CDC6 is a prognostic biomarker and correlated with immune infiltrates in glioma

Lnc-RAINY regulates genes involved in radiation susceptibility through DNA:DNA:RNA triplex-forming interactions and has tumor therapeutic potential in lung cancers

Lung cancer is the leading cause of cancer related deaths worldwide. Unfortunately, radiation resistance remains a major problem facing lung cancer patients. Recently, we identified a group of long non-coding RNAs (lncRNAs) known as linc-SPRY3 RNAs, expressed on the Y-chromosome, which play a role in radiation sensitivity by decreasing tumor burden in vitro and in vivo after radiation. In this study, we found that the linc-SPRY3 RNAs are one large lncRNA that we named Radiation Induced Y-chromosome linked long non-coding RNA (lnc-RAINY). Through ATAC-seq and immunoprecipitation experiments, we show that lnc-RAINY interacts with DNA in a triple helix to induce chromatin remodeling and gene expression. We also identified that lnc-RAINY regulates CDC6 and CDC25A expression affecting senescence induction, cell migration patterns, and cell cycle regulation. Furthermore, the administration of Lnc-RAINY encapsulated in FDA-approved nanoparticles into a lung cancer patient-derived xenograft model dramatically reduces tumor progression demonstrating therapeutic potential.

20(S)-ginsenoside Rg3 suppresses gastric cancer cell proliferation by inhibiting E2F-DP dimerization

BACKGROUND

Gastric cancer (GC) is a common and aggressive malignancy, with treatment options often limited by drug resistance and the adverse effects of targeted therapies and immunotherapy. Ginsenoside Rg3, a bioactive compound derived from ginseng, has shown promise in inhibiting the growth of various tumor types, including GC. However, the molecular mechanisms underlying its therapeutic effects against GC remain insufficiently understood.

OBJECTIVE

This study aimed to elucidate the molecular mechanisms underlying the anti-cancer effects of ginsenoside Rg3 against GC.

METHODS

To explore the molecular mechanisms underlying Rg3's anti-GC effects, RNA sequencing (RNA-Seq) was conducted to identify potential Rg3-regulated targets. The interaction between Rg3 and E2F was analyzed using several approaches, including the cellular thermal shift assay (CETSA), Rg3-PEGA pull-down, Rg3 pull-down protein mass spectrometry, and 3D molecular docking. Additionally, quantitative reverse transcription PCR (qRT-PCR), co-transfection followed by immunoprecipitation, Western blotting, flow cytometry, Annexin V-FITC staining, Hoechst staining, and luciferase reporter assays were employed to elucidate the molecular effects of Rg3. The inhibitory effect of Rg3 on GC proliferation was assessed through colony formation assays in vitro and tumor xenograft experiments in C57BL/6 mice in vivo.

RESULTS

Rg3-mediated gene expression profiling in GC cells revealed several transcription factors, including E2F, and biological processes potentially influenced by Rg3. Consistent with these findings, Rg3 suppressed E2F expression and impeded GC cell proliferation by inducing G1/S cell cycle arrest, reducing cell growth both in vitro and in vivo, enhancing apoptosis, and inhibiting CDC6 transactivation. CETSA and Rg3 pull-down assays confirmed an interaction between Rg3 and E2F. Additionally, 3D molecular docking analysis demonstrated that Rg3 binds with high affinity to E2F at the heterodimeric domain via hydrogen bonding, potentially disrupting E2F-DP heterodimer formation and subsequently inhibiting cell cycle gene expression. In agreement with this, Rg3-treated GC cells exhibited reduced expression of cyclin D1, CDK4, cyclin A, CDK1, and CDK2. Moreover, Rg3 activated the tumor suppressors p53 and p21, further inhibiting RB phosphorylation by suppressing cyclin/CDK activity, thereby blocking transcription of G1/S transition-related genes.

CONCLUSION

This study provides the first evidence that Rg3 directly binds to E2F proteins, disrupting E2F-DP heterodimer formation and inhibiting the transcription of E2F-DP-regulated target genes. Furthermore, Rg3 activates the p53-p21 pathway while suppressing the cyclin/CDK-RB signaling pathway, effectively inhibiting cancer cell proliferation. These findings highlight a potential therapeutic strategy for developing small molecules structurally similar to Rg3 to target tumors with high E2F expression.

Gene regulatory networks analysis for the discovery of prognostic genes in gliomas

Gliomas are the most common and aggressive primary tumors of the central nervous system. Dysregulated transcription factors (TFs) and genes have been implicated in glioma progression, yet these tumors' overall structure of gene regulatory networks (GRNs) remains undefined. We analyzed transcriptional data from 989 primary gliomas in The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to address this. GRNs were reconstructed using the RTN package which identifies regulons-sets of genes regulated by a common TF based on co-expression and mutual information. Regulon activity was evaluated through Gene Set Enrichment Analysis. Elastic net regularization and Cox regression identified 31 and 32 prognostic genes in the TCGA and CGGA datasets, respectively, with 11 genes overlapping, many of which are associated with neural development and synaptic processes. GAS2L3, HOXD13, and OTP demonstrated the strongest correlations with survival outcomes among these. Single-cell RNA-seq analysis of 201,986 cells revealed distinct expression patterns for these genes in glioma subpopulations, particularly oligoprogenitor cells. This study uncovers key GRNs and prognostic genes in gliomas, offering new insights into tumor biology and potential therapeutic targets.

Cell division cycle 6 ​is an independent prognostic biomarker in breast cancer

Cell division cycle 6 (CDC6) is a cell cycle protein involved in cell cycle control, DNA replication and cancer cell apoptosis. This study investigated the prognostic value of CDC6 in breast cancer (BC) utilising large well-characterised cohorts of early-stage ​BC. CDC6 messenger RNA (mRNA) was assessed using the Molecular Taxonomy of the Breast Cancer International Consortium (n=1980), the Cancer Genome Atlas (n=854) and Kaplan-Meier plotter (n=4,929) cohorts. CDC6 protein expression was evaluated using immunohistochemistry in a large (n=951) well-characterised Nottingham BC cohort. The associations between CDC6, clinicopathological parameters, molecular features and patient outcomes were assessed. High CDC6 expression positively correlated with dysregulation of key BC-related genes, including gene involved in cell cycle, DNA damage repair, epithelial cell migration, ​and tumour microenvironment control, as well as with markers characteristic of the basal-like phenotype (CK5, CK14 and CK17). High CDC6 mRNA and protein expression were associated with clinicopathological parameters characteristic of aggressive behaviour, including high tumour grade, large tumour size, the presence of lymphovascular invasion and hormone receptor negativity. High CDC6 protein expression was an independent predictor of poor outcome [p=0.007; hazard ratio (HR)=1.3; 95% confidence interval (CI) 1.2-1.9). This study indicates that CDC6 is an independent prognostic biomarker in BC. These results warrant further functional validation for CDC6 as a potential therapeutic target in BC.

