H2B gene family: A prognostic biomarker and correlates with immune infiltration in glioma

Prognostic implications of glucose metabolism pathways in colon adenocarcinoma: a comprehensive outlook on the molecular landscape and immunotherapy

BACKGROUND

Colon adenocarcinoma (COAD) is a common and aggressive cancer characterized by significant metabolic alterations, particularly in glucose metabolism. Identifying key genes and pathways involved in glucose metabolism could provide valuable prognostic biomarkers and therapeutic targets.

METHODS

Clinical and transcriptomic data for patients with COAD were obtained from TCGA and validated using external datasets (GSE17536 and GSE39582). Seventeen glucose metabolism-related pathways were selected from the MSigDB and analysed using ssGSEA. WGCNA was used to identify key gene modules. Prognostic genes were selected via univariate Cox regression, Lasso-Cox regression, and multivariate Cox regression. Model validation was conducted using independent datasets. Immunotherapy prediction and immune infiltration analyses were also performed. A-NEK9 knockdown cell line was established using SW1116 and SW480 cell lines. The effect of NEK9 on COAD was evaluated in vivo and in vitro. The effects of NEK9 on glucose uptake and lactate production were also assessed.

RESULTS

A prognostic model based on five glucose metabolism-related genes (NEK9, HS2ST1, AC016394.3, H2BC21, and MIR23A) was developed. The model demonstrated strong predictive value, with high-risk patients showing poorer survival outcomes in both the TCGA and external validation cohorts. Additionally, lower risk scores were associated with better responses to immunotherapy, as indicated by TIDE and SubMap analyses. These findings were further validated through ROC analysis, which revealed robust predictive performance for immunotherapy response across multiple cohorts. NEK9 promoted the proliferation and tumour angiogenesis of SW1116 and SW480 cells, inhibited apoptosis, and enhanced glucose uptake and lactate production in tumour cells. NEK9 knockdown significantly inhibited the tumorigenic ability of COAD in mice.

CONCLUSIONS

This study highlights the role of glucose metabolism in COAD and presents a novel prognostic model based on glucose metabolism-related genes. The model has potential clinical applications for predicting survival and guiding immunotherapy decisions in patients with COAD.

Development and validation of a multiple myeloma diagnostic model based on systemic lupus erythematosus-associated genes and identification of specific genes

BACKGROUND

Monoclonal immunoglobulins are commonly found in multiple myeloma (MM), a prevalent hematologic malignancy that is currently incurable. In recent years, the association between systemic lupus erythematosus (SLE), an autoimmune disease, and MM has garnered increasing attention. However, there remains a lack of in-depth research regarding the interactions between these two conditions and their potential pathogenic mechanisms. Therefore, in order to improve the identification of MM associated with SLE, this work attempts to clarify the pathogenic pathways that are shared by MM and SLE and to develop corresponding diagnostic models.

METHODS

This study employs a comprehensive bioinformatics analysis combined with machine learning techniques to extract relevant data from public databases. We used GO and KEGG pathway analyses to investigate the functionalities and pathway enrichments of the DEGs that we found in MM and SLE populations. Furthermore, we used the STRING database to build a PPI network for the intersecting genes and the cytoHubba plugin in Cytoscape software to identify important genes with biological significance. To establish a diagnostic model for SLE-related MM, we compared 113 combinations of 12 machine learning algorithms, ultimately determining the optimal model.

RESULTS

Our analysis identified 63 intersecting genes, with 31 exhibiting upregulated expression and 32 showing downregulated expression. The selection of key genes indicated that nine genes met the criteria of having both Degree and MCC values exceeding 3, among which seven (CDH1, IL4, AURKB, HGF, H2BC9, AREG, TJP1) have previously been confirmed to have direct associations with MM. Notably, H2BC5 was identified as a specific gene associated with SLE-related MM. Our findings revealed that elevated expression of H2BC5 is significantly correlated with an increased risk of MM, suggesting its potential critical role in the diagnosis and identification of this malignancy.

