Dynamic changes of Bacterial Microbiomes in Oropharynx during Infection and Recovery of COVID-19 Omicron Variant.

Oropharyngeal microbiomes play a significant role in the susceptibility and severity of COVID-19, yet the role of these microbiomes play for the development of COVID-19 Omicron variant have not been reported. A total of 791 pharyngeal swab samples were prospectively included in this study, including 297 confirmed cases of Omicron variant (CCO), 222 confirmed case of Omicron who recovered (CCOR), 73 confirmed cases of original strain (CCOS) and 199 healthy controls (HC). All samples completed MiSeq sequencing. The results showed that compared with HC, conditional pathogens increased in CCO, while acid-producing bacteria decreased. Based on six optimal oropharyngeal operational taxonomy units (OTUs), we constructed a marker microbial classifier to distinguish between patients with Omicron variant and healthy people, and achieved high diagnostic efficiency in both the discovery queue and the verification queue. At same time, we introduced a group of cross-age infection verification cohort and Omicron variant subtype XBB.1.5 branch, which can be accurately distinguished by this diagnostic model. We also analyzed the characteristics of oropharyngeal microbiomes in two subgroups of Omicron disease group-severity of infection and vaccination times, and found that the change of oropharyngeal microbiomes may affect the severity of the disease and the efficacy of the vaccine. In addition, we found that some genera with significant differences gradually increased or decreased with the recovery of Omicron variant infection. The results of Spearman analysis showed that 27 oropharyngeal OTUs were closely related to 6 clinical indexes in CCO and HC. Finally, we found that the Omicron variant had different characterization of oropharyngeal microbiomes from the original strain. Our research characterizes oropharyngeal microbiomes of Omicron variant cases and rehabilitation cases, successfully constructed and verified the non-invasive diagnostic model of Omicron variant, described the correlation between microbial OTUs and clinical indexes. It was found that the infection of Omicron variant and the infection of original strain have different characteristics of oropharyngeal microbiomes.

Safety and immunogenicity of CoronaVac in healthy adults: A prospective observational multicenter real-world study in Henan Province, China.

Vaccination has emerged as the primar approach for managing the COVID-19 pandemic. Despite certain clinical trials reporting the safety and immunogenicity of CoronaVac, additional multicenter real-world studies are still necessary. In this study, we recruited 506 healthy volunteers who were not infected with COVID-19 or vaccinated. Each participant provided peripheral blood samples three times: prior to the first dose of vaccine, prior to the second dose, and 8 weeks following the second dose. Ultimately, 388 participants completed the entire follow-up process. No serious adverse events were observed among any of the participants. Within 1 week of vaccination, 13.4% of participants experienced systemic adverse reactions, with fatigue (5.93%) and dizziness (3.35%) being the most frequent. Although some clinical indicators, including creatinine, significantly changed after vaccination (p < 0.05), the mean of all altered indicators remained within the normal range. The positive rates of neutralizing antibodies (NAb), IgG, and IgM were 12.3%, 18.85%, and 5.24% prior to the second dose, respectively; and 57.99%, 86.34%, and 2.32% at 8 weeks following the second dose, respectively. Additionally, seven indicators, such as sex, age, and BMI, were significantly correlated with NAb (p < 0.05). Finally, a prediction model was developed based on age, monocytes, and alanine aminotransferase (ALT) with an AUC value of 87.56% in the train set and 80.71% in the test set. This study demonstrated that safety and immunogenicity of CoronaVac were good. The prediction model based on the baseline clinical characteristics prior to vaccination can help to develop more suitable vaccination strategies.

Nanosecond pulse effectively ablated hepatocellular carcinoma with alterations in the gut microbiome and serum metabolites.

Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death in the world. Nanosecond pulsed electric fields (nsPEFs) have emerged as a new treatment for cancer. This study aims to identify the effectiveness of nsPEFs in the treatment of HCC and analyze the alterations in the gut microbiome and serum metabonomics after ablation. Methods: C57BL/6 mice were randomly divided into three groups: healthy control mice (n = 10), HCC mice (n = 10), and nsPEF-treated HCC mice (n = 23). Hep1-6 cell lines were used to establish the HCC model in situ. Histopathological staining was performed on tumor tissues. The gut microbiome was analyzed by 16S rRNA sequencing. Serum metabolites were analyzed by liquid chromatography-mass spectrometry (LC-MS) metabolomic analysis. Spearman's correlation analysis was carried out to analyze the correlation between the gut microbiome and serum metabonomics. Results: The fluorescence image showed that nsPEFs were significantly effective. Histopathological staining identified nuclear pyknosis and cell necrosis in the nsPEF group. The expression of CD34, PCNA, and VEGF decreased significantly in the nsPEF group. Compared with normal mice, the gut microbiome diversity of HCC mice was increased. Eight genera including Alistipes and Muribaculaceae were enriched in the HCC group. Inversely, these genera decreased in the nsPEF group. LC-MS analysis confirmed that there were significant differences in serum metabolism among the three groups. Correlation analysis showed crucial relationships between the gut microbiome and serum metabolites that are involved in nsPEF ablation of HCC. Conclusion: As a new minimally invasive treatment for tumor ablation, nsPEFs have an excellent ablation effect. The alterations in the gut microbiome and serum metabolites may participate in the prognosis of HCC ablation.

The effect of the intratumoral microbiome on tumor occurrence, progression, prognosis and treatment.

In the past few decades, great progress has been achieved in the understanding of microbiome-cancer interactions. However, most of the studies have focused on the gut microbiome, ignoring how other microbiomes interact with tumors. Emerging evidence suggests that in many types of cancers, such as lung cancer, pancreatic cancer, and colorectal cancer, the intratumoral microbiome plays a significant role. In addition, accumulating evidence suggests that intratumoral microbes have multiple effects on the biological behavior of tumors, for example, regulating tumor initiation and progression and altering the tumor response to chemotherapy and immunotherapy. However, to fully understand the role of the intratumoral microbiome in cancer, further investigation of the effects and mechanisms is still needed. This review discusses the role of intratumoral bacteria in tumorigenesis and tumor progression, recurrence and metastasis, as well as their effect on cancer prognosis and treatment outcome, and summarizes the relevant mechanisms.

Serum metabolome alterations in patients with early nonalcoholic fatty liver disease.

BACKGROUND: Although metabolomic analysis for patients with nonalcoholic fatty liver disease (NAFLD) was a promising approach to identify novel biomarkers as targets for the diagnosis of NAFLD, the serum metabolomics profile of early-stage NAFLD patients from central China remain unknown. OBJECTIVE: The aim of the present study was to explore the metabolic characteristics of patients with early-stage NAFLD based on the ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) technology, to identify differential metabolites and perform functional analysis, and especially, to establish a novel early NAFLD clinical diagnostic tool. RESULTS: Compared with healthy controls, serum metabolite species increased significantly in early stage NAFLD patients. Expression of 88 metabolites including 1-naphthylmethanol, rosavin, and theophylline were up-regulated in early NAFLD, while 68 metabolites including 2-hydroxyphenylacetic acid and lysophosphatidylcholine (24:1(15Z)) were down-regulated. The early NAFLD classifier achieved a strong diagnostic efficiency in the discovery phases (80.99%) and was successfully verified in the validation phases (75.23%). CONCLUSIONS: These results advance our understanding about the composition and biological functions of serum metabolites of early NAFLD. In addition, serum metabolic markers can serve as an efficient diagnostic tool for the early-stage NAFLD.

Characterization of oral and gut microbiome and plasma metabolomics in COVID-19 patients after 1-year follow-up.