Mapping glioma progression: single-cell RNA sequencing illuminates cell-cell interactions and immune response variability

BACKGROUND

Glioma, the most common primary cancer of the central nervous system, characterizes significant heterogeneity, presenting major challenges for therapeutic approaches and prognosis. In this study, the interactions between malignant glioma cells and macrophages/monocytes, as well as their influence on tumor progression and treatment responses, were explored using comprehensive single-cell RNA sequencing analysis.

METHODS

RNA-seq data from the TCGA and CGGA databases were integrated and an in-depth analysis of glioma samples was performed using single-cell RNA sequencing, functional enrichment analysis, developmental trajectory analysis, cell-cell communication analysis, and gene regulatory network analysis. Furthermore, a prognostic model based on risk scores was developed, and its predictive performance was assessed through immune cell infiltration analysis and immune treatment response evaluation.

RESULTS

Fourteen distinct glioma cellular subpopulations, seven primary cell types, and four macrophage/monocyte subtypes were identified. Developmental trajectory analysis offered insights into the origins and heterogeneity of malignant cells as well as macrophages/monocytes. Cell communication analysis revealed the interaction of macrophages and monocytes with malignant cells through several pathways, including the macrophage migration inhibitory factor and secreted phosphoprotein 1 pathways, engaging in key ligand-receptor interactions that influence tumor behavior. Categorization based on these communication characteristics was significantly correlated with overall survival. Immune cell infiltration analysis highlighted variations in immune cell abundance across different subgroups, possibly linked to differing responses to immunotherapy. This predictive model, comprising 29 prognostic genes, demonstrated high accuracy and robustness across multiple independent cohorts.

CONCLUSION

This study reveals the complex heterogeneity of the glioma microenvironment and enhances the understanding of diverse characteristics of glioma cell subsets. At the same time, it lays a foundation for the development of therapeutic strategies and prognostic models targeting the glioma microenvironment.

Comprehensive multi-omics analysis showed that CDC6 is a potential prognostic and immunotherapy biomarker for multiple cancer types including HCC

BACKGROUND

Cell division cycle 6 (CDC6) is a member of the AAA+ ATPase family and has chaperone-like activity. Many studies have shown that CDC6 plays an important role in cancer development and progression.

METHODS

Explored CDC6 mRNA and protein expression in normal human tissues and tumors using TCGA, GTEx, and HPA. The role of CDC6 in cancer was analyzed using multiple web platforms and software, including R, cBioPortal, UALCAN, SangerBox and others. Finally, CCK-8, EdU assays and Transwell assays were used to verify the effects of CDC6 knockdown on HCC cell proliferation, migration, and invasion.

RESULTS

CDC6 expression was upregulated in most cancers and was associated with poorer prognosis. RNA methylation may play an important role in CDC6 epigenetic modification. CDC6 was significantly positively associated with CD4+ Th2 cells and MDSC in a variety of tumors. Furthermore, immunomodulatory genes are strongly associated with CDC6 expression in most tumor types. CDC6 has higher predictive value than B. Clonality and TMB, and its expression is significantly positively correlated with TMB/MSI and DNAss/RNAss, and is closely related to cell cycle events. Down-regulation of CDC6 can inhibit proliferation, migration and invasion of HCC cells.

CONCLUSIONS

CDC6 is associated with the occurrence and progression of multiple cancer types by regulating the cell cycle. It holds promise as a diagnostic and prognostic biomarker for cancer, and offers potential in immunomodulatory and targeted therapies.

Targeting GDF15 to enhance immunotherapy efficacy in glioblastoma through tumor microenvironment-responsive CRISPR-Cas9 nanoparticles

Despite the outstanding clinical success of immunotherapy, its therapeutic efficacy in glioblastoma (GBM) is still limited. To identify critical regulators of GBM immunity, we constructed a mouse single-guide RNA (sgRNA) library corresponding to all disease-related immune genes, and performed an in vivo CRISPR knockout (KO) screen in syngeneic GBM mouse models. We demonstrated that the deletion of GDF15 in GBM cells ameliorated the immunosuppressive tumor microenvironment (TME) and enhanced the antitumor efficacy of immune checkpoint blockade (ICB) response. Moreover, we designed unique nanoparticles for efficient encapsulation of CRISPR-Cas9, noninvasive brain delivery and tumor cell targeting, demonstrating an effective and safe strategy for GDF15 gene therapy. The CRISPR-Cas9 nanoparticles, known as ANPSS (Cas9/sgRNA), are easily created by enclosing a single Cas9/sgRNA complex in a polymer shell that is sensitive to glutathione. This shell also contains a dual-action ligand that aids in crossing the blood‒brain barrier, targeting tumor cells, and selectively releasing Cas9/sgRNA. Our encapsulating nanoparticles demonstrated promising GBM targeting, resulting in high GDF15 gene editing efficiency within brain tumors while showing minimal off-target gene editing in high-risk tissues. Treatment with ANPSS (Cas9/sgGDF15) effectively halted tumor growth, reversed immune suppression, and enhanced the efficacy of ICB therapy. These results emphasize the potential role of GDF15 in modulating the immune microenvironment and enhancing the effectiveness of current immunotherapy strategies for GBM.

