Dysbiosis of human tumor microbiome and aberrant residence of Actinomyces in tumor-associated fibroblasts in young-onset colorectal cancer

Gut microbial subtypes and clinicopathological value for colorectal cancer

BACKGROUND

Gut bacteria are related to colorectal cancer (CRC) and its clinicopathologic characteristics.

OBJECTIVE

To develop gut bacterial subtypes and explore potential microbial targets for CRC.

METHODS

Stool samples from 914 volunteers (376 CRCs, 363 advanced adenomas, and 175 normal controls) were included for 16S rRNA sequencing. Unsupervised learning was used to generate gut microbial subtypes. Gut bacterial community composition and clustering effects were plotted. Differences of gut bacterial abundance were analyzed. Then, the association of CRC-associated bacteria with subtypes and the association of gut bacteria with clinical information were assessed. The CatBoost models based on gut differential bacteria were constructed to identify the diseases including CRC and advanced adenoma (AA).

RESULTS

Four gut microbial subtypes (A, B, C, D) were finally obtained via unsupervised learning. The characteristic bacteria of each subtype were Escherichia-Shigella in subtype A, Streptococcus in subtype B, Blautia in subtype C, and Bacteroides in subtype D. Clinical information (e.g., free fatty acids and total cholesterol) and CRC pathological information (e.g., tumor depth) varied among gut microbial subtypes. Bacilli, Lactobacillales, etc., were positively correlated with subtype B. Positive correlation of Blautia, Lachnospiraceae, etc., with subtype C and negative correlation of Coriobacteriia, Coriobacteriales, etc., with subtype D were found. Finally, the predictive ability of CatBoost models for CRC identification was improved based on gut microbial subtypes.

CONCLUSION

Gut microbial subtypes provide characteristic gut bacteria and are expected to contribute to the diagnosis of CRC.

Ecosystemic Approach to Understanding Gut Microbiome-Mediated Prevention of Colorectal Cancer

Humans and their associated microorganisms coexist in complex symbiotic relationships. Continuously advancing research is demonstrating the crucial role of host-associated microbiota in the pathophysiology and etiology of disease and in mediating the prevention thereof. As an exemplar, the gut microbiota, especially colonic bacteria, have been extensively studied in colorectal cancer (CRC), and the growing body of evidence establishes new oncomicrobes and their oncometabolites associated with the initiation and promotion of carcinogenesis. Herein, we discuss the importance of approaching the gut microbiome as an ecosystem rather than an assortment of individual factors, especially in the context of cancer prevention. Furthermore, we argue that a dietary pattern effectively drives multiple nodes of the gut microbial ecosystem toward disease- or health-promoting qualities. In the modern circumstances of excessive consumption of ultraprocessed and animal-based foods and concomitant escalation of chronic disease burden worldwide, we focus on whole food-derived dietary fiber as a key to establishing a health-promoting eubiosis in the gut.

Intratumoral Microbiome: Foe or Friend in Reshaping the Tumor Microenvironment Landscape?

The role of the microbiome in cancer and its crosstalk with the tumor microenvironment (TME) has been extensively studied and characterized. An emerging field in the cancer microbiome research is the concept of the intratumoral microbiome, which refers to the microbiome residing within the tumor. This microbiome primarily originates from the local microbiome of the tumor-bearing tissue or from translocating microbiome from distant sites, such as the gut. Despite the increasing number of studies on intratumoral microbiome, it remains unclear whether it is a driver or a bystander of oncogenesis and tumor progression. This review aims to elucidate the intricate role of the intratumoral microbiome in tumor development by exploring its effects on reshaping the multileveled ecosystem in which tumors thrive, the TME. To dissect the complexity and the multitude of layers within the TME, we distinguish six specialized tumor microenvironments, namely, the immune, metabolic, hypoxic, acidic, mechanical and innervated microenvironments. Accordingly, we attempt to decipher the effects of the intratumoral microbiome on each specialized microenvironment and ultimately decode its tumor-promoting or tumor-suppressive impact. Additionally, we portray the intratumoral microbiome as an orchestrator in the tumor milieu, fine-tuning the responses in distinct, specialized microenvironments and remodeling the TME in a multileveled and multifaceted manner.

