Background Glioblastoma is one of the most common brain cancers in adults, and is characterized by recurrence and little curative effect. An effective treatment for glioblastoma patients remains elusive worldwide. 7-methylguanosine (m7G) is a common RNA modification, and its role in tumors has become a research hotspot. Methods By searching for differentially expressed genes related to m7G, we generated a prognostic signature via cluster analysis and established classification criteria of high and low risk scores. The effectiveness of classification was validated using the Non-negative matrix factorization (NMF) algorithm, and repeatedly verified using training and test groups. The dimension reduction method was used to clearly show the difference and clinical significance of the data. All analyses were performed via R (version 4.1.2). Results According to the signature that included four genes (TMOD2, CACNG2, PLOD3, and TMSB10), glioblastoma patients were divided into high and low risk score groups. The survival rates between the two groups were significantly different, and the predictive abilities for 1-, 3-, and 5-year survivals were effective. We further established a Nomogram model to further examine the signature,as well as other clinical factors, with remaining significant results. Our signature can act as an independent prognostic factor related to immune-related processes in glioblastoma. Conclusions Our research addresses the gap in knowledge in the m7G and glioblastoma research fields. The establishment of a prognostic signature and the extended analysis of the tumor microenvironment, immune correlation, and tumor mutation burden further suggest the important role of m7G in the development and development of this disease. This work will provide support for future research.

Clear cell renal cell carcinoma (ccRCC) has become a common malignant cancer with increasing incidence rate and high recurrence risk in genitourinary oncology around the world. Recently, miRNAs were identified to affect pathogenesis, development, molecular functions, and prognosis of ccRCC. In this study, microRNA-184-5p (miR-184-5p) was identified from three independent ccRCC cohorts and was determined as a significantly distinct prognostic biomarker. Relative miR-184-5p expression was found in A-498 and 786-O ccRCC cells compared with HK-2 cells. After ccRCC cells were transfected with miR-184-5p mimics or inhibitor, biological abilities of miR-184-5p in tumor cell proliferation, cycle, apoptosis and invasion were determined. Additionally, we confirmed the direct relationship between miR-184-5p and NUS1 dehydrodolichyl diphosphate synthase subunit (NUS1) by using the Luciferase reporter and rescue assays. These results indicated that the expression level of miR-184-5p in human ccRCC cells and tissues was reduced, and the up-regulation of miR-184-5p regulated A-498 and 786-O cell proliferation, invasion and apoptosis by directly targeting NUS1. These findings may provide new theoretical targets for treatment strategies and drug development of ccRCC.

Background: Given that long non-coding RNAs (lncRNAs) involved in the tumor initiation or progression of the endometrium and that competing endogenous RNA (ceRNA) plays an important role in increasingly more biological processes, lncRNA-mediated ceRNA is likely to function in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). Our present study aimed to explore the potential molecular mechanisms for the prognosis of UCEC through a lncRNA-mediated ceRNA network. Methods: The transcriptome profiles and corresponding clinical profiles of UCEC dataset were retrieved from Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) databases respectively. Differentially expressed genes (DEGs) in UCEC samples were identified via "Edge R" package. Then, an integrated bioinformatics analysis including functional enrichment analysis, tumor infiltrating immune cell (TIIC) analysis, Kaplan-Meier curve, Cox regression analysis were conducted to analyze the prognostic biomarkers. Results: In the CPTAC dataset of UCEC, a ceRNA network comprised of 36 miRNAs, 123 lncRNAs and 124 targeted mRNAs was established, and 8 of 123 prognostic-related Differentially Expressed long noncoding RNAs (DElncRNAs) were identified. While in the TCGA dataset, a ceRNA network comprised of 38 miRNAs, 83 lncRNAs and 110 targeted mRNAs was established, and 2 of 83 prognostic-related DElncRNAs were identified. After filtered by risk grouping and Cox regression analysis, 10 prognosticrelated lncRNAs including LINC00443, LINC00483, C2orf48, TRBV11-2, MEG-8 were identified. In addition, 33 survival-related Differentially Expressed messenger RNA (DEmRNAs) in two ceRNA networks were further validated in the Human Protein Atlas Portal (HPA) database. Finally, six lncRNA/miRNA/ mRNA axes were established to elucidate prognostic regulatory roles in UCEC. Conclusions: Several prognostic lncRNAs are identified and prognostic model of lncRNA-mediated ceRNA network is constructed, which promotes the understanding of UCEC development mechanisms and potential therapeutic targets.

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