CENPA promotes clear cell renal cell carcinoma progression and metastasis via Wnt/β-catenin signaling pathway

Multifunctional histone variants in genome function

Histones are integral components of eukaryotic chromatin that have a pivotal role in the organization and function of the genome. The dynamic regulation of chromatin involves the incorporation of histone variants, which can dramatically alter its structural and functional properties. Contrary to an earlier view that limited individual histone variants to specific genomic functions, new insights have revealed that histone variants exert multifaceted roles involving all aspects of genome function, from governing patterns of gene expression at precise genomic loci to participating in genome replication, repair and maintenance. This conceptual change has led to a new understanding of the intricate interplay between chromatin and DNA-dependent processes and how this connection translates into normal and abnormal cellular functions.

Single-cell and bulk RNA sequencing analysis reveals CENPA as a potential biomarker and therapeutic target in cancers

BACKGROUND

Cancer remains one of the most significant public health challenges worldwide. A widely recognized hallmark of cancer is the ability to sustain proliferative signaling, which is closely tied to various cell cycle processes. Centromere Protein A (CENPA), a variant of the standard histone H3, is crucial for selective chromosome segregation during the cell cycle. Despite its importance, a comprehensive pan-cancer bioinformatic analysis of CENPA has not yet been conducted.

METHODS

Data on genomes, transcriptomes, and clinical information were retrieved from publicly accessible databases. We analyzed CENPA's genetic alterations, mRNA expression, functional enrichment, association with stemness, mutations, expression across cell populations and cellular locations, link to the cell cycle, impact on survival, and its relationship with the immune microenvironment. Additionally, a prognostic model for glioma patients was developed to demonstrate CENPA's potential as a biomarker. Furthermore, drugs targeting CENPA in cancer cells were identified and predicted using drug sensitivity correlations and protein-ligand docking.

RESULTS

CENPA exhibited low levels of gene mutation across various cancers. It was found to be overexpressed in nearly all cancer types analyzed in TCGA, relative to normal controls, and was predominantly located in the nucleus of malignant cells. CENPA showed a strong association with the cancer cell cycle, particularly as a biomarker for the G2 phase. It also emerged as a valuable diagnostic and prognostic biomarker across multiple cancer types. In glioma, CENPA demonstrated reliable prognostic potential when used alongside other prognostic factors. Additionally, CENPA was linked to the immune microenvironment. Drugs such as CD-437, 3-Cl-AHPC, Trametinib, BI-2536, and GSK461364 were predicted to target CENPA in cancer cells.

CONCLUSION

CENPA serves as a crucial biomarker for the cell cycle in cancers, offering both diagnostic and prognostic value.

Identification of CENPM as a key gene driving adrenocortical carcinoma metastasis via physical interaction with immune checkpoint ligand FGL1

BACKGROUND

Distant metastasis occurs in the majority of adrenocortical carcinoma (ACC), leading to an extremely poor prognosis. However, the key genes driving ACC metastasis remain unclear.

METHODS

Weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were conducted to identify ACC metastasis-related genes. Data from RNA-seq and microarray were analyzed to reveal correlations of the CENPM gene with cancer, metastasis, and survival in ACC. Immunohistochemistry was used to assess CENPM protein expression. The impact of CENPM on metastasis behaviour was verified in ACC (H295R and SW-13) cells and xenograft NPG mice. DIA quantitative proteomics analysis, western blot, immunofluorescence, and co-immunoprecipitation assay were performed to identify the downstream target of CENPM.

RESULTS

Among the 12 035 analyzed genes, 363 genes were related to ACC metastasis and CENPM was identified as the hub gene. CENPM was upregulated in ACC samples and associated with metastasis and poor prognosis. Knockdown of CENPM inhibited proliferation, invasion, and migration of ACC cells and suppressed liver metastasis in xenograft NPG mice. Collagen-containing extracellular matrix signalling was primarily downregulated when CENPM was knocked down. FGL1, important components of ECM signalling and immune checkpoint ligand of LAG3, were downregulated following CENPM silence, overexpressed in human advanced ACC samples, and colocalized with CENPM. Physical interaction between CENPM and FGL1 was identified. Overexpression of FGL1 rescued migration and invasion of CENPM knockdown ACC cells.

CONCLUSIONS

CENPM is a key gene in driving ACC metastasis. CENPM promotes ACC metastasis through physical interaction with the immune checkpoint ligand FGL1. CENPM can be used as a new prognostic biomarker and therapeutic target for metastatic ACC.

HIGHLIGHTS

CENPM is the key gene that drives ACC metastasis, and a robust biomarker for ACC prognosis. Silencing CENPM impedes ACC metastasis in vitro and in vivo by physical interaction with immune checkpoint ligand FGL1. FGL1 is overexpressed in ACC and promotes ACC metastasis.

CENPE is a diagnostic and prognostic biomarker for cervical cancer

Cervical squamous cell carcinoma (CESC) is a common cancer in women. Despite advancements in early diagnosis through high-risk human papillomavirus (HPV) screening, challenges remain in predicting and treating the disease. Hence, the identification of novel biomarkers for prognosis and therapeutic targets is crucial. CENPE, a microtubule-end directed motor protein that accumulates during the G2 phase, is recognized for its involvement in promoting cancer growth and progression. However, its specific role in CESC remains unclear. This research investigated the expression of CENPE in CESC utilizing data from The Cancer Genome Atlas (TCGA), which was further validated through gene expression profiles, the Human Protein Atlas (HPA), and clinical data. The study utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and immune infiltration analysis to elucidate the role of CENPE in CESC. Additionally, Protein-protein interaction (PPI) networks and competing endogenous RNA (CeRNA) networks involving CENPE and its differentially expressed genes were established. Furthermore, Kaplan-Meier survival analysis was conducted to evaluate the impact of CENPE on patient prognosis. Our study revealed an upregulation of CENPE expression in cervical cancer tissues, which promotes the progression of CESC through IL-6-mediated PI3K-Akt and MAPK signaling pathways. The significant associations with ACNG3, LY6H, and SLC6A7 suggest that CENPE may play a role in tumor growth and metastasis, potentially involving the nervous system. Moreover, the correlations with ARIH1, KDM1A, KDM5B, and NSD3 indicate that CENPE could be a promising target for drug development. Our analysis of the ROC curve demonstrated a high diagnostic accuracy of CENPE in CESC (AUC: 0.997, CI: 0.990-1.000). Subgroup analysis highlighted substantial effects in patients under 50 years old, those with a height under 160 cm, individuals in peri- and post-menopausal stages, and patients in clinical stages 1 and 4. Additionally, COX regression analysis indicated that older age, lower BMI, and higher CENPE expression are associated with decreased 1-year, 3-year, and 5-year survival rates. In conclusion, CENPE emerges as a crucial factor in the initiation and advancement of cervical cancer, showing potential as a novel target for therapeutic interventions.

