FGF14-AS2 accelerates tumorigenesis in glioma by forming a feedback loop with miR-320a/E2F1 axis

MiR-320a Acts as a Tumor Suppressor in Somatotroph Pituitary Neuroendocrine Tumors by Targeting BCAT1

INTRODUCTION

Aberrant miR-320a has been reported to be involved in the tumorigenesis of several cancers. In our previous study, we identified the low expression of circulating miR-320a in patients with somatotroph pituitary neuroendocrine tumor (PitNET); however, the role of miR-320a in somatotroph PitNET proliferation is still unclear.

METHODS

Cell viability and colony formation assays were used to detect the effect of miR-320a and BCAT1 on GH3 cells. TargetScan was used to identify the target genes of miR-320a. Dual-luciferase reporter gene assay was used to explore the relation between miR-320a and BCAT1. Transcriptome and proteome analyses were performed between somatotroph PitNETs and healthy controls. The expression level of miR-320a in somatotroph PitNETs were detected by RT-qPCR and Western blot.

RESULTS

miR-320a mimics inhibit cell proliferation, while miR-320a inhibitors promote cell proliferation in GH3 cells. An overlap analysis using a Venn diagram revealed that BCAT1 is the only target gene of miR-320a overexpressed in somatotroph PitNETs compared to healthy controls, as revealed by both microarray and proteomics results. A dual-luciferase reporter gene assay showed that miR-320a may bind to the BCAT1-3'UTR. The transfection of miR-320a mimics downregulated the expression and miR-320a inhibitors and upregulated the expression of BCAT1 in GH3 cells. The interference of BCAT1 expression in GH3 cells downregulated cell proliferation and growth. Pan-cancer analyses demonstrated that high BCAT1 expression often indicates a poor prognosis.

CONCLUSION

Our findings illustrate that miR-320a may function as a tumor suppressor and BCAT1 may promote tumor progression. miR-320a may inhibit the growth of somatotroph PitNETs by targeting BCAT1.

m6A reader YTHDF3 is associated with clinical prognosis, related RNA signatures and immunosuppression in gastric cancer

BACKGROUND

YTHDF3 as a N6-methyladenosine (m6A) reader participates in the development and progression of multiple cancer types, however, the prognosis, molecular biology and immune infiltration of YTHDF3 in gastric cancer (GC) have not been investigated.

METHODS

The YTHDF3 expression profile and clinicopathological parameters of stomach adenocarcinoma (STAD) were downloaded from TCGA. The online websites and databases such as GEPIA2, cBioPortal, UALCAN, ImmuCellAl, xCell, TISIDB, GSCA were utilized for analysis of the association of YTHDF3 with STAD, including clinical prognosis, WGCNA and LASSO Cox regression analysis. Further functional assays such as RT-qPCR, Western blot, immunohistochemistry (IHC), immunofluorescence (IF), CCK-8, colony formation, EdU and Transwell assays were performed to determine the role of YTHDF3 in GC.

RESULTS

We found that YTHDF3 was upregulated in STAD tissue samples ascribed to its copy number amplification and associated with poor prognosis in patients with STAD. GO and KEGG analyses showed that YTHDF3-related differential genes were predominantly enriched in the proliferation, metabolism and immune signaling pathways. Knockdown of YTHDF3 repressed the growth and invasion of GC cells by inhibition of PI3K/AKT signaling. We then identified YTHDF3-related lncRNAs, miRNAs and mRNAs, and constructed their prognostic signatures in patients with STAD. Moreover, YTHDF3 associated with tumor immune infiltration such as CD8+ T cells, macrophages, Tregs, MHC molecules and chemokines, upregulated PD-L1 and CXCL1 and exerted a response to the immunotherapy in GC.

CONCLUSIONS

YTHDF3 upregulation indicates poor prognosis and promotes GC cell growth and invasion by activating PI3K/AKT pathway and regulating immune microenvironment. The established YTHDF3-related signatures highlight the association of YTHDF3 with the clinical prognosis and immune cell infiltration in GC.

