APLP2, RRM2, and PRC1: New Putative Markers for the Differential Diagnosis of Thyroid Follicular Lesions

Circulating small extracellular vesicle-based miRNA classifier for follicular thyroid carcinoma: a diagnostic study

BACKGROUND

The diagnosis of follicular thyroid carcinoma (FTC) prior to surgery remains a major challenge in the clinic.

METHODS

This multicentre diagnostic study involved 41 and 150 age- and sex-matched patients in the training cohort and validation cohort, respectively. The diagnostic properties of circulating small extracellular vesicle (sEV)-associated and cell-free RNAs were compared by RNA sequencing in the training cohort. Subsequently, using a quantitative real-time polymerase chain reaction (qRT‒PCR) assay, high-quality candidates were identified to construct an RNA classifier for FTC and verified in the validation cohort. The parallel expression, stability and influence of the RNA classifier on surgical strategy were also investigated.

RESULTS

The diagnostic properties of sEV long RNAs, cell-free long RNAs and sEV microRNAs (miRNAs) were comparable and superior to those of cell-free miRNAs in RNA sequencing. Given the clinical application, the circulating sEV miRNA (CirsEV-miR) classifier was developed from five miRNAs based on qRT‒PCR data, which could well identify FTC patients (area under curve [AUC] of 0.924 in the training cohort and 0.844 in the multicentre validation cohort). Further tests revealed that the CirsEV-miR score was significantly correlated with the tumour burden, and the levels of sEV miRNAs were also higher in sEVs from the FTC cell line, organoid and tissue. Additionally, circulating sEV miRNAs remained constant after different treatments, and the addition of the CirsEV-miR classifier as a biomarker improves the current surgical strategy.

CONCLUSIONS

The CirsEV-miR classifier could serve as a noninvasive, convenient, specific and stable auxiliary test to help diagnose FTC following ultrasonography.

Identification and validation of a ferroptosis-related gene to predict survival outcomes and the immune microenvironment in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related death worldwide. Ferroptosis, a form of cell death characterized by iron-dependent lipid peroxidation. However, the involvement of ferroptosis in the regulation of immune cell infiltration and its immunotherapeutic efficacy in LUAD remain unclear.

Methods

The Cancer Genome Atlas (TCGA) LUAD cohort was used to assess the survival prognosis of FRGs and construct a seven-gene risk signature. Correlation tests, difference tests, and a cluster analysis were performed to explore the role of FRGs in the immune microenvironment and their immunotherapeutic efficacy in LUAD. The effects of FRGs on LUAD cells were assessed by Western blot, iron assay, and lipid peroxidation assay.

Results

The seven-gene risk signatures of patients with LUAD were established and validated. FRG clustering based on 70 differentially expressed FRGs was associated with the immune microenvironment and indicated potential immune subtypes of LUAD. The seven-gene risk signature was an independent prognostic factor for LUAD and was used to divide the LUAD cohort into a high-risk and a low-risk group. Immunocyte infiltration levels, immune checkpoints, and immunotherapy response rates were significantly different between the two groups. Patients with high risk scores had lower overall levels of immunocyte infiltration but higher immunotherapy response rates. The key gene ribonucleotide reductase subunit M2 (RRM2) was associated with LUAD prognosis, which may be related to its ability to regulate the infiltration levels of activated mast cells and activated CD4 memory T cells. In addition, RRM2 was involved in ferroptosis, and its expression was up regulated in lung cancer tissues and the LUAD cell lines. Silencing RRM2 can inhibit the proliferation and induce ferroptosis of H1975 cells suggesting that silencing RRM2 could promote ferroptosis in H1975 cells.

Conclusion

Our results revealed RRM2 as a promising biomarker and therapeutic target associated with tumor immune infiltration in patients with LUAD.

Bioinformatics analysis of the circRNA-miRNA-mRNA network for non-small cell lung cancer

OBJECTIVE

Non-small cell lung cancer (NSCLC) accounts for approximately 80% of all lung cancers, but its pathogenesis has not been fully elucidated. Therefore, it is valuable to explore the pathogenesis of NSCLC to improve diagnosis and identify novel treatment biomarkers.

METHODS

Circular (circ)RNA, micro (mi)RNA, and gene expression datasets of NSCLC were analyzed to identify those that were differentially expressed between tumor and healthy tissues. Common genes were found and pathway enrichment analyses were performed. Survival analysis was used to identify hub genes, and their level of methylation and association with immune cell infiltration were analyzed. Finally, an NSCLC circRNA-miRNA-mRNA network was constructed.

RESULTS

Eight miRNAs and 211 common genes were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that cell projection morphogenesis, blood vessel morphogenesis, muscle cell proliferation, and synapse organization were enriched. Ten hub genes were found, of which the expression of DTL and RRM2 was significantly related to NSCLC patient prognosis. Significant methylation changes and immune cell infiltration correlations with DTL and RRM2 were also detected.

CONCLUSIONS

hsa_circ_0001947/hsa-miR-637/RRM2 and hsa_circ_0072305/hsa-miR-127-5p/DTL networks were constructed, and identified molecules may be involved in the occurrence and development of NSCLC.

Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value

Glioblastoma is a common malignant tumor in the central nervous system with an extremely poor outcome; understanding the mechanisms of glioblastoma at the molecular level is essential for clinical treatment. In the present study, we used bioinformatics analysis to identify potential biomarkers associated with prognosis in glioblastoma and elucidate the underlying mechanisms. The result revealed that 552 common genes were differentially expressed between glioblastoma and normal tissues based on TCGA, GSE4290, and GSE 50161 datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network were carried out to gain insight into the actions of differentially expressed genes (DEGs). As a result, 20 genes (CALB1, CDC20, CDCA8, CDK1, CEP55, DLGAP5, KIF20A, KIF4A, NDC80, PBK, RRM2, SYN1, SYP, SYT1, TPX2, TTK, VEGFA, BDNF, GNG3, and TOP2A) were found as hub genes via CytoHubba in Cytoscape and functioned mainly by participating in cell cycle and p53 signaling pathway; among them, RRM2 and CEP55 were considered to have relationship with the prognosis of glioblastoma, especially RRM2. High expression of RRM2 was consistent with shorter overall survival time. In conclusion, our study displayed the bioinformatic analysis methods in screening potential oncogenes in glioblastoma and underlying mechanisms. What is more is that we successfully identified RRM2 as a novel biomarker linked with prognosis, which might be expected to be a promising target for the therapy of glioblastoma.