Aberrant MEK5 Signalling Promotes Renal Cancer Development via mTOR Activation

Purpose: Renal cell carcinoma is one of the most incident malignancies globally. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. However, comprehensive clinical treatment has some limitations. Therefore, exploring the pathogenesis and identifying novel therapeutic targets are required urgently. MEK5 has been reported to play an essential role in the development of various cancers. However, no study has evaluated its role and specific mechanisms in ccRCC. Methods: Using the ONCOMINE database, MEK5 expression in ccRCC and normal tissues was compared. ccRCC and adjacent normal tissues were collected from fourteen ccRCC patients, and ccRCC expression was assessed by qPCR and immunohistochemistry. MEK5 overexpression and knockdown plasmids were constructed and transfected into ccRCC cells. CCK8, wound-healing assay, and clone formation assay were performed to examine the cell proliferation, migration, and clone formation ability of ccRCC cells. Furthermore, a western blot was performed to verify the regulation and influence of MEK5 on the mTOR signaling pathway. The MEK5 small molecule inhibitor BIX was used to treat cells, followed by CCK8, wound-healing assay, clone formation, and flow cytometry assay to examine the cell proliferation, migration, clone formation ability, apoptosis, and cell cycle. Finally, a murine subcutaneous tumor model was constructed, and the effect and safety of BIX were evaluated in-vivo.Results: The ONCOMINE database indicated that the MEK5 expression in ccRCC was significantly higher than the normal tissues, further confirmed in clinical specimens. The knockdown of MEK5 markedly inhibited the ability of ccRCC cell proliferation, colony formation, and migration. In contrast, MEK5 overexpression promoted cell proliferation, colony formation, and migration. Western blotting showed that overexpression of MEK5 can further activate the mTOR signaling pathway. The MEK5 inhibitor, BIX treatment of ccRCC, significantly inhibited cell proliferation, arrested the cell cycle in the G0/G1 phase, induced apoptosis, and effectively inhibited cell migration and clone formation. BIX also showed an excellent anti-tumor effect and favorable safety profile in murine models.Conclusions: MEK5 regulated the mTOR signal pathway and regulated the cell proliferation, cycle, migration, clone formation of ccRCC. Targeted inhibition of MEK5 had a good anti-tumor effect and favorable safety profile, providing new directions for ccRCC therapy.

Identification of key genes for HNSCC from public databases using bioinformatics analysis

Background The cause and underlying molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) are unclear. Our study aims to identify the key genes associated with HNSCC and reveal potential biomarkers. Methods In this study, the expression profile dataset GSE83519 of the Gene Expression Omnibus database and the RNA sequencing dataset of HNSCC of The Cancer Genome Atlas were included for analysis. Sixteen differentially expressed genes were screened from these two datasets using R software. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) was then adopted for survival analysis, and finally, three key genes related to the overall survival of HNSCC patients were identified. Furthermore, we verified these three genes using the Oncomine database and from real-time PCR and immunohistochemistry results from HNSCC tissues. Results The expression data of 44 samples from GSE83519 and 545 samples from TCGA-HNSC were collected. Using bioinformatics, the two databases were integrated, and 16 DEGs were screened out. Gene Ontology (GO) enrichment analysis showed that the biological functions of DEGs focused primarily on the apical plasma membrane and regulation of anoikis. Kyoto Encyclopedia of Genes and Genomes (KEGG) signalling pathway analysis showed that these DEGs were mainly involved in drug metabolism-cytochrome P450 and serotonergic synapses. Survival analysis identified three key genes, CEACAM5, CEACAM6 and CLCA4, that were closely related to HNSCC prognosis. The Oncomine database, qRT–PCR and IHC verified that all 3 key genes were downregulated in most HNSCC tissues compared to adjacent normal tissues. Conclusions This study indicates that integrated bioinformatics analyses play an important role in screening for differentially expressed genes and pathways in HNSCC, helping us better understand the biomarkers and molecular mechanism of HNSCC.

