Good Prognostic Value Research Articles

A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway

BackgroundExosome small RNAs are believed to be involved in the pathogenesis of cancer, but their role in breast cancer is still unclear. This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated them.MethodPeripheral blood samples from breast cancer screening positive and negative people were used for small RNA sequencing of plasma exosomes. The differences in the expression of small RNAs between the two groups were compared. We used machine learning algorithms to analyze small RNAs with significant differences between the two groups, fit the model through training sets, and optimize the model through testing sets. We recruited new research subjects as validation samples and used PCR-based quantitative detection to validate the key small RNAs screened by the machine learning model. Finally, target gene prediction and functional enrichment analysis were performed on these key RNAs.ResultsThe machine learning model incorporates six small RNAs: piR-36,340, piR-33,161, miR-484, miR-548ah-5p, miR-4282, and miR-6853-3p. The area under the ROC curve (AUC) of the machine learning model in the training set was 0.985 (95% CI = 0.948-1), while the AUC in the test set was 0.972 (95% CI = 0.882–0.995). RT-qPCR was used to detect the expression levels of these key small RNAs in the validation samples, and the results revealed that their expression levels were significantly different between the two groups (P < 0.05). Through target gene prediction and functional enrichment analysis, it was found that the functions of the target genes were enriched mainly in the chemokine signaling pathway.ConclusionThe combination of six plasma exosome small RNAs has good prognostic value for women with positive breast cancer by imaging screening. The chemokine signaling pathway may be involved in the early stage of breast cancer. It is worth further exploring whether small RNAs mediate chemokine signaling pathways in the pathogenesis of breast cancer through the delivery of exosomes.

DSN1 may predict poor prognosis of lower-grade glioma patients and be a potential target for immunotherapy

ABSTRACT DSN1 has been previously found to be positively correlated with various cancers. However, the effect of DSN1 or its methylation on the prognosis, molecular characteristics, and immune cell infiltration of low-grade glioma (LGG) has not yet been studied. We obtained 1046 LGG samples from the The Cancer Genome Atlas, The Chinese Glioma Genome Atlas (CGGA) microarray, and CGGA RNA-Seq databases. Bioinformatic methods (gene set enrichment analysis (GSEA), chi-square test, multivariate), and laboratory validation were used to investigate DSN1 in LGG. The expression levels of DSN1 mRNA and protein in LGG were substantially higher than those in normal brain tissue, and their expression was negatively regulated by methylation. The survival time of patients with low expression of DSN1 and cg12601032 hypermethylation was considerably prolonged. DSN1 was a risk factor, and of good diagnostic and prognostic value for LGG. Importantly, the expression of DSN1 is related to many types of tumor-infiltrating immune cells and has a positive correlation with PDL1. DSN1 promoted the activation of multiple cancer-related pathways, such as the cell cycle. Additionally, knockdown of DSN1 substantially inhibited the proliferation and invasion of LGG cells. To the best of our knowledge, this study is the first comprehensive analysis of the mechanism of DSN1 leading to poor prognosis of LGG, which provides a new perspective for revealing the pathogenesis of LGG. DSN1 or its methylation has diagnostic value for the prognosis of glioma, and may become a new biological target of anti-tumor immunotherapy.

CSIG-35. RAB3B DICTATES MTORC1/S6 SIGNALING IN CHORDOMA AND PREDICTS RESPONSE TO MTORC1-TARGETED THERAPY

Abstract Chordoma is a rare mesenchymal malignancy, exhibiting a high tendency to postoperative recurrence and poor prognosis. To date, its tumorigenic regulatory mechanisms remain elusive leading to a lack of effective therapeutic targets and drug sensitivity indicators. Here, via transcriptome and proteome analyses, we unveiled RAB3B as the prominent oncogenic regulator in chordoma. Multidimensional tissue samples further indicated its overexpression and enhancer-associated high transcriptional activity in chordoma. Notably, RAB3B ablation attenuated the chordoma cell growth, stemness, migration and tumorigenicity in vivo and in vitro. Through determining the RAB3B-mediated program in chordoma, we identified that RAB3B was highly associated with PI3K-AKT-mTOR signaling and directly bound to S6. Mechanistically, RAB3B increased the S6K-mediated phosphorylation of S6 by physically interacting with dephosphorylase DUSP12, to block its dephosphorylating of p-S6 specifically at S235/236. Pharmacological targeting mTORC1 pathway via dactolisib and rapamycin dramatically impeded the RAB3B-induced chordoma cell oncogenesis, while RAB3B knockout desensitized mTORC1 inhibition. Clinically, RAB3B/p-S6 suggested a good prognostic value and response to mTORC1 inhibitors for chordoma patients. Altogether, this work uncovers RAB3B as a novel activator of mTORC1 pathway, and suggests the oncogenic and predictive roles of RAB3B/p-S6 in chordoma, indicating therapeutic potentials of mTORC1-targeted therapy in advanced chordoma patients with aberrant RAB3B/p-S6 hyperactivation.

