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.

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).

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.

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 R 2-score of a 10-fold CV of training data can reach 96.38%, and the R 2-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.

Novel Model for Cascading Failure Based on Degree Strength and Its Application in Directed Gene Logic Networks.

A novel model for cascading failures in a directed logic network based on the degree strength at a node was proposed. The definitions of in-degree and out-degree strength of a node were initially reconsidered, and the load at a nonisolated node was proposed as the ratio of in-degree strength to out-degree strength of the node. The cascading failure model based on degree strength was applied to the logic network for three types of cancer including adenocarcinoma of lung, prostate cancer, and colon cancer based on their gene expression profiles. In order to highlight the differences between the three networks by the cascading failure mechanism, we used the largest-scale cascades and the cumulative cascade probability to depict the damage. It was found that the cascading failures caused by hubs are usually larger. Furthermore, the result shows that propagations against the networks were correlated with the structures motifs of connected logical doublets. Finally, some genes were selected based on cascading failure mechanism. We believe that these genes may be involved in the occurrence and development of three types of cancer.

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.

Modeling Gene Networks in Saccharomyces cerevisiae Based on Gene Expression Profiles.

Detailed and innovative analysis of gene regulatory network structures may reveal novel insights to biological mechanisms. Here we study how gene regulatory network in Saccharomyces cerevisiae can differ under aerobic and anaerobic conditions. To achieve this, we discretized the gene expression profiles and calculated the self-entropy of down- and upregulation of gene expression as well as joint entropy. Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions. Four structural parameters such as average degree, average clustering coefficient, average shortest path, and average betweenness were used to compare the structure of the corresponding aerobic and anaerobic logic networks. Five genes were identified to be putative key components of the two energy metabolisms. Furthermore, community analysis using the Newman fast algorithm revealed two significant communities for the aerobic but only one for the anaerobic network. David Gene Functional Classification suggests that, under aerobic conditions, one such community reflects the cell cycle and cell replication, while the other one is linked to the mitochondrial respiratory chain function.