Univariate Cox Regression Analysis Research Articles

Development and validation of a nomogram for predicting outcomes in ovarian cancer patients with liver metastases

PurposeTo develop and validate a nomogram for predicting the overall survival (OS) of ovarian cancer patients with liver metastases (OCLM).MethodsThis study identified 821 patients in the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly divided in a ratio of 7:3 into a training cohort (n = 574) and a validation cohort (n = 247). Clinical factors associated with OS were assessed using univariate and multivariate Cox regression analyses, and backward stepwise regression was applied using the Akaike information criterion (AIC) to select the optimal predictor variables. The nomogram for predicting the OS of the OCLM patients was constructed based on the identified prognostic factors. Their prediction ability was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curves analysis (DCA) in both the training and validation cohorts.ResultsWe identified factors that predict OS for OCLM patients and constructed a nomogram based on the data. The ROC, C-index, and calibration analyses indicated that the nomogram performed well over the 1, 2, and 3-year OS in both the training and validation cohorts. Additionally, in contrast to the External model from multiple perspectives, our model shows higher stability and accuracy in predictive power. DCA curves, NRI, and IDI index demonstrated that the nomogram was clinically valuable and superior to the External model.ConclusionWe established and validated a nomogram to predict 1,2- and 3-year OS of OCLM patients, and our results may also be helpful in clinical decision-making.

Surgical and Oncologic Outcomes of Primary Tumor Resection in Patients with Small Intestinal Neuroendocrine Tumors: Results from a Single-Center Series Over a 15-Year Period.

The role of prophylactic primary tumor resection (PTR) in patients with small intestinal neuroendocrine tumors (SI-NETs) and unresectable liver metastases is a matter of debate. We aimed to evaluate outcomes in patients with SI-NETs who underwent PTR, according to the presence of metastasis and symptoms from primary. Data from patients who underwent PTR for SI-NETs from a single referral center (2007-2023) were prospectively collected. Patients were divided into three groups: non-metastatic (M0) and metastatic with primary tumor-related symptoms (MS) or metastatic asymptomatic (MA). Kaplan-Meier curves for overall survival (OS) and event-free survival (EFS) were generated and compared by group. Univariate and multivariable Cox regression analyses were performed to assess the significance of risk factors. Of 147 patients, 53 were M0, 23 were MS, and 71 were MA. Median follow-up was 26 months. The 5- and 10-year OS estimates were 100% and 100% in M0 patients, 82 and 21% in MS patients, and 89 and 80% in MA patients, respectively (p<0.001). Median EFS was 91 months overall and 43,126 months and not reached for the MS, MA, and M0 groups, respectively (p<0.001). In multivariable analysis, MS (hazard ratio [HR] 3.92, 95% confidence interval [CI] 1.71-9.52), functioning tumors (HR 1.98, 95% CI 1.03-3.81), and G2 grade (HR 3.20, 95% CI 1.64-6.85) were associated with a shorter EFS. In this large retrospective series, the MS group had a significantly worse OS and EFS compared with M0 and MA. This finding suggests that prophylactic PTR might benefit SI-NET patients with unresectable liver metastases, as symptom development could impair long-term prognosis after surgery.

A real-world study: third-line treatment options for metastatic colorectal cancer

BackgroundNumerous third-line treatment options exist for colorectal cancer. This study aims to assess the efficacy and safety of third-line therapies, including TKIs (fruquintinib, regorafenib) combined with PD-1 inhibitors, and trifluridine/tipiracil combined with bevacizumab, in patients with refractory microsatellite stable metastatic colorectal cancer who have progressed or are intolerant following standard first- and second-line treatments.Materials and methodsThis retrospective analysis collected data from patients with microsatellite stable advanced colorectal adenocarcinoma, diagnosed through histopathology and treated at Henan Provincial Cancer Hospital from May 2019 to April 2023. We compared the efficacy and safety of fruquintinib combined with PD-1 inhibitors, regorafenib combined with PD-1 inhibitors, and trifluridine/tipiracil combined with bevacizumab.ResultsAmong 60 eligible patients with refractory microsatellite stable metastatic colorectal adenocarcinoma, 29 (48.3%) received fruquintinib combined with PD-1 inhibitors, 15 (25%) received regorafenib combined with PD-1 inhibitors, and 16 (26.7%) received trifluridine/tipiracil combined with bevacizumab. The average follow-up period was 12.6 months (ranging from 2.3 to 37.6 months). After third-line treatment, the overall objective response rate (ORR) was 8.6%, and the disease control rate (DCR) was 78.6%. The median overall survival (OS) for the regorafenib, fruquintinib, and trifluridine/tipiracil groups was 19.2 months, 14.0 months, and 16.2 months, respectively, with no statistically significant differences observed. However, there were statistically significant differences in progression-free survival (PFS); the median PFS for the regorafenib group was 6.3 months, for the fruquintinib group was 4.2 months, and for the trifluridine/tipiracil group was 5.4 months. Pairwise comparisons indicated that the PFS for the regorafenib group was similar to that for the trifluridine/tipiracil group, both of which were superior to the fruquintinib group. Cox univariate regression analysis revealed that the presence of liver and peritoneal metastases was associated with PFS in third-line treatment.ConclusionIn the third-line treatment of colorectal cancer, regorafenib combined with PD-1 inhibitors and trifluridine/tipiracil combined with bevacizumab showed superiority over fruquintinib combined with PD-1 inhibitors in terms of PFS, but no statistically significant difference in OS was noted among the three groups.