The peritumoral edema index and related mechanisms influence the prognosis of GBM patients.

Peritumoral brain edema (PTBE) represents a characteristic phenotype of intracranial gliomas. However, there is a lack of consensus regarding the prognosis and mechanism of PTBE. In this study, clinical imaging data, along with publicly available imaging data, were utilized to assess the prognosis of PTBE in glioblastoma (GBM) patients, and the associated mechanisms were preliminarily analyzed. We investigated relevant imaging features, including edema, in GBM patients using ITK-SNAP imaging segmentation software. Risk factors affecting progression-free survival (PFS) and overall survival (OS) were assessed using a Cox proportional hazard regression model. In addition, the impact of PTBE on PFS and OS was analyzed in clinical GBM patients using the Kaplan-Meier survival analysis method, and the results further validated by combining data from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Finally, functional enrichment analysis based on TCIA and TCGA datasets identified several pathways potentially involved in the mechanism of edema formation. This study included a total of 32 clinical GBM patients and 132 GBM patients from public databases. Univariate and multivariate analyses indicated that age and edema index (EI) are independent risk factors for PFS, but not for OS. Kaplan-Meier curves revealed consistent survival analysis results between IE groups among both clinical patients and TCIA and TCGA patients, suggesting a significant effect of PTBE on PFS but not on OS. Furthermore, functional enrichment analysis predicted the involvement of several pathways related mainly to cellular bioenergetics and vasculogenic processes in the mechanism of PTBE formation. While these novel results warrant confirmation in a larger patient cohort, they support good prognostic value for PTBE assessment in GBM. Our results indicate that a low EI positively impacts disease control in GBM patients, but this does not entirely translate into an improvement in OS. Multiple genes, signaling pathways, and biological processes may contribute to the formation of peritumoral edema in GBM through cytotoxic and vascular mechanisms.

Effect of left atrial function on cardiovascular outcome in non-dilated left ventricular cardiomyopathy

Abstract Background This study aimed to determine whether atrial function and strains can provide the greatest or joint incremental prognostic value in patients with non-dilated left ventricular cardiomyopathy (NDLVC) over a long follow-up period. Methods One hundred sixty-four NDLVC patients were included retrospectively. Comprehensive clinical evaluation and echocardiographic investigations were obtained, including measurements of strain derived left atrial (LA) reservoir, conduit, booster strain. All patients were followed up for adverse cardiac events (MACE) including all-cause mortality, hospitalization due to heart failure aggravation. The predictors of MACE were examined with univariable and multivariable Cox regression analysis. Subsequently, nested Cox regression models were built to evaluate the incremental prognostic value of strain parameters. Patient survival was illustrated by Kaplan–Meier curves and differences were evaluated by log-rank test. Results During a median follow-up of 3.8 years, MACEs were identified in 34 (21%) patients. Cox regression models showed that the predictive value of LA conduit strain was independent from and incremental to LAVi and baseline variables. Age, E/e', RASP and LA reservoir and conduit strain were univariably associated with the outcome (all P &amp;lt; 0.05). LA conduit strain was a stronger predictor of outcome compared with reservoir strain. LA conduit strain, age, RASP remained associated in the multivariable model (LA conduit strain HR: 2.14 [95% CI: 1.37-3.87; P &amp;lt; 0.007]; age HR: 1.05 [95% CI: 1.01-1.10; P = 0.020]; and RASP HR: 1.07 [95% CI: 1.01-1.14; P = 0.021]), whereas LVEF, LAVi were not. Adding LA conduit strain to other independent predictors (age and LAVi) improved the prediction of MACEs. (p &amp;lt; 0.05). Also, when a Kaplan Meier graph was obtained for the occurrence of MACE based on LA conduit strain 17%, a significant difference was shown between the two groups. Conclusion LA conduit strain provided the good prognostic value and added incremental value to conventional clinical predictors for patients with NDLVC.Incremental value of LA strainKM according to LA strain 17%

Identification of immune feature genes and intercellular profiles in diabetic cardiomyopathy

