Machine learning constructs a T cell-related signature for predicting prognosis and drug sensitivity in ovarian cancer.

BACKGROUND: The leading cause of death related to gynecologic cancer is ovarian cancer, which typically has a poor prognosis. T cells are referred to as key mediators of immunosurveillance and tumor eradication, and unbalanced regulation or lack of T cells in tumors result in immunotherapy resistance. METHODS: The identification of T cell related markers depended on single-cell RNA-seq analysis. Using data from multiple datasets, including TCGA, GSE14764, GSE26193, GSE26712, and GSE140082, we constructed a prognostic signature called TRS (T cell-related signature) using 10 different machine learning algorithms. The correlation between TRS and drug sensitivity were analyzed using the data from GSE91061 and IMvigor210 dataset. RESULTS: PlsRcox method based TRS was as a risk factor for the clinical outcome of ovarian cancer patients. In comparison with stage, grade and many prognostic signatures, the performance of our TRS in evaluating the clinical outcome was better in ovarian cancer. TRS-based risk score showed distinct association with the level of ESTIMATE score, immune-related function score and immune cells. Moreover, TRS could be used to predict the immunotherapy response and chemotherapy response in ovarian cancer. CONCLUSION: In conclusion, we constructed a powerful TRS in ovarian cancer, which could accurately predict the clinical outcome of patients and be used to predict the immunotherapy response and chemotherapy response.

Carbon emissions in China's urban agglomerations: spatio-temporal patterns, regional inequalities, and driving forces.

Urban agglomerations are the centers of carbon emissions. However, research on sector-specific carbon emissions in different urban agglomerations is still limited. Drawing on the data of China's six urban agglomerations in 2005, 2010, and 2015, this study investigates the spatio-temporal patterns, regional inequalities, and driving forces of total, industrial, transportation, and residential carbon emissions. The study found that Beijing-Tianjin-Hebei was the total and sectoral emission center among the studied urban agglomerations. Additionally, regional carbon inequalities gradually decreased, implying a growing regional synergistic carbon pattern. The driving forces of carbon emissions, including population, GDP, energy intensity, secondary industry, tertiary industry, foreign investment, urbanization, and green coverage, varied across sectors and regions. Notably, foreign investment could lead to lower carbon emissions in less developed agglomerations like Beijing-Tianjin-Hebei, the Central Plains, and the middle reaches of the Yangtze River, whereas more developed agglomerations like the Yangtze River Delta and the Pearl River Delta benefited less from foreign investment. Besides, ChengYu has good ecological conditions and sustainable development modes, which linked urbanization and green space to reduced carbon emissions in the industrial sector. The findings can help formulate differentiated carbon policy and support sustainable development.

Comprehensive Analysis of the Expression and Prognosis for MCM4 in Uterine Corpus Endometrial Carcinoma.

Background: Mini chromosome maintenance protein 4 (MCM4) belongs to the family of mini chromosome maintenance proteins (MCMs) that plays a crucial role in DNA replication and cell cycle regulation. Given that MCM4 has been reported to be aberrantly expressed in a variety of tumor tissues, and is strongly associated with poor patient prognosis, it has rarely been reported in uterine corpus endometrial carcinoma (UCEC). Methods: We explored the role of MCM4 in UCEC through multi-omics analysis, including gene expression levels, survival prognosis, the biological function of interacting proteins, immune infiltration, and diagnostic value. Finally, these results were confirmed by biological experiments. Results: MCM4 was highly expressed in various malignancies including UCEC compared to normal samples and was associated with poor prognosis in patients with UCEC [including OS (HR = 1.74, p = 0.009), PFI (HR = 1.73, p = 0.002), PFI (HR = 2.23, p = 0.003)]. In the Cox regression analysis, MCM4 was an independent prognostic biomarker. Further studies showed those interacting proteins of MCM4 were enriched in DNA repair and cell cycle. Moreover, high expression of MCM4 was accompanied by lower infiltration of immune cells such as Treg cells and B cells. The distribution of MCM4 expression in molecular and immune subtypes was significantly different (p < 0.05), with high expression in the copynumber high (CN_HIGH) molecular subtype and the IFN-gamma dominant (C2) immune subtype. RT-qPCR and immunohistochemistry results also showed that MCM4 expression was significantly upregulated in endometrial cancer tissues and negatively correlated with patient prognosis (p < 0.05). Subsequent biological experiments confirmed that MCM4 promoted cell growth and invasion and inhibited apoptosis in vitro. Conclusion: Therefore, MCM4 could be a new potential biomarker for UCEC.

