scholarly journals Identification of Immune Infiltration-related Gene Signature Associated with Prognosis and Immune Features in Stomach Adenocarcinoma

2021 ◽  
Author(s):  
Ya Yang ◽  
Xintan Zhang ◽  
Tingxuan Li ◽  
Yue Zhang ◽  
Xiaoxiao Zuo
Keyword(s):  
Gene Expression ◽  
Clinical Data ◽  
Expression Profiles ◽  
Prognostic Score ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Validation Dataset ◽  
Prognostic Signature ◽  
Survival Prognosis ◽  
Stomach Adenocarcinoma

Abstract Background: Immune infiltrated genes (IIGs) have been identified to associated with the prognosis of various cancers, but their expression and prognostic significance remain largely unclear in stomach adenocarcinoma (STAD).Methods: Gene expression profiles and clinical data of STAD patients were downloaded from The Cancer Genome Atlas (TCGA) as a training dataset (n = 375) and Gene Expression Omnibus (GEO) databases as a validation dataset (n = 300). Construction of high and low immune cell infiltration groups was performed by single sample gene set enrichment analysis (ssGSEA) and evaluated by ESTIMATE algorithm-derived immune scores. The overlapping differentially expressed genes (DEGs) in tumor vs. normal and Immunity-H vs. Immunity-L were selected as differentially expressed immune infiltrated genes (DEIIGs), which were used to construct DEIIG prognostic signature and its performance was validated using validation dataset. Moreover, the association between clinical data and immune features were explored. Furthermore, ADH4 and ANGPT2 were selected for analyzing their expression and prognostic values in STAD patients.Results: A total of 191 overlapping DEGs, including 6 lnRNAs and 185 mRNA were identified. Consecutively, 9 DEIIG prognostic signature (LINC00843, ADH4, ANGPT2, APOA1, ASLC2, GFRA1, KIAA1549L, MTTP and PROC) were identified as risk signature and Kaplan-Meier curve and ROC curve verified its performance in TCGA and GEO datasets. Total five clinical outcomes (age, pathologic T, radiotherapy, tumor recurrence and prognostic score model status) were identified to be associated with the survival prognosis of STAD patients. The TIMER algorithm revealed that B cell, T cell CD4+, neutrophil, macrophage and myeloid dendritic cell were positively correlated with STAD prognosis, while CD8+ was negatively correlated with STAD prognosis. Additionally, we validated that higher ADH4 and lower ANGPT2 predicted better survival prognosis in STAD patients.Conclusion: We constructed and verified a robust signature of nine DEIIG prognostic signature for the prediction of STAD patient survival.

scholarly journals Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Guoliang Jia ◽  
Zheyu Song ◽  
Zhonghang Xu ◽  
Youmao Tao ◽  
Yuanyu Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cutaneous Melanoma ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Validation Dataset ◽  
Survival Prognosis ◽  
Gene Markers ◽  
Clinical Prognosis ◽  
Logit Regression

Abstract Background Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. Methods We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein–protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. Results Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. Conclusion The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

scholarly journals The Expression of Human Placental Genes Related to Carotenoid/retinoid Metabolism and Pathways (P02-005-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Xiaoming Gong ◽  
Lewis Rubin
Keyword(s):  
Gene Expression ◽  
Microarray Analysis ◽  
Fetal Development ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Retinoid Metabolism ◽  
Expression Of Genes ◽  
Β Carotene

Abstract Objectives Carotenoid/retinoids status and metabolism are essential for normal placental and fetal development. Both deficiencies and excess of retinoids and some carotenoids are associated with adverse pregnancy outcomes, such as preeclampsia and preterm birth. A group of important genes involved in regulating carotenoid/retinoid metabolism and maternal to fetal transfer in human placenta. The objective of this study is to analyze (a) the expression of genes critical for regulating carotenoid/retinoid metabolism and maternal-fetal transport in human trophoblasts and (b) placental transcriptional profiles of these pathways in response to carotenoid exposure. Methods Human cytotrophoblasts (CTBs) were isolated from term placentas. CTB RNA was used to analyze the expression of genes involved in carotenoid/retinoid metabolism and pathways by qRT-PCT. First trimester-like trophoblasts (HTR-8/SVneo) were treated with either β-carotene or lycopene. RNAs were isolated and gene expression were analyzed by DNA microarrays. Results Human CTBs express retinoid metabolism and pathways-related genes, including Stra6, Lrat, Rdh5, Rdh10, Aldh1a1, Aldh1a2, Aldh1a3, Aldh8a1, Cyp26a1, and Cyp26b1, but not carotenoid metabolism genes, BCO1 and BCO2. Microarray analysis of placental gene expression profile revealed a total of 872 and 756 differentially expressed genes, respectively, compared to the control. Gene set enrichment analysis and functional annotation clustering was performed to characterize the genes differentially expressed in either β-carotene or lycopene-treated HTR-8/SVneo cells. Many known retinoid metabolism related genes and genes involved in regulation of retinoid signaling were found, and the expression profiles of these genes were markedly different in response to β-carotene treatments. Finally, the qRT-PCR and microarray analysis results showed similar gene expression patterns of carotenoid/retinoid metabolism and pathways. Conclusions These findings suggest that placental expression of genes involved in retinoid metabolism and transport in trophoblasts is critical for regulating retinoid homeostasis during placental and fetal development. Carotenoid exposure in early placental development, significantly modify the placenta gene expression related to retinoid pathways and maternal to fetal transfer. Funding Sources NIH HD421174.

