Construction of a prognostic model for breast cancer based on moonlighting genes.

Breast cancer (BRCA) is a highly heterogeneous disease, with significant differences in prognosis among patients. Existing biomarkers and prognostic models have limited ability to predict BRCA prognosis. Moonlighting genes regulate tumor progression and are associated with cancer prognosis. This study aimed to construct a moonlighting gene-based prognostic model for BRCA. We obtained differentially expressed genes (DEGs) in BRCA from The Cancer Genome Atlas and intersected them with moonlighting genes from MoonProt to acquire differential moonlighting genes. GO and KEGG results showed main enrichment of these genes in the response of BRCA cells to environmental stimuli and pentose phosphate pathway. Based on moonlighting genes, we conducted drug prediction and validated results through cellular experiments. After ABCB1 knockdown, viability and proliferation of BRCA cells were significantly enhanced. Based on differential moonlighting genes, BRCA was divided into three subgroups, among which cluster2 had the highest survival rate and immunophenoscore and relatively low tumor mutation burden. TP53 had the highest mutation frequency in cluster2 and cluster3, while PIK3CA had a higher mutation frequency in cluster1, with the majority being missense mutations. Subsequently, we established an 11-gene prognostic model in the training set based on DEGs among subgroups using univariate Cox regression, LASSO regression, and multivariable Cox regression analyses. Model prognostic performance was verified in GEO, METABRIC and ICGC validation sets. In summary, this study obtained three BRCA moonlighting gene-related subtypes and constructed an 11-gene prognostic model. The 11-gene BRCA prognostic model has good predictive performance, guiding BRCA prognosis for clinical doctors.

Subtyping and prognostic model construction based on vesicle-mediated transport-related genes in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is impacted by various environmental and genetic variables. Dysregulation of vesicle-mediated transport-related genes (VMTRGs) has been observed in many malignancies, but their effect on prognosis in CRC remains unclear. METHODS: CRC samples were clustered into varying subtypes per differential expression of VMTRGs. R package was utilized to explore differences in survival, immune, and drug sensitivity among different disease subtypes. According to differentially expressed genes (DEGs) between subtypes, regression analysis was employed to build a riskscore model and identify independent prognostic factors. The model was validated through a Gene Expression Omnibus (GEO) dataset. Immune landscape, immunophenoscore (IPS), and Tumor Immune Dysfunction and Exclusion (TIDE) scores for different risk groups were calculated. RESULTS: Two subtypes of CRC were identified based on VMTRGs, which showed significant differences in survival rates, immune cell infiltration abundance, immune functional activation levels, and immune checkpoint expression levels. Cluster2 exhibited higher sensitivity to anti-tumor drugs such as Nilotinib, Cisplatin, and Oxaliplatin compared to Cluster1. DEGs were mainly enriched in biological processes such as epidermis development, epidermal cell differentiation, and receptor-ligand activity, and signaling pathways like pancreatic secretion. The constructed 13-gene riskscore model demonstrated good predictive ability for CRC patients' prognosis. Furthermore, differences in immune landscape, IPS, and TIDE scores were observed among different risk groups. CONCLUSION: This study successfully obtained two CRC subtypes with distinct survival statuses and immune levels based on differential expression of VMTRGs. A 13-gene risk model was constructed. The findings had important implications for prognosis and treatment of CRC.

Development of a breast cancer prognostic model based on vesicle-mediated transport-related genes to predict immune landscape and clinical drug therapy.

