Cistrome Database Research Articles

Calcitriol alleviates noise-induced hearing loss by regulating the ATF3/DUSP1 signalling pathway

BackgroundCalcitriol (Cal) is the most active metabolite of vitamin D and has antioxidant and anti-inflammatory properties. The aim of this study was to investigate the role of Cal in noise-induced hearing loss (NIHL) to further elucidate the mechanism of noise-induced oxidative stress in the mouse cochlea. MethodsC57BL/6 J mice were given six intraperitoneal injections of Cal (500 ng/kg/d). After 14 days of noise exposure, auditory brainstem response (ABR) thresholds, and the cochlear outer hair cell loss rate were analysed to evaluate auditory function. Real-time fluorescence quantitative PCR, immunofluorescence and western blotting were performed in vitro after the treatment of cochlear explants with 100 µM tert-butyl hydroperoxide (TBHP) for 2.5 h and HEI-OC1 cells with 250 µM TBHP for 1.5 h. ResultsIn vivo experiments confirmed that Cal pretreatment mitigated NIHL and outer hair cell death. The in vitro results demonstrated that Cal significantly reduced TBHP-induced cochlear auditory nerve fibre degradation and spiral ganglion neuron damage. Moreover, treatment with Cal inhibited the expression of oxidative stress-related factors (3-NT and 4-HNE) and DNA damage-related factors (γ-H2A.X) and attenuated TBHP-induced apoptosis in cochlear explants and HEI-OC1 cells. A total of 1479 upregulated genes and 1443 downregulated genes were screened in cochlear tissue 1 h after noise exposure. The level of transcription factor 3 (ATF3) was significantly elevated in HEI-OC1 cells after TBHP stimulation. Gene Transcription Regulation Database （GTRD）and Cistrome database analyses revealed that the downstream target gene of ATF3 is dual specificity phosphatase 1 (DUSP1). Cistrome DB Toolkit database results showed that the transcription factor of DUSP1 was ATF3. In addition, the ChIP-PCR results indicated that ATF3 might be a direct transcription factor of DUSP1. ConclusionThe results of our study suggest that Cal attenuates NIHL and inhibits noise-induced apoptosis by regulating the ATF3/DUSP1 signalling pathway.

Potential marker genes for chronic obstructive pulmonary disease revealed based on single-cell sequencing and Mendelian randomization analysis.

Progress is being made in the prevention and treatment of chronic obstructive pulmonary disease (COPD), but it is still unsatisfactory. With the development of genetic technology, validated genetic information can better explain COPD. The study utilized scRNA-seq and Mendelian randomization analysis of eQTLs to identify crucial genes and potential mechanistic pathways underlying COPD pathogenesis. Single-cell sequencing data were used to identify marker genes for immune cells in the COPD process. Data on eQTLs for immune cell marker genes were obtained from the eQTLGen consortium. To estimate the causal effect of marker genes on COPD, we selected an independent cohort (ukb-b-16751) derived from the UK Biobank database for two-sample Mendelian randomization analysis. Subsequently, we performed immune infiltration analysis, gene set enrichment analysis (GSEA), and co-expression network analysis on the key genes. The 154 immune cell-associated marker genes identified were mainly involved in pathways such as vacuolar cleavage, positive regulation of immune response and regulation of cell activation. Mendelian randomization analysis screened four pairs of marker genes (GZMH, COTL1, CSTA and CD14) were causally associated with COPD. These four key genes were significantly associated with immune cells. In addition, we have identified potential transcription factors associated with these key genes using the Cistrome database, thus contributing to a deeper understanding of the regulatory network of these gene expressions. This eQTLs Mendelian randomization study identified four key genes (GZMH, COTL1, CSTA, and CD14) causally associated with COPD, providing new insights for prevention and treatment of COPD.

Clinicopathological role of Cyclin A2 in uterine corpus endometrial carcinoma: Integration of tissue microarrays and ScRNA-Seq.