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer

The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I-IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44). Untargeted plasma metabolomics and 16S rRNA amplicon sequencing (microbiome analysis) of tumor tissue were performed. We fit DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies) to construct a supervised machine-learning classifier using paired multi-omics (microbiome and metabolomics) data and identify associations unique to eoCRC. A differential association network analysis was also performed. Distinct clustering patterns emerged in multi-omic dimension reduction analysis. The metabolomics classifier achieved an AUC of 0.98, compared to AUC 0.61 for microbiome-based classifier. Circular correlation technique highlighted several key associations. Metabolites glycerol and pseudouridine (higher abundance in individuals with aoCRC) had negative correlations with Parasutterella, and Ruminococcaceae (higher abundance in individuals with eoCRC). Cholesterol and xylitol correlated negatively with Erysipelatoclostridium and Eubacterium, and showed a positive correlation with Acidovorax with higher abundance in individuals with eoCRC. Network analysis revealed different clustering patterns and associations for several metabolites e.g.: urea cycle metabolites and microbes such as Akkermansia. We show that multi-omics analysis can be utilized to study host-microbiome correlations in eoCRC and demonstrates promising biomarker potential of a metabolomics classifier. The distinct host-microbiome correlations for urea cycle in eoCRC may offer opportunities for therapeutic interventions.

Obesity and early-onset colorectal cancer risk: emerging clinical evidence and biological mechanisms

Early-onset colorectal cancer (EOCRC) is defined as diagnosed at younger than 50 years of age and indicates a health burden globally. Patients with EOCRC have distinct risk factors, clinical characteristics, and molecular pathogenesis compared with older patients with CRC. Further investigations have identified different roles of obesity between EOCRC and late-onset colorectal cancer (LOCRC). Most studies have focused on the clinical characteristics of obesity in EOCRC, therefore, the mechanism involved in the association between obesity and EOCRC remains inconclusive. This review further states that obesity affects the carcinogenesis of EOCRC as well as its development and progression, which may lead to obesity-related metabolic syndrome, intestinal dysbacteriosis, and intestinal inflammation.

The value of intratumoral microbiota in the diagnosis and prognosis of tumors

Intratumoral microbiota (ITM) are microorganisms present in tumor cells. ITM participate in tumor development by affecting tumor cells directly and the tumor microenvironment (TME), indirectly. Alterations in ITM instigate changes in tumor DNA, activate oncogenic pathways, induce tumor inflammatory responses, disrupt normal immune activity, and facilitate the secretion of effectors leading to tumor progression, metastasis, or diminished therapeutic effects. ITM varies significantly in different types of cancer cells and disease states. The presence of certain ITM serves as a predictor of various disease states. Thus, ITM predicts tumorigenesis, tumor grade, treatment efficacy, and prognosis, making it a potential tumor biomarker. The present study aimed to determine the mechanisms by which ITM affects tumor development, especially through the TME; highlight the significant potential of ITM in enhancing tumor diagnosis and prognosis; and outline future directions for ITM research, with a focus on the development of innovative tumor markers.

