Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma

Ren, Zhigang; Li, Ang; Jiang, Jianwen; Zhou, Lin; Yu, Zujiang; Lü, Haifeng; Xie, Haiyang; Chen, Xiaolong; Shao, Li; Zhang, Ruiqing; Xu, Shaoyan; Zhang, Hua; Cui, Guangying; Chen, Xinhua; Sun, Ranran; Wen, Hao; Lerut, Jan; Kan, Quancheng; Li, Lanjuan; Zheng, Shusen

doi:10.1136/gutjnl-2017-315084

Cited by 543 publications

(545 citation statements)

References 47 publications

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“…Previous studies had shown that gut microbiota could be regarded as potential noninvasive biomarkers for diagnosing nonpsychiatric diseases, such as hepatocellular carcinoma, [35] type 2 diabetes, [36] and colorectal cancer. Our study has achieved satisfied classification efficacy for distinguishing BD patients from HCs based on random forest classification models.…”

Section: Discussionmentioning

confidence: 99%

Gut Microbiota Changes in Patients with Bipolar Depression

et al. 2019

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This study aims to characterize the gut microbiota in depressed patients with bipolar disorder (BD) compared with healthy controls (HCs), to examine the effects of quetiapine treatment on the microbiota, and to explore the potential of microbiota as a biomarker for BD diagnosis and treatment outcome. Analysis of 16S‐ribosomal RNA gene sequences reveals that gut microbial composition and diversity are significantly different between BD patients and HCs. Phylum Bacteroidetes and Firmicutes are the predominant bacterial communities in BD patients and HCs, respectively. Lower levels of butyrate‐producing bacteria are observed in untreated patients. Microbial composition changes following quetiapine treatment in BD patients. Notably, 30 microbial markers are identified on a random forest model and achieve an area under the curve (AUC) of 0.81 between untreated patients and HCs. Ten microbial markers are identified with the AUC of 0.93 between responder and nonresponder patients. This study characterizes the gut microbiota in BD and is the first to evaluate microbial changes following quetiapine monotherapy. Gut microbiota‐based biomarkers may be helpful in BD diagnosis and predicting treatment outcome, which need further validations.

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Section: Discussionmentioning

confidence: 99%

Gut Microbiota Changes in Patients with Bipolar Depression

et al. 2019

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“…higher in SHS individuals than in the controls, indicating that bacteria could be potential biomarkers for SHS.Several diseases can be diagnosed using intestinal microbiota.Faecal microbiota can be used to discriminate Parkinson's disease patients from controls with an AUC of 0.81 (95% CI: 0.72-0.90), a sensitivity of 75.60% and a specificity of 77.80% 61. The intestinal microbiota was also used for the diagnosis of hypertension (AUC = 0.91, 95% CI: 0.75-1.00)21 and early hepatocellular carcinoma (AUC = 0.81, 95% CI: 0.74-0.87) 62. Additionally, the microbiome was used for the diagnosis of several diseases, including CFS (AUC = 0.89)28 and ileal Crohn's disease (AUC = 0.97) 39.…”

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confidence: 99%

Association of suboptimal health status with intestinal microbiota in Chinese youths

Sun

Zhang

et al. 2019

J Cellular Molecular Medi

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Suboptimal health status (SHS) is a physical state between health and disease and is characterized by the symptoms of health complaints, general weakness and low energy within a period of 3 months. 1 A comprehensive Suboptimal Health Status Questionnaire-25 (SHSQ-25) was used to assess SHS, with the SHSQ-25 accounting for the multidimensionality of SHS by assessing the following: (a) fatigue, (b) the cardiovascular system, (c) the digestive tract, (d) the immune system and (e) mental status. 2 To date, the SHSQ-25 has AbstractSuboptimal health status (SHS), a physical state between health and disease, is a subclinical and reversible stage of chronic disease. Previous studies have shown alterations in the intestinal microbiota in patients with some chronic diseases. This study aimed to investigate the association between SHS and intestinal microbiota in a casecontrol study with 50 SHS individuals and 50 matched healthy controls. Intestinal microbiota was analysed by MiSeq 250PE. Alpha diversity of intestinal microbiota in SHS individuals was higher compared with that of healthy controls (Simpson index, W = 2238, P = .048). Beta diversity was different between SHS and healthy controls (P = .018). At the phylum level, the relative abundance of Verrucomicrobia was higher in the SHS group than that in the controls (W = 2201, P = .049). Compared with that of the control group, nine genera were significantly higher and five genera were lower in abundance in the SHS group (all P < .05). The intestinal microbiota, analysed by a random forest model, was able to distinguish individuals with SHS from the controls, with an area under the curve of 0.79 (95% confidence interval: 0.77-0.81). We demonstrated that the alteration of intestinal microbiota occurs with SHS, an early stage of disease, which might shed light on the importance of intestinal microbiota in the primary prevention of noncommunicable chronic diseases. K E Y W O R D S16S rRNA, intestinal microbiota, LEfSe analysis, random forest tree, suboptimal health status

