A novel microRNA signature for pathological grading in�lung adenocarcinoma based on TCGA and GEO data

Yang, Zhiyu; Yin, Hongkun; Shi, Lei; Qian, Xiaohua

doi:10.3892/ijmm.2020.4526

Cited by 11 publications

(12 citation statements)

References 44 publications

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“…Considering that the abnormal expression of miRNA can affect the molecular functions and biological processes of a variety of tumors, many attempts have been made to use miRNA as a biomarker to accurately predict the diagnosis and prognosis of cancers. [20][21][22][23] Up to now, several studies have also focused on the discovery of miRNAs as biomarkers for nasopharyngeal cancer diagnosis. Huo Zhang et al identified a 7-miRNA signature in plasma for nasopharyngeal cancer detection.…”

Section: Discussionmentioning

confidence: 99%

A novel microRNA-based signature predicts prognosis among nasopharyngeal cancer patients

Wang

et al. 2020

Exp Biol Med (Maywood)

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Nasopharyngeal cancer is one of the most common malignant tumors in the head and neck. Identification of promising miRNA biomarkers might benefit a lot to the detection of nasopharyngeal carcinoma. miRNA expression profile and clinical information were obtained from two microarray profiling data sets from the Gene Expression Omnibus (GEO) database. miRNA signature model was constructed via univariate Cox survival analysis, multivariate Cox survival analysis, and least absolute shrinkage and selection operator Cox regression analysis. Kaplan–Meier curve, area under the curve (AUC), decision curve analysis, Box plot, and nomogram were used to evaluate the prognosis of the model to patients. 67 up-regulated and 93 down-regulated miRNAs were identified from GEO microarray data sets ( P < 0.05). A three-miRNA signature (has-miR-142-3p, has-miR-29c, and has-miR-30e) was obviously associated with the overall survival of nasopharyngeal carcinoma patients ( P < 0.001). The AUCs for the signature were 0.74, 0.7 for the training set and external validation set. The AUC of disease free survival and distant metastasis-free survival were also high. The model has better clinical independence and has better clinical prediction effect when combined with clinical characteristics ( P < 0.0001). Compared with the published models, our model had a higher AUC. Our results revealed that a three-miRNA signature was a potential novel prognostic biomarker for nasopharyngeal carcinoma. Impact statement Nasopharyngeal cancer is one of the most common malignant tumors in the head and neck. Identification of promising miRNA biomarkers might benefit a lot to the detection of nasopharyngeal carcinoma. A three-miRNA signature (has-miR-142-3p, has-miR-29c, and has-miR-30e) was obviously associated with the overall survival of nasopharyngeal carcinoma patients. The model has better clinical independence and has better clinical prediction effect when combined with clinical characteristics. Our results revealed that a three-miRNA signature was a potential novel prognostic biomarker for nasopharyngeal carcinoma.

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Section: Discussionmentioning

confidence: 99%

A novel microRNA-based signature predicts prognosis among nasopharyngeal cancer patients

Wang

et al. 2020

Exp Biol Med (Maywood)

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“…Predicting NSCLC subtypes by identifying specific genotypes is a potentially powerful method that can be recommended as a special marker for IHC-stained slides. Therefore, several references determined the sensitivity and specificity of genetic markers to discriminate the subtypes of NSCLCs, as shown in Table 2 (31)(32)(33)(34)(35)(36)(37). A retrospective study selected the genetic features from 77 ADCs and 73 SCCs to construct the support vector machine (SVM) and random forest (RF) classifier (32).…”

Section: Diagnosing Histologic Subtypes Of Nsclcs Based On Digital Histopathologic Slides and Gene Profilesmentioning

confidence: 99%

“…Gene expression variants have been reported to have critical roles in tumor progression and metastasis, thus suggesting the feasibility of genetic biomarkers for the detection and classification of NSCLC subsets (37). Although the gene profiles have been identified as predictors of clinical diagnosis or NSCLC outcomes, whether gene profiles can be used as pathologic staging marker has not been established.…”

