Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study

Tang, Xin; Li, Ying Qin; Liang, Shumin; Jiang, Wei; Liu, Fang; Ge, Wen Xiu; TANG, Ling; Ma, Jun; He, Qiuming; Yang, Xiao; Zhang, Yuan; Wen, Xin; Zhang, Jian; Wang, Ya Qin; Zhang, Pan Pan; Sun, Ying; Yun, Jing Ping; Zeng, Jing; Li, Li; Liu, Li Zhi; Liu, Na

doi:10.1016/s1470-2045(18)30080-9

Cited by 231 publications

(213 citation statements)

References 35 publications

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“…An array‐based database to identify survival‐associated mRNAs for prognostic prediction is significant and urgently needed to guide tailored therapy for patients with BC . In our study, we used GEO array data to screen differential mRNAs in a discovery set and selected 56 significant mRNAs according to the likelihood of patients with distant metastasis.…”

Section: Discussionmentioning

confidence: 99%

“…An array-based database to identify survival-associated mRNAs for prognostic prediction is significant and urgently needed to guide tailored therapy for patients with BC. 20,21,23 In our study, we used GEO array data to screen differential mRNAs in a discovery set and selected 56 significant mRNAs according to the likelihood of patients Our study results show that the evaluation of four mRNAs expression could be a vital tool for the management of BC patients, which can guide the stratification of patients that may recur or metastasis, aiding in decision making for tailored therapy, and then ultimately contributing to an improvement in survival rates.…”

Section: Discussionmentioning

confidence: 99%

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Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Xie

Wang

Shi

et al. 2018

J Cellular Molecular Medi

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Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Xie

Wang

Shi

et al. 2018

J Cellular Molecular Medi

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“…Statistically significant variables in the univariate analyses were entered into multivariable Cox regression analysis. The independent significance of different clinical factors were tested by the multivariate Cox regression analysis, of which the P value larger than 0.05 was removed from the analysis . Kaplan‐Meier method was used to calculate time‐to‐event data, and 95% confidence interval (CI) was calculated using Greenwood's formula.…”

Section: Methodsmentioning

confidence: 99%

Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy

et al. 2019

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Background To explore the prognostic value and the role for treatment decision of pathological microscopic features in patients with nasopharyngeal carcinoma (NPC) using the method of deep learning. Methods The pathological microscopic features were extracted using the software QuPath (version 0.1.3. Queen's University) in the training cohort (Guangzhou training cohort, n = 843). We used the neural network DeepSurv to analyze the pathological microscopic features (DSPMF) and then classified patients into high‐risk and low‐risk groups through the time‐dependent receiver operating characteristic (ROC). The prognosis accuracy of the pathological feature was validated in a validation cohort (n = 212). The primary endpoint was progression‐free survival (PFS). Results We found 429 pathological microscopic features in the H&E image. Patients with high‐risk scores in the training cohort had shorter 5‐year PFS (HR 10.03, 6.06‐16.61; P < .0001). The DSPMF (C‐index: 0.723) had the higher C‐index than the EBV DNA (C‐index: 0.612) copies and the N stage (C‐index: 0.593). Furthermore, induction chemotherapy (ICT) plus concomitant chemoradiotherapy (CCRT) had better 5‐year PFS to those received CCRT (P < .0001) in the high‐risk group. Conclusion The DSPMF is a reliable prognostic tool for survival risk in patients with NPC and might be able to guide the treatment decision.

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“…25,26 Afterwards, we conducted a log rank test to determine the difference between the low-and high-risk groups. Then, we performed univariate Cox regression analysis to determine the relationship between patient survival and immune gene expression.…”

Section: Immune Prognostic Model Construction and Validationmentioning

confidence: 99%

“…X-tile software (version: 3.6.1) was used to identify the optimum cut-off value for dividing patients into low-and high-risk groups according to the highest χ 2 value defined in the Mantel-Cox test. 25,26 Afterwards, we conducted a log rank test to determine the difference between the low-and high-risk groups. We plotted a Kaplan-Meier OS curve for both groups and calculated the hazard rate (HR).…”

Section: Immune Prognostic Model Construction and Validationmentioning

confidence: 99%

Systematic construction and validation of an immune prognostic model for lung adenocarcinoma

Luo

Lei

Zhang

et al. 2019

J Cellular Molecular Medi

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Lung adenocarcinoma (LUAD), the most common non-small-cell lung cancer, is characterized by a dense lymphocytic infiltrate, which indicates that the immune system plays an active role in the development and growth of this cancer. However, no investigations to date have proposed robust models for predicting survival outcome for patients with LUAD in terms of tumour immunology. A total of 761 LUAD patients were included in this study, in which the database of The Cancer Genome Atlas (TCGA) was utilized for discovery, and the Gene Expression Omnibus (GEO) database was utilized for validation. Bioinformatics analysis and R language tools were utilized to construct an immune prognostic model and annotate biological functions.Lung adenocarcinoma showed a weakened immune phenotype compared with adjacent normal tissues. Immune-related gene sets were profiled, an immune prognostic model based on 2 immune genes (ANLN and F2) was developed with the TCGA database to distinguish cases as having a low or high risk of unfavourable prognosis, and the model was verified with the GEO database. The model was prognostically significant in stratified cohorts, including stage I-II, stage III-IV and epidermal growth factor receptor (EGFR) mutant subsets, and was considered to be an independent prognostic factor for LUAD. Furthermore, the low-and high-risk groups showed marked differences in tumour-infiltrating leucocytes, tumour mutation burden, aneuploidy and PD-L1 expression. In conclusion, an immune prognostic model was proposed for LUAD that is capable of independently identifying patients at high risk for poor survival, suggesting a relationship between local immune status and prognosis. K E Y W O R D Sbiomarkers, immune prognostic model, immunology, lung adenocarcinoma, overall survival

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Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study

Cited by 231 publications

References 35 publications

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy

Systematic construction and validation of an immune prognostic model for lung adenocarcinoma

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