The cell division cycle associated 8 (CDCA8) is a crucial component of the chromosome passenger complex (CPC). It has been implicated in the regulation of cell dynamic localization during mitosis. However, its role in hepatocellular carcinoma (HCC) is not clearly known. In this study, data of 374 patients with HCC were retrieved from the Cancer Genome Atlas (TCGA) database. Pan analysis of Gene Expression Profiling Interactive Analysis (GEPIA) database was performed to profile the mRNA expression of CDCA8 in HCC. Then, the Kaplan‐Meier plotter database was analysed to determine the prognostic value of CDCA8 in HCC. In addition, samples of tumour and adjacent normal tissues were collected from 88 HCC patients to perform immunohistochemistry (IHC), reverse transcription‐quantitative polymerase chain reaction (qRT‐PCR) and Western blotting. The results obtained from bioinformatic analyses were validated through CCK‐8 assay, EdU assay, colony formation assay, cell cycle assays and Western blotting experiments. Analysis of the Kaplan‐Meier plotter database showed that high expression of CDCA8 may lead to poor overall survival (OS, p = 4.06e‐05) in patients with HCC. For the 88 patients with HCC, we found that stages and grades appeared to be strongly linked with CDCA8 expression. Furthermore, the high expression of CDCA8 was found to be correlated with poor OS (p = 0.0054) and progression‐free survival (PFS, p = 0.0009). In vitro experiments revealed that inhibition of CDCA8 slowed cell proliferation and blocked the cell cycle at the G0/G1 phase. In vivo experiments demonstrated that inhibition of CDCA8 inhibited tumour growth. Finally, blockade of CDCA8 reduced the expression levels of cyclin A2, cyclin D1, CDK4, CDK6, Ki67 and PCNA. And, there is an interaction between CDCA8 and E2F1. In conclusion, this research demonstrates that CDCA8 may serve as a biomarker for early diagnosis and prognosis prediction of HCC patients. In addition, CDCA8 could be an effective therapeutic target in HCC.

Pancreatic cancer (PC) is a leading cause of cancer‐related mortality globally. Though increasing evidence has demonstrated that circular RNAs (circRNAs) are linked to the development and progression of cancers, the biological functions of circRNAs in PC remain largely unexplored so far. Based on previous studies, Hsc_circ_0075829 (circ_0075829) was screened out and then further identified in PC clinical specimens and cell lines by real‐time PCR. After the stability tests, a series of in vitro and in vivo functional experiments were performed to investigate the role of circ_0075829 in PC development. Furthermore, fluorescent in situ hybridization (FISH), bioinformatics tools, dual‐luciferase assays and rescue experiments were conducted to clarify the regulatory mechanisms of circ_0075829 in SW1990 and BxPC‐3 cells. Compared with paracancerous tissues, the expression of circ_0075829 was increased in PC tissues, which was positively correlated with the clinical features of PC. Knockdown of circ_0075829 significantly suppressed the proliferative, migratory and invasive rates of SW1990 and BxPC‐3 cells both in vitro and in vivo. Bioinformatics analysis and dual‐luciferase reporter gene assay indicated that circ_0075829 could bind to miR‐1287‐5p. Mechanism research and rescue experiments demonstrated that circ_0075829 could regulate the LAMTOR3/p‐ERK signalling pathway via sponging miR‐1287‐5p in PC cell lines. Our data reveal that the circ_0075829 could facilitate the proliferation and metastasis of PC through circ_0075829/miR‐1287‐5p/LAMTOR3 axis.

Pancreatic cancer (PC) is a life-threatening cancer with increasing incidence in developed countries. Reports indicate that tRNA-derived fragments (tRFs) are possible therapeutic targets and biomarkers for cancer treatment. Nonetheless, the effect of tRF-Leu-AAG on PC is unclear. This study aims to explore the role of tRF-Leu-AAG and upstream frameshift mutant 1 (UPF1) in the development of PC and its potential underlying mechanisms. High-throughput second-generation sequencing techniques were used to detect the expression of tRFs in cancerous and adjacent normal tissues from PC patients. The role of tRF-Leu-AAG proliferation in PC cells was investigated via the Cell Counting Kit-8 (CCK8) assay. The effect of tRF-Leu-AAG on the invasion and migration ability of PC cells was also determined by the transwell assay. Thereafter, the downstream target genes of tRF-Leu-AAG were comprehensively predicted using bioinformatics analysis databases. We also used the Dual-Luciferase Reporter assay to assess the nexus between tRF-Leu-AAG and UPF1. Eventually, Western Blot was used to validate the expression of UPF1 in PC cells. A total of 33 tRF expressions significantly varied from PC patients. RT-qPCR confirmed that the expression of tRF-Leu-AAG was observably up-regulated in PC cells as compared to the control cells. Importantly, knockdown of tRF-Leu-AAG observably inhibited cell proliferation, migration, and invasion. Furthermore, according to the predicted frameshift database results, the UPF1 acted as downstream target genes for tRF-Leu-AAG and significantly down-regulated UPF1 expression. Graphical Abstract

Background: The incidence of thyroid cancer has been on a rise. Papillary thyroid cancer (PTC) is the most common type of malignant thyroid tumor and accounts for approximately 85% of thyroid cancer cases. Although the genetic background of PTC has been studied extensively, relatively little is known about the role of small noncoding RNAs (sncRNAs) in PTC. tRNA‐derived fragments (tRFs) represent a newly discovered class of sncRNAs that exist in many species and play key roles in various biological processes.Methods: In this study, we used high‐throughput next‐generation sequencing technology to analyze the expression of tRFs in samples from PTC tissues and normal tissues. We selected four tRFs to perform qPCR to determine the expression levels of these molecules and make bioinformatic predictions.Results: We identified 53 unique tRFs and transfer RNA halves (tsRNAs). The 10 most upregulated tRFs and tsRNAs were tRF‐39‐I6D3887S1RMH5MI2, tRF‐21‐2E489B3RB, tRF‐18‐JMRPFQDY, tRF‐17‐202L2YF, tRF‐17‐VBY9PYJ, tRF‐18‐YRRHQFD2, tRF‐21‐WE884U1DD, tRF‐41‐EX2Z10I9BZBZOS4YB, tRF‐39‐HPDEXK7S1RNS9MI2, and tRF‐20‐1SS2P46I. The 10 most downregulated tRFs and tsRNAs were tRF‐31‐HQ9M739P8WQ0B, tRF‐43‐5YXENDBP1IUUK7VZV, tRF‐38‐RZYQHQ9M739P8WD8, tRF‐25‐9M739P8WQ0, tRF‐33‐V6Z3M8ZLSSXUD6, tRF‐27‐MY73H3RXPLM, tRF‐26‐DBNIB9I1KQ0, tRF‐38‐Z9HMI8W47W1R7HX, tRF‐40‐Z6V6Z3M8ZLSSXUOL, and tRF‐39‐YQHQ9M739P8WQ0EB. qPCR verification of cell lines and tissue samples yielded results consistent with the sequencing analysis. As tRF‐39 expression showed the maximum difference between PTC cells and normal cells, we chose this tRF to predict targets and perform functional tRF and tsRNA enrichment analysis.Conclusion: In this study, we provided a comprehensive catalog of tRFs involved in PTC and assessed the abnormal expression of these fragments. Through qPCR verification, tRF‐39‐0VL8K87SIRMM12E2 was found to be the most significantly upregulated tRF. Further tRF and enrichment analysis revealed that tRF‐39 was mostly enriched in the “metabolic pathways.” These preliminary findings can be used as the basis for further research studies based on the functional role of tRFs in patients with PTC.