PHF6 promotes the progression of endometrial carcinoma by increasing cancer cells growth and decreasing T-cell infiltration.

Uterine corpus endometrial carcinoma (UCEC) is the most common cancer of the female reproductive tract. The overall survival of advanced and recurrent UCEC patients is still unfavourable nowadays. It is urgent to find a predictive biomarker and block tumorgenesis at an early stage. Plant homeodomain finger protein 6 (PHF6) is a key player in epigenetic regulation, and its alterations lead to various diseases, including tumours. Here, we found that PHF6 expression was upregulated in UCEC tissues compared with normal tissues. The UCEC patients with high PHF6 expression had poor survival than UCEC patients with low PHF6 expression. PHF6 mutation occurred in 12% of UCEC patients, and PHF6 mutation predicted favourable clinical outcome in UCEC patients. Depletion of PHF6 effectively inhibited HEC-1-A and KLE cell proliferation in vitro and decreased HEC-1-A cell growth in vivo. Furthermore, high PHF6 level indicated a subtype of UCECs characterized by low immune infiltration, such as CD3+ T-cell infiltration. While knockdown of PHF6 in endometrial carcinoma cells increased T-cell migration by promoting IL32 production and secretion. Taken together, our findings suggested that PHF6 might play an oncogenic role in UCEC patients. Thus, PHF6 could be a potential biomarker in predicting the prognosis of UCEC patients. Depletion of PHF6 may be a novel therapeutic strategy for UCEC patients.

Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis.

BACKGROUND: The development of deep learning (DL) algorithms for use in dentistry is an emerging trend. Periodontitis is one of the most prevalent oral diseases, which has a notable impact on the life quality of patients. Therefore, it is crucial to classify periodontitis accurately and efficiently. This systematic review aimed to identify the application of DL for the classification of periodontitis and assess the accuracy of this approach. METHODS: A literature search up to November 2023 was implemented through EMBASE, PubMed, Web of Science, Scopus, and Google Scholar databases. Inclusion and exclusion criteria were used to screen eligible studies, and the quality of the studies was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Random-effects inverse-variance model was used to perform the meta-analysis of a diagnostic test, with which pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) were calculated, and a summary receiver operating characteristic (SROC) plot was constructed. RESULTS: Thirteen studies were included in the meta-analysis. After excluding an outlier, the pooled sensitivity, specificity, positive LR, negative LR and DOR were 0.88 (95%CI 0.82-0.92), 0.82 (95%CI 0.72-0.89), 4.9 (95%CI 3.2-7.5), 0.15 (95%CI 0.10-0.22) and 33 (95%CI 19-59), respectively. The area under the SROC was 0.92 (95%CI 0.89-0.94). CONCLUSIONS: The accuracy of DL-based classification of periodontitis is high, and this approach could be employed in the future to reduce the workload of dental professionals and enhance the consistency of classification.

lncRNA TRHDE-AS1 Correlated with Genomic Landscape and Clinical Outcome in Glioma.

The role of lncRNA in cancer development has received more and more attention in research. A variety of lncRNAs are associated with the occurrence and development of glioma. However, the role of TRHDE-AS1 in glioma is still unknown. In this study, we explored the role of TRHDE-AS1 in glioma through bioinformatic methods. We first identified an association between TRHDE-AS1 and tumor prognosis in pan-cancer analysis. Subsequently, the expression levels of TRHDE-AS1 in various clinical types of glioma were compared, and significant differences were found in pathological classification, WHO classification, molecular classification, IDH mutation, and age stratification. We analyzed the genes co-expressed with TRHDE-AS1 in glioma. In the functional analysis of TRHDE-AS1, we found that TRHDE-AS1 may be involved in the regulation of synapse-related functions. In glioma cancer driver gene correlation analysis, it was also found that TRHDE-AS1 was significantly correlated with the expression levels of multiple driver genes such as TP53, BRAF, and IDH1. By comparing the mutant profiles of the high and low TRHDE-AS1 groups, we also found that there may be differences in TP53 and CIC gene mutations in low-grade gliomas. Subsequent correlation analysis between TRHDE-AS1 and glioma immune microenvironment showed that the expression level of TRHDE-AS1 was correlated with a variety of immune cells. Therefore, we believe that TRHDE-AS1 is involved in the occurrence and development of glioma and has the ability to predict the prognosis of glioma as a biomarker of glioma.