&lt;p&gt;The Clinical Significance of &lt;em&gt;PPEF1&lt;/em&gt; as a Promising Biomarker and Its Potential Mechanism in Breast Cancer&lt;/p&gt;

Ye, Ting; Wan, Xue; Li, Jingyuan; Feng, June; Guo, Jinglan; Li, Chang-Jiu; Liu, Jinbo

doi:10.2147/ott.s229432

Cited by 5 publications

(4 citation statements)

References 52 publications

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“…Finally, while the molecular function of phosphatase encoded by PPEF1 is not known in detail, it has been connected to apoptosis regulation and the response to calcium (Ca 2+ is also second messenger controlling cell death) 64 . It exerts a tumorigenic role in breast cancer 65 and was found to be overexpressed in pancreatic carcinoma 66 which confirms both its prognostic value in LUAD (poorer survival when highly expressed) and literature data regarding lung cancer 64 .…”

Section: Discussionsupporting

confidence: 76%

Prognostic significance of AP-2α/γ targets as cancer therapeutics

Kołat

Kałuzińska

Płuciennik

2022

Sci Rep

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Identifying genes with prognostic importance could improve cancer treatment. An increasing number of reports suggest the existence of successful strategies based on seemingly “untargetable” transcription factors. In addition to embryogenesis, AP-2 transcription factors are known to play crucial roles in cancer development. Members of this family can be used as prognostic factors in oncological patients, and AP-2α/γ transcription factors were previously investigated in our pan-cancer comparative study using their target genes. The present study investigates tumors that were previously found similar with an emphasis on the possible role of AP-2 factors in specific cancer types. The RData workspace was loaded back to R environment and 3D trajectories were built via Monocle3. The genes that met the requirement of specificity were listed using top_markers(), separately for mutual and unique targets. Furthermore, the candidate genes had to meet the following requirements: correlation with AP-2 factor (through Correlation AnalyzeR) and validated prognostic importance (using GEPIA2 and subsequently KM-plotter or LOGpc). Eventually, the ROC analysis was applied to confirm their predictive value; co-dependence of expression was visualized via BoxPlotR. Some similar tumors were differentiated by AP-2α/γ targets with prognostic value. Requirements were met by only fifteen genes (EMX2, COL7A1, GRIA1, KRT1, KRT14, SLC12A5, SEZ6L, PTPRN, SCG5, DPP6, NTSR1, ARX, COL4A3, PPEF1 and TMEM59L); of these, the last four were excluded based on ROC curves. All the above genes were confronted with the literature, with an emphasis on the possible role played by AP-2 factors in specific cancers. Following ROC analysis, the genes were verified using immunohistochemistry data and progression-related signatures. Staining differences were observed, as well as co-dependence on the expression of e.g. CTNNB1, ERBB2, KRAS, SMAD4, EGFR or MKI67. In conclusion, prognostic value of targets suggested AP-2α/γ as candidates for novel cancer treatment. It was also revealed that AP-2 targets are related to tumor progression and that some mutual target genes could be inversely regulated.

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Section: Discussionsupporting

confidence: 76%

Prognostic significance of AP-2α/γ targets as cancer therapeutics

Kołat

Kałuzińska

Płuciennik

2022

Sci Rep

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“…In addition, COL10A1 is a carcinogenic marker involving the epithelial-to-mesenchymal transition in GC, which is regulated by the transforming growth factor beta1 (TGF-β1)-SRY-box transcription factor 9 (SOX9) axis ( 49 ). Along with being an unfavorable prognostic marker, PPEF1 is negatively associated with OS and metastasis-free survival of breast cancer ( 50 ). Lin et al.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Analyses of Immune Subtypes of Stomach Adenocarcinoma for mRNA Vaccination

