A three microRNA‐based prognostic signature for small cell lung cancer overall survival

Hu, Yan; Xin, Shaobin; Ma, Jing; Wang, Hui; Zhang, Heng; Liu, Jindong

doi:10.1002/jcb.28159

Cited by 16 publications

(13 citation statements)

References 43 publications

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“…Based on this fourteen-mRNA prognostic nomogram, we developed an online precision medicine predictive tool named the Smart Cancer Survival Predictive System, which can generate full-time mortality risk prediction curves for one specific patient at the individual level. Several prognostic models have been built for predicting the prognosis of lung cancer patients (Xie & Xie, 2019;Yan et al, 2018;Zuo et al, 2019). However, these previous prognostic models could only provide prognostic information for a special subgroup at the group level and failed to provide individual mortality risk prediction at the individual level.…”

Section: Discussionmentioning

confidence: 99%

“…Several prognostic models have been built for predicting the prognosis of lung cancer patients ( Xie & Xie, 2019 ; Yan et al, 2018 ; Zuo et al, 2019 ). However, these previous prognostic models could only provide prognostic information for a special subgroup at the group level and failed to provide individual mortality risk prediction at the individual level.…”

Section: Discussionmentioning

confidence: 99%

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Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools

Lin

Jiang

et al. 2021

PeerJ

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Background Individual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients. Methods Multivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model. Results There were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744–0.828]), 0.736 (95% CI [0.694–0.778]) and 0.766 (95% CI [0.724–0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients. Conclusion The current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools

Lin

Jiang

et al. 2021

PeerJ

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“…However, none of these prognostic www.nature.com/scientificreports www.nature.com/scientificreports/ expression signatures have been applied to clinical practice because of their uncertain performance on clinical samples. Furthermore, some studies were devoted to the excavation of prognostic markers from the perspective of microRNA or lncRNA profile, and a few prognostic signatures were proposed [24][25][26] ; however, their clinical performance remains to be evaluated.…”

Section: Discussionmentioning

confidence: 99%

Identification of prognostic signature of non–small cell lung cancer based on TCGA methylation data

Wang

Zhang

2020

Sci Rep

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Non-small lung cancer (NSCLC) is a common malignant disease with very poor outcome. Accurate prediction of prognosis can better guide patient risk stratification and treatment decision making, and could optimize the outcome. Utilizing clinical and methylation/expression data in The Cancer Genome Atlas (TCGA), we conducted comprehensive evaluation of early-stage NSCLC to identify a methylation signature for survival prediction. 349 qualified cases of NSCLC with curative surgery were included and further grouped into the training and validation cohorts. We identified 4000 methylation loci with prognostic influence on univariate and multivariate regression analysis in the training cohort. KEGG pathway analysis was conducted to identify the key pathway. Hierarchical clustering and WGCNA co-expression analysis was performed to classify the sample phenotype and molecular subtypes. Hub 5′-C-phosphate-G-3′ (CpG) loci were identified by network analysis and then further applied for the construction of the prognostic signature. The predictive power of the prognostic model was further validated in the validation cohort. Based on clustering analysis, we identified 6 clinical molecular subtypes, which were associated with different clinical characteristics and overall survival; clusters 4 and 6 demonstrated the best and worst outcomes. We identified 17 hub CpG loci, and their weighted combination was used for the establishment of a prognostic model (RiskScore). The RiskScore significantly correlated with post-surgical outcome; patients with a higher RiskScore have worse overall survival in both the training and validation cohorts (P < 0.01). We developed a novel methylation signature that can reliably predict prognosis for patients with NSCLC.

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“…Zheng et al built a three‐miRNA signature (miR‐1293, miR‐873, and miR‐1914) of LUAD using the TCGA database. Yan et al also found a 3‐miRNA signature (miR‐194, miR‐608, and miR‐9) using 42 SCLC patients acquired from the GEO dataset. Zhang et al performed a high‐throughput polymerase chain reaction assay to obtained a five‐serum‐miRNA signature, serving as a predictor of advanced NSCLC patients' prognosis.…”

Section: Discussionmentioning

confidence: 99%

Molecular characterization of lung cancer: A two‐miRNA prognostic signature based on cancer stem‐like cells related genes

Yan-ping

Yang

Shen

et al. 2019

J of Cellular Biochemistry

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Lung cancer is one of the deadliest cancers worldwide. To increase the survival rate of lung cancer, it is necessary to explore specific prognosis markers. More and more evidence finds that noncoding RNA is closely associated with the survival of lung cancer, and cancer stem cells (CSCs) also play a significant role in the progress of lung cancer. The objective of this study is to find CSLCs genes that affect the prognosis of lung cancer. The differential expression of long noncoding RNAs (lncRNAs), microRNAs (miRNAs), messenger RNAs (mRNAs) in the Cancer Genome Atlas (TCGA) database and differential expression data from microarray of CD326+ and CD326− A549 cell are intersected to identify stable and consistent expression genes (2 lncRNAs, 15 miRNAs, and 134 mRNAs). The intersection of lncRNAs and miRNAs is analyzed by univariate and multivariate Cox regression to obtained prognostic genes. Two miRNAs (miR‐30b‐5p and miR‐29c‐3p) are significantly correlated with the overall survival rate. Then using these two miRNAs to construct a risk score model as a prognosis signature of lung cancer. Subsequently, we analyzed the association between two miRNAs and clinical information of lung cancer patients, of which T stage, Neoplasm cancer and risk score (P < .05) can be used as independent prognostic indicators of lung cancer. Finally, target genes of 2 miRNAs and 134 mRNAs were annotated with Gene Ontology and analyzed with Kyoto Encyclopedia of Genes and Genomes pathway, and verified with the GEO database. In summary, this study illustrates the role of miRNAs in the promotion of lung cancer by CSCs, which is important to find molecular biomarkers of lung cancer.

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A three microRNA‐based prognostic signature for small cell lung cancer overall survival

Cited by 16 publications

References 43 publications

Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools

Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools

Identification of prognostic signature of non–small cell lung cancer based on TCGA methylation data

Molecular characterization of lung cancer: A two‐miRNA prognostic signature based on cancer stem‐like cells related genes

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