Prognostic Model Research Articles

M1 macrophage-related prognostic model by combining bulk and single-cell transcriptomic data in NSCLC

M1 macrophage-related prognostic model by combining bulk and single-cell transcriptomic data in NSCLC

PET/computed tomography radiomics combined with clinical features in predicting sarcopenia and prognosis of diffuse large B-cell lymphoma.

The study aimed to assess the role of 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) radiomics combined with clinical features using machine learning (ML) in predicting sarcopenia and prognosis of patients with diffuse large B-cell lymphoma (DLBCL). A total of 178 DLBCL patients (118 and 60 applied for training and test sets, respectively) who underwent pretreatment 18F-FDG PET/CT were retrospectively enrolled. Clinical characteristics and PET/CT radiomics features were analyzed, and feature selection was performed using univariate logistic regression and correlation analysis. Sarcopenia prediction models were built by ML algorithms and evaluated. Besides, prognostic models were also developed, and their associations with progression-free survival (PFS) and overall survival (OS) were identified. Fourteen features were finally selected to build sarcopenia prediction and prognosis models, including two clinical (maximum standard uptake value of muscle and BMI), nine PET (seven gray-level and two first-order), and three CT (three gray-level) radiomics features. Among sarcopenia prediction models, combined clinical-PET/CT radiomics features models outperformed other models; especially the support vector machine algorithm achieved the highest area under curve of 0.862, with the sensitivity, specificity, and accuracy of 79.2, 83.3, and 78.3% in the test set. Furthermore, the consistency index based on the prognostic models was 0.753 and 0.807 for PFS and OS, respectively. The enrolled patients were subsequently divided into high-risk and low-risk groups with significant differences, regardless of PFS or OS (P < 0.05). ML models incorporating clinical and PET/CT radiomics features could effectively predict the presence of sarcopenia and assess the prognosis in patients with DLBCL.

Which Scoring System Best Predicts Long-term Survival in Patients with Spinal Metastasis in the Era of Targeted Systemic Treatment? A Comparative Study of Eight Prognostic Models.

Retrospective observational study. To evaluate the accuracy of eight scoring systems, including the Tomita, modified Tokuhashi, modified Bauer, Rades, Oswestry Spinal Risk index (OSRI), Lei, New England Spinal Metastasis Score, and Skeletal Oncology Research Group (SORG) nomogram, for predicting long-term survival of patients with spinal metastasis. Predicting the prognosis of spinal metastasis is vital for surgical decisions, yet the effectiveness of existing scoring systems in identifying long-term survival remains unclear. 456 cases were finally included. Prognostic scores were compared with survival outcomes. Receiver operating characteristic (ROC) curves were analyzed for the entire cohort and across three distinct time periods to evaluate the area under the curve (AUC) for 1-year and 2-year survival, alongside Harrell's C-statistic. The mean patient age was 58.9 years, and the median survival time was 8.6 months. For the entire cohort, the SORG nomogram, OSRI, and modified Tokuhashi scores yielded Harrell's C-index values of 0.64, 0.63, and 0.62, respectively. For 1-year survival prediction, the SORG nomogram, OSRI, and modified Tokuhashi score demonstrated moderate discriminative power, with AUC values of 0.72, 0.71, and 0.70, respectively. Similarly, for 2-year survival prediction, the modified Tokuhashi score, SORG nomogram, and OSRI also revealed moderate discriminative power, with AUC values of 0.73, 0.72, and 0.70, respectively. For patients who underwent surgery in the most recent period, OSRI demonstrated the highest predictive accuracy for 1-year survival, with a Harrell's C-index of 0.63 and an AUC of 0.68, and 2-year survival, with a Harrell's C-index of 0.63 and an AUC of 0.64. Most scoring systems exhibited low discriminative power, with only the SORG nomogram, OSRI, and modified Tokuhashi scores demonstrating moderate power for predicting long-term survival. In the most recent period, the OSRI demonstrated the highest predictive accuracy for both 1-year and 2-year survival.