BACKGROUND Diabetic cardiomyopathy (DCM) is a multifaceted cardiovascular disorder in which immune dysregulation plays a pivotal role. The immunological molecular mechanisms underlying DCM are poorly understood. AIM To examine the immunological molecular mechanisms of DCM and construct diagnostic and prognostic models of DCM based on immune feature genes (IFGs). METHODS Weighted gene co-expression network analysis along with machine learning methods were employed to pinpoint IFGs within bulk RNA sequencing (RNA-seq) datasets. Single-sample gene set enrichment analysis (ssGSEA) facilitated the analysis of immune cell infiltration. Diagnostic and prognostic models for these IFGs were developed and assessed in a validation cohort. Gene expression in the DCM cell model was confirmed through real time-quantitative polymerase chain reaction and western blotting techniques. Additionally, single-cell RNA-seq data provided deeper insights into cellular profiles and interactions. RESULTS The overlap between 69 differentially expressed genes in the DCM-associated module and 2483 immune genes yielded 7 differentially expressed immune-related genes. Four IFGs showed good diagnostic and prognostic values in the validation cohort: Proenkephalin (Penk) and retinol binding protein 7 (Rbp7), which were highly expressed, and glucagon receptor and inhibin subunit alpha, which were expressed at low levels in DCM patients (all area under the curves &gt; 0.9). SsGSEA revealed that IFG-related immune cell infiltration primarily involved type 2 T helper cells. High expression of Penk (P &lt; 0.0001) and Rbp7 (P = 0.001) was detected in cardiomyocytes and interstitial cells and further confirmed in a DCM cell model in vitro . Intercellular events and communication analysis revealed abnormal cellular phenotype transformation and signaling communication in DCM, especially between mesenchymal cells and macrophages. CONCLUSION The present study identified Penk and Rbp7 as potential DCM biomarkers, and aberrant mesenchymal-immune cell phenotype communication may be an important aspect of DCM pathogenesis.

Role and clinical value of serum hsa_tsr011468 in lung adenocarcinoma.

Transfer RNA‑derived small RNAs (tsRNAs) are novel non‑coding RNAs that are associated with the pathogenesis of various diseases. However, their association with lung adenocarcinoma (LUAD) has not been studied comprehensively. Therefore, the present study aimed to explore the diagnostic value of a tsRNA, hsa_tsr011468, in LUAD. The OncotRF database was used to screen tsRNAs and reverse transcription‑quantitative PCR (RT‑qPCR) was performed to detect the expression levels of hsa_tsr011468 in various samples. Subsequently, the diagnostic and prognostic values of hsa_tsr011468 for LUAD were determined via receiver operating characteristic (ROC) curve and survival curve analyses, and by assessing clinicopathological parameters. In addition, both nuclear and cytoplasmic RNA were extracted to assess the location of hsa_tsr011468. The OncotRF database identified high expression of hsa_tsr011468 in LUAD. In addition, the results of RT‑qPCR showed that the relative expression levels of hsa_tsr011468 in the serum and tissues of patients with LUAD were higher than those in normal controls. Furthermore, its expression was lower in postoperative serum samples than in preoperative serum samples from patients with LUAD. ROC and survival curves indicated that hsa_tsr011468 had good diagnostic and prognostic value. Furthermore, the clinicopathological analysis revealed that hsa_tsr011468 was associated with tumor size. In addition, hsa_tsr011468 was mainly localized in the cytoplasm of LUAD cells. The present study indicated that hsa_tsr011468 has good diagnostic value and, therefore, could be employed as a serum marker for LUAD.

The Use of Biochemical Markers in Covid-19 Infection for Detecting Disease Severity and Patient Outcome