A Distinguished Roadmap of Fibroblast Senescence in Predicting Immunotherapy Response and Prognosis Across Human Cancers

The resistance of tumors to immune checkpoint inhibitors (ICI) may be intricately linked to cellular senescence, although definitive clinical validation remains elusive. In this study, comprehensive pan-cancer scRNA-seq analyses identify fibroblasts as exhibiting the most pronounced levels of cellular senescence among tumor-associated cell populations. To elucidate this phenomenon, a fibroblast senescence-associated transcriptomic signature (FSS), which correlated strongly with protumorigenic signaling pathways and immune dysregulation that fosters tumor progression, is developed. Leveraging the FSS, the machine learning (ML) framework demonstrates exceptional accuracy in predicting ICI response and survival outcomes, achieving superior area under curve (AUC) values across validation, testing, and in-house cohorts. Strikingly, FSS consistently outperforms established signatures in predictive robustness across diverse cancer subtypes. From an integrative analysis of 17 CRISPR/Cas9 libraries, CDC6 emerges as a pivotal biomarker for pan-cancer ICI response and prognostic stratification. Mechanistically, experimental evidence reveals that CDC6 in tumor cells orchestrates fibroblast senescence via TGF-β1 secretion and oxidative stress, subsequently reprogramming the tumor microenvironment and modulating ICI response. These findings underscore the translational potential of targeting fibroblast senescence as a novel therapeutic strategy to mitigate immune resistance and enhance antitumor efficacy.

Exploring Bioinformatics Tools to Analyze the Role of CDC6 in the Progression of Polycystic Ovary Syndrome to Endometrial Cancer by Promoting Immune Infiltration

Cell division cycle 6 (CDC6) is essential for the initiation of DNA replication in eukaryotic cells and contributes to the development of various human tumors. Polycystic ovarian syndrome (PCOS) is a reproductive endocrine disease in women of childbearing age, with a significant risk of endometrial cancer (EC). However, the role of CDC6 in the progression of PCOS to EC is unclear. Therefore, we examined CDC6 expression in patients with PCOS and EC. We evaluated the relationship between CDC6 expression and its prognostic value, potential biological functions, and immune infiltrates in patients with EC. In vitro analyses were performed to investigate the effects of CDC6 knockdown on EC proliferation, migration, invasion, and apoptosis. CDC6 expression was significantly upregulated in patients with PCOS and EC. Moreover, this protein caused EC by promoting the aberrant infiltration of macrophages into the immune microenvironment in patients with PCOS. A functional enrichment analysis revealed that CDC6 exerted its pro-cancer and pro-immune cell infiltration functions via the PI3K-AKT pathway. Moreover, it promoted EC proliferation, migration, and invasion but inhibited apoptosis. This protein significantly reduced EC survival when mutated. These findings demonstrate that CDC6 regulates the progression of PCOS to EC and promotes immune infiltration.

Correlations between 14-gene RNA-level assay and clinical and molecular features in resectable non-squamous non-small cell lung cancer: a cross-sectional study

Background

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Accurate risk stratification is essential for optimizing treatment strategies. A 14-gene RNA-level assay of lung cancer, which involves quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of formalin-fixed paraffin-embedded (FFPE) tissue samples, offers a promising approach. The aim of our study was to assess the relationships between risk stratification, as determined by a 14-gene RNA-level assay, and various clinical and molecular characteristics.

Methods

We retrospectively collected the preoperative clinical information and molecular testing information from 102 resectable non-squamous NSCLC patients. The 14-gene RNA-level assay was performed by extracting RNA from FFPE samples, followed by reverse transcription and quantification via quantitative polymerase chain reaction (qPCR) to assess the expression levels of 11 cancer-associated genes and three housekeeping genes. These gene expression levels were used to calculate a risk score, enabling patient stratification into distinct risk groups. Based on the 14-gene risk stratification, we analyzed the correlations between the clinical and molecular characteristics across the high-, medium-, and low-risk groups.

Results

A total of 102 patients were included in the study. The mean age was 55.19 years, 67 (65.7%) patients were female, and 18 (17.6%) had a smoking history. The 14-gene risk stratification classified patients into low-risk (n=63), intermediate-risk (n=25), and high-risk (n=14) groups. No significant differences were observed in baseline demographics between the three risk groups. High-risk patients had significantly higher mean computed tomography (CT) value (P=0.01) and enhanced CT value (P=0.02) compared to low-risk patients. Genomic profiling of 89 patients revealed specific mutations that were significantly associated with the higher-risk groups. Tumor mutational burden (TMB) was higher in higher-risk groups (P=0.007). In clinically low-risk patients (n=85) as recognized by the NCCN guidelines, the 14-gene risk stratification model reclassified 30 out from the 85 clinically low-risk patients, with 19 placed in the medium-risk group and 11 in the high-risk group, while the remaining samples were still classified as low-risk. Additionally, we found that three patients who were not recommended for adjuvant therapy by the Multiple-gene INdex to Evaluate the Relative benefit of Various Adjuvant therapies (MINERVA) model were classified as high risk and 13 as intermediate risk.

Conclusions

Our results indicate that 14-gene RNA-level assay is correlated with specific genetic mutations, including TP53, KRAS, and LRP1B. These insights provide a stronger foundation for integrating molecular risk assessment with clinical and imaging data, offering more comprehensive information to guide more targeted and effective adjuvant therapy strategies in the future management of lung cancer.

Identifying the prognostic significance of mitophagy-associated genes in multiple myeloma: a novel risk model construction

