Interplay of chronic obstructive pulmonary disease and colorectal cancer development: unravelling the mediating role of fatty acids through a comprehensive multi-omics analysis.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) patients often exhibit gastrointestinal symptoms, A potential association between COPD and Colorectal Cancer (CRC) has been indicated, warranting further examination. METHODS: In this study, we collected COPD and CRC data from the National Health and Nutrition Examination Survey, genome-wide association studies, and RNA sequence for a comprehensive analysis. We used weighted logistic regression to explore the association between COPD and CRC incidence risk. Mendelian randomization analysis was performed to assess the causal relationship between COPD and CRC, and cross-phenotype meta-analysis was conducted to pinpoint crucial loci. Multivariable mendelian randomization was used to uncover mediating factors connecting the two diseases. Our results were validated using both NHANES and GEO databases. RESULTS: In our analysis of the NHANES dataset, we identified COPD as a significant contributing factor to CRC development. MR analysis revealed that COPD increased the risk of CRC onset and progression (OR: 1.16, 95% CI 1.01-1.36). Cross-phenotype meta-analysis identified four critical genes associated with both CRC and COPD. Multivariable Mendelian randomization suggested body fat percentage, omega-3, omega-6, and the omega-3 to omega-6 ratio as potential mediating factors for both diseases, a finding consistent with the NHANES dataset. Further, the interrelation between fatty acid-related modules in COPD and CRC was demonstrated via weighted gene co-expression network analysis and Kyoto Encyclopedia of Genes and Genomes enrichment results using RNA expression data. CONCLUSIONS: This study provides novel insights into the interplay between COPD and CRC, highlighting the potential impact of COPD on the development of CRC. The identification of shared genes and mediating factors related to fatty acid metabolism deepens our understanding of the underlying mechanisms connecting these two diseases.

Prioritizing drug targets in systemic lupus erythematosus from a genetic perspective: a druggable genome-wide Mendelian randomization study.

OBJECTIVES: Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with an unsatisfactory state of treatment. We aim to explore novel targets for SLE from a genetic standpoint. METHODS: Cis-expression quantitative trait loci (eQTLs) for whole blood from 31,684 samples provided by the eQTLGen Consortium as well as two large SLE cohorts were utilized for screening and validating genes causally associated with SLE. Colocalization analysis was employed to further investigate whether changes in the expression of risk genes, as indicated by GWAS signals, influence the occurrence and development of SLE. Targets identified for drug development were evaluated for potential side effects using a phenome-wide association study (PheWAS). Based on the multiple databases, we explored the interactions between drugs and genes for drug prediction and the assessment of current medications. RESULTS: The analysis comprised 5427 druggable genes in total. The two-sample Mendelian randomization (MR) in the discovery phase identified 20 genes causally associated with SLE and validated 8 genes in the replication phase. Colocalization analysis ultimately identified five genes (BLK, HIST1H3H, HSPA1A, IL12A, NEU1) with PPH4 > 0.8. PheWAS further indicated that drugs acting on BLK and IL12A are less likely to have potential side effects, while HSPA1A and NEU1 were associated with other traits. Four genes (BLK, HSPA1A, IL12A, NEU1) have been targeted for drug development in autoimmune diseases and other conditions. CONCLUSIONS: .This study identified five genes as therapeutic targets for SLE. Repurposing and developing drugs targeting these genes is anticipated to improve the existing treatment state for SLE. Key Points • We identified five gene targets of priority for the treatment of SLE, with BLK and IL12A indicating fewer side effects. • Among the existing drugs that target these candidate genes, Ustekinumab, Ebdarokimab, and Briakinumab (targeting the IL12 gene) and CD24FC (targeting HSPA1A) may potentially be repurposed for the treatment of SLE.

Prediction of distant metastasis and survival prediction of gastric cancer patients with metastasis to the liver, lung, bone, and brain: research based on the SEER database.

BACKGROUND: Gastric cancer (GC) is a globally important disease. It is the 5th most common malignancy and the 4th most common cause of death from cancer in the world. Patients with GC are often at an advanced stage when they are first diagnosed, and their overall prognosis is poor due to locally advanced and distant metastasis. This study sought to establish a predictive model of GC distant metastasis and survival that can be used to guide individualized treatment. METHODS: Patients diagnosed with GC from the Surveillance, Epidemiology, and End Results database were enrolled in the study. Univariate and multivariate logistic regression analyses were used to identify risk and prognostic factors for GC patients with distant metastasis. The factors were then used to construct nomograms to predict the probability of distant metastasis and the survival time of GC patients. Receiver operating characteristic (ROC) curve and decision curve analyses were used to verify the prediction ability of the nomograms. RESULTS: We established a comprehensive nomogram to predict the survival time of GC patients and 4 nomograms to predict distant metastasis. Nomograms could help oncologists to formulate treatment strategies and provide hospice care under an overall management model. CONCLUSIONS: Establishing a prediction model for distant metastasis and the survival of GC patients is of great clinical significance. The prediction of distant metastasis could help clinicians to make individualized assessments of patients and formulate individualized examination measures. Survival prediction models could help oncologists to formulate good treatment strategies and provide hospice care.

The establishment of a hub differential gene model to predict prognosis in stage II and III right- and left-sided colon cancer patients.

BACKGROUND: It is now recognized that the symptoms of colon cancer differ according to whether the tumor is located on the left or right side of the patient. The results of the present study point to the differences in the tissue and embryonic origins of left- and right-sided colon cancer that cause the variations in molecular typing. The research purpose of this study is to establish a core differential gene scoring model and proved its effect. METHODS: We downloaded transcriptome data and clinical information from The Cancer Genome Atlas (TCGA). A total of 243 patients in stages II and III were grouped according to the colon cancer site. Then we screened for differential transcriptome products. The corresponding differential gene were performing a corresponding protein interaction analysis. We used 12 algorithms in Cytoscape to calculate the hub genes and a total of 37 hub genes were obtained finally. We extracted the first principal component value (PC1) of the hub genes to evaluate the effectiveness of screening. Cox regression analysis was performed for the differential genes. Finally, we performed a prognostic analysis on right-sided colon cancer patients using the BST2 gene, PC1 and relevant clinical information. RESULTS: After screening for differentially expressed genes, 37 hub genes were obtained with appropriate algorithms. PC1 showed differences in hub genes between left- and right-sided colon cancer patients. BST2 and 31 other genes were identified as significant by Cox regression analysis and were significantly mutated in patients with right-sided colon cancer. Finally, we selected the BST2 gene and relevant clinical information as the prognostic factors to build a scoring model. The prediction effect of the model was satisfied. CONCLUSIONS: We constructed a prognostic model based on BST2, PC1, and other relevant clinical information and proved its good effect.