Clinical relevance of somatic mutations in Chinese lung adenocarcinoma and their prognostic implications for survival.

BACKGROUND: To comprehensively elucidate the genomic and mutational features of lung cancer cases, and lung adenocarcinoma (LUAD), it is imperative to conduct ongoing investigations into the genomic landscape. In this study, we aim to analyze the somatic mutation profile and assessed the significance of these informative genes utilizing a retrospective LUAD cohort. METHODS: A total of 247 Chinese samples were analyzed to exhibit the tumor somatic genomic alterations in patients with LUAD. The Cox regression analysis was employed to identify prognosis-related genes and establish a predictive model for stratifying patients with LUAD. RESULTS: In the Dianjiang People's Hospital (DPH) cohort, the top five frequent mutated genes were (Epidermal growth factor receptor) EGFR (68%), TP53 (30%), RBM10 (13%), LRP1B (9%), and KRAS (9%). Of which, EGFR is a mostly altered driver gene, and most mutation sites are located in tyrosine kinase regions. Oncogene pathway alteration and mutation signature analysis demonstrated the RTK-RAS pathway alteration, and smoking was the main carcinogenic factor of the DPH cohort. Furthermore, we identified 34 driver genes in the DPH cohort, including EGFR (68%), TP53 (30.4%), RBM10 (12.6%), KRAS (8.5%), LRP1B (8.5%), and so on, and 45 Clinical Characteristic-Related Genes (CCRGs) were found to closely related to the clinical high-risk factors. We developed a Multiple Parameter Gene Mutation (MPGM) risk model by integrating critical genes and oncogenic pathway alterations in LUAD patients from the DPH cohort. Based on publicly available LUAD datasets, we identified five genes, including BRCA2, Anaplastic lymphoma kinase (ALK), BRAF, EGFR, and Platelet-Derived Growth Factor Receptor Alpha (PDGFRA), according to the multivariable Cox regression analysis. The MPGM-low group showed significantly better overall survival (OS) compared to the MPGM-high group (p < 0.0001, area under the curve (AUC) = 0.754). The robust performance was validated in 55 LUAD patients from the DPH cohort and another LUAD dataset. Immune characteristics analysis revealed a higher proportion of primarily DCs and mononuclear cells in the MPGM-low risk group, while the MPGM-high risk group showed lower immune cells and higher tumor cell infiltration. CONCLUSION: This study provides a comprehensive genomic landscape of Chinese LUAD patients and develops an MPGM risk model for LUAD prognosis stratification. Further follow-up will be performed for the patients in the DPH cohort consistently to explore the resistance and prognosis genetic features.

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.

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.

Analyzing the Functional Roles and Immunological Features of Chemokines in COAD.

Chemokines are key proteins that regulate cell migration and immune responses and are essential for modulating the tumor microenvironment. Despite their close association with colon cancer, the expression patterns, prognosis, immunity, and specific roles of chemokines in colon cancer are still not fully understood. In this study, we investigated the mutational features, differential expression, and immunological characteristics of chemokines in colon cancer (COAD) by analyzing the Tumor Genome Atlas (TCGA) database. We clarified the biological functions of these chemokines using Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. By univariate and multivariate COX regression analyses, we developed chemokine-based prognostic risk models. In addition, using Gene Set Enrichment Analysis (GSEA) and Gene Set Variant Analysis (GSVA), we analyzed the differences in immune responses and signaling pathways among different risk groups. The results showed that the mutation rate of chemokines was low in COAD, but 25 chemokines were significantly differentially expressed. These chemokines function in several immune-related biological processes and play key roles in signaling pathways including cytokine-cytokine receptor interactions, NF-kappa B, and IL-17. Prognostic risk models based on CCL22, CXCL1, CXCL8, CXCL9, and CXCL11 performed well. GSEA and GSVA analyses showed significant differences in immune responses and signaling pathways across risk groups. In conclusion, this study reveals the potential molecular mechanisms of chemokines in COAD and proposes a new prognostic risk model based on these insights.

