Dynamic changes of the immune microenvironment in the development of gastric cancer caused by inflammation.

Precancerous lesions typically precede gastric cancer (GC), but the molecular mechanisms underlying the transition from these lesions to GC remain unclear. Therefore, it is urgent to understand this transition from precancerous lesions to GC, which is crucial for the early diagnosis and treatment of GC. In this study, we merged multiple single-cell RNA sequencing datasets to investigate the molecular changes in distinct cell types associated with the progression of GC.

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

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. Consideration of microbial abundance changes and microbial interactions facilitates the identification of microbial markers of tumour recurrence and metastasis.

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.

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.

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.

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.

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.

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.

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.

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.