A predicted risk score based on the expression of 16 autophagy‑related genes for multiple myeloma survival

Zhu, Fanxiu; Wang, Xiaotao; Zeng, Hui‐Qiong; Yin, Zhihua; Ye, Zhi‐Zhong

doi:10.3892/ol.2019.10881

Cited by 27 publications

(35 citation statements)

References 82 publications

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“…Autophagy is an important mechanism in the processes of transporting damaged, denatured or aging proteins, digestion, and degradation of organelles. 15 These processes are mediated by autophagy-related genes (ARGs). Previous studies have identified more than 200 ARGs, directly or indirectly participating in the process of autophagy.…”

Section: Introductionmentioning

confidence: 99%

An autophagic gene‐based signature to predict the survival of patients with low‐grade gliomas

Chen

Han

et al. 2021

Cancer Medicine

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Section: Introductionmentioning

confidence: 99%

An autophagic gene‐based signature to predict the survival of patients with low‐grade gliomas

Chen

Han

et al. 2021

Cancer Medicine

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“…Two published risk models were selected, one of which was a 16-gene signature [ 23 ] and the other was a 6-gene signature [ 24 ], compared with our 7-lncRNA signature. In order to make the model comparable, we use multifactor Cox analysis to calculate the risk score of the training set samples based on the corresponding genes in the model, evaluate the ROC of the two models, and divide the samples into high according to the optimal threshold.…”

Section: Resultsmentioning

confidence: 99%

“…We select 2 published related risk models, one of which is a 16-gene signature [ 23 ] and the other is a 6-gene signature [ 24 ], which was compared with our 7-lncRNA signature. The ROC and Kaplan-Meier (KM) survival curves of the published models in the training set and the C-index of the three models are plotted to compare the optimal models.…”

Section: Methodsmentioning

confidence: 99%

Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

Zhong

Liu

et al. 2020

BioMed Research International

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Background. An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods. Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results. In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion. In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

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“…A nine-gene prognostic signature ( HLA-DPB1 , TOP2A , FABP5 , CYP1B1 , IGHM , FANCI , LYZ , HMGN5 , and BEND6 ) related to the ISS stage of MM was developed via weighted gene co-expression network analysis (WGCNA) and Lasso ( Liu et al, 2018 ). A 16-gene ( ATIC , BNIP3L , CALCOCO2 , DNAJB1 , DNAJB9 , EIF4EBP1 , EVA1A , FKBP1B , FOXO1 , FOXO3 , GABARAP , HIF1A , NCKAP1 , PRKAR1A , TM9SF1 , and SUPT20H ) prognostic model related to autophagy was also established by Lasso ( Zhu et al, 2019 ). Moreover, a six-gene risk score model ( ZNF486 , EPHA5 , RP11.326C3.15 , DUSP6 , DUSP10 , and TRIAP1 ) for the prognostic prediction of PI-treated myeloma patients was developed by the random survival forest variable hunting (RSF-VH) algorithm ( Liu et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Super-Enhancer Associated Five-Gene Risk Score Model Predicts Overall Survival in Multiple Myeloma Patients

et al. 2020

Front. Cell Dev. Biol.

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Multiple myeloma (MM) is a malignant plasma cell tumor with high heterogeneity, characterized by anemia, hypercalcemia, renal failure, and lytic bone lesions. Although various powerful prognostic factors and models have been exploited, the development of more accurate prognosis and treatment for MM patients is still facing many challenges. Given the essential roles of super-enhancer (SE) associated genes in the tumorigenesis of MM, we tried to initially screen and identify the significant prognostic factors from SE associated genes in MM by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis using GSE24080 and GSE9782 datasets. Risk score model of five genes including CSGALNACT1, FAM53B, TAPBPL, REPIN1, and DDX11, was further constructed and the Kaplan-Meier (K-M) curves showed that the low-risk group seems to have better clinical outcome of survival compared to the high-risk group. Time-dependent receiver operating characteristic (ROC) curves presented the favorable performance of the model. An interactive nomogram consisting of the five-gene risk group and eleven clinical traits was established and identified by calibration curves. Therefore, the risk score model of SE associated five genes developed here could be used to predict the prognosis of MM patients, which may assist the clinical treatment of MM patients in the future.

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A predicted risk score based on the expression of 16 autophagy‑related genes for multiple myeloma survival

Cited by 27 publications

References 82 publications

An autophagic gene‐based signature to predict the survival of patients with low‐grade gliomas

An autophagic gene‐based signature to predict the survival of patients with low‐grade gliomas

Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

Super-Enhancer Associated Five-Gene Risk Score Model Predicts Overall Survival in Multiple Myeloma Patients

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