Assessing the Predictive Accuracy of EORTC, CUETO and EAU Risk Stratification Models for High-Grade Recurrence and Progression after Bacillus Calmette-Guérin Therapy in Non-Muscle-Invasive Bladder Cancer.

The currently available EORTC, CUETO and EAU2021 risk stratifications were originally developed to predict recurrence and progression in non-muscle-invasive bladder cancer (NMIBC). However, they have not been validated to differentiate between high-grade (HG) and low-grade (LG) recurrence-free survival (RFS), which are distinct events with specific implications. We aimed to evaluate the accuracy of available risk models and identify additional risk factors for HG RFS and PFS among NMIBC patients treated with Bacillus Calmette-Guérin (BCG). We retrospectively included 171 patients who underwent transurethral resection of the bladder tumor (TURBT), of whom 73 patients (42.7%) experienced recurrence and 29 (17%) developed progression. Initially, there were 21 low-grade and 52 high-grade recurrences. EORTC2006, EORTC2016 and CUETO recurrence scoring systems lacked accuracy in the prediction of HG RFS (C-index 0.63/0.55/0.59, respectively). EAU2021 risk stratification, EORTC2006, EORTC2016, and CUETO progression scoring systems demonstrated low to moderate accuracy (C-index 0.59/0.68/0.65/0.65) in the prediction of PFS. In the multivariable analysis, T1HG at repeat TURBT (HR = 3.17 p < 0.01), tumor multiplicity (HR = 2.07 p < 0.05), previous history of HG NMIBC (HR = 2.37 p = 0.06) and EORTC2006 progression risk score (HR = 1.1 p < 0.01) were independent predictors for HG RFS. To conclude, available risk models lack accuracy in predicting HG RFS and PFS in -NMIBC patients treated with BCG.

An Aging-Related lncRNA Signature Establishing for Breast Cancer Prognosis and Immunotherapy Responsiveness Prediction.

Purpose: Emerging evidence demonstrates the vital role of aging and long non-coding RNAs (lncRNAs) in breast cancer (BC) progression. Our study intended to develop a prognostic risk model based on aging-related lncRNAs (AG-lncs) to foresee BC patients' outcomes. Patients and Methods: 307 aging-related genes (AGs) were sequenced from the TCGA project. Then, 697 AG-lncs were identified by the co-expression analysis with AGs. Using multivariate and univariate Cox regression analysis, and LASSO, 6 AG-lncs, including al136531.1, mapt-as1, al451085.2, otud6b-as1, tnfrsf14-as1, and linc01871, were validated to compute the risk score and establish a risk signature. Expression levels of al136531.1, mapt-as1, al451085.2, tnfrsf14-as1, and linc01871 were higher in low-risk BC patients, whereas otud6b-as1 expression was higher in high-risk BC patients. In the training and testing set, high-risk patients performed shorter PFI, OS, and DFS than low-risk patients. Results: Our risk signature had the highest concordance index among other established prognostic signatures and displayed ideal predictive ability for 1-, 3- and 5-year patient OS in the nomogram. Additionally, BC patients with different risk score levels showed different immune statuses and responses to immunotherapy via GSEA, ssGSEA, ESTIMATE algorithm, and TIDE algorithm analysis. Of note, the qRT-PCR analysis validated that these 6 AG-lncs expressed quite differentially in BC tissues at various clinical stages. Conclusion: The risk signature of 6 AG-lncs might offer a novel prognostic biomarker and promisingly enhance BC immunotherapy's effectiveness.

Revealing prognostic insights of programmed cell death (PCD)-associated genes in advanced non-small cell lung cancer.

