Univariate Cox Research Articles

Multi-omics analysis revealed the regulation mode of intratumor microorganisms and microbial signatures in gastrointestinal cancer.

Gastrointestinal cancer is one of the most common malignant tumors in the world, and its incidence rate is always high. In recent years, research has shown that microorganisms may play a broad role in the diagnosis, pathogenesis, and treatment of cancer. In this study, samples were first classified according to the microbial expression data of Gastrointestinal cancer, followed by functional enrichment and Immunoassay. In order to better understand the role of intratumor microorganisms in the prognosis, we screened gene signatures and constructed risk model through univariate cox and lasso regression and multivariable cox, then screened microbial signatures using zero-inflated model regression model and constructed risk index (RI), and finally predicted the immunotherapeutic effect of the risk model. The results indicate that the composition of tumor microorganisms in the C3 subtype is closely related to tumor angiogenesis, and there is a significant difference in the proportion of innate and acquired immune cells between the C2 and C1 subtypes, as well as differences in the physiological functions of immune cells. There are significant differences in the expression of microbial signatures between high and low risk subtypes, with 9 microbial signatures upregulated in high risk subtypes and 15 microbial signatures upregulated in low risk subtypes. These microbial signatures were significantly correlated with the prognosis of patients. The results of immunotherapy indicate that immunotherapy for high-risk subtypes is more effective. Overall, we analyze from the perspective of microorganisms within tumors, pointing out new directions for the diagnosis and treatment of cancer.

Abstract 13333: Review of Simultaneous Kidney and Heart Transplant After 2018: UNOS Data

Introduction: In October 2018, the Organ Procurement and Transplant Network (OPTN) adopted a 6-tier system for organ allocation where priority was given to critically ill patients, especially those on Extra Corporeal Membrane Oxygenation (ECMO) support. The guideline lacks information about multiorgan transplant due to weak evidence on Simultaneous Kidney and Heart transplant (SKHT) data. Hence with this study, we aim to review outcomes of patients on ECMO at the time of SKHT. Method: This was a retrospective study of adult SKHT recipients between 2018-2022 from the OPTN database. The study excluded individuals who underwent simultaneous multiple organ transplants, isolated heart transplants, as well as those with incomplete outcome information. The cohort was stratified by ECMO need at the time of transplant. Univariate cox regression analysis was performed for predictors of worse outcomes. Patient survival was compared using Kaplan Meier (KM) curve and log rank test. Results: A total of 1383 patients were included in this study, of which 72 (5.2%) were on ECMO bridge. On review of baseline characteristics, patients on ECMO had mean wait time of 65 (± 190) days with higher mortality rate post-transplant and a lower mean survival time as compared to the non-ECMO counterparts (398 (± 404) vs 545 (± 462) days, p= 0.008). On analysis of KM curve, SKHT recipients on ECMO bridge had higher mortality within the first 10 months of transplant and plateaued after that. The only predictors associated with worst outcomes among ECMO recipients were dialysis requirement and development of stroke post-transplant. On the contrary, Left Ventricular assisted device (LVAD) use prior to transplant was associated with 70% reduction in mortality in non-ECMO group (HR: 0.30 (95% CI: 0.11 - 0.83, p=0.02)) in this cohort. Conclusion: Following the change in guidelines, around 5.2% of the total SKHT recipients were on the ECMO bridge. Dialysis requirement and development of stroke post-transplant was independently associated with higher mortality compared to non ECMO group. On the contrary, patients on LVAD support at the time of transplant have had better outcomes.Figure 1:

Epidemiology and Independent Prognostic Factors of Patients with Hepatosplenic T-Cell Lymphoma over the Past 2 Decades