BACKGROUND: Vesicle-mediated transport, vital for substance exchange and intercellular communication, is linked to tumor initiation and progression. This work was designed to study the role of vesicle-mediated transport-related genes (VMTRGs) in breast cancer (BC)prognosis. METHODS: Univariate Cox analysis was utilized to screen prognosis-related VMTRGs. BC samples underwent unsupervised clustering based on VMTRGs to analyze survival, clinical factors, and immune cell abundance across different subtypes. We constructed a risk model using univariate Cox and LASSO regression analysis, with validation conducted using GEO datasets. Subsequently, we performed tumor mutational burden analysis, and immune landscape analysis on both groups. Ultimately, we conducted immunophenoscore (IPS) scoring to forecast immunotherapy and performed drug sensitivity analysis. RESULTS: We identified 102 VMTRGs associated with BC prognosis. Using these 102 VMTRGs, BC patients were classified into 3 subtypes, with Cluster3 patients showing significantly better survival rates. We constructed a prognostic model for BC based on 12 VMTRGs that effectively predicted patient survival. Riskscore was an independent prognostic factor for BC patients. According to median risk score, high-risk group (HRG) had higher TMB values. The immune landscape of the HRG exhibited characteristics of cold tumor, with higher immune checkpoint expression levels and lower IPS scores, whereas Gemcitabine, Nilotinib, and Oxaliplatin were more suitable for treating low-risk group. CONCLUSION: We classified BC subtypes and built a prognostic model based on VMTRGs. The genes in the prognostic model may serve as potential targets for BC therapy.

EPHB2 as a key mediator of glioma progression: Insights from microenvironmental receptor ligand-related prognostic gene signature.

Malignant gliomas, characterized by pronounced heterogeneity, a complex microenvironment, and a propensity for relapse and drug resistaniguree, pose significant challenges in oncology. This study aimed to investigate the prognostic value of Ligand and Receptor related genes (LRRGs) within the glioma microenvironment. An intersection of 71 ligand-related genes (LRGs) and 2628 receptor-related genes (RRGs) yielded a total of 69 LRRGs. Utilizing the least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic RiskScore model comprising 28 LRRGs was constructed. The model demonstrated robust prognostic value, further validated in the TCGA-GBMLGG dataset. Subsequent analyses included differential gene expression, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment (GSEA), and gene set variation (GSVA) within RiskScore groups. Additionally, evaluations of PPI, mRNA-RBP, mRNA-TF, and mRNA-drug interaction networks were conducted. Four hub genes were identified through differential expression analysis of the 28 LRRGs across various GSE datasets. A multivariate Cox prognostic model was constructed for nomogram analysis, gene mutation analysis, and related expression distribution. This study underscores the role of LRRGs in intercellular communication within the glioma microenvironment and identifies four hub genes crucial for prognostic assessment in clinical glioma patients. These findings offer a potential evaluation framework for glioma patients, enhancing our understanding of the disease and informing future therapeutic strategies.

Identification of hypoxia-related gene signatures based on multi-omics analysis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and one of the malignancies with the highest incidence rate and mortality worldwide. Hypoxia is a typical feature of tumour microenvironment (TME), which affects the progression of LUAD from multiple molecular levels. However, the underlying molecular mechanisms behind LUAD hypoxia are not fully understood. In this study, we estimated the level of hypoxia by calculating a score based on 15 hypoxia genes. The hypoxia scores were relatively high in LUAD patients with poor prognosis and were bound up with tumour node metastasis (TNM) stage, tumour size, lymph node, age and gender. By comparison of high hypoxia score group and low hypoxia score group, 1820 differentially expressed genes were identified, among which up-regulated genes were mainly about cell division and proliferation while down-regulated genes were primarily involved in cilium-related biological processes. Besides, LUAD patients with high hypoxia scores had higher frequencies of gene mutations, among which TP53, TTN and MUC16 had the highest mutation rates. As for DNA methylation, 1015 differentially methylated probes-related genes were found and may play potential roles in tumour-related neurobiological processes and cell signal transduction. Finally, a prognostic model with 25 multi-omics features was constructed and showed good predictive performance. The area under curve (AUC) values of 1-, 3- and 5-year survival reached 0.863, 0.826 and 0.846, respectively. Above all, our findings are helpful in understanding the impact and molecular mechanisms of hypoxia in LUAD.

Identification of PPAR-related differentially expressed genes liver hepatocellular carcinoma and construction of a prognostic model based on data analysis and molecular docking.

