Intersection Differentially Expressed Genes Research Articles

Mongolian HCC vs. Caucasian HCC: The Metabolic Reprogramming Process in Mongolian HCC is an Interesting Difference.

Racial/ethnic and region disparities in incidence and mortality are obviously in liver cancer. Mongolia has the highest reported incidence and mortality of hepatocellular carcinoma (HCC) in the world, while the incidence of HCC is relatively low in the United States, but differences in their molecular characteristics remain largely elusive. Here we report differentially expressed genes (DEGs) in Mongolian hepatocellular carcinoma and in Caucasian HCC and their intersection DEGs, as well as their corresponding signaling pathways in Mongolian and Caucasian hepatocellular carcinoma patients based on the transcriptome sequences from Gene Expression Omnibus (GEO) database. We got 908 up-regulated genes and 1946 down-regulated genes in Mongolian HCC, 1244 up-regulated genes and 1912 down-regulated genes in Caucasian HCC, 254 Co-upregulated genes and 1035 co-downregulated genes in Mongolian and Caucasian. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that most of the genes with altered expression levels in Mongolian HCC participate in biological processes that involve metabolic reprogramming of various substances, accounting for about one-third of all biological processes. In particular, multiple amino acid biosynthesis and metabolic processes appear to be specific in Mongolian HCC compared with Caucasian HCC. The biological processes they share include those in which most immune cells are involved and cell cycle-related biological processes. In addition, we also found the genes UPP2, PCK1, GLYAT, GNMT, ADH1B and HPD, encode for key metabolic enzymes, whose expression level up-regulated or down-regulated more than 5 times in Mongolian HCC and was dramatically correlated with survival in Mongolian HCC (p value < 0.01), More importantly, these molecules are potential targets for some metabolic antitumor drugs. This study not only makes up for the shortcomings of previous studies on liver cancer, which paid more attention to its commonality, but ignored the specificity of liver cancer in different races and regions. More importantly, the purpose of this study is to identify robust molecular subclasses and information with underlying unique tumor biology. And this study may have important implications for the study of the pathogenic factors and molecular mechanisms of hepatocellular carcinoma and the precise therapy of Mongolian HCC.

Identification of Novel Biomarkers With Diagnostic Value and Immune Infiltration in Burn Injury.

Burn injury is an intractable problem in the field of surgery where screening relevant target genes and exploring pathological mechanisms through bioinformatic methods has become a necessity. Herein, we integrated three burn injury mRNA microarray datasets from the Gene Expression Omnibus database to analyze the hub differentially expressed genes (DEGs) between burn injury patient samples and healthy human samples; we conducted multiple functional enrichment analyses and constructed the protein–protein interaction (PPI) network. Finally, we evaluated the immune infiltration in the burn injury microenvironment. A total of 84 intersection DEGs (32 upregulated and 52 downregulated) were screened in burn injury patients via integrated analyses. Upregulated genes were primarily enriched in regulation of T cell activation, regulation of response to DNA damage stimulus, positive regulation of innate immune response, positive regulation of defense response. We also identified 10 hub genes from the PPI network (CCNB2, MYO10, TTK, POLQ, VASP, TIMP1, CDK16, MMP1, ZYX, and PKMYT1). Next, we found that 22 immune cells were substantially changed during the burn injury by CIBERSORT. In addition, we verified that VASP and POLQ are two novel diagnostic markers in burn processes with high diagnostic efficacy via immunohistochemistry. In summary, we identified several key genes involved in burn injury and provided a favorable basis for elucidating the molecular mechanisms of burn injury through comprehensive bioinformatic analysis.

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

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.

Identification of AGT and CD44 in methotrexate-resistant colorectal cancer and reversal of methotrexate-resistance

This study aims to screen out hub genes in 2 methotrexate-resistant colorectal cancer (CRC) cells (HT29 and Caco2), compared with parental CRC cells and reverse methotrexate-resistance in methotrexate-resistant CRC. GEO database and R software were utilized to analyze the gene expression profiles GSE11440 and GSE16066. Venn diagram was used to identify intersection differentially expressed genes (DEGs) between GSE11440 and GSE16066. Protein-protein interaction (PPI) was utilized to screen out central node genes. Hub genes were determined by volcano graphs, heatmaps and box plots. The functional enrichment analysis was exhibited with DAVID. The GEPIA was used to obtain survival curves to analyze association between patient prognosis and hub genes. Western blotting was used to detect the expressions of hub genes. CCK-8 assay was used to show MTX-resistant CRC cell viability following CD44 inhibitor (THIQ) and AGT inhibitor (O6-BG) treatments. In our results, there were 180 intersection DEGs between GSE11440 and GSE16066. CD44 and AGT were screened out as hub genes by PPI, heatmaps, volcano and box plots. In the 2 MTX-resistant CRC cells, the expressions of CD44 and AGT were up-regulated compared with parental CRC cells. The results of western blotting showed that CD44 and AGT were up-regulated in MTX-resistant HT29 and Caco2 cells compared with parental CRC cells. CCK-8 assay results showed that the combination of MTX with O6-BG or THIQ could significantly reduce the activity of MTX-resistant CRC cells. This research screened out CD44 and AGT in MTX-resistant CRC cells by bioinformatics and suggested that the combination of MTX with O6-BG or THIQ could enhance the sensitivity of MTX-resistant CRC cells to MTX. This research provides a new strategy for overcoming MTX-resistance in CRC.