Background: Hepatosplenic T-cell lymphoma (HSTCL) is a rare and aggressive peripheral T-cell lymphoma with historically dismal outcomes, representing less than one percent of non-Hodgkin lymphomas. Given its rarity, the true incidence of HSTCL is unknown and most data have been extrapolated through case reports. To the best of our knowledge, the largest and most up to date study addressing the epidemiology and outcomes of patients with HSTCL in the United States covered a period from 1996 to 2014, with a sample size of 122 patients. The aim of this study is to investigate the clinical characteristics, survival outcomes, and independent prognostic factors of patients with neuroblastoma over the past two decades with a larger sample size. Methods: A total of 186 patients diagnosed with HSTCL, between 2000 and 2017, were ultimately enrolled in our study by retrieving data from the Surveillance, Epidemiology, and End Results (SEER) database. We analyzed demographics, clinical characteristics, and overall mortality (OM) as well as cancer-specific mortality (CSM) of HSTCL. Variables with a p value <0.01 in the univariate Cox regression were incorporated into the multivariate Cox model to determine the independent prognostic factors, with a hazard ratio (HR) of greater than 1 representing adverse prognostic factors. Results: Our cohort had a male predominance (68.82%). Most patients were diagnosed between the age of 40- and 59-year-old (36.02%) and HSTCL was least common among patients 80 years old and older (6.45%). Non-Hispanic whites (60.75%) and non-Hispanic blacks (20.97%) were most represented. Univariate cox proportional hazard regression analyses of factors affecting all-cause mortality revealedhigher overall mortality among non-Hispanic black patients (HR= 1.97, 95% CI 1.27-3.07, p<0.01). CSM was higher among non-Hispanic blacks (HR= 2.34, 95% CI 1.47-3.72, p<0.01) and patients with distant metastasis (HR= 2.23, 95% CI 1.24-4.02, p<0.01). Multivariate cox proportional hazard regression analyses of factors affecting CSM revealedhigher mortality in patients aged 80 or older (HR= 4.72, 95% CI 1.14-19.54, p<0.05) and non-Hispanic blacks (HR= 2.02, 95% CI 1.08-3.7, p<0.05). Surgery, radiation and B symptoms did not affect the overall mortality, nor did they affect the CSM. Conclusion: Overall the prognosis of this rare malignancy is very dismal. In this United States population-based retrospective cohort study using the SEER database, we found that non-Hispanic blacks and patients 80 and above had a higher CSM. Furthermore, cancer directed surgery, marital status and distant metastasis did not affect mortality. While one would expect older patients to have a poorer prognosis, it remains unclear why one racial/ethnic group would be at higher risk as well. This data paves the way for larger prospective studies addressing factors associated with worse prognosis in non-Hispanic blacks, such as delay in treatment, which has been shown to worsen mortality in this racial/ethnic group in other malignancies.

Myelomonocytic/Monocytic Subtypes Are More Resistant to Venetoclax-Based Therapy in Acute Myeloid Leukemia Patients: A Monocentric, Real-Life Retrospective Study

Background： Venetoclax(VEN)-based regimens have seen broad adoption in newly diagnosed patients with acute myeloid leukemia (AML) who are not candidates for intensive induction chemotherapy. Nonetheless, a sizable proportion of AML patients do not respond. Finding biomarkers that can predict treatment results is crucial. FAB (French, American, British) classification system provides a well-described and clinically associated means to segregate AML patients by virtue of myeloid differentiation status. Whether FAB classification can be used as a predictor marker for VE-based therapy is controversial. In the Chinese population of patients with AML, we conducted this single center retrospective study to examine the predictive value of monocytic and myelomonocytic differentiation on the response to VEN-based treatment. Methods: The clinical features, treatment information, and clinical outcomes of AML patients who received at least one dose of VEN treatment at West China Hospital for more than seven days between January 1, 2019, and September 8, 2022 were retrospectively examined in this study. We classified the patients into two groups based on the FAB classification system: the primitive group (includes M0/M1/M2, abbreviated as PRI) and the myelomonocytic/monocytic group (includes M4/M5, abbreviated as M/M). AML response to VEN therapy was determined by the 2017 European Leukemia Net response criteria after one cycle of VEN treatment. The overall response rate (ORR) was defined as the combination of CR, CRi, PR. From the first day of VEN use until the patient dies or the end of follow-up, the overall survival (OS) time is calculated. Logistic regression and COX regression were used to examine the variables that influence ORR and survival. All statistical tests were two-sided, and p-values of 0.05 or lower were considered significant. All statistical analyses were performed using RStudio 4.2.2. Results: There were 267 patients in all, with 162 newly diagnosed (M/M n=69; PRI n=93) and 85 relapsed/refractory (M/M n=44; PRI n=41). The ORR for M/M is 63.77%, while that for PRI is 84.95% among newly diagnosed patients (p=0.018). The ORR for M/M is 40.91% and PRI is 68.29% among relapsed/refractory patients (p=0.0114, Figure1). In addition, we identified (i) FAB classification M/M subtypes, (ii) previously diagnosed MDS/MPN, (iii) IDH1/IDH2 mutations, (iv) relapsed/refractory event, (v) transfusion requirement, and (vi) TP53 mutation as significant factors predicting the response of all patients using univariate logistic regression analysis. M/M subtypes and MDS/MPN also trended toward independent predictors in the multivariate analysis. We identified (i) FAB Classification M/M subtype, (ii) adverse risk AMLs according to the 2022 ELN classification, (iii) CEBPA mutation, and (iv) TP53 mutation as significant factors predicting the risk of all patients using univariate COX regression analysis. M/M subtype and TP53 also trended toward independent predictors in the multivariate analysis (Table1). OS was significantly longer in the PRI than in the M/M(median OS: 621 days vs 266 days, hazard ratio 0.56, 95% CI: 0.39-0.80, p=0.0011). We compared the overall survival of various illness conditions; OS still was significantly longer in the PRI group than in the M/M group among newly diagnosed patients (median OS: newly diagnosed: 1124 days vs 295 days, HR: 0.47, 95% CI: 0.24-0.83, p=0.0017; refractory/relapsed: 204 days vs 266 days, HR 0.68: 95% CI: 0.45-1.97, p=0.72). Conclusion： Our research demonstrates that the response to VEN may differ depending on how advanced the AML is, with PRI patients being more sensitive and M/M patients being more resistant, particularly in newly diagnosed individuals. Previous research has discovered that AML cells with Monocyte/erythroid/Megakaryocyte differentiation are less sensitive to BCL-2 inhibition in vitro, indicating that the differentiation stage of AML may affect its sensitivity to BCL-2 family inhibitors. This is likely due to an increase in MCL1 family expression and a decrease in BCL2 family expression during cell differentiation. In conclusion, M/M subtype may be able to predict VENs resistance and the worse prognosis for patients treated with VEN-based regimens. Legends to figures Figure 1: Differences in ORR and NR (no response) between M/M and PRI in newly diagnosed and relapsed/refractory patients Table1: Regression analysis results