The management of patients with advanced non-small cell lung cancer (NSCLC) presents significant challenges due to cancer cells' intricate and heterogeneous nature. Programmed cell death (PCD) pathways are crucial in diverse biological processes. Nevertheless, the prognostic significance of cell death in NSCLC remains incompletely understood. Our study aims to investigate the prognostic importance of PCD genes and their ability to precisely stratify and evaluate the survival outcomes of patients with advanced NSCLC. We employed Weighted Gene Co-expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), univariate and multivariate Cox regression analyses for prognostic gene screening. Ultimately, we identified seven PCD-related genes to establish the PCD-related risk score for the advanced NSCLC model (PRAN), effectively stratifying overall survival (OS) in patients with advanced NSCLC. Multivariate Cox regression analysis revealed that the PRAN was the independent prognostic factor than clinical baseline factors. It was positively related to specific metabolic pathways, including hexosamine biosynthesis pathways, which play crucial roles in reprogramming cancer cell metabolism. Furthermore, drug prediction for different PRAN risk groups identified several sensitive drugs explicitly targeting the cell death pathway. Molecular docking analysis suggested the potential therapeutic efficacy of navitoclax in NSCLC, as it demonstrated strong binding with the amino acid residues of C-C motif chemokine ligand 14 (CCL14), carboxypeptidase A3 (CPA3), and C-X3-C motif chemokine receptor 1 (CX3CR1) proteins. The PRAN provides a robust personalized treatment and survival assessment tool in advanced NSCLC patients. Furthermore, identifying sensitive drugs for distinct PRAN risk groups holds promise for advancing targeted therapies in NSCLC.

Identification and validation of a novel risk model based on cuproptosis­associated m6A for head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a prevalent cancer with a poor survival rate due to anatomical limitations of the head and a lack of reliable biomarkers. Cuproptosis represents a novel cellular regulated death pathway, and N6-methyladenosine (m6A) is the most common internal RNA modification in mRNA. They are intricately connected to tumor formation, progression, and prognosis. This study aimed to construct a risk model for HNSCC using a set of mRNAs associated with m6A regulators and cuproptosis genes (mcrmRNA). METHODS: RNA-seq and clinical data of HNSCC patients from The Cancer Genome Atlas (TCGA) database were analyzed to develop a risk model through the least absolute shrinkage and selection operator (LASSO) analysis. Survival analysis and receiver operating characteristic (ROC) analysis were performed for the high- and low-risk groups. Additionally, the model was validated using the GSE41613 dataset from the Gene Expression Omnibus (GEO) database. GSEA and CIBERSORT were applied to investigate the immune microenvironment of HNSCC. RESULTS: A risk model consisting of 32 mcrmRNA was developed using the LASSO analysis. The risk score of patients was confirmed to be an independent prognostic indicator by multivariate Cox analysis. The high-risk group exhibited a higher tumor mutation burden. Additionally, CIBERSORT analysis indicated varying levels of immune cell infiltration between the two groups. Significant disparities in drug sensitivity to common medications were also observed. Enrichment analysis further unveiled significant differences in metabolic pathways and RNA processing between the two groups. CONCLUSIONS: Our risk model can predict outcomes for HNSCC patients and offers valuable insights for personalized therapeutic approaches.

Dynamic Risk Model for the Medical Treatment of Graves' Hyperthyroidism according to Treatment Duration.

Background: Changes in thyrotropin receptor antibody (TRAb) levels are associated with the clinical outcomes of Graves' hyperthyroidism. However, the effects of the patterns of TRAb changes on patient prognosis according to the treatment duration of antithyroid drugs (ATDs) are not well established. Methods: In this retrospective cohort study, 1,235 patients with Graves' hyperthyroidism who were treated with ATDs for more than 12 months were included. Patients were divided into two groups according to treatment duration: group 1 (12-24 months) and group 2 (>24 months). Risk prediction models comprising age, sex, and either TRAb levels at ATD withdrawal (model A) or patterns of TRAb changes (model B) were compared. Results: The median treatment duration in groups 1 (n=667, 54%) and 2 (n=568, 46%) was 17.3 and 37.1 months, respectively. The recurrence rate was significantly higher in group 2 (47.9%) than in group 1 (41.4%, P=0.025). Group 2 had significantly more goiter, thyroid eye disease, and fluctuating and smoldering type of TRAb pattern compared with group 1 (all P<0.001). The patterns of TRAb changes were an independent risk factor for recurrence after adjusting for other confounding factors in all patients, except in group 1. Integrated discrimination improvement and net reclassification improvement analyses showed that model B performed better than model A in all patients, except in group 1. Conclusion: The dynamic risk model, including the patterns of TRAb changes, was more suitable for predicting prognosis in patients with Graves' hyperthyroidism who underwent longer ATD treatment duration.

