Consensus Clustering Analysis Research Articles

Identification of angiogenesis-related subtypes, the development of prognostic models, and the landscape of tumor microenvironment infiltration in colorectal cancer.

Background: Angiogenesis is one of the most prominent markers of cancer progression and contributes to tumor metastasis and prognosis. Anti-angiogenic drugs have proven effective in treating metastatic colorectal cancer. However, there is some uncertainty regarding the potential role of angiogenesis-related genes in the tumor microenvironment. Methods: We analyzed 1,214 colorectal cancer samples to identify alterations in angiogenesis-related genes (ARGs), and then correlated angiogenesis with clinical features, prognosis, and TME. The ARGs expression profiles in colorectal cancer were analyzed using three computational methods (CIBERSORT, ssGSEA, and MCPcounter) and provided a systematic immune landscape. Patients with CRC were classified into two subtypes based on consensus clustering analysis of angiogenesis-related genes. The revealed differentially expressed genes between the two subtypes were used to create and validate ARGscore prognostic models. In addition, we collected eight colorectal cancer patient specimens and performed RT-qPCR to validate the signature gene expression. Results: We assessed the expression patterns of ARGs in colorectal cancer. We identified two molecular subtypes and confirmed that the expression of ARGs was associated with prognosis and TME characteristics. Based on differentially expressed genes between subtypes, we constructed ARGscore and evaluated their predictive power for the survival of colorectal cancer patients. We also developed an accurate nomogram to make the ARGscore more clinically useful. In addition, ARGscore was significantly correlated with microsatellite instability, cancer stem cells, and chemotherapeutic drug sensitivity. Patients with ARGscore-low characterized by immune activation and microsatellite instability high had a better prognosis. Conclusion: ARGs expression influenced the prognosis, clinicopathological features, and tumor stromal immune microenvironment in colorectal cancer. We developed a new risk model, ARGscore, for the treatment and prognosis of CRC patients and validated its promising predictive power. These findings will enable us to understand colorectal cancer better, assess prognoses, and develop more effective treatment options.

Developing an m5C regulator-mediated RNA methylation modification signature to predict prognosis and immunotherapy efficacy in rectal cancer.

Currently, a very small number of patients with colorectal cancer (CRC) respond to immune checkpoint inhibitor (ICI) treatment. Therefore, there is an urgent need to investigate effective biomarkers to determine the responsiveness to ICI treatment. Recently, aberrant 5-methylcytosine (m5C) RNA modification has emerged as a key player in the pathogenesis of cancer. Thus, we aimed to explore the predictive signature based on m5C regulator-related genes for characterizing the immune landscapes and predicting the prognosis and response to therapies. The Cancer Genome Atlas (TCGA) cohort was used as the training set, while GEO data sets, real-time quantitative PCR (RT-qPCR) analysis from paired frozen tissues, and immunohistochemistry (IHC) data from tissue microarray (TMA) were used for validation. We constructed a novel signature based on three m5C regulator-related genes in patients with rectal adenocarcinoma (READ) using a least absolute shrinkage and selection operator (LASSO)-Cox regression and unsupervised consensus clustering analyses. Additionally, we correlated the three-gene signature risk model with the tumor immune microenvironment, immunotherapy efficiency, and potential applicable drugs. The m5C methylation-based signature was an independent prognostic factor, where low-risk patients showed a stronger immunoreactivity phenotype and a superior response to ICI therapy. Conversely, the high-risk patients had enriched pathways of cancer hallmarks and presented immune-suppressive state, which demonstrated that they are more insensitive to immunotherapy. Additionally, the signature markedly correlated with drug susceptibility. We developed a reliable m5C regulator-based risk model to predict the prognosis, clarify the molecular and tumor microenvironment status, and identify patients who would benefit from immunotherapy or chemotherapy. Our study could provide vital guidance to improve prognostic stratification and optimize personalized therapeutic strategies for patients with rectal cancer.

Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer.