Promising dawn in tumor microenvironment therapy: engineering oral bacteria

Despite decades of research, cancer continues to be a major global health concern. The human mouth appears to be a multiplicity of local environments communicating with other organs and causing diseases via microbes. Nowadays, the role of oral microbes in the development and progression of cancer has received increasing scrutiny. At the same time, bioengineering technology and nanotechnology is growing rapidly, in which the physiological activities of natural bacteria are modified to improve the therapeutic efficiency of cancers. These engineered bacteria were transformed to achieve directed genetic reprogramming, selective functional reorganization and precise control. In contrast to endotoxins produced by typical genetically modified bacteria, oral flora exhibits favorable biosafety characteristics. To outline the current cognitions upon oral microbes, engineered microbes and human cancers, related literatures were searched and reviewed based on the PubMed database. We focused on a number of oral microbes and related mechanisms associated with the tumor microenvironment, which involve in cancer occurrence and development. Whether engineering oral bacteria can be a possible application of cancer therapy is worth consideration. A deeper understanding of the relationship between engineered oral bacteria and cancer therapy may enhance our knowledge of tumor pathogenesis thus providing new insights and strategies for cancer prevention and treatment.

Effect of different forms of tobacco on the oral microbiome in healthy adults: a systematic review

Objective

The study aimed to evaluate the impact of tobacco use on the composition and functions of the oral microbiome in healthy adult humans.

Methods

We conducted a systematic search on PubMed, Web of Science, and Cinhal databases for literature published until 15 December 2023, to identify studies that have evaluated the oral microbiome with culture-independent next-generation techniques comparing the oral microbiome of tobacco users and non-users. The search followed the PECO format. The outcomes included changes in microbial diversity and abundance of microbial taxa. The quality assessment was performed using the Newcastle-Ottawa Scale (NOS) (PROSPERO ID CRD42022340151).

Results

Out of 2,435 articles screened, 36 articles satisfied the eligibility criteria and were selected for full-text review. Despite differences in design, quality, and population characteristics, most studies reported an increase in bacterial diversity and richness in tobacco users. The most notable bacterial taxa enriched in users were Fusobacteria and Actinobacteria at the phylum level and Streptococcus, Prevotella, and Veillonella at the genus level. At the functional level, more similarities could be noted; amino acid metabolism and xenobiotic biodegradation pathways were increased in tobacco users compared to non-users. Most of the studies were of good quality on the NOS scale.

Conclusion

Tobacco smoking influences oral microbial community harmony, and it shows a definitive shift towards a proinflammatory milieu. Heterogeneities were detected due to sampling and other methodological differences, emphasizing the need for greater quality research using standardized methods and reporting.

Systematic Review Registration

CRD42022340151.

Analysis of differences in intestinal flora associated with different BMI status in colorectal cancer patients

BACKGROUND

Overweight is known to be an important risk factor for colorectal cancer (CRC), and the differences in intestinal flora among CRC patients with different BMI status have not been clearly defined. The purpose of this study was to elucidate the differences in the abundance, composition and biological function of intestinal flora in CRC patients with different BMI status.

METHOD

A total of 170 CRC patients were included and grouped according to the BMI data of CRC patients. BMI ≥ 24 kg/m2 was defined as overweight group, and BMI within the range of 18.5-23.9 kg/m2 was defined as normal weight group. Preoperative stool collection of patients in both groups was used for 16S rRNA sequencing. Total RNA was extracted from 17 CRC tumor tissue samples for transcriptome sequencing, and then CIBERSORT algorithm was used to convert the transcriptome data into the relative content matrix of 22 kinds of immune cells, and the correlation between different intestinal flora and immune cells and immune-related genes under different BMI states was analyzed. Finally, we identified BMI-related differential functional pathways and analyzed the correlation between these pathways and differential intestinal flora.

RESULT

There was no significant difference in α diversity and β diversity analysis between overweight group and normal weight group. Partial least square discriminant analysis (PLS-DA) could divide the flora into two different clusters according to BMI stratification. A total of 33 BMI-related differential flora were identified by linear discriminant effect size analysis (LEfSe), among which Actinomyces, Desulfovibrio and Bacteroides were significantly enriched in overweight group. ko00514: Other types of O-glycan biosynthesis are significantly enriched in overweight group. There was a significant positive correlation between Clostridium IV and Macrophages M2 and T cells regulatory (Tregs). There was a significant negative correlation with Dendritic cells activated and T cells CD4 memory activated.

