Gut microbiota differences in Island Hispanic Puerto Ricans and mainland non-Hispanic whites during chemoradiation for rectal cancer: A pilot study

Integrating clinical and cross-cohort metagenomic features: a stable and non-invasive colorectal cancer and adenoma diagnostic model

Background: Dysbiosis is associated with colorectal cancer (CRC) and adenomas (CRA). However, the robustness of diagnostic models based on microbial signatures in multiple cohorts remains unsatisfactory. Materials and Methods: In this study, we used machine learning models to screen metagenomic signatures from the respective cross-cohort datasets of CRC and CRA (selected from CuratedMetagenomicData, each disease included 4 datasets). Then select a CRC and CRA data set from the CuratedMetagenomicData database and meet the requirements of having both metagenomic data and clinical data. This data set will be used to verify the inference that integrating clinical features can improve the performance of microbial disease prediction models. Results: After repeated verification, we selected 20 metagenomic features that performed well and were stably expressed within cross-cohorts to represent the diagnostic role of bacterial communities in CRC/CRA. The performance of the selected cross-cohort metagenomic features was stable for multi-regional and multi-ethnic populations (CRC, AUC: 0.817-0.867; CRA, AUC: 0.766-0.833). After clinical feature combination, AUC of our integrated CRC diagnostic model reached 0.939 (95% CI: 0.932-0.947, NRI=30%), and that of the CRA integrated model reached 0.925 (95%CI: 0.917-0.935, NRI=18%). Conclusion: In conclusion, the integrated model performed significantly better than single microbiome or clinical feature models in all cohorts. Integrating cross-cohort common discriminative microbial features with clinical features could help construct stable diagnostic models for early non-invasive screening for CRC and CRA.

Gut Microbiome and Its Associations With Acute and Chronic Gastrointestinal Toxicities in Cancer Patients With Pelvic Radiation Therapy: A Systematic Review

Aim

Pelvic radiation therapy (RT) can impact the gut microbiome in patients with cancer and result in gastrointestinal (GI) toxicities. The purpose of this systematic review was to describe the effects of RT on the gut microbiome and the associations between the gut microbiome and GI toxicities in patients treated with pelvic RT.

Methods

PubMed, Embase, and Web of Science databases were searched from their earliest records to August 2020. The articles screening process adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Mixed Method Assessment Tool was used to assess the methodological quality for each included study. All study findings were synthesized and presented in narrative format. Thirteen studies were included. The gut microbiome of fecal samples was analyzed using 16S rRNA sequencing approaches.

Results

There were disparities in alpha and beta diversities that existed across the studies. Divergent results were found among various phyla, including Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Cyanobacteria, Fusobacteria, and Deinococcus-Thermus. Moreover, alteration in the gut microbiome diversity and abundance related to cancer treatment was associated with pelvic toxicities, specifically diarrhea. Following treatment, increases in the abundance of Bacteroides was associated with diarrhea and radiation enteritis.

Conclusions

Pelvic RT can disrupt the diversity and abundance of commensal gut microorganisms. A dysbiotic gut microbiome showed a promising association with radiation enteritis through alterations of the intestinal barrier function, innate immunity, and intestinal repair mechanisms; however, confounders, such as diet, were not thoroughly addressed.

Gut microbiota and fatigue in rectal cancer patients: a cross-sectional pilot study

CONTEXT

Although microbial-mediated disturbance of intestinal mucosal homeostasis (dysbiosis) is believed to contribute to the pathogenesis of chemotherapy and radiotherapy (CRT)-related fatigue, potential differences in the gut microbial diversity and in the abundance of gut microbial taxa between fatigued and non-fatigued patients have not been adequately examined, particularly in the rectal cancer population.

PURPOSE

In this cross-sectional study, we aim to examine the differences in (a) gut microbial diversity and gut microbial abundances and (b) predicted functional pathways of the gut microbiome between rectal cancer participants with and without fatigue at the end of CRT.

METHODS

Rectal cancer patients (n = 50) provided stool samples for 16S rRNA gene sequencing and symptom ratings for fatigue at the end of CRT. Gut microbiome data were analyzed using QIIME2, LEfSe, and the R statistical package.

RESULTS

Fatigued (n = 35) participants showed enriched bacterial abundances of Eubacterium, Streptococcus, Adlercreutzia, and Actinomyces, as well as enriched abundances of the microbial sucrose degradation pathway, compared to non-fatigued patients at the end of CRT (n = 15).

