CDT1 facilitates metastasis in prostate cancer and correlates with cell cycle regulation

Chromatin Licensing and DNA Replication Factor 1 (CDT1) Is a Potential Prognostic Biomarker Involved in the Malignant Biological Behavior of Glioma

Glioma is the primary malignant tumor with the highest incidence rate in the adult central nervous system. The application of bioinformatics methods to analyze the RNA sequences of multiple gliomas revealed that the CDT1 gene has a significant impact on the cell cycle of glioma cells. Subsequently, we comprehensively and systematically investigated the expression of CDT1 in gliomas through bioinformatics analysis, clinical tissue specimens, and in vitro functional experiments. Our study is the first to report the expression of CDT1 in glioma. Our findings demonstrate that CDT1 plays a crucial role in the proliferation and invasion of glioma. Additionally, our bioinformatics analysis identified several other genes and signaling pathways that are dysregulated in multifocal gliomas, providing potential targets for further research and drug development.

Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States

Prostate cancer is a significant health concern and the most commonly diagnosed cancer in men worldwide. Understanding the complex process of prostate tumor evolution and progression is crucial for improved diagnosis, treatments, and patient outcomes. Previous studies have focused on unraveling the dynamics of prostate cancer evolution using phylogenetic or lineage analysis approaches. However, those approaches have limitations in capturing the complete disease process or incorporating genomic and transcriptomic variations comprehensively. In this study, we applied a novel computational approach to derive a prostate cancer progression model using multidimensional data from 497 prostate tumor samples and 52 tumor-adjacent normal samples obtained from The Cancer Genome Atlas study. The model was validated using data from an independent cohort of 545 primary tumor samples. By integrating transcriptomic and genomic data, our model provides a comprehensive view of prostate tumor progression, identifies crucial signaling pathways and genetic events, and uncovers distinct transcription signatures associated with disease progression. Our findings have significant implications for cancer research and hold promise for guiding personalized treatment strategies in prostate cancer.

SIGNIFICANCE

We developed and validated a progression model of prostate cancer using >1,000 tumor and normal tissue samples. The model provided a comprehensive view of prostate tumor evolution and progression.

Network pharmacology and experimental validation to explore the role and potential mechanism of Liuwei Dihuang Decoction in prostate cancer

OBJECTIVE

To evaluate the anti-tumor effector of Liuwei Dihuang Decoction (LWDHD) in prostate cancer (PCa) and explore the potential mechanism using experimental validation, network pharmacology, bioinformatics analysis, and molecular docking.

METHODS

CCK test, Clone formation assay and wound-healing assays were used to determine the effect of LWDHD on prostate cancer growth and metastasis. The active ingredients and targets of LWDHD were obtained from the TCMSP database, and the relevant targets were selected by GeneCards, OMIM and DisGeNET databases for PCa. The cross-targets of drugs and disease were imported into the STRING database to construct protein interactions. The network was also visualized using Cytoscape software and core targets are screened using the Network Analyzer plug-in. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed using R software. TCGA database was used to analyze the correlation of bioinformatics genes. AutoDock vina was used to predict the molecular docking and binding ability of active ingredients to key targets. Through WB and q-PCR experiments, the above gene targets were detected to verify the effect of LWDHD on PCa.

RESULTS

CCK and scratch tests confirmed that LWDHD could inhibit the proliferation, invasion and migration of prostate cancer cells. Clone formation experiments showed that LWDHD inhibited the long-term proliferative capacity of PC3 cells. LWDHD and PCa had a total of 99 common targets, establishing a "drug-ingredient-common target" network. Through GO and KEGG enrichment analysis, PI3K/AKT, MAPK, TP53 pathway, MYC, TNF pathway and other signaling pathways were found. Bioinformatics analysis showed that MYC gene was highly expressed and CCND1 and MAPK1 were low expressed in prostate cancer tissues. In addition, TP53, AKT1, MYC, TNF and CCND1 were positively correlated with MAPK1, among which AKT1 and CCND1 were most closely correlated with MAPK1. Molecular docking results showed that quercetin, kaempferol, β-sitosterol and other main active ingredients of LWDHD treatment for PCa were combined with core proteins MAPK1 and AKT1 well. WB and q-PCR results showed that LWDHD inhibited the expression of PI3K and AKT in PC3 cells.

CONCLUSION

The mechanism of LWDHD therapy for PCa is a multi-target and multi-pathway complex process, which may be related to the biological processes mediated by MAPK1 and AKT1 pathways, such as cell proliferation and inhibition of metastasis, and the regulation of signaling pathways. The PI3K/AKT signaling pathway may be a central pathway of LWDHD to inhibit prostate cancer proliferation.