Exploration on the construction of a bladder cancer prognostic model based on disulfidptosis-related lncRNAs and its clinical significance.

Disulfidptosis is a novel programmed cell death mode that has been reported to play a role in oncogenesis. Increasing evidences suggest that the long non-coding RNAs (lncRNAs) play crucial roles in the initiation and progression of bladder cancer (BLCA). However, the role and prognostic value of disulfidptosis-related lncRNAs in BLCA remain unknown.The aim of this study was to construct and validate a disulfidptosis-related lncRNA risk model for predicting the prognosis of BLCA patients. A risk model consisting of 5 disulfidptosis-related lncRNAs was developed to predict the prognosis of BLCA patients. The overall survival (OS) of BLCA patients in the high-risk group was significantly shorter than that in the low-risk group (P < 0.05). The effectiveness of this model was validated using the receiver operating characteristic (ROC) curve analysis, and this model proved superior in prognostic accuracy compared with other clinical features. Furthermore, the tumor immune dysfunction and exclusion (TIDE) score in the high-risk group was significantly higher than that in the low-risk group, suggesting that the high-risk group had a less favorable response to immunotherapy. Simultaneously, patients in the low-risk group exhibited significantly higher sensitivity to CTLA-4 monoclonal antibody therapy compared to those in the high-risk group, suggesting potential benefits of immunotherapy for patients in the low-risk group. The combination of high risk and low tumor mutational burden (TMB) could further shortened the OS of BLCA patients. Lastly, the drug sensitivity analysis revealed that the BLCA cells in the high-risk group showed an increased sensitivity to cisplatin, sunitinib, cetuximab, axitinib, docetaxel, saracatinib, vinblastine and pazopanib compared with those in the low-risk group. According to the Quantitative real time PCR results, we found that five lncRNAs of the risk model were more highly expressed in BCa cell lines than human immortalized uroepithelial cell line. The disulfidptosis-related lncRNA risk model has a valuable effect in assessing the prognosis of BLCA patients.

Development and validation of a prognostic model for cachexia in postoperative gastric cancer patients with low nutritional risk: a dual-center retrospective cohort study.

Gastric cancer can lead to excessive catabolism in patients. After undergoing gastric surgery, patients may experience additional unintended weight loss, resulting in severe malnutrition and potentially cachexia. We selected and incorporated patients from two centers. Cohort 1 (n = 1393) served as the development cohort, while cohort 2 (n = 501) was designated as an external validation cohort. Within cohort 1, 70% of the patients were utilized for model training, with the remaining 30% reserved for internal validation. The training set initially underwent univariate logistic regression, followed by multivariate logistic regression. The factors ultimately incorporated were used to construct the model and create nomograms. The discriminative ability was assessed using ROC curves in all three datasets, calibration curves were used to evaluate consistency, and decision curves analysis was performed to assess the clinical application value. The model incorporated 12 factors, specifically: age (OR = 1.07), preoperative BMI (OR = 0.89), surgery type (Total Gastrectomy (TG), OR = 1.83), chemotherapy (yes, OR = 1.52), stage (III, OR = 1.40), anastomotic obstruction (yes, OR = 6.85), Postsurgical Gastroparesis Syndrome (PGS) (yes, OR = 2.27), albumin (OR = 0.95), hemoglobin (OR = 0.98), triglycerides (OR = 0.36), CRP (OR = 1.07), and Neutrophil to Lymphocyte Ratio (NLR) (OR = 1.05). The model demonstrated robust performance in ROC with AUC values of 0.805 in the training set, 0.767 in the validation set, and 0.795 in Cohort 2. Calibration curves in all three datasets exhibited a high degree of concordance between actual and predicted probabilities. Decision curve analysis (DCA) indicated that the model holds substantial clinical utility across all three datasets. We have developed a predictive model for cachexia in patients undergoing gastric cancer surgery. This model enables healthcare professionals to accurately estimate the risk of cachexia in postoperative patients with nutritional deficits, allowing for timely nutritional interventions to enhance patient quality of life and prognosis.