scholarly journals Integrative DNA Methylation and Gene Expression Analysis Reveals Candidate Biomarkers Associated with Dichotomized Response to Chemoimmunotherapy in Diffuse Large B-Cell Lymphoma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 22-22
Author(s):  
Ellen K. Kendall ◽  
Manishkumar S. Patel ◽  
Sarah Ondrejka ◽  
Agrima Mian ◽  
Yazeed Sawalha ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
B Cell Lymphoma ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Gene Sets

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. While 60% of DLBCL patients achieve complete remission with frontline therapy, relapsed/refractory (R/R) DLBCL patients have a poor prognosis with median overall survival below one year, necessitating investigation into the biological principles that distinguish cured from R/R DLBCL. Recent analyses have identified unfavorable molecular signatures when accounting for gene expression, copy number alterations and mutational profiles in R/R DLBCL. However, an integrative analysis of the relationship between epigenetic and transcriptomic changes has yet to be described. In this study, we compared baseline methylation and gene expression profiles of DLBCL patients with dichotomized clinical outcomes. Methods: Diagnostic DLBCL biopsies were obtained from two patient cohorts: patients who relapsed or were refractory following chemoimmunotherapy ("R/R"), and patients who entered durable clinical remission following therapy ("cured"). The median age for R/R and cured cohorts were 62 (range 35-86) years vs. 64 (range 28-83) years (P= 0.27). High-intermediate or high IPI scores were present in 14 vs. 6 patients (P= 0.08) in the R/R and cured cohorts, respectively. All patients were treated with frontline R-CHOP or R-EPOCH. DNA and RNA were extracted simultaneously from formalin-fixed, paraffin embedded biopsy samples. An Illumina 850k Methylation Array was used to identify DNA methylation levels in 29 R/R patients and 20 cured patients. RNA sequencing was performed on 9 R/R patients and 7 cured patients at diagnosis using Illumina HiSeq4000. Differentially methylated probes were identified using the DMRcate package, and differentially expressed genes were identified using the DESeq2 package. Gene set enrichment analysis was performed using canonical pathway gene sets from MSigDB. Results: At the time of diagnosis, we found significant epigenetic and transcriptomic differences between cured and R/R patients. Comparing cured to R/R samples, there were 8,159 differentially methylated probes (FDR<0.05). Differentially methylated regions between R/R and cured cohorts overlap with genes previously identified as mutation hotspots in DLBCL. Upon comparing transcriptomic profiles between R/R and cured, 267 genes were found to be differentially expressed (Log2FC>|1| and FDR<0.05). Gene set enrichment analysis revealed gene sets related to cell cycle, membrane trafficking, Rho and Rab family GTPase function, and transcriptional regulation were upregulated in the R/R samples. Gene sets related to innate immune signaling, Type I and II interferon signaling, fatty acid and carbohydrate metabolism were upregulated in the cured samples. To identify genes likely to be regulated by specific changes in methylation, we selected genes that were both differentially expressed and differentially methylated between the R/R and cured cohorts. In the R/R samples, 13 genes (ARMC5, ARRDC1, C12orf57, CCSER1, D2HGDH, DUOX2, FAM189B, FKBP2, KLF5, MFSD10, NEK8, NT5C, and WDR18) were significantly hypermethylated and underexpressed when compared to cured specimens, suggesting that epigenetic silencing of these genes is associated with lack of response to chemoimmunotherapy. In contrast, 12 genes (ATP2B1, C15orf41, FAM102B, FAM3C, FHOD3, FYTTD1, GPR180, KIAA1841, LRMP, MEF2A, RRAS2, and TPD52) were significantly hypermethylated and underexpressed in cured patients, suggesting that epigenetic silencing of these genes is favorable for treatment response. Many of these epigenetically modified genes have been previously implicated in cancer biology, including roles in NOTCH signaling, chromosomal instability, and biomarkers of prognosis. Conclusions: This is the first integrative epigenetic and transcriptomic analysis of diagnostic biopsies from cured and R/R DLBCL patients following chemoimmunotherapy. At the time of diagnosis, both the methylation and gene expression profiles significantly differ between patients that enter durable remission as opposed to those who are R/R to therapy. Soon, the hypomethylating agent CC-486 (i.e. oral azacitidine) will be explored in combination with mini-R-CHOP for older DLBCL patients in whom DNA methylation is likely increased. These data support the use of hypomethylating agents to potentially restore sensitivity of DLBCL to chemoimmunotherapy. Disclosures Hsi: Eli Lilly: Research Funding; Abbvie: Research Funding; Miltenyi: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; CytomX: Consultancy, Honoraria. Hill:Celgene: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Takeda: Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding.