Analyzing the involvement of diverse cell death-related genes in diffuse large B-cell lymphoma using bioinformatics techniques.

Diffuse large B-cell lymphoma (DLBCL) stands as the most prevalent subtype of non-Hodgkin's lymphoma and exhibits significant heterogeneity. Various forms of programmed cell death (PCD) have been established to have close associations with tumor onset and progression. To this end, this study has compiled 16 PCD-related genes. The investigation delved into genes linked with prognosis, constructing risk models through consecutive application of univariate Cox regression analysis and Lasso-Cox regression analysis. Furthermore, we employed RT-qPCR to validate the mRNA expression levels of certain diagnosis-related genes. Subsequently, the models underwent validation through KM survival curves and ROC curves, respectively. Additionally, nomogram models were formulated employing prognosis-related genes and risk scores. Lastly, disparities in immune cell infiltration abundance and the expression of immune checkpoint-associated genes between high- and low-risk groups, as classified by risk models, were explored. These findings contribute to a more comprehensive understanding of the role played by the 16 PCD-associated genes in DLBCL, shedding light on potential novel therapeutic strategies for the condition.

Expression of Anoikis-Related Genes and Potential Biomarkers in Colon Cancer Based on RNA-seq and scRNA-seq.

Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience recurrence and metastasis. Therefore, it is urgent to better predict the prognosis of CC patients and identify new biomarkers. Recent studies have shown that anoikis-related genes (ARGs) play a significant role in the progression of many tumors. Hence, it is essential to confirm the role of ARGs in the development and treatment of CC by integrating scRNA-seq and transcriptome data. This study integrated transcriptome and single-cell sequencing (scRNA-seq) data from CC samples to evaluate patient stratification, prognosis, and ARG expression in different cell types. Specifically, differential expression of ARGs was identified through consensus clustering to classify CC subtypes. Subsequently, a CC risk model composed of CDKN2A, NOX4, INHBB, CRYAB, TWIST1, CD36, SERPINE1, and MMP3 was constructed using prognosis-related ARGs. Finally, using scRNA-seq data of CC, the expression landscape of prognostic genes in different cell types and the relationship between important immune cells and other cells were explored. Through the above analysis, two CC subtypes were identified, showing significant differences in prognosis and clinical factors. Subsequently, a risk model comprising aforementioned genes successfully categorized all CC samples into two risk groups, which also exhibited significant differences in prognosis, clinical factors, involved pathways, immune landscape, and drug sensitivity. Multiple pathways (cell adhesion molecules (CAMs), and extracellular matrix (ECM) receptor interaction) and immune cells/immune functions (B cell naive, dendritic cell activate, plasma cells, and T cells CD4 memory activated) related to CC were identified. Furthermore, it was found that prognostic genes were highly expressed in various immune cells, and B cells exhibited more and stronger interaction pathways with other cells. The results of this study may provide references for personalized treatment and potential biomarker identification in CC.

Nomogram model for predicting cancer-specific mortality in patients with early-onset colorectal cancer: a competing risk analysis insight from the SEER database and an external validation cohort.

