Incident heart failure in chronic kidney disease: proteomics informs biology and risk stratification.

BACKGROUND AND AIMS: Incident heart failure (HF) among individuals with chronic kidney disease (CKD) incurs hospitalizations that burden patients and health care systems. There are few preventative therapies, and the Pooled Cohort equations to Prevent Heart Failure (PCP-HF) perform poorly in the setting of CKD. New drug targets and better risk stratification are urgently needed. METHODS: In this analysis of incident HF, SomaScan V4.0 (4638 proteins) was analysed in 2906 participants of the Chronic Renal Insufficiency Cohort (CRIC) with validation in the Atherosclerosis Risk in Communities (ARIC) study. The primary outcome was 14-year incident HF (390 events); secondary outcomes included 4-year HF (183 events), HF with reduced ejection fraction (137 events), and HF with preserved ejection fraction (165 events). Mendelian randomization and Gene Ontology were applied to examine causality and pathways. The performance of novel multi-protein risk models was compared to the PCP-HF risk score. RESULTS: Over 200 proteins were associated with incident HF after adjustment for estimated glomerular filtration rate at P < 1 × 10-5. After adjustment for covariates including N-terminal pro-B-type natriuretic peptide, 17 proteins remained associated at P < 1 × 10-5. Mendelian randomization associations were found for six proteins, of which four are druggable targets: FCG2B, IGFBP3, CAH6, and ASGR1. For the primary outcome, the C-statistic (95% confidence interval [CI]) for the 48-protein model in CRIC was 0.790 (0.735, 0.844) vs. 0.703 (0.644, 0.762) for the PCP-HF model (P = .001). C-statistic (95% CI) for the protein model in ARIC was 0.747 (0.707, 0.787). CONCLUSIONS: Large-scale proteomics reveal novel circulating protein biomarkers and potential mediators of HF in CKD. Proteomic risk models improve upon the PCP-HF risk score in this population.

Integrating bulk and single-cell data to predict the prognosis and identify the immune landscape in HNSCC.

The complex interplay between tumour cells and the tumour microenvironment (TME) underscores the necessity for gaining comprehensive insights into disease progression. This study centres on elucidating the elusive the elusive role of endothelial cells within the TME of head and neck squamous cell carcinoma (HNSCC). Despite their crucial involvement in angiogenesis and vascular function, the mechanistic diversity of endothelial cells among HNSCC patients remains largely uncharted. Leveraging advanced single-cell RNA sequencing (scRNA-Seq) technology and the Scissor algorithm, we aimed to bridge this knowledge gap and illuminate the intricate interplay between endothelial cells and patient prognosis within the context of HNSCC. Here, endothelial cells were categorized into Scissorhigh and Scissorlow subtypes. We identified Scissor+ endothelial cells exhibiting pro-tumorigenic profiles and constructed a prognostic risk model for HNSCC. Additionally, four biomarkers also were identified by analysing the gene expression profiles of patients with HNSCC and a prognostic risk prediction model was constructed based on these genes. Furthermore, the correlations between endothelial cells and prognosis of patients with HNSCC were analysed by integrating bulk and single-cell sequencing data, revealing a close association between SHSS and the overall survival (OS) of HNSCC patients with malignant endothelial cells. Finally, we validated the prognostic model by RT-qPCR and IHC analysis. These findings enhance our comprehension of TME heterogeneity at the single-cell level and provide a prognostic model for HNSCC.

Differential genetic mutations and immune cell infiltration in high- and low-risk STAD: Implications for prognosis and immunotherapy efficacy.

