A meta-analysis of tissue microbial biomarkers for recurrence and metastasis in multiple cancer types

Background. Local recurrence and distant metastasis are the main causes of death in patients with cancer. Only considering species abundance changes when identifying markers of recurrence and metastasis in patients hinders finding solutions.Hypothesis. Consideration of microbial abundance changes and microbial interactions facilitates the identification of microbial markers of tumour recurrence and metastasis.Aim. This study aims to simultaneously consider microbial abundance changes and microbial interactions to identify microbial markers of recurrence and metastasis in multiple cancer types.Method. One thousand one hundred and six non-RM (patients without recurrence and metastasis within 3 years after initial surgery) tissue samples and 912 RM (patients with recurrence or metastasis within 3 years after initial surgery) tissue samples representing 11 cancer types were collected from The Cancer Genome Atlas (TCGA).Results. Tumour tissue bacterial composition differed significantly among 11 cancers. Among them, the tissue microbiome of four cancers, head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD) and uterine corpus endometrial carcinoma (UCEC), showed relatively good performance in predicting recurrence and metastasis in patients, with areas under the receiver operating characteristic curve (AUCs) of 0.78, 0.74, 0.91 and 0.93, respectively. Considering both species abundance changes and microbial interactions for the four cancers, a combination of nine genera (Niastella, Schlesneria, Thioalkalivibrio, Phaeobacter, Sphaerotilus, Thiomonas, Lawsonia, Actinobacillus and Spiroplasma) performed best in predicting patient survival.Conclusion. Taken together, our results imply that comprehensive consideration of microbial abundance changes and microbial interactions is helpful for mining bacterial markers that carry prognostic information.

Intratumoral Microbiota-Host Interactions Shape the Variability of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma in Recurrence and Metastasis

Differences in tissue microbiota-host interaction between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) about recurrence and metastasis have not been well studied. In this study, we performed bioinformatics analyses to identify the genes and tissue microbes significantly associated with recurrence or metastasis. All lung cancer patients were divided into the recurrence or metastasis (RM) group and the nonrecurrence and nonmetastasis (non-RM) group according to whether or not they had recurred or metastasized within 3 years after the initial surgery. Results showed that there were significant differences between LUAD and LUSC in gene expression and microbial abundance associated with recurrence and metastasis. Compared with non-RM, the bacterial community of RM had a lower richness in LUSC. In LUSC, host genes significantly correlated with tissue microbe, whereas host-tissue microbe interaction in LUAD was rare. Then, we established a novel multimodal machine learning model based on genes and microbes to predict the recurrence and metastasis risk of a LUSC patient, which achieves an area under the curve (AUC) of 0.81. In addition, the predicted risk score was significantly associated with the patient's survival. IMPORTANCE Our study elucidates significant differences in RM-associated host-microbe interactions between LUAD and LUSC. Besides, the microbes in tumor tissue could be used to predict the RM risk of LUSC, and the predicted risk score is associated with patients' survival.

MLSpatial: A machine-learning method to reconstruct the spatial distribution of cells from scRNA-seq by extracting spatial features

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) technologies allow us to interrogate the state of an individual cell within its microenvironment. However, prior to sequencing, cells should be dissociated first, making it difficult to obtain their spatial information. Since the spatial distribution of cells is critical in a few circumstances such as cancer immunotherapy, we present MLSpatial, a novel computational method to learn the relationship between gene expression patterns and spatial locations of cells, and then predict cell-to-cell distance distribution based on scRNA-seq data alone.

RESULTS

We collected the drosophila embryo dataset, which contains both the fluorescence in situ hybridization (FISH) data and single cell RNA-seq (scRNA-seq) data of drosophila embryo. The FISH data provided the spatial position of 3039 cells and the expression of 84 genes for each cell. The scRNA-seq data contains the expressions of 8924 genes in 1297 high-quality cells with cell location unknown. For a comparison, we also collected the MERFISH data of 645 osteosarcoma cells with cell location and the expression status of 10,050 genes known. For each data, the cells were randomly divided into a training set and a test set, in the ratio of 7:3. The cell-to-cell distances our model extracted had a higher correspondence (i.e., correlation coefficient 0.99) with those of the real situation than those of existing methods in the FISH data of drosophila embryo. However, in the osteosarcoma data, our model captured the spatial relationship between cells, with a correlation of 0.514 to that of the real situation. We also applied the model trained using the FISH data of drosophila embryo into the single cell data of drosophila embryo, for which the real location of cells are unknown. The reconstructed pseudo drosophila embryo and the real embryo (as shown by the FISH data) had a high similarity in the spatial distribution of gene expression.