Background: Early-onset colorectal cancer (EOCRC) is increasing in incidence and poses a growing threat. Urgent research is needed, especially in survival analysis, to enhance comprehension and treatment strategies. This study aimed to explore the risk factors associated with cancer-specific mortality (CSM) and other-cause mortality (OCM) in patients with EOCRC. Additionally, the study aimed to develop a nomogram predicting CSM using a competitive risk model and validate its accuracy through the use of training, using internal and external cohorts. Methods: Data from EOCRC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database (2008-2017). EOCRC patients who were treated at a tertiary hospital in northeast China between 2014 and 2020 were also included in the study. The SEER data were divided into the training and validation sets at a 7:3 ratio. A univariate Cox regression model was employed to identify prognostic factors. Subsequently, multivariate Cox regression models were applied to ascertain the presence of independent risk factors. A nomogram was generated to visualize the results, which were evaluated using the concordance index (C-index), area under the curve (AUC), and calibration curves. The clinical utility was assessed via decision curve analysis (DCA). Results: Multivariable Cox regression analysis demonstrated that factors such as race, tumor differentiation, levels of carcinoembryonic antigen (CEA), marital status, histological type, American Joint Committee on Cancer (AJCC) stage, and surgical status were independent risk factors for CSM in EOCRC patients. In addition, age, gender, chemotherapy details, CEA levels, marital status, and AJCC stage were established as independent risk factors for OCM in individuals diagnosed with EOCRC. A nomogram was developed using the identified independent risk factors, demonstrating excellent performance with a C-index of 0.806, 0.801, and 0.810 for the training, internal validation, and external validation cohorts, respectively. The calibration curves and AUC further confirmed the accuracy and discriminative ability of the nomogram. Furthermore, the DCA results indicated that the model had good clinical value. Conclusions: In this study, a competing risk model for CSM was developed in EOCRC patients. The model demonstrates a high level of predictive accuracy, providing valuable insights into the treatment decision-making process.

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.

Outcomes of Isolated Tricuspid Valve Surgery: A Society of Thoracic Surgeons Analysis and Risk Model.

BACKGROUND: To provide patients and surgeons with clinically relevant information, the Society of Thoracic Surgeons Adult Cardiac Surgery Database (STS-ACSD) was queried to develop a risk model for isolated tricuspid valve (TV) operations. METHODS: All patients in the STS-ACSD undergoing isolated TV repair or replacement (N=13,587; age 48.3±18.4 years) were identified (7/2017-6/2023). Multivariable logistic regression accounting for TV replacement versus repair was used to model eight operative outcomes: mortality, morbidity and/or mortality, stroke, renal failure, reoperation, prolonged ventilation, short and prolonged hospital stay. Model discrimination (C-statistic) and calibration were assessed using 9-fold cross-validation. RESULTS: The isolated TV study population included 41.1% repairs (N=5,583; age 52.6±18.1 years) and 58.9% replacements (N=8,004; age 45.3±18.0 years). Overall predicted risk of operative mortality was 5.6%, similar in repairs and replacements (5.5% and 5.7%, respectively); as was the predicted risk of composite morbidity and mortality (28.2% and 26.8%). Replacements were generally younger patients with a higher endocarditis prevalence than repairs (45.7% vs. 21.1%). The model yielded a C-statistic of 0.81 for mortality and 0.76 for the composite of morbidity and mortality, with excellent observed-to-expected calibration that was comparable in all sub-cohorts and predicted risk decile groups. CONCLUSIONS: A new STS risk model has been developed for isolated TV surgery. The current mortality of isolated TV operations is lower than previously observed. This risk prediction model and these contemporary outcomes provide a new benchmark for current and future isolated TV interventions.

A Competing Risk Nomogram for Prediction of Prognosis in Patients With Primary Squamous Cell Thyroid Carcinoma.

Objective: Primary squamous cell thyroid carcinoma (PSCTC) is an extremely rare carcinoma, accounting for less than 1% of all thyroid carcinomas. However, the factors contributing to PSCTC outcomes remain unclear. This study aimed to identify the prognostic factors and develop a prognostic predictive model for patients with PSCTC. Methods: The analysis included patients diagnosed with thyroid carcinoma between 1975 and 2016 from the Surveillance, Epidemiology, and End Results database. Prognostic differences among the 5 pathological types of thyroid carcinomas were analyzed. To determine prognostic factors in PSCTC patients, the Cox regression model and Fine-Gray competing risk model were utilized. Based on the Fine-Gray competing risk model, a nomogram was established for predicting the prognosis of patients with PSCTC. Results: A total of 198,757 thyroid carcinoma patients, including 218 PSCTC patients, were identified. We found that PSCTC and anaplastic thyroid cancer had the worst prognosis among the 5 pathological types of thyroid carcinoma (P < .001). According to univariate and multivariate Cox regression analyses, age (71-95 years) was an independent risk factor for poorer overall survival and disease-specific survival in PSCTC patients. Using Fine-Gray regression analysis, the total number of in situ/malignant tumors for patient (Number 1) (≥2) was identified as an independent protective factor for prognosis of PSCTC. The area under the curve, the concordance index (C-index), calibration curves and decision curve analysis revealed that the nomogram was capable of predicting the prognosis of PSCTC patients accurately. Conclusion: The competing risk nomogram is highly accurate in predicting prognosis for patients with PSCTC, which may help clinicians to optimize individualized treatment decisions.

