PPFIA1 expression associates with poor response to endocrine treatment in luminal breast cancer

Alfarsi, Lutfi; Ansari, Rokaya El; Craze, Madeleine L.; Masisi, Brendah K.; Ellis, Ian O.; Rakha, Emad A.; Green, Andrew

doi:10.1186/s12885-020-06939-6

Cited by 16 publications

(16 citation statements)

References 32 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…explored the prognostic value of PPFIA1 alone or in combination with TMEM16A and FADD in patients with invasive ductal breast carcinoma, and they found that the combined expression was signi cantly associated with perineural invasion and a low disease-free survival rate. A recent study showed that the expression of PPFIA1 is closely related to poor response to endocrine treatment in luminal breast cancer [22]. Since PPFIA1 was found to be involved in the development of ESCC, we believe that PPFIA1 is likely to have a great impact on the survival of patients with ESCC.…”

Section: Discussionmentioning

confidence: 81%

High Expression of PPFIA1 Is Associated With Tumor Progression and a Poor Prognosis in Esophageal Squamous Cell Carcinoma

Zhang

Jia

Yang

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: PTPRF interacting protein alpha 1 (PPFIA1) is reportedly related to the occurrence and progression of several types of malignancies. However, its role in esophageal squamous cell carcinoma (ESCC) remains unknown. We aimed to investigate the expression and clinical value of PPFIA1 in ESCC.Methods: The Oncomine, Gene Expression Profiling Enrichment Analysis (GEPIA), and Gene Expression Omnibus (GEO) databases were utilized to explore PPFIA1 mRNA expression in esophageal cancer. The associations of PPFIA1 expression with clinicopathological variables and prognosis were evaluated in the GSE53625 dataset and verified in quantitative real-time polymerase chain reaction (qRT-PCR)-based cDNA array and immunohistochemistry (IHC)-based tissue microarray (TMA) datasets. The interactions between PPFIA1 and other genes based on the protein-protein interaction (PPI) network was analyzed via the STRING website.Results: PPFIA1 expression was obviously upregulated in ESCC tissues versus adjacent normal tissues according to online database analyses (all P<0.05). High PPFIA1 expression was significantly associated with several clinicopathological features, including tumor size, histological grade, tumor invasion depth, lymph node metastasis, and tumor-node-metastasis (TNM) stage. High PPFIA1 expression was related to worse outcomes and was identified as an independent prognostic indicator of overall survival (OS) in ESCC patients GSE53625 dataset, P=0.004; cDNA array dataset, P<0.001; TMA dataset, P=0.039). PPI analysis demonstrated that PPFIA1 was highly correlated with multiple genes, including UNC13B, RAB3A, PTPRD, and SYT1.Conclusion: PPFIA1 may be associated with ESCC progression and could be used as a biomarker for prognostic evaluation in ESCC patients.

show abstract

Section: Discussionmentioning

confidence: 81%

High Expression of PPFIA1 Is Associated With Tumor Progression and a Poor Prognosis in Esophageal Squamous Cell Carcinoma

Zhang

Jia

Yang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To clarify the functions of DEGs, we further performed functional enrichment analysis. The proteins translated by DEGs were mainly located in extracellular regions and these genes were primarily implicated in tumor-related biological processes such as cell adhesion, wound healing, and extracellular matrix organization (Yu et al, 2019;Alfarsi et al, 2020;Liao et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

A Prognostic Signature Constructed by CTHRC1 and LRFN4 in Stomach Adenocarcinoma

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…To better clarify the functions of DEGs, we further performed functional enrichment analysis. The proteins translated by DEGs were mainly located in extracellular regions and these genes were primarily implicated in tumor-related biological processes such as cell adhesion, wound healing and extracellular matrix organization [16][17][18].…”

Section: Discussionmentioning

confidence: 99%

A prognostic model constructed by CTHRC1 and LRFN4 in Stomach adenocarcinoma by Bioinformatics Analysis

Han

Zhu

Guan

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Stomach adenocarcinoma (STAD) is the most common histological type of stomach cancer, which causes a considerable number of deaths worldwide. This study specifically aimed to identify potential biomarkers and reveal the underlying molecular mechanisms. Methods Gene expression profiles 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 data of STAD cases with both RNA sequencing and clinical information of The Cancer Genome Atlas (TCGA) were obtained from Genomic Data Commons (GDC) data portal. Survival curves were analyzed by the Kaplan-Meier method, univariate Cox regression analysis and multivariate Cox regression were performed using ‘survival’ package. CIBERSORT algorithm used approach to characterize the 22 human immune cell composition. Gene expression profiles microarray data and clinical information were downloaded from GEO database to validate prognostic model. Results Three public datasets including 90 STAD patients and 43 healthy controls were used and 44 genes were differentially expressed in all three datasets. These genes were primarily implicated in biological processes including cell adhesion, wound healing and extracellular matrix organization. Seven out of 44 genes showed significant survival differences based on their expression differences. CTHRC1 and LRFN4 were eventually used to constructed risk score and prognostic model by univariate Cox regression and stepwise multivariate Cox regression in The Cancer Genome Atlas (TCGA)-STAD dataset. The group having high risk scores and the group having low risk scores had significant differences in the infiltration level of multiple immune cells including CD4 memory resting T cells, M2 macrophages, memory B cells, resting dendritic cells, eosinophils, and gamma delta T cells. Multivariate Cox regression analyses indicated that the risk score was an independent predictor after adjusting for age, sex, and tumor stage. At last, the model was verified and evaluated by another independent dataset and showed a good classification effect. Conclusions The present study constructed the prognostic model by expression of CTHRC1 and LRFN4 for the first time via comprehensive bioinformatics analysis, which may provide clinical guidance and potential therapeutic targets for STAD.

show abstract

PPFIA1 expression associates with poor response to endocrine treatment in luminal breast cancer

Cited by 16 publications

References 32 publications

High Expression of PPFIA1 Is Associated With Tumor Progression and a Poor Prognosis in Esophageal Squamous Cell Carcinoma

High Expression of PPFIA1 Is Associated With Tumor Progression and a Poor Prognosis in Esophageal Squamous Cell Carcinoma

A Prognostic Signature Constructed by CTHRC1 and LRFN4 in Stomach Adenocarcinoma

A prognostic model constructed by CTHRC1 and LRFN4 in Stomach adenocarcinoma by Bioinformatics Analysis

Contact Info

Product

Resources

About