Differences in susceptibility to chlorantraniliprole between Chilo suppressalis (Lepidoptera: Crambidae) and two dominant parasitic wasps collected from Sichuan Province, China.

Chilo suppressalis Walker (Lepidoptera: Crambidae) is one of the most destructive pests occurring in the rice-growing regions of Asia. Parasitoids, mainly egg parasitoids, have been of interest for several years even with practical used cases. Therefore, the potential impact of insecticides on natural enemies needs great attention. In this study, chlorantraniliprole was evaluated for its impact on C. suppressalis and two dominant parasitic wasps. Bioassays showed that chlorantraniliprole had negligible toxicity to Eriborus terebrans but was significantly toxic to Chelonus munakatae; the mortality exceeded 50% when the concentration reached 46.83 ng/cm2. Enzyme assays suggested that the significantly different carboxylesterase activity may be involved in the high-level detoxification metabolism of E. terebrans. According to the results of enzyme gene correlation analysis, P450s may be the dominant factor in the detoxification metabolism of C. munakatae. In addition, the ryanodine receptor C-terminus of C. suppressalis (CsRyR), C. munakatae (CmRyR) and E. terebrans (EtRyR) were successfully cloned. Different amino acids at resistance mutation I4758 M between susceptible C. suppressalis (I) and parasitic wasps (M) may be related to susceptibility differences. Simulated docking showed that CsRyR and CmRyR can interact with chlorantraniliprole but not EtRyR. More interaction forces were formed between CsRyR and chlorantraniliprole than CmRyR. Furthermore, a Pi-Pi T-shape formed between 73PHE in CsRyR and the benzene ring in chlorantraniliprole. These results indicated that both detoxification metabolism and the target site could mediate the susceptibility difference between C. suppressalis and its parasitic wasps.

Deep learning model for classifying endometrial lesions.

BACKGROUND: Hysteroscopy is a commonly used technique for diagnosing endometrial lesions. It is essential to develop an objective model to aid clinicians in lesion diagnosis, as each type of lesion has a distinct treatment, and judgments of hysteroscopists are relatively subjective. This study constructs a convolutional neural network model that can automatically classify endometrial lesions using hysteroscopic images as input. METHODS: All histopathologically confirmed endometrial lesion images were obtained from the Shengjing Hospital of China Medical University, including endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyps, and submucous myomas. The study included 1851 images from 454 patients. After the images were preprocessed (histogram equalization, addition of noise, rotations, and flips), a training set of 6478 images was input into a tuned VGGNet-16 model; 250 images were used as the test set to evaluate the model's performance. Thereafter, we compared the model's results with the diagnosis of gynecologists. RESULTS: The overall accuracy of the VGGNet-16 model in classifying endometrial lesions is 80.8%. Its sensitivity to endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyp, and submucous myoma is 84.0%, 68.0%, 78.0%, 94.0%, and 80.0%, respectively; for these diagnoses, the model's specificity is 92.5%, 95.5%, 96.5%, 95.0%, and 96.5%, respectively. When classifying lesions as benign or as premalignant/malignant, the VGGNet-16 model's accuracy, sensitivity, and specificity are 90.8%, 83.0%, and 96.0%, respectively. The diagnostic performance of the VGGNet-16 model is slightly better than that of the three gynecologists in both classification tasks. With the aid of the model, the overall accuracy of the diagnosis of endometrial lesions by gynecologists can be improved. CONCLUSIONS: The VGGNet-16 model performs well in classifying endometrial lesions from hysteroscopic images and can provide objective diagnostic evidence for hysteroscopists.

WbaP is required for swarm motility and intramacrophage multiplication of Salmonella Enteritidis spiC mutant by glucose use ability.

