CD8+ T Cell Co-Expressed Genes Correlate With Clinical Phenotype and Microenvironments of Urothelial Cancer

Multifaceted investigations of PSMB8 provides insights into prognostic prediction and immunological target in thyroid carcinoma

The Proteasome 20S subunit beta 8 (PSMB8) is an integral element of the immunoproteasome complex, playing a pivotal role in antigen processing. Despite its significance, the contributory role of PSMB8 in oncogenesis, particularly in thyroid carcinoma (THCA), has not been well-characterized. To address this gap in knowledge, our study endeavored to delineate the potential associations between PSMB8 and THCA. Transcriptomic profiles and clinical data of patients with THCA were retrieved from The Cancer Genome Atlas (TCGA) database to facilitate comprehensive analysis. Complementary resources from additional online databases were utilized to augment the study. Logistic regression analysis was employed to elucidate the relationship between PSMB8 and various clinicopathological parameters. Uni/multivariate Cox regression analyses were conducted to ascertain the independent prognostic factors for THCA patient outcomes. Quantitative polymerase chain reaction (qPCR) and western blot assays were employed to verify the expression level of PSMB8 in vitro. Our study demonstrated that PSMB8 was significantly upregulated in THCA, with its overexpression correlating with lymph node metastasis, extrathyroidal extension, and favorable prognosis. Immunohistochemistry substantiated a higher PSMB8 protein presence in THCA tissue compared to the normal, supporting its potential role as a moderately accurate diagnostic biomarker. Logistic regression analysis identified PSMB8 as a significant indicator of the N1 stage, classical histological subtype, and extrathyroidal extension. Age, T stage, and PSMB8 were further determined as independent prognostic factors for THCA. Functional investigations linked PSMB8 to immune processes, evidenced by its association with increased immune cell infiltration and higher stromal/immune scores, as well as a positive co-expression with several immune checkpoints. A constructed predicted competing endogenous RNA (ceRNA) network implicated PSMB8 in complex post-transcriptional regulation. Finally, in vitro assays confirmed the upregulation of PSMB8, underscoring its relevance in THCA and as a target for future research. Our work has preliminarily appraised PSMB8 as a biomarker with certain prognostic and diagnostic significance, and as a potential target for immunotherapy in THCA.

Disulfidptosis related immune genes drive prognostic model development and tumor microenvironment characterization in bladder urothelial carcinoma

The intricate nature and varied forms of bladder urothelial carcinoma (BLCA) highlight the need for new indicators to define tumor prognosis. Disulfidptosis, a novel form of cell death, is closely linked to BLCA progression, prognosis, and treatment outcomes. Our current goal is to develop a novel disulfidptosis-related immune prognostic model to enhance BLCA treatment strategies. Utilizing RNA-seq data from The Cancer Genome Atlas (TCGA) , which included 419 patients (19 normal, 400 tumor), we performed weighted gene co-expression network analysis (WGCNA) to identify disulfidptosis-associated immune genes. Through multivariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) regularization, we established a disulfidptosis-related immune risk scoring system. A nomogram combining risk score and clinical features predicted prognosis. Model performance was validated through survival curve analysis and independent validation cohort. Immune checkpoints, cell infiltration, and tumor mutation load were assessed. Differential gene enrichment analysis was conducted. Prognostic genes were validated via in vitro experiments. Eight immune genes related to disulfidptosis were identified and verified in BLCA prognosis. A prognostic model outperformed previous ones in predicting overall survival (OS) for high- and low-risk groups. Patients with low-risk scores had higher OS rates and tumor mutation burden (TMB) compared to high-risk score patients. CD4 memory T cells, CD8 T cells, M1 macrophages, and resting NK cells were found to be higher in the low-risk group. Immune checkpoint inhibitor (ICI) treatment may be more effective for the low-risk score group. High-risk score group exhibited stronger correlation with cancer malignant pathways. Knocking out tumor necrosis factor receptor superfamily member 12 A (TNFRSF12A) inhibits BLCA cell proliferation and invasion while overexpressing it has the opposite effect. We constructed a novel risk score model that combines disulfidptosis and immune genes, demonstrating good prognostic prediction performance. We discovered and verified that the TNFRSF12A gene is an oncogene in BLCA, which may help provide personalized guidance for individualized treatment and immunotherapy selection for BLCA patients to a certain extent.

Immunoproteasome subunits are novel signatures for predicting efficacy of immunotherapy in muscle invasive bladder cancer

BACKGROUND

How to select muscle-invasive bladder cancer (MIBC) patients who are sensitive to immunotherapy is an unmet medical need. This study aimed to explore the role of immunoproteasome subunits as a novel signature for predicting efficacy of immunotherapy in MIBC.

METHODS

The expression profile of immunoproteasome subunits of MIBC and normal tissues was evaluated from data of The Cancer Genome Atlas (TCGA) and of the Chongqing University Cancer Hospital (CQUCH) cohort. Survival analysis and response to immunotherapy was further explored and compared between immunoproteasome subunitshigh and immunoproteasome subunitslow MIBC patients in the TCGA, the CQUCH and the IMvigor210 cohort. The association of the expression of immunoproteasome subunits with immune checkpoint molecules and the tumor immune microenvironment was explored by immunohistochemistry staining and bioinformatic analysis in MIBC of these three cohorts.

RESULTS

The expression of the immunoproteasome subunits PSMB8, PSMB9 and PSMB10 was significantly upregulated in MIBC. MIBC patients with high expression of immunoproteasome subunits, especially high expression of PSMB9, showed a trend of prolonged overall and progression free survival, which was further significantly improved in response to immunotherapy. Bioinformatics and immunohistochemistry staining revealed a positive correlation of the expression of immunoproteasome subunits with the expression of immune checkpoint molecules, with T cell activation and with T cell-mediated cytotoxicity.

CONCLUSIONS

Immunoproteasome subunits, in particular PSMB9, are immune microenvironment-related molecules of MIBC and are promising signatures for survival prediction in response to immunotherapy of MIBC.

Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer

OBJECTIVE

The evaluation of the efficacy of immunotherapy is of great value for the clinical treatment of bladder cancer. Graph Neural Networks (GNNs), pathway analysis and multi-omics analysis have shown great potential in the field of cancer diagnosis and treatment.

METHODS

A GNNs model was constructed to predict the immunotherapy response and identify key pathways. Based on the genes of key pathways, bioinformatic methods were used to generate a simple linear scoring model, namely responseScore. The intrinsic mechanism of responseScore was explored from the perspectives of multi-omics analysis. The relationship between each gene involved in responseScore and prognosis was also explored. Transfection experiments with human bladder cancer cells were used to investigate the biological effects of PSMB9 gene.

RESULTS

The final GNNs model had an AUC of 0.785 on the training set and an AUC of 0.839 on the validation set. R-HSA-69620 and others were identified as key pathways. ResponseScore had a good performance in predicted the immunotherapy response and prognosis. Analysis results from genetic variation, pathways and tumor microenvironment, showed that responseScore was significantly associated with immune cell infiltration and anti-tumor immunity. The results of single-cell analysis showed that responseScore was closely related to the functional state of natural killer cells. Compared with the PCDH-NC group, cell migration and proliferation were significantly inhibited while cell apoptosis increased in the PCDH-PSMB9 group.

CONCLUSION

The GNNs predictive model and responseScore constructed in this study can reflect the immunotherapy response and prognosis of bladder cancer patients. ResponseScore can also reflect features such as tumor microenvironment, antitumor immunity, and natural killer cell function status in bladder cancer. PSMB9 was identified as a significant gene for prognosis. High expression of PSMB9 can inhibit bladder cancer cell migration and proliferation while increasing cell apoptosis.

Characterizing m6A modification factors and their interactions in colorectal cancer: implications for tumor subtypes and clinical outcomes

BACKGROUND

The study aims to comprehensively combine colorectal cancer data cohorts in order to analyze the effects of various DNA methylation-coding genes on colorectal cancer patients. The annual incidence and mortality of colorectal cancer are very high, and there are no effective treatments for advanced colorectal cancer. DNA methylation is a method widely used to regulate epigenetics in the molecular mechanism study of tumors.

METHOD

Three single-cell cohorts GSE166555, GSE146771, and EMTAB8107, and five transcriptome cohorts GSE17536, GSE39582, GSE72970, and TCGA-CRC (TCGA-COAD and TCGA-READ) were applied in this study. 2 erasers (ALKBH5 and FTO), There are 7 writers (METTL3, METTL14, WTAP, VIRMA, RBM15, RBM15B, and ZC3H13) and 11 readers (YTHDC1, IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF3, YTHDC2, and HNRNPA2B1, YTHDF2, HNRNPC and RBMX), a total of 20 M6A regulators, were used as the basis of the dataset in this study and were applied to the construction of molecular typing and prognostic models. Drugs that are differentially sensitive in methylation-regulated gene-related prognostic models were identified using the ConsensusClusterPlus package, which was also used to identify distinct methylation regulatory expression patterns in colorectal cancer and to model the relationship between tissue gene expression profiles and drug IC50 values. Finally, TISCH2 assessed which immune cells were significantly expressed with M6A scores. The immunosuppression of M6A methylation is spatially explained.

RESULTS

This study used data from 583 CRC patients in the TCGA-CRC cohort. Firstly, the mutation frequency and CNV variation frequency of 20 m6A modification-related factors were analyzed, and the corresponding histogram and heat map were drawn. The study next analyzed the expression variations between mutant and wild forms of the VIRMA gene and explored differences in the expression of these variables in tumor and normal tissues. In addition, the samples were divided into different subgroups by molecular clustering method based on m6A modification, and each subgroup's expression and clinicopathological characteristics were analyzed. Finally, we compared prognostic differences, tumor microenvironment (TME) characteristics, immune cell infiltration, and gene function enrichment among different subpopulations. We also developed a colorectal cancer m6A-associated gene signature and validated its prognostic effects across multiple cohorts. Finally, using single-cell RNA sequencing data, we confirmed that tumor cells show elevated expression of m6A-related gene signatures.

DISCUSSION

This study explored the mutation frequency, expression differences, interactions, molecular clustering, prognostic effect, and association with tumor characteristics of m6A modification-related factors in CRC and validated them at the single-cell level. These results clarify the association between m6A alteration and colorectal cancer (CRC) and offer important insights into the molecular recognition and management of cancer.

Identification of unique cell type responses in pancreatic islets to stress

Diabetes involves the death or dysfunction of pancreatic β-cells. Analysis of bulk sequencing from human samples and studies using in vitro and in vivo models suggest that endoplasmic reticulum and inflammatory signaling play an important role in diabetes progression. To better characterize cell type-specific stress response, we perform multiplexed single-cell RNA sequencing to define the transcriptional signature of primary human islet cells exposed to endoplasmic reticulum and inflammatory stress. Through comprehensive pair-wise analysis of stress responses across pancreatic endocrine and exocrine cell types, we define changes in gene expression for each cell type under different diabetes-associated stressors. We find that β-, α-, and ductal cells have the greatest transcriptional response. We utilize stem cell-derived islets to study islet health through the candidate gene CIB1, which was upregulated under stress in primary human islets. Our findings provide insights into cell type-specific responses to diabetes-associated stress and establish a resource to identify targets for diabetes therapeutics.