This study investigates genetic mutations and immune cell dynamics in stomach adenocarcinoma (STAD), focusing on identifying prognostic markers and therapeutic targets. Analysis of TCGA-STAD samples revealed C > A as the most common single nucleotide variant (SNV) in both high and low-risk groups. Key mutated driver genes included TTN, TP53 and MUC16, with frame-shift mutations more prevalent in the low-risk group and missense mutations in the high-risk group. Interaction analysis of hub genes such as C1QA and CD68 showed significant correlations, impacting immune cell infiltration patterns. Using ssGSEA, we found higher immune cell infiltration (B cells, CD4+ T cells, CD8+ T cells, DC cells, NK cells) in the high-risk group, correlated with increased risk scores. xCell algorithm results indicated distinct immune infiltration levels between the groups. The study's risk scoring model proved effective in prognosis prediction and immunotherapy efficacy assessment. Key molecules like CD28, CD27 and SLAMF7 correlated significantly with risk scores, suggesting potential targets for high-risk STAD patients. Drug sensitivity analysis showed a negative correlation between risk scores and sensitivity to certain treatments, indicating potential therapeutic options for high-risk STAD patients. We also validated the carcinogenic role of RPL14 in gastric cancer through phenotypic experiments, demonstrating its influence on cancer cell proliferation, invasion and migration. Overall, this research provides crucial insights into the genetic and immune aspects of STAD, highlighting the importance of a risk scoring model for personalized treatment strategies and clinical decision-making in gastric cancer management.

Prognostic and immunotherapeutic implications of bilirubin metabolism-associated genes in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major subtype of non-small-cell lung cancer and accompanies high mortality rates. While the role of bilirubin metabolism in cancer is recognized, its specific impact on LUAD and patient response to immunotherapy needs to be elucidated. This study aimed to develop a prognostic signature of bilirubin metabolism-associated genes (BMAGs) to predict outcomes and efficacy of immunotherapy in LUAD. We analysed gene expression data from The Cancer Genome Atlas (TCGA) to identify survival-related BMAGs and construct a prognostic model in LUAD. The prognostic efficacy of our model was corroborated by employing TCGA-LUAD and five Gene Expression Omnibus datasets, effectively stratifying patients into risk-defined cohorts with marked disparities in survival. The BMAG signature was indeed an independent prognostic determinant, outperforming established clinical parameters. The low-risk group exhibited a more favourable response to immunotherapy, highlighted by increased immune checkpoint expression and immune cell infiltration. Further, somatic mutation profiling differentiated the molecular landscapes of the risk categories. Our screening further identified potential drug candidates preferentially targeting the high-risk group. Our analysis of critical BMAGs showed the tumour-suppressive role of FBP1, highlighting its suppression in LUAD and its inhibitory effects on tumour proliferation, migration and invasion, in addition to its involvement in cell cycle and apoptosis regulation. These findings introduce a potent BMAG-based prognostic indicator and offer valuable insights for prognostication and tailored immunotherapy in LUAD.

Prognostic value of oxidative stress-related genes in colorectal cancer and its correlation with tumor immunity.

Oxidative stress (OS) plays an essential role in chronic diseases such as colorectal cancer (CRC). In this study, we aimed to explore the relation between oxidative stress-related genes and CRC prognosis and their involvement in the immune microenvironment. Totally 101 OS-related genes were selected from the MsigDB database. Then, univariate Cox regression was used to explore the prognostic value of the selected genes correlated with the CRC patient survival in the TCGA database. A total of 9 prognostic OS-related genes in CRC were identified. Based on consensus clustering, CRC patients were then categorized into two molecular subtypes. A prognostic risk model containing 8 genes was established using Lasso regression, and CRC patients were divided into high or low-risk groups based on the median risk scores. The predictive value of the 8 genes in CRC prognosis was validated using ROC curves, which indicate that CTNNB1, STK25, RNF112, SFPQ, MMP3, and NOL3 were promising prognostic biomarkers in CRC. Furthermore, the immune cell infiltration levels in different risk groups or CRC subtypes were analyzed. We found that the high-risk or C1 subtype had immunosuppressive microenvironment, which might explain the unfavorable prognosis in the two groups of CRC patients. Additionally, functional experiments were conducted to investigate the effects of OS-related genes on CRC cell proliferation, stemness, and apoptosis. We found that CTNNB1, HSPB1, MMP3, and NOL3 were upregulated in CRC tissues and cells. Knockdown of CTNNB1, HSPB1, MMP3, and NOL3 significantly suppressed CRC cell proliferation, stemness and facilitated CRC cell apoptosis. In conclusion, we established prognostic CRC subtypes and an eight-gene risk model, which may provide novel prognostic indicators and benefit the design of individualized therapeutic strategies for CRC patients.

