Comprehensive analysis of the MIR4435-2HG/miR-1-3p/MMP9/miR-29-3p/DUXAP8 ceRNA network axis in hepatocellular carcinoma

AbstractA growing number of studies have shown that competitive endogenous RNA (ceRNA) regulatory networks might play important roles during the process of hepatocellular carcinoma (HCC). This study assessed the role of the ceRNA network in immune cell infiltration in HCC. Immune-related gene sets were downloaded from Molecular Signatures Database, and differentially expressed genes were screened based on TCGA HCC transcriptome data. The corresponding miRNAs with low expression and good prognostic implications, and the corresponding lncRNAs with high expression and poor prognostic were identified to construct ceRNA networks. The networks were utilized for clinical correlation analysis and risk model construction, and the CIBERSORT algorithm was applied to assess immune cell infiltration. In this study, the mRNA-miRNA-lncRNA model was used to construct a ceRNA network in HCC using immune-related differentially expressed mRNAs. Assessment of the MIR4435-2HG/hsa-miR-1-3p/MMP9/hsa-miR-29-3p/DUXAP8 ceRNA network axis in HCC showed that a high risk/poor prognosis was significantly correlated with tumor stage and invasion depth. MMP9 was positively correlated with resting M0 macrophages and NK cells and negatively correlated with activated mast cells, resting mast cells, monocytes and activated NK cells. DUXAP8 was positively correlated with M2 macrophages and negatively correlated with MIR4435-2HG, which was positively correlated with M2 macrophages and negatively correlated with activated mast cells, CD8 T cells and follicular helper T cells. The correlation of the MIR4435-2HG/hsa-miR-1-3p/MMP9/hsa-miR-29-3p/DUXAP8 ceRNA network axis with immune cell infiltration provides further information on the mechanism of HCC development. The result might improve our understanding the interactions between immune related genes and non-coding RNAs in the occurrence and development of HCC, and the relevant RNAs might be used as diagnostic and prognostic biomarkers and molecular targets in HCC patients.

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Identification of the Key Serum Biomarkers to Diagnose and Predict Metastasis of Osteosarcoma Based on the Analysis of Immune Cell Infiltration Characteristics

10.21203/rs.3.rs-1047570/v1 ◽

2021 ◽

Author(s):

Zhihao Chen ◽

Liubing Li ◽

Ziyuan Li ◽

Xi Wang ◽

Mingxiao Han ◽

...

Keyword(s):