Impact of “Cell-of-Origin” on Outcome after Axicabtagene-Ciloleucel CAR-T Cell Therapy in Relapsed/Refractory Aggressive Non-Hodgkin B-Cell Lymphomas

Introduction: Despite significant advances in characterizing aggressive B-cell non-Hodgkin lymphomas (aNHL) based on molecular and genetic signatures, the Hans algorithm, determined by immunohistochemistry, remains the most commonly used approach to differentiate aNHLs based on the cell-of-origin (COO) into germinal center B-cell (GCB) and non-GCB types. Although the Hans algorithm has demonstrated reliability as a predictor of better outcomes in newly diagnosed GCB cases, conflicting evidence has emerged, particularly in the relapsed/refractory (R/R) setting, and there are no definitive data about the impact of COO in patients (pts) receiving CAR-T cells. Thus, we conducted a retrospective analysis to evaluate the impact of COO extended to R/R aNHLs who underwent treatment with axicabtagene-ciloleucel (axi-cel). Methods: We retrospectively collected data from pts with R/R aNHLs treated with axi-cel at Fred Hutchinson Cancer Center between February 2018 and August 2022. The Hans algorithm, using CD10, BCL6 and MUM1 markers, was employed to stratify the pts into two cohorts: GCB and non-GCB. Paired biopsies of diagnosis and relapse before CAR-T eligibility were reviewed at our institution. Response to CAR-T was assessed at one month per Lugano PET/CT criteria. Univariate cox proportional hazard regression was used to estimate associations between baseline factors and outcomes. Statistically significant factors ( p<.05) in univariate analysis were further evaluated by multivariate analysis. Continuous variables were compared by Mann-Whitney U test and categorical variables by Fischer's exact test. Progression-free survival (PFS) was calculated from the date of CAR-T cell infusion to the date of death from any cause, disease progression or relapse. Overall survival (OS) was calculated from the date of CAR-T cell infusion to the date of death from any cause. Results: Sixty-four pts were included in this analysis, with an equal distribution of pts in the two cohorts: 32 with GCB subtype and 32 with non-GCB subtype. Baseline pts' characteristics and histology subtypes are listed in Table 1. Transformed follicular lymphoma and primary mediastinal B-cell lymphoma were not included in this analysis. The presence of bulky disease (defined as >5cm diameter) was higher in the GCB subtype (56% vs 22%, p<.001). Although primary refractory cases were more frequent in the GCB cohort (87.5 vs 66%), statistical significance was not reached (NR)( p=.07). The same proportion of pts received bridging therapy in the two groups (53% vs 53%, p=.99). Pts evaluable for response assessment were 62. Median time to best response was 29 days (range, 21-186) for GCB pts and 31 days (range, 22-182) for non-GCB pts ( p=.91). The overall response rate was 70% for GCBs (34.5% complete response [CR], 34.5% partial response [PR]) and 75% for non-GCBs (44% CR, 31% PR). After a median follow-up of 24.5 months (range, 9-121), comparable outcomes were observed for both the COO groups: despite the estimated median OS of 14 months (95% CI, 4.73-NR) for GCB vs 40 months (95% CI, 12.55-67.46) for non-GCB, we did not find any significant differences ( p=.73) (Figure 1A). Estimated median PFS was 10 months (95% confidence interval [CI], 3.5-16) for GCB and 11 months (95% CI, 0-24) for non-GCB ( p=.57) (Figure 1B). The 2-year OS and PFS were: GCB, 45% (95% CI, 30-69) and 44% (95% CI, 29-67) respectively; non-GCB, 55% (95% CI, 38-79) and 29% (95% CI, 16-55), respectively. Treatment-related toxicities did not differ between GCB and non-GCB, with cytokine-release syndrome rates of 97% vs 94% ( p=.99) and neurotoxicity rates of 53% vs 62.5% ( p=.61), respectively. The incidence of infectious complications was 25% vs 19% ( p=.76). On univariate analysis, the impact of COO on OS (HR=0.82, 95% CI 0.39-1.73, p=.60) and PFS (HR=1.18, 95% CI 0.61-2.28, p=.62) was undetermined. After adjusting for bulky disease, elevated LDH, absolute lymphocyte count≤1000/uL, performance status≥2, >2 lines of therapy and response status before axi-cel infusion, the impact of COO on OS (adjusted HR [aHR]=1.73, 95% CI 0.71-4.22, p=.23) and PFS (aHR=2.01, 95% CI 0.93-4.35, p=.08), remained undetermined. Conclusion: We could not confirm an association between COO, as determined by Hans algorithm, and outcomes in R/R aNHLs who received axi-cel at our center. Prospective analyses on larger datasets and other types of CAR-T products are needed.