Comprehensive pan-cancer analysis of inflammatory age-clock-related genes as prognostic and immunity markers based on multi-omics data.

Inflammatory age (iAge) is a vital concept for understanding the intricate interplay between chronic inflammation and aging in the context of cancer. However, the importance of iAge-clock-related genes (iAge-CRGs) across cancers remains unexplored. This study aimed to explore the mechanisms and applications of these genes across diverse cancer types. We analyzed profiling data from over 10,000 individuals, covering 33 cancer types, 750 small molecule drugs, and 24 immune cell types. We focused on DCBLD2's function at the single-cell level and computed an iAge-CRG score using GSVA. This score was correlated with cancer pathways, immune infiltration, and survival. A signature was then derived using univariate Cox and LASSO regression, followed by ROC curve analysis, nomogram construction, decision curve analysis, and immunocytochemistry. Our comprehensive analysis revealed epigenetic, genomic, and immunogenomic alterations in iAge-CRGs, especially DCBLD2, leading to abnormal expression. Aberrant DCBLD2 expression strongly correlated with cancer-associated fibroblast infiltration and prognosis in multiple cancers. Based on GSVA results, we developed a risk model using five iAge-CRGs, which proved to be an independent prognostic index for uveal melanoma (UVM) patients. We also systematically evaluated the correlation between the iAge-related signature risk score and immune cell infiltration. iAge-CRGs, particularly DCBLD2, emerge as potential targets for enhancing immunotherapy outcomes. The strong correlation between abnormal DCBLD2 expression, cancer-associated fibroblast infiltration, and patient survival across various cancers underscores their significance. Our five-gene risk signature offers an independent prognostic tool for UVM patients, highlighting the crucial role of these genes in suppressing the immune response in UVM.Kindly check and confirm whether the corresponding affiliation is correctly identified.I identified the affiliation is correctly.thank you.Per style, a structured abstract is not allowed so we have changed the structured abstract to an unstructured abstract. Please check and confirm.I confirm the abstract is correctly ,thank you.

Development and validation of a nomogram for predicting specific mortality risk: A study of competing risk model based on real endometrial cancer patients.

OBJECTIVE: This study aimed to construct a competing risk prediction model for predicting specific mortality risks in endometrial cancer patients from the SEER database based on their demographic characteristics and tumor information. METHODS: We collected relevant clinical data on patients with histologically confirmed endometrial cancer in the SEER database between 2010 and 2015. Univariate and multivariate competing risk models were used to analyze the risk factors for endometrial cancer-specific death, and a predictive nomogram was constructed. C-index and receiver operating characteristic curve (ROC) at different time points were used to verify the accuracy of the constructed nomogram. RESULTS: There were 26 109 eligible endometrial cancer patients in the training cohort and 11 189 in the validation cohort. Univariate and multivariate analyses revealed that Age, Marriage, Grade, Behav, FIGO, Size, Surgery, SurgOth, Radiation, ParaAortic_Nodes, Peritonea, N positive, DX_liver, and DX_lung were independent prognostic factors for specific mortality in endometrial cancer patients. Based on these factors, a nomogram was constructed. Internal validation showed that the nomogram had a good discriminative ability (C-index = 0.883 [95% confidence interval [CI]: 0.881-0.884]), and the 1-, 3-, and 5-year AUC values were 0.901, 0.886 and 0.874, respectively. External validation indicated similar results (C-index = 0.883 [95%CI: 0.882-0.883]), and the 1-, 3-, and 5- AUC values were 0.908, 0.885 and 0.870, respectively. CONCLUSION: We constructed a competing risk model to predict the specific mortality risk among endometrial cancer patients. This model has favorable accuracy and reliability and can provide a reference for the development and update of endometrial cancer prognostic risk assessment tools.