CONCLUSION

MLSpatial can accurately restore the relative position of cells from scRNA-seq data; however, the performance depends on the type of cells. The trained model might be useful in reconstructing the spatial distributions of single cells with only scRNA-seq data, provided that the scRNA-seq data and the FISH data are under similar background (i.e., the same tissue with similar disease background).

Bacterial biomarkers capable of identifying recurrence or metastasis carry disease severity information for lung cancer

Background

Local recurrence and distant metastasis are the main causes of death in patients with lung cancer. Multiple studies have described the recurrence or metastasis of lung cancer at the genetic level. However, association between the microbiome of lung cancer tissue and recurrence or metastasis remains to be discovered. Here, we aimed to identify the bacterial biomarkers capable of distinguishing patients with lung cancer from recurrence or metastasis, and how it related to the severity of patients with lung cancer.

Methods

We applied microbiome pipeline to bacterial communities of 134 non-recurrence and non-metastasis (non-RM) and 174 recurrence or metastasis (RM) samples downloaded from The Cancer Genome Atlas (TCGA). Co-occurrence network was built to explore the bacterial interactions in lung cancer tissue of RM and non-RM. Finally, the Kaplan–Meier survival analysis was used to evaluate the association between bacterial biomarkers and patient survival.

Results

Compared with non-RM, the bacterial community of RM had lower richness and higher Bray–Curtis dissimilarity index. Interestingly, the co-occurrence network of non-RM was more complex than RM. The top 500 genera in relative abundance obtained an area under the curve (AUC) of 0.72 when discriminating between RM and non-RM. There were significant differences in the relative abundances of Acidovorax, Clostridioides, Succinimonas, and Shewanella, and so on between RM and non-RM. These biomarkers played a role in predicting the survival of lung cancer patients and were significantly associated with lung cancer stage.

Conclusion

This study provides the first evidence for the prediction of lung cancer recurrence or metastasis by bacteria in lung cancer tissue. Our results highlights that bacterial biomarkers that distinguish RM and non-RM are also associated with patient survival and disease severity.

Identifying cancer tissue-of-origin by a novel machine learning method based on expression quantitative trait loci

Cancer of unknown primary (CUP) refers to cancer with primary lesion unidentifiable by regular pathological and clinical diagnostic methods. This kind of cancer is extremely difficult to treat, and patients with CUP usually have a very short survival time. Recent studies have suggested that cancer treatment targeting primary lesion will significantly improve the survival of CUP patients. Thus, it is critical to develop accurate yet fast methods to infer the tissue-of-origin (TOO) of CUP. In the past years, there are a few computational methods to infer TOO based on single omics data like gene expression, methylation, somatic mutation, and so on. However, the metastasis of tumor involves the interaction of multiple levels of biological molecules. In this study, we developed a novel computational method to predict TOO of CUP patients by explicitly integrating expression quantitative trait loci (eQTL) into an XGBoost classification model. We trained our model with The Cancer Genome Atlas (TCGA) data involving over 7,000 samples across 20 types of solid tumors. In the 10-fold cross-validation, the prediction accuracy of the model with eQTL was over 0.96, better than that without eQTL. In addition, we also tested our model in an independent data downloaded from Gene Expression Omnibus (GEO) consisting of 87 samples across 4 cancer types. The model also achieved an f1-score of 0.7-1 depending on different cancer types. In summary, eQTL was an important information in inferring cancer TOO and the model might be applied in clinical routine test for CUP patients in the future.

A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data

Purpose

Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency.

Methods

After deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set.

Results

Selecting features with around 800 genes for training, the R²-score of a 10-fold CV of training data can reach 96.38%, and the R²-score of test data can reach 83.3%.

Conclusion

These findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors’ location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions.

Extracellular vesicles promote esophageal cancer progression by delivering lncZEB1-AS1 between cells

OBJECTIVE

To explore the expression of extracellular vesicle-derived lncZEB1-AS1 in esophageal cancer and its role in esophageal cancer progression.

