A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study.

BACKGROUND: The pretherapeutic differentiation of subtypes of primary intracranial germ cell tumours (iGCTs), including germinomas (GEs) and nongerminomatous germ cell tumours (NGGCTs), is essential for clinical practice because of distinct treatment strategies and prognostic profiles of these diseases. This study aimed to develop a deep learning model, iGNet, to assist in the differentiation and prognostication of iGCT subtypes by employing pretherapeutic MR T2-weighted imaging. METHODS: The iGNet model, which is based on the nnUNet architecture, was developed using a retrospective dataset of 280 pathologically confirmed iGCT patients. The training dataset included 83 GEs and 117 NGGCTs, while the retrospective internal test dataset included 31 GEs and 49 NGGCTs. The model's diagnostic performance was then assessed with the area under the receiver operating characteristic curve (AUC) in a prospective internal dataset (n = 22) and two external datasets (n = 22 and 20). Next, we compared the diagnostic performance of six neuroradiologists with or without the assistance of iGNet. Finally, the predictive ability of the output of iGNet for progression-free and overall survival was assessed and compared to that of the pathological diagnosis. RESULTS: iGNet achieved high diagnostic performance, with AUCs between 0.869 and 0.950 across the four test datasets. With the assistance of iGNet, the six neuroradiologists' diagnostic AUCs (averages of the four test datasets) increased by 9.22% to 17.90%. There was no significant difference between the output of iGNet and the results of pathological diagnosis in predicting progression-free and overall survival (P = .889). CONCLUSIONS: By leveraging pretherapeutic MR imaging data, iGNet accurately differentiates iGCT subtypes, facilitating prognostic evaluation and increasing the potential for tailored treatment.

The relationship between circulating tumor cells in peripheral blood and clinical characteristics of pediatric neuroblastoma and prognostic evaluation.

This study investigates the correlation between circulating tumor cells (CTCs) in peripheral blood and the clinical characteristics and prognosis of advanced pediatric neuroblastoma (NB). We conducted a retrospective analysis of 144 children with advanced NB who underwent comprehensive treatment. Detailed clinical data were collected, and CTCs were detected using a negative enrichment method combined with immunofluorescence technology. Prognostic evaluation criteria and cutoff values for CTCs were established using ROC curve analysis. Univariate and Cox multivariate regression analyses identified independent risk factors impacting prognosis. Patients were categorized into high and low-expression groups based on optimal cutoff values determined with X-tile software. The high expression group had a significantly higher incidence of disease progression (p < 0.001), maximum tumor diameter ≥10 cm (p = 0.004), undifferentiated subtype (p = 0.034), and stage IV disease (p = 0.007) compared to the low expression group. CTCs were notably higher in patients with progression compared to those with mitigation (p < 0.001), in those with maximum tumor diameter ≥10 cm compared to <10 cm (p < 0.001), and in stage IV compared to stage III patients (p = 0.036). The AUC values for maximum tumor diameter, degree of differentiation, and tumor stage were 0.703, 0.669, 0.574, and 0.598, respectively. The detection of CTCs provides significant insights into the clinical characteristics and prognosis of advanced pediatric NB, highlighting its potential as a prognostic tool.

Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing computed tomography radiomics and deep-learning features of primary lesions and peripheral lymph nodes.

Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In our study, we developed a prognostic prediction model for stage II CRC by fusing radiomics and deep-learning (DL) features of primary lesions and peripheral lymph nodes (LNs) in computed tomography (CT) scans. First, two CT radiomics models were built using primary lesion and LN image features. Subsequently, an information fusion method was used to build a fusion radiomics model by combining the tumor and LN image features. Furthermore, a transfer learning method was applied to build a deep convolutional neural network (CNN) model. Finally, the prediction scores generated by the radiomics and CNN models were fused to improve the prognosis prediction performance. The disease-free survival (DFS) and overall survival (OS) prediction areas under the curves (AUCs) generated by the fusion model improved to 0.76 ± 0.08 and 0.91 ± 0.05, respectively. These were significantly higher than the AUCs generated by the models using the individual CT radiomics and deep image features. Applying the survival analysis method, the DFS and OS fusion models yielded concordance index (C-index) values of 0.73 and 0.9, respectively. Hence, the combined model exhibited good predictive efficacy; therefore, it could be used for the accurate assessment of the prognosis of stage II CRC patients. Moreover, it could be used to screen out high-risk patients with poor prognoses, and assist in the formulation of clinical treatment decisions in a timely manner to achieve precision medicine.

