Circadian gene CSNK1D promoted the progression of hepatocellular carcinoma by activating Wnt/β-catenin pathway via stabilizing Dishevelled Segment Polarity Protein 3

Centrosome amplification-related signature correlated with immune microenvironment and treatment response predicts prognosis and improves diagnosis of hepatocellular carcinoma by integrating machine learning and single-cell analyses

BACKGROUND

Centrosome amplification is a well-recognized oncogenic driver of tumor initiation and progression across a variety of malignancies and has been linked with tumor aggressiveness, metastasis, and adverse prognosis. Nevertheless, the significance of centrosome amplification in HCC is not well understood.

METHODS

The TCGA dataset was downloaded for centrosome amplification-related signature construction using the LASSO-penalized Cox regression algorithm, while the ICGC dataset was obtained for signature validation. Single-cell RNA sequencing from GSE149614 was analyzed to profile gene expression and the liver tumor niche.

RESULTS

A total of 134 centrosome amplification-related prognostic genes in HCC were detected and 6 key prognostic genes (SSX2IP, SPAG4, SAC3D1, NPM1, CSNK1D, and CEP55) among them were screened out to construct a signature with both high sensitivity and specificity in diagnosis and prognosis of HCC patients. The signature, as an independent factor, was associated with frequent recurrences, high mortality rates, advanced clinicopathologic features, and high vascular invasions. Moreover, the signature was intimately associated with cell cycle-related pathways and TP53 mutation profile, suggesting its underlying role in accelerating cell cycle progression and leading to liver cancer development. Meanwhile, the signature was also closely correlated with immunosuppressive cell infiltration and immune checkpoint expression, making it a vital immunosuppressive factor in the tumor microenvironment. Upon single-cell RNA sequencing, SSX2IP and SAC3D1 were found to be specially expressed in liver cancer stem-like cells, where they promoted cell cycle progression and hypoxia.

CONCLUSIONS

This study provided a direct molecular link of centrosome amplification with clinical characteristics, tumor microenvironment, and clinical drug-response, highlighting the critical role of centrosome amplification in liver cancer development and therapy resistance, thereby providing valuable insights into prognostic prediction and therapeutic response of HCC.

The Circadian Clock as a Potential Biomarker and Therapeutic Target in Gastrointestinal Cancers

The circadian clock consists of a hierarchical multi-oscillator network of intracellular and intercellular mechanisms throughout the body that contributes to anticipating metabolic activity and maintaining system homeostasis in response to environmental cues and intrinsic stimuli. Over the past few years, genetic variations of core clock genes have been associated with cancer risk in several epidemiological studies. A growing number of epidemiological research studies have demonstrated a direct correlation between the disturbance of circadian rhythms and the growth of tumors, indicating that shift workers are more susceptible to malignancies of the colon, prostate, ovarian, breast, lung, and liver. One of the most related cancers with circadian rhythm is Gastrointestinal (GI) cancer, which is a leading cause of cancer-related mortality nowadays. The aim of this review was to demonstrate the effect of the clock gene network on the growth of GI cancer, providing molecular targets for GI cancer treatment, possible prognostic biomarkers, and guidance for treatment choices.

Integrated Bioinformatic Analysis of Differentially Expressed Genes Associated with Wound Healing

OBJECTIVE

Wound healing is a complex process involving the coordinated interaction of various genes and molecular
pathways. The study aimed to uncover novel therapeutic targets, biomarkers and candidate genes for drug development
to improve successful wound repair interventions.
Materials and Methods: This study is a network-meta analysis study. Nine wound healing microarray datasets obtained
from the Gene Expression Omnibus (GEO) database were used for this study. Differentially expressed genes (DEGs)
were described using the Limma package and shared genes were used as input for weighted gene co-expression
network analysis. The Gene Ontology analysis was performed using the EnrichR web server, and construction of a
protein-protein interaction (PPI) network was achieved by the STRING and Cytoscape.
Results: A total of 424 DEGs were determined. A co-expression network was constructed using 7692 shared genes
between nine data sets, resulting in the identification of seven modules. Among these modules, those with the top 20
genes of up and down-regulation were selected. The top down-regulated genes, including TJP1, SEC61A1, PLEK,
ATP5B, PDIA6, PIK3R1, SRGN, SDC2, and RBBP7, and the top up-regulated genes including RPS27A, EEF1A1,
HNRNPA1, CTNNB1, POLR2A, CFL1, CSNk1E, HSPD1, FN1, and AURKB, which can potentially serve as therapeutic
targets were identified. The KEGG pathway analysis found that the majority of the genes are enriched in the "Wnt
signaling pathway".
Conclusion: In our study of nine wound healing microarray datasets, we identified DEGs and co-expressed modules
using WGCNA. These genes are involved in important cellular processes such as transcription, translation, and posttranslational
modifications. We found nine down-regulated genes and ten up-regulated genes, which could serve as
potential therapeutic targets for further experimental validation. Targeting pathways related to protein synthesis and cell
adhesion and migration may enhance wound healing, but additional experimental validation is needed to confirm the
effectiveness and safety of targeted interventions.