Comprehensive analysis of the interaction of antigen presentation during anti-tumour immunity and establishment of AIDPS systems in ovarian cancer

There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation-associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival-SVM). Then, in the train cohort, the prediction model was fitted using a leave-one cross-validation (LOOCV) technique, which involved 101 different possible combinations of prognostic genes. Seven validation data sets (GSE26193, GSE26712, GSE30161, GSE63885, GSE9891, GSE140082 and ICGC_OV_AU) were compared and analysed, and the C-index was calculated. Finally, we collected 32 published ovarian cancer prognostic models (including mRNA and lncRNA). All data sets and prognostic models were subjected to a univariate Cox regression analysis, and the C-index was calculated to demonstrate that the antigen presentation process should be the core criterion for evaluating ovarian cancer prognosis. In a univariate Cox regression analysis, 22 prognostic genes were identified based on the expression profiles of 283 genes involved in antigen presentation and the intersection of genes (p < 0.05). AIDPS were developed by our machine learning-based integration method, which was applied to these 22 genes. One hundred and one prediction models are fitted using the LOOCV framework, and the C-index is calculated for each model across all validation sets. Interestingly, RSF + Lasso was the best model overall since it had the greatest average C-index and the highest C-index of any combination of models tested on the validated data sets. In comparing external cohorts, we found that the C-index correlated AIDPS method using the RSF + Lasso method in 101 prediction models was in contrast to other features. Notably, AIDPS outperformed the vast majority of models across all data sets. Antigen-presenting anti-tumour immune pathways can be used as a representative gene set of ovarian cancer to track the prognosis of patients with cancer. The antigen-presenting model obtained by the RSF + Lasso method has the best C-INDEX, which plays a key role in developing antigen-presenting targeted drugs in ovarian cancer and improving the treatment outcome of patients.

The predictive efficacy of programmed cell death in immunotherapy of melanoma: A comprehensive analysis of gene expression data for programmed cell death biomarker and therapeutic target discovery

In this study, genes linked to prognosis in skin cutaneous melanoma (SKCM) involved in programmed cell death (PCD) were identified and confirmed and prognostic models based on these genes were constructed. Acquisition and analysis of clinical data and RNA sequencing information from The Cancer Genome Atlas-SKCM (TCGA-SKCM) and Sangerbox databases, gene expression data for 477 tumor samples and 2 normal samples were successfully gathered. The patients were separated into two clusters based on consensus clustering of PCD-related genes, with Cluster A having greater tumor purity, ESTIMATE score, immune score, and matrix score, and Cluster B having a significantly distinct pattern of immune cell infiltration. The use of gene set enrichment analysis and weighted correlation network analysis showed significant associations between certain genes and factors such as tumor mutation burden, age, stage, grade, and tumor subtype. Finally, based on the 12 genes selected by Least Absolute Shrinkage and Selection Operator regression analysis (STAT3, IRF2, SLC7A11, ZEB1, LIPT1, PML, GCH1, GYS1, ABCC1, XBP1, TFAP2C, NOX4), a prognostic model of PGD-related genes was constructed. The effectiveness of the model's prognostic value was confirmed through survival analysis, time-dependent receiver operating characteristic curve, single-factor Cox regression analysis, and nomogram. We also verified the relationship between the GCH1 and MKI67 expression by wet experiment. This model has high prediction accuracy in SKCM patients and can provide a reference for clinical treatment.

The role of PKP1 in tumor progression in melanoma: Analysis of a cell adhesion-related model

The incidence rate of melanoma varies across regions, with Europe, the United States, and Australia having 10-25, 20-30, and 50-60 cases per 1 00 000 people. In China, patients with melanoma exhibit different clinical manifestations, pathogenesis, and outcomes. Current treatments include surgery, adjuvant therapy, and immune checkpoint inhibitors. Nonetheless, complications may arise during treatment. Melanoma development is heavily reliant on cell adhesion molecules (CAMs), and studying these molecules could provide new research directions for metastasis and progression. CAMs include the integrin, immunoglobulin, selectin, and cadherin families, and they affect multiple processes, such as maintenance, morphogenesis, and migration of adherens junction. In this study, a cell adhesion-related risk prognostic signature was constructed using bioinformatics methods, and survival analysis was performed. Plakophilin 1 (PKP1) was observed to be crucial to the immune microenvironment and has significant effects on melanoma cell proliferation, migration, invasion, and the cell cycle. This signature demonstrates high reliability and has potential for clinical applications.

The inflammatory response-related robust machine learning signature in endometrial cancer: Based on multi-cohort studies

Uterine corpus endometrial carcinoma (UCEC) is a prevalent form of cancer in women, affecting the inner lining of the uterus. Inflammation plays a crucial role in the progression and prognosis of cancer, making it important to identify inflammatory response-related subtypes in UCEC for targeted therapy and personalized medicine. This study discovered significant variation in immune response within UCEC tumors based on molecular subtypes of inflammatory response-related genes. Subtype A showed a more favorable prognosis and better response to immunotherapies like anti-CTLA4 and anti-PDCD1 therapy. Functional analysis revealed subtype-specific differences in immune response, with subtype A exhibiting higher expression of genes related to cytokine signaling pathways, NK cell-mediated cytotoxicity pathways and inflammatory processes. Subtype A also showed increased sensitivity to three chemotherapeutic agents. A 12-gene inflammatory response-related signature was found to have prognostic value for 1, 2 and 3 year survival in UCEC patients. Additionally, a validated machine learning-based signature demonstrated significant differences in clinical traits between low-risk and high-risk cohorts. Elevated risk scores were associated with higher pathological grading, older age, advanced stage and immune subtype C2. Low-risk groups had higher infiltration of immune cell types such as CD8 + T cells and activated CD4 + cells. However, the abundance of cytotoxic immune cells decreased with increasing risk scores. Finally, PCR was applied to test the different expression in P2PX4. P2RX4 knockdown inhibited the proliferation and proliferation of the endometrial carcinoma Ishikawa cell line. In conclusion, this developed signature can serve as a clinical prediction index and reveal distinct immune expression patterns. Ultimately, this study has the potential to enhance targeted therapy and personalized medicine for UCEC patients.

Metabolism-related gene vaccines and immune infiltration in ovarian cancer: A novel risk score model of machine learning

BACKGROUND

The present study aims to develop a metabolic gene signature to evaluate the survival rate of ovarian cancer (OC) patients and analyze the potential mechanisms of metabolic genes in OC because the difficulty in early detection of OC often leads to poor treatment outcomes.

METHODS

A non-negative matrix factorization algorithm was applied to determine molecular subtypes according to metabolism genes. To build a risk prognosis model, least absolute shrinkage and selection operator multivariate Cox analysis was carried out with weighted correlation network analysis (WCGNA). Glycolytic flux and mitochondrial function were evaluated by conducting seahorse analysis.

