Analysis Of Gene Expression Datasets Research Articles

Systems and computational analysis of gene expression datasets reveals GRB-2 suppression as an acute immunomodulatory response against enteric infections in endemic settings.

Enteric infections are a major cause of under-5 (age) mortality in low/middle-income countries. Although vaccines against these infections have already been licensed, unwavering efforts are required to boost suboptimalefficacy and effectiveness in regions that are highly endemic to enteric pathogens. The role of baseline immunological profiles in influencing vaccine-induced immune responses is increasingly becoming clearer for several vaccines. Hence, for the development of advanced and region-specific enteric vaccines, insights into differences in immune responses to perturbations in endemic and non-endemic settings become crucial. For this reason, we employed a two-tiered system and computational pipeline (i) to study the variations in differentially expressed genes (DEGs) associated with immune responses to enteric infections in endemic and non-endemic study groups, and (ii) to derive features (genes) of importance that keenly distinguish between these two groups using unsupervised machine learning algorithms on an aggregated gene expression dataset. The derived genes were further curated using topological analysis of the constructed STRING networks. The findings from these two tiers are validated using multilayer perceptron classifier and were further explored using correlation and regression analysis for the retrieval of associated gene regulatory modules. Our analysis reveals aggressive suppression of GRB-2, an adaptor molecule integral for TCR signaling, as a primary immunomodulatory response against S. typhi infection in endemic settings. Moreover, using retrieved correlation modules and multivariant regression models, we found a positive association between regulators of activated T cells and mediators of Hedgehog signaling in the endemic population, which indicates the initiation of an effector (involving differentiation and homing) rather than an inductive response upon infection. On further exploration, we found STAT3 to be instrumental in designating T-cell functions upon early responses to enteric infections in endemic settings. Overall, through a systems and computational biology approach, we characterized distinct molecular players involved in immune responses to enteric infections in endemic settings in the process, contributing to the mounting evidence of endemicity being a major determiner of pathogen/vaccine-induced immune responses. The gained insights will have important implications in the design and development of region/endemicity-specific vaccines.

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques.

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen-Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.

Integrative analysis of gene expression datasets in corneal endothelium samples of Fuchs endothelial corneal dystrophy

FECD is an age-related progressive ocular disorder characterized by the gradual loss of corneal endothelial cells. Although the exact pathogenesis of FECD remains incompletely understood, differentially expressed genes in the corneal endothelium of FECD patients compared to controls have been reported in several studies. However, a consensus regarding consistently affected genes in FECD has not been established. To address this issue, we conducted a comprehensive meta-analysis incorporating five studies with data that met our predefined inclusion criteria. The combined dataset included 41 FECD patients and 26 controls. We conducted study-level analyses, followed by a meta-analysis, and subsequent functional enrichment analysis targeting the topmost DEGs. Our findings revealed a total of 1537 consistently dysregulated genes in the corneal endothelium of FECD patients. Notably, only 14.6% (224/1537) of these DEGs had been previously identified as statistically significant in individual datasets. Functional enrichment analysis revealed that the upregulated DEGs were significantly enriched in immune-related signaling pathways, with a particularly high enrichment in “The NLRP3 inflammasome” and “Inflammasomes” pathways. In conclusion, we successfully identify a set of consistently dysregulated genes in FECD, which are associated with both established and novel biological pathways. This study highlights the importance of further investigating the role of inflammasomes in FECD pathogenesis and exploring strategies to modulate inflammasome activation for the management of this debilitating condition.

Abstract 1460: Co-expression network analysis reveals male breast cancer specific transcriptional module