Data-Driven Remaining Useful Life Estimation

This paper explores the development and application of data-driven prognostic models for estimating the Remaining Useful Life (RUL) of Nuclear Power Plant (NPP) condensers experiencing tube fouling. Due to the unavailability of run-to-failure industry sensor data, we utilized simulated data generated by the Asherah Nuclear Power Plant Simulator (ANS), initially designed by the International Atomic Energy Agency (IAEA) and programmed in Simulink for cyber security simulations. ANS's adaptability allows it to simulate Pressurized Water Reactor (PWR) behaviors given a time series of operating conditions and to introduce degradation modules to mimic fouling effects. Our study compares two primary approaches applied to data generated by ANS: inference-based and direct prediction methods. The selected inference-based approach estimates the health state of the condenser using a pipeline formed by an Auto Associative Kernel Regressor and a Hidden Markov Model (HMM), which subsequently combines the state estimates with its parameters to predict the RUL. The direct prediction method employs a Gradient Boosting Regressor Decision Tree (GBRDT) to map input variables directly to RUL. Our findings demonstrate the efficacy and limitations of each method through the case study, providing valuable insights for the adoption of data-driven RUL estimation techniques in industrial and energy applications.

Comprehensive Analysis and Verification of the Prognostic Significance of Cuproptosis-Related Genes in Colon Adenocarcinoma.

Colon adenocarcinoma (COAD) is a frequently occurring and lethal cancer. Cuproptosis is an emerging type of cell death, and the underlying pathways involved in this process in COAD remain poorly understood. Transcriptomic and clinical data for COAD patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We investigated alterations in DNA and chromatin of cuproptosis-related genes (CRGs) in COAD. In order to identify predictive differentially expressed genes (DEGs) and various molecular subtypes, we used consensus cluster analysis. Through univariate, multivariate, and Lasso Cox regression analyses, four CRGs were identified. A risk prognostic model for cuproptosis characteristics was constructed based on four CRGs. This study also examined the association between the risk score and the tumor microenvironment (TME), the immune landscape, and drug sensitivity. We distinguished two unique molecular subtypes using consensus clustering analysis. We discovered that the clinical characteristics, prognosis, and TME cell infiltration characteristics of patients with multilayer CRG subtypes were all connected. The internal and external evaluations of the predicted accuracy of the prognostic model built using data derived from a cuproptosis risk score were completed at the same time. A nomogram and a clinical pathological analysis make it more useful in the field of medicine. A significant rise in immunosuppressive cells was observed in the high cuproptosis risk score group, with a correlation identified between the cuproptosis risk score and immune cell infiltration. Despite generally poor prognoses, the patients with a high cuproptosis risk but low tumor mutation burden (TMB), cancer stem cell (CSC) index, or microsatellite instability (MSI) may still benefit from immunotherapy. Furthermore, the cuproptosis risk score positively correlated with immune checkpoint gene expression. Analyzing the potential sensitivity to medications could aid in the development of clinical chemotherapy regimens and decision-making. CRGs are the subject of our in-depth study, which exposed an array of regulatory mechanisms impacting TME. In addition, we performed additional data mining into clinical features, prognosis effectiveness, and possible treatment medications. COAD's molecular pathways will be better understood, leading to more precise treatment options.

Single-cell sequencing unveils mitophagy-related prognostic model for triple-negative breast cancer

BackgroundTriple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer lacking hormone receptors and HER2 expression, leading to limited treatment options and poor prognosis. Mitophagy, a selective autophagy process targeting damaged mitochondria, plays a complex role in cancer progression, yet its prognostic significance in TNBC is not well understood.MethodsThis study utilized single-cell RNA sequencing data from the TCGA and GEO databases to identify mitophagy-related genes (MRGs) associated with TNBC. A prognostic model was developed using univariate Cox analysis and LASSO regression. The model was validated across multiple independent cohorts, and correlations between MRG expression, immune infiltration, and drug sensitivity were explored.ResultsNine key MRGs were identified and used to stratify TNBC patients into high-risk and low-risk groups, with the high-risk group showing significantly worse survival outcomes. The model demonstrated strong predictive accuracy across various datasets. Additionally, the study revealed a correlation between higher MRG expression levels and increased immune cell infiltration, as well as potential responsiveness to specific chemotherapeutic agents.ConclusionThe mitophagy-related prognostic model offers a novel method for predicting outcomes in TNBC patients and highlights the role of mitophagy in influencing the tumor microenvironment, with potential applications in personalized treatment strategies.