T Cells ◽

Cd8 T Cells ◽

Immune Cells ◽

Immune Cell ◽

Serum Biomarkers ◽

Differentially Expressed ◽

Cell Infiltration ◽

Diagnostic Model ◽

Immune Cell Infiltration ◽

Differentially Expressed Mirnas

Abstract Background: The potential functions of circular RNAs (circRNAs) and micro RNAs (miRNAs) in osteosarcoma (OS) have not been fully elucidated. Especially, the behavior and mechanism of immune responses in OS development and progression have not been fully demonstrated. It was reported that circRNAs and miRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of many cancers. This study aimed to identify novel key serum biomarkers to diagnose and predict metastasis of OS based on the analysis of immune cell infiltration characteristics.Methods: The differentially-expressed circRNAs (DEcircRNAs), differentially-expressed miRNAs (DEmiRNAs),and differentially-expressed mRNAs (DEmRNAs) of human OS were investigated based on the microarray data downloaded from Gene Expression Omnibus (GEO) datasets. Then, we analyzed immune characteristics pattern of tumor-infiltrating immune cells in OS. On this basis, we identified statistically-significant transcription factors and performed pathway enrichment analysis. Subsequently, we constructed protein-protein interaction (PPI) and competitive endogenous RNA (ceRNA) networks. Moreover, the biological characteristic of targets in ceRNA networks was proposed. Finally, the expression and diagnostic capability of these potential biomarkers from ceRNA network were confirmed by RT-qPCR in patients’ serum.Results: Seven differentially-expressed circRNAs (DEcircRNAs), 166 differentially-expressed miRNAs (DEmiRNAs) and 175 differentially-expressed mRNAs (DEmRNAs) were identified in total. The highest level of infiltration in OS patients were M0 macrophages, M2 macrophages and CD8+ T cells. Further, M0 macrophages and CD8+ T cells were showed the largest negative correlation coefficients. These significant immune characteristics pattern of tumor-infiltrating immune cells were revealed by the principal component analysis in OS. Moreover, we found 185 statistically-significant transcription factors in which the main significant molecules show the potential in immunotherapy of OS. Hsa-circ-0010220, hsa-miR-326, hsa-miR-338-3p, and FAM98A from ceRNA networks associated with immune cell infiltration were confirmed as the potential novel biomarkers for OS diagnosis, of which FAM98A could distinguish and predict metastasis. Most importantly, a novel diagnostic model consisting of the four promising biomarkers (hsa-circ-0010220, hsa-miR-326, hsa-miR-338-3p, and FAM98A) was highlighted with 0.928 AUC value.Conclusions: In summary, the potenial serum biomarkers to diagnose and predict metastasis of OS based on the analysis of immune cell infiltration characteristics were found, and a novel diagnostic model consisting of four promising serum biomarkers was proposed firstly. These results provided a new perspective for the immunotherapy of OS.

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POS0363 IDENTIFICATION OF MOLECULAR PHENOTYPES AND IMMUNE CELL INFILTRATION IN PSORIATIC ARTHRITIS PATIENTS’ SKIN TISSUES BY INTEGRATED BIOINFORMATICS ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.2171 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 411.1-411

Author(s):

T. Cheng ◽

S. X. Zhang ◽

J. Qiao ◽

R. Zhao ◽

S. Song ◽

...

Keyword(s):

T Cells ◽

Psoriatic Arthritis ◽

Nk Cells ◽

Bioinformatics Analysis ◽

Immune Cell ◽

Functional Enrichment ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Signature Genes ◽

Molecular Phenotypes

Background:Psoriatic arthritis (PsA) is an inflammatory musculoskeletal disease associated with cutaneous psoriasis1. Heterogeneity of clinical manifestation often makes differential diagnosis difficult 2. Thus, the underlying molecular pathogenesis of PsA need to be further studied to diagnose early and ensure optimal management of arthritis and key comorbidities.Objectives:This research was conducted to identify molecular phenotypes and immune infiltration in the skin tissues of psoriatic arthritis patients according to bioinformatics analysis.Methods:The mRNA expression profiles of GSE13355 (116 samples), GSE14905 (56 samples) and GSE30999 (162 samples) were obtained from the publicly GEO databases. Non-negative matrix factorization (NMF), functional enrichment and cibersort algorithm were applied to illustrate the conditions of PsA patients’ skin tissues for classification after screening the differentially expressed genes (DEGs) between lesion biopsy and non-lesion biopsy.Results:Two subsets (Sub1 and Sub2) were identified and validated by NMF typing of 612 detected DEGs (Figure 1a). A total of 54 signature genes (18 in Sub1 and 36 in Sub2) were obtained (Figure 1b). GO and KEGG enrichment analysis showed the signature genes in Sub1 were mainly involved in proliferation and differentiation of immune cells, whereas genes in Sub2 were related to humoral immune response mediated by antimicrobial peptide (Figure 1c.1d). Further, immune cell infiltration results revealed Sub2 had higher levels of resting NK cells (P<0.001), macrophages M1(P<0.001), resting mast cells (P<0.001) and regulatory T cells (P<0.001) but lower concentrations of activated CD4+ memory T cells (P<0.001), activated NK cells (P<0.05), activated dendritric cells(P<0.001), eosinophils (P<0.05) and neutrophil (P<0.001) (Figure 1e).Conclusion:The pathogenesis of psoriatic arthritis is related to both cellular immunity and humoral immunity. It is indispensable to adjust the treatment strategies according to patient’s immune status.References:[1]Ritchlin CT, Colbert RA, Gladman DD. Psoriatic Arthritis. The New England journal of medicine 2017;376(10):957-70. doi: 10.1056/NEJMra1505557 [published Online First: 2017/03/09].[2]Veale DJ, Fearon U. The pathogenesis of psoriatic arthritis. Lancet (London, England) 2018;391(10136):2273-84. doi: 10.1016/s0140-6736(18)30830-4 [published Online First: 2018/06/13].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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GRB10 and E2F3 as Diagnostic Markers of Osteoarthritis and Their Correlation with Immune Infiltration