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“…In a gut microbial study of patients with irritable bowel syndrome, based on machine learning and fivefold cross‐validation to reduce the complexity of sequencing data, gut microbial signature was used to distinguish healthy individuals and patients with different degrees of symptoms, using a LASSO regression model . Furthermore, gut microbial signature has also been used, in addition to cross‐validation and random forest analysis, to identify potential microbial biomarkers for early diagnosis of hepatocarcinoma and breast cancer . Obviously, robust statistical approaches could overcome the weakness of the difference on relative abundance between groups approach, and help identify key taxa.…”

Section: Discussionmentioning

confidence: 99%

“…Several published studies have pointed out that key taxa may exist to support bacterial clustering. [36][37][38][39] To prove this hypothesis, machine learning approaches have been used widely in microbial studies. This could help identify signature microbiota, which could be utilized as diagnostic markers to predict progression of diseases, such as irritable bowel syndrome, 37 breast cancer, 39 and hepatocarcinoma.…”

Section: Discussionmentioning

confidence: 99%

“…This could help identify signature microbiota, which could be utilized as diagnostic markers to predict progression of diseases, such as irritable bowel syndrome, 37 breast cancer, 39 and hepatocarcinoma. 38 In a gut microbial study of patients with irritable bowel syndrome, based on machine learning and fivefold cross-validation to reduce the complexity of sequencing data, gut microbial signature was used to distinguish healthy individuals and patients with different degrees of symptoms, using a LASSO regression model. 37 Furthermore, gut microbial signature has also been used, in addition to cross-validation and random forest analysis, to identify potential microbial biomarkers for early diagnosis of hepatocarcinoma and breast cancer.…”

Section: Discussionmentioning

confidence: 99%

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Fecal microbiota as a noninvasive biomarker to predict the tissue iron accumulation in intestine epithelial cells and liver

et al. 2020

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Iron is an essential trace mineral required for growth, metabolism, and immune response. Dysregulation of iron homeostasis is linked with the development and progression of various diseases. Iron accumulation is associated with inflammatory diseases and cancer, while iron deficiency leads to the growth retardation. Several studies have suggested that iron imbalance results in alteration of gut microbiota, leading to the disruption of microbial diversity, the increase of pathogen abundance, and the induction of intestinal inflammation. However, in screening studies done in the past decades, the association between the iron availability and gut microbiota has not been systemically explored. Furthermore, a noninvasive and convenient approach to determine the iron levels in tissues is lacking. In the present study, a murine model for iron dysregulation was established. 16S rRNA amplicon sequencing and bioinformatic algorithms were used to identify the key taxa. Using the key taxa identified and machine learning models, we established an easily accessible prediction model, which could accurately distinguish between iron-deprived or ironfortified condition. This prediction model could precisely predict the iron level of the intestinal epithelial cells and the liver and could be used for early diagnosis of iron dysbiosis-related diseases, in a noninvasive manner, in the future.

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Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma

Cited by 543 publications

References 47 publications

Gut Microbiota Changes in Patients with Bipolar Depression

Gut Microbiota Changes in Patients with Bipolar Depression

Association of suboptimal health status with intestinal microbiota in Chinese youths

Fecal microbiota as a noninvasive biomarker to predict the tissue iron accumulation in intestine epithelial cells and liver

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