Centromere Protein A (CENPA) Regulates Metabolic Reprogramming in the Colon Cancer Cells by Transcriptionally Activating Karyopherin Subunit Alpha 2 (KPNA2)

CENPA facilitates glioma stem cell stemness and suppress ferroptosis to accelerate glioblastoma multiforme progression by promoting GBP2 transcription

The function of glioma stem cells (GSCs) is closely related to the progression of glioblastoma multiforme (GBM). Centromere protein A (CENPA) has been confirmed to be related to the poor prognosis of GBM patients. However, whether CENPA regulates GSCs function to mediate GBM progression is still unclear. GSCs were isolated from GBM cells. The expression of CENPA and guanylate-binding protein 2 (GBP2) was examined by quantitative real-time PCR and western blot. GSCs proliferation and stemness were assessed using EdU assay and sphere formation assay. Cell ferroptosis was evaluated by detecting related factors. The interaction between CENPA and GBP2 was analyzed by ChIP assay and dual-luciferase reporter assay. Animal experiments were conducted to measure the effect of CENPA knockdown on the tumorigenicity of GSCs in vivo. CENPA was upregulated in GBM tissues and GSCs. CENPA knockdown inhibited GSCs proliferation, stemnness, and promoted ferroptosis. GBP2 was overexpressed in GBM tissues and GSCs, and CENPA enhanced GBP2 transcription by binding to its promoter region. CENPA overexpression accelerated GSCs proliferation and stemnness and suppressed ferroptosis, while GBP2 knockdown reversed these effects. Downregulation of CENPA reduced the tumorigenicity of GSCs by decreasing GBP2 expression in vivo. In conclusion, CENPA enhanced GBP2 transcription to increase its expression, thus accelerating GSCs proliferation and stemnness and repressing ferroptosis. Our findings promote a new idea for GBM treatment.

Targeting colorectal cancer at the level of nuclear pore complex

BACKGROUND

Nuclear pore complexes (NPCs) are the architectures entrenched in nuclear envelop of a cell that regulate the nucleo-cytoplasmic transportation of materials, such as proteins and RNAs for proper functioning of a cell. The appropriate localization of proteins and RNAs within the cell is essential for its normal functionality. For such a complex transportation of materials across the NPC, around 60 proteins are involved comprising nucleoporins, karyopherins and RAN system proteins that play a vital role in NPC's structure formation, cargo translocation across NPC, and cargoes' rapid directed transportation respectively. In various cancers, the structure and function of NPC is often exaggerated, following altered expressions of its nucleoporins and karyopherins, affecting other proteins of associated signaling pathways. Some inhibitors of karyopherins at present, have potential to regulate the altered level/expression of these karyopherin molecules.

AIM OF REVIEW

This review summarizes the data from 1990 to 2023, mainly focusing on recent studies that illustrate the structure and function of NPC, the relationship and mechanisms of nucleoporins and karyopherins with colorectal cancer, as well as therapeutic values, in order to understand the pathology and underlying basis of colorectal cancer associated with NPC. This is the first review to our knowledge elucidating the detailed updated studies targeting colorectal cancer at NPC. The review also aims to target certain karyopherins, Nups and their possible inhibitors and activators molecules as a therapeutic strategy.

KEY SCIENTIFIC CONCEPTS OF REVIEW

NPC structure provides understanding, how nucleoporins and karyopherins as key molecules are responsible for appropriate nucleocytoplasmic transportation. Many studies provide evidences, describing the role of disrupted nucleoporins and karyopherins not only in CRC but also in other non-hematological and hematological malignancies. At present, some inhibitors of karyopherins have therapeutic potential for CRC, however development of more potent inhibitors may provide more effective therapeutic strategies for CRC in near future.