CONCLUSIONS

Differentially abundant microbial taxa were identified in fatigued and non-fatigued rectal cancer participants at the end of CRT. However, the exact role of these taxa (and identification of species) in the biology of CRT-related fatigue remains to be examined.

Gut Microbiota and Depressive Symptoms at the End of CRT for Rectal Cancer: A Cross-Sectional Pilot Study

BACKGROUND

The role of alterations in gut microbiota composition (termed dysbiosis) has been implicated in the pathobiology of depressive symptoms; however, evidence remains limited. This cross-sectional pilot study is aimed at exploring whether depressive symptom scores changed during neoadjuvant chemotherapy and radiation therapy to treat rectal cancer, and if gut microbial taxa abundances and predicted functional pathways correlate with depressive symptoms at the end of chemotherapy and radiation therapy.

METHODS

40 newly diagnosed rectal cancer patients (ages 28-81; 23 males) were assessed for depressive symptoms using the Hamilton Rating Scale for Depression (HAM-D) and provided stool samples for 16S rRNA sequencing. Gut microbiome data were analyzed using QIIME2, and correlations and regression analyses were performed in R.

RESULTS

Participants had significantly higher depressive symptoms at the end as compared to before CRT. The relative abundances of Gemella, Bacillales Family XI, Actinomyces, Streptococcus, Lactococcus, Weissella, and Leuconostocaceae were positively correlated (Spearman's rho = 0.42 to 0.32), while Coprobacter, Intestinibacter, Intestimonas, Lachnospiraceae, Phascolarctobacterium, Ruminiclostridium, Ruminococcaceae (UCG-005 and uncultured), Tyzzerella, and Parasutterella (Spearman's rho = -0.43 to - 0.31) were negatively correlated with HAM-D scores. Of the 14 predicted MetaCyc pathways that correlated with depressive symptom scores at the end of CRT, 11 (79%) were associated with biosynthetic pathways.

CONCLUSIONS

Significant bacterial taxa and predicted functional pathways correlated with depressive symptoms at the end of chemotherapy and radiation therapy for rectal cancer which warrants further examination and replication of our findings.

Role of Carbon Monoxide in Host-Gut Microbiome Communication

Nature is full of examples of symbiotic relationships. The critical symbiotic relation between host and mutualistic bacteria is attracting increasing attention to the degree that the gut microbiome is proposed by some as a new organ system. The microbiome exerts its systemic effect through a diverse range of metabolites, which include gaseous molecules such as H₂, CO₂, NH₃, CH₄, NO, H₂S, and CO. In turn, the human host can influence the microbiome through these gaseous molecules as well in a reciprocal manner. Among these gaseous molecules, NO, H₂S, and CO occupy a special place because of their widely known physiological functions in the host and their overlap and similarity in both targets and functions. The roles that NO and H₂S play have been extensively examined by others. Herein, the roles of CO in host-gut microbiome communication are examined through a discussion of (1) host production and function of CO, (2) available CO donors as research tools, (3) CO production from diet and bacterial sources, (4) effect of CO on bacteria including CO sensing, and (5) gut microbiome production of CO. There is a large amount of literature suggesting the "messenger" role of CO in host-gut microbiome communication. However, much more work is needed to begin achieving a systematic understanding of this issue.

Factors Affecting the Severity of Fatigue during Radiotherapy for Prostate Cancer; an Exploratory Study

INTRODUCTION

Limited studies have examined potential risk factors associated with the fatigue experience of a sample of Puerto Rican men treated with radiotherapy for non-metastatic prostate cancer. Identifying these factors may provide initial information about targets for individualized interventions, leading to more effective management of fatigue in this population.

PURPOSE

To examine the relationship of age, body max index, depressive symptoms, physical activity, and sleep disturbance with fatigue during radiotherapy for prostate cancer.

METHODS

Twenty six participants completed five inventories: demographic intake, health form, the Functional Assessment of Cancer-Therapy-fatigue, Patient-Reported Outcome Measures Information System-Sleep disturbance, and the International Physical Activity Questionnaire-Short Form before, middle/days 19-21 and completion/days 38-42 of radiotherapy. The principal investigator rated the Hamilton depression scale. Descriptive statistics were performed. Interactions and influence of variables on fatigue were assessed using bivariate correlation and multiple linear regression, respectively.

RESULTS

At each study time point, sleep disturbance and depressive symptoms were strongly correlated with each other and fatigue. The linear combination of sleep disturbance and depressive symptoms was significantly related to fatigue.

CONCLUSION

Given the high association of sleep disturbance and depressive symptoms with fatigue, clinicians should assess and develop interventions to manage these symptoms altogether.