Machine learning-based classification of deubiquitinase USP26 and its cell proliferation inhibition through stabilizing KLF6 in cervical cancer.

OBJECTIVE: We aim to accurately distinguish ubiquitin-specific proteases (USPs) from other members within the deubiquitinating enzyme families based on protein sequences. Additionally, we seek to elucidate the specific regulatory mechanisms through which USP26 modulates Krüppel-like factor 6 (KLF6) and assess the subsequent effects of this regulation on both the proliferation and migration of cervical cancer cells. METHODS: All the deubiquitinase (DUB) sequences were classified into USPs and non-USPs. Feature vectors, including 188D, n-gram, and 400D dimensions, were extracted from these sequences and subjected to binary classification via the Weka software. Next, thirty human USPs were also analyzed to identify conserved motifs and ascertained evolutionary relationships. Experimentally, more than 90 unique DUB-encoding plasmids were transfected into HeLa cell lines to assess alterations in KLF6 protein levels and to isolate a specific DUB involved in KLF6 regulation. Subsequent experiments utilized both wild-type (WT) USP26 overexpression and shRNA-mediated USP26 knockdown to examine changes in KLF6 protein levels. The half-life experiment was performed to assess the influence of USP26 on KLF6 protein stability. Immunoprecipitation was applied to confirm the USP26-KLF6 interaction, and ubiquitination assays to explore the role of USP26 in KLF6 deubiquitination. Additional cellular assays were conducted to evaluate the effects of USP26 on HeLa cell proliferation and migration. RESULTS: 1. Among the extracted feature vectors of 188D, 400D, and n-gram, all 12 classifiers demonstrated excellent performance. The RandomForest classifier demonstrated superior performance in this assessment. Phylogenetic analysis of 30 human USPs revealed the presence of nine unique motifs, comprising zinc finger and ubiquitin-specific protease domains. 2. Through a systematic screening of the deubiquitinase library, USP26 was identified as the sole DUB associated with KLF6. 3. USP26 positively regulated the protein level of KLF6, as evidenced by the decrease in KLF6 protein expression upon shUSP26 knockdown in both 293T and Hela cell lines. Additionally, half-life experiments demonstrated that USP26 prolonged the stability of KLF6. 4. Immunoprecipitation experiments revealed a strong interaction between USP26 and KLF6. Notably, the functional interaction domain was mapped to amino acids 285-913 of USP26, as opposed to the 1-295 region. 5. WT USP26 was found to attenuate the ubiquitination levels of KLF6. However, the mutant USP26 abrogated its deubiquitination activity. 6. Functional biological assays demonstrated that overexpression of USP26 inhibited both proliferation and migration of HeLa cells. Conversely, knockdown of USP26 was shown to promote these oncogenic properties. CONCLUSIONS: 1. At the protein sequence level, members of the USP family can be effectively differentiated from non-USP proteins. Furthermore, specific functional motifs have been identified within the sequences of human USPs. 2. The deubiquitinating enzyme USP26 has been shown to target KLF6 for deubiquitination, thereby modulating its stability. Importantly, USP26 plays a pivotal role in the modulation of proliferation and migration in cervical cancer cells.

Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method.

The Src Homology 2 (SH2) domain plays an important role in the signal transmission mechanism in organisms. It mediates the protein-protein interactions based on the combination between phosphotyrosine and motifs in SH2 domain. In this study, we designed a method to identify SH2 domain-containing proteins and non-SH2 domain-containing proteins through deep learning technology. Firstly, we collected SH2 and non-SH2 domain-containing protein sequences including multiple species. We built six deep learning models through DeepBIO after data preprocessing and compared their performance. Secondly, we selected the model with the strongest comprehensive ability to conduct training and test separately again, and analyze the results visually. It was found that 288-dimensional (288D) feature could effectively identify two types of proteins. Finally, motifs analysis discovered the specific motif YKIR and revealed its function in signal transduction. In summary, we successfully identified SH2 domain and non-SH2 domain proteins through deep learning method, and obtained 288D features that perform best. In addition, we found a new motif YKIR in SH2 domain, and analyzed its function which helps to further understand the signaling mechanisms within the organism.

Expression of ALDH1 plays the important role during generation and progression in human cervical cancer.