Prognostic and Immune Landscape Analysis of Ubiquitination-related Genes in Hepatocellular Carcinoma: Based on Bulk and Single-cell RNA Sequencing Data.

Background: Despite significant advances in tumor immunotherapy, hepatocellular carcinoma (HCC) remains a malignancy with a challenging prognosis. The increasing research emphasizes the crucial role of ubiquitination in tumor immunotherapy. However, the establishment of prognostic signatures based on ubiquitination-related genes (UbRGs) and their role in immunotherapy are still lacking in HCC. Methods: We employed datasets from TCGA and GEO for transcriptome differential expression analysis and single-cell RNA sequencing analysis. Applying weighted gene co-expression network analysis, cox regression, lasso, selection and visualization of the most relevant features, and gradient boosting machine, we identified hub UbRGs as a gene signature to develop a prognostic model. We evaluated the predictive utility concerning clinical characteristics as well as its role in the immune landscape and immunotherapy potential. Additionally, western blotting, reverse transcription-quantitative PCR, and immunofluorescence were employed to detect the expression and sub-localization of hub genes. Results: Three hub UbRGs (BOP1, CDC20, and UBE2S) were identified as a gene signature. In particular, the high-risk group exhibited notable characteristics, including higher tumor mutation burden, enrichment in immune-related pathways, up-regulation immune checkpoint, and higher immunity scores. Treatment response to immunotherapy varied based on the expression of PD-1 and CTLA-4. Furthermore, single-cell data analysis revealed heterogeneous expression of hub UbRGs across different cell subtypes, while cytological experiments provided additional confirmation of the high expression of hub UbRGs in HCC. Conclusion: Our study provides valuable insights into the identification of novel ubiquitination-related biomarkers with potential applications for prognosis, immunotherapy prediction, and drug sensitivity in HCC.

Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model.

Objective: Positive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals. Methods: Using a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. A priori, we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples. Results: We assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set. Conclusion: We developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals.

Development and experimental validation of an energy metabolism-related gene signature for diagnosing of osteoporosis.

Osteoporosis is usually caused by excessive bone resorption and energy metabolism plays a critical role in the development of osteoporosis. However, little is known about the role of energy metabolism-related genes in osteoporosis. This study aimed to explore the important energy metabolism-related genes involved in the development of osteoporosis and develop a diagnosis signature for osteoporosis. The GSE56814, GSE62402, and GSE7158 datasets were downloaded from the NCBI Gene Expression Omnibus. The intersection of differentially expressed genes between high and low levels of body mineral density (BMD) and genes related to energy metabolism were screened as differentially expressed energy metabolism genes (DE-EMGs). Subsequently, a DE-EMG-based diagnostic model was constructed and differential expression of genes in the model was validated by RT-qPCR. Furthermore, a receiver operating characteristic curve and nomogram model were constructed to evaluate the predictive ability of the diagnostic model. Finally, the immune cell types in the merged samples and networks associated with the selected optimal DE-EMGs were constructed. A total of 72 overlapped genes were selected as DE-EMGs, and a five DE-EMG based diagnostic model consisting B4GALT4, ADH4, ACAD11, B4GALT2, and PPP1R3C was established. The areas under the curve of the five genes in the merged training dataset and B4GALT2 in the validation dataset were 0.784 and 0.790, respectively. Moreover, good prognostic prediction ability was observed using the nomogram model (C index = 0.9201; P = 5.507e-14). Significant differences were observed in five immune cell types between the high- and low-BMD groups. These included central memory, effector memory, and activated CD8 T cells, as well as regulatory T cells and activated B cells. A network related to DE-EMGs was constructed, including hsa-miR-23b-3p, DANCR, 17 small-molecule drugs, and two Kyoto Encyclopedia of Genes and Genomes pathways, including metabolic pathways and pyruvate metabolism. Our findings highlighted the important roles of DE-EMGs in the development of osteoporosis. Furthermore, the DANCR/hsa-miR-23b-3p/B4GALT4 axis might provide novel molecular insights into the process of osteoporosis development.