Identification and validation of lung adenocarcinoma (LUAD)-associated targets for monascin from the extracts of Monascus purpureus-fermented rice: Compound preparation, high-throughput genome sequencing and bioinformatics analysis

Despite the remarkable bioactivities exhibited by monascin against several tumors, its therapeutic targets remain unexplored. In this study, our objective is to identify therapeutic targets based on the antitumor activity of monascin against lung adenocarcinoma (LUAD) A549 cells. The compound monascin was derived from extracts of Monascus purpureus-fermented rice. Its chemical structure was determined using spectroscopic methods, while the physicochemical properties of monascin were investigated through thermal behavior analysis. Herein, antitumor studies indicated that monascin significantly inhibited the viability of A549 cells, with an IC50 value of 2.05 μM; RNA-sequencing revealed 12 up-regulated and 29 down-regulated genes as differentially expressed genes (DEGs) for A549 cells in response to monascin; RT-qPCR validation supported the plausibility of the RNA-sequencing results. Moreover, utilizing data from the Cancer Genome Atlas (TCGA) database, univariate and multivariate Cox regression analyses provided evidence supporting GPR37 and FAM83A as potential candidate genes for predicting disease progression in LUAD patients. Additionally, the LASSO Cox regression and nomogram analyses confirmed that FAM83A and GPR37 genes had the potential to predict overall survival for LUAD patients. Finally, molecular docking studies suggested that monascin could interact with both GPR37 and FAM83A proteins through the hydrogen bonding and hydrophobic interactions; Western blotting assays indicated that monascin inhibited the expression levels of both GPR37 and FAM83A proteins. Overall, the clinical prognosis of GPR37 and FAM83A highlights the rationale for targeting these specific genes with monascin. Additionally, there findings provide compelling evidence for the ploypharmacological properties of monascin against LUAD.

A Novel Prognostic Risk Score Model Based on RNA Editing Level in Lower-Grade Glioma

BackgroundLower-grade glioma (LGG) refers to WHO grade 2 and 3 gliomas. Surgery combined with radiotherapy and chemotherapy can significantly improve the prognosis of LGG patients, but tumor progression is still unavoidable. As a form of posttranscriptional regulation, RNA editing (RE) has been reported to be involved in tumorigenesis and progression and has been intensively studied recently. MethodsSurvival data and RE data were subjected to univariate and multivariate Cox regression analysis and lasso regression analysis to establish an RE risk score model. A nomogram combining the risk score and clinicopathological features was built to predict the 1-, 3-, and 5-year survival probability of patients. The relationship among ADAR1, SOD2 and SOAT1 was verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) ResultsA risk model associated with RE was constructed and patients were divided into different risk groups based on risk scores. The model demonstrated strong prognostic capability, with the area under the ROC curve (AUC) values of 0.882, 0.938, and 0.947 for 1-, 3-, and 5-year survival predictions, respectively. Through receiver operating characteristic curve (ROC) curves and calibration curves, it was verified that the constructed nomogram had better performance than age, grade, and risk score in predicting patient survival probability. Apart from this functional analysis, the results of correlation analyses between risk differentially expressed genes (RDEGs) and RE help us to understand the underlying mechanism of RE in LGG. ADAR may regulate the expression of SOD2 and SOAT1 through gene editing. ConclusionIn conclusion, this study establishes a novel and accurate 17-RE model and a nomogram for predicting the survival probability of LGG patients. ADAR may affect the prognosis of glioma patients by influencing gene expression.