Untreated Vestibular Schwannoma: Analysis of the Determinants of Growth.

The growth rate of sporadic VS varies considerably, posing challenges for consistent clinical management. This systematic review examines data on factors associated with VS growth, following a protocol registered in the PROSPERO database. The analysis reveals that key predictors of tumor growth include tumor location, initial size, and specific clinical symptoms such as hearing loss and imbalance. Additionally, several studies suggest that growth observed within the first year may serve as an indicator of subsequent progression, enabling the earlier identification of high-risk cases. Emerging factors such as the posture swing test and MRI signal intensity have also been identified as novel predictors that could further refine growth assessments. Our meta-analysis confirms that tumor location, initial size, cystic components, and vestibular symptoms are closely linked to the likelihood of VS growth. This review provides valuable guidance for clinicians in identifying patients who may require closer monitoring or early intervention. By integrating these predictive factors into clinical practice, this review supports more personalized treatment and contributes to the development of more accurate prognostic models for managing untreated sporadic VS.

The Value of Primary Tumor Resection in Patients with Liver Metastases: A 10-Year Outcome.

This study aimed to analyze the impact of primary tumor resection (PTR) on the prognosis of four common primary tumors with liver metastases, and to develop a prognostic model to visualize the PTR benefit rate of patients with liver metastases. Patients diagnosed with colorectal cancer liver metastases (CRLM), pancreatic cancer liver metastases (PLM), gastric cancer liver metastases (GLM), and breast cancer liver metastases (BLM) between 2004 and 2015 were retrospectively reviewed from the Surveillance, Epidemiology, and End Results (SEER) database and assigned to either the surgery or non-surgery groups. A 1:1 propensity score matching (PSM) was performed. Surgical patients who survived longer than the median cancer-specific survival (CSS) time for non-surgery patients constituted the benefit group. Logistic regression was conducted to explore the independent factors affecting surgical benefit, and a nomogram was established. A total of 21,928 patients with liver metastases were included. After PSM for surgery and non-surgery patients, we found that PTR had a significant impact on the overall survival (OS) and CSS of CRLM, PLM, and BLM patients. In CRLM patients, age (p<0.001), primary site (p=0.006), grade (p=0.009), N stage (p=0.034), and histology (p=0.006) affected the surgical benefit. In BLM patients, the independent factors were age (p=0.002), race (p=0.020), and radiotherapy (p=0.043). And in PLM patients, chemotherapy was an independent factor associated with a survival benefit from PTR. PTR improved OS and CSS in patients with CRLM, PLM, and BLM. A predictive model was established to identify suitable candidates for PTR in CRLM patients.

Development and Validation of a Novel Classification System and Prognostic Model for Open Traumatic Brain Injury: A Multicenter Retrospective Study.

