Determining the Clinical Value and Critical Pathway of GTPBP4 in Lung Adenocarcinoma Using a Bioinformatics Strategy: A Study Based on Datasets from The Cancer Genome Atlas

Foot-and-mouth disease virus VP1 degrades YTHDF2 through autophagy to regulate IRF3 activity for viral replication

Many viruses, including foot-and-mouth disease virus (FMDV), can promote the degradation of host proteins through macroautophagy/autophagy, thereby promoting viral replication. However, the regulatory mechanism between autophagy and innate immune responses is not fully understood during FMDV infection. Here, we found that the host GTPBP4/NOG1 (GTP binding protein 4) is a negative regulator of innate immune responses. GTPBP4 deficiency promotes the antiviral innate immune response, resulting in the ability of GTPBP4 to promote FMDV replication. Meanwhile, GTPBP4-deficient mice are more resistant to FMDV infection. To antagonize the host's antiviral immunity, FMDV structural protein VP1 promotes the expression of GTPBP4, and the 209th site of VP1 is responsible for this effect. Mechanically, FMDV VP1 promotes autophagy during virus infection and interacts with and degrades YTHDF2 (YTH N6-methyladenosine RNA binding protein F2) in an AKT-MTOR-dependent autophagy pathway, resulting in an increase in GTPBP4 mRNA and protein levels. Increased GTPBP4 inhibits IRF3 binding to the Ifnb/Ifn-β promoter, suppressing FMDV-induced type I interferon production. In conclusion, our study revealed an underlying mechanism of how VP1 negatively regulates innate immunity through the autophagy pathway, which would contribute to understanding the negative regulation of host innate immune responses and the function of GTPBP4 and YTHDF2 during FMDV infection.Abbreviation: 3-MA:3-methyladenine; ACTB: actin beta; ATG: autophagy related; ChIP:chromatin immunoprecipitation; CQ: chloroquine; DAPI:4',6-diamidino-2-phenylindole; dpi: days post-infection; EV71:enterovirus 71; FMDV: foot-and-mouth disease virus; GTPBP4/NOG1: GTPbinding protein 4; HIF1A: hypoxia inducible factor 1 subunit alpha;hpt:hours post-transfection; IFNB/IFN-β:interferon beta; IRF3: interferon regulatory factor 3; MAP1LC3/LC3:microtubule associated protein 1 light chain 3; MAVS: mitochondriaantiviral signaling protein; MOI: multiplicity of infection; MTOR:mechanistic target of rapamycin kinase; m6A: N(6)-methyladenosine;qPCR:quantitativePCR; SIRT3:sirtuin 3; SQSTM1/p62: sequestosome 1; STING1: stimulator ofinterferon response cGAMP interactor 1; siRNA: small interfering RNA;TBK1: TANK binding kinase 1; TCID50:50% tissue culture infectious doses; ULK1: unc-51 like autophagyactivating kinase 1; UTR: untranslated region; WT: wild type; YTHDF2:YTH N6-methyladenosine RNA binding protein F2.

Role of Bioinformatics Analysis in Early Differential Diagnosis of Ovarian Cancer

In order to solve the problem of early differential diagnosis of ovarian cancer, this paper proposes the role of bioinformatics analysis in early differential diagnosis of ovarian cancer. This method uses bioinformatics methods to mine the existing data in the tumor database and obtain tumor-related molecules. It is an efficient method to obtain effective biomarkers, screen signal pathway molecules, and reveal the internal mechanism of tumor occurrence and development. Using this method can greatly improve the efficiency and reliability of screening diagnosis, prognosis, and treatment targets. The results showed that 5821 new lncRNA transcripts and 4611 new lncRNA genes were identified by lncScore from the assembled transcripts. 10 new lncRNA transcripts and 174 new lncRNA genes were found to be differentially expressed in ovarian cancer.

Pan-cancer analysis revealed the significance of the GTPBP family in cancer

Background: At present, cancer is still one of the principal diseases to represent a serious danger to human health. Although research on the pathogenesis and treatment of cancer is progressing rapidly, the current knowledge on this topic is far from sufficient. Some tumors with poor prognoses lack effective prognostic biomarkers.