Endometrial cancer (EC) is women's fourth most common malignant tumor. Neddylation plays a significant role in many diseases; however, the effect of neddylation and neddylation-related genes (NRGs) on EC is rarely reported. In this study, we first used MLN4924 to affect the activation of neddylation in different cell lines (Ishikawa and HEC-1-A) and determined the critical role of neddylation-related pathways for EC progression. Subsequently, we screened 17 prognostic NRGs based on expression files of the TCGA-UCEC cohort. Based on unsupervised consensus clustering analysis, patients with EC were classified into two neddylation patterns (C1 and C2). In terms of prognosis, substantial differences were observed between the two patterns. Compared with C2, C1 exhibited low levels of immune infiltration and promoted tumor progression. More importantly, based on the expression of 17 prognostic NRGs, we transformed nine machine-learning algorithms into 89 combinations. The random forest (RSF) was selected to construct the neddylation-related risk score according to the average C-index of different cohorts. Notably, our score had important clinical implications for EC. Patients with high scores have poor prognoses and a cold tumor state. In conclusion, neddylation-related patterns and scores can distinguish tumor microenvironment (TME) and prognosis and guide personalized treatment in patients with EC.

Key success factors for small design offices in the bidding process

ABSTRACT Competition in the design office market is high, therefore it is important to be aware of the conditions determining the competitive advantage, thus increasing the bidding effectiveness. For this reason, the aim of the article is to identify key success factors (KSF) increasing successful bidding of a small design office. The factors were identified and then analysed based on a literature review, expert interviews and using Group Consensus Cluster Analysis (GCCA) – the module of the AHP method clustering a group of decision makers into smaller subgroups with higher consensus. To examine the managerial implications, a KSF analysis was conducted from the perspective of the bidding effectiveness of the selected small design office. The results indicate that ‘technical skills and experience’ is the factor most influencing the potential of a small design office. Nevertheless the entire proposed procedure provides important guidelines on how to use the organisation's strengths for its development and what needs to be improved in its operation. HIGHLIGHTS Detailed analysis of key success factors (KSFs) of the design office operation. KSFs subjected to the AHP method using Group Consensus Cluster Analysis (GCCA). Managerial implications based on KSF analysis from the perspective of the bidding effectiveness of the selected small design office. Combining AHP method with the KSF analysis provides the company with important guidelines regarding strategic efforts.

Construction of a TTN Mutation-Based Prognostic Model for Evaluating Immune Microenvironment, Cancer Stemness, and Outcomes of Colorectal Cancer Patients.

Colorectal cancer (CRC) is one of the commonest cancers worldwide. As conventional biomarkers cannot clearly define the heterogeneity of CRC, it is essential to establish novel prognostic models. For the training set, data pertaining to mutations, gene expression profiles, and clinical parameters were obtained from the Cancer Genome Atlas. Consensus clustering analysis was used to identify the CRC immune subtypes. CIBERSORT was used to analyze the immune heterogeneity across different CRC subgroups. Least absolute shrinkage and selection operator regression was used to identify the genes for constructing the immune feature-based prognostic model and to determine their coefficients. A gene prognostic model was then constructed to predict patient outcomes; the model was then externally validated using data from the Gene Expression Omnibus. As a high-frequency somatic mutation, the titin (TTN) mutation has been identified as a risk factor for CRC. Our results demonstrated that TTN mutations have the potential to modulate the tumor microenvironment, converting it into the immunosuppressive type. In this study, we identified the immune subtypes of CRC. Based on the identified subtypes, 25 genes were selected for prognostic model construction; a prediction model was also constructed, and its prediction accuracy was tested using the validation dataset. The potential of the model in predicting immunotherapy responsiveness was then explored. TTN-mutant and TTN-wild-type CRC demonstrated different microenvironment features and prognosis. Our model provides a robust immune-related gene prognostic tool and a series of gene signatures for evaluating the immune features, cancer stemness, and prognosis of CRC.

Identification and Validation of Molecular Subtype and Prognostic Signature for Bladder Cancer Based on Neutrophil Extracellular Traps

Neutrophil extracellular traps (NETs) could promote tumor growth and distant metastases. Molecular subtypes of bladder cancer were identified with consensus cluster analysis. A NETs-related prognostic signature was constructed with LASSO cox regression analysis. As a result, we identified three subtypes of bladder cancer, which had a distinct difference in prognosis, immune microenvironment, TIDE score, mRNAsi score and IC50 score. We also developed a prognostic signature based on 5 NETs-related genes, which had a good performance in clinical outcome prediction of bladder cancer. These results may provide more data about the vital role of NETs in bladder cancer.