Open traumatic brain injury (OTBI) is associated with high mortality and morbidity; however, the classification of these injuries and the determination of patient prognosis remain uncertain, hindering the selection of optimal treatment strategies. This study aimed to develop and validate a novel OTBI classification system and a prognostic model for poor prognosis. This retrospective study included patients with isolated OTBI who received treatment at three large medical centersin China between January 2020 and June 2022 as the training set. Data on patients with OTBI collected at the Fuzong Clinical Medical College of Fujian Medical University between July 2022 and June 2023 were used as the validation set. Clinical parameters, including clinical data at admission, radiological and laboratory findings, details of surgical methods, and prognosis were collected. Prognosis was assessed through a dichotomized Glasgow Outcome Scale (GOS). A novel OTBI classification was proposed, categorizing patients based on a combination of intracranial hematoma and midline shift observed on imaging, and logistic regression analyses were performed to identify risk factors associated with poor prognosis and to investigate the association between the novel OTBI classification and prognosis. Finally, a nomogram suitable for clinical application was established and validated. Multivariable logistic regression analysis identified OTBI classification type C (p<0.001), a Glasgow Coma Scale score (GCS)≤8 (p<0.001), subarachnoid hemorrhage (SAH) (p=0.004), subdural hematoma (SDH) (p=0.011), and coagulopathy (p=0.020) as independent risk factors for poor prognosis. The addition of the OTBI classification to a model containing all the other identified prognostic factors improved the predictive ability of the model (Z=1.983; p=0.047). In the validation set, the model achieved an area under the curve (AUC) of 0.917 [95% confidence interval (CI)=0.864-0.970]. The calibration curve closely approximated the ideal curve, indicating strong predictive performance of the model. The implementation of our proposed OTBI classification system and its use alongside the other prognostic factors identified here may improve the prediction of patient prognosis and aid in the selection of the most suitable treatment strategies.

Vapor kinetic energy for the detection and understanding of atmospheric rivers

Poleward water vapor transport in the midlatitudes mainly occurs in meandering filaments of intense water vapor transport, spanning thousands of kilometers long and hundreds of kilometers wide and moving eastward. The water vapor filaments are known as atmospheric rivers (ARs). They can cause extreme wind gusts, intense precipitation, and flooding along densely populated coastal regions. Many recent studies about ARs focused on the statistical analyses of ARs, but a process-level understanding of ARs remains elusive. Here we show that ARs are streams of air with enhanced vapor kinetic energy (VKE) and derive a governing equation for Integrated VKE to understand what contributes to the evolution of ARs. We find that ARs grow mainly because of potential energy conversion to kinetic energy, decay largely owing to condensation and turbulence, and the eastward movement is primarily due to horizontal advection of VKE. Our VKE framework complements the integrated vapor transport framework, which is popular for identifying ARs but lacks a prognostic equation for understanding the physical processes.

A preliminary study of developing an MRI-based model for postoperative recurrence prediction and treatment direction of intrahepatic cholangiocarcinoma.

To establish an MRI-based predictive model for postoperative recurrence in intrahepatic cholangiocarcinoma (iCCA) and further to evaluate the model utility in treatment direction for neoadjuvant and adjuvant therapies. Totally 114 iCCA patients with curative surgery were retrospectively included, including 38 patients in the neoadjuvant treatment, traditional surgery, and adjuvant treatment groups for each. Predictive variables associated with postoperative recurrence were identified using univariate and multivariate Cox regression analyses, and a prognostic model was formulated. Recurrence-free survival (RFS) curves were compared using log-rank test between MRI-predicted high-risk and low-risk iCCAs stratified by the optimal threshold. Tumor multiplicity (hazard ratio (HR) = 1.671 [95%CI 1.036, 2.695], P = 0.035), hemorrhage (HR = 2.391 [95%CI 1.189, 4.810], P = 0.015), peri-tumor diffusion-weighted hyperintensity (HR = 1.723 [95%CI 1.085, 2.734], P = 0.021), and positive regional lymph node (HR = 2.175 [95%CI 1.295, 3.653], P = 0.003) were independently associated with postoperative recurrence; treatment group was not significantly related to recurrence (P > 0.05). Independent variables above were incorporated for the recurrence prediction model, the 1-year and 2-year time-dependent area under the curve values were 0.723 (95%CI 0.631, 0.815) and 0.725 (95%CI 0.634, 0.816), respectively. After risk stratification, the MRI-predicted high-risk iCCA patients had higher cumulative incidences of recurrence and worse RFS than the low-risk patients (P < 0.001 for both). In the MRI-predicted high-risk patients, neoadjuvant therapy was associated with improved all-stage RFS (P = 0.034), and adjuvant therapy was associated with improved RFS after 4months (P = 0.014). The MRI-based iCCA recurrence predictive model may serve as a decision-making tool for both personalized prognostication and therapy selection.

