Construction of epithelial-mesenchymal transition related miRNAs signatures as prognostic biomarkers in gastric cancer patients
Xiao, J.; Zhang, F.; Liu, W.; Zang, W.
Show abstract
AimTo identify the potential post-healing EMT related miRNAs associated with lymph node metastatic gastric cancer (LNMGC). MethodsBoth RNA expression and clinical medical data were obtained from the TCGA dataset. We performed differential expression and normalization analysis of miRNAs. Cox linear regression model confirmed the differentially expressed miRNAs (DEmiRNAs) and clinical medical parameters related to overall survival (OS). The role of target genes of DEmiRNAs was determined according to the role enrichment analysis. ResultsWe obtained a total of 7531 DEmRNAs and 267 DEmRNAs, of which 185 DEmRNAs were down-regulated and 82 DEmRNAs were up-regulated. We randomly divided the LMNGC cases (n=291) into a training group (n=207) and a test group (n=84). The results showed that a total of 103, 11, 13 and 83 overlapping genes were associated with hsa-mir-141-3p, hsa-mir-4664-3p, hsa-mir-125b-5p and hsa-mir-7-5p, respectively. Kaplan-Meier determined that these four miRNAs can effectively distinguish high-risk and low-risk groups, and have a good indicator role (all p<0.05). Multivariate cox regression analysis also showed that EMT-related miRNA predictive model and lymph node metastasis were both prognostic risk factors (all p<0.05). The ROC curve showed that this feature had high accuracy (AUC>0.7, p<0.05). In addition, KEGG analysis showed that EMT-related pathways were mainly enriched in HIF-1 signaling pathway and focal adhesion. ConclusionsOur study demonstrated that EMT-related miRNAs could serve as independent prognostic markers in pN1-3 GC patients.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Bioinformatics analysis of immune-related prognostic genes and immunotherapy in renal clear cell carcinoma 97%
- Identification of cuproptosis and ferroptosis-related subtypes and development of a prognostic signature in colon cancer 97%
- Research of the mechanism on miRNA193 in exosomes promotes cisplatin resistance in esophageal cancer cells 96%
Similar papers in this journal
- Patient stratification of clear cell renal cell carcinoma using the global transcription factor activity landscape derived from RNA-seq data 96%
- Pyroptosis-related gene signatures can robustly diagnose skin cutaneous melanoma and predict the prognosis 96%
- Establishment of a prognosis prediction model based on pyroptosis-related signatures associated with the immune microenvironment and molecular heterogeneity in clear cell renal carcinoma 96%
Similar papers in this journal
- Upregulation of ARHGAP9 is correlated with poor prognosis and immune infiltration in clear cell renal cell carcinoma 97%
- Correlation Analysis of Breast Cancer Molecular Characteristics and Epithelial-Mesenchymal Transition in Circulating Tumor Cells: Based on Clinical Cases 91%
- Global research hotspots and frontier trends of epigenetic modifications in autoimmune diseases: a bibliometric analysis from 2012 to 2022 90%
Similar papers in this journal
- DNMT family induced down-regulation of NDRG1 via DNA methylation and clinicopathological significance in gastric cancer 97%
- Construction of competing endogenous RNA interaction networks as prognostic markers in metastatic melanoma 95%
- Ursolic acid improves the bacterial community mapping of the intestinal tract in liver fibrosis mice 94%
Similar papers in this journal
- Mapping of single-cell landscape of acral melanoma and analysis of molecular regulatory network of tumor microenvironment 96%
- Tumor Purity-Related Genes for Predicting the Prognosis and Drug Sensitivity of DLBCL Patients 95%
- Molecular Feature-Based Classification of Retroperitoneal Liposarcoma: A Prospective Cohort Study 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.