SQLE promotes osteosarcoma progression via activating TGFβ/SMAD signaling pathway

BackgroundThe prognosis of advanced osteosarcoma (OS) has remained stagnant in last decades, requiring the identification of novel therapeutic targets. Recently, much attention was paid to the role of squalene epoxidase (SQLE), a rate-limiting enzyme in cholesterol metabolism, in the field of oncology, while the specific role of SQLE in OS has not been sufficiently elucidated. The present study aims to investigate the role of SQLE in the progression of OS and explore the potential mechanisms. MethodsThe expression levels of SQLE in OS tissues and adjacent normal tissues were compared using bioinformatic methods and experiments. Kaplan-Meier survival analysis and univariate and multivariate Cox analysis were performed to detect the association of SQLE expression and patient’ prognosis. Stably cell lines with SQLE knockdown or overexpression were constructed by lentivirus infection. CCK-8, colony formation, scratch healing, and Transwell invasion assays were carried out to explore the effect of SQLE knockdown or overexpression on the proliferation, migration, and invasion of OS cells. Gene set enrichment analysis was conducted to reveal signaling pathways associated with SQLE expression. The effect of SQLE on TGFβ/SMAD signaling pathway were explored by Western blot assay. ResultsHere, we found a notable rise of SQLE expression in OS tissues and cell lines. Survival analysis showed that individuals with high SQLE expression had a lower median overall survival time compared to those with low SQLE expression. Univariate and multivariate Cox regression analyses showed that SQLE might have the potency to serve as an independently prognostic biomarker in OS. Loss- and gain-of-function experiments indicated that silence of SQLE suppressed OS cell proliferation, migration, and invasion, while overexpression of SQLE exerted the opposite effects. Mechanistically, TGF-β signaling pathway was identified as the downstream pathway of SQLE through bioinformatic methods, and the results of Western blot assay showed that SQLE positively regulated the activity of TGFβ1/SMAD2/3 signaling in OS. Resue experiments demonstrated that SB431542, a small molecule that inhibits TGFβ/SMAD signaling, could partly reverse the promoting effects of SQLE on OS cell proliferation, migration, and invasion. ConclusionOur results provided preliminary evidences that SQLE was a tumor-promoting factor and prognosis predictor in OS. SQLE promoted OS cell proliferation, migration, and invasion via activating TGFβ/SMAD signaling and targeting SQLE might be a potential strategy for the treatment of OS.

Ferroptosis-related lncRNA AL136084.3 is associated with NUPR1 in bladder cancer

BackgroundLncRNAs are critical regulators of bladder cancer (BLCA), and ferroptosis is a newly discovered cell death responsible for mediating apoptosis and tumorigenesis. The present study aims to establish a prognostic signature of differentially expressed ferroptosis-related lncRNAs (DEFRlncRNAs) and explore the DEFRlncRNA associated with NUPR1 in BLCA.MethodsDEFRlncRNAs in BLCA patients were screened using univariate and multivariate Cox and LASSO regression analyses. In vitro experiments were performed to detect the regulatory effects of DEFRlncRNAs on BLCA cells. A prognostic signature of DEFRlncRNAs in BLCA was created and validated. Moreover, we used RNA-binding protein immunoprecipitation (RIP) to evaluate the correlated DEFRlncRNA with NUPR1.ResultsA prognostic signature involving 18 DEFRlncRNAs in BLCA was created. Overexpression of AL355353.2 or knockdown of AL136084.3 promoted apoptosis in 5637 and T24 cells in vitro. Results from Starbase database estimated that AL136084.3 was positively associated with NUPR1 (R = 0.229, p < 0.001). RIP analysis revealed the reciprocal binding of NUPR1 and AL136084.3 in BLCA.ConclusionThe identified FRlncRNA pair signature has a good prognostic and clinical predictive value. The ferroptosis-related lncRNA AL136084.3 is correlated with NUPR1 in BLCA.

Novel signature of ferroptosis-related long non-coding RNA to predict lower-grade glioma overall survival

BackgroundFerroptosis is a novel type of programmed cell death in various tumors; however, underlying mechanisms remain unclear. We aimed to develop ferroptosis-related long non-coding RNA (FRlncRNA) risk scores to predict lower-grade glioma (LGG) prognosis and to conduct functional analyses to explore potential mechanisms.MethodsLGG-related RNA sequencing data were extracted from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Pearson correlation analysis was used to identify the FRlncRNAs, univariate Cox regression analysis was for identify the prognostic FRlncRNAs, and then intersection FRlncRNAs were screened between TCGA and CGGA. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to develop a risk score to predict LGG prognosis.ResultsA total of nine FRlncRNAs were screened to construct the novel prognostic risk score of LGG, and high-risk score patients had a worse overall survival than low-risk score patients both in TCGA and CGGA datasets. The risk score was quite correlated with clinicopathological characteristics (age, WHO grade, status of MGMT Methtlation, IDH mutation, 1p/19q codeletion, and TMB), and could promote current molecular subtyping systems. Comprehensive analyses revealed that signaling pathways of B-cell receptor and T-cell receptor, immune cells of macrophage cell and CD4+ T cell, tumor microenvironment of stroma score and immune score, and immune checkpoints of PD-1, PD-L1, and CTLA4 were all enriched in the high-risk score group.ConclusionThe nine FRlncRNAs risk scores was a promising biomarker to predict the LGG’s prognosis and distinguish the characteristics of molecular and immune.

Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer

BackgroundThe tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC).MethodsAfter downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, we classified the molecular subtypes using the “NMF” R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA).ResultsFrom data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group (P < 0.01). ROC curve analysis demonstrated strong predictive performance, with 1-year, 3-year, and 5-year AUC values ranging from 0.654 to 0.702 across different cohorts. The model accurately predicted survival outcomes across subgroups with varying clinical features, and its predictive accuracy was validated through a nomogram.ConclusionsWe developed a prognostic model based on TME-related genes in NSCLC. Our 18-gene TME signature can effectively predict the prognosis of NSCLC with high accuracy.

Comprehensive characterization of neddylation related genes in cutaneous melanoma identified a novel prognostic signature for treatment outcomes, immune landscape

Neddylation, as a post-translational modification, has garnered significant attention in various tumor types recently. Few studies have investigated the involvement of neddylation-related genes (NRGs) in cutaneous melanoma (CM). Our study aims to identify prognostic NRGs and investigate their potential roles in CM. The RNA-sequencing data and corresponding clinical data of CM patients were retrieved from The Cancer Genome Atlas (TCGA) database, while the expression profiles of 812 normal skin specimens were obtained from the Genotype-Tissue Expression (GTEx) database. The neddylation-related genes (NRGs) were extracted from the Molecular Signatures Database (MSigDB). We identified differentially expressed NRGs in CM and determined neddylation-related prognostic genes through univariate Cox regression analysis. We constructed a novel NRGs signature using LASSO-COX. The accuracy and utility of the NRGs signature were evaluated via a variety of statistical methods. Bioinformatics tools were employed to investigate the differential biological functions and signaling pathways among distinct risk groups. The expression levels of NRGs were analyzed through RT-qPCR experiments conducted in vitro. Finally, we identified an 8-NRGs signature in CM. Our prognostic model exhibited a high predictive capability for outcomes. The differences in the proportions of immune cells among subgroups were statistically significant. The in vitro experiments indicated significant differences in the expression of our NRGs. The 8-NRGs signature serves as a prognostic model for CM. Importantly, the novel biological prognostic model holds potential for personalized therapy in CM patients.

A novel disulfidptosis-related LncRNA prognostic risk model: predicts the prognosis, tumor microenvironment and drug sensitivity in esophageal squamous cell carcinoma

BackgroundDisulfidptosis is a newly discovered type of cell death that differs from apoptosis, necrosis, ferroptosis and other death modes and is closely related to the occurrence and progression of tumors. However, the predictive potential and biological characteristics of disulfidptosis-related lncRNAs (DRGs-lncRNAs) in esophageal squamous cell carcinoma (ESCC) are unclear.MethodsRNA transcriptome data, clinical information and mutation data for ESCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation and Cox regression analyses were used to identify the DRGs-lncRNAs associated with overall survival (OS). LASSO regression analysis was used to construct the prognostic model. A nomogram was created to predict the prognosis of patients with ESCC. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to identify the signaling pathways associated with the model. TIMER, CIBERSORT, ESTIMATE and other methods were used to analyze immune infiltration, immune function, immune checkpoints and drug sensitivity. The tumor mutation burden (TMB) were assessed between different risk groups. Real-time polymerase chain reaction (RT‒PCR) was used to detect the expression of DRGs-lncRNAs in ESCC cell lines.ResultsA total of 155 lncRNAs significantly associated with disulfidptosis were identified. Through univariate Cox regression analysis, LASSO regression analysis and multivariate Cox regression analysis, 9 lncRNAs with independent prognostic significance were selected, and a prognosis model was established. Survival analysis with the prognostic model revealed that there were obvious differences in survival between the high- and low-risk groups. Further analysis revealed that the immune microenvironment, immune infiltration, immune function, immune checkpoints, and drug sensitivity significantly differed between the high-risk and low-risk groups. Patients who exhibited both high risk and high tumor mutation burden (TMB) survived shorter, while those who fell into the low risk and low TMB categories survived longer. In addition, RT‒PCR analysis revealed differential expression of DRG lncRNAs between ESCC cell lines and esophageal epithelial cell lines.ConclusionsWe established a DRG-lncRNA prognostic model that can be used to predict the prognosis, tumor mutation burden, immune cell infiltration, and drug sensitivity of ECSS patients. The results of this study provide valuable insights into the understanding of ESCC and provide valuable assistance for the individualized treatment of ESCC patients.

Preoperative neutrophil-to-lymphocyte ratio (NLR) value as a predictor of recurrence of non-muscle-invasive bladder cancer.

To predict oncological outcomes and select appropriate treatments for non-muscle-invasive bladder cancer (NMIBC), pre-treatment predictors such as neutrophil-to-lymphocyte ratio (NLR) are being used. This study aims to evaluate whether NLR is an independent predictor of disease and disease recurrence in NMIBC patients. In this prospective clinical study, from March 2018 to March 2023, preoperative NLR values were monitored in 99 patients newly diagnosed with NMIBC, who were initially treated with trans-urethral resection of bladder tumor (TURBT) and adjuvant intravesical therapy. To evaluate the best NLR cutoff points to predict recurrence, the ROC (receiver operating characteristic) curve and the Youden index were used. The monitoring period was 24 months. With the cutoff value of NLR = 1.73, there is a statistically significant correlation (p = 0.008) between the NLR value and the increased risk of recurrence. Univariate and multivariate Cox regression analyses show the significant prognostic impact of NLR on the recurrence of the disease. The value of NLR > 1.73 is a significant preoperative predictor in risk assessment and will help with proper selection of treatment in the high- and intermediate-risk groups of patients.

Negative Association Between Schizophrenia and Subsequent Cancer Diagnoses—A Retrospective Cohort Study from Germany

Background: Since previous studies have reported contradictory findings regarding the relationship between schizophrenia and cancer, we evaluated the association between schizophrenia and cancer diagnoses. Methods: In this retrospective cohort study, the IQVIA Disease Analyzer database was utilized to examine the incidence of cancer among patients aged over 18 years diagnosed with schizophrenia in German general practices from 2005 to 2022. Patients with schizophrenia were compared with those without the condition, with adjustments made for age, sex, index year of diagnosis, average annual practitioners visit frequency, and comorbidity. Kaplan–Meier curves were used to analyze the 10-year cumulative incidence of schizophrenia and cancer in total amongst patients with and without schizophrenia. Univariate Cox regression analysis was performed to calculate Hazard Ratios (HR) of cancer risk and their 95% confidence intervals (CI) of cancer in total and of specific cancer types. Results: Patients with schizophrenia (N = 13.711) had a lower incidence of cancer diagnosis compared to those without (N = 68.555). Specifically, 10.4% of patients with schizophrenia and 12.5% of patients without the condition were diagnosed with cancer (p &lt; 0.001). Cox regression analysis showed a significant association between schizophrenia and subsequent cancer in the total population (HR: 0.82; 95% CI: 0.76–0.90), and among men (HR: 0.70; 95% CI: 0.61–0.80), but not among women (HR: 0.94, 95% CI: 0.84–1.04). Analyses stratified by cancer type and sex revealed a strong and significant association between schizophrenia and a decreased risk of prostate cancer in men (HR: 0.38; 95% CI: 0.24–0.61). Furthermore, there was also a negative association between schizophrenia and colorectal cancer risk in men, but statistical significance was not reached (HR: 0.58; 95% CI: 0.37–0.93). Conclusions: This study demonstrates negative associations between schizophrenia and subsequent cancer, and more specifically in men for prostate and colorectal cancer. However, further research is required to explore the underlying reasons for these associations.

A nomogram for predicting early mortality in patients with traumatic brain injury requiring mechanical ventilation based on clinical laboratory data

We developed an intuitive and user-friendly nomogram based on clinical laboratory data for early outcome predictions for patients with traumatic brain injury requiring mechanical ventilation who were admitted to the intensive care unit for the first time. We included 625 patients from the Medical Information Mart for Intensive Care IV 2.2 database as the training cohort and 107 patients from the Affiliated Dongyang Hospital of Wenzhou Medical University as the external validation cohort. The least absolute shrinkage and selection operator regression analysis, combined with univariate and multivariate Cox regression analyses, was used to identify independent risk factors. Age, sex, partial pressure of carbon dioxide, white blood cell count, creatinine levels, prothrombin time, and partial thromboplastin time were included in the nomogram. The area under the receiver operating characteristic curve was 0.811 for the training cohort and 0.770 for the external validation cohort. The calibration curves, clinical decision curve analysis, and clinical impact curve demonstrated that the nomogram had a good goodness-of-fit and clinical utility. We developed a nomogram based on clinical laboratory data for predicting the early (14-day) mortality rate of patients with traumatic brain injury requiring mechanical ventilation, which may help assess patient risk and decision-making.

Predicting survival in bladder cancer with a novel apoptotic gene-related prognostic model

BackgroundApoptosis and apoptotic genes play a critical role in the carcinogenesis and progression of bladder cancer. However, there is no prognostic model established by apoptotic genes.MethodsMessenger RNA (mRNA), Expression data, and related clinical data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. After extracting the apoptosis-related genes, the survival-related apoptosis genes were screened by univariate Cox regression analysis in the TCGA cohort. Following the Least Absolute Shrinkage and Selection Operator (LASSO) regression method, these genes were modeled by multivariate Cox analysis. The predictive abilities of the Apoptosis-Related Gene Model (ARGM) for overall survival (OS) rate, disease-specific survival (DSS) measures, and progression-free survival (PFS) were verified by the Kaplan–Meier(K-M)survival analysis and time-dependent Receiver Operating Characteristic (ROC) curve. Functional enrichment analyses were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG). CIBERSORT and Single-Sample Gene Set Enrichment Analysis (ssGSEA) were used to calculate the type of immune cell infiltration and immune functions. The model’s predictive ability for immunotherapy were evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and the Imvigor210 study.The single-cell sequencing was used to display the expression level of the ARGM.Finally,qRT-PCR was executed to validate the expression level of ARGM.ResultsSeveral apoptosis genes were identified through the model, including ANXA1, CASP6, CD2, F2, PDGFRB, SATB1, and TSPO. The prognostic value of the model for OS, DSS, and PFS were verified using the TCGA and GEO cohort. The model can predict patient response to immunotherapy treatment as established through the model’s score which was linked to different types of immune cell infiltration and identified significant differences in the signal pathways between high-risk and low-risk groups. Nomogram variables, prompted from ARGM and clinical parameters, also generate a high predictive value for patient survival.ConclusionOurestablished apoptosis-related gene model (ARGM) has a substantial predictive value for prognosis and immunotherapy of bladder cancer. It may help with clinical consultation, clinical stratification, and treatment selection. The immune infiltration status and signal pathway of different risk groups also provide direction for further research.

Study on predicting new onset heart failure events in patients with hypertrophic cardiomyopathy using machine learning algorithms based on clinical and magnetic resonance features

Objective: To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magnetic resonance (CMR) features based on machine learning algorithms. Methods: The study was a retrospective cohort study. Patients with a confirmed diagnosis of HCM who underwent CMR examinations at Beijing Anzhen Hospital from May 2017 to March 2021 were selected and randomly divided into the training set and the validation set in a ratio of 7∶3. Clinical data and CMR parameters (including conventional parameters and radiomics features) were collected. The endpoint events were heart failure hospitalization and heart failure death, with follow-up ending in January 2023. Features with high stability and P value<0.05 in univariate Cox regression analysis were selected. Subsequently, three machine learning algorithms-random forest, decision tree, and XGBoost-were used to build heart failure event prediction models in the training set. The model performance was then evaluated using the independent validation set, with the performance assessed based on the concordance index. Results: A total of 462 patients were included, with a median age of 51 (39, 62) years, of whom 332 (71.9%) were male. There were 323 patients in the training set and 139 in the validation set. The median follow-up time was 42 (28, 52) months. A total of 44 patients (9.5% (44/462)) experienced endpoint events (8 cases of heart failure death and 36 cases of heart failure hospitalization), with 31 events in the training set and 13 in the validation set. Univariate Cox regression analysis identified 39 radiomic features, 4 conventional CMR parameters (left ventricular end-diastolic volume index, left ventricular end-systolic volume index, left ventricular ejection fraction, and late gadolinium enhancement ratio), and 1 clinical feature (history of non-sustained ventricular tachycardia) that could be included in the machine learning model. In the prediction models built with the training set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.966 (95%CI 0.813-0.995), 0.956 (95%CI 0.796-0.992), and 0.973 (95%CI 0.823-0.996), respectively. In the validation set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.854 (95%CI 0.557-0.964), 0.706 (95%CI 0.399-0.896), and 0.703 (95%CI 0.408-0.890), respectively. Conclusion: Integrating clinical and CMR features of HCM patients through machine learning aids in predicting heart failure events, with the random forest model showing superior performance.

Identification of a novel prognostic signature for breast cancer derived from post-translational ubiquitin and ubiquitin-like modification-related genes.

Ubiquitin and ubiquitin-like (UUL) modifications play pleiotropic functions and are subject to fine regulatory mechanisms frequently altered in cancer. However, the comprehensive impact of UUL modification on breast cancer remains unclear. Transcriptomic and clinical data of breast cancer were downloaded from TCGA and GEO databases. Molecular subtyping of breast cancer was conducted using the NMF and CIBERSORT algorithms. Prognostic genes were identified via univariate, lasso and multivariate Cox regression analyses. Clinical pathological features, immune cell infiltration, immune therapeutic response and chemotherapy drug sensitivity were compared between groups using the Wilcoxon test. Survival analysis was performed using the Kaplan-Meier method and log-rank test. A total of 63 UUL modification-related genes were differentially expressed, with 29 up-regulated and 34 down-regulated genes. These genes were used to generate two UUL modification patterns that exhibited significant differences in prognostic features and immune cell infiltration. The UUL modification patterns were associated with 2038 differentially expressed genes that were significantly enriched in nuclear division, chromosome segregation, neuroactive ligand-receptor interaction, cell cycle, and other biological processes. Of these genes, 425 were associated with breast cancer prognosis, which enabled the classification of breast cancer into two clusters with significantly distinct prognoses. We developed a prognostic model, UULscore, which comprised nine genes and showed a significant correlation with partial immune cell infiltration. Furthermore, UULscore demonstrated potential predictive value in breast cancer overall survival prediction, immune therapeutic response, and chemotherapy drug sensitivity. UULscore, stage, radiotherapy, and chemotherapy were identified as independent prognostic factors for breast cancer. Based on these factors, a nomogram model was constructed, which demonstrated exceptional prognostic predictive performance. The present study identified two UUL modification-derived molecular subtypes in breast cancer, and have successfully constructed a risk-scoring model that holds potential value in prognosis, immune infiltration, immune therapeutic response, and chemotherapy sensitivity.

Neoadjuvant chemoradiotherapy versus neoadjuvant chemo-immunotherapy for locally advanced esophageal squamous cell carcinoma

Objective: To compare the clinical efficacy of neoadjuvant chemoradiotherapy (nCRT) and neoadjuvant chemo-immunotherapy (nCIT) for locally advanced esophageal squamous cell carcinoma (ESCC). Methods: Clinical data of patients who received nCRT or nCIT followed by esophagectomy for locally advanced ESCC between January 2010 and December 2022 were retrospectively collected from Zhejiang Cancer Hospital, with 155 patients in the nCRT group and 470 patients in the nCIT group. Propensity score matching (PSM) was performed in the two groups. After PSM, 120 patients were allocated to the nCRT group and 192 patients to the nCIT group. The pathological response and disease recurrence were compared between the two groups after PSM. Log rank test were used to compare the survival outcomes before and after PSM. Univariate and multivariate Cox regression analyses were performed to identify the prognostic factors for locally advanced ESCC. Results: After PSM, the R0 resection rate in the nCRT group and the nCIT group was 93.3% (112/120) and 93.8% (180/192), respectively, with no statistical significance (P=0.884). However, the pathological complete response rate in the nCRT group [36.7% (44/120)] was higher than that in the nCIT group [21.4% (41/192), P=0.003]. For patients with R0 resection, the major recurrence pattern was distant metastasis [18.8% (21/112)] in the nCRT group, while the pattern was locoregional recurrence [12.2% (22/180)] in the nCIT group. The 3-year disease-free survival rates were 52.7% and 66.1% (P=0.022) and the 3-year overall survival rates were 59.2% and 75.5% (P=0.002) in the nCRT and nCIT groups, respectively. Multivariate Cox regression analysis also revealed that the neoadjuvant therapy mode was an independent prognostic factor for patients with locally advanced ESCC. Compared with nCRT, nCIT could significantly prolong disease-free survival (HR=0.58, 95% CI: 0.40-0.86) and overall survival (HR=0.53, 95% CI: 0.35-0.79). Conclusion: These results suggest that nCIT could significantly improve disease-free survival rate and overall survival rate over nCRT in locally advanced ESCC, even with lower pathological complete response rate.