A novel immune checkpoint score system for prognostic evaluation in pancreatic adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) remains a lethal malignancy making the detection of novel prognostic biomarkers urgent. Limited studies have investigated the predictive capability of immune checkpoints in PAAD. METHOD: Gene expression data and correlative clinical information of PAAD cohort were obtained from public databases, including TCGA, ICGC, GTEX and GEO databases. Risk factors were screened and used to establish a risk score model through LASSO and Cox regression analyses. The prognostic ability of the risk score model was demonstrated. The association between risk score with immune cells infiltration, immune checkpoint genes expression, immunogenic cell death, somatic mutations and signaling pathways enrichment were analysed. scRNA-seq data were collected to confirmed the immune checkpoints expression in PAAD samples. The prognosis prediction ability of OX40/TNFRSF4 was identified. The mRNA and protein expression of OX40 in our clinical specimens were examined by RT-PCR and IHC method and its prognosis ability was verified. RESULTS: First of all, the difference of immune microenvironment between pancreatic cancer and adjacent tissues was shown. A risk score system based on three immune checkpoints (OX40, TNFSF14 and KIR3DL1) was established. The risk score model was an independent prognostic factor and performed well regarding overall survival (OS) predictions among PAAD patients. A nomogram was established to facilitate the risk model application in clinical prognosis. Immune cells including naive B cells, CD8+ T cells and Tregs were negatively correlated with the risk score. The risk score was associated with expression of immune checkpoint genes, immunogenic cell death related genes and somatic mutations. Glycolysis processes, IL-2-STAT5, IL-6-STAT3, and mTORC1 signaling pathways were enriched in the high-risk score group. Furthermore, scRNA-seq data confirmed that TNFRSF4, TNFSF14 and KIR3DL1 were expressed on immune cells in PAAD samples. We then identified OX40 as an independent prognosis-related gene, and a higher OX40 expression was associated with increased survival rate and immune environment change. In 84 PAAD clinical specimens collected from our center, we confirmed that higher OX40 mRNA expression levels were related to a good prognosis. The protein expression of OX40 on tumor-infiltrating immune cells (TIICs), endothelial cells and tumor cells was verified in PAAD tissues by immunohistochemistry (IHC) method. CONCLUSIONS: Overall, our findings strongly suggested that the three-immune checkpoints score system might be useful in the prognosis and design of personalized treatments for PAAD patients. Finally, we identified OX40 as an independent potential biomarker for PAAD prognosis prediction.

Overexpression of ferritin light chain as a poor prognostic factor for breast cancer.

BACKGROUND: Ferritin light chain (FTL) is involved in tumor progression, but the specific molecular processes by which FTL affects the development of breast cancer (BRCA) have remained unknown. In this research, the clinicopathological significance of FTL overexpression in BRCA was investigated. METHODS: To investigate the role of FTL in BRCA, we utilized multiple online databases to analyse FTL expression levels in BRCA. Next, we reviewed the expression and localization of the FTL protein in BRCA by immunohistochemistry (IHC), Western blot (WB) and immunofluorescence (IF) staining. To assess the impact of FTL on patient prognosis, we conducted Kaplan‒Meier, univariate and multivariate survival analyses. The relationship between FTL and immune infiltration in BRCA was also analysed in the TISCH and SangerBox databases. MTT, malondialdehyde (MDA) and reactive oxygen species (ROS) assays were carried out to investigate the molecular mechanisms of FTL action in BRCA cells. RESULTS: FTL was significantly upregulated in BRCA compared to normal tissues. Its expression significantly linked to histological grade (P = 0.038), PR expression (P = 0.021), Her2 expression (P = 0.012) and Ki-67 expression (P = 0.040) in patients with BRCA. Furthermore, the expression of the FTL protein was higher in the BRCA cell lines than in the normal breast cells and mainly localized in the cytoplasm. Compared to patients with a low level of FTL expression, patients with a high level of FTL expression showed lower overall survival (OS). More convincingly, univariate and multivariate statistical analyses revealed that FTL expression (P = 0.000), ER expression (P = 0.036) and Her2 expression (P = 0.028) were meaningful independent prognostic factors in patients with BRCA. FTL was associated with immune infiltration in BRCA. Functional experiments further revealed that FTL knockdown inhibited the capacity of proliferation and increased the level of oxidative stress in BRCA cells. CONCLUSIONS: Overexpression of FTL was associated with the progression of BRCA. FTL overexpression may become a biomarker for the evaluation of poor prognosis in patients with BRCA.