Ferroptosis-Related lncRNA to Predict the Clinical Outcomes and Molecular Characteristics of Kidney Renal Papillary Cell Carcinoma.

Kidney renal papillary cell carcinoma (KIRP) is a highly heterogeneous type of kidney cancer, resulting in limited effective prognostic targets for KIRP patients. Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in the regulation of ferroptosis and iron metabolism, making them potential targets for the treatment and prognosis of KIRP. In this study, we constructed a ferroptosis-related lncRNA risk score model (FRM) based on the TCGA-KIRP dataset, which represents a novel subtype of KIRP not previously reported. The model demonstrated promising diagnostic accuracy and holds potential for clinical translation. We observed significant differences in metabolic activities, immune microenvironment, mutation landscape, ferroptosis sensitivity, and drug sensitivity between different risk groups. The high-risk groups exhibit significantly higher fractions of cancer-associated fibroblasts (CAFs), hematopoietic stem cells (HSC), and pericytes. Drugs (IC50) analysis provided a range of medication options based on different FRM typing. Additionally, we employed single-cell transcriptomics to further analyze the impact of immune invasion on the occurrence and development of KIRP. Overall, we have developed an accurate prognostic model based on the expression patterns of ferroptosis-related lncRNAs for KIRP. This model has the potential to contribute to the evaluation of patient prognosis, molecular characteristics, and treatment modalities, and can be further translated into clinical applications.

Ovarian carcinoma immune-related microRNA affects the heterogeneity of the endocrine microenvironment and anti-tumor immune pattern.

BACKGROUND: The eighth-leading cause of cancer-related mortality and the seventh-most prevalent malignancy in women globally is ovarian cancer (OV). However, 5-year survival expectancy after conventional treatment is not good. Therefore, there is an urgent need for novel signatures to guide the designation of therapeutic schemes for OV patients. METHODS: We used univariate Cox analysis to screen hormone secretion regulation axis-related microRNAs (miRNAs), least absolute shrinkage and selection operator analysis to select candidate miRNAs and multivariate Cox analysis to build the risk model. To evaluate possible route and functional differences, enrichment analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the differentially expressed genes (DEGs) across various risk groups. We compared Tumor Immune Dysfunction and Exclusion (TIDE) scores across risk categories by analyzing immune cell infiltration, immune checkpoint gene expression, immunological function and TIDE scores. In the end, we determined the half maximal inhibitory concentration (IC50 ) of chemotherapy and targeted medicines for individual patients. Cell assays were determined to test the migration of the miRNA-target genes and western blotting was used to test the correlation of the miRNA-target genes and the pathways. RESULTS: We finally identified hormone secretion regulation axis-related 13 microRNAs to build a risk model. The validation of observed and anticipated values revealed a fair level of agreement. To evaluate the molecular pathways between various groups in accordance with the GO and KEGG analyses, we then discovered 173 DEGs between distinct risk groups. The risk score was shown to be inversely related to the number of immune cells, including myeloid dendritic, granulocytes, M1 and M2 macrophages, B cells, t-lymphocytes, and CD4+ and CD8+ cells, suggesting that immune cells are more frequent in the low-risk group. Immune cell infiltration investigation yielded these results. Finally, we recognized 11 chemotherapeutic drugs and 30 novels targeted drugs on the basis of IC50 between the different risk groups. GJB5 was determined to be the mir-219 target gene and was identified as promoting the cell cycle process. In addition, hormone secretion regulation axis related miRNAs were reported to affects the heterogeneity of endocrine microenvironment and anti-tumor immune pattern. CONCLUSIONS: In conclusion, a 13-miRNA prognostic model was constructed to know the immune status, prognosis, immunotherapeutic response and anti-tumor drug sensitivity for OV, which provides theoretical guidance for the effective and individualized treatment of OV patients.