The comprehensive expression level and potential molecular role of Cyclin A2 (CCNA2) in uterine corpus endometrial carcinoma (UCEC) remains undiscovered. UCEC and normal endometrium tissues from in-house and public databases were collected for investigating protein and messenger RNA expression of CCNA2. The transcription factors of CCNA2 were identified by the Cistrome database. The prognostic significance of CCNA2 in UCEC was evaluated through univariate and multivariate Cox regression as well as Kaplan-Meier curve analysis. Single-cell RNA-sequencing (scRNA-seq) analysis was performed to explore cell types in UCEC, and the AUCell algorithm was used to investigate the activity of CCNA2 in different cell types. A total of 32 in-house UCEC and 30 normal endometrial tissues as well as 720 UCEC and 165 control samples from public databases were eligible and collected. Integrated calculation showed that the CCNA2 expression was up-regulated in the UCEC tissues (SMD = 2.43, 95% confidence interval 2.23∼2.64). E2F1 and FOXM1 were identified as transcription factors due to the presence of binding peaks on transcription site of CCNA2. CCNA2 predicted worse prognosis in UCEC. However, CCNA2 was not an independent prognostic factor in UCEC. The scRNA-seq analysis disclosed five cell types: B cells, T cells, monocytes, natural killer cells, and epithelial cells in UCEC. The expression of CCNA2 was mainly located in B cells and T cells. Moreover, CCNA2 was active in T cells and B cells using the AUCell algorithm. CCNA2 was up-regulated and mainly located in T cells and B cells in UCEC. Overexpression of CCNA2 predicted unfavorable prognosis of UCEC.

Abstract 872: Development of a nomogram and prognostic model based on immune-related genes in kidney renal papillary cell carcinoma

Abstract Background: Kidney renal papillary cell carcinoma (KIRP) is frequently associated with an unfavorable prognosis for affected individuals. Unfortunately, there has been insufficient exploration in search for a reliable prognosis signature and predictive indicators to forecast outcomes for KIRP patients. Thus, we have used bioinformatics of existing transcriptomic and health data to identify survival-related and prognostic immune genes. Methods: Processed RNA-seq FPKM data of 289 KIRP and 32 adjacent normal KIRP tissues were downloaded from the TCGA data portal. We downloaded 2499 immune-related genes via the Immunology Database and Analysis portal database and used the Cistrome database to identify and extract 318 total transcription factors (TFs). The differentially expressed immune-related genes and TFs were detected using the Wilcoxon test method. R was used to generate heatmaps and volcano plots. R was also used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Multivariate Cox regression analysis was implemented to investigate the prognostic value of differentially expressed immune-related genes used to construct the predictive model and nomogram. Kaplan-Meier analysis was performed to compare overall survival. We used Tumor Immune Estimation Resource (TIMER) and the Human Protein Atlas database (HPA) to validate expression and explore associations between a prognostic model and immune cell infiltration. Statistical analyses were performed using R; a p-value < 0.05 was considered to be significant. Results: A total of 368 immune-related genes and 60 TFs were identified as differentially expressed in KIRP tissues compared with normal tissues. Of the 368, 23 were found to be related to overall survival. GO and KEGG analysis suggested that these prognostic immune-related genes mainly participated in the ERK1 and ERK2 cascades, Rap1 signaling pathway, and the PI3K-Akt signaling pathway - pathways shown to be significantly correlated with the development of cancer. Of these 23 genes, 9 (GRN, PDGFRB, APOH, BIRC5, CCL19, RETN, HTR3A, PTGER1, TRAV39) were identified from Cox regression to be statistically significant prognostic-related genes. Survival analysis showed that a model based on these 9 prognostic-related genes has high predictive performance. Our constructed nomogram has good predictive power and clinical practicability. Immunohistochemistry results show that APOH, BIRC5, CCL19 and GRN were significantly increased in kidney cancer. B cells and CD4+ T cells were positively correlated with risk score model. Conclusion: We were able to create a prognostic model based on 9 immune-related genes correlated with overall survival in KIRP. We hope that this work helps to provide some insight into therapeutic approaches and prognostic predictors of KIRP. Citation Format: Adrian Lim, Mouad Edderkaoui, Yi Zhang, Stephen J. Pandol, Yan Ou. Development of a nomogram and prognostic model based on immune-related genes in kidney renal papillary cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 872.