NUF2 is associated with cancer stem cell characteristics and a potential drug target for prostate cancer

Background

Cancer stem cells are characterized by self-renewal, clonal tumor initiation capacity, and treatment resistance, which play essential roles in the tumor progression of prostate cancer (PCa). In this study, we aim to explore the features of cancer stemness and characterize the expression of stem cell-related genes for PCa.

Methods

We downloaded RNA-seq data and related clinical information from The Cancer Genome Atlas (TCGA) database. The mRNA stemness index (mRNAsi) was analyzed for various clinical features, overall survival (OS), and disease-free survival (DFS), and a weighted gene co-expression network analysis (WGCNA) was performed to identify crucial gene modules and key genes, which may play a role in CSCs. The key gene functions were verified using multiple databases, including the TCGA and Gene Expression Omnibus database (GEO). Next, we explored the potential function of the modules and genes obtained using WGCNA using an enrichment analysis. Finally, we performed in vitro experiments for further verification.

Results

We found that mRNAsi were higher in PCa tissues than in normal tissues, and the mRNAsi were closely related to the clinical characteristics of PCa. A total of 16 key genes associated with the mRNAsi scores were identified by WGCNA analysis, including NCAPG, NEK2, DLGAP5, CENPA, CENPF, TPX2, GTSE1, KIF4A, NEIL3, CDC25C, UBE2C, CDCA5, MELK, SKA3, NUF2, and BIRC5. These genes were explicitly highly expressed in PCa across TCGA cancers and were validated in 3 independent GEO PCa datasets. The functional annotations of the key genes were linked with the cell proliferation processes. NUF2 may be a potential biomarker for PCa. In vitro experiments showed that knockdown NUF2 reduced the proliferation and migration of PCa cells.

Conclusion

The 16 key genes identified in this study significantly correlate with PCa stem cell characteristics and showed prognosis-oriented effects in PCa patients. Further, the NUF2 gene may be used as a drug target for treating PCa.

CLIP170 enhancing FOSL1 expression via attenuating ubiquitin-mediated degradation of β-catenin drives renal cell carcinoma progression

Protein interactions are fundamental for all cellular metabolic activities. Cytoplasmic linker protein 170 (CLIP170) plays diverse roles in cellular processes and the development of malignant tumors. Renal cell carcinoma (RCC) poses a significant challenge in oncology owing to its invasive nature, metastatic potential, high recurrence rates, and poor prognosis. However, the specific mechanisms and roles of CLIP170 underlying its involvement in RCC progression remain unclear. The findings of this study revealed a significant upregulation of CLIP170 in RCC tumor tissues. Elevated CLIP170 expression correlated positively with advanced clinical and pathological stages and was associated with poor overall survival in RCC patients. Functional assays in vitro demonstrated that elevated CLIP170 levels enhanced RCC cell proliferation, migration and invasion. Mechanistically, 4D-label free proteomics library identified that CLIP170 increased the level of FOSL1 in the Wnt signaling pathway. Immunoprecipitation and molecular docking were performed to unveil that CLIP170 formed a complex with β-catenin, inhibiting β-catenin degradation via the ubiquitin-proteasome pathway. Elevated β-catenin levels within RCC cells played a central role in promoting the transcriptional expression of FOSL1, thereby facilitating RCC cell proliferation and epithelial-mesenchymal transition (EMT) progression. In vivo investigations corroborated these findings, illustrating that CLIP170 regulated β-catenin and FOSL1 expression, driving tumor growth in RCC. This study highlights the crucial role of CLIP170 in promoting FOSL1 expression by preventing β-catenin ubiquitination and degradation, thus promoting RCC tumor progression. It suggests the CLIP170/β-catenin/FOSL1 axis as a potential therapeutic target for RCC treatment.

Centromeres in cancer: Unraveling the link between chromosomal instability and tumorigenesis

Centromeres are critical structures involved in chromosome segregation, maintaining genomic stability, and facilitating the accurate transmission of genetic information. They are key in coordinating the assembly and help keep the correct structure, location, and function of the kinetochore, a proteinaceous structure vital for ensuring proper chromosome segregation during cell division. Abnormalities in centromere structure can lead to aneuploidy or chromosomal instability, which have been implicated in various diseases, including cancer. Accordingly, abnormalities in centromeres, such as structural rearrangements and dysregulation of centromere-associated proteins, disrupt gene function, leading to uncontrolled cell growth and tumor progression. For instance, altered expression of CENP-A, CENP-E, and others such as BUB1, BUBR1, MAD1, and INCENP, have been shown to ascribe to centromere over-amplification, chromosome missegregation, aneuploidy, and chromosomal instability; this, in turn, can culminate in tumor progression. These centromere abnormalities also promoted tumor heterogeneity by generating genetically diverse cell populations within tumors. Advanced techniques like fluorescence in situ hybridization (FISH) and chromosomal microarray analysis are crucial for detecting centromere abnormalities, enabling accurate cancer classification and tailored treatment strategies. Researchers are exploring strategies to disrupt centromere-associated proteins for targeted cancer therapies. Thus, this review explores centromere abnormalities in cancer, their molecular mechanisms, diagnostic implications, and therapeutic targeting. It aims to advance our understanding of centromeres' role in cancer and develop advanced diagnostic tools and targeted therapies for improved cancer management and treatment.

Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment

PURPOSE

We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment.

METHODS

The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results.

RESULTS

At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05).

CONCLUSION

The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.

Roles of Histone H2B, H3 and H4 Variants in Cancer Development and Prognosis

Histone variants are the paralogs of core histones (H2A, H2B, H3 and H4). They are stably expressed throughout the cell cycle in a replication-independent fashion and are capable of replacing canonical counterparts under different fundamental biological processes. Variants have been shown to take part in multiple processes, including DNA damage repair, transcriptional regulation and X chromosome inactivation, with some of them even specializing in lineage-specific roles like spermatogenesis. Several reports have recently identified some unprecedented variants from different histone families and exploited their prognostic value in distinct types of cancer. Among the four classes of canonical histones, the H2A family has the greatest number of variants known to date, followed by H2B, H3 and H4. In our prior review, we focused on summarizing all 19 mammalian histone H2A variants. Here in this review, we aim to complete the full summary of the roles of mammalian histone variants from the remaining histone H2B, H3, and H4 families, along with an overview of their roles in cancer biology and their prognostic value in a clinical context.