A novel model incorporating chromatin regulatory factors for risk stratification, prognosis prediction, and characterization of the microenvironment in Wilms tumor.

BACKGROUND: Wilms tumor, also known as nephroblastoma, a pediatric most-frequent malignant-kidney tumor, may be regulated and influenced by transcriptional and epigenetic mechanisms. Chromatin regulatory factors (CRs) play key roles in epigenetic regulation. The present study aimed to explore the involvement of CRs in the development of nephroblastoma. METHODS: RNA-sequencing and clinical information of nephroblastoma samples were obtained by downloading data from the TARGET database. The Limma package was utilized to perform differential expression analysis of genes (DEGs) between the tumor group and the control group. A Venn map was used for intersection of differential genes and CRs and to perform Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of DEGs using the clusterProfiler package. LASSO and Cox analyses were used to construct CR-related risk models and were evaluated based on clinical parameters. A receiver operating characteristic curve was employed to assess the diagnostic performance of risk model. Furthermore, we used a single-sample gene set enrichment analysis algorithm for immune cell infiltration analysis. Finally, to confirm the transcriptome expression of pivotal genes in human nephroblastoma cell lines, a quantitative real-time PCR was employed. RESULTS: Fifteen key CRs were obtained through analysis in nephroblastoma and then the risk model based on 13 important CRs was constructed using the transcriptome data of nephroblastoma. Using the risk model, pediatric nephroblastoma patients were stratified into high- and low-risk groups based on their individual risk scores. The risk score of CRs can predict adverse outcomes in pediatric nephroblastoma, and this gene cluster is closely related to various immunity characteristics of nephroblastoma. Moreover, the nephroblastoma cell line exhibited higher expression levels of prognostic genes (VRK1, ARNTL, RIT1, PRDM6, and TSPY1) compared to the HEK293 T cell line. CONCLUSIONS: The risk characteristics derived from CRs have tremendous significance in predicting prognosis and guiding clinical classification and intervention strategies for pediatric nephroblastoma.

Prognostic value of genomic mutation signature associated with immune microenvironment in southern Chinese patients with esophageal squamous cell carcinoma.

The genomic landscape of esophageal squamous cell cancer (ESCC), as well as its impact on the regulation of immune microenvironment, is not well understood. Thus, tumor samples from 92 patients were collected from two centers and subjected to targeted-gene sequencing. We identified frequently mutated genes, including TP53, KMT2C, KMT2D, LRP1B, and FAT1. The most frequent mutation sites were ALOX12B (c.1565C > T), SLX4 (c.2786C > T), LRIG1 (c.746A > G), and SPEN (c.6915_6917del) (6.5%). Pathway analysis revealed dysregulation of cell cycle regulation, epigenetic regulation, PI3K/AKT signaling, and NOTCH signaling. A 17-mutated gene-related risk model was constructed using random survival forest analysis and showed significant prognostic value in both our cohort and the validation cohort. Based on the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE) algorithm, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm, and the MCPcounter algorithm, we found that the risk score calculated by the risk model was significantly correlated with stimulatory immune checkpoints (TNFSF4, ITGB2, CXCL10, CXCL9, and BTN3A1; p < 0.05). Additionally, it was significantly associated with markers that are important in predicting response to immunotherapy (CD274, IFNG, and TAMM2; p < 0.05). Furthermore, the results of immunofluorescence double staining showed that patients with high risk scores had a significantly higher level of M2 macrophage than those with low risk scores (p < 0.05). In conclusion, our study provides insights into the genomic landscape of ESCC and highlights the prognostic value of a genomic mutation signature associated with the immune microenvironment in southern Chinese patients with ESCC.

Validation of Variables for Use in Pediatric Obesity Risk Score Development in Demographically and Racially Diverse US Cohorts.

