Abstract:Pancreatic ductal adenocarcinoma (PDAC) is an extremely malignant tumor. The immune profile of PDAC and the immunologic milieu of its tumor microenvironment (TME) are unique; however, the mechanism of how the TME engineers the carcinogenesis of PDAC is not fully understood. This study is aimed at better understanding the relationship between the immune infiltration of the TME and gene expression and identifying potential prognostic and immunotherapeutic biomarkers for PDAC. Analysis of data from The Cancer Gen… Show more
“…Furthermore, we used another plugin of Cytoscape, cytoHubba, to identify the hub genes. Four of its algorithms—the Maximum Neighborhood Component, Maximal Clique Centrality, Edge Percolated Component, and Degree—were used to calculate the top 20 hub genes ( Chin et al, 2014 ; Luan et al, 2020 ). The results of the four algorithms intersected and are shown in a Venn diagram ( Figure 7B ).…”
ObjectiveThe multisystem involvement and high heterogeneity of systemic lupus erythematosus (SLE) lead to great challenges in its diagnosis and treatment. The purpose of this study was to find new lncRNAs in peripheral blood mononuclear cells of SLE patients by transcriptome sequencing and explore their potential as biomarkers and their correlation with clinical features.Materials and MethodsTranscriptome sequencing was used to screen differentially expressed lncRNAs (DELs) and mRNAs (DEMs). The expression of these selected lncRNAs and mRNAs in SLE patients and healthy controls was verified by qPCR. DAVID and WebGestalt were used to perform enrichment analysis. Cytoscape was used to construct a protein–protein network, a coexpression network, and a competitive endogenous RNA network to reveal the regulatory mechanisms of lncRNAs at the transcriptome level.ResultsA total of 1737 DELs and 4078 DEMs were identified between SLE patients and healthy controls. Ten lncRNAs and eight genes were verified by qPCR in a larger sample set. The lncRNA NONHSAT101022.2 was significantly downregulated in SLE patients and was also significantly related to the activity and severity of disease. The upregulated genes were enriched in defense and the immune response, while the downregulated genes were mainly enriched in SLE-related pathways. Topology network analysis revealed that the lncRNAs were involved in regulation at the transcriptome level, including acting directly on mRNA or indirectly affecting gene expression by acting on miRNA.ConclusionIn this work, we identified many mRNAs and novel lncRNAs by transcriptome sequencing. The functions and regulatory mechanisms of these lncRNAs were analyzed by bioinformatic methods. The novel lncRNA NONHSAT101022.2 is significantly downregulated in SLE patients and is significantly related to the activity and severity of disease. Additionally, we propose that NONHSAT101022.2 may enhance the signal transduction of β2-AR by cis regulating LMBRD2, inducing NK cells to produce high levels of IFN-γ and thereby exacerbating SLE.
“…Furthermore, we used another plugin of Cytoscape, cytoHubba, to identify the hub genes. Four of its algorithms—the Maximum Neighborhood Component, Maximal Clique Centrality, Edge Percolated Component, and Degree—were used to calculate the top 20 hub genes ( Chin et al, 2014 ; Luan et al, 2020 ). The results of the four algorithms intersected and are shown in a Venn diagram ( Figure 7B ).…”
ObjectiveThe multisystem involvement and high heterogeneity of systemic lupus erythematosus (SLE) lead to great challenges in its diagnosis and treatment. The purpose of this study was to find new lncRNAs in peripheral blood mononuclear cells of SLE patients by transcriptome sequencing and explore their potential as biomarkers and their correlation with clinical features.Materials and MethodsTranscriptome sequencing was used to screen differentially expressed lncRNAs (DELs) and mRNAs (DEMs). The expression of these selected lncRNAs and mRNAs in SLE patients and healthy controls was verified by qPCR. DAVID and WebGestalt were used to perform enrichment analysis. Cytoscape was used to construct a protein–protein network, a coexpression network, and a competitive endogenous RNA network to reveal the regulatory mechanisms of lncRNAs at the transcriptome level.ResultsA total of 1737 DELs and 4078 DEMs were identified between SLE patients and healthy controls. Ten lncRNAs and eight genes were verified by qPCR in a larger sample set. The lncRNA NONHSAT101022.2 was significantly downregulated in SLE patients and was also significantly related to the activity and severity of disease. The upregulated genes were enriched in defense and the immune response, while the downregulated genes were mainly enriched in SLE-related pathways. Topology network analysis revealed that the lncRNAs were involved in regulation at the transcriptome level, including acting directly on mRNA or indirectly affecting gene expression by acting on miRNA.ConclusionIn this work, we identified many mRNAs and novel lncRNAs by transcriptome sequencing. The functions and regulatory mechanisms of these lncRNAs were analyzed by bioinformatic methods. The novel lncRNA NONHSAT101022.2 is significantly downregulated in SLE patients and is significantly related to the activity and severity of disease. Additionally, we propose that NONHSAT101022.2 may enhance the signal transduction of β2-AR by cis regulating LMBRD2, inducing NK cells to produce high levels of IFN-γ and thereby exacerbating SLE.