Blocking the WNT/β-catenin pathway in cancer treatment:pharmacological targets and drug therapeutic potential

The WNT/β-catenin signaling pathway plays crucial roles in tumorigenesis and relapse, metastasis, drug resistance, and tumor stemness maintenance. In most tumors, the WNT/β-catenin signaling pathway is often aberrantly activated. The therapeutic usefulness of inhibition of WNT/β-catenin signaling has been reported to improve the efficiency of different cancer treatments and this inhibition of signaling has been carried out using different methods including pharmacological agents, short interfering RNA (siRNA), and antibodies. Here, we review the WNT-inhibitory effects of some FDA-approved drugs and natural products in cancer treatment and focus on recent progress of the WNT signaling inhibitors in improving the efficiency of chemotherapy, immunotherapy, gene therapy, and physical therapy. We also classified these FDA-approved drugs and natural products according to their structure and physicochemical properties, and introduced briefly their potential mechanisms of inhibiting the WNT signaling pathway. The review provides a comprehensive understanding of inhibitors of WNT/β-catenin pathway in various cancer therapeutics. This will benefit novel WNT inhibitor development and optimal clinical use of WNT signaling-related drugs in synergistic cancer therapy.

The Impact of DAXX, HJURP and CENPA Expression in Uveal Melanoma Carcinogenesis and Associations with Clinicopathological Parameters

Uveal melanomas (UMs) represent rare malignant tumors associated with grim prognosis for the majority of patients. DAXX (Death Domain-Associated Protein), HJURP (Holliday Junction Recognition Protein) and CENPA (Centromere Protein A) proteins are implicated in epigenetic mechanisms, now in the spotlight of cancer research to better understand the molecular background of tumorigenesis. Herein, we investigated their expression in UM tissues using immunohistochemistry and explored possible correlations with a multitude of clinicopathological and survival parameters. The Cancer Genome Atlas Program (TCGA) was used for the investigation of their mRNA levels in UM cases. Nuclear DAXX expression correlated with an advanced T-stage (p = 0.004), while cytoplasmic expression marginally with decreased disease-free survival (DFS) (p = 0.084). HJURP nuclear positivity also correlated with advanced T-status (p = 0.054), chromosome 3 loss (p = 0.042) and increased tumor size (p = 0.03). More importantly, both nuclear and cytoplasmic HJURP immunopositivity correlated with decreased overall survival (OS) (p = 0.011 and 0.072, respectively) and worse DFS (p = 0.071 and 0.019, respectively). Lastly, nuclear CENPA overexpression was correlated with presence of irido-corneal angle involvement (p = 0.015) and loss of chromosome 3 (p = 0.041). Nuclear and cytoplasmic CENPA immunopositivity associated with decreased OS (p = 0.028) and DFS (p = 0.018), respectively. HJURP and CENPA mRNA overexpression exhibited strong association with tumor epithelioid histology and was linked to worse prognosis. Our results show the compounding role of DAXX, HJURP and CENPA in UM carcinogenesis, designating them as potential biomarkers for assessing prognosis and possible targets for novel therapeutic interventions.

Insights into immune microenvironment and therapeutic targeting in androgen-associated prostate cancer subtypes

Prostate cancer, one of the most prevalent malignancies among men worldwide, is intricately linked with androgen signaling, a key driver of its pathogenesis and progression. Understanding the diverse expression patterns of androgen-responsive genes holds paramount importance in unraveling the biological intricacies of this disease and prognosticating patient outcomes. In this study, utilizing consensus clustering analysis based on the expression profiles of androgen-responsive genes, prostate cancer patients from the TCGA database were stratified into two distinct subtypes, denoted as C1 and C2. Notably, the C1 subtype demonstrates a significant upregulation of certain genes, such as CGA and HSD17B12, along with a shorter progression-free survival duration, indicating a potentially unfavorable prognosis. Further analyses elucidated the immune infiltration disparities, mutation landscapes, and gene functional pathways characteristic of each subtype. Through integrated bioinformatics approaches and machine learning techniques, key genes such as BIRC5, CENPA, and MMP11 were identified as potential therapeutic targets, providing novel insights into tailored treatment strategies. Additionally, single-cell transcriptome analysis shed light on the heterogeneous expression patterns of these genes across different cell types within the tumor microenvironment. Furthermore, virtual screening identified candidate drugs targeting the BIRC5 receptor, offering promising avenues for drug development. Collectively, these findings deepen our understanding of prostate cancer biology, paving the way for personalized therapeutic interventions and advancing the quest for more effective treatments in prostate cancer management.

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.

Renal cancer: signaling pathways and advances in targeted therapies

Renal cancer is a highlyheterogeneous malignancy characterized by rising global incidence and mortalityrates. The complex interplay and dysregulation of multiple signaling pathways,including von Hippel-Lindau (VHL)/hypoxia-inducible factor (HIF), phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR), Hippo-yes-associated protein (YAP), Wnt/ß-catenin, cyclic adenosine monophosphate (cAMP), and hepatocyte growth factor (HGF)/c-Met, contribute to theinitiation and progression of renal cancer. Although surgical resection is thestandard treatment for localized renal cancer, recurrence and metastasiscontinue to pose significant challenges. Advanced renal cancer is associatedwith a poor prognosis, and current therapies, such as targeted agents andimmunotherapies, have limitations. This review presents a comprehensiveoverview of the molecular mechanisms underlying aberrant signaling pathways inrenal cancer, emphasizing their intricate crosstalk and synergisticinteractions. We discuss recent advancements in targeted therapies, includingtyrosine kinase inhibitors, and immunotherapies, such as checkpoint inhibitors.Moreover, we underscore the importance of multiomics approaches and networkanalysis in elucidating the complex regulatory networks governing renal cancerpathogenesis. By integrating cutting-edge research and clinical insights, this review contributesto the development of innovative diagnostic and therapeutic strategies, whichhave the potential to improve risk stratification, precision medicine, andultimately, patient outcomes in renal cancer.

Investigation of differentially expressed genes related to cellular senescence between high-risk and non-high-risk groups in neuroblastoma

Object

This study aims to identify differentially expressed genes (DEGs) between high-risk and non-high-risk groups in neuroblastoma (NB), construct a prognostic model, and establish a risk score formula.

