ABSTRACT We re-analyzed the data from a recent large-scale study that reported strong correlations between DNA signatures of microbial organisms and 33 different cancer types and that created machine-learning predictors with near-perfect accuracy at distinguishing among cancers. We found at least two fundamental flaws in the reported data and in the methods: (i) errors in the genome database and the associated computational methods led to millions of false-positive findings of bacterial reads across all samples, largely because most of the sequences identified as bacteria were instead human; and (ii) errors in the transformation of the raw data created an artificial signature, even for microbes with no reads detected, tagging each tumor type with a distinct signal that the machine-learning programs then used to create an apparently accurate classifier. Each of these problems invalidates the results, leading to the conclusion that the microbiome-based classifiers for identifying cancer presented in the study are entirely wrong. These flaws have subsequently affected more than a dozen additional published studies that used the same data and whose results are likely invalid as well. IMPORTANCE Recent reports showing that human cancers have a distinctive microbiome have led to a flurry of papers describing microbial signatures of different cancer types. Many of these reports are based on flawed data that, upon re-analysis, completely overturns the original findings. The re-analysis conducted here shows that most of the microbes originally reported as associated with cancer were not present at all in the samples. The original report of a cancer microbiome and more than a dozen follow-up studies are, therefore, likely to be invalid. Recent reports showing that human cancers have a distinctive microbiome have led to a flurry of papers describing microbial signatures of different cancer types. Many of these reports are based on flawed data that, upon re-analysis, completely overturns the original findings. The re-analysis conducted here shows that most of the microbes originally reported as associated with cancer were not present at all in the samples. The original report of a cancer microbiome and more than a dozen follow-up studies are, therefore, likely to be invalid.

Major data analysis errors invalidate cancer microbiome findings

We re-analyzed the data from a recent large-scale study that reported strong correlations between microbial organisms and 33 different cancer types, and that created machine learning predictors with near-perfect accuracy at distinguishing among cancers. We found at least two fundamental flaws in the reported data and in the methods: (1) errors in the genome database and the associated computational methods led to millions of false positive findings of bacterial reads across all samples, largely because most of the sequences identified as bacteria were instead human; and (2) errors in transformation of the raw data created an artificial signature, even for microbes with no reads detected, tagging each tumor type with a distinct signal that the machine learning programs then used to create an apparently accurate classifier. Each of these problems invalidates the results, leading to the conclusion that the microbiome-based classifiers for identifying cancer presented in the study are entirely wrong. These flaws have subsequently affected more than a dozen additional published studies that used the same data and whose results are likely invalid as well.

Abstract Solid tumors are complex entities that actively shape their microenvironment to create a supportive environment for their own growth. Angiogenesis and immune suppression are two key characteristics of this tumor microenvironment. Despite attempts to deplete tumor blood vessels using antiangiogenic drugs, extensive vessel pruning has shown limited efficacy. Instead, a targeted approach involving the judicious use of drugs at specific time points can normalize the function and structure of tumor vessels, leading to improved outcomes when combined with other anticancer therapies. Additionally, normalizing the immune microenvironment by suppressing immunosuppressive cells and activating immunostimulatory cells has shown promise in suppressing tumor growth and improving overall survival. Based on these findings, many studies have been conducted to normalize each component of the tumor microenvironment, leading to the development of a variety of strategies. In this review, we provide an overview of the concepts of vascular and immune normalization and discuss some of the strategies employed to achieve these goals. 

Normalization of the tumor microenvironment by harnessing vascular and immune modulation to achieve enhanced cancer therapy

