The High Expression of PTPRH Is Associated with Poor Prognosis of Human Lung Adenocarcinoma

Chen, Aifeng; Ding, Shibiao; Shen, Xiaoqiang; Lin, Xu'ai

doi:10.1155/2021/9932088

Cited by 7 publications

(10 citation statements)

References 22 publications

(23 reference statements)

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“…Existing studies have reported the relationship between PTPRH and LUAD. For example, Chen et al [ 31 ] observed that PTPRH was overexpressed in the LUAD tissue and served as an independent prognostic factor for LUAD. Previous studies indicated the prognostic value of regulator of G protein signaling 20 (RGS20) in patients with LUAD, and it might be a novel prognostic marker for LUAD [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma

Li²,

Tian

et al. 2023

Genetics Research

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Background. Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD. Methods. RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465. Results. A prognostic model based on eight LNM-related genes (ANGPTL4, BARX2, GPR98, KRT6A, PTPRH, RGS20, TCN1, and TNS4) was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues. Conclusion. Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications.

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Section: Discussionmentioning

confidence: 99%

Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma

Li²,

Tian

et al. 2023

Genetics Research

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“…[54] Study had shown that PTPRH was correlated with a poor prognosis in LUAD patients. [55] TNS4 is a putative oncogene in LUAD and gastric cancer. [56,57] In many cancers, we can detect abnormal MET tyrosine kinase receptor signaling.…”

Section: Discussionmentioning

confidence: 99%

Identification of metabolism-related gene signature in lung adenocarcinoma

Wang,

Wang

2023

Medicine

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Aim: Lung cancer is one of the most common cancers in China and has a high mortality rate. Most patients who are diagnosed have lost the opportunity to undergo surgery. Aberrant metabolism is closely associated with tumorigenesis. We aimed to identify an effective metabolism-related prediction model for assessing prognosis based on the cancer genome atlas (TCGA) and GSE116959 databases. Methods: TCGA and GSE116959 datasets from Gene Expression Omnibus were used to obtain lung adenocarcinoma (LUAD) data. Additionally, we captured metabolism-related genes (MRGs) from the GeneCards database. First, we extracted differentially expressed genes using R to analyze the LUAD data. We then selected the same differentially expressed genes, including 168 downregulated and 77 upregulated genes. Finally, 218 differentially expressed MRGs (DEMRGs) were included to perform functional enrichment analysis and construct a protein–protein interaction network with the help of Cytoscape and Search Tool for the Retrieval of Interacting Genes database. Cytoscape was used to visualize the intensive intervals in the network. Then univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses, which assisted in identifying the overall survival (OS)-related DEMRGs and building a 10-DEMRG prognosis model, were performed. The prognostic values, tumor immunity relevance, and molecular mechanism were further investigated. A nomogram incorporating signature, age, gender, and TNM stage was established. Results: A 10-DEMRG model was established to forecast the OS of LUAD through Least Absolute Shrinkage and Selection Operator regression analysis. This prognostic signature stratified LUAD patients into low-risk and high-risk groups. The receiver operating characteristic curve and K–M analysis indicated good performance of the DEMRGs signature at predicting OS in the TCGA dataset. Univariate and multivariate Cox regression also revealed that the DEMRGs signature was an independent prognosis factor in LUAD. We noticed that the risk score was substantially related to the clinical parameters of LUAD patients, covering age and stage. Immune analysis results showed that risk score was associated with some immune cells and immune checkpoints. Nomogram also verified the clinical value of the DEMRGs signature. Conclusion: In this study, we constructed a DEMRGs signature and established a prognostic nomogram that is robust and reliable to predict OS in LUAD. Overall, the findings could help with therapeutic customization and personalized therapies.

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“…Chen et al once validated the prognostic value of PTPRH in LUAD tissues. The transcription as well as the protein level of PTPRH was found to be noticeably upregulated in LUAD tissues, as demonstrated by qRT-PCR and immunohistochemistry, respectively [ 40 ]. The role of DKK1 in cancer development remains unelucidated.…”

Section: Discussionmentioning

confidence: 99%

A Connexin-Based Biomarker Model Applicable for Prognosis and Immune Landscape Assessment in Lung Adenocarcinoma

Yin

Ding

2022

Journal of Oncology

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Purpose. Gap junction protein (Connexin) family is the basic unit of cellular connection, whose multiple members were recently demonstrated to be associated with tumor progression. However, the expression pattern and prognostic value of connexin in lung adenocarcinoma (LUAD) have not yet been elucidated. Methods. Consensus cluster algorithm was first applied to determine a novel molecular subtype in LUAD based on connexin genes. The differentially expressed genes (DEGs) between two clusters were obtained to include in Cox regression analyses for the model construction. To examine the predictive capacity of the signature, survival curves and ROC plots were conducted. We implemented GSEA method to uncover the function effects enriched in the risk model. Moreover, the tumor immune microenvironment in LUAD was depicted by CIBERSORT and ssGSEA methods. Results. The integrated LUAD cohort (TCGA-LUAD and GSE68465) were clustered into two subtypes ( C 1 = 217 and C 2 = 296 ) based on 21 connexins and the clinical outcomes of LUAD cases in the two clusters showed remarkable discrepancy. Next, we collected 222 DEGs among two subclusters to build a prognostic model using stepwise Cox analyses. Our proposed model consisted of six genes that accurately forecast patient outcomes and differentiate patient risk. GSEA indicated that high-risk group was involved in tumor relevant pathways were activated in high-risk group, such as PI3K/AKT signaling, TGF-β pathway, and p53 pathway. Furthermore, LUAD cases with high-risk presented higher infiltration level of M2 macrophage and neutrophil, suggesting high-risk group were more likely to generate an immunosuppressive status. Conclusion. Our data identified a novel connexin-based subcluster in LUAD and further created a risk signature which plays a central part in prognosis assessment and clinical potency.

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The High Expression of PTPRH Is Associated with Poor Prognosis of Human Lung Adenocarcinoma

Cited by 7 publications

References 22 publications

Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma

Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma

Identification of metabolism-related gene signature in lung adenocarcinoma

A Connexin-Based Biomarker Model Applicable for Prognosis and Immune Landscape Assessment in Lung Adenocarcinoma

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