BackgroundColon adenocarcinoma (COAD) is a fatal disease, and its cases are constantly increasing worldwide. Further, the therapeutic and management strategies for patients with COAD are still unsatisfactory due to the lack of accurate patient classification and prognostic models. Therefore, our study aims to identify prognostic markers in patients with COAD and construct a cell subtype-specific prognostic model with high accuracy and robustness.MethodsSingle-cell transcriptomic data of six samples were retrieved from the Gene expression omnibus (GEO) database. The cluster annotation and cell-cell communication analysis identified enterocytes as a key player mediating signal communication networks. A four-gene signature prognostic model was constructed based on the enterocyte-related differentially expressed genes (ERDEGs) in patients with COAD of the Cancer Genome Atlas cohort. The prognostic model was validated on three external validation cohorts from the GEO database. The correlation between immune cell infiltration, immunotherapy response, drug sensitivity, and the four-gene signature prognostic model was investigated. Finally, immunohistochemistry (IHC) was performed to determine the expression of the four genes.ResultsWe found that the proportion of epithelial cells was obviously large in COAD samples. Further analysis of epithelial cells showed that the activity of the enterocytes was highest in the cell-cell communication network. Based on enterocyte data, 30 ERDEGs were identified and a 4-gene prognostic model including CPM, CLCA4, ELOVL6, and ATP2A3 was developed and validated. The risk score derived from this model was considered as an independent variable factor to predict overall survival. The patients were divided into high- and low-risk groups based on the median riskscore value. The correlation between immune cell infiltration, immunotherapy response, immune status, clinical characteristics, drug sensitivity, and risk score was analyzed. IHC confirmed the expression of signature genes in tissues from normal individuals, patients with polyps, and COAD.ConclusionIn this study, we constructed and validated a novel four-gene signature prognostic model, which could effectively predict the response to immunotherapy and overall survival in patients with COAD. More importantly, this model provides useful insight into the management of colon cancer patients and aids in designing personalized therapy.

Background: The plasma membrane provides a highly dynamic barrier for cancer cells to interact with their surrounding microenvironment. Membrane tension, a pivotal physical property of the plasma membrane, has attracted widespread attention since it plays a role in the progression of various cancers. This study aimed to identify a prognostic signature in colon cancer from membrane tension-related genes (MTRGs) and explore its implications for the disease. Methods: Bulk RNA-seq data were obtained from The Cancer Genome Atlas (TCGA) database, and then applied to the differentially expressed gene analysis. By implementing a univariate Cox regression and a LASSO-Cox regression, we developed a prognostic model based on four MTRGs. The prognostic efficacy of this model was evaluated in combination with a Kaplan–Meier analysis and receiver operating characteristic (ROC) curve analysis. Moreover, the relationships between the signature and immune cell infiltration, immune status, and somatic mutation were further explored. Lastly, by utilizing single-cell RNA-seq data, cell type annotation, pseudo-time analysis, drug sensitivity, and molecular docking were implemented. Results: We constructed a 4-MTRG signature. The risk score derived from the model was further validated as an independent variable for survival prediction. Two risk groups were divided based on the risk score calculated by the 4-MTRG signature. In addition, we observed a significant difference in immune cell infiltration, such as subsets of CD4 T cells and macrophages, between the high- and low-risk groups. Moreover, in the pseudo-time analysis, TIMP1 was found to be more highly expressed with the progression of time. Finally, three small molecule drugs, elesclomol, shikonin, and bryostatin-1, exhibited a binding potential to TIMP-1. Conclusions: The novel 4-MTRG signature is a promising biomarker in predicting clinical outcomes for colon cancer patients, and TIMP1, a member of the signature, may be a sensitive regulator of the progression of colon cancer.

Background Epidemiological studies have indicated a potential link between the gut microbiome and autoimmune liver disease (AILD) such as autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC). The relationship between the gut microbiome and autoimmune liver disease is still uncertain due to confounding variables. In our study, we aim to shed light on this relationship by employing a two-sample Mendelian randomization approach. Methods We conducted a two-sample Mendelian randomization (MR) study using the R package "TwoSampleMR". The exposure data consisted of genetic variants associated with 194 bacterial traits obtained from the MiBioGen consortium. Summary statistics for AILD were obtained from the GWAS Catalog website. Furthermore, a series of sensitivity analyses were performed to validate the initial MR results. Results There were two, four and three bacteria traits associated with an increased risk of AIH. PBC, and PSC respectively. In contrast, there were five, two and five bacteria traits associated with a decreased risk for AIH, PBC and PSC. Notably, the genus_Clostridium_innocuum_group showed a negative association with AIH (OR = 0.67, 95% CI: 0.49–0.93), and the genus_Actinomyces was found to be genetically associated with a decreased risk of PSC (OR = 0.62, 95% CI: 0.42–0.90). Conclusions Our study identified the causal impact of specific bacterial features on the risk of AILD subtypes. Particularly, the genus_Clostridium_innocuum_group and the genus_Actinomyces demonstrated significant protective effects against AIH and PSC respectively. These findings provide further support for the potential use of targeted probiotics in the management of AILD.

Background: Plasma membrane provides a highly dynamic barriers for cancer cells to interact with their surrounding microenvironment. Membrane tension, a pivotal physical property of plasma membrane, has attracted more and more attention since it plays a role in the progression of various cancers. However, membrane tension related genes (MTRGs) involved in the regulation of colon cancer are not thoroughly understood. This study aimed to identify a prognostic MTRG signature in colon cancer and explore its implications for the disease.Methods: Bulk and Single-cell RNA-seq data and relevant clinical information were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. 44 membrane tension related genes (MTRGs) were obtained through literature. Analysis of differential expressed MTRGs was performed on the RNA-Seq data. By implementing a univariate Cox regression and a LASSO-Cox regression, we developed a prognostic model based on 4 MTRGs. We evaluated the prognostic efficacy of this model using Kaplan–Meier survival curve analysis and receiver operating characteristic (ROC) curve analysis. Moreover, the relationships between this signature and immune cell infiltration, immune status, somatic mutation, pseudo cell differentiation and drug sensitivity were further explored.Results: A 4-MTRG signature was constructed. Risk score derived from the model was further validated as an independent variable for survival prediction. Two risk groups were classified based on the risk score calculated by the 4-MTRG signature. Additionally, we observed a significant difference in immune cells infiltration, such as subsets of CD4 T cells and macrophages, between the high- and low groups. Moreover, in the pseudo-time analysis, TIMP1 was found to express higher as time goes on. Finally, three small molecule drugs, elesclomol, shikonin and bryostatin-1, exhibited binding potential to TIMP-1.Conclusion: The proposed 4-MTRG signature is a promising biomarker to predict clinical outcomes and therapeutic responses in colon cancer patients.