BACKGROUND: Due to the outbreak and rapid spread of coronavirus disease 2019 (COVID-19), more than 160 million patients have become convalescents worldwide to date. Significant alterations have occurred in the gut and oral microbiome and metabonomics of patients with COVID-19. However, it is unknown whether their characteristics return to normal after the 1-year recovery. METHODS: We recruited 35 confirmed patients to provide specimens at discharge and one year later, as well as 160 healthy controls. A total of 497 samples were prospectively collected, including 219 tongue-coating, 129 stool and 149 plasma samples. Tongue-coating and stool samples were subjected to 16S rRNA sequencing, and plasma samples were subjected to untargeted metabolomics testing. RESULTS: The oral and gut microbiome and metabolomics characteristics of the 1-year convalescents were restored to a large extent but did not completely return to normal. In the recovery process, the microbial diversity gradually increased. Butyric acid-producing microbes and Bifidobacterium gradually increased, whereas lipopolysaccharide-producing microbes gradually decreased. In addition, sphingosine-1-phosphate, which is closely related to the inflammatory factor storm of COVID-19, increased significantly during the recovery process. Moreover, the predictive models established based on the microbiome and metabolites of patients at the time of discharge reached high efficacy in predicting their neutralizing antibody levels one year later. CONCLUSIONS: This study is the first to characterize the oral and gut microbiome and metabonomics in 1-year convalescents of COVID-19. The key microbiome and metabolites in the process of recovery were identified, and provided new treatment ideas for accelerating recovery. And the predictive models based on the microbiome and metabolomics afford new insights for predicting the recovery situation which benefited affected individuals and healthcare.

Dysbiosis in the Human Microbiome of Cholangiocarcinoma.

Cholangiocarcinoma (CCA) is the most common malignant tumor of the biliary system with a very poor prognosis. The human microbiome, which is the sum of the genetic information of human microorganisms, plays an important role in regulating the digestion, absorption, immune response, and metabolism of the host. Increasing evidence indicates a close relationship between CCA and the human microbiome. Specific alterations occur in the human microbiome of patients with CCA. Therefore, in this review, we aimed to summarize the recent evidence on dysbiosis in the human microbiome of CCA. Then, we generalized the effect of Helicobacter pylori on CCA. Additionally, the potential mechanism of human microbial dysbiosis promoted the progress of CCA, and its precancerous disease was also explored. Furthermore, the possibility of the human microbiome as a diagnostic and therapeutic target of CCA was discussed.

RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown. METHODS: The differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis. RESULTS: A total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed. CONCLUSION: Our study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

Characterization of the Human Oropharyngeal Microbiomes in SARS-CoV-2 Infection and Recovery Patients.

Respiratory tract microbiome is closely related to respiratory tract infections, while characterization of oropharyngeal microbiome in recovered coronavirus disease 2019 (COVID-19) patients is not studied. Herein, oropharyngeal swabs are collected from confirmed cases (CCs) with COVID-19 (73 subjects), suspected cases (SCs) (36), confirmed cases who recovered (21), suspected cases who recovered (36), and healthy controls (Hs) (140) and then completed MiSeq sequencing. Oropharyngeal microbial α-diversity is markedly reduced in CCs versus Hs. Opportunistic pathogens are increased, while butyrate-producing genera are decreased in CCs versus Hs. The classifier based on eight optimal microbial markers is constructed through a random forest model and reached great diagnostic efficacy in both discovery and validation cohorts. Notably, the classifier successfully diagnosed SCs with positive IgG antibody as CCs and is demonstrated from the perspective of the microbiome. Importantly, several genera with significant differences gradually increase and decrease along with recovery from COVID-19. Forty-four oropharyngeal operational taxonomy units (OTUs) are closely correlated with 11 clinical indicators of SARS-CoV-2 infection and Hs based on Spearman correlation analysis. Together, this research is the first to characterize oropharyngeal microbiota in recovered COVID-19 cases and suspected cases, to successfully construct and validate the diagnostic model for COVID-19 and to depict the correlations between microbial OTUs and clinical indicators.

Oral Microbiome Characteristics in Patients With Autoimmune Hepatitis.