The role of dysbiosis in the development and progression of oral potentially malignant disorders (OPMDs) remains largely unknown. Here, we aim to characterize and compare the oral microbiome of homogeneous leucoplakia (HL), proliferative verrucous leukoplakia (PVL), oral squamous cell carcinoma (OSCC), and OSCC preceded by PVL (PVL-OSCC). Fifty oral biopsies from HL (n = 9), PVL (n = 12), OSCC (n = 10), PVL-OSCC (n = 8), and healthy (n = 11) donors were obtained. The sequence of the V3–V4 region of the 16S rRNA gene was used to analyze the composition and diversity of bacterial populations. In the cancer patients, the number of observed amplicon sequence variants (ASVs) was lower and Fusobacteriota constituted more than 30% of the microbiome. PVL and PVL-OSCC patients had a higher abundance of Campilobacterota and lower Proteobacteria than any other group analyzed. A penalized regression was performed to determine which species were able to distinguish groups. HL is enriched in Streptococcus parasanguinis, Streptococcus salivarius, Fusobacterium periodonticum, Prevotella histicola, Porphyromonas pasteri, and Megasphaera micronuciformis; PVL is enriched in Prevotella salivae, Campylobacter concisus, Dialister pneumosintes, and Schaalia odontolytica; OSCC is enriched in Capnocytophaga leadbetteri, Capnocytophaga sputigena, Capnocytophaga gingivalis, Campylobacter showae, Metamycoplasma salivarium, and Prevotella nanceiensis; and PVL-OSCC is enriched in Lachnospiraceae bacterium, Selenomonas sputigena, and Prevotella shahii. There is differential dysbiosis in patients suffering from OPMDs and cancer. To the best of our knowledge, this is the first study comparing the oral microbiome alterations in these groups; thus, additional studies are needed.

/pdf/on-the-oral-microbiome-of-oral-potentially-malignant-and-1s4hngf7.pdf

On the Oral Microbiome of Oral Potentially Malignant and Malignant Disorders: Dysbiosis, Loss of Diversity, and Pathogens Enrichment

The potential role of cholesterol in Parkinson's disease neuropathology: perpetrator or victim.

Microbiome has been shown to substantially contribute to some cancers. However, the diagnostic implications of microbiome in head and neck squamous cell carcinoma (HNSCC) remain unknown.To identify the molecular difference in the microbiome of oral and non-oral HNSCC, primary data was downloaded from the Kraken-TCGA dataset. The molecular differences in the microbiome of oral and non-oral HNSCC were identified using the linear discriminant analysis effect size method.In the study, the common microbiomes in oral and non-oral cancers were Fusobacterium, Leptotrichia, Selenomonas and Treponema and Clostridium and Pseudoalteromonas, respectively. We found unique microbial signatures that positively correlated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in oral cancer and positively and negatively correlated KEGG pathways in non-oral cancer. In oral cancer, positively correlated genes were mostly found in prion diseases, Alzheimer disease, Parkinson disease, Salmonella infection, and Pathogenic Escherichia coli infection. In non-oral cancer, positively correlated genes showed Herpes simplex virus 1 infection and Spliceosome and negatively correlated genes showed results from PI3K-Akt signaling pathway, Focal adhesion, Regulation of actin cytoskeleton, ECM-receptor interaction and Dilated cardiomyopathy.These results could help in understanding the underlying biological mechanisms of the microbiome of oral and non-oral HNSCC. Microbiome-based oncology diagnostic tool warrants further exploration. 

/pdf/microbial-and-molecular-differences-according-to-the-1s0nrmpk.pdf

Microbial and molecular differences according to the location of head and neck cancers

Oxysterols are oxygenated derivatives of cholesterol that contain an additional hydroxy, epoxide, or ketone group in the sterol nucleus and/or a hydroxyl group in the side chain of the cholesterol molecule. 27-Hydroxycholesterol (27HC) is a side-chain oxysterol that is oxygenated at the 27th carbon atom of cholesterol. The oxysterol (27HC) is produced via oxidation by sterol 27-hydroxylase (CYP27A1) and metabolized via oxysterol 7a-hydroxylase (CYP7B1) for bile acid synthesis in the liver. A previous study has demonstrated that treatment with the alternative Estrogen receptor alpha (ERα) ligand 27HC induces ERα-dependent hematopoietic stem cell (HSC) mobilization. In addition, Cyp27a1-deficient mice demonstrate significantly reduced 27HC levels and HSC mobilization. Here, we report that exogenous 27HC treatment leads to a substantial reduction in the hematopoietic stem and progenitor cell (HSPC) population owing to significantly increased reactive oxygen species (ROS) levels and apoptosis in the bone marrow (BM). However, 27HC does not influence the population of mature hematopoietic cells in the BM. Furthermore, exogenous 27HC treatment suppresses cell growth and promotes ROS production and apoptosis in leukemic cells. Moreover, acute myeloid leukemia (AML) patients with high CYP7B1 expression (expected to have inhibition of 27HC) had significantly shorter survival than those with low CYP7B1 expression (expected to have an elevation of 27HC). Single-cell RNA-sequencing (scRNA seq) analysis revealed that the expression of CYP7B1 was significantly increased in AML patients. Thus, our study suggests that 27HC may serve as a potent agent for regulating pools of HSPCs and may have an application as a novel therapeutic target for hematological malignancies. Collectively, pharmacological inhibition of CYP7B1 (expected to have an elevation of 27HC) would potentially have fewer long-term hematological side effects, particularly when used in combination with chemotherapy or radiation for the treatment of leukemia patients. 

