Weighted gene co-expression network analysis and prognostic analysis identifies hub genes and the molecular mechanism related to head and neck squamous cell carcinoma

Li, Qiuli; Chen, Weichao; Song, Ming; Chen, Wenkuan; Yang, Zhongyuan; Yang, Ankui

doi:10.1080/15384047.2018.1564560

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…Over the past several years, the discoveries of novel and informative genes by using bioinformatics methods have provided valuable information in the diagnosis of malignancies ( He et al, 2017 ; Meulekamp et al, 2017 ). At present, WGCNA has been widely used to screen out novel and effective molecular biomarkers in the bioinformatics field ( Gu et al, 2019 ; Li et al, 2019 ; Lv et al, 2021 ). In our previous study, we have made great efforts to use WGCNA as the main method to identify prognosis biomarkers for bladder cancer ( Yan et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Yan

Guo

et al. 2021

Front. Cell Dev. Biol.

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Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = −0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

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Section: Discussionmentioning

confidence: 99%

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Yan

Guo

et al. 2021

Front. Cell Dev. Biol.

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“…With the development of high-throughput sequencing and bioinformatics, more and more bioinformatics methods have been used to develop novel biomarkers associated with tumor progression [24], survival [25] or prognosis [26] of malignancies, which might greatly aid the early diagnosis and evaluation of prognosis in malignant tumors. Among these methods, WGCNA has been successfully applied in the identification of novel diagnostic and prognostic biomarkers for malignancies [27,28]. We have made great efforts to apply this method in finding out prognosis biomarkers of patients with BC in our previous study [29].…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer

Yan¹,

Fu²,

Guo³

et al. 2020

J. Cancer

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Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to construct an eight-gene signature by establishing a LASSO Cox model. Moreover, we reckoned the risk score (RS) of each sample, through which we could divide samples into two groups (the high-risk and low-risk groups) and verify the signature, in the training set and 3 validation sets (internal test set, GSE13507and E-MTAB-4321). This signature could distinguish between the high-and low-risk patients well (survival analysis: P = 0.015; AUC: 0.61 at 1 year, 0.61 at 3 years and 0.61 at 5 years). In the validation sets, this signature also showed good performance, which was consistent with the training test. Furthermore, we plotted a nomogram to predict the possibility of the overall survival (OS) and three calibration curves to predict the effectiveness of the nomogram, which suggested good value and clinical utility of the nomogram. In conclusion, we established an eight-gene signature, which was probably effective in the prediction of prognosis of patients with BC.

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“…Over the past several years, the discoveries of novel and informative genes by using bioinformatics methods have provided valuable information in diagnosis of malignancies [4]. In present, WGCNA has been widely used to screen out novel and effective molecular biomarkers in bioinformatics eld [28,29]. In our previous study, we have made great efforts to use WGCNA as the main method to identify prognosis biomarkers for bladder cancer [30].…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of a novel prognosis prediction model in adrenocortical carcinoma by integrative bioinformatics analysis and statistics and machine learning

Yan

Guo

et al. 2020

Preprint

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Background: Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a gene signature to predict the prognosis for ACC. Methods: Firstly, “WGCNA” package was used to construct a co-expression network and screen key module. Secondly, survival associated genes were identified by performing survival analysis. Thirdly, regression models were constructed by using the Ridge, ELASTIC-NET, and LASSO methods. Time-dependent ROC analysis, Cox regression analysis, GSEA, DCA and nomogram were performed to validate the model. Fourthly, mutations and CNVs of genes in the model were explored. Finally, LDA, KNN, SVM, PPI network and meta-analysis were used screened and validated meaningful prognostic biomarkers.Results: Two key modules were selected and 93 survival associated genes were identified. Furthermore, 11 models were constructed and two models were further selected, which were validated in each dataset (training set, internal validation set, GSE19750, and GSE76021). Model 2 was further identified as the best model (training set: survival analysis: p < 0.0001; AUC: 0.92 at 1 year, 0.91 at 3 years and 0.95 at 5 years). In genes in the best model, MKI67 was altered most (12%). Six hub genes were further analyzed by constructing a PPI network and validated by meta-analysis. Conclusion: In summary, we constructed and validated a prognostic multi-gene model and six powerful prognostic biomarkers, which might be useful instruments for predicting the prognosis of ACC patients.

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Weighted gene co-expression network analysis and prognostic analysis identifies hub genes and the molecular mechanism related to head and neck squamous cell carcinoma

Abstract: Yang (2019) Weighted gene co-expression network analysis and prognostic analysis identifies hub genes and the molecular mechanism related to head and neck squamous cell carcinoma,

Cited by 9 publications

References 29 publications

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer

Identification and validation of a novel prognosis prediction model in adrenocortical carcinoma by integrative bioinformatics analysis and statistics and machine learning

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