CONCLUSIONS

The richness and diversity of intestinal flora of CRC patients may be related to different BMI status, and the enrichment of Actinomyces, Desulphurvibrio and Bacteroides may be related to overweight status of CRC patients. The tumor microenvironment in which BMI-related differential flora resides has different immune landscapes, suggesting that some intestinal flora may affect the biological process of CRC by regulating immune cell infiltration and immune gene expression, but further experiments are needed to confirm this.

Identification of consensus head and neck cancer-associated microbiota signatures: a systematic review and meta-analysis of 16S rRNA and The Cancer Microbiome Atlas datasets

Introduction. Multiple reports have attempted to describe the tumour microbiota in head and neck cancer (HNSC).Gap statement. However, these have failed to produce a consistent microbiota signature, which may undermine understanding the importance of bacterial-mediated effects in HNSC.Aim. The aim of this study is to consolidate these datasets and identify a consensus microbiota signature in HNSC.Methodology. We analysed 12 published HNSC 16S rRNA microbial datasets collected from cancer, cancer-adjacent and non-cancer tissues to generate a consensus microbiota signature. These signatures were then validated using The Cancer Microbiome Atlas (TCMA) database and correlated with the tumour microenvironment phenotypes and patient's clinical outcome.Results. We identified a consensus microbial signature at the genus level to differentiate between HNSC sample types, with cancer and cancer-adjacent tissues sharing more similarity than non-cancer tissues. Univariate analysis on 16S rRNA datasets identified significant differences in the abundance of 34 bacterial genera among the tissue types. Paired cancer and cancer-adjacent tissue analyses in 16S rRNA and TCMA datasets identified increased abundance in Fusobacterium in cancer tissues and decreased abundance of Atopobium, Rothia and Actinomyces in cancer-adjacent tissues. Furthermore, these bacteria were associated with different tumour microenvironment phenotypes. Notably, high Fusobacterium signature was associated with high neutrophil (r=0.37, P<0.0001), angiogenesis (r=0.38, P<0.0001) and granulocyte signatures (r=0.38, P<0.0001) and better overall patient survival [continuous: HR 0.8482, 95 % confidence interval (CI) 0.7758-0.9273, P=0.0003].Conclusion. Our meta-analysis demonstrates a consensus microbiota signature for HNSC, highlighting its potential importance in this disease.

In-depth metaproteomics analysis of tongue coating for gastric cancer: a multicenter diagnostic research study

BACKGROUND

Our previous study revealed marked differences in tongue images between individuals with gastric cancer and those without gastric cancer. However, the biological mechanism of tongue images as a disease indicator remains unclear. Tongue coating, a major factor in tongue appearance, is the visible layer on the tongue dorsum that provides a vital environment for oral microorganisms. While oral microorganisms are associated with gastric and intestinal diseases, the comprehensive function profiles of oral microbiota remain incompletely understood. Metaproteomics has unique strength in revealing functional profiles of microbiota that aid in comprehending the mechanism behind specific tongue coating formation and its role as an indicator of gastric cancer.

METHODS

We employed pressure cycling technology and data-independent acquisition (PCT-DIA) mass spectrometry to extract and identify tongue-coating proteins from 180 gastric cancer patients and 185 non-gastric cancer patients across 5 independent research centers in China. Additionally, we investigated the temporal stability of tongue-coating proteins based on a time-series cohort. Finally, we constructed a machine learning model using the stochastic gradient boosting algorithm to identify individuals at high risk of gastric cancer based on tongue-coating microbial proteins.

RESULTS

We measured 1432 human-derived proteins and 13,780 microbial proteins from 345 tongue-coating samples. The abundance of tongue-coating proteins exhibited high temporal stability within an individual. Notably, we observed the downregulation of human keratins KRT2 and KRT9 on the tongue surface, as well as the downregulation of ABC transporter COG1136 in microbiota, in gastric cancer patients. This suggests a decline in the defense capacity of the lingual mucosa. Finally, we established a machine learning model that employs 50 microbial proteins of tongue coating to identify individuals at a high risk of gastric cancer, achieving an area under the curve (AUC) of 0.91 in the independent validation cohort.