Five-gene prognostic model based on autophagy-dependent cell death for predicting prognosis in lung adenocarcinoma

Non-small cell lung adenocarcinoma (LUAD) is the predominant form of lung cancer originating from lung epithelial cells, making it the most prevalent pathological type. Currently, reliable indicators for predicting treatment efficacy and disease prognosis are lacking. Despite extensive validation of autophagy-dependent cell death (ADCD) in solid tumor studies and its correlation with immunotherapy effectiveness and cancer prognosis, systematic research on ADCD-related genes in LUAD is limited. We utilized AddModuleScore, ssGSEA, and WGCNA to identify genes associated with ADCD across single-cell and bulk transcriptome datasets. The TCGA dataset, comprising 598 cases, was randomly divided into training and validation sets to develop an ADCD-related LUAD prediction model. Internal validation was performed using the TCGA validation set. For external validation, datasets GSE13213 (119 LUAD samples), GSE26939 (115 LUAD samples), GSE29016 (39 LUAD samples), and GSE30219 (86 LUAD samples) were employed. We evaluated the model’s accuracy and effectiveness in predicting prognostic risk. Additionally, CIBERSORT, ESTIMATE, and ssGSEA techniques were used to explore immunological characteristics, drug response, and gene expression in LUAD. Real-time RT-PCR was conducted to assess variations in mRNA expression levels of the gene XCR1 between cancerous and normal tissues in 10 lung cancer patients. We identified 249 genes associated with autophagy-dependent cell death (ADCD) at both single-cell and bulk transcriptome levels. Univariate COX regression analysis revealed that 18 genes were significantly associated with overall survival (OS). Using LASSO-Cox analysis, we developed an ADCD signature based on five genes (BIRC3, TAP1, SLAMF1, XCR1, and HLA-DMB) and created the ADCD-related risk scoring system (ADCDRS). Validation of this model demonstrated its ability to predict disease prognosis and its correlation with clinical characteristics, immune cell infiltration, and the tumor microenvironment. To enhance clinical applicability, we integrated an ADCDRS nomogram. Furthermore, we identified potential drugs targeting specific risk subgroups. We successfully identified a model based on five ADCD genes to predict disease prognosis and treatment efficacy in LUAD, as well as to assess the tumor immune microenvironment. An efficient and practical ADCDRS nomogram was designed.

Contribution of blowing-snow sublimation to the surface mass balance of Antarctica

Abstract. Blowing-snow sublimation is a key boundary layer process in polar regions and is the major ablation term in the surface mass balance (SMB) of the Antarctic ice sheet. This study updates the blowing-snow model in the Regional Atmospheric Climate Model (RACMO), version 2.3p3, incorporating blowing-snow sublimation into the prognostic equations for temperature and water vapour. These updates address numerical artefacts in the previous model version by replacing the uniformly discretised ice particle radius distribution, which limited the maximum ice particle radius to ≤ 50 µm, with a non-uniform distribution covering radii from 2 to 300 µm without additional computational overhead. The improved model is validated against meteorological observations from site D47 in Adélie Land, East Antarctica. The updates fix the numerical artefacts, successfully predicting the power-law variation in the blowing-snow flux with wind speed while improving the prediction of its magnitude. Additionally, a qualitative comparison with CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite data shows that RACMO accurately forecasts the spatial pattern of monthly blowing-snow frequencies. The model also yields an average blowing-snow layer depth of 230±116 m at D47, matching typical satellite observation values. Results reveal that, without blowing snow, sublimation in Antarctica mainly occurs in summer (October–March), with minimal surface sublimation in winter (April–September). Introducing the blowing-snow model creates an additional sublimation mechanism primarily contributing in winter. From 2000–2012, model-integrated blowing-snow sublimation averaged 175±7 Gt yr−1, a 52 % increase from the previous version. Total sublimation, summing blowing-snow and surface sublimation, reached 234±10 Gt yr−1, 47 % higher than in simulations without the blowing-snow model. This increase leads to a 1.2 % reduction in the Antarctic ice sheet's integrated SMB. Additionally, changes in sublimation in coastal and lower escarpment zones underscore the importance of the model updates for Antarctic climatology.

Predictive Prognostic Model for Hepatocellular Carcinoma Based on Seven Genes Participating in Arachidonic Acid Metabolism.

The occult onset and rapid progression of hepatocellular carcinoma (HCC) lead to an unsatisfactory overall survival (OS) rate. Established prognostic predictive models based on tumor-node-metastasis staging and predictive factors do not report satisfactory predictive efficacy. Arachidonic acid plays pivotal roles in biological processes including inflammation, regeneration, immune modulation, and tumorigenesis. We, therefore, constructed a prognostic predictive model based on seven genes linked to arachidonic acid metabolism, using samples of HCC patients from databases to analyze the genomic profiles. We also assessed the predictive stability of the constructed model. Sample data of 365 patients diagnosed with HCC were extracted from The Cancer Genome Atlas (TCGA, training set) and HCCDB18, GSE14520, and GSE76427 databases (validation sets). Patient samples were clustered using ConsensusClusterPlus analysis based on the expression levels of 12 genes involved in arachidonic acid metabolism that were significantly associated with HCC prognosis. Differentially expressed genes (DEGs) within different clusters were distinguished and compared using WebGestaltR. Immunohistochemistry (IHC) analysis was performed using a human HCC tissue microarray (TMA). Tumor immune microenvironment assessment was performed using ESTIMATE, ssGSEA, and TIDE. Samples of patients with HCC were classified into three clusters, with significant differences in OS. Cluster 2 showed the best prognosis, whereas cluster 1 presented the worst. The three clusters showed significant differences in immune infiltration. We then performed Cox and LASSO regression analyses, which revealed CYP2C9, G6PD, CDC20, SPP1, PON1, TRNP1, and ADH4 as prognosis-related hub genes, making it a simplified prognostic model. TMA analysis for the seven target genes showed similar results of regression analyses. The high-risk group showed a significantly worse prognosis and reduced immunotherapy efficacy. Our model showed stable prognostic predictive efficacy. This seven-gene-based model showed stable outcomes in predicting HCC prognosis as well as responses to immunotherapy.

Construction of a prognostic model based on cuproptosis-related patterns for predicting survival, immune infiltration, and immunotherapy efficacy in breast cancer: Cuproptosis-based prognostic modeling in breast cancer.

Breast cancer is the most common and lethal malignancy among women worldwide. Cuproptosis, a newly identified copper-dependent cell death, is closely associated with cancer development. However, its regulatory mechanisms in breast cancer are not well studied. This study aims to establish a prognostic model for breast cancer to improve risk stratification. The mRNA expression data was downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering identified patterns based on cuproptosis-related genes. Key genes were screened using Weighted Gene Co-Expression Network Analysis and differentially expressed gene analysis. A prognostic model was constructed using Cox regression and evaluated with time-dependent receiver operating characteristic and Kaplan-Meier analyses. Functional pathways, immune cell infiltration, and other tumor characteristics were also analyzed. Two distinct cuproptosis patterns were identified. The top 21 differentially expressed genes, significantly associated with survival, were used to construct the prognostic model. The risk score has a negative correlation with survival. Enrichment analysis showed immune-related pathways enriched in the low-risk group, which also had more immune cell infiltration, higher stromal component, lower tumor purity, and lower tumor heterogeneity. Finally, significant differences of half maximal inhibitory concentration were also observed between patients in high- and low-risk groups who received chemotherapy and targeted therapy drugs. These findings in our study may provide evidence for further research and individualized management of breast cancer.

Establishing a Novel Pyroptosis-Related Gene Signature and Predicting Chemical Drugs for Papillary Thyroid Cancer.