Materials and methods

The NB dataset GSE49710 (n = 498) from the GEO database served as the training cohort to select DEGs between high-risk and non-high-risk NB groups. Cellular senescence-related genes were obtained from the Aging Atlas database. Intersection genes from both datasets were identified as key genes of cellular senescence-related genes (SRGs). A prognostic model was constructed using Univariate Cox regression analysis and the Lasso algorithm with SRGs. Validation was performed using the E-MTAB-8248 cohort (n = 223). The expression levels of AURKA and CENPA were evaluated via RT-qPCR in two clinical NB sample groups.

Results

Eight SRGs were identified, and a prognostic model comprising five genes related to cellular senescence was constructed. AURKA and CENPA showed significant expression in clinical samples and were closely associated with cellular senescence.

Conclusion

The prognostic model consisted with five cellular senescence related genes effectively predicts the prognosis of NB patients. AURKA and CENPA represent promising targets in NB for predicting cellular senescence, offering potential insights for NB therapy.

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.

EXO1/P53/SREBP1 axis-regulated lipid metabolism promotes prostate cancer progression

Prostate cancer (PCa) is one of the most common malignant tumors affecting the male genitourinary system. However, there is currently a lack of effective treatments for patients with advanced prostate cancer, which significantly impacts men's overall health. Exonuclease 1 (EXO1), a protein with mismatch repair and recombination functions, has been found to play a vital role in various diseases. In our study, we discovered that EXO1 acts as a novel biomarker of PCa, which promotes prostate cancer progression by regulating lipid metabolism reprogramming in prostate cancer cells. Mechanistically, EXO1 promotes the expression of SREBP1 by inhibiting the P53 signaling pathway. In summary, our findings suggest that EXO1 regulated intracellular lipid reprogramming through the P53/SREBP1 axis, thus promoting PCa progression. The result could potentially lead to new insights and therapeutic targets for diagnosing and treating PCa.

CENPA knockdown restrains cell progression and tumor growth in breast cancer by reducing PLA2R1 promoter methylation and modulating PLA2R1/HHEX axis

BACKGROUND

Breast cancer is a lethal malignancy affecting females worldwide. It has been reported that upregulated centromere protein A (CENPA) expression might indicate unfortunate prognosis and can function as a prognostic biomarker in breast cancer. This study aimed to investigate the accurate roles and downstream mechanisms of CENPA in breast cancer progression.

METHODS

CENPA protein levels in breast cancer tissues and cell lines were analyzed by Western blot and immunohistochemistry assays. We used gain/loss-of-function experiments to determine the potential effects of CENPA and phospholipase A2 receptor (PLA2R1) on breast cancer cell proliferation, migration, and apoptosis. Co-IP assay was employed to validate the possible interaction between CENPA and DNA methyltransferase 1 (DNMT1), as well as PLA2R1 and hematopoietically expressed homeobox (HHEX). PLA2R1 promoter methylation was determined using methylation-specific PCR assay. The biological capabilities of CENPA/PLA2R1/HHEX axis in breast cancer cells was determined by rescue experiments. In addition, CENPA-silenced MCF-7 cells were injected into mice, followed by measurement of tumor growth.

RESULTS

CENPA level was prominently elevated in breast cancer tissues and cell lines. Interestingly, CENPA knockdown and PLA2R1 overexpression both restrained breast cancer cell proliferation and migration, and enhanced apoptosis. On the contrary, CENPA overexpression displayed the opposite results. Moreover, CENPA reduced PLA2R1 expression through promoting DNMT1-mediated PLA2R1 promoter methylation. PLA2R1 overexpression could effectively abrogate CENPA overexpression-mediated augment of breast cancer cell progression. Furthermore, PLA2R1 interacted with HHEX and promoted HHEX expression. PLA2R1 knockdown increased the rate of breast cancer cell proliferation and migration but restrained apoptosis, which was abrogated by HHEX overexpression. In addition, CENPA silencing suppressed tumor growth in vivo.

CONCLUSION

CENPA knockdown restrained breast cancer cell proliferation and migration and attenuated tumor growth in vivo through reducing PLA2R1 promoter methylation and increasing PLA2R1 and HHEX expression. We may provide a promising prognostic biomarker and novel therapeutic target for breast cancer.

NEIL3 promotes cell proliferation of ccRCC via the cyclin D1-Rb-E2F1 feedback loop regulation

Nei endonuclease VIII-like 3 (NEIL3), a novel tumor-related gene, is differentially expressed and involved in pathophysiological processes in multiple tumors. However, the potential biological functions and molecular mechanisms of NEIL3 in human clear cell renal cell carcinoma (ccRCC) have not been identified. In this research, we demonstrated that NEIL3, transcriptionally activated by E2F1, served as an oncogene to facilitate cell proliferation and cell cycle progression and contribute to tumorigenesis via the cyclin D1-Rb-E2F1 feedback loop in ccRCC. First, we found that NEIL3 expression was upregulated in ccRCC tissues and cell lines compared with matched adjacent nontumor tissues and renal tubular epithelial cells and was also positively correlated with adverse clinicopathological characteristics, such as advanced cancer stages and higher tumor grades, and acted as an independent prognostic marker in ccRCC. Mechanistically, we demonstrated that NEIL3 promoted cell proliferation, DNA replication and cell cycle progression in vitro and tumor growth in vivo. Furthermore, we found that NEIL3 overexpression activated the cyclin D1-Rb-E2F1 pathway, and the E2F1 upregulation transcriptionally activated NEIL3 expression, thus forming a feedback loop. In addition, there was a positive correlation between NEIL3 and E2F1 expression in clinical specimens of ccRCC. Taken together, our results suggest that NEIL3 serves as a proto-oncogene in ccRCC and presents as a novel candidate for ccRCC diagnosis and treatment.

Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and treatment strategies for ccRCC. This study aims to create a pathomics model to predict OS in ccRCC patients. In this study, data from ccRCC patients in the TCGA database were used as a training set, with clinical data serving as a validation set. Pathological features were extracted from H&E-stained slides using PyRadiomics, and a pathomics model was constructed using the non-negative matrix factorization (NMF) algorithm. The model's predictive performance was assessed through Kaplan-Meier (KM) survival curves and Cox regression analysis. Additionally, differential gene expression, gene ontology (GO) enrichment analysis, immune infiltration, and mutational analysis were conducted to investigate the underlying biological mechanisms. A total of 368 pathomics features were extracted from H&E-stained slides of ccRCC patients, and a pathomics model comprising two subtypes (Cluster 1 and Cluster 2) was successfully constructed using the NMF algorithm. KM survival curves and Cox regression analysis revealed that Cluster 2 was associated with worse OS. A total of 76 differential genes were identified between the two subtypes, primarily involving extracellular matrix organization and structure. Immune-related genes, including CTLA4, CD80, and TIGIT, were highly expressed in Cluster 2, while the VHL and PBRM1 genes, along with mutations in the PI3K-Akt, HIF-1, and MAPK signaling pathways, exhibited mutation rates exceeding 40% in both subtypes. The machine learning-based pathomics model effectively predicts the OS of ccRCC patients and differentiates between subtypes. The critical roles of the immune-related gene CTLA4 and the PI3K-Akt, HIF-1, and MAPK signaling pathways offer new insights for further research on the molecular mechanisms, diagnosis, and treatment strategies for ccRCC.

Identification and immune landscape analysis of fatty acid metabolism genes related subtypes of gastric cancer

Fatty acid metabolism (FAM) is associated with prognosis and immune microenvironment remodeling in many tumors. It is currently unknown how FAM affects the immunological microenvironment and prognosis of Gastric cancer (GC). Therefore, the current work aims to categorize GC samples based on the expression status of genes involved in FAM and to identify populations that might benefit from immunotherapy. In total, 50 FAM genes associated with overall survival (OS) were determined through univariate Cox proportional hazard regression analysis by mining the public TCGA and GEO databases. The GSE84437 and TCGA-STAD cohort samples were divided into two clusters using the "NMF" R package. According to the survival curve, patients in Cluster-1 showed considerably longer OS than those in Cluster-2. Patients in Cluster-1 exhibited earlier T stages, more intestinal GCs, and were older. MSI molecular subtypes were mainly distributed in Cluster-1, while GS molecular subtypes were distributed primarily in Cluster-2. There were 227 upregulated and 22 down-regulated genes (logFC > 1 or logFC < - 1, FDR < 0.05) in Cluster-2 compared with Cluster-1. One hub module (edges = 64, nodes = 12) was identified with a module score of 11.636 through Cytoscape plug-in MCODE. KEGG and GO analysis showed that the hub genes were associated with the cell cycle and cell division. Different immune cell infiltrates profile, and immune pathway enrichment existed between the subtypes. In conclusion, the current findings showed that practically all immunological checkpoint and immunoregulatory genes were elevated in patients with Cluster-2 GC, indicating that FAM subtypes may be crucial in GC immunotherapy.

CENPE and LDHA were potential prognostic biomarkers of chromophobe renal cell carcinoma

BACKGROUND

Most sarcomatoid differentiated renal cell carcinoma was differentiated from Chromophobe renal cell carcinoma (KICH) and related to a bad prognosis. Thus, finding biomarkers is important for the therapy of KICH.

METHODS

The UCSC was used for determining the expression of mRNA and miRNA and clinical data in KICH and normal samples. KEGG and GO were used for predicting potential function of differently expressed genes (DEGs). Optimal prognostic markers were determined by Lasso regression. Kaplan-Meier survival, ROC, and cox regression were used for assessing prognosis value. GSEA was used for predicting potential function of markers. The relations between markers and immune cell infiltration were determined by Pearson method. The upstream miRNA of markers was predicted in TargetScan and DIANA.

RESULTS

The 6162 upregulated and 13,903 downregulated DEGs were identified in KICH. Further CENPE and LDHA were screened out as optimal prognostic risk signatures. CENPE was highly expressed while LDHA was lowly expressed in KICH samples, and the high expressions of 2 genes contributed to bad prognosis. The functions of CENPE and LDHA were mainly enriched in proliferation related pathways such as cell cycle and DNA replication. In addition, the correlation of 2 genes with immune infiltrates in KICH was also observed. Finally, we found that has-miR-577 was the common upstream of 2 genes and the binding sites can be predicted.

CONCLUSION

CENPE and LDHA were identified as the important prognostic biomarkers in KICH, and they might be involved in the proliferation of cancer cell.

TTC13 expression and STAT3 activation may form a positive feedback loop to promote ccRCC progression

Background

Renal cell carcinoma (RCC) originates from renal tubular epithelial cells and is mainly classified into three histological types, including clear cell renal cell carcinoma (ccRCC) which accounts for about 75% of all kidney cancers and is characterized by its strong invasiveness and poor prognosis. Hence, it is imperative to understand the mechanisms underlying the occurrence and progression of ccRCC to identify effective biomarkers for the early diagnosis and the prognosis prediction.

Methods

The mRNA level of TTC13 was quantified by RT-PCR, while the protein level was determined by western blot and immunohistochemistry (IHC) staining. Cell proliferation was measured by cck-8, and cell apoptosis was detected by flow cytometry. The binding of STAT3 to the promoter region of TTC13 was determined by the luciferase reporter assay and chip experiments. STAT3 nuclear translocation was assessed by immunofluorescence staining.

Results

We found that TTC13 was up-regulated in ccRCC, and TTC13 promoted cell proliferation as well as inhibited cell apoptosis and autophagy of ccRCC through wnt/β-catenin and IL6-JAK-STAT3 signaling pathways. Furthermore, TTC13 might play a role in the immune infiltration and immunotherapy of ccRCC. Mechanistically, STAT3 activated the transcription of TTC13 gene.

Conclusions

STAT3 directly regulated TTC13 expression through a positive feedback loop mechanism to promote ccRCC cell proliferation as well as reduce cell apoptosis and autophagy. These findings suggested new and effective therapeutic targets for more accurate and personalized treatment strategies.

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.

CENPA functions as a transcriptional regulator to promote hepatocellular carcinoma progression via cooperating with YY1

The centromere proteins (CENPs), a critical mitosis-related protein complexes, are involved in the kinetochore assembly and chromosome segregation. In this study, we identified that CENPA was significantly up-regulated in HCC and highly expressed CENPA correlated with poor prognosis for HCC patients. Knockdown of CENPA inhibited HCC cell proliferation and tumor growth in vitro and in vivo. Mechanistically, CENPA transcriptionally activated and cooperated with YY1 to drive the expression of cyclin D1 (CCND1) and neuropilin 2 (NRP2). Moreover, we identified that CENPA can be lactylated at lysine 124 (K124). The lactylation of CENPA at K124 promotes CENPA activation, leading to enhanced expression of its target genes. In summary, CENPA function as a transcriptional regulator to promote HCC via cooperating with YY1. Targeting the CENPA-YY1-CCND1/NRP2 axis may provide candidate therapeutic targets for HCC.