YTHDC1-dependent m6A modification modulated FOXM1 promotes glycolysis and tumor progression through CENPA in triple-negative breast cancer

Triple-negative breast cancer (TNBC) exhibits heightened aggressiveness compared with other breast cancer (BC) subtypes, with earlier relapse, a higher risk of distant metastasis, and a worse prognosis. Transcription factors play a pivotal role in various cancers. Here, we found that factor forkhead box M1 (FOXM1) expression was significantly higher in TNBC than in other BC subtypes and normal tissues. Combining the findings of Gene Ontology (GO) enrichment analysis and a series of experiments, we found that knockdown of the FOXM1 gene attenuated the ability of TNBC cells to proliferate and metastasize both in vivo and in vitro. In addition, Spearman's test showed that FOXM1 significantly correlated with glycolysis-related genes, especially centromere protein A (CENPA) in datasets (GSE76250, GSE76124, GSE206912, and GSE103091). The effect of silencing FOXM1 on the inhibition of CENPA expression, TNBC proliferation, migration, and glycolysis could be recovered by overexpression of CENPA. According to MeRIP, the level of m6A modification on FOMX1 decreased in cells treated with cycloleucine (a m6A inhibitor) compared with that in the control group. The increase in FOXM1 expression caused by YTHDC1 overexpression could be reversed by the m6A inhibitor, which indicated that YTHDC1 enhanced FOXM1 expression depending on m6A modification. Therefore, we concluded that the YTHDC1-m6A modification/FOXM1/CENPA axis plays an important role in TNBC progression and glycolysis.

CENPA promotes glutamine metabolism and tumor progression by up-regulating SLC38A1 in endometrial cancer

Glutamine addiction is a significant hallmark of metabolic reprogramming in tumors and is crucial to the progression of cancer. Nevertheless, the regulatory mechanisms of glutamine metabolism in endometrial cancer (EC) remains elusive. In this research, we found that elevated expression of CENPA and solute carrier family 38 member 1 (SLC38A1) were firmly associated with worse clinical stage and unfavorable outcomes in EC patients. In addition, ectopic overexpression or silencing of CENPA could either enhance or diminish glutamine metabolism and tumor progression in EC. Mechanistically, CENPA directly regulated the transcriptional activity of the target gene, SLC38A1, leading to enhanced glutamine uptake and metabolism, thereby promoting EC progression. Notably, a prognostic model utilizing the expression levels of CENPA and SLC38A1 genes independently emerged as a prognostic factor for EC. More importantly, CENPA and SLC38A1 were significantly elevated and positively correlated, as well as indicative of poor prognosis in multiple cancers. In brief, our study confirmed that CENPA is a critical transcription factor involved in glutamine metabolism and tumor progression through modulating SLC38A1. This revelation suggests that targeting CENPA could be an appealing therapeutic approach to address pan-cancer glutamine addiction.

CENPA-driven STMN1 Transcription Inhibits Ferroptosis in Hepatocellular Carcinoma

Background and Aims

The growing knowledge of ferroptosis has suggested the regulatory role of ferroptosis in hepatocellular carcinoma (HCC), but the pertinent molecular mechanisms remain unclear. Herein, this study investigated the mechanistic basis of ferroptosis-related genes (ferrGenes) in the growth of HCC.

Methods

Differentially expressed human ferrGenes and tumor-related transcription factors (TFs) were obtained from the The Cancer Genome Atlas (TCGA) dataset and the GTEx dataset. Spearman method-based correlation analysis were conducted to construct TF-ferrGene coexpression regulatory network. Key genes associated with prognosis were singled out with Lasso regression and multivariate Cox analysis to construct the prognostic risk model. Then the accuracy and independent prognostic ability of the model were evaluated. Expression of CENPA and STMN1 was determined in clinical HCC tissues and HCC cells, and their binding was analyzed with dual-luciferase and chromatin immunoprecipitation (ChIP) assays. Furthermore, ectopic expression and knockdown assays were performed in HCC cells to assess the effect of CENPA and STMN1 on ferroptosis and malignant phenotypes.