Abstract Introduction: According to the American Cancer Society, An estimated 2,710 men are diagnosed with breast cancer annually of which about 530 die due to the disease annually in the United States. Most male breast tumors are estrogen receptor positive. Risk factors include age, radiation exposure, BRCA1/2 gene mutations, Klinefelter syndrome, testicular disorders, diabetes, gynecomastia (enlarged breast) etc. Due to low testing and incidence rate and lack of bioinformatics approaches focused on identification of drivers of rare cancer, little is known about this disease in men than in women. Because of this knowledge-gap there is no therapy that is specifically tailored for male breast cancer, leading to nineteen percent higher mortality in male than female with breast cancer. Method: In the past, gene expression analysis has revealed differential pathways in male than in female breast cancer, but these studies compared male and female tumors without considering innate (sex-specific) differences in the mammary tissues between the two sexes. To overcome these challenges, we have utilized publicly available tumor (E-TABM-810, TCGA-Breast) and normal (GTEX - mammary) datasets to identify pathways and gene co-expression networks enriched in specific transcription factor (TF) targets that are specific to male breast tumors . Findings from this analysis is further validated in male breast tumor patient derived xenograft (PDX) model. Result: Using differential pathway analysis corrected by sex, Type I diabetes mellitus is the only Hallmark pathway signature that is significantly downregulated in male tumors compared to both female tumors and normal mammary tissue. No pathway is distinctively up-regulated in male tumors. Co-expression network analysis of gene expression datasets identified unique TF-driven networks that are specific to male breast tumors. This analysis identified a network of ELK1 and DMRT1 TF targets that are unregulated and downregulated in male breast tumors respectively. This was further validated in PDX dataset. Conclusion: Based on our results, we hypothesize that loss of DMRT1 TF activity is potentially associated with occurrence of male breast tumors. The role of DMRT1 in sex differentiation and gonadal differentiation has been explored, but its role in male breast tumor formation is not as well characterized. Thus, our analysis provides novel insight in transcription factors associated with incidence of this rare form of cancer in men. Citation Format: Xuxu Gou, Junkai Wang, Jonathan T. Lei, Eric J. Jaehnig, Meenakshi Anurag. Co-expression network analysis reveals male breast cancer specific transcriptional module [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 1460.

Exploration of the role of Cuproptosis genes and their related long non-coding RNA in clear cell renal cell carcinoma: a comprehensive bioinformatics study

Clear cell renal cell carcinoma is a common malignant tumor of the urinary system. The mechanism of its occurrence and development is unknown, and there is currently few effective comprehensive predictive markers for prognosis and treatment response. With the discovery of a new cell death process – cuproptosis drew the attention of researchers. We constructed a model for the prediction of clinical prognosis and immunotherapy response through integrative analysis of gene expression datasets from KIRC samples in The Cancer Genome Atlas (TCGA) database. During the course of the study, we found that cuproptosis genes are significantly differentially expressed between clear cell renal cell carcinoma samples and normal samples. Based on this, we put forward the prognostic model for cuproptosis gene related-long non-coding RNA. And through various statistic and external independent cohorts, we proved that the model is accurate and stable, worthy of clinical application and further exploration and validation.

ERBB and P-glycoprotein inhibitors break resistance in relapsed neuroblastoma models through P-glycoprotein.

Chemotherapy resistance is a persistent clinical problem in relapsed high-risk neuroblastomas. We tested a panel of 15 drugs for sensitization of neuroblastoma cells to the conventional chemotherapeutic vincristine, identifying tariquidar, an inhibitor of the transmembrane pump P-glycoprotein (P-gp/ABCB1), and the ERBB family inhibitor afatinib as the top resistance breakers. Both compounds were efficient in sensitizing neuroblastoma cells to vincristine in trypan blue exclusion assays and in inducing apoptotic cell death. The evaluation of ERBB signaling revealed no functional inhibition, that is, dephosphorylation of the downstream pathways upon afatinib treatment but direct off-target interference with P-gp function. Depletion of ABCB1, but not ERRB4, sensitized cells to vincristine treatment. P-gp inhibition substantially broke vincristine resistance invitro and invivo (zebrafish embryo xenograft). The analysis of gene expression datasets of more than 50 different neuroblastoma cell lines (primary and relapsed) and more than 160 neuroblastoma patient samples from the pediatric precision medicine platform INFORM (Individualized Therapy For Relapsed Malignancies in Childhood) confirmed a pivotal role of P-gp specifically in neuroblastoma resistance at relapse, while the ERBB family appears to play a minor part.

An Analysis of Transcriptomic Burden Identifies Biological Progression Roadmaps for Hematological Malignancies and Solid Tumors.