A novel anoikis-related gene signature predicts in lung adenocarcinoma

ObjectiveTo generate a prognostic model prognosis based on anoikis-related genes (ANRGs) expression for Lung adenocarcinoma (LUAD), an exploration of the prognostic value of ANRGs in LUAD was conducted. MethodsBased on the expression matrix of genes from the TCGA database, we built the co-expressed modules by weighted gene co-expression network analysis (WGCNA). Then we identified the differentially expressed ANRGs (DE-ANGs) between LUAD and normal samples by the WGCNA results, DEGs, and the 345 ANRGs. The biofunction of the DE-ANRGs was interpreted using the GO and KEGG databases. Univariate and multivariate regression models were used to verify whether the risk model could serve as an independent prognostic factor. A nomogram was utilized to predict overall survival (OS) in LUAD. ResultsThe expression of 56 DE-ANRGs was significantly different in tumor tissues. We established a 4-ANRG prognostic signature. In the TCGA cohort and the external GSE31210 validation cohort, the OS of the high-risk group was lower than that of the low-risk group significantly. Moreover, the prediction performance of the risk model was excellently verified by the ROC curve. In addition, both univariate COX and multivariate Cox analyses indicated that risk score could act as an independent prognostic factor for LUAD patients. The calibration curve and C-index demonstrated that the nomogram was satisfactory in predicting 1, 3- and 5-year survival in LUAD patients. ConclusionsOur study developed a novel prognostic signature of 4 ANRGs with Excellent prognostic performance for LUAD patients.

Clinical characteristics and prognostic impact of immune checkpoint inhibitor-associated myocarditis in advanced non-small cell lung cancer.

Myocarditis is a rare immune-related adverse events (irAEs) with high mortality rates, with few reports on its clinical characteristics and prognostic impact. This study designed to explore the associations between cardiac parameters and outcomes of myocarditis in advanced non-small cell lung cancer (NSCLC) who treated with immune checkpoint inhibitor (ICI). Fourteen patients diagnosed with ICI-associated myocarditis by clinicians were admitted to the study analysis. By Cox univariate and multivariate survival analyses, potential risk factors for the development of severe myocarditis were identified. Survival analysis was also performed to explore the prognosis of patients with myocarditis. Among patients with myocarditis, higher B-type natriuretic peptide (BNP) levels (P= 0.04) and conduction block (P= 0.03) were associated with progression to severe myocarditis. In addition, high lactate dehydrogenase (LHD) levels (P= .04) and myocarditis onset within 2 months (P= 0.02) were prognostic factors of severe myocarditis. The median progression-free survival (PFS) time and median overall survival (OS) time for all patients were 5.9 months and 18.5 months, respectively. However, there were no statistical differences between mild and severe cohorts in terms of PFS and OS (PFS: 4.5 vs. 8.5 months, P= 0.17; OS: 21.3 vs. 18.5months, P= 0.36). And we found that the earlier occurrence of myocarditis, worse PFS prognosis (4.5 months vs. 10.5 months, P= 0.008), while no difference in OS (18.5 months vs. 21.3 months, P= 0.35). Compared to mild myocarditis, severe myocarditis presented with higher BNP levels and cardiac conduction abnormalities. In addition, patients with mild and early myocarditis tended to have better survival rates.

Clinical implications of heat shock protein 70 in oral carcinogenesis and prediction of progression and recurrence in oral squamous cell carcinoma patients: a retrospective clinicopathological study

