Pan-cancer analysis of Chromobox (CBX) genes for prognostic significance and cancer classification

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer

BACKGROUND

Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.

AIM

To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.

METHODS

Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model.

RESULTS

More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.

CONCLUSION

This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.

The predictive significance of chromobox family members in prostate cancer in humans

PURPOSE

The Chromobox (CBX) family proteins are crucial elements of the epigenetic regulatory machinery and play a significant role in the development and advancement of cancer. Nevertheless, there is limited understanding regarding the role of CBXs in development or progression of prostate cancer (PCa). Our objective is to develop a unique prognostic model associated with CBXs to improve the accuracy of predicting outcomes of patients with PCa.

METHODS

Data from TCGA and GEO databases were analyzed to assess differential expression, prognostic value, gene pathway enrichment, and immune cell infiltration. COX regression analysis was utilized to identify the independent prognostic factors that impact disease-free survival (DFS). The expression of CBX2 and FOXP3+ cells infiltration was verified by immunohistochemical staining of clinical tissue sections. In vitro proliferation, migration and invasion assay were conducted to examine the function of CBX2. RNA-seq was employed to examine the CBX2 related pathway enrichment.

RESULTS

CBX2, CBX3, CBX4, and CBX8 were upregulated, while CBX6 and CBX7 were downregulated in PCa tissues. CBXs expression varied by stage and grade. Elevated expression of CBX1, CBX2, CBX3, CBX4 and CBX8 is correlated with poor outcome. CBX2 expression, T stage, and Gleason score were independent prognostic factors. The expression level of CBX2 in PCa tissues was significantly higher than that in adjacent normal tissues. More Treg infiltration was observed in the group with high CBX2 expression. CBX2 expression affected PCa cell growth, migration, and invasion.

CONCLUSIONS

CBX2 is involved in the development and advancement of PCa, suggesting its potential as a reliable prognostic indicator for PCa patients.

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning-based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.

Uncovering the role of aquaporin and chromobox family members as potential biomarkers in head and neck squamous cell carcinoma via integrative multiomics and in silico approach

Head and neck squamous cell carcinoma (HNSC) is a diverse group of tumors arising from oral cavity, oropharynx, larynx, and hypopharynx squamous epithelium, posing significant morbidity. Aquaporins (AQPs) are membrane proteins forming water channels, some associated with carcinomas. Chromobox (CBX) family is known to modulate physiological and oncological processes. In our study, we analyzed AQPs and CBXs having significant expression followed by their prognostic and mutational assessment. Next, we performed enrichment and tumor infiltration analysis followed by HPA validation. Lastly, we established a 3-node miRNA-TF-mRNA regulatory network and performed protein-protein docking of the highest-degree subnetwork motif between TF and mRNA. Significant upregulation of CBX3/2 and downregulation of AQP3/5/7 correlated with poor overall survival (OS) in HNSC patients. The most significant pathway, GO-BP, GO-MF, and GO-CC terms associated with AQP3 and CBX3 were passive transport by aquaporins, response to vitamin, glycerol channel activity, and condensed chromosome, centromeric region. AQP3 negatively correlated with [Formula: see text] T cells, positively with [Formula: see text] T cells and B cells, and negatively with tumor purity, whereas CBX3 positively correlated with [Formula: see text] T cells, negatively with [Formula: see text] T cells and B cells, and positively with tumor purity. Three-node miRNA-TF-mRNA regulatory network revealed a highest-degree subnetwork motif comprising one TF (SMAD3), one miRNA (miR-423-5p), and one mRNA (AQP3). Protein-protein interaction studies suggested a direct interaction between AQP3 and Smad3 proteins. We concluded that AQP3 and CBX3 hold potential as treatment strategies and individual prognostic biomarkers, while further protein-protein interaction studies of AQP3 could offer insights into its interactions with Smad3 proteins.

Disulfidptosis-related Protein RPN1 may be a Novel Anti-osteoporosis Target of Kaempferol

BACKGROUND

Osteoporosis (OP) is an age-related skeletal disease. Kaempferol can regulate bone mesenchymal stem cells (BMSCs) osteogenesis to improve OP, but its mechanism related to disulfidptosis, a newly discovered cell death mechanism, remains unclear.

OBJECTIVE

The study aimed to investigate the biological function and immune mechanism of disulfidptosis- related ribophorin I (RPN1) in OP and to experimentally confirm that RPN1 is the target for the treatment of OP with kaempferol.

METHODS

Differential expression analysis was conducted on disulfide-related genes extracted from the GSE56815 and GSE7158 datasets. Four machine learning algorithms identified disease signature genes, with RPN1 identified as a significant risk factor for OP through the nomogram. Validation of RPN1 differential expression in OP patients was performed using the GSE56116 dataset. The impact of RPN1 on immune alterations and biological processes was explored. Predictive ceRNA regulatory networks associated with RPN1 were generated via miRanda, miRDB, and TargetScan databases. Molecular docking estimated the binding model between kaempferol and RPN1. The targeting mechanism of kaempferol on RPN1 was confirmed through pathological HE staining and immunohistochemistry in ovariectomized (OVX) rats.

RESULTS

RPN1 was abnormally overexpressed in the OP cohort, associated with TNF signaling, hematopoietic cell lineage, and NF-kappa B pathway. Immune infiltration analysis showed a positive correlation between RPN1 expression and CD8+ T cells and resting NK cells, while a negative correlation with CD4+ naive T cells, macrophage M1, T cell gamma delta, T cell follicular helper cells, activated mast cells, NK cells, and dendritic cells, was found. Four miRNAs and 17 lncRNAs associated with RPN1 were identified. Kaempferol exhibited high binding affinity (-7.2 kcal/mol) and good stability towards the RPN1. The experimental results verified that kaempferol could improve bone microstructure destruction and reverse the abnormally high expression of RPN1 in the femur of ovariectomized rats.