Diagnostics ◽

10.3390/diagnostics10030171 ◽

2020 ◽

Vol 10 (3) ◽

pp. 171 ◽

Cited By ~ 3

Author(s):

Ya-Jun Deng ◽

En-Hui Ren ◽

Wen-Hua Yuan ◽

Guang-Zhi Zhang ◽

Zuo-Long Wu ◽

...

Keyword(s):

T Cells ◽

Mast Cells ◽

Immune Cells ◽

Immune Cell ◽

Diagnostic Markers ◽

Cell Infiltration ◽

Recursive Feature Elimination ◽

Support Vector ◽

Resting Cells ◽

Immune Cell Infiltration

This study aimed to find potential diagnostic markers for osteoarthritis (OA) and analyze the role of immune cells infiltration in this pathology. We used OA datasets from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination algorithms were used to screen and verify the diagnostic markers of OA. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in OA tissues, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. A total of 458 DEGs were screened in this study. GRB10 and E2F3 (AUC = 0.962) were identified as diagnostic markers of OA. Immune cell infiltration analysis found that resting mast cells, T regulatory cells, CD4 memory resting T cells, activated NK cells, and eosinophils may be involved in the OA process. In addition, GRB10 was correlated with NK resting cells, naive CD4 + T cells, and M1 macrophages, while E2F3 was correlated with resting mast cells. In conclusion, GRB10 and E2F3 can be used as diagnostic markers of osteoarthritis, and immune cell infiltration plays an important role in the occurrence and progression of OA.

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Identification of uterine leiomyosarcoma-associated hub genes and immune cell infiltration pattern using weighted co-expression network analysis and CIBERSORT algorithm

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02333-z ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Xiaoqing Shen ◽

Zhujuan Yang ◽

Songwei Feng ◽

Yi Li

Keyword(s):

Mast Cells ◽

Network Analysis ◽

Nk Cells ◽

Immune Cell ◽

Cell Infiltration ◽

Uterine Leiomyosarcoma ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Limma Package ◽

M1 Type

Abstract Background While 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. Methods Firstly, 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. Results WGCNA 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. Conclusion A 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.

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The Landscape of Immune Cell Infiltration in the Glomerulus of Diabetic Nephropathy: Evidence Based on Bioinformatics

10.21203/rs.3.rs-554679/v1 ◽

2021 ◽

Author(s):

Wei ZHOU ◽

Yaoyu LIU ◽

Qinghong HU ◽

Jiuyao ZHOU ◽

Hua LIN

Keyword(s):

T Cells ◽

Diabetic Nephropathy ◽

Mast Cells ◽

Protein Interactions ◽

Immune Cell ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Immune Infiltration