Construction of Two Independent RAB Family-Based Scoring Systems Based on Machine Learning Algorithms and Definition of RAB13 as a Novel Therapeutic Target for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) remains a global health challenge with a low early diagnosis rate and high mortality. The Rab GTPase (RAB) family plays an essential role in the occurrence and progression of HCC. Nonetheless, a comprehensive and systematic investigation of the RAB family has yet to be performed in HCC. We comprehensively assessed the expression landscape and prognostic significance of the RAB family in HCC and systematically correlated these RAB family genes with tumor microenvironment (TME) characteristics. Then, three RAB subtypes with distinct TME characteristics were determined. Using a machine learning algorithm, we further established a RAB score to quantify TME features and immune responses of individual tumors. Moreover, to better evaluate patient prognosis, we established a RAB risk score as an independent prognostic factor for patients with HCC. The risk models were validated in independent HCC cohorts and distinct HCC subgroups, and their complementary advantages guided clinical practice. Furthermore, we further confirmed that the knockdown of RAB13, a pivotal gene in risk models, suppressed HCC cell proliferation and metastasis by inhibiting the PI3K/AKT signaling pathway, CDK1/CDK4 expression, and epithelial-mesenchymal transition. In addition, RAB13 inhibited the activation of JAK2/STAT3 signaling and the expression of IRF1/IRF4. More importantly, we confirmed that RAB13 knockdown enhanced GPX4-dependent ferroptosis vulnerability, highlighting RAB13 as a potential therapeutic target. Overall, this work revealed that the RAB family played an integral role in forming HCC heterogeneity and complexity. RAB family-based integrative analysis contributed to enhancing our understanding of the TME and guided more effective immunotherapy and prognostic evaluation.

Clinical value of serum miR-92 and miR-122 expression level combined with pulmonary ultrasound score in the prognosis of neonatal acute respiratory distress syndrome.

PURPOSE: To explore the value of the expression of serum miR-92 and miR-122 combined with lung ultrasound score (LUS) in the prognosis of neonatal acute respiratory distress syndrome (ARDS). PATIENTS AND METHODS: This study involved 148 neonatal ARDS cases from January 2018 to October 2021, of which 77 children were discharged from hospital and 31 died. The children with ARDS were classified according to disease severity based on X-ray examination as mild (n = 69 cases) and severe (n = 39 cases). The expression of serum miR-92 and miR-122 was detected by real-time fluorescence quantitative PCR and the LUS score was recorded. The data were subjected to ROC curve analysis and Pearson correlation analysis. RESULTS: The expression of serum miR-92, miR-122, and LUS score in the patients that died were significantly higher than in those who survived (p < 0.05). These indicators were also significantly higher in the severe disease group compared to the mild disease group (p < 0.05). ROC curve showed that serum miR-92 and miR-122 combined with the LUS score had the largest area under the curve (0.920, 95% CI: 0.860-0.977) for predicting death, with a sensitivity and specificity of 92.0% and 87.0%, respectively. Pearson correlation analysis showed that the expression levels of serum miR-92 and miR-122 were positively correlated with the LUS score (all p < 0.01). CONCLUSIONS: The increased expression of serum miR-92 and miR-122 is related to the severity and prognosis of children with ARDS, combined with the LUS score are of value to predict the prognosis of children with ARDS.