OBJECTIVE: To evaluate the performance of childhood obesity prediction models in four independent cohorts in the United States, using previously validated variables obtained easily from medical records as measured in different clinical settings. STUDY DESIGN: Data from four prospective cohorts, Latinx, Eating, and Diabetes (LEAD); Stress in Pregnancy Study (SIPS); Project Viva; and Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) were used to test childhood obesity risk models and predict childhood obesity by ages 4 through 6, using five clinical variables (maternal age, maternal pre-pregnancy body mass index, birth weight Z-score, weight-for-age Z-score change, and breastfeeding), derived from a previously validated risk model and as measured in each cohort's clinical setting. Multivariable logistic regression was performed within each cohort, and performance of each model was assessed based on discrimination and predictive accuracy. RESULTS: The risk models performed well across all four cohorts, achieving excellent discrimination. The area under the receiver operator curve (AUROC) was 0.79 for CHAMACOS and Project Viva, 0.83 for SIPS, and 0.86 for LEAD. At a 50th percentile threshold, the sensitivity of the models ranged from 12 to 53%, and specificity was greater than or equal to 90%. The negative predictive values (NPV) were ≥ 80% for all cohorts, and the positive predictive values (PPV) ranged from 62-86%. CONCLUSION: All four risk models performed well in each independent and demographically diverse cohort, demonstrating the utility of these five variables for identifying children at high risk for developing early childhood obesity in the United States.

Telomerase related molecular subtype and risk model reveal immune activity and evaluate prognosis and immunotherapy response in prostate cancer.

BACKGROUND: Prostate cancer ranks among the six most lethal malignancies worldwide. Telomerase, a reverse transcriptase enzyme, plays a pivotal role in extending cellular telomeres and is intimately associated with cell proliferation and division. However, the interconnection between prostate cancer and telomerase-related genes (TEASEs) remains unclear. METHODS: Somatic mutations and copy number alterations of TEASEs were comprehensively analyzed. Subsequently, the transcripts of prostate cancer patients in TCGA and GEO databases were integrated to delineate new molecular subtypes. Followed by constructing a risk model containing nine characteristic genes through Lasso regression and Cox prognostic analysis among different subtypes. Various aspects including prognosis, tumor microenvironment (TME), landscape of immunity, tumor mutational burden (TMB), stem cell correlation, and median inhibitory concentration amongst different risk groups were compared. Finally, the expression, prognosis, and malignant biological behavior of ZW10 interactor (ZWINT) in vitro was explored. RESULTS: TEASEs exhibited a notably high mutation frequency. Three distinct molecular subtypes and two gene subclusters based on TEASEs were delineated, displaying significant associations with prognosis, immune function regulation, and clinical characteristics. Low-risk patients demonstrated superior prognosis and better response to immunotherapy. Conversely, high-risk patients exhibited higher TMB and stronger stem cell correlations. It was also found that the patients' sensitivity to chemotherapy agents was impacted by the risk score. Finally, ZWINT's potential as a novel diagnostic and prognostic biomarker for prostate cancer was validated. CONCLUSIONS: TEASEs play a pivotal role in modulating immune regulation and immunotherapeutic responses, thereby significantly impacting the diagnosis, prognosis, and treatment strategies for affected patients.

Basement membrane-associated gene expression as a predictor of survival in oral cancer.

BACKGROUND: This study sought to investigate the prognostic value of basement membrane (BM)-associated gene expressions in oral cancer. METHODS: We harvested and integrated data on BM-associated genes (BMGs), the oral cancer transcriptome, and clinical information from public repositories. After identifying differentially expressed BMGs, we used Cox and Lasso regression analyses to create a BMG-based risk score for overall survival at various intervals. We then validated this score using the GSE42743 cohort as a validation set. The prognostic potential of the risk scores and their relations to clinical features were assessed. Further, we conducted functional pathway enrichment, immune cell infiltration, and immune checkpoint analyses to elucidate the immunological implications and therapeutic potential of the BMG-based risk score and constituent genes. To confirm the expression levels of the BMG LAMA3 in clinical samples of oral cancer tissue, we performed quantitative real-time PCR (qRT-PCR) and immunohistochemical staining. RESULTS: The BMGs LAMA3, MMP14, and GPC2 demonstrated notable prognostic significance, facilitating the construction of a BMG-based risk score. A higher risk score derived from BMGs correlated with a poorer survival prognosis for oral cancer patients. Moreover, the risk-associated BMGs exhibited a significant relationship with immune function variability (P < 0.05), discrepancies in infiltrating immune cell fractions, and immune checkpoint expressions (P < 0.05). The upregulated expression levels of LAMA3 in oral cancer tissues were substantiated through qRT-PCR and immunohistochemical staining. CONCLUSION: The BMG-based risk score emerged as a reliable prognostic tool for oral cancer, meriting further research for validation and potential clinical application.

