Upregulation of ADAMDEC1 correlates with tumor progression and predicts poor prognosis in non-small cell lung cancer (NSCLC) via the PI3K/AKT pathway

IER5L is a Prognostic Biomarker in Pan-Cancer Analysis and Correlates with Immune Infiltration and Immune Molecules in Non-Small Cell Lung Cancer

Purpose

Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases. Immediate early response 5 like (IER5L) plays crucial roles in progression and prognosis for several tumors, but its role in NSCLC remains unclear.

Patients and Methods

Gene expression and mutation profiles, DNA methylation data, and clinical information for cancers were downloaded from multiple databases. Relative expression, prognostic value, and correlation with disease progression of IER5L were analyzed in multiple cancers, including NSCLC. Upstream mechanisms were explored using a transcriptional network. Functional enrichment analysis, protein-protein interaction network, and gene set enrichment analysis were applied to study downstream mechanisms. Correlations of IER5L with immune infiltration, immune molecules, methylation status, and tumor mutation burden (TMB) were analyzed using R language. Finally, quantitative polymerase chain reaction (qPCR) and single-cell RNA sequencing (scRNA seq) analysis were performed to validate IER5L expression in NSCLC.

Results

Pan-cancer analysis displayed that IER5L expression was upregulated in multiple cancers and was associated with disease prognosis and progression, including NSCLC, which was validated using qPCR. scRNA seq analysis showed that multiple cells had increased IER5L expression. An EGR1-hsa-miR-8075-IER5L network was constructed for NSCLC. A total of 191 DEGs were identified between the two IER5L groups, which were significantly enriched in biological process of action potential, sodium ion transport, and regulation of membrane potential. Increased IER5L expression was primarily enriched in cell cycle, NOTCH signaling pathway, and oxidative phosphorylation pathway, and was correlated with increased regulatory T cells and neutrophils, elevated levels of immune molecules, and higher TMB.

Conclusion

Our findings show that increased IER5L expression was correlated with progression and prognosis in multiple cancers as well as with immune infiltration and immune molecules in NSCLC. Thus, IER5L is a prognostic biomarker in multiple cancers and may correlate with immunotherapeutic response in NSCLC.

Knockdown of ADAMDEC1 ameliorates ox-LDL-induced endothelial cell injury and atherosclerosis progression

This study was designed to investigate the role of a disintegrin and metalloproteinase domain-like protein decysin 1 (ADAMDEC-1) in atherosclerosis (AS). The Gene Expression Omnibus (GEO) database was utilized to identify differentially expressed genes (DEGs) between carotid atheroma plaque and carotid tissue adjacent atheroma plaque obtained from AS patients. Gene functional enrichment analysis was conducted on DEGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). QRT-PCR was employed to quantify mRNAs expression. AS animal model was established using ApoE-/- mice; serum triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels were detected. Aortic sinus atherosclerotic lesions were observed using H&E staining and Oil Red O staining. ADAMDEC-1 was silenced using small interfering RNAs (siRNAs) in human vascular smooth muscle cells (HVSMCs). Cell proliferation, migration, and cell cycle progression were detected by cell count kit-8 (CCK8), 5-ethynyl-2'-deoxyuridine (EDU), wound scratch healing assay, transwell assay, and flow cytometry, respectively. Western blot was used to evaluate various protein expression levels. Our results showed that ADAMDEC-1 was highly expressed in the serum of AS patients, consistent with the in silico results. The elevated TG, LDL-C, and HDL-C levels along with H&E and Oil Red O staining confirmed the successful establishment of the AS mouse model. ADAMDEC-1 expression was also elevated in AS mice. ADAMDEC-1 knockdown in HVSMCs suppressed cell proliferation, inhibited the expression of proliferating cell nuclear antigen (PCNA), and reduced the levels of matrix metalloproteinases (MMP2 and MMP9) proteins. Protein-protein interaction (PPI) analysis indicated that ADAMDEC-1 was associated with CXCL9, CCR5, TNF-α, TNFR1, and NF-κB-p50. The expression levels of CXCL9, CCR5, TNF-α, TNFR1, and NF-κB-p50 increased, while ADAMDEC-1 knockdown attenuated the expression of these proteins. Our study findings substantiate that ADAMDEC-1 may represent a novel target for AS.

