A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy

Asakura, Keisuke; Kadota, Tsukasa; Matsuzaki, Juntaro; Yoshida, Yukihiro; Yamamoto, Yusuke; Nakagawa, Kazuo; Takizawa, Satoko; Aoki, Yasumichi; Nakamura, Eiji; Miura, Junichiro; Sanada, Hiromi; Kato, Ken; Watanabe, Shun; Ochiya, Takahiro

doi:10.1038/s42003-020-0863-y

Cited by 88 publications

(67 citation statements)

References 51 publications

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“…For example, miR-29b, miR-210, miR-335-5p, and miR-2861 were shown to enhance this process [15][16][17][18], whereas miR-125a-3p, miR-145-5p, miR-106b-5p, and miR-17-5p exert suppressive effects [19][20][21]. MiR-17-3p is variably expressed in different cancer types [22][23][24] but its role in osteogenesis has not been previously reported. The MC3T3-E1 preosteoblast cell line has been widely used for in vitro studies of osteogenesis.…”

Section: Discussionmentioning

confidence: 99%

MiR‐17‐3p inhibits osteoblast differentiation by downregulating Sox6 expression

Chen

et al. 2020

FEBS Open Bio

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An in vitro model of osteogenesis was established by treating MC3T3‐E1 murine preosteoblast cells with BMP2. MiR‐17‐3p inhibited the differentiation of MC3T3‐E1 cells. Sox6 was confirmed to be a target gene of miR‐17‐3p, and the inhibitory effect of miR‐17‐3p on osteoblast differentiation was reversed by Sox6. These results suggest potential implications for the treatment of orthopedic disorders.

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Section: Discussionmentioning

confidence: 99%

MiR‐17‐3p inhibits osteoblast differentiation by downregulating Sox6 expression

Chen

et al. 2020

FEBS Open Bio

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“…Most of survival prediction models developed so far separate the patients into two groups (high and low survival rate). [20][21][22] However, by combination of the strati cation models, the patients can be identi ed more accurately. In our study, we developed two prediction models that divide the patients into two and three groups, respectively (Figs.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Prediction of Survival Prognosis in Cervical Cancer

Ding

Lang

Zou

et al. 2020

Preprint

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Backgroud: Accurately forecasting the prognosis could improve therapeutic management of cancer patients, however, the currently used clinical features are difficult to provide enought information. The purpose of this study is to develop a survival prediction model for cervical cancer patients with big data and machine learning algorithms. Results: The cancer genome atlas cervical cancer data, including the expression of 1046 microRNAs and the clinical information of 309 cervical and endocervical cancer and 3 control samples, were downloaded. Missing values and outliers imputation, samples normalization, log transformation and features scaling were performed for preprocessing and 3 control, 2 metastatic samples and 707 microRNAs with missing values ≥ 20% were excluded. By Cox Proportional-Hazards analysis, 55 prognosis-related microRNAs (20 positively and 35 negatively correlated with survival) were identified. K-means clustering analysis showed that the cervical cancer samples can be separated into two and three subgroups with top 20 identified survival-related microRNAs for best stratification. By Support Vector Machine algorithm, two prediction models were developed which can segment the patients into two and three groups with different survival rate, respectively. The models exhibite high performance : for two classes, Area under the curve = 0.976 (training set), 0.972 (test set), 0.974 (whole data set); for three classes, AUC = 0.983, 0.996 and 0.991 (group1, 2 and 3 in training set), 0.955, 0.989 and 0.991 (group 1, 2 and 3 in test set), 0.974, 0.993 and 0.991 (group 1, 2 and 3 in whole data set) .Conclusion: The survival prediction models for cervical cancer were developed. The patients with very low survival rate (≤ 40%) can be separated by the three classes prediction model first. The rest patients can be identified by the two classes prediction model as high survival rate (≈ 75%) and low survival rate (≈ 50%).

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“…While the field is in relative infancy, with no commercially validated tests, the identification of circulating biomarkers predicative of radiation response holds tremendous potential. MicroRNAs (miRNAs) are differentially regulated in a number of disease types and following exposure to ionizing radiation; they therefore offer a potential biomarker to predict treatment response in cancer [ 87 , 88 , 89 ]. A radiotherapeutic response predication was developed for patients with lower-grade glioma (LGG), based on the expression of five miRNAs.…”

Section: Predictive Biomarkers Of Radiation Responsementioning

confidence: 99%

Radiation Response in the Tumour Microenvironment: Predictive Biomarkers and Future Perspectives

Byrne

Tambe

Coulter

2021

JPM

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Radiotherapy (RT) is a primary treatment modality for a number of cancers, offering potentially curative outcomes. Despite its success, tumour cells can become resistant to RT, leading to disease recurrence. Components of the tumour microenvironment (TME) likely play an integral role in managing RT success or failure including infiltrating immune cells, the tumour vasculature and stroma. Furthermore, genomic profiling of the TME could identify predictive biomarkers or gene signatures indicative of RT response. In this review, we will discuss proposed mechanisms of radioresistance within the TME, biomarkers that may predict RT outcomes, and future perspectives on radiation treatment in the era of personalised medicine.

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A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy

Cited by 88 publications

References 51 publications

MiR‐17‐3p inhibits osteoblast differentiation by downregulating Sox6 expression

MiR‐17‐3p inhibits osteoblast differentiation by downregulating Sox6 expression

Machine Learning-Based Prediction of Survival Prognosis in Cervical Cancer

Radiation Response in the Tumour Microenvironment: Predictive Biomarkers and Future Perspectives

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