Thyroid cancer risk prediction model using m6A RNA methylation regulators: integrated bioinformatics analysis and histological validation.

Epigenetic reprogramming has been reported to play a critical role in the progression of thyroid cancer. RNA methylation accounts for more than 60% of all RNA modifications, and N6-methyladenosine (m6A) is the most common modification of RNAs in higher organisms. The purpose of this study was to explore the related modification mode of m6A regulators construction and its evaluation on the clinical prognosis and therapeutic effect of thyroid cancer. The levels of 23 m6A regulators in The Cancer Genome Atlas (TCGA) were analyzed. Differentially expressed genes (DEGs) and survival analysis were performed based on TCGA-THCA clinicopathological and follow-up information, and the mRNA levels of representative genes were verified using clinical thyroid cancer data. In order to detect the effects of m6A regulators and their DEGs, consensus cluster analysis was carried out, and the expression of different m6A scores in Tumor Mutation Burden (TMB) and immune double antibodies (PD-1 antibody and CTLA4 antibody) were evaluated to predict the correlation between m6A score and thyroid cancer tumor immunotherapy response. Different expression patterns of m6A regulatory factors were detected in thyroid cancer tumors and normal tissues, and several prognoses related m6A genes were obtained. Two different m6A modification patterns were determined by consensus cluster analysis. Two different subgroups were established by screening overlapping DEGs between two m6A clusters, with cluster A having the best prognosis. According to the m6A score extracted from DEGs, thyroid cancer patients can be divided into high and low score subgroups. Patients with lower m6A score have longer survival time and better clinical features. The relationship between m6A score and Tumor Mutation Burden (TMB) and its correlation with the expression of PD-1 antibody and CTLA4 antibody proved that m6A score could be used as a potential predictor of the efficacy of immunotherapy in thyroid cancer patients. We screened DEGs from cluster m6A and constructed a highly predictive model with prognostic value by dividing TCGA-THCA into two different clusters and performing m6A score analysis. This study will help clarify the overall impact of m6A modification patterns on thyroid cancer progression and formulate more effective immunotherapy strategies.

Multilevel proteomic analyses reveal molecular diversity between diffuse-type and intestinal-type gastric cancer

Diffuse-type gastric cancer (DGC) and intestinal-type gastric cancer (IGC) are the major histological types of gastric cancer (GC). The molecular mechanism underlying DGC and IGC differences are poorly understood. In this research, we carry out multilevel proteomic analyses, including proteome, phospho-proteome, and transcription factor (TF) activity profiles, of 196 cases covering DGC and IGC in Chinese patients. Integrative proteogenomic analysis reveals ARIDIA mutation associated with opposite prognostic effects between DGC and IGC, via diverse influences on their corresponding proteomes. Systematical comparison and consensus clustering analysis identify three subtypes of DGC and IGC, respectively, based on distinct patterns of the cell cycle, extracellular matrix organization, and immune response-related proteins expression. TF activity-based subtypes demonstrate that the disease progressions of DGC and IGC were regulated by SWI/SNF and NFKB complexes. Furthermore, inferred immune cell infiltration and immune clustering show Th1/Th2 ratio is an indicator for immunotherapeutic effectiveness, which is validated in an independent GC anti-PD1 therapeutic patient group. Our multilevel proteomic analyses enable a more comprehensive understanding of GC and can further advance the precision medicine.

Identification of tumor antigens and immune subtypes of acute myeloid leukemia for mRNA vaccine development.