Liver hepatocellular carcinoma (LIHC) is a significant global health issue with limited treatment options. In this study, single-cell RNA sequencing (scRNA-seq) data were used to explore the molecular mechanisms of LIHC development and identify potential targets for therapy. The expression of peroxisome proliferator-activated receptors (PPAR)-related genes was analysed in LIHC samples, and primary cell populations, including natural killer cells, T cells, B cells, myeloid cells, endothelial cells, fibroblasts and hepatocytes, were identified. Analysis of the differentially expressed genes (DEGs) between normal and tumour tissues revealed significant changes in gene expression in various cell populations. PPAR activity was evaluated using the 'AUCell' R software, which indicated higher scores in the normal versus the malignant hepatocytes. Furthermore, the DEGs showed significant enrichment of pathways related to lipid and glucose metabolism, cell development, differentiation and inflammation. A prognostic model was then constructed using 8 PPARs-related genes, including FABP5, LPL, ACAA1, PPARD, FABP4, PLIN1, HMGCS2 and CYP7A1, identified using least absolute shrinkage and selection operator-Cox regression analysis, and validated in the TCGA-LIHC, ICGI-LIRI and GSE14520 datasets. Patients with low-risk scores had better prognosis in all cohorts. Based on the expression of the eight model genes, two clusters of patients were identified by ConsensusCluster analysis. We also predicted small-molecule drugs targeting the model genes, and identified perfluorohexanesulfonic acid, triflumizole and perfluorononanoic acid as potential candidates. Finally, wound healing assay confirmed that PPARD can promote the migration of liver cancer cells. Overall, our study offers novel perspectives on the molecular mechanisms of LIHC and potential areas for therapeutic intervention, which may facilitate the development of more effective treatment regimens.

Colorectal cancer subtyping and prognostic model construction based on interleukin-related genes.

Members of the interleukin (IL) family are closely linked to cancer development and progression. However, research on the prognosis of colorectal cancer (CRC) related to IL is still lacking. This study investigated new CRC prognostic markers and offered new insights for CRC prognosis and treatment. CRC-related data and IL gene data were collected from public databases. Sample clustering was done with the NMF package to divide samples into different subtypes. Differential, enrichment, survival, and immune analyses were conducted on subtypes. A prognostic model was constructed using regression analysis. Drug sensitivity analysis was performed using GDSC database. Western blot analysis was performed to assess the effect of IL-7 on the JAK/STAT signaling pathway. Flow cytometry was used to examine the impact of IL-7 on CD8+ T cell apoptosis. Two CRC subtypes based on IL-associated genes were obtained. Cluster 1 had a higher survival rate than cluster 2, and they showed differences in some immune levels. The two clusters were mainly enriched in the JAK-STAT signaling pathway, T helper 17 cell differentiation, and the IL-17 signaling pathway. An 11-gene signature was built, and risk score was an independent prognosticator for CRC. The low-risk group showed a higher sensitivity to nine common targeted anticancer drugs. Western blot and flow cytometry results demonstrated that IL-7 could phosphorylate STAT5 and promote survival of CD8+ T cells. In conclusion, this study divided CRC samples into two IL-associated subtypes and obtained an 11-gene signature. In addition, targeted drugs that may improve the prognosis of patients with CRC were identified. These findings are of paramount importance for patient prognosis and CRC treatment.NEW & NOTEWORTHY We identified two clusters with significant survival differences in colorectal cancer (CRC) based on interleukin-related genes, constructed an 11-gene risk score model that can independently predict the prognosis of CRC, and explored some targeted drugs that may improve the prognosis of patients with CRC. The results of this study have important implications for the prognosis and treatment of CRC.

From bioinformatics to clinical applications: a novel prognostic model of cuproptosis-related genes based on single-cell RNA sequencing data in hepatocellular carcinoma.