Prognostic value of pulmonary ultrasound score combined with plasma miR-21-3p expression in patients with acute lung injury.

PURPOSE: The aim of this study was to explore the value of the combination between lung ultrasound score (LUS) and the expression of plasma miR-21-3p in predicting the prognosis of patients with acute lung injury (ALI). PATIENTS AND METHODS: A total of 136 ALI patients were divided into survival (n = 86) and death groups (n = 50), or into low/middle-risk (n = 77) and high-risk groups (n = 59) according to APACHE II scores. Bioinformatics was used to explore the mechanism of action of miR-21-3p in humans. Real-time fluorescent quantitative PCR was used to detect the expression of miR-21-3p in plasma, and LUS was recorded. Receiver operator characteristic (ROC) curve and Pearson correlation were also used. RESULTS: The LUS and expression level of plasma miR-21-3p in the death and high-risk groups were significantly higher than those in the survival and low/middle-risk groups (p < 0.01 and p < 0.05). miR-21-3p expression leads to pulmonary fibrosis and promotes the deterioration of ALI patients by regulating fibroblast growth factor and other target genes. ROC curve analysis showed that the best cutoff values for LUS and plasma miR-21-3p expression were 18.60 points and 1.75, respectively. LUS score and miR-21-3p combined predicted the death of ALI patients with the largest area under the curve (0.907, 95% CI: 0.850-0.964), with sensitivity and specificity of 91.6% and 85.2%, respectively. The expression level of plasma miR-21-3p was positively correlated with LUS in the death group (r = 0.827, p < 0.01). CONCLUSION: LUS and expression level of miR-21-3p in plasma are correlated with the severity and prognosis of ALI patients, and their combination has a high value for the prognostic assessment of ALI patients.

Targeted next-generation sequencing of adult gliomas for retrospective prognostic evaluation and up-front diagnostics.

AIMS: We aimed to reclassify a population-based cohort of 529 adult glioma patients to evaluate the prognostic impact of the 2016 World Health Organization (WHO) central nervous system tumour classification. Moreover, we evaluated the feasibility of gene panel next-generation sequencing (NGS) in daily diagnostics of 225 prospective glioma patients. METHODS: The retrospective cohort was reclassified according to WHO 2016 criteria by immunohistochemistry for IDH-R132H, fluorescence in situ hybridization for 1p/19q-codeletion and gene panel NGS. All tumours of the prospective cohort were subjected to NGS analysis up-front. RESULTS: The entire population-based cohort was successfully reclassified according to WHO 2016 criteria. NGS results were obtained for 98% of the prospective patients. Survival analyses in the population-based cohort confirmed three major prognostic subgroups, that is, isocitrate dehydrogenase (IDH)-mutant and 1p/19q-codeleted oligodendrogliomas, IDH-mutant astrocytomas and IDH-wildtype glioblastomas. The distinction between WHO grade II and III was prognostic in patients with IDH-mutant astrocytoma. The survival of patients with IDH-wildtype diffuse astrocytomas carrying TERT promoter mutation and/or EGFR amplification overlapped with the poor survival of IDH-wildtype glioblastoma patients. CONCLUSIONS: Gene panel NGS proved feasible in daily diagnostics. In addition, our study confirms the prognostic role of glioma classification according to WHO 2016 in a large population-based cohort. Molecular features of glioblastoma in IDH-wildtype diffuse glioma were linked to poor survival corresponding to IDH-wildtype glioblastoma patients. The distinction between WHO grade II and III retained prognostic significance in patients with IDH-mutant diffuse astrocytic gliomas.

A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center.