PAX5 aberrant expression incorporated in MIPI-SP risk scoring system exhibits additive value in mantle cell lymphoma

Mantle cell lymphoma (MCL) is a subtype of non-Hodgkin lymphoma with highly heterogeneous clinical courses. Paired-box 5 (PAX5), the regulator of B cell differentiation and growth, is abnormally expressed in several types of cancers. Herein, we explored the prognostic value of PAX5 in MCL by comprehensively analyzing the clinical features and laboratory data of 82 MCL cases. PAX5 positivity was associated with shorter overall survival (OS; p = 0.011) and was identified as an independent prognostic factor in MCL patients. The elevated β2-MG (p = 0.027) and advanced Mantle Cell Lymphoma International Prognostic Index (MIPI) score (p = 0.014) were related to positive PAX5 expression. The MIPI-SP risk scoring system was established and exhibited a superior prognostic value for OS depending on an area under the curve (AUC) of 0.770 (95% CI, 0.658-0.881) than MIPI score. Bioinformatic analysis of PAX5-related genes supported the mechanistic roles of PAX5 in MCL. This study provides insight into the potential role of PAX5 in MCL, and the novel risk scoring system MIPI-SP optimizes the risk stratification and facilitates prognosis evaluation in MCL patients. KEY MESSAGES: • Paired-box 5 positivity indicated adverse prognosis in mantle cell lymphoma patients. • Positive PAX5 expression was related to MIPI score and β2-MG in MCL patients. • MIPI-SP risk scoring system has superior prognostic value than MIPI score in MCL.

Stearoyl-CoA Desaturases1 Accelerates Non-Small Cell Lung Cancer Metastasis by Promoting Aromatase Expression to Improve Estrogen Synthesis

Metastases contribute to the low survival rate of non-small cell lung cancer (NSCLC) patients. Targeting lipid metabolism for anticancer therapies is attractive. Accumulative evidence shows that stearoyl-CoA desaturases1 (SCD1), a key enzyme in lipid metabolism, enables tumor metastasis and the underlying mechanism remains unknown. In this study, immunohistochemical staining of 96 clinical specimens showed that the expression of SCD1 was increased in tumor tissues (p < 0.001). SCD1 knockdown reduced the migration and invasion of HCC827 and PC9 cells in transwell and wound healing assays. Aromatase (CYP19A1) knockdown eliminated cell migration and invasion caused by SCD1 overexpression. Western blotting assays demonstrated that CYP19A1, along with β-catenin protein levels, was reduced in SCD1 knocked-down cells, and estrogen concentration was reduced (p < 0.05) in cell culture medium measured by enzyme-linked immunosorbent assay. SCD1 overexpression preserving β-catenin protein stability was evaluated by coimmunoprecipitation and Western blotting. The SCD1 inhibitor A939572, and a potential SCD1 inhibitor, grape seed extract (GSE), significantly inhibited cell migration and invasion by blocking SCD1 and its downstream β-catenin, CYP19A1 expression, and estrogen concentration. In vivo tumor formation assay and a tail vein metastasis model indicated that knockdown of SCD1 blocked tumor growth and metastasis. In conclusion, SCD1 could accelerate metastasis by maintaining the protein stability of β-catenin and then promoting CYP19A1 transcription to improve estrogen synthesis. SCD1 is expected to be a promised therapeutic target, and its novel inhibitor, GSE, has great therapeutic potential in NSCLC.

A pan-cancer landscape of centromere proteins in tumorigenesis and anticancer drug sensitivity

BACKGROUND

During mitosis and meiosis, centromere proteins (CENPs) play a key role in proper chromosome segregation. Abnormal expression of CENPs leads to chromosome instability, which is the main cause of tumorigenesis.

METHODS

To elucidate the functional characteristics of CENPs in pan-cancer, we comprehensively analyzed the expression landscape of CENPs and their relationships with patient survival, genomic alterations, tumor immunity, tumor microenvironment, and anticancer drug sensitivity. The expression patterns and signaling pathways of CENPs were identified through multiple bioinformatics mining and experimental verification. GEPIA2 and PrognoScan were implemented to evaluate the prognostic value of CENPs. The molecular functions of CENPs in pan-cancer were comprehensively assessed using cBioPortal, GSCA, ImmuCellAI, CellMiner, the ROC plotter tool and TIDE.

RESULTS

The results showed that CENPs were upregulated in most tumors compared with normal tissues. We confirmed this conclusion by immunohistochemistry and real-time quantitative PCR. Survival analysis revealed a significant association between high CENP expression and a poor prognosis. CENP expression is related to genome alterations, copy number variation, single nucleotide variation and methylation. Among CENP family genes, CENPF and CENPE are mutated at high frequencies in various tumors, while CENPM and CENPA are less frequently mutated. Furthermore, CENPs regulate the tumor mutational burden, stemness, and microsatellite instability, and are associated with tumor immunity. Most importantly, we revealed that CENP family gene expression was correlated with chemosensitivity and immunotherapy responses.

CONCLUSION

These findings may clarify the role of CENPs in cancer progression and antitumor drug sensitivity and provide evidence for CENPs as a potential target in pan-cancer.

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.

Comprehensive analysis of protein phosphatase 1 regulatory inhibitor subunit 14B, a molecule related to tumorigenesis, poor prognosis, and immune cell infiltration in lung adenocarcinoma

OBJECTIVE

To explore the relationship between Protein Phosphatase 1 Regulatory Inhibitor Subunit 14B (PPP1R14B) and the occurrence of lung adenocarcinoma (LUAD).

METHOD

PPP1R14B expression was investigated using various databases, and its molecular functions and pathways were evaluated using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). Then, the correlation between tumor mutations and PPP1R14B expression was analyzed. Furthermore, the regulation network and expression pathway axes of PPP1R14B were constructed. The correlation analysis between PPP1R14B and immune cell infiltration was performed using deconvolution algorithm analysis and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical (IHC) staining of the clinical samples were used for expression validation.

RESULTS

PPP1R14B showed high expression in tumor tissue. PPP1R14B was associated with T and N stages and poor prognosis and was linked to the cell cycle, DNA repair, and low immune response. High PPP1R14B expression was associated with high tumor mutation rates. The upstream and downstream genes of PPP1R14B were identified, along with the construction of a protein-protein interaction network (PPI network) and the expression pathway axes of PPP1R14B. PPP1R14B expression was associated with poor immune cell infiltration and a negative correlation between PPP1R14B and mast cell and eosinophil infiltration.