A novel TCGA-validated programmed cell-death-related signature of ovarian cancer.

BACKGROUND: Ovarian cancer (OC) is a gynecological malignancy tumor with high recurrence and mortality rates. Programmed cell death (PCD) is an essential regulator in cancer metabolism, whose functions are still unknown in OC. Therefore, it is vital to determine the prognostic value and therapy response of PCD-related genes in OC. METHODS: By mining The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Genecards databases, we constructed a prognostic PCD-related genes model and performed Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic (ROC) curve for its predictive ability. A nomogram was created via Cox regression. We validated our model in train and test sets. Quantitative real-time PCR (qRT-PCR) was applied to identify the expression of our model genes. Finally, we analyzed functional analysis, immune infiltration, genomic mutation, tumor mutational burden (TMB) and drug sensitivity of patients in low- and high-risk group based on median scores. RESULTS: A ten-PCD-related gene signature including protein phosphatase 1 regulatory subunit 15 A (PPP1R15A), 8-oxoguanine-DNA glycosylase (OGG1), HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 (HERC1), Caspase-2.(CASP2), Caspase activity and apoptosis inhibitor 1(CAAP1), RB transcriptional corepressor 1(RB1), Z-DNA binding protein 1 (ZBP1), CD3-epsilon (CD3E), Clathrin heavy chain like 1(CLTCL1), and CCAAT/enhancer-binding protein beta (CEBPB) was constructed. Risk score performed well with good area under curve (AUC) (AUC3 - year =0.728, AUC5 - year = 0.730). The nomogram based on risk score has good performance in predicting the prognosis of OC patients (AUC1 - year =0.781, AUC3 - year =0.759, AUC5 - year = 0.670). Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the erythroblastic leukemia viral oncogene homolog (ERBB) signaling pathway and focal adhesion were enriched in the high-risk group. Meanwhile, patients with high-risk scores had worse OS. In addition, patients with low-risk scores had higher immune-infiltrating cells and enhanced expression of checkpoints, programmed cell death 1 ligand 1 (PD-L1), indoleamine 2,3-dioxygenase 1 (IDO-1) and lymphocyte activation gene-3 (LAG3), and were more sensitive to A.443,654, GDC.0449, paclitaxel, gefitinib and cisplatin. Finally, qRT-PCR confirmed RB1, CAAP1, ZBP1, CEBPB and CLTCL1 over-expressed, while PPP1R15A, OGG1, CASP2, CD3E and HERC1 under-expressed in OC cell lines. CONCLUSION: Our model could precisely predict the prognosis, immune status and drug sensitivity of OC patients.

Exploring and clinical validation of prognostic significance and therapeutic implications of copper homeostasis-related gene dysregulation in acute myeloid leukemia.

The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

OBJECTIVES: Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. RESULTS: A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. CONCLUSION: The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

Long-term reliability of the phospholamban (PLN) p.(Arg14del) risk model in predicting major ventricular arrhythmia: a landmark study.