CONCLUSION

A new molecular framework for the early diagnosis of MM, especially in SLE patients, is established by this study. Our findings highlight H2BC5 as a possible biomarker that merits more research into how it contributes to the development of MM. By identifying the shared pathogenic mechanisms between SLE and MM, our research offers new perspectives for future clinical interventions and personalized therapies.

Upregulation of MMP3 Promotes Cisplatin Resistance in Ovarian Cancer

Most women with ovarian cancer (OC) develop resistance to platinum chemotherapy, posing a significant challenge to treatment. Matrix metalloproteinase 3 (MMP3) is overexpressed in High-Grade Serous Ovarian Cancer (HGSOC) and is associated with poor survival outcomes; however, its role in platinum resistance remains underexplored. We evaluated the baseline and cisplatin-induced MMP3 transcript and protein levels in cisplatin-resistant OC cells, revealing significantly higher MMP3 levels in cisplatin-resistant cells than in cisplatin-sensitive cells. siRNA-mediated MMP3 knockdown in cisplatin-resistant OC cells significantly reduced viability, proliferation, and invasion, and these effects were further enhanced when combined with cisplatin treatment, indicating a possible synergistic impact on reducing cancer cell aggressiveness; however, chemical MMP3 inhibition did not replicate these effects. RNA sequencing of MMP3-siRNA-treated cisplatin-resistant HGSOC cells revealed 415 differentially expressed genes (DEGs) compared to the negative control, with an additional 440 DEGs identified in MMP3-siRNA HGSOC cells treated in combination with cisplatin. These DEGs were enriched in pathways related to cell cycle regulation, apoptosis, metabolism, stress response, and extracellular matrix organization. Co-immunoprecipitation-coupled mass spectroscopy (IP-MS) identified MMP3-interacting proteins that may contribute to cell survival and chemoresistance in cisplatin-resistant OC. While MMP3-siRNA monotherapy did not reduce tumor growth in vivo, its combination with cisplatin significantly inhibited tumor growth in a cisplatin-resistant HGSOC xenograft model. These findings underscore the multifaceted role of MMP3 in cisplatin resistance, suggesting its involvement in critical cellular processes driving chemoresistance and highlighting the challenges associated with direct MMP3 targeting in therapeutic strategies.

Prognostic model for cervical cancer based on apoptosis-related genes

This study attempts to develop a novel apoptosis-related predictive model for cervical cancer. Differentially expressed apoptosis-related genes were identified using TCGA, GEO, and MSigDB databases. A 13-gene prognostic model was constructed using multiple regression analyses. The low-risk group exhibited low tumor purity and high ESTIMATE and immune scores. Most of the immune checkpoints in the low-risk group were expressed at higher levels than those in the high-risk group. The low-risk group also had relatively more infiltrating immune cells. An independent prognostic model pertaining to cell apoptosis has been built by this work, which performs well in prediction.

Evaluating H2BC9 as a potential diagnostic and prognostic biomarker in head and neck squamous cell carcinoma

BACKGROUND

Histone H2B is highly expressed in many types of cancers and is involved in cancer development. H2B clustered histone 9 (H2BC9), a member of the H2B family, plays critical roles in gene expression regulation, chromosome structure, DNA repair stability, and cell cycle regulation. However, the diagnostic and prognostic value of H2BC9 in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study aimed to evaluate the potential diagnostic and prognostic value of H2BC9 in HNSCC and investigate its biological role using bioinformatics.

METHODS

The expression pattern and diagnostic value of H2BC9 in HNSCC were explored using UCSC Xena and GEO database. H2BC9 expression was validated using the Human Protein Atlas database, qRT-PCR, and western blotting. Prognostic value was assessed using Kaplan-Meier curves, Cox regression analysis, and a nomogram. Drug sensitivity was predicted using the R package pRRophetic, and molecular interactions were analyzed using the DepMap database. The impact of H2BC9 on HNSCC cells was further investigated through in vitro experiments.

