Cox Regression Analysis Research Articles

Clinical characteristics and prognosis analysis of gastric cancer patients with bone metastasis

Objectives: To investigate the clinical characteristics and prognosis of bone metastasis of gastric cancer, analyze the influencing factors of bone metastasis and the effects of different treatment methods, and provide a basis for early detection and treatment optimization of bone metastasis of gastric cancer. Methods: A total of 142 gastric cancer patients with bone metastasis admitted to the First Hospital of Lanzhou University from January 2011 to December 2021 were enrolled, including 60 cases of simple bone metastasis and 82 cases of bone metastasis combined with extraosseous metastasis. 142 patients with stage Ⅲgastric cancer without distant metastasis and 142 gastric cancer patients with visceral metastasis admitted to this hospital during the same period were also enrolled for comparison. Logistic regression analysis was used to determine the influencing factors of bone metastasis, and the Cox proportional hazards regression model was used to evaluate the influencing factors of overall survival (OS) of patients with bone metastasis. Results: Among the 142 patients with bone metastasis, poorly differentiated adenocarcinoma was the main type (123 cases), and 45 patients had simultaneous bone metastasis. Rib metastasis (100 cases), spine metastasis (88 cases), and pelvis metastasis (84 cases) were more common. A total of 110 patients had multiple bone metastasis, and 82 patients had extraosseous metastasis. Results of the stage Ⅲ gastric cancer group, the visceral metastasis group, the bone metastasis group, and the bone metastasis with extraosseous metastasis group were compared. There were significant differences in age, degree of differentiation, Borrmann type, alkaline phosphatase, lactate dehydrogenase, serum calcium, alanine aminotransferase, aspartate aminotransferase, creatine kinase isoenzyme, lymphocyte, hemoglobin, platelet, CEA, CA19-9, and CA724 (all P<0.05). Multivariate logistic regression analysis showed that Borrmann type was an independent protective factor of bone metastasis of gastric cancer (type 3: OR=0.07, 95%CI: 0.01-0.64, P=0.018). Alkaline phosphatase (OR=2.54, 95% CI: 1.07-6.01, P=0.034), serum calcium (OR=2.71, 95% CI: 1.15-6.41, P=0.023), creatine kinase isoenzyme (OR=16.33, 95% CI: 1.83-145.58, P=0.012), platelet (OR=10.08, 95% CI:1.89-53.85, P=0.007), and CA19-9 (OR=2.40, 95% CI: 1.14-5.05, P=0.021) were independent risk factors of bone metastasis of gastric cancer. The median OS of the stage Ⅲ gastric cancer group, the visceral metastasis group, the bone metastasis group, and the bone metastasis with extrabony group were 47, 13, 18, and 6 months, respectively, and the difference was statistically significant (P<0.001). The median OS of patients with bone metastasis only who underwent primary tumor surgery was 33 months, better than 6 months of patients without surgery (P=0.048). Multivariate Cox regression analysis showed that extraosseous metastasis (HR=2.45, 95% CI: 1.56-3.85, P<0.001) and decreased hemoglobin (HR=1.54, 95%CI: 1.02-2.34, P=0.042) were independent risk factors of OS of gastric cancer patients with bone metastasis. Conclusions: The prognosis of gastric cancer patients with bone metastasis alone is significantly better than that of other stage Ⅳ patients. For such patients, surgery on the primary site combined with chemotherapy after full evaluation may prolong the survival time.

Novel lactylation-related signature to predict prognosis for pancreatic adenocarcinoma

BACKGROUND Lactate, previously considered a metabolic byproduct, is pivotal in cancer progression and maintaining the immunosuppressive tumor microenvironment. Further investigations confirmed that lactate is a primary regulator, introducing recently described post-translational modifications of histone and non-histone proteins, termed lysine lactylation. Pancreatic adenocarcinomas are characterized by increased glycolysis and lactate accumulation. However, our understanding of lactylation-related genes in pancreatic adenocarcinomas remains limited. AIM To construct a novel lactylation-related gene signature to predict the survival of patients with pancreatic cancer. METHODS RNA-seq and clinical data of pancreatic adenocarcinoma (PDAC) were obtained from the GTEx (Genotype-Tissue Expression) and TCGA (The Cancer Genome Atlas) databases via Xena Explorer, and GSE62452 datasets from GEO. Data on lactylation-related genes were obtained from publicly available sources. Differential expressed genes (DEGs) were acquired by using R package “DESeq2” in R. Univariate COX regression analysis, LASSO Cox and multivariate Cox regressions were produced to construct the lactylation-related prognostic model. Further analyses, including functional enrichment, ESTIMATE, and CIBERSORT, were performed to analyze immune status and treatment responses in patients with pancreatic cancer. PDAC and normal human cell lines were subjected to western blot analysis under lactic acid intervention; two PDAC cell lines with the most pronounced lactylation were selected. Subsequently, RT-PCR was employed to assess the expression of LRGs genes; SLC16A1, which showed the highest expression, was selected for further investigation. SLC16A1-mediated lactylation was analyzed by immunofluorescence, lactate production analysis, colony formation, transwell, and wound healing assays to investigate its role in promoting the proliferation and migration of PDAC cells. In vivo validation was performed using an established tumor model. RESULTS In this study, we successfully identified 10 differentially expressed lactylation-related genes (LRGs) with prognostic value. Subsequently, a lactylation-related signature was developed based on five OS-related lactylation-related genes (SLC16A1, HLA-DRB1, KCNN4, KIF23, and HPDL ) using Lasso Cox hazard regression analysis. Subsequently, we evaluated the clinical significance of the lactylation-related genes in pancreatic adenocarcinoma. A comprehensive examination of infiltrating immune cells and tumor mutation burden was conducted across different subgroups. Furthermore, we demonstrated that SLC16A1 modulates lactylation in pancreatic cancer cells through lactate transport. Both in vivo and in vitro experiments showed that decreasing SLC16A1 Level and its lactylation significantly inhibited tumor progression, indicating the potential of targeting the SLC16A1/ Lactylation-associated signaling pathway as a therapeutic strategy against pancreatic adenocarcinoma. CONCLUSION We constructed a novel lactylation-related prognostic signature to predict OS, immune status, and treatment response of patients with pancreatic adenocarcinoma, providing new strategic directions and antitumor immunotherapies.

Endoscopic and Surgical Treatment of Gastrointestinal Neuroendocrine Neoplasms: A Population-based Comparative Study.

Controversy surrounds the indications for endoscopic treatment (ET) versus surgery in addressing gastrointestinal neuroendocrine neoplasms (GI-NENs). This paper aims to compare the long-term survival prognosis between ET and surgery for patients with GI-NENs. A retrospective analysis of GI-NEN patients diagnosed between 2000 and 2020 was conducted using the SEER database. Overall survival (OS) and cancer-specific survival (CSS) were estimated using the Kaplan-Meier method. Prognostic factors were assessed through univariate and multivariate Cox regression analyses. Propensity score matching (PSM) was employed to adjust for clinical variables. This study encompassed 12,016 patients with GI-NENs, with 3732 patients (31.1%) undergoing ET and 8284 patients (68.9%) opting for surgery. The rectum was the most frequent location for ET, while the small bowel was the predominant site for surgery. Both the ET and surgical groups exhibited similar overall survival risk and cancer-specific survival risk before and after matching. No significant differences in the 1-year, 3-year, 5-year, and 10-year OS and CSS rates were observed between the ET and surgery groups after PSM. Nevertheless, subgroup analysis revealed a significantly better CSS in the ET group than the surgery group, particularly in stage I and tumors sized <10mm (P<0.01). In the colon subgroup, the OS and CSS of the ET group were superior to those of the surgery group (P<0.05). Endoscopic treatment and surgery demonstrate comparable long-term survival prognoses for treating GI-NENs. ET emerges as a viable option for patients averse to surgical interventions.

Association of atrial mechanical dispersion with atrial fibrillation recurrence following catheter ablation: results of the ASTRA-AF pilot study.

For patients with symptomatic drug-refractory atrial fibrillation (AF), catheter ablation to achieve rhythm control is an important therapeutic option. The atrial mechanical dispersion measured as standard deviation of the time to peak strain (SD-TPS) is associated with the risk of AF recurrence following catheter ablation. The study cohort prospectively enrolled n = 132 consecutive patients with paroxysmal (n = 88) or persistent AF (n = 44) presenting for de novo pulmonary vein isolation (PVI) and followed for 1year. We related left atrial (LA) volume, LA ejection fraction, SD-TPS, and global longitudinal strain of the left ventricle and clinical variables (sex, age, and type of AF) to AF recurrence. Kaplan-Meier curves showed higher AF recurrence rate with an increase of SD-TPS with the calculated cut-off of 38.6ms. Uni- and multivariable Cox regression analysis could show that SD-TPS had the highest relevance regarding AF recurrence with a HR of 1.05 (95% CI, 1.01; 1.09, p = 0.01) and HR of 1.05 (95% CI, 1.01; 1.09, p = 0.02) per 10ms increase. In the additional analyses for the model including the clinical variables age, sex, and type of AF with paroxysmal or persisting AF, SD-TPS did only show a trend and after adjusting for covariates, SD-TPS showed a HR of 1.04 (95% CI, 0.99; 1.09, p = 0.09) per 10ms increase. Atrial mechanical dispersion was associated with recurrent AF.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82–0.84) and a balanced accuracy of 0.78 (95% CI 0.77–0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40–0.74). Quantitative PCR validated LEF1-AS1’s adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

Highly expressed of BID indicates poor prognosis and mediates different tumor microenvironment characteristics in clear cell renal cell carcinoma

BackgroundStudies have found that BH3 interacting domain death agonist (BID) is closely related to the occurrence and development of many kinds of tumors. However, little attention has been paid to the situation of BID in clear cell renal cell carcinoma (ccRCC). So, our aim was to explore the effect of BID in ccRCC.MethodsSurvival analysis, ROC curve, correlation analysis and Cox regression analysis were executed to analyze the prognostic value and clinical correlation of BID in ccRCC. The risk prognosis model was constructed in the training cohort and further validated in the internal testing cohort, ICGC cohort, and GEO cohort. Transcriptome sequencing and immunohistochemical staining of clinical specimens were used to validate the results of bioinformatics analysis. The GSEA, ESTIMATE algorithm, CIBERSORT algorithm, ssGSEA, TIDE score, correlation and difference analysis were used to analyze the effects of BID on immune infiltration in tumor microenvironment (TME).ResultsBID was highly expressed in ccRCC tissues, which was verified by transcriptome sequencing and immunohistochemical staining of clinical specimens. Patients with high expression of BID had a worse prognosis. BID is an independent prognostic factor for ccRCC. The prognostic model based on BID can accurately predict the prognosis of patients in different cohorts. In addition, the expression levels of BID was closely related to immunomodulatory molecules such as PD-1, LAG3, and CTLA4. Enrichment analysis indicated that BID was significantly enriched in immune-related responses and cancer-related pathways. The change of BID expression mediates different characteristics of immune infiltration in TME.ConclusionsBID is highly expressed in ccRCC, which is a reliable biomarker of ccRCC prognosis. It is closely related to TME, and may be a potential target for immunotherapy in patients with ccRCC.

Tertiary lymphoid structures as a potential prognostic biomarker for combined hepatocellular-cholangiocarcinoma.

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA), as a rare primary hepatic tumor, is challenging to accurately assess in terms of the clinical outcomes and prognostic risk factors in patients. This study aimed to clarify the function of tertiary lymphoid structure (TLS) status in predicting the outcome of cHCC-CCA and to preliminarily explore the possible mechanism of TLS formation. The TLSs, with different spatial distributions and densities, of 137 cHCC-CCA were quantified, and their association with prognosis was assessed by Cox regression and Kaplan-Meier analyses. We further validated TLS possible efficacy in predicting immunotherapy responsiveness in two cHCC-CCA case reports. TLS composition and its relationship to CXCL12 expression were analysed by fluorescent multiplex immunohistochemistry. A high intratumoural TLS score was correlated with prolonged survival, whereas a high TLS density in adjacent tissue indicated a worse prognosis in cHCC-CCA. Mature TLSs were related to favorable outcomes and showed more CD8 + T cells infiltrating tumor tissues. We further divided the cHCC-CCA patients into four immune grades by combining the peri-TLS and intra-TLS, and these grades were an independent prognostic factor. In addition, our reported cases suggested a potential value of TLS in predicting immunotherapy response in cHCC-CCA patients. Our findings suggested that CXCL12 expression in cHCC-CCA tissue was significantly correlated with TLS presence. The spatial distribution and density of TLSs revealing the characteristics of the cHCC-CCA immune microenvironment, significantly correlated with prognosis and provided a potential immunotherapy response biomarker for cHCC-CCA.

Identification of a methyltransferase-related long noncoding RNA signature as a novel prognosis biomarker for lung adenocarcinoma.

Lung adenocarcinoma (LUAD) accounts for a high proportion of tumor deaths globally, while methyltransferase-related lncRNAs in LUAD were poorly studied. In our study, we focused on two distinct cohorts, TCGA-LUAD and GSE3021, to establish a signature of methyltransferase-related long non-coding RNAs (MeRlncRNAs) in LUAD. We employed univariate Cox and LASSO regression analyses as our main analytical tools. The GSE30219 cohort served as the validation cohort for our findings. Furthermore, to explore the differential pathway enrichments between groups stratified by risk, we utilized Gene Set Enrichment Analysis (GSEA). Additionally, single-sample GSEA (ssGSEA) was conducted to assess the immune infiltration landscape within each sample. Reverse transcription quantitative PCR (RT-qPCR) was also performed to verify the expression of prognostic lncRNAs in both clinically normal and LUAD samples. In LUAD, we identified a set of 32 MeRlncRNAs. We further narrowed our focus to six prognostic lncRNAs to develop gene signatures. The TCGA-LUAD cohort and GSE30219 were utilized to validate the risk score model derived from these signatures. Our analysis showed that the risk score served as an independent prognostic factor, linked to immune-related pathways. Additionally, the analysis of immune infiltration revealed that the immune landscape in high-risk groups was suppressed, which could contribute to poorer prognoses. We also constructed a regulatory network comprising 6 prognostic lncRNAs, 19 miRNAs, and 21 mRNAs. Confirmatory RT-qPCR results aligned with public database findings, verifying the expression of these prognostic lncRNAs in the samples. The prognostic gene signature of LUAD associated with MeRlncRNAs that we provided, may offer us a comprehensive picture of the prognosis prediction for LUAD patients.

Programmed Cell Death-Related Gene Signature Associated with Prognosis and Immune Infiltration and the Roles of HMOX1 in the Proliferation and Apoptosis were Investigated in Uveal Melanoma.

Uveal melanoma (UVM) is the most common primary ocular malignancy, with a wide range of symptoms and outcomes. The programmed cell death (PCD) plays an important role in tumor development, diagnosis, and prognosis. There is still no research on the relationship between PCD-related genes and UVM. A novel PCD-associated prognostic model is urgently needed to improve treatment strategies. We aim to screen PCD-related prognostic signature and investigate its proliferation ability and apoptosis in UVM cells. The clinical information and RNA-seq data of the UVM patients were collected from the TCGA cohort. All the patients were classified using consensus clustering by the selected PCD-related genes. After univariate Cox regression and PPI network analysis, the prognostic PCD-related genes were then submitted to the LASSO regression analysis to build a prognostic model. The level of immune infiltration of 8-PCD signature in high- and low-risk patients was analyzed using xCell. The prediction on chemotherapy and immunotherapy response in UVM patients was assessed by GDSC and TIDE algorithm. CCK-8, western blot and Annexin V-FITC/PI staining were used to explore the roles of HMOX1 in UVM cells. A total of 8-PCD signature was constructed and the risk score of the PCD signature was negatively correlated with the overall survival, indicating strong predictive ability and independent prognostic value. The risk score was positively correlated with CD8 Tcm, CD8 Tem and Th2 cells. Immune cells in high-risk group had poorer overall survival. The drug sensitivity demonstrated that cisplatin might impact the progression of UVM and better immunotherapy responsiveness in the high-risk group. Finally, Overespression HMOX1 (OE-HMOX1) decreased the cell viability and induced apoptosis in UVM cells. Recuse experiment results showed that ferrostatin-1 (fer-1) protected MP65 cells from apoptosis and necrosis caused by OE-HMOX1. The PCD signature may have a significant role in the tumor microenvironment, clinicopathological characteristics, prognosis and drug sensitivity. More importantly, HMOX1 depletion greatly induced tumor cell growth and inhibited cell apoptosis and fer-1 protected UVM cells from apoptosis and necrosis induced by OE-HMOX1. This work provides a foundation for effective therapeutic strategy in tumour treatment.

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma.

To investigate the utility of deep learning (DL) automated segmentation-based MRI radiomic features and clinical-radiological characteristics in predicting early recurrence after curative resection of single hepatocellular carcinoma (HCC). This single-center, retrospective study included consecutive patients with surgically proven HCC who underwent contrast-enhanced MRI before curative hepatectomy from December 2009 to December 2021. Using 3D U-net-based DL algorithms, automated segmentation of the liver and HCC was performed on six MRI sequences. Radiomic features were extracted from the tumor, tumor border extensions (5 mm, 10 mm, and 20 mm), and the liver. A hybrid model incorporating the optimal radiomic signature and preoperative clinical-radiological characteristics was constructed via Cox regression analyses for early recurrence. Model discrimination was characterized with C-index and time-dependent area under the receiver operating curve (tdAUC) and compared with the widely-adopted BCLC and CNLC staging systems. Four hundred and thirty-four patients (median age, 52.0 years; 376 men) were included. Among all radiomic signatures, HCC with 5 mm tumor border extension and liver showed the optimal predictive performance (training set C-index, 0.696). By incorporating this radiomic signature, rim arterial phase hyperenhancement (APHE), and incomplete tumor "capsule," a hybrid model demonstrated a validation set C-index of 0.706 and superior 2-year tdAUC (0.743) than both the BCLC (0.550; p < 0.001) and CNLC (0.635; p = 0.032) systems. This model stratified patients into two prognostically distinct risk strata (both datasets p < 0.001). A preoperative imaging model incorporating the DL automated segmentation-based radiomic signature with rim APHE and incomplete tumor "capsule" accurately predicted early postsurgical recurrence of a single HCC. The DL automated segmentation-based MRI radiomic model with rim APHE and incomplete tumor "capsule" hold the potential to facilitate individualized risk estimation of postsurgical early recurrence in a single HCC. A hybrid model integrating MRI radiomic signature was constructed for early recurrence prediction of HCC. The hybrid model demonstrated superior 2-year AUC than the BCLC and CNLC systems. The model categorized the low-risk HCC group carried longer RFS.

Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses.

Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.

Development and internal validation of a clinical prediction model for osteopenia in Chinese middle-aged and elderly men: a prospective cohort study

BackgroundEarly identification of patients at risk of osteopenia is an essential step in reducing the population at risk for fractures. We aimed to develop and validate a prediction model for osteopenia in Chinese middle-aged and elderly men that provides individualized risk estimates.MethodsIn this prospective cohort study, 1109 patients who attend regular physical examinations in the Second Medical Centre of Chinese PLA General Hospital were enrolled from 2015.03 to 2015.09. The baseline risk factors included dietary habits, exercise habits, medical histories and medication records. Osteopenia during follow-up were collected from Electronic Health Records (EHRs) and telephone interviews. Internal validation was conducted using bootstrapping to correct the optimism. The independent sample T-test analysis, Mann_Whitney U test, Chi-Square Test and multivariable Cox regression analysis were utilized to identify predictive factors for osteopenia in Chinese middle-aged and elderly men. A nomogram based on the seven variables was built for clinical use. Concordance index (C-index), receiver operating characteristic curve (ROC), decision curve analysis (DCA) and calibration curve were used to evaluate the efficiency of the nomogram.ResultsThe risk factors included in the prediction model were bone mineral density at left femoral neck (LNBMD), hemoglobin (Hb), serum albumin (ALB), postprandial blood glucose (PBG), fatty liver disease (FLD), smoking and tea consumption. The C-index for the risk nomogram was 0.773 in the prediction model, which presented good refinement. The AUC of the risk nomogram at different time points ranged from 0.785 to 0.817, exhibiting good predictive ability and performance. In addition, the DCA showed that the nomogram had a good clinical application value. The nomogram calibration curve indicated that the prediction model was consistent.ConclusionsOur study provides a novel nomogram and a web calculator that can effectively predict the 7-year incidence risk of osteopenia in Chinese middle-aged and elderly men. It is convenient for clinicians to prevent fragility fractures in the male population.

Inter-relationships of risk factors and pathways associated with all-cause mortality in patients with chronic schizophrenia

IntroductionOf all psychiatric disorders, schizophrenia is associated with the highest risk of all-cause mortality. This study aimed to investigate independent risk factors for all-cause mortality in patients with chronic schizophrenia. In addition, the possible causal inter-relationships among these independent risk factors and all-cause mortality were also explored.MethodsWe conducted an analysis of 1,126 patients with chronic schizophrenia from our psychiatric department from April 2003 to August 2022, and retrospectively reviewed their medical records. The study endpoint was all-cause mortality. Baseline clinical characteristics including sociodemographic data, biochemical data, lifestyle factors, comorbidities and antipsychotic treatment were examined with Cox proportional hazards analysis.ResultsThe all-cause mortality rate was 3.9% (44 patients). Multivariate Cox regression analysis revealed that several factors were independently associated with all-cause mortality, including diabetes mellitus (DM), hypertension, heart failure, gastroesophageal reflux disease (GERD), peptic ulcer disease, ileus, underweight, fasting glucose, triglycerides, albumin, and hemoglobin. Structural equation modeling (SEM) analysis revealed that several factors had statistically significant direct effects on all-cause mortality. Heart failure, hypertension, underweight, age at onset, and ileus showed positive direct effects, while albumin and hemoglobin demonstrated negative direct effects. In addition, several factors had indirect effects on all-cause mortality. GERD indirectly affected all-cause mortality through ileus, and peptic ulcer disease had indirect effects through albumin and ileus. Ileus, underweight, DM, and hypertension also exhibited indirect effects through various pathways involving albumin, hemoglobin, and heart failure. Overall, the final model, which included these factors, explained 13% of the variability in all-cause mortality.DiscussionThese results collectively suggest that the presence of DM, hypertension, heart failure, GERD, peptic ulcer disease, ileus, and underweight, along with lower levels of albumin or hemoglobin, were independently associated with all-cause mortality. The SEM analysis further revealed potential causal pathways and inter-relationships among these risk factors contributing to all-cause mortality in patients with chronic schizophrenia.

Geriatric Nutritional Risk Index is associated with adverse outcomes in patients with hypertension: the Fukushima Cohort study.

Malnutrition is reportedly associated with adverse clinical outcomes in various populations. However, associations between nutritional status and adverse outcomes in patients with hypertension have not been sufficiently elucidated. We therefore aimed to investigate the impact of nutritional status as evaluated by the Geriatric Nutritional Risk Index (GNRI) on adverse outcomes in patients with hypertension. We conducted a retrospective cohort study of 1588 hypertensive patients enrolled in the Fukushima Cohort Study. Participants were categorized into tertiles (T1-T3) according to GNRI at baseline. The primary endpoint of the present study was a kidney event, defined as a combination of a 50% decline in eGFR from baseline and end-stage kidney disease requiring kidney replacement therapy. Associations between GNRI and kidney events were assessed using Kaplan-Meier curves and multivariate Cox regression analyses. Median age was 64 years, 55% were men, median eGFR was 63.1 mL/min/1.73 m2, and median GNRI was 101.3. The lower GNRI group (T1) showed an increased incidence of kidney events in the Kaplan-Meier curve analysis. Compared to the highest GNRI group (T3), lower GNRI carried a higher risk of kidney events for both T2 (hazard ratio [HR] 1.38, 95% confidence interval [CI] 0.71-2.68) and T1 (HR 3.59, 95%CI 1.96-6.63). Similar relationships were observed for risks of all-cause death and cardiovascular events. Lower GNRI was associated with kidney events, all-cause death, and cardiovascular events in patients with hypertension. Nutritional status as evaluated by GNRI could offer a simple and useful predictor of adverse outcomes in this population.

Computed tomography machine learning classifier correlates with mortality in interstitial lung disease

BackgroundA machine learning classifier system, Fibresolve, was designed and validated as an adjunct to non-invasive diagnosis in idiopathic pulmonary fibrosis (IPF). The system uses a deep learning algorithm to analyze chest computed tomography (CT) imaging. We hypothesized that Fibresolve is a useful predictor of mortality in interstitial lung diseases (ILD). MethodsFibresolve was previously validated in a multi-site >500-patient dataset. In this analysis, we assessed the usefulness of Fibresolve to predict mortality in a subset of 228 patients with IPF and other ILDs in whom follow up data was available. We applied Cox regression analysis adjusting for the Gender, Age, and Physiology (GAP) score and for other known predictors of mortality in IPF. We also analyzed the role of Fibresolve as tertiles adjusting for GAP stages. ResultsDuring a median follow-up of 2.8 years (range 5 to 3434 days), 89 patients died. After adjusting for GAP score and other mortality risk factors, the Fibresolve score significantly predicted the risk of death (HR: 7.14; 95% CI: 1.31–38.85; p = 0.02) during the follow-up period, as did forced vital capacity and history of lung cancer. After adjusting for GAP stages and other variables, Fibresolve score split into tertiles significantly predicted the risk of death (p = 0.027 for the model; HR 1.37 for 2nd tertile; 95% CI: 0.77–2.42. HR 2.19 for 3rd tertile; 95% CI: 1.22–3.93). ConclusionsThe machine learning classifier Fibresolve demonstrated to be an independent predictor of mortality in ILDs, with prognostic performance equivalent to GAP based solely on CT images.

Sleep disturbances, cognitive decline, and AD biomarkers alterations in early Parkinson's disease.

We aimed to investigate whether each type of sleep disturbances (i.e., pRBD, EDS, and insomnia) is specifically associated with faster decline in global cognition and different cognitive domains among de novo PD patients. We also assessed the influence of sleep disturbances on core AD CSF biomarkers alterations and conversion to dementia. Prospectively longitudinal data were obtained from the PPMI cohort. Sleep disturbances and cognition ability were assessed by questionnaires at baseline and follow-up visits. Generalized linear mixed models were utilized to assess the effect of sleep disturbances on cognitive decline and core AD CSF biomarkers change. The associations between sleep disturbances and conversion to dementia were analyzed using Cox regression analysis. Baseline pRBD was associated with faster decline in global cognition and all cognitive domains, including verbal episodic memory, visuospatial ability, executive function, language, and processing speed. EDS was associated with faster decline in three cognitive domains, including verbal episodic memory, executive function/working memory, and processing speed. Insomnia was associated with faster decline in global cognition and verbal episodic memory. Meanwhile, pRBD and EDS were associated with longitudinal decrease of CSF Aβ42. Baseline pRBD increased the risk of conversion to dementia. The risk of dementia in PD patients with multiple sleep disturbances also increased compared with those without sleep disturbance. Sleep disturbances (i.e., pRBD, EDS, and insomnia) were associated with cognitive decline in early PD. EDS and pRBD were associated with decrease of CSF Aβ42. Moreover, pRBD was associated with conversion to dementia.

Incidence and predictors of post‐surgery atrial fibrillation occurrence: A cohort study in 53,387 patients

AbstractIntroductionAtrial fibrillation (AF) represents the most common arrhythmia in the postoperative setting. We aimed to investigate the incidence of postoperative AF (POAF) and determine its predictors, with a specific focus on inflammation markers.MethodsWe performed a retrospective single tertiary center cohort study including consecutive adult patients who underwent a major surgical procedure between January 2016 and January 2020. Patients were divided into four subgroups according to the type of surgery.ResultsAmong 53,387 included patients (79.4% male, age 64.5 ± 9.5 years), POAF occurred in 570 (1.1%) with a mean latency after surgery of 3.4 ± 2.6 days. Ninety patients died (0.17%) after a mean of 13.7 ± 8.4 days. The 28‐day arrhythmia‐free survival was lower in patients undergoing lung and cardiovascular surgery (p &lt; .001). Patients who developed POAF had higher levels of C‐reactive protein (CRP) (0.70 ± 0.03 vs. 0.40 ± 0.01 log10 mg/dl; p &lt; .001). In the multivariable Cox regression analysis, adjusting for confounding factors, CRP was an independent predictor of POAF [HR per 1 mg/dL increase in log‐scale = 1.81 (95% CI 1.18–2.79); p = .007]. Moreover, independent predictors of POAF were also age (HR/1 year increase = 1.06 (95% CI 1.04–1.08); I &lt; .001), lung and cardiovascular surgery (HR 23.62; (95% CI 5.65–98.73); p &lt; .001), and abdominal and esophageal surgery (HR 6.26; 95% CI 1.48–26.49; p = .013).ConclusionsLung and cardiovascular surgery had the highest risk of POAF in the presented cohort. CRP was an independent predictor of POAF and postsurgery inflammation may represent a major driver in the pathophysiology of the arrhythmia.