CONCLUSION

This study reveals high PPP1R14B expression in LUAD, its contribution to poor prognosis, molecular function, biological pathways, and impact on immune cell infiltration, and provides great insight into the role of PPP1R14B in LUAD tumorigenesis.

AIM2 promotes renal cell carcinoma progression and sunitinib resistance through FOXO3a-ACSL4 axis-regulated ferroptosis

Renal cell carcinoma (RCC) is a serious threat to people's health due to its rapid progression, and patients easily develop resistance to targeted therapy. The absent in melanoma 2 (AIM2) is a receptor protein that has recently been proposed to play an important role in various diseases. In this study, AIM2 was identified as a new biomarker of RCC and promoted RCC progression and sunitinib resistance in an inflammasome-independent manner. Mechanistically, AIM2 promoted FOXO3a phosphorylation and proteasome degradation, thereby reducing its transcriptional effect on ACSL4 and inhibiting ferroptosis. In summary, AIM2 promoted RCC progression and sunitinib resistance through FOXO3a-ACSL4 axis-regulated ferroptosis, which could provide new ideas and therapeutic targets for RCC diagnosis and treatment.

Mitochondrial metabolic reprogramming-mediated immunogenic cell death reveals immune and prognostic features of clear cell renal cell carcinoma

Background

Mitochondrial metabolic reprogramming (MMR)-mediated immunogenic cell death (ICD) is closely related to the tumor microenvironment (TME). Our purpose was to reveal the TME characteristics of clear cell renal cell carcinoma (ccRCC) by using them.

Methods

Target genes were obtained by intersecting ccRCC differentially expressed genes (DEGs, tumor VS normal) with MMR and ICD-related genes. For the risk model, univariate COX regression and K-M survival analysis were used to identify genes most associated with overall survival (OS). Differences in the TME, function, tumor mutational load (TMB), and microsatellite instability (MSI) between high and low-risk groups were subsequently compared. Using risk scores and clinical variables, a nomogram was constructed. Predictive performance was evaluated by calibration plots and receiver operating characteristics (ROC).

Results

We screened 140 DEGs, including 12 prognostic genes for the construction of risk models. We found that the immune score, immune cell infiltration abundance, and TMB and MSI scores were higher in the high-risk group. Thus, high-risk populations would benefit more from immunotherapy. We also identified the three genes (CENPA, TIMP1, and MYCN) as potential therapeutic targets, of which MYCN is a novel biomarker. Additionally, the nomogram performed well in both TCGA (1-year AUC=0.862) and E-MTAB-1980 cohorts (1-year AUC=0.909).

Conclusions

Our model and nomogram allow accurate prediction of patients' prognoses and immunotherapy responses.

KPNA2 promotes angiogenesis by regulating STAT3 phosphorylation

PURPOSE

Angiogenesis is involved in many pathological and physiological processes and is mainly driven by hypoxia. Karyopherin subunit alpha 2 (KPNA2), a member of the nuclear transport protein family, was recently shown to be induced by hypoxia in various types of tumours, so we aimed to investigate the role and mechanism of KPNA2 in angiogenesis under hypoxia.

MATERIALS AND METHODS

After overexpression or knockdown of KPNA2 in human umbilical vein endothelial cells (HUVEC) by adenovirus vector infection, the tube formation, proliferation and migration of HUVEC under hypoxia were detected by tubule formation assay, 5-ethynyl-2'-deoxyuridine (EdU) staining and Transwell assay, respectively. After overexpression or knockdown of KPNA2 in a murine hindlimb ischemia model by local injection of purified adenovirus vector into the gastrocnemius muscle, blood flow changes were examined with a laser Doppler system. Changes in KPNA2-binding proteins under hypoxia were detected by immunoprecipitation-mass spectrometry (IP-MS) and co-immunoprecipitation (Co-IP). The effect of KPNA2 on signal transducer and activator of transcription 3 (STAT3) was detected by Western blotting and quantitative RT‒PCR.

RESULTS

KPNA2 was upregulated in the HUVEC hypoxia model and murine hindlimb ischemia model. Overexpression of KPNA2 increased the proliferation, migration and tube formation of HUVEC under hypoxia, while knockdown of KPNA2 reduced the proliferation, migration and tube formation of HUVEC. Overexpression of KPNA2 promoted the restoration of blood flow in the murine hindlimb ischemia model, while knockout of KPNA2 inhibited the restoration of blood flow in the murine hindlimb ischemia model. Mechanistically, hypoxia promoted the binding of STAT3 to KPNA2. Overexpression of KPNA2 promoted STAT3 phosphorylation and then upregulated vascular endothelial growth factor (VEGF) and angiopoietin 2(ANGPT2), whereas knockdown of KPNA2 inhibited STAT3 phosphorylation and then downregulated VEGF and ANGPT2.

CONCLUSION

Our study demonstrates that hypoxia promotes the binding of STAT3 to KPNA2 and KPNA2 promotes angiogenesis under hypoxia by promoting the binding of STAT3 and JAK1 and regulating STAT3 phosphorylation.

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.

Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes

Objective

Increasing studies have indicated that senescence was associated with tumorigenesis and progression. Lower-grade glioma (LGG) presented a less invasive nature, however, its treatment efficacy and prognosis prediction remained challenging due to the intrinsic heterogeneity. Therefore, we established a senescence-related signature and investigated its prognostic role in LGGs.

Methods

The gene expression data and clinicopathologic features were from The Cancer Genome Atlas (TCGA) database. The experimentally validated senescence genes (SnGs) from the CellAge database were obtained. Then LASSO regression has been performed to build a prognostic model. Cox regression and Kaplan-Meier survival curves were performed to investigate the prognostic value of the SnG-risk score. A nomogram model has been constructed for outcome prediction. Immunological analyses were further performed. Data from the Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data (REMBRANDT), and GSE16011 were used for validation.

Results

The 6-SnG signature has been established. The results showed SnG-risk score could be considered as an independent predictor for LGG patients (HR=2.763, 95%CI=1.660-4.599, P<0.001). The high SnG-risk score indicated a worse outcome in LGG (P<0.001). Immune analysis showed a positive correlation between the SnG-risk score and immune infiltration level, and the expression of immune checkpoints. The CGGA datasets confirmed the prognostic role of the SnG-risk score. And Kaplan-Meier analyses in the additional datasets (CGGA, REMBRANDT, and GSE16011) validated the prognostic role of the SnG-signature (P<0.001 for all).