Biological paths of tumor progression are difficult to predict without time-series data. Using median shift and abacus transformation in the analysis of RNA sequencing data sets, natural patient stratifications were found based on their transcriptomic burden (TcB). Using gene-behavior analysis, TcB groups were evaluated further to discover biological courses of tumor progression. We found that solid tumors and hematological malignancies (n = 4179) share conserved biological patterns, and biological network complexity decreases at increasing TcB levels. An analysis of gene expression datasets including pediatric leukemia patients revealed TcB patterns with biological directionality and survival implications. A prospective interventional study with PI3K targeted therapy in canine lymphomas proved that directional biological responses are dynamic. To conclude, TcB-enriched biological mechanisms detected the existence of biological trajectories within tumors. Using this prognostic informative novel informatics method, which can be applied to tumor transcriptomes and progressive diseases inspires the design of progression-specific therapeutic approaches.

Nucleotides modulate synoviocyte proliferation and osteoclast differentiation in macrophages with potential implications for rheumatoid arthritis.

The online version contains supplementary material available at 10.1007/s13205-021-03052-8.

Bioinformatic analysis reveals in common genes between colorectal cancer and recurrent colorectal malignancy

Colorectal cancer has been ranked as the second cause of death in the United States of America. Bioinformatic analysis of gene expression datasets including GSE10402 and GSE110224 were complemented to identify differentially expressed genes between recurrent and colorectal cancer patients. A total of 73 overlapped differentially expressed genes were screened out that were specifically changed in compare with recurrent colorectal cancer tissues. Most of the pathways were enriched in genes contributing in signaling, nuclear transcription factor complex and ubiquitin protein ligase activity. Protein-protein construction was complemented and additionally 10 hub-genes were selected in protein-protein interaction construction. As a conclusion, our results may possibly help to divulge the manner which colorectal cancer tissues may be changed into the recurrent cancer cells and other researchers can investigate more target genes in clinical treatments.

Altered Expression of CCR10 Indicates Malignant Transformation of Bone Marrow Plasma Cells

Introduction. Phenotypic analysis of tumor cells by flow cytometry is now essential for diagnosis and treatment of hematological malignancies, including multiple myeloma (MM). Surface molecules such as CD19, CD56, CD27 and CD45 have been used to distinguish normal bone marrow (BM) plasma cells from malignant plasma cells (MM cells). Molecules that are relatively specific to plasma cells (CD38 and SLAMF7) are considered targets for antibody-based therapies in MM. Among the vast numbers of surface molecules, chemokine receptors have been attracting much attention as the regulators of migration and tissue localization of plasma cells and MM cells. CXCR4 (C-X-C chemokine receptor type 4) has been reported to guide the movement of plasma cells and myeloma cells to the BM, where its ligand CXCL12 (C-X-C motif chemokine 12) is abundantly produced. CCR10 (C-C motif chemokine receptor 10) is another chemokine receptor, whose ligand is CCL27 (C-C motif chemokine ligand 27) and CCL28, also known to be involved in cell homing and migration. CCR10 has been reported to be highly expressed on mucosal derived plasma cells, however its expression in MM cells and its role within the BM microenvironment has not been well studied. In this study, we analyzed the expression of CCR10 on BM lymphocytes from patients of plasma cell disorders in order to understand its significance in the BM microenvironment.Materials and methods. We analyzed a public available gene expression dataset (GSE 22886) in order to study CCR10 gene expression in human plasma cells. To examine CCR10 gene expression in myeloma cells, we utilized the gene expression data set of multiple cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE: http://www.broadinstitute.org/ccle). Multi-color flow cytometry was performed to study CCR10 expression on bone marrow lymphocytes. BM plasma cells were determined as CD38++ CD138+ or CD38++ SLAMF7+. Monoclonal plasma cells (myeloma clones) were determined as CD19- CD56- or CD19- CD56+ cells and polyclonal plasma cells (normal plasma cells) as CD19+ CD56- cells. CCR10 expression of BM plasma cells, B cells, CD4 T cells, CD8 T cells and NK cells of MGUS, newly diagnosed MM, relapsed-refractory MM patients were analyzed.Results. Gene expression dataset analysis showed that, among the normal leukocyte subsets, plasma cells had the highest CCR10 gene expression compared to other leukocytes (p<0.001). Myeloma cell lines showed the highest expression of CCR10 gene expression among all the other cancer cell lines, indicating the specificity of CCR10 expression in MM cells. Multi-color flow cytometry showed significantly high expression of CCR10 on BM plasma cells compared to other lymphocytes in the bone marrow of MGUS patients (p<0.0001). Within the MGUS BM plasma cells, monoclonal plasma cells had significantly lower CCR10 expression compared to polyclonal plasma cells (p<0.05). Additionally, monoclonal plasma cells from relapsed-refractory MM patients had significantly lower CCR10 expression compared to those from MGUS and newly diagnosed MM patients. These data indicate a role of CCR10 decrease in malignant plasma cells leading to MM progression. We further analyzed other surface molecules that may be related to CCR10 on monoclonal plasma cells. CCR10 expression did not correlate with CD56 expression, while CD45- monoclonal plasma cells tend to have lower CCR10 expression compared to the CD45+ counterparts in newly diagnosed MM patients (p<0.001).Conclusions. Gene expression dataset analysis and multi-color flow cytometry have showed that CCR10 is highly expressed in normal plasma cells. However, we show here for the first time that plasma cells start to decrease CCR10 expression at malignant transformation and during disease progression. Since, CCL27, the ligand of CCR10, is known to induce chemotaxis to plasma cells, loss of CCR10 may lead to migration or metastasis of MM cells to different sites. Further research is now on-going to elucidate the significance and mechanism of altered CCR10 expression in MM cells. This study may lead to a better understanding of MM biology and to the development of a novel strategy to target malignant plasma cells. DisclosuresNo relevant conflicts of interest to declare.