Screening and Identification of Key Microenvironment-Related Genes in Non-functioning Pituitary Adenoma.

PurposeNon-functioning pituitary adenoma (NFPA) is a very common type of intracranial tumor, which can be locally invasive and can have a high recurrence rate. The tumor microenvironment (TME) shows a high correlation with tumor pathogenesis and prognosis. The current study aimed to identify microenvironment-related genes in NFPAs and assess their prognostic value.Methods73 NFPA tumor samples were collected from Beijing Tiantan Hospital and transcriptional expression profiles were obtained through microarray analysis. The immune and stromal scores of each sample were calculated through the ESTIMATE algorithm, and the patients were divided into high and low immune/stromal score groups. Intersection differentially expressed genes (DEGs) were then obtained to construct a protein–protein interaction (PPI) network. Potential functions and pathways of intersection DEGs were then analyzed through Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. The prognostic value of these genes was evaluated. The quantitative real-time polymerase chain reaction in another set of NFPA samples was used to confirm the credibility of the bioinformatics analysis.ResultsThe immune/stromal scores were significantly correlated with cavernous sinus (CS) invasion. The Kaplan–Meier curve indicated that the high immune score group was significantly related to poor recurrence-free survival. We identified 497 intersection DEGs based on the high vs. low immune/stromal score groups. Function enrichment analyses of 497 DEGs and hub genes from the PPI network showed that these genes are mainly involved in the immune/inflammatory response, T cell activation, and the phosphatidylinositol 3 kinase-protein kinase B signaling pathway. Among the intersection DEGs, 88 genes were further verified as significantly expressed between the CS invasive group and the non-invasive group, and five genes were highly associated with NFPA prognosis.ConclusionWe screened out a series of critical genes associated with the TME in NFPAs. These genes may play a fundamental role in the development and prognosis of NFPA and may yield new therapeutic targets.

Identification of prognostic genes in uveal melanoma microenvironment.

BackgroundUveal melanoma (UM) is the most common primary intraocular malignancy in adults. Many previous studies have demonstrated that the infiltrating of immune and stromal cells in the tumor microenvironment contributes significantly to prognosis.MethodsDataset TCGA-UVM, download from TCGA portal, was taken as the training cohort, and GSE22138, obtained from GEO database, was set as the validation cohort. ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to initial screen for potential prognostic genes. Then these genes entered into the validation cohort for validation using the same methods as that in the training cohort. Moreover, we conducted correlation analyses between the genes obtained in the validation cohort and the status of chromosome 3, chromosome 8q, and tumor metastasis to get prognosis genes. At last, the immune infiltration analysis was performed between the prognostic genes and 6 main kinds of tumor-infiltrating immune cells (TICs) for understanding the role of the genes in the tumor microenvironment.Results959 intersection DEGs were found in the UM microenvironment. Kaplan-Meier and Cox analysis was then performed in the training and validation cohorts on these DEGs, and 52 genes were identified with potential prognostic value. After comparing the 52 genes to chromosome 3, chromosome 8q, and metastasis, we obtained 21 genes as the prognostic genes. The immune infiltration analysis showed that Neutrophil had the potential prognostic ability, and almost every prognostic gene we had identified was correlated with abundances of Neutrophil and CD8+ T Cell.ConclusionsIdentifying 21 prognosis genes (SERPINB9, EDNRB, RAPGEF3, HFE, RNF43, ZNF415, IL12RB2, MTUS1, NEDD9, ZNF667, AZGP1, WARS, GEM, RAB31, CALHM2, CA12, MYEOV, CELF2, SLCO5A1, ISM1, and PAPSS2) could accurately identify patients' prognosis and had close interactions with Neutrophil in the tumor environment, which may provide UM patients with personalized prognosis prediction and new treatment insights.