Cervical cancer which is caused by persistent infection with oncogenic human papillomavirus (HPV), is the third most common cancer. HPV infection causes the progression of the normal cervix to cervical intraepithelial neoplasia (CIN) because it often occurs at the function conversion of the cervical squamous epithelium and columnar epithelium zone, further to invasive carcinoma. The difference in the ALDH1 expression was very significant. With the progression of cervical cancer, reports explained obviously increased nuclear and cytoplasm ALDH1 staining in comparisons of cervical carcinomas and normal cervix (P < 0.0001), cervical carcinomas compared with CIN (P = 0.0002). Therefore, ALDH1 as a stem marker, not only resists cervical cancer but also resists in normal cervix and CIN tissues. Developing an experimental method to discover cervical cancer earlier is feasible. Furthermore, the ALDH1 was expressed in human cervical cancer cell lines (Hela, SiHa, CaSki, HT-3, and C33A) together with western blot and immunocytochemical analysis. ALDH1 plays a significant role in nuclear and cytoplasm staining by immunochemistry in single or clustered HT-3 and C33A cells. However, western blot and immunochemical analysis did not detect ALDH1 in HeLa or CaSki, SiHa cells. We also discovered that there were no remarkable differences in age, tumor size, clinical TNM staging, multiple pelvic lymph node metastasis, or histological staging (p > 0.05) between the ALDH1-positive groups in 100 cervical cancer tissues. But after the control variable age, different ALDH rating survival function contrasted, it can be concluded that the higher ALDH1 scores with the survival of patients with the worse condition.

Analysis Effects of Pender Health Promotion Model Education on Health Behaviors and Maternal and Infant of Cardiac Disease in Pregnancy.

Pregnancy with cardiac disease includes pregnancy in women with a history of heart disease and cardiac disease during pregnancy. Therefore, we aim to investigate the effect of Pender health promotion model (HPM) on the health behaviors and maternal and fetal outcomes in pregnant patients with cardiac disease. 80 pregnant women with cardiac disease were enrolled in the study from January 2016 to December 2017. 40 patients who received HPM health education were selected as treatment group by convenient sampling method. 40 patients were selected to receive routine health education. After 12 weeks of education, the awareness of health knowledge before and after education, health behavior compliance, level of pre-partum cardiac function, and maternal and fetal outcomes were compared between the two groups. After education, the knowledge rate in observation group was higher than that in control group (P < 0.01, P < 0.05); the compliance of health behavior in observation group was higher than that in control group (P < 0.01, P < 0.05); before delivery, the rate of cardiac function(I+II) in observation group was 67.50% and significantly lower than 42.50% in control group (P < 0.05); after education, the incidence rate of severe heart failure, cesarean delivery, premature delivery and full-term infants with low body mass in observation group was significantly lower than that in control group (P < 0.05). The Pender health promotion model can improve the knowledge of health knowledge and compliance of health behaviors of pregnancy with cardiac disease effectively, and it can help reduce heart burden and improve maternal and child outcomes.

Genetic and epigenetic characteristics in ovarian tissues from polycystic ovary syndrome patients with irregular menstruation resemble those of ovarian cancer.

BACKGROUND: Irregular menstruation is clinically associated with an increased risk for ovarian cancer and disease-related mortality. This relationship remains poorly understood, and a mechanism explaining it has yet to be described. METHODS: Ovarian tissues from women with polycystic ovary syndrome (PCOS) and regular menstruation (n = 10) or irregular menstruation (n = 10) were subjected to DNA methylation sequencing, real-time PCR array, whole-exome sequencing, and bioinformatics analysis. RESULTS: We demonstrated that ovarian tissue from PCOS patients with irregular menstruation displayed global DNA hypomethylation, as well as hypomethylation at several functionally and oncologically significant regions. Furthermore, we showed that several cancer-related genes were aberrantly expressed in ovarian tissue from patients with irregular menstruation, and that their mRNA and microRNA profiles shared appreciable levels of coincidence with those from ovarian cancer tissue. We identified multiple point mutations in both the BRCA1 and MLH1 genes in patients with irregular menstruation, and predicted the potential pathogenicity of these mutations using bioinformatics analyses. CONCLUSIONS: Due to the nature of ovarian cancer, it is important to broaden our understanding of the pathogenesis and risk factors of the disease. Herein, we provide the first description of a genetic and epigenetic basis for the clinical relationship between irregular menstruation and an increased risk for ovarian cancer.