“…Several groups have proposed CSMD3 (CUB and Sushi Multiple Domains 3) as a frequently mutated gene in human LUSC, resulting in an increased proliferation of airway epithelial cells [27,37,38]. FBN1 (Fibrillin-1), an extracellular matrix glycoprotein that serves as a structural component of calcium-binding microfibrils, seems to play an important role in tumor-related immune infiltration and has been proposed as a prognostic and predictive biomarker for immune therapy against PDAC [39]. Finally, CSF2RB (Colony Stimulating Factor 2 Receptor Subunit Beta) has been shown to regulate EMT in human lung cancer cells [40,41].…”
There is a paucity of adequate mouse models and cell lines available to study lung squamous cell carcinoma (LUSC). We have generated and characterized two models of phenotypically different transplantable LUSC cell lines (UN-SCC679 and UN-SCC680) derived from an N-nitroso-tris-chloroethylurea (NTCU) chemically-induced mouse model in A/J mice. Furthermore, we genetically characterized and compared both LUSC cell lines by performing whole exome and RNA sequencing. These experiments revealed similar genetic and transcriptomic patterns that may correspond to the classical LUSC human subtype. In addition, we compared the immune landscape generated by both tumor cells lines in vivo and assessed their response to immune checkpoint inhibition. The differences between the two cell lines are a good model for the remarkable heterogeneity of human squamous cell carcinoma. Study of the metastatic potential of these models revealed that both cell lines represent the human LUSC organotropism to the brain, bones, liver and adrenal glands. In summary, we have generated a very valuable cell line tools for LUSC research that recapitulates the complexity of the human disease.
“… 19 The expression of FBN1 is considered to be positively correlated with tumor-related immune infiltration. 20 Together, these results demonstrated HRD PC harbors a specific genetic mutation spectrum. Figure 3 Multi-omics analysis in HRD (A) Top: differences in the distribution of aneuploidy score, TMB, SNV neoantigens, number of fraction, and HRD score between HRD and non-HRD groups in TCGA.…”
Pancreatic cancer (PC) with homologous recombination deficiency (HRD) has been reported to benefit from poly ADP-ribose polymerase (PARP) inhibitors. However, accurate identification of HRD status for PC patients from the transcriptional level is still a great challenge. Here, based on a relative expression ordering (REO)-based algorithm, we developed an HRD signature including 24 gene pairs (24-GPS) using PC transcriptional profiles from The Cancer Genome Atlas (TCGA). HRD samples classified by 24-GPS showed worse overall survival (p = 4.4E-3 for TCGA; p = 1.2E-3 for International Cancer Genome Consortium-Australia cohort; p = 6.4E-2 for
GSE17891
; p = 7.5E-2 for
GSE57495
) and higher HRD scores than non-HRD samples (p = 1.4E-4). HRD samples showed highly unstable genomic characteristics and also displayed HRD-related alterations at the epigenomic and proteomic levels. Moreover, HRD cell lines identified by 24-GPS tended to be sensitive to PARP inhibitors (p = 6.6E-2 for olaparib; p = 2.6E-3 for niraparib). Compared with the non-HRD group, the HRD group presented lower immune scores and CD4/CD8 T cell infiltration proportion. Interestingly, PC tumor cells with co-inhibition of PARP-related genes and
ATR
showed reduced survival ability. In conclusion, 24-GPS can robustly identify PC patients with HRD status at the individualized level.
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