Objective: The objective of this study was to identify key molecules including small nucleolar RNAs (snoRNAs) and small nucleolar RNA host genes (SNHGs) involved in pancreatic cancer.Methods: First, we screened the differentially expressed snoRNAs (DEsnoRNAs) and trend-related snoRNAs based on the cancer genome atlas (TCGA) dataset for pancreatic cancer, and then performed methylation correlation analysis, survival analysis, and extraction of snoRNA host genes for Gene ontology (GO) functional and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Next, DESNHGs and trend-related SNHGs were screened according to the TCGA dataset for pancreatic cancer, and a competing endogenous RNA (ceRNA) network was constructed for pathway and functional enrichment analysis. Results: A total of eight DEsnoRNAs and 93 trend-related snoRNAs were extracted. Then, ten host genes of the snoRNAs were identified. Functional analysis suggested that the ten host genes were significantly enriched in several GO terms including mitotic chromosome condensation and endocytosis pathway. SNORA38B was considered to associate with survival and prognosis. The SNORD17 and SNORA11 were considered to negatively correlate with methylation. In addition, two trend-related SNHGs were extracted. Additionally, a ceRNA network was constructed with 11 miRNAs, one lncRNAs, and one mRNA. SNHG24 mainly correlated with GnRH secretion and neuroactive ligand-receptor interaction in pancreatic cancer. Conclusion: The identified snoRNAs and SNHGs could serve as potential markers for the early detection of pancreatic cancer.
Objective : This study was designed to identify the differentially expressed mRNA, microRNA (miRNA), and long non-coding RNA (lncRNA) and their functions in pancreatic cancer (PC). Methods: The expression data of PC and normal samples were downloaded from the GEO database. The expression data of pancreatic head (H), body (B), and tail (T) were downloaded from the TCGA database. After data preprocessing, the differential analyses between PC vs. Normal, H vs. B, H vs. T, and T vs. B were performed. Overlapping genes between PC vs. Normal and the different locations (the union of genes among T vs. B, T vs. H, and B vs. H) were selected. The competing endogenous RNAs (ceRNA) network was constructed based on co-expression analysis and prediction of targets, followed by functional enrichment analysis. Construction of an mRNA prognosis risk model and screening of prognostic factors were performed using Cox univariate/multivariate regression analysis, followed by Nomogram model construction. Finally, the gene-drug interactions were predicted for the DE-mRNA. Results: A five-mRNA prognostic model (GRHL2+CACNA1A+GRM1+UPK1B+PKHD1) was constructed, and the risk score was relatively increased with the increased expression of the GRHL2, PKHD1, and UPK1B, and the decreased expression of CACNA1A and GRM1. Compared with pancreatic body/tail cancer, the expression of GRHL2 was increased, while the expression of CACNA1A and GRM1 was decreased in pancreatic head cancer. LncRNA AC006369.2-miR-146a-5p-CACNA1A/GRM1 was a regulatory axis in the ceRNA network. Verapamil was predicted to be an antagonist of CACNA1A. Conclusion: Our results provide a new direction for the accurate diagnosis and treatment of PC and for investigating the mechanism of PC.
Objective. To screen some RNAs that correlated with colorectal cancer (CRC). Methods. Differentially expressed miRNAs, lncRNAs, and mRNAs between cancer tissues and normal tissues in CRC were identified using data from the Gene Expression Omnibus (GEO) database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interactions (PPIs) were performed to do the functional enrichment analysis. And a lncRNA-miRNA-mRNA network was constructed which correlated with CRC. RNAs in this network were subjected to analyze the relationship with the patient prognosis. Results. A total of 688, 241, and 103 differentially expressed genes (diff-mRNA), diff-lncRNA, and diff-miRNA were obtained between cancer tissues and normal tissues. A total of 315 edges were obtained in the ceRNA network. lncRNA RP11-108K3.2 and mRNA ONECUT2 correlated with prognosis. Conclusion. The identified RNAs and constructed ceRNA network could provide great sources for the researches of therapy of the CRC. And the lncRNA RP11-108K3.2 and mRNA ONECUT2 may serve as a novel prognostic predictor of CRC.
Background: Colorectal cancer (CRC) is the third most common malignancy in the world and metastasis is responsible for a major proportion of the cancer-related deaths in CRC patients.Aims: To construct machine learning models for predicting lymph node and distant metastases in colorectal cancer and analyze biological functions features of metastasis-related genes.Methods: RNA-seq and miRNA-seq data as well as corresponding clinical data from colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed RNAs (DE-RNAs) in non-LNM (N0) and LNM (N1/N2) as well as non-distant metastases (M0) and distant metastases (M1) were analyzed. Six machine learning models including logistic regression (LR), random forest (RF), support vector machine (SVM), Catboost, gradient boosting decision tree (GBDT), and artificial neural network (NN) were constructed to predict cancer metastasis and the feature genes of the optimal model were further analyzed by functional enrichment, protein-protein interaction (PPI) network, and drug-target analyses.Results: Differential RNA expression profiles of LNM and non-LNM as well as M0 vs. M1 were observed in both COAD and READ samples. NN model was determined to be the optimal model for predicting distant metastases, while Catboost and LR models were the optimal models for predicting LNM in COAD and READ samples, respectively. PPI analysis indicated that KIR2DL4, chemokine-related genes CXCL9/10/11/13 and CCL25, and gamma-aminobutyric acid (GABA) receptor genes (GABRR1, GABRB2 and GABRA3) were key genes in metastasis. In addition, atorvastatin and eszopiclone were identified as potential therapeutic agents as they target these genes.Conclusions: We constructed six machine learning models for predicting colorectal cancer metastases and identify the optimal model. We analyzed biological functions features of metastasis-related RNAs in colorectal cancer.
Background: Patients with colorectal cancer (CRC) are at risk of malnutrition. Gut microbes and microbial metabolites are involved in the initiation and development of CRC.Purpose: To investigate serum protein levels in CRC patients and explore the role of gut microbes and microbial metabolites in CRCs complicated with different serum albumin levels.Methods: Overall, 398 CRC patients and same number of healthy volunteers in Huzhou Central Hospital from January 2016 to December 2018 were recruited to compare serum protein levels. The serological indicators were detected by Abbott Automatic Biochemical Analyzer(HCHL-YQ-SH-01). A total of 30 and 56 stool samples from CRC patients were used to detect intestinal microbes and microbial metabolites, respectively. Bacterial 16S V3-V4 and fungal ITS ribosomal DNA genes were sequenced and gas chromatography/mass spectrometry (GC/MS) was performed to detect microbial metabolites. Results: Some serum protein-related indicators in the CRC group were lower than those in the control group (p<0.05). The total protein and albumin levels in colon cancer patients were lower than those in rectal cancer patients (p<0.05). The higher abundance of Sutterella is correlated with lower serum albumin level in CRCs. There were statistically significant differences in the abundance of fungi including Agaricomycetes, Simplicillium, Sclerotiniaceae, and Exophiala among patients with different serum albumin levels. Multiple gut bacteria and fungi are closely related to serum albumin levels.We found some characteristic microbial metabolites in CRCs complicated with different serum protein levels.Conclusions: The different serum albumin levels were associated with the gut microbes and microbial metabolites in CRCs. It may provide novel ideas for basic research and clinical application.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.