Identification of a Novel Tumor Microenvironment–Associated Eight-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma

BackgroundLung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Accumulating evidence suggests the tumor microenvironment is correlated with the tumor progress and the patient’s outcome. This study aimed to establish a gene signature based on tumor microenvironment that can predict patients’ outcomes for LUAD.MethodsDataset TCGA-LUAD, downloaded from the TCGA portal, were taken as training cohort, and dataset GSE72094, obtained from the GEO database, was set as validation cohort. In the training cohort, ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan–Meier analysis and univariate Cox regression model were performed on intersection DEGs to preliminarily screen prognostic genes. Besides, the LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan–Meier, Cox, and receiver operating characteristic curve (ROC) analyses. In addition, the correlation between tumor mutational burden (TMB) and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment.ResultsAn eight-gene signature was built, and it was examined by Kaplan–Meier analysis, revealing that a significant overall survival difference was seen. The eight-gene signature was further proven to be independent of other clinico-pathologic parameters via the Cox regression analyses. Moreover, the ROC analysis demonstrated that this signature owned a better predictive power of LUAD prognosis. The eight-gene signature was correlated with TMB. Furthermore, GSEA and immune infiltrating analyses showed that the exact pathways related to the characteristics of eight-genes signature, and identified the vital roles of Mast cells resting and B cells naive in the prognosis of the eight-gene signature.ConclusionIdentifying the eight-gene signature (INSL4, SCN7A, STAP1, P2RX1, IKZF3, MS4A1, KLRB1, and ACSM5) could accurately identify patients’ prognosis and had close interactions with Mast cells resting and B cells naive, which may provide insight into personalized prognosis prediction and new therapies for LUAD patients.

Identification of key genes and construction of CircRNA–miRNA–mRNA regulatory networks in osteoarthritis

Background: Osteoarthritis (OA) is one of the most frequent degenerative joint diseases with high rate of disability, but its mode of action remains largely unclear. The current study was aimed at identifying key genes and molecular mechanism in OA. Gene expression datasets (GSE1919, GSE55235, GSE55457) were downloaded from Gene Expression Omnibus for integrated bioinformatics analysis. Differentially expressed genes (DEGs) in OA synovial tissues were identified using GeoDiver and GEO2R. Gene ontology enrichment analyses were undertaken via FunRich and Metascape. Also, Gene Set Enrichment Analysis was performed using miRWalk3.0. Subsequently, pathways interrelation analysis of hub genes was carried out using plug-in ClueGO v2.3.3. Additionally, circRNA- miRNA-mRNA regulatory networks were visualized using Cytoscape. Results: A total of 508 DEGs were obtained from three GSE datasets, of these five intersection DEGs (TNFAIP3, VEGFA, GADD45B, SIK1, KLF9) were shared by three GSE datasets. Intersection DEGs were significantly enriched in LKB1 signaling events, signaling events mediated by focal adhesion kinase, and PDGFR-beta signaling pathway. Enrichment analysis for all the DEGs showed that they mainly enriched in inflammatory response, cytokine production, blood vessel development, stress response, osteoclast differentiation, and MAPK signaling pathway. A total of 39 genes were regarded as hub genes and pathways interrelation analysis indicated that hub genes mainly enriched in TNF signaling pathway, IL-17 signaling pathway, and NF-kappa B signaling pathway. Conclusions: The current study revealed the potential key genes, pathways, and circRNA-miRNA-mRNA regulatory networks in OA, which may contribute to a more comprehensive understanding of OA pathogenesis.

Genes and pathways identified in thyroid carcinoma based on bioinformatics analysis

The objective of this study was to investigate the key genes and pathways associated with thyroid carcinoma. Based on the microarray data of GSE27155, we identified the differentially expressed genes (DEGs) between four types of thyroid carcinoma samples (papillary carcinoma (PTC), oncocytic carcinoma (OTC), follicular carcinoma (FTC) and anaplastic carcinoma (ATC)) and normal controls. With the obtained DEGs, we performed gene functional interaction (FI) network analysis. Then we conducted Venn diagram analysis to identify the intersection and specific DEGs of the four types of thyroid carcinomas. The intersections DEGs were performed by functional enrichment and transcription factor (TF) prediction analyses. These specific DEGs were performed by pathway enrichment analysis. There were respectively 323, 318, 118 and 1005 DEGs identified in PTC, OTC, FTC and ATC. Twelve sub-network modules were extracted based on gene FI network analysis and eight thyroid carcinoma-associated DEGs were involved in the network, such as TIMP1. Based on the Venn diagram analysis, 27 common DEGs were identified, such as HMGB3 which was regulated by TF of NKX3-1. There were 149 PTC-specific DEGs (like CLDN1), 160 OTC-specific DEGs, 94 FTC-specific DEGs (like PPARG), and 789 ATC-specific DEGs (like CDK1). They were enriched in some pathways, such as Cell cycle, Citrate cycle, and Oxidative phosphorylation. TIMP1, HMGB3, CLDN1, CDK1 and PPARG as well as pathways of Cell cycle, Citrate cycle, and Oxidative phosphorylation may play important roles in the progression of thyroid carcinoma.