Acute myeloid leukemia (AML) is a highly aggressive hematological malignancy, and there has not been any significant improvement in therapy of AML over the past several decades. The mRNA vaccines have become a promising strategy against multiple cancers, however, its application on AML remains undefined. In this study, we aimed to identify novel antigens for developing mRNA vaccines against AML and explore the immune landscape of AML to select appropriate patients for vaccination. Genomic data and gene mutation data were retrieved from TCGA, GEO and cBioPortal, respectively. GEPIA2 was used to analyze differentially expressed genes. The single cell RNA-seq database Tumor Immune Single-cell Hub (TISCH) was used to explore the association between the potential tumor antigens and the infiltrating immune cells in the bone marrow. Consensus clustering analysis was applied to identify distinct immune subtypes. The correlation between the abundance of antigen presenting cells and the expression level of antigens was evaluated using Spearman correlation analysis. The characteristics of the tumor immune microenvironment in each subtype were investigated based on single-sample gene set enrichment analysis. Five potential tumor antigens were identified for mRNA vaccine from the pool of overexpressed and mutated genes, including CDH23, LRP1, MEFV, MYOF and SLC9A9, which were associated with infiltration of antigen-presenting immune cells (APCs). AML patients were stratified into two immune subtypes Cluster1 (C1) and Cluster2 (C2), which were characterized by distinct molecular and clinical features. C1 subtype demonstrated an immune-hot and immunosuppressive phenotype, while the C1 subtype had an immune-cold phenotype. Furthermore, the two immune subtype showed remarkably different expression of immune checkpoints, immunogenic cell death modulators and human leukocyte antigens. CDH23, LRP1, MEFV, MYOF and SLC9A9 were potential antigens for developing AML mRNA vaccine, and AML patients in immune subtype 1 were suitable for vaccination.

Diagnostic, clustering, and immune cell infiltration analysis of m6A regulators in patients with sepsis

RNA N6-methladenosine (m6A) regulators are required for a variety of biological processes, including immune responses, and increasing evidence indicates that their dysregulation is closely associated with many diseases. However, the potential roles of m6A regulators in sepsis remain unknown. We comprehensively analyzed the transcriptional variations in and interactions of 26 m6A regulators in sepsis based on the Gene Expression Omnibus (GEO) database. A random forest (RF) model and nomogram were established to predict the occurrence and risk of sepsis in patients. Then, two different m6A subtypes were defined by consensus clustering analysis, and we explored the correlation between the subtypes and immune cells. We found that 17 of the 26 m6A regulators were significantly differentially expressed between patients with and without sepsis, and strong correlations among these 17 m6A regulators were revealed. Compared with the support vector machine (SVM) model, the RF model had better predictive ability, and therefore was used to construct a reliable nomogram containing 10 candidate m6A regulators to predict the risk of sepsis in patients. In addition, a consensus clustering algorithm was used to identify two different subtypes of m6A, which helped us distinguish different levels of immune cell infiltration and inflammation in patients with sepsis. Comprehensive analysis of m6A regulators in sepsis revealed their potential roles in sepsis occurrence, immune cell infiltration and inflammation in patients with sepsis. This study may contribute to the development of follow-up treatment strategies for sepsis.

Development and validation of a pyroptosis-related genes signature for risk stratification in gliomas.

Background: Glioma is a highly heterogeneous disease, causing the prognostic prediction a challenge. Pyroptosis, a programmed cell death mediated by gasdermin (GSDM), is characterized by cell swelling and the release of inflammatory factors. Pyroptosis occurs in several types of tumor cells, including gliomas. However, the value of pyroptosis-related genes (PRGs) in the prognosis of glioma remains to be further clarified. Methods: In this study, mRNA expression profiles and clinical data of glioma patients were acquired from TCGA and CGGA databases, and one hundred and eighteen PRGs were obtained from the Molecular Signatures Database and GeneCards. Then, consensus clustering analysis was performed to cluster glioma patients. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to establish a polygenic signature. Functional verification of the pyroptosis-related gene GSDMD was achieved by gene knockdown and western blotting. Moreover, the immune infiltration status between two different risk groups were analyzed through the "gsva" R package. Results: Our results demonstrated that the majority of PRGs (82.2%) were differentially expressed between lower-grade gliomas (LGG) and glioblastoma (GBM) in the TCGA cohort. In univariate Cox regression analysis, eighty-three PRGs were shown to be associated with overall survival (OS). A five-gene signature was constructed to divide patients into two risk groups. Compared with patients in the low-risk group, patients in the high-risk group had obviously shorter OS (p < 0.001). Also, we found that the high-risk group showed a higher infiltrating score of immune cells and immune-related functions. Risk score was an independent predictor of OS (HR > 1, p < 0.001). Furthermore, knockdown of GSDMD decreased the expression of IL-1β and cleaved caspase-1. Conclusion: Our study constructed a new PRGs signature, which can be used to predict the prognosis of glioma patients. Targeting pyroptosis might serve as a potential therapeutic strategy for glioma.