A predictive biomarker panel for bone metastases: Liquid biopsy approach

Bone metastases is one of the common metastatic site and leading cause of cancer-related mortality in progressive cancer patients. The purpose of the present study is to establish a liquid biopsy based multi-gene classifier and associated signalling pathways for early diagnosis of bone metastases. We used publically available microarray datasets and analysed them in a platform/chip-specific manner using GeneSpring software. Analyses of gene expression datasets identified 15 consistently over-expressed genes with statistical significance. Further, expression profile of same set of 15 genes were compared in breast and lung cancer exosome derived mRNA with (n = 10) and without (n = 10) bone metastases against healthy controls. ROC curve analysis performed individually for all the 15 genes shortlisted the 4 most relevant genes (HSP90AA1, SPP1, IL3, and PTK2) with higher sensitivity and specificity in both cancers. This liquid biopsy-based bone metastases predictor using multi-gene panel is a unique approach with potential clinical applications for effective management of aggressive cancers.

Nuclear factor IB is downregulated in vulvar squamous cell carcinoma (VSCC): Unravelling differentially expressed genes in VSCC through gene expression dataset analysis.

Vulvar squamous cell carcinoma (VSCC) comprises two distinct etiopathological subtypes: i) Human papilloma virus (HPV)-related VSCC, which arises via the precursor high grade squamous intraepithelial lesion (HSIL); and ii) HPV-independent VSCC, which arises via precursor, differentiated vulvar intraepithelial neoplasia (dVIN), driven by TP53 mutations. However, the mechanism of carcinogenesis of VSCC is poorly understood. The current study aimed to gain insight into VSCC carcinogenesis by identifying differentially expressed genes (DEGs) for each VSCC subtype. The expression of certain DEGs was then further assessed by performing immunohistochemistry (IHC) on whole tissue sections of VSCC and its precursors. Statistical analysis of microarrays was performed on two independent gene expression datasets (GSE38228 and a study from Erasmus MC) on VSCC and normal vulva. DEGs were identified that were similarly (up/down) regulated with statistical significance in both datasets. For HPV-related VSCCs, this constituted 88 DEGs, and for HPV-independent VSCCs, this comprised 46 DEGs. IHC was performed on VSCC (n=11), dVIN (n=6), HSIL (n=6) and normal vulvar tissue (n=7) with i) signal transducer and activator of transcription 1 (STAT1; an upregulated DEGs); ii) nuclear factor IB (NFIB; a downregulated DEG); iii) p16 (to determine the HPV status of tissues); and iv) p53 (to confirm the histological diagnoses). Strong and diffuse NFIB expression was observed in the basal and para-basal layers of normal vulvar tissue, whereas NFIB expression was minimal or completely negative in dVIN and in both subtypes of VSCC. In contrast, no discernable difference was observed in STAT1 expression among normal vulvar tissue, dVIN, HSIL or VSCC. By leveraging bioinformatics, the current study identified DEGs that can facilitate research into VSCC carcinogenesis. The results suggested that NFIB is downregulated in VSCC and its relevance as a diagnostic/prognostic biomarker deserves further exploration.

