Potential four‑miRNA signature associated with T stage and prognosis of patients with pancreatic ductal adenocarcinoma identified by co‑expression analysis

A Six-microRNA Signature Nomogram for Preoperative Prediction of Tumor Deposits in Colorectal Cancer

Purpose

Tumor deposits (TDs) are acknowledged negative prognostic factors in colorectal cancer (CRC), and their pathogenesis remains a puzzle. This study aimed to construct and validate a nomogram available for preoperative TDs prediction in CRC patients.

Patients and Methods

Patients from the Surveillance, Epidemiology, and End Results (SEER) and the cancer genome atlas (TCGA) databases were randomly divided into training and validation sets according to the sample size ratio of 7:3. Univariate logistic regression was performed for identifying differentially expressed microRNAs between TDs and non-TDs. Nomograms for TDs prediction were developed from the multivariate logistic regression model with least absolute shrinkage and selection operator and were validated internally in terms of accuracy, calibration, and clinical utility. Based on the target genes, pathways tightly associated with TDs were selected using enrichment analysis.

Results

Six clinicopathologic factors and expressions of six microRNAs (miR-614, miR-1197, miR-4770, miR-3136, miR-3173, and miR-4636) differed significantly between TDs and non-TDs CRC patients from the SEER and TCGA training sets. We compared potential prediction discrimination between two nomograms: a clinicopathologic nomogram and a six-microRNA signature nomogram. The six-microRNA signature nomogram revealed better accuracy than the clinicopathologic one for TDs prediction (AUC values of 0.96 and 0.93 in the validation cohort). The calibration plots and decision curve analysis demonstrated that the six-microRNA signature nomogram had better validity and a greater prognostic benefit versus the clinicopathologic one for TDs prediction. Calcium signaling pathways were closely associated with roles of the six microRNAs in TDs of CRC patients.

Conclusion

The six-microRNA signature nomogram can be used as an efficient tool for preoperative TDs prediction in CRC patients.

Identifying novel biomarkers of the pediatric influenza infection by weighted co-expression network analysis

Background

Despite the high yearly prevalence of Influenza, the pathogenesis mechanism and involved genes have not been fully known. Finding the patterns and mapping the complex interactions between different genes help us to find the possible biomarkers and treatment targets.

Methods

Herein, weighted gene co-expression network analysis (WGCNA) was employed to construct a co-expression network among genes identified by microarray analysis of the pediatric influenza-infected samples.

Results

Three of the 38 modules were found as the most related modules to influenza infection. At a functional level, we found that the genes in these modules regulate the immune responses, protein targeting, and defense to virus. Moreover, the analysis of differentially expressed genes disclosed 719 DEGs between the normal and infected subjects. The comprehensive investigation of genes in the module involved in immune system and viral defense (yellow module) revealed that SP110, HERC5, SAMD9L, RTP4, C19orf66, HELZ2, EPSTI1, and PHF11 which were also identified as DEGs (except C19orf66) have the potential to be as the biomarkers and also drug targeting for the treatment of pediatric influenza.

Conclusions

The WGCN analysis revealed co-expressed genes which were involved in the innate immune system and defense to virus. The differentially expressed genes in the identified modules can be considered for designing drug targets. Moreover, modules can help to find pathogenesis routes in the future.

MiR-193a-3p inhibits pancreatic ductal adenocarcinoma cell proliferation by targeting CCND1

Background: MicroRNAs (miRNAs) could modulate gene expression at the posttranscriptional level by promoting mRNA degradation or blocking mRNA translation, thus affecting the occurrence and development of cancer. Methods: In this work, qRT-PCR was conducted to detect the expression of miR-193a-3p and CCND1. The ability of cell proliferation was evaluated via CCK-8 assay. Cell apoptosis and cell cycle distribution were detected by flow cytometry. Bioinformatic techniques were employed to research the regulatory relationship between miR-193a-3p and target genes. The relationship between miR-193a-3p and CCND1 was verified via dual-luciferase reporter assays. Results: MiR-193a-3p expression in pancreatic ductal adenocarcinoma (PDAC) tissue was significantly lower than in non-cancerous tissue. After overexpressing miR-193a-3p in PDAC cells, their multiplication ability was significantly inhibited, apoptosis was accelerated, and the cell cycle was blocked in the G1 and G2/M phases. CCND1 was confirmed to have a targeted relationship with miR-193a-3p. Moreover, CCND1 expression was significantly lower in PDAC cells with an overexpression of miR-193a-3p. Conclusions: MiR-193a-3p targeted CCND1 to suppress tumor growth in PDAC cells. MiR-193a-3p may function as a tumor inhibitor in PDAC development, which could offer a promising therapeutic and prognostic strategy for PDAC treatment.