Autoimmune hepatitis (AIH) is a common cause of liver cirrhosis. To identify the characteristics of the oral microbiome in patients with AIH, we collected 204 saliva samples including 68 AIH patients and 136 healthy controls and performed microbial MiSeq sequencing after screening. All samples were randomly divided into discovery cohorts (46 AIH and 92 HCs) and validation cohorts (22 AIH and 44 HCs). Moreover, we collected samples of 12 AIH patients from Hangzhou for cross-regional validation. We described the oral microbiome characteristics of AIH patients and established a diagnostic model. In the AIH group, the oral microbiome diversity was significantly increased. The microbial communities remarkably differed between the two groups. Seven genera, mainly Fusobacterium, Actinomyces and Capnocytophaga, were dominant in the HC group, while 51 genera, Streptococcus, Veillonella and Leptotrichia, were enriched in the AIH group. Notably, we found 23 gene functions, including Membrane Transport, Carbohydrate Metabolism, and Glycerolipid metabolism that were dominant in AIH and 31 gene functions that prevailed in HCs. We further investigated the correlation between the oral microbiome and clinical parameters. The optimal 5 microbial markers were figured out through a random forest model, and the distinguishing potential achieved 99.88% between 46 AIH and 92 HCs in the discovery cohort and 100% in the validation cohort. Importantly, the distinguishing potential reached 95.55% in the cross-regional validation cohort. In conclusion, this study is the first to characterize the oral microbiome in AIH patients and to report the successful establishment of a diagnostic model and the cross-regional validation of microbial markers for AIH. Importantly, oral microbiota-targeted biomarkers may be able to serve as powerful and noninvasive diagnostic tools for AIH.

Alterations in the human oral and gut microbiomes and lipidomics in COVID-19.

OBJECTIVE: To characterise the oral microbiome, gut microbiome and serum lipid profiles in patients with active COVID-19 and recovered patients; evaluate the potential of the microbiome as a non-invasive biomarker for COVID-19; and explore correlations between the microbiome and lipid profile. DESIGN: We collected and sequenced 392 tongue-coating samples, 172 faecal samples and 155 serum samples from Central China and East China. We characterised microbiome and lipid molecules, constructed microbial classifiers in discovery cohort and verified their diagnostic potential in 74 confirmed patients (CPs) from East China and 37 suspected patients (SPs) with IgG positivity. RESULTS: Oral and faecal microbial diversity was significantly decreased in CPs versus healthy controls (HCs). Compared with HCs, butyric acid-producing bacteria were decreased and lipopolysaccharide-producing bacteria were increased in CPs in oral cavity. The classifiers based on 8 optimal oral microbial markers (7 faecal microbial markers) achieved good diagnostic efficiency in different cohorts. Importantly, diagnostic efficacy reached 87.24% in the cross-regional cohort. Moreover, the classifiers successfully diagnosed SPs with IgG antibody positivity as CPs, and diagnostic efficacy reached 92.11% (98.01% of faecal microbiome). Compared with CPs, 47 lipid molecules, including sphingomyelin (SM)(d40:4), SM(d38:5) and monoglyceride(33:5), were depleted, and 122 lipid molecules, including phosphatidylcholine(36:4p), phosphatidylethanolamine (PE)(16:0p/20:5) and diglyceride(20:1/18:2), were enriched in confirmed patients recovery. CONCLUSION: This study is the first to characterise the oral microbiome in COVID-19, and oral microbiomes and lipid alterations in recovered patients, to explore their correlations and to report the successful establishment and validation of a diagnostic model for COVID-19.

Gut Microbiome Distinguishes Patients With Epilepsy From Healthy Individuals.

Objective: The gut microecosystem is the largest microecosystem in the human body and has been proven to be linked to neurological diseases. The main objective of this study was to characterize the fecal microbiome, investigate the differences between epilepsy patients and healthy controls, and evaluate the potential efficacy of the fecal microbiome as a diagnostic tool for epilepsy. Design: We collected 74 fecal samples from epilepsy patients (Eps, n = 24) and healthy controls (HCs, n = 50) in the First Affiliated Hospital of Zhengzhou University and subjected the samples to 16S rRNA MiSeq sequencing and analysis. We set up a train set and a test set, identified the optimal microbial markers for epilepsy after characterizing the gut microbiome in the former and built a diagnostic model, then validated it in the validation group. Results: There were significant differences in microbial communities between the two groups. The α-diversity of the HCs was higher than that of the epilepsy group, but the Venn diagram showed that there were more unique operational taxonomic unit (OTU) in the epilepsy group. At the phylum level, Proteobacteria and Actinobacteriota increased significantly in Eps, while the relative abundance of Bacteroidota increased in HCs. Compared with HCs, Eps were enriched in 23 genera, including Faecalibacterium, Escherichia-Shigella, Subdoligranulum and Enterobacteriaceae-unclassified. In contrast, 59 genera including Bacteroides, Megamonas, Prevotella, Lachnospiraceae-unclassified and Blautia increased in the HCs. In Spearman correlation analysis, age, WBC, RBC, PLT, ALB, CREA, TBIL, Hb and Urea were positively correlated with most of the different OTUs. Seizure-type, course and frequency are negatively correlated with most of the different OTUs. In addition, twenty-two optimal microbial markers were identified by a fivefold cross-validation of the random forest model. In the established train set and test set, the area under the curve was 0.9771 and 0.993, respectively. Conclusion: Our study was the first to characterize the gut microbiome of Eps and HCs in central China and demonstrate the potential efficacy of microbial markers as a noninvasive biological diagnostic tool for epilepsy.