BACKGROUND: The incidence rate of adenocarcinoma of the oesophagogastric junction (AEG) has significantly increased over the past decades, with a steady increase in morbidity. The aim of this study was to explore a variety of clinical factors to judge the survival outcomes of AEG patients. METHODS: We first obtained the clinical data of AEG patients from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and least absolute shrinkage and selection operator (LASSO) regression models were used to build a risk score system. Patient survival was analysed using the Kaplan-Meier method and the log-rank test. The specificity and sensitivity of the risk score were determined by receiver operating characteristic (ROC) curves. Finally, the internal validation set from the SEER database and external validation sets from our center were used to validate the prognostic power of this model. RESULTS: We identified a risk score system consisting of six clinical features that can be a good predictor of AEG patient survival. Patients with high risk scores had a significantly worse prognosis than those with low risk scores (log-rank test, P-value < 0.0001). Furthermore, the areas under ROC for 3-year and 5-year survival were 0.74 and 0.75, respectively. We also found that the benefits of chemotherapy and radiotherapy were limited to stage III/IV AEG patients in the high-risk group. Using the validation sets, our novel risk score system was proven to have strong prognostic value for AEG patients. CONCLUSIONS: Our results may provide new insights into the prognostic evaluation of AEG.

Prognostic Potential of the Panfungal Marker (1 → 3)-ß-D-Glucan in Invasive Mycoses Patients.

We analyze the prognostic potential of (1 → 3)-ß-D-glucan (BG) levels in predicting clinical outcomes in patients with invasive fungal infections, on a population undergoing 253 episodes (177 with positive and 76 with negative outcome). Using linear regression analysis, we assessed the prognostic potential of kinetically evaluated BG levels and we found an overall sensitivity and specificity of 68 and 82%, respectively. Moreover, using an interpretative algorithm based on two distinct cutoff values, we were able to predict the outcome in 84% of the studied population with a diagnostic accuracy of 82%.

The risk stratification and prognostic evaluation of soluble programmed death-1 on patients with sepsis in emergency department.

OBJECTIVE: To evaluate the efficacy of soluble programmed death-1 (sPD-1) for risk stratification and prediction of 28-day mortality in patients with sepsis, we compared serum sPD-1 with procalcitonin (PCT), C-reactive protein (CRP), and the Mortality in Emergency Department Sepsis (MEDS) score. METHODS: A total of 60 healthy volunteers and 595 emergency department (ED) patients were recruited for this prospective cohort study. According to the severity of their condition on ED arrival, the patients were allocated to the systemic inflammatory response syndrome group (130 cases), sepsis group (276 cases), severe sepsis group (121 cases), and septic shock group (68 cases). In addition, all patients with sepsis were also divided into the survivor group (349 cases) and nonsurvivor group (116 cases) according to the 28-day outcomes. RESULTS: When the severity of sepsis increased, the levels of sPD-1 gradually increased. The levels of sPD-1, PCT, CRP and the MEDS score were also higher in the nonsurvivor group compared to the survivor group. Logistic regression suggested that sPD-1, PCT, and the MEDS score were independent risk factors for 28-day mortality of patients with sepsis. Area under the curve (AUC) of sPD-1, PCT and the MEDS score for 28-day mortality was 0.725, 0.693, and 0.767, respectively, and the AUC was improved when all 3 factors were combined (0.843). CONCLUSION: Serum sPD-1 is positively correlated with the severity of sepsis, and it is valuable for risk stratification of patients and prediction of 28-day mortality. Combining sPD-1 with PCT and the MEDS score improves the prognostic evaluation.

Prognostic factors in patients with metastatic spine tumors derived from lung cancer-a novel scoring system for predicting life expectancy.

BACKGROUND: Recently, molecule-targeting and bone-modifying agents have improved the treatment outcomes of lung cancer-derived metastatic spine tumors. Therefore, the prognostic factors for such tumors were examined, and novel scoring systems for predicting the life expectancy of patients with such tumors were proposed. METHODS: In 207 patients with lung cancer-derived metastatic spine tumors (surgery 49; conservative therapy 158), we retrospectively examined the factors that influenced the post-treatment survival time (age, sex, the affected site, pathology, general condition, the number of extraspinal bone metastases, the number of spinal metastases, the presence/absence of major internal organ metastasis, paralysis state, the total Tokuhashi score, the serum alkaline phosphatase level, the serum carcinoembryonic antigen level, molecule-targeting drug treatment, and bone-modifying agent treatment). Based on the results, we devised novel scoring systems for predicting the prognosis of such patients. RESULTS: Univariate analyses showed that the pathology of the primary lung tumor, the patient's general condition and paralysis state, and the presence/absence of molecule-targeting drug treatment significantly influenced survival. We performed a Cox regression analysis of these four factors and developed criteria for a novel scoring system based on the patient's general condition and paralysis state, which exhibited significance in the regression analysis. A retrospective review indicated that the consistency rate between predicted life expectancy and actual survival was 67.3%. When criteria based on the four factors that exhibited significance in the univariate analyses were adopted, the consistency rate was 76.2%. CONCLUSION: The patient's general condition and paralysis state, the pathology of the primary lung tumor, and molecule-targeting drug treatment influenced survival among patients with lung cancer-derived metastatic spine tumors. Novel scoring systems based on these four factors were proposed.

A retrospective prognostic evaluation using unsupervised learning in the treatment of COVID-19 patients with hypertension treated with ACEI/ARB drugs.

Introduction: This study aimed to evaluate the prognosis of patients with COVID-19 and hypertension who were treated with angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor B (ARB) drugs and to identify key features affecting patient prognosis using an unsupervised learning method. Methods: A large-scale clinical dataset, including patient information, medical history, and laboratory test results, was collected. Two hundred patients with COVID-19 and hypertension were included. After cluster analysis, patients were divided into good and poor prognosis groups. The unsupervised learning method was used to evaluate clinical characteristics and prognosis, and patients were divided into different prognosis groups. The improved wild dog optimization algorithm (IDOA) was used for feature selection and cluster analysis, followed by the IDOA-k-means algorithm. The impact of ACEI/ARB drugs on patient prognosis and key characteristics affecting patient prognosis were also analysed. Results: Key features related to prognosis included baseline information and laboratory test results, while clinical symptoms and imaging results had low predictive power. The top six important features were age, hypertension grade, MuLBSTA, ACEI/ARB, NT-proBNP, and high-sensitivity troponin I. These features were consistent with the results of the unsupervised prediction model. A visualization system was developed based on these key features. Conclusion: Using unsupervised learning and the improved k-means algorithm, this study accurately analysed the prognosis of patients with COVID-19 and hypertension. The use of ACEI/ARB drugs was found to be a protective factor for poor clinical prognosis. Unsupervised learning methods can be used to differentiate patient populations and assess treatment effects. This study identified important features affecting patient prognosis and developed a visualization system with clinical significance for prognosis assessment and treatment decision-making.

The Role of Troponin in the Diagnosis and Treatment of Acute Pulmonary Embolism: Mechanisms of Elevation, Prognostic Evaluation, and Clinical Decision-Making.

Acute pulmonary embolism (APE) is a cardiovascular disease with severe consequences, wherein cardiac troponin (Tn) plays a pivotal role in diagnosis and treatment. This article reviews the various roles of Tn in managing APE. It looks at how Tn levels increase, their importance in predicting outcomes, and their use in making clinical decisions. Studies indicate that an elevation in Tn is primarily associated with right ventricular overload, ischemia, and necrosis, changes that directly reflect the extent of right ventricular dysfunction and myocardial injury. Elevated levels of Tn are significantly correlated with both short-term and long-term mortality risks in patients with APE, serving as crucial indicators for prognostic assessment and guiding therapeutic strategies. International guidelines recommend integrating Tn testing with clinical scoring and echocardiography to optimize treatment decisions in patients with APE. Despite the significant value of Tn determination in the management of APE, further research is needed to standardize its application. This paper emphasizes future research directions, including exploring the application of Tn in different patient subgroups with APE and its potential combined use with other biomarkers.

NR5A2 gene affects the overall survival of LUAD patients by regulating the activity of CSCs through SNP pathway by OCLR algorithm and immune score.

Objective: Differentially expressed genes (DEGs) in lung adenocarcinoma (LUAD) tumor stem cells were screened, and the biological characteristics of NR5A2 gene were investigated. Methods: The expression and prognosis of NR5A2 in human LUAD were predicted and analyzed through bioinformatics analysis from a human cancer database. Gene expression and clinical data of LUAD tumor and normal lung tissues were obtained from The Cancer Genome Atlas (TCGA) database, and DEGs associated with lung cancer tumor stem cells (CSCs) were screened. Univariate and multivariate Cox regression models were used to screen and establish prognostic risk prediction models. The immune function of the patients was scored according to the model, and the relative immune functions of the high- and low-risk groups were compared to determine the difference in survival prognosis between the two groups. In addition, we calculated the index of stemness based on the transcriptome of the samples using one-class linear regression (OCLR). Results: Bioinformatics analysis of a clinical cancer database showed that NR5A2 was significantly decreased in human LUAD tissues than in normal lung tissues, and the decrease in NR5A2 gene expression shortened the overall survival and progression-free survival of patients with LUAD. Conclusion: The NR5A2 gene may regulate LUAD tumor stem cells through selective splicing mutations, thereby affecting the survival and prognosis of patients with lung cancer, and the NR5A2 gene may regulate CSCs through single nucleotide polymorphism.

Identification of an immune-related signature as a prognostic classifier for patients with early-stage head and neck squamous cell carcinoma.

Background: Head and neck squamous cell carcinoma (HNSCC) is the most common type and accounts for 90% of all head and neck cancer cases. Despite advances in early diagnosis and treatment strategies-chemotherapy, surgical resection, and radiotherapy-5-year survival remains grim. For patients with early-stage HNSCC, accurately predicting clinical outcomes is challenging. Considering the pivotal role of the immune system in HNSCC, we developed a reliable immune-related gene signature (IRGS) and explored its predictive accuracy in patients with early-stage HNSCC. Methods: We examined immune gene expression profiles and clinical information from 230 early-stage HNSCC specimens, including 100 cases from The Cancer Genome Atlas (TCGA), 49 cases from the Gene Expression Omnibus (GEO; GSE65858), and 81 cases from an independent clinical cohort. The prognostic signature was constructed using Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) Cox algorithm. We also explored the IRGS-related biological pathways and immune landscape using bioinformatics analysis. Results: A nine-immune-gene signature was generated to significantly stratify patients into high and low-risk groups. High risk patients exhibited shorter survival time [hazard ratio (HR) =13.795, 95% confidence interval (CI): 3.275-58.109, P<0.001]. The signature demonstrated robust prognostic ability in the training and validation sets and could independently predict overall survival (OS) and relapse-free survival (RFS). Subsequently, the receiver operating characteristic (ROC) curve and C-index confirmed the signature's predictive accuracy compared to clinical parameters. Additionally, cases classified as low risk showed more immune cell infiltration than high-risk cases. Conclusions: Our novel IRGS is a reliable and robust classifier for accurate patient stratification and prognostic evaluation. Future studies will attempt to affirm the signature's clinical application to early-stage HNSCC.

Prognostic evaluation and staging optimization of the Mayo Additive Staging System (MASS) in real world for newly diagnosed multiple myeloma patients.

OBJECTIVE: To explore the prognostic value of the Mayo Additive Staging System (MASS) in real-world patients with newly diagnosed multiple myeloma(MM). METHODS: The clinical data of 307 patients with newly diagnosed MM from August 2015 to June 2022 were retrospectively analyzed. Survival analysis was conducted for each subgroup according to the MASS. The MASS was compared to the original staging systems to evaluate its prognostic value. Patients in the high-risk group were further stratified. RESULTS: Patients were divided into MASS stages I (93 cases), II (91 cases), and III (123 cases), with differences in overall survival (OS) and progression-free survival (PFS) among all groups (p < 0.0001). Patients were grouped according to treatment regimen, age, transplant status, renal function, and bone destruction; with differences in OS and PFS among patients at each MASS stage in all subgroups (P ≤ 0.05). The MASS was also used for further risk stratification of patients with Mayo Myeloma Stratification and Risk-adjusted Treatment Stratification System 3.0 (mSMART3.0) and Revised International Staging System (R-ISS). Furthermore, in the MASS high-risk group, patients with scores of 2 and 3 vs 4 had OS of 23.7 and 10.1 months (P = 0.004), and PFS of 17.6 and 8.2 months (P = 0.004), respectively. Patients in the high-risk complex karyotype group not covered by SMART staging criteria had shorter OS and PFS than those in the mSMART3.0 high-risk and MASS stage III groups. CONCLUSION: The prognostic value of the MASS in patients with MM has been verified, and has better evaluation efficiency than the SMART and R-ISS systems.