RESULTS

H2BC9 was markedly upregulated in HNSCC cell lines and tissues. High expression of H2BC9 was correlated with advanced-stage disease and poor prognosis. KEGG analysis linked H2BC9 to cell cycle regulation and DNA replication. H2BC9 expression influenced the drug sensitivity of paclitaxel, docetaxel, cisplatin, and 5-fluorouracil. Key molecules, such as TONSL, PITX2, NOTCH1, and H2BC10, were positively correlated with H2BC9 expression. Silencing H2BC9 suppressed cell proliferation, induced G2/M cell cycle arrest, and enhanced apoptosis and DNA damage in HNSCC cells.

CONCLUSION

We demonstrated that H2BC9 expression may be associated with HNSCC development and prognosis. These findings may provide a potential therapeutic target for HNSCC.

Potential impact of epithelial splicing regulatory protein 1 (ESRP1) associated with tumor immunity in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a prevalent and highly malignant gastrointestinal tumor. Therefore, exploring the mechanisms of drug resistance and immune pathways in PAAD is crucial for clinical treatment. In this study, a total of 497 differentially expressed genes (DEGs) were identified between normal and PAAD samples, and which were enriched to 117 GO terms and 7 functional pathways. Subsequently, 5 overall survival-related DEGs (ESRP1, KRT6A, H2BC11, H2BC4 and KLK) was generated using Cox hazards regression analysis in TCGA dataset. Furthermore, the weighted gene co-expression network analysis revealed a strong association between ESRP1 and PAAD among 5 survival-related DEGs. Patients were divided into two clusters based on ESRP1 expression levels, and low ESRP1 expression existed stronger immune infiltration and higher expression of immunomodulatory targets than high ESRP1 expression by single-sample gene set enrichment analysis, which indicated that low ESRP1 expression was associated with longer survival compared to high ESRP1 expression. Finally, our study also found that immune cells distribution and immunomodulatory targets gene expression in the GEO dataset were similar to the TCGA cohort. Overall, our findings suggest that ESRP1 may play a role in influencing immune contexture and regulating immune function of PAAD patients by integrating data from various databases. SIGNIFICANCE: Utilizing TCGA and GEO datasets, this study uncovers the significant impact of epithelial splicing regulatory protein 1 (ESRP1) on PAAD. ESRP1 emerges as a key regulator of immune function, influencing tumor microenvironment and immune cell infiltration. Cluster analysis shows that low ESRP1 expression correlates with enhanced immune activity, predicting better prognosis. This discovery suggests that ESRP1 can serve as a potential biomarker for the prognosis of PAAD, offering new insights into personalized immunotherapy by influencing immune regulation and tumor progression.

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

Proteomic Analysis of Domestic Cat Blastocysts and Their Secretome Produced in an In Vitro Culture System without the Presence of the Zona Pellucida

Domestic cat blastocysts cultured without the zona pellucida exhibit reduced implantation capacity. However, the protein expression profile has not been evaluated in these embryos. The objective of this study was to evaluate the protein expression profile of domestic cat blastocysts cultured without the zona pellucida. Two experimental groups were generated: (1) domestic cat embryos generated by IVF and cultured in vitro (zona intact, (ZI)) and (2) domestic cat embryos cultured in vitro without the zona pellucida (zona-free (ZF group)). The cleavage, morula, and blastocyst rates were estimated at days 2, 5 and 7, respectively. Day 7 blastocysts and their culture media were subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS). The UniProt Felis catus database was used to identify the standard proteome. No significant differences were found in the cleavage, morula, or blastocyst rates between the ZI and ZF groups (p > 0.05). Proteomic analysis revealed 22 upregulated and 20 downregulated proteins in the ZF blastocysts. Furthermore, 14 proteins involved in embryo development and implantation were present exclusively in the culture medium of the ZI blastocysts. In conclusion, embryo culture without the zona pellucida did not affect in vitro development, but altered the protein expression profile and release of domestic cat blastocysts.

The application of weighted gene co-expression network analysis and support vector machine learning in the screening of Parkinson's disease biomarkers and construction of diagnostic models

Background

This study aims to utilize Weighted Gene Co-expression Network Analysis (WGCNA) and Support Vector Machine (SVM) algorithm for screening biomarkers and constructing a diagnostic model for Parkinson's disease.