Risk factor analysis and nomogram development and verification for medullary carcinoma of the colon using SEER database

Medullary Carcinoma of the Colon (MCC) is a rare histological subtype of colon cancer, and there is currently no recognized optimal treatment plan for it, with its prognosis remaining unclear. The aim of this study is to analyze the independent prognostic factors for MCC patients and develop and validate nomograms to predict overall survival (OS). A total of 760 patients newly diagnosed with MCC from 2004 to 2020 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly allocated to a training group and a validation group in a 7:3 ratio. Univariate and multivariable Cox regression analyses were conducted to identify prognostic factors and construct nomograms. The nomogram prediction model was evaluated and validated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The study found that elderly women are more susceptible to MCC, and the ascending colon and cecum are the most common sites of involvement. MCC is poorly differentiated, with stages II and III being the most common. Surgery is the primary treatment for MCC. The prognosis for patients with stage IV MCC is poor, with a median survival time of only 10 months. Independent prognostic factors for MCC include age, N stage, M stage, surgery, chemotherapy, and tumor size. Among them, age < 75 years and completion of chemotherapy were protective factors for colon medullary carcinoma, while N2 (HR = 2.18, 95%CI 1.40–3.38), M1 (HR = 3.31, 95%CI 2.01–5.46), no surgery (HR = 27.94, 95%CI 3.69–211.75), and tumor diameter > 7 cm (HR = 1.66, 95%CI 1.20–2.30) were risk factors for colon medullary carcinoma. The results of ROC, AUC, calibration curves, and DCA demonstrate that the nomogram prediction model exhibits good predictive performance. We have updated the demographic characteristics of colon medullary carcinoma and identified age, N staging, M staging, surgery, chemotherapy and tumor size as independent prognostic factors for colon medullary carcinoma. Additionally, we have established nomograms for prognostic prediction. These nomograms can provide personalized predictions and serve as valuable references for clinical decision-making.

The mitochondria-related gene risk mode revealed p66Shc as a prognostic mitochondria-related gene of glioblastoma.

Numerous studies have highlighted the pivotal role of mitochondria-related genes (MRGs) in the initiation and progression of glioblastoma (GBM). However, the specific contributions of MRGs coding proteins to GBM pathology remain incompletely elucidated. The identification of prognostic MRGs in GBM holds promise for the development of personalized targeted therapies and the enhancement of patient prognosis. We combined differential expression with univariate Cox regression analysis to screen prognosis-associated MRGs in GBM. Based on the nine MRGs, the hazard ratio model was conducted using a multivariate Cox regression algorithm. SHC-related survival, pathway, and immune analyses in GBM cohorts were obtained from the Biomarker Exploration of the Solid Tumor database. The proliferation and migration of U87 cells were measured by CCK-8 and transwell assay. Apoptosis in U87 cells was evaluated using flow cytometry. Confocal microscopy was employed to measure mitochondrial reactive oxygen species (ROS) levels and morphology. The expression levels of SHC1 and other relevant proteins were examined via western blotting. We screened 15 prognosis-associated MRGs and constructed a 9 MRGs-based model. Validation of the model's risk score confirmed its efficacy in predicting the prognosis of patients with GBM. Furthermore, analysis revealed that SHC1, a constituent MRG of the prognostic model, was upregulated and implicated in the progression, migration, and immune infiltration of GBM. In vitro experiments elucidated that p66Shc, the longest isoform of SHC1, modulates mitochondrial ROS production and morphology, consequently promoting the proliferation and migration of U87 cells. The 9 MRGs-based prognostic model could predict the prognosis of GBM. SHC1 was upregulated and correlated with the prognosis of patients by involvement in immune infiltration. Furthermore, in vitro experiments demonstrated that p66Shc promotes U87 cell proliferation and migration by mediating mitochondrial ROS production. Thus, p66Shc may serve as a promising biomarker and therapeutic target for GBM.

A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels

Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.