The Alterations and Potential Roles of MCMs in Breast Cancer

The minichromosome maintenance (MCM) protein family plays a key role in eukaryotic DNA replication and has been confirmed to be associated with the occurrence and progression of many tumors. However, the expression levels, functions, and prognostic values of MCMs in breast cancer (BC) have not been clearly and systematically explained. In this article, we studied the transcriptional levels of MCMs in BC based on the Oncomine database. Kaplan-Meier plotter was used to analyze prognostic value of MCMs in human BC patients. Furthermore, we constructed a MCM coexpression gene network and performed functional annotation analysis through DAVID to reveal the functions of MCMs and coexpressed genes. The data showed that the expression of MCM2–8 and MCM10 but not MCM1 and MCM9 was upregulated in BC. Kaplan-Meier plotter analysis revealed that high transcriptional levels of MCM2, MCM4–7, and MCM10 were significantly related to low relapse-free survival (RFS) in BC patients. In contrast, high levels of MCM1 and MCM9 predicted high RFS for BC patients. This study suggests that MCM2, MCM4–7, and MCM10 possess great potential to be valuable prognostic biomarkers for BC and that MCM1 and MCM9 may serve as potential treatment targets for BC patients.

Bioinformatic Analysis of Key Genes Related to Tumor Microenvironment in Hepatocellular Carcinoma

Background: Tumor microenvironment (TME) plays important roles in the development of different types of cancer. However, the critical regulatory members of TME related to hepatocellular carcinoma (HCC) remain unclear. In this study, a bioinformatic analysis based on Cancer Genome Atlas (TCGA) and Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) datasets was conducted to predict the key genes affecting TME in HCC.Material and Methods: First, 340 patients and 20531 genes’ expression data with ESTIMATE scores were filtered and combined to identify differentially expressed genes. Next, protein-protein interaction (PPI) network and functional enrichment analysis were conducted to find hub genes. Then, log-rank test and functional enrichment analysis were conducted on the consensus genes and hub genes. Finally, Kaplan-Meier curves of the hub genes were drawn. As verification, those genes were searched on Oncomine database.Results: Among all differentially expressed genes, 916 genes were expressed in both the immune and stromal groups. The Gene Ontology (GO) terms they enriched were T cell activation, leukocyte migration, collagen-containing matrix, external side of plasma membrane, receptor ligand and activator activity. Cytokine-cytokine receptor interaction was the most significant Kyoto Encyclopedia of Genes and Genomes (KEGG) term. Furthermore, cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 hub genes were low expressed in 304 patients, participating in a variety of responses including immune response−activating cell surface receptor signaling, immune response−activating signal transduction, clathrin−coated vesicle membrane, immune receptor activity， peptide binding and amide binding pathways. Their low expression was also verified on Oncomine database.Conclusion: cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 participated in many aspects related to TME, and their low expression constructs a signature, may predict a poor 5 years’ survival in hepatocellular carcinoma.

The Effects of Autophagy-Related Genes and lncRNAs in Therapy and Prognosis of Colorectal Cancer

Cellular autophagy plays an important role in the occurrence and development of colorectal cancer (CRC). Whether autophagy-related genes and lncRNAs can be used as ideal markers in CRC is still controversial. The purpose of this study is to identify novel treatment and prognosis markers of CRC. We downloaded transcription and clinical data of CRC from the GEO (GSE40967, GSE12954, GSE17536) and TCGA database, screened for differentially autophagy-related genes (DEAGs) and lncRNAs, constructed prognostic model, and analyzed its relationship with immune infiltration. TCGA and GEO datasets (GSE12954 and GSE17536) were used to validate the effect of the model. Oncomine database and Human Protein Atlas verified the expression of DEAGs. We obtained a total of 151 DEAGs in three verification sets collaboratively. Then we constructed a risk prognostic model through Lasso regression to obtain 15 prognostic DEAGs from the training set and verified the risk prognostic model in three verification sets. The low-risk group survived longer than the high-risk group. Age, gender, pathological stage, and TNM stage were related to the prognostic risk of CRC. On the other hand, BRAF status, RFS event, and tumor location are considered as most significant risk factors of CRC in the training set. Furthermore, we found that the immune score of the low-risk group was higher. The content of CD8 + T cells, active NK cells, macrophages M0, macrophages M1, and active dendritic cells was noted more in the high-risk group. The content of plasma cells, resting memory CD4 + T cells, resting NK cells, resting mast cells, and neutrophil cells was higher in the low-risk group. After all, the Oncomine database and immunohistochemistry verified that the expression level of most key autophagy-related genes was consistent with the results that we found. In addition, we obtained six lncRNAs co-expressed with DEAGs from the training set and found that the survival time was longer in the low-risk group. This finding was verified in the verification set and showed same trend to the results mentioned above. In the final analysis, these results indicate that autophagy-related genes and lncRNAs can be used as prognostic and therapeutic markers for CRC.