RESULTS

On the basis of metabolism-related genes, the two subtypes of OC samples present in The Cancer Genome Atlas database were distinguished. An analysis of WGCNA identified 1056 genes. Lastly, a 10-gene signature (CMAS, ADH1B, PLA2G2D, BHMT, CACNA1C, AADAC, ALOX12, CYP2R1, SCN1B and ME1) was constructed that demonstrated promising performance in predicting outcome in patients with OC. The RiskScore of the gene signature was linked to microenvironment cell infiltration and immune checkpoint. Higher RiskScores were associated with poorer results for OC patients. Seahorse analysis shows the influence of CMAS in cell energy metabolism.

CONCLUSIONS

In the present study, a novel marker for evaluating the survival of OC patients was developed through the creation of a gene signature incorporating metabolism-related genes. Our knowledge of immunotherapy and microenvironment cell infiltration may be enriched by evaluating metabolism-related gene modification patterns.

Peroxisome proliferator-activated receptors signature reveal the head and neck squamous cell carcinoma energy metabolism phenotype and clinical outcome

BACKGROUND

Peroxisome proliferator activating receptors (PPARs) are important regulators of nuclear hormone receptor function, and they play a key role in biological processes such as lipid metabolism, inflammation and cell proliferation. However, their role in head and neck squamous cell carcinoma (HNSC) is unclear.

METHODS

We used multiple datasets, including TCGA-HNSC, GSE41613, GSE139324, PRJEB23709 and IMVigor, to perform a comprehensive analysis of PPAR-related genes in HNSC. Single-cell sequencing data were preprocessed using Seurat packets, and intercellular communication was analyzed using CellChat packets. Functional enrichment analysis of PPAR-related genes was performed using ClusterProfile and GSEA. Prognostic models were constructed using LASSO and Cox regression models, and immunohistochemical analyses were performed using human protein mapping (The Human Protein Atlas).

RESULTS

Our single-cell RNA sequencing analysis revealed distinct cell populations in HNSC, with T cells having the most significant transcriptome differences between tumors and normal tissues. The PPAR features were higher in most cell types in tumor tissues compared with normal tissues. We identified 17 PPAR-associated differentially expressed genes between tumors and normal tissues. A prognostic model based on seven PPAR-associated genes was constructed with high accuracy in predicting 1, 2 and 3 year survival in patients with HNSC. In addition, patients with a low risk score had a higher immune score and a higher proportion of T cells, CD8+ T cells and cytotoxic lymphocytes. They also showed higher immune checkpoint gene expression, suggesting that they might benefit from immunotherapy. PPAR-related genes were found to be closely related to energy metabolism.

CONCLUSIONS

Our study provides a comprehensive understanding of the role of PPAR related genes in HNSC. The identified PPAR features and constructed prognostic models may serve as potential biomarkers for HNSC prognosis and treatment response. In addition, our study found that PPAR-related genes can differentiate energy metabolism and distinguish energy metabolic heterogeneity in HNSC, providing new insights into the molecular mechanisms of HNSC progression and therapeutic response.

Exploration of the prognostic effect of costimulatory genes in bladder cancer

BACKGROUND

A prognostic model of bladder cancer was constructed based on costimulatory molecules, and its stability and accuracy were verified in different datasets.

METHOD

The expression profile of bladder cancer RNA and the corresponding clinical data in The Cancer Genome Atlas (TCGA) database were analyzed employing computational biology, and a prognostic model was constructed for costimulating molecule-related genes. The model was applied in GSE160693, GSE176307, Xiangya_Cohort, GSE13507, GSE19423, GSE31684, GSE32894, GSE48075, GSE69795 and GSE70691 in TCGA dataset and Gene Expression Omnibus database. The role of costimulating molecules in bladder cancer tumor subtypes was also explored. By consistent cluster analysis, bladder cancer in the TCGA dataset was categorized into two subtypes: C1 and C2. The C1 subtype exhibited a poor prognosis, high levels of immune cell infiltration and significant enrichment of natural killer cells, T cells and dendritic cells in the C1 subtype. In addition, the ImmuneScore calculated by the ESTIMATE algorithm differed greatly between the two subtypes, and the ImmuneScore of the C1 subtype was greater than the C2 subtype in a significant manner.

RESULTS

This study also assessed the relationship between costimulating molecules and immunotherapy response. The high-risk group responded poorly to immunotherapy, with significant differences in the amount of most immune cells between the two groups. Further, three indices of the ESTIMATE algorithm and 22 immune cells of the CIBERSORT algorithm were significantly correlated with risk values. These findings suggest the potential value of costimulating molecules in predicting immunotherapy response.

CONCLUSION

A costimulatory molecule-based prognostic model for bladder cancer was established and validated across multiple datasets. This model introduces a novel mode for tailoring treatments to each individual with bladder cancer, and offers valuable insights for informed clinical choices. Simultaneously, this research also delved into the significance of costimulating molecules within distinct bladder cancer subtypes, shedding novel insights into improving immunotherapy strategies for the treatment of bladder cancer.

Integrative analysis of cuproptosis-associated genes for predicting immunotherapy response in single-cell and multi-cohort studies

BACKGROUND

The role of genes associated with the cuproptosis cell signaling pathway in prognosis and immunotherapy in ovarian cancer (OC) has been extensively investigated. In this study, we aimed to explore these mechanisms and establish a prognostic model for patients with OC using bioinformatics techniques.

METHODS

We obtained the single cell sequencing data of ovarian cancer from the Gene Expression Omnibus (GEO) database and preprocessed the data. We analyzed a variety of factors including cuproptosis cell signal score, transcription factors, tumorigenesis and progression signals, gene set variation analysis (GSVA) and intercellular communication. Differential gene analysis was performed between groups with high and low cuproptosis cell signal scores, as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Using bulk RNA sequencing data from The Cancer Genome Atlas, we used the least absolute shrinkage and selection operator (LASSO)-Cox algorithm to develop cuproptosis cell signaling pathword-related gene signatures and validated them with GEO ovarian cancer datasets. In addition, we analyzed the inherent rules of the genes involved in building the model using a variety of bioinformatics methods, including immune-related analyses and single nucleotide polymorphisms. Molecular docking is used to screen potential therapeutic drugs. To confirm the analysis results, we performed various wet experiments such as western blot, cell counting kit 8 (CCK8) and clonogenesis tests to verify the role of the Von Willebrand Factor (VWF) gene in two ovarian cancer cell lines.

