Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection

Fan, Xuemeng; Wang, Yaolai

doi:10.1186/s12859-019-2739-z

Cited by 4 publications

(4 citation statements)

References 42 publications

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“…According to mass spectrometry (MS) analysis, 15 proteins involved in a variety of biological process, including protein degradation, cell cycle, transcriptional regulation, stress factor, RNA splicing, and protein degradation, were identified as SIRT7 interacting candidates ( Figures 1 C and 1D). These genes in black were reported to interact with SIRT7 among the previous researches ( Fan et al., 2019 ; Grob et al., 2009 ; Iyer-Bierhoff et al., 2018 ; Jiang et al., 2017 ; Qi et al., 2018 ), and the rest need to be verified by IP methods. USP39, an SR-related protein and deubiquitinating enzyme, was detected in the SIRT7 pull-down products, suggesting that USP39 might interact with SIRT7.…”

Section: Resultsmentioning

confidence: 83%

An NAD+-Dependent Deacetylase SIRT7 Promotes HCC Development Through Deacetylation of USP39

Dong

et al. 2020

iScience

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Summary Ubiquitin specific protease 39 (USP39), an ortholog of Sad1p in yeast, is essential for spliceosome assembly during pre-mRNA splicing in human. Although it is known that USP39 is upregulated and plays an oncogenic role in hepatocellular carcinoma (HCC), the underlying mechanism remains unknown. The results of this study demonstrated that USP39 can be acetylated by the histone acetyltransferase MYST1, which is required for its proteasome-mediated degradation by Von Hippel-Lindau protein. In HCC cells, USP39 interacts with and is deacetylated by the lysine deacetylase sirtuin 7 (SIRT7). Notably, the deacetylation of USP39 by SIRT7 promotes its stability and thereby accelerates HCC cell proliferation and tumorigenesis in vitro and in vivo . Our data demonstrated a novel mechanism by which SIRT7 modulates the deacetylation of USP39 to promote HCC development, thus providing an effective anti-tumor therapeutic strategy for HCC.

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Section: Resultsmentioning

confidence: 83%

An NAD+-Dependent Deacetylase SIRT7 Promotes HCC Development Through Deacetylation of USP39

Dong

et al. 2020

iScience

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“…Furthermore, using the novel Bayesian hierarchical Cox proportional hazards model, most of the 14 prognostic genes we found can be explained in terms of basic study and population study. It is reported that CPS1, IGFBP3, MCM5, MCM7, NT5E, PLK1, PTTG1, SERPINB5, TXNRD1 and TYMS were associated with the prognosis of lung adenocarcinoma or non-small cell lung cancer, which was also included in our 14 genetic findings 27 – 36 . Basic studies found that NME4 and POLR3G were related to tumorigenesis and the progression of lung adenocarcinoma 37 , 38 .…”

Section: Discussionmentioning

confidence: 79%

A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model

Sun

Chu

et al. 2022

Sci Rep

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There have been few investigations of cancer prognosis models based on Bayesian hierarchical models. In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects of mRNAs on the prognosis of patients with lung adenocarcinoma. Based on the identified mRNAs, we can build a prognostic model combining mRNAs and clinical features, allowing us to explore new molecules with the potential to predict the prognosis of lung adenocarcinoma. The mRNA data (n = 594) and clinical data (n = 470) for lung adenocarcinoma were obtained from the TCGA database. Gene set enrichment analysis (GSEA), univariate Cox proportional hazards regression, and the Bayesian hierarchical Cox proportional hazards model were used to explore the mRNAs related to the prognosis of lung adenocarcinoma. Multivariate Cox proportional hazard regression was used to identify independent markers. The prediction performance of the prognostic model was evaluated not only by the internal cross-validation but also by the external validation based on the GEO dataset (n = 437). With the Bayesian hierarchical Cox proportional hazards model, a 14-gene signature that included CPS1, CTPS2, DARS2, IGFBP3, MCM5, MCM7, NME4, NT5E, PLK1, POLR3G, PTTG1, SERPINB5, TXNRD1, and TYMS was established to predict overall survival in lung adenocarcinoma. Multivariate analysis demonstrated that the 14-gene signature (HR 3.960, 95% CI 2.710–5.786), T classification (T1, reference; T3, HR 1.925, 95% CI 1.104–3.355) and N classification (N0, reference; N1, HR 2.212, 95% CI 1.520–3.220; N2, HR 2.260, 95% CI 1.499–3.409) were independent predictors. The C-index of the model was 0.733 and 0.735, respectively, after performing cross-validation and external validation, a nomogram was provided for better prediction in clinical application. Bayesian hierarchical Cox proportional hazards models can be used to integrate high-dimensional omics information into a prediction model for lung adenocarcinoma to improve the prognostic prediction and discover potential targets. This approach may be a powerful predictive tool for clinicians treating malignant tumours.

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“…e occurrence and development of cancer are often accompanied by complex interactions between genes and changes in their products. is complexity may be one of the main obstacles hindering clinical diagnosis [32]. Nowadays, with the rapid development of high-throughput sequencing technology and the reduction of costs, the biomedical field has accumulated massive amounts of data.…”

Section: Applicationsmentioning

confidence: 99%

Clustering Ensemble Technology Based on Granular Computing to Extract Cervical Cancer Predictors

Wang

2022

Mathematical Problems in Engineering

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Background. Cervical cancer is the most common gynecological malignancy, and its incidence has tended to be younger in recent years. Through the analysis of high-throughput expression data, the identification of key genes in cancer and healthy individuals as predictors of cervical cancer is of great significance for the early detection and early treatment of cervical cancer. Method. Granular computing is a concept and computing paradigm to deal with problems through information granulation, and the process of granulation can be realized by means of clustering. Based on this, this paper proposes an AB method to obtain representative elements in a multiattribute data system. First, the evaluation index FHEI of the clustering structure is introduced, and Algorithm 1 is designed to obtain the optimal clustering structure of each attribute of the data system and use it as the base cluster. Secondly, based on the clustering ensemble technology of granular computing, Algorithm 2 is designed with the help of the concept of information entropy. The algorithm takes the base cluster as the input to obtain the optimal ensemble clustering structure. Finally, using the nearest center principle, the representative elements of each class in the optimal ensemble clustering structure are obtained. Results. In this paper, the differentially expressed genes (DEGs) are screened out by using the gene expression data of cervical cancer, and the scores of the four interaction relationships among the DEGs are used as a multiattribute data system and input into the AB method. The five representative elements obtained are RTTN, SAMD10, ZNF207, WAC, and METTL14, which are the predictors of cervical cancer. The classification accuracy of these predictors is as high as 98.82%. This paper also conducts a comparative study between the AB method and other classical methods on six independent gene expression datasets. The results show that the number of predictors obtained by the AB method is small but has a high classification accuracy in the classification of patient samples.

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Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection

Cited by 4 publications

References 42 publications

An NAD+-Dependent Deacetylase SIRT7 Promotes HCC Development Through Deacetylation of USP39

An NAD+-Dependent Deacetylase SIRT7 Promotes HCC Development Through Deacetylation of USP39

A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model

Clustering Ensemble Technology Based on Granular Computing to Extract Cervical Cancer Predictors

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