Identification of PKP 2/3 as potential biomarkers of ovarian cancer based on bioinformatics and experiments

Gao, Lingling; Li, Xiao; Guo, Qin; Nie, Xin; Hao, Yingying; Liu, Qing; Liu, Juanjuan; Zhu, Liping; Yan, Limei; Lin, Bei

doi:10.1186/s12935-020-01602-3

Cited by 9 publications

(7 citation statements)

References 62 publications

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“… 24 PKP2 was elucidated to initially affect tumorigenesis, aggressiveness, malignant biological behavior, and immune infiltration of ovarian cancer. 25 These results correspond to the risk ratio of the feature genes we analyzed, whereas the observation of TMPRSS11F and MARCH4 is less explored. KRT81, a hair keratin, has become a biomarker of breast cancer and was revealed to promote cancer cell migration and invasion.…”

Section: Discussionmentioning

confidence: 82%

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Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes

Wang

et al. 2022

Clinical Laboratory Analysis

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Background Integrin β (ITGB) superfamily plays an essential role in the intercellular connection and signal transmission. It was exhibited that overexpressing of ITGB family members promotes the malignant progression of lung adenocarcinoma (LUAD), but the relationship between ITGB superfamily and the LUAD prognosis remains unclear. Methods In this study, the samples were assigned to different subgroups utilizing non‐negative matrix factorization clustering according to the expression of ITGB family members in LUAD. Kaplan–Meier (K‐M) survival analysis revealed the significant differences in the prognosis between different ITGB subgroups. Subsequently, we screened differentially expressed genes among different subgroups and conducted univariate Cox analysis, random forest feature selection, and multivariate Cox analysis. 9‐feature genes (FAM83A, AKAP12, PKP2, CYP17A1, GJB3, TMPRSS11F, KRT81, MARCH4, and STC1) in the ITGB superfamily were selected to establish a prognostic assessment model for LAUD. Results In accordance with the median risk score, LUAD samples were divided into high‐ and low‐risk groups. The receiver operating characteristic (ROC) curve of LUAD patients’ survival was predicted via K‐M survival curve and principal component analysis dimensionality reduction. This model was found to have a favorable performance in LUAD prognostic assessment. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differentially expressed genes between groups and Gene Set Enrichment Analysis (GSEA) of intergroup samples confirmed that the high‐ and low‐risk groups had evident differences mainly in the function of extracellular matrix (ECM) interaction. Risk score and univariate and multivariate Cox regression analyses of clinical factors showed that the prognostic model could be applied as an independent prognostic factor for LUAD. Then, we draw the nomogram of 1‐, 3‐, and 5‐year survival of LUAD patients predicted with the risk score and clinical factors. Calibration curve and clinical decision curve proved the favorable predictive ability of nomogram. Conclusion We constructed a LUAD prognostic risk model based on the ITGB superfamily, which can provide guidance for clinicians on their prognostic judgment.

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Section: Discussionmentioning

confidence: 82%

“…PKP2, a member of the p120ctn family of cell adhesion molecules, enhances cell proliferation, migration, and invasion by activating the EGFR signaling pathway in LUAD 24 . PKP2 was elucidated to initially affect tumorigenesis, aggressiveness, malignant biological behavior, and immune infiltration of ovarian cancer 25 …”

Section: Discussionmentioning

confidence: 99%

Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes

Wang

et al. 2022

Clinical Laboratory Analysis

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“…Plakophilin-2 ( PKP2 ) was initially identified as a desmosomal protein, but further studies have revealed that it is localized in the cytoplasm and nucleus as well [ 28 ]. PKP2 expression is upregulated in many cancers, such as lung [ 29 ], ovarian [ 30 ], glioma [ 31 ], and bladder cancers [ 32 ]. Increased PKP2 expression was also related to a malignant phenotype and poor prognosis in some cancers [ 29 – 32 ].…”

Section: Discussionmentioning

confidence: 99%

A glycolysis-related two-gene risk model that can effectively predict the prognosis of patients with rectal cancer