https://www.nature.com/articles/s41419-022-05360-0.pdf

Role of reactive oxygen species in regulating 27-hydroxycholesterol-induced apoptosis of hematopoietic progenitor cells and myeloid cell lines

Abstract Several derivatives derived from the oxime structure have been reported as potential anticancer agents in various cancers. Here, we first tested a novel oxime-containing derivative of 2-((2,4,5-trifluorobenzyl)oxy)benzaldehyde oxime (TFOBO) to evaluate its anticancer effect in myeloid leukemic cells. Compared to (2-((2,4,5-trifluorobenzyl)oxy)phenyl)methanol (TFOPM), the oxime derivative TFOBO suppresses leukemic cell growth by significantly increasing reactive oxygen species (ROS) levels and cell death. Leukemic cells treated with TFOBO displayed apoptotic cell death, as indicated by nuclear condensation, DNA fragmentation, and annexin V staining. TFOBO increases Bax/Bcl2 levels, caspase9, and caspase3/7 activity and decreases mitochondrial membrane potential. ROS production was reduced by N -acetyl- l -cysteine, a ROS scavenger, diphenyleneiodonium chloride, a nicotinamide adenine dinucleotide phosphate (NADPH) oxidase inhibitor, after exogenous TFOBO treatment. ROS inhibitors protect leukemic cells from TFOBO-induced cell death. Thus, our study findings suggest that TFOBO promotes apoptosis by modulating ROS and regulating NADPH oxidase activity. Collectively, the oxime-containing derivative TFOBO is a novel therapeutic drug for myeloid leukemia. 

/pdf/oxime-derivative-tfobo-promotes-cell-death-by-modulating-kyabaixy.pdf

Oxime derivative TFOBO promotes cell death by modulating reactive oxygen species and regulating NADPH oxidase activity in myeloid leukemia

The homing of M1 and M2 macrophages may play distinct roles in the tumor microenvironment (TME). However, these roles of macrophages in the TME remain unclear. We downloaded RNA sequencing data from The Cancer Genome Atlas (TCGA) database for patients with CRC. Subsequently, Kaplan-Meier survival curves were generated to assess the differential infiltration of M1 and M2 macrophages based on CRC location. Differentially expressed gene (DEG) and functional analyses were performed to screen the roles of DEGs. Critical prognostic genes were identified using least absolute shrinkage and selection operator regression. The risk scores were calculated for each patient. In patients with right-sided CRC, reduced M1 macrophage infiltration was associated with poor prognosis. M1 macrophage infiltration positively correlated with CD8+ T cell infiltration. A risk model was developed and validated for performance using GSE103479 and GSE72970. Nine genes were identified as independent prognostic genes that could be potential biomarkers for effectively predicting survival in patients with right-sided CRC. Kaplan-Meier curves for overall survival and progression-free survival analyses revealed that the high-risk group of patients with right-sided CRC had a poor prognosis. This novel M1 macrophage-related risk model may provide a gene signature for predicting the survival outcomes of patients with right-sided CRC and facilitate further studies examining the relationship between infiltration of M1 macrophages and the prognosis of such patients.

Risk stratification of patients with right-sided colorectal cancer based on the tumor-infiltrating M1 macrophage.

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Microbial and molecular differences according to the location of head and neck cancers

Role of reactive oxygen species in regulating 27-hydroxycholesterol-induced apoptosis of hematopoietic progenitor cells and myeloid cell lines

Oxime derivative TFOBO promotes cell death by modulating reactive oxygen species and regulating NADPH oxidase activity in myeloid leukemia

Risk stratification of patients with right-sided colorectal cancer based on the tumor-infiltrating M1 macrophage.