Multiple myeloma (MM) is a highly heterogeneous hematological malignancy that is currently incurable. Individualized therapeutic approaches based on accurate risk assessment are essential for improving the prognosis of MM patients. Nevertheless, current prognostic models for MM exhibit certain limitations and prognosis heterogeneity still an unresolved issue. Recent studies have highlighted the pivotal involvement of mitochondrial autophagy in the development and drug sensitivity of MM. This study seeks to conduct an integrative analysis of the prognostic significance and immune microenvironment of mitophagy-related signature in MM, with the aim of constructing a novel predictive risk model. GSE4581 and GSE47552 datasets were acquired from the Gene Expression Omnibus database. MM-differentially expressed genes (DEGs) were identified by limma between MM samples and normal samples in GSE47552. Mitophagy key module genes were obtained by weighted gene co-expression network analysis in the Cancer Genome Atlas (TCGA)-MM dataset. Mitophagy DEGs were identified by the overlap genes between MM-DEGs and mitophagy key module genes. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, and a risk model was subsequently constructed based on these prognostic genes. Subsequently, the MM samples were stratified into high- and low-risk groups based on their median risk scores. The validity of the risk model was further evaluated using the GSE4581 dataset. Moreover, a nomogram was developed using the independent prognostic factors identified from the risk score and various clinical indicators. Additionally, analyses were conducted on immune infiltration, immune scores, immune checkpoint, and chemotherapy drug sensitivity. The 17 mitophagy DEGs were obtained by intersection of 803 MM-DEGs and 1084 mitophagy key module genes. Five prognostic genes (CDC6, PRIM1, SNRPB, TOP2A, and ZNF486) were selected via LASSO and univariate cox regression analyses. The predictive performance of the risk model, which was constructed based on the five prognostic genes, demonstrated favorable results in both TCGA-MM and GSE4581 datasets as indicated by the receiver operating characteristic (ROC) curves. In addition, calibration curve, ROC curve, and decision curve analysis curve corroborated that the nomogram exhibited superior predictive accuracy for MM. Furthermore, immune analysis results indicated a significant difference in stromal scores of two risk groups categorized on median risk scores. And four immune checkpoints (CD274, CTLA4, LAG3, and PDCD1LG2) showed significant differences in different risk groups. The analysis of chemotherapy drug sensitivity revealed that etoposide and doxorubicin, which target TOP2A, exhibited superior treatment outcomes in the high-risk group. A novel prognostic model for MM was developed and validated, demonstrating significant potential in predicting patient outcomes and providing valuable guidance for personalized immunotherapy counseling.

Construction of a novel model based on PVT1-MYC duet-related genes for predicting survival and characterization of the tumor microenvironment in pancreatic cancer

Pancreatic cancer is an extremely malignant tumor. PVT1 and MYC signaling has been considered as a therapeutic target recently. Nonetheless, the prognostic values and critical regulatory networks of PVT1-MYC duet in pancreatic cancer remain unclear. Firstly, we identified PVT1-MYC duet-related genes using public databases. Then we analyzed our Hi-C and ChIP-seq data to confirm PVT1-MYC duet. We performed LASSO regression and multivariate Cox regression analysis to build a prognostic model whose effectiveness and robustness were validated by Cox regression, ROC analysis, calibration curve, and nomogram. Besides, we conducted functional enrichment analyses, mutation profiles analyses and the immune features analyses to compare low- and high-risk group. Functional enrichment analyses revealed that several terms associated with cancer progression were enriched in the high-risk group. Mutation profile analysis showed that high-risk group had higher tumor mutation burden, and immune analysis demonstrated high-risk group had more immunosuppressive tumor microenvironment. Finally, we detected PVT1 expression in pancreatic cancer and paracancer tissues from the PUMCH cohort, which showed that PVT1 was significantly upregulated in pancreatic cancer and associated with invasion, metastasis, and poor prognosis. We further performed transwell and proliferation assays and found that PVT1, CDC6, and COL17A1 could promote migration or proliferation of PDAC cells. This study constructed a prognostic model based on three PVT1-MYC duet-related genes, which had a significant potential in predicting the prognosis and tumor microenvironment of pancreatic cancer. These results suggested that targeting PVT1-MYC duet or its regulatory processes could be a therapeutic option with great interests.

Comprehensive pan-cancer analysis reveals CDC6 as a potential immunomodulatory agent and promising therapeutic target in pancreatic cancer

BACKGROUND

CDC6 is critical in DNA replication initiation, but its expression patterns and clinical implications in cancer are underexplored. This study uses multi-omics data from The Cancer Genome Atlas (TCGA) to comprehensively analyze CDC6 across various cancers, aiming to evaluate its potential as a prognostic biomarker and explore its role in immunotherapy.

METHODS

By leveraging multi-omics data from TCGA, we conducted a comprehensive analysis of CDC6 expression across a variety of cancer types. Least absolute shrinkage and selection operator (LASSO) regression was employed to assess the association of CDC6 with key molecules implicated in pancreatic cancer.

RESULTS

CDC6 expression was found to be significantly upregulated across a broad spectrum of cancers. High levels of CDC6 expression were associated with poor prognosis in several cancer types. Notable associations were observed between CDC6 expression and tumor mutational burden (TMB), microsatellite instability (MSI), as well as immune cell infiltration. Co-expression analysis revealed significant associations between CDC6 and prevalent immune checkpoint genes. A risk model incorporating CDC6-related genes, including CCNA1, CCNA2, CCND1, CCND2, CDC25B, CDC6, and CDK2, was developed for pancreatic cancer.

CONCLUSIONS

CDC6 emerges as a promising prognostic biomarker and a potential target for immunotherapy across various cancers, including pancreatic cancer. It appears to modulate immune responses across cancer types, highlighting its regulatory role. Further exploration into the biological functions and clinical implications of CDC6 is warranted.

Comprehensive analysis of aberrantly methylated differentially expressed genes and validation of CDC6 in melanoma

BACKGROUND

Skin Cutaneous Melanoma (SKCM) is a highly aggressive malignant tumor with a significant increase in mortality upon metastasis. The molecular mechanisms driving melanoma progression remain largely unclear. Recent studies have highlighted the importance of epigenetic alterations, especially DNA methylation, in melanoma development. This study aims to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) in genome-wide profiles between primary and metastatic melanoma.

METHODS

Gene expression profiling datasets GSE8401 and gene methylation profiling datasets GSE86355 were collected from the GEO database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were systematically identified. Integration of DEGs and DMGs yielded a set of MeDEGs, which subsequently underwent functional enrichment analysis. The protein-protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Survival analysis was used to select prognostic hub genes. In addition, 37 SKCM and 37 normal skin tissues from the First Affiliated Hospital of Soochow University (FAHSU) were collected for immunohistochemical (IHC) staining and evaluation. Furthermore, DNA methylation patterns of CDC6 were analyzed. To validate these findings, SKCM cell cultures were utilized to elucidate the expression and behavioral characteristics of CDC6. Additionally, gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted for CDC6.