et al. 2022

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BackgroundAlthough messenger RNA (mRNA) vaccines have unique advantages against multiple tumors, mRNA vaccine targets in stomach adenocarcinoma (STAD) remain unknown. The potential effectiveness of mRNA vaccines is closely associated with the tumor immune infiltration microenvironment. The present study aimed to identify tumor antigens of STAD as mRNA vaccine targets and systematically determine immune subtypes (ISs) of STAD that might be suitable for immunotherapy.MethodsGene expression profiles and clinical data of patients with gastric cancer were downloaded from The Cancer Genome Atlas (TCGA; n = 409) and the Gene Expression Omnibus (GEO; n = 433), and genomic data were extracted from cBioPortal. Differential gene expression was analyzed using the limma package, genetic alterations were visualized using maftools, and prognosis was analyzed using ToPP. Correlations between gene expression and immune infiltration were calculated using TIMER software, and potential ISs were identified using ConsensusClusterPlus. Functional enrichment was analyzed in clusterProfiler, and r co-expression networks were analyzed using the weighted gene co-expression network analysis (WGCNA) package in R.ResultsOverexpression of the prognostic and highly mutated antigens ADAMTS18, COL10A1, PPEF1, and STRA6 was associated with infiltration by antigen-presenting cells in STAD. Five ISs (IS1–IS5) in STAD with distinct prognoses were developed and validated in TCGA and GEO databases. The tumor mutational burden and molecular and clinical characteristics significantly differed among IS1–IS5. Both IS1 and IS2 were associated with a high mutational burden, massive infiltration by immune cells, especially antigen-presenting cells, and better survival compared with the other subtypes. Both IS4 and IS5 were associated with cold immune infiltration and correlated with advanced pathological stages. We analyzed the immune microenvironments of five subtypes of immune modulators and biomarkers to select suitable populations for mRNA vaccination and established four co-expressed key modules to validate the characteristics of the ISs. Finally, the correlation of these four mRNA vaccine targets with the transcription factors of DC cells, including BATF3, IRF4, IRF8, ZEB2, ID2, KLF4, E2-2, and IKZF1, were explored to reveal the underlying mechanisms.ConclusionsADAMTS18, COL10A1, PPEF1, and STRA6 are potential mRNA vaccine candidates for STAD. Patients with IS1 and IS2 are suitable populations for mRNA vaccination immunotherapy.

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“…The 50 genes (listed in the Supplementary Table 8) discovered by the proposed model include 16 genes, IQGAP3 (Hu et al, 2019), KIF4A (Xue et al, 2018), TSHZ2 (Yamamoto et al, 2011), MKI67 (Schmidt et al, 2007), TNXB (Hu et al, 2009), KIFC1 (Ogden et al, 2017), KDM5B (Catchpole et al, 2011), PPEF1 (Ye et al, 2020), RYR3 (Shrestha et al, 2012), TMEM132C , FANCD2 (Barroso et al, 2006), ATAD2 (Kalashnikova et al, 2010), KIF26B (Wang et al, 2013), BRCA2 (Wooster et al, 1995), BLM (Arora et al, 2015), and ARFGEF (Kim et al, 2011), which are reported to be directly associated with breast cancer by previous researches. Although the other 14 genes have not been verified by biological experiments, we further analyze the Gene Ontology and pathway enrichment to explore their impact on the tumor formation and progression.…”

Section: Biomarkers and Go/pathway Analysismentioning

confidence: 99%

Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression

Jiang

Jin

2021

Front. Genet.

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Exploring the molecular mechanisms of breast cancer is essential for the early prediction, diagnosis, and treatment of cancer patients. The large scale of data obtained from the high-throughput sequencing technology makes it difficult to identify the driver mutations and a minimal optimal set of genes that are critical to the classification of cancer. In this study, we propose a novel method without any prior information to identify mutated genes associated with breast cancer. For the somatic mutation data, it is processed to a mutated matrix, from which the mutation frequency of each gene can be obtained. By setting a reasonable threshold for the mutation frequency, a mutated gene set is filtered from the mutated matrix. For the gene expression data, it is used to generate the gene expression matrix, while the mutated gene set is mapped onto the matrix to construct a co-expression profile. In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. In the stage of evaluation, we propose a weighted metric to modify the traditional accuracy to solve the sample imbalance problem. We apply the proposed method to The Cancer Genome Atlas breast cancer data and identify a mutated gene set, among which the implicated genes are oncogenes or tumor suppressors previously reported to be associated with carcinogenesis. As a comparison with the integrative network, we also perform the optimal model on the individual gene expression and the gold standard PMA50. The results show that the integrative network outperforms the gene expression and PMA50 in the average of most metrics, which indicate the effectiveness of our proposed method by integrating multiple data sources, and can discover the associated mutated genes in breast cancer.

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<p>The Clinical Significance of <em>PPEF1</em> as a Promising Biomarker and Its Potential Mechanism in Breast Cancer</p>

Cited by 5 publications

References 52 publications

Prognostic significance of AP-2α/γ targets as cancer therapeutics

Prognostic significance of AP-2α/γ targets as cancer therapeutics

Comprehensive Analyses of Immune Subtypes of Stomach Adenocarcinoma for mRNA Vaccination

Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression

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