PATIENTS AND METHODS

The extracellular vesicles (EVs) from esophageal cancer patients (n = 26) and normal subjects (n = 26) were isolated by differential centrifugation. The expression of lncZEB1-AS1 in EVs was detected by Real-time PCR (polymerase chain reaction). The clinical data of normal subjects and patients were analyzed. In addition, the concentration of EVs and lncZEB1-AS1 in blood samples from normal subjects and esophageal cancer patients were assessed. After co-culture of esophageal cancer cell line EC109 and EVs with or without lncZEB1-AS1 knockdown, cell proliferation was detected by CCK-8 assay. The possible target microRNAs of lncZEB1-AS1 in cytoplasm were predicted with miRcode, followed by correlation analysis of lncZEB1-AS1 and miR-214. Through literature review, lncZEB1-AS1 positively regulates ZEB1 expression, which was consistent with our result.

RESULTS

Quantitative Real-time PCR showed that the serum levels of EVs and the content of lncZEB1-AS1 in EVs from esophageal cancer patients were significantly higher than those in normal controls. LncZEB1-AS1 was overexpressed in esophageal cancer cells co-cultured with EVs of esophageal cancer patients. CCK-8 results indicated that EC109 cells co-cultured with EVs of esophageal cancer patients had stronger proliferative capacity. miRcode showed that miR-214 ranked the first of microRNAs that lncZEB1-AS1 might target, and miR-214 expression was significantly increased after lncZEB1-AS1 knockdown in EC109. After overexpressing lncZEB1-AS1 in EC109 or co-culturing EVs of esophageal cancer patients with EC109 cells, we found that lncZEB1-AS1 positively regulates ZEB1. In contrast, interfering with the expression of lncZEB1-AS1 in esophageal cancer cell lines can effectively reduce the expression of ZEB1.

CONCLUSIONS

EVs in the peripheral blood from esophageal cancer patients promote esophageal cancer progression by delivering lncZEB1-AS1 to esophageal cancer cells and targeting miR-214.

Effects of habitat fragmentation on the genetic diversity and differentiation of Dendrolimus punctatus (Lepidoptera: Lasiocampidae) in Thousand Island Lake, China, based on mitochondrial COI gene sequences

Thousand Island Lake (TIL) is a typical fragmented landscape and an ideal model to study ecological effects of fragmentation. Partial fragments of the mitochondrial cytochrome oxidase subunit I gene of 23 island populations of Dendrolimus punctatus in TIL were sequenced, 141 haplotypes being identified. The number of haplotypes increased significantly with the increase in island area and shape index, whereas no significant correlation was detected between three island attributes (area, shape and isolation) and haplotype diversity. However, the correlation with number of haplotypes was no longer significant when the 'outlier' island JSD (the largest island) was not included. Additionally, we found no significant relationship between geographic distance and genetic distance. Geographic isolation did not obstruct the gene flow among D. punctatus populations, which might be because of the high dispersal capacity of this pine moth. Fragmentation resulted in the conversion of large and continuous habitats into isolated, small and insular patches, which was the primary effect on the genetic diversity of D. punctatus in TIL. The conclusion to emphasize from our research is that habitat fragmentation reduced the biological genetic diversity to some extent, further demonstrating the importance of habitat continuity in biodiversity protection.

Structural Comparison of Gene Relevance Networks for Breast Cancer Tissues in Different Grades

BACKGROUND

The breast is an important biological system of human with two distinct states, i.e. normal and tumoral. Research on breast cancer could be based on systematic modeling to contrast the system structures of these two states.

OBJECTIVE

We use mutual information for the construction of the gene network of breast tissues and normal tissues. These gene networks are analyzed, compared as well as classified. We also identify structural key genes that may play significant roles in the formation of breast cancer.

METHOD

Gene networks are constructed using with mutual information values. Four structural parameters, namely node degree, clustering coefficient, shortest path length and standard betweenness centrality, are used for analyzing the gene networks. Support vector machine is used to classify the gene networks into normal and disease states. Genes with standard betweenness centrality of greater than 0.3 are identified as possibly significant in the development of breast cancer.

RESULT

The classification of the gene networks into normal and disease states suggest that the vectors of parameters are linearly separable by any combinations of these four structural parameters. In addition, the six genes BAK1, RRAD, LCN2, EGFR, ZAP70 and FOSB are identified to possibly play significant roles in the formation of breast cancer.

CONCLUSION

In this work, four structural parameters have been generalized to the relevance networks. These parameters are found to distinguish gene networks of normal and cancerous breast tissues at different thresholds. In addition, the six genes identified may motivate further studies and research in breast cancer.