RESULTS

Based on single-cell data analysis, we found that endothelial cells and fibroblasts showed active substance synthesis and signaling pathway activation in OC, which further promoted immune cell suppression, cancer cell proliferation and metastasis. Ovarian cancer has a high tendency to metastasize, and cancer cells cooperate with other cells to promote disease progression. We developed a signature consisting of eight cuproptosis-related genes (CRGs) (MAGEF1, DNPH1, RARRES1, NBL1, IFI27, VWF, OLFML3 and IGFBP4) that predicted overall survival in patients with ovarian cancer. The validity of this model is verified in an external GEO validation set. We observed active infiltrating states of immune cells in both the high- and low-risk groups, although the specific cells, genes and pathways of activation differed. Gene mutation analysis revealed that TP53 is the most frequently mutated gene in ovarian cancer. We also predict small molecule drugs associated with CRGs and identify several potential candidates. VWF was identified as an oncogene in ovarian cancer, and the protein was expressed at significantly higher levels in tumor samples than in normal samples. The high-score model of the cuproptosis cell signaling pathway was associated with the sensitivity of OC patients to immunotherapy.

CONCLUSIONS

Our study provides greater insight into the mechanisms of action of genes associated with the cuproptosis cell signaling pathway in ovarian cancer, highlighting potential targets for future therapeutic interventions.

Prognostic role of long non-coding RNA USP30-AS1 in ovarian cancer: insights into immune cell infiltration in the tumor microenvironment

Ovarian cancer represents a formidable gynecologic malignancy bearing a dismal prognosis owing to the dearth of reliable early detection approaches and a high recurrence rate. Long non-coding RNAs (lncRNAs) have garnered immense attention as key orchestrators involved in diverse biological processes and take part in cancer initiation and progression. The present study investigated the potential significance of lncRNA USP30-AS1 in ovarian cancer prognosis, as well as its putative association with immune cell infiltration in tumor immune microenvironment (TIME). By analyzing publicly available datasets, we identified six lncRNAs with prognostic prediction ability, including USP30-AS1. The results revealed a significant positive correlation of USP30-AS1 expression with the infiltration of immune cells such as Th1 cells, TFH, CD8 T cells, B cells, antigen-presenting dendritic cells (aDC), and plasmacytoid dendritic cells (pDC) in ovarian cancer specimens. These findings provide compelling evidence of the potential involvement of lncRNA in the regulation of the TME in ovarian carcinoma. The outcomes from this study underscore the potential of USP30-AS1 as a promising prognostic biomarker for ovarian cancer. Additionally, the findings offer significant insights into the plausible role of lncRNAs in modulating immune activities, thus adding to our understanding of the disease biology. Additional investigations are necessary to unravel the molecular mechanisms underpinning these connections and validate the results seen in independent cohorts and experimental models.

A novel machine learning-based programmed cell death-related clinical diagnostic and prognostic model associated with immune infiltration in endometrial cancer

Background

To explore the underlying mechanism of programmed cell death (PCD)-related genes in patients with endometrial cancer (EC) and establish a prognostic model.

Methods

The RNA sequencing data (RNAseq), single nucleotide variation (SNV) data, and corresponding clinical data were downloaded from TCGA. The prognostic PCD-related genes were screened and subjected to consensus clustering analysis. The two clusters were compared by weighted correlation network analysis (WGCNA), immune infiltration analysis, and other analyses. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct the PCD-related prognostic model. The biological significance of the PCD-related gene signature was evaluated through various bioinformatics methods.

Results

We identified 43 PCD-related genes that were significantly related to prognoses of EC patients, and classified them into two clusters via consistent clustering analysis. Patients in cluster B had higher tumor purity, higher T stage, and worse prognoses compared to those in cluster A. The latter generally showed higher immune infiltration. A prognostic model was constructed using 11 genes (GZMA, ASNS, GLS, PRKAA2, VLDLR, PRDX6, PSAT1, CDKN2A, SIRT3, TNFRSF1A, LRPPRC), and exhibited good diagnostic performance. Patients with high-risk scores were older, and had higher stage and grade tumors, along with worse prognoses. The frequency of mutations in PCD-related genes was correlated with the risk score. LRPPRC, an adverse prognostic gene in EC, was strongly correlated with proliferation-related genes and multiple PCD-related genes. LRPPRC expression was higher in patients with higher clinical staging and in the deceased patients. In addition, a positive correlation was observed between LRPPRC and infiltration of multiple immune cell types.

Conclusion

We identified a PCD-related gene signature that can predict the prognosis of EC patients and offer potential targets for therapeutic interventions.

Novel methylation-related long non-coding RNA clinical outcome prediction method: the clinical phenotype and immune infiltration research in low-grade gliomas

Background

Recent studies have suggested that long non-coding RNAs (lncRNAs) may play crucial role in low-grade glioma; however, the underlying mechanisms linking them to epigenetic methylation remain unclear.

Methods

We downloaded expression level data for regulators associated with N1 methyladenosine (m1A), 5-methyladenine (m5C), and N6 methyladenosine (m6A) (M1A/M5C/M6A) methylation from the Cancer Genome Atlas-low-grade glioma (TCGA-LGG) database. We identified the expression patterns of lncRNAs, and selected methylation-related lncRNAs using Pearson correlation coefficient>0.4. Non-negative matrix dimensionality reduction was then used to determine the expression patterns of the methylation-associated lncRNAs. We constructed a weighted gene co-expression network analysis (WGCNA) network to explore the co-expression networks between the two expression patterns. Functional enrichment of the co-expression network was performed to identify biological differences between the expression patterns of different lncRNAs. We also constructed prognostic networks based on the methylation presence in lncRNAs in low-grade gliomas.