Expression of the Androgen Receptor Governs Radiation Resistance in a Subset of Glioblastomas Vulnerable to Antiandrogen Therapy.

New approaches are needed to overcome intrinsic therapy resistance in glioblastoma (GBM). Because GBMs exhibit sexual dimorphism and are reported to express steroid hormone receptors, we reasoned that signaling through the androgen receptor (AR) could mediate therapy resistance in GBM, much as it does in AR-positive prostate and breast cancers. We found that nearly half of GBM cell lines, patient-derived xenografts (PDX), and human tumors expressed AR at the transcript and protein level-with expression levels overlapping those of primary prostate cancer. Analysis of gene expression datasets also revealed that AR expression is higher in GBM patient samples than normal brain tissue. Multiple clinical-grade antiandrogens slowed the growth of and radiosensitized AR-positive GBM cell lines and PDXs in vitro and in vivo Antiandrogens blocked the ability of AR-positive GBM PDXs to engage adaptive transcriptional programs following radiation and slowed the repair of radiation-induced DNA damage. These results suggest that combining blood-brain barrier permeable antiandrogens with radiation may have promise for patients with AR-positive GBMs.

Multiple Gene Expression Dataset Analysis Reveals Toll-Like Receptor Signaling Pathway is Strongly Associated With Chronic Obstructive Pulmonary Disease Pathogenesis

Chronic obstructive pulmonary disease is a complex pulmonary disease that causes airflow obstruction in humans. To identify the core genes in COPD pathogenesis, seven diverse microarray datasets (GSE475, GSE1122, GSE1650, GSE3212, GSE8823, GSE37768, and GSE22148) were downloaded from the gene expression omnibus database. All the datasets were analyzed independently with the R/Bioconductor package to screen the differentially expressed genes (DEGs). The gene ontology and pathway enrichment analysis were performed for the acquired DEGs using DAVID (Database for Annotation, Visualization, and Integrated Discovery). Further protein-protein interaction network was constructed for the DEGs and their potential hub genes and sub-networks were identified using Cytoscape software. From the selected seven datasets, 188 overlapped DEGs were perceived eventually based on considering the repetitive genes between at-least one dataset. Gene Ontology analysis reveals that most of the DEGs were significantly enriched in immune response, inflammatory response, extracellular region, lipid binding, cytokine, and chemokine activity. Moreover, genes from the sub-network analysis were again submitted to the DAVID server to validate the results which uncover the Toll-like receptor signaling pathway was significantly enriched and all the genes present in this pathway were likewise detected as hub genes from Cytoscape software. CXCL9, CXCL10, CXCL11, CCL4, TLR7, and SPP1 hub genes in the toll-like receptor signaling pathway were explored in this study as potential biomarker genes associated with COPD pathogenesis.

PI3K-Akt pathway mediated antiviral mechanism in silkworm Antheraea mylitta.

Bombyx mori nucleopolyhedrosis virus (BmNPV) is a highly pathogenic virus to domestic silkworm Bombyx mori, often causing severe economic losses to silk industry. Interestingly, there are no documented reports of NPV infection in the wild silkworm Antheraea mylitta. Analysis of gene expression datasets and comparative genomic analysis of sequence datasets from B. mori and A. mylitta was undertaken to unravel the potential immune-related proteins and immune pathways involved. The B. mori silkworm races Sarupat and CSR-2 which are resistant and susceptible to BmNPV respectively were selected and challenged with virus to study BmNPV resistance related genes and their expression profile. The genes were filtered to isolate membrane proteins, their sequences were retrieved from UniProt and were compared against A. mylitta using BLASTp to search for similarity. Major proteins were putative defence proteins. Further, KEGG database was used to check for the presence of differentially regulated proteins in certain metabolic pathways. The analysis of the pathways was carried out using cytoscape software based on betweeness centrality and stress. The PI3K-Akt pathway was found to be the hub of all signals triggered during the course of NPV infection. We analyzed how the NPV infection modulates PI3K-Akt signaling by gene expression studies of the key regulator of the pathway i.e. Akt using real-time PCR in A. mylitta. A significant upregulation of Akt expression from 72h post infection reaching its peak with a 2.30 fold change at 120h pi clearly indicates an enhanced level of immune response in host towards the viral infection.