BackgroundOral cancer is a common cause of death worldwide. The search for novel biomarkers for oral cancer is an ongoing struggle. Prognostic biomarkers are of great importance in diagnosis, and prediction of the cancer outcome. There are several disagreements in oral cancer studies over the role of heat shock proteins as prognostic markers. The current study investigated HSP70 expression in diverse tissues ranging from normal oral mucosa to dysplastic oral epithelium and oral squamous cell carcinoma to determine its role in oral carcinogenesis. Moreover, HSP70 was evaluated concerning different prognostic parameters to determine its capability in predicting cancer progression. Recurrence of tumor was recorded, and patients` disease-free survival was calculated and analyzed considering HSP70 expression to determine the potential utility of HSP70 immuno-expression in predicting recurrence.MethodsA retrospective study was accomplished on 50 cases of OSCC. Biopsies from the cancerous tissue, the free surgical margin, and the normal oral mucosa were used. The grading of dysplastic epithelium and OSCCs followed the criteria of WHO classification (2017). The clinicopathological and follow-up records for each patient were retrieved. Pearson’s Chi-square test, one-way ANOVA, and post hoc tests were used to analyze the variance of HSP70 immuno-expression concerning different parameters. The Kaplan–Meier method was used to compute and visualize disease-free survival, and the log-rank test was used to analyze the data. With Cox regression, univariate and multivariate survival analyses were run. A P-value of 0.05 or less was regarded as statistically significant.ResultsA significant increased expression of HSP70 was observed as the tissue progressed from normal to dysplastic epithelium, and carcinoma (P = 0.000). HSP70 revealed a significant increased expression by progression from mild to severe dysplasia (P = 0.023), and also from well to moderately and poorly differentiated carcinoma (P = 0.000). High HSP70 immuno-expression was significantly associated with progression of OSCC; large-sized tumors (P = 0.002), advanced TNM clinical stages (P = 0.001), positive nodal involvement (P = 0.001), presence of recurrence (P = .008), and reduced DFS (P = 0.014).ConclusionHSP70 has a crucial contribution to oral carcinogenesis, and its immune-expression could potentially be used as predictor of progression and recurrence of OSCC patients.Trial registration: Retrospectively registered.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. Retrospective. 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. 3 TECHNICAL EFFICACY: Stage 3.

Prognostic visualization model for primary pulmonary sarcoma: a SEER-based study

Primary pulmonary sarcoma (PPS) is a rare and poor prognostic malignancy that results from current clinical studies are lacking. Our study aimed to investigate the prognostic factors of PPS and to construct a predictive nomogram that predict the overall survival (OS) rate. We extracted data on patients diagnosed with PPS from 2010 to 2019 in the SEER database. A total of 169 patients were included after screening by inclusion and exclusion criteria. Univariate and multivariate COX regression analyses showed that age, pathological grade, liver metastasis, surgical intervention, and chemotherapy influenced the prognosis. We constructed the prediction model nomogram based on these factors. Moreover, the results of the internal and external ROC curves, calibration curves, and DCA plots confirmed that the model has good discrimination, accuracy, and clinical practice efficacy. The present study is the first population-based study to explore the factors affecting the prognosis of PPS. We established a novel prognostic nomogram to predict the OS rate, which can help to make proper clinical decisions.

Relation between red blood cell distribution width and 30-day in-hospital mortality of patients with ventilator-associated pneumonia

BackgroundEpidemiological studies have demonstrated an association between red blood cell distribution width (RDW) and the prognosis of pneumonia-associated diseases. However, prognostic value of RDW in patients with ventilator-associated pneumonia (VAP) has yet to be investigated. This study aimed to explore the association between RDW and in-hospital mortality in VAP patients and explore predictive value of RDW for VAP patients.MethodsThis retrospective cohort study included 1,543 VAP patients from the Medical Information Mart for Intensive Care IV database 2008-2019. The primary outcome was considered to 30-day in-hospital mortality of VAP patients in this study. Non-high RDW level group was defined as <15 %, and high RDW level group as ≥15%. The possible confounding factors were screened by least absolute shrinkage and selection operator regression. Univariate and multivariate COX regression analyses were used for the assessment on the association of RDW and 30-day in-hospital mortality in VAP patients. We also performed subgroup analyses. Furthermore, a comparative analysis of RDW and sequential organ failure assessment (SOFA) score and simplified acute physiology score II (SAPS II) were performed by receiver operating characteristic (ROC) curves.ResultsThe 30-day in-hospital mortality of VAP patients was approximately 19.05%. After adjusting all confounding factors, high RDW was associated with 30-day in-hospital mortality among VAP patients by using non-high RDW as the reference [hazard ratio (HR) =1.29, 95% confidence interval (CI): 1.01-1.63]. Additionally, the relationship was also robust in several populations, such as patients were younger than 60 years, or had not a history of congestive heart failure, or had a history of sepsis, or had not received renal replacement therapy, or had a duration of mechanical ventilation for more than 7 days. The result of ROC indicated that RDW had a better prognostic value in predicting 30-day in-hospital mortality for VAP patients than SOFA score and SAPS II score.ConclusionHigh RDW level is associated with an increased 30-day in-hospital mortality. The RDW is a promising biomarker in predicting 30-day in-hospital mortality for patients admitted to the ICU, regardless of VAP.