Results

We identified 44 regulators by literature review. Using a correlation coefficient greater than 0.4, we identified 2330 lncRNAs, among which 108 lncRNAs with independent prognostic values were further screened using univariate Cox regression at P< 0.05. Functional enrichment of the co-expression networks revealed that regulation of trans-synaptic signaling, modulation of chemical synaptic transmission, calmodulin binding, and SNARE binding were mostly enriched in the blue module. The calcium and CA2 signaling pathways were associated with different methylation-related long non-coding chains. Using the Least Absolute Shrinkage Selector Operator (LASSO) regression analysis, we analyzed a prognostic model containing four lncRNAs. The model's risk score was 1.12 *AC012063 + 0.74 * AC022382 + 0.32 * AL049712 + 0.16 * GSEC. Gene set variation analysis (GSVA) revealed significant differences in mismatch repair, cell cycle, WNT signaling pathway, NOTCH signaling pathway, Complement and Cascades, and cancer pathways at different GSEC expression levels. Thus, these results suggest that GSEC may be involved in the proliferation and invasion of low-grade glioma, making it a prognostic risk factor for low-grade glioma.

Conclusion

Our analysis identified methylation-related lncRNAs in low-grade gliomas, providing a foundation for further research on lncRNA methylation. We found that GSEC could serve as a candidate methylation marker and a prognostic risk factor for overall survival in low-grade glioma patients. These findings shed light on the underlying mechanisms of low-grade glioma development and may facilitate the development of new treatment strategies.

Sequence Variants in PSMB8/PSMB9 Immunoproteasome Genes and Risk of Urothelial Bladder Carcinoma

BACKGROUND

The PSMB8 and PSMB9 immunoproteasome genes are essential in cell processes, such as decisions on cell survival or death, the cell cycle, and cellular differentiation. Because recent evidence has demonstrated an immunological role for proteasomes in various malignancies, including urothelial bladder carcinoma (UBC), we evaluated single nucleotide polymorphisms (SNPs) in PSMB9 and PSMB8. We determined any associations between these SNPs and susceptibility to UBC in the Saudi community.

METHODS

Samples of genomic DNA were taken from buccal cells of 111 patients with UBC and 78 healthy controls. TaqMan Real-Time PCR was used to determine genotype distributions and allele frequencies for the PSMB9 rs17587 G>A and PSMB8 rs2071543 G>T SNPs. We used SNPStats (https://www.snpstats.net) to choose each SNP's best interactive inheritance model.

RESULTS

The PSMB9 rs17587 SNP was associated with the risk of UBC (odds ratio [OR] = 5.21, P < 0.0001). In contrast, the PSMB8 rs2071543 SNP showed no association with UBC risk (OR = 1.13, P = 0.7871). In terms of genotypic distribution, the rs17587 G>A SNP was more frequent in UBC cases than controls in both the dominant (OR = 7.5; 95% confidence interval, 3.7-15.1; P = 0.0051) and recessive (OR = 17.11, 95% confidence interval 5.1-57.4; P = 0.0026) models. Genotypic distribution of the PSMB8 rs2071543 G>T SNP was not significantly different between cases and controls in any interactive inheritance models (P > 0.05).

CONCLUSION

These results suggest a potential role for PSMB9 as a biomarker for increased UBC risk. Discovering more genetic variants within immunoproteasome genes related to antigen presentation could help further our understanding of this risk.

TAP1, a potential immune-related prognosis biomarker with functional significance in uveal melanoma

BACKGROUND

TAP1 is an immunomodulation-related protein that plays different roles in various malignancies. This study investigated the transcriptional expression profile of TAP1 in uveal melanoma (UVM), revealed its potential biological interaction network, and determined its prognostic value.

METHODS

CIBERSORT and ESTIMATE bioinformatic methods were used on data sourced from The Cancer Genome Atlas database (TCGA) to determine the correlation between TAP1 expression, UVM prognosis, biological characteristics, and immune infiltration. Gene set enrichment analysis (GSEA) was used to discover the signaling pathways associated with TAP1, while STRING database and CytoHubba were used to construct protein-protein interaction (PPI) and competing endogenous RNA (ceRNA) networks, respectively. An overall survival (OS) prognostic model was constructed to test the predictive efficacy of TAP1, and its effect on the in vitro proliferation activity and metastatic potential of UVM cell line C918 cells was verified by RNA interference.

RESULTS

There was a clear association between TAP1 expression and UVM patient prognosis. Upregulated TAP1 was strongly associated with a shorter survival time, higher likelihood of metastasis, and higher mortality outcomes. According to GSEA analysis, various immunity-related signaling pathways such as primary immunodeficiency were enriched in the presence of elevated TAP1 expression. A PPI network and a ceRNA network were constructed to show the interactions among mRNAs, miRNAs, and lncRNAs. Furthermore, TAP1 expression showed a significant positive correlation with immunoscore, stromal score, CD8+ T cells, and dendritic cells, whereas the correlation with B cells and neutrophils was negative. The Cox regression model and calibration plots confirmed a strong agreement between the estimated OS and actual observed patient values. In vitro silencing of TAP1 expression in C918 cells significantly inhibited cell proliferation and metastasis.

CONCLUSIONS

This study is the first to demonstrate that TAP1 expression is positively correlated with clinicopathological factors and poor prognosis in UVM. In vitro experiments also verified that TAP1 is associated with C918 cell proliferation, apoptosis, and metastasis. These results suggest that TAP1 may function as an oncogene, prognostic marker, and importantly, as a novel therapeutic target in patients with UVM.