Hepatitis B Virus X Protein Induces RHAMM-Dependent Motility in Hepatocellular Carcinoma Cells via PI3K-Akt-Oct-1 Signaling.

Chronic hepatitis B virus (HBV) infection is a major risk factor for the development of hepatocellular carcinoma (HCC), which represents one of the most common cancers worldwide. Recent studies suggest that HBV's protein X (HBx) plays a crucial role in HCC development and progression. Earlier, genome-wide analysis identified that the receptor for hyaluronan-mediated motility (RHAMM) represents a putative oncogene and is overexpressed in many human cancers, including HCC. However, the mechanism underlying RHAMM upregulation and its role in tumorigenesis remain unclear. Here, we show that ectopic expression of HBx activates the PI3K/Akt/Oct-1 pathway and upregulates RHAMM expression in HCC cells. HBx overexpression leads to dissociation of C/EBPβ from the RHAMM gene promoter, thereby inducing RHAMM upregulation. RHAMM knockdown attenuates HBx-induced cell migration and invasion in vitro. In mice, HBx promotes cancer cell colonization via RHAMM upregulation, resulting in enhanced metastasis. Analysis of gene expression datasets reveals that RHAMM mRNA level is upregulated in patients with HCC with poor prognosis. IMPLICATIONS: These results indicate that RHAMM expression is upregulated by HBx, a process that depends on the inhibition of C/EBPβ activity and activation of the PI3K/Akt/Oct-1 pathway. These results have several implications for the treatment of HBV-positive HCC involving upregulation of RHAMM and cancer metastasis. VISUAL OVERVIEW: http://mcr.aacrjournals.org/content/molcanres/18/3/375/F1.large.jpg.

FOXP1 negatively regulates tumor infiltrating lymphocyte migration in human breast cancer.

BackgroundFOXP1, a transcriptional regulator of lymphocyte development, is abnormally expressed in some human tumors. This study investigated FOXP1-mediated regulation of tumor infiltrating lymphocytes (TIL) in untreated primary breast cancer (BC).MethodsFOXP1 expression was analyzed in tissues from primary untreated breast tumors, BC cell lines and the METABRIC gene expression BC dataset. Cytokine and chemokine expression and lymphocyte migration in response to primary tumor supernatants (SN) was compared between FOXP1hi and FOXP1lo primary BC.FindingFOXP1 expression was higher in estrogen receptor positive compared to negative BC. FOXP1hi tumors were significantly associated with lower TIL and fewer tertiary lymphoid structures (TLS) compared to FOXP1lo BC. Silencing FOXP1 in BC cell lines positively impacted cytokine and chemokine expression with the inverse effect associated with overexpression. CXCL9, CXCL10, CXCL11, CXCL13, CX3CL, CCL20, IL2, IL21, GZMB and IFNG expression decreased while IL10 and TGFβ increased in FOXP1hi compared to FOXP1lo primary BC. Lymphocyte migration using primary BC supernatants detected decreased mobility toward FOXP1hi supernatants. FOXP1lo BC expresses higher levels of chemokines driving TIL migration. The METABRIC gene expression dataset analysis show FOXP1 expression is associated with unfavorable BC outcomes.InterpretationThese data identify FOXP1 as an important negative regulator of immune responses in BC via its regulation of cytokine and chemokine expression.FundBelgian Fund for Scientific Research (FNRS 3.4513.12F) and Opération Télévie (7.4636.13F and 7.4609.15F), Fonds J.C. Heuson and Fonds Lambeau-Marteaux.

Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer.

Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in general and in different breast cancer stages, as well as to explore their functions in tumor development. For integrated analysis, a total of 5 gene expression profiling datasets for 3 different stages of breast cancer (stages I–III) were downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information. Pre-processing of these datasets was performed using the Robust Multi-array Average algorithm and global renormalization was performed for all studies. Differentially expressed genes between breast cancer patients and controls were estimated using the empirical Bayes algorithm. The Database for Annotation, Visualization and Integrated Discovery web server was used for analyzing the enrichment of the differentially expressed genes in Gene Ontology terms of the category biological process and in Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, breast cancer target genes were downloaded from the Thomson Reuters Integrity Database. We merged these target genes with the genes in breast cancer datasets. Analysis of anti-breast cancer gene networks was performed using the Genome-scale Integrated Analysis of Gene Networks in Tissues web server. The results demonstrated that the normal functions of the cell cycle, cell migration and cell adhesion were altered in all stages of breast cancer. Furthermore, 12 anti-breast cancer genes were identified to be dysregulated in at least one of the three stages. Among all of these genes, ribonucleotide reductase regulatory subunit M2 (RRM2) exhibited the highest degree of interaction with other interacting genes. Analysis of the network interactions revealed that the transcription factor of RRM2 is crucial for cancer development. Other genes, including mucin 1, progesterone receptor and cyclin-dependent kinase 5 regulatory subunit associated protein 3, also exhibited a high degree of interaction with the associated genes. In conclusion, several key anti-breast cancer genes identified in the present study are mainly associated with the regulation of the cell cycle, cell migration, cell adhesion and other cancer-associated cell functions, particularly RRM2.

