A five‐microRNA signature for individualized prognosis evaluation and radiotherapy guidance in patients with diffuse lower‐grade glioma

Zhang, Jianhua; Hou, Ruiqin; Pan, Yuhualei; Gao, Yuhan; Yang, Ying; Tian, Wenqin; Zhu, Yanbing

doi:10.1111/jcmm.15377

Cited by 16 publications

(11 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, a three-lncRNA signature 41 and a five-microRNA signature 42 were used as independent biomarkers for predicting LGGs patient outcomes after radiotherapy. Patients who had higher risk scores tended to have shorter survival times after radiotherapy treatment.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of an immune-related gene signature for predicting the radiosensitivity of lower-grade gliomas

Yan

Zhao

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Radiotherapy is an important treatment modality for lower-grade gliomas (LGGs) patients. This analysis was conducted to develop an immune-related radiosensitivity gene signature to predict the survival of LGGs patients who received radiotherapy. The clinical and RNA sequencing data of LGGs were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Lasso regression analyses were used to construct a 21-gene signature to identify the LGGs patients who could benefit from radiotherapy. Based on this radiosensitivity signature, patients were classified into a radiosensitive (RS) group and a radioresistant (RR) group. According to the Kaplan–Meier analysis results of the TCGA dataset and the two CGGA validation datasets, the RS group had a higher overall survival rate than that of the RR group. This gene signature was RT-specific and an independent prognostic indicator. The nomogram model performed well in predicting 3-, and 5-year survival of LGGs patients after radiotherapy by this gene signature and other clinical factors (age, sex, grade, IDH mutations, 1p/19q codeletion). In summary, this signature is a powerful supplement to the prognostic factors of LGGs patients with radiotherapy and may provide an opportunity to incorporate individual tumor biology into clinical decision making in radiation oncology.

show abstract

Section: Discussionmentioning

confidence: 99%

Development and validation of an immune-related gene signature for predicting the radiosensitivity of lower-grade gliomas

Yan

Zhao

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…According to median risk scores, all LUAD samples were divided into two groups, involving in high-risk and low-risk groups in different cohorts. Kaplan Meier (KM) curves were plotted to compare the prognostic difference ( 41 ). The area under the curve (AUC) which performed from receiver operating characteristic (ROC) curve analysis for overall survival (OS) was employed to designate the predictive efficiency ( 42 ).…”

Section: Methodsmentioning

confidence: 99%

A novel epithelial-mesenchymal transition (EMT)-related gene signature of predictive value for the survival outcomes in lung adenocarcinoma

et al. 2022

View full text Add to dashboard Cite

Lung adenocarcinoma (LUAD) is a remarkably heterogeneous and aggressive disease with dismal prognosis of patients. The identification of promising prognostic biomarkers might enable effective diagnosis and treatment of LUAD. Aberrant activation of epithelial-mesenchymal transition (EMT) is required for LUAD initiation, progression and metastasis. With the purpose of identifying a robust EMT-related gene signature (E-signature) to monitor the survival outcomes of LUAD patients. In The Cancer Genome Atlas (TCGA) database, least absolute shrinkage and selection operator (LASSO) analysis and cox regression analysis were conducted to acquire prognostic and EMTrelated genes. A 4 EMT-related and prognostic gene signature, comprising dickkopf-like protein 1 (DKK1), lysyl oxidase-like 2 (LOXL2), matrix Gla protein (MGP) and slit guidance ligand 3 (SLIT3), was identified. By the usage of datum derived from TCGA database and Western blotting analysis, compared with adjacent tissue samples, DKK1 and LOXL2 protein expression in LUAD tissue samples were significantly higher, whereas the trend of MGP and SLIT3 expression were opposite. Concurrent with upregulation of epithelial markers and downregulation of mesenchymal markers, knockdown of DKK1 and LOXL2 impeded the migration and invasion of LUAD cells. Simultaneously, MGP and SLIT3 silencing promoted metastasis and induce EMT of LUAD cells. In the TCGA-LUAD set, receiver operating characteristic (ROC) analysis indicated that our risk model based on the identified E-signature was superior to those reported in literatures. Additionally, the E-signature carried robust prognostic significance. The validity of prediction in the E-signature was validated by the three independent datasets obtained from Gene Expression Omnibus (GEO) database. The probabilistic nomogram including the E-signature, pathological T stage and N stage was constructed and the nomogram demonstrated satisfactory discrimination and calibration. In LUAD patients, the E-signature risk score was associated with T stage, N stage, M stage and TNM stage. GSEA Frontiers in Oncology frontiersin.org 01

show abstract

“…where N is the number of prognosis-related mRNAs, mRNAExp is the expression value of prognosis-related mRNA, and the coefficient of prognosis-related mRNA is derived from Cox regression. The prognostic RS model was selected for its largest area under the curve (AUC) value from all the combinations (Zhang et al, 2020).…”

Section: The Process Of Developing Prognostic Models Through Statistics and Machine-learning Methodsmentioning

confidence: 99%

Development of a Four-mRNA Expression-Based Prognostic Signature for Cutaneous Melanoma

et al. 2021

View full text Add to dashboard Cite

We aim to find a biomarker that can effectively predict the prognosis of patients with cutaneous melanoma (CM). The RNA sequencing data of CM was downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training group and test group. Survival statistical analysis and machine-learning approaches were performed on the RNA sequencing data of CM to develop a prognostic signature. Using univariable Cox proportional hazards regression, random survival forest algorithm, and receiver operating characteristic (ROC) in the training group, the four-mRNA signature including CD276, UQCRFS1, HAPLN3, and PIP4P1 was screened out. The four-mRNA signature could divide patients into low-risk and high-risk groups with different survival outcomes (log-rank p < 0.001). The predictive efficacy of the four-mRNA signature was confirmed in the test group, the whole TCGA group, and the independent GSE65904 (log-rank p < 0.05). The independence of the four-mRNA signature in prognostic prediction was demonstrated by multivariate Cox analysis. ROC and timeROC analyses showed that the efficiency of the signature in survival prediction was better than other clinical variables such as melanoma Clark level and tumor stage. This study highlights that the four-mRNA model could be used as a prognostic signature for CM patients with potential clinical application value.

show abstract

A five‐microRNA signature for individualized prognosis evaluation and radiotherapy guidance in patients with diffuse lower‐grade glioma

Cited by 16 publications

References 44 publications

Development and validation of an immune-related gene signature for predicting the radiosensitivity of lower-grade gliomas

Development and validation of an immune-related gene signature for predicting the radiosensitivity of lower-grade gliomas

A novel epithelial-mesenchymal transition (EMT)-related gene signature of predictive value for the survival outcomes in lung adenocarcinoma

Development of a Four-mRNA Expression-Based Prognostic Signature for Cutaneous Melanoma

Contact Info

Product

Resources

About