A Novel Three-miRNA Signature Identified Using Bioinformatics Predicts Survival in Esophageal Carcinoma

MiR-199a-5p Decreases Esophageal Cancer Cell Proliferation Partially through Repression of Jun-B

MicroRNA (miR)-199a-5p has been shown to function as a tumor suppressor in some malignancies but its role in esophageal cancer is poorly understood. To further explore its role in esophageal cancer, we sought to investigate the interaction between miR-199a-5p and Jun-B, an important component of the AP1 transcription factor, which contains a potential binding site for miR-199a-5p in its mRNA. We found that levels of miR-199a-5p are reduced in both human esophageal cancer specimens and in multiple esophageal cancer cell lines compared to esophageal epithelial cells. Jun-B expression is correspondingly elevated in these tumor specimens and in several cell lines compared to esophageal epithelial cells. Jun-B mRNA expression and stability, as well as protein expression, are markedly decreased following miR-199a-5p overexpression. A direct interaction between miR-199a-5p and Jun-B mRNA was confirmed by a biotinylated RNA-pull down assay and luciferase reporter constructs. Either forced expression of miR-199a-5p or Jun-B silencing led to a significant decrease in cellular proliferation as well as in AP-1 promoter activity. Our results provide evidence that miR-199a-5p functions as a tumor suppressor in esophageal cancer cells by regulating cellular proliferation, partially through repression of Jun B.

Predictive value of an exosomal microRNA-based signature for tumor immunity in cervical cancer patients treated with chemoradiotherapy

Resistance of cervical cancer to radiotherapy with concurrent chemotherapy (CCRT) results in a poor prognosis. To identify new biomarkers for predicting the treatment response and prognosis, we explored exosomal microRNA (miRNA) expression signatures associated with the outcome of cervical cancer patients treated with CCRT. Exosomes were isolated from the plasma of 45 patients prior to CCRT during 2014-2020, and miRNA analysis was performed by next-generation sequencing. At a median follow-up of 38 months, 26 patients were recurrence free, 15 patients had died of the disease, and 4 patients received salvage chemotherapy due to distant metastasis. Of the 2522 miRNAs detected, 9 (miR-148a-5p, 1915-3p, 3960, 183-5p, 196b-5p, 200c-3p, 182-5p, 374a-5p, and 431-5p) showed differential expression between the recurrence-free and recurrence groups. Patients were divided into high- and low-risk groups according to the cutoff of the miRNAs-based risk score calculated from respective expression levels. The high-risk group had significantly worse disease-specific survival than the low-risk group (p < 0.001). In addition, miR-374a-5p and miR-431-5p expression showed a weak inverse correlation with tumor-infiltrating CD8+ and FOXP3+ T cells, suggesting a potential inhibitory effect on CCRT by suppressing tumor immunity. This miRNA signature could improve non-invasive monitoring and personalized treatment for cervical cancer.

Role of Bioinformatics Analysis in Early Differential Diagnosis of Ovarian Cancer

In order to solve the problem of early differential diagnosis of ovarian cancer, this paper proposes the role of bioinformatics analysis in early differential diagnosis of ovarian cancer. This method uses bioinformatics methods to mine the existing data in the tumor database and obtain tumor-related molecules. It is an efficient method to obtain effective biomarkers, screen signal pathway molecules, and reveal the internal mechanism of tumor occurrence and development. Using this method can greatly improve the efficiency and reliability of screening diagnosis, prognosis, and treatment targets. The results showed that 5821 new lncRNA transcripts and 4611 new lncRNA genes were identified by lncScore from the assembled transcripts. 10 new lncRNA transcripts and 174 new lncRNA genes were found to be differentially expressed in ovarian cancer.

A Prognostic Signature Based on Immunogenomic Profiling Offers Guidance for Esophageal Squamous Cell Cancer Treatment

Our study aimed to develop an immune prognostic signature that could provide accurate guidance for the treatment of esophageal squamous cell cancer (ESCC). By implementing Single-Sample Gene Set Enrichment Analysis (ssGSEA), we established two ESCC subtypes (Immunity High and Immunity Low) in GSE53625 based on immune-genomic profiling of twenty-nine immune signature. We verified the reliability and reproducibility of this classification in the TCGA database. Immunity High could respond optimally to immunotherapy due to higher expression of immune checkpoints, including PD1, PDL1, CTLA4, and CD80. We used WGCNA analysis to explore the underlying regulatory mechanism of the Immunity High group. We further identified differentially expressed immune-related genes (CCR5, TSPAN2) in GSE53625 and constructed an independent two-gene prognostic signature we internally validated through calibration plots. We established that high-risk ESCC patients had worse overall survival (P=0.002, HR=2.03). Besides, high-risk ESCC patients had elevated levels of infiltrating follicle-helper T cells, naïve B cells, and macrophages as well as had overexpressed levels of some immune checkpoints, including B3H7, CTLA4, CD83, OX40L, and GEM. Moreover, through analyzing the Genomics of Drug Sensitivity in Cancer (GDSC) database, the high-risk group demonstrated drug resistance to some chemotherapy and targeted drugs such as paclitaxel, gefitinib, erlotinib, and lapatinib. Furthermore, we established a robust nomogram model to predict the clinical outcome in ESCC patients. Altogether, our proposed immune prognostic signature constitutes a clinically potential biomarker that will aid in evaluating ESCC outcomes and promote personalized treatment.

Construction and Investigation of MicroRNA-mRNA Regulatory Network of Gastric Cancer with Helicobacter pylori Infection

Background

Helicobacter pylori (H. pylori) is a common human pathogen, which is closely correlated with gastric cancer (GC). However, the mechanism of H. pylori-related GC has not been elucidated. This study aimed to explore the role of H. pylori infection in GC and find biomarkers for early diagnosis of H. pylori-related GC.

Methods

We identified differentially expressed microRNAs (DEMs) and genes (DEGs) from the Gene Expression Omnibus (GEO) dataset, constructed microRNA-(miRNA-)mRNA expression networks, analyzed the function and signal pathway of cross-genes, analyzed the relations between cross-genes and GC prognosis with the Cancer Genome Atlas (TCGA) data, and verified the expression of cross-genes in patients with H. pylori infection.

Results

22 DEMs and 68 DEGs were identified in GSE197694 and GSE27411 dataset. 16 miRNAs and 509 genes were involved in the expression network, while the cross-genes of the network were mainly enriched in MAP kinase (MAPK) signaling pathway and TGF-beta signaling pathway. Patients with higher expression of hsa-miR-196b-3p, CALML4, or SMAD6 or lower expression of PITX2 or TGFB2 had better outcomes than those with lower expression of hsa-miR-196b-3p, CALML4, or SMAD6 or higher expression of PITX2 or TGFB2 (P < 0.05). Patients with H. pylori infection had a higher expression of hsa-miR-196b-3p and CALML4 than those without H. pylori infection (P < 0.05).

Conclusion

The study of miRNA-mRNA expression network would provide molecular support for early diagnosis and treatment of H. pylori-related GC.