Methods: Firstly, the Wilcoxon test was used to analyse the expression of GTPBP1-GTPBP10 in cancerous and normal tissues. Subsequently, we explored the expression of GTPBP1-10 in cancer by way of a paired t-test and plotted the survival curve using KM and univariate Cox regression analysis to explore the relationship between GTPBP1-10 and the prognosis of cancer. We then explored the significance of the GTPBP family in the tumor microenvironment.

Results: The results showed that many members of the GTPBP family are differentially expressed in a variety of cancers and alter the prognosis of a number of cancers. Members of the GTPBP family may serve as novel prognostic markers for these tumors. Moreover, members of the GTPBP family are correlated with the immune microenvironment of tumors, which is valuable in terms of adding to our understanding of the mechanisms of tumor genesis. Finally, we identified drugs showing a high correlation with GTPBP family members, which are therefore conducive to the development of GTPBP family member-based treatment regimens.

Conclusions: The 10 members of the GTPBP family have prognostic value in multiple tumor types and are associated with the immune microenvironment. Our study may provide a reference for the diagnosis and treatment of tumors.

Upregulation of GTPBP4 Promotes the Proliferation of Liver Cancer Cells

RESULTS

The GTPBP4 has upregulated expression in liver cancer patients (P < 0.01), but there was no difference in its expression in patients with different clinicopathological stages. The expression of GTPBP4 increased with the increase of cancer metastasis in lymph nodes (P < 0.01). Liver cancer patients with upregulated expression of GTPBP4 showed a shorter overall survival rate (P=0.02). GTPBP4 is closely related to genes such as NIFK, WDR12, and RPF2, and these genes are involved in life processes such as GTP binding and rRNA processing. The upregulated expression of GTPBP4 promotes the proliferation of liver cancer cells and promotes the growth of tumors in mice, while the downregulated expression of GTPBP4 inhibits the proliferation of liver cancer cells and inhibits the growth of tumors in mice.

CONCLUSION

The expression of GTPBP4 is upregulated in liver cancer patients and affects the overall survival rate of patients. The upregulated expression of GTPBP4 promotes the proliferation of liver cancer cells and the growth of tumors.

mRBioM: An Algorithm for the Identification of Potential mRNA Biomarkers From Complete Transcriptomic Profiles of Gastric Adenocarcinoma

Purpose

In this work, an algorithm named mRBioM was developed for the identification of potential mRNA biomarkers (PmBs) from complete transcriptomic RNA profiles of gastric adenocarcinoma (GA).

Methods

mRBioM initially extracts differentially expressed (DE) RNAs (mRNAs, miRNAs, and lncRNAs). Next, mRBioM calculates the total information amount of each DE mRNA based on the coexpression network, including three types of RNAs and the protein-protein interaction network encoded by DE mRNAs. Finally, PmBs were identified according to the variation trend of total information amount of all DE mRNAs. Four PmB-based classifiers without learning and with learning were designed to discriminate the sample types to confirm the reliability of PmBs identified by mRBioM. PmB-based survival analysis was performed. Finally, three other cancer datasets were used to confirm the generalization ability of mRBioM.

Results

mRBioM identified 55 PmBs (41 upregulated and 14 downregulated) related to GA. The list included thirteen PmBs that have been verified as biomarkers or potential therapeutic targets of gastric cancer, and some PmBs were newly identified. Most PmBs were primarily enriched in the pathways closely related to the occurrence and development of gastric cancer. Cancer-related factors without learning achieved sensitivity, specificity, and accuracy of 0.90, 1, and 0.90, respectively, in the classification of the GA and control samples. Average accuracy, sensitivity, and specificity of the three classifiers with machine learning ranged within 0.94–0.98, 0.94–0.97, and 0.97–1, respectively. The prognostic risk score model constructed by 4 PmBs was able to correctly and significantly (^∗∗∗p < 0.001) classify 269 GA patients into the high-risk (n = 134) and low-risk (n = 135) groups. GA equivalent classification performance was achieved using the complete transcriptomic RNA profiles of colon adenocarcinoma, lung adenocarcinoma, and hepatocellular carcinoma using PmBs identified by mRBioM.

Conclusions

GA-related PmBs have high specificity and sensitivity and strong prognostic risk prediction. MRBioM has also good generalization. These PmBs may have good application prospects for early diagnosis of GA and may help to elucidate the mechanism governing the occurrence and development of GA. Additionally, mRBioM is expected to be applied for the identification of other cancer-related biomarkers.