Colon adenocarcinoma (COAD) is one of the most frequent types of cancer worldwide. Disulfidptosis has been identified as a new mode of cell death recently. The goal of this study was to explore the possibility of a connection between disulfidptosis and COAD. RNA sequencing data from COAD patients were retrieved from the The Cancer Genome Atlas (TCGA) database for this investigation. R software and various methods were used to identify disulfidptosis-related lncRNAs (DRLs) in COAD, and a prognostic model was created based on 6 DRLs (AP003555.1, AL683813.1, SNHG7, ZEB1-AS1, AC074212.1, RPL37A-DT). The prognostic model demonstrated a good accuracy in predicting the prognosis of COAD patients, according to receiver operating characteristic (ROC) curve and Concordance index (C-index) analyses. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed significant differences in biological functions and signaling pathways involved in differential genes in risk subgroups, including protein − DNA complex subunit organization, Hippo signaling pathway, Wnt signaling pathway. TIDE analysis was done on risk groupings in this study, and it found that patients in the high-risk group had more immune escape potential and were less probable to react to immunotherapy. Real-time quantitative pcr (qRT-PCR) was used to identify the relatively high expression of 6 DRLs in colon cancer cell lines. In summary, 6 DRLs were identified as possible novel molecular therapy targets for COAD in this investigation. This prognostic model has the potential to be a novel tool for forecasting COAD prognosis in clinical practice, as well as providing new insights on the potential function and mechanism of disulfidptosis in the COAD process.

Ulcerative colitis (UC) is one of the high‐risk pathogenic factors for colorectal cancer (CRC). However, the shared gene and signalling mechanisms between UC and CRC remain unclear. The goal of this study was to delve more into the probable causal relationship between UC and CRC. CRC and UC datasets were downloaded from the Gene Expression Omnibus database. Using R software and Perl, differentially expressed genes (DEGs) in both UC and CRC tissues were re‐annotated and screened. The biological activities and signalling pathways involved in DEGs were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The STRING database and Cytoscape software were used to construct the gene interaction network. A total of 384 DEGs were selected for further investigation, and functional analysis revealed that inflammatory and immunological responses were crucial in the development of the two diseases. Moreover, the top 15 key genes involved in the UC and CRC were screened using cytoHubba, including IL1B, CXCL10, CCL20, MMP9, ICAM1, CCL4, CXCR1, MMP3, TLR2, PTGS2, IL1RN, IL6, COL1A2, TIMP1 and CXCL1. The identification of these genes in the present study may provide a novel perspective for the prediction, prevention and personalized medicine of UC and CRC patients.

Histone lysine demethylases (KDMs) have been reported in various malignances, which affect transcriptional regulation of tumor suppressor or oncogenes. However, the relationship between KDMs and formation of tumor microenvironment (TME) in gastric cancer (GC) remain unclear and need to be comprehensively analyzed.In the present study, 24 KDMs were obtained and consensus molecular subtyping was performed using the "NMF" method to stratify TCGA-STAD into three clusters. The ssGSEA and CIBERSORT algorithms were employed to assess the relative infiltration levels of various cell types in the TME. The KDM_score was devised to predict patient survival outcomes and responses to both immunotherapy and chemotherapy.Three KDM genes-related molecular subtypes were Figured out in GC with distinctive clinicopathological and prognostic features. Based on the robust KDM genes-related risk_score and nomogram, established in our work, GC patients’ clinical outcome can be well predicted. Furthermore, low KDM genes-related risk_score exhibited the more effective response to immunotherapy and chemotherapy.This study characterized three KDM genes-related TME pattern with unique immune infiltration and prognosis by comprehensively analyses of transcriptomic profiling. Risk_score was also built to help clinicians decide personalized anticancer treatment for GC patients, including in prediction of immunotherapy and chemotherapy response for patients.

Background Ulcerative colitis (UC) is one of the high-risk pathogenic factors for colorectal cancer (CRC). However, the underlying molecular mechanisms of ulcerative colitis-associated colorectal cancer (UC-CRC) remain unclear. Therefore, identifying novel biomarkers and therapeutic targets in the evolution of UC-CRC from a predictive, preventive, and personalized medicine (PPPM) perspective is of great significance. Methods CRC and UC datasets were downloaded from the Gene Expression Omnibus database. Using R software and Perl, differentially expressed genes (DEGs) in both UC and CRC tissues were re-annotated and screened. The biological activities and signaling pathways involved in DEGs were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The STRING database and Cytoscape software were used to construct the gene interaction network. Results A total of 384 DEGs were selected for further investigation, and functional analysis revealed that inflammatory and immunological responses were crucial in the development of the two diseases. Moreover, the top 15 key genes involved in the UC-CRC were screened using cytoHubba, including IL1B, CXCL10, CCL20, MMP9, ICAM1, CCL4, CXCR1, MMP3, TLR2, PTGS2, IL1RN, IL6, COL1A2, TIMP1, and CXCL1. Conclusion The identification of these genes in the present study may provide a novel perspective for the prediction, prevention, and personalized medicine of UC and CRC patients.

Purpose To analyze the risk factors of adult patients with type 2 diabetes mellitus complicated with hypertension in Jiangsu province, and establish an individualized risk prediction model of hypertension. Methods We analyzed 485 newly diagnosed patients with type 2 diabetes mellitus from 2020 to 2021 in Lianshui County People's Hospital, Huai'an City, Jiangsu province, China. According to the ratio of 7:3, they were randomly divided into training group and validation group. After primary screening by univariate analysis, the predicted variables were determined by multivariate analysis. The nomogram model of hypertension was constructed and evaluated by receiver operating characteristic curve (ROC curve), calibration curve and decision curve analysis(DCA). Results This study investigated 485 people, and the prevalence of hypertension was 56.90%. Residential area, systolic blood pressure, family history of diabetes and uric acid are independent risk factors of hypertension among adults in Jiangsu province (P < 0.05). The area under ROC curve (AUC) of hypertension risk prediction model in training group and validation group were 0.7401 and 0.7392, respectively, and the calibration curves showed excellent consistency. DCA shows that the training group shows a great positive rate of return in the risk range of 13%~57% and 58%~100%. Conclusions The prediction model based on the related risk factors of hypertension among adult residents in Jiangsu province has excellent accuracy and clinical application value. It can provide a more intuitive way to assess the risk of diabetic patients with hypertension, and has guiding significance for the prevention and treatment of hypertension.