Experimental study of peripheral-blood pro-surfactant protein B for screening non-small cell lung cancer

He, Yong; Jiang, Zhenjie; Tong, Fengzhi; Li, Mingwu; Yin, Xing-ru; Hu, S. Jack; Wang, Linlin

doi:10.1590/s0102-865020170070000008

Cited by 10 publications

(9 citation statements)

References 25 publications

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“…This is in line with our conclusion. SFTPB has also been found to be reduced in patients with other lung diseases such as lung cancer (He et al, 2017) and chronic interstitial lung disease (Amin et al, 2001). Meanwhile, we also found that artificial alterations of SFTPB levels affected the expression of inflammatory cytokines.…”

Section: Discussionmentioning

confidence: 51%

Pulmonary surfactant‐associated protein B regulates prostaglandin‐endoperoxide synthase‐2 and inflammation in chronic obstructive pulmonary disease

Wang

Gao

et al. 2021

Experimental Physiology

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Pulmonary surfactant-associated protein B (SFTPB) is a critical protein for lung homeostasis, and polymorphism of its gene is associated with chronic obstructive pulmonary disease (COPD). However, few studies have so far confirmed the functional involvement of SFTPB in COPD. Serum SFTPB and inflammatory cytokine levels were measured in 54 patients with acute exacerbation of COPD and 29 healthy controls. A549 cells were induced using 10% cigarette smoke extract (CSE) and treated with dexamethasone to investigate the effect of inflammation on SFTPB expression, and the effect of SFTPB overexpression and silencing on inflammatory cytokines was measured using real-time PCR and enzyme-linked immunosorbent assay. SFTPB expression was assessed in mouse lung tissues using immunofluorescence. Serum levels of SFTPB were significantly lower in COPD patients than in controls (P = 0.009).

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Section: Discussionmentioning

confidence: 51%

Pulmonary surfactant‐associated protein B regulates prostaglandin‐endoperoxide synthase‐2 and inflammation in chronic obstructive pulmonary disease

Wang

Gao

et al. 2021

Experimental Physiology

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“…In previous study, RHOV is an atypical RHO GTPase that has been nominated as upregulated in non-small cell lung cancer in a minor study [47]. Pro-SFTPB is over expressed in non-small cell lung cancer, especially in LUAD [48]. In a multivariate analysis of patients with LUAD, DKK1 was independently associated with poor survival [49].…”

Section: Discussionmentioning

confidence: 96%

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

Geng

Meng

et al. 2020

J. Cancer

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Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genome Atlas (TCGA) were divided into TCGA cohort I (n = 338) and II (n = 168). The cohort I was used for model construction, and the cohort II and data from Gene Expression Omnibus (GSE72094 cohort, n = 393; GSE11969 cohort, n = 149) were utilized for validation. First, the survival-related seed genes were selected from the cohort I using the machine learning model (random survival forest, RSF), and then in order to improve prediction accuracy, the forward selection model was utilized to identify the prognosis-related key genes among the seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system was constructed by using these key genes in the cohort II, the GSE72094 cohort and the GSE11969 cohort, and the evaluation metrics such as HR, p value and C-index were calculated to validate the proposed method. Third, the developed approach was compared with the previous five prediction models. Finally, bioinformatics analyses (pathway, heatmap, protein-gene interaction network) have been applied to the identified seed genes and key genes. Results and Conclusion:Based on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that formed the prognostic gene expression signature. These sixteen key genes could achieve a strong power for prognostic prediction of LUAD patients in cohort II (HR = 3.80, p = 1.63e-06, C-index = 0.656), and were further validated in the GSE72094 cohort (HR = 4.12, p = 1.34e-10, C-index = 0.672) and GSE11969 cohort (HR = 3.87, p = 6.81e-07, C-index = 0.670). The experimental results of three independent validation cohorts showed that compared with the traditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effectively improved the prediction accuracy of LUAD prognosis, and the derived model was also superior to the other five existing prediction models. KEGG pathway analysis found eleven of the sixteen genes were associated with Nicotine addiction. Thirteen of the sixteen genes were reported for the first time as the LUAD prognosis-related key genes. In conclusion, we developed a sixteen-gene prognostic marker for LUAD, which may provide a powerful prognostic tool for precision oncology.

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“…For example, SFTPB is major component of pulmonary surfactant and is secreted by both alveolar type II and club lung epithelial cells. SFTPB is over expressed in non-small cell lung cancer, especially in lung adeno-carcinoma ( 49 ). Moreover, pro-SFTPB in plasma, the precursor of SFTPB, was an independent predictor of lung cancer ( 50 ).…”

Section: Discussionmentioning

confidence: 99%

A Novel mRNA-miRNA Regulatory Sub-Network Associated With Prognosis of Metastatic Clear Cell Renal Cell Carcinoma

Yang

Miao

Bai³

et al. 2021

Front. Oncol.

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BackgroundClear cell renal cell carcinoma (ccRCC) is a urinary disease with high incidence. The high incidence of metastasis is the leading cause of death in patients with ccRCC. This study was aimed to identify the gene signatures during the metastasis of ccRCC.MethodsTwo datasets, including one gene expression profile dataset and one microRNA (miRNA) expression profile dataset, were downloaded from Gene Expression Omnibus (GEO) database. The integrated bioinformatics analysis was performed using the (limma) R package, miRWalk, DAVID, STRING, Kaplan-Meier plotter databases. Quantitative real-time polymerase chain reaction (qPCR) was conducted to validate the expression of differentially expressed genes (DEGs) and DE-miRNAs.ResultsIn total, 84 DEGs (68 up-regulated and 16 down-regulated) and 41 DE-miRNAs (24 up-regulated and 17 down-regulated) were screened from GSE22541 and GSE37989 datasets, respectively. Furthermore, 11 hub genes and 3 key miRNAs were identified from the PPI network, including FBLN1, THBS2, SCGB1A1, NKX2-1, COL11A1, DCN, LUM, COL1A1, COL6A3, SFTPC, SFTPB, miR-328, miR-502, and miR-504. The qPCR data showed that most of the selected genes and miRNAs were consistent with that in our integrated analysis. A novel mRNA-miRNA network, SFTPB-miR-328-miR-502-miR-504-NKX2-1 was found in metastatic ccRCC after the combination of data from expression, survival analysis, and experiment validation.ConclusionIn conclusion, key candidate genes and miRNAs were identified and a novel mRNA-miRNA network was constructed in ccRCC metastasis using integrated bioinformatics analysis and qPCR validation, which might be utilized as diagnostic biomarkers and molecular targets of metastatic ccRCC.

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Experimental study of peripheral-blood pro-surfactant protein B for screening non-small cell lung cancer

Cited by 10 publications

References 25 publications

Pulmonary surfactant‐associated protein B regulates prostaglandin‐endoperoxide synthase‐2 and inflammation in chronic obstructive pulmonary disease

Pulmonary surfactant‐associated protein B regulates prostaglandin‐endoperoxide synthase‐2 and inflammation in chronic obstructive pulmonary disease

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

A Novel mRNA-miRNA Regulatory Sub-Network Associated With Prognosis of Metastatic Clear Cell Renal Cell Carcinoma

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