CONCLUSIONS

We characterized the alterations in tongue-coating proteins among gastric cancer patients and constructed a gastric cancer screening model based on microbial-derived tongue-coating proteins. Tongue-coating proteins are shown as a promising indicator for identifying high-risk groups for gastric cancer. Video Abstract.

Microbiota: A key factor affecting and regulating the efficacy of immunotherapy

BACKGROUND

Immunotherapy has made significant progress in cancer treatment; however, the responsiveness to immunotherapy varies widely among patients. Growing evidence has demonstrated the role of the gut microbiota in the efficacy of immunotherapy.

MAIN BODY

Herein, we summarise the changes in the microbiota in different cancers under various immunotherapies. The microbial-host signal transmission on immunotherapeutic responses and mechanisms associated with microbial translocation to tumours in the context of immunotherapy are also discussed. In addition, we have highlighted the clinical application value of methods for regulating the microbiota. Finally, we elaborate on the relationship between the microbiota, host and immunotherapy, and provide potential directions for future research.

CONCLUSION

Different microbiota cause changes in the tumour microenvironment through microbial signals thereby affecting immunotherapy efficacy. Translocation of gut microbiota and the role of extraintestinal microbiota in immunotherapy deserve attention. Microbiota regulation is a novel strategy for combination therapy with immunotherapy. Although there are several aspects that deserve further refinement and exploration with regard to administration and clinical translation. Nevertheless, it is foreseeable that the microbiota will become an integral part of cancer treatment.

Interaction between intratumoral microbiota and tumor mediates the response of neoadjuvant therapy for rectal cancer

Background

Previous observations have demonstrated that the response to neoadjuvant chemoradiotherapy (nCRT) is highly variable in patients with locally advanced rectal cancer (LARC). Recent studies focusing on the intratumoral microbiota of colorectal cancer have revealed its role in oncogenesis and tumor progression. However, limited research has focused on the influence of intratumoral microbiota on the nCRT of LARC.

Methods

We explored the microbial profiles in the tumor microenvironment of LARC using RNA-seq data from a published European cohort. Microbial signatures were characterized in pathological complete response (pCR) and non-pCR groups. Multi-omics analysis was performed between intratumor microbiomes and transcriptomes.

Results

Microbial α and β diversity were significantly different in pCR and non-pCR groups. Twelve differential microbes were discovered between the pCR and non-pCR groups, six of which were related to subclusters of cancer-associated fibroblasts (CAFs) associated with extracellular matrix formation. A microbial risk score based on the relative abundance of seven differential microbes had predictive value for the nCRT response (AUC = 0.820, p < 0.001).

Conclusion

Our study presents intratumoral microbes as potential independent predictive markers for the response of nCRT to LARC and demonstrates the underlying mechanism by which the interaction between intratumoral microbes and CAFs mediates the response to nCRT.

Salivary and fecal microbiota: potential new biomarkers for early screening of colorectal polyps

Objective

Gut microbiota plays an important role in colorectal cancer (CRC) pathogenesis through microbes and their metabolites, while oral pathogens are the major components of CRC-associated microbes. Multiple studies have identified gut and fecal microbiome-derived biomarkers for precursors lesions of CRC detection. However, few studies have used salivary samples to predict colorectal polyps. Therefore, in order to find new noninvasive colorectal polyp biomarkers, we searched into the differences in fecal and salivary microbiota between patients with colorectal polyps and healthy controls.