SPATS2, negatively regulated by miR-145-5p, promotes hepatocellular carcinoma progression through regulating cell cycle.

Spermatogenesis associated serine rich 2 (SPATS2) has been reported to contribute to the tumorigenesis of multiple malignancies. The molecular function of SPATS2 in hepatocellular carcinoma (HCC) is still not fully understood. In this study, we aimed to investigate the expression pattern and function roles of SPATS2 in HCC. The regulation of SPATS2 expression was also explored. We found that SPATS2 was highly expressed in HCC tissues in comparison with that in adjacent normal tissues. High expression of SPATS2 was associated with vascular invasion, advanced TNM stages, tumor multiplicity, and poor survival. Functionally, SPATS2 was found to promote the proliferation and metastasis of HCC cells both in vitro and in vivo, while knockdown of SPATS2 enhanced apoptosis and G1 arrest of HCC cells in vitro. Mechanistically, bioinformatics analysis revealed that MiR-145-5p directly targeted SPATS2 and functional rescue experiments verified that MiR-145-5p overexpression could abolish the effect of SPATS2 on the regulation of HCC malignant phenotype. Taken together, our findings suggest that SPATS2 functions as an oncogene in HCC. The MiR-145-5p/SPATS2 axis provides a novel mechanism underlying HCC progression and may serve as a potential therapeutic target for HCC.

Fecal Microbiomes Distinguish Patients With Autoimmune Hepatitis From Healthy Individuals.

Objective: The intestinal microbiome is associated with various autoimmune diseases. Regional difference is the main influencing factor of intestinal microbial difference. This study aimed to identify the differences in fecal microbiome between autoimmune hepatitis (AIH) patients and healthy controls (HCs) in Central China, and to validate the efficacy of fecal microbiome as a diagnostic tool for AIH. Design: We collected 115 fecal samples from AIH patients (N = 37) and HCs (N = 78) in Central China and performed gene sequencing. Fecal microbiomes were characterized and microbial markers for AIH were identified. Results: Fecal microbial diversity showed a downward trend in AIH compared with HCs. Fecal microbial communities significantly differed between both groups. At the phylum level, Verrucomicrobia abundance was significantly increased, while Lentisphaerae and Synergistetes were significantly decreased in the AIH patients vs. the HCs. Compared to the HCs, 15 genera, including Veillonella, Faecalibacterium, and Akkermansia, were enriched, while 19 genera, such as Pseudobutyrivibrio, Lachnospira, and Ruminococcaceae, were decreased in the AIH patients. Ten genera, including Veillonella, Faecalibacterium, and Akkermansia, predominated in the AIH patients. Five microbial biomarkers were deemed optimal diagnostic tools for AIH. The probability of disease was significantly increased in AIH group vs. HCs, achieving 83.25% value of area under the curve. Conclusion: We present the characteristics of AIH patients in Central China for the first time. Five microbial biomarkers, including Lachnospiraceae, Veillonella, Bacteroides, Roseburia, and Ruminococcaceae, achieved a high potential distinguishing AIH patients from HCs.

A novel predicted model for hypertension based on a large cross-sectional study.

Hypertension is a global public health issue and leading risk for death and disability. It is urgent to search novel methods predicting hypertension. Herein, we chose 73158 samples of physical examiners in central China from June 2008 to June 2018. After strict exclusion processes, 33570 participants with hypertension and 35410 healthy controls were included. We randomly chose 70% samples as the train set and the remaining 30% as the test set. Clinical parameters including age, gender, height, weight, body mass index, triglyceride, total cholesterol, low-density lipoprotein, blood urea nitrogen, uric acid, and creatinine were significantly increased, while high-density lipoprotein was decreased in the hypertension group versus controls. Nine optimal markers were identified by a logistic regression model, and achieved AUC value of 76.52% in the train set and 75.81% in the test set for hypertension. In conclusions, this study is the first to establish predicted models for hypertension using the logistic regression model in Central China, which provide risk factors and novel prediction method to predict and prevent hypertension.