Results

The prognostic risk model constructed based on the eight TF-ferrGene regulatory network-related genes accurately predicted the prognosis of HCC patients. It was strongly related to the clinical characteristics of HCC patients. Moreover, CENPA/STMN1 might be a key TF-ferrGene regulatory network in ferroptosis of HCC. CENPA and STMN1 were overexpressed in HCC tissues and cells. Additionally, CENPA facilitated STMN1 transcription by binding to STMN1 promoter, thus facilitating the malignant phenotypes and suppressing the ferroptosis of HCC cells.

Conclusions

Taken together, CENPA curbs the ferroptosis of HCC cells by upregulating STMN1 transcription, thereby promoting HCC growth.

High expression of centromere protein A and its molecular mechanism and clinical significance in prostate cancer: A study based on data mining and immunohistochemistry

The progression of prostate cancer (PCa) leads to poor prognosis. However, the molecular mechanism of PCa is still not completely clear. This study aimed to elucidate the important role of centromere protein A (CENPA) in PCa. Large numbers of bulk RNA sequencing (RNA-seq) data and in-house immunohistochemistry data were used in analysing the expression level of CENPA in PCa and metastatic PCa (MPCa). Single-cell RNA-seq data was used to explore the expression status of CENPA in different prostate subpopulations. Enrichment analysis was employed to detect the function of CENPA in PCa. Clinicopathological parameters analysis was utilised in analysing the clinical value of CENPA. The results showed that CENPA was upregulated in PCa (standardised mean difference [SMD] = 0.83, p = 0.001) and MPCa (SMD = 0.61, p = 0.029). CENPA was overexpressed in prostate cancer stem cells (CSCs) with androgen receptor (AR) negative compared to epithelial cells with AR positive. CENPA may influence the development of PCa through affecting cell cycle. Patients with nodal metastasis had higher expression level of CENPA. And patients with high CENPA expression had poor disease-free survival. Taken together, Overexpression of CENPA may influence the development of PCa by regulating cell cycle and promoting metastasis.

The influence of COVID-19 on colorectal cancer was investigated using bioinformatics and systems biology techniques

Introduction

Coronavirus disease 2019 (COVID-19) is a global pandemic and highly contagious, posing a serious threat to human health. Colorectal cancer (CRC) is a risk factor for COVID-19 infection. Therefore, it is vital to investigate the intrinsic link between these two diseases.

Methods

In this work, bioinformatics and systems biology techniques were used to detect the mutual pathways, molecular biomarkers, and potential drugs between COVID-19 and CRC.

Results

A total of 161 common differentially expressed genes (DEGs) were identified based on the RNA sequencing datasets of the two diseases. Functional analysis was performed using ontology keywords, and pathway analysis was also performed. The common DEGs were further utilized to create a protein-protein interaction (PPI) network and to identify hub genes and key modules. The datasets revealed transcription factors-gene interactions, co-regulatory networks with DEGs-miRNAs of common DEGs, and predicted possible drugs as well. The ten predicted drugs include troglitazone, estradiol, progesterone, calcitriol, genistein, dexamethasone, lucanthone, resveratrol, retinoic acid, phorbol 12-myristate 13-acetate, some of which have been investigated as potential CRC and COVID-19 therapies.

Discussion

By clarifying the relationship between COVID-19 and CRC, we hope to provide novel clues and promising therapeutic drugs to treat these two illnesses.

Upregulation of CENPM is associated with poor clinical outcome and suppression of immune profile in clear cell renal cell carcinoma

BACKGROUND

The response of advanced clear cell renal cell carcinoma (ccRCC) to immunotherapy is still not durable, suggesting that the immune landscape of ccRCC still needs to be refined, especially as some molecules that have synergistic effects with immune checkpoint genes need to be explored.