Methods

In this case-control study, we collected salivary and fecal samples from 33 patients with colorectal polyps (CP) and 22 healthy controls (HC) between May 2021 and November 2022. All samples were sequenced using full-length 16S rRNA sequencing and compared with the Nucleotide Sequence Database. The salivary and fecal microbiota signature of colorectal polyps was established by alpha and beta diversity, Linear discriminant analysis Effect Size (LEfSe) and random forest model analysis. In addition, the possibility of microbiota in identifying colorectal polyps was assessed by Receiver Operating Characteristic Curve (ROC).

Results

In comparison to the HC group, the CP group's microbial diversity increased in saliva and decreased in feces (p < 0.05), but there was no significantly difference in microbiota richness (p > 0.05). The principal coordinate analysis revealed significant differences in β-diversity of salivary and fecal microbiota between the CP and HC groups. Moreover, LEfSe analysis at the species level identified Porphyromonas gingivalis, Fusobacterium nucleatum, Leptotrichia wadei, Prevotella intermedia, and Megasphaera micronuciformis as the major contributors to the salivary microbiota, and Ruminococcus gnavus, Bacteroides ovatus, Parabacteroides distasonis, Citrobacter freundii, and Clostridium symbiosum to the fecal microbiota of patients with polyps. Salivary and fecal bacterial biomarkers showed Area Under ROC Curve of 0.8167 and 0.8051, respectively, which determined the potential of diagnostic markers in distinguishing patients with colorectal polyps from controls, and it increased to 0.8217 when salivary and fecal biomarkers were combined.

Conclusion

The composition and diversity of the salivary and fecal microbiota were significantly different in colorectal polyp patients compared to healthy controls, with an increased abundance of harmful bacteria and a decreased abundance of beneficial bacteria. A promising non-invasive tool for the detection of colorectal polyps can be provided by potential biomarkers based on the microbiota of the saliva and feces.

Oral antibiotics and mechanical bowel preparation for colorectal surgery: A prospective observational study of surgical site infection and microbial analysis

PURPOSE

Surgical site infections (SSIs) are common in colorectal surgery. Mechanical bowel preparation (MBP) in conjunction with oral antibiotics (OABs) have been shown to reduce SSI rates. It however is still unclear which OABs to use, and how this can be implemented in practice.

METHODS

This is a prospective observational study carried out in Swansea Bay University Health Board during 2019-2021, evaluating the introduction of OABs in a stepwise manner on the incidence of SSI in major colorectal surgery. A control group having MBP only was compared to two OAB groups: one group had MBP plus metronidazole only and the second MBP plus metronidazole and neomycin. A 30-day follow-up after surgery was ascertained via chart review and telephone contact. Logistic regression was performed to estimate the relation between OAB use and SSI, with adjustment for confounding. In a subset of patients, faecal samples were analysed through 16S rRNA amplicon sequencing before and after OAB treatment, depicting the impact of the gut microbiome.

RESULTS

In total 160 patients were analysed: 46 patients had MBP only, whilst 76 patients had MBP plus metronidazole only and 38 patients had MBP with metronidazole/neomycin. The SSI rate in the entire cohort was 33.8%, whilst the adjusted ORs for the single- and dual-OAB groups were 0.76 (95% CI: 0.17-1.81) and 0.50 (95% CI: 0.17-1.52). The microbial analysis demonstrated that the relative abundance for many bacterial genera was changed before and after OAB treatment, but no link with SSI development could be shown.

CONCLUSIONS

Introduction of OABs in conjunction with MBP in colorectal surgery is feasible, and may potentially lead to lower rates of SSI, as well as altering the community structure of the faecal microbiome. More research is needed, especially considering different OABs and mechanistic studies of the gut microbiome in the context of colorectal surgery.

