Machine learning methods revealed the roles of immune-metabolism related genes in immune infiltration, stemness, and prognosis of neuroblastoma.

BACKGROUND: Immunometabolism plays an important role in neuroblastoma (NB). However, the mechanism of immune-metabolism related genes (IMRGs) in NB remains unclear. This study aimed to explore the effects of IMRGs on the prognosis, immune infiltration and stemness of patients with NB using machine learning methods. METHODS: R software (v4.2.1) was used to identify the differentially expressed IMRGs, and machine learning algorithm was used to screen the prognostic genes from IMRGs. Then we constructed a prognostic model and calculated the risk scores. The NB patients were grouped according to the prognosis scores. In addition, the genes most associated with the immune infiltration and stemness of NB were analyzed by weighted gene co-expression network analysis (WGCNA). RESULTS: There were 89 differentially expressed IMRGs between the MYCN amplification and the MYCN non-amplification group, among which CNR1, GNAI1, GLDC and ABCC4 were selected by machine learning algorithm to construct the prognosis model due to their better prediction effect. Both the K-M survival curve and the 5-year Receiver operating characteristic (ROC) curve indicated that the prognosis model could predict the prognosis of NB patients, and there was significant difference in immune infiltration between the two groups according to the median of risk score. CONCLUSIONS: We verified the effects of IMRGs on the prognosis, immune infiltration and stemness of NB. These findings could provide help for predicting prognosis and developing immunotherapy in NB.

Analysis and validation of aging-related genes in prognosis and immune function of glioblastoma.

BACKGROUND: Glioblastoma (GBM) is a common malignant brain tumor with poor prognosis and high mortality. Numerous reports have identified the correlation between aging and the prognosis of patients with GBM. The purpose of this study was to establish a prognostic model for GBM patients based on aging-related gene (ARG) to help determine the prognosis of GBM patients. METHODS: 143 patients with GBM from The Cancer Genomic Atlas (TCGA), 218 patients with GBM from the Chinese Glioma Genomic Atlas (CGGA) of China and 50 patients from Gene Expression Omnibus (GEO) were included in the study. R software (V4.2.1) and bioinformatics statistical methods were used to develop prognostic models and study immune infiltration and mutation characteristics. RESULTS: Thirteen genes were screened out and used to establish the prognostic model finally, and the risk scores of the prognostic model was an independent factor (P < 0.001), which indicated a good prediction ability. In addition, there are significant differences in immune infiltration and mutation characteristics between the two groups with high and low risk scores. CONCLUSION: The prognostic model of GBM patients based on ARGs can predict the prognosis of GBM patients. However, this signature requires further investigation and validation in larger cohort studies.

The m6A methyltransferase METTL3 affects cell proliferation and migration by regulating YAP expression in Hirschsprung disease.

BACKGROUND: METTL3, an mRNA m6A methyltransferase, has been implicated in various steps of mRNA metabolism, such as stabilization, splicing, nuclear transportation, translation, and degradation. However, whether METTL3 dysregulation is involved in Hirschsprung disease (HSCR) development remains unclear. In this study, we preliminarily elucidated the role of METTL3 in HSCR and sought to identify the associated molecular mechanism. METHODS: The gene expression levels of YAP and several methyltransferases, demethylases, and effectors were evaluated by RT-qPCR. Protein levels were evaluated by western blot and immunohistochemistry. Cell proliferation and migration were detected by CCK-8 and Transwell assays, respectively. The overall levels of m6A modification were determined by colorimetry. RESULTS: We found that m6A levels were reduced in the stenotic intestinal tissue of patients with HSCR. When METTL3 was knocked down in SH-SY5Y and HEK-293T cells, the proliferative and migratory abilities of the cells were inhibited, m6A modification levels were reduced, and YAP expression was increased. Importantly, YAP and METTL3 expression displayed a negative correlation in both cell lines as well as in HSCR tissue. CONCLUSIONS: Our results provide evidence for an interaction between METTL3 and YAP in HSCR, and further suggest that METTL3 is involved in the pathogenesis of HSCR by regulating neural crest cell proliferation and migration upstream of YAP.

Association of rs2435357 and rs2506030 polymorphisms in RET with susceptibility to hirschsprung disease: A systematic review and meta-analysis.

Background: There are numerous published studies on the association between RET polymorphisms and susceptibility to Hirschsprung disease (HSCR). However, some of the results are inconsistent and the studies were conducted with small sample sizes. Therefore, we performed a meta-analysis to clarify the relationship. Methods: Relevant data were retrieved from PubMed, Web of Science, Cochrane Library, EMBASE, CNKI, and Google Scholar according to PRISMA guidelines. Odds ratios (OR) were calculated to assess susceptibility to HSCR. Meanwhile, heterogeneity and publication bias were also calculated by R software package (version 4.2.1). The protocol was published in PROSPERO (CRD42022348940). Results: A total of 12 studies were included in the meta-analysis and comprised 12 studies on the RET polymorphism rs2435357 (1,939 subjects and 3,613 controls) and 7 studies on the RET polymorphism rs2506030 (1,849 patients with HSCR and 3,054 controls). The analysis revealed that rs2435357 [A vs. G: odds ratio (OR) = 3.842, 95% confidence interval (CI) 2.829-5.220; AA vs. GG: OR = 2.597, 95% CI 1.499-4.501; AA + AG vs. GG: OR = 6.789, 95% CI 3.0711-14.9973; AA vs. AG + GG: OR = 8.156, 95%CI 5.429-12.253] and rs2506030 (A vs. G: OR = 0.519, 95% CI 0.469-0.573; AA vs. GG: OR = 0.543, 95% CI 0.474-0.623; AA + AG vs. GG: OR = 0.410, 95% CI 0.360-0.468; AA vs. AG + GG: OR = 0.361, 95%CI 0.292-0.447) were significantly associated with susceptibility to HSCR. Conclusions: The polymorphisms rs2435357 and rs2506030 in the RET may be related to susceptibility to HSCR, of which rs2435357 (T > C) is the causal locus and rs2506030 (A > G) is the protective locus. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier:CRD42022348940.

[Determination of total nitrogen content in fresh tea leaf using visible-near infrared spectroscopy].

To monitor tea tree growth and nitrogen nutrition in tea leaves, visible-near infrared spectroscopy was used to determine total nitrogen content. One hundred eleven fresh tea leaves of different nitrogen levels were sampled according to different tea type, plant age, leaf age, leaf position and soil nutrients, which covered a wide range of nitrogen content. Visible-near infrared reflectance spectra were scanned under the sunlight with a portable spectroradiometer (ASD FieldSpec 3) in field. The software of NIRSA developed by Jiangsu University was used to establish the calibration models and prediction models, which included spectra data editing, preprocessing, sample analysis, spectrogram comparison, calibration model and prediction model, analysis reporting and system configuration Eighty six samples were used to establish the calibration model with the preprocessing of first/second-order derivative plus moving average filter and the algorithm of PLS regression, stepwise regression, principal component regression, PLS regression plus artificial neural network and so on The result shows that the PLS regression calibration model with 7 principal component factors after the preprocessing of first-order derivative plus moving average filter is the best and correspondingly the root mean square error of calibration is 0. 973. Twenty five unknown samples were used to establish the prediction model and the correlation coefficient between predicted values and real values is 0.8881, while the root mean square error of prediction is 0. 130 4 with the mean relative error of 4.339%. Therefore, visible-near infrared spectroscopy has a huge potential for the determination of total nitrogen content in fresh tea leaves in a rapid and nondestructive way. Consequently, the technique can be significant to monitoring the tea tree growth and fertilization management.