TRIM59 as a novel molecular biomarker to predict the prognosis of patients with NSCLC

Liao, Ming; Gao, Zhaojia; Zhu, Tao; Mao, Xiaoliang; Wang, Yeming; Yuan, Kai; Tong, Jichun

doi:10.3892/ol.2019.11199

Cited by 9 publications

(8 citation statements)

References 38 publications

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“…Besides, proteins, such as tripartite motif-containing protein59 (TRIM59), minichromosome maintenance protein 2 (MCM2) and CD271 were highly expressed, while the expression of interferon-induced proteins with tetratricopeptide repeats 2 (IFIT2) was decreased in LUSCC cell lines or tissues [12]. The overall survival (OS) of patients with high TRIM59, MCM2, CD271 expression or low IFIT2 expression were significantly better than the opposite group [12][13][14][15][16]. However, the sensitivity and specificity was not satisfactory using one biomarker.…”

Section: Ivyspringmentioning

confidence: 99%

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Fang¹,

Liu²,

Weng³

et al. 2020

Int. J. Med. Sci.

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Lung squamous cell carcinoma (LUSCC), as the major type of lung cancer, has high morbidity and mortality rates. The prognostic markers for LUSCC are much fewer than lung adenocarcinoma. Besides, protein biomarkers have advantages of economy, accuracy and stability. The aim of this study was to construct a protein prognostic model for LUSCC. The protein expression data of LUSCC were downloaded from The Cancer Protein Atlas (TCPA) database. Clinical data of LUSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 237 proteins were identified from 325 cases of LUSCC patients based on the TCPA and TCGA database. According to Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, a prognostic prediction model was established which was consisted of 6 proteins (CHK1_pS345, CHK2, IRS1, PAXILLIN, BRCA2 and BRAF_pS445). After calculating the risk values of each patient according to the coefficient of each protein in the risk model, the LUSCC patients were divided into high risk group and low risk group. The survival analysis demonstrated that there was significant difference between these two groups (p= 4.877e−05). The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.699, which suggesting that the prognostic risk model could effectively predict the survival of LUSCC patients. Univariate and multivariate analysis indicated that this prognostic model could be used as independent prognosis factors for LUSCC patients. Proteins co-expression analysis showed that there were 21 proteins co-expressed with the proteins in the risk model. In conclusion, our study constructed a protein prognostic model, which could effectively predict the prognosis of LUSCC patients.

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Section: Ivyspringmentioning

confidence: 99%

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Fang¹,

Liu²,

Weng³

et al. 2020

Int. J. Med. Sci.

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“…This phenomenon is a unique energy metabolism that exists in cancer cells. In recent years, many biomarkers for LUSC have been discovered, including glycolysis-associated genes such as kininogen 1 (KNG1) [ 8 ] and tripartite motif-containing protein 59 (TRIM59) [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

et al. 2021

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Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

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“…In recent years, many biomarkers, including glycolysis-associated genes, for LUSC have been determined, such as kininogen 1 (KNG1) 8 and tripartite motif-containing protein 59 (TRIM59). 9 With the development of high-throughput sequencing, various patient genome databases were constructed, which makes us have a deep understanding of genomic changes. Based on database mining, an increasing number of biomarkers were identi ed that were related to survival of patients with cancer.…”

Section: Introductionmentioning

confidence: 99%

A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

Huang

Zhang

Gong

et al. 2020

Preprint

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Background: Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of major histological subtypes. Although, numerous biomarkers were found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is not sufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival of patients with LUSC.Methods: The mRNA expression files and clinical information of LUSC were obtained from The Cancer Genome Atlas (TCGA) dataset.Results: Based on Gene set enrichment analysis (GSEA), we found 5 glycolysis-related gene sets were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were conducted to choose prognostic-related gene signature. Based on Cox proportional regression model, a risk score of three-gene signature (including HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. We found that a risk score of three-gene signature was an independent of prognostic indicator in LUSC using multivariate Cox regression analysis. Additionally, based on the cBioPortal database, the rate of alterations in HKDC1, ALDH7A1, and MDH1 genes were 1.9%, 1.1%, and 5% in LUSC patients, respectively. Conclusion: In conclusion, a glycolysis-based three-gene signature could serve as a novel biomarker in predicting prognosis of patients with LUSC, which provided more gene targets to cure LUSC patients.

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TRIM59 as a novel molecular biomarker to predict the prognosis of patients with NSCLC

Cited by 9 publications

References 38 publications

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

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