METHODS

The expression levels of CENPM and its relationship with clinicopathological features were explored using the ccRCC dataset from TCGA and GEO databases. Quantitative polymerase chain reaction (qPCR) analysis was performed to validate the expression of CENPM in renal cancer cell lines. Kaplan-Meier analysis, COX regression analysis and Nomogram construction were used to systematically evaluate the prognostic potential of CENPM in ccRCC. Besides, single gene correlation analysis, protein-protein interaction (PPI) network, genetic ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to predict the biological behaviour of CENPM and the possible signalling pathways involved. Finally, a comprehensive analysis of the crosstalk between CENPM and immune features in the tumor microenvironment was performed based on the ssGSEA algorithm, the tumor immune dysfunction and exclusion (TIDE) algorithm, the TIMER2.0 database and the TISIDB database.

RESULTS

CENPM was significantly upregulated in ccRCC tissues and renal cancer cell lines and was closely associated with poor clinicopathological features and prognosis. Pathway enrichment analysis revealed that CENPM may be involved in the regulation of the cell cycle in ccRCC and may have some crosstalk with the immune microenvironment in tumors. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. Furthermore, the TISIDB database provides evidence that not only CENPM is positively associated with immune checkpoint genes such as CTLA4, PDCD1, LAG3, TIGIT, but also chemokines and receptors (such as CCL5, CXCL13, CXCR3, CXCR5) may be responsible for the malignant phenotype of CENPM in ccRCC. Meanwhile, predictions based on the TIDE algorithm support that patients with high CENPM expression have a worse response to immunotherapy.

CONCLUSIONS

The upregulation of CENPM in ccRCC predicts a poor clinical outcome, and this malignant phenotype may be associated with its exacerbation of the immunosuppressive state in the tumor microenvironment.

Exploration of an Prognostic Signature Related to Endoplasmic Reticulum Stress in Colorectal Adenocarcinoma and Their Response Targeting Immunotherapy

Background: Endoplasmic reticulum (ER) stress plays a pro-apoptotic role in colorectal adenocarcinoma (COAD). This study aimed to develop a novel ER-stress-related prognostic risk model for COAD and provide support for COAD cohorts with different risk score responses to immune checkpoint inhibitor therapies. Methods: TCGA-COAD and GSE39582 were included in this prospective study. Univariate and multivariate Cox analyses were performed to identify prognostic ER stress-related genes (ERSGs). Accordingly, the immune infiltration landscape and immunotherapy response in different risk groups were assessed. Finally, the expression of prognostic genes in 10 normal and 10 COAD tissue samples was verified using reverse transcription-quantitative polymerase chain reaction. Results: Eight prognostic genes were selected to establish an ERSG-based signature in the training set of the TCGA-COAD cohort. The accuracy of this was confirmed using a testing set of TCGA-COAD and GSE39582 cohorts. Gene set variation analysis indicated that differential functionality in high-low-risk groups was related to immune-related pathways. Corresponding to this, CD36, TIMP1, and PTGIS were significantly associated with 19 immune cells with distinct proportions between the different risk groups, such as central memory CD4T cells and central memory CD8T cells. Moreover, the risk score was considered effective for predicting the clinical response to immunotherapy, and the immunotherapy response was significantly and negatively correlated with the risk score of individuals with COAD. Furthermore, the immune checkpoint inhibitor treatment was less effective in the high-risk group, where the expression levels of PD-L1 and tumor immune dysfunction and exclusion scores in the high-risk group were significantly increased. Finally, the experimental results demonstrated that the expression trends of prognostic genes in clinical samples were consistent with the results from public databases. Conclusion: Our study established a novel risk signature to predict the COAD prognosis of patients and provide theoretical support for the clinical treatment of COAD.