USP4 inhibits p53 and NF-κB through deubiquitinating and stabilizing HDAC2

Histone deacetylases (HDACs) are major epigenetic modulators involved in a broad spectrum of human diseases including cancers. As HDACs are promising targets of cancer therapy, it is important to understand the mechanisms of HDAC regulation. In this study, we show that ubiquitin-specific peptidase 4 (USP4) interacts directly with and deubiquitinates HDAC2, leading to the stabilization of HDAC2. Accumulation of HDAC2 in USP4-overexpression cells leads to compromised p53 acetylation as well as crippled p53 transcriptional activation, accumulation and apoptotic response upon DNA damage. Moreover, USP4 targets HDAC2 to downregulate tumor necrosis factor TNFα-induced nuclear factor (NF)-κB activation. Taken together, our study provides a novel insight into the ubiquitination and stability of HDAC2 and uncovers a previously unknown function of USP4 in cancers.

Lentivirus-mediated knockdown of tumor protein D52-like 2 inhibits glioma cell proliferation

TPD52L2 (tumor protein D52-like 2) is a member of TPD52 family which has been implicated in multiple human cancers. Recently, TPD52 protein was shown to be associated with several malignancies, but very little is known about the function of TPD52L2 in cancers, especially in glioma to date, and its roles in glioma occurrence and progression remain to be elucidated. In the present study, we employed lentivirus-mediated RNA interference (RNAi) to knock down TPD52L2 expression in human glioma cell line U251. We found that knockdown of TPD52L2 significantly not only inhibited cell proliferation and colony formation, but also induced G0/G1 cell cycle arrest in vitro. Taken together, these findings suggest that TPD52L2 might play an important role in glioma tumorigenesis.

JNK phosphorylation promotes natural degeneration of cervical endplate chondrocytes by down-regulating expression of ANK

BACKGROUND

Endplate degeneration leads to accelerated degeneration of the intervertebral disc. The importance of endplate chondrocytes in this process is unclear. Many cellular processes in chondrocytes are controlled by activated c-Jun N-terminal kinases (JNK) and protein kinase B (AKT). However, the involvement of their pathways in the degeneration process needs to be elucidated.

AIM

To study activation of JNK and AKT signaling pathways and their significance for degeneration of endplate chondrocytes, as well as involvement of progressive ankylosis protein (ANK) in this process.

MATERIALS AND METHODS

Rat primary chondrocytes were grown to confluence and subcultured until passage 4. Morphological appearances (microscope, hematoxylin & eosin staining, toluidine blue staining) and proliferation rates of cells (MTT test) were observed. Further, levels of type II collagen, aggrecan, phosphorylated JNK and AKT, total JNK, AKT and ANK were evaluated by qPCR, flow cytometry and Western blot assays. Furthermore, inhibition experiments with SP600125, the JNK inhibitor, were carried out in the passage 4 cells to assess the effects of the JNK pathway on natural degeneration of endplate chondrocytes.

RESULTS

The proliferative speed of endplate chondrocytes progressively decreased during passaging. Expressions of type II collagen and aggrecan were significantly decreased with cells at higher passages. Furthermore, phosphorylation of JNK, but not AKT, was significantly up-regulated and accompanied by reduced ANK expression. Inhibition of the JNK pathway increased expression of type II collagen, aggrecan and ANK and facilitated proliferation rates.

CONCLUSIONS

Phosphorylation of JNK promotes natural degeneration of cervical endplate chondrocytes, likely by down-regulating ANK expression.

Contrast-enhanced ultrasound of hepatocellular carcinoma: Correlation of washout time and angiogenesis

BACKGROUND

The use of the pure blood pool ultrasound contrast agent SonoVue (Bracco, Italy) with specific ultrasound imaging software has enabled the dynamic visualization of tumor microcirculation.

AIM

The present study was designed to investigate the washout time of hepatocellular carcinoma (HCC) and correlate it with angiogenesis parameters.

METHODS

Thirty-one surgically confirmed HCC cases were prospectively evaluated with contrast-enhanced ultrasound (CEUS), and parameters such as wash-in time, peak enhancement time and washout time were determined offline. We also calculated microvessel density and the percentage of microvessel area (MVA) and compared CEUS parameters between a well differentiated group and a poorly to moderately differentiated group. The Spearman rank order correlation method was used to analyze the relationship between washout time and angiogenesis parameters.

RESULTS

The washout time was longer in well differentiated HCC patients compared to those with poorly to moderately differentiated HCC (p < 0.05). In addition, the washout time of HCC was positively correlated with the percentage of MVA (r = 0.510).

CONCLUSIONS

Given that the percentage of MVA was positively correlated with tumor blood volume, washout time may be associated with HCC blood volume.

