Identification of serum prognostic marker miRNAs and construction of microRNA-mRNA networks of esophageal cancer

miR-508-5p regulates macrophage polarization via targeting TSGA10 to promote malignant behavior in esophageal cancer cells

BACKGROUND

Esophageal cancer (EC) is among the deadliest malignancies in humans, with various miRNAs shown to regulate its progression by targeting distinct genes. miR-508-5p was identified as being linked to the malignant behavior of various tumors. Nevertheless, the precise role and mechanism of miR-508-5p in esophageal cancer (EC) remain ambiguous.

OBJECTIVE

This investigation focuses on the role and mechanism of the miR-508-5p/TSGA10 axis in the progression of EC.

METHODS

The expression of miR-508-5p and TSGA10 in EC cell lines was evaluated using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). Cell transfection techniques were used to knock down miR-508-5p and observe its effects on cell proliferation, migration, invasion, and apoptosis. A dual-luciferase reporter gene assay was conducted to verify the targeting relationship of miR-508-5p with TSGA10. Co-culture studies were undertaken to examine the regulatory effect of the miR-508-5p/TSGA10 axis on the polarization state of tumor-associated macrophages (TAMs) and the malignant behavior of EC cells.

RESULTS

The expression of miR-508-5p was significantly elevated in EC cells. Knocking down miR-508-5p curbed cell proliferation, migration, and invasion while promoting apoptosis. TSGA10 was validated as a primary target gene of miR-508-5p. miR-508-5p knockdown could inhibit the M2 polarization of TAMs by upregulating TSGA10, thereby suppressing the tumorigenic behavior of EC cells.

CONCLUSION

miR-508-5p promotes the M2 polarization of TAMs and enhances the malignant behavior of EC cells by inhibiting TSGA10.

Correction: Identification of serum prognostic marker miRNAs and construction of microRNA-mRNA networks of esophageal cancer

[This corrects the article DOI: 10.1371/journal.pone.0255479.].

Preanalytic Methodological Considerations and Sample Quality Control of Circulating miRNAs

As miRNAs emerge as potential circulating biomarkers for the diagnosis or prognosis of a wide variety of diseases, the quantification of miRNA necessitates careful preanalytic considerations and sample quality control becomes crucial. This study comprehensively analyzed the profiles of 356 miRNAs by quantitative RT-PCR in various blood sample types, with various processing protocols. The comprehensive analysis investigated the correlations of individual miRNAs with certain confounding factors. On the basis of these profiles, a panel of 7 miRNAs was established for the quality control of samples corresponding to hemolysis and platelet contamination. The panel was used to investigate the confounding impacts based on the size of the blood collection tube, the centrifugation protocol, post-freeze-thaw spinning, and whole blood storage. A standard dual-spin workflow for the processing of blood had been established for optimal sample quality. The real-time stability of 356 miRNAs was also investigated with demonstration of the temperature and time-induced miRNA degradation profile. Stability-related miRNAs were identified from real-time stability study and further incorporated into the quality control panel. This quality control panel enables the assessment of sample quality for more robust and reliable detection of circulating miRNAs.

Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes

G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methylations, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune-related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs.