Univariate Cox Regression Analysis Research Articles

Association between urinary incontinence and mortality risk among US adults: a prospective cohort study

BackgroundUrinary incontinence is frequent in the general population and affects men and women of all ages. UI carries an unsuspected load on the healthcare system. To investigate whether urinary incontinence is associated with all-cause, cardiovascular disease (CVD) and cancer mortality among US adults.MethodsThe analyzed data was collected from the 2007–2016 National Health and Nutrition Examination Survey (NHANES) data. A total of 25,182 US participants with complete information about follow-up information and urinary incontinence (UI) were included in this cohort study. Univariate Cox regression analyses, the UpSet plot, multivariate Cox regression analysis, subgroup analysis of all-cause mortality, and Kaplan-Meier survival curve were employed to support the research objectives.ResultsA total of 25,182 participants had a mean age of 49.8 ± 17.8 years, with 49.3% of them being male and 50.7% of them being female. In the unadjusted model, we found that the risk of all-cause mortality increased by 12% (95% CI 1.03–1.22, P = 0.008), the risk of CVD mortality increased by 21% (95% CI 1.07–1.36, P = 0.002), and the risk of Cancer mortality increased by 8% (95% CI 0.95–1.22, P = 0.243) among individuals with urinary incontinence. After adjusting for multiple variables, we found that the risk of all-cause mortality in patients with urinary incontinence decreased by 1% (95% CI 0.9–1.09), but this decrease was statistically insignificant (P = 0.868), and the risk of Cancer mortality decreased by 3% (95% CI 0.84–1.12, P = 0.686). The association between urinary incontinence and mortality risk were stable in stratified analyses.ConclusionsIn our study, we found that urinary incontinence itself is an independent risk factor for death. The association between urinary incontinence and mortality became less significant after adjusting for covariates, this is a common occurrence in statistical analysis. Future research, with larger sample sizes and more robust study designs, is needed to further elucidate the complex relationship between urinary incontinence and mortality risk.

Key platelet genes play important roles in predicting the prognosis of sepsis

Sepsis is a life-threatening organ malfunction induced by an imbalanced immunological reaction to infection in the host. Many studies have utilized traditional RNA sequencing (RNA-seq) data to identify important biological targets to predict sepsis prognosis. However, alterations in core cells and functional status cannot be effectively detected in sepsis patients. The goal of this study was to identify key cells through single-cell RNA-seq (scRNA-seq), and combine bulk RNA-seq data and multiple algorithm analysis to construct a stable prognostic model for sepsis. The scRNA-seq and bulk RNA-seq data from sepsis patients were collected from the Gene Expression Omnibus (GEO) database. The R package “Seurat” was used to process the scRNA-seq data. Cell communication was investigated using the R package “CellChat”. The pseudo-time of the cells was calculated using the R package “monocle”. The R package “limma” was used to identify differentially expressed genes (DEGs) between the sepsis group and the control group. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules. Eight kinds of machine learning and 90 algorithm combinations were used to construct the prognostic model for sepsis. Quantitative real-time PCR (qRT‒PCR) was performed to determine the expression of key genes in the cecal ligation and puncture (CLP)-induced sepsis mouse model. The immunological status and related properties of DEGs were then investigated in the high- and low-risk groups delineated by the model. By combining the scRNA-seq data from nine samples, 13 clusters and 9 cell types were identified. CellChat analysis revealed that the number and strength of interactions between platelets and a variety of cells increased. We identified key platelet genes from the scRNA-seq data and combined these genes and the results of differential analysis and WGCNA of the bulk RNA-seq data. After univariate Cox regression analysis, we calculated the Cindex of the model constructed by the combination of 90 algorithms, and we finally determined the “CoxBoost + Lasso” combination. Multivariate Cox regression was used to construct the final prognostic model. The qRT-PCR results revealed significant differences in five key prognostic genes between the CLP and sham groups. The data was classified into high- and low-risk groups based on the model score. The high-risk group had a poorer survival rate and less immune infiltration. We identified the importance of platelets in sepsis patients through scRNA-seq, and established prognostic models with key genes that were identified via scRNA-seq combined with bulk RNA-seq analysis. The results of this model were closely associated with patient survival rates and immunological status and this model is useful for the prognostic management of sepsis.

Application of a risk score model based on glycosylation-related genes in the prognosis and treatment of patients with low-grade glioma

IntroductionLow-grade gliomas (LGG) represent a heterogeneous and complex group of brain tumors. Despite significant progress in understanding and managing these tumors, there are still many challenges that need to be addressed. Glycosylation, a common post-translational modification of proteins, plays a significant role in tumor transformation. Numerous studies have demonstrated a close relationship between glycosylation modifications and tumor progression. However, the biological function of glycosylation-related genes in LGG remains largely unexplored. Their potential roles within the LGG microenvironment are also not well understood.MethodsWe collected RNA-seq data and scRNA-seq data from patients with LGG from TCGA and GEO databases. The glycosylation pathway activity scores of each cluster and each patient were calculated by irGSEA and GSVA algorithms, and the differential genes between the high and low glycosylation pathway activity score groups were identified. Prognostic risk profiles of glycosylation-related genes were constructed using univariate Cox and LASSO regression analyses and validated in the CGGA database.ResultsAn 8 genes risk score signature including ASPM, CHI3L1, LILRA4, MSN, OCIAD2, PTGER4, SERPING1 and TNFRSF12A was constructed based on the analysis of glycosylation-related genes. Patients with LGG were divided into high risk and low risk groups according to the median risk score. Significant differences in immunological characteristics, TIDE scores, drug sensitivity, and immunotherapy response were observed between these groups. Additionally, survival analysis of clinical medication information in the TCGA cohort indicated that high risk and low risk groups have different sensitivities to drug therapy. The risk score characteristics can thus guide clinical medication decisions for LGG patients.ConclusionOur study established glycosylation-related gene risk score signatures, providing new perspectives and approaches for prognostic prediction and treatment of LGG.

Clinicopathological, immunohistochemical and therapeutic approaches on survival in patients with epithelioid glioblastoma: Institutional experience in the management of 58 patients

Epithelioid glioblastoma (Ep-GBM) is a rare variant of glioblastoma characterized by a high recurrence rate and poor prognosis. Currently, there is no established standard treatment for Ep-GBM. Therefore, we identified 58 Ep-GBM cases to investigate these characteristics and identify the possible prognostic factors of survival. There were 30 male and 28 female patients with a median age of 39 years. Headaches and dizziness were the most common clinical symptom. The tumor is most frequently located in the temporal lobe (36.2%). The positivity rate for BRAF-V600E is 56.9% (33/58), for MGMT is 56.9% (33/58), and for INI-1 is 75% (30/40). Tumor recurrence was observed in 39 patients. The median progression-free survival (PFS) of all patients was 12.7 months, while the median overall survival (OS) was 29.1 months. Additionally, the median survival time after recurrence was 14.3 months. Both univariate and multivariate COX regression analyses revealed that individuals who received more than six cycles of adjuvant oral temozolomide experienced a longer median PFS compared to those who received fewer cycles. Characteristics associated with poorer PFS included tumor dissemination prior to initial surgery. Additionally, both analyses identified tumor dissemination, radiotherapy and adjuvant oral temozolomide as predictors of OS. Notably, for patients with recurrent Ep-GBM, reoperation was shown to significantly increase survival time after recurrence. In conclusion, the standard Stupp regimen is also applicable to patients with Ep-GBM, extending adjuvant oral temozolomide could further improve survival for Ep-GBM patients, reoperation may also prolong survival for recurrent Ep-GBM.

Prognostic significance of pretreatment PET parameters in inoperable, node-positive NSCLC patients with poor prognostic factors undergoing hypofractionated radiotherapy: a single-institution retrospective study

BackgroundNode-positive non-small cell lung cancers (NSCLCs) present a challenge for treatment decisions, particularly in patients ineligible for concurrent chemoradiotherapy (CRT) due to poor performance status and compromised lung function. We aimed to investigate the prognostic value of pretreatment positron emission tomography (PET) parameters in high-risk patients undergoing hypofractionated radiotherapy.MethodsA retrospective analysis was conducted on 42 consecutive patients with inoperable node-positive NSCLC, who underwent hypofractionated radiotherapy between 2014 and 2021 at a single institution. Clinical, treatment-related, and [18F]FDG PET-based parameters were correlated with progression-free survival (PFS) and overall survival (OS). Median dichotomisation was performed to establish risk groups. Statistical analyses included univariable and multivariable Cox regression and Kaplan-Meier survival analyses.ResultsAfter a median follow-up of 47.1 months (range: 0.5-101.7), the median PFS and OS were 11.5 months (95% CI: 7.4-22.0), and 24.3 months (95% CI: 14.1-31.8). In univariable Cox regression analysis, significant predictors of PFS included receipt of salvage systemic treatment (p=0.007), SUVmax (p=0.032), and tMTV (p=0.038). Similarly, ECOG-PS (p=0.014), Histology (p=0.046), and tMTV (p=0.028) were significant predictors of OS. Multivariable Cox regression analysis (MVA) identified SUVmax as a significant predictor for PFS [HR: 2.29 (95% CI: 1.02-5.15); p=0.044]. For OS, ECOG-PS remained a significant prognosticator [HR: 3.53 (95% CI: 1.49-8.39); p=0.004], and tMTV approached significance [HR: 2.24 (95% CI: 0.95-5.26); p=0.065]. Furthermore, the high tMTV group exhibited a median PFS of 5.3 months [95% CI: 2.8-10.4], while the low tMTV group had a PFS of 15.2 months [95% CI: 10.1-33.5] (p=0.038, log-rank test). Median OS was 33.5 months [95% CI: 18.3-56.8] for tMTV ≤ 36.6 ml vs. 14.1 months [95% CI: 8.1-27.2] for tMTV > 36.6 ml (p=0.028, log-rank test).ConclusionPretreatment PET parameters, especially tMTV, hold promise as prognostic indicators in NSCLC patients undergoing hypofractionated radiotherapy. The study highlights the potential of PET metrics as biomarkers for patient stratification.

T cell exhaustion methylation signature drives differential immune responses in glioblastoma.

Methylation-related signatures play crucial roles in tumorigenesis and progression. However, their roles in the immune response in primary glioblastoma (GBM) remains unclear. We analyzed the differential expression of specific members of T cell exhaustion-related pathways in GBM from the perspective of T cell exhaustion. We further screened for significantly negatively correlated methylation sites as candidate methylation markers for T cell exhaustion. Using consensus clustering, we divided the samples into two categories with significant differences in overall survival (OS). We then performed univariate and multivariate Cox regression analyses to construct the T Cell Exhaustion Methylation (TEXM) signature. Finally, we confirmed that this signature served as an independent prognostic factor, and further characterized it in terms of drug resistance and immunotherapy. We identified 95 significantly differentially expressed T cell exhaustion-related genes and 51 methylation markers associated with T cell exhaustion. The cancer samples were classified according to methylation site markers, thus indicating two subtypes with significant differences in OS: subtype A and subtype B. Tumor scores, stromal scores, tumor purity, and ESTIMATE scores all showed significant differences between subtypes (P < 0.05). Univariate Cox regression analysis identified five methylation sites significantly associated with OS, and multivariate Cox regression analysis was used to construct the TEXM signature model by using these five methylation sites. Significant differences in OS were found between the groups with high and low TEXM signature scores, on the basis of calculation of the TEXM signature scores of tumor samples and using the median score to divide them into high and low score groups. Survival analysis revealed that the high score group had poorer OS and DFS than the low score group in the validation set. Notably, we observed a significant difference in drug sensitivity between the high and low TEXM signature score groups, with the high score group showing higher drug resistance and poorer prognosis. The tumor immune state, as predicted with Tracking Tumor Immunophenotype (TIP), revealed significant differences in antitumor immune scores between the high and low TEXM signature score groups. Finally, we identified 43 significantly differentially regulated metabolism-associated biological processes. The epigenetic methylation-related TEXM signature plays a key role in driving differential immune responses in GBM.

The prognostic significance of synchronous metastasis in glioblastoma multiforme patients: a propensity score-matched analysis using SEER data

BackgroundGlioblastoma multiforme (GBM) with synchronous metastasis(SM) is a rare occurrence. We extracted the data of GBM patients from the SEER database to look into the incidence of SM in GBM, determine the prognostic significance of SM in GBM, and assess therapeutic options for patients presenting with SM.MethodsFrom 2004 to 2015, information on GBM patients was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The propensity score matching (PSM) method was employed to mitigate confounding factors between SM and non-SM groups, subsequently investigating the prognostic significance of SM in patients with GBM. Multivariate Cox proportional hazards regression analyses were employed to identify independent prognostic variables for GBM patients with SM. A forest plot was used to visualize the results.ResultsA cohort of 19,708 patients was obtained from the database, among which 272 (1.4%) had SM at the time of diagnosis. Following PSM at a 3:1 ratio, in both univariate and multivariate cox regression analysis, SM (HR = 1.27, 95% CI: 1.09–1.46) was found to be an independent predictive predictor for GBM patients. Furthermore, the Cox proportional hazard forest plot demonstrated that independent risk variables for GBM patients with SM included age (Old vs. Young, HR = 1.44, 95% CI: 1.11–1.88), surgery (biopsy vs. no surgery, HR = 0.67, 95% CI: 0.46–0.96;Subtotal resection vs. no surgery, HR = 0.47, 95% CI: 0.32–0.68;Gross total resection vs. no surgery, HR = 0.44, 95% CI: 0.31–0.62), radiotherapy (HR = 0.58, 95% CI: 0.41–0.83), and chemotherapy (HR = 0.51, 95% CI: 0.36–0.72).ConclusionThe predictive value of SM in GBM was determined by this propensity-matched analysis using data from the SEER database. Radiotherapy, chemotherapy, and surgery constitute an effective treatment regimen for patients with SM. A more positive approach toward the use of aggressive treatment for GBM patients with SM may be warranted.

A disulfidptosis-related lncRNA signature for analyzing tumor microenvironment and clinical prognosis in hepatocellular carcinoma

IntroductionDisulfidptosis is a recently identified form of non-apoptotic programmed cell death which distinguishes itself from classical cell death pathways. However, the prognostic implications of disulfidptosis-related long non-coding RNAs (DRLs) and their underlying mechanisms in hepatocellular carcinoma (HCC) remain largely unexplored.MethodsIn this study, we leveraged RNA-sequencing data and clinical information of HCC patients from the TCGA database. Through expression correlation and prognostic correlation analyses, we identified a set of top-performing long non-coding RNAs. Subsequently, a 5-DRLs predictive signature was established by conducting a Lasso regression analysis.ResultsThis signature effectively stratified patients into high- and low-risk groups, revealing notable differences in survival outcomes. Further validation through univariate and multivariate Cox regression analyses confirmed that the risk score derived from our signature independently predicted the prognosis of HCC patients. Moreover, we observed significant disparities in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups, shedding light on the potential connection between immune-related mechanisms and disulfidptosis. Notably, the signature also exhibited predictive value in the context of chemotherapeutic drug sensitivity and immunotherapy efficacy for HCC patients. Finally, we performed experimental validation at both cellular and patient levels and successfully induced a disulfidptosis phenotype in HCC cells.DiscussionIn general, this multifaceted approach provides a comprehensive overview of DRLs profiles in HCC, culminating in the establishment of a novel risk signature that holds promise for predicting prognosis and therapy outcomes of HCC patients.

RNA-seq and bulk RNA-seq data analysis of cancer-related fibroblasts (CAF) in LUAD to construct a CAF-based risk signature

Angiogenesis, metastasis, and resistance to therapy are all facilitated by cancer-associated fibroblasts (CAFs). A CAF-based risk signature can be used to predict patients’ prognoses for Lung adenocarcinoma (LUAD) based on CAF characteristics. The Gene Expression Omnibus (GEO) database was used to gather signal-cell RNA sequencing (scRNA-seq) data for this investigation. The GEO and TCGA databases were used to gather bulk RNA-seq and microarray data for LUAD. The scRNA-seq data were analyzed using the Seurat R program based on the CAF markers. Our goal was to use differential expression analysis to discover differentially expressed genes (DEGs) across normal and tumor samples in the TCGA dataset. Pearson correlation analysis was utilized to discover prognostic genes related with CAF, followed by univariate Cox regression analysis. Using Lasso regression, a risk signature based on CAF-related prognostic genes was created. A nomogram model was created based on the clinical and pathological aspects. 5 CAF clusters were identified in LUAD, 4 of which were associated with prognosis. From 2811 DEGs, 1002 genes were found to be significantly correlated with CAF clusters, which led to the creation of a risk signature with 8 genes. These 8 genes were primarily connected with 41 pathways, such as antigen paocessing and presentation, apoptosis, and cell cycle. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for LUAD, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of LUAD. CAF-based risk signatures can be effective in predicting the prognosis of LUAD, and they may provide new strategies for cancer treatments by interpreting the response of LUAD to immunotherapy.

Establishment and Validation of Prognostic Nomograms for Nonmetastatic Melanoma of the Limbs—A SEER-Based Study

Background Malignant melanoma, a highly aggressive skin cancer, has remarkable incidence and mortality nowadays. This study aims to explore prognostic factors associated with nonmetastatic cutaneous melanoma of the limbs and to develop nomograms for predicting overall survival (OS) and cancer-specific survival (CSS). Methods The study cohort was derived from the Surveillance, Epidemiology, and End Results database. Univariate Cox regression, Lasso regression, and multivariate Cox regression analyses were conducted to identify prognostic factors and construct nomograms. The receiver operating characteristic (ROC) curve, time-dependent C-index, calibration curve, decision curve analysis (DCA) and Kaplan-Meier method were used to evaluate the accuracy and clinical applicability of the nomograms. Results A total of 15,606 patients were enrolled. Multivariate analysis identified several prognostic factors for OS and CSS including age, sex, histologic type, N stage, tumor thickness, depth of invasion, mitotic rate, ulceration, surgery of primary site, systemic therapy, race, and number of lymph nodes examined. A nomogram incorporating 12 independent predictors for OS was developed, with a C-index of 0.866 (95% confidence interval [CI]: 0.858–0.874) in the training cohort and 0.853 (95% CI: 0.839–0.867) in validation. For CSS, 10 independent predictors and one related factor were included, yielding a C-index of 0.913 (95% CI: 0.903–0.923) in the training cohort and 0.922 (95% CI: 0.908–0.936) in validation. The ROC curve, time-dependent C-index, calibration curve, DCA, and K-M plot demonstrated favorable discrimination, calibration, and clinical utility. Conclusion The developed nomograms provide a precise and personalized predictive tool for risk management of patients with nonmetastatic limb melanoma.

Association of Granulocyte Colony-Stimulating Factor Treatment with Risk of Brain Metastasis in Advanced Stage Breast Cancer

The routine use of granulocyte colony-stimulating factor (GCSF) is not recommended for the prevention or treatment of chemotherapy-induced neutropenia or febrile neutropenia because risks associated with certain types of cancers, distant organ metastases, and primary tumor growth cannot be excluded. We examined the association between GCSF use and the incidence of brain metastasis (BM), as well as BM-free survival (BMFS). This retrospective cohort study included 121 stage IV breast cancer patients without confirmed BM at the time of diagnosis and who received at least one course of systematic chemotherapy or target therapy at a tertiary teaching hospital between 1 January 2014 and 31 December 2022. The effect of GCSF use on BM was assessed with other confounding factors in Cox regression analyses. In this retrospective cohort, patients who received GCSF treatment had a significantly higher incidence of BM than those who did not (34.9% vs. 13.8%, p = 0.011). Univariate Cox regression analysis showed that GCSF use, menopause status, hormone treatment, HER2 treatment, cumulative dosage, dosage density, and neutropenia were independent risk factors for BMFS (p &lt; 0.05). GCSF users had a higher risk of BM (adjusted HR: 2.538; 95% CI: 1.127–5.716, p = 0.025) than nonusers. BM risk was significantly associated with those with neutropenia (RR: 1.84, 95% CI: 1.21, 2.80) but not with those without neutropenia (RR: 0.59, 95% CI: 0.41–0.84, Interaction p-value &lt; 0.05). The higher the dose density of GCSF, the higher the risk compared with those who do not use GCSF (p for trend &lt; 0.01). These preliminary results suggest that GCSF is associated with BM in patients with stage IV breast cancer who did not have BM at initial diagnosis. Further comprehensively designed large-scale observational studies are needed to confirm our preliminary results.

Construction and validation of cell cycle-related prognostic genetic model for glioblastoma

Glioblastoma (GBM) is a common primary malignant brain tumor and the prognosis of these patients remains poor. Therefore, further understanding of cell cycle-related molecular mechanisms of GBM and identification of appropriate prognostic markers and therapeutic targets are key research imperatives. Based on RNA-seq expression datasets from The Cancer Genome Atlas database, prognosis-related biological processes in GBM were screened out. Gene Set Variation Analysis (GSVA), LASSO-COX, univariate and multivariate Cox regression analyses, Kaplan–Meier survival analysis, and Pearson correlation analysis were performed for constructing a predictive prognostic model. A total of 58 cell cycle-related genes were identified by GSVA and analysis of differential expression between GBM and control samples. By univariate Cox and LASSO regression analyses, 8 genes were identified as prognostic biomarkers in GBM. A nomogram with superior performance to predict the survival of GBM patients was established regarding risk score, cancer status, recurrence type, and mRNAsi. This study revealed the prognostic value of cell cycle-related genes in GBM. In addition, we constructed a reliable model for predicting the prognosis of GBM patients. Our findings reinforce the relationship between cell cycle and GBM and may help improve the prognostic assessment of patients with GBM. Our predictive prognostic model, based on independent prognostic factors, enables tailored treatment strategies for GBM patients. It is particularly useful for subgroups with uncertain prognosis or treatment challenges.

Clinical impacts of concomitant left atrial appendage occlusion during mitral valve surgery in patients with mitral regurgitation

Surgical occlusion of the left atrial appendage (LAA) during cardiac surgery in patients with atrial fibrillation (AF) is known to reduce thromboembolism. However, data on the clinical significance of LAA occlusion (LAAO) in patients with mitral regurgitation (MR) are lacking. A total of 237 AF patients with chronic severe MR who underwent mitral valve (MV) surgery were retrospectively analyzed. Patients were divided into two groups according to concomitant LAAO or LAA preservation. The primary outcome was a composite of all-cause death and thromboembolic events (ischemic stroke or systemic embolism). The LAA was surgically occluded in 98 (41%) patients and preserved in 139 (59%) patients. During the follow-up period (median, 37 months), 29 primary outcomes occurred. In the Kaplan–Meyer analysis, the LAA preservation group showed a greater cumulative incidence of the primary outcome (P = 0.002) and thromboembolic events (P = 0.003) than the LAAO group. In the univariate Cox regression analysis, coronary artery disease, CHA2DS2-VASc score, a cauliflower-shaped LAA, Maze, and no LAAO were significantly associated with the primary outcome. In the multivariate Cox regression analysis, concomitant LAAO was significantly linked to the primary outcome (hazard ratio [HR]: 0.30, 95% confidence interval [CI]: 0.10–0.91, P = 0.033) and thromboembolic events (HR: 0.19, 95% CI: 0.04–0.87, P = 0.032). These benefits from LAAO were consistent, even after propensity score–matched analysis. For patients undergoing surgery for chronic MR who also have AF, concomitant surgical LAAO is associated with favorable clinical outcome.

Patterns of Recurrence after Curative Intent Hepatic Resection for Colorectal Liver Metastasis

BackgroundImproved surgical techniques and more intensive systemic therapy have increased the number of oligometastatic colorectal cancer patients eligible for resection, but a significant percentage of these patients will ultimately recur. Furthermore, distinct recurrence patterns have been associated with different outcomes. MethodsData for 195 patients who underwent curative-intent colorectal liver metastasis (CRLM) resection from 2016-2022 at Johns Hopkins Hospital were retrospectively collected. Cox regression univariate and multivariate analysis identified features associated with survival outcomes. Association between risk factors and site of recurrences was conducted via logistic regression with initial recurrences grouped into intrahepatic-only, extrahepatic-only, and concurrent intra- and extrahepatic recurrence. ResultsThe 1- and 2-year recurrence free survival (RFS) rates were 46% and 22% respectively. The 1- and 2-year overall survival (OS) rates were 95% and 88% respectively. Median OS was 71.7 months. Multivariate analysis identified age <60 years old, N2 nodal status, R1 liver margin, and higher preoperative carcinoembryonic antigen as top prognostic factors for worse RFS. Additionally, patients with left-sided primary tumors had higher risk of intrahepatic-only recurrence while mutant KRAS was associated with higher risk of extrahepatic recurrence with or without liver recurrence. Discussion or ConclusionThese results from a recent cohort of patients treated with current standard of care therapies identifies features associated with elevated recurrence risk and specific recurrence patterns. These insights into CRLM recurrence patterns support a larger prospective study to identify subgroups of patients who may require additional therapies to prevent recurrence.

Is DEB-TACE as locoregional therapy before liver transplantation for hepatocellular carcinoma effective?

BackgroundThe objectives were to assess the safety and efficacy of drug-eluting bead transarterial chemoembolization (DEB-TACE) as locoregional therapy (LRT) in hepatocellular carcinoma (HCC) before liver transplantation (LT) beyond Hangzhou criteria (HC) and to analyze the prognostic factors.MethodsForty patients with HCC beyond HC who received DEB-TACE only before LT were retrospectively analyzed between January 2017 and December 2022. Data on patient demographics, disease characteristics, treatment response, and adverse events (AE) were collected. Overall survival (OS) and recurrence-free survival (RFS) were evaluated with Kaplan-Meier curves. Univariate and multivariate Cox regression analyses were performed to identify factors independently associated with RFS and OS.ResultsAll patients successfully underwent LT following DEB-TACE with a mean interval of 2.3 months. The objective response rates (ORRs) for these patients following DEB-TACE was 82.5%. The primary AE was post-embolization syndrome (PES), with affected patients experiencing grades I and II. The median RFS and OS were 12.0 months (95%CI: 0.0-30.1) and 52.0 months (95%CI: 11.8–92.2) over the follow-up period until December 2022. The 2-year RFS and OS rates were 42.5%, and 67.5%. Multivariate analyses revealed Child-Pugh classification (HR = 6.24; 95%CI,1.83–21.24; P = 0.01) and macrovascular invasion (MAV) (HR = 3.89; 95%CI,1.07–14.15; P = 0.04) were both significant independent predictors of OS.ConclusionsDEB-TACE can serve as a safe and effective LRT in HCC patients beyond HC before LT, and can improve the prognosis of patients, especially without MAV. The higher Child–Pugh classification and MAV are independent prognostic factors after LT.

The prognostic role of whole-body volumetric positron emission tomography/computed tomography parameters in treatment naive colorectal cancer patients with liver metastases.

The present study aimed to predict the prognostic role of quantitative 18F-fluorodeoxyglucose PET/computed tomography parameters such as maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) obtained from primary tumor, lymph node metastases, and liver metastasis (LM) in patients with colorectal LM (CLM). The research was designed as a retrospective study and 66 patients with CLM were enrolled between January 2017 and December 2018. Primary tumor SUVmax (PSUVmax), liver SUVmax (LSUVmax), and lymph node SUVmax (LnSUVmax) values obtained from the primary tumor, liver, and lymph nodes were recorded. In addition, total MTV (TMTV) and total TLG (TTLG) values were obtained by summing the values obtained from the primary tumor (PMTV and PTLG), lymph nodes (LnMTV and LnTLG), and liver (LMTV and LTLG). Univariate and multivariate Cox regression analysis was used to measure the effects of prognostic variables on mortality and survival. In univariate Cox regression analysis, PMTV (P = 0.001), LnMTV (P = 0.008), LnTLG (P = 0.008), LnSUVmax (P = 0.047), and TTLG (P = 0.038) were identified as prognostic factors for overall survival. No statistically significant relationship was found between MTV and TLG values of LM and overall survival. In multivariate analysis, PMTV (P = 0.022) was identified as an independent prognostic factor. In conclusion, our study demonstrated that the PMTV value used in evaluating treatment-naive patients diagnosed with CLM is an independent prognostic factor for survival. Our results need to be confirmed with more studies involving more patients.

Leukocyte Indices as Markers of Inflammation and Predictors of Outcome in Heart Failure with Preserved Ejection Fraction

Background: The pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF) is suggested to be influenced by inflammation. Leukocyte indices, including the neutrophil–lymphocyte ratio (NLR), the monocyte–lymphocyte ratio (MLR), and the pan-immune inflammation value (PIV), can be utilized as biomarkers of systemic inflammation. Their prognostic utility is yet to be fully understood. Methods: Between December 2010 and May 2023, patients presenting to a tertiary referral center for HFpEF were included into a prospective registry. The association of the NLR, MLR, and PIV with the composite endpoint of all-cause mortality and HF-related hospitalization was tested utilizing Cox regression analysis. Results: In total, 479 patients (median 74.3, interquartile range (IQR): 69.22–78.3 years, 27.8% male) were included. Patients were observed for 43 (IQR: 11–70) months, during which a total of 267 (55.7%) patients met the primary endpoint. In a univariate Cox regression analysis, an above-the-median NLR implied a hazard ratio (HR) of 1.76 (95%-confidence interval (CI): 1.38–2.24, p &lt; 0.001), an MLR of 1.46 (95%-CI: 1.14–1.86, p = 0.003), and a PIV of 1.67, 95%-CI: 1.30–2.13, p &lt; 0.001) for the composite endpoint. After adjustment in a step-wise model, the NLR (HR: 1.81, 95%-CI: 1.22–2.69, p = 0.003), the MLR (HR: 1.57, 95%-CI: 1.06–2.34, p = 0.026), and the PIV (HR: 1.64, 95%-CI: 1.10–2.46, p = 0.015) remained significantly associated with the combined endpoint. Conclusions: The NLR, the MLR, and the PIV are simple biomarkers independently associated with outcomes in patients with HFpEF.

Construction and Validation of a Prognostic Model Based on Pyroptosis-related Genes in Bladder Cancer.

Bladder cancer (BCa) is a highly prevalent disease with a poor prognosis. There is no better forecasting method for it yet. Current studies demonstrate that pyroptosis is involved in the development and progression of various cancers. This study employed bioinformatics techniques to analyze the data of BCa patients obtained from the TCGA and GEO databases in order to construct a prognostic risk model. The TCGA dataset was used for the training set, and the multiple external datasets (including GSE13507, GSE31684, GSE48075, IMvigor210, and GSE32894) were applied as the validation sets. Prognostic-associated pyroptosis genes screened by univariate Cox regression analysis were utilized to construct the lasso Cox regression model. GO and KEGG analysis results identified the selected genes that are primarily involved in the inflammation and cell death processes. The related patients were grouped into low- and high-risk groups. Kaplan-Meier survival analysis was performed to compare survival differences between the risk groups. The accuracy of this risk prediction model was assessed by ROC. We also applied the Human Protein Atlas (HPA) to detect the protein expression of these genes. Subsequently, qRT-PCR was performed to verify the expression of these model genes. There are 29 pyroptosis-related genes with significant expression differences between BCa and corresponding adjacent tissues, and 11 genes (SH2D2A, CHMP4C, MRFAP1L1, GBP2, EHBP1, RAD9A, ANXA1, TMEM109, HEYL, APOL2, ORMDL1) were picked by univariate and LASSO Cox regression analysis. Immunological cell infiltration and ssGSEA results further indicated that the low and high-risk groups were substantially correlated with the immune status of BCa patients. According to TCGA and multiple external datasets, Kaplan-Meier survival curves showed the overall survival rate of the high-risk group to be decreased. ROC curves showed the model established to be accurate and reliable. Moreover, the HPA database also demonstrated the verification of the modeled genes' expression in BCa and normal bladder tissue using the HPA database. qRT-PCR results also suggested the up-regulated EHBP1 and down-regulated RAD9A mRNA expression levels to be confirmed in 15 pairs of BCa and corresponding adjacent tissues. This study presents the development and validation of a novel gene signature associated with pyroptosis, which holds the potential for predicting patient outcomes in BCa and providing insights into the immune microenvironment of BCa.

Correlation between prognosis and peripheral blood levels of NLRP3 and triglyceride-glucose index after myocardial ischemia-reperfusion injury

ObjectiveWe aim to investigate the association between prognosis and outcomes following myocardial ischemia-reperfusion injury, as well as peripheral blood levels of NLRP3 and the triglyceride-glucose index (TyG).MethodsA total of 100 patients who underwent emergency coronary intervention following myocardial infarction confirmed by coronary angiography at our hospital between October 2021 and May 2023 were included in this study. Patients were stratified into two groups based on their prognoses: the control group (n = 73), which did not experience new myocardial infarctions or require hospitalization for heart failure or suffer sudden cardiac death post-interventional treatment; and the observation group (n = 27), which experienced one or more cardiovascular events post-treatment. Patient demographics were obtained from clinical records while biochemical analyses assessed peripheral blood triglycerides, blood glucose levels, and TyG index. Additionally, ELISA measurements determined levels of NLRP3 as well as inflammatory factors IL-6, TNF-α, and CRP in peripheral blood samples. Cardiac function was evaluated according to NYHA standards. Univariable Cox regression analysis identified factors influencing patient prognosis while Pearson correlation analysis examined relationships among prognosis, outcomes following myocardial ischemia-reperfusion injury, TyG index, and peripheral blood NLRP3.ResultsNo significant differences were observed in the general characteristics between the two patient groups (P > 0.05). However, the observation group exhibited higher levels of peripheral blood triglycerides, blood glucose, and TyG index compared to the control group (P < 0.05). Additionally, levels of NLRP3 and inflammatory factors IL-6, TNF-α, and CRP were elevated in the observation group compared to the control group (P < 0.05). Cardiac function impairment was more pronounced in the observation group (P < 0.05). Notably, TyG index and peripheral blood NLRP3 demonstrated higher risk ratios compared to other biomarkers (P < 0.05), indicating their significance in prognosis and outcomes. Elevated levels of NLRP3 and TyG index were associated with poorer recovery of cardiac function, increased rehospitalization rates, and higher mortality (P < 0.05).ConclusionElevated NLRP3 levels and an increased TyG index are strongly associated with impaired cardiac function and heightened risk of cardiovascular events. These findings suggest that these biomarkers may serve as crucial prognostic indicators following myocardial ischemia-reperfusion injury.

Risk stratification and overall survival prediction in extensive stage small cell lung cancer after chemotherapy with immunotherapy based on CT radiomics

The prognosis of extensive-stage small cell lung cancer is usually poor. In this study, a combined model based on pre-treatment CT radiomics and clinical features was constructed to predict the OS of extensive-stage small cell lung cancer after chemotherapy with immunotherapy.Clinical data of 111 patients with extensive stage small-cell lung cancer who received first-line immunotherapy combined with chemotherapy in our hospital from December 2019 to December 2021 were retrospectively collected. Finally, 93 patients were selected for inclusion in the study, and CT images were obtained through PACS system before treatment. All patients were randomly divided into a training set (n = 66) and a validation set (n = 27). Images were imported into ITK-SNAP to outline areas of interest, and Python software was used to extract radiomics features. A total of 1781 radiomics features were extracted from each patient’s images. The feature dimensions were reduced by MRMR and LASSO methods, and the radiomics features with the greatest predictive value were screened. The weight coefficient of radiomics features was calculated, and the linear combination of the feature parameters and the weight coefficient was used to calculate Radscore. Univariate cox regression analysis was used to screen out the factors significantly associated with prognosis from the radiomics and clinical features, and multivariate cox regression analysis was performed to establish the prognosis prediction model of extensive stage small cell lung cancer. The degree of metastases was selected as a significant clinical prognostic factor by univariate cox regression analysis. Seven radiomics features with significance were selected by LASSO-COX regression analysis, and the Radscore was calculated according to the coefficient of the radiomics features. An alignment diagram survival prediction model was constructed by combining Radscore with the number of metastatic lesions. The study population was stratified into those who survived less than 11 months, and those with a greater than 11 month survival. The C-index was 0.722 (se = 0.044) and 0.68(se = 0.074) in the training and the validation sets, respectively. The Log_rank test results of the combination model were as follows: training set: p < 0.0001, validation set: p = 0.00042. In this study, a combined model based on radiomics and clinical features could predict OS in patients with extensive stage small cell lung cancer after chemotherapy with immunotherapy, which could help guide clinical treatment strategies.