Interaction, immune infiltration characteristics and prognostic modeling of efferocytosis-related subtypes in glioblastoma

BackgroundEfferocytosis is a biological process in which phagocytes remove apoptotic cells and vesicles from tissues. This process is initiated by the release of inflammatory mediators from apoptotic cells and plays a crucial role in resolving inflammation. The signals associated with efferocytosis have been found to regulate the inflammatory response and the tumor microenvironment (TME), which promotes the immune escape of tumor cells. However, the role of efferocytosis in glioblastoma multiforme (GBM) is not well understood and requires further investigation.MethodsIn this study, we conducted a comprehensive analysis of 22 efferocytosis-related genes (ERGs) by searching for studies related to efferocytosis. Using bulk RNA-Seq and single-cell sequencing data, we analyzed the expression and mutational characteristics of these ERGs. By using an unsupervised clustering algorithm, we obtained ERG clusters from 549 GBM patients and evaluated the immune infiltration characteristics of each cluster. We then identified differential genes (DEGs) in the two ERG clusters and classified GBM patients into different gene clusters using univariate cox analysis and unsupervised clustering algorithms. Finally, we utilized the Boruta algorithm to screen for prognostic genes and reduce dimensionality, and the PCA algorithm was applied to create a novel efferocytosis-related scoring system.ResultsDifferential expression of ERGs in glioma cell lines and normal cells was analyzed by rt-PCR. Cell function experiments, on the other hand, validated TIMD4 as a tumor risk factor in GBM. We found that different ERG clusters and gene clusters have distinct prognostic and immune infiltration profiles. The ERG signature we developed provides insight into the tumor microenvironment of GBM. Patients with lower ERG scores have a better survival rate and a higher likelihood of benefiting from immunotherapy.ConclusionsOur novel efferocytosis-related signature has the potential to be used in clinical practice for risk stratification of GBM patients and for selecting individuals who are likely to respond to immunotherapy. This can help clinicians design appropriate targeted therapies before initiating clinical treatment.

Prognostic evaluation of system immune-inflammatory index and prognostic nutritional index in double expressor diffuse large B-cell lymphoma.

Predicting MYC and BCL2 double-expressor lymphoma prognosis using the system immune-inflammatory index (SII) and prognostic nutritional index (PNI) (DEL). From January 2015 to December 2021, 281 diffuse large B-cell lymphoma (DLBCL) wax blocks were used to make tissue chips. Screening double expressor lymphoma (DEL) instances involved immunocytochemistry and fluorescence in situ hybridization. Academic analysis used clinicopathological characteristics and follow-up data. SII, PNI, and DEL prognosis were correlated using univariate and multivariate cox regression analysis. The median age of 78 DEL patients is 60 (range: 43-74). SII and PNI cut-off values of 603.5, 3.07, and 144 predict PFS and OS well. Lower SII is associated with longer PFS (HR for SII = 0.34, 95% CI 0.15-0.76, P = 0.006; HR for NLR = 0.46, 95% CI 0.22-0.99, P = 0.048; HR for PLR = 0.39, 95% CI 0.17-0.94, P = 0.025; LMR = 0.39, 95%, CI 0.17-0.94, P = 0.025) and OS (HR for SII = 0.16, 95% CI 0.05-0.51, P = 0.005; HR for PNI = 0.20, 95% CI 0.06-0.62, P = 0.002). SII and PNI are promising predictors for twofold expressor DLBCL. Combining these increase prediction accuracy.

Comprehensive multi-omics analysis of tryptophan metabolism-related gene expression signature to predict prognosis in gastric cancer.

Introduction: The 5-year survival of gastric cancer (GC) patients with advanced stage remains poor. Some evidence has indicated that tryptophan metabolism may induce cancer progression through immunosuppressive responses and promote the malignancy of cancer cells. The role of tryptophan and its metabolism should be explored for an in-depth understanding of molecular mechanisms during GC development. Material and methods: We utilized the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset to screen tryptophan metabolism-associated genes via single sample gene set enrichment analysis (ssGSEA) and correlation analysis. Consensus clustering analysis was employed to construct different molecular subtypes. Most common differentially expressed genes (DEGs) were determined from the molecular subtypes. Univariate cox analysis as well as lasso were performed to establish a tryptophan metabolism-associated gene signature. Gene Set Enrichment Analysis (GSEA) was utilized to evaluate signaling pathways. ESTIMATE, ssGSEA, and TIDE were used for the evaluation of the gastric tumor microenvironment. Results: Two tryptophan metabolism-associated gene molecular subtypes were constructed. Compared to the C2 subtype, the C1 subtype showed better prognosis with increased CD4 positive memory T cells as well as activated dendritic cells (DCs) infiltration and suppressed M2-phenotype macrophages inside the tumor microenvironment. The immune checkpoint was downregulated in the C1 subtype. A total of eight key genes, EFNA3, GPX3, RGS2, CXCR4, SGCE, ADH4, CST2, and GPC3, were screened for the establishment of a prognostic risk model. Conclusion: This study concluded that the tryptophan metabolism-associated genes can be applied in GC prognostic prediction. The risk model established in the current study was highly accurate in GC survival prediction.