Identification of Gene Signature-Related Oxidative Stress for Predicting Prognosis of Colorectal Cancer.

Colorectal cancer (CRC) is the third most common cancer. Nearly a decade of studies had shown that cancer regimens tailored to molecular and pathological features lead to improved overall survival. Oxidative stress (OS) refers to a state in which oxidation and antioxidant effects are unbalanced in the body. However, the molecular mechanism of OS-related CRC remains unclear. Univariate Cox regression analysis gained OS signature genes related to CRC prognosis, and then, different CRC molecular subtypes were obtained by consensus clustering analysis. Differential expression analysis and least absolute shrinkage and selection operator (LASSO) algorithm were used to obtain prognostic-related signature genes. Significantly, risk score was calculated by RiskScore = Σβi × Expi. Moreover, the Kaplan-Meier survival analysis, immune cell infiltration, and sensitivity to treatment regimens were performed to assess the model's validity and adaptability. Finally, RiskScore incorporated clinicopathological features to further improve prognostic models and survival prediction. 63 OS-related prognostic genes were obtained, and four distinct molecular subtypes of CRC were identified based on the expression characteristics. 230 differentially expressed genes (DEGs) between different molecular subtypes were compressed by LASSO algorithm, and finally, 6 OS-related genes were obtained. The Kaplan-Meier survival analysis indicated that the high RiskScore groups had poorer prognosis and the RiskScore model showed better predictive performance in all three other independent datasets. Moreover, immunotherapy/chemosensitivity analysis found that the low-risk group was more sensitive to different treatment options and could achieve better treatment outcomes. Oxidative stress-related RiskScore model built in this work has good predictive performance for CRC.

Construction and validation of a cuproptosis-related prognostic model for glioblastoma.

Cuproptosis, a newly reported type of programmed cell death, takes part in the regulation of tumor progression, treatment response, and prognosis. But the specific effect of cuproptosis-related genes (CRGs) on glioblastoma (GBM) is still unclear. The transcriptome data and corresponding clinical data of GBM samples were downloaded from the TCGA and GEO databases. R software and R packages were used to perform statistical analysis, consensus cluster analysis, survival analysis, Cox regression analysis, Lasso regression analysis, and tumor microenvironment analysis. The mRNA and protein expression levels of model-related genes were detected by RT-qPCR and Western blot assays, respectively. The expression profile of CRGs in 209 GBM samples from two separate datasets was obtained. Two cuproptosis subtypes, CRGcluster A and CRGcluster B, were identified by consensus cluster analysis. There were apparent differences in prognosis, tumor microenvironment, and immune checkpoint expression levels between the two subtypes, and there were 79 prognostic differentially expressed genes (DEGs). According to the prognostic DEGs, two gene subtypes, geneCluster A and geneCluster B, were identified, and a prognostic risk score model was constructed and validated. This model consists of five prognostic DEGs, including PDIA4, DUSP6, PTPRN, PILRB, and CBLN1. Ultimately, to improve the applicability of the model, a nomogram was established. Patients with GBM in the low-risk cluster have a higher mutation burden and predict a longer OS than in the high-risk group. Moreover, the risk score was related to drug sensitivity and negatively correlated with the CSC index. We successfully constructed a cuproptosis-related prognostic model, which can independently predict the prognosis of GBM patients. These results further complement the understanding of cuproptosis and provide new theoretical support for developing a more effective treatment strategy.