Identification and Characterization of an Ageing-Associated 13-lncRNA Signature That Predicts Prognosis and Immunotherapy in Hepatocellular Carcinoma

Background

In cancer pathology, cell senescence not only alters cell function but also reshapes the immune microenvironments in tumours. However, the association between cell senescence, tumour microenvironment, and disease progression of hepatocellular carcinoma (HCC) is yet to be fully understood. Therefore, the role of cell senescence-related genes and long noncoding RNAs (lncRNAs) in evaluating the clinical prognosis and immune cell infiltration (ICI) of HCC patients requires further investigation.

Methods

The limma R package was utilised to investigate differentially expressed genes according to the multiomics data. The CIBERSORT R package was utilised to assess ICI, and unsupervised cluster analysis was conducted using the R software's ConsensusClusterPlus package. A polygenic prognostic model of lncRNAs was constructed by conducting univariate and least absolute shrinkage and selection operator (Lasso) cox proportional-hazards regression analyses. The time-dependent receiver operating characteristic (ROC) curves were used for validation. We utilised the survminer R package to evaluate the tumour mutational burden (TMB). Moreover, the gene set enrichment analysis (GSEA) helped in pathway enrichment analysis, and the immune infiltration level of the model was evaluated using the IMvigor210 cohort.

Results

The identification of 36 prognosis-related genes was achieved based on their differential expression between healthy and liver cancer tissues. Liver cancer individuals were categorised into 3 independent senescence subtypes using the gene list, revealing considerable survival differences (variations). We observed that the prognosis of patients in the ARG-ST2 subtype was substantially better as compared to that in the ARG-ST3 subtype. Differences were observed in gene expression profiles among the three subtypes, with the differentially expressed genes predominantly associated with cell cycle control. The enrichment of upregulated genes in the ARG-ST3 subtype was observed in pathways related to biological processes, for instance, organelle fission, nuclear division, and chromosome recombination. ICI in the ARG-ST1 and ARG-ST2 subtypes, with relatively better prognosis, was substantially higher as compared to the ARG-ST3 subtype. Furthermore, a risk-score model, which can be employed as a reliable prognostic factor in an independent manner for individuals suffering from liver cancer, was constructed based on 13 cell senescence-related lncRNAs (MIR99AHG, LINC01224, LINC01138, SLC25A30AS1, AC006369.2, SOCS2AS1, LINC01063, AC006037.2, USP2AS1, FGF14AS2, LINC01116, KIF25AS1, and AC002511.2). The individuals with higher risk scores had noticeably poor prognoses in contrast with those having low-risk scores. Moreover, increased levels of TMB and ICI were observed in individuals with low-risk scores and gaining more benefit from immune checkpoint therapy.

Conclusion

Cell senescence is an essential factor in HCC onset and progression. We identified 13 senescence-related lncRNAs as HCC prognostic markers, which can help understand their function in the onset and progression of HCC and guide clinical diagnosis and treatment.

Screening of Hub Genes in Hepatocellular Carcinoma Based on Network Analysis and Machine Learning

Hepatocellular carcinoma (LIHC) is the fifth common cancer worldwide, and it requires effective diagnosis and treatment to prevent aggressive metastasis. The purpose of this study was to construct a machine learning-based diagnostic model for the diagnosis of liver cancer. Using weighted correlation network analysis (WGCNA), univariate analysis, and Lasso-Cox regression analysis, protein-protein interactions network analysis is used to construct gene networks from transcriptome data of hepatocellular carcinoma patients and find hub genes for machine learning. The five models, including gradient boosting, random forest, support vector machine, logistic regression, and integrated learning, were to identify a multigene prediction model of patients. Immunological assessment, TP53 gene mutation and promoter methylation level analysis, and KEGG pathway analysis were performed on these groups. Potential drug molecular targets for the corresponding hepatocellular carcinomas were obtained by molecular docking for analysis, resulting in the screening of 2 modules that may be relevant to the survival of hepatocellular carcinoma patients, and the construction of 5 diagnostic models and multiple interaction networks. The modes of action of drug-molecule interactions that may be effective against hepatocellular carcinoma core genes CCNA2, CCNB1, and CDK1 were investigated. This study is expected to provide research ideas for early diagnosis of hepatocellular carcinoma.