Abstract Background Corona viridea is a family of viruses that cause illness such as respiratory diseases or gastrointestinal diseases. Respiratory diseases can range from common cold to more severe diseases such as, Middle East Respiratory Syndrome (MERS-CoV) and severe Acute Respiratory Syndrome (SARS-CoV). In December 2019, the city of Wuhan in China became the epicentre of unexplainable cases of pneumonia, which in January 2020 were identified as a new coronavirus, turning rapidly into a major problem of public health worldwide. This pathogen, corresponding to a beta coronavirus, is made up of single chains of positive RNA belonging to the large Coronaviridae subfamily and has the ability to infect mammals and other animals. Aim of the Work to use different biomarkers to evaluate if they are able to predict clinical outcomes and correlate with the severity of COVID-19 disease. Patients and Methods This retrospective study was conducted on COVID-19 infection patients who were admitted in Ain Shams University Isolation for 6 months from 1st of April 2021 to 31st of October 2021. It included hospitalized isolated patients in “Ain Shams University Isolation Hospital” with COVID-19 infection confirmed by “Polymerase chain reaction (PCR) positive for SARS-CoV2”. Results Results in the current study show that LDH was significantly increased in patients who died compared to patients who recovered (p = 0.023) indicating a good prognostic value. Procalcitonin also showed good prognostic value being higher in patients who died than in patients who recovered. This study showed no significant relation between severity or outcome of disease and liver function tests including AST and ALT (p &amp;gt; 0.05), nor severity or outcome of disease and kidney function tests including BUN and serum creatinine. There was also no significant relation between serum ferritin and D-dimer levels and the severity or outcome of disease. Conclusion This work suggests that there is clear evidence of how the levels of some biomarkers may alternate according to severity of COVID-19 infection. Biomarkers of prognostic significance such as LDH and PCT have shown remarkable clinical implications for ineffective management, monitoring, and assessment of the severity of disease in COVID- 19 patients. This can be used as an adjunct in clinical practice to guide treatment and admission to ICU. Doing so may improve prognosis and minimise the mortality rates.

Identification of Necroptosis-related Molecular Subtypes and Construction of Necroptosis-related Gene Signature for Glioblastoma Multiforme.

Necroptosis is a highly regulated and genetically controlled process, and therefore, attention has been paid to the exact effects of this disorder on a variety of diseases, including cancer. An in-depth understanding of the key regulatory factors and molecular events that trigger necroptosis can not only identify patients at risk of cancer development but can also help to develop new treatment strategies. This study aimed to increase understanding of the complex role of necroptosis in glioblastoma multiforme (GBM) and provide a new perspective and reference for accurate prediction of clinical outcomes and gene-targeted therapy in patients with GBM. The objective of this study was to analyze the gene expression profile of necroptosis regulatory factors in glioblastoma multiforme (GBM) and establish a necroptosis regulatory factor-based GBM classification and prognostic gene signature to recognize the multifaceted impact of necroptosis on GBM. The necroptosis score of the glioblastoma multiforme (GBM) sample in TCGA was calculated by ssGSEA, and the correlation between each gene and the necroptosis score was calculated. Based on necroptosis score-related genes, unsupervised consensus clustering was employed to classify patients. The prognosis, tumor microenvironment (TME), genomic changes, biological signal pathways and gene expression differences among clusters were analyzed. The gene signature of GBM was constructed by Cox and LASSO regression analysis of differentially expressed genes (DEGs). Based on 34 necroptosis score-related genes, GBM was divided into two clusters with different overall survival (OS) and TME. A necroptosis-related gene signature (NRGS) containing 8 genes was developed, which could stratify the risk of GBM in both the training set and verification set and had good prognostic value. NRGS and age were both independent prognostic indicators of GBM, and a nomogram developed by the integration of both of them showed a better predictive effect than traditional clinical features. In this study, patients from public data sets were divided into two clusters and the unique TME and molecular characteristics of each cluster were described. Furthermore, an NRGS was constructed to effectively and independently predict the survival outcome of GBM, which provides some insights for the implementation of personalized precision medicine in clinical practice.

Identification of circadian clock-related immunological prognostic index and molecular subtypes in prostate cancer

BackgroundEvidence suggests that the circadian clock (CIC) is among the important factors for tumorigenesis. We aimed to provide new insights into CIC-mediated molecular subtypes and gene prognostic indexes for prostate cancer (PCa) patients undergoing radical prostatectomy (RP) or radical radiotherapy (RT).MethodsPCa data from TCGA was analyzed to identify differentially expressed genes (DEGs) with significant fold changes and p-values. A prognostic index called CIC-related gene prognostic index (CICGPI) was developed through clustering methods and survival analysis and validated on multiple data sets. The diagnostic accuracy of CICGPI for resistance to chemotherapy and radiotherapy was confirmed. Additionally, the interaction between tumor immune environment and CICGPI score was explored, along with their correlation with prognosis.ResultsTOP2A, APOE, and ALDH2 were used to classify the PCa patients into two subtypes. Cluster 2 had a higher risk of biochemical recurrence (BCR) than cluster 1 for PCa patients undergoing RP or RT. A CIC-related gene prognostic index (CICGPI) was constructed using the above three genes for PCa patents in the TCGA database. The CICGPI score showed good prognostic value in the TCGA database and was externally confirmed by PCa patients in GSE116918, MSKCC2010 and GSE46602. In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988). For gene set variation analysis, we observed that both beta alanine metabolism and limonene and pinene degradation were upregulated in cluster 1 for PCa patients undergoing RP or RT. For PCa patients undergoing RP, cell cycle, homologous recombination, mismatch repair, and DNA replication were upregulated in cluster 2. A strongly positive relationship between cancer-related fibroblasts and CICGPI score was observed in PCa patients undergoing RP or RT. Moreover, a high density of CAFs was highly closely associated with poorer BCR-free survival of PCa patients.ConclusionsIn this study, we established CIC-related immunological prognostic index and molecular subtypes, which might be useful for the clinical practice.