scholarly journals Construction of a novel immune gene-based model for prognosis prediction of colorectal cancer

2021 ◽  
Author(s):  
Bin Xie ◽  
jie lin
Keyword(s):  
Gene Expression ◽  
Cox Regression ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Gene Set Enrichment Analysis ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Risk Of Death ◽  
Immune Related Genes

Abstract Background Colon adenocarcinoma (COAD) is the third leading cause of cancer-related death. Although surgical treatment and chemotherapy of COAD have made significant progress, its immunotherapy also has great potential, nowadays. Methods Gene expression profiles and clinical data of COAD patients were obtained from The Cancer Genome Atlas_Colon Adenocarcinoma (TCGA_COAD) and Gene Expression Omnibus (GEO) databases, which were further detected for immune-related genes. Immune-related genes were downloaded from Immunology Database and Analysis Portal (ImmPort). LASSO Cox regression analysis was utilized to analyze the immune-related prognostic signature. The prognostic value of the signature was validated by ROC curve. To further detected the potential pathway about immune-related genes in COAD patients, Gene Set Enrichment Analysis (GESA) was used to identify the most significant pathways. Results Finally, a total of 436 immune-related mRNA were identified. Eleven prognosis-related genes were selected to establish an immune-related prognostic signature, which divided patients into high and low risk groups. Several biological processes, such as immune response was enriched. Moreover, our prognosis model has better performance in predicting the 1-, 3-, 5- and 8-years overall survival (OS) for patients from the TCGA and GEO cohort. Also, the complicated signature obtained by combining immune-related signatures with clinical factors provides a more accurate OS predicting compared with individual molecular signatures. Conclusion We have established a prognostic signature consisting of 11 immune-related genes, which may be potential biomarkers for identifying COAD with a high risk of death. Then, the possibility including immunotherapy in personalized COAD treatment was evaluated.

scholarly journals Development and Validation of a Prognostic Signature Based on Immune Genes in Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yu Chen ◽  
Hao Lin ◽  
Ya-Nan Pi ◽  
Xi-Xi Chen ◽  
Hu Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Prognostic Score ◽  
Progression Free Survival ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Validation Dataset ◽  
Prognostic Signature ◽  
Immune Genes ◽  
Immune Signature

BackgroundCervical cancer is one of the most common types of gynecological malignancies worldwide. This study aims to develop an immune signature to predict survival in cervical cancer.MethodThe gene expression data of 296 patients with cervical cancer from The Cancer Genome Atlas database (TCGA) and immune-related genes from the Immunology Database and Analysis Portal (ImmPort) database were included in this study. The immune signature was developed based on prognostic genes. The validation dataset was downloaded from the Gene Expression Omnibus (GEO) database.ResultThe immune signature namely immune-based prognostic score (IPRS) was developed with 229 genes. Multivariate analysis revealed that the IPRS was an independent prognostic factor for overall survival (OS) and progression-free survival (PFS) in patients with cervical cancer. Patients were stratified into high IPRS and low IPRS groups, and those in the high IPRS group were associated with better survival, which was validated in the validation set. A nomogram with IPRS and stage was constructed to predict mortality in cervical cancer.ConclusionsWe developed a robust prognostic signature IPRS that could be used to predict patients’ survival outcome.

scholarly journals A Bioinformatics Approach to Identifying Potential Biomarkers for Cryptosporidium parvum: A Coccidian Parasite Associated with Fetal Diarrhea

Vaccines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1427
Author(s):  
Mumdooh J. Sabir ◽  
Ross Low ◽  
Neil Hall ◽  
Majid Rasool Kamli ◽  
Md. Zubbair Malik
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Cryptosporidium Parvum ◽  
Early Stage ◽  
Expression Profiles ◽  
Interaction Network ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Data Sets ◽  
Gene Interaction Network