Section: Predicting Pathologic Stages Of Nsclcs Based On Gene Profilesmentioning

confidence: 99%

A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations

Li¹,

Chen²,

Wu³

et al. 2021

J Thorac Dis

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Objective: To summarize the current evidence regarding the applications, workflow, and limitations of artificial intelligence (AI) in the management of patients pathologically-diagnosed with lung cancer.Background: Lung cancer is one of the most common cancers and the leading cause of cancer-related deaths worldwide. AI technologies have been applied to daily medical workflow and have achieved an excellent performance in predicting histopathologic subtypes, analyzing gene mutation profiles, and assisting in clinical decision-making for lung cancer treatment. More advanced deep learning for classifying pathologic images with minimal human interactions has been developed in addition to the conventional machine learning scheme. Methods: Studies were identified by searching databases, including PubMed, EMBASE, Web of Science, and Cochrane Library, up to February 2021 without language restrictions. Conclusions: A number of studies have evaluated AI pipelines and confirmed that AI is robust and efficacious in lung cancer diagnosis and decision-making, demonstrating that AI models are a useful tool for assisting oncologists in health management. Although several limitations that pose an obstacle for the widespread use of AI schemes persist, the unceasing refinement of AI techniques is poised to overcome such problems. Thus, AI technology is a promising tool for use in diagnosing and managing lung cancer.

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“…With the application of gene sequencing in tumours, miRNAs can be considered as new biomarkers in patient prognosis prediction and drug resistance (11)(12)(13). In addition, integration of multiple miRNAs may be more efficient than a single miRNA for prognosis prediction (14,15). A large number of studies have identified a series of miRNA signatures that can serve as potential biomarkers for LUAD patient prognosis prediction (15,16).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, integration of multiple miRNAs may be more efficient than a single miRNA for prognosis prediction (14,15). A large number of studies have identified a series of miRNA signatures that can serve as potential biomarkers for LUAD patient prognosis prediction (15,16). It is well known that miRNAs have important roles in the regulation of gene expression, either through a single miRNA that regulates the expression of multiple genes or through the combination of several miRNAs that finely regulate the expression of a gene, which in turn regulates various physiological processes, cellular functions, and signalling pathways (17,18).…”

Section: Introductionmentioning

confidence: 99%

An 11-Gene Signature Risk-Prediction Model Based on Prognosis-Related miRNAs and Their Target Genes in Lung Adenocarcinoma

Zhou

Ding

et al. 2021

Front. Oncol.

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Aberrant expression of microRNAs may affect tumorigenesis and progression by regulating their target genes. This study aimed to construct a risk model for predicting the prognosis of patients with lung adenocarcinoma (LUAD) based on differentially expressed microRNA-regulated target genes. The miRNA sequencing data, RNA sequencing data, and patients’ LUAD clinical data were downloaded from the The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs and genes were screened out by combining differential analysis with LASSO regression analysis to further screen out miRNAs associated with patients’ prognosis, and target gene prediction was performed for these miRNAs using a target gene database. Overlapping gene screening was performed for target genes and differentially expressed genes. LASSO regression analysis and survival analysis were then used to identify key genes. Risk score equations for prognostic models were established using multifactorial COX regression analysis to construct survival prognostic models, and the accuracy of the models was evaluated using subject working characteristic curves. The groups were divided into high- and low-risk groups according to the median risk score, and the correlation with the clinicopathological characteristics of the patients was observed. A total of 123 up-regulated miRNAs and 22 down-regulated miRNAs were obtained in this study. Five prognosis-related miRNAs were screened using LASSO regression analysis and Kaplan-Meier method validation, and their target genes were screened with the overlap of differentially expressed genes before multifactorial COX analysis finally resulted in an 11-gene risk model for predicting patient prognosis. The area under the ROC curve proved that the model has high accuracy. The 11-gene risk-prediction model constructed in this study may be an effective predictor of prognosis.

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A novel microRNA signature for pathological grading in�lung adenocarcinoma based on TCGA and GEO data

Cited by 11 publications

References 44 publications

A novel microRNA-based signature predicts prognosis among nasopharyngeal cancer patients

A novel microRNA-based signature predicts prognosis among nasopharyngeal cancer patients

A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations

An 11-Gene Signature Risk-Prediction Model Based on Prognosis-Related miRNAs and Their Target Genes in Lung Adenocarcinoma

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