Low expression of PINK1 and PARK2 predicts poor prognosis in patients with esophageal squamous cell carcinoma

BackgroundThe Parkinson’s disease (PD) gene family expression is strongly linked to tumor development and progression; PINK1 and PARK2 are essential members of the PD gene family. However, the relationship between PINK1 and PARK2 and esophageal squamous cell carcinoma (ESCC) remains unknown. This research aims to clarify the prognostic value of PINK1 and PARK2 in ESCC.MethodsPINK1 and PARK2 protein levels in 232 ESCC specimens, and 125 matched adjacent normal tissues were detected by immunohistochemistry. The relationship between PINK1 and PARK2 protein expression and clinicopathological features were analyzed. Kaplan–Meier survival analysis was performed to estimate the prognostic value of the PINK1 and PARK2 proteins in patients. Cox univariate and multivariate analyses were used to assess the risk factors affecting the OS for patients with ESCC.ResultsPINK1 and PARK2 had low expression in ESCC. Patients with low PINK1 had worse differentiation and advanced T and TNM stages. Lower PARK2 expression was linked to lymph node metastases and an advanced TNM stage. Furthermore, reduced PINK1 and PARK2 levels were associated with a poor prognosis for ESCC. Cox univariate and multivariate analyses revealed that PINK1, PARK2, and tumor size were closely associated with the prognosis of patients with ESCC, and PARK2 was an independent risk factor for patients with ESCC. Finally, the PINK1 and PARK2 proteins were closely related and shared the same signal pathway.ConclusionsPINK1 and PARK2 could work as tumor suppressors in ESCC and are likely to become new treatment targets for ESCC.

Correlation between serum lipid levels and endocrine resistance in patients with ER-positive breast cancer.

Lipid metabolism may be involved in the development of endocrine drug resistance in ER-positive (ER+) breast cancer (BC). This study aimed to investigate the relationship between serum lipid levels, risk stratification of dyslipidemia, and endocrine resistance. We collected the data from 166 ER + breast cancer patients who received endocrine therapy (ET). 73 of 166 patients (44.0%)developed endocrine resistance. Univariate and multivariate COX regression were conducted to explore the potential factors affecting endocrine resistance in BC. The clinical T stage, mean serum lipid levels in ET progression-free-survival (total cholesterol, triglycerides, low-density lipoprotein cholesterol, apolipoprotein A, and triglycerides/high-density lipoprotein cholesterol) were correlated with endocrine resistance (R = 0.214, P = .006; R = 0.268, P < .001; R = 0.182, P = .019;R = 0.197, P = .011; R = 0.211, P = .006; R = 0.159, P < .041). Clinical stage, triglycerides (TG) in endocrine therapy progression-free-survival (ePFS) and low-density lipoprotein cholesterol (LDL-C) in ePFS were independent predictors of endocrine resistance (P < .05; OR = 1.406, CI 1.108-1.783, P < .05; OR = 1.309, CI 1.026-1.669, P < .05, respectively). Moreover, in clinical stage III, the ePFS was worse in patients with in the high-risk and extremely high-risk group the median ePFS time was 8.0 months (95% CI: 1.140-14.860, P < .05). Clinical stage, TG in ePFS and LDL-C in ePFS may act as a new predictive biomarker for endocrine resistance in BC. The lipid levels of BC patients should be closely monitored throughout the treatment process, and patients with dyslipidemia should receive treatment immediately.

Development of nomograms for predicting the survival of intestinal-type gastric adenocarcinoma patients after surgery

Intestinal-type gastric adenocarcinoma (IGA) is a common phenotype of gastric cancer. Currently, few studies have constructed nomograms that may predict overall (OS) and cancer-specific survival (CSS) probability after surgery. This study is to establish novel nomograms for predicting the survival of IGA patients who received surgery. A total of 1814 IGA patients who received surgery between 2000 and 2018 were selected from Surveillance, Epidemiology, and End Results database and randomly assigned to the training and validating sets at a ratio of 7:3. Then univariate and multivariate cox regression analyses were performed to screen significant indictors for the construction of nomograms. The calibration curve, the area under the receiver operating characteristic (receiver operating characteristic, ROC) curve (the area under curve, AUC), C-index, net reclassification index (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) curves were applied to assess the performance of the model. The significant outcomes of multivariate analysis revealed that ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, radiotherapy, number of regional nodes examined, number of regional nodes positive) were demonstrated to construct the nomogram for OS and ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, chemotherapy, number of regional nodes examined, number of regional nodes positive) for CSS. The calibration and AUC uncovered their favorable predictive performance. Subsequently, C-index, NRI, IDI and DCA curves further validated the predicative superiority of nomograms over 7th AJCC Stage System. The validated nomogram provides more reliable OS and CSS predictions for postoperative IGA patients with good accuracy, which can help surgeons in treatment decision-making and prognosis evaluation.