Abstract 3154: Integrating DNA damage and mutation enrichment to understand mutagenesis at transcription factor binding sites

Abstract The buildup of ultraviolet (UV) light-induced DNA damage, such as Cyclobutane Pyrimidine Dimers (CPDs), is a crucial precursor to genetic mutations and the development of skin melanoma. These damaged sites and mutations are known to not be randomly distributed; in fact, previous research suggests potential relationships between damaged/mutated sites and transcription factors (TFs), proteins that regulate gene expression by binding to target sequences across the genome. To examine relationships between UV damages and TF binding sites on a genomic level, we integrated data from the Duke iMADS database of TF binding sites, CPD damage maps across the human genome, ATAC-seq accessibility data from the Cistrome database, and mutation data from Australian melanoma patients (from the ICGC consortium). We developed a computational workflow to measure the spatial distribution of damage/mutations at TF binding sites. Our analysis showed a strong association between the positions of CPD damages and mutations within the binding sites of ETS1, but not E2F or c-Myc, with the peaks in damage/mutations primarily residing at the central core of the ETS1 binding sites. The creation of such an analytic pipeline will enable the further quantitative genome-wide study of the relationships between the degrees of damages/mutations within TF binding sites, chromatin accessibility, and other genomic contexts. With these results, we can better understand the mechanisms involved in accumulating damages and mutations and how they progress to cancer, aging, and senescence. Citation Format: Bo Chi, Raluca Gordan. Integrating DNA damage and mutation enrichment to understand mutagenesis at transcription factor binding sites [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3154.

Development of an immune-related gene prognostic risk model and identification of an immune infiltration signature in the tumor microenvironment of colon cancer

BackgroundColon cancer is a common and highly malignant tumor. Its incidence is increasing rapidly with poor prognosis. At present, immunotherapy is a rapidly developing treatment for colon cancer. The aim of this study was to construct a prognostic risk model based on immune genes for early diagnosis and accurate prognostic prediction of colon cancer.MethodsTranscriptome data and clinical data were downloaded from the cancer Genome Atlas database. Immunity genes were obtained from ImmPort database. The differentially expressed transcription factors (TFs) were obtained from Cistrome database. Differentially expressed (DE) immune genes were identified in 473 cases of colon cancer and 41 cases of normal adjacent tissues. An immune-related prognostic model of colon cancer was established and its clinical applicability was verified. Among 318 tumor-related transcription factors, differentially expressed transcription factors were finally obtained, and a regulatory network was constructed according to the up-down regulatory relationship.ResultsA total of 477 DE immune genes (180 up-regulated and 297 down-regulated) were detected. We developed and validated twelve immune gene models for colon cancer, including SLC10A2, FABP4, FGF2, CCL28, IGKV1-6, IGLV6-57, ESM1, UCN, UTS2, VIP, IL1RL2, NGFR. The model was proved to be an independent prognostic variable with good prognostic ability. A total of 68 DE TFs (40 up-regulated and 23 down-regulated) were obtained. The regulation network between TF and immune genes was plotted by using TF as source node and immune genes as target node. In addition, Macrophage, Myeloid Dendritic cell and CD4+ T cell increased with the increase of risk score.ConclusionWe developed and validated twelve immune gene models for colon cancer, including SLC10A2, FABP4, FGF2, CCL28, IGKV1-6, IGLV6-57, ESM1, UCN, UTS2, VIP, IL1RL2, NGFR. This model can be used as a tool variable to predict the prognosis of colon cancer.

Identification of Prognostic and Predictive Biomarkers and Druggable Targets among 205 Antioxidant Genes in 21 Different Tumor Types via Data-Mining.