Dysregulated m6A-Related Regulators Are Associated With Tumor Metastasis and Poor Prognosis in Osteosarcoma.

Background: Osteosarcoma (OS) is the most common primary bone tumor. The disease has a poor prognosis due to the delay in the diagnosis and the development of metastasis. N6-Methyladenosine (m6A)-related regulators play an essential role in various tumors. In this study, a comprehensive analysis was conducted to elucidate the relationship between the expression profiles of m6A-related molecules and the clinical outcome of OS patients. Materials and Methods: Public genome datasets and a tissue microarray (TMA) cohort were used to analyze the mRNA and protein expression levels of m6A regulators. Next, immunofluorescence (IF) analysis was used to determine the subcellular localization of m6A-related molecules. Kaplan-Meier and Cox regression analyses were performed to confirm the prognostic value of m6A-related molecules in OS. A comprehensive bioinformatic analysis was conducted to identify the potential molecular mechanisms mediated by m6A modification in OS. Results: We found that m6A-related regulator expression was dysregulated in OS tissues, especially in metastatic tumor tissues. Low expression of METTL3, METTL14, and YTHDF2 and high expression of KIAA1429 and HNRNPA2B1 were significantly associated with poor prognosis in the TMA cohort. Simultaneously, the genome meta-cohort analysis revealed that low expression of FTO and METTL14 and high expression of METTL3, HNRNPA2B1, and YTHDF3 were associated with poor prognosis in OS. Cox regression analysis showed that HNRNPA2B1 might be an independent risk factor for OS. Bioinformatic analysis indicated that m6A regulators might be involved in OS progression through humoral immune response and cell cycle pathways. Conclusion: M6A-related regulators are frequently dysregulated and correlate with metastasis and prognosis in OS. M6A-related regulators may serve as novel therapeutic targets and prognostic biomarkers for OS.

The Function of the HGF/c-Met Axis in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, leading to a large global cancer burden. Hepatocyte growth factor (HGF) and its high-affinity receptor, mesenchymal epithelial transition factor (c-Met), are closely related to the onset, progression, and metastasis of multiple tumors. The HGF/c-Met axis is involved in cell proliferation, movement, differentiation, invasion, angiogenesis, and apoptosis by activating multiple downstream signaling pathways. In this review, we focus on the function of the HGF/c-Met axis in HCC. The HGF/c-Met axis promotes the onset, proliferation, invasion, and metastasis of HCC. Moreover, it can serve as a biomarker for diagnosis and prognosis, as well as a therapeutic target for HCC. In addition, it is closely related to drug resistance during HCC treatment.

Human microbiome is a diagnostic biomarker in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer mortality worldwide. Increasing evidence indicates a close relationship between HCC and the human microbiota. Herein, we reviewed the important potential of the human microbiota as a diagnostic biomarker of HCC. DATA SOURCES: Several innovative studies have investigated the characteristics of the gut and oral microbiomes in patients with HCC and proposed that the human microbiome has the potential to be a diagnostic biomarker of HCC. Literature from February 1999 to February 2019 was searched in the PubMed database using the keywords "microbiota" or "microbiome" or "microbe" and "liver cancer" or "hepatocellular carcinoma", and the results of clinical and experimental studies were analyzed. RESULTS: Specific changes occur in the human microbiome of patients with HCC. Moreover, the gut microbiome and oral microbiome can be used as non-invasive diagnostic biomarkers for HCC. Furthermore, they also have certain diagnostic potential for precancerous diseases of HCC. The diagnostic potential of the blood microbiota and ascites microbiota in HCC will be gradually discovered in the future. CONCLUSIONS: The human microbiome is valuable to the diagnosis of HCC and provides a novel strategy for targeted therapy of HCC. The human microbiome may be widely used in the diagnosis, treatment and prognosis for multiple system diseases or cancers in the future.