CONCLUSION

RPN1 may be a new diagnostic biomarker in patients with OP, and may serve as a new target for kaempferol to improve OP.

Abnormal dynamic functional connectivity in the hippocampal subregions of patients with untreated moderate-to-severe obstructive sleep apnea

OBJECTIVE

To investigate the dynamic change characteristics of dynamic functional connectivity (dFC) between the hippocampal subregions (anterior and posterior) and other brain regions in obstructive sleep apnoea (OSA) and its relationship with cognitive function, and to explore whether these characteristics can be used to distinguish OSA from healthy controls (HCs).

METHODS

Eighty-five patients with newly diagnosed moderate-to-severe OSA and 85 HCs were enrolled. All participants underwent resting-state functional magnetic resonance imaging (fMRI). The difference between dFC values between the hippocampal subregions and other brain regions in OSA patients and HCs was compared using the two-sample t tests. Correlation analyses were used to assess the relationship between dFC, clinical data, and cognitive functions in OSA patients. dFC values from different brain regions were used as classification features to distinguish between the two groups using a support vector machine.

RESULTS

Compared with HCs, the dFC values between the left anterior hippocampus and right culmen of the cerebellum anterior lobe, right anterior hippocampus and left lingual gyrus, and left posterior hippocampus and left precentral gyrus were significantly lower, and the dFC values between the left posterior hippocampus and precuneus were significantly higher in OSA patients. The dFC values between the left posterior hippocampus and the precuneus of OSA patients were associated with sleep-related indicators and Montreal Cognitive Assessment scores. Support vector machine analysis results showed that dFC values in different brain regions could distinguish OSA patients from HCs.

CONCLUSION

dFC patterns between the hippocampal subregions and other brain regions were altered in patients with OSA, including the cerebellum, default mode networks, sensorimotor networks, and visual function networks, which is possibly associated with cognitive decline. In addition, the dFC values of different brain regions could effectively distinguish OSA patients from HCs. These findings provide new perspectives on neurocognition in these patients.

CBX4 deletion promotes tumorigenesis under KrasG12D background by inducing genomic instability

Chromobox protein homolog 4 (CBX4) is a component of the Polycomb group (PcG) multiprotein Polycomb repressive complexes 1 (PRC1), which is participated in several processes including growth, senescence, immunity, and tissue repair. CBX4 has been shown to have diverse, even opposite functions in different types of tissue and malignancy in previous studies. In this study, we found that CBX4 deletion promoted lung adenocarcinoma (LUAD) proliferation and progression in KrasG12D mutated background. In vitro, over 50% Cbx4L/L, KrasG12D mouse embryonic fibroblasts (MEFs) underwent apoptosis in the initial period after Adeno-Cre virus treatment, while a small portion of survival cells got increased proliferation and transformation abilities, which we called selected Cbx4-/-, KrasG12D cells. Karyotype analysis and RNA-seq data revealed chromosome instability and genome changes in selected Cbx4-/-, KrasG12D cells compared with KrasG12D cells. Further study showed that P15, P16 and other apoptosis-related genes were upregulated in the primary Cbx4-/-, KrasG12D cells due to chromosome instability, which led to the large population of cell apoptosis. In addition, multiple pathways including Hippo pathway and basal cell cancer-related signatures were altered in selected Cbx4-/-, KrasG12D cells, ultimately leading to cancer. We also found that low expression of CBX4 in LUAD was associated with poorer prognosis under Kras mutation background from the human clinical data. To sum up, CBX4 deletion causes genomic instability to induce tumorigenesis under KrasG12D background. Our study demonstrates that CBX4 plays an emerging role in tumorigenesis, which is of great importance in guiding the clinical treatment of lung adenocarcinoma.

Pan-Cancer Analysis of the Expression and Prognostic Value of S-Phase Kinase-Associated Protein 2

BACKGROUND: S-Phase Kinase-Associated Protein 2 (SKP2) is essential in modulating metabolism processes, cell proliferation, and carcinogenesis DUE to its capacity to ubiquitinate and degrade various tumor-suppressive substrates. However, the actual biological and mechanism significance of SKP2 in the development of tumors and as a possible therapeutic target remains to be completely understood. AIM: This study aimed to explore the potential roles of the SKP2 gene in the oncologic pathogenesis of various cancers through an in-depth pan-cancer analysis including gene expression assessment, survival analysis, genetic alteration, and enrichment analysis. METHODS: Public databases including the Cancer Genome Atlas database, Genotype-Tissue Expression Project database, cBioPortal database, Gene Expression Profiling Interactive Analysis 2 database, Tumor Immune Estimation Resource version 2.0 database, and STRING database were used to detect the SKP2 expression, molecular mechanism, and its association with the prognosis across pan-cancer. RESULTS: SKP2 was significantly highly expressed in most types of cancers and was substantially correlated to the poor survival of patients with specific cancers based on the log-rank test. SKP2 had the highest frequency of alteration in lung cancer and amplification was the most common genetic alteration type. Finally, SKP2-related genes were identified and enrichment analyses were conducted. CONCLUSION: This study presented the first demonstration of the pan-cancer landscape of abnormal SKP2 expression, it could potentially serve as a predictive indicator and prospective therapeutic target.