Methods

Firstly, we conducted WGCNA analysis on gene expression data from Parkinson's disease patients and control group using three GEO datasets (GSE8397, GSE20163, and GSE20164) to identify gene modules associated with Parkinson's disease. Then, key genes with significantly differential expression from these gene modules were selected as candidate biomarkers and validated using the GSE7621 dataset. Further functional analysis revealed the important roles of these genes in processes such as immune regulation, inflammatory response, and cell apoptosis. Based on these findings, we constructed a diagnostic model by using the expression data of FLT1, ATP6V0E1, ATP6V0E2, and H2BC12 as inputs and training and validating the model using SVM algorithm.

Results

The prediction model demonstrated an AUC greater than 0.8 in the training, test, and validation sets, thereby validating its performance through SMOTE analysis. These findings provide strong support for early diagnosis of Parkinson's disease and offer new opportunities for personalized treatment and disease management.

Conclusion

In conclusion, the combination of WGCNA and SVM holds potential in biomarker screening and diagnostic model construction for Parkinson's disease.

Synergistic Interaction of the Class IIa HDAC Inhibitor CHDI0039 with Bortezomib in Head and Neck Cancer Cells

In contrast to class I/IIb/pan histone deacetylase inhibitors (HDACi), the role of class IIa HDACi as anti-cancer chemosensitizing agents is less well understood. Here, we studied the effects of HDAC4 in particular and the class IIa HDACi CHDI0039 on proliferation and chemosensitivity in Cal27 and cisplatin-resistant Cal27CisR head and neck squamous cell cancer (HNSCC). HDAC4 and HDAC5 overexpression clones were generated. HDAC4 overexpression (Cal27_HDAC4) increased proliferation significantly compared to vector control cells (Cal27_VC). Chicken chorioallantoic membrane (CAM) studies confirmed the in vitro results: Cal27_HDAC4 tumors were slightly larger than tumors from Cal27_VC, and treatment with CHDI0039 resulted in a significant decrease in tumor size and weight of Cal27_HDAC4 but not Cal27_VC. Unlike class I/pan-HDACi, treatment with CHDI0039 had only a marginal impact on cisplatin cytotoxicity irrespective of HDAC4 and HDAC5 expression. In contrast, the combination of CHDI0039 with bortezomib was synergistic (Chou–Talalay) in MTT and caspase 3/7 activation experiments. RNAseq indicated that treatment with CHDI0039 alters the expression of genes whose up- or downregulation is associated with increased survival in HNSCC patients according to Kaplan–Meier data. We conclude that the combination of class IIa HDACi with proteasome inhibitors constitutes an effective treatment option for HNSCC, particularly for platinum-resistant cancers.

Heterogeneous pattern of gene expression driven by TTN mutation is involved in the construction of a prognosis model of lung squamous cell carcinoma

Objective

To investigate the impact that TTN mutation had on the gene heterogeneity expression and prognosis in patients with lung adenocarcinoma.

Methods

In this study, the Cancer Genome Atlas (TCGA) dataset was used to analyze the TTN mutations in lung adenocarcinoma. Lung adenocarcinoma data was collected from the TCGA database, clinical information of patients was analyzed, and bioinformatics statistical methods were applied for mutation analysis and prognosis survival analysis. The results were verified using the GEO dataset.

Results

The incidence of TTN mutations in lung adenocarcinoma was found to be 73%, and it was related to the prognosis of lung adenocarcinoma. Ten genes were screened with significant contributions to prognosis. A prognosis model was constructed and verified by LASSO COX analysis in the TCGA and GEO datasets based on these ten beneficial factors. The independent prognostic factor H2BC9 for TTN mutation-driven gene heterogeneity expression was screened through multi-factor COX regression analysis.

Conclusion

Our data showed that the gene heterogeneity expression, which was driven by TTN mutations, prolonged the survival of lung adenocarcinoma patients and provided valuable clues for the prognosis of TTN gene mutations in lung adenocarcinoma.