The ferroptosis signature predicts the prognosis and immune microenvironment of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a cancer with a high metastatic rate and poor prognosis. Growing studies suggest that ferroptosis take part in the development of tumours. At the same time, the connection between ferroptosis-related genes (FRGs) and the prognosis of NPC remains unclear. In this study, we explored the dysregulated FRGs between normal control and tumour samples of NPC. Firstly, 14 of 36 differentially expressed FRGs were identified in NPC tissues compared to normal tissues, among which ABCC1, GLS2, CS and HMGCR were associated with poor prognosis for patients. The four ferroptosis genes were used for consensus cluster analysis and two risk-related FRGs (ABCC1 and GLS2) were used in a risk model. The ROC curve revealed the good predictive performance of this risk signature. Multivariate analysis revealed that risk score and intratumoral TILs were independent risk factors linked to prognosis. Additionally, our results suggested that the risk signature was attached to the immune microenvironment. Moreover, the NPC patients with high risk were sensitive to chemotherapeutic drugs including axitinib, docetaxel, embelin, epothilone.B, parthenolide, thapsigargin, tipifarnib, vinorelbine. Finally, the expression of ABCC1 and GLS2 was validated in NPC tissues using immunohistochemistry. Together, these results revealed ferroptosis may be a potential biomarker in NPC and representing a promising future direction in prognosis and therapeutic strategy for the treatment of NPC.

Expression Profiles of Cuproptosis-Related Genes Determine Distinct Subtypes of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent subtype of pancreatic cancer and one of the most malignant tumors worldwide. Due to the heterogeneity of its genomics and proteomics, the prognosis of PDAC remains disappointing despite advances in surgery and medicines. Recently, a novel form of programmed cell death, cuproptosis, was proposed, although its role in PDAC has not been investigated. This study aimed to quantify the expression of cuproptosis-related genes and characterize the novel subtypes of PDAC. To evaluate the pattern of cuproptosis in PDAC, the gene expression data and clinical information of 372 samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A consensus cluster analysis was performed using the transcriptional levels, genetic alterations, and individual prognostic values of seven pre-selected cuproptosis-related genes (DLAT, LIPT1, FDX1, DLD, PDHB, PDHA1, and LIAS) to identify the novel subtypes associated with cuproptosis in PDAC. A univariate Cox regression analysis was used to determine the significant prognostic indicators and cuproptosis scores among the differentially expressed genes (DEGs) between the dividing subclusters, followed by a principal component analysis. The prognostic values, immune profiles, treatment sensitivities, and cuproptosis scores were evaluated between the different subgroups. Seven cuproptosis-related genes showed aberrant expression levels and genetic alterations in the PDAC tumor microenvironment. Among them, LIPT1, LIAS, DLAT, PDHA1, and DLD were significantly correlated with overall survival. Based on the expression profiles of the seven cuproptosis-related genes, three cuproptosis clusters (Clusters A, B, and C) were identified, which were represented by different clinicopathologic features, gene expression levels, and biological processes. A total of 686 DEGs were identified among the three cuproptosis clusters, of which 35 prognosis-related DEGs were selected to further classify the PDAC samples into two subgroups with different survival rates, clinicopathologic features, immune infiltration levels, and drug sensitivities. Higher cuproptosis scores were associated with a significantly poorer prognosis. The cuproptosis subtypes, scores, and relevant genes represent valuable information for assessing the heterogeneity, treatment, and prognosis of PDAC.

Distinct Subtypes of Hepatorenal Syndrome and Associated Outcomes as Identified by Machine Learning Consensus Clustering