Cryptosporidium parvum (C. parvum) is a protozoan parasite known for cryptosporidiosis in pre-weaned calves. Animals and patients with immunosuppression are at risk of developing the disease, which can cause potentially fatal diarrhoea. The present study aimed to construct a network biology framework based on the differentially expressed genes (DEGs) of C. parvum infected subjects. In this way, the gene expression profiling analysis of C. parvum infected individuals can give us a snapshot of actively expressed genes and transcripts under infection conditions. In the present study, we have analyzed microarray data sets and compared the gene expression profiles of the patients with the different data sets of the healthy control. Using a network medicine approach to identify the most influential genes in the gene interaction network, we uncovered essential genes and pathways related to C. parvum infection. We identified 164 differentially expressed genes (109 up- and 54 down-regulated DEGs) and allocated them to pathway and gene set enrichment analysis. The results underpin the identification of seven significant hub genes with high centrality values: ISG15, MX1, IFI44L, STAT1, IFIT1, OAS1, IFIT3, RSAD2, IFITM1, and IFI44. These genes are associated with diverse biological processes not limited to host interaction, type 1 interferon production, or response to IL-gamma. Furthermore, four genes (IFI44, IFIT3, IFITM1, and MX1) were also discovered to be involved in innate immunity, inflammation, apoptosis, phosphorylation, cell proliferation, and cell signaling. In conclusion, these results reinforce the development and implementation of tools based on gene profiles to identify and treat Cryptosporidium parvum-related diseases at an early stage.

scholarly journals A Prognostic Signature Constructed by CTHRC1 and LRFN4 in Stomach Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Songling Han ◽  
Wei Zhu ◽  
Weili Yang ◽  
Qijie Guan ◽  
Chao Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Real Time ◽  
Real Time Pcr ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Quantitative Real Time Pcr ◽  
Stomach Adenocarcinoma ◽  
Geo Database

BackgroundStomach adenocarcinoma (STAD) is the most common histological type of stomach cancer, which causes a considerable number of deaths worldwide. This study aimed to identify its potential biomarkers with the notion of revealing the underlying molecular mechanisms.MethodsGene expression profile microarray data were downloaded from the Gene Expression Omnibus (GEO) database. The “limma” R package was used to screen the differentially expressed genes (DEGs) between STAD and matched normal tissues. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for function enrichment analyses of DEGs. The STAD dataset from The Cancer Genome Atlas (TCGA) database was used to identify a prognostic gene signature, which was verified in another STAD dataset from the GEO database. CIBERSORT algorithm was used to characterize the 22 human immune cell compositions. The expression of LRFN4 and CTHRC1 in tissues was determined by quantitative real-time PCR from the patients recruited to the present study.ResultsThree public datasets including 90 STAD patients and 43 healthy controls were analyzed, from which 44 genes were differentially expressed in all three datasets. These genes were implicated in biological processes including cell adhesion, wound healing, and extracellular matrix organization. Five out of 44 genes showed significant survival differences. Among them, CTHRC1 and LRFN4 were selected for construction of prognostic signature by univariate Cox regression and stepwise multivariate Cox regression in the TCGA-STAD dataset. The fidelity of the signature was evaluated in another independent dataset and showed a good classification effect. The infiltration levels of multiple immune cells between high-risk and low-risk groups had significant differences, as well as two immune checkpoints. TIM-3 and PD-L2 were highly correlated with the risk score. Multiple signaling pathways differed between the two groups of patients. At the same time, the expression level of LRFN4 and CTHRC1 in tissues analyzed by quantitative real-time PCR were consistent with the in silico findings.ConclusionThe present study constructed the prognostic signature by expression of CTHRC1 and LRFN4 for the first time via comprehensive bioinformatics analysis, which provided the potential therapeutic targets of STAD for clinical treatment.