OBJECTIVE AND METHODS: To ascertain the connection between cuproptosis-related genes (CRGs) and the prognosis of hepatocellular carcinoma (HCC) via single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data, relevant data were downloaded from the GEO and TCGA databases. The differentially expressed CRGs (DE-CRGs) were filtered by the overlaps in differentially expressed genes (DEGs) between HCC patients and normal controls (NCs) in the scRNA-seq database, DE-CRGs between high- and low-CRG-activity cells, and DEGs between HCC patients and NCs in the TCGA database. RESULTS: Thirty-three DE-CRGs in HCC were identified. A prognostic model (PM) was created employing six survival-related genes (SRGs) (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) via univariate Cox regression analysis and LASSO. The predictive ability of the model was validated via a nomogram and receiver operating characteristic curves. Research has employed tumor immune dysfunction and exclusion as a means to examine the influence of PM on immunological heterogeneity. Macrophage M0 levels were significantly different between the high-risk group (HRG) and the low-risk group (LRG), and a greater macrophage level was linked to a more unfavorable prognosis. The drug sensitivity data indicated a substantial difference in the half-maximal drug-suppressive concentrations of idarubicin and rapamycin between the HRG and the LRG. The model was verified by employing public datasets and our cohort at both the protein and mRNA levels. CONCLUSION: A PM using 6 SRGs (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) was developed via bioinformatics research. This model might provide a fresh perspective for assessing and managing HCC.

Utilizing a novel model of PANoptosis-related genes for enhanced prognosis and immune status prediction in kidney renal clear cell carcinoma.

Kidney renal clear cell carcinoma (KIRC) is the most common histopathologic type of renal cell carcinoma. PANoptosis, a cell death pathway that involves an interplay between pyroptosis, apoptosis and necroptosis, is associated with cancer immunity and development. However, the prognostic significance of PANoptosis in KIRC remains unclear. RNA-sequencing expression and mutational profiles from 532 KIRC samples and 72 normal samples with sufficient clinical data were retrieved from the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using differentially expressed genes (DEGs) related to PANoptosis in the TCGA cohort and was validated in a Gene Expression Omnibus (GEO) cohorts. Incorporating various clinical features, the risk model remained an independent prognostic factor in multivariate analysis, and it demonstrated superior performance compared to unsupervised clustering of the 21 PANoptosis-related genes alone. Further mutational analysis showed fewer VHL and more BAP1 alterations in the high-risk group, with alterations in both genes also associated with patient prognosis. The high-risk group was characterized by an unfavorable immune microenvironment, marked by reduced levels of CD4 + T cells and natural killer cells, but increased M2 macrophages and regulatory T cells. Finally, the risk model was predictive of response to immune checkpoint blockade, as well as sensitivity to sunitinib and paclitaxel. The PANoptosis-related risk model developed in this study enables accurate prognostic prediction in KIRC patients. Its associations with the tumor immune microenvironment and drug efficacy may offer potential therapeutic targets and inform clinical decisions.

Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma.

Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging studies demonstrated NETs contributed to cancer progression and metastases in multiple ways. This study intends to provide a prognostic NETs signature and therapeutic target for lung adenocarcinoma (LUAD) patients. Consensus cluster analysis performed by 38 reported NET-related genes in TCGA-LUAD cohorts. Then, WGCNA network was conducted to investigate characteristics genes in clusters. Seven machine learning algorithms were assessed for training of the model, the optimal model was picked by C-index and 1-, 3-, 5-year ROC value. Then, we constructed a NETs signature to predict the overall survival of LUAD patients. Moreover, multi-omics validation was performed based on NETs signature. Finally, we constructed stable knockdown critical gene LUAD cell lines to verify biological functions of Phospholipid Scramblase 1 (PLSCR1) in vitro and in vivo. Two NETs-related clusters were identified in LUAD patients. Among them, C2 cluster was provided as "hot" tumor phenotype and exhibited a better prognosis. Then, WGCNA network identified 643 characteristic genes in C2 cluster. Then, Coxboost algorithm proved its optimal performance and provided a prognostic NETs signature. Multi-omics revealed that NETs signature was involved in an immunosuppressive microenvironment and predicted immunotherapy efficacy. In vitro and in vivo experiments demonstrated that knockdown of PLSCR1 inhibited tumor growth and EMT ability. Besides, cocultural assay indicated that the knockdown of PLSCR1 impaired the ability of neutrophils to generate NETs. Finally, tissue microarray (TMA) for LUAD patients verified the prognostic value of PLSCR1 expression. In this study, we focus on emerging hot topic NETs in LUAD. We provide a prognostic NETs signature and identify PLSCR1 with multiple roles in LUAD. This work can contribute to risk stratification and screen novel therapeutic targets for LUAD patients.

Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data.

BACKGROUND: Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses the synthesis of DNA and RNA, is a pivotal cellular biochemical process that significantly impacts both the progression and therapeutic strategies of colorectal cancer METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed to calculate scores related to nucleotide metabolism. Cell developmental trajectory analysis and intercellular interaction analysis were utilized to explore the metabolic characteristics and communication patterns of different epithelial cells. These findings were further validated using spatial transcriptome RNA sequencing (stRNA-seq). A risk model was constructed using expression profile data from TCGA and GEO cohorts to optimize clinical decision-making. Key nucleotide metabolism-related genes (NMRGs) were functionally validated by further in vitro experiments. RESULTS: In both scRNA-seq and stRNA-seq, colorectal cancer (CRC) exhibited unique cellular heterogeneity, with myeloid cells and epithelial cells in tumor samples displaying higher nucleotide metabolism scores. Analysis of intercellular communication revealed enhanced signaling pathways and ligand-receptor interactions between epithelial cells with high nucleotide metabolism and fibroblasts. Spatial transcriptome sequencing confirmed elevated nucleotide metabolism states in the core region of tumor tissue. After identifying differentially expressed NMRGs in epithelial cells, a risk prognostic model based on four genes effectively predicted overall survival and immunotherapy outcomes in patients. High-risk group patients exhibited an immunosuppressive microenvironment and relatively poorer prognosis and responses to chemotherapy and immunotherapy. Finally, based on data analysis and a series of cellular functional experiments, ACOX1 and CPT2 were identified as novel therapeutic targets for CRC. CONCLUSION: In this study, a comprehensive analysis of NMRGs in CRC was conducted using a combination of single-cell sequencing, spatial transcriptome sequencing, and high-throughput data. The prognostic model constructed with NMRGs shows potential as a standalone prognostic marker for colorectal cancer patients and may significantly influence the development of personalized treatment approaches for CRC.

Clinical features and prognostic model for viral encephalitis after allogeneic haematopoietic stem cell transplantation.

The objective of this study was to identify independent prognostic factors of viral encephalitis (VE) after allogeneic haematopoietic stem cell transplantation (allo-HSCT) and establish a prognostic model to identify post-transplant VE patients with a greater likelihood of mortality. Among 5380 patients in our centre from 2014 to 2022, 211 patients who developed VE after allo-HSCT were reviewed in this retrospective study. Prognostic factors were selected, and a prognostic model was constructed using Cox regression analysis. The model was subsequently validated and estimated using the area under the receiver operating characteristic curve (AUC), a calibration plot and decision curve analysis (DCA). Glasgow Coma Scale score <9, lesions >3 lobes on magnetic resonance imaging and severe thrombocytopenia were identified as independent prognostic risk factors for VE patients who underwent allo-HSCT. The prognostic model GTM (GTM is an abbreviation for a model composed of three risk factors: GCS score <9, severe thrombocytopenia [platelet count <20 000 per microliter], and lesions >3 lobes on MRI) was established according to the regression coefficients. The validated internal AUC was 0.862 (95% confidence interval [CI], 0.773-0.950), and the external AUC was 0.815 (95% CI, 0.708-0.922), indicating strong discriminatory ability. Furthermore, we constructed calibration plots that demonstrated good consistency between the predicted outcomes and the observed outcomes. DCA exhibited high accuracy in this system, leading to potential benefits for patients.