Predictors of In-Hospital Mortality in Older Inpatients with Suspected Infection.

OBJECTIVES: To determine the rate and predictors of death in older individuals with suspected infection at any time during hospital stay in a geriatric acute ward and the prognostic ability of different tools [quick Sequential (Sepsis-related) Organ Failure Assessment (qSOFA), Modified and National Early Warning Scores (MEWS) and (NEWS)] in such population. DESIGN: Prospective observational single-center cohort study. SETTING AND PARTICIPANTS: Among patients admitted to an acute geriatric unit of an Italian University Hospital with at least 1 sepsis risk factor, all subjects with suspected infection at admission or during hospital stay (defined as antibiotic prescription and associated culture test) were considered. METHODS: A geriatric assessment including comorbidity and social, functional, and cognitive status was performed for each patient. Clinical parameters were evaluated at least twice daily throughout hospital stay; qSOFA, MEWS, and NEWS were derived, with positive cutoffs set at ≥2, ≥5, and ≥7, respectively. RESULTS: Among 305 older inpatients (median age 86.0 years, 49.2% female), 21% died during hospital stay. Sepsis was diagnosed in 31.8% of the overall sample and in 64.1% of deceased patients. Deceased patients showed a significantly higher prevalence of prior institutionalization, functional dependence, cognitive impairment, and multimorbidity. The prognostic accuracy of the qSOFA score at infection onset was only fair (area under the receiver operating characteristic curve 0.72; 95% CI, 0.65-0.79, P < .001) and comparable with that of MEWS and NEWS. After multivariable analysis, in-hospital death was positively associated with male sex [odds ratio (OR), 2.11; 95% CI, 1.01-4.44; P = .048] and abnormal white blood cells count (OR, 4.93; 95% CI, 2.36-10.29; P < .001), platelet count (OR, 2.61; 95% CI, 1.10-6.16; P = .029) and serum creatinine (OR, 2.70; 95% CI, 1.30-5.61; P = .008), along with any of the score considered, and negatively associated with autonomy in instrumental activities (OR, 0.78; 95% CI, 0.68-0.90; P < .001). CONCLUSIONS: Prognosis in older inpatients with infection or sepsis appears to be determined both by the geriatric characteristics and by the severity of the acute event, expressed by recommended tools and blood test results.

Necroptosis-related signatures identify two distinct hepatocellular carcinoma subtypes: Implications for predicting drug sensitivity and prognosis.

Background: Necroptosis is associated with oncogenesis, tumor immunity and progression. This research aims to investigate the association of necroptosis-related genes with drug sensitivity and prognosis in hepatocellular carcinoma (HCC). Methods: Based on necroptosis-related signatures, HCC patients retrieved from the TCGA database were categorized. Survival outcomes, mutation profile, immune microenvironment, and drug sensitivity between HCC subtypes were further compared. Then, LASSO analysis was performed to construct a necroptosis-related prognostic signature, which was further evaluated using another independent cohort. Results: A total of 371 patients with HCC could be categorized into two necroptosis-related subtypes. About 36% of patients were allocated to subtype A, with worse survival, more mutant TP53, and a lower likelihood of immunotherapy response. In contrast, patients in subtype B had a favorable prognosis, with lower expression of immunosuppressive signatures but a lower abundance of B and CD8+ T-cell infiltration. The prognostic risk score calculated using the expression levels of nine genes involved in the necroptosis pathway (MLKL, FADD, XIAP, USP22, UHRF1, CASP8, RIPK3, ZBP1, and FAS) showed a significant association with tumor stage, histologic grade, and Child‒Pugh score. Additionally, the risk score model was proven to be accurate in both the training and independent external validation cohorts and performed better than the TNM staging system and three well-recognized risk score models. Conclusions: Based on necroptosis-related signatures, we identified two HCC subtypes with distinctive immune microenvironments, mutation profiles, drug sensitivities, and survival outcomes. A novel well-performing prognostic model was further constructed.