IP-score correlated to endogenous tumour antigen peptide processing: A candidate clinical response score algorithm of immune checkpoint inhibitors therapy in multiple cohorts

The processing of endogenous tumour antigen peptides was essential for anti-tumour immunity in the tumour microenvironment. A high degree of Endogenous tumour antigen peptide processing has been demonstrated to improve the prognosis of carcinoma patients. However, there is insufficient evidence to prove its effect on the clinical response to immune checkpoint inhibitor therapy. To undertake a more in-depth analysis of the effects of the aforementioned genes on immunotherapy, we constructed a gene set evaluation score system relevant to tumour endogenous antigen peptide therapy using the GSVA approach. This rating mechanism is known as IP score (IPs). Immediately afterwards, we used the TCGA pan-cancer cohorts to conduct a comprehensive analysis of 6 genes in the IPs, and the analysis results showed that these six genes were related to the proportion of CD8⁺ T lymphocytes in a variety of solid tumours. As a prognostic protective factor for solid tumours, patients had better prognosis outcomes in the group with high expression levels of the above genes. We analysed the differential expression of six genes between immune checkpoint inhibitor treatment response and disease progression groups using several treatment cohorts. The results revealed that after treatment with PD-1 or CTLA4 inhibitors, the expression levels of the above six genes were comparatively high in the effective group, but the expression of the signature genes was dramatically downregulated in the ICI-insensitive groups. This indicates that the 6 genes are related to the clinical response to ICI treatment. Finally, we used the GSVA method to evaluate the above signatures, and the results showed that PDCD1, CTAL4, CD274 and LAG3 were significantly higher expressed in the IPs high-expression group; therefore, based on the processing of endogenous antigenic peptides in tumours, a predictive score of clinical response to immune checkpoint inhibitor therapy composed of 6 genes(PSMB8/PSMB9/PSMB10/PSME1/PSME2/IRF1) was constructed, and the role of each independent variable in the signature in the solid tumour microenvironment and the impact on ICI treatment were comprehensively analysed. This study provides a candidate evaluation score for predicting clinical response to immune checkpoint inhibitor therapy.

FBXO6 regulates the antiviral immune responses via mediating alveolar macrophages survival

Inducing early apoptosis in alveolar macrophages is one of the strategies influenza A virus (IAV) evolved to subvert host immunity. Correspondingly, the host mitochondrial protein nucleotide-binding oligomerization domain-like receptor (NLR)X1 is reported to interact with virus polymerase basic protein 1-frame 2 (PB1-F2) accessory protein to counteract virus-induced apoptosis. Herein, we report that one of the F-box proteins, FBXO6, promotes proteasomal degradation of NLRX1, and thus facilitates IAV-induced alveolar macrophages apoptosis and modulates both macrophage survival and type I interferon (IFN) signaling. We observed that FBXO6-deficient mice infected with IAV exhibited decreased pulmonary viral replication, alleviated inflammatory-associated pulmonary dysfunction, and less mortality. Analysis of the lungs of IAV-infected mice revealed markedly reduced leukocyte recruitment but enhanced production of type I IFN in Fbxo6-/- mice. Furthermore, increased type I IFN production and decreased viral replication were recapitulated in FBXO6 knockdown macrophages and associated with reduced apoptosis. Through gain- and loss-of-function studies, we found lung resident macrophages but not bone marrow-derived macrophages play a key role in the differences FBXO6 signaling pathway brings in the antiviral immune response. In further investigation, we identified that FBXO6 interacted with and promoted the proteasomal degradation of NLRX1. Together, our results demonstrate that FBXO6 negatively regulates immunity against IAV infection by enhancing the degradation of NLRX1 and thus impairs the survival of alveolar macrophages and antiviral immunity of the host.

PSME2 identifies immune-hot tumors in breast cancer and associates with well therapeutic response to immunotherapy

Breast cancer (BrCa) is a heterogeneous disease, which leads to unsatisfactory prognosis in females worldwide. Previous studies have proved that tumor immune microenvironment (TIME) plays crucial roles in oncogenesis, progression, and therapeutic resistance in Breast cancer. However, biomarkers related to TIME features have not been fully discovered. Proteasome activator complex subunit 2 (PSME2) is a member of proteasome activator subunit gene family, which is critical to protein degradation mediated by the proteasome. In the current research, we comprehensively analyzed the expression and immuno-correlations of Proteasome activator complex subunit 2 in Breast cancer. Proteasome activator complex subunit 2 was significantly upregulated in tumor tissues but associated with well prognosis. In addition, Proteasome activator complex subunit 2 was overexpressed in HER2-positive Breast cancer but not related to other clinicopathological features. Interestingly, Proteasome activator complex subunit 2 was positively related to immune-related processes and identified immuno-hot TIME in Breast cancer. Specifically, Proteasome activator complex subunit 2 was positively correlated with immunomodulators, tumor-infiltrating immune cells (TIICs), immune checkpoints, and tumor mutation burden (TMB) levels. Moreover, the positive correlation between Proteasome activator complex subunit 2 and PD-L1 expression was confirmed in a tissue microarray (TMA) cohort. Furthermore, in an immunotherapy cohort of Breast cancer, patients with pathological complete response (pCR) expressed higher Proteasome activator complex subunit 2 compared with those with non-pathological complete response. In conclusion, Proteasome activator complex subunit 2 is upregulated in tumor tissues and correlated with the immuno-hot tumor immune microenvironment, which can be a novel biomarker for the recognition of tumor immune microenvironment features and immunotherapeutic response in Breast cancer.

Analysis on methylation and expression of PSMB8 and its correlation with immunity and immunotherapy in lung adenocarcinoma

Aim: To find biomarkers for immunity and immunotherapy in lung adenocarcinoma (LUAD) through multiomics analysis. Materials & methods: The multiomics data of patients with LUAD were downloaded from the TCGA and GEO databases. CIBERSORT, quanTIseq, ESTIMATEScore, k-means clustering, gene set enrichment analysis, gene set variation analysis, immunophenoscore and logistic regression were used in this study. Results: PSMB8 HypoMet-HighExp group patients have more active immune-related pathways, more antitumor immune cells, less protumor immune cells, higher immunophenoscore and longer progression-free survival of immune checkpoint inhibitor therapy than HyperMet-LowExp group. In multivariate analysis, PSMB8 showed an independent value. Conclusion: The combination of DNA methylation and mRNA expression of PSMB8 could independently distinguish types of tumor immune microenvironment and predict programmed cell death protein 1/programmed cell death-ligand 1 inhibitors' effects in patients with LUAD.