The predictive value of pan-immune inflammatory index for early recurrence of atrial fibrillation after cryoablation

ObjectiveThe pan-immune inflammatory (PIV) index holds prognostic value for cardiovascular diseases. This study aimed to investigate the predictive value of the PIV index regarding recurrence of atrial fibrillation (AF) after cryoballoon ablation (CBA).MethodsThe study included 307 patients with AF. Four inflammatory markers, namely, the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation (SII) index, and PIV index, were used as indicators. COX regression analysis was conducted to evaluate the predictive value of AF recurrence after CBA. A receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to evaluate the discriminative power of the indicators.ResultThe PIV index [94.9 (168.9,504.9) vs. 143.2 (98.2,210.6), P < 0.01] and SII index [366.3 (256.6,491.9) vs. 569.9 (658.1,438.4), P < 0.01] were significantly higher in the recurrence group. Univariable COX regression analysis showed that these four indices, persistent AF, and left atrial diameter (LAD) were all associated with AF recurrence. In multivariable regression analysis, the PIV index, persistent AF, and LAD (all P < 0.05) were independent predictors of postoperative AF recurrence. The ROC curve analysis showed that the PIV index had a higher predictive value for AF recurrence (AUC = 0.768, P < 0.01, 95% CI: 0.696–0.840) than the SII index and NLR. Kaplan–Meier analysis showed that patients with a PIV index > 260.7 had a higher recurrence rate at 1-year follow-up (P < 0.01). Subgroup analysis indicated that PIV had a predictive value in patients with different types of AF.ConclusionPIV index may be a potential biomarker for predicting relapse in patients with AF after CBA.

Exploiting potential molecular compounds for treating testicular seminoma by targeting immune related genes

BackgroundIn cases of advanced seminoma, up to 30% of patients may manifest cisplatin resistance, necessitating aggressive salvage therapy, with a consequent 50% risk of mortality attributable to cancer. Nevertheless, beyond chemotherapy and radiotherapy, no further therapeutic modalities have been implemented for these patients.MethodsThe study commenced with the identification of differentially expressed immune-related genes, which were subsequently subjected to clustering using WGCNA. Prognostic signature construction ensued through the execution of univariable Cox regression, lasso regression, and multivariable Cox regression analyses. To validate the prognostic signature, the TCGA-TGCT and GSE99420 cohorts were employed, with assessments conducted via PFS, C-index, DCA, and ROC analyses. Subsequent exploration of the immune landscape and potential immunotherapeutic applications was undertaken through Cibersort and TIDE analyses. Molecular docking and dynamics simulation techniques were then employed for screening potential molecular compounds. Validation of these findings was pursued through in vitro and vivo assays.ResultsCTLA4, SNX17, and TMX1 were selected to construct the signature. Patients in the high-risk group exhibited diminished progression-free survival rates. The AUC for predicting survival at 1, 3, and 5 years was 0.802, 0.899, and 0.943, respectively, surpassing those of other risk factors, such as lymphovascular invasion and T stage. The C-index for the risk score was 0.838. Decision curve analysis (DCA) suggests that incorporating lymphovascular invasion and the risk score yields the most favorable decision-making outcomes for patients. Moreover, individuals classified as high-risk may derive greater benefit from immunotherapy. Molecular compounds including Rutin, ICG-001, and Doxorubicin can selectively target CTLA4, SNX17, and TMX1, respectively, thereby inhibiting the proliferation and invasive capabilities of seminoma tumor cells in vitro and vivo.ConclusionThe signature initially constructed based on immune-related genes shows promise for predicting outcomes and assessing the efficacy of immunotherapy in seminoma patients. Rutin, ICG-001, and Doxorubicin have demonstrated potential to target these signature genes and inhibit tumor cell viability.Graphical