CDC20 as a new therapeutic target for treating ovarian cancer: an integrated bioinformatics analysis

Background: There were 313959 cases of newly diagnosed ovarian cancer (OC) and 207252 new deaths for OC in 2020 and OC lacks effective treatment options. Therefore, identifying novel therapeutic targets is imminent. Here, we use an integrated bioinformatics analysis to key genes involved in ovarian cancer and reveal potential therapeutic targets.Methods: GSE105437, GSE14407 and GSE18520 downloaded from Gene Expression Omnibus (GEO) were used to screen differentially expressed genes (DEGs). Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to predict the potential functions of the DEGs. Protein-protein interaction network (PPI) was drawn through STRING database and select CDC20 having the highest degrees of connectivity as the potential therapeutic target. Oncomine database and quantitative Real-time RT-PCR (RT-qPCR) of the ovarian tissues were used to validate the mRNA expression of CDC20. We use Gene Set Enrichment Analysis (GSEA) software to explore the potential biological function of CDC20 in OC. Results: A total of 821 DEGs were obtained, including 497 upregulated genes and 324 downregulated genes. Functional and pathway enrichment analyses indicated the DEGs were mainly involved in DNA-binding transcription activator activity, tubulin binding, microtubule binding, cell cycle, Wnt signaling pathway, p53 signaling pathway, and metabolism changes. Oncomine database analysis and RT-qPCR showed that CDC20 is significantly upregulated in OC tissues. GSEA analysis showed that CDC20 may regulate OC via cell cycle, citrate and TCA cycle, Oxidative phosphorylation and ubiquitin mediated proteolysis pathways. Conclusion: The results of the present study deduced that CDC20 is overexpressed in OC and may be a promising therapeutic target for the treatment of OC.

CDC20 as a new therapeutic target for treating ovarian cancer: an integrated bioinformatics analysis

Background There were 313959 cases of newly diagnosed ovarian cancer (OC) and 207252 new deaths for OC in 2020 and OC lacks effective treatment options. Therefore, identifying novel therapeutic targets is imminent. Here, we use an integrated bioinformatics analysis to key genes involved in ovarian cancer and reveal potential therapeutic targets. Methods GSE105437, GSE14407 and GSE18520 downloaded from Gene Expression Omnibus (GEO) were used to screen differentially expressed genes (DEGs). Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to predict the potential functions of the DEGs. Protein-protein interaction network (PPI) was drawn through STRING database and select CDC20 having the highest degrees of connectivity as the potential therapeutic target. Oncomine database and quantitative Real-time RT-PCR (RT-qPCR) of the ovarian tissues were used to validate the mRNA expression of CDC20. We use Gene Set Enrichment Analysis (GSEA) software to explore the potential biological function of CDC20 in OC. Results A total of 821 DEGs were obtained, including 497 upregulated genes and 324 downregulated genes. Functional and pathway enrichment analyses indicated the DEGs were mainly involved in DNA-binding transcription activator activity, tubulin binding, microtubule binding, cell cycle, Wnt signaling pathway, p53 signaling pathway, and metabolism changes. Oncomine database analysis and RT-qPCR showed that CDC20 is significantly upregulated in OC tissues. GSEA analysis showed that CDC20 may regulate OC via cell cycle, citrate and TCA cycle, Oxidative phosphorylation and ubiquitin mediated proteolysis pathways. Conclusion The results of the present study deduced that CDC20 is overexpressed in OC and may be a promising therapeutic target for the treatment of OC.