Liu

Zhou

et al. 2022

Hum Genomics

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Background Aerobic glycolysis is an emerging hallmark of cancer. Although some studies have constructed glycolysis-related prognostic models of colon adenocarcinoma (COAD) based on The Cancer Genome Atlas (TCGA) database, whether the COAD glycolysis-related prognostic model is appropriate for distinguishing the prognosis of rectal adenocarcinoma (READ) patients remains unknown. Exploring critical and specific glycolytic genes related to READ prognosis may help us discover new potential therapeutic targets for READ patients. Results Three gene sets, HALLMARK_GLYCOLYSIS, REACTOME_GLYCOLYSIS and REACTOME_REGULATION_OF_GLYCOLYSIS_BY_FRUCTOSE_2_6_BISPHOSPHATE_METABOLISM, were both significantly enriched in both COAD and READ through glycolysis-related gene set enrichment analysis (GSEA). We found that six genes (ANKZF1, STC2, SUCLG2P2, P4HA1, GPC1 and PCK1) were independent prognostic genes in COAD, while TSTA3 and PKP2 were independent prognostic genes in READ. Glycolysis-related prognostic model of COAD and READ was, respectively, constructed and assessed in COAD and READ. We found that the glycolysis-related prognostic model of COAD was not appropriate for READ, while glycolysis-related prognostic model of READ was more appropriate for READ than for COAD. PCA and t-SNE analysis confirmed that READ patients in two groups (high and low risk score groups) were distributed in discrete directions based on the glycolysis-related prognostic model of READ. We found that this model was an independent prognostic indicator through multivariate Cox analysis, and it still showed robust effectiveness in different age, gender, M stage, and TNM stage. A nomogram combining the risk model of READ with clinicopathological characteristics was established to provide oncologists with a practical tool to evaluate the rectal cancer outcomes. GO enrichment and KEGG analyses confirmed that differentially expressed genes (DEGs) were enriched in several glycolysis-related molecular functions or pathways based on glycolysis-related prognostic model of READ. Conclusions We found that a glycolysis-related prognostic model of COAD was not appropriate for READ, and we established a novel glycolysis-related two-gene risk model to effectively predict the prognosis of rectal cancer patients.

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“…PKP2 has been reported to be overexpressed in all adenocarcinomas [ 25 , 27 ]. High PKP2 expression was reported to be involved in the development of various cancers, including bladder [ 38 ], lung [ 39 ], and ovarian cancers [ 24 ]. However, the role of PKP2 in OS has rarely been studied, except for that reported by He et al .…”

Section: Discussionmentioning

confidence: 99%

“…We uploaded the top 50 most upregulated genes in OS from the GSE11416 data series to the string database, and identified 17 that were shown to interact closely ( Figure 1a ). Among the 17 genes, we found that PKP2 was upregulated in cancers and accelerated cancer development [ 24–28 ]. Nonetheless, only one study has revealed the positive role of PKP2 in OS [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Long non-coding RNA HCG11 enhances osteosarcoma phenotypes by sponging miR-1245b-5p that directly inhibits plakophilin 2

et al. 2021

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Long non-coding RNA (lncRNA) HCG11 can regulate various cancers through the ceRNA network. However, its role in osteosarcoma (OS) remains unknown. The HOS and Saos-2 cell lines were used for in vitro analyses. HCG11 and plakophilin 2 (PKP2) silencers, a miR-1245b-5p mimic, and a miR-1245b-5p inhibitor were utilized for the regulation analysis of lncRNA HCG11, miR-1245b-5p, and PKP2. Cell Counting Kit-8, wound healing, and transwell assays were used for cell proliferation, migration, and invasion analyses, and caspase-3 activity assay was used to measure cell apoptosis. The expression levels of lncRNA HCG11, miR-1245b-5p, and PKP2 were evaluated by quantitative real-time PCR and Western blotting. The distribution of lncRNA HCG11 was assessed using the RNA-FISH assay. The sponging and targeting roles of HCG11 and PKP2 on miR-1245b-5p were confirmed by dual-luciferase reporter analysis. An RNA immunoprecipitation assay was used to assess the binding between lncRNA HCG11 and miRNA-1245b-5p. We found that the lncRNA HCG11 was significantly upregulated in OS. LncRNA HCG11 silencing inhibits OS progression by repressing cell proliferation, migration, and invasion, and promoting cell apoptosis. RNA-FISH analysis indicated that lncRNA HCG11 was located in the cytoplasm. Mechanistic experiments showed that lncRNA HCG11 sponges miR-1245b-5p and negatively regulates miR-1245b-5p expression. Upregulated lncRNA HCG11 promotes proliferation, migration, and invasion, and inhibits apoptosis by inhibiting miR-1245b-5p in OS cells. PKP2 was verified as a target gene of miR-1245b-5p. Upregulated PKP2 promotes proliferation, migration, and invasion, and inhibits apoptosis by inhibiting miR-1245b-5p in OS. In conclusion, the HCG11/miR-1245b-5p/PKP2 axis promotes OS expression by promoting cell proliferation, migration, and invasion, and inhibiting apoptosis.

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Identification of PKP 2/3 as potential biomarkers of ovarian cancer based on bioinformatics and experiments

Cited by 9 publications

References 62 publications

Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes

Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes

A glycolysis-related two-gene risk model that can effectively predict the prognosis of patients with rectal cancer

Long non-coding RNA HCG11 enhances osteosarcoma phenotypes by sponging miR-1245b-5p that directly inhibits plakophilin 2

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