Comprehensive analysis of LD-related genes signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

BackgroundLipid droplets (LD) in renal clear cell carcinoma (ccRCC)play a crucial role in lipid metabolism and immune response modulation. The purpose of this study was to create a LD-related signature to predict prognosis and guide the immunotherapy and targeted therapy in ccRCC patients.MethodsWe conducted a comprehensive analysis using transcriptional profiles and clinical data obtained from The Cancer Genome Atlas (TCGA). LD-related genes were identified from existing literature and the GeneCards database, and differentially expressed genes were determined. Sequentially, we conducted Cox regression analysis and Lasso regression analysis, to establish a prognostic risk model. The performance of the risk model was evaluated using Kaplan–Meier (KM) analysis and time-dependent receiver operating characteristic (ROC) analysis. Additionally, gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, and immunophenoscore (IPS) algorithm were used to assess the tumor microenvironment (TME) and treatment response.ResultsWe constructed a risk signature with four LD-related genes in the TCGA dataset, which could be an independent prognostic factor in ccRCC patients. Then, patients were classified into two risk groups and exhibited notable differences in overall survival (OS), progression-free survival (PFS), and TME characteristics. Furthermore, we developed a comprehensive nomogram based on clinical features, which demonstrated good prognostic predictive value. According to the results of GSEA analysis, immune-related pathways were found to be significantly enriched in the high-risk group. Additionally, the high-risk group displayed high levels of immune cell infiltration, TMB and IPS scores, indicating better efficacy of immune checkpoint inhibitors (ICIs). Finally, high-risk demonstrated reduced IC50 values compared to the low-risk counterpart for specific targeted and chemotherapeutic drugs, suggesting that the patients receiving these targeted drugs in high-risk group had better treatment outcomes.ConclusionsOur findings suggested that the LD-related gene signature could potentially predict the prognosis of ccRCC patients. Additionally, it showed promise for predicting responses to immunotherapy and targeted therapy in ccRCC patients. These insights might potentially have guided the clinical management of these patients, but further validation and broader data analysis are needed to confirm these preliminary observations.

Construction of thyroid cancer classification and iodine metabolism related diagnostic model using thyroid differentiation score and bioinformation.

To more accurately diagnose and treat patients with different subtypes of thyroid cancer, we constructed a diagnostic model related to the iodine metabolism of THCA subtypes. THCA expression profiles, corresponding clinicopathological information, and single-cell RNA-seq were downloaded from TCGA and GEO databases. Genes related to thyroid differentiation score were obtained by GSVA. Through logistic analyses, the diagnostic model was finally constructed. DCA curve, ROC curve, machine learning, and K-M analysis were used to verify the accuracy of the model. qRT-PCR was used to verify the expression of hub genes in vitro. There were 104 crossover genes between different TDS and THCA subtypes. Finally, 5 genes (ABAT, CHEK1, GPX3, NME5, and PRKCQ) that could independently predict the TDS subpopulation were obtained, and a diagnostic model was constructed. ROC, DCA, and RCS curves exhibited that the model has accurate prediction ability. K-M and subgroup analysis results showed that low model scores were strongly associated with poor PFI in THCA patients. The model score was significantly negatively correlated with T cell follicular helper. In addition, the diagnostic model was significantly negatively correlated with immune scores. Finally, the results of qRT-PCR corresponded with bioinformatics results. This diagnostic model has good diagnostic and prognostic value for THCA patients, and can be used as an independent prognostic indicator for THCA patients.