Abstract BackgroundIncreasing evidence suggests that immune cell infiltration contributes to the pathogenesis and progression of diabetic nephropathy (DN). We aim to unveil the immune infiltration pattern in the glomerulus of DN and provide potential targets for immunotherapy. MethodsInfiltrating percentage of 22 types of immune cell in the glomerulus tissues were estimated by the CIBERSORT algorithm based on three transcriptome datasets mined from the GEO database. Differentially expressed genes (DEGs) were identified by the “limma” package. Then immune-related DEGs were identified by intersecting DEGs with immune-related genes (downloaded from Immport database). The protein-protein interactions of Immune-related DEGs were explored using the STRING database and visualized by Cytoscape. The enrichment analyses for KEGG pathways and GO terms were carried out by the gene set enrichment analysis (GSEA) method. Results9 types of immune cell were revealed to be significantly altered in the glomerulus tissues of DN (Up: B cells memory, T cells CD4 naive, Macrophages M2, Dendritic cells resting, Mast cells resting, Mast cells activated; Down: NK cells resting, Monocytes, Neutrophils). Correlation analysis revealed that immune infiltration act as a complicated and tightly regulated network, among which T cells gamma delta and T cells CD4 naive show the most synergistic effect (r = 0.58, p ＜ 0.001); meanwhile, T cells CD8 and T cells CD4 memory resting show the most competitive effect (r = - 0.67, p ＜ 0.001). Several pathways related to immune were significantly activated. Moreover, 6 hub genes with a medium to strong correlation with renal function (eGFR) were identified (ALB, EGF, FOS, CXCR1, CXCR2, CCL2). ConclusionIn the glomerulus of DN, the immune infiltration pattern changed significantly. A complicated and tightly regulated network of immune cells exists in the pathological of DN. The hub genes identified here will facilitate the development of immunotherapy.

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SFTPA1 is a potential prognostic biomarker correlated with immune cell infiltration and response to immunotherapy in lung adenocarcinoma

Cancer Immunology Immunotherapy ◽

10.1007/s00262-021-02995-4 ◽

2021 ◽

Author(s):

Lu Yuan ◽

Xixi Wu ◽

Longshan Zhang ◽

Mi Yang ◽

Xiaoqing Wang ◽

...

Keyword(s):

Lung Cancer ◽

T Cells ◽

Lung Adenocarcinoma ◽

Expression Profile ◽

Immune Cell ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Regulatory Pathways ◽

Chronic Lung Diseases ◽

Potential Biomarker

AbstractPulmonary surfactant protein A1 (SFTPA1) is a member of the C-type lectin subfamily that plays a critical role in maintaining lung tissue homeostasis and the innate immune response. SFTPA1 disruption can cause several acute or chronic lung diseases, including lung cancer. However, little research has been performed to associate SFTPA1 with immune cell infiltration and the response to immunotherapy in lung cancer. The findings of our study describe the SFTPA1 expression profile in multiple databases and was validated in BALB/c mice, human tumor tissues, and paired normal tissues using an immunohistochemistry assay. High SFTPA1 mRNA expression was associated with a favorable prognosis through a survival analysis in lung adenocarcinoma (LUAD) samples from TCGA. Further GeneOntology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that SFTPA1 was involved in the toll-like receptor signaling pathway. An immune infiltration analysis clarified that high SFTPA1 expression was associated with an increased number of M1 macrophages, CD8+ T cells, memory activated CD4+ T cells, regulatory T cells, as well as a reduced number of M2 macrophages. Our clinical data suggest that SFTPA1 may serve as a biomarker for predicting a favorable response to immunotherapy for patients with LUAD. Collectively, our study extends the expression profile and potential regulatory pathways of SFTPA1 and may provide a potential biomarker for establishing novel preventive and therapeutic strategies for lung adenocarcinoma.

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Enhanced CD4+ and CD8+ T cell infiltrate within convex hull defined pancreatic islet borders as autoimmune diabetes progresses

Scientific Reports ◽

10.1038/s41598-021-96327-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Alexander J. Dwyer ◽

Jacob M. Ritz ◽

Jason S. Mitchell ◽

Tijana Martinov ◽

Mohannad Alkhatib ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Convex Hull ◽

Pancreatic Islets ◽

Immune Cell ◽

Nod Mice ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Analytical Strategy ◽