The utilization of multi-dimensional patient data to subtype hepatorenal syndrome (HRS) can individualize patient care. Machine learning (ML) consensus clustering may identify HRS subgroups with unique clinical profiles. In this study, we aim to identify clinically meaningful clusters of hospitalized patients for HRS using an unsupervised ML clustering approach. Consensus clustering analysis was performed based on patient characteristics in 5564 patients primarily admitted for HRS in the National Inpatient Sample from 2003-2014 to identify clinically distinct HRS subgroups. We applied standardized mean difference to evaluate key subgroup features, and compared in-hospital mortality between assigned clusters. The algorithm revealed four best distinct HRS subgroups based on patient characteristics. Cluster 1 patients (n = 1617) were older, and more likely to have non-alcoholic fatty liver disease, cardiovascular comorbidities, hypertension, and diabetes. Cluster 2 patients (n = 1577) were younger and more likely to have hepatitis C, and less likely to have acute liver failure. Cluster 3 patients (n = 642) were younger, and more likely to have non-elective admission, acetaminophen overdose, acute liver failure, to develop in-hospital medical complications and organ system failure, and to require supporting therapies, including renal replacement therapy, and mechanical ventilation. Cluster 4 patients (n = 1728) were younger, and more likely to have alcoholic cirrhosis and to smoke. Thirty-three percent of patients died in hospital. In-hospital mortality was higher in cluster 1 (OR 1.53; 95% CI 1.31-1.79) and cluster 3 (OR 7.03; 95% CI 5.73-8.62), compared to cluster 2, while cluster 4 had comparable in-hospital mortality (OR 1.13; 95% CI 0.97-1.32). Consensus clustering analysis provides the pattern of clinical characteristics and clinically distinct HRS phenotypes with different outcomes.

N6-methyladenine regulator-mediated RNA methylation modification patterns in immune microenvironment regulation of osteoarthritis.

Background: Osteoarthritis is a common chronic degenerative disease, and recently, an increasing number of studies have shown that immunity plays an important role in the progression of osteoarthritis, which is exacerbated by local inflammation. The role of N6-methyladenine (m6A) modification in immunity is being explored. However, the role of m6A modification in regulating the immune microenvironment of osteoarthritis remains unknown. In this study, we sought to discuss the association between the N6-methyladenine (m6A) modification and the immune microenvironment of osteoarthritis. Methods: First, the data and gene expression profiles of 139 samples, including 33 healthy samples and 106 osteoarthritis samples, were obtained from the Genetics osteoARthritis and Progression (GARP) study. Then the differences in m6A regulators between healthy individuals and osteoarthritis patients were analyzed. The correlation between m6A regulators and immune characteristics was also investigated by single-sample gene set enrichment analysis (ssGSEA). Principal component analysis (PCA), Gene Set Variation Analysis (GSVA) enrichment analysis, weighted gene coexpression network analysis (WGCNA), and Associated R packages were used to identify the m6A phenotype and its biological functions. Results: A total of 23 m6A regulators were involved in this study. We found a close correlation between most m6A regulators in all samples as well as in osteoarthritis samples. VIRMA and LRPPRC were the most highly correlated m6A regulators and showed a positive correlation, whereas VIRMA and RBM15B were the most negatively correlated. M6A regulators are associated with osteoarthritis immune characteristics. For example, MDSC cell abundance was strongly correlated with RBM15B and HNRNPC. Meanwhile, RBM15B and HNRNPC were important effectors of natural killer cell immune responses. IGFBP3 is an important regulator of cytolytic activity immune function. We performed an unsupervised consensus cluster analysis of the osteoarthritis samples based on the expression of 23 m6A regulators. Three different m6A subtypes of osteoarthritis were identified, including 27 samples in subtype C1, 21 samples in subtype C2, and 58 samples in subtype C3. Different m6A subtypes have unique biological pathways and play different roles in the immune microenvironment of osteoarthritis. Conclusion: The m6A modification plays a crucial role in the diversity and complexity of the immune microenvironment in osteoarthritis.

Identification of tumor antigens and immune landscapes for bladder urothelial carcinoma mRNA vaccine.