Salmonella spp. can survive and replicate in macrophage cells to cause persistent infection, SpiC is a necessary T3SS effector, but its pathogenic mechanism is still not known completely. In our study, Salmonella Enteritidis spiC mutant (SEΔspiC) was found to have stronger swarming motility and intramacrophage hyperproliferation which was closely related to glucose metabolism. SEΔspiC wbaP::Tn5 mutant was screened out by transposon mutagenesis, which had weaker swarming motility and intramacrophage replication ability than SEΔspiC in the presence of glucose. Bioinformatics displayed that undecaprenyl-phosphate galactose phosphotransferase (Wbap), encoded by wbaP gene, was a key enzyme for glucose metabolism and Lipopolysaccharide(LPS) synthesis, which confirmed our outcome that Wbap was involved in intramacrophage replication ability by glucose use in addition to swarming motility based on SEΔspiC. This discovery will further promote the understanding of the interaction between wbaP gene and spiC gene and the intracellular Salmonella replication mechanism.

Bioinformatics Identification of the Expression and Clinical Significance of E2F Family in Endometrial Cancer.

BACKGROUND: Besides being one of the most prevalent cancers among women, incidence and mortality rates of endometrial cancer (EC) are still increasing. The E2F family of transcriptional factors is involved in cell differentiation, apoptosis, and inhibition of DNA damage response, thus affecting growth and invasion of tumor cells. METHODS: We used multiple bioinformatics tools to explore the role of E2F family in endometrial cancer. RESULTS: The expression of E2F1/2/3/7/8 was significantly upregulated in endometrial cancer tissues, converse to E2F4, which was downregulated. Methylation downregulates all E2Fs except for E2F2. Accordingly, E2F1/2/3/5/7/8 are potential diagnostic biomarkers for EC. In particular, EC patients displaying upregulated E2F1, and E2F3 expression had a worse overall survival and relapse-free survival. E2F8, E2F7, and E2F1 were the top three, most-frequently altered genes in endometrial cancer. E2F family activates apoptosis pathways, regulates cell cycle, and impairs DNA damage response pathways. Drug-sensitivity analysis demonstrated that the level of E2F2/3/8 negatively correlated with drug resistance. Meanwhile, immune infiltrations analysis revealed that E2F family is associated with recruitment of several immune cells. Enrichment analysis on its part revealed that the E2F family is mainly associated with cell cycle, sequence-specific DNA binding, nuclear transcription factor complex, PI3K-Akt signaling, and p53 signaling pathway. We also identified multiple E2Fs-associated miRNA and kinase targets in endometrial cancer. CONCLUSION: Our study revealed the unique expression signature and clinical significance of E2F family in EC, demonstrating the potential clinical utility of these transcription factors (TF) in endometrial cancer.

BTG1 inhibits malignancy as a novel prognosis signature in endometrial carcinoma.

BACKGROUND: Endometrial carcinoma (EC) is one of the three major malignant tumors of the female reproductive system. In recent years, the incidence and mortality rate of EC have increased. B-cell translocation gene 1 (BTG1) is an anti-proliferation gene that regulates the occurrence and development of a variety of tumors, but there is no research regarding this gene in EC. METHODS: Based on The Cancer Genome Atlas (TCGA) database, we used a variety of bioinformatics tools and databases to explore the expression and prognosis of BTG1. We verified expression and prognosis of BTG1 in EC using qRT-PCR and analyzed the relevant clinicopathological parameters. We functionally enriched BTG1 and related genes in EC patients through the bioinformatics website and analyzed miRNA targets of BTG1 and interacting protein networks. Cell proliferation, wound healing, transwell invasion, and cell apoptosis assays were used to detect the effects of BTG1 on the malignant biological behavior of endometrial carcinoma cells (ECCs). The effect of BTG1 on the epithelial-to-mesenchymal transition (EMT) process was detected using western blot. RESULTS: We analyzed the expression and prognosis of BTG1 based on TCGA and found that low expression of BTG1 was associated with poor EC prognosis. The qRT-PCR suggested that BTG1 had low expression in EC. BTG1 expression was significantly correlated with overall survival (OS) shortening. Clinicopathological analysis suggested that expression of BTG1 was related to invasion depth and the International Federation of Gynecology and Obstetrics (FIGO) stage. EC pathological tissue type, fertility history, lymphatic metastasis, menopause, estrogen receptor (ER), progesterone receptor (PR), and age of diagnosis were not related. Functional enrichment analysis showed that BTG1 plays an important role in regulating embryonic development, tumorigenesis, apoptosis, and cell cycle. Biological behavior experiments suggest that BTG1 inhibits proliferation, migration, and invasion of ECCs, and promotes apoptosis of ECCs. Western blot indicated that BTG1 inhibited the EMT process of ECCs. CONCLUSIONS: BTG1, as a tumor suppressor gene, plays an important role in the occurrence and development of EC. We believe that BTG1 can be used as a potential prognostic biomarker for EC.

