The prognostic significance of epoxide hydrolases in colorectal cancer.

Colorectal cancer (CRC) is a common malignant cancer. Epoxide hydrolases (EHs) are involved in the development of cancer by regulating epoxides, but their relationship with CRC is unclear. We used multiple datasets to confirm the expression of different EPHX family members in CRC tissues, and to explore their association with different clinicopathologic characteristics.

Prognostic cellular senescence-related lncRNAs patterns to predict clinical outcome and immune response in colon cancer.

Background: Cellular senescence (CS) is believed to be a major factor in the evolution of cancer. However, CS-related lncRNAs (CSRLs) involved in colon cancer regulation are not fully understood. Our goal was to create a novel CSRLs prognostic model for predicting prognosis and immunotherapy and exploring its potential molecular function in colon cancer.

Exploration of bacterial lipopolysaccharide-related genes signature based on T cells for predicting prognosis in colorectal cancer.

Purpose: The intratumoral microorganisms participates in the progression and immunotherapy of colorectal cancer (CRC). However, due to technical limitations, the impact of microorganisms on CRC has not been fully understood. Therefore, we conducted a systematic analysis of relationship between bacterial lipopolysaccharide (LPS)-associated genes and immune cells to explore new biomarkers for predicting the prognosis of CRC.

Constructing an immune-related prognostic signature for predicting prognosis and immune response in hepatocellular carcinoma.

Background: Currently, there are few studies on immune-related prognostic analysis of hepatocellular carcinoma (HCC). Our aim was to establish an immune-correlated prognostic model for HCC.

Methods: Immune-associated cells were obtained from the scRNA-seq dataset (GSE149614) of HCC.

Direct prediction of antimicrobial resistance in by metagenomic next-generation sequencing.

Objective: has strong drug resistance and can tolerate a variety of antibiotics, which is a major problem in the management of antibiotic-resistant infections. Direct prediction of multi-drug resistance (MDR) resistance phenotypes of isolates and clinical samples by genotype is helpful for timely antibiotic treatment.

Methods: In the study, whole genome sequencing (WGS) data of 494 isolates were used to screen key anti-microbial resistance (AMR)-associated genes related to imipenem (IPM), meropenem (MEM), piperacillin/tazobactam (TZP), and levofloxacin (LVFX) resistance in by comparing genes with copy number differences between resistance and sensitive strains.

Identifying important microbial biomarkers for the diagnosis of colon cancer using a random forest approach.

Colon cancer is one of the most common cancers, with 30-50 % of patients returning or metastasizing within 5 years of treatment. Increasingly, researchers have highlighted the influence of microbes on cancer malignant activity, while no studies have explored the relationship between colon cancer and the microbes in tumors. Here, we used tissue and blood samples from 67 colon cancer patients to identify pathogenic microorganisms associated with the diagnosis and prediction of colon cancer and evaluate the predictive performance of each pathogenic marker and its combination based on the next-generation sequencing data by using random forest algorithms.

Ferroptosis-related genes are considered as potential targets for CPAP treatment of obstructive sleep apnea.

Obstructive sleep apnea (OSA) is a common syndrome characterized by upper airway dysfunction during sleep. Continuous positive airway pressure (CPAP) is the most frequently utilized non-surgical treatment for OSA. Ferroptosis play a crucial role in the physiological diseases caused by chronic intermittent hypoxia, but its involvement in the development of OSA and the exact mechanisms have incompletely elucidated.

Comparative analyses of the prognosis, tumor immune microenvironment, and drug treatment response between left-sided and right-sided colon cancer by integrating scRNA-seq and bulk RNA-seq data.

Background: In this study, we compared the prognosis, tumor immune microenvironment (TIM), and drug treatment response between left-sided (LCC) and right-sided (RCC) colon cancer to predict outcomes in patients with LCC and RCC.

Methods: Based on identified differentially expressed genes and using single-cell RNA sequencing data, we constructed and validated a prognostic model for LCC and RCC patients in the TCGA-COAD cohort and GSE103479 cohort. Moreover, we compared the differences of TIM characteristics and drug treatment response between LCC and RCC patients.

Identification of m6A-related lncRNAs as prognostic signature within colon tumor immune microenvironment.

Background: The current study probed prognosis-related potential for m6A-related lncRNAs signatures within colon tumor immune microenvironment (TIM).

Methods: After downloading transcriptomic datasets for colon cancer (CC) patients from The Cancer Genome Atlas (TCGA), they were divided, in a 1:1 ratio, within training or test datasets. m6A-related lncRNAs were then scrutinized across such dataset using Pearson correlation assessment before generating a m6A-related lncRNAs prognosis-related model using the training dataset.

Exploring immune-related signatures for predicting immunotherapeutic responsiveness, prognosis, and diagnosis of patients with colon cancer.

The present study focused on identifying the immune-related signatures and exploring their performance in predicting the prognosis, immunotherapeutic responsiveness, and diagnosis of patients with colon cancer. Firstly, the immunotherapeutic response-related differential expressed genes (DEGs) were identified by comparing responders and non-responders from an anti-PD-L1 cohort using the edgeR R package. Then, the immunotherapeutic response related DEGs was intersected with immune-related genes (IRGs) to obtain the immunotherapeutic response and immune-related genes (IRIGs).

Exploring Immune-Related Prognostic Signatures in the Tumor Microenvironment of Colon Cancer.

Colon cancer is a common malignant tumor with poor prognosis. The aim of this study is to explore the immune-related prognostic signatures and the tumor immune microenvironment of colon cancer. The mRNA expression data of TCGA-COAD from the UCSC Xena platform and the list of immune-related genes (IRGs) from the ImmPort database were used to identify immune-related differentially expressed genes (DEGs).

Construction of a novel methylation-related prognostic model for colorectal cancer based on microsatellite status.

The present study aimed to construct a novel methylation-related prognostic model based on microsatellite status that may enhance the prognosis of colorectal cancer (CRC) from methylation and microsatellite status perspective. DNA methylation and mRNA expression data with clinical information were downloaded from The Cancer Genome Atlas (TCGA) data set. The samples were divided into microsatellite stability and microsatellite instability group, and CIBERSORT was used to assess the immune cell infiltration characteristics.