Comprehensive bioinformatics and machine learning analysis identify VCAN as a novel biomarker of hepatitis B virus-related liver fibrosis

Yuan, Mengqin; Han, Xue; Yao, Lichao; Liu, Pingji; Jiang, Yingan; Li, Lanjuan

doi:10.3389/fmolb.2022.1010160

Cited by 3 publications

(3 citation statements)

References 33 publications

(32 reference statements)

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“…It has been used in breast carcinoma, lung adenocarcinoma, and tumor cell proliferation (Song and Zhang 2015;Yin et al 2022). Machine learning algorithms including support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) have been used to find key genes in stroke (Zheng et al 2022) and breast cancer (Yuan et al 2022). However, few studies have combined MEGENA with machine learning algorithms to find key genes in CA.…”

Section: Introductionmentioning

confidence: 99%

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Qin

Liu

et al. 2023

Funct Integr Genomics

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Neurological prognostication after cardiac arrest (CA) is important to avoid pursuing futile treatments for poor outcome and inappropriate withdrawal of life-sustaining treatment for good outcome. To predict neurological outcome after CA through biomarkers in peripheral blood mononuclear cells, four datasets were downloaded from the Gene Expression Omnibus database. GSE29546 and GSE74198 were used as training datasets, while GSE92696 and GSE34643 were used as verification datasets. The intersection of differentially expressed genes and hub genes from multiscale embedded gene co-expression network analysis (MEGENA) was utilized in the machine learning screening. Key genes were identified using support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF). The results were validated using receiver operating characteristic curve analysis. An mRNA-miRNA network was constructed. The distribution of immune cells was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Five biomarkers were identified as predictors for neurological outcome after CA, with an area under the curve (AUC) greater than 0.7: CASP8 and FADD-like apoptosis regulator (CFLAR), human protein kinase X (PRKX), miR-483-5p, let-7a-5p, and let-7c-5p. Interestingly, the combination of CFLAR minus PRKX showed an even higher AUC of 0.814. The mRNA-miRNA network consisted of 30 nodes and 76 edges. Statistical differences were found in immune cell distribution, including neutrophils, NK cells active, NK cells resting, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD8, B cells memory, and mast cells resting between individuals with good and poor neurological outcome after CA. In conclusion, our study identified novel predictors for neurological outcome after CA. Further clinical and laboratory studies are needed to validate our findings.

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Section: Introductionmentioning

confidence: 99%

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Qin

Liu

et al. 2023

Funct Integr Genomics

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“…It employs regularization to enhance prediction accuracy [ 26 ]. R package “glmnet” were applied for LASSO, which was performed by 10-fold cross-validation to adjust the optimal penalty parameter λ [ 27 ]. The response type was set as binomial, and the α was set as 1.…”

Section: Methodsmentioning

confidence: 99%

Predicting the Progress of Tuberculosis by Inflammatory Response-Related Genes Based on Multiple Machine Learning Comprehensive Analysis

Peng

Duan

et al. 2023

Journal of Immunology Research

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Background. Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, affects approximately one-quarter of the global population and is considered one of the most lethal infectious diseases worldwide. The prevention of latent tuberculosis infection (LTBI) from progressing into active tuberculosis (ATB) is crucial for controlling and eradicating TB. Unfortunately, currently available biomarkers have limited effectiveness in identifying subpopulations that are at risk of developing ATB. Hence, it is imperative to develop advanced molecular tools for TB risk stratification. Methods. The TB datasets were downloaded from the GEO database. Three machine learning models, namely LASSO, RF, and SVM-RFE, were used to identify the key characteristic genes related to inflammation during the progression of LTBI to ATB. The expression and diagnostic accuracy of these characteristic genes were subsequently verified. These genes were then used to develop diagnostic nomograms. In addition, single-cell expression clustering analysis, immune cell expression clustering analysis, GSVA analysis, immune cell correlation, and immune checkpoint correlation of characteristic genes were conducted. Furthermore, the upstream shared miRNA was predicted, and a miRNA–genes network was constructed. Candidate drugs were also analyzed and predicted. Results. In comparison to LTBI, a total of 96 upregulated and 26 downregulated genes related to the inflammatory response were identified in ATB. These characteristic genes have demonstrated excellent diagnostic performance and significant correlation with many immune cells and immune sites. The results of the miRNA–genes network analysis suggested a potential role of hsa-miR-3163 in the molecular mechanism of LTBI progressing into ATB. Moreover, retinoic acid may offer a potential avenue for the prevention of LTBI progression to ATB and for the treatment of ATB. Conclusion. Our research has identified key inflammatory response-related genes that are characteristic of LTBI progression to ATB and hsa-miR-3163 as a significant node in the molecular mechanism of this progression. Our analyses have demonstrated the excellent diagnostic performance of these characteristic genes and their significant correlation with many immune cells and immune checkpoints. The CD274 immune checkpoint presents a promising target for the prevention and treatment of ATB. Furthermore, our findings suggest that retinoic acid may have a role in preventing LTBI from progressing to ATB and in treating ATB. This study provides a new perspective for differential diagnosis of LTBI and ATB and may uncover potential inflammatory immune mechanisms, biomarkers, therapeutic targets, and effective drugs in the progression of LTBI into ATB.

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“…The rapid development of bioinformatics and the arrival of the big data era provide researchers with powerful tools [11][12][13][14][15]. In this study, we analyzed data gathered from The Cancer Genome Atlas Program (TCGA) database and the Gene Expression Omnibus (GEO) database to illustrate the underlying correlation between RCC and Ktx, as well as find the possible biological mechanism between them.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Underlying Biological Association and Targets between Rejection of Renal Transplant and Renal Cancer

Chen

Liu

et al. 2023

International Journal of Genomics

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Background. Post-renal transplant patients have a high likelihood of developing renal cancer. However, the underlying biological mechanisms behind the development of renal cancer in post-kidney transplant patients remain to be elucidated. Therefore, this study aimed to investigate the underlying biological mechanism behind the development of renal cell carcinoma in post-renal transplant patients. Methods. Next-generation sequencing data and corresponding clinical information of patients with clear cell renal cell carcinoma (ccRCC) were obtained from The Cancer Genome Atlas Program (TCGA) database. The microarray data of kidney transplant patients with or without rejection response was obtained from the Gene Expression Omnibus (GEO) database. In addition, statistical analysis was conducted in R software. Results. We identified 55 upregulated genes in the transplant patients with rejection from the GEO datasets (GSE48581, GSE36059, and GSE98320). Furthermore, we conducted bioinformatics analyses, which showed that all of these genes were upregulated in ccRCC tissue. Moreover, a prognosis model was constructed based on four rejection-related genes, including PLAC8, CSTA, AIM2, and LYZ. The prognosis model showed excellent performance in prognosis prediction in a ccRCC cohort. In addition, the machine learning algorithms identified 19 rejection-related genes, including PLAC8, involved in ccRCC occurrence. Finally, the PLAC8 was selected for further research, including its clinical and biological role. Conclusion. In all, our study provides novel insight into the transition from the rejection of renal transplant to renal cancer. Meanwhile, PLAC8 could be a potential biomarker for ccRCC diagnosis and prognosis in post-kidney transplant patients.

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Comprehensive bioinformatics and machine learning analysis identify VCAN as a novel biomarker of hepatitis B virus-related liver fibrosis

Cited by 3 publications

References 33 publications

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Predicting the Progress of Tuberculosis by Inflammatory Response-Related Genes Based on Multiple Machine Learning Comprehensive Analysis

Investigation of Underlying Biological Association and Targets between Rejection of Renal Transplant and Renal Cancer

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