Islet Mass

AbstractThe notion that T cell insulitis increases as type 1 diabetes (T1D) develops is unsurprising, however, the quantitative analysis of CD4+ and CD8+ T cells within the islet mass is complex and limited with standard approaches. Optical microscopy is an important and widely used method to evaluate immune cell infiltration into pancreatic islets of Langerhans for the study of disease progression or therapeutic efficacy in murine T1D. However, the accuracy of this approach is often limited by subjective and potentially biased qualitative assessment of immune cell subsets. In addition, attempts at quantitative measurements require significant time for manual analysis and often involve sophisticated and expensive imaging software. In this study, we developed and illustrate here a streamlined analytical strategy for the rapid, automated and unbiased investigation of islet area and immune cell infiltration within (insulitis) and around (peri-insulitis) pancreatic islets. To this end, we demonstrate swift and accurate detection of islet borders by modeling cross-sectional islet areas with convex polygons (convex hulls) surrounding islet-associated insulin-producing β cell and glucagon-producing α cell fluorescent signals. To accomplish this, we used a macro produced with the freeware software ImageJ equipped with the Fiji Is Just ImageJ (FIJI) image processing package. Our image analysis procedure allows for direct quantification and statistical determination of islet area and infiltration in a reproducible manner, with location-specific data that more accurately reflect islet areas as insulitis proceeds throughout T1D. Using this approach, we quantified the islet area infiltrated with CD4+ and CD8+ T cells allowing statistical comparison between different age groups of non-obese diabetic (NOD) mice progressing towards T1D. We found significantly more CD4+ and CD8+ T cells infiltrating the convex hull-defined islet mass of 13-week-old non-diabetic and 17-week-old diabetic NOD mice compared to 4-week-old NOD mice. We also determined a significant and measurable loss of islet mass in mice that developed T1D. This approach will be helpful for the location-dependent quantitative calculation of islet mass and cellular infiltration during T1D pathogenesis and can be combined with other markers of inflammation or activation in future studies.

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Development of an Immune-Related Gene Signature for Prognosis in Melanoma

Frontiers in Oncology ◽

10.3389/fonc.2020.602555 ◽

2021 ◽

Vol 10 ◽

Author(s):

Jia-An Zhang ◽

Xu-Yue Zhou ◽

Dan Huang ◽

Chao Luan ◽

Heng Gu ◽

...

Keyword(s):

T Cells ◽

Immune Cell ◽

Treatment Method ◽

Gene Signature ◽

Cell Infiltration ◽

Venn Diagram ◽

Immune Cell Infiltration ◽

Tumor Purity ◽

New Treatment ◽

Immune Related Genes

Melanoma remains a potentially deadly malignant tumor. The incidence of melanoma continues to rise. Immunotherapy has become a new treatment method and is widely used in a variety of tumors. Original melanoma data were downloaded from TCGA. ssGSEA was performed to classify them. GSVA software and the "hclust" package were used to analyze the data. The ESTIMATE algorithm screened DEGs. The edgeR package and Venn diagram identified valid immune-related genes. Univariate, LASSO and multivariate analyses were used to explore the hub genes. The "rms" package established the nomogram and calibrated the curve. Immune infiltration data were obtained from the TIMER database. Compared with that of samples in the high immune cell infiltration cluster, we found that the tumor purity of samples in the low immune cell infiltration cluster was higher. The immune score, ESTIMATE score and stromal score in the low immune cell infiltration cluster were lower. In the high immune cell infiltration cluster, the immune components were more abundant, while the tumor purity was lower. The expression levels of TIGIT, PDCD1, LAG3, HAVCR2, CTLA4 and the HLA family were also higher in the high immune cell infiltration cluster. Survival analysis showed that patients in the high immune cell infiltration cluster had shorter OS than patients in the low immune cell infiltration cluster. IGHV1-18, CXCL11, LTF, and HLA-DQB1 were identified as immune cell infiltration-related DEGs. The prognosis of melanoma was significantly negatively correlated with the infiltration of CD4+ T cells, CD8+ T cells, dendritic cells, neutrophils and macrophages. In this study, we identified immune-related melanoma core genes and relevant immune cell subtypes, which may be used in targeted therapy and immunotherapy of melanoma.