Conclusion

The SnG-related prognostic model could predict the survival of LGG accurately. This study proposed a novel indicator for predicting the prognosis of LGG and provided potential therapeutic targets.

CENP-A is a potential prognostic biomarker and correlated with immune infiltration levels in glioma patients

Background: Centromeric protein A (CENP-A), an essential protein involved in chromosomal segregation during cell division, is associated with several cancer types. However, its role in gliomas remains unclear. This study examined the clinical and prognostic significance of CENP-A in gliomas.

Methods: Data of patients with glioma were collected from the Cancer Genome Atlas. Logistic regression, the Kruskal–Wallis test, and the Wilcoxon signed-rank test were performed to assess the relationship between CENP-A expression and clinicopathological parameters. The Cox regression model and Kaplan–Meier curve were used to analyze the association between CENP-A and survival outcomes. A prognostic nomogram was constructed based on Cox multivariate analysis. Gene set enrichment analysis (GSEA) was conducted to identify key CENP-A-related pathways and biological processes.

Results:CENP-A was upregulated in glioma samples. Increased CENP-A levels were significantly associated with the world health organization (WHO) grade [Odds ratio (OR) = 49.88 (23.52–129.06) for grade 4 vs. grades 2 and 3], primary therapy outcome [OR = 2.44 (1.64–3.68) for progressive disease (PD) and stable disease (SD) vs. partial response (PR) and complete response (CR)], isocitrate dehydrogenase (IDH) status [OR = 13.76 (9.25–20.96) for wild-type vs. mutant], 1p/19q co-deletion [OR = 5.91 (3.95–9.06) for no codeletion vs. co-deletion], and age [OR = 4.02 (2.68–6.18) for > 60 vs. ≤ 60]. Elevated CENP-A expression was correlated with shorter overall survival in both univariate [hazard ratio (HR): 5.422; 95% confidence interval (CI): 4.044–7.271; p < 0.001] and multivariate analyses (HR: 1.967; 95% CI: 1.280–3.025; p < 0.002). GSEA showed enrichment of numerous cell cycle-and tumor-related pathways in the CENP-A high expression phenotype. The calibration plot and C-index indicated the favorable performance of our nomogram for prognostic prediction in patients with glioma.

Conclusion: We propose a role for CENP-A in glioma progression and its potential as a biomarker for glioma diagnosis and prognosis.

Integrated analysis of senescence-associated genes in pancreatic ductal adenocarcinoma

Background: Cellular senescence plays a critical role in the occurrence and development, and immune modulation of cancer. This research primarily investigated the role of senescence-associated genes (SAGs) in the survival and tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC).

Methods: From the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, the gene expression profiles and clinical data of PDAC samples were downloaded. SAGs in the TCGA cohort were used to build a novel prognostic model and validated in the ICGC cohort. The relationship of signature with the immune landscape, tumor mutational burden (TMB), as well as the sensitivity of different therapies, was explored. Moreover, a nomogram was developed to predict the overall survival of PDAC patients.

Results: A prognostic signature was constructed on basis of three SAGs, and patients in the low-risk score group had a longer survival time. The accuracy of the signature to distinguish different score groups was confirmed through principal component analysis (PCA) and the Receiver operator curves curve. The mRNA expression of the three signature genes was also verified in normal pancreatic and PDAC cell lines by RT-qPCR. The signature could independently predict the prognosis of PDAC patients and had broad applicability. Meanwhile, the nomogram predicted that 1- and 3-years survival rates were in good agreement with the observed overall survival rates. Low-risk patients had lower tumor mutational burden, and low-TMB patients had a better prognosis. Low- and high-risk patients exhibit distinct immune cell infiltration and immune checkpoint changes. By further analyzing the risk score, patients in the low-risk group were more responsive to immunotherapy and a variety of commonly used chemotherapeutic drugs.

Conclusion: The prognostic signature can well predict the prognosis and assess the possibility of immunotherapy in personalized PDAC treatment.

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.

CENP-A Regulation and Cancer

In mammals, CENP-A, a histone H3 variant found in the centromeric chromatin, is critical for faithful chromosome segregation and genome integrity maintenance through cell divisions. Specifically, it has dual functions, enabling to define epigenetically the centromere position and providing the foundation for building up the kinetochore. Regulation of its dynamics of synthesis and deposition ensures to propagate proper centromeres on each chromosome across mitosis and meiosis. However, CENP-A overexpression is a feature identified in many cancers. Importantly, high levels of CENP-A lead to its mislocalization outside the centromere. Recent studies in mammals have begun to uncover how CENP-A overexpression can affect genome integrity, reprogram cell fate and impact 3D nuclear organization in cancer. Here, we summarize the mechanisms that orchestrate CENP-A regulation. Then we review how, beyond its centromeric function, CENP-A overexpression is linked to cancer state in mammalian cells, with a focus on the perturbations that ensue at the level of chromatin organization. Finally, we review the clinical interest for CENP-A in cancer treatment.

Identification of a new five-gene risk score for risk stratification and prognosis prediction in HCC

Approximately 80% of primary liver cancer (PLC) is hepatocellular carcinoma (HCC), and the prognosis of HCC patients is unfavorable. Further studies are required to develop new prognostic tools for predicting the HCC patients' prognosis. The univariate Cox and LASSO regression were utilized to develop the multi-gene risk score. Single-sample Gene Set Enrichment Analysis (ssGSEA) was employed to assess differences of immune functions and cells. The model performance was evaluated by calibration curve and receiver operating characteristic curve (ROC). And qRT-PCR was utilized to evaluate the genes expression in clinical samples. Finally, a novel five-gene (KIF20A, CENPA, HMMR, G6PD, and ADH4) risk score was developed. Based on the median value of patients' risk scores, patients were divided into two groups: high-risk group and low-risk group. The Overall survival (OS) of patients in high-risk group was obviously poorer than that in the low-risk group. And the five-gene risk score was an independent risk factor correlated with patients' OS. Besides, a nomogram consisting of TNM stage and risk score was established. The results of decision curve, calibration curve, and ROC presented that the prognostic risk score and the nomogram had great predictive capability. Besides, ADH4's mRNA was reduced in HCC tissues, while the mRNA of KIF20A, CENPA, HMMR, and G6PD were overexpressed in HCC tissues. We developed a novel five-gene risk score that could predict HCC patients' prognosis. And these five genes could be promising therapeutic targets for HCC. The five-gene risk score and nomogram may be useful prognostic tools for HCC.