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

2018 ◽  
Vol 21 (2) ◽  
pp. 74-83
Author(s):  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Yu-Heng Chen ◽  
Yu-Ching Hsu ◽  
Hung-I Harry Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

scholarly journals Comprehensive Comparison of Amnion Stromal Cells and Chorion Stromal Cells by RNA-Seq

2021 ◽  
Vol 22 (4) ◽  
pp. 1901
Author(s):  
Brielle Jones ◽  
Chaoyang Li ◽  
Min Sung Park ◽  
Anne Lerch ◽  
Vimal Jacob ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stromal Cells ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Principal Component ◽  
Differentially Expressed ◽  
Inflammatory Factor ◽  
Next Generation Sequencing Technology ◽  
Angiogenic Potential ◽  
Anti Inflammatory

Mesenchymal stromal cells derived from the fetal placenta, composed of an amnion membrane, chorion membrane, and umbilical cord, have emerged as promising sources for regenerative medicine. Here, we used next-generation sequencing technology to comprehensively compare amniotic stromal cells (ASCs) with chorionic stromal cells (CSCs) at the molecular and signaling levels. Principal component analysis showed a clear dichotomy of gene expression profiles between ASCs and CSCs. Unsupervised hierarchical clustering confirmed that the biological repeats of ASCs and CSCs were able to respectively group together. Supervised analysis identified differentially expressed genes, such as LMO3, HOXA11, and HOXA13, and differentially expressed isoforms, such as CXCL6 and HGF. Gene Ontology (GO) analysis showed that the GO terms of the extracellular matrix, angiogenesis, and cell adhesion were significantly enriched in CSCs. We further explored the factors associated with inflammation and angiogenesis using a multiplex assay. In comparison with ASCs, CSCs secreted higher levels of angiogenic factors, including angiogenin, VEGFA, HGF, and bFGF. The results of a tube formation assay proved that CSCs exhibited a strong angiogenic function. However, ASCs secreted two-fold more of an anti-inflammatory factor, TSG-6, than CSCs. In conclusion, our study demonstrated the differential gene expression patterns between ASCs and CSCs. CSCs have superior angiogenic potential, whereas ASCs exhibit increased anti-inflammatory properties.

4 Molecularly guided multiplexed digital spatial analysis reveals differential gene expression profiles in the WNT-β-catenin pathway between melanoma and prostate tumors

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A4-A4
Author(s):  
Anushka Dikshit ◽  
Dan Zollinger ◽  
Karen Nguyen ◽  
Jill McKay-Fleisch ◽  
Kit Fuhrman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Molecule ◽  
Spatial Information ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Fluorescence Assay ◽  
Differentially Expressed ◽  
Tumor Type ◽  
Prostate Tumors

BackgroundThe canonical WNT-β-catenin signaling pathway is vital for development and tissue homeostasis but becomes strongly tumorigenic when dysregulated. and alter the transcriptional signature of a cell to promote malignant transformation. However, thorough characterization of these transcriptomic signatures has been challenging because traditional methods lack either spatial information, multiplexing, or sensitivity/specificity. To overcome these challenges, we developed a novel workflow combining the single molecule and single cell visualization capabilities of the RNAscope in situ hybridization (ISH) assay with the highly multiplexed spatial profiling capabilities of the GeoMx™ Digital Spatial Profiler (DSP) RNA assays. Using these methods, we sought to spatially profile and compare gene expression signatures of tumor niches with high and low CTNNB1 expression.MethodsAfter screening 120 tumor cores from multiple tumors for CTNNB1 expression by the RNAscope assay, we identified melanoma as the tumor type with the highest CTNNB1 expression while prostate tumors had the lowest expression. Using the RNAscope Multiplex Fluorescence assay we selected regions of high CTNNB1 expression within 3 melanoma tumors as well as regions with low CTNNB1 expression within 3 prostate tumors. These selected regions of interest (ROIs) were then transcriptionally profiled using the GeoMx DSP RNA assay for a set of 78 genes relevant in immuno-oncology. Target genes that were differentially expressed were further visualized and spatially assessed using the RNAscope Multiplex Fluorescence assay to confirm GeoMx DSP data with single cell resolution.ResultsThe GeoMx DSP analysis comparing the melanoma and prostate tumors revealed that they had significantly different gene expression profiles and many of these genes showed concordance with CTNNB1 expression. Furthermore, immunoregulatory targets such as ICOSLG, CTLA4, PDCD1 and ARG1, also demonstrated significant correlation with CTNNB1 expression. On validating selected targets using the RNAscope assay, we could distinctly visualize that they were not only highly expressed in melanoma compared to the prostate tumor, but their expression levels changed proportionally to that of CTNNB1 within the same tumors suggesting that these differentially expressed genes may be regulated by the WNT-β-catenin pathway.ConclusionsIn summary, by combining the RNAscope ISH assay and the GeoMx DSP RNA assay into one joint workflow we transcriptionally profiled regions of high and low CTNNB1 expression within melanoma and prostate tumors and identified genes potentially regulated by the WNT- β-catenin pathway. This novel workflow can be fully automated and is well suited for interrogating the tumor and stroma and their interactions.GeoMx Assays are for RESEARCH ONLY, not for diagnostics.

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