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

KIF11 serves as a cell cycle mediator in childhood acute lymphoblastic leukemia

OBJECTIVE

To identify key gene in childhood acute lymphoblastic leukemia (ALL) through weighted gene co-expression network analysis (WGCNA), and their enriched biological functions and signaling pathways.

METHODS

Array data of the GSE73578 dataset, involving 46 childhood ALL samples, were acquired from the Gene Expression Omnibus (GEO) database. Hub modules associated with childhood ALL were screened out by WGCNA. Enriched biological functions and signaling pathways were then identified by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Hub genes were selected by overlapping those between down-regulated genes in GSE73578, GSE4698 and the hub module. Guilt by association (GBA) was adopted to verify the function of the identified KIF11 gene and to predict its target genes. Regulatory effects of KIF11 on the proliferation and cell cycle progression of ALL in vitro were determined by cytological experiments.

RESULTS

WGCNA showed that the yellow module was the most relevant to childhood ALL treatment, containing 698 genes that were enriched in cell division, mitotic nuclear division, DNA replication and DNA repair, cell cycle, DNA replication and the P53 signaling pathway. The KIF11 gene was screened out and predicted as a cell cycle mediator in childhood ALL. Knockdown of KIF11 in ALL cells inhibited cell proliferation and arrested cell cycle progression in G2/M phase.

CONCLUSIONS

The KIF11 gene is critical in the treatment process of childhood ALL, which is a promising therapeutic target for childhood ALL.

ECM1 regulates the resistance of colorectal cancer to 5-FU treatment by modulating apoptotic cell death and epithelial-mesenchymal transition induction

5-Fluorouracil (5-FU) chemoresistance is a persistent impediment to the efficient treatment of many types of cancer, yet the molecular mechanisms underlying such resistance remain incompletely understood. Here we found CRC patients resistant to 5-FU treatment exhibited increased extracellular matrix protein 1 (ECM1) expression compared to CRC patients sensitive to this chemotherapeutic agent, and higher levels of ECM1 expression were correlated significantly with shorter overall survival and disease-free survival. 5-FU resistant HCT15 (HCT15/FU) cells expressed significantly higher levels of ECM1 relative to parental HCT15 cells. Changes in ECM1 expression altered the ability of both parental and HCT15/FU cells to tolerate the medication in vitro and in vivo via processes associated with apoptosis and EMT induction. From a mechanistic perspective, knocking down and overexpressing ECM1 in HCT15/FU and HCT15 cell lines inhibited and activated PI3K/AKT/GSK3β signaling, respectively. Accordingly, 5-FU-induced apoptotic activity and EMT phenotype changes were affected by treatment with PI3K/AKT agonists and inhibitors. Together, these data support a model wherein ECM1 regulates CRC resistance to 5-FU via PI3K/AKT/GSK3β pathway-mediated modulation of apoptotic resistance and EMT induction, highlighting ECM1 as a promising target for therapeutic intervention for efforts aimed at overcoming chemoresistance in CRC patients.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

BHLHE41 Overexpression Alleviates the Malignant Behavior of Colon Cancer Cells Induced by Hypoxia via Modulating HIF-1α/EMT Pathway

Objective. BHLHE41 has been shown to be a marker of tumorigenesis. Colon cancer (CC) is a common malignant tumor of colonic mucosa. This study mainly explored the mechanism of BHLHE41 in alleviating malignant behavior of hypoxia-induced CC cells. Methods. The levels of BHLHE41 in CC and normal cell lines were tested by Western blot and qRT-PCR. After, CC cells were subjected to hypoxia treatment and BHLHE41 overexpression transfection, and the BHLHE41 expression, the effect of BHLHE41 on CC cell viability, apoptosis, migration, and invasion and cell cycle were tested by qRT-PCR and relevant cell functional experiments. HIF-1α and epithelial-mesenchymal transition- (EMT-) related proteins were tested by Western blot. Moreover, CC tumor-bearing model was established in nude mice, and the effect of BHLHE41 on the tumor was evaluated by measuring the tumor volume and weight. Then, the expressions of BHLHE41 and EMT-related proteins were detected by immunohistochemistry and Western blot. Results. Western blot and qRT-PCR showed that BHLHE41 was lowly expressed in CC cells. BHLHE41 overexpression could inhibit the hypoxia-induced CC cell viability, migration, and invasion, induce apoptosis, and alter cell cycle. Besides, BHLHE41 overexpression could enhance the levels of E-cadherin but reduce the levels of HIF-1α, N-cadherin, vimentin, and MMP9 in hypoxia-induced CC cells. Moreover, BHLHE41 overexpression reduced tumor volume, weight, and EMT-related proteins levels in tumor tissues. Conclusions. BHLHE41 overexpression could mitigate the malignant behavior of hypoxia-induced CC via modulating the HIF-1α/EMT pathway.