE plugin. The correlation between tumor immune cell infiltration and TCIRG1 expression was explored using the TIMER database. Additionally, the correlation between TCIRG1 and the gene signature of immune infiltration was explored through TIMER and Gene Expression Profiling Interactive Analysis. External validation of TCIRG1 expression according to immune signatures in GBM was performed using the GSE16011 dataset with the GlioVis online tool. It was found that TCIRG1 expression was increased in GBM and numerous malignant tumors and may serve as a biomarker of the mesenchymal subtype of GBM. GO category analysis of positively correlated genes revealed that TCIRG1 was correlated with the immune response in GBM. PPI network and module analyses also supported the potential function of TCIRG1 in the local immune response. The expression of TCIRG1 was associated with various immune markers. It was therefore speculated that TCIRG1 is associated with glioma malignancy and may be a marker of unfavorable prognosis in patients with GBM, and it could be regarded as a prognostic biomarker and an indicator of immune infiltration in GBM.

Project description:Background: Increasing evidence supports that competing endogenous RNAs (ceRNAs) and tumor immune infiltration act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking. Methods: RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of HCC downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis), and lncRNAs (DELs) to establish a ceRNA regulatory network. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways were analyzed to functionally annotate these DEMs. A multigene-based risk signature was developed utilizing least absolute shrinkage and selection operator method (LASSO) algorithm. Moreover, survival analysis and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (TIMER, XCELL, MCPcounter, QUANTISEQ, CIBERSORT, EPIC, and CIBERSORT-ABS) were utilized to characterize tumor immune microenvironment (TIME). Finally, the mutation data were analyzed by employing "maftools" package. Results: In total, 136 DELs, 128 DEMis, and 2,028 DEMs were recognized in HCC. A specific lncRNA-miRNA-mRNA network consisting of 3 lncRNAs, 12 miRNAs, and 21 mRNAs was established. A ceRNA-based prognostic signature was established to classify samples into two risk subgroups, which presented excellent prognostic performance. In additional, prognostic risk-clinical nomogram was delineated to assess risk of individual sample quantitatively. Besides, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was revealed. Conclusion: In this work, comprehensive analyses of ceRNAs coexpression network will facilitate prognostic prediction, delineate complexity of TIME, and contribute insight into precision therapy for HCC.

Dataset Information

Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma.

Publications

Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma.

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