Clinicopathological role of Cyclin A2 in uterine corpus endometrial carcinoma: Integration of tissue microarrays and ScRNA-Seq.

BACKGROUND: The comprehensive expression level and potential molecular role of Cyclin A2 (CCNA2) in uterine corpus endometrial carcinoma (UCEC) remains undiscovered. METHODS: UCEC and normal endometrium tissues from in-house and public databases were collected for investigating protein and messenger RNA expression of CCNA2. The transcription factors of CCNA2 were identified by the Cistrome database. The prognostic significance of CCNA2 in UCEC was evaluated through univariate and multivariate Cox regression as well as Kaplan-Meier curve analysis. Single-cell RNA-sequencing (scRNA-seq) analysis was performed to explore cell types in UCEC, and the AUCell algorithm was used to investigate the activity of CCNA2 in different cell types. RESULTS: A total of 32 in-house UCEC and 30 normal endometrial tissues as well as 720 UCEC and 165 control samples from public databases were eligible and collected. Integrated calculation showed that the CCNA2 expression was up-regulated in the UCEC tissues (SMD = 2.43, 95% confidence interval 2.23∼2.64). E2F1 and FOXM1 were identified as transcription factors due to the presence of binding peaks on transcription site of CCNA2. CCNA2 predicted worse prognosis in UCEC. However, CCNA2 was not an independent prognostic factor in UCEC. The scRNA-seq analysis disclosed five cell types: B cells, T cells, monocytes, natural killer cells, and epithelial cells in UCEC. The expression of CCNA2 was mainly located in B cells and T cells. Moreover, CCNA2 was active in T cells and B cells using the AUCell algorithm. CONCLUSION: CCNA2 was up-regulated and mainly located in T cells and B cells in UCEC. Overexpression of CCNA2 predicted unfavorable prognosis of UCEC.

Identification of the key genes and mechanisms associated with transcatheter arterial chemoembolisation refractoriness in hepatocellular carcinoma.

BACKGROUND: Transcatheter arterial embolisation (TACE) is the primary treatment for intermediate-stage hepatocellular carcinoma (HCC) patients while some HCC cases have shown resistance to TACE. AIM: To investigate the key genes and potential mechanisms correlated with TACE refractoriness in HCC. METHODS: The microarray datasets of TACE-treated HCC tissues, HCC and non-HCC tissues were collected by searching multiple public databases. The respective differentially expressed genes (DEGs) were attained via limma R package. Weighted gene co-expression network analysis was employed for identifying the significant modules related to TACE non-response. TACE refractoriness-related genes were obtained by intersecting up-regulated TACE-associated and HCC-associated DEGs together with the genes in significant modules related to TACE non-response. The key genes expression in the above two pairs of samples was compared respectively via Wilcoxon tests and standard mean differences model. The prognostic value of the key genes was evaluated by Kaplan-Meier curve. Multivariate analysis was utilised to investigate the independent prognostic factor in key genes. Single-cell RNA (scRNA) sequencing analysis was conducted to explore the cell types in HCC. TACE refractoriness-related genes activity was calculated via AUCell packages. The CellChat R package was used for the investigation of the cell-cell communication between the identified cell types. RESULTS: HCC tissues of TACE non-responders (n = 66) and TACE responders (n = 81), HCC (n = 3941) and non-HCC (n = 3443) tissues were obtained. The five key genes, DLG associated protein 5 (DLGAP5), Kinesin family member 20A (KIF20A), Assembly factor for spindle microtubules (ASPM), Kinesin family member 11 (KIF11) and TPX2 microtubule nucleation factor (TPX2) in TACE refractoriness-related genes, were identified. The five key genes were all up-regulated in the TACE non-responders group and the HCC group. High expression of the five key genes predicted poor prognosis in HCC. Among the key genes, TPX2 was an independent prognostic factor. Four cell types, hepatocytes, embryonic stem cells, T cells and B cells, were identified in the HCC tissues. The TACE refractoriness-related genes expressed primarily in hepatocytes and embryonic stem cells. Hepatocytes, as the providers of ligands, had the strongest interaction with embryonic stem cells that provided receptors. CONCLUSION: Five key genes (DLGAP5, KIF20A, ASPM, KIF11 and TPX2) were identified as promoting refractory TACE. Hepatocytes and embryonic stem cells were likely to boost TACE refractoriness.

EcTracker: Tracking and elucidating ectopic expression leveraging large-scale scRNA-seq studies.

Dramatic genomic alterations, either inducible or in a pathological state, dismantle the core regulatory networks, leading to the activation of normally silent genes. Despite possessing immense therapeutic potential, accurate detection of these transcripts is an ever-challenging task, as it requires prior knowledge of the physiological gene expression levels. Here, we introduce EcTracker, an R-/Shiny-based single-cell data analysis web server that bestows a plethora of functionalities that collectively enable the quantitative and qualitative assessments of bona fide cell types or tissue-specific transcripts and, conversely, the ectopically expressed genes in the single-cell ribonucleic acid sequencing datasets. Moreover, it also allows regulon analysis to identify the key transcriptional factors regulating the user-selected gene signatures. To demonstrate the EcTracker functionality, we reanalyzed the CRISPR interference (CRISPRi) dataset of the human embryonic stem cells differentiated into endoderm lineage and identified the prominent enrichment of a specific gene signature in the SMAD2 knockout cells whose identity was ambiguous in the original study. The key distinguishing features of EcTracker lie within its processing speed, availability of multiple add-on modules, interactive graphical user interface and comprehensiveness. In summary, EcTracker provides an easy-to-perform, integrative and end-to-end single-cell data analysis platform that allows decoding of cellular identities, identification of ectopically expressed genes and their regulatory networks, and therefore, collectively imparts a novel dimension for analyzing single-cell datasets.