MicroRNAs that regulate PTEN as potential biomarkers in colorectal cancer: a systematic review

Liu, Jianrong; Fei, Ke; Chen, Tingting; Zhou, Qingde; Weng, Lingling; Tan, Jun; Shen, Weixing; Li, Liu; Zhou, Jin‐Yong; Chen, Xu; Cheng, Haibo; Zhou, Jin‐Rong

doi:10.1007/s00432-020-03172-3

Cited by 27 publications

(17 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this way, early malignancy determination and recognition clinical imaging is a robust method. Without a doubt, clinical imaging has been generally utilized for early malignancy discovery, checking, and follow-up after the medicines [ 44 , 101 , 102 ].…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Kumar

Gupta

Singla³

et al. 2021

Arch Computat Methods Eng

129

View full text Add to dashboard Cite

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

show abstract

Section: Related Workmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Kumar

Gupta

Singla³

et al. 2021

Arch Computat Methods Eng

129

View full text Add to dashboard Cite

show abstract

“…DNA methylation is one of the most frequently occurring epigenetic modifications, which plays a crucial role in regulating gene expression and genome function (7). A series of studies have reported significant biomarkers for predicting the prognosis of CRC patients at different omics levels, including DNA methylation (8), microRNAs (9), gene expression (10), and proteins (11). These studies are based on single-level OMICS to consider the complicated process of tumor development (12).…”

Section: Introductionmentioning

confidence: 99%

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Huang

Zhang

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

BackgroundAberrant DNA methylation is a critical regulator of gene expression and plays a crucial role in the occurrence, progression, and prognosis of colorectal cancer (CRC). We aimed to identify methylation-driven genes by integrative epigenetic and transcriptomic analysis to predict the prognosis of CRC patients.MethodsMethylation-driven genes were selected for CRC using a MethylMix algorithm and LASSO regression screening strategy, and were further used to construct a prognostic risk-assessment model. The Cancer Genome Atlas (TCGA) database was obtained as the training set for both the screening of methylation-driven genes and the effect of genes signature on CRC prognosis. Then, the prognostic genes signature was validated in three independent expression arrays of CRC data from Gene Expression Omnibus (GEO).ResultsWe identified 143 methylation-driven genes, of which the combination of BATF, PHYHIPL, RBP1, and PNPLA4 expression levels was screened as a better prognostic model with the best area under the curve (AUC) (AUC = 0.876). Compared with patients in the low-risk group, CRC patients in the high-risk group had significantly poorer overall survival in the training set (HR = 2.184, 95% CI: 1.404–3.396, P < 0.001). Similar results were observed in the validation set. Moreover, VanderWeele’s mediation analysis indicated that the effect of methylation on prognosis was mediated by the levels of their expression (HRindirect = 1.473, P = 0.001, Proportion mediated, 69.10%).ConclusionsWe identified a four-gene prognostic signature by integrative analysis and developed a risk-assessment model that is significantly associated with patients’ survival. Methylation-driven genes might be a potential prognostic signature for CRC patients.

show abstract

“…In the past few decades, miRNAs have become essential post-transcriptional regulators of gene expression ( 13 , 14 ). Studies have shown that the abnormal expression of miRNA may be related to the inflammatory response in patients with neonatal sepsis.…”

Section: Discussionmentioning

confidence: 99%

miR-141 is negatively correlated with TLR4 in neonatal sepsis and regulates LPS-induced inflammatory responses in monocytes

Lin

Wang

2021

Braz J Med Biol Res

View full text Add to dashboard Cite

Neonatal sepsis is an inflammatory system syndrome and a main cause of neonatal mortality. However, there is a lack of ideal biomarkers for early neonatal sepsis diagnosis. The aim of this study was to evaluate the clinical significance of miR-141 in sepsis in neonates, and explore the regulatory effects of miR-141 on inflammation in monocytes. This study used qRT-PCR to calculate the expression of miR-141 in the serum of septic neonates. The diagnostic values of procalcitonin (PCT) and serum miR-141 were evaluated by receiver operating characteristic (ROC) curves. The relationship between miR-141 and TLR4 was determined using luciferase reporter assay. An inflammation model was established using monocytes with lipopolysaccharide (LPS) treatment. ELISA assay was used to analyze the levels of pro-inflammatory cytokines. The expression of miR-141 in neonatal sepsis was significantly lower than healthy controls. ROC curves showed that miR-141 had diagnostic accuracy. LPS stimulation in monocytes led to a decrease in the expression of miR-141. A luciferase reporter assay proved that miR-141 targeted TLR4, and a negative correlation of miR-141 with TLR4 was found in septic neonates. ELISA results demonstrated that the overexpression of miR-141 inhibited LPS-induced inflammation in monocytes. In conclusion, serum decreased miR-141 expression served as a candidate diagnostic biomarker of neonatal sepsis. TLR4 is a target gene of miR-141, which may mediate the inhibitory effects of miR-141 overexpression on LPS-induced inflammation in monocytes. Therefore, miR-141 is expected to be a potential diagnostic biomarker and a therapeutic target in neonatal sepsis.

show abstract

MicroRNAs that regulate PTEN as potential biomarkers in colorectal cancer: a systematic review

Cited by 27 publications

References 65 publications

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

miR-141 is negatively correlated with TLR4 in neonatal sepsis and regulates LPS-induced inflammatory responses in monocytes

Contact Info

Product

Resources

About