ADAMDEC1 induces EMT and promotes colorectal cancer cells metastasis by enhancing Wnt/β-catenin signaling via negative modulation of GSK3β

Colorectal cancer (CRC) is a highly invasive malignant tumor, with a high proliferative capacity and is prone to epithelial-mesenchymal transition (EMT) and subsequent metastasis. A disintegrin and metalloproteinase domain-like decysin 1 (ADAMDEC1) is a proteolytically active metzincin metalloprotease that is invested in extracellular matrix remodeling, cell adhesion, invasion, and migration. However, the effects of ADAMDEC1 on CRC are unclear. The purpose of this research is to investigate the expression and biological role of ADAMDEC1 in CRC. We found that ADAMDEC1 was substantially elevated in both clinical samples and CRC cell lines. Likewise, ADAMDEC1 can enhance CRC cell proliferation, migration, and invasion while inhibiting apoptosis. Interestingly, we discovered that exogenous ADAMDEC1 overexpression triggered epithelial-mesenchymal transition (EMT) in CRC cells, as evidenced by alterations in E-cadherin, N-cadherin, and vimentin expression. In ADAMDEC1-knockdown or ADAMDEC1-overexpressing CRC cells, the Western blotting analysis revealed that downstream targets of Wnt signaling, along with β-catenin, Wnt 4, LEF1, Cyclin D1, and c-Myc, were down-regulated or up-regulated. Furthermore, inhibition of the Wnt/β-catenin pathway by FH535 negated the effect of ADAMDEC1 overexpression on EMT and CRC cell proliferation. Further mechanistic research revealed that ADAMDEC1 knockdown might up-regulate GSK3β and inactivate the Wnt/β-catenin pathway, accompanied by suppressing the expression of β-catenin. Additionally, the blockage of GSK3β by CHIR 99021 markedly abolished the inhibitory effect of ADAMDEC1 knockdown on Wnt/β-catenin signaling. In summary, our findings first indicate that ADAMDEC1 promotes CRC metastasis by negatively regulating GSK3β, activating the Wnt/β-catenin signaling pathway, and inducing EMT, suggesting its potential utility as a therapeutic target for the treatment of metastatic CRC.

Design, Synthesis and Biological Evaluation of 6-(Imidazo[1,2-a]pyridin-6-yl)quinazoline Derivatives as Anticancer Agents via PI3Kα Inhibition

Aberrant expression of the phosphatidylinositol 3-kinase (PI3K) signalling pathway is often associated with tumourigenesis, progression and poor prognosis. Hence, PI3K inhibitors have attracted significant interest for the treatment of cancer. In this study, a series of new 6-(imidazo[1,2-a]pyridin-6-yl)quinazoline derivatives were designed, synthesized and characterized by 1H NMR, 13C NMR and HRMS spectra analyses. In the in vitro anticancer assay, most of the synthetic compounds showed submicromolar inhibitory activity against various tumour cell lines, among which 13k is the most potent compound with IC50 values ranging from 0.09 μΜ to 0.43 μΜ against all the tested cell lines. Moreover, 13k induced cell cycle arrest at G2/M phase and cell apoptosis of HCC827 cells by inhibition of PI3Kα with an IC50 value of 1.94 nM. These results suggested that compound 13k might serve as a lead compound for the development of PI3Kα inhibitor.

ADAMDEC1 accelerates GBM progression via activation of the MMP2-related pathway

The ADAM (a disintegrin and metalloprotease) gene-related family including ADAM, ADAMTS, and ADAM-like decysin-1 has been reported to play an important role in the pathogenesis of multiple diseases, including cancers (lung cancer, gliomas, colorectal cancer, and gastrointestinal cancer). However, its biological role in gliomas remains largely unknown. Here, we aimed to investigate the biological functions and potential mechanism of ADAMDEC1 in gliomas. The mRNA and protein expression levels of ADAMDEC1 were upregulated in glioma tissues and cell lines. ADAMDEC1 showed a phenomenon of “abundance and disappear” expression in gliomas and normal tissues in that the higher the expression of ADAMDEC1 presented, the higher the malignancy of gliomas and the worse the prognosis. High expression of ADAMDEC1 was associated with immune response. Knockdown of ADAMDEC1 could decrease the proliferation and colony-forming ability of LN229 cells, whereas ADAMDEC1 overexpression has opposite effects in LN229 cells in vitro. Furthermore, we identified that ADAMDEC1 accelerates GBM progression via the activation of the MMP2 pathway. In the present study, we found that the expression levels of ADAMDEC1 were significantly elevated compared with other ADAMs by analyzing the expression levels of ADAM family proteins in gliomas. This suggests that ADAMDEC1 has potential as a glioma clinical marker and immunotherapy target.

Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer

Background

Lung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80–85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).

Methods

NSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan–Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature.

Results

In total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC.

Conclusion

Our study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.