Comprehensive Characterization of the Regulatory Landscape of Adrenocortical Carcinoma: Novel Transcription Factors and Targets Associated with Prognosis

We reconstructed a transcriptional regulatory network for adrenocortical carcinoma (ACC) using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA)-ACC cohort. We investigated the association of transcriptional regulatory units (regulons) with overall survival, molecular phenotypes, and immune signatures. We annotated the ACC regulons with cancer hallmarks and assessed single sample regulon activities in the European Network for the Study of Adrenal Tumors (ENSAT) cohort. We found 369 regulons associated with overall survival and subdivided them into four clusters: RC1 and RC2, associated with good prognosis, and RC3 and RC4, associated with worse outcomes. The RC1 and RC3 regulons were highly correlated with the 'Steroid Phenotype,' while the RC2 and RC4 regulons were highly correlated with a molecular proliferation signature. We selected two regulons, NR5A1 (steroidogenic factor 1, SF-1) and CENPA (Centromeric Protein A), that were consistently associated with overall survival for further downstream analyses. The CENPA regulon was the primary regulator of MKI-67 (a marker of proliferation KI-67), while the NR5A1 regulon is a well-described transcription factor (TF) in ACC tumorigenesis. We also found that the ZBTB4 (Zinc finger and BTB domain-containing protein 4) regulon, which is negatively associated with CENPA in our transcriptional regulatory network, is also a druggable anti-tumorigenic TF. We anticipate that the ACC regulons may be used as a reference for further investigations concerning the complex molecular interactions in ACC tumors.

Glycometabolism-related gene signature of hepatocellular carcinoma predicts prognosis and guides immunotherapy

Hepatocellular carcinoma (HCC) is a severe cancer endangering human health. We constructed a novel glycometabolism-related risk score to predict prognosis and immunotherapy strategies in HCC patients. The HCC data sets were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, and the glycometabolism-related gene sets were obtained from the Molecular Signature Database. The least absolute contraction and selection operator (LASSO) regression model was used to construct a risk score based on glycometabolism-related genes. A simple visual nomogram model with clinical indicators was constructed and its effectiveness in calibration, accuracy, and clinical value was evaluated. We also explored the correlation between glycometabolism-related risk scores and molecular pathways, immune cells, and functions. Patients in the low-risk group responded better to anti-CTLA-4 immune checkpoint treatment and benefited from immune checkpoint inhibitor (ICI) therapy. The study found that glycometabolism-related risk score can effectively distinguish the prognosis, molecular and immune-related characteristics of HCC patients, and may provide a new strategy for individualized treatment.

Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma

Background

The initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.

Methods

The activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.

Results

Patients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.

Conclusions

Our study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.

A combined signature of glycolysis and immune landscape predicts prognosis and therapeutic response in prostate cancer

Prostate cancer (PCa) is a common malignancy that poses a major threat to the health of men. Prostate-specific antigen (PSA) and its derivatives, as FDA-approved detection assays, are insufficient to serve as optimal markers for patient prognosis and clinical decision-making. It is widely acknowledged that aberrant glycolytic metabolism in PCa is related to tumor progression and acidifies the tumor microenvironment (TME). Considering the non-negligible impacts of glycolysis and immune functions on PCa, we developed a combined classifier in prostate cancer. The Glycolysis Score containing 19 genes and TME Score including three immune cells were created, using the univariate and multivariate Cox proportional hazards model, log-rank test, least absolute shrinkage and selection operator (LASSO) regression analysis and the bootstrap approach. Combining the glycolysis and immunological landscape, the Glycolysis-TME Classifier was then constructed. It was observed that the classifier was more accurate in predicting the prognosis of patients than the current biomarkers. Notably, there were significant differences in metabolic activity, signaling pathways, mutational landscape, immunotherapeutic response, and drug sensitivity among the Glycolysis^high/TME^low, Mixed group and Glycolysis^low/TME^high identified by this classifier. Overall, due to the significant prognostic value and potential therapeutic guidance of the Glycolysis-TME Classifier, we anticipate that this classifier will be clinically beneficial in the management of patients with PCa.