Identification of the Diagnostic Biomarker VIPR1 in Hepatocellular Carcinoma Based on Machine Learning Algorithm

Song, Guowen; Xu, Chen-rui; Liu, Yanming; Zhang, Yulin; Li, Na; Wang, Fei-tong; Ding, Liang; Jian, Niu

doi:10.1155/2022/2469592

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…ECM1 is a glycoprotein that was identi ed as a candidate biomarker with high diagnostic value, but not associated with overall survival, as in the study by Song Ge et al 21 . The current study found that ECM1 can downregulate E-cadherin expression and upregulate Vimentin expression 22 , promote migration and invasion of HCC cells by inducing epithelial-mesenchymal transition (EMT), while knockdown of ECM1 inhibited HCC cell function 23 .…”

Section: Discussionmentioning

confidence: 99%

Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration

Liu,

Zhang,

Xiao

et al. 2023

Preprint

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Objective The diagnosis of hepatocellular carcinoma (HCC) remains challenging for clinicians. Machine learning approaches and big data analysis are viable strategies to identify HCC diagnostic markers. Materials and methods For this study, we downloaded mRNA expression profiles of HCC from the GEO database and used random forest and machine learning algorithms such as Lasso to screen for reliable diagnostic genes. DO, KEGG, GSEA enrichment analysis for exploring differential gene function, disease pathways. CIBERSORT was performed to calculate the immune cell infiltration of HCC and to calculate the correlation of diagnostic genes with immune cells. Results The results indicated that ECM1, NPC1L1, and RSPO3 were downregulated in HCC compared with the normal group (P < 0.05), and furthermore, ECM1, NPC1L1, and RSPO3 had a high diagnostic value for HCC in both the training and test cohorts (AUC > 0.75). Immuno-infiltration analysis revealed that ECM1 and RSPO3 were highly positively correlated with neutrophil and macrophage M2, whereas they were negatively correlated with Tregs. Conclusion The present study identifies ECM1, NPC1L1, and RSPO3 as new diagnostic biomarkers for HCC based on normal and disease samples from HCC and correlated with immune cell infiltration.

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Section: Discussionmentioning

confidence: 99%

Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration

Liu,

Zhang,

Xiao

et al. 2023

Preprint

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“…Currently, especially in 2022-2023, there is a significant increase in the number of studies addressing aspects of the use of ML and DL technologies in the context of solving the problem of early diagnosis of cancer [3][4][5][6][7][11][12][13][14]20,26,37,38,[60][61][62][63][64][65][66]. At the same time, many of them are aimed at solving the problem of diagnosing ODs on the basis of biomarkers, including blood protein markers [13,14,[20][21][22][23][24]27,28,37,38,60].…”

Section: Related Workmentioning

confidence: 99%

Decision-Making on the Diagnosis of Oncological Diseases Using Cost-Sensitive SVM Classifiers Based on Datasets with a Variety of Features of Different Natures

Demidova

2024

Mathematics

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This paper discusses the problem of detecting cancer using such biomarkers as blood protein markers. The purpose of this research is to propose an approach for making decisions in the diagnosis of cancer through the creation of cost-sensitive SVM classifiers on the basis of datasets with a variety of features of different nature. Such datasets may include compositions of known features corresponding to blood protein markers and new features constructed using methods for calculating entropy and fractal dimensions, as well as using the UMAP algorithm. Based on these datasets, multiclass SVM classifiers were developed. They use cost-sensitive learning principles to overcome the class imbalance problem, which is typical for medical datasets. When implementing the UMAP algorithm, various variants of the loss function were considered. This was performed in order to select those that provide the formation of such new features that ultimately allow us to develop the best cost-sensitive SVM classifiers in terms of maximizing the mean value of the metric MacroF1−score. The experimental results proved the possibility of applying the UMAP algorithm, approximate entropy and, in addition, Higuchi and Katz fractal dimensions to construct new features using blood protein markers. It turned out that when working with the UMAP algorithm, the most promising is the application of a loss function on the basis of fuzzy cross-entropy, and the least promising is the application of a loss function on the basis of intuitionistic fuzzy cross-entropy. Augmentation of the original dataset with either features on the basis of the UMAP algorithm, features on the basis of the UMAP algorithm and approximate entropy, or features on the basis of approximate entropy provided the creation of the three best cost-sensitive SVM classifiers with mean values of the metric MacroF1−score increased by 5.359%, 5.245% and 4.675%, respectively, compared to the mean values of this metric in the case when only the original dataset was utilized for creating the base SVM classifier (without performing any manipulations to overcome the class imbalance problem, and also without introducing new features).

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“…A 2023 study demonstrated the ability of machine learning to interpret cis-diol metabolic fingerprinting for precise diagnosis of primary liver cancer [30]. Ge et al recently discovered VIPR1 as an early diagnostic biomarker through machine learning from microarray datasets [31]. This gene is involved in glycogen metabolism and immune system regulation.…”

Section: Ai-assisted Biomarker Detectionmentioning

confidence: 99%

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Mansur

Vrionis

Charles

et al. 2023

Cancers

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Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

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Identification of the Diagnostic Biomarker VIPR1 in Hepatocellular Carcinoma Based on Machine Learning Algorithm

Cited by 6 publications

References 52 publications

Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration

Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration

Decision-Making on the Diagnosis of Oncological Diseases Using Cost-Sensitive SVM Classifiers Based on Datasets with a Variety of Features of Different Natures

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

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