The present study aimed to construct a novel pyroptosis-related gene signature to predict the prognosis of papillary thyroid cancer (PTC). The gene expression level and survival and prognosis information of PTC were obtained from TCGA. The differentially expressed pyroptosis-related genes (DEPs) between cancer and control groups were selected, followed by subtype analysis. A prognostic model was built using LASSO regression analysis. The samples were then divided into high- and low-risk groups, and the differences in immune cell distribution in different risk groups were compared. The chemical drugs associated with genes in the prognostic model were extracted from the Comparative Toxicogenomics Database. A total of 31 DEPs were selected, and 3 different subtypes were obtained. A prognostic model based on 6 pyroptosis-related genes was constructed. The risk grouping was significantly correlated with the actual prognosis, and the model was found to be an independent prognostic factor. Six immune cells with significant differences in distribution in different risk groups were screened. CGP52608 could target four genes in the prognostic model, including GSDMB, NLRC4, IL1A, and IL6. The present study constructed a pyroptosis-related gene signature that could predict the prognosis of PTC. Additionally, this signature was correlated with tumor immunity.

Development and validation of prognostic model for metastatic castration-resistant prostate cancer patients treated with first-line abiraterone or enzalutamide.

IntroductionOver the years, several prognostic models were developed in patients receiving chemotherapy for metastatic castration resistant prostate cancer (mCRPC), while data on androgen-receptor signaling inhibitors (ARSI) in a real-world setting are limited. Patients and methodsWe compared a consecutive series of 565 mCRPC patients receiving first-line ARSI at four high-volume Italian Centers (development set) to an external series of 180 patients receiving the same treatment at another Italian high-volume Center (training set), between 2011 and 2022.Sixteen clinical and baseline laboratory variables were selected to develop a prognostic model. Patients were categorized into risk groups according to the number of independent factors positively associated with overall survival (OS). ResultsIn the development cohort, after a median follow-up of 21.1 months, the median OS was 30.4 months (95% CI 27.5-33.4). At the multivariate analysis, seven variables [age, prostate specific antigen (PSA) doubling time, baseline levels of hemoglobin, PSA, time to castration resistance, ECOG PS and bone metastases number) were included into the final model.The median OS was 13.4, 25.7 and 46.4 months in poor (0-2 factors), intermediate (3-4 factors) and good (≥5 factors) prognosis group, respectively.The application of the model to the validation set confirmed its ability to prognosticate for OS. The model c-indexes were 0.68 (95% CI 0.64-0.72) and 0.75 (95% CI 0.68–0.81) in the development and validation cohort, respectively. ConclusionsOur model, based on clinical and laboratory variables readily assessable in clinical practice, might prognosticate the OS of mCRPC patients receiving first-line ARSI. Micro AbstractA retrospective study comparing a development to a validation cohort of mCRPC patients receiving ARSI found a new prognostic model. It included seven clinical and baseline laboratory variables and confirmed its ability to prognosticate for OS.

Identification and Validation of a Novel PANoptosis-related Gene Signatured for Osteosarcoma as Prognostic Model.

To construct and validate a prognostic model for osteosarcoma prognostication and therapeutic potential of PANoptosis- related genes. Observational study. Place and Duration of the Study: Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, from August 2021 to January 2024. Transcriptomic data from the GEO and TARGET databases were utilised to construct and validate a prognostic model for osteosarcoma. The analysis involved the use of the LASSO Cox-regression method with the Glmnet R package to identify key PANoptosis-related genes. Differential gene expression analysis was conducted using the Limma R package, and model validation was performed using Kaplan-Meier survival analysis and time-dependent ROC curves. This model, derived from five key PANoptosis-related genes, demonstrated significant predictive capability for patient survival across training and validation cohorts. Further analysis confirmed the model's effectiveness and identified metastasis stage and risk scores as the robust independent prognostic indicators. The prognostic model offers a novel tool for osteosarcoma prognostication and underscores the therapeutic potential of targeting PANoptosis-related pathways. PANoptosis-related genes, Osteosarcoma, Prognosis, Bioinformatics, Tumour micro-environment.