Exploring Core Genes by Comparative Transcriptomics Analysis for Early Diagnosis, Prognosis, and Therapies of Colorectal Cancer

Human pan-cancer analysis of the predictive biomarker for the CDKN3

BACKGROUND

Cell cycle protein-dependent kinase inhibitor protein 3 (CDKN3), as a member of the protein kinase family, has been demonstrated to exhibit oncogenic properties in several tumors. However, there are no pan-carcinogenic analyses for CDKN3.

METHODS

Using bioinformatics tools such as The Cancer Genome Atlas (TCGA) and the UCSC Xena database, a comprehensive pan-cancer analysis of CDKN3 was conducted. The inverstigation encompassed the examination of CDKN3 function actoss 33 different kinds of tumors, as well as the exploration of gene expressions, survival prognosis status, clinical significance, DNA methylation, immune infiltration, and associated signal pathways.

RESULTS

CDKN3 was significantly upregulated in most of tumors and correlated with overall survival (OS) of patients. Methylation levels of CDKN3 differed significantly between tumors and normal tissues. In addition, infiltration of CD4 + T cells, cancer-associated fibroblasts, macrophages, and endothelial cells were associated with CDKN3 expression in various tumors. Mechanistically, CDKN3 was associated with P53, PI3K-AKT, cell cycle checkpoints, mitotic spindle checkpoint, and chromosome maintenance.

CONCLUSION

Our pan-cancer analysis conducted in the study provides a comprehensive understanding of the involvement of CDKN3 gene in tumorigenesis. The findings suggest that targeting CDKN3 may potentially lead to novel therapeutic strategies for the treatment of tumors.

Knockdown of TOP2A suppresses IL-17 signaling pathway and alleviates the progression of ulcerative colitis

BACKGROUND

Ulcerative colitis (UC) is a chronic inflammatory disease of the colonic mucosa, with a gradually increasing incidence. Therefore, it is necessary to actively seek targets for the treatment of UC.

METHODS

Common differentially expressed genes (DEGs) were screened from two microarray data sets related to UC. Protein-protein interaction network was constructed to find the hub genes. The UC mouse model and cell model were induced by dextran sulfate sodium (DSS). The pathological changes of colon tissue were observed by hematoxylin-eosin staining. Immunohistochemistry and immunofluorescence were performed to detect the expressions of Ki67 and Claudin-1. The performance of mice was observed by disease activity index (DAI). The effect of TOP2A on proliferation, inflammation, oxidative stress, and interleukin-17 (IL-17) signaling pathway in UC model was measured by cell counting kit-8, enzyme-linked immunosorbent assay, and western blot.

RESULTS

Through bioinformatics analysis, 295 common DEGs were screened, and the hub gene TOP2A was selected. In UC model, there was obvious inflammatory cell infiltration in the colon and less goblet cells, while si-TOP2A lessened it. More Ki67 positive cells and less Claudin-1 positive cells were observed in UC model mice. Furthermore, knockdown of TOP2A increased the body weight and colon length of UC mice, while the DAI was decreased. Through in vivo and in vitro experiments, knockdown of TOP2A also inhibited inflammation and IL-17 signaling pathway, and promoted proliferation in DSS-induced NCM460 cells.

CONCLUSION

Knockdown of TOP2A alleviated the progression of UC by suppressing inflammation and inhibited IL-17 signaling pathway.

Identification of crucial anoikis-related genes as novel biomarkers and potential therapeutic targets for lung adenocarcinoma via bioinformatic analysis and experimental verification

Lung adenocarcinoma (LUAD) is a malignant tumor of the respiratory system that has a poor 5-year survival rate. Anoikis, a type of programmed cell death, contributes to tumor development and metastasis. The aim of this study was to develop an anoikis-based stratified model, and a multivariable-based nomogram for guiding clinical therapy for LUAD. Through differentially expressed analysis, univariate Cox, LASSO Cox regression, and random forest algorithm analysis, we established a 4 anoikis-related genes-based stratified model, and a multivariable-based nomogram, which could accurately predict the prognosis of LUAD patients in the TCGA and GEO databases, respectively. The low and high-risk score LUAD patients stratified by the model showed different tumor mutation burden, tumor microenvironment, gemcitabine sensitivity and immune checkpoint expressions. Through immunohistochemical analysis of clinical LUAD samples, we found that the 4 anoikis-related genes (PLK1, SLC2A1, ANGPTL4, CDKN3) were highly expressed in the tumor samples from clinical LUAD patients, and knockdown of these genes in LUAD cells by transfection with small interfering RNAs significantly inhibited LUAD cell proliferation and migration, and promoted anoikis. In conclusion, we developed an anoikis-based stratified model and a multivariable-based nomogram of LUAD, which could predict the survival of LUAD patients and guide clinical treatment.

Comprehensive Analysis Reveals the Potential Roles of CDKN3 in Pancancer and Verification in Endometrial Cancer

Background

Cyclin-dependent kinase inhibitor 3 (CDKN3) has been studied in many cancers. However, the comprehensive and systematic pancancer analysis of CDKN3 genes is still lacking.

Methods

Data were downloaded from online databases. R was used for analysis of the differential expression and gene alteration of CDKN3 and of the associations between CDKN3 expression and survival, signaling pathways, and drug sensitivity. Clinical samples and in vitro experiments were selected for verification.

Results

CDKN3 expression was higher in most types of cancers, and this phenotype was significantly correlated with poor survival. CDKN3 showed gene alterations and copy number alterations in many cancers and associated with some immune-related pathways and factors. Drug sensitivity analysis elucidated that CDKN3 could be a useful marker for therapy selection. Clinical samples elucidated CDKN3 expressed high in endometrial cancer tissue. In vitro studies showed that CDKN3 induced pro-tumor effect in immune environment and facilitated endometrial cancer cell proliferation and G1/S phase transition.

Conclusion

CDKN3 has been shown to be highly expressed in most types of cancers and promoted cancer cell progression. CDKN3 may serve as a novel marker in clinical diagnosis, treatment, and prognosis prediction in future.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.