Development of a Prognostic Model for Early Breast Cancer Integrating Neutrophil to Lymphocyte Ratio and Clinical-Pathological Characteristics.

BACKGROUND: Breast cancer-related inflammation is critical in tumorigenesis, cancer progression, and patient prognosis. Several inflammatory markers derived from peripheral blood cells count, such as the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), and systemic immune-inflammation index (SII) are considered as prognostic markers in several types of malignancy. METHODS: We investigate and validate a prognostic model in early patients with breast cancer to predict disease-free survival (DFS) based on readily available baseline clinicopathological prognostic factors and preoperative peripheral blood-derived indexes. RESULTS: We analyzed a training cohort of 710 patients and 2 external validation cohorts of 980 and 157 patients with breast cancer, respectively, with different demographic origins. An elevated preoperative NLR is a better DFS predictor than others scores. The prognostic model generated in this study was able to classify patients into 3 groups with different risks of relapse based on ECOG-PS, presence of comorbidities, T and N stage, PgR status, and NLR. CONCLUSION: Prognostic models derived from the combination of clinicopathological features and peripheral blood indices, such as NLR, represent attractive markers mainly because they are easily detectable and applicable in daily clinical practice. More comprehensive prospective studies are needed to unveil their actual effectiveness.

Identification of neutrophil-related genes and development of a prognostic model for cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma is a prevalent gastrointestinal tumor with limited effective early diagnostic methods. The role of neutrophils in the context of cholangiocarcinoma remains largely unexplored. METHODS: A comprehensive analysis was performed on a cohort of cholangiocarcinoma samples (TCGA-CHOL) from the TCGA database to investigate the relationship between cholangiocarcinoma and neutrophils. Methodologies included single-sample gene set enrichment analysis (ssGSEA), differential expression analysis, weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA). RESULTS: The study identified a significant decrease of neutrophils in cholangiocarcinoma via ssGSEA. WGCNA and differential expression analysis led to the identification of a neutrophil-related gene module comprised of 1059 genes. Cluster 1, showing a higher proportion of neutrophils, was linked to better survival outcomes. GSEA disclosed downregulation of complement, inflammatory response and interferon response pathways in Cluster 2, hinting at possible cholangiocarcinoma development triggers. A notable upregulation of PD1, PD-L1 and CTLA4 was observed in Cluster 1, suggesting potential benefits from immunotherapy. A prognostic model was developed based on clinical data and expression levels of three prognostic genes (SOWAHD, TNFAIP8 and EBF3) showing satisfactory discrimination, calibration and clinical benefits. An overexpression of TNFAIP8 in cholangiocarcinoma cells was found, with its knockdown significantly inhibiting cell proliferation and migration. CONCLUSIONS: This study elucidates a neutrophil-related gene module and prognostic genes, offering insights into the role of neutrophils in cholangiocarcinoma development and progression. It also introduces a clinical prediction model for enhanced prognosis assessment. These findings may lay the groundwork for the development of innovative therapeutic strategies in cholangiocarcinoma treatment.

An m1A/m6A/m5C-associated long non-coding RNA signature: Prognostic and immunotherapeutic insights into cervical cancer.

BACKGROUND: Cervical cancer (CC) remains a significant clinical challenge, even though its fatality rate has been declining in recent years. Particularly in developing countries, the prognosis for CC patients continues to be suboptimal despite numerous therapeutic advances. METHODS: Using The Cancer Genome Atlas database, we extracted CC-related data. From this, 52 methylation-related genes (MRGs) were identified, leading to the selection of a 10 long non-coding RNA (lncRNA) signature co-expressed with these MRGs. R programming was employed to filter out the methylation-associated lncRNAs. Through univariate, least absolute shrinkage and selection operator (i.e. LASSO) and multivariate Cox regression analysis, an MRG-associated lncRNA model was constructed. The established risk model was further assessed via the Kaplan-Meier method, principal component analysis, functional enrichment annotation and a nomogram. Furthermore, we explored the potential of this model with respect to guiding immune therapeutic interventions and predicting drug sensitivities. RESULTS: The derived 10-lncRNA signature, linked with MRGs, emerged as an independent prognostic factor. Segmenting patients based on their immunotherapy responses allowed for enhanced differentiation between patient subsets. Lastly, we highlighted potential compounds for distinguishing CC subtypes. CONCLUSIONS: The risk model, associated with MRG-linked lncRNA, holds promise in forecasting clinical outcomes and gauging the efficacy of immunotherapies for CC patients.