Identification of OAF and PVRL1 as candidate genes for an ocular anomaly characterized by Peters anomaly type 2 and ectopia lentis

Keratolenticular dysgenesis (KLD) and ectopia lentis are congenital eye defects. The aim of this study is the identification of molecular genetic alterations responsible for those ocular anomalies with neurologic impairment in an individual with a de novo balanced chromosome translocation t(11;18)(q23.3;q11.2)dn. Disruption of OAF, the human orthologue of the Drosophila oaf, by the 11q23.3 breakpoint results in reduced expression of this transcriptional regulator. Furthermore, four most likely nonfunctional chimeric transcripts comprising up to OAF exon 3, derived from the der(11) allele, have also been identified. This locus has been implicated by publicly available genome-wide association data in corneal disease and corneal topography. The expression of the poliovirus receptor-related 1(PVRL1) or nectin cell adhesion molecule 1 (NECTIN1), a paralogue of nectin cell adhesion molecule 3 (PVRL3) associated with congenital ocular defects, situated 500 kb upstream from 11q23.3 breakpoint, is increased. The 18q11.2 breakpoint is localized between cutaneous T-cell lymphoma-associated antigen 1(CTAGE1) and retinoblastoma binding protein 8 (RBBP8) genes. Genomic imbalance that could contribute to the observed phenotype was excluded. Analysis of gene expression datasets throughout normal murine ocular lens embryogenesis suggests that OAF expression is significantly enriched in the lens from early stages of development through adulthood, whereas PVRL1 is lens-enriched until E12.5 and then down-regulated. This contrasts with the observation that the proposita's lymphoblastoid cell lines exhibit low OAF and high PVRL1 expression as compared to control, which offers further support that the alterations described above are most likely responsible for the clinical phenotype. Finally, gene interaction topology data for PVRL1 also agree with our proposal that disruption of OAF by the translocation breakpoint and misregulation of PVRL1 due to a position effect contribute to the observed ocular and neurological phenotype.

Conserved Gene Microsynteny Unveils Functional Interaction Between Protein Disulfide Isomerase and Rho Guanine-Dissociation Inhibitor Families

Protein disulfide isomerases (PDIs) support endoplasmic reticulum redox protein folding and cell-surface thiol-redox control of thrombosis and vascular remodeling. The family prototype PDIA1 regulates NADPH oxidase signaling and cytoskeleton organization, however the related underlying mechanisms are unclear. Here we show that genes encoding human PDIA1 and its two paralogs PDIA8 and PDIA2 are each flanked by genes encoding Rho guanine-dissociation inhibitors (GDI), known regulators of RhoGTPases/cytoskeleton. Evolutionary histories of these three microsyntenic regions reveal their emergence by two successive duplication events of a primordial gene pair in the last common vertebrate ancestor. The arrangement, however, is substantially older, detectable in echinoderms, nematodes, and cnidarians. Thus, PDI/RhoGDI pairing in the same transcription orientation emerged early in animal evolution and has been largely maintained. PDI/RhoGDI pairs are embedded into conserved genomic regions displaying common cis-regulatory elements. Analysis of gene expression datasets supports evidence for PDI/RhoGDI coexpression in developmental/inflammatory contexts. PDIA1/RhoGDIα were co-induced in endothelial cells upon CRISP-R-promoted transcription activation of each pair component, and also in mouse arterial intima during flow-induced remodeling. We provide evidence for physical interaction between both proteins. These data support strong functional links between PDI and RhoGDI families, which likely maintained PDI/RhoGDI microsynteny along > 800-million years of evolution.