AIMS: Recently, a genetic variant-specific prediction model for phospholamban (PLN) p.(Arg14del)-positive individuals was developed to predict individual major ventricular arrhythmia (VA) risk to support decision-making for primary prevention implantable cardioverter defibrillator (ICD) implantation. This model predicts major VA risk from baseline data, but iterative evaluation of major VA risk may be warranted considering that the risk factors for major VA are progressive. Our aim is to evaluate the diagnostic performance of the PLN p.(Arg14del) risk model at 3-year follow-up. METHODS AND RESULTS: We performed a landmark analysis 3 years after presentation and selected only patients with no prior major VA. Data were collected of 268 PLN p.(Arg14del)-positive subjects, aged 43.5 ± 16.3 years, 38.9% male. After the 3 years landmark, subjects had a mean follow-up of 4.0 years (± 3.5 years) and 28 (10%) subjects experienced major VA with an annual event rate of 2.6% [95% confidence interval (CI) 1.6-3.6], defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. The PLN p.(Arg14del) risk score yielded good discrimination in the 3 years landmark cohort with a C-statistic of 0.83 (95% CI 0.79-0.87) and calibration slope of 0.97. CONCLUSION: The PLN p.(Arg14del) risk model has sustained good model performance up to 3 years follow-up in PLN p.(Arg14del)-positive subjects with no history of major VA. It may therefore be used to support decision-making for primary prevention ICD implantation not merely at presentation but also up to at least 3 years of follow-up.

Construction and Validation of Novel Ferroptosis-related Risk Score Signature and Prognostic Prediction Nomogram for Patients with Colorectal Cancer.

Background: Colorectal cancer (CRC) has a high morbidity and mortality. Ferroptosis is a phenomenon in which metabolism and cell death are closely related. The role of ferroptosis-related genes in the progression of CRC is still not clear. Therefore, we screened and validated the ferroptosis-related genes which could determine the prevalence, risk and prognosis of patients with CRC. Methods: We firstly screened differentially expressed ferroptosis-related genes by The Cancer Genome Atlas (TCGA) database. Then, these genes were used to construct a risk-score model using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The function and prognosis of the ferroptosis-related genes were confirmed using multi-omics analysis. The gene expression results were validated using publicly available databases and qPCR. We also used publicly available data and ferroptosis-related genes to construct a prognostic prediction nomogram. Results: A total of 24 differential expressed genes associated with ferroptosis were screened in this study. A three-gene risk score model was then established based on these 24 genes and GPX3, CDKN2A and SLC7A11 were selected. The significant prognostic value of this novel three-gene signature was also assessed. Furthermore, we conducted RT-qPCR analysis on cell lines and tissues, and validated the high expression of CDKN2A, GPX3 and low expression of SLC7A11 in CRC cells. The observed mRNA expression of GPX3, CDKN2A and SLC7A11 was consistent with the predicted outcomes. Besides, eight variables including selected ferroptosis related genes were included to establish the prognostic prediction nomogram for patients with CRC. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations. Also, the time-dependent AUC (>0.7) indicated satisfactory discriminative ability of the nomogram. Conclusions: The present study constructed and validated a novel ferroptosis-related three-gene risk score signature and a prognostic prediction nomogram for patients with CRC. Also, we screened and validated the ferroptosis-related genes GPX3, CDKN2A, and SLC7A11 which could serve as novel biomarkers for patients with CRC.

Effect of marital status on the survival outcomes of cervical cancer: a retrospective cohort study based on SEER database.

BACKGROUND: Cervical cancer is the fourth most common malignant tumor troubling women worldwide. Whether marital status affects the prognosis of cervical cancer is still unclear. Here, we investigate the prognostic value of marital status in patients with cervical cancer based on the seer database. MATERIAL/METHODS: The demographic and clinical data of patients with cervical cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1975 to 2017. Patients were divided into two groups (married and unmarried) according to marital status, and then the clinical characteristics of each group were compared using the chi-square test. Propensity score matching (PSM) was used to reduce differences in baseline characteristics. The overall survival (OS) and cervical cancer-specific survival (CCSS) were assessed by the Kaplan-Meier method, univariate and multivariate Cox regression models, and stratified analysis. Moreover, univariate and multivariate competing risk regression models were performed to calculate hazard ratios (HR) of death risk. RESULTS: A total of 21,148 patients were included in this study, including 10,603 married patients and 10,545 unmarried patients. Married patients had better OS(P < 0.05) and CCSS (P < 0.05) compared to unmarried patients, and marital status was an independent prognostic factor for both OS (HR: 0.830, 95% CI: 0.798-0.862) and CCSS (HR: 0.892, 95% CI: 0.850-0.937). Moreover, after eliminating the competing risk, married patients (CCSD: HR:0.723, 95% CI: 0.683-0.765, P < 0.001) had a significantly decreased risk of death compared to unmarried patients. In stratified analysis, the married patients showed better OS and CCSS than the unmarried patients diagnosed in 1975-2000 and 2001-2017. CONCLUSIONS: Being married was associated with a favorable prognosis of cervical cancer, and marital status was an independent prognostic factor for cervical cancer.