Unveiling the immunogenomic landscape of cholangiocarcinoma: Identifying new prognostic markers and therapeutic targets based on CCL5 expression.

BACKGROUND: Cholangiocarcinoma (CCA) stands as an aggressive malignancy of the biliary tract. The interplay between the tumor and immune system plays a pivotal role in disease progression and treatment outcomes. Hence, the present study aimed to extensively explore the immunogenomic landscape of CCA, with the objective of unveiling unique molecular and immunological signatures that could guide personalized therapeutic approaches. METHODS: The study collected data from The Cancer Genome Atlas databases, performed gene set variation analysis for the chemokine ligand 5 (CCL5) high/low expression group, conducted principal component analysis, gene set enrichment analysis enrichment and mutation pattern analysis, generated a heatmap, and performed cox regression analysis. RESULTS: The two discrete subpopulations were found to exhibit contrasting mutational and immunogenomic characteristics, emphasizing the heterogeneity of CCA. These subsets also showed pronounced discrepancies in the infiltration of immune cells, indicating diverse interactions with the tumor immune microenvironment. Furthermore, the dissimilarities in mutational patterns were observed within the two CCA subgroups, with PBRM1 and BAP1 emerging as the most frequently mutated genes. In addition, a prognostic framework was formulated and validated utilizing the expression profiles of COX16 and RSAD2 genes, effectively segregating patients into high-risk and low-risk cohorts. Furthermore, the connections between immune-related parameters and these risk groups were identified, underscoring the potential significance of the immune microenvironment in patient prognosis. In vitro experiments have shown that COX16 promotes the proliferation and metastasis of CCA cells, whereas RSAD2 inhibits it. CONCLUSIONS: The present study provides an intricate depiction of the immunogenomic landscape of CCA based on CCL5 expression, thereby paving the way for novel immunotherapy strategies and prognostic assessment.

Multiomics blueprint of PANoptosis in deciphering immune characteristics and prognosis stratification of glioma patients.

BACKGROUND: As the most prevalent primary brain tumor in adults, glioma accounts for the majority of all central nervous system malignant tumors. The concept of PANoptosis is a relatively new, underlining the interconnection and synergy among three distinct pathways: pyroptosis, apoptosis and necroptosis. METHODS: We performed single-cell annotations of glioma cells and determined crucial signaling pathways through cell chat analysis. Using least absolute shrinkage and selection operator (LASSO) and Cox analyses, we identified a gene set with prognostic values. Our model was validated using independent external cohort. In addition, we employed single-sample gene set enrichment analysis and xCell analyses to describe the detailed profile of infiltrated immune cells and depicted the gene mutation landscape in the two groups. RESULTS: We identified seven distinct cell clusters in glioma samples, including oligodendrocyte precursor cells (OPCs), myeloid cells, tumor cells, oligodendrocytes, astrocytes, vascular cells and neuronal cells. We found that myeloid cells showed the highest PANoptosis activity. An intense mutual cell communication pattern between the tumor cells and OPCs and oligodendrocytes was observed. Differentially expressed genes between the high-PANoptosis and low-PANoptosis cell groups were obtained, which were enriched to actin cytoskeleton, cell adhesion molecules and gamma R-mediated phagocytosis pathways. We determined a set of five genes of prognostic significance: SAA1, SLPI, DCX, S100A8 and TNR. The prognostic differences between the two groups in the internal and external sets were found to be statistically significant. We found a marked correlation between S100A8 and activated dendritic cell, macrophage, mast cell, myeloid derived suppressor cell and Treg infiltration. Moreover, we have observed a significant increase of PTEN mutation in the high risk (HR) group of glioma patients. CONCLUSIONS: In the present study, we have constructed a prognostic model that is based on the PANoptosis, and we have demonstrated its significant efficacy in stratifying patients with glioma. This innovative prognostic model offers novel insights into precision immune treatments that could be used to combat this disease and improve patient outcomes, thereby providing a new avenue for personalized treatment options.