RESULTS

In our study, we discovered 120 hypomethylated-upregulated genes and 212 hypermethylated-downregulated genes. The hypomethylated-upregulated genes were notably associated with biological processes such as spindle assembly checkpoint signaling, mitotic spindle assembly, and negative regulation of mitotic metaphase/anaphase transition. Our pathway analysis revealed significant enrichment in pathways related to dilated cardiomyopathy, amino sugar metabolism, progesterone-mediated oocyte maturation, and chemical carcinogenesis. Conversely, hypermethylated-downregulated genes were found to be enriched in processes like epidermis development, keratinocyte differentiation, and skin development. Additionally, pathway analysis highlighted associations with estrogen signaling, Staphylococcus aureus infection, axon guidance, and arachidonic acid metabolism. Following the establishment of PPI networks and survival analysis, we identified 11 prognostic hub genes: CCNA2, CDC6, CDCA3, CKS2, DTL, HJURP, KRT5, KRT14, KRT15, KRT16, and NEK2. Notably, among the 11 hub genes, our findings indicate that CDC6 plays a pivotal role in enhancing the proliferation, migration, and invasion capabilities of melanoma cells in vitro.

CONCLUSIONS

Our comprehensive genomic analyses reveal that genes with aberrant methylation exhibit differential expression during the transition from primary to metastatic melanoma. The identified genes, especially CDC6, which plays a crucial role in enhancing melanoma cell proliferation, migration, and invasion, provide valuable insights into potential methylation-based biomarkers. These findings could contribute significantly to advancing precision medicine in SKCM.

Novel prognostic alternative splicing events in colorectal Cancer: Impact on immune infiltration and therapy response

OBJECTIVE

This study aims to comprehensively analyze alternative splicing (AS) features in colorectal cancer (CRC) using integrative multi-omics and to elucidate their relationship with the CRC immune microenvironment.

METHODS

Transcriptomic data, clinical information, and Percent Spliced In (PSI) values of AS events for CRC patients were obtained from The Cancer Genome Atlas (TCGA) and TCGA SpliceSeq databases. Differentially expressed AS events were identified. Univariate Cox analysis was used to pinpoint prognosis-related AS events. A prognostic risk model was developed and validated using multivariate Cox analysis, patient survival analysis, and the area under the receiver operating characteristic (ROC) curve (AUC). Gene Set Enrichment Analysis (GSEA), immune infiltration, immunotherapy, chemotherapy sensitivity analyses, and regulatory relationships between AS events and splicing factors (SFs) were conducted. Single-cell sequencing was used to study the distribution of key factors. siRNA and overexpression vectors were utilized to silence/overexpress BCAS1 in CRC cells and evaluate their effects on cell growth, migration, and invasion. Furthermore, the interaction between BCAS1 and ANO7 pre-mRNA was investigated using RIP-PCR.

RESULTS

82 prognosis-related AS events were identified in CRC patients. A 15-AS prognostic model was constructed, which correlated with immune cell infiltration and showed differences in immunotherapy and chemotherapy sensitivity. BCAS1 was identified as a potential regulator of the ANO7|58341|AT splicing event in CRC. Single-cell sequencing analysis revealed the distribution of BCAS1 and ANO7 in cancer stem cells. In vitro experiments demonstrated that overexpression of BCAS1 and silencing of ANO7 inhibit the proliferation, migration, and invasion of CRC cells. Moreover, BCAS1 suppresses the progression of CRC by modulating ANO7 alternative splicing.

CONCLUSION

This study provides new insights into the role of alternative splicing in colorectal cancer, particularly the potential regulatory action of BCAS1 on the ANO7|58341|AT splicing event. It also identifies the impact of alternative splicing on the tumor microenvironment and potential implications for immunotherapy, highlighting its relevance for the in-depth study and treatment of CRC.

CDC6 overexpression contributes to the malignant phenotype of glioma via IL6/JAK2/STAT3 signaling

Glioma, a prevalent primary tumor of the central nervous system, is targeted by molecular therapies aiming to intervene in specific genes and signaling pathways to inhibit tumor growth and spread. Our previous bioinformatics study revealed that significant CDC6 overexpression in gliomas was closely correlated with poor patient prognosis. Through qPCR, western blotting, and immunohistochemistry, we will further validate CDC6 expression in clinical glioma specimens, while the effects of silencing and overexpressing CDC6 in the U87 and LN229 glioma cell lines on malignancy will be assessed through MTS, EdU, transwell, and migration assays. Luciferase reporter assays, ChIP, qPCR, and western blotting were used to explore the upstream and downstream molecular mechanisms of CDC6. Our study confirmed the abnormal overexpression of CDC6 in gliomas, particularly in glioblastomas. CDC6 promotes glioma cell activity, proliferation, invasion, and migration by activating the IL6-mediated JAK2/STAT3 signaling pathway. The transcription Factor E2F8 directly regulates CDC6 transcription, playing a crucial role in its abnormal overexpression in gliomas. This research provides vital evidence supporting CDC6 as a molecular target for glioma therapy.

TGIF2 is a potential biomarker for diagnosis and prognosis of glioma

Background

TGFB-induced factor homeobox 2 (TGIF2), a member of the Three-Amino-acid-Loop-Extension (TALE) superfamily, has been implicated in various malignant tumors. However, its prognostic significance in glioma, impact on tumor immune infiltration, and underlying mechanisms in glioma development remain elusive.

Methods

The expression of TGIF2 in various human normal tissues, normal brain tissues, and gliomas was investigated using HPA, TCGA, GTEx, and GEO databases. The study employed several approaches, including Kaplan-Meier analysis, ROC analysis, logistic regression, Cox regression, GO analysis, KEGG analysis, and GSEA, to explore the relationship between TGIF2 expression and clinicopathologic features, prognostic value, and potential biological functions in glioma patients. The impact of TGIF2 on tumor immune infiltration was assessed through Estimate, ssGSEA, and Spearman analysis. Genes coexpressed with TGIF2 were identified, and the protein-protein interaction (PPI) network of these coexpressed genes were constructed using the STRING database and Cytoscape software. Hub genes were identified using CytoHubba plugin, and their clinical predictive value was explored. Furthermore, in vitro experiments were performed by knocking down and knocking out TGIF2 using siRNA and CRISPR/Cas9 gene editing, and the role of TGIF2 in glioma cell invasion and migration was analyzed using transwell assay, scratch wound-healing assay, RT-qPCR, and Western blot.