(1) Background: Oxidative stress is crucial in carcinogenesis and the response of tumors to treatment. Antioxidant genes are important determinants of resistance to chemotherapy and radiotherapy. We hypothesized that genes involved in the oxidative stress response may be valuable as prognostic biomarkers for the survival of cancer patients and as druggable targets. (2) Methods: We mined the KM Plotter and TCGA Timer2.0 Cistrome databases and investigated 205 antioxidant genes in 21 different tumor types within the context of this investigation. (3) Results: Of 4347 calculations with Kaplan-Meier statistics, 84 revealed statistically significant correlations between high gene expression and worse overall survival (p < 0.05; false discovery rate ≤ 5%). The tumor types for which antioxidant gene expression was most frequently correlated with worse overall survival were renal clear cell carcinoma, renal papillary cell carcinoma, and hepatocellular carcinoma. Seventeen genes were clearly overexpressed in tumors compared to their corresponding normal tissues (p < 0.001), possibly qualifying them as druggable targets (i.e., ALOX5, ALOX5AP, EPHX4, G6PD, GLRX3, GSS, PDIA4, PDIA6, PRDX1, SELENOH, SELENON, STIP1, TXNDC9, TXNDC12, TXNL1, TXNL4A, and TXNRD1). (4) Conclusions: We concluded that a sub-set of antioxidant genes might serve as prognostic biomarkers for overall survival and as druggable targets. Renal and liver tumors may be the most suitable entities for this approach.

Construction and validation of a robust prognostic model based on immune features in sepsis.

Sepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis. The differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis. A total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value <0.01, 69 prognostic DEIRGs were obtained and the potential molecular mechanisms of DEIRGs were also clarified. According to multivariate Cox regression analysis, 22 DEIRGs were further identified to construct the prognostic model and identify patients with high risk. The Kaplan-Meier survival analysis showed that high-risk groups have higher 28-day mortality than low-risk groups (P=1.105e-13). The AUC value was 0.879 which symbolized that the prognostic model had a better accuracy to predict the 28-day mortality. The external datasets also prove that the prognostic model had an excellent prediction value. Furthermore, the results of correlation analysis showed that patients with Mars1 might have higher risk scores than Mars2-4 (P=0.002). According to the previous study, Mars1 endotype was characterized by immunoparalysis. Thus, the sepsis patients in high-risk groups might exist the immunosuppression. Between the high-risk and low-risk groups, circulating immune cells types were significantly different, and risk score was significantly negatively correlated with naive CD4+ T cells (P=0.019), activated NK cells (P=0.0045), monocytes (P=0.0134), and M1 macrophages (P=0.0002). Our study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.

CisCross: A gene list enrichment analysis to predict upstream regulators in Arabidopsis thaliana.

Having DNA-binding profiles for a sufficient number of genome-encoded transcription factors (TFs) opens up the perspectives for systematic evaluation of the upstream regulators for the gene lists. Plant Cistrome database, a large collection of TF binding profiles detected using the DAP-seq method, made it possible for Arabidopsis. Here we re-processed raw DAP-seq data with MACS2, the most popular peak caller that leads among other ones according to quality metrics. In the benchmarking study, we confirmed that the improved collection of TF binding profiles supported a more precise gene list enrichment procedure, and resulted in a more relevant ranking of potential upstream regulators. Moreover, we consistently recovered the TF binding profiles that were missing in the previous collection of DAP-seq peak sets. We developed the CisCross web service (https://plamorph.sysbio.ru/ciscross/) that gives more flexibility in the analysis of potential upstream TF regulators for Arabidopsis thaliana genes.