A pan-cancer analysis of the association of METRN with prognosis and immune infiltration in human tumors

BackgroundMeteorin (METRN) is expressed predominantly in the central nervous system (CNS), where it functions by regulating glial cell differentiation and promoting axonal elongation. Nonetheless, its function within tumors is still not well understood. In this study, we focused on investigating its expression across various cancers and delving deeper into how METRN expression correlates with prognosis and immune infiltration. MethodsWe explored METRN expression patterns in pan-cancers utilizing data obtained from the UCSC Xena and TCGA. In addition, analyses of survival and clinical association were conducted for tumors where METRN could affect the prognosis. Subsequently, nomogram models were constructed for sarcoma (SARC) and prostate adenocarcinoma (PRAD) to verify METRN's prognostic value in tumors. Furthermore, we also discussed the link between METRN and immune infiltration. As far as mechanisms are concerned, functional enrichment analysis was conducted to analyze the functional components and signaling pathways involved in METRN. ResultsThis study found that METRN was abnormally expressed in various tumors, closely connected with the prognosis and clinical characteristics of several tumors, and had good prognostic value. Moreover, analysis of immune infiltration revealed that METRN interacts with multiple immune cells, with alterations in the immune microenvironment potentially influencing tumor prognosis. Enrichment analysis indicates that METRN may influence tumorigenesis and progression through immune-related pathways. ConclusionTo sum up, our study demonstrates that METRN can be a prospective predictive biomarker in diverse cancer types and a promising target for cancer immunotherapy for pan-cancer.

Evaluation of Serum Procalcitonin as a Diagnostic and Prognostic Biomarker for Sepsis in Major Burn Patients: A Prospective Study

Abstract Background Sepsis in burns worsens the patient’s prognosis and increases the risk of organ failure and death. Multiple organ dysfunction syndrome (MODS), which is a direct response to sepsis is the main reason for death in burn patients. Identifying early sepsis is very important, given that every 6 h delay in the diagnosis of sepsis reduces survival by 10%. Complexity in diagnosing sepsis in the burn is due to the systemic response to the burn itself clinically simulating sepsis. Aim of the Work The aim of this study is: To investigate the diagnostic validity of PCT in burn sepsis as an early diagnostic tool and to identify its prognostic value in major burn patients with sepsis. Patients and Methods The study was a prospective study carried out in the Burn Intensive Care Unit (ICU) of Ain Shams University Hospitals on 30 patients that were admitted from October 2021 to April 2022 with major burn (more than 20% of TBSA) were included and the Local Ethics Committee approved the study, and informed consent was obtained from all participants or their guardians. Results It had been revealed that the first 3 samples have no significant increase in PCT levels in relation to sepsis. However, the following 2 samples which had been withdrawn on day 5 show a significant increase in PCT levels, and these on day 7 on admission are highly significant with a median (IQR) range of 9 ng/dl and median (IQR) range of 13.2 ng/dl respectively. Moreover, it had been noted that the PCT level on admission was significant for the prognosis of death with a median (IQR) range of 1.65 ng/dl while in survivor patients the median was 0.45 ng/dl. Conclusion Our study demonstrated that PCT level in major burn patients is a promising diagnostic biomarker in detecting sepsis that could facilitate ideal management and initiate proper antimicrobial therapy and good prognostic value as an early predictor of mortality.

Stratification of Lung Adenocarcinoma Patients Based on In Silico and Immunohistochemistry Analyses of Oxidative Stress-Related Genes.

Background: Lung adenocarcinoma (LUAD) remains heterogeneous in the prognosis of patients; oxidative stress (OS) has been widely linked to cancer progression. Therefore, it is necessary to explore the prognostic value of the OS-associated genes in LUAD. Methods: An OS-associated prognostic signature was developed using the Cox regression and random forest model in The Cancer Genome Atlas-LUAD dataset. Kaplan-Meier (K-M) survival curve and time-dependent receiver operating characteristic (tROC) curves were applied to evaluate and validate the predictive accuracy of this signature among the training and testing cohorts. A nomogram was constructed and also verified by the concordance index (C-index), calibration curves, and tROC curves, respectively. ESTIMATE algorithm and CIBERSORT algorithms were conducted to explore the signature's immune characteristics. Core target genes of the prognostic signature were identified in the protein-protein interaction network. Results: A six OS-associated prognostic gene signature (CDC25C, ERO1A, GRIA1, TERT, CAV1, BDNF) was developed. The tROC and K-M survival curves in the training and testing cohorts revealed that the signature had good and robust predictive capability to predict the overall survival of LUAD patients. Meanwhile, the risk score was an independent prognostic factor influencing patients' overall survival. The results of the C-index (0.714), calibration curves, and the 1-, 2-, and 3-year tROC curves (area under the curve = 0.703, 0.737, and 0.723, respectively) suggested that the nomogram had good predictive efficacy and prognostic value for LUAD. Then, the authors found that the high-risk group may be depletion or loss of antitumor function of immune cells. Finally, 10 core genes of the signature were predicted. Conclusion: Their study may provide a novel understanding for the identification of prognostic stratification in LUAD patients, as well as the regulation of OS-associated genes in LUAD progression.