Results

TGIF2 mRNA was found to be upregulated in 21 cancers, including glioma. High expression of TGIF2 was associated with malignant phenotypes and poor prognosis in glioma patients, indicating its potential as an independent prognostic factor. Furthermore, elevated TGIF2 expression positively correlated with cell cycle regulation, DNA synthesis and repair, extracellular matrix (ECM) components, immune response, and several signaling pathways that promote tumor progression. TGIF2 showed correlations with Th2 cells, macrophages, and various immunoregulatory genes. The hub genes coexpressed with TGIF2 demonstrated significant predictive value. Additionally, in vitro experiments revealed that knockdown and knockout of TGIF2 inhibited glioma cell invasion, migration and suppressed the epithelial-mesenchymal transition (EMT) phenotype.

Conclusion

TGIF2 emerges as a potential biomarker for glioma, possibly linked to tumor immune infiltration and EMT.

Prognostic value and immune landscapes of immunogenic cell death-associated lncRNAs in lung adenocarcinoma

Immunogenic cell death (ICD) has been demonstrated to activate T cells to kill tumor cells, which is closely related to tumor development, and long noncoding RNAs (lncRNAs) are also involved. However, it is not known whether ICD-related lncRNAs are associated with the development of lung adenocarcinoma (LUAD). We downloaded ICD-related genes from GeneCards and the transcriptome statistics of LUAD patients from The Cancer Genome Atlas (TCGA) and subsequently developed and verified a predictive model. A successful model was used together with other clinical features to construct a nomogram for predicting patient survival. To further study the mechanism of tumor action and to guide therapy, we performed enrichment analysis, tumor microenvironment analysis, somatic mutation analysis, drug sensitivity analysis and real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Nine ICD-related lncRNAs with significant prognostic relevance were selected for model construction. Survival analysis demonstrated that overall survival was substantially shorter in the high-risk group than in the low-risk group (P < 0.001). This model was predictive of prognosis across all clinical subgroups. Cox regression analysis further supported the independent prediction ability of the model. Ultimately, a nomogram depending on stage and risk score was created and showed a better predictive performance than the nomogram without the risk score. Through enrichment analysis, the enriched pathways in the high-risk group were found to be primarily associated with metabolism and DNA replication. Tumor microenvironment analysis suggested that the immune cell concentration was lower in the high-risk group. Somatic mutation analysis revealed that the high-risk group contained more tumor mutations (P = 0.00018). Tumor immune dysfunction and exclusion scores exhibited greater sensitivity to immunotherapy in the high-risk group (P < 0.001). Drug sensitivity analysis suggested that the predictive model can also be applied to the choice of chemotherapy drugs. RT-qPCR analysis also validated the accuracy of the constructed model based on nine ICD-related lncRNAs. The prognostic model constructed based on the nine ICD-related lncRNAs showed good application value in assessing prognosis and guiding clinical therapy.

Molecular and clinical characterization of ANG expression in gliomas and its association with tumor-related immune response

Background

Angiogenin (ANG) has been widely reported as a crucial molecular regulator in multiple malignancies. However, its role in gliomagenesis remains unclear. This study aimed to investigate the molecular and clinical characterization of ANG expression at transcriptome level and the association with glioma-related immune response.

Methods

A total of 301 glioma samples with mRNA microarray data (CGGA301) was obtained from the official website of CGGA project for yielding preliminary results, followed by validation in two independent RNAseq datasets, including TCGA with 697 samples and CGGA325 with 325 patients. Moreover, CGGA single-cell RNAseq (scRNAseq) data were analyzed to identify differential and dynamic ANG expression in different cells. Immunohistochemistry was performed to evaluate ANG protein expression across different WHO grades in a tissue microarray (TMA). Figure generation and statistical analysis were conducted using R software.

Results

ANG expression was associated with clinical features, malignant phenotypes, and genomic alterations. Based on significantly correlated genes of ANG, subsequent gene ontology (GO) and gene set enrichment analysis (GSEA) concordantly pointed to the significant association of ANG in immune-related biological processes. Moreover, ANG showed robust correlations with canonical immune checkpoint molecules, including PD1 signaling, CTLA4, TIM3, and B7H3. Gene sets variation analysis (GSVA) found that ANG was particularly associated with activities of macrophages and antigen presentation cells (APCs) in both LGG and GBM across different datasets. Furthermore, the higher-ANG milieu seemed to recruit monocyte-macrophage lineage and dendritic cells into the glioma microenvironment. According to scRNAseq analysis, ANG was mainly expressed by neoplastic cells and tumor-associated macrophages (TAMs) and was correlated with the initiation and progression of tumor cells and the polarization of TAMs. Finally, Kaplan-Meier plots demonstrated that higher expression of ANG was significantly correlated with shorter survival in gliomas. Cox regression analysis further confirmed ANG as an independent predictor of prognosis for gliomas of all three datasets.

Conclusion

ANG is significantly correlated with a range of malignant and aggressive characteristics in gliomas and reveals considerable prognostic value for glioma patients. ANG seems to be primarily associated with immune activities of macrophages and APCs in gliomas. Furthermore, ANG is mainly expressed in neoplastic cells and TAMs and is involved in the initiation and progression of neoplastic cells as well as macrophage polarization.

Identified RP2 as a prognostic biomarker for glioma, facilitating glioma pathogenesis mainly via regulating tumor immunity

Glioma is the most common primary intracranial tumor in the central nervous system, with a high degree of malignancy and poor prognosis, easy to recur, difficult to cure. The mutation of Retinitis Pigmentosa 2 (RP2) can cause retinitis pigmentosa, it is a prognostic factor of osteosarcoma, however, its role in glioma remains unclear. Based on the data from TCGA and GTEx, we identified RP2 as the most related gene for glioma by WGCNA, and used a series of bioinformatics analyses including LinkedOmics, GSCA, CTD, and so on, to explore the expression of RP2 in glioma and the biological functions it is involved in. The results showed that RP2 was highly expressed in glioma, and its overexpression could lead to poor prognosis. In addition, the results of enrichment analysis showed that RP2 was highly correlated with cell proliferation and immune response. And then, we found significant enrichment of Macrophages among immune cells. Furthermore, our experiments have confirmed that Macrophages can promote the development of glioma by secreting or influencing the secretion of some cytokines. Moreover, we investigated the influence of RP2 on the immunotherapy of glioma and the role of m6A modification in the influence of RP2 on glioma. Ultimately, we determined that RP2 is an independent prognostic factor that is mainly closely related to immune for glioma.