Development of a prognostic signature of patients with esophagus adenocarcinoma by using immune-related genes

BackgroundEsophageal adenocarcinoma (EAC) is an aggressive malignancy with a poor prognosis. The immune-related genes (IRGs) are crucial to immunocytes tumor infiltration. This study aimed to construct a IRG-related prediction signature in EAC.MethodsThe related data of EAC patients and IRGs were obtained from the TCGA and ImmPort database, respectively. The cox regression analysis constructed the prediction signature and explored the transcription factors regulatory network through the Cistrome database. TIMER database and CIBERSORT analytical tool were utilized to explore the immunocytes infiltration analysis.ResultsThe prediction signature with 12 IRGs (ADRM1, CXCL1, SEMG1, CCL26, CCL24, AREG, IL23A, UCN2, FGFR4, IL17RB, TNFRSF11A, and TNFRSF21) was constructed. Overall survival (OS) curves indicate that the survival rate of the high-risk group is significantly shorter than the low-risk group (P = 7.26e−07), and the AUC of 1-, 3- and 5- year survival prediction rates is 0.871, 0.924, and 0.961, respectively. Compared with traditional features, the ROC curve of the risk score in the EAC patients (0.967) is significant than T (0.57), N (0.738), M (0.568), and Stage (0.768). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are accurate by the combined analysis of the risk score, Sex, M stage, and Stage (The AUC of 1- and 3-years are 0.911, and 0.853).ConclusionThe 12 prognosis-related IRGs might be promising therapeutic targets for EAC.

A Novel Immune-Related lncRNA-Based Model for Survival Prediction in Clear Cell Renal Cell Carcinoma.

Background Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction. Methods The transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature. Results Consequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8+ T cells, resting mast cells, and naïve B cells were significantly correlated with the signature. Conclusion Therefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.

MotifGenie: a Python application for searching transcription factor binding sequences using ChIP-Seq datasets.

Next generation sequencing enabled the fast accumulation of genomic data at public repositories. This technology also made it possible to better understand the regulation of gene expression by transcription factors (TFs) and various chromatin-associated proteins through the integration of chromatin immunoprecipitation (ChIP-Seq). The Cistrome Project has become one of the indispensable research portals for biologists to access and analyze data generated with thousands of ChIP-Seq experiments. Integrative motif analysis on shared binding regions among a set of experiments is not yet achievable despite a set of search and analysis tools provided by Cistrome via its web interface and the Galaxy framework. We implemented a python command-line tool for searching binding sequences of a TF common to multiple ChIP-Seq experiments. We use the peaks in the Cistrome database as identified by MACS 2.0 for each experiment and identify shared peak regions in a genomic locus of interest. We then scan these regions for binding sequences using a binding motif of a TF obtained from the JASPAR database. MotifGenie is developed in collaboration with molecular biologists and its findings are corroborated by laboratory experiments. MotifGenie is freely available at https://github.com/ceragoguztuzun/MotifGenie.

Broadening our understanding of genetic risk for scleroderma/systemic sclerosis by querying the chromatin architecture surrounding the risk haplotypes

BackgroundGenetic variants in the human leukocyte antigen (HLA) locus contribute to the risk for developing scleroderma/systemic sclerosis (SSc). However, there are other replicated loci that also contribute to genetic risk for SSc, and it is unknown whether genetic risk in these non-HLA loci acts primarily on the vasculature, immune system, fibroblasts, or other relevant cell types. We used the Cistrome database to investigate the epigenetic landscapes surrounding 11 replicated SSc associated loci to determine whether SNPs in these loci may affect regulatory elements and whether they are likely to impact a specific cell type.MethodsWe mapped 11 replicated SNPs to haplotypes and sought to determine whether there was significant enrichment for H3K27ac and H3K4me1 marks, epigenetic signatures of enhancer function, on these haplotypes. We queried pathologically relevant cell types: B cells, endothelial cells, fibroblasts, monocytes, and T cells. We then identified the topologically associated domains (TADs) that encompass the SSc risk haplotypes in primary T cells to identify the full range of genes that may be influenced by SSc causal SNPs. We used gene ontology analyses of the genes within the TADs to gain insight into immunologic functions that might be affected by SSc causal SNPs.ResultsThe SSc-associated haplotypes were enriched (p value < 0.01) for H3K4me1/H3K27ac marks in monocytes. Enrichment of one of the two histone marks was found in B cells, fibroblasts, and T cells. No enrichment was identified in endothelial cells. Ontological analyses of genes within the TADs encompassing the risk haplotypes showed enrichment for regulation of transcription, protein binding, activation of T lymphocytes, and proliferation of immune cells.ConclusionsThe 11 non-HLA SSc risk haplotypes queried are highly enriched for H3K4me1/H3K27ac-marked regulatory elements in a broad range of immune cells and fibroblasts. Furthermore, in immune cells, the risk haplotypes belong to larger chromatin structures encompassing genes that regulate a wide array of immune processes associated with SSc pathogenesis. Though importance of the vasculature in the pathobiology of SSc is widely accepted, we were unable to find evidence for genetic influences on endothelial cell function in these regions.