Predicting potential microbe-disease associations based on multi-source features and deep learning

Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can affect tumorigenesis and metastasis by regulating the tumor microenvironment. However, there are still large gaps in the clinical observation of the microbiota in disease. Although biological experiments are accurate in identifying disease-associated microbes, they are also time-consuming and expensive. The computational models for effective identification of diseases related microbes can shorten this process, and reduce capital and time costs. Based on this, in the paper, a model named DSAE_RF is presented to predict latent microbe-disease associations by combining multi-source features and deep learning. DSAE_RF calculates four similarities between microbes and diseases, which are then used as feature vectors for the disease-microbe pairs. Later, reliable negative samples are screened by k-means clustering, and a deep sparse autoencoder neural network is further used to extract effective features of the disease-microbe pairs. In this foundation, a random forest classifier is presented to predict the associations between microbes and diseases. To assess the performance of the model in this paper, 10-fold cross-validation is implemented on the same dataset. As a result, the AUC and AUPR of the model are 0.9448 and 0.9431, respectively. Furthermore, we also conduct a variety of experiments, including comparison of negative sample selection methods, comparison with different models and classifiers, Kolmogorov-Smirnov test and t-test, ablation experiments, robustness analysis, and case studies on Covid-19 and colorectal cancer. The results fully demonstrate the reliability and availability of our model.

The Microbiome-TIME Axis: A Host of Possibilities

Cancer continues to be a significant source of mortality and morbidity worldwide despite progress in cancer prevention, early detection, and treatment. Fortunately, immunotherapy has been a breakthrough in the treatment of many cancers. However, the response to immunotherapy treatment and the experience of associated side effects varies significantly between patients. Recently, attention has been given to understanding the role of the tumor immune microenvironment (TIME) in the development, progression, and treatment response of cancer. A new understanding of the role of the microbiota in the modulation of the TIME has further complicated the story but also unlocked a new area of adjuvant therapeutic research. The complex balance of tumor-permissive and tumor-suppressive immune environments requires further elucidation in order to be harnessed as a therapeutic target. Because both the TIME and the microbiome show importance in these areas, we propose here the concept of the "microbiome-TIME axis" to review the current field of research and future directions.

Effects of chemotherapy and immunotherapy on microbial diversity in TME and engineered bacterial-mediated tumor therapy

Tumor microbiota is a group of microorganisms located in tumor tissues with rich diversity that can promote tumorigenesis and development, and different types of tumors have different tumor microbiotas, which has important implications for tumor research, detection, and clinical treatment. In this review, we examine the diversity of the tumor microbiota, discuss the impact of chemotherapy and immunotherapy on tumor microbiota diversity, and summarize recent advances in the use of genetically engineered bacteria for the treatment of tumors. In addition, we propose key questions that need to be further addressed by the tumor microbiota.

Colorectal cancer and gut microbiota studies in China

Colorectal cancer (CRC) is the third most common malignant tumor worldwide. The incidence and mortality rates of CRC have been increasing in China, possibly due to economic development, lifestyle, and dietary changes. Evidence suggests that gut microbiota plays an essential role in the tumorigenesis of CRC. Gut dysbiosis, specific pathogenic microbes, metabolites, virulence factors, and microbial carcinogenic mechanisms contribute to the initiation and progression of CRC. Gut microbiota biomarkers have potential translational applications in CRC screening and early diagnosis. Gut microbiota-related interventions could improve anti-tumor therapy's efficacy and severe intestinal toxic effects. Chinese researchers have made many achievements in the relationship between gut microbiota and CRC, although some challenges remain. This review summarizes the current evidence from China on the role of gut microbiota in CRC, mainly including the gut microbiota characteristics, especially Fusobacterium nucleatum and Parvimonas micra, which have been identified to be enriched in CRC patients; microbial pathogens such as F. nucleatum and enterotoxigenic Bacteroides fragilis, and P. micra, which Chinese scientists have extensively studied; diagnostic biomarkers especially F. nucleatum; therapeutic effects, including microecological agents represented by certain Lactobacillus strains, fecal microbiota transplantation, and traditional Chinese medicines such as Berberine and Curcumin. More efforts should be focused on exploring the underlying mechanisms of microbial pathogenesis of CRC and providing novel gut microbiota-related therapeutic and preventive strategies.