Plasma-metabolite-based machine learning is a promising diagnostic approach for esophageal squamous cell carcinoma investigation

Chen, Zhongjian; Huang, Xiancong; Gao, Yun; Zeng, Su; Mao, Weimin

doi:10.1016/j.jpha.2020.11.009

Cited by 14 publications

(5 citation statements)

References 44 publications

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“…The area under the ROC curve was 0.887 (26). Chen et al (27) developed a new diagnosis approach for esophageal squamous cell carcinoma (ESCC) in 2020, using a machine learning system with plasma metabolomics. The study combined plasma metabolomics with machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

Lin

et al. 2022

Front. Oncol.

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ObjectiveUsing visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated.MethodsOn April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used.ResultsA total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal “New England Journal of Medicine” of Medicine, General & Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future.ConclusionsAn increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.

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Section: Introductionmentioning

confidence: 99%

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

Lin

et al. 2022

Front. Oncol.

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“…Elevated levels of IS have also been found in the urine of both mouse models and patients with gastric and cervical cancer, indicating the interplay of this toxin in a wide span of cancers [ 25 , 26 ]. In a recent metabolomics study performed in 88 esophageal squamous cell cancer (ESCC) patients and 52 healthy controls, Chen et al have demonstrated that IS is one of the 6 circulating metabolites showing a higher diagnostic accuracy (0.885) in detecting ESCC [ 27 ]. In another metabolomics study conducted on renal cell cancer (RCC) patients, the levels of IS in kidney cancer tissue were associated with reduced compensatory renal cell growth with unfavorable effects on both renal function and clinical outcome in RCC patients [ 28 ].…”

Section: Resultsmentioning

confidence: 99%

Possible Effects of Uremic Toxins p-Cresol, Indoxyl Sulfate, p-Cresyl Sulfate on the Development and Progression of Colon Cancer in Patients with Chronic Renal Failure

Paola

Izhar

et al. 2023

Genes

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Chronic kidney disease (CKD) induces several systemic effects, including the accumulation and production of uremic toxins responsible for the activation of various harmful processes. Gut dysbiosis has been widely described in CKD patients, even in the early stages of the disease. The abundant discharge of urea and other waste substances into the gut favors the selection of an altered intestinal microbiota in CKD patients. The prevalence of bacteria with fermentative activity leads to the release and accumulation in the gut and in the blood of several substances, such as p-Cresol (p-C), Indoxyl Sulfate (IS) and p-Cresyl Sulfate (p-CS). Since these metabolites are normally eliminated in the urine, they tend to accumulate in the blood of CKD patients proportionally to renal impairment. P-CS, IS and p-C play a fundamental role in the activation of various pro-tumorigenic processes, such as chronic systemic inflammation, the increase in the production of free radicals and immune dysfunction. An up to two-fold increase in the incidence of colon cancer development in CKD has been reported in several studies, although the pathogenic mechanisms explaining this compelling association have not yet been described. Based on our literature review, it appears likely the hypothesis of a role of p-C, IS and p-CS in colon cancer development and progression in CKD patients.

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“…However, both YTHDF1 and HNRNPC act as m6A readers, and they need to identify methylated genes or m6A writers to promote tumor development, therefore, we next performed a PPI analysis of both YTHDF1 and HNRNPC in the transcriptome data of our hospital, respectively, where the connection between HNRNPC and METTL3 has been constructed. Methyltransferase-like protein 3 (METTL3) is the most important component of the m6A MTC and is highly conserved in eukaryotes from yeast to humans ( Chen Z et al, 2021 ). METTL3 has also been confrimed to be highly expressed in ESCC and is associated with poor prognosis in esophageal cancer ( Xia et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of m6a-related signature genes in esophageal squamous cell carcinoma by machine learning method

et al. 2023

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Background: We aimed to construct and validate the esophageal squamous cell carcinoma (ESCC)-related m6A regulators by means of machine leaning.Methods: We used ESCC RNA-seq data of 66 pairs of ESCC from West China Hospital of Sichuan University and the transcriptome data extracted from The Cancer Genome Atlas (TCGA)-ESCA database to find out the ESCC-related m6A regulators, during which, two machine learning approaches: RF (Random Forest) and SVM (Support Vector Machine) were employed to construct the model of ESCC-related m6A regulators. Calibration curves, clinical decision curves, and clinical impact curves (CIC) were used to evaluate the predictive ability and best-effort ability of the model. Finally, western blot and immunohistochemistry staining were used to assess the expression of prognostic ESCC-related m6A regulators.Results: 2 m6A regulators (YTHDF1 and HNRNPC) were found to be significantly increased in ESCC tissues after screening out through RF machine learning methods from our RNA-seq data and TCGA-ESCA database, respectively, and overlapping the results of the two clusters. A prognostic signature, consisting of YTHDF1 and HNRNPC, was constructed based on our RNA-seq data and validated on TCGA-ESCA database, which can serve as an independent prognostic predictor. Experimental validation including the western and immunohistochemistry staining were further successfully confirmed the results of bioinformatics analysis.Conclusion: We constructed prognostic ESCC-related m6A regulators and validated the model in clinical ESCC cohort as well as in ESCC tissues, which provides reasonable evidence and valuable resources for prognostic stratification and the study of potential targets for ESCC.

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Plasma-metabolite-based machine learning is a promising diagnostic approach for esophageal squamous cell carcinoma investigation

Cited by 14 publications

References 44 publications

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

Possible Effects of Uremic Toxins p-Cresol, Indoxyl Sulfate, p-Cresyl Sulfate on the Development and Progression of Colon Cancer in Patients with Chronic Renal Failure

Identification of m6a-related signature genes in esophageal squamous cell carcinoma by machine learning method

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