Identify a DNA Damage Repair Gene Signature for Predicting Prognosis and Immunotherapy Response in Cervical Squamous Cell Carcinoma

The DNA damage repair (DDR) genes are increasingly gaining attention as potential therapeutic targets in cancers. In this study, we identified the DDR genes associated with the tumor mutation burden (TMB) and prognosis of cervical squamous cell carcinoma (CESC) based on The Cancer Genome Atlas (TCGA) database. Through LASSO Cox regression, the prognostic signature involving five DDR genes (ACTR2, TEX12, UBE2V1, HSF1, and FBXO6) was established, and the risk score was identified as an independent risk factor for CESC. The nomogram consisting of the five genes accurately predicted the overall survival (OS) and the immunotherapeutic response of CESC patients. Finally, the loss of the copies of the transcription factor (TF) SP140 in CESC patients may decrease the expression of FBXO6, improve DNA repair function, and reduce the diversity of neoantigens, thereby lowering the response to immunotherapies. Therefore, the DDR gene signature is a novel prognostic model and a biomarker for immunotherapies in CESC patients.

Anti-PD-L1 and anti-CD73 combination therapy promotes T cell response to EGFR-mutated NSCLC

Treatment with anti-PD-1 and anti-PD-L1 therapies has shown durable clinical benefit in non-small cell lung cancer (NSCLC). However, patients with NSCLC with epidermal growth factor receptor (EGFR) mutations do not respond as well to treatment as patients without an EGFR mutation. We show that EGFR-mutated NSCLC expressed higher levels of CD73 compared with EGFR WT tumors and that CD73 expression was regulated by EGFR signaling. EGFR-mutated cell lines were significantly more resistant to T cell killing compared with WT cell lines through suppression of T cell proliferation and function. In a xenograft mouse model of EGFR-mutated NSCLC, neither anti-PD-L1 nor anti-CD73 antibody alone inhibited tumor growth compared with the isotype control. In contrast, the combination of both antibodies significantly inhibited tumor growth, increased the number of tumor-infiltrating CD8+ T cells, and enhanced IFN-γ and TNF-α production of these T cells. Consistently, there were increases in gene expression that corresponded to inflammation and T cell function in tumors treated with the combination of anti-PD-L1 and anti-CD73. Together, these results further support the combination of anti-CD73 and anti-PD-L1 therapies in treating EGFR-mutated NSCLC, while suggesting that increased T cell activity may play a role in response to therapy.

Identification of a novel signature based on unfolded protein response-related gene for predicting prognosis in bladder cancer

BACKGROUND

The unfolded protein response (UPR) served as a vital role in the progression of tumors, but the molecule mechanisms of UPR in bladder cancer (BLCA) have been not fully investigated.

METHODS

We identified differentially expressed unfolded protein response-related genes (UPRRGs) between BLCA samples and normal bladder samples in the Cancer Genome Atlas (TCGA) database. Univariate Cox analysis and the least absolute shrinkage and selection operator penalized Cox regression analysis were used to construct a prognostic signature in the TCGA set. We implemented the validation of the prognostic signature in GSE13507 from the Gene Expression Omnibus database. The ESTIMATE, CIBERSORT, and ssGSEA algorithms were used to explore the correlation between the prognostic signature and immune cells infiltration as well as key immune checkpoints (PD-1, PD-L1, CTLA-4, and HAVCR2). GDSC database analyses were conducted to investigate the chemotherapy sensitivity among different groups. GSEA analysis was used to explore the potential mechanisms of UPR-based signature.

RESULTS

A prognostic signature comprising of seven genes (CALR, CRYAB, DNAJB4, KDELR3, CREB3L3, HSPB6, and FBXO6) was constructed to predict the outcome of BLCA. Based on the UPRRGs signature, the patients with BLCA could be classified into low-risk groups and high-risk groups. Patients with BLCA in the low-risk groups showed the more favorable outcomes than those in the high-risk groups, which was verified in GSE13507 set. This signature could serve as an autocephalous prognostic factor in BLCA. A nomogram based on risk score and clinical characteristics was established to predict the over survival of BLCA patients. Furthermore, the signature was closely related to immune checkpoints (PD-L1, CTLA-4, and HAVCR2) and immune cells infiltration including CD8⁺ T cells, follicular helper T cells, activated dendritic cells, and M2 macrophages. GSEA analysis indicated that immune and carcinogenic pathways were enriched in high-risk group.

CONCLUSIONS

We identified a novel unfolded protein response-related gene signature which could predict the over survival, immune microenvironment, and chemotherapy response of patients with bladder cancer.

Effect of unfolded protein response on the immune infiltration and prognosis of transitional cell bladder cancer

Background: Bladder cancer (BC) is one of the most common human malignancies worldwide. Previous researches have shown that the unfolded protein response (UPR) pathway could contribute to the tumorigenesis of BC. However, the role of UPR in the immune infiltration, progression, and prognosis of BC is unclear.Methods: The GSVA and ssGSEA methods were used for assessing the UPR score and immune cells infiltration score in three BC public datasets, respectively. The relationship between the UPR pathway and clinicopathological characteristics was analyzed by the Kruskal-Wallis, Wilcox test, and log-rank test. The association of the UPR pathway with various tumor-infiltrating immune cells was evaluated with the correlation analysis. Univariate Cox regression analysis was performed to identify risk factors significantly associated with prognosis. The predictive models were built based on risk factors and visualized with nomograms. The performance of our models was evaluated with the calibration curve, Harrell's concordance index (c-index), and receiver operating characteristic (ROC) analysis.Results: We found that the UPR pathway and many UPR-related genes were significantly associated with the pathologic grade, tumor type, and invasive progression of transitional cell bladder cancer (TCBC), and a high UPR score predicted a poor prognosis in patients. The UPR score was positively correlated with the infiltration abundance of many tumor immune cells in TCBC. Besides, we constructed predictive models based on the UPR score, and good performance was observed, with c-indexes ranging from 0.74 to 0.87.Conclusions: Our study proved that the UPR pathway may have an important impact on the progression, prognosis, and tumor immune infiltration in TCBC, and the models we built may provide effective and reliable guides for prognosis assessment and treatment decision-making for TCBC patients.