Multi-parameter gene expression profiling of peripheral blood for early detection of hepatocellular carcinoma

Xie, Hui; Xue, Yao-Qin; Liu, Peng; Zhang, Pengjun; Tian, Shengtao; Zhao, Yang; Guo, Zhi Hou; Wang, Huaming

doi:10.3748/wjg.v24.i3.371

Cited by 9 publications

(6 citation statements)

References 35 publications

(31 reference statements)

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“…Xie ( 40 ) utilized gene expression profiles from peripheral blood to develop an artificial neural network (ANN) model that could differentiate HCC patients from the control group with a sensitivity of 96% and specificity of 86%. Harpreet ( 41 ) utilized a large-scale transcriptomic analysis dataset containing a total of 2,316 HCC samples and 1,665 non-tumor tissue samples to identify HCC samples using machine learning, with an accuracy ranging from 93% to 98%.…”

Section: Discussionmentioning

confidence: 99%

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Jiang,

Zhou,

Jiang

et al. 2024

Front. Oncol.

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Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

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Section: Discussionmentioning

confidence: 99%

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Jiang,

Zhou,

Jiang

et al. 2024

Front. Oncol.

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“…PFN1 was mainly responsible for the polymerization of actin filaments and responds to extracellular signals, which were associated with cell proliferation and motility ( Witke, 2004 ). Xie et al (2018) suggested that PFN1 was a risk factor for poor prognosis in HCC. This was consistent with our findings.…”

Section: Discussionmentioning

confidence: 99%

Integrated analysis of single-cell and bulk RNA-sequencing reveals tumor heterogeneity and a signature based on NK cell marker genes for predicting prognosis in hepatocellular carcinoma

Gan

et al. 2023

Front. Pharmacol.

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Background: Natural killer (NK) cells are a type of innate immune cell that recognize and eliminate tumor cells and infected cells, without prior sensitization or activation. Herein, we aimed to construct a predictive model based on NK cell-related genes for hepatocellular carcinoma (HCC) patients and assess the feasibility of utilizing this model for prognosis prediction.Methods: Single-cell RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database to identify marker genes of NK cells. Univariate Cox and lasso regression were performed to further establish a signature in the TCGA dataset. Subsequently, qPCR and immunohistochemistry (IHC) staining were employed to validate the expression levels of prognosis signature genes in HCC. The effectiveness of the model was further validated using two external cohorts from the GEO and ICGC datasets. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, and biological function were compared for different genetic subtypes and risk groups. Finally, molecular docking was performed to evaluate the binding affinity between the hub gene and chemotherapeutic drugs.Results: A total of 161 HCC-related NK cell marker genes (NKMGs) were identified, 28 of which were significantly associated with overall survival in HCC patients. Based on differences in gene expression characteristics, HCC patients were classified into three subtypes. Ten prognosis genes (KLRB1, CD7, LDB2, FCER1G, PFN1, FYN, ACTG1, PABPC1, CALM1, and RPS8) were screened to develop a prognosis model. The model not only demonstrated excellent predictive performance on the training dataset, but also were successfully validated on two independent external datasets. The risk scores derived from the model were shown to be an independent prognosis factor for HCC and were correlated with pathological severity. Moreover, qPCR and IHC staining confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis. Finally, molecular docking revealed favorable binding energies between the hub gene ACTG1 and chemotherapeutic drugs.Conclusion: In this study, we developed a model for predicting the prognosis of HCC based on NK cells. The utilization of NKMGs as innovative biomarkers showed promise in the prognosis assessment of HCC.

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“…Unlike the aforementioned single-factor analysis, a multi-factor analysis approach can establish a higher diagnostic value (Ning et al, 2021). Xie et al (2018) constructed an expression detection system based on the GeXP system for nine genes: GPC3, HGF, ANXA1, FOS, SPAG9, HSPA1B, CXCR4, PFN1, and CALR. The team developed a multi-parametric gene expression analysis method by combining logistic regression analysis, discriminant analysis, classification trees, and DNNs to model the diagnosis of groups of earlystage HCC patients and healthy controls by routinizing the area under the curve (AUC), sensitivity, and specificity.…”

Section: Serologymentioning

confidence: 99%

Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework

Shen

Wu²,

Su³

et al. 2023

Front. Genet.

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Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which is increasingly used in the diagnosis, treatment, and prognosis prediction of HCC, an automated approach to HCC status classification is promising. AI integrates labeled clinical data, trains on new data of the same type, and performs interpretation tasks. Several studies have shown that AI techniques can help clinicians and radiologists be more efficient and reduce the misdiagnosis rate. However, the coverage of AI technologies leads to difficulty in which the type of AI technology is preferred to choose for a given problem and situation. Solving this concern, it can significantly reduce the time required to determine the required healthcare approach and provide more precise and personalized solutions for different problems. In our review of research work, we summarize existing research works, compare and classify the main results of these according to the specified data, information, knowledge, wisdom (DIKW) framework.

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Multi-parameter gene expression profiling of peripheral blood for early detection of hepatocellular carcinoma

Cited by 9 publications

References 35 publications

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Integrated analysis of single-cell and bulk RNA-sequencing reveals tumor heterogeneity and a signature based on NK cell marker genes for predicting prognosis in hepatocellular carcinoma

Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework

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