Nomograms based on the novel platelet index score predict postoperative prognosis in endometrial cancer.

OBJECTIVE: To identify preoperative platelet indexes with prognostic value and to develop and validate nomograms for predicting the survival of endometrial cancer (EC) patients. METHODS: A total of 1198 women who received primary surgical treatment between January 2008 and January 2017 were included in the study. Data were randomly divided into a training set (70%, N = 840) and an external validation set (30%, n = 358). Cox regression analysis was performed in the training cohort to identify independent prognostic factors and develop nomograms for survival rate prediction. RESULTS: High platelet count (PLT ≥350), high mean platelet volume (MPV ≥8.8) and low platelet distribution width (PDW <12.1) were independently associated with poor RFS and OS. PLT, MPV and PDW were thus incorporated in an innovative score called the platelet index score (PIS). The PIS was also an independent indicator, which was related to histology, lymph-vascular space invasion, lymph node involvement and FIGO stage (P = 0.007, P = 0.042, P < 0.001 and P < 0.001, respectively). Furthermore, we developed and validated two nomograms based on Cox regression models. The discriminative ability and calibration of the nomograms revealed good predictive ability, as indicated by the C-indexes and calibration plots. Moreover, both the IDI and NRI were improved. CONCLUSIONS: Nomograms based on the PIS and clinicopathological features accurately predict recurrence-free survival and overall survival for EC patients.

The population growth, development and metabolic enzymes of the white-backed planthopper, Sogatella furcifera (Hemiptera: Delphacidae) under the sublethal dose of triflumezopyrim.

Triflumezopyrim, a new nicotinic acetylcholine receptor (nAChR) inhibition, can effectively control piercing-sucking insect pests such as white-backed planthopper (Sogatella furcifera). At present, there has been no reports on the effects of triflumezopyrim on the population growth and development of S. furcifera. In this experiment, an age-stage two-sex life table was used to evaluate the impact of triflumezopyrim on the biological parameters of S. furcifera. The results showed that the adult preoviposition period (APOP) and total preoviposition period (TPOP) of the F1 generation were significantly higher than those of the F0 and F4 generations, on the contrary the average fecundity, intrinsic rate of increase (r) and finite rate of increase (λ) of the F4 generation were higher than those of the F0 and F1 generations. The results of synergists and enzyme activities indicated that the CarE and P450 activities in the F4 generation were significantly higher than those in the F0 generation (P < 0.05). The protein contents of vitellogenin (Vg) and vitellogenin receptor (VgR) and relative expression quality of VgR in the F4 female adults were also significantly higher than those in the F0 generation (P < 0.05). These results showed that triflumezopyrim at a low concentration could promote the growth and reproduction of S. furcifera, and that may provide a reference for the rational use of triflumezopyrim in the future.

Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer.

BACKGROUND: Endometrial cancer (EC) is one of the three major gynecological malignancies. Numerous biomarkers that may be associated with survival and prognosis have been identified through database mining in previous studies. However, the predictive ability of single-gene biomarkers is not sufficiently specific. Genetic signatures may be an improved option for prediction. This study aimed to explore data from The Cancer Genome Atlas (TCGA) to identify a new genetic signature for predicting the prognosis of EC. METHODS: mRNA expression profiling was performed in a group of patients with EC (n = 548) from TCGA. Gene set enrichment analysis was performed to identify gene sets that were significantly different between EC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Quantitative real-time-PCR was used to verify the reliability of the expression of selected mRNAs. Subsequent multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan-Meier survival estimates and the log-rank test were used to validate the significance of risk parameters for prognosis prediction. RESULT: Nine genes associated with glycolysis (CLDN9, B4GALT1, GMPPB, B4GALT4, AK4, CHST6, PC, GPC1, and SRD5A3) were found to be significantly related to overall survival. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. Based on the nine-gene signature, the 548 patients with EC were divided into high/low-risk subgroups. The prognostic ability of the nine-gene signature was not affected by other factors. CONCLUSION: A nine-gene signature associated with cellular glycolysis for predicting the survival of patients with EC was developed. The findings provide insight into the mechanisms of cellular glycolysis and identification of patients with poor prognosis in EC.