Develop a Novel Nomogram to Predict Respiratory Failure in Acute Pancreatitis at Early Stage

Qiu, Xinze; Fu, Hai-Xia; Li, Zhiyang; Wei, Xiaohe; Wu, Jiangni; Huang, Jiean; Liu, Shiquan

doi:10.7754/clin.lab.2021.210826

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, to improve the prediction power, the nomogram was constructed via “rms” package, including age, gender, TNM stage and risk score. [16] ROC curves and calibration curves were performed to assess the discrimination and deviation of nomogram, respectively.…”

Section: Methodsmentioning

confidence: 99%

Identification and verification of m6A-related miRNAs correlated with prognosis and immune microenvironment in colorectal cancer

Qiu,

Chen,

Huang

et al. 2023

Medicine

Self Cite

View full text Add to dashboard Cite

It’s well known that N6-methyladenosine (m6A) modification is the most abundant modification in multiple RNA species. miRNAs play important roles in m6A modification and are closely related with occurrence and development of colorectal cancer (CRC). Thus, the aim of this study was to identify the prognostic value of m6A-related miRNAs and explore the correlation between the miRNAs and immune microenvironment in CRC. The differentially expressed m6A regulators and differentially expressed miRNAs between CRC tissues and adjacent normal tissues were identified based on TCGA dataset, and the m6A-related miRNAs were screened. The CRC patients from TCGA were randomized (1:1) into training set and validation set, and the risk score was established in the training set. Next, risk score was verified in the validation set and GSE92928 from GEO datasets. Besides, the relationship among tumor mutational burden, immune microenvironment and risk score were analyzed. What’s more, RT-qPCR were used to explore the expression levels of the miRNAs in risk score between SW480 and SW620. A total of 29 m6A-related miRNAs were screened out, and a 5-differentially expressed miRNAs risk score was established. Kaplan–Meier analysis and ROC curves revealed the risk score could predict the prognosis of CRC, accurately. Similarly, the patients in the high-risk group had shorter overall survival in GSE92928. The risk score was relevant with the tumor mutational burden and immune infiltration, and the expression of HAVCR2 was significant difference between 2 risk groups. The expression levels of miR-328-3p, miR-3934-5p, miR-664b-5p and miR-3677-3p were down-regulated in SW620 compared with SW480, only the expression level of miR-200c-5p was up-regulated in SW620. The findings provided the new insights into the correlation between miRNAs and m6A regulators. The m6A-related miRNAs could predict the prognosis of CRC and provide the valuable information of immunotherapy in CRC patients.

show abstract

Section: Methodsmentioning

confidence: 99%

Identification and verification of m6A-related miRNAs correlated with prognosis and immune microenvironment in colorectal cancer

Qiu,

Chen,

Huang

et al. 2023

Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…The models were analyzed using the "DALEX" R package (version 2.3.0) to plot residual distributions, and algorithmic power box line plots to obtain the best model from which biomarkers were obtained (Floyd et al, 2022). The nomogram of biomarkers was constructed using the "RMS" (version 6.1-0) R package to derive the relationship between biomarkers and diseases, and the calibration curves plotted by "RMS" (version 6.1-0) R package were used to validate the model (Qiu et al, 2022).…”

Section: Identification Of Biomarkers By Machine Learning Algorithmsmentioning

confidence: 99%

Comprehensive bioinformatics analysis reveals biomarkers of DNA methylation-related genes in varicose veins