Integration of two-dimensional echocardiography: A novel risk indicator for ST-segment elevation myocardial infarction.

We aim to integrate the parameters of two-dimensional (2D) echocardiography and identify the high-risk population for all-cause mortality in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI). The study involved a retrospective cohort population with STEMI who were admitted to Yongchuan Hospital of Chongqing Medical University between January 2016 and January 2019. Baseline data were collected, including 2D echocardiography parameters and left ventricular ejection fraction (LVEF). The parameters of 2D echocardiography were subjected to cluster analysis. Logistic regression models were employed to assess univariate and multivariate adjusted odds ratios (ORs) of cluster information in relation to all-cause mortality. Four logistic regression models were generated, utilizing cluster information, clinical variables, clinical variables in conjunction with LVEF, and clinical variables in conjunction with LVEF and cluster information as predictive variables, respectively. The area under the curve (AUC) were utilized to evaluate the incremental risk stratification value of cluster information. The study included 633 participants with 28.8% female, a mean age of 65.68±11.98years. Over the course of a 3-year follow-up period, 108 (17.1%) patients experienced all-cause mortality. Utilizing cluster analysis of 2D echocardiography parameters, the patients were categorized into two distinct clusters, with statistically significant differences observed in most clinical variables, echocardiography, and survival outcomes between the clusters. Multivariate regression analysis revealed that cluster information was independently associated with the risk of all-cause mortality with adjusted OR 7.33 (95% confidence interval [CI] 3.99-14.06, P<0.001). The inclusion of LVEF enhanced the predictive capacity of the model utilized with clinical variables with AUC 0.848 (95% CI 0.809-0.888) versus AUC 0.872 (95% CI 0.836-0.908) (P<0.001), and the addition of cluster information further improved its predictive performance with AUC 0.906 (95% CI 0.878-0.934, P<0.001). This cluster analysis was translated into a free available online calculator (https://app-for-mortality-prediction-cluster.streamlit.app/). The 2D echocardiographic diagnostic information based on cluster analysis had good prognostic value for STEMI population, which was helpful for risk stratification and individualized intervention.

Identification of a signature of histone modifiers in kidney renal clear cell carcinoma.

Kidney renal clear cell carcinoma (KIRC) is a cancer that is closely associated with epigenetic alterations, and histone modifiers (HMs) are closely related to epigenetic regulation. Therefore, this study aimed to comprehensively explore the function and prognostic value of HMs-based signature in KIRC. HMs were first obtained from top journal. Then, the mRNA expression profiles and clinical information in KIRC samples were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (Lasso) analysis were implemented to find prognosis-related HMs and construct a risk model related to the prognosis in KIRC. Kaplan-Meier analysis was used to determine prognostic differences between high- and low-risk groups. Immune infiltration and drug sensitivity analysis were also performed between high- and low-risk groups. Eventually, 8 HMs were successfully identified for the construction of a risk model in KIRC. The results of the correlation analysis between risk signature and the prognosis showed HMs-based signature has good prognostic value in KIRC. Results of immune analysis of risk models showed there were significant differences in the level of immune cell infiltration and expression of immune checkpoints between high- and low-risk groups. The results of the drug sensitivity analysis showed that the high-risk group was more sensitive to several chemotherapeutic agents such as Sunitinib, Tipifarnib, Nilotinib and Bosutinib than the low-risk group. In conclusion, we successfully constructed HMs-based prognostic signature that can predict the prognosis of KIRC.

Right Ventricular Strain Derived from Cardiac MRI Feature Tracking for the Diagnosis and Prognosis of Arrhythmogenic Right Ventricular Cardiomyopathy.

