&lt;p&gt;Identification and Validation of Reference Genes Selection in Ovarian Cancer Exposed to Hypoxia&lt;/p&gt;

Yan, Wenying; Xie, Mei; Li, Rong; Hu, Hsiao Yun; Tang, Biao; Shen, Jie

doi:10.2147/ott.s249733

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…Loss of Rpl13a snoRNAs alters mitochondrial metabolism, reduces reactive oxygen species levels, increases glucose-stimulated insulin secretion from pancreatic islets, and enhances systemic glucose tolerance [ 72 ]. Previous studies have reported that under normoxic conditions, the most stably expressed genes in ovarian cancer cells are GAPDH/TBP, while under hypoxic conditions, the most stable candidate housekeeping genes are RPL13A/SDHA [ 73 ]. ITGAM encodes the α chain of integrin αMβ2 (CD11b).…”

Section: Discussionmentioning

confidence: 99%

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

Zhao,

Wang,

Zhao

et al. 2024

BMC Genomics

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Background There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC. Methods Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors. Results The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1. Conclusion This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

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Section: Discussionmentioning

confidence: 99%

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

Zhao,

Wang,

Zhao

et al. 2024

BMC Genomics

View full text Add to dashboard Cite

show abstract

“…Loss of Rpl13a snoRNAs alters mitochondrial metabolism, reduces reactive oxygen species levels, increases glucose-stimulated insulin secretion from pancreatic islets, and enhances systemic glucose tolerance [58]. Previous studies have reported that under normoxic conditions, the most stably expressed genes in ovarian cancer cells are GAPDH/TBP, while under hypoxic conditions, the most stable candidate housekeeping genes are RPL13A/SDHA [59]. With each new scienti c discovery, the structure of the GO resource continually evolves to incorporate biological knowledge of gene functions and to constantly improve its ability to re ect the latest state of biological understanding [70].…”

Section: Discussionmentioning

confidence: 99%

Single-Cell and Transcriptomic Analyses Reveal the Influence of Diabetes on Ovarian Cancer

Zhao,

Wang,

Zhao

et al. 2023

Preprint

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Background:There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC. Methods:Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets GSE184880 and GSE165816 from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data from GSE40595 and GSE29142. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses, including transcription factor (TF)–genes interaction network analysis and microRNA (miRNA)–genes coregulation network analysis, were performed based on the core targets. Results:Immune cell infiltration analysis of mRNA expression data revealed significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Conclusion: This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

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“…Moreover, this enzyme has been shown to be a key molecule in multiple biological functions, including molecular chaperoning,protein folding,protein tra cking,immune modulation,and cell signaling [6]. Studies have also reported that PPIA is involved in several diseases, including kawasaki disease [7],amyotrophic lateral sclerosis [8],atherosclerosis,myocardial infarction [9],severe sepsis [10],vascular smooth muscle cell disease [11],rheumatoid arthritis and HIV infection [12].Moreover,several studies have shown that PPIA is expressed in some cancers,including lung cancer,nasopharyngeal carcinoma [13],gastric adenocarcinoma [14],thyroid carcinoma [15],neuroblastoma [16],ovarian cancer [17],pancreatic cancer [18],breast cancer,colorectal cancer [19][20],melanoma [21],liver hepatocellular carcinoma [22].Many researchers have assessed PPIA function in cancer.It has been suggested that PPIA plays an important role in the whole pathological process of cancer development.High expression levels of PPIA were associated with decreased overall survival in liver hepatocellular carcinoma,colorectal cancer,low-grade glioma, acute myeloid leukemia.However,in gastric adenocarcinoma, high expression levels of CypA are positively correlated with invasiveness and distant metastasis.Therefore,both low and high expression of PPIA are associated with tumor prognosis.…”

Section: Introductionmentioning

confidence: 99%

Identification Of PPIA As A Prognostic Biomarker Of Lung Adenocarcinoma From Bioinformatics Analysis

Chen

et al. 2022

Preprint

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Background: We aim to investigate the expression of the peptidylprolyl isomerase A (PPIA) gene in lung adenocarcinoma(LUAD),and to investigate the diagnostic, prognostic value and correlation with immune infiltrates of PPIA in LUAD.Methods: Transcriptional expression profiles of PPIA between LUAD tissues and normal tissues were downloaded from the Cancer Genome Atlas (TCGA).The PPIA protein expression was assessed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) .Receiver operating characteristic(ROC) curve was used to differentiate LUAD from adjacent normal tissues.Kaplan-Meier method was conducted to assess the effect of PPIA on survival.Protein-protein interaction (PPI) networks were constructed by the STRING.Functional enrichment analyses were performed using the“ClusterProfiler”package.The relationship between PPIA mRNA expression and immune infiltrates was determined by tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB).Results: The expression of PPIA in LUAD tissues was significantly upregulated than those in adjacent normal tissues. High PPIA mRNA expression was associated with lymph node metastases and high TNM stage. The ROC curve analysis showed that PPIA had an AUC value of 0.804(95% CI: 0.764-0.843).Kaplan-Meier survival analysis showed LUAD patients with high-PPIA had a worse prognosis than those with low-PPIA(mOS: 42.2months VS 87.2months,P＜0.001). Correlation analysis indicated PPIA mRNA expression was correlated with immune infiltrates.PPIA expression had correlations with CD4+ T cell (r = -0.29, P = 5.51e-11),macrophage M1 (r = 0.106, P = 1.84e-02), and B cell (r = -0.181, P =5.40e-5).Conclusion: Our research showed that PPIA could be used as a prognostic biomarker.Up-regulation of PPIA expression is significantly related to poor prognosis and immune infiltration of LUAD and may be a potential therapeutic molecular target for LUAD patients.

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<p>Identification and Validation of Reference Genes Selection in Ovarian Cancer Exposed to Hypoxia</p>

Cited by 9 publications

References 21 publications

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

Single-Cell and Transcriptomic Analyses Reveal the Influence of Diabetes on Ovarian Cancer

Identification Of PPIA As A Prognostic Biomarker Of Lung Adenocarcinoma From Bioinformatics Analysis

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