Effect of gut microbiome regulated Taohong Siwu Decoction metabolism on glioma cell phenotype

Introduction

To establish a new model for exploring the mechanism of the gut microbiome and drug metabolism, we explored whether Taohong Siwu Decoction acts after metabolism by intestinal flora under the premise of clarifying the interaction between intestinal flora and drug metabolism.

Methods

Taohong Siwu Decoction (TSD) was fed to germ-free mice and conventional mice, respectively. The serum from both groups of mice was removed and co-cultured with glioma cells in vitro. The co-cultured glioma cells were compared separately for changes at the RNA level using RNA-seq technology. The genes of interest in the comparison results were selected for validation.

Results

The differences in the phenotypic alterations of glioma cells between serum from TSD-fed germ-free mice and normal mice were statistically significant. In vitro experiments showed that Taohong Siwu Decoction-fed normal mouse serum-stimulated glioma cells, which inhibited proliferation and increased autophagy. RNA-seq analysis showed that TSD-fed normal mouse serum could regulate CDC6 pathway activity in glioma cells. The therapeutic effect of TSD is significantly influenced by intestinal flora.

Conclusion

The treatment of tumors by TSD may be modulated by intestinal flora. We established a new method to quantify the relationship between intestinal flora and the regulation of TSD efficacy through this study.

Quantitative proteomics study on the changes of egg white of yellow preserved primary chicken eggs soaked in alkali solution

In order to investigate the changes of egg white of primary chicken eggs after being soaked in alkali solution, the tandem mass tags (TMT)-labeled quantitative proteomic technology combined with bioinformatics was conducted in this study. The results indicated that 100 differentially expressed proteins (DEPs) in yellow preserved primary egg white (YPPEW), 75 of which were highly and significantly correlated with the quality traits of YPPEW (| r | ≥ 0.9000, P < 0.01). Most of DEPs were involved in cellular processes by binding in extracellular space. Six pathways revealed the potential anti-inflammatory, anti-virus, anti-cancer and neuromodulatory mechanism of YPPEW. The current research provided a theoretical basis for the further study on YPPEW.

Long Non-Coding RNA BNIP3 Inhibited the Proliferation of Bovine Intramuscular Preadipocytes via Cell Cycle

The intramuscular fat (or marbling fat) content is an essential economic trait of beef cattle and improves the flavor and palatability of meat. Several studies have highlighted the correlation between long non-coding RNAs (lncRNAs) and intramuscular fat development; however, the precise molecular mechanism remains unknown. Previously, through a high-throughput sequencing analysis, we found a lncRNA and named it a long non-coding RNA BNIP3 (lncBNIP3). The 5' RACE and 3' RACE explored 1945 bp total length of lncBNIP3, including 1621 bp of 5'RACE, and 464 bp of 3'RACE. The nucleoplasmic separation and FISH results explored the nuclear localization of lncBNIP3. Moreover, the tissue expression of lncBNIP3 was higher in the longissimus dorsi muscle, followed by intramuscular fat. Furthermore, down-regulation of lncBNIP3 increased the 5-Ethynyl-2'- deoxyuridine (EdU)-EdU-positive cells. The flow cytometry results showed that the number of cells in the S phase was significantly higher in preadipocytes transfected with si-lncBNIP3 than in the control group (si-NC). Similarly, CCK8 results showed that the number of cells after transfection of si-lncBNIP3 was significantly higher than in the control group. In addition, the mRNA expressions of proliferative marker genes CyclinB1 (CCNB1) and Proliferating Cell Nuclear Antigen (PCNA) in the si-lncBNIP3 group were significantly higher than in the control group. The Western Blot (WB) results also showed that the protein expression level of PCNA transfection of si-lncBNIP3 was significantly higher than in the control group. Similarly, the enrichment of lncBNIP3 significantly decreased the EdU-positive cells in the bovine preadipocytes. The results of flow cytometry and CCK8 assay also showed that overexpression of lncBNIP3 inhibited the proliferation of bovine preadipocytes. In addition, the overexpression of lncBNIP3 significantly inhibited the mRNA expressions of CCNB1 and PCNA. The WB results showed that the overexpression of lncBNIP3 significantly inhibited the expression of the CCNB1 protein level. To further explore the mechanism of lncBNIP3 on the proliferation of intramuscular preadipocytes, RNA-seq was performed after interference with si-lncBNIP3, and 660 differentially expressed genes (DEGs) were found, including 417 up-regulated DEGs and 243 down-regulated DEGs. The KEGG pathway analysis showed that the cell cycle was the most significant pathway for the functional enrichment of DEGs, followed by the DNA replication pathway. The RT-qPCR quantified the expression of twenty DEGs in the cell cycle. Therefore, we speculated that lncBNIP3 regulated intramuscular preadipocyte proliferation through the cell cycle and DNA replication pathways. To further confirm this hypothesis, the cell cycle inhibitor Ara-C was used to inhibit DNA replication of the S phase in intramuscular preadipocytes. Herein, Ara-C and si-lncBNIP3 were simultaneously added to the preadipocytes, and the CCK8, flow cytometry, and EdU assays were performed. The results showed that the si-lncBNIP3 could rescue the inhibitory effect of Ara-C in the bovine preadipocyte proliferation. In addition, lncBNIP3 could bind to the promoter of cell division control protein 6 (CDC6), and down-regulation of lncBNIP3 promoted the transcription activity and the expression of CDC6. Therefore, the inhibitory effect of lncBNIP3 on cell proliferation might be understood through the cell cycle pathway and CDC6 expression. This study provided a valuable lncRNA with functional roles in intramuscular fat accumulation and revealed new strategies for improving beef quality.