Purpose To demonstrate the myocardial strain characteristics of patients with arrhythmogenic right ventricular cardiomyopathy (ARVC), based on revised Task Force Criteria (rTFC), and to explore the prognostic value of strain analysis in ARVC. Materials and Methods This retrospective study included 247 patients (median age, 38 years [IQR, 28-48 years]; 167 male, 80 female) diagnosed with ARVC, based on rTFC, between 2014 and 2018. Patients were divided into "possible" (n =25), "borderline" (n = 40), and "definite" (n = 182) ARVC groups following rTFC. Biventricular global strain parameters were calculated using cardiac MRI feature tracking (FT). The primary outcome was defined as a composite of cardiovascular events, including cardiovascular death, heart transplantation, and appropriate implantable cardioverter defibrillator discharge. Univariable and multivariable cumulative logistic regression and Cox proportional hazards regression analysis were used to evaluate the diagnostic and prognostic value of right ventricle (RV) strain parameters. Results Patients with definite ARVC had significantly reduced RV global strain in all three directions compared with possible or borderline groups (all P < .001). RV global longitudinal strain (GLS) was an independent predictor for disease (odds ratio, 1.09 [95% CI: 1.02, 1.16]; P = .009). During a median follow-up of 3.4 years (IQR, 2.0-4.9 years), 55 patients developed primary end point events. Multivariable analysis showed that RV GLS was independently associated with the occurrence of cardiovascular events (hazard ratio, 1.15 [95% CI: 1.07, 1.24]; P < .001). Kaplan-Meier analysis showed that patients with RV GLS worse than median had a higher risk of combined cardiovascular events (log-rank P < .001). Conclusion RV GLS derived from cardiac MRI FT demonstrated good diagnostic and prognostic value in ARVC. Keywords: MR Imaging, Image Postprocessing, Cardiac, Right Ventricle, Cardiomyopathies, Arrhythmogenic Right Ventricular Cardiomyopathy, Revised Task Force Criteria, Cardiovascular MR, Feature Tracking, Cardiovascular Events Supplemental material is available for this article. © RSNA, 2024.

A novel signature incorporating genes related to lipid metabolism and immune for prognostic and functional prediction of breast cancer.

Breast cancer prognosis and functioning have not been thoroughly examined in relation to immunological and lipid metabolism. However, there is a lack of prognostic and functional analyses of the relationship between lipid metabolism and immunity in breast cancer. DEGs in breast cancer were obtained from UCSC database, and lipid metabolism and immune-related genes were obtained from GSEA and Immune databases. A predictive signature was constructed using univariate Cox and LASSO regression on lipid metabolism and immune-related DEGs. The signature's prognostic significance was assessed using Kaplan-Meier, time-dependent ROC, and risk factor survival scores. Survival prognosis, therapeutic relevance, and functional enrichment were used to mine model gene biology. We selected IL18, which has never been reported in breast cancer before, in the signature to learn more about its function, potential to predict outcome, and immune system role. RT-PCR was performed to verify the true expression level of IL18. A total of 136 DEGs associated with breast cancer responses to both immunity and lipid metabolism. Nine key genes (CALR, CCL5, CEPT, FTT3, CXCL13, FLT3, IL12B, IL18, and IL24, p < 1.6e-2) of breast cancer were identified, and a prognostic was successfully constructed with a good predictive ability. IL18 in the model also had good clinical prognostic guidance value and immune regulation and therapeutic potential. Furthermore, the expression of IL18 was higher than that in paracancerous tissue. A unique predictive signature model could effectively predict the prognosis of breast cancer, which can not only achieve survival prediction, but also screen out key genes with important functional mechanisms to guide clinical drug experiments.

Diagnostic biomarkers in knee osteoarthritis: Based on bioinformatics and experimental verification in vivo and in vitro.

Knee osteoarthritis (KOA) is a multifactorial whole-joint disease with a high rate of disability. Considering the complexity of KOA, there is an urgent need to discover new molecular pathological markers and multi-target treatment strategies. Two datasets, GSE51588 and GSE57218, were downloaded from the Gene Expression Omnibus database and screened for differentially expressed genes (DEGs) using the Gene Expression Omnibus 2R (GEO2R). Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed. A protein-protein interaction (PPI) network was constructed and hub genes were identified using Molecular Complex Detection (MCODE). Receiver-operating characteristic curves (ROC) were plotted for the genes, and their prognostic values were evaluated. The expression levels of the hub genes in the monosodium iodoacetate (MIA)-induced KOA rat model and lipopolysaccharide (LPS)-stimulated C28/I2 cells were verified using reverse transcription quantitative real-time PCR (RT-qPCR). Overall, 33 DEGs were up-regulated and 6 DEGs were down-regulated in the two datasets. A total of 12 hub genes were identified, including COL15A1, THY1, COL1A1, COL5A1, CTHRC1, MXRA5, FN1, COL1A2, COL3A1, SPARC, COL8A1, and COL2A1, which all could be used as biomarkers to differentiate KOA samples from healthy controls. More importantly, we found that THY1, CTHRC1, SPARC, and COL8A1 were significantly upregulated in vivo and in vitro compared with the controls (p < .01). The expression levels of THY1, CTHRC1, SPARC, and COL8A1 were elevated and had good prognostic values as biomarkers in KOA.