Identification of novel disulfidptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of skin cutaneous melanoma patients.

BACKGROUND: Skin cutaneous melanoma (SKCM) is an aggressive form of malignant melanoma with poor prognosis and high mortality rates. Disulfidptosis is a newly discovered cell death regulatory mechanism caused by the abnormal accumulation of disulfides. This unique pathway is guiding significant new research to understand cancer progression for targeted treatment. However, the correlation between disulfidptosis with long non-coding RNAs (lncRNAs) in SKCM remains unknown at present. METHODS: The Cancer Genome Atlas database furnished lncRNA expression data and clinical information for SKCM patients. Pearson correlation and Cox regression analyses identified disulfidptosis-related lncRNAs associated with SKCM prognosis. ROC curves and a nomogram validated the model. TME, immune infiltration, GSEA analysis, immune checkpoint gene expression profiling, and drug sensitivity were assessed in high and low-risk groups. Consistent clustering categorized SKCM patients for personalized clinical treatment guidance. RESULTS: A total of twelve disulfidptosis-related lncRNAs were identified for the development of prognosis prediction models. The area under the curve (AUC) values of the ROC curve and the nomogram provided reliable discrimination to evaluate the prognostic potential for SKCM patients. The TME played a crucial role in tumorigenesis, progression and prognosis, and the risk scores were closely related to immune cell infiltration. Meanwhile, the combination of chemotherapy, targeted therapy, and immunotherapy was recommended for low-risk patients based on drug sensitivity and immune efficacy analyses. CONCLUSION: We identified a risk model of twelve disulfidptosis-related lncRNAs that could be used to predict the prognosis of SKCM patients and help guide immunotherapy and chemotherapy for personalized treatment plans.

High cardiovascular mortality risk among older merkel cell carcinoma patients.

OBJECTIVE: Previous research has primarily focused on the incidence and mortality rates of Merkel cell carcinoma (MCC), neglecting the examination of cardiovascular mortality (CVM) risk among survivors, particularly older patients. This study aims to assess the risk of CVM in older individuals diagnosed with MCC. METHODS: Data pertaining to older MCC patients were obtained from the Surveillance, Epidemiology, and End Results database (SEER). CVM risk was measured using standardized mortality ratio (SMR) and cumulative mortality. Multivariate Fine-Gray's competing risk model was utilized to evaluate the risk factors contributing to CVM. RESULTS: Among the study population of 2,899 MCC patients, 465 (16.0%) experienced CVM during the follow-up period. With the prolongation of the follow-up duration, the cumulative mortality rate for CVM reached 27.36%, indicating that cardiovascular disease (CVD) became the second most common cause of death. MCC patients exhibited a higher CVM risk compared to the general population (SMR: 1.69; 95% CI: 1.54-1.86, p < 0.05). Notably, the SMR for other diseases of arteries, arterioles, and capillaries displayed the most significant elevation (SMR: 2.69; 95% CI: 1.16-5.29, p < 0.05). Furthermore, age at diagnosis and disease stage were identified as primary risk factors for CVM, whereas undergoing chemotherapy or radiation demonstrated a protective effect. CONCLUSION: This study emphasizes the significance of CVM as a competing cause of death in older individuals with MCC. MCC patients face a heightened risk of CVM compared to the general population. It is crucial to prioritize cardiovascular health starting from the time of diagnosis and implement personalized CVD monitoring and supportive interventions for MCC patients at high risk. These measures are essential for enhancing survival outcomes.