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.

Bioinformatics-based analysis of programmed cell death pathway and key prognostic genes in gastric cancer: Implications for the development of therapeutics.

BACKGROUND: Gastric cancer (GC) represents a major global health burden as a result of its high incidence and poor prognosis. The present study examined the role of the programmed cell death (PCD) pathway and identified key genes influencing the prognosis of patients with GC. METHODS: Bioinformatics analysis, machine learning techniques and survival analysis were systematically integrated to identify core prognostic genes from the The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset. A prognostic model was then developed to stratify patients into high-risk and low-risk groups, and further validated in the GSE84437 dataset. The model also demonstrated clinical relevance with tumor staging and histopathology. Immune infiltration analysis and the potential benefits of immunotherapy for each risk group were assessed. Finally, subgroup analysis was performed based on the expression of three key prognostic genes. RESULTS: Three core prognostic genes (CAV1, MMP9 and MAGEA3) were identified. The prognostic model could effectively differentiate patients into high-risk and low-risk groups, leading to significantly distinct survival outcomes. Increased immune cell infiltration was observed in the high-risk group, and better potential for immunotherapy outcomes was observed in the low-risk group. Pathways related to cancer progression, such as epithelial-mesenchymal transition and tumor necrosis factor-α signaling via nuclear factor-kappa B, were enriched in the high-risk group. By contrast, the low-risk group showed a number of pathways associated with maintenance of cell functionality and immune responses. The two groups differed in gene mutation patterns and drug sensitivities. Subgroup analysis based on the expression of the three key genes revealed two distinct clusters with distinct survival outcomes, tumor immune microenvironment characteristics and pathway enrichment. CONCLUSIONS: The present study offers novel insights into the significance of PCD pathways and identifies key genes associated with the prognosis of patients with GC. This robust prognostic model, along with the delineation of distinct risk groups and molecular subtypes, provides valuable tools for risk stratification, treatment selection and personalized therapeutic interventions for GC.

Machine learning reveals PANoptosis as a potential reporter and prognostic revealer of tumour microenvironment in lung adenocarcinoma.

Lung adenocarcinoma (LUAD), a prominent lung cancer subtype, has an underexplored relationship with PANoptosis, a recently discovered mode of tumour cell death. This study incorporated iron death, copper death, scorch death, necrotizing apoptosis and bisulfide death into a pan-death gene set (PANoptosis) and conducted single-cell analysis of scRNA-seq data from 11 LUAD samples. Differentially expressed genes were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. Univariate COX regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen PANoptosis key genes for constructing an LUAD risk model. The model's prognostic performance was evaluated using survival curves, risk scores and validation in the Gene Expression Omnibus database. The study also explored the correlation between risk scores, tumour biological function, immunotherapy, drug sensitivity and immune infiltration. The SMS gene in the PANoptosis model was silenced in two LUAD cell lines for cellular validation. Single-cell analysis revealed eight major cell types and several PANoptosis genes significantly associated with LUAD survival. The risk model demonstrated strong prognostic performance and association with immune infiltration, suggesting PANoptosis involvement in LUAD tumour immunity. Cellular validation further supported these findings. The PANoptosis key risk genes are believed to be closely related to the tumour microenvironment and immune regulation of LUAD, potentially providing valuable insights for early diagnosis and clinical treatment, and broader applications in other tumours and complex diseases.