Systematic analysis of cuproptosis-related genes in immunological characterization and predictive drugs in Alzheimer's disease

Objectives

This study aimed to make a systematic analysis of cuproptosis-related genes (CRGs) in immunological characterization and predictive drugs in Alzheimer's disease (AD) through bioinformatics and biological experiments.

Methods

The molecular clusters related to CRGs and associated immune cell infiltrations in AD were investigated. The diagnostic models were constructed for AD and different AD subtypes. Moreover, drug prediction and molecular docking were also performed. Subsequently, a molecular dynamics (MD) simulation was conducted to further verify the findings. Finally, RT-qPCR validation was performed.

Results

The characterization of 12 AD-related CRGs was evaluated in AD, and a diagnostic model for AD showed a satisfying discrimination power based on five CRGs by LASSO regression analysis. The dysregulated CRGs and activated immune responses partially differed between patients with AD and healthy subjects. Furthermore, two molecular subtypes (clusters A and B) with different immune infiltration characteristics in AD were identified. Similarly, a diagnostic model for different AD subtypes was built with nine CRGs, which achieved a good performance. Molecular docking revealed the optimum conformation of CHEMBL261454 and its target gene CSNK1D, which was further validated by MD simulation. The RT-qPCR results were consistent with those of the comprehensive analysis.

Conclusion

This study systematically elucidated the complex relationship between cuproptosis and AD, providing novel molecular targets for treatment and diagnosis biomarkers of AD.

Prognostic value and immunological role of CSNK1D in human cancers

CSNK1D, also known as CK1δ, is a crucial gene involved in various biological processes such as cell cycle, transcriptional regulation, apoptosis, cell polarity, and cell motility. It is associated with different cancers and neurodegenerative diseases. This study aimed to investigate the role of CSNK1D in multiple human cancers, particularly hepatocellular carcinoma (HCC), through in vitro experiments. The research utilized various online resources and databases like UCSC, NCBI, GEPIA2, HPA, cBioPortal, SangerBox, UALCAN, and TCGA for analyzing CSNK1D expression, prognosis significance, immune features, and gene alterations in cancers. RT-PCR was employed to evaluate CSNK1D expression in normal liver and liver cancer cell lines. In vitro experiments, including CCK-8, Edu, wound healing, and Transwell assays, were conducted to assess CSNK1D's biological function in HCC cells. Results demonstrated consistent upregulation of CSNK1D in various tumors. Heightened CSNK1D expression correlated with reduced overall survival and disease-free survival rates in different cancer patient cohorts. Significant associations were found between CSNK1D expression levels and immune cell infiltrations, immune checkpoint inhibitors, tumor mutation burden, and microsatellite instability across multiple malignancies. Notably, statistical analyses using TCGA and ICGC data identified CSNK1D as a robust and independent prognostic biomarker in HCC. Inhibiting CSNK1D expression effectively hindered cell proliferation, migration, and invasion in cellular experiments. In conclusion, this study suggests that CSNK1D may serve as a biomarker for tumor prognosis and immunotherapy. It influences the proliferation and metastasis of HCC cells.

Identification of a tissue resident memory CD8 T cell-related risk score signature for colorectal cancer, the association with TME landscapes and therapeutic responses

Backgrounds: The tissue resident memory CD8 T cell (Trm) constitutes an important component of the local immunity. In the context of malignant tumors, mounting evidence also supports the potential anti-tumor property of this cell subset. Therefore, identification of Trm marker genes and exploration of the causative effect of Trm in shaping tumor microenvironment (TME) heterogeneity might provide novel insights for the comprehensive management of cancer patients. Methods: By dissecting a single T cell transcriptome dataset, we acquired marker genes for Trm, which were latter applied to bulk RNA sequencing profiles of two large colorectal cancer (CRC) patient cohorts downloaded from TCGA and GEO databases. First, colorectal cancer patients were divided into different Trm clusters using consensus clustering algorithm. Then, we established a Trm-related gene (TRMRG) risk score signature and tested its efficacy in predicting prognosis for colorectal cancer patients. Moreover, a sequence of rigorous and robust analyses were also carried out to investigate the potential role of Trm-related gene risk score in tumor microenvironment remodeling and therapeutic utility of it in colorectal cancer treatment. Results: A total of 49 Trm marker genes were identified by analyzing single cell RNA sequencing profiles. First, colorectal cancer patients were successfully classified into two Trm clusters with significant heterogeneity in functional enrichment patterns and tumor microenvironment landscapes. Then, we developed a Trm-related gene risk score signature and divided patients into different risk levels. High risk patients were characterized by attenuated immunogenicity, weakened sensitivity to immunotherapy, as well as adverse clinical outcomes. While low risk patients with advantages in survival exhibited increased immunogenicity, stronger metabolic activity and improved immunotherapeutic responses. Conclusion: Through combinatorial analysis of single cell and bulk RNA sequencing data, the present study identified Trm to play a non-negligible role in regulating the complexity and heterogeneity of tumor microenvironment for colorectal cancer. Moreover, the Trm-related gene risk score signature developed currently was corroborated to be tightly correlated with prognosis and therapeutic responses of colorectal cancer patients, thus exhibiting potential application value for clinical practice.