AP4M1 as a prognostic biomarker associated with cell proliferation, migration and immune regulation in hepatocellular carcinoma

BackgroundAP4M1 is a protein-coding gene that plays a crucial role in transporter activity, recognition, and hereditary-associated diseases, but it’s largely unknown in cancers.MethodsThe expression level of AP4M1 in cancers was investigated by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and the correlation between AP4M1 and hepatocellular carcinoma (HCC) clinicopathological parameters were analyzed. Univariate and multifactorial COX regression analyses were performed to clarify the prognostic value of AP4M1 in HCC. The correlation between AP4M1 and immune cell infiltration was analyzed using single-sample Gene Set Enrichment Analysis (ssGSEA). Besides, we verified the biological function of AP4M1 by applying Cell Counting Kit-8 (CCK8), colony formation, and transwell assays.ResultsThe expression of AP4M1 was significantly elevated in HCC and was correlated with patients’ pathological grades, AFP, and BMI. Kaplan-Meier survival curves indicated that patients with AP4M1 overexpression had worse overall survival. Univariate and multivariate COX regression analyses showed that AP4M1 was an independent risk factor affecting the prognosis of HCC. In addition, we observed that AP4M1 positively correlated with most immune checkpoint suppressor genes in HCC. Moreover, in vitro experiments further confirmed that AP4M1 could promote the proliferation and invasion of HCC.ConclusionsAP4M1 is highly expressed and associated with poor prognosis in HCC. AP4M1 is closely related to cancer-immune regulation and could be a novel target for HCC, and guiding new strategies for the diagnosis and treatment of HCC patients.

Comparison of Treatment and Prognosis Between Follicular Variant Papillary Thyroid Carcinoma and Classical Papillary Thyroid Carcinoma.

This cohort study evaluated the associations of different treatments with the prognosis of follicular variant papillary thyroid carcinoma (FVPTC) and classical papillary thyroid carcinoma (CPTC) patients. The data of 69034 PTC patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. The 5-year mortality of CPTC and FVPTC patients receiving surgery, radiation and combination therapy were compared. The univariable and multivariable cox proportional risk models explored the associations between different treatments and the 5-year mortality in CPTC and FVPTC patients. The 5-year mortality of CPTC patients was 2.81% and FVPTC patients was 2.47%. Compared with CPTC receiving lobectomy and/or isthmectomy, those not receiving surgery were associated with increased risk of 5-year mortality [Hazards ratio (HR)=3.27, 95% confidence interval (CI): 2.55-4.20] while total thyroidectomy was correlated with reduced risk of 5-year mortality (HR=0.67, 95%CI: 0.55-0.80). Radioactive iodine (RAI) was linked with decreased risk of 5-year mortality in CPTC patients (HR=0.57, 95%CI: 0.50-0.65). CPTC patients undergoing both surgery and radiation were related to decreased risk of 5-year mortality compared with those receiving surgery only (HR=0.55, 95%CI: 0.48-0.63). CPTC patients receiving neither surgery nor radiation (HR=4.53, 95%CI: 3.72-5.51) or those receiving radiation (HR=1.98, 95%CI: 1.13-3.48) were correlated with elevated risk of 5-year mortality. The elevated risk of 5-year mortality in FVPTC patients was reduced in those undergoing RAI (HR=0.63, 95%CI: 0.51-0.76). In conclusion, combination therapy was associated with decreased risk of 5-year mortality in CPTC and FVPTC patients, which might provide a reference for the management of these patients.

Machine learning reveals PANoptosis as a potential reporter and prognostic revealer of tumour microenvironment in lung adenocarcinoma.

Lung adenocarcinoma (LUAD), a prominent lung cancer subtype, has an underexplored relationship with PANoptosis, a recently discovered mode of tumour cell death. This study incorporated iron death, copper death, scorch death, necrotizing apoptosis and bisulfide death into a pan-death gene set (PANoptosis) and conducted single-cell analysis of scRNA-seq data from 11 LUAD samples. Differentially expressed genes were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. Univariate COX regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen PANoptosis key genes for constructing an LUAD risk model. The model's prognostic performance was evaluated using survival curves, risk scores and validation in the Gene Expression Omnibus database. The study also explored the correlation between risk scores, tumour biological function, immunotherapy, drug sensitivity and immune infiltration. The SMS gene in the PANoptosis model was silenced in two LUAD cell lines for cellular validation. Single-cell analysis revealed eight major cell types and several PANoptosis genes significantly associated with LUAD survival. The risk model demonstrated strong prognostic performance and association with immune infiltration, suggesting PANoptosis involvement in LUAD tumour immunity. Cellular validation further supported these findings. The PANoptosis key risk genes are believed to be closely related to the tumour microenvironment and immune regulation of LUAD, potentially providing valuable insights for early diagnosis and clinical treatment, and broader applications in other tumours and complex diseases.