Bladder urothelial carcinoma (BLCA) is associated with high mortality and recurrence. Although mRNA-based vaccines are promising treatment strategies for combating multiple solid cancers, their efficacy against BLCA remains unclear. We aimed to identify potential effective antigens of BLCA for the development of mRNA-based vaccines and screen for immune clusters to select appropriate candidates for vaccination. Gene expression microarray data and clinical information were retrieved from The Cancer Genome Atlas and GSE32894, respectively. The mRNA splicing patterns were obtained from the SpliceSeq portal. The cBioPortal for Cancer Genomics was used to visualize genetic alteration profiles. Furthermore, nonsense-mediated mRNA decay (NMD) analysis, correlation analysis, consensus clustering analysis, immune cell infiltration analysis, and weighted co-expression network analysis were conducted. Six upregulated and mutated tumor antigens related to NMD, and infiltration of APCs were identified in patients with BLCA, including HP1BP3, OSBPL9, SSH3, ZCCHC8, FANCI, and EIF4A2. The patients were subdivided into two immune clusters (IC1 and IC2) with distinct clinical, cellular and molecular features. Patients in IC1 represented immunologically 'hot' phenotypes, whereas those in IC2 represented immunologically 'cold' phenotypes. Moreover, the survival rate was better in IC2 than in IC1, and the immune landscape of BLCA indicated significant inter-patient heterogeneity. Finally, CALD1, TGFB3, and ANXA6 were identified as key genes of BLCA through WGCNA analysis, and their mRNA expression levels were measured using qRT-PCR. HP1BP3, OSBPL9, SSH3, ZCCHC8, FANCI, and EIF4A2 were identified as potential antigens for developing mRNA-based vaccines against BLCA, and patients in IC2 might benefit more from vaccination.

Construction and Verification of the Molecular Subtype and a Novel Prognostic Signature Based on Inflammatory Response-Related Genes in Uveal Melanoma.

The significance of inflammation in tumorigenesis and progression has become prominent. This study aimed to construct and validate the molecular subtype and a novel prognostic signature based on inflammatory response-related genes in uveal melanoma (UM). Patients from the TCGA, GSE84976, and GSE22138 UM cohorts were enrolled. According to the consensus cluster analysis, patients were divided into two molecular subtypes, namely IC1 and IC2. Survival curves showed that patients in IC1 had a better prognosis. The IC2 subgroup had higher levels of immune cell infiltration and more enriched immunological pathways. There were statistical differences in the immune-inflammation microenvironment, immune checkpoint genes expression, and drug sensitivity. The prognostic signature constructed based on inflammatory response-related genes exhibited a stable predictive power. Multivariate analysis confirmed that the signature was a prognostic factor independent of clinical characteristics. Functional analyses showed that the high-risk group was associated with immunological response, inflammatory cell activation, and tumor-related signal pathways. The riskscore had a negative relationship with tumor purity and was positively correlated with immune and stromal scores. Furthermore, the prognostic signature could sensitively predict the response to drug treatments. In conclusion, the prognostic signature might aid in stratifying patients at risk premised on the prognosis and immunotherapy sensitivity.

Characteristics of immune clusters and cell abundance in patients with different subtypes of nonparoxysmal atrial fibrillation

Atrial fibrillation (AF) is the most common sustained arrhythmia in clinical practice. Inflammation plays an important role in the initiation and perpetuation of AF. The present study was conducted to characterize immune clusters in nonparoxysmal AF and to distinguish immune subtypes of nonparoxysmal AF. Immune-related algorithms (CIBERSORT, ESTIMATE, and ssGSEA) were used to evaluate the immune cluster characterization and cell abundance, and multivariable logistics analysis was performed to determine the most relevant immune cells. We identified differentially expressed genes (DEGs) and used consensus clustering analysis to identify nonparoxysmal AF subtypes. Weighted gene coexpression network analysis (WGCNA) was used for finding highly correlated gene sets and attach to external sample traits. And it was conducted twice to identify the immune- and subtype- related modules. Finally, Metascape was used to compare the biological functions of the two nonparoxysmal AF subtypes we obtained. CytoHubba was used to identify the hub genes of these two subtypes. Based on the results of bioinformatics analysis, regulatory T cells, resting NK cells, active mast cells and neutrophils were considered to be closely related to nonparoxysmal AF. The brown module was identified as the most relevant module to the above immune cells by WGCNA. We identified two major nonparoxysmal AF subtypes by consensus clustering analysis and their enriched biological functions by Metascape. The hub genes are TYROBP, PTPRC, ITGB2, SPI1, PLEK, and CSF1R in permanent AF and JAM3, S100P, ARPC5, TRIM34, and GREB1L in persistent AF. This study revealed two major nonparoxysmal AF subtypes and eleven hub genes, which provide potential therapeutic targets for anti-inflammatory treatments of nonparoxysmal AF.