Association of HLA genes with ankylosing spondylitis in Han population of eastern China

Ankylosing spondylitis (AS) is a chronic inflammatory disorder with a multifactorial genetic basis. HLA-B27 was reported with the greatest susceptibility to AS but did not act alone. The aim of this study was to search for other gene(s) associated with AS independently of HLA-B27 using 13 microsatellite markers spanning 1.5 Mb from locus TAP1 to HLA-Cw and a single-nucleotide polymorphism marker within NFkappaBIL1 gene promoter. Genotyping for microsatellites was performed in 175 AS patients of eastern Chinese and 219 ethnically matched healthy controls using polymerase chain reaction with fluorescence-labelled primers, whereas the SNP marker was genotyped by direct DNA sequencing. Allele as well as haplotype frequencies were compared between cases and controls, and a linkage disequilibrium analysis was performed to estimate the LD relationship between the candidate regions. The frequencies of alleles D6S2811*128, STR_MICA*A5.1 and D6S2672*109, as well as haplotypes D6S2811*128-D6S2927*213-D6S2810*340, D6S2927* 221-D6S2810*350-MICA*A5.1, and D6S2810*350-MICA*A5.1-D6S2800* 136 were significantly increased in B27-positive AS patients when compared with B27-positive controls. The results indicated that there may be other gene(s) within the HLA region, especially around locus HLA-B or HLA-Cw, with susceptibility to AS independently of HLA-B27.

Effects of a Single Nucleotide Polymorphism on the Expression of Human Tumor Necrosis Factor-alpha

Tumor necrosis factor (TNF)-alpha plays a prominent role in inflammations and is a proinflammatory cytokine that has been implicated in the pathogenesis of autoimmune and infectious diseases. Recent association studies have found that the TNF-alpha-857T allele was associated with several disorders. Here we demonstrate, with reporter genes under the control of the two allelic TNF-alpha promoters, that the minor allele -857T is a much stronger transcriptional activator than the major allele -857C in RAW264.7 cell line in response to lipopolysaccharide stimulation. However, the result was not consistent in HeLa cell line. Furthermore, for the quantitative analysis of TNF-alpha synthesis between the -857C/C genotype from healthy subjects and the -857C/T genotype from AS patients, the quantitative reverse transcription-polymerase chain reaction and enzyme-linked immunosorbent assay were performed separately. There was no significant difference between the two groups at the level of mRNA and protein. These results show that this polymorphism may have a direct effect on TNF-alpha regulation in a tissue-specific manner, and apart from the polymorphism at -857 in the TNF-alpha promoter, there may be other factors affecting the expression of TNF-alpha.

Separation and determination of ephedrine alkaloids and tetramethylpyrazine in Ephedra sinica Stapf by gas chromatography-mass spectrometry

A simple, sensitive, and reliable method using gas chromatography (GC)-mass spectrometry (MS) is developed for the simultaneous determination of ephedrine alkaloids and 2,3,5,6-tetramethylpyrazine (TMP) in Ephedra sinica Stapf. The sample is extracted with ethyl ether and submitted to GC-MS for identification and quantitation without derivatization. The column used for GC is an HP-5 (30.0 m x 250 microm x 0.25 microm, 5% phenyl methyl siloxane), and the carrier gas is helium. The detection limits for ephedrine, pseudoephedrine, and TMP are 0.4 ng 0.7 ng, and 0.02 ng (signal-to-noise ratio of 3), respectively. The reproducibility of the total procedure is proved to be acceptable (RSD < 2%), and the recoveries are above 93%.

Analysis of tetramethylpyrazine in Ephedrae herba by gas chromatography-mass spectrometry and high-performance liquid chromatography

A simple and reliable HPLC method was developed for the determination of 2,3,5,6-tetramethylpyrazine (TMP) in Ephedrae herba. Further identification of TMP was achieved using GC-MS. The mobile phase used was methanol-water-35% acetic acid (35:65:0.5, v/v/v) at a flow-rate of 0.8 ml/min. The detection wavelength was set at 290 nm. The linear range of the peak area calibration curve of TMP was 2.64-264 mg/l (r=0.9987) and the recovery for TMP in Ephedrae herba extracts was 101.1-106.9%. The relative standard deviations of retention time and peak area were 0.18 and 1.5% (n=6), respectively. The detection limit of TMP was 0.03 mg/l. The contents of TMP in Ephedrae herba could easily be determined within 10 min.