A novel immune-related prognostic signature in epithelial ovarian carcinoma.

The immune response is associated with the progression and prognosis of epithelial ovarian cancer (EOC). However, the roles of infiltrated immune cells and immune-related genes (IRGs) in EOC have not been reported comprehensively. In the current study, the differentially expressed genes (DEGs) were filtered based on the integrated gene expression data acquired from The University of California at Santa Cruz (UCSC) Genome Browser. Then, IRGs and transcriptional factors (TFs) were screened based on the ImmPort database and Cistrome database. A total of 501 differentially expressed IRGs, and 76 TFs were detected. A TF-mediated network was constructed by univariate Cox analysis to reveal the potential regulatory mechanisms of IRGs. Next, a nine immune-based prognostic risk model using nine IRGs (PI3, CXCL10, CXCL11, LCN6, CCL17, CCL25, MIF, CX3CR1, and CSPG5) was established. Based on the risk score worked out from the signature, the EOC patients could be classified into low-risk and high-risk groups. Furthermore, the immune landscapes, elevated by the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm and the Tumor Immune Estimation Resource (TIMER) database, effectuated different patterns in two groups. Thus, an immune-based prognostic risk model of EOC elucidates the immune status in the tumor microenvironment, and hence, could be used for prognosis.

DUOX2 As a Potential Prognostic Marker which Promotes Cell Motility and Proliferation in Pancreatic Cancer.

DUOX2 has been reported to highly express in several types of cancers. However, the prognostic significance and the biological function of DUOX2 expression with pancreatic cancer (PC) still remain unclear. The present study is aimed at investigating whether DUOX2 could act as a novel biomarker of prognosis and evaluating its effect on PC cell progression. The mRNA and protein expression of DUOX2 in PC cells and tissues were assessed by quantitative real-time PCR (RT-qPCR) and immunohistochemistry. The effect of DUOX2 expression on PC cell motility and proliferation was evaluated in vitro. The correlation between DUOX2 mRNA expression and clinicopathological features and its prognostic significance were analyzed according to the Gene Expression Profiling Interactive Analysis (GEPIA) website based on The Cancer Genome Atlas (TCGA) and the GTEx databases combined with our clinical information. According to bioinformatics analysis, we forecasted the upstream transcription factors (TFs) and microRNA (miRNA) regulatory mechanism of DUOX2 in PC. The expression of DUOX2 at transcriptional and protein level was dramatically increased in PC specimens when compared to adjacent nontumor specimens. Functionally, DUOX2 knockdown inhibited cell motility and proliferation activities. Our clinical data revealed that the patients had better postoperative overall survival (OS) with lower expression of DUOX2, which is consistent with GEPIA data. Multivariate analysis revealed that high DUOX2 expression was considered as an independent prognostic indicator for OS (P = 0.031). Based on Cistrome database, the top 5 TFs of each positively and negatively association with DUOX2 were predicted. hsa-miR-5193 and hsa-miR-1343-3p targeting DUOX2 were forecasted from TargetScan, miRDB, and DIANA-TarBase databases, which were negatively correlated with OS (P = 0.043 and P = 0.0088, respectively) and DUOX2 expression (P = 0.0093 and P = 0.0032, respectively) in PC from TCGA data. These findings suggest that DUOX2 acts as a promising predictive biomarker and an oncogene in PC, which could be a therapeutic target for PC.

Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Breast Invasive Carcinoma

BackgroundBone is the most common metastatic site of Breast invasive carcinoma (BRCA). In this study, the bone metastasis-specific regulation network of BRCA was constructed based on prognostic stemness-related signatures (PSRSs), their upstream transcription factors (TFs) and downstream pathways.MethodsClinical information and RNA-seq data of 1,080 primary BRCA samples (1,048 samples without bone metastasis and 32 samples with bone metastasis) were downloaded from The Cancer Genome Atlas (TCGA). The edgeR method was performed to identify differential expressed genes (DEGs). Next, mRNA stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR). To analyze DEGs by classification, similar genes were integrated into the same module by weighted gene co-expression network analysis (WGCNA). Then, univariate and multivariate Cox proportional hazard regression were applied to find the PSRSs. Furthermore, PSRSs, 318 TFs obtained from Cistrome database and 50 hallmark pathways quantified by GSVA were integrated into co-expression analysis. Significant co-expression patterns were used to construct the bone metastasis-specific regulation network. Finally, spatial single-cell RNA-seq and chromatin immunoprecipitation sequence (ChIP-seq) data and multi-omics databases were applied to validate the key scientific hypothesis in the regulation network. Additionally, Connectivity Map (CMap) was utilized to select the potential inhibitors of bone metastasis-specific regulation network in BRCA.ResultsBased on edgeR and WGCNA method, 43 PSRSs were identified. In the bone metastasis-specific regulation network, MAF positively regulated CD248 (R = 0.435, P < 0.001), and hallmark apical junction was the potential pathway of CD248 (R = 0.353, P < 0.001). This regulatory pattern was supported by spatial single-cell RNA sequence, ChIP-seq data and multi-omics online databases. Additionally, alexidine was identified as the possible inhibitor for bone metastasis of BRCA by CMap analysis.ConclusionPSRSs played important roles in bone metastasis of BRCA, and the prognostic model based on PSRSs showed good performance. Especially, we proposed that CD248 was the most significant PSRS, which was positively regulated by MAF, influenced bone metastasis via apical junction pathway. And this axis might be inhibited by alexidine, which providing a potential treatment strategy for bone metastasis of BRCA.

Identifying the p65-Dependent Effect of Sulforaphene on Esophageal Squamous Cell Carcinoma Progression via Bioinformatics Analysis.

Understanding the mechanism by which sulforaphene (SFE) affects esophageal squamous cell carcinoma (ESCC) contributes to the application of this isothiocyanate as a chemotherapeutic agent. Thus, we attempted to investigate SFE regulation of ESCC characteristics more deeply. We performed gene set enrichment analysis (GSEA) on microarray data of SFE-treated ESCC cells and found that differentially expressed genes are enriched in TNFα_Signaling_via_the_NFκB_Pathway. Coupled with the expression profile data from the GSE20347 and GSE75241 datasets, we narrowed the set to 8 genes, 4 of which (C-X-C motif chemokine ligand 10 (CXCL10), TNF alpha induced protein 3 (TNFAIP3), inhibin subunit beta A (INHBA), and plasminogen activator, urokinase (PLAU)) were verified as the targets of SFE. RNA-sequence (RNA-seq) data of 182 ESCC samples from The Cancer Genome Atlas (TCGA) were grouped into two phenotypes for GSEA according to the expression of CXCL10, TNFAIP3, INHBA, and PLAU. The enrichment results proved that they were all involved in the NFκB pathway. ChIP-seq analyses obtained from the Cistrome database indicated that NFκB-p65 is likely to control the transcription of CXCL10, TNFAIP3, INHBA, and PLAU, and considering TNFAIP3 and PLAU are the most significantly differentially expressed genes, we used chromatin immunoprecipitation-polymerase chain reaction (ChIP-PCR) to verify the regulation of p65 on their expression. The results demonstrated that SFE suppresses ESCC progression by down-regulating TNFAIP3 and PLAU expression in a p65-dependent manner.

Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma

BackgroundUterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis particularly at an advanced stage. Herein, this study aims to construct prognostic markers of UCEC based on immune-related genes to predict the prognosis of UCEC.MethodsWe analyzed expression data of 575 UCEC patients from The Cancer Genome Atlas database and immune genes from the ImmPort database, which were used for generation and validation of the signature. We constructed a transcription factor regulatory network based on Cistrome databases, and also performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using the Cox regression analysis. We then constructed and verified a prognostic signature. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content.ResultsThe immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic signature revealed a ten-gene prognostic signature, comprising of PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC. This signature showed a strong prognostic ability in both the training and testing sets and thus can be used as an independent tool to predict the prognosis of UCEC. In addition, levels of B cells and neutrophils were significantly correlated with the patient’s risk score, while the expression of ten genes was associated with immune cell infiltrates.ConclusionsIn summary, the ten-gene prognostic signature may guide the selection of the immunotherapy for UCEC.

Identification of a Prognostic Model Based on Immune-Related Genes of Lung Squamous Cell Carcinoma.

Immune-related genes (IRGs) play considerable roles in tumor immune microenvironment (IME). This research aimed to discover the differentially expressed immune-related genes (DEIRGs) based on the Cox predictive model to predict survival for lung squamous cell carcinoma (LUSC) through bioinformatics analysis. First of all, the differentially expressed genes (DEGs) were acquired based on The Cancer Genome Atlas (TCGA) using the limma R package, the DEIRGs were obtained from the ImmPort database, whereas the differentially expressed transcription factors (DETFs) were acquired from the Cistrome database. Thereafter, a TFs-mediated IRGs network was constructed to identify the candidate mechanisms for those DEIRGs in LUSC at molecular level. Moreover, Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, was conducted for exploring those functional enrichments for DEIRGs. Besides, univariate as well as multivariate Cox regression analysis was conducted for establishing a prediction model for DEIRGs biomarkers. In addition, the relationship between the prognostic model and immunocytes was further explored through immunocyte correlation analysis. In total, 3,599 DEGs, 223 DEIRGs, and 46 DETFs were obtained from LUSC tissues and adjacent non-carcinoma tissues. According to multivariate Cox regression analysis, 10 DEIRGs (including CALCB, GCGR, HTR3A, AMH, VGF, SEMA3B, NRTN, ENG, ACVRL1, and NR4A1) were retrieved to establish a prognostic model for LUSC. Immunocyte infiltration analysis showed that dendritic cells and neutrophils were positively correlated with IRGs, which possibly exerted an important part within the IME of LUSC. Our study identifies a prognostic model based on IRGs, which is then used to predict LUSC prognosis and analyze immunocyte infiltration. This may provide a novel insight for exploring the potential IRGs in the IME of LUSC.

Development of an Immune-Related Risk Signature in Patients with Bladder Urothelial Carcinoma.

With recent advances in immunooncology and tumor microenvironment, the treatment landscape of bladder urothelial carcinoma has been changing dramatically. We aim to construct an immune gene-related signature which can predict BLCA patients' overall survival. Transcriptomic data of BLCA patients was downloaded from The Cancer Genome Atlas database, and immune-related genes were downloaded from the Immunology Database and Analysis Portal database. Prognostic immune-related genes were identified. We then constructed and validated an immune gene-related signature. Tumor-related transcription factors were downloaded from the Cistrome database, and a network between them and prognostic immune-related genes was generated. Cox's proportional hazards model and the Kaplan–Meier survival analysis were performed to assess our signature's prognostic ability. Relationship between the signature and patients' clinicopathologic features was then explored to validate its clinical value. We further downloaded concentration of six types of immune cells from the Tumor Immune Estimation Resource database to explore immune-related potential mechanisms of the signature.