High throughput sequencing revealed enhanced cell cycle signaling in SLE patients

The multi-system involvement and high heterogeneity of systemic lupus erythematosus (SLE) pose great challenges to its diagnosis and treatment. The purpose of the current study is to identify genes and pathways involved in the pathogenesis of SLE. High throughput sequencing was performed on the PBMCs from SLE patients. We conducted differential gene analysis, gene ontology (GO) analysis, kyoto encyclopedia of genes and genomes (KEGG) analysis, and quantitative real-time PCR (qRT-PCR) verification. Protein-protein interaction (PPI) analysis, alternative splicing analysis, and disease correlation analysis were conducted on some key pathogenic genes as well. Furthermore, si-CDC6 was used for transfection and cell proliferation was monitored using a cell counting kit-8 (CCK-8) assay. We identified 2495 differential genes (1494 upregulated and 1001 downregulated) in SLE patients compared with healthy controls. The significantly upregulated genes were enriched in the biological process-related GO terms of the cell cycle, response to stress, and chromosome organization. KEGG enrichment analysis revealed 7 significantly upregulated pathways including SLE, alcoholism, viral carcinogenesis, cell cycle, proteasome, malaria, and transcriptional misregulation in cancer. We successfully verified some differential genes on the SLE pathway and the cell cycle pathway. CDC6, a key gene in the cell cycle pathway, had remarkably higher MXE alternative splicing events in SLE patients than that in controls, which may explain its significant upregulation in SLE patients. We found that CDC6 participates in the pathogenesis of many proliferation-related diseases and its levels are positively correlated with the severity of SLE. Knockdown of CDC6 suppressed the proliferation of Hela cells and PBMCs from SLE patients in vitro. We identified SLE-related genes and their alternative splicing events. The cell cycle pathway and the cell cycle-related biological processes are over-activated in SLE patients. We revealed a higher incidence of MXE events of CDC6, which may lead to its high expression in SLE patients. Upregulated cell cycle signaling and CDC6 may be related to the hyperproliferation and pathogenesis of SLE.

CCNF is a potential pancancer biomarker and immunotherapy target

Background

CCNF catalyzes the transfer of ubiquitin molecules from E2 ubiquitin-conjugating enzymes to target proteins, thereby regulating the G1/S or G2/M transition of tumor cells. Thus far, CCNF expression and its potential as a pancancer biomarker and immunotherapy target have not been reported.

Methods

TCGA datasets and the R language were used to analyze the pancancer gene expression, protein expression, and methylation levels of CCNF; the relationship of CCNF expression with overall survival (OS), recurrence-free survival (RFS), immune matrix scores, sex and race; and the mechanisms for posttranscriptional regulation of CCNF.

Results

CCNF expression analysis showed that CCNF mRNA expression was higher in cancer tissues than in normal tissues in the BRCA, CHOL, COAD, ESCA, HNSC, LUAD, LUSC, READ, STAD, and UCEC; CCNF protein expression was also high in many cancer tissues, indicating that it could be an important predictive factor for OS and RFS. CCNF overexpression may be caused by CCNF hypomethylation. CCNF expression was also found to be significantly different between patients grouped based on sex and race. Overexpression of CCNF reduces immune and stromal cell infiltration in many cancers. Posttranscriptional regulation analysis showed that miR-98-5p negatively regulates the expression of the CCNF gene.

Conclusion

CCNF is overexpressed across cancers and is an adverse prognostic factor in terms of OS and RFS in many cancers; this phenomenon may be related to hypomethylation of the CCNF gene, which could lead to cancer progression and worsen prognosis. In addition, CCNF expression patterns were significantly different among patients grouped by sex and race. Its overexpression reduces immune and stromal cell infiltration. miR-98-5p negatively regulates CCNF gene expression. Hence, CCNF is a potential pancancer biomarker and immunotherapy target.

Role of estrogen receptor signaling pathway-related genes in diffuse large B-cell lymphoma and identification of key targets via integrated bioinformatics analysis and experimental validation

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy. Epidemiologically, the incidence of DLBCL is higher in men, and the female sex is a favorable prognostic factor, which can be explained by estrogen. This study aimed to explore the potential targets of the estrogen receptor (ER) signaling pathway and provide a meaningful way to treat DLBCL patients. Datasets were obtained from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). Representative gene sets estrogen receptor pathways, and growth regulatory pathways were identified based on Gene Set Enrichment Analysis (GSEA) analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for function and pathway analysis. STRING and Cytoscape were used to construct the interaction network, and the MCODE plug-in performed the module analysis. GEPIA, TCGA, and LOGpc databases were used for expression and predictive analysis. The Human Protein Atlas (HPA) database was used to analyze the protein expression levels, cBioPortal was used to explore genetic alterations, and ROC analysis and prognostic assessment were used to predict the diagnostic value of genes. Finally, BJAB cells were treated with ER inhibitor fulvestrant and specific shRNA, and the expression of hub genes was verified by RT-qPCR. We identified 81 overlapping DEGs and CDC6, CDC20, KIF20A, STIL, and TOP2A as novel biomarkers affecting the prognosis of DLBCL. In addition, the STAT and KRAS pathways are considered potential growth regulatory pathways. These results hold promise for new avenues for the treatment of DLBCL patients.

Screening for Biomarkers for Progression from Oral Leukoplakia to Oral Squamous Cell Carcinoma and Evaluation of Diagnostic Efficacy by Multiple Machine Learning Algorithms

The aim of the study is to identify key genes during the progression from oral leukoplakia (OL) to oral squamous cell carcinoma (OSCC) and predict effective diagnoses. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to identify seven genes associated with the progression from OL to OSCC. Twelve machine learning algorithms including k-nearest neighbor (KNN), neural network (NNet), and extreme gradient boosting (XGBoost) were used to construct multi-gene models, which revealed that each model had good diagnostic efficacy. The functional mechanism or the pathways associated with these genes were evaluated using enrichment analysis, subtype clustering, and immune infiltration analysis. The enrichment analysis revealed that the genes enriched were associated with the cell cycle, cell division, and intracellular energy metabolism. The immunoassay results revealed that the genes primarily affected the infiltration of proliferating T cells and macrophage polarization. Finally, a nomogram and Kaplan-Meier survival analysis were used to predict the prognostic efficacy of key genes in OSCC patients. The results showed that genes could predict the prognosis of the patients, and patients in the high-risk group had a poor prognosis. Our study identified that the seven key genes, including DHX9, BCL2L12, RAD51, MELK, CDC6, ANLN, and KIF4A, were associated with the progression from OL to OSCC. These genes had good diagnostic efficacy and could be used as potential biomarkers for the prognosis of OSCC patients.