Liu²,

Liu³

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

Background: Patients with Varicose veins (VV) show no obvious symptoms in the early stages, and it is a common and frequent clinical condition. DNA methylation plays a key role in VV by regulating gene expression. However, the molecular mechanism underlying methylation regulation in VV remains unclear.Methods: The mRNA and methylation data of VV and normal samples were obtained from the Gene Expression Omnibus (GEO) database. Methylation-Regulated Genes (MRGs) between VV and normal samples were crossed with VV-associated genes (VVGs) obtained by weighted gene co-expression network analysis (WGCNA) to obtain VV-associated MRGs (VV-MRGs). Their ability to predict disease was assessed using receiver operating characteristic (ROC) curves. Biomarkers were then screened using a random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). Next, gene set enrichment analysis (GSEA) was performed to explore the functions of biomarkers. Furthermore, we also predicted their drug targets, and constructed a competing endogenous RNAs (ceRNA) network and a drug target network. Finally, we verified their mRNA expression using quantitative real-time polymerase chain reaction (qRT-PCR).Results: Total three VV-MRGs, namely Wnt1-inducible signaling pathway protein 2 (WISP2), Cysteine-rich intestinal protein 1 (CRIP1), and Odd-skipped related 1 (OSR1) were identified by VVGs and MRGs overlapping. The area under the curves (AUCs) of the ROC curves for these three VV-MRGs were greater than 0.8. RF was confirmed as the optimal diagnostic model, and WISP2, CRIP1, and OSR1 were regarded as biomarkers. GSEA showed that WISP2, CRIP1, and OSR1 were associated with oxidative phosphorylation, extracellular matrix (ECM), and respiratory system functions. Furthermore, we found that lncRNA MIR17HG can regulate OSR1 by binding to hsa-miR-21-5p and that PAX2 might treat VV by targeting OSR1. Finally, qRT-PCR results showed that the mRNA expression of the three genes was consistent with the results of the datasets.Conclusion: This study identified WISP2, CRIP1, and OSR1 as biomarkers of VV through comprehensive bioinformatics analysis, and preliminary explored the DNA methylation-related molecular mechanism in VV, which might be important for VV diagnosis and exploration of potential molecular mechanisms.

show abstract

“…However, the scoring systems and models mentioned previously were not developed for predicting respiratory failure, and there are doubts about their efficacy in predicting the risk of respiratory failure in patients with sepsis. Some scholars have developed a model for predicting the risk of respiratory failure in patients with pancreatitis (Qiu et al, 2022). However, as the model is not aimed at patients with sepsis, there are doubts about its efficacy in predicting the risk of respiratory failure in patients with sepsis.…”

Section: Introduction Backgroundmentioning

confidence: 99%

Establishment and validation of a predictive model for respiratory failure within 48 h following admission in patients with sepsis: a retrospective cohort study

Wang,

Chen,

Wang

2023

Front. Physiol.

View full text Add to dashboard Cite

Objective: The objective of this study is to identify patients with sepsis who are at a high risk of respiratory failure.Methods: Data of 1,738 patients with sepsis admitted to Dongyang People’s Hospital from June 2013 to May 2023 were collected, including the age at admission, blood indicators, and physiological indicators. Independent risk factors for respiratory failure during hospitalization in the modeling population were analyzed to establish a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative ability, the GiViTI calibration graph was used to evaluate the calibration, and the decline curve analysis (DCA) curve was used to evaluate and predict the clinical validity. The model was compared with the Sequential Organ Failure Assessment (SOFA) score, the National Early Warning Score (NEWS) system, and the ensemble model using the validation population.Results: Ten independent risk factors for respiratory failure in patients with sepsis were included in the final logistic model. The AUC values of the prediction model in the modeling population and validation population were 0.792 and 0.807, respectively, both with good fit between the predicted possibility and the observed event. The DCA curves were far away from the two extreme curves, indicating good clinical benefits. Based on the AUC values in the validation population, this model showed higher discrimination power than the SOFA score (AUC: 0.682; p < 0.001) and NEWS (AUC: 0.520; p < 0.001), and it was comparable to the ensemble model (AUC: 0.758; p = 0.180).Conclusion: Our model had good performance in predicting the risk of respiratory failure in patients with sepsis within 48 h following admission.

show abstract

Develop a Novel Nomogram to Predict Respiratory Failure in Acute Pancreatitis at Early Stage

Cited by 3 publications

References 0 publications

Identification and verification of m6A-related miRNAs correlated with prognosis and immune microenvironment in colorectal cancer

Identification and verification of m6A-related miRNAs correlated with prognosis and immune microenvironment in colorectal cancer

Comprehensive bioinformatics analysis reveals biomarkers of DNA methylation-related genes in varicose veins

Establishment and validation of a predictive model for respiratory failure within 48 h following admission in patients with sepsis: a retrospective cohort study

Contact Info

Product

Resources

About