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Glucose-6-Phosphate Dehydrogenase is a Prognostic Biomarker and Correlated with Immune Infiltrates in Hepatocellular Carcinoma

10.21203/rs.3.rs-1232206/v1 ◽

2022 ◽

Author(s):

Yang Bu ◽

Kejun Liu ◽

Yiming Niu ◽

Ji Hao ◽

Lei Cui ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Tumor Microenvironment ◽

Liver Cancer ◽

Immune Cell ◽

Cox Regression ◽

Functional Enrichment ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Potential Biomarker ◽

Glucose 6 Phosphate Dehydrogenase

Abstract Background: Glucose-6-phosphate dehydrogenase (G6PD) plays an important role in the metabolic and immunological aspects of tumors. In hepatocellular carcinoma (HCC), the alteration of tumor microenvironment influences recurrence and metastasis. We extracted G6PD-related data from public databases of HCC tissues and used a bioinformatics approach to explore the correlation between G6PD expression and clinicopathological features and prognosis of immune cell infiltration in HCC.Methods: We extract G6PD expression information from TCGA and GEO databases in liver cancer tissues and normal tissues, validated by immunohistochemistry, and the correlation between G6PD expression and clinical features is analyzed, and the clinical significance of G6PD in liver cancer is assessed by Kaplan-Meier, Cox regression and prognostic line graph models. Functional enrichment analysis is performed by protein-protein interaction (PPI) network, GO/KEGG, GSEA and G6PD-associated differentially expressed genes (DEGs). TIMER and ssGSEA packages are used to assess the correlation between expression and the level of immune cell infiltration.Results: Our results show that G6PD expression is significantly upregulated in hepatocellular carcinoma tissues (P < 0.001). G6PD expression is associated with histological grade, pathological stage, T-stage, vascular infiltration and AFP level (P < 0.05); HCC patients in the low G6PD expression group had longer overall survival and better prognosis compared with the high G6PD expression group (P < 0.05). The level of G6PD expression also affects the levels of macrophages, unactivated dendritic cells, B cells, and follicular helper T cells in the tumor microenvironment.Conclusion: High expression of G6PD is a potential biomarker for poor prognosis of hepatocellular carcinoma, and G6PD may be a target for immunotherapy of HCC.

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Identification of Potential Therapeutic Targets and Molecular Regulatory Mechanisms for Osteoporosis by Bioinformatics Methods

BioMed Research International ◽

10.1155/2021/8851421 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Li Zhang ◽

Yunlong Yang ◽

Dechun Geng ◽

Yonghua Wu

Keyword(s):

Immune Cell ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Differential Analysis ◽

Ppi Network ◽

The Difference ◽

And Control

Background. Osteoporosis is characterized by low bone mass, deterioration of bone tissue structure, and susceptibility to fracture. New and more suitable therapeutic targets need to be discovered. Methods. We collected osteoporosis-related datasets (GSE56815, GSE99624, and GSE63446). The methylation markers were obtained by differential analysis. Degree, DMNC, MCC, and MNC plug-ins were used to screen the important methylation markers in PPI network, then enrichment analysis was performed. ROC curve was used to evaluate the diagnostic effect of osteoporosis. In addition, we evaluated the difference in immune cell infiltration between osteoporotic patients and control by ssGSEA. Finally, differential miRNAs in osteoporosis were used to predict the regulators of key methylation markers. Results. A total of 2351 differentially expressed genes and 5246 differentially methylated positions were obtained between osteoporotic patients and controls. We identified 19 methylation markers by PPI network. They were mainly involved in biological functions and signaling pathways such as apoptosis and immune inflammation. HIST1H3G, MAP3K5, NOP2, OXA1L, and ZFPM2 with higher AUC values were considered key methylation markers. There were significant differences in immune cell infiltration between osteoporotic patients and controls, especially dendritic cells and natural killer cells. The correlation between MAP3K5 and immune cells was high, and its differential expression was also validated by other two datasets. In addition, NOP2 was predicted to be regulated by differentially expressed hsa-miR-3130-5p. Conclusion